"Research in Action" explores the dynamic world of life sciences, covering drug discovery, clinical trials, therapeutic development, and the pivotal role of real-world data and technology in connecting clinical research with patient care. Hear insightful conversations with scientists, clinicians, and leaders from pharma, biotech, and health.
Site
RSS
Apple
Classements récents
Dernières positions dans les classements Apple Podcasts et Spotify.
Liens partagés entre épisodes et podcasts
Liens présents dans les descriptions d'épisodes et autres podcasts les utilisant également.
Découvrez des podcasts liées à Research in Action. Explorez des podcasts avec des thèmes, sujets, et formats similaires. Ces similarités sont calculées grâce à des données tangibles, pas d'extrapolations !
Where are the biggest opportunities to leverage AI in cancer diagnosis and treatment? What are the biggest barriers remaining to move away from a one-treatment-fits-all approach to treating cancer? And how are AI, radiomics, machine learning and deep learning helping to understand which patients will respond best to which treatments? We will learn all that and more in this episode of Research in Action with Otavio Clark, M.D. Ph.D. and Principal Research Consultant at Oracle Life Sciences.
Transforming public health with unstructured data and NLP in FDA's Sentinel Initiative
Saison 2 · Épisode 12
mardi 23 juillet 2024 • Durée 35:01
What is the MOSAIC-NLP project around structured and unstructured EHR data? Why is structured data not really enough for drug safety studies? And to what degree is NLP speeding up access to data and research results? We will learn all that and more in this episode of Research in Action with Dr. Darren Toh, Professor at Harvard Medical School and Principal Investigator at Sentinel Operations Center.
00;00;00;00 - 00;00;26;14 What is the MOSAIC and LP project around structured and unstructured data? Why is structured data not really enough for drug safety studies? And to what degree is NLP speeding up access to data and research results? We'll find all that out and more on this episode of Research in Action. Hello and welcome to Research in Action, brought to you by Oracle Life Sciences.
00;00;26;14 - 00;00;50;14 I'm Mike Stiles. And today our guest is Dr. Darren Toh, professor at Harvard Medical School and principal investigator at Sentinel Operations Center. He's got a lot of expertise in Pharmacoepidemiology as well as comparative effectiveness research and real-world data. So, Darren, really glad to have you with us today. Thank you. My pleasure to be here. Well, tell us how you wound up where you are today.
00;00;50;14 - 00;01;26;22 What what attracted you in the beginning to public health? Good question. So I trained in pharmacy originally, and I got my Masters degree in Pharmaceutical Outcomes Research at a University of Chicago, Illinois, Chicago. And it's where I first learned about a field called Pharmacoepidemiology, which sort of very interesting to me because I like to solve problems with methods and data and pharmacoepidemiology.
00;01;26;22 - 00;02;00;29 It seems to be able to teach me how to do that. So I got into the program at the Harvard School of Public Health, and when I was finishing up, I was deciding between staying in academia and going somewhere and getting a real job. And that's when I found out about an opportunity within my current organization and I've heard great things about this organization.
00;02;00;29 - 00;02;29;26 So I thought I would give it a try. And the timing turned out to be perfect because when I joined, our group was responding to a request for proposal for what is called a mini sentinel pilot, which ultimately became the sentinel system that we have today. So I've been involved in the Sentinel system since the very beginning or before we began.
00;02;29;28 - 00;03;02;25 And for the past 15 years I've been with the system and the program and because I really like its public health mission and I'm also very drawn to the dedication of FDA, our partners and my colleagues to make this a successful program. Well, so now here you are, a principal investigator. What exactly is the Sentinel Operations Center? What's what's the mission there and what part do you specifically play in it?
00;03;02;27 - 00;03;52;26 Sentinel is a pretty unique system because it is a congressionally mandated system. So the Congress passed what is called the FDA Amendments Act in 2007. And within that FDA, the Congress asked FDA to create a new program to complement FDA existing systems to monitor medical product safety and more specifically, the Congress, US FDA, to create a post-market risk identification and analysis system that will be using data from multiple sources that will cover at least 1 million lives to to look at the safety of medical products after they are approved and marketed.
00;03;52;28 - 00;04;33;07 So in response to this congressional mandate, FDA launched what is called a Sentinel initiative in 2008 and in 2009 as I mentioned, FDA issued its request for proposal to launch the Mini Sentinel Pilot program, and the program grew into the sentinel system that we have today. So it's for my involvement. It sort of grew over time. So when I joined, as I mentioned, we were responding to this request for a proposal and we were very lucky to be awarded the contract.
00;04;33;09 - 00;05;04;05 So when it was starting, I serve as a one of the many epidemiologists on the team and I led several studies and I gradually took on more leadership responsibility and became the principal investigator of the Sentinel Operations Center in 2022. So I've been very fortunate to have a team of very professional and very dedicated colleagues within the operations center.
00;05;04;05 - 00;05;27;26 So on a day to day basis, we work with FDA to make sure that we can help them answer the questions they would like to get addressed. And we also work with our partners to make sure that they have the resources that they need to answer the questions for FDA. And most of the time I'm just the cheerleader in chief just to share my colleagues and our collaborators.
00;05;27;28 - 00;06;11;23 Now that's great. And and then specifically, there's the Mosaic NLP project that you're involved with. What is that trying to achieve and what are the collaborations being leveraged to get that done? So Sentinel Systems has always had access to medical claims data and electronic health record data or year data. One of the main goals for the current sentinel system is to incorporate even more data, both structured and unstructured, into the sentinel system and to combine it with advanced analytic methods so that FDA can answer even more regulatory questions.
00;06;11;25 - 00;06;40;09 So the Mosaic and NLP project was one of the projects that FDA funded to accomplish this goal. So the main goal of this project is to demonstrate how billing claims and data from multiple sources when combined with advanced machine learning and natural language processing methods, could be used to extract useful information from unstructured clinical data to perform a more robust drug safety assessment.
00;06;40;11 - 00;07;21;18 When we tried to launch this project, we decided that we would issue our own request for proposal. So there was an open and competitive process, and Oracle, together with their collaborators, were selected to lead this project. So I want to talk in broad or general terms right now about data sharing, the standards and practices around that. It kind of feels silly for anyone to say it's not needed, that we can get a comprehensive view and analysis of diseases and how they're impacting the population without it.
00;07;21;20 - 00;07;46;15 NIH is on board. It updated the DMS policy to promote data sharing. You know, the FDA obviously is leaning into this. So is data sharing now happening and advancing research as expected, or are there still hang ups? So I think we are making good progress. So I think the good news is data are just being accrued at an unprecedented rate.
00;07;46;17 - 00;08;28;21 So there are just so much data now for us to potentially access and analyze. There's always this concern about proper safeguard of individual privacy. And through our work, we also became very appreciative of other considerations, for example, the fishery responsibilities of the delivery systems and payers to protect patient data and make sure that they are used properly. So you mentioned the recent changes, including in data management, ensuring policy, which I think are moving us in the right direction.
00;08;28;26 - 00;08;56;23 But if you look closer at the NIH policy, it makes special considerations for proprietary data. So I would say that we have made some progress, but access to proprietary data remains very challenging. And the FDA, the NIH policy doesn't actually fully resolve that yet. When you think about the people who do make that argument for limited data sharing, they do mostly talk about what you just said about patient privacy.
00;08;56;23 - 00;09;25;20 IT proprietary data. Pharma is especially sensitive to that, I would imagine. So how do we incentivize the reluctant how can we ease their risks and concerns or can we? Yeah, it's a tough question. I think that this require a multi-pronged approach and I can only comment on some aspects of this. So I would say that at least based on our experience, the willingness or ability to share data often depends on the purpose.
00;09;25;23 - 00;09;55;29 That is, why do we need the data? Many data partners participate in Sentinel because of its public health mission, and our consideration is how would the data be used again, Is there proper safeguard of patient privacy and institutional interest? There are other ways to share data. For example, instead of asking the data to come to us, we can send analysis to where the data is.
00;09;56;06 - 00;10;34;22 And that is actually the principle follow by federated system like Sentinel. So we don't pull the data centrally. We send an analysis to the data partners and only get back what we need it. And it's usually in the summary level format. So that actually encourages more data sharing instead of less sharing. I would say that recent advances in some domains, such as tokenization and encryption, might also reduce some concern about a data sharing, a patient privacy concerns in academic settings.
00;10;34;29 - 00;11;24;26 We've been talking a lot about days, for example, for individual who collect the data and the people I propose to offer them authorship or proper acknowledgment if they are willing to share their data. But that is not sufficient in many cases outside of academic settings. If you look at what is happening in the past ten years or so, there are now a lot of what people call data aggregators that are able to bring together data from multiple delivery systems or health plans, and they seem to be able to develop a pretty effective model to convince the data provider to share that data in some way.
00;11;24;29 - 00;11;55;28 And a way to do that could be to help these data providers to manage their data more efficiently or to help them identify individuals who might be eligible for clinical trials. More quickly. So there are some incentives that we could think of to allow people to to share that data more openly but personally, I think that scientific data should be considered public good and hopefully that will become a reality one day.
00;11;56;00 - 00;12;23;21 Yeah, that's really interesting because it sounds like it's both a combination of centralized and decentralized tactics in terms of of data sharing and gathering. Why is it so important to use unstructured data in pharmacoepidemiology studies? And does NLP really make a huge difference in overcoming the limitations and extracting that data? So in the past, I think that that's true.
00;12;23;21 - 00;12;58;07 Now, many pharmaco epidemiologic studies rely on data. They are not collected for research purposes. So we use a lot of medical claims, data that are maintained by payers. We use each our data that are maintained by delivery systems. So this data are not created for research purposes and much of this data, at least for claim, is data stored in structured format using established coding systems like ICD ten.
00;12;58;10 - 00;13;39;06 Coding system and structured data sometimes are not granular enough for a given drug safety study and certain data or set of variables that are required for claims reimbursements or other business purposes might not be collected at all. And people felt that, well, maybe the information that we need could be extracted from unstructured data because as part of clinical care, the physicians or nurse practitioner or the health care provider might include that information in the notes, but use user data also pretty messy, especially that unstructured data.
00;13;39;08 - 00;14;05;25 So instead of going through the unstructured notes manually to extract this information manually, technique by natural language processing could help us do this task much more efficiently so that we can mind a larger model of unstructured data. Well, obviously, when it comes to real world evidence, you're a fan. Tell us what excites you about using it to complement clinical research.
00;14;05;25 - 00;14;42;07 Get us more evidence based insights and help practitioners make better decisions. Yeah, that's a great question. Yes, I'm a fan of so I personally don't quite like the dichotomy between conventional, randomized, controlled trial and real world data studies because they actually sit along a continuum. But is true that conventional randomized trials cannot address all the questions in clinical practice.
00;14;42;09 - 00;15;30;17 So that's where real data and real data studies come in, because real data like we discussed come from clinical practice. So they capture what happens in day to day clinical practice. So if we are thoughtful enough, we will be able to analyze the data properly and generate useful information to fill some of the knowledge gap. The truth is we have been using real data throughout the lifecycle of medical product development for many years now, ranging from understanding the natural history or burden of diseases to using real data as controls for single arm trials, and that we have been doing this before the term real data became popular.
00;15;30;19 - 00;15;57;11 So I see real data to complement what we could do in conventional randomized trials. So real data studies don't replace clinical trials. I see them to be complementary, and real data studies sometimes are the only way for us to get certain evidence. We already talked about Mosaic and LP that project, but I kind of want to go a little deeper with it.
00;15;57;11 - 00;16;42;02 The idea is to tackle the challenges of using link data structured and unstructured at scale. Tell us about a use case for that project and why it was chosen for this project. We actually, Cerner proposed to use the association between Montelukast, which is an asthma drug and neuropsychiatric events as a motivating example. It is also important to note that the project is not designed to answer this particular safety question, because if you look at the label of Montelukast, there's also already a box warning on neuropsychiatric events.
00;16;42;02 - 00;17;18;26 So FDA already has some knowledge about this being a potential adverse event associated with the medication. The reason why or recalls is has proposed this project was because we actually did look at this association in a previous sentinel study that only used structured data, although the study provided provided some very useful information. We also recognized that certain information that we needed was available in such a data, but may be available in unstructured data.
00;17;18;28 - 00;17;42;18 So if we are able to get more data from unstructured data, we might be able to understand this association better. So that's why this motivating example was chosen. Well, this is an Oracle podcast and Oracle is involved in Mosaic, so I think it's fair to ask you about the technology challenges that are involved in what you're trying to do.
00;17;42;19 - 00;18;17;24 What does the technology have to be able to do for you to experience success? So Mosaic in LP is I was at a very ambitious project because it is using an LP to extract multiple variables that are important for the study. That includes the study outcome, which when you look at it, is a composite of multiple clinical outcomes and it's also trying to extract important covariates that could help us reduce the bias associated with real data study.
00;18;17;26 - 00;19;01;24 So I think technology comes in well is powerful in many ways. First, thanks to technology, the project is able to access very large amount of data from millions of patients who seek care in more than 100 healthcare delivery systems across the country. So this was hard to imagine maybe ten or 15 years ago. But now we have access to lots and lots of data at our fingertips because of advances in technology, because of the large amount and the complexity of the data methods side and LP becomes even more important.
00;19;01;26 - 00;19;33;19 And for this project, we are also particularly interested in whether an LP algorithm developed in one year trial system could be applied to another system, which has been a challenge in our field because each year our system is created very differently. So one, an algorithm that works in one system might not work in another. So we are hoping that through advanced methods and technology, we will be able to address this problem.
00;19;33;21 - 00;19;57;15 So without this technology advances, we might not be able to do this study as efficiently as we could all So the task might might not be possible. So where are we going with this? I mean, let's say the project is a success. What will that mean in terms of the FDA's goals and how NLP gets applied in medical therapeutics safety surveillance?
00;19;57;18 - 00;20;38;03 The hope is that Sentinel system can answer even more questions than it can address today. And the way that we are trying to accomplish that is to see whether or how this complex, unstructured data, we combine it with advanced analytic methods can help us answer questions that could not be addressed by structured data alone. I think through this project we also learned a lot about how the challenges associated with analyzing a very large amount of data from multiple sources.
00;20;38;06 - 00;21;11;14 Again, service data is compiled from more than 100 systems, so it is big but also very complex. And in many of our studies we really need that large amount of data just to be able to answer the question because we may be focusing on rare exposures or real come. So you really need to start with very large from our data just to get to maybe the ten patients that are taking a medication.
00;21;11;17 - 00;21;44;15 And what you learn with Mosaic, can that get applied to addressing other public health issues like disparate ease and asthma diagnosis and treatment, especially when you think about diverse groups? Yeah, that's a great question. So is the project is not designed to address these important questions, but if we are able to better understand the completeness of social drivers of health in these data sources, then we will be able to leverage this data to answer these questions in the future.
00;21;44;18 - 00;22;04;26 I think about how a project like this gets a evaluated at various steps along the way. I guess that's my question. How I mean, what what methods are used to ensure the validity of real world evidence? So the good news is in the past few decades we have been using real data, even though we might not be using the term.
00;22;04;28 - 00;22;36;22 So there's been a lot of progress in the field to improve the validity of Real-World Data studies. So we now have a pretty good framework to identify fit for purpose data, and we also have very good understanding of appropriate design and analytic methods. So to target trial emulation and propensity score methods. So this project and many other projects in Sentinel are following this principle.
00;22;36;24 - 00;23;14;03 And one thing to also note that this project is also following the overall sentinel principle in transparency. So everything we do will be in the public domain to allow people to reproduce, so replicate the analysis. So the protocol is available in public domain, and when we are done with the study, everything will be made publicly available. So that's one way to make sure that the the work at least is reproducible or replicable.
00;23;14;05 - 00;23;43;00 And through that process, we hope to be able to improve the validity of this study. And what about comparisons? How do you compare the results from different data sources like claims data, structured data? You know, I extracted unstructured data, all of that. How was that done, the comparisons? So if you're talking about the Mosaic and LP study, so we have a pretty structured approach to address that question.
00;23;43;02 - 00;24;13;14 So we are using this proven principle of changing one thing and keeping everything else fixed to see what happens. So the project will start by using only claims data to replicate the previously done Sentinel study. And then we are going to add on such data to see whether the results are different. And then we add on an LP extract that unstructured data one at a time to see whether the results change.
00;24;13;21 - 00;24;40;24 So by fixing everything else to be constant and changing one thing, we'll be able to assess the added value of each how data, both structure and structure. And that's how we are going to do it within the Mosaic and LP study. And then what about scalability? How would you make sure the NLP models that you develop are scalable and transportable across all these different health systems of which there are many?
00;24;40;27 - 00;25;10;10 Yeah. The question again is about transport ability. So one thing that is unique about this study, as we briefly discussed earlier, was that the the survey yesterday to actually come from multiple healthcare systems. So the end up models that we are developing will be trained in tune on a sample of patients from this system and not from a single hospital network.
00;25;10;10 - 00;25;42;18 So at the development phase, we are already taking into account the potential diversity of different delivery system. And as part of this project, we also include another delivery system to apply and test the method as part of the transport ability assessment. So we are doing that to make sure that the LPI models that we are developing for this project will be useful for other system as well.
00;25;42;20 - 00;26;12;29 Unknown There is a larger question about computational resources, so that will be the issue that would still need to be addressed because a train and tuning this and NLP models within such a huge amount of data requires a lot of computing resources. So that is something that we could only partially address in our study. But if we want to apply or do the same thing in our system, that would be something to consider.
00;26;13;02 - 00;26;43;13 We talked a little bit about the collaboration with your tech partner, but these things usually have so many stakeholders and disciplines and silos. Tell us first why collaboration is a good thing and unavoidable anyway, and then what the challenges of collaboration are. Maybe some tips on how to best make them work. The problems that we face, at least many of the problems that I face quite complex and they require expertise from multiple domains.
00;26;43;13 - 00;27;18;19 So that calls for collaboration from multiple stakeholders. And we always have our blind spots. So we only see things in a certain way and we always miss things. So that's why I think collaboration is important. But it's really hard sometimes because we all have our priorities and perspectives and sometimes they don't align. And I also learned throughout the years that we don't communicate enough and we may also not have time to communicate or we may be under pressure to deliver.
00;27;18;21 - 00;27;47;21 So all of that sort of contribute to the challenges of collaborating effectively, especially when you collaborate across disciplines, because we might be using different languages to mean the same thing or use the same term to describe different things. So even though we can all speak the same language less English, we might not be talking about the same thing and not communicate at all.
00;27;47;21 - 00;28;17;25 Because because we are using different joggers and terminology. So that has been tough. But I think we are getting better. And so I think that it is for us within the center of operation center, we try to communicate honestly and respectfully and we try to understand different perspectives and we try to find common ground. And but I think ultimately what brings us together is that we have a shared common goal.
00;28;17;27 - 00;28;44;17 A lot of the work that we do. So for music and NLP, we are all trying to answer the same question, which is that how do we use unstructured data and advanced analytic methods to answer safety question? So once we apply on this common goal, things become easier because we start to understand each other better or be able to communicate more effectively.
00;28;44;19 - 00;29;19;16 Just out of curiosity, what are the different stakeholders involved in Mosaic? Who falls on the roster? we have people from different disciplines, so we have experts in natural language processing and artificial intelligence. We have epidemiologists, both statisticians, clinicians, we experts in psychiatric conditions and respiratory disease. We have data scientists, we have engineers, we have project managers. So it's a very big group of individuals with different expertise in this project.
00;29;19;18 - 00;29;46;14 Well, you probably noticed Oracle's really thrown itself into and committed huge resources to health and life sciences. Things got really exciting with the acquisition of Cerner and Cerner and Visa. What's Oracle doing right and what do you think it should be doing to make itself even more valuable in health and life sciences? Well, this is a great but very difficult question, so I cannot comment too much what Oracle is doing or will be doing.
00;29;46;17 - 00;30;23;06 But I can say more generally that there have been a number of technology companies that have tried to foray into health or life sciences. I would say with mixed results. And one reason is that our health care system remains highly fragmented and complex, so it takes a lot of energy to break the status quo. So you probably know that we were one of the last countries in the world to transition from ICD nine to ICD ten coding system, and we are soon going to move into the ICD 11 system.
00;30;23;06 - 00;31;00;05 So I'll be interested to see whether the US is ready for that. And that again, is maybe a reflection of just how complex and fragmented our system is and disruptive innovation and I think are great, but they may or may not translate into successes when they applied to health care. That is not to say tempesta mistake. I'm actually pretty optimistic that the perspectives and solutions and ideas brought by technology companies could help us solve a lot of problems that we have today.
00;31;00;07 - 00;31;31;26 But I think that it will be good to engage people who will be struggling with these issues early on and to work together with them to develop solutions that are not just good on paper, but also feasible in practice. So at least in my very limited experience, we have seen some very cool technology that ended up not being useful for health care just because it's very hard to change what people have been doing.
00;31;31;28 - 00;31;56;09 So again, disruptive innovations are good, but sometimes it's just very hard to adopt, at least not quickly enough for for us to see meaningful changes. Yeah, that's really fascinating. It's, you know, it is disruptive innovation, but it's not always applicable to the to the goals you're pursuing. But it does feel like technology where that's concerned, the future is coming at us faster and faster.
00;31;56;11 - 00;32;32;21 So what are the technologies that are most interesting to you? Is it A.I. or what big advances in public health do you see coming? Maybe sooner than we thought. Yeah. Yeah. You know, I feel like you said some of this came too fast. Like, I wish I. And closer to retirement, I don't worry about this. But so even though I say disruptive innovation sometime might not work in health care, but I will say generative A.I. seems to be a recent exception.
00;32;32;24 - 00;33;10;14 So I would say that generative is definitely on the list of things that surprised me in a very nice way. I will also say that the continue fast accrual of better real data is also something that excites me and the continue recognition or increased recognition of the potential real data of. It's also something that I think is good to have for things that came sooner than I found it again, generative.
00;33;10;19 - 00;33;44;13 AI So if you ask me when, we'll be ready for generally. AI Last year or two years ago, I would say not yet, but now we in the era where everything seems possible. So I remain extremely optimistic about generative in some of these last language models that will help us analyze unstructured data even more efficiently. Well, therein it's deeply fascinating and exciting stuff.
00;33;44;14 - 00;34;10;27 Thanks again for letting me pester you with these questions. If our listeners want to learn more about Sentinel, Operation Center or Mosaic or you, what's the best way for them to do that? So Sentinel has a poverty website where we post everything that we do. So is Sentinel initiative dot org. So I am a member of the Department of Population Medicine at Harvard Medical School.
00;34;10;29 - 00;35;00;16 So our website's population is a thought, but these would be two places that would be very informative for audience. Who wants to know more? All right. We appreciate that. And to our listeners, go ahead and subscribe to the show. Feel free to listen to past episodes because they are free. There's a lot to learn here. And if you want to learn more about how Oracle can accelerate your own life sciences research, just go to Oracle dot com slash life dash sciences and we'll see you next time on Research in Action.
Data hippies, real-world evidence, and precision medicine
Saison 2 · Épisode 3
mardi 6 février 2024 • Durée 35:38
What does a data hippie believe about the democratization of data? What role do technology companies, government, academia, industry, and other stakeholders play in life sciences and discovery? And how might walking clinical trials lead to improved precision medicine? We will get the answers to those questions and more in this episode with Dr. Chris Boone, the GVP of Research Services at Oracle Life Sciences. Chris has held some prominent roles at AbbVie and Pfizer, influencing health economics, medical epidemiology, and real-world data and evidence. He is an adjunct assistant professor at NYU, engaged in national health data committees, and serves on several boards including the American Heart Association.
What does a data hippie believe about the democratization of data? What role should tech companies, government and other stakeholders play in life sciences? Discoveries? And how might walking clinical trials lead to improved precision medicine? We'll get those answers and more on this episode of Research and Action in the lead.
00;00;24;03 - 00;00;43;21
Hello and welcome to Research and Action, brought to you by Oracle Life Sciences. I'm Mike Stiles. And today we're going right to the source when it comes to finding out what Oracle is doing in the life sciences space, what does a company like Oracle have to contribute? Why is it in the space? What does it and the rest of us have to gain from its involvement?
00;00;43;21 - 00;01;09;03
Those are the kinds of questions will be throwing at Dr. Chris Boon, newly appointed EVP of Research Services at Oracle Life Sciences. Chris has held some prominent roles at AbbVie and Pfizer, influencing health economics, medical epidemiology and real world data and evidence. He is an adjunct assistant professor at NYU, engaged in national health data committees and serves on several boards, including the American Heart Association.
Advancing rare disease research with a patient-centered approach
Saison 2 · Épisode 2
mercredi 24 janvier 2024 • Durée 39:53
What is the rare Gaucher disease and how does it impact patients, families, and life sciences? Is enough emphasis being placed on research and discovery for rare diseases? And what are the patient-centered approaches that best serve those battling rare diseases? We will get those answers and more in this episode with Tanya Collin-Histed, CEO of the International Gaucher Alliance. Tanya has been a longtime driving force in supporting patients with rare diseases and advocating for world-class healthcare. Her work has been nothing short of groundbreaking and she’s become the go-to person for patients, medical practitioners, industry, and governing bodies. As a mother of a child with Gaucher disease, she brings a unique, first-hand, and compassionate approach.
What is the rare gosh disease? Is enough emphasis being placed on rare diseases? And what are the patient centered approaches that best serve those battling rare diseases? We'll get those answers and more on research in action in the lead to the world. Hello and welcome to another episode of Research and Action, brought to you by Oracle Life Sciences.
00;00;25;09 - 00;00;49;25
I'm Mike Stiles. And today we have a truly inspiring guest. Tanya calling his dad, CEO of the International Gosh Alliance, has been a long time driving force in supporting patients with rare diseases and advocating for world class health care. Our work has been nothing short of groundbreaking. She's actually become quite the go to person for patients, medical practitioners, industry governing bodies.
00;00;50;03 - 00;00;52;01
Tanya, thanks so much for being with us today.
00;00;52;14 - 00;00;55;04
Thanks, Mike. It's an absolute pleasure to be here.
Automation, innovation, and the future of drug safety
Saison 2 · Épisode 1
mardi 9 janvier 2024 • Durée 35:37
International Data Corporation reports safety caseloads are increasing by 30% to 50% each year, and emerging technology will be the only way to keep up. But how are powerful technologies like generative AI advancing safety and pharmacovigilance? Is touchless case processing a good or bad thing? And how do we balance AI, automation, and the human touch? We will get answers to those questions and more in this episode with Bruce Palsulich, Vice President of Safety Solutions at Oracle Life Sciences. His portfolio includes Argus Safety, the industry-leading adverse event case processing and analytics solution, and Empirica Signal, the standard for signal detection and risk management. He has more than 30 years of experience in the healthcare and life sciences industry, including 25 in pharmacovigilance.
00;00;00;00 - 00;00;13;22 What is pharmacovigilance? How can technology best handle the tracking of adverse drug events? And is touchless case processing a good or a bad idea? We'll get those answers and more on this episode of Research in Action.
00;00;15;01 - 00;00;18;28 The lead, the Building.
00;00;20;10 - 00;00;48;22 Hello, welcome to Research in Action, brought to you by Oracle Life Sciences. I'm Mike Stiles. Today we are talking with Bruce Palsulich, vice president of Safety Solutions at Oracle Life Sciences. Bruce's portfolio includes Argus Safety, the industry leading adverse event, case processing and analytics solution, and empirical signal, the standard for signal detection and risk management. He's got more than 30 years of experience in the healthcare and life sciences industry, including 25 and pharmacovigilance.
00;00;49;02 - 00;01;03;25 Now, why is that important? Well, International Data Corporation reports safety caseloads are increasing 30 to 50% each year. Bruce is intimately involved in tackling that volume. So, Bruce, thanks for thanks for being with us today.
00;01;04;05 - 00;01;06;00 Yeah, thanks, Mike. Happy to be here. 00;01;06;16 - 00;01;17;04 Yeah. Let's get acquainted with you first. How did Life's path bring you into life sciences technology? How did you kind of wind up at Oracle and what are you tasked with getting done there? 00;01;17;29 - 00;01;50;08 You know, back back when I was still in university, I actually started off doing software development and consulting with a medical device company. And so early in my career, it was working on the actual embedded software that controlled medical devices. And early on ended up joining a consulting firm that started off doing engineering, consulting on medical devices, and eventually working towards quality software and regulatory submissions. 00;01;50;24 - 00;02;17;04 And so came to Oracle in 2009. So we had acquired a company that was that small engineering startup that I mentioned. And this is the company that originally developed Argus Safety, so I managed the team that developed Argus safety originally and through my time at Oracle, I jumped out of a safety for a little while. 00;02;17;04 - 00;02;42;24 For about four years I was running our healthcare strategy. That was when we had a much smaller healthcare footprint than we now have with our acquisition of Cerner. But at the time we did a lot of things in sort of what was called health-information exchange, sort of the foundation for national platforms under Australia and Singapore and multiple provinces in Canada. 00;02;43;09 - 00;02;51;18 And after doing that for about four years and then I came back to the safety side of the business about ten years ago or so. 00;02;52;03 - 00;03;02;25 Well, did you always see yourself doing something in medicine and life sciences, like when you were younger, or did this was this a life path that kind of surprised you? 00;03;03;08 - 00;03;29;12 You know, I ommitted the part where for four years I actually worked in aerospace. So I even though when I was still at university, I started off in medical devices. I did take a job in aerospace for four years. But that's sort of left a hollow feeling and not the same sort of mission driven purpose. When you do have a role that's within the broader health care or clinical development. 00;03;29;12 - 00;03;55;04 So, you know, I think many people like myself that, you know, whether you're on the vendor side or whether you're on the the pharma side of drug safety or pharmacovigilance or even broader clinical development, I think you do appreciate that there's there's a calling and you feel more purpose driven life. I suppose working in a field that's helping individuals, helping patients. 00;03;55;26 - 00;04;13;27 Well, for our audience, and I'm deflecting because our audience is smart, this is mostly for me. Let's just level set. What's what's the main goal of safety and pharmacovigilance? And I imagine safety standards would apply across every step in that drug development process. 00;04;14;10 - 00;04;46;07 Yeah. So drug safety and pharmacovigilance is really trying to understand the safety of drugs that are under both clinical development as well as once they complete their clinical development and are approved for broad market use. And so clinical trials really focus on safety and efficacy, but that's done under controlled conditions with a limited number of patients and and sort of restricted patients as well. 00;04;46;07 - 00;05;27;27 And once a marketed drug is approved, it's going to be exposed to significantly more patients. And so during a clinical development, a clinical trial, if you had an adverse event that occurs in one out of 10,000 people, that's that's sort of defined as a rare adverse event or adverse reaction. You can imagine if you gave that to a billion people, maybe, for instance, in the example of the COVID vaccines, Now that rare adverse event that's only occurring in one out of 10,000 people is actually occurring 10,000 times in a billion people. 00;05;27;27 - 00;05;42;04 And so so really, you know, pharmacovigilance is looking at and trying to understand that benefit risk and manage that risk when it's being exposed under real world conditions to to actual patients. 00;05;42;24 - 00;06;11;20 So the study of a drug is hardly done after it's approved by the FDA and goes out into the public, the public market, that monitoring is still happening while safety is paramount, It can't be easy. I mean, for whatever reason, the public does seem to expect perfection without risk when it comes to their drugs. So, I mean, what are the biggest challenges that Pharmacovigilance and the industry has to deal with currently? 00;06;12;04 - 00;06;50;12 So, you know, getting back to sort of those controlled conditions that are under clinical trials, for instance, typically you're not looking at pediatric or children exposure. Quite often you're not dealing with elderly patients or immune compromised patients or patients taking multiple medications. You know, do you have the diversity within your clinical trials such that you're getting genetic differences that might exist within different populations and such? 00;06;50;12 - 00;07;21;16 And so so all of those are exposures that are going to occur during broad use of those products once they get approved. And so so pharmacovigilance is really trying to, you know, track that, trying to collect as many adverse reactions that occur. It's trying to evaluate whether or not those events truly are a reaction that's related to the drug that's being studied and the drug of interest. 00;07;21;16 - 00;07;46;15 Or is it just occurring, for instance, within the general background rate that you would expect within within a patient population? And so all of that analysis is to try and understand, is it more than correlation that just, you know, we have an adverse event that occurred with a drug? Is that coincidence or is that related to other drugs you're taking? 00;07;46;15 - 00;08;13;16 Is that a progression of the disease that the patient is taking a medication for, or is it something that is actually induced by by the drug of interest? And how serious is that reaction? And is that something that should be, you know, updated on the prescribing information that's tracked along with a drug and the, you know, communication and education that's done to the health care community. 00;08;13;16 - 00;08;16;08 So they understand the risks associated with the drug. 00;08;16;28 - 00;08;46;17 So I get the challenge, which is that in a clinical trial to get a drug approved and on the market, there's no way to cover every possible circumstance and every type of person and every type of situation where, like you said, there are other actions with other drugs. And I already get the enormity of the challenge of keeping track of all of those people, all of those interactions, all of those adverse effects. 00;08;46;20 - 00;08;59;13 I imagine technology is tackling those challenges, right, Or at least helping to tackle them. For instance, like how can we better efficiently do data management? How does that play a big role in tackling these problems? 00;08;59;28 - 00;09;24;29 Yeah, So the you know, we talked about the increasing volumes somewhat. It's still generally estimated that somewhere on the order of between five and 10% of the actual adverse events that occur are actually reported. And so many people might just say, well, I felt dizzy when I took that and so I stopped taking it. And, you know, did you ever tell your doctor, Well, no, I just manage that on my own. 00;09;24;29 - 00;09;56;21 So so really part of the challenge is how can you make it easier to collect a higher number of of these adverse reactions that actually occur? How can you reduce the burden on both the patient and on a health care professional to report those? The other is that, you know, we want to move beyond the handling and the workflow of processing these individual adverse event reports and get to a more of the emphasis being placed on driving or deriving insights from the data itself. 00;09;56;21 - 00;10;18;20 So so we want to make, as we deliver our own solutions, we want to make the pharma companies more efficient at being able to handle these sort of transactions. But with the real value out of that of then more, more effort and more value can be derived from the insights. From the data itself. 00;10;19;10 - 00;10;37;03 Yeah. I mean, there's a need to track adverse events that are happening all the time. The volume and the sources of that data increases exponentially. So you kind of touched on it there. How do you go about not just effectively managing the data flow but actually making it actionable? 00;10;37;14 - 00;11;18;12 So I think part part of this is, is within an ecosystem where perceptions are changing. And I'll say when I entered the field, you know, back in the mid nineties and such, the perception was sort of like an ostrich putting their head in the sand or something. And, and I don't want to know about what hasn't specifically been reported and, and Pharmacovigilance and drug safety was really looked at as sort of a a tax on the business a cost of doing business and wasn't appreciated as a valuable information asset that can be leveraged, you know, within a biopharma organization. 00;11;18;12 - 00;12;00;26 And so now I think PV data being an expensively curated data set, is now looked as a valuable information asset within organizations. It can be used to identify new indications, it can be used to inform drug discovery and portfolio prioritization. I think more and more we're seeing safety used as a competitive differentiator and certainly we saw that with the COVID vaccines and those that were commercially successful versus those that perhaps were perceived as having a more risks associated with those. 00;12;00;26 - 00;12;26;19 And towards this, I think, you know, we're looking at, you know, how can advances in data science, technology, things like machine learning, predictive models, generative AI, how can they be leveraged in order to process and be able to make use of these increasing volumes of information as well as diverse sources of adverse event information as well? 00;12;27;07 - 00;12;42;22 Yeah, that's where I want to go next. Are you seeing cloud based platforms and AI transforming pharmacovigilance? I mean kind of balance the hope and the hype for me. How do you see those technologies changing, how we approach drug safety and in like, say, the next decade or so? 00;12;43;05 - 00;13;18;23 So I really think and not not even just in this field, but in all fields, if you look at sort of the proliferation and the scaling of accumulation of data and information, it really requires new methods to approach that. So I do think that things like the large language models like Generative AI, are really going to be transformational into how we leverage this data and information specifically within health care and life science, but but also broader, I think, as a global population. 00;13;18;23 - 00;13;50;04 But so you can imagine even things like, you know, querying the data versus the natural language conversation, you know, perhaps you could ask how rare is this actual event or how does the rate of this adverse event compare for my drug versus other drugs within the same therapeutic class or given the volume of adverse events for this drug in 2023, how might how many reports might we expect to receive in in 2024? 00;13;50;04 - 00;14;26;08 Or are there clusters of patients that appear to be more likely to have this adverse event than other patients? And could you describe those differences? And so those I think, are all sort of examples that we're going to move from strictly having skills of of a data science list or query builder, a developer and such accessing data to sort of expose those questions of the data closer to the the individuals that are forming the question. 00;14;26;08 - 00;15;06;24 And so I think right now, you know, we really don't know what sort of insights or what sort of interactions are going to exist between these diverse data sources that are going to lead towards improved insights, improve patient safety. You know, we really want to, you know, identify what drugs work for, what patients and inversely know which patients shouldn't be exposed to certain drugs and and what characteristics, what scientific information is out there already, both broadly, you know, basic chemistry, genomics, pharmacokinetics, things like that. 00;15;07;12 - 00;15;10;29 But then bring that down to the experience of an individual patient. 00;15;11;18 - 00;15;24;29 Well, you've talked before about touchless case processing and what that could look like in the future. Tell us what that is and what companies should be doing now to start transitioning to that kind of model. 00;15;25;17 - 00;15;55;05 So I think sometimes the the phrase touchless case processing can sound a little scary, you know, that humans are going to be completely out of the loop and such. And I think the industry is generally looking for something a little bit more incremental. So we're not looking to say all cases should now be touchless. We're looking at things like, well, perhaps non-serious cases that don't provide a lot of new scientific information. 00;15;55;05 - 00;16;37;28 Perhaps those should be handled automatically by the system, perhaps for drugs that are well understood or have been on the market for a long time. Perhaps those would be better candidates for having automated case processing then things that are going to be a new a new drug on the market with less experience and exposure, perhaps cases that are received electronically and, you know, or cases from partners, you know, quite often they'll be global relationships between one pharma who partners with another pharma to to market that product in another region of the world. 00;16;37;28 - 00;17;03;22 And so you're receiving adverse event cases from this partner who who is originating those from patients or health care professionals. But if you're receiving that from a partner, you probably trust that they're sending it to you and maybe you can process that item automatically. The other is, is I think again, people get get a bit concerned if you say, well, this is going to be end to end and no human ever touched it. 00;17;03;22 - 00;17;35;06 And now we're going to be reporting this. You know, it doesn't necessarily have to be end to end. It can be the high volume of effort activities like doing the actual data entry. It can be decision support to support perhaps the causal assessments or to assess whether or not this is team serious or to look at is this an adverse event that's already listed on the the product label or prescribing information So it can be, you know, specific work steps are workflow steps. 00;17;35;06 - 00;18;15;27 Could be touchless, but overall, you know where it is appropriate. I think we still want humans in the loop to to oversee the process overall. So I think there are tremendous opportunities again, to take repetitive non value added processes out of and automate those from from requiring human effort to process those and allow the humans to focus on, you know, insights and focus on more value rather than these repetitive steps that that computers are well suited to be able to process as well. 00;18;15;27 - 00;18;39;08 You said something earlier, and that's very legitimate that, you know, a lot of patients will start taking a drug and experience some kind of adverse reaction to it and then just stop and not even tell their doctor about it. No one's ever going to know about the adverse reaction that they had. So there's even a reliability factor on the part of the patients and their willingness to report. 00;18;39;27 - 00;19;05;15 How far away are we from being able to have essentially a digital model of patients that drugs can be tested on? I mean, am I going way far ahead in the world of science fiction where in Silico gets kicked up a notch and safety procedures are tested on not real people, but essentially digital versions of patients? 00;19;05;15 - 00;19;35;22 Yeah, I think this whole concept and people may have heard the term digital twin and such is is obviously very interesting and I think we'll have certain benefit. I think, you know, certainly, you know, establishing toxicity and such would much better be supported through some of these models than than experimenting on on animals or on humans in order to establish toxicities and such. 00;19;35;22 - 00;20;12;08 And so so I think, you know, it's going to start from sort of the bottoms up that way when you're looking at those types of exposures. And I think as we get again, as we sort of stitch together these diverse data sources and have tools to be able to look for correlations and linkages that that are there, that would be difficult for humans to ascertain, then I think, you know, that will allow us to sort of advance these digital models that that represent a human response to medications and such. 00;20;12;08 - 00;20;44;18 So I think that's something that is definitely being advanced and we have pockets of that, and those pockets will ultimately end up being combined into a larger simulation of, you know, humans. So yeah, it's certainly an interesting area. And even myself, you know, it took me a while to sort of get my head around what that concept of digital twin and how that's going to benefit clinical development as well as is health care overall. 00;20;45;16 - 00;21;06;07 Well, we touched on the balance of hope and hype, but there's another balance here that you also touched on a bit. It feels like we want every advantage that technologies and automation and machines can bring us, but then we only trust those things up to a point. We do want human experience, human judgment and expertise to kind of have the final word. 00;21;06;07 - 00;21;13;22 So how do you view where that balance is now between tech and human? What gets us to the lowest error rates? 00;21;14;07 - 00;21;47;22 So I think, you know, one of the perception challenges that exists right now is that people think the humans are probably doing a better job than they really are right now. So if you gave the same health care record source document to five different people and said, you know, take from this piece of paper and enter it into the system, you would probably end up you would not end up with five identical versions of data entry from abstraction from that source medical record. 00;21;47;22 - 00;22;15;14 And so, you know, which one of those five is right. And what's the error rate there? And so I think you would normally say that humans are going to be somewhere on the order of six or 7% error rate for that type of work. And so even in manual processing is adverse event cases, typically there's going to be some sort of QC sampling that's trying to keep a handle on detecting errors and keep a handle on the overall process and such. 00;22;15;14 - 00;22;43;04 And so looking at how, you know, automation or machine learning is going to apply similar things are going to occur. You still want some checks and balances in order to know that you still have control of the automated process and things that are getting into medical judgment. I still think we we want to stick within what we would say is sort of augmented processing or decision support. 00;22;43;04 - 00;23;27;27 Speaker 3 So you want to provide assistance to the person making those judgments and say the system has determined that we think this might be related to this drug and based on these factors, why we think that might lead to that decision. Again, it would be up to the health care professional to make the final judgment there. So I think we are you're trying to bring the facts, bring the the right parameters and such into view so that the human can make the best decision, given the data points and the assessments that are being being suggested by by the system. 00;23;28;04 - 00;24;04;15 So I think we're still, you know, I was listening to NPR yesterday and they had a discussion on self-driving cars and there are self-driving cars ever going to get to the same accuracy and insights of of a human. And I think, you know, this is similar here, although probably, you know, certainly a different problem than looking at real time sensors in forming a automated self-driving car, but trying to look at human experience, human judgment, you know, how do we model some of those? 00;24;04;26 - 00;24;16;25 I think right now we'll stay in this augmented decision support mode for many of these, you know, clinical medical decisions and certainly leave the final judgment up to a clinician. 00;24;16;25 - 00;24;34;24 So, yeah, I remain terrified of human drivers. So in your role at Oracle Life Sciences, how is Oracle specifically leveraging these emerging technologies that we talked about like AI and big data to enhance drug safety and pharmacovigilance? 00;24;35;10 - 00;25;15;20 So there's a number of technologies and that's that's one of the benefits of being part of the broader Oracle, is that, you know, you kind of have all of these other areas and big areas of investment in AI and data science and high capacity compute and large language models and generative AI. And so so we get to it's like going to the toy store or something and decide which which things already have been built that you get to pull off the shelf and decide how we could apply those into our area of drug safety and pharmacovigilance. 00;25;15;20 - 00;25;45;13 And so, for instance, we just added the translation facility and, you know, out of the box in our Argus Cloud, you now have a translate button and it doesn't sound like a big deal, but if you were using an external tool before and then had to cut and paste and you were doing that 20 or 30 times within an adverse event report case to report it to local regions, just taking out that cut and paste and making it as a button straight in the system. 00;25;45;13 - 00;26;07;27 And by default we'll hook it up to the Oracle Cloud Translation Service. But if you wanted to hook it up to Google or you wanted to get up to a life science translation service, you could do that as well. Again, we're trying to look for where there are bottlenecks and we're trying to go out and look at where can we leverage an investment that Oracle's already making and then apply that into our specific field. 00;26;07;27 - 00;26;52;00 And, and part of that's what's exciting about our acquisition of Cerner is that, you know, I may have had a use case that sounded interesting in Pharmacovigilance. Maybe it's a case narrative generation or a case narrative is not all that different than a discharge summary for a health care record, or if you're doing a health care referral letter for referring the patient to a specialist and giving a summary of their their specific case and such, that's not that different than perhaps auto generating a letter that is a follow up request for collecting additional information on an adverse event case and so on. 00;26;52;00 - 00;27;17;28 Many of these there's there's overlap and we're able to team up with the teams that are focused on the health care use cases and add on our life science use cases and, you know, really benefit both teams or sometimes health care is leading the charge and sometimes life science is leading the charge. But ultimately that power together is like a multiplier, not not addition. 00;27;17;28 - 00;27;31;14 And and I think is a big benefit. And one of the big benefits of our of our acquisition of Cerner and the fact that we now are a leading health care company, in addition to, you know, what we've traditionally done in life science. 00;27;32;06 - 00;27;48;28 Yeah, there are a lot of industry players in life science. So is is what you describe what makes Oracle a real differentiator in the space when it comes to safety and pharmacovigilance? So things like combined assets and the Cerner acquisition. 00;27;49;11 - 00;28;25;29 Yeah I think there's there's a couple of things. One is sort of foundational with our cloud infrastructure and capacity there. For instance, we have high capacity compute and GPUs and just within our drug safety solution area, you know, we have two GP2 cloud instances available, dedicated 100% to our use and that's multimillion dollar worth of compute that we have dedicated to to our team of data scientists working to NPV. 00;28;25;29 - 00;28;58;05 And that would be difficult, not impossible, but difficult for a lot of other vendors to sort of dedicate that sort of compute capacity in such just to their life science use cases. Now the other I think is is around, you know, the acquisition of Cerner. So we talked about we now have a point of care footprint. So where, you know, clinicians are using Cerner software as the electronic health record when they're interacting with patients. 00;28;58;05 - 00;29;28;19 And so if we want to collect information as part of that point of care relationship, we can do that if we want to leverage, You know, we have something that's called the Learning Health Network that has a electronic health record, real world data asset. And so companies that our health systems sign on to use this because they they want a few benefits, they want access to clinical trials. 00;29;28;19 - 00;29;55;07 So they want their their patients and such to be able to be included within cohort selection and recruitment, site selection and recruitment for clinical trials. They also want to understand how they're delivery of care matches against other health systems across the country and eventually across the globe. So that they can sort of benchmark and compare how they're doing. 00;29;55;16 - 00;30;23;05 So that ends up creating this research data asset That, for instance, is very important for drug safety and pharmacovigilance, so that if you have a particular risk or an adverse event that's been reported against your drug or therapy, that you can then go out and say, well, is that just a correlation? Is there enough information within these individual cases to establish causality to the drug, actually cause that adverse reaction? 00;30;23;18 - 00;31;11;09 Or do I really need to go investigate that and understand its usage within the, you know, electronic health care record or claims data? And so so that's one of the areas that we are really focused on right now of sort of benefiting this better together with with the combined assets of and expertise between Oracle and Cerner is how can we leverage that real world data to understand and investigate risks that have been reported in adverse event reports to be able to go out and and understand real world usage there and and look at and understand how many patients are taking this drug, how many patients potentially had this reaction? 00;31;11;25 - 00;31;31;17 How many patients generally have this reaction not taking our drug, you know, understand those background rates and such. And so it's another level of understanding of the benefit risk once you have not only the adverse event reports, but the ability to research these within a real world dataset also. 00;31;32;03 - 00;31;38;03 Okay. I've got one more question for you. The all those warnings at the end of the pharma TV ads, is that because of you? 00;31;38;24 - 00;32;07;22 Well, ultimately, you know, I feel like sometimes we're plane name that tune or something. So a commercial comes on and I'll say, Oh, that's a pharma access to pharma y company. And you know, I'm usually right on naming the drug to that company. But, but it is, it is vitally important, you know, what is being done and where traditionally pharmacovigilance has sort of been a retrospective. 00;32;07;22 - 00;33;02;11 What can we learn after it has occurred? We're really trying to move towards what or labeling as precision pharmacovigilance, which is better understand that safety profile, better understand that risk benefit profile, not at these broad population levels that might be by by gender and age group, but getting down to smaller and smaller subpopulations and ultimately ideally to be able to go back and impact proactively the care of an individual patient where we might be able to identify based on a certain patient characteristics, a patient history, genomic marker, current labs, other concomitant medications they may be on presently, that maybe there is a higher risk to that individual patient of therapy versus therapy and provide that 00;33;02;11 - 00;33;39;18 information to the clinician that's treating the patient at that point of care. So so we intend to continue to drive towards that advances in drug safety that can improve overall population level help, but want to drive that down to to the ability to inform care around an individual patient. And thus, you know, when we see and hear those commercials and we hear the list of adverse events that are potentially associated with that drug, to give us better context, to say, well, what does that mean for me as bruise versus what does that mean for Mike? 00;33;39;18 - 00;33;51;15 And maybe one of us needs to be concerned and maybe one of us doesn't, and wouldn't that be great rather than just hear the list and and know that randomly that might be meaningful or not so obvious? 00;33;51;15 - 00;34;08;19 It's a vital part of drug development. And it's been interesting to hear what approaches are being taken and who's leading them. We appreciate you being on the show. For those who are interested in Pharmacovigilance and their interest has been tweaked, is there any way they can connect with you or get more information on what's going on? 00;34;09;09 - 00;34;51;14 So for me, I can be reached at Bruce.Palsulich@oracle.com. If you're on any one of your web search engines, you could just search on Oracle pharmacovigilance. The other is that we do have a community that we call the Oracle Safety Consortium. So if you search on Oracle Safety Consortium, you'll come up with and that's sort of our end user community where we have regular monthly events and such that are discussing industry, but as well as Oracle Solutions and how we're addressing the needs of industry through this sort of peer consortium group as well. 00;34;51;14 - 00;35;00;09 So those are sort of three ways that you could either follow up with me individually or learn more what we're doing here in Oracle for drug safety and Pharmacovigilance. 00;35;00;24 - 00;35;29;25 All right, we've got it. And if you want to see if Oracle can accelerate your life sciences research, just head over to Oracle dot com slash life dash sciences and you'll probably find out what you need to know. Don't forget to subscribe to this show and join us next time for Research in Action.
Patient empowerment, digital innovation, and rare diseases
Saison 1 · Épisode 21
jeudi 5 octobre 2023 • Durée 44:00
How is clinical research becoming more patient-focused and more convenient for patients to participate in clinical trials? Why is a decentralized approach especially important concerning rare diseases? And how will digital innovation advance the way clinical research is conducted? We will learn those answers and more in this episode with Scott Schliebner, an innovative life sciences executive with 30 years of experience across the biopharma, CRO, medtech, and non-profit sectors. With a strategic and consultative approach to building and growing life science businesses, Scott has developed relationships, partnerships and collaborations that have driven commercial success. His vast experience includes leveraging real-world data and real-world evidence (RWE/RWD), leading technological innovation, and driving patient-focused paradigms to accelerate clinical drug development. Scott is an active board member, advisor, and mentor and his passions lie with infusing data and innovation into life sciences organizations—especially where rare diseases are concerned. He is currently the leading executive at Rare Clinical.
How is clinical research becoming more patient focused and more convenient for patients to participate in? Why is a decentralized approach especially important when researching rare diseases? And what is the most likely future for how clinical research is conducted? We'll get the answers to all that and more on Research in Action.
00;00;24;23 - 00;00;48;24
Hello and welcome back to Research in Action, brought to you by Oracle. I'm Mike Stiles and our guest today is Scott Schliebner. Scott is a leader and innovative life sciences executive with 30 years experience across biopharma, CROs, medtech, and nonprofit. He's developed relationships, partnerships and collaborations that have driven commercial success with a strategic and consultative approach to building and growing life science businesses.
Biotech startup working with Oracle to innovate for pharma
Saison 1 · Épisode 20
mercredi 20 septembre 2023 • Durée 42:54
How is academia fostering research that later turns into startup companies? What are new computational powers bringing to in silico drug design? And what is MoveableType methodology and why should pharma be excited about it? We will learn those answers and more in this episode with Lance Westerhoff, President and General Manager of QuantumBio. QuantumBio is a biotech startup operating in the vast field of drug discovery and molecular design. As President and GM, Lance oversees QuantumBio’s day-to-day management including the research, development, and deployment of advanced technology, as well as strategic partnerships and business development. Lance earned his PhD in Chemistry at Penn State University, and he is an entrepreneur, computational biochemist, and published scientist with projects involving the synergistic application of quantum mechanics and molecular mechanics in the life and pharmaceutical sciences. QuantumBio recently earned a Small Business Innovation Research (SBIR) grant from the NIH to run calculations for their MovableType methodology research, which they will be working with Oracle on that research project, and we talk about that and much more in this episode. -------------------------------------------------------- Episode Transcript:
00;00;00;00 - 00;00;26;06
How was academia fostering research that later turns into startup companies? What are new computational powers bringing to in Silico drug design and what is moveable type methods? And why should pharma be excited about it? We'll get those answers and more on research and action in the lead. The leading scene. Hello and welcome to Research and Action, brought to you by Oracle for Research.
00;00;26;06 - 00;01;00;18
I'm Mike Stiles. And today our guest is Lance Wester Hof, who is president and general manager of Quantum Bio. That's a biotech startup that operates in the field of drug discovery and molecular design. Lance oversees day to day management, including the research, development and deployment of advanced technology, as well as strategic partnerships and business development. He earned his Ph.D. in chemistry at Penn State, and he's an entrepreneur, a computational biochemist and published scientist with projects involving the synergistic application of quantum mechanics and molecular mechanics in the life and pharmaceutical sciences.
Science, Research, and Reaching the UN SDGs
Saison 1 · Épisode 19
mercredi 6 septembre 2023 • Durée 43:34
What are the 17 United Nations Sustainable Development Goals? What are the biggest challenges in pursuing and achieving those goals? How does technology play a role? And what’s the best way for government, academia, and industry to cooperate and collaborate in support of fundamental research? We will learn those answers and more in this episode with Declan Kirrane, the Chairman of the Science Summit at the United Nations General Assembly, and founder and managing director of ISC Intelligence in Science. Declan has more than 25 years of experience as a global senior advisor to governments and industry on science research, science policy and related regulation. He has been actively promoting a more significant role for science within the context of the United Nations General Assembly since 2010. This has culminated in the annual Science Summit within the context of the UN’s General Assembly. The focus of the Summit is on the role and contribution of science to attain the United Nations Sustainable Development Goals – or SDGs. The current edition – UNGA78 - takes place from September 12-29, and will bring together thought leaders, scientists, technologists, policymakers, philanthropists, journalists, and community leaders to increase health science and citizen collaborations to promote the importance of supporting science. And we are thrilled that Oracle will be part of the Science Summit with a few of our executives speaking and attending, including Alison Derbenwick Miller, global head and VP of Oracle for Research.
What are the United Nations Sustainable Development Goals? What are the biggest challenges in pursuing and achieving those goals? And what's the best way for government, academia and industry to cooperate and collaborate in support of basic research? We'll get the answers to all this and more on Research in Action.
00;00;23;02 - 00;00;49;08
Talking AI, Computer Vision, Autism, and Small Data Problems
Saison 1 · Épisode 18
mercredi 16 août 2023 • Durée 36:27
How is computer vision being used to spot autism symptoms much earlier in children? What is augmented cognition? And how can you use AI to make data models work even with small data sets? We will learn those answers and more in this episode with Dr. Sarah Ostadabbas. Dr. Ostadabbas is an associate professor in Electrical and Computer Engineering at Northeastern University, where she is also the director of the Augmented Cognition Laboratory (ACLab), which works at the intersection of computer vision, pattern recognition, and machine learning. Before joining Northeastern, she was a post-doctoral researcher at Georgia Tech and earned her Ph.D. at the University of Texas at Dallas. A renowned expert in the field, her research focuses on the goal of enhancing human information-processing capabilities through the design of adaptive interfaces based on rigorous models using machine learning and computer vision algorithms. With over 100 peer-reviewed publications, Professor Ostadabbas has received recognition and awards from prestigious government agencies such as the National Science Foundation (NSF), the Department of Defense (DoD) as well as several private industries. In 2022, she received an NSF CAREER award to use artificial intelligence for the early detection of autism, which she is working on with Oracle for Research. http://www.oracle.com/research --------------------------------------------------------- Episode Transcript:
00;00;00;00 - 00;00;26;15
How are computer vision and contactless techniques spotting signs of autism much earlier in children? What is augmented cognition and how can you use AI to make data models work, even with small datasets? We'll find all that out and more in this episode of Research in Action. Hello and welcome back to Research in Action, brought to you by Oracle for Research.
00;00;26;15 - 00;00;50;10
I'm Mike Stiles, and today we have with us Dr. Sarah Ostadabbas, an Associate Professor in the Electrical & Computer Engineering Department Northeastern University, where she's also director of the Augmented Cognition Laboratory (ACLab), which works at the intersection of computer vision, pattern recognition and machine learning. Before joining Northeastern, she was a postdoctoral researcher at Georgia Tech and got her Ph.D. at the University of Texas at Dallas.
Cloud technology’s impact on epidemiology research and public health
Saison 1 · Épisode 17
mercredi 12 juillet 2023 • Durée 43:05
How do the latest technologies impact epidemiology, clinical research, and public health? What kind of progress has there been in collaboration, open data, and citizen science? And in what ways can digital health appropriately supplement healthcare with the human touch? We will get the answers to these questions and more in this episode with Christine Ballard, a professionally trained epidemiologist specializing in clinical research and a Research Advocate at Oracle for Research. Christine has her Master of Public Health and is currently pursuing her Ph.D. in pharmacoepidemiology at UNC Chapel Hill. Her vast experience includes stints as an assistant professor and clinical research roles at Wake Forest Baptist Health, the University of Rochester Medical Center, and the New York State Department of Public Health. You can learn more about Oracle for Research here: http://www.oracle.com/research
What challenges do epidemiology researchers face in getting solutions to the public? What kind of progress has there been in collaboration, open data and citizen science? And in what ways can digital health appropriately supplement health care with the human touch? We'll get the answers to these questions and more on this episode of Research in Action, brought to you by Oracle for Research.
00;00;24;23 - 00;00;57;18
Hello. Welcome back to another episode of Research in Action, brought to you by Oracle for Research. I'm Mike Stiles, and today we have Christine Ballard with us. Christine is a professionally trained epidemiologist, specialized in clinical research. She has her master of public health and is working on her Ph.D. and pharmacoepidemiology at UNC-Chapel Hill. Her rather vast background includes stints as an assistant professor and clinical research roles at Wake Forest Baptist Health, University of Rochester Medical Center, and the New York State Department of Public Health.
Apple Podcasts
🇩🇪 Allemagne - techNews
18/06/2026
#89
🇩🇪 Allemagne - techNews
17/06/2026
#76
🇩🇪 Allemagne - techNews
16/06/2026
#52
🇩🇪 Allemagne - techNews
15/06/2026
#38
00;01;09;03 - 00;01;14;18
So Chris, you're obviously a very busy person, so we really appreciate your time today.
00;01;15;21 - 00;01;17;02
Thanks, Mike. I'm happy to be here.
00;01;17;11 - 00;01;30;01
Before we get started, tell us about your new role at Oracle and how you see scientific and industry expertise as kind of a winning combination with technology.
00;01;30;01 - 00;01;50;15
Yeah, that's a great question. And I think this is a very fascinating point in our health care and life sciences history. I mean, it's about but I'll start a bit with who I am and what exactly I do as the group vice President of Research Services. I get the great honor and privilege of leading our research services organization formerly known as Cerner.
00;01;50;15 - 00;02;17;14
And these are within the Hawk Oracle Life Sciences Organization. This particular organization has been primarily focused on data analytics and research, right? So in many respects it represents the convergence, if you will, of scientific clinical industry and technology expertise, which I think is pretty much nirvana for where we are with the future of evidence generation in our industry.
00;02;17;14 - 00;02;35;06
And so I'm extremely excited and honored to be able to sort of usher this organization and Oracle into this new realm and fully integrate all the great technologies that Oracle has with all the expertise and expertise and capabilities that that we've had in this R&D as a team.
00;02;35;26 - 00;02;53;21
Yeah, it sounds like there's a lot of people involved and buy in as necessary from a lot of different areas, from researchers to academia to technology. How are you finding the the openness and the willingness to include Oracle in these major efforts?
00;02;54;07 - 00;03;22;05
You know, it's interesting because I feel that the industry is very, very, very hungry for and interested even and curious. Maybe that's a better term for what Oracle will do in this space. I mean, I mean, I think after the Cerner acquisition, people became very intrigued of what Oracle could do, right? Because they sort of they think about the technologies, the advanced technologies that Oracle has, whether it be in a cloud computing automation and these great things.
00;03;22;28 - 00;03;54;26
They think about the clinical trial management platforms that it has. And now you have an electronic health record organization, a capability in addition to a research organization. So it does put Oracle at it's sort of an end of one really. I mean, there's no other company in industry that can can can make those sort of claims and to be true, but also have the ability to sort of drive transformation and how we think about clinical care as well as clinical research with all of the technologies we have at our disposal.
00;03;54;26 - 00;04;03;12
So I think it's a it's a very exciting time and I think that, you know, there's no better place to be right now than Oracle as it pertains to what we can do.
00;04;03;27 - 00;04;18;06
Well, there's no question how large a role data has played in your life and career, But you don't even call yourself a data nerd. Like most folks, you've actually referred to yourself as a data hippie. So what does that mean? Do you live in a van or something?
00;04;18;06 - 00;04;43;03
I think you're right about everything except the van part. But now the term data hippie, you know, it resonates pretty much with my career journey. You know, specifically going back to I first adopted the name back in 2014. I was leading a public private partnership called Health Data Consortium in D.C. but we were really focused on advocating for it and pushing this whole concept of open data and health care, I think.
00;04;43;11 - 00;05;04;19
And really which is sort of the genesis of this idea of the democratization of health data, really. And it was supposed to drive, obviously, innovation that would lead to higher quality patient care and making it more accessible and doing all these other things. In a modern times, though, I think we've sort of we've sort of moved past that a bit, right?
00;05;05;09 - 00;05;29;27
So I think now if I if I think about my, you know, current vision, it's just really about creating a system where you have the use of open, accessible data as a transformative force for the greater good of patients and ultimately the entire global health care system. So, I mean, I hope that there are more people I know that there are more people out there that share this vision, too.
00;05;29;27 - 00;05;37;01
So technically their data hit these just like I am, and we all are champions for this idea of the open exchange of health data.
00;05;37;01 - 00;05;51;04
Well, so if you're a proponent for data hippies, are you up against the man, the man being those who want more siloed proprietary data management? Why or why would anybody be resistant to this open access to data for all that you're talking about?
00;05;51;23 - 00;06;16;09
I think we sort of created a system that sort of has perverse incentives and, you know, and granted, I do believe that there are certain situations that warrant protecting the data privacy for individuals. So I'm not saying that everybody's data should be accessible to the masses for whatever they wanted to do with it. But I also think, too, that there is an opportunity to make data accessible for the public good.
00;06;16;09 - 00;06;34;29
I mean, if you go back to the pandemic, one of the one of the and there weren't very many, but one of the silver linings during the pandemic was this idea of global data sharing in order to sort of move faster with what would be the development of the vaccines, as well as treatment and therapies for for COVID, right?
00;06;35;00 - 00;07;03;18
I mean, that was only made possible by the free flow of data, right? So I think that what we have to do is create an incentive structure. We have to make people understand the value of data. I think you'll find that many folks became extremely educated on how the clinical trial process works and life sciences, but also the idea that using their data can actually be contributory to something that affects all of humanity.
00;07;03;18 - 00;07;25;09
And I think that and that's really where we are. So this whole that fragmented proprietary data, siloed nature that we existed in had, you know, has actually worked against us in many respects. And I think we're at a place in time where history will define what we do, what we've done, and the free flow of health data so critical to human health to be opposed to it.
00;07;25;27 - 00;07;49;14
Well, you mentioned that the the pandemic, what very few things good came out of it. But one of those good things is a more open approach to data and data sharing. What had to happen to make those walls come down that quickly was that a government instituted thing or did the industry itself decide we can't operate status quo and get a vaccine out there?
00;07;50;00 - 00;08;08;02
I think it was all of the above. I mean, but really I think it was more a genuine concern from all parties to really address this pandemic head on. And we knew that not one sector could could address it by themselves. Right. So you knew the public sector can do it by itself. Perhaps the private sector couldn't do it by itself.
00;08;08;02 - 00;08;33;12
So this idea of forming these collaborations, these partnerships, was was critical to sort of advancing science in the way that we knew it and that the way that we'll continue to practice it today, but also in a way that you know, we also had to engage even the community, the broader community, you had to educate people on what on public health matters that some may or may not have been concerned about in the past.
00;08;33;12 - 00;08;56;20
So you have this idea of engaging the private sector, the private sector engaged in a public sector, the public sector and the private sector engaging the public at large. And those are the things that were necessary to make this happen. I mean, and then it makes issues such as what you talk about data sharing a bit easier for people when they understand what the clear purpose is behind the sharing of their health data.
00;08;57;07 - 00;09;08;15
Well, obviously, you wound up at Oracle. How did that come about? What did you see out Oracle that presented great opportunities for what you want to accomplish, both personally and professionally in life sciences?
00;09;09;02 - 00;09;35;27
You know, it's interesting, and I would say that there is a short and a long answer to that question. But but but the short answer is, is that I actually didn't know much about what Oracle was doing and the health care and life sciences space prior to the Oracle Health Conference that I attended in September. And I was invited out to be part of the panel to sort of discuss the future of clinical trials and some innovations that we've seen.
00;09;36;09 - 00;09;57;28
And then I got an opportunity to listen to my Sicilian and Sima and a number of folks really talk about what their perspective, their world view on the future of health care life sciences looked like and what Oracle was actually doing to advance it. Right. And so, you know, you couldn't help but walk away feeling inspired by what this organization was seeking to do.
00;09;57;28 - 00;10;27;26
And so I had a bit of an epiphany while attending this meeting. And and just the idea of joining an organization that sort of shared my my sort of personal vision and values around the industry. And it's aligned with my professional goals. It only makes sense. Right. And it didn't it didn't hurt that the organization sort of believes in fostering innovation and and driving meaningful change and utilizing data and digital technologies to create a better world.
00;10;27;26 - 00;10;53;11
And and that that is what resonated with me. And the other pieces of that that I would say is that you know, I mentioned Mike Mike earlier but and Sima but you know, another person that I would add into that Ms.. Mix is David Fineberg. And I think that having inspirational leadership who have who are very passionate and committed about addressing these tough challenges that we have within the health care industry, I think is critical.
00;10;53;28 - 00;11;10;15
It makes all of our work feel more meaningful. Right? And so as a person who prides himself on being passionate about this industry and passionate about its transformation, it was it's great to partner up with with senior leaders who share that same passion and that same vision.
00;11;11;04 - 00;11;37;03
Well, it seems like technology is playing a bigger and bigger role as a solution to so many of the problems the world has and that we as a people have. Is it that the path to more, better and more accessible health care data is more likely to come from the private tech industry like the oracles of the world, than from some of the other traditional players like academia and government?
00;11;37;18 - 00;12;03;06
Again, I still think it's a multistakeholder approach that's necessary. I mean, you could you can point to the significance of academia. What we've seen with some of the peer reviewed journals and how they thought about the idea of of incentivizing folks to share their data with their publications. Right. And, you know, and sort of a de-emphasized in this need to say that you need to you need to keep your data proprietary.
00;12;03;06 - 00;12;24;15
It's your intellectual property and therefore, you know, it sort of affects their their ability to to move up in the ranks and their universities. But I also think the private sector, we've been at an interesting time the last decade where there's been a tremendous amount of focus on how to monetize data, which is sort of a disincentive for the free flow of data, which is sort of where I end with it.
00;12;24;15 - 00;12;51;24
Right. And I think that, you know, just by a lot of the things that we've seen even from a public sector perspective and regulators and the 21st Century Cures Act, for example, is a way that, you know, you've seen how the impact of working together and collaborating with both regulators, industry and academic researchers and how it essentially facilitates that necessary cooperation that we need.
00;12;52;03 - 00;12;59;17
And that's just one example of how I think that we've seen progression in this particular space.
00;13;00;07 - 00;13;15;15
Well, you brought up something kind of interesting. How do you work through the balance of the need to monetize on the part of private industry versus if that element weren't there, how far we could get, how fast with data sharing?
00;13;16;02 - 00;13;37;27
You know, it's interesting, a few several years ago I was I was quoted in one of the periodicals where I said that we were at a farm. I was at a in a data arms race and essentially what I was meaning by that is that we were looking to amass as many datasets as we possibly got. Granted, we weren't going to use all this data, nor could we even make sense of all the data that we were accumulating.
00;13;38;06 - 00;13;57;09
So what you're hearing, my personal belief is that people pay for insights, noxious, raw data, right? I think that it seems, you know, that that the raw data is the valued asset. And it could be if you actually know what to do with it. But I think that what people are looking for is insights to really drive decision making, right.
00;13;57;15 - 00;14;14;02
Whether that be at the policy level, whether it be at the clinical care level, whether it be at the research level, whether it be in the investment level. However you want to think about it. And I think that there's still the monetization of valuable insights is still there and that still should be very much a part of it.
00;14;14;09 - 00;14;32;07
And it doesn't mean that I'm opposed to the idea of monetizing raw data. I don't want that that belief to be out there. It's just that I believe that there is a greater good that we're all striving for and the more that we can get to an interoperable state within our industry, the better off patients will be in the long run.
00;14;32;10 - 00;14;35;16
And that's really sort of my core belief.
00;14;35;16 - 00;14;41;00
So you've talked in the past sometimes about a walking clinical trial. What do you mean by that?
00;14;41;19 - 00;15;04;11
Yeah, I mean, so the walk in clinical trial is sort of synonymous in my mind, at least with this concept of an app one trials that we've heard or some people refer to it, a single patient trials. And really what we're I think we are is that we're at a space where, you know, digital technologies have advanced and have been adopted and they're rather ubiquitous, you know, in our society as we think about it.
00;15;04;11 - 00;15;40;21
And so we're constantly accumulating data passively about patients and their environments and their lifestyles and their health conditions and even their medical histories. And we now have the ability to better understand and maybe in many ways be preventative with how we think about personal care. Right? I mean, so you get to understand quickly, which gets into this this this sort of world of precision medicine, where essentially treatment approaches are personalized to that individual based on their genes or their environment or lifestyle.
00;15;41;10 - 00;16;00;03
And I think this idea of being a walk, a walk in clinical trial, which is for all intents and purposes, is a paradigm shift from the way we thought about clinical trials in the past. But I think we now have all the technologies there to be able to embrace this concept. Does it apply in every single situation? Absolutely not.
00;16;00;12 - 00;16;12;26
Right. But I do think, and especially in the rare diseases space, there is a tremendous opportunity to do an app. One trials are walking clinical trials as often as I would describe it.
00;16;12;26 - 00;16;25;00
You mentioned precision medicine. What's your vision of precision medicine and how close or far away are we from it? What remains to be done to get us significantly closer to that vision?
00;16;25;23 - 00;16;42;17
I think we're a lot closer than we were, say, even five years ago. Right. And I think one of the biggest drivers for that has to be that direct to consumer genetic testing that folks are able to get. I mean, and the fact that the cost of doing that is much less. We've had clinical data from each US for a very long time.
00;16;43;09 - 00;17;10;19
We now have many of these sort of digital health apps available on mobile devices that can capture a lot of data about patients and in sort of a pro or a patient reported outcome format. So if you take the data that we have in the past, if you take the data that we collect from patients directly, if you take the genetic data, if you take the environmental data, we know a lot more about patients than we've ever known before, which actually gets us a lot closer.
00;17;10;19 - 00;17;31;10
And I think where we are is then is maybe in lacking some of the technological capabilities. The technology is out there, but it's not fully utilized. Don't embrace at this point. And, you know, and I think that's where technologies such as DNA, I you know, quantum computing and other tech that we are really excited about come into play.
00;17;31;18 - 00;17;44;05
And I think that once we're able to make sense of all the data that we collect on each individual, you know, then the better insight we can get and then the more personal your health care treatment plan will be for them.
00;17;44;23 - 00;18;10;18
Yeah, it sounds like the kind of future we all think about and that we believe that we should eventually get to, which is real time personalized diagnostics and treatment. But what I heard you just say, I think, is that technology is actually the the current barrier to that and that that technology exists. We just haven't applied it properly yet.
00;18;10;18 - 00;18;35;23
Yes and no. I actually think the bigger barriers, honestly, are going to be probably around the data privacy concerns that many folks have. And and this sort of core issue of informed consent, I do believe the technologies are there. Right. So that that is not a rate limiting factor in this case. I think that the probably the bigger concern and for many folks is the privacy concerns.
00;18;36;04 - 00;18;58;24
So I think as we move closer to the future of precision health, where individuals are providing these massive amounts of personal health data, it'll become increasingly more important that they fully understand and they consent to the use of their data for these secondary purposes. Right. And so I think that to me is the larger issue at this point.
00;19;00;08 - 00;19;10;22
Well, before we achieve the highest ideals of precision medicine, we first kind of have to start doing a better job of making clinical trials more patient centered, don't we? Isn't that step one?
00;19;11;17 - 00;19;44;25
It is step one. Yeah. I mean, I think we have to move to a paradigm where patients are not just seen as subjects, but as active contributors with valuable insights. Right. And and in order to get closer to that, I mean, we have to sort of create frameworks where it allows for engagement of diverse patient groups, and it takes into consideration these sort of variabilities that exist, whether it be varying health literacy levels, accessibility needs, geographic constraints.
00;19;45;04 - 00;20;15;01
All of these things have to play. And, you know, it's just all about building cultural sensitivity into how we think about study design for trials and whether it be randomized clinical trials or this whole end of one trials or walking clinical trial concept that we were discussing earlier. So I think that this is all important and it's not just purely a health equity play, it is in large part, but it's not purely that it's really about patient centeredness, and patient centricity is what some people will refer to it as.
00;20;15;09 - 00;20;18;13
And I think that's the world that we are trying to move closer to.
00;20;19;09 - 00;20;31;02
Well, you just kind of alluded to the fact that clinical trials aren't necessarily as diverse as they need to be. Right. We're not testing a significant number of populations.
00;20;31;15 - 00;20;33;00
Yeah. Yeah, absolutely.
00;20;33;05 - 00;20;40;12
And is that a is that a data problem? Is that a culture problem? Is it a privacy concerns problem?
00;20;41;19 - 00;20;59;17
You know, there's a number of reasons why I think, you know, a diversity of clinical trials is lack over the years. I mean, you know, what people tend to latch on to is this concept of trust. And while I do think that's part of it, I think that's actually probably a smaller piece of it than what we all want to accept.
00;21;00;11 - 00;21;31;09
The reality is, is that it's more of a awareness and a sort of an accessibility challenge, right? I mean, awareness, meaning that the vast majority of us will identify clinical trial opportunities from our primary care physicians or our specialists or whomever we're getting receive in our care from if they are not aware and or if they're not incentivized to really push this this notion of being part of a certain clinical trial, then typically the patients are never even aware that that is a possibility.
00;21;32;00 - 00;22;01;08
And then I think the other piece of this is that you get into some rather stringent inclusion and exclusion criteria for people that sort of exclude them. I'll give you an example. I am a hyper tension patient, a sort of a post-COVID condition that I've been dealing with the last couple of years. I mean, and so as part of that, I could easily be excluded from from an opportunity to be part of a clinical trial just by having that sort of chronic condition, if you will.
00;22;01;27 - 00;22;33;19
And so I think there's an issue of awareness and availability of the trial. You know, you know, sort of incentivizing or making it bring in the idea of this trial to the physician, making making them aware. There's also an idea of revisiting some of the inclusion exclusion criteria that are associated with being part of a clinical trial and acknowledging that certain subgroups, you know, may or may not be able to be part of that.
00;22;33;27 - 00;22;56;15
There's also a challenge of moving or shifting closer to more virtual trials. Some people call them decentralized clinical trials. And the idea basically is just the idea of utilizing remote monitoring or digital technologies to make it more accessible to people. I mean, that that's at the heart of it. And so I think that there's a number there's a myriad of factors that I think all contribute to it.
00;22;56;28 - 00;23;32;27
It's not simply trust, but that doesn't sort of diminish the significance of the trust issue in certain communities. But it also it but but it does mean that we have to acknowledge that there are other challenges that we must actively address, too. And and I think that's what you're seeing more of from many of the research or the sponsoring or organizations who are facilitating many of these trials is they're acknowledging that there are many that there are other issues that they have to address to want to sort of increase their recruitment efforts and also retain people to be part of those trials.
00;23;32;27 - 00;23;52;23
Well, going back to your data happy mantra of collaborating around readily accessible data, how open do you feel the industry is to that kind of multi-stakeholder collaboration? Like is that the role that government might have to play with policies like the CURES Act? I don't want to say force, but push that a little.
00;23;52;23 - 00;24;18;12
Yeah, no, I think the government plays a very critical role in their ability to sort of convene all the different stakeholder groups at the table and to really have a, you know, an honest and we'll say courageous conversation about many of these issues. And so it's not to say that they're simply just a convener, but I do think their convening power is critical in this state of forming many of these multi-stakeholder collaborations.
00;24;18;26 - 00;24;56;15
I also think that, you know, the idea that the government can play a role in facilitating these public private partnerships that we've seen around the globe in Europe, for example, they formed the Innovative Medicines Initiative, where they're essentially facilitating collaborations between the European Union, pharmaceutical companies and other stakeholders to really accelerate the development of next generation medicines. And, you know, so in addition to and then within the U.S., obviously you have the curious act, but you also have the single initiative, which has been something around drug safety that's been around for a while.
00;24;56;15 - 00;25;15;17
I think the World Health Organization has been collaborating with different governments around the world and NGOs. I just think that everyone recognizes how critical these multi-stakeholder collaborations are to really advance health care in the way that we all know it should be. And you won't get any pushback from anybody on that.
00;25;16;03 - 00;25;29;05
Yeah, so there's regulation, but there's also the carrot. What could be done to incentivize data sharing? How do you make everybody happy? From patients to researchers to profit to nonprofit to pharma, to IP holders, etc.?
00;25;29;25 - 00;25;52;02
Yeah, I mean, I think that the incentive structure has to be sort of multipronged, right? I mean, there is no one carrot for all the different stakeholder groups. I think recognizing that there is a diverse need, there are diverse needs and concerns from both for profit organizations as well as nonprofit organizations is the first thing we have to do, right?
00;25;52;02 - 00;26;27;04
So acknowledgment and acceptance is is key. I also think that you have to start thinking about it. You know, the reward systems or the incentives, Right. For pharmaceutical companies, for example, who are involved in some, you know, therapeutic development that their incentives may have to be aligned with some of their commercial objectives as well. Right. So if you think about what the role that policy can play, potentially, it could be, whether it be extended patent protections or tax benefits for companies that share data, especially data when it's coming from their clinical trials.
00;26;27;13 - 00;26;59;20
That could be one step. You know, it sort of addresses those concerns around competition and IP that many companies have. Right. While at the same time you're hoping that it accelerates the drug development and improves trial diversity, which is what we were just discussing. And I think that, you know, as it pertains to the the sort of academic community, you know, traditionally the the career advancement model was based primarily on publications.
00;26;59;20 - 00;27;32;09
I'm not going to say solely, but publications is obviously a big area of concern. So we have to recognize that that is the incentive and how do you shift that thinking to to sort of reward those those researchers who are more actively contributing their data to a larger repository for research purposes? And I think that those are the those things are essential not just for advancing health care, but also getting us closer to this precision medicine, which is what we all want.
00;27;32;27 - 00;27;45;04
Real world data and real world evidence are gaining prominence. What's your vision for how we use that to advance health care and life sciences? And is that where Oracle and Tech best plays a role?
00;27;45;27 - 00;28;13;09
I don't think that's the only place that Oracle can play a role. But let me answer the first part of your question first. I think that it's been incredibly exciting as someone who's been in the sort of real world Data Group 11, a space for for around so very long time and and to see it sort of come to prominence the way it has and and its ability to sort of transform how we do how we generate evidence and what evidence and how decision making, how decisions are made based on real world evidence.
00;28;13;09 - 00;28;34;05
I mean, we've seen it play a critical role in understanding disease, understanding patient outcomes, understanding the benefits and risk of certain treatments that are in the market, all of these things. And now we're at a space where, you know, you even have the ability to use real world data and where will evidence to streamline the drug development process?
00;28;34;05 - 00;29;05;06
So you're using it for a protocol design or are you using it to support post-market surveillance activities or facilitating regulatory decisions? As we discussed earlier with the 21st Century Cures? So I think my vision for it is that we continue to incorporate it, that we create this sort of learning health care system that we all that we all desire, where we're not only just generate the evidence, but the evidence is being fed back and to, you know, providers for clinical care policy.
00;29;05;13 - 00;29;23;23
So many of their policy decisions, so on and so forth. And we continuously collect that data and we also continue to learn from it. So I think ultimately that's really where it is. But with Oracle, you know, going back to my point about it being in such a unique position, we have this cloud infrastructure, we have these data management capabilities.
00;29;23;23 - 00;29;46;27
We have. Are it just sort of puts that puts the organization in a unique position to sort of really co innovate in this space. And I and I and I think that you'll also see the ability to sort of build these very sort of novel patient registries around certain diseases that allow us to learn more about it than we ever knew before, leveraging many of those technologies.
00;29;46;27 - 00;30;06;09
So I think I think in summary, I would say that the the integration and utilization of of real world evidence represent, you know, the new frontier in advancing health care and life sciences. And and I have no doubt that Oracle is ideally suited to be at the forefront of that transformation.
00;30;06;09 - 00;30;24;19
Well, we touched on precision medicine. We have all these new ways to monitor patients real time. And we read about the work being done in genomics. How do you balance the excitement of what can be done against the inevitable concerns about privacy and ethics, which you brought up before? Isn't it a lot like a lot of tech products?
00;30;24;19 - 00;30;42;24
Yes. You get great benefit from them, but the cost is a lot of trust that people have to have. And I'm not just talking about the population's having enough trust to participate in clinical trials, but overall, the level of trust that's needed to make open data and data sharing work.
00;30;43;08 - 00;31;09;16
Yeah, I mean, I think balancing that promise, what the pitfalls is, is obviously critical. And and then now when we start to get into this whole idea of using genomic data that really sort of scares people. I mean, we had a situation not long ago where one of those sort of genomic providers was hacked. Right. And so that that data tends to be highly personal and sensitive and can be misused that place in the wrong hands.
00;31;09;16 - 00;31;34;19
So we always want to keep that in mind. I think one of the and so it only elevates this issue of privacy as being a top concern for many folks. And I think that we have to this is where regulators come in right by it. You know, ensuring the the robust data protection and privacy laws that are safeguarding, you know, this information in a way that people feel comfortable with it.
00;31;34;19 - 00;32;00;11
Right. But I also think, too, you know, one of the things that's often that's near and dear to me that's often overlooked is really some of the ethical, ethical concerns with the misuse of the data. Right? So we have these privacy concerns, but this is where it does become a health equity issue. If if that data is misused like it's used to sort of discriminate against certain cell populations, then that is a huge problem.
00;32;00;16 - 00;32;40;10
Right? Some people are concerned about the use of it for whether it be in employment decisions, whether it be potentially in legal situations, insurance situations, you name it. And I think that that becomes a I would say that's also an added concern for people in addition to sort of the privacy issue. So I think where we are as an industry is that we got to pay more attention to providing the education and the literacy surrounding how health data is being used, how genomic data is being used, and what this concept of precision medicine really means and how it benefits them.
00;32;40;21 - 00;32;53;27
Right. I think that education is key, transparency is key and consent are key, right? I mean, so those are that those are the key instruments I think we have to use in order to really advance and address those concerns.
00;32;54;14 - 00;33;15;22
Well, looking ahead, what technologies do you think hold the most promise for clinical research and drug development? Like Oracle's focus is on generative AI and automation? One of their focuses are those the things that are going to really fling the doors open and lead us to getting effective drugs and treatments to market much faster than what we have now.
00;33;16;09 - 00;33;40;21
I do I do believe that, yes, the answer to that question, and I think but not only that right. I do think DNA and drug discovery is great. I mean, we can we can use the models to generate novel compounds, maybe even simulate their interactions with biological targets. That is how you speed up the identification of some of these viable drug candidates.
00;33;40;21 - 00;34;02;15
I think automation in clinical trials should address some of the efficiency challenges that we've had. And and also, you hope, reduce the likelihood of human error. As you know, when it comes to sort of the data capture aspects of it we've been using and, you know, the whole idea of natural language processing and literature search for a while.
00;34;02;15 - 00;34;24;25
Right. And I think that's another huge opportunity as we sort of automate much of that. I think quantum computing is exciting, too, you know, I mean, it's just and it's an we're still in the nascent stages of it, but, you know, right now, the way it's looking, the potential to unlock some of the challenges that we have a drug development can be addressed with quantum computing.
00;34;24;25 - 00;34;26;10
So I think that's pretty exciting as well.
00;34;26;25 - 00;34;42;25
Well, Chris, thanks for coming on the show today, giving us a glimpse of how you see the future of technology collaborate action and data sharing shaping up to revolutionize clinical trials and health care. If those listening want to learn more about you or Oracle's initiatives, how can they best do that?
00;34;43;03 - 00;35;03;25
Yeah, I mean, you can shoot me an email or you can find me on LinkedIn or even X. My my handle this data happy on both of those social media channels. Also have a personal website out there. Chris Bowen, SI.com, if you're interested in connecting that way, But I'm very easy to follow, so I look forward to connecting with everyone.
00;35;04;05 - 00;35;32;25
Fantastic. If you are interested in Oracle's contributions to life sciences research, just take a look at Oracle dot com slash life Sciences. Also be sure to subscribe to the show so you can be here for the next episode of Research in Action.
00;00;55;19 - 00;01;05;11
Well, before we get into the incredible work you're doing, let's get a baseline understanding of exactly what Gaucher disease is and just how rare it is.
00;01;06;00 - 00;01;37;11
Okay. Well, as a caregiver, I'll give a lay lay version to you. So it's a genetic condition and it's inherited. It's caused by a storage disorder. And that is because people with Gaucher have a deficiency in an enzyme. And the function of that enzyme is that it's in the body to break down substances. And because there isn't enough of that enzyme, the substances store in different parts of the body.
00;01;37;25 - 00;02;05;17
And it really does depend on what type of disease you have to how the disease affects you. But all patients can have a large liver and spleen. They get anemia, they get bruising where the blood doesn't clot properly and bone pain and bone damage due to the cells being in their bone marrow where which is where the blood cells are made.
00;02;06;02 - 00;02;42;09
Now, for patients who have type two and type three, there's also brain involvement and that really ranges from patient to patient. But that can include things like cognitive impairment, seizures, hearing and sight loss, unsteadiness in their movements and tremors. Now, it's it's a rare disease, as you say, and roughly it's around one in 100,000. However, this will different differ from region to region and also from type to type.
00;02;42;21 - 00;03;11;18
So historically, type one cases, disease is the most prevalent. Then we go into type three and then type two is like what we would call ultra ultra. However, as we become a much more globally connected community, we are seeing that there are many more patients with type two and Type three in Asia, whereas in sort of Europe and the West, we see more Type one patients.
00;03;12;05 - 00;03;27;29
Yeah, well it sounds like just that one issue, the the deficiency of that enzyme can cause countless problems all over the body. It already makes it obvious why this is such a difficult disease to get a handle on.
00;03;28;17 - 00;04;03;25
Yeah, absolutely. And I think the thing is, is that often when patients become ill and they go to maybe their general practitioner, you know, and they describe the, you know, how they feel that there are lots of things that could be wrong with patients. And therefore often patients have what we call a sort of diagnostic journey, a diagnostic odyssey where it will take a long period of time for them to actually get diagnosed.
00;04;04;12 - 00;04;12;04
If someone is diagnosed and they do get a correct diagnosis for, gosh, what are the typical outcomes?
00;04;12;20 - 00;04;38;28
Wow, that's a good question. So again, this goes back to whether or not you have type one, Type two or type three as a rare disease. We are incredibly lucky. So over 30 years ago, there was a medicine developed called enzyme replacement therapy, and this was developed and it what it does is it puts the deficient enzyme back into the patient's body.
00;04;39;06 - 00;05;00;14
So it's a bit like, you know, when you've been men. Tom So you've been, you know, you start up or your waist and, you know, you put it to one side and then the binmen come and they empty it, and then you start to store it up again. Well, of course, it's a bit man dotcom, you know, that storage gets more and more and more and starts to affect the average around it.
00;05;00;20 - 00;05;34;04
So that that's a sort of good analogy for go phase disease. But because this enzyme replacement therapy was developed and it was it's like an infusion. So patients either have it once a week or once a fortnight, it puts the enzyme back into the body, gets rid of all the storage and a significant proportion of patients. If they get treatment early on and they get the right dose of treatment, then they can actually live really good lives with great outcomes.
00;05;34;10 - 00;06;05;22
Now here it's important to say that enzyme replacement therapy is for the non neurological aspects of the disease. So that is your liver, your spleen, your bones. Now it doesn't cross the blood brain barrier. So the type for patients with type two and type three, they still have the all the neurological aspects of of the disease. So if you're type one, it will depend on where you live in the world, whether or not you get treatment.
00;06;05;22 - 00;06;13;28
And that's some issue. But if you do get treatment and you get good clinical care, then you can expect to have a relative normal life.
00;06;14;18 - 00;06;31;20
Well, and unfortunately, the reason the world has you as such a strong advocate is that this is a disease faced by your own daughter. Tell us about her, what her symptoms were when they started showing up and that journey that you mentioned of getting properly diagnosed and treated.
00;06;32;10 - 00;07;01;05
Of course, yeah. This is this is going back a few years ago now. So in 1995, Maddie was my daughter, Maddie was 15 months old. And it was towards the end of the year and we just noticed that she just wasn't that well. And she had quite a low mood and a cold. And, you know, like many pet parents, you know, she was was still quite young.
00;07;01;05 - 00;07;29;11
So we took her to the doctors and they were like, yeah, she's got a you know, she's got a throat infection, She's got ear infection. You know, hear the antibiotics go away If she doesn't get any better, come back. So a week goes by, ten days go by. She's she's not any better. So we took her back and at that point, the general practitioner said she's very pale, if you notice that she's very pale.
00;07;29;25 - 00;07;47;12
And we was like, Well, yeah, we have noticed, but that's why we just thought it was part of her not not feeling great. So he said, I'll tell you what he said, I think we should you should go to the local doctor, local hospital and they'll do a hemoglobin C what her, her blood types are, and we'll take it from there.
00;07;48;13 - 00;08;20;01
Well, that from that morning, basically, we went on a three month journey to the local hospital. Her hemoglobin was 6.4, where it should be around 12. She was admitted she had a number of blood transfusions. On examination, they found out she had a large liver and spleen. We were given the diagnosis of leukemia. So that was obviously very, very challenging for us as a family.
00;08;20;02 - 00;08;55;05
She was our first born. Now, at this point in time, we lived not far from London, and the local hospital had shared care for pediatric pediatric oncology with Great Ormond Street Hospital, who most people would have heard of. So we were taken by ambulance to Great Ormond Street Hospital. We were admitted onto the oncology ward and it was like a little conveyor belt of all these little children going through for Beaumaris to aspirations so that they could give her a final diagnosis.
00;08;56;05 - 00;09;23;16
Actually, after waiting a number of hours, we were told that she didn't have leukemia, but they suspected that she had something called Go Shay's Disease, which was a very rare disease. Now, you will remember I previously set about this diagnostic journey and diagnostic odyssey, and it takes a long time to be diagnosed. Now, ours was not a typical one for a patient with rare disease.
00;09;24;02 - 00;09;56;11
And it goes back to what I said about there being that new medicine in the early 1990s. And because it was approved, the company put investment into awareness and actually Great Ormond Street Hospital had become a center of excellence for Go Shay's Disease. And they had a very, very good doctor there. And actually that doctor cared for Maddie until she was 18 years old at Great Ormond Street when she transferred to the adult hospital at the Royal Free.
00;09;57;23 - 00;10;27;19
Now, you know, we were lucky because when they did that bone aspiration for leukemia, because of their expertise, they noticed the sort of shape and the pattern of the cell and that's why she was diagnosed with Go Shay's Disease. And actually from the first visit to the doctor at the end of 1995 to her first infusion of the new medicine to help with her liver and spleen, it was only actually approximately six weeks.
00;10;28;04 - 00;10;59;20
So we were, you know, we were very lucky. We did stay in Great Ormond Street for three months because her liver and spleen were so large. She underwent a what we call a partial splenectomy. So she had most of her spleen removed. She was severely underweight and her breathing was very shallow at the time of of admission. So we were in Great Ormond Street for a long time when she when she was first diagnosed.
00;10;59;20 - 00;11;15;06
And actually she was on a feeding tube for about a year afterwards just to build her back up. But, you know, if you can have a great diagnosis, then I think we were extremely lucky. In that case.
00;11;15;23 - 00;11;37;28
I would absolutely have to agree. I mean, what a horrible thing for Maddie to have to go through and for your family to have to go through. But, you know, it sounds like you landed in exactly the right place to be with the right people. And then based on my own experience, you know, it's not uncommon to go beyond just caring for your loved one and want to make a bigger impact to help others like them.
00;11;37;28 - 00;11;48;05
So walk me through your own thought process. What did you see the need was and how did you first go about exploring what role you could play in it beyond Maddie?
00;11;48;15 - 00;12;17;11
Yeah. So, you know, when Maddie was first diagnosed, I, you know, it took me almost a year really, to be strong enough to do more than just survive. To be honest, My, my, my, my marriage failed, and I actually became a single mum, which is not uncommon for patients or families that have children with with chronic conditions. But I did have a good job and a great family and friends who were there for me.
00;12;17;11 - 00;12;47;18
So when Maddie was diagnosed with type three, Go Shay's Disease, it's obviously have has neurological involvement. There was literally no information out there for me as a parent. And when I went to the library, it said Death within a year. And nobody had ever heard about it. So for me, you know, I set out to develop information for patients and parents so that they wouldn't be in that situation.
00;12;47;28 - 00;13;16;19
But also I set out to sort of develop and build a community in the UK, and that was really for support, for support for me, support for Maddie and support for others. Now, around five years before Maddie was diagnosed, that was the UK and Shay's Association was actually set up. Now it was set up because of this new medicine that had come up for type one Gaucher disease.
00;13;17;12 - 00;13;41;20
And as patients were going to the hospital and having treatment, they were talking to each other. And this organization set up. So there was already an established group in the UK and I decided to join them and then they invited me to sit on the board as a sort of representative for patients with type two and Type three.
00;13;42;09 - 00;14;04;10
And they asked me to do that really, because everybody else on the board had type one Gaucher disease. And if you're a patient or caregiver with a with type two or type three, it really is it's almost like a completely different disease. So I think they saw the benefits of me having the benefits of of having me on the on the board.
00;14;04;10 - 00;14;31;16
I did have a lot of support from the founders of the UK Association, Susan Lewis and Jeremy Emmanuel, and also Maddie's consultant doctor Elodie, who was really great in terms of educating me about the disease, what was going on in research, who the the doctors were, where the other patients were. So it was really a sort of collaborative effort.
00;14;32;13 - 00;15;02;29
And, you know, I started to bring patients and parents together on family days out and conferences and sort of listened to the challenges. And then we write books on education, trying to find out what was going on in research, the developments, and really how best to go about sort of trying to improve patient outcomes, whatever that looks like, to to to to a patient.
00;15;02;29 - 00;15;27;05
You know, were there any new treatments? Was there ever going to be a cure? And it was really about putting information, you know, putting my feelers out there, getting known, getting people to talk to me and, you know, feeding all that back to to the community. I became a board member of the UK Association in 2005, and I started working for them then.
00;15;27;25 - 00;16;03;01
And actually I remained working in the UK as well as sort of then going into the European and global state until about 2018. And in terms of my work, European and internationally, again before my time actually back in 1994, again because of this new treatment, seven patient advocates for this disease invited themselves to a European meeting where doctors and researchers were talking about this disease.
00;16;03;21 - 00;16;31;07
And these advocates sort of formed an anarchy of of patient of a patient group, because they you know, they saw they had common interests and goals. And by working together, they could see that they would have a much, much stronger voice. And that European sort of group of patients soon turned into a sort of international group of patients.
00;16;31;07 - 00;16;57;11
And today that's nine is the international alliance. We'll be celebrating 30 years next year. And I am the CEO of the International Gaucher Alliance and have been involved, you know, since 2008. Really, I sort of got my foot in the door in the UK and then slowly learned a lot and then sort of started to get my foot in the door in Europe and internationally.
00;16;57;29 - 00;17;24;24
But I think when I when I really think about why I did what I did and why I became a patient advocate, it really does go back to Maddie being born in the UK, you know, and she had access to treatment and good clinical care. And to me I wanted to try and make sure that wherever other patients lived in the world, that they too could have this.
00;17;25;12 - 00;18;02;06
And because treatments were so successful for many patients that, you know, there was a hope for those patients to have a future, but also that they didn't feel alone. Having a rare disease can be very lonely. And for many patients that I work with, I will never, ever meet them. But they know that there is somebody out there who's advocating on their behalf and the if they're feeling down or helpless and have nowhere to go in their own community, then actually there is somebody who does care.
00;18;02;28 - 00;18;31;19
Yeah, it is a tremendous resource and sorely needed, not just for Gaucher disease but for others. Actually, the challenges faced by those battling Gaucher disease are so similar to those overall who have or are caregiving. For someone who has a rare disease, as you just touched on it a little bit, but talk to me about what it's like to live in that world where you have something very serious, but because it's rare, you kind of feel emphasis isn't being placed on it.
00;18;31;28 - 00;18;35;00
And it can it can feel quite isolating, right?
00;18;35;08 - 00;19;00;15
Yeah. So I think I would start by saying people say you've got what I've never heard of. What is it? What is it? And, you know, this is the reality for patients and their and their caregivers, because you absolutely have to become your own advocate or the advocate of your your child. And you have to fight for everything.
00;19;00;24 - 00;19;29;01
And every time you go for an appointment, you have to again, go through what it is, how it affects. And then they're interested. You are an interesting case and that in itself is is is very, very challenging. And I think, you know, this is why patient organizations are so important because they provide the support that patients need, that, you know, that pastoral or support that time.
00;19;29;01 - 00;19;58;11
Somebody to talk to who knows how you feel and has often been through that situation. But they also provide information and advice so that they can empower you and patients and, you know, for better outcomes. But, you know, I rare diseases don't have the coverage of of more common diseases. And you may live in a country where they're just really on any other patients.
00;19;58;11 - 00;20;22;18
Speaker 2
So it can be really, really lonely. I went to Ireland many years ago with a member of our board from the UK and we actually have met a couple of patients now in Ireland and there was a gentleman there and he was in his fifties, so he'd had chase disease for 30 odd years and he had never met another patient.
00;20;23;05 - 00;20;47;25
Wow. You know, that is in a place like Ireland. So you can see how how lonely and isolating it can be not only for the patient but also for their family. I think you hear when we think about, you know, the environment we live in now, you know, social media like Facebook can be very important. Linking patients and families.
00;20;47;25 - 00;21;18;05
And often they become, you know, online communities sharing stories and advice. And we see that a lot in negotiate community. You know, type three that my daughter has many years ago there was one type three patient in in Lithuania. So, you know, by using things like social media that that parent could come into a community and sort of be part of a wider family.
00;21;18;21 - 00;21;39;27
One of the biggest things that affected us many other patients is that, you know, because it is a rare disease and people don't know a lot about it, it means that people have an unknown future. You know, when Mary was diagnosed, we read death. We knew within a year and we were told not to make any future plans.
00;21;40;14 - 00;22;10;10
Parents are told to take their children home and just enjoy life. You know, it's a physical and real emotional rollercoaster. And the thing is, you can't get out of it. It's exhausting. It's not something you can, you know, you can sort of just put to one side. It's part of everything you do. And the thing is, is reality is, is that if you don't fight as a family, nobody will fight for you.
00;22;11;01 - 00;22;57;05
And I think like many rare diseases, you know, mental health is a massive challenge for patient and caregiver community. And, you know, we are seeing much more attention being given to this topic, which is good. But like anything in life, it's patchy and not available to all. So importantly, you know, I see my role as sort of being that people often spend a lot of time, you know, just WhatsApp, a new families or people sending little videos and just having a chat and that enables me to sort of hopefully help them make them not feel so alone, but also enables me to sort of link them up with other people that I might know that, you know, they may get support from.
00;22;59;25 - 00;23;28;06
Yeah, it sounds like what families need most is connection information and ongoing research. But have you found that the perception isn't necessarily true, that there are actually medical practitioners who are working on and who are captivated by finding solutions to rare diseases? Because that sounds like what you've found. And specifically, have we gotten anywhere in terms of advanced treatments since the early nineties?
00;23;28;23 - 00;23;29;01
So. You know, I think, you know, we are really lucky and go chase disease. You know, we we work in an environment where, you know, our doctors and researchers are really committed to improve patient lives and together as a community, you know, physicians and patients, we've really developed a great global community. You've got the international Law Alliance, which is an umbrella organization, and we have 58 member countries, plus we work in another 25 or 30 countries with patients.
00;24;06;13 - 00;24;46;09
And then you have the International Working Group on Disease, which is a sort of platform for clinicians and research is globally who are interested in in Gaucher disease. And actually we do a lot of work together on things like guidelines, consensuses, meetings, and we often body doctors are who are new in go chase disease with, you know, people from the Iwg day, for instance, there was a doctor in Kenya who'd got a new patients, had never treated a patient before.
00;24;46;15 - 00;25;19;27
So we were able to, you know, buddy them up with somebody from the UK who had a lot of experience. We there is a lack of like diagnostic testing in Zambia. So we know doctors in Brazil that would do testing free of charge for patients. So we link those those doctors up and you know, there's we're working a lot in Africa at the moment and there's a lot of education to be done.
00;25;20;07 - 00;25;58;00
So we recently did a online educational session which actually got 1200 doctors from Kenya and the surrounding countries that are interested in pediatric medicine. And one of our doctors from the Iwg today did that educational session for us. So there's a lot of of volunteering, there's a lot of joint working, there's a lot of preceptorship. And, you know, it's really a great collaborative environment now, you know, that's 30 years in the making.
00;25;58;16 - 00;26;30;25
But, you know, over the last few years we've seen a real acceleration, you know, in reaching areas of the world that maybe before were cut off. You know, we didn't know that there were patients there. So that's all really positive. And, you know, we patients disease is a is a bit of a success story really in some ways because for type one patients, enzyme replacement therapy has just completely changed the patient, a patient's life.
00;26;31;13 - 00;27;04;14
You know, they have been able to get access to treatment. They have been able to get access to, you know, doctors that are very knowledgeable. And, you know, they've been able to get on with their lives, to have families, to have careers, to run marathons. And, you know, enzyme replacement therapy that came on over 30 years ago has been followed for type one Déchets disease, an oral therapy called substrate reduction therapy.
00;27;04;14 - 00;27;35;16
So we've gone from, you know, weekly or fortnightly infusions to taking a pill once or twice a day, which has transformed patients lives. And actually at the moment where we are again mostly for type one, there are a number of clinical trials for gene therapy for type one. So, you know, we've gone the real sort of like infusion pill and now we're potentially looking at a one off treatment for Gay Shea's disease.
00;27;35;25 - 00;28;07;06
Now most of that is for type one. There is currently one study for type two and one study for type three. So, you know, that's an area still where we are trying to find out how to address the neurological involvement in patients. And there's still a long way to go to go for that. I think the other thing I would say is that one of the challenges for us as a global community is that, yes, we are 30 years down the road from that first enzyme replacement therapy.
00;28;07;19 - 00;29;01;05
But if you are born in Kenya or Zambia or Cuba or Pakistan or Jordan, treatment is not available through reimbursement. So that is where there is still a lot of work to do. Now, there are opportunities for patients to get treatments, and that's through charitable access programs. And that's one of the things that we as an organization, the International Gateway Alliance, do a lot with the companies who manufacture these therapies is that we work with them and they have a number of slots for charitable access where patients can be given treatment, a lifelong treatment where there's no river reimbursement in their countries so that they can have that future and that life.
00;29;01;05 - 00;29;25;05
You know, we've talked to guests several times on this podcast about patient centered research and citizen science. And it feels like, especially with rare diseases, there needs to be a tighter collaboration where patients and their families are more involved and work more closely and directly with researchers. Are you seeing that happening and is the research side leaning into it?
00;29;25;16 - 00;30;04;07
So I think the this is an area that we have been really strong on in as I described earlier, you have the patient community and you have the clinical community who have sort of grown up together and are working very, very closely together. And then you've got the research community and the sort of pharma community who, you know, are not big pharma, they are small pharma, and they recognize that patient centered research and and patient centered support needs to run through everything they do.
00;30;04;23 - 00;30;36;06
And what we see is that we see that we are invited to have a seat at the table from very, very early on to really understand the condition, to understand the challenges that patients and their families face, to understand what's important to patients and what areas they're still very little known about. And we can we deliver that to the different stakeholders.
00;30;36;21 - 00;31;17;10
And basically we are seen as an equal partner and, you know, things like the development of research projects, the development of clinical trial design, the patient community are really co-creators in this. I think this is an area where we have had a lot of success. So I think generally in rare diseases that you see that there is a real recognition of the value of having patients and patient advocates at the table from the very beginning, because ultimately whatever you're doing, you want it to go as smoothly as possible and you want it to have the biggest effect.
00;31;17;22 - 00;31;39;11
Now, if you get everybody around the table at the very beginning, you're more likely to see that happen. Whereas if you get to a point and then you say, Oh, actually maybe we should invite a few patients or patient caregivers or advocates around, see what they think, and then you're having to go back or you're not actually doing that.
00;31;39;24 - 00;32;07;23
And then I think, you know, we're seeing more encouragement and necessity by health technology assessments and marketing for licensing like the FDA and EMA to say, you know, where is your involvement from your patient community? How did you know your patient? How did you work with your patient community? What is important to patients? What do they want you to address?
00;32;07;23 - 00;32;16;03
Well, in fact, I think you've partnered with Cerner and Visa, which is now part of Oracle Life Sciences, for some research efforts. What does that partnership look like?
00;32;16;21 - 00;32;43;08
Yeah, so this is really, really exciting. So as I've said, you know, a lot of work in progress has been done for patients with type one diabetes disease, not so much for type two and Type three. And I've been in this community for 26 years and I've sadly seen many of our patients lose their lives to type two and type ricochets disease now because it is very rare.
00;32;43;27 - 00;33;20;12
No single center has enough patients to study. There is a real lack of natural history data on these patients. And actually when you look at patients, even when they have like the same genotype, they have completely different phenotypes. You know, they the way that the disease presents itself in them is completely different. And therefore, it's a really, really challenging disease to really understand and see how you could potentially develop a therapy that is safe and effective.
00;33;20;21 - 00;33;54;03
But also, you know, how should we be managing these patients clinically and what are the care and support do these patients need to function in society? So what we did as an international alliance is that we had an idea to set up a patient led patient owned registry just for type two and three disease. And for the last couple of years, we have been working with what was Sunrun Visa and now Oracle, in setting up a registry called Guardian.
00;33;54;13 - 00;34;27;07
And Guardian collects patient reported data from patients and caregivers around the world on the way that Type two and Type three patients disease affect patients in their everyday life. And the way that we set it up was we work collaboratively with interviewing patients, doing focus groups, finding out what was important to them, and then developing the question, as in Guardian and the role of Sutter and Visa.
00;34;27;07 - 00;34;53;26
Oracle is that they are the they are the the organization that provide all of that support to us in terms of running the registry as a patient organization. We had a vision, we had an idea, we had the passion, but we didn't have the skills and resources in in-house to run a registry. And that is where Sutter and Visa Oracle have come in and we've developed this partnership.
00;34;54;11 - 00;35;14;21
Well. So on the research side, obviously clinical trials are where the rubber meets the road. If I have a rare disease, is it getting easier for me to participate and find a clinical trial if I want to? And how easy is it now for researchers to find the patients willing to participate in these trials?
00;35;14;21 - 00;35;48;13
So so we do have some trials for go shows disease, but because it's a rare disease, it's not like working in, you know, oncology or diabetes or anything like that. So, you know, there are not a lot of trials, but there are trials. And, you know, the thing is, is that we have a lot of centers of excellence around where patients go to have their disease managed.
00;35;49;01 - 00;36;35;17
So the this is an ideal opportunity where you have cohorts of patients to be able to raise awareness and make patients aware that, you know, there are clinical trials for their type of disease. I've got to say the one of the challenges is that most clinical trials are done in the West, whereas we have huge unmet need in the east of the world and that comes down to expertise, but it also comes down to it's easier to run a clinical trial in Europe than it is in Africa, which is something that we as a patient organization are trying to work with.
00;36;36;00 - 00;37;22;22
Those interested in bringing clinical trials to shades disease to try and have a different approach. And I think because we have the International Working Group on disease and we have a very strong patient organization and we have a few really good pharma companies that are interested in Roche's disease, We, you know, we work together. So I think that it's not easy to develop trials, but but actually it's also not easy to recruit for clinical trials because patients have also had enzyme replacement therapy or substrate reduction therapy for a number of years now.
00;37;22;22 - 00;37;53;04
And these drugs have been really, really good. They do what they say on the tin and patients are living a really good quality of life. So actually recruiting patients into these clinical trials can be quite challenging, particularly when you're thinking about new technologies and patients want to know about safety and efficacy and the benefits of switching from a a safe therapy, which they've been on for for years into something that is experimental.
00;37;53;19 - 00;38;08;22
So as an organization, you know, we are really trying to raise awareness and share information on clinical trials and educate patients on clinical trials, you know, to support their decision making.
00;38;09;12 - 00;38;33;11
Well, Tanni, it's been so great hearing your story, hearing Maddie's story. There are real people and human beings behind these rare diseases, and you've done such great work bringing that vibe to health care and the work being done on pragmatic solutions. I'm going to bet that listeners will want to learn more about you and what you're doing. Is there any way they can get more information or get in touch with you and the Alliance?
00;38;34;00 - 00;39;03;17
Yeah. So like most organizations, we have a great website which is Geisha Airlines dot org and we can also be found on social media platforms like Facebook and other sorts of Instagram. So we, you know, we do have a really good social media presence for you to do is is is find out where the newsletter is or click on the join us on on Facebook.
00;39;03;17 - 00;39;13;27
And you know, we have somebody in our group that works specifically on communication and we try and share not only the work we do, but work that our partners do.
00;39;14;06 - 00;39;47;16
Perfect. Well, thank you so much again. And if you faithful listener want to find out how Oracle can simplify and accelerate your life sciences research, just check out Oracle dot com slash Life Sciences. Subscribe to the show so you don't miss anything and we will see you again next time on Research in Action.
00;00;48;27 - 00;01;16;06
Scott got a lot of experience, including leveraging real-world data and real-world evidence, leading technological innovation and driving patient focused paradigms to accelerate clinical drug development. And he's an active board member, advisor and mentor, and he's all about infusing data and innovation into life sciences organizations, especially where rare disease is are concerned. And last but certainly not least, Scott is the leading executive at Rare Clinical.
00;01;16;09 - 00;01;35;01
Scott, we're glad to have you with us. Thanks for letting me grill you with all these questions. Thank you, Mike. My pleasure to be here with you. Well, let's start at the beginning. A fine place to start. What got you into the field of clinical research and drug discovery and why this special focus on rare diseases? Yeah, great.
00;01;35;01 - 00;02;04;13
It's a great place to start. I think, like a lot of my colleagues in this clinical research, clinical drug development profession, a lot of us sort of find our way into this field as there aren't necessarily a lot of like formal training programs or pathways necessarily. So for me, I was in graduate school, I was doing some more like I would call more basic science, more basic research that I found my one day struggling to.
00;02;04;16 - 00;02;22;00
As I was writing a grant for a professor, I found myself struggling to justify why, why this was really important. I kept saying to myself, Yeah, this doesn't really seem very applied. Is this really make a big difference? I, I can't convince myself this is critical. How am I going to convince a funder of our grant that this is really important?
00;02;22;00 - 00;02;44;17
And it kind of was a little bit of a light bulb moment for me that made me realize while I loved the field of research, I needed to be doing something that was more applied and could have a little bit more of a direct impact upon people. So it sort of led me to the clinical drug development space and clinical trials, and I got started back.
00;02;44;17 - 00;03;12;21
It's been a couple of decades now as I've been around for a little while, but it got started in a sort of like a biotech clinical research setting, helping to design and manage clinical trials and have been sort of engaged and passionate about this industry ever said. So it's been it's been a fun ride. But again, like a lot of people in this space, I think I stumbled into clinical research, maybe not accidentally, but, but, but there's not an obvious clear entry point for some of us.
00;03;12;23 - 00;03;33;25
Yeah. So I get that you, you got into the bio research space and drug development and those kind of things develop that interest. And I get that you wanted to make a real impact that you could feel like you were making a difference. Is that where the focus on rare diseases came into play or when did that? Yeah, thanks for following up on that part of the question.
00;03;33;25 - 00;04;12;02
I think that, yeah, after having been in the industry for a little while, you know, about, I don't know, this was probably like 12 years ago or something. Rare diseases at that time were really still a little bit. They weren't certainly a hot and sexy topic like they are today in 2023. But I came across some patients, I came across some patient groups, and I also came across a couple of clinical trials and I realized that what we were trying to do and what was required really to function and develop drugs in the space of rare diseases really required, honestly, a completely different, really way of operating a completely different paradigm than what we
00;04;12;02 - 00;04;48;16
were doing in most of clinical drug development. And with, you know, with our biopharma industry being pretty risk averse. That's a theme I think you'll hear come up probably a lot today. In our conversation. There hadn't been a lot of appetite or initiative around trying different approaches or looking at things differently. And these rare disease studies for sort of a countless sort of logistical and medical and scientific reasons really require a very different approach of, you know, you're talking about small populations that are geographically dispersed.
00;04;48;16 - 00;05;23;21
You're talking about patients that may have they may have to go through a diagnostic odyssey. A lot of people don't know about these disease states. There's a host of challenges that kind of come together and create a scenario that is even more complicated than your average challenging clinical trial. So also, when you look at the fact that there's something like 10,000 individual rare diseases individually, they're all rare little sub populations, but taken together they make up about 10% of the U.S. population and about 10% of the global population.
00;05;23;21 - 00;05;50;24
So it's it's a big area of unmet medical need. When you look at it from a big picture perspective, when you drill down into individual disease states, individual patient populations, you notice that these patients and families don't have any therapies, they don't have any treatments, they don't have a lot of hope sometimes. And clinical trials. And this world is really their only source of hope at times.
00;05;50;24 - 00;06;10;22
It's less of an experiment and more of a care or treatment option for rare disease patients. And so I found myself really immersed and passionate about this area and felt like it was a space that really needed new approaches. And I've been happy to kind of delve into that and try to make a difference there. And what is the state of that research like?
00;06;10;24 - 00;06;39;24
Is there reason for people with rare diseases to have hope? For instance, there are people in my family who have ankylosing spondylitis, which is a relatively rare form of arthritis. Is it appropriate for them to have hope that in their lifetime something's going to happen? Or are these populations so small and the research to develop drugs for it's so difficult that, you know, we're looking at 50, 60 years in the future before we make any progress.
00;06;39;25 - 00;06;55;20
Yeah, it's a great question. I mean, there really is a really broad spectrum here when we talk about rare diseases. We have such a such a large number of them. I think that the short answer is there is hope. And in a lot of cases that hope is in front of us or is on the very near horizon.
00;06;55;22 - 00;07;18;09
There certainly are other scenarios where another disease states where it's going to take a while and that hope is a little further out to be seen. But the good news is that we've well, there's been a lot of mobilization, there's been a lot of innovation and a lot of attention devoted to rare diseases over the last decade, 15 years, we've seen a lot of drug approvals.
00;07;18;11 - 00;07;39;24
We've seen a lot of companies, we've seen a lot of investment in biopharma biotech firms come into the rare disease space, whether they are small little biotech startups or whether they're the big pharma of the world. Everyone sees this as an opportunity to help develop drugs and help people. And in an area that really needs as much help as we can provide.
00;07;39;24 - 00;08;15;29
So there's a lot of hope. Some of these disease states are a little more clear than others. We understand the biology and the genetics, and maybe we can develop targeted therapies that help these patients some of these other more obscure, ultra rare or nano rare diseases. We're still learning who the patients are and how do we diagnose them and before we can develop a drug and show that it works and that it's safe in those populations, we need to first even understand a little bit about the natural history of some of these diseases and how they progressed kind of on their own and what kind of end points we would want to choose and some
00;08;15;29 - 00;08;37;27
things like that. But the bottom line is that there's a lot of hope, there's a lot of progress, there's a lot of activity, there's a lot of investment. I think there's a fair amount of awareness. We've seen a lot of progress here with people, with people and organizations and industry really getting into the space. Of course. With that said, there's a lot more that we can be doing.
00;08;37;27 - 00;09;02;21
There are a lot of disease states that really need some more attention and more funding and more research. But from where I sit, we've made some great strides and I hope to kind of keep accelerating that progress. Well, science is kind of inherently a social enterprise, but despite that, scientists and clinicians seem to work mostly behind closed doors, maybe even a little too far removed from the people they're actually working to help.
00;09;02;23 - 00;09;24;27
The pandemic changed a lot of things, but for one thing, Big pharma got kind of pushed out of its risk averse comfort zone because they had to speed the science and adopt more openness. So what do you think COVID did to innovation in the clinical research space? What changes are permanent and which ones aren't? Yeah, this is a fantastic topic.
00;09;24;27 - 00;09;57;11
We could we could spend a lot of time on those, I think. Well, necessity being the mother of invention, I think that COVID presented a lot of unique challenges and picking on my risk averse colleagues who may not want to necessarily try something new or go out on a limb with some sort of more risky, unproven approach. COVID forced us to reconsider how we were doing things, and it forced us to keep clinical trials going in a in a manner and keep them operating.
00;09;57;11 - 00;10;32;15
When we couldn't go to clinics or go to hospitals or when we had to social distance. And it forced us to really rethink a paradigm that had not really changed in many decades. So if you rewind a little bit to pre-COVID, there was several movements out there around creating more patient focused approaches. So this idea that you mentioned science being inherently a social enterprise, I envision a lot of clinical protocols and clinical trials being they're often developed in a little bit of a bubble.
00;10;32;18 - 00;11;01;01
Sometimes I'll joke and say in a conference room in New Jersey, right, these clinical trials come to life and are sketched out and designed in a little bit of an insulated bubble of sorts that don't really take into account the perspective and the input and the needs and the voice of the end users, namely the patients themselves. So similarly to the fact that, you know, you may have an iPhone sitting there on your desk next to you.
00;11;01;04 - 00;11;26;27
Apple, of course, didn't design a camera to put on their phone and say, let's see if people want to use a camera. The camera was designed, of course, by consumer demand and designed for the people using it. Ironically, even though science and even though clinical drug development is completely dependent upon patients participating in clinical trials, we rely on them and their data to move things forward.
00;11;26;29 - 00;12;06;22
They're very rarely considered actually, in the design process. Right. That's the irony, is that we rely on them. We must have their participation, but they're kind of an afterthought historically when it comes to designing a clinical trial and thinking about how to implement it. So that being sort of the baseline of how we've operated COVID hits and all of a sudden our world is interrupted and some of these novel approaches that had been being developed, these mobile health platforms, early COVID, we were talking about how can we make clinical trials, quote unquote virtual or hybrid with some of the language we were using.
00;12;06;24 - 00;12;35;18
How do we instead of requiring patients to maybe travel long distances to a clinical site or an academic medical center? Sometimes it could be a weekly visit for 52 weeks. A lot of times that's not going down to your neighborhood primary care physician. It might be driving into Manhattan and going to Memorial Sloan-Kettering every Friday afternoon and taking time off of work and away from your family and your children to participate in the clinical trial.
00;12;35;21 - 00;13;02;09
The bottom line, clinical trials were really not designed for patients, often not realistic and often not feasible. So when we ran into COVID and the challenges that kind of shutting down hospitals sort of created, it forced us to adopt this what we ended up naming decentralized clinical trial paradigm, and it forced us to think through, well, how do we bring clinical trials to patients themselves?
00;13;02;11 - 00;13;25;15
So this was a long, long overdue need that was out there. Again, we've been operating in a little bit of an archaic fashion for quite a while, putting out studies that were not realistic for patients. But when the world around us sort of crashed down and we needed a new approach, we adopted this more patient focused paradigm simply out of need, I think.
00;13;25;18 - 00;14;05;21
So there's been some traction with this. Decentralized clinical trials have become a common term in this industry. We have the Decentralized Trials and Research Alliance that was formed in 2020. There's a lot of companies on investment that have come up to develop this new paradigm of, instead of these critical end users, patients, instead of having them have to travel long distances and inconvenience themselves and for a scenario to be very hard for them, let's create something that revolves around them, that's create a patient centered, patient focused approach where we bring trials to patients in their homes.
00;14;05;21 - 00;14;28;07
And so we do this via some tools like apps on our phone and a mobile health platforms. We do this, of course, now via telehealth and things like that that have become much more routine and regular. We're able to collect data remotely. We're able to push out sort of questionnaires and quality of life and clinical outcome assessments to patients wherever they are.
00;14;28;11 - 00;14;53;23
We're able to send nurses to their home to help them administer drug or check on how they're doing or evaluate their symptoms. So this transition that COVID has sort of forced us into, again, necessity being the mother of invention has forced a more patient focused approach that I personally think we've been really long overdue. So I think of that as a little bit of a silver lining of the pandemic.
00;14;53;25 - 00;15;24;08
There's been some good progress, I think, made as a result of that. But these technologies that you're talking about that are used to monitor these patients in these trials, like maybe wearable technologies or whatever, what's the reliability level of that? Does a lot of this rely on the patient just reporting accurately what's going on? Well, as we've come out of the pandemic now, the interesting sort of dynamic is does this new model really stick around when it's not as needed as it was during COVID?
00;15;24;11 - 00;16;06;16
Do we continue to move forward in this new innovative approach, or do we revert back to the way it was? And along with that, are these sort of challenges maybe, and complications that come along with this decentralized approach, like remote data capture, lots of data sources coming from various areas, various directions, wearables, as you've said. And there's also this kind of push also to not only engage patients where they are, but also to collect data in a little bit of a real world setting in this, you know, real world evidence, real world data, you know, kind of concept around, you know, we're conducting clinical trials that are controlled and we're we're looking at specific variables
00;16;06;16 - 00;16;25;12
and we're looking to evaluate safety and efficacy. But at some point, if this new therapy is going to be approved and patients are are using this in their daily regular life, how it's better for us to also know what that's going to be like, like to study that real world experience now even in the context of a clinical trial.
00;16;25;12 - 00;16;44;27
And so you are even seeing regulators like the FDA encourage the collection of more real world data for that to be used to supplement more controlled clinical trial data. So you're having a little bit of a mixture here and a little bit of an evolution, I would say, in terms of the data and evidence that we're bringing in.
00;16;44;29 - 00;17;12;08
But your point, there's there's lots of challenges with that as well. Now we have a lot of different technology platforms. We have a lot of data sources that need to interface and communicate and integrate. There can be complexities with having lots of different technology platforms for clinical sites and patients to use. You mentioned the reliability component. Are we using instruments that are validated and have been used over and over and we can trust?
00;17;12;10 - 00;17;39;17
So with this new approach of collecting more data, collecting data from different sources, collecting real world data, we also have to make sure that we're integrating it well, that we're not overly burdening both the clinical sites and the patients. But the trend that we're seeing is that each year now, clinical trials become a little bit more complex. They're collecting more and more data each year.
00;17;39;20 - 00;18;10;11
Data points in these clinical protocols are becoming more extensive. Right? So, you know, that's more data that becomes a little bit more of a burden for a lot of people. And it also takes us back to this, this mantra of, okay, let's remember the patients again, because if you're going back to my example, if you're a scientist or a drug developer in a conference room in New Jersey, you may very well want to collect all kinds of interesting data points that you think are going to be informative to you.
00;18;10;11 - 00;18;41;03
And if everybody sort of pours in all their sort of data requests, you can see this sort of ballooning, mushrooming clinical trial becoming overly burdensome. And let's not forget that each of those measurements require something of a patient. So there needs to be some kind of balance here between gathering more data, real world evidence controlled clinical trial data, but also remembering that, you know, the patients are producing that data and let's not make things overly complicated here.
00;18;41;03 - 00;18;59;00
So there's a little bit of a dynamic there that I feel like we're right in the midst of it. I'm interested to see how this kind of plays out a little bit. Well, yeah, And that's being seen everywhere. There is a ton of data. Companies are drowning in data. Some of them know what to do with it, how to handle and process it, process it.
00;18;59;00 - 00;19;22;20
Some don't, but it is crucial. There are challenges and benefits to being data focused, especially where sensitive data is concerned. Things like evolving standards. I mean, is too much data a problem? Are these supposedly connected platforms causing redundancies and other headaches? Yeah, I mean, I think so. Probably depends on who you ask, right? I'm sure a lot of people would say more data, the better.
00;19;22;20 - 00;19;45;16
Let's get everything we can. It'll inform us better on a patient experience or on the effects of a drug. At the same time, though, right, there's logistical challenges. Where are these pipes of data running into in the while? It's happening across all kinds of industries in the clinical research, clinical drug development space, you also have the added piece of patient privacy.
00;19;45;19 - 00;20;11;07
And in this country, HIPA and the need to, you know, keep patients data safe, private, de-identified as well. So when you're implementing different platforms that are collecting data at a clinical site, but maybe also at a patient's home and also maybe a wearable as a patient is, you know, walking around and just their daily life and collecting data via that.
00;20;11;09 - 00;20;34;22
Where do these all come into? How are they getting analyzed? Is it secure? We can very easily and very quickly make things more complicated than we needed to. I personally think we should be mindful of not collecting every possible data point we might have an interest in. We might not even know what we want to do with that data today, but that might be nice to have down the road.
00;20;34;24 - 00;20;56;18
I do realize that's great for science and discovery and exploration, but again, in the context of a clinical trial, you know, the environment we're in right now with clinical trials is that more than 60% of all clinical trials are behind schedule due to enrollment. And even when patients do enroll, we also have a lot of patients dropping out and not staying on studies.
00;20;56;18 - 00;21;27;09
So the retention of patients is also a problem. And so while we balance collecting more data, while we balance more complicated, complex clinical protocols as we're increasing the burden at times on patients and I use this patient word a lot, but really we're talking about people, right? We're talking about people like like Mike and I who have jobs, who have lives, who have hobbies, who have families, work people, and we have other commitments in our lives.
00;21;27;09 - 00;21;50;24
And we can't just drop everything to participate in a clinical trial. So the the concept of if you build it, they will come doesn't really apply here really anymore. And we need to be thinking about building things that will appeal to people and be realistic and feasible for patients, both in terms of time, commitment and travel and there might be you know, there might be emotional issues.
00;21;50;24 - 00;22;14;12
There's all kinds of domains of burden that we've learned. And this balance of like, wow, we can collect data from anything anywhere, any time, that's great, but let's maybe we need to real that and a little bit here and balance that out with what can a human being really handle And are we overly burdening patients because we're finding that people are just declining to participate, which is actually.
00;22;14;14 - 00;22;40;16
So this idea of more data better can actually be slowing down drug development in a way because clinical trials are delayed now due to enrollment. So there's an interesting dynamic there that I think progress in this area, real world evidence, remote data capture, lots of data sources, is also creating a more complex environment that I think is giving giving some patients pause to like before and roll in that study.
00;22;40;16 - 00;23;09;24
I'm going to I'm going to think twice. So really interesting thing that's really very fluid and dynamic. I think this is kind of changing on a monthly basis these days. Well, let's turn back to rare diseases, because I think we can all agree plenty of innovation is needed to make a dent in those. But what kind of innovations and as I hear you talk, it's like, okay, there's challenges to this patient centered research, decentralized research as it is now.
00;23;09;24 - 00;23;37;12
You're talking about rare diseases where the population is small to begin with, it seems. I mean, what are the most effective things we could be doing? Is decentralized trials the answer? Yeah. Well, I think, you know, in that rare space, you're right, clinical trials are challenging. Clinical trials are behind schedule. Clinical trials might be overly complex. And I think that happens in worlds where we could be dealing with a fairly common, you know, diabetes or arthritis study.
00;23;37;14 - 00;24;03;10
When you overlay those challenges on a disease area and on a patient population, that is rare or ultra rare, you know, it becomes magnified, it becomes extra challenging. You know, some of those challenges include things like, well, by the nature of the name rare diseases, there's very few patients with that disease. Sometimes those patients and some of these disease states are very, very small.
00;24;03;12 - 00;24;25;08
And people are, of course, geographically spread out. So if you wanted to learn more about a disease, study it, run a clinical trial, what have you. You know, you're not just opening up a clinical center in New York City and enrolling all those patients. You might be needing to go to multiple countries around the globe just to be able to find a couple dozen patients.
00;24;25;10 - 00;24;50;26
So you've got a dispersed and few and far between patient issue is challenging to come to come up with. And again, by the nature of, you know, the definition, there are physicians and our principal investigators in clinical research sometimes don't see a lot of these patients. So for those of you listening that have that know a little bit about the rare disease space, you'll know that that's the symbol of rare diseases is the zebra.
00;24;51;02 - 00;25;13;17
And the reason for the zebra symbol is, you know, back in the fifties, there were some physicians that were training medical students, and they used to use the phrase that, you know, trying to teach their medical students that, you know, keep things simple, Don't overly think it don't overly complicated. If you think you're sort of stumbling upon a diagnosis, you're probably right.
00;25;13;17 - 00;25;34;13
So the phrase was, you know, if you hear hoof beats, it's probably a horse. Right. And the rare, rare disease community, of course, sort of shudders at that idea and says, well, hold on a second here. You know, if you hear hoof beats, don't forget that 10% of those diseases could be a zebra. Right. And the zebra or the rare disease has been one to not forget about.
00;25;34;13 - 00;25;55;17
So we we also have challenges with physicians that may come across a case or a patient like that once a year or once every couple of years. So there might be a limited knowledge about the disease and the care of those patients. It also makes those patients being diagnosed initially really, really challenging. I mentioned earlier the diagnostic odyssey.
00;25;55;19 - 00;26;19;18
It's not uncommon for a rare disease, patient or family to see seven, eight, nine, ten physician ones go to all kinds of different hospitals and institutions and have a variety of sort of tests done to figure out even what they have. Right. They're craving a diagnosis so they can figure out what's going on with them so that then they can start to think about how do I get this treated.
00;26;19;18 - 00;26;44;22
So there's there's challenges around diagnosis, There's challenges around how do you develop a drug in this new rare disease where clinical trials never been done before? What what are the end points? What kind of benefit do we need to see if it's a genetic or life threatening disease? Does that change our, you know, the the efficacy safety sort of ratio we're willing to accept?
00;26;44;22 - 00;27;10;07
Right. So whole bunch of challenges there in that space. Again, though, I think there's been amazing progress. We've even seen things like the FDA and other regulators be more flexible and encourage innovation. They realize it's hard in this area and, you know, there aren't that many patients and conducting clinical trials and finding those patients and collecting data is extra challenging and we're not going to get that much of it.
00;27;10;07 - 00;27;53;14
So I've seen the FDA be very flexible and responsive and kind of partnering and collaborating with industry to help because we all want to kind of move these therapies forward and help these patients. So lots and lots of progress, but extra challenges. And I think it really creates an environment here where the rare disease space has really been the tip of the spear, both with decentralized clinical trials, with maybe some of these remote data capture paradigms, because again, necessity being the mother of invention, just to just to manage to do this in the rare disease space does require us to think differently and approach things differently and be creative and I think the
00;27;53;14 - 00;28;13;25
success we're seeing in this space, I'm hoping that other parts of our industry see that and also can adopt a little bit of innovation in their own little areas as well. Well, I'm always looking for good stories. Do you have some examples of where patient driven data really changed the trajectory of drug discovery for a specific rare disease?
00;28;13;27 - 00;28;49;06
Well, there's been a lot of good examples of this patient focused approaches that we're talking about, this idea of get outside of that conference room bubble, incorporate the patient voice and perspective into your drug development just to the point of. So if you're developing a, you know, a drug and you have a clinical trial, you're looking at endpoints, you're looking to write to measure the effect of your drug on, say, some kind of performance factor, You know, when you look at how we're going to develop that, the FDA may want a certain endpoint that is validated and it's the gold standard.
00;28;49;06 - 00;29;21;15
And they're looking for industry to kind of measure that and hopefully exceed that in their trial. The drug developers and industry, you know, they're often thinking about what's more what's realistic. Does it take us five years to achieve that endpoint? Is there something that's sub or is there a surrogate, is there a biomarker? And then interesting, when you talk to patients, you know, you hear this a lot of diseases, well, we'll use what's called the six minute walk test, which has been talked about ad nauseum as a measure of a patient's sort of performance.
00;29;21;15 - 00;29;44;20
The how far can you walk in the six minute space? And if you have, you know, any kind of respiratory or cardiac condition or maybe even something neuromuscular going on, it really limits your ability to kind of perform and walk in that that test that's been used as a standard for a time that's very controversial, that, you know, the FDA is required at a Times or industry has wanted to pursue it.
00;29;44;23 - 00;30;06;14
And then when you actually sometimes talk to patients themselves and say, you know, they'll look at the endpoint in a trial as measuring and say, well, that's not really relevant to my life. You know, if I'm able to walk across my kitchen and reach my arms up and get something out of the cupboard, that would be a dramatic, meaningful benefit to me, right?
00;30;06;20 - 00;30;27;22
Just the ability to do that on a day to day basis would change my life. And is there a way to measure something there that's maybe a little bit more relevant for patients, but also balances out what our scientists and our regulators and our industry professionals want, I think is so there's often a dynamic there around what are we going to measure?
00;30;27;22 - 00;30;53;07
There might even you might even argue there's a there's another arm in there that's maybe our our payers, right. Are insurance companies, what are they wanting to see, What's the benefit there? Because this drug will cost something. So it can't just be safe and we think it works. Is it working enough? Is the cost benefit enough? So there creates multiple kind of perspectives on how do we measure the effectiveness and and from what from whose perspective are we measuring it.
00;30;53;07 - 00;31;18;16
So that's pretty interesting. You're seeing more and more, you know, even in FDA new drug review meetings, you're seeing patients come up to podiums and share their experience on the trial, share their experience on the difference this drug made. There's been a lot of, you know, in the muscular dystrophy space, it's a really challenging disease, and drug development has been exceedingly difficult.
00;31;18;16 - 00;31;39;16
So it's been a lot of interesting stories there around not only the six minute walk test, but also just hearing patient voices, hearing the impact a drug has had on a patient or a family or a child. And there's often some competing data here around, well, the seemed to work for patients, but it didn't hit the primary endpoint that we wanted it to.
00;31;39;16 - 00;31;59;15
Yet there's some benefit here and we end up in some kind of gray area. And it's been an interesting kind of discussion and dialog we see playing out in those areas. So yeah, kind of competing priorities in a way. How do we find like what works for everyone? Now I'm going to ask you to put on your Nostradamus hat and predict the future.
00;31;59;23 - 00;32;22;05
Thinking about how things are trending now, what role will patients have in the next ten years of drug discovery? Will it be a story of, Hey, we found better ways to do decentralized trials and involve patients and that? Or does it shift to, hey, most of this stuff is done in silico with AI and that's how we do our research.
00;32;22;07 - 00;32;48;27
Yeah, fascinating. Yeah, I think the patient focus, the emphasis on being patient focused and considering patients, I feel like, you know, ten, 12 years ago was a really novel concept. I think we've made incredible strides in that area to where that's not a foreign idea anymore. You still may have some some people in the industry questioning the value of that, or can patients really inform their disease?
00;32;48;29 - 00;33;10;17
You know, a lot of us think the patients are truly the experts in their space. And there's been a lot, I think, to be there's advance things that informed us a lot by including that patient voice, patient perspective, bringing patients and patient panels to the to have a seat in that New Jersey conference room I referenced. Right. To actually provide their input early.
00;33;10;17 - 00;33;34;19
We're starting to see that more and more. And I think that's having impact and progress on making sure that we're creating things that are feasible for patients. So, you know, my crystal ball here on my desk sees us continuing to do more and more of that. I think there's a nice trend. I think rare disease, again, has been a space where we've kind of led the charge with that out of necessity.
00;33;34;21 - 00;33;56;02
But I think you'll see that extend into most other therapeutic areas. It's logical, right? It's good science. Consider your end user. Consider the people you're trying to help. Make sure what we're building or delivering or or trying to execute works for the people. You know, it's our Apple. Our iPhone works for the people who are actually using it right?
00;33;56;04 - 00;34;21;25
So I think we're going to see more and more of that, the decentralized clinical trial sort of paradigm that popped up as a necessity as a solution during COVID, you know, really has been like the hot sort of term and topic in our industry for the last two years. Again, if you picture if you if you zoom out, you see an industry that is risk averse and has been conducting clinical trials the same way for several decades.
00;34;21;28 - 00;34;45;25
We have a pandemic, we adjust, we adopt some new technologies. You know, from where I sit, I see this pendulum swinging back toward the middle a little bit. I feel like decentralized clinical trials are being looked at, maybe with a little more scrutiny and skepticism. You know, was this something that we just had to do short term? Is there really value in doing things that way?
00;34;45;25 - 00;35;19;25
Is there really are a slide back to your earlier point, Mike, Are we are we making things more complicated at times? Right. When you start to think about a patient focused approach where we collect data remotely, where we have nurses visit a patient's home, where we have things on telehealth, maybe we go into a clinical site as well, kind of introduces a few more different variables, and it makes not only the data more complicated, but if you're a patient, you might suddenly have six, seven, eight touch touchpoints of Who do I contact?
00;35;19;25 - 00;35;40;10
Is that the is it the help desk for the app or is it my nursing group or is it the clinical site or is it the sponsor or the CRO? So we're I think we've made some things a little bit more complicated and I think some of the risk averse folks in our industry are sort of looking at decentralized clinical trials saying, I don't know about this.
00;35;40;10 - 00;35;58;15
I wonder if maybe we're going to this pendulum will swing back and maybe all clinical trials will have maybe a little bit of a DCT component in them, maybe a little bit of remote data capture, maybe a little bit of consent, maybe a little bit of telehealth, and maybe that'll be just kind of the way we do things.
00;35;58;17 - 00;36;15;28
And we won't be looking at a regular clinical trial or a full DC trial. It'll be a little bit maybe about a mixture would be my thought so, But I do think we're going to continue to emphasize patients more and more. I think there's value in that. I think it's important. I think it's the right way to operate and the right thing to do.
00;36;16;01 - 00;36;33;16
Again, I think in any kind of industry, right, you want to be paying attention to your to your customers, right. And thinking about what they need and what's realistic for them. And I think our industry has a long way to go still in that area. So but again, with my crystal ball, I'm encouraged. I'm excited about where we're headed.
00;36;33;19 - 00;36;54;21
Well, getting back to the technology, especially how researchers figure out what tools to use, making decisions like legacy on premises systems versus cloud, what do you see? Are there mostly hybrid strategies out there, or is everything moving to the cloud? What are the pros and cons of each? Well, I'm the strongest technical person when it comes to things like that.
00;36;54;21 - 00;37;15;18
I do tend to focus on how we can collect data with patients, and I do a lot with wearables and some of these mobile platforms. I mean, it feels like, you know, the people I talk to, it feels like the legacy and on premises systems are, you know, are going away. It feels like everyone's in the cloud. Granted, I don't know, Mike, I live in Seattle.
00;37;15;18 - 00;37;37;16
I have Microsoft and Amazon down the street from me, so maybe it's just a little bit of a a bias with where I am geographically, but it feels like the cloud is where everything is going. I'm sure there are some folks listening who know a little bit more about the details than me, but that's my take. Yeah, well, you have been a longtime partner of Oracle, and in fact, you were at the Oracle Health Conference in September.
00;37;37;18 - 00;38;07;16
You're often participating in Oracle Life Sciences webinars. And I guess this goes back to the question of, you know, what is the industry's role in this thing? What has it been like working with Oracle and what role do you see companies like that playing in clinical research and drug discovery? Yeah, well, it's been a great partnership working with Oracle, both working with folks like yourself and your colleagues, but also leveraging a lot of the good technology solutions that Oracle developed.
00;38;07;19 - 00;38;38;17
I think that, you know, not to be a broken record, but again, risk averse drug development industry here. I think that a lot of innovation can occur from organizations like Oracle all pushing forward new technology, new concepts, new approaches. I'm a strong believer that the innovation in the space occurs from, you know, technology companies, service providers, bringing new solutions to biopharma sponsors.
00;38;38;17 - 00;39;05;18
So Oracle's been a group like that that has moved forward with different electronic data capture systems, you know, sort of the regulatory safety database solutions, things like CTMS's, the platforms that are being created are making us more efficient, they're making us more effective When it comes to the trials themselves. They're allowing data to be captured and integrated better from lots of different sources.
00;39;05;21 - 00;39;29;15
We talked about those challenges to that. But I think the direction is is a really good positive one. So Oracle and, you know, organizations like Oracle, I think are the ones really driving that. They're the ones to be to present new bright, shiny objects, new innovative technology and new solutions to pharma, and to say this will help move things along faster for you.
00;39;29;15 - 00;39;52;05
And with the you know, what the pressures around, you know, right now, the pressures around fundraising, the pressures around firms, maybe like prior noticing different sort of compounds in their pipeline, the challenges in the industry right now, I think everyone's looking for better, smarter, faster solutions. And that's where I think some technology and some innovation can come in.
00;39;52;05 - 00;40;15;01
And I think that's where Oracle will continue to make a really, really strong difference. Well, embrace my own shortcomings as a host. I never assume I've properly picked my guests brains. So what are you thinking about most these days? What's the big question or challenge you think we're facing? Or maybe there's just something you think it's important for our brilliant listeners to know.
00;40;15;04 - 00;40;35;25
And oh boy, we've covered some really interesting topics around, you know, data being captured. Is there too much data out there? How do we integrate data from all these all these different sources? I feel like that is a that is a topic that won't be going away anytime soon. Right? That is something that I think we're going to struggle with for a while of.
00;40;35;28 - 00;41;16;22
We want to capture data from all these new sources. How do we manage it, integrate it, validated, keep it secure. And then, of course, when you have this group of data, we're seeing, of course, artificial intelligence and machine learning and all kinds of derivatives of that occurring with really every data set we have. So that's I think the other big piece is how do we how do we as we collect more and more data, what kind of systems do we have built that are going to help us learn from that and inform us and hopefully within the context of clinical trials and drug development, all of the data we're collecting should allow us to design
00;41;16;22 - 00;41;44;25
the next trial better for the next one to be a little more smarter, to be a little more efficient or a little faster applying this. Maybe upstream into some of the drug discovery pre-clinical stages when we're really looking at new drug candidates, what might work? I think being able to leverage that data and be smart and inform ourselves to be able to accelerate drug to drug discovery in drug development.
00;41;44;28 - 00;42;12;15
I'm hoping that that's where we see great progress, because there are patients out there that really can't wait. And their only hope is that new therapy coming from a clinical trial. And right now, our process just takes a long time. So to that point, do you think do you think A.I. is overhyped? Like if I'm someone with a rare disease and I'm hearing about A.I. and I think, Oh, this is it, this is going to get the therapy or the drug treatment I need in the next year.
00;42;12;16 - 00;42;34;01
Yeah, I think I think that you should be realistic, right? I think that, you know, the AML is going to be those are going to be sort of tools and approaches that are going to be a big part of, like you said, probably most industries and our daily life everywhere. I don't know if that's going to be, you know, a magic bullet that is suddenly going to, like, revolutionize drug development overnight and move things along in a year.
00;42;34;09 - 00;42;53;17
I would temper expectations probably a little bit, but I'd be hopeful that this will help us and this will help us move faster and be smarter. But yeah, maybe that's not a maybe that's not a silver bullet that fixes everything. I think that's another tool in our arsenal to kind of move things forward faster. Well, Scott, thanks again for your time today.
00;42;53;19 - 00;43;15;07
If anyone wants to learn more about you or what you've talked about or a rare clinical, what's the best way for them to do that? Yeah, absolutely. It's been my pleasure to be here today. I've really enjoyed it. Really enjoyed the topics in the conversation. You know, my email address, I guess will put an email out. There is a Scott Schliebner – I’ll spell out Schliebner
00;43;15;12 - 00;43;40;19
@ msn.com. You can also find me on LinkedIn. Scott should leave her and be happy to engage, support, help you out there in any way that I can. Great. We appreciate that. If you are interested in how Oracle can accelerate your research and data needs, all you have to do is check out Oracle dot com. And join us again next time for research and action.
00;01;00;20 - 00;01;24;09
In fact, Quantum Bio earned a small business innovation research grant from the NIH to run calculations for their movable type methodology. Research. They'll be working with Oracle on that project. So, Lance, we're really glad to have you with us. Certainly. Well, thank you for having me. I look forward to the discussion. Well, listeners, I hope you're ready to get into the weeds because we're going to get into chemistry quantum and all the exciting things that are becoming possible.
00;01;24;12 - 00;01;44;12
And it's all emerging science and technology. So keep listening. You'll be well caught up. But to start, we're always interested in what got you, Lance, and what you're doing. What was that professional and personal journey like? Certainly. Yeah, well, and actually, I when I first started things out or I just started really putting my head around what I wanted to do for a living.
00;01;44;15 - 00;02;06;22
Science was actually pretty far from from the discussion or my thought process I'd actually started is as a semiprofessional professional amateur theater geek, doing a lot of five local theater, that sort of thing. I worked at a local Renaissance fair, you know, those sorts of things that that that people that wanted to go more into the the arts.
00;02;06;22 - 00;02;24;22
If you will, you're really wanted to do. And then one day I was when I was in high school and starting to think about what I wanted to do for a living, it just kind of dawned on me that, you know, you could be the best actor in the world and be very successful as a and have a lot of a lot of great enjoyment.
00;02;24;24 - 00;02;46;20
But if you don't catch a break, you can have all sorts of professional and financial difficulties throughout life. And so I started looking at what classes I did well in in high school or what I was doing well. And at that time I was in 10th grade and of course it was the sciences biology at the time. And at the same time I was I had always been into computers.
00;02;46;23 - 00;03;09;06
And so I think my first computer was a Vic 20, which I believe as I, as I looked up, just came out in 1980. So so that kind of puts it perspective that I was about six years old, and so I knew that I would want to do something with computers, something with biology. So then I started really setting up my my high school career for that, for that sort of background.
00;03;09;06 - 00;03;36;13
I studied some theater on the side. Theater is always fun, but, you know, that was where I focused my energy. Then I went to college. I ended up majoring in biochemistry and computer science with an eye towards doing exactly what I'm doing now. And so my wife always jokes with me that and she knew me then too, that, you know, I wanted to do something that most people, including her at the time, had never heard of before, and that was computational biochemistry or computational chemistry.
00;03;36;18 - 00;03;54;15
And so I spent my years in college, you know, certainly learned a lot in biology. I was I was more focused on the biology versus the chemistry side of things, you know, And of course, like I said, with the comp sci. But then when I went, when I started looking at grad school, I had already met my future advisor at the time.
00;03;54;15 - 00;04;18;14
His name is Kenny Myers began at Penn State at the time and now he's he's moved on as well. But I actually had met him a couple of years before I graduated from college and, you know, started talking to him. And then we ended up I decided that was the lab that I wanted to work in. You know, once I went to Penn State and so as I settled in into graduate school again, that would have been in 1998 when I had started grad school.
00;04;18;16 - 00;04;45;15
By about 2000, 2001, you know, I was really starting to think about and talking to him, of course, at the same time about the possibility of starting a company. And I had already done started some companies back then, back in, I guess you could say the the college years doing, you know, web design for people you know back when the web was very, very young and and just getting started those sorts of jobs.
00;04;45;15 - 00;05;15;02
And so I had already had an understanding of of the basics of of getting a business started. And so at that time, then, you know, Katie and myself and then another person began the companies really to focus on commercializing the linear scaling semi semi empirical quantum mechanics technology from from his lab and spinning that out again as as a company that's really focused on applying these methods to drug discovery working in the pharmaceutical space.
00;05;15;05 - 00;05;42;13
Yeah, I've been calling quantum bio a startup, but it's actually pretty established. It's spun out of Penn State in 2002. How did the company come to be and what does it aim to do? What were your highest aspirations for it? Well, I'll tell you, when you're around that long and you've done, you know, a lot of say, ups and downs, we we we always joke with our investors and everything else on the topic that you're really you know, there's a lot of trial by fire when it comes to entrepreneurship and that is part of the process.
00;05;42;13 - 00;06;07;00
And so you become very comfort, comfortable with trying different things, seeing what works, what doesn't work, and learning from mistakes and moving forward. And so when we first spun out the company, it was very focused on a we have a patent that's associated with it, which was a quantum scoring based methodology that again was published probably about that same time frame.
00;06;07;00 - 00;06;28;09
You know, you know, early 2000s. We thought this was going to be the greatest technology that was going to be known to man or whatever and was going to be very successful in pharma. And I think what we learned was that, you know, trying to just develop a academic software package and commercialize it, it's well, it takes a lot more than just a good idea.
00;06;28;10 - 00;06;50;22
You know, you really need to understand, you know, how software is put together. You need to not necessarily focus on it from an academic perspective, answering academic questions, if you will, and really focus more on your client is what the client really needs to do and how much time and effort they're willing to spend on that. And so that's how we learned.
00;06;50;22 - 00;07;10;11
We had a couple of hard lessons along the way, you know, that, you know, these things had to evolve a little bit more, so on and so forth. And so, you know, we certainly but the good news was at the same time we were bringing on clients and, you know, we've we've had made a lot of friends, you know, a lot of folks that we could work with, collaborators, so on and so forth.
00;07;10;13 - 00;07;41;25
And then over the years as we really put our heads around this and understand how things had to progress, I begun to work with the the National Institutes of Health, and I took over the general management of the company at that time and then really focused on raising funding specifically for development of new technologies. And so we've been able to raise probably on the order of about 8 million or so dollars from the Spire program over the over the last several years.
00;07;41;27 - 00;08;12;12
And again, that is focused very specifically on development of new technologies for pharmaceutical research. And we also then at the same time, we expanded beyond just the scoring methodology that we had done, and now we're in the free energy space, the X-ray crystallography space, the nuclear magnetic resonance space. So we've been able to, you could say, grow that that nugget or completely redevelop that nugget and then now expand to into a different lot of different applications.
00;08;12;20 - 00;08;36;05
And that then becomes our key. Then for from a business perspective is now we're focused very much on applications of these technologies. And in order to solve problems, you know, the more interviews we do on the show, the more I'm seeing startups come out of academia as some kind of deliberate ecosystem that's been set up where colleges are basically incubating research startups that then go on to take off.
00;08;36;05 - 00;09;00;08
What what are the universities get out of that? Well, I mean, I think it obviously depends on on a case by case. You know, there's but but in terms of the generalities, those sorts of deals can take a lot of different looks, I guess you could say. So in our case, you know, most of the focus was that you would be bringing out a technology that was developed in the lab.
00;09;00;10 - 00;09;23;08
Again, as I already mentioned, an academic way to solve a problem is oftentimes different than a industrial way to solve a problem. So there's a lot of that R&D that needs to go into developing a technology. And so that's that's one aspect. Certainly the and so therefore, getting that technology out to more people, you know, tends to be a key benefit for universities.
00;09;23;11 - 00;09;48;14
I think the other, of course, you know, is it comes down to a monetary, you know, very oftentimes thanks to the by double act and so on and so forth, there is a requirement that they take this technology and put it out to the world and actually take some sort of monetary value. And so you get a certain amount of of ownership in that in whatever downstream company comes about.
00;09;48;16 - 00;10;18;25
And so that's how generally universities, you know, develop, they pay for their intellectual property offices and so on and so forth by actually investing in those companies. And usually not necessarily, again, invest in cash as much as investing technology into those companies. And then they get they get a certain percentage of the ownership. And so as we as we move forward, then the the certainly it's oftentimes students, grad students, postdocs, faculty, they get the experience and enjoyment of spinning out of business.
00;10;18;25 - 00;10;41;11
So therefore they get some of that training that goes along with it. But then I think also the university gets some some financial benefit then as well. Well, how do you think that relationship is working? Because entrepreneurs are very unique creatures who put it that way. And a lot of PhDs aren't really entrepreneur material. Or better to say, it's not something that comes naturally to them or that they aspire to be.
00;10;41;13 - 00;11;04;08
That's got to be hard. Throwing themselves into business, especially in something like pharma, which is really regulated and competitive. Right, Right. Well, it actually it's interesting. I would actually argue that actually very oftentimes PhDs, especially in the sciences, I again, I can't really speak for, you know, the humanities, but in the sciences very often they are actually entrepreneurial in terms of their spirit.
00;11;04;08 - 00;11;26;04
Now, again, that might manifest itself in different ways. So a great example of that is a professor at a research university. They generally need to raise their own funding to support their labs. They are extremely it's really important that they go out and do quote unquote marketing in the form of going to conferences and conventions and and writing papers and so on and so forth.
00;11;26;06 - 00;11;50;15
And so that that entrepreneurial spirit, I think, is actually pretty strong. And certainly anybody that's in the sciences and, you know, using the scientific method is, you know, already pretty comfortable with risk and uncertainty because, of course, a lot of what we do stems from hypotheses that some of them don't work out. And so, again, that's something that, you know, a lot of entrepreneurs, you know, would be would understand very well.
00;11;50;17 - 00;12;15;02
And so I think that there is a lot of that spirit. I think the difference then oftentimes is, you know, a lot of folks maybe don't necessarily understand a priori or at the beginning of, well, how do I actually raise funding, raise my own capital in order to develop a new a new company out of that? And so it's those aspects that I think where the university can be a really big benefit.
00;12;15;04 - 00;12;38;27
You know, they they kind of provide that little that little test bed or that little seed bed, if you will. They allow that the people to take risks, that it's a little harder to do if, again, you're out on your own. And so I think that that ends up being where a lot of the benefit comes from. And I think that therefore, then they can really kind of play to those strengths and then grow from there and build out from there.
00;12;39;00 - 00;13;04;29
In terms of the pharmaceutical space, absolutely, very highly regulated. That tends to be a an impediment often. I mean, I think it certainly has benefits, but I think the the, the minus of it is you generally have to have a lot more infrastructure that can support understanding of those regulations, understanding of how those those regulations work, how that game is played, quote unquote.
00;13;05;01 - 00;13;24;28
And so I think that part does tend to be a little more difficult. And again, that is where, you know, a university or even just a corporate partner can take a lot of that push, a lot of that heft in terms of competition. You know, I think that's something that I think we we all like and appreciate. These things should be highly competitive.
00;13;25;00 - 00;13;48;26
But the regulation is something that, you know, like I said, there's always this expense that goes along with it and legal fees and so on and so forth. Computational drug design or in silico has shown promise in enhancing the success rates of drug candidates and saving time and money. Now, of course, it can't replace experimental bench work, but in silico platforms like Quantum Bio, they're starting to be seen even by regulators as having a lot of potential.
00;13;48;28 - 00;14;13;21
I mean, the NIH is even funding you. What is so exciting about in Silico drug discovery and design and and why are folks excited about quantum bio? That's a really you know, I think it goes to a core question or a core core benefit that you're absolutely right. I mean, in Silico back, as I mentioned at the beginning, that back in the day was really not something that people fully understood.
00;14;13;28 - 00;14;39;17
They saw maybe a lot of promise there, but there was a lot of concern, you know, is the are the computational methods just going to replace all all lab bench work? And of course, that's nonsense. Of course, lab bench work is is is critical as well. And at the same time, there's a lot of evolution still going on in the field where we were still trying to fully understand how to.
00;14;39;17 - 00;15;11;12
And I think we're still fully trying to fully understand how proteins and ligands interact with each other, how all these different molecules interact with each other. And by expanding that or as that continue to evolve, if you will, and mature, then it really has now become a core aspect of the of any pretty much any pharmaceutical company out there is likely going to have, you know, a number of computational chemists that are working on projects on a day to day basis.
00;15;11;15 - 00;15;32;16
You know, this is now a critical part, a core part of the drug discovery process where again, back back before it was probably more something that folks kept an eye on. They they wanted to see what sort of ideas would be coming out of it. But oftentimes is that as the saying would go, you know, you're you're only going to give them a certain amount of time to give a result.
00;15;32;16 - 00;15;59;02
And if if not, I'm just going to go ahead and go to the lab and make it anyway. And so methods like the ones that we work with and and develop have now, really, like I said, they've become ubiquitous. You know, they are something that's that's part of the drug discovery or pharmaceutical space. And now then where we focus them is laser on very specific specific solutions to very specific types of problems.
00;15;59;04 - 00;16;19;03
And so and so therefore, we play to our strengths and then we very oftentimes then partner with other software companies that then are applying to theirs. And so in in our world, collaboration between pharma, between other software companies is is just part of the day to day. You know, we need to make sure that our software works well with theirs.
00;16;19;03 - 00;16;43;08
They're doing what they do really, really well, and we're doing what we do really, really well. Then that then provides a solution or a set of solutions that really gets added into a toolbox for the pharmaceutical space. And so a firm before and so a practitioner in the pharmaceutical world will have any number of tools. Ours is one of them that that would be solving the types of problems that they need to solve on a day to day basis.
00;16;43;11 - 00;17;10;10
Pharma, it seems to me, moves really slowly and cautiously, sometimes frustratingly slow toward emerging innovations. And silica has been around a long time, so they got used to that. But now computational power has made it a real for something with enormous potential. So how is pharma acting now? Are they do they get it? Are they fully adopting it to modernize and speed drug discovery?
00;17;10;12 - 00;17;34;01
Yes. Oh yeah. I think clearly that's the case. You know, I think now it's it's really like I said a few minutes ago, I mean, I think it's it's it's a critical part of the R&D process now. I would be surprised if there's any pharmaceutical company doing direct or R&D efforts in the world that that doesn't have, you know, at least some computational chemistry muscle.
00;17;34;08 - 00;18;05;08
Because, again, I think what what a lot of it is we can test we can test hypotheses in the computer that are either difficult to test in at the lab lab bench or that are just expensive or that are, you know, maybe a little, little early in the sense that things that we might we try to what we want to try to build a hypothesis as far as possible before you start throwing a whole bunch of chemicals at it, you know, chemicals and the people and so on and so forth.
00;18;05;08 - 00;18;42;20
Obviously there are costs. And so what you try to do is you want to optimize their time as much as possible, not maybe flush quite as many chemicals down the drain and really focus on solving the cases or addressing the the solutions that you really need to address in order to understand how a molecule is interacting. And so if you think about it this way, a, the drug discovery process takes, you know, billions of dollars and they go through untold hundreds, thousands of potential compounds to finally get to that one compound that's going to be the winner.
00;18;42;22 - 00;19;12;09
Anything that we can do or to to understand and better impact decisions, the better for the whole process it gets. Obviously, the process hopefully gets cheaper, but much more importantly is it gets more, more efficient. We start solving problems quicker so that way we can get that drug to market much, much quicker. Do The pandemic changed the dynamic in terms of how important it is to get to discovery, get drugs to market really, really quickly?
00;19;12;09 - 00;19;47;05
I mean, was that was it any kind of wake up call for speeding discovery? Absolutely. Yeah. I think the pandemic was a you know, it really showed, I think, where computational chemistry can can, you know, really adds to the whole whole process of drug discovery. And so, I mean, Grady, just on the very simplest level, even where as we had as we were doing was shut down in the very early stages of the of the pandemic, you know, people a lot of pharmaceutical companies had to also shut down.
00;19;47;05 - 00;20;10;20
They had to you know, people couldn't come in to the lab. They couldn't work in the lab. So even on the simplest level, computational chemists could continue working. You know, we didn't have to go into the lab. You know, we continued to do the the modeling, the the R&D to really understand what was going on with COVID and understand how the virus is working and getting structure very, very early on.
00;20;10;20 - 00;20;46;22
And so I can remember very early on being a party to online virtual meetings, conferences, meetings with clients and partners and collaborators where we were actively discussing how are we going to solve this problem, How can we as a field add value and solve this problem while, again, everybody else was effectively locked down in their houses and so very quickly there was structure that had come out of this this these these sorts of efforts where we understood what the structure looked like very early on.
00;20;46;24 - 00;21;15;13
And that was just an amazing there's a whole websites and papers and books that are that are dedicated to that topic of just the process of coming up with structure, understanding how this thing was going to work, and then therefore then we could take that and move forward into here are the therapeutics. At that time we didn't necessarily fully recognize that we would get to any sort of vaccine as quickly as we did, and I think that was an amazing accomplishment in itself.
00;21;15;15 - 00;21;34;02
But we were at that time we were still talking about, well, this is the way that this virus works. How can we develop therapeutics in order to address it? And so that that effort, you know, I think was a definite success. And it really showed what computational chemistry can do, especially, like I said, in a very hard situation that we were dealing with as a world.
00;21;34;05 - 00;21;59;28
You're now each research grant is for your movable type methodology research. Now, I don't know what that is, but I know it's patented so I can't steal it yet. So tell me what that methodology is. Certainly. Certainly, Yeah. So this this methodology was actually one of the kind of we were alluding to earlier was it was developed in the lab, in the academic lab originally, and we licensed it from Michigan State University.
00;22;00;03 - 00;22;23;14
At that time. We took it, you know, licensed it, redeveloped it, re-implemented it from the ground up, so on and so forth, actually partnered with the university and to really get their expertise as we were doing it. So I think that was a win and that was certainly a showed a nice core collaboration between a university and an industrial partner and how that can be very successful.
00;22;23;21 - 00;22;44;21
And from there then we've we've now taken that technology and brought that to the world. And now it is something that we're marketing directly to pharma, to biotech, to CEOs, consultants, that sort of thing. Now the question to answer the question of what it is or what it's doing, it's effectively it's it's what's called a free energy simulation method.
00;22;44;23 - 00;23;06;09
The way that that that modeling generally works are simulations. Work is they usually take a lot of CPU time, a lot of GPU time actually, and you model how to molecules interact with each other over time. And when I say over time, it's you're generally modeling nanoseconds or picoseconds worth of time that's in the solution, if you will.
00;23;06;16 - 00;23;40;12
But this is taking weeks or excuse me, I should say weeks anymore, but, but certainly hours. The days in order to get to get to those results, it's something that then you're modeling those interactions and how that that these two molecules or three or four or however many are interacting through time. And what that gives us an understanding of is when it's happening this way and in the quote unquote test tube, in this case a computational test, you or the in silico test tube, it mimics what is likely happening also within the body.
00;23;40;14 - 00;24;19;23
And so by doing that, we can inject the different and I say again, a kind of error code, inject this all obviously computationally or virtually inject a different molecule into this solution and allow that interactions, those interactions to happen. And by modeling those those changes over time, we can see that this interaction with or this particular molecule and again, I'm just talking about the protein ligand space just for this example, but a little molecule with a big molecule, how those two are interacting with each other and if they interact and actually bind together, well then that's, that's a possible drug.
00;24;19;26 - 00;24;35;17
Yeah, we might we might have just found $1,000,000,000 drug alternatively, and this is what usually happens is it falls apart. You know, it didn't, it didn't actually interact. And so that those are the failures, those are the things that then you would tell the bench chemist, I probably don't make that one, make this one, this one looked like it interacted.
00;24;35;17 - 00;24;54;05
Well, that one didn't. But the problem is, as I alluded to, that takes a lot of time that that interaction, all of those interactions takes a lot of time, a computational time. And of course, time is is expense both in terms of the the lab chemist that's maybe waiting for an answer and that person's only going to wait so long.
00;24;54;08 - 00;25;22;01
Also, on the other hand, and it's just the amount of, you know, paying the keep the lights on there, the air conditioning, so on and so forth that goes along with the with the computer. And so what we do then what moveable type does is it says, well, now hold on a minute, Do we need to do all of that modeling or can we start with some key starting points and that maybe maybe they would be what are what we call doc positions or just positions of that ligand within the active site?
00;25;22;03 - 00;25;48;20
Or maybe we actually run shorter simulations and we take snapshots and then we take those points and we say, okay, now we're going to use our method again, what's called movable type to basically smear or blur the interactions between the protein and the ligand In this case. What that does then is that effectively mimics that binding. It mimics all of these local very, very local sampling interactions.
00;25;48;20 - 00;26;22;05
And so what that does for us is we don't have to do the long simulations in order to get an understanding of binding between that that protein and ligand. And therefore our hope would be awe. And what we're seeing is that we can now inject that that that virtual drug in quicker get a quicker understanding of is this good or bad, is this a good molecule or a bad molecule from a binding perspective and then pass that that information down, then to the the medicinal chemistry of the bench chemist to then then go ahead and make the winners versus the losers.
00;26;22;12 - 00;26;55;11
And so the methodology itself is something that then can answer these questions much quicker, that then we can then move a potential drug down that line much, much faster. As I mentioned, Oracle is partnering with Quantum Bio on this NIH research. But in in what way? What does that look like? So that so that's that's I think a core critical aspect that I think is, you know, hopefully, you know, beginning to come out of this this discussion is that a lot of what we do and a lot of what our our field does is a lot of collaboration.
00;26;55;16 - 00;27;24;12
It's a lot of going back and forth between between different entities. No one person or no one organization tends to bring everything to the table and so in our case, what we need is we need the computational muscle. You know, we have the the the algorithms, the software, if you will, of the part of this aspect. But what all of our our methods need is, is some level of modeling or simulation.
00;27;24;15 - 00;27;56;27
And that does take a certain amount of CPU time. Now, we could of course, go and buy a bunch of computers and we of course have some computers that, you know, cluster of our own that we, that we can do some basic testing on. But in this particular project where we're running not just, you know, ten or 20 dynamics calculations in order to get those little snapshots, but we're actually running hundreds of dynamics calculations where now again, each one because now as part of the project, we're comparing to conventional dynamics calculations.
00;27;56;27 - 00;28;23;06
So we still have to run those long simulations to give us a baseline. But then what we do then is we take a take snapshots along the way, with the idea being that hopefully, depending upon the success of this project, we won't need to run all of those long calculations. We could run maybe 20% of the time and still get similar predictive capabilities versus, you know, what the conventional method is.
00;28;23;12 - 00;28;53;00
And so what Oracle is bringing to the table is that ability to run these that computational muscle, that hardware, and that goes along with it, of actually running all of those calculations in parallel. Okay. So now I'm going to put us in our nonexistent time machine and fly forward to the day all of your research is done. If you prove that 90% of the time quantum bio's methodology can get the same or better results with just 25% of the computational time, what does that mean?
00;28;53;07 - 00;29;15;04
What would a Fama exact see? And that that gets them really, really stoked. So that that is, that is savings in terms in a number of different ways. One of course is is the obvious one and that is the just the cost of running computers or cloud computing or again, building their own computers and having to call them and show them and so on and so forth.
00;29;15;11 - 00;29;35;15
So that's the obvious one. That's the obvious best benefit. But there's also an opportunity cost that as I've as I've alluded to, is that if you can't answer a question, if you can't answer a medicinal chemist's question fairly quickly, they will go ahead and make the compound anyway. Of course. I mean, they still have to do their job.
00;29;35;15 - 00;29;56;07
They still have their own hypotheses that they need to test. And so we want to really address both those costs that if a if a calculation is going to take a conventional again, not with movable type, but a conventional calculation may take, you know, again, hours to days, you know, maybe a week or so potentially to get to get an understanding.
00;29;56;09 - 00;30;21;21
That's something that a lot of folks it becomes just problematic to wait for. You know, why not? Why don't we just go ahead and move forward? Why should we wait that long to test hypothesis so we can cut that time down to basically over almost over lunchtime, you know, over a short period of time. Now you're now you've actually given the tool, a tool that then the client can now solve more hypotheses.
00;30;21;27 - 00;30;52;29
They can try more hypotheses, they can they can squirt more virtual chemical into a virtual box and get their answer quicker to then separate the the garbage from from the good stuff during that that downstream process. And so that's that's really where we're coming in is and that's where there's a huge amount of upside benefit to that pharmaceutical company by cutting down that cost, not just the cost but the time that it takes to get to that.
00;30;53;04 - 00;31;15;26
That final answer when researchers are thinking about, okay, what tools am I going to need to use? Am I stuck with a legacy system? Should I use on premises or go with the cloud? Will a hybrid strategy work? What were your answers to those questions? What did you see as the pros and cons of those choices? Well, so I think I mean, I think the answer is probably in this in the short term is all of the above.
00;31;15;28 - 00;31;41;16
You know, I don't think anybody's looking for necessarily a panacea that that this is the one thing that's going to work for everyone. What I do see is that when it comes to having on site clusters, like I said, they do serve a purpose. We we have one of our own as well. That they are a benefit when it comes to that very quick turnaround testing, you know, almost like that workflow testing, making sure that things are working the way that that they're supposed to work.
00;31;41;19 - 00;32;08;28
But when things shift to production, it doesn't matter where that that hardware resides really as long as the the machine is secure and the the partner is secure, which again, Oracle would be, would be, you know, a partner that would provide that security where we can submit those jobs from anywhere as we were talking about back in the with the COVID discussion, people would be literally sitting on the beach and they would be running calculations.
00;32;08;28 - 00;32;29;22
You know, it really doesn't matter to a computational chemist where that hardware is. We have long since done away with the idea of of that. It has to be a local workstation or a local machine and so I think to computational chemists I think it yeah, the cloud definitely makes a lot of sense and, and that's certainly where I think we see a lot of benefit.
00;32;29;25 - 00;32;52;02
The other side of it then too of, of having on the cloud is then generally someone else is taking care of maintaining the system, upgrading the system both on a hardware and a software basis, addressing again, security needs, all of those that infrastructure and that overhead definitely tends to go out the window and we worry about or we we addressed that the partner is taking care of that.
00;32;52;04 - 00;33;11;08
When it comes to the I guess you could say the the management or what what magic management tends to see then is they might see things a little differently where they would they might see it as well. Yes, that's all a benefit. But then they, they still sometimes are in the the belief of risk, you know, of risk management.
00;33;11;08 - 00;33;43;25
Is the cloud more risky than just having it on a local and a local cluster? And that, I think, is probably a good debate. It's debatable. I do. I would say that one benefit of, you know, getting a cloud provider, somebody an organization like Oracle or some of the others that are focused almost exclusively on make it, making sure that they're maintaining a good secure system, you know probably might be better than your, you know, the the local i.t person that you may have hired for you to take care of your own, your own machine.
00;33;43;25 - 00;34;12;03
So, you know, so so my point is that, you know, I think that there's a lot of benefits to, to the cloud. I think that's certainly where things are going. There's, you know, maybe a little bit of concern and again in the in the farmers space about risk. But I think that is really starting to fall away as as more and more of of us vendors, myself included, are shifting a lot of our attention on to the cloud and allowing clients to run things on the cloud.
00;34;12;03 - 00;34;34;04
I think it's it's like anything else, it's it's a feedback loop. A company's success running on the cloud. They're going to continue to run it on the cloud, and especially as they can push through more and more hypotheses that much quicker. So just like computational power took in Silico to a whole other level, now we have this new great leap forward or backwards or sideways, depending on what you think.
00;34;34;04 - 00;34;56;21
With AI, what is under hyped and overhyped about emerging technologies like AI and quantum computing, for that matter? Yes. So, you know, these are these are certainly two very, very hot topics. You know, I think that in some respects, especially when it comes to quantum computing, I think the that one is still, I think a technology of a of a whole lot of promise.
00;34;56;21 - 00;35;16;07
And we're not sure necessarily where it is all going to go. I mean, there are there are obvious benefits of the ideas of, you know, being able to solve multiple hypotheses at once and do things in a Uber parallel level when it comes to quantum computing. So I think that is it is going to happen, but the progression is is on the slower side.
00;35;16;07 - 00;35;48;08
I mean, we were we were talking about quantum computing back in probably discussions we're having is happening in the lab in 2000. You know, so that's something we've certainly been talking about for a long time. The question has always been, you know, where it's going to be. And I think it's a it is a slower moving evolution and we're going to see where things are in the next 510, you know, so so on in terms of artificial intelligence, that's that's here And now, you know, most folks in in certainly in my space is become a tool in the toolbox.
00;35;48;10 - 00;36;09;00
You know, I think everybody sees the strengths of it. Everybody sees the the abilities of it. They see it also see the weaknesses of it. You know, we're still falling for the interpolation versus extrapolation issue that, you know, AI is pretty good at at solving problems. It's already seen it's it's a little harder to use it to solve solve problems it hasn't seen.
00;36;09;02 - 00;36;30;19
But that being said, it is now something that people use to push their their hypotheses through a little bit quicker or get a better understanding of what they should be studying. And that would be the go to kind of saying or belief is A.I. isn't going to necessarily take your job. What it is probably somebody that uses A.I. is going to take your job.
00;36;30;21 - 00;36;56;10
So we've talked a lot about computing power. And you know, as we've seen, our incredible computing power is now in just about everybody's hands. And in research, we're starting to hear more about patient led research or citizen science. So I guess my question is, what place do you think citizen science has in research? Is it is it real or is it mostly just pretending to let non-scientists have a role?
00;36;56;12 - 00;37;19;25
No, I think it I mean, I think it's real. You know, I think they're it's like anything else. There are pluses and minuses. I think there's it's always a plus to, You know, for folks, this is an, if you will, the person on the street, the layman or whatever term you want to use to better understand the scientific method and the process and what we're really doing, what scientists are really doing.
00;37;19;25 - 00;37;51;20
And very often it's kind of like developing a program or software. The best way to learn a programing language is actually to sit down and do it, you know, from a just a day to day experience perspective. I think the more people understand what science is, what it's doing, the process, that's what's behind it, the better, I think where the, you know, some of the really neat things that citizen scientists or citizens bring or the the layman brings, if you will, they bring a different perspective.
00;37;51;21 - 00;38;17;23
You know, and I think that perspective is a benefit. Things that we have to be a little more careful about, I think, is that your folks can be wrong, scientists can be wrong. Anybody can be wrong when we're going through and part of the scientific method is actually an in embracing the potential of being wrong. You're actually saying that, yes, this is something that we could be incorrect on when we're trained for that.
00;38;17;25 - 00;38;33;29
You can find some difficulty, I guess if you're if you're a, you know, someone that really starts getting behind what they believe, sometimes it can be difficult to shake them. That could be a maybe what I'm trying to say is maybe an embrace of your bias. There's a certain amount of bias that that we work into. Anything is the scientists.
00;38;33;29 - 00;38;57;12
I think they they could end up falling into that bias maybe a little bit stronger. But what do you find yourself thinking about most these days? What's an either looming question in your mind or what's something that maybe you wish researchers like our audience knew? Oh, yeah. I mean, I think one certainly right now just with how, you know, I don't want to go into unnecessary politics and that sort of that sort of aspect.
00;38;57;12 - 00;39;19;08
But I think, you know, something I wish that, you know, a lot of people did understand was that, yes, scientists are people like anyone else, but we don't necessarily know all the answers. It's not about knowing all of the answers. What it is is about using a system, using a method in order to figure out those answers, something that I certainly think about a lot.
00;39;19;10 - 00;39;38;00
You know, and it kind of what we were just talking about a few minutes ago is that how to get more people to understand that method and how that method works and really address the types of questions that the big questions that we have as a society. You know, these these this method is the way to do it.
00;39;38;06 - 00;40;02;00
And, you know, I think we certainly have a great track record in solving problems. Do you think that faith in science and particularly in public health officials took a hit during the pandemic? I mean, people put a lot of stock in what they say. And if they are coming out there and confidently getting it wrong, what kind of damage does that?
00;40;02;03 - 00;40;23;06
I think that if if folks who understood what again, the process of science is all about, I don't think that that it would have been nearly as hard. But yes, I would say that there there has been a hit and I don't think that's necessarily all that controversial at this point. You know, and I think that is something that does need to be addressed.
00;40;23;08 - 00;40;46;06
There has to be a recognition that, for one, mistakes were made. I think there's also a recognition that part of that is in better communication. You know, people are smart enough to understand risk. They're smart enough to understand what is and is not risky to them. And so I think that was probably the part that that really was was undermined the most.
00;40;46;13 - 00;41;12;10
A great analogy that we use a lot, actually, when we're talking about predicting molecules and predicting binding is there used to be a time when when a computational chemist was expected to come in and say, this is the right answer and this is the wrong answer, and that is really started to evolve now to actually I would say that that just started it's now has become more looking almost like the weather report analogy to the weather report.
00;41;12;10 - 00;41;35;27
Weather report talks. Yeah. You don't you don't usually watch Al Roker or whoever you're going to watch in the morning and they tell you that it's going to rain. What it's usually is, is some percentage. It's a percent chance. And so I think people do have an understanding of risk or they can understand risk, but the health officials need to appreciate that and they need to be honest with what the risks are.
00;41;36;00 - 00;41;53;12
Maybe focus more on that perspective, allow people then to to make choices that are going to work for them. Well, that's thanks again for being with us today. I'm really glad you chose science over acting, because if you dare, you'd be walking a picket line right now and you'd probably be very hungry. That's right. It might be. That might be the case.
00;41;53;16 - 00;42;13;20
Well, I'm sure we're going to bring you back because we do want updates on that research project you're doing with Oracle. If want to learn more about you or quantum bio, how can they do that? Absolutely. We are certainly we're we're on LinkedIn as as most folks in our field are. So that's always a good place. Certainly if you Google Quantum bio, you'll you'll come across our our website.
00;42;13;23 - 00;44;33;25
Most of all of our updates are there and certainly you're welcome to subscribe to our list through that. Then we get announcements, then that sort of thing of, of what we're doing at the time, from time to time. All right, perfect. If you are interested in how Oracle can simplify and accelerate your research, check out Oracle dot com slash research and make it a point to check out the next research in action.
Hi, and welcome back to Research and Action, brought to you by Oracle for Research. I'm Mike Stiles and today's distinguished guest is Declan Kirrane, who is the chairman of the Science Summit at the United Nations General Assembly and the founder and managing director of ISC Intelligence and Science. And we're talking to a guy with more than 25 years of experience as a global senior advisor to governments and industry on science research, science policy and regulation around science.
00;00;49;10 - 00;01;17;07
Declan has been promoting a bigger role for science in the context of the U.N. General Assembly since 2010, and that's led to an annual science summit that focuses on the role and contribution of science to reach the United Nations Sustainable Development Goals or SDGs. The current edition UNGA 78 is happening September 12th through 29th and will bring together thought leaders, scientists, technologists, policymakers, philanthropists, journalists and community leaders.
00;01;17;09 - 00;01;37;02
We'll talk about increasing health science and citizen collaborations and why it's important to support science overall. Now, Oracle's actually going to be part of that science summit a few of the executives will be there speaking, including Alison Derbenwick Miller, who's global head and VP of Oracle for Research. Declan, thank you so much for being with us today.
00;01;37;08 - 00;01;58;13
Thanks, Michael. Great to be here. Thank you for the opportunity. Delighted to be here. What we want to hear all about the science summit at the U.N. General Assembly. But before we go there, tell me what got you not just into science, but science policies and your role in creating this summit? Well, first is, I suppose, the simple answer to that is happenstance.
00;01;58;13 - 00;02;21;10
I have to tell you, it was not planned. My primary degree is the history of art. And then I did law and probably needed a job after all of that. And then as a lot of people did in the late, late eighties, emigrated to the U.S. of A and on the basis that there was nothing going on in Ireland.
00;02;21;10 - 00;02;51;23
So opportunity beckoned and therefore from that worked on Wall Street and at a boutique mutual fund company. And then between one thing and another, I ended up in a in a boutique similar boutique company in Paris. And from that to Greece and from that, I got into more consulting side of things and from that started working for global multilateral bodies such as the World Bank and the IMF on a contract basis.
00;02;51;23 - 00;03;23;25
And then from that got more into telecoms and from that into into science coming out. And I suppose from the area of telecoms, infrastructure and data rather than, if you like, a bank scientist. And I suppose my history of art background gave me a wonderful perspective on policy, at least that's what I argue. And, and from that I got very interested and from the insights, but partly because the European Commission invited me and a couple of others to set up a dissemination service.
00;03;23;25 - 00;03;57;19
It's called Cordis. Cordis and the Cordis Information Service was designed by the European Commission to provide information on ongoing collaborative research and to provide information on publicly funded research opportunities in the course. The reason the European Union did that was to was to ensure that the information resulting from funding they're providing reached a very, very wide audience. So my job was to to do that and we built that out and that brought me into the area of science policy.
00;03;57;22 - 00;04;27;19
And I gradually began to understand the huge importance of science policy. And of course, 20 years ago science policy was not a thing, you know, it doesn't really exist in terms of policy making headlines, but it gradually came to be and as you know, it's it's part of the lexicon now. A lot of governments around the world have science policy priorities, and it's recognized as a driver for economic development and global competitiveness and driving solutions to global challenges.
00;04;27;19 - 00;04;51;05
So sciences is a thing, but 20 years ago it wasn't. So it's a relatively recent and I began quickly to appreciate the policy dimension of that, and that led me to work on policy that led me to understand policy mechanisms. And, you know, from my standpoint, I mean, there's no point in looking at some global challenges or many global challenges from a national perspective.
00;04;51;12 - 00;05;21;24
Really, it has to be global, it has to be international. That led me to engage with the United Nations. And from that, we just started to build from, as you say, from 2010, to start to build, engage with nations. And I really want to stress these were designed to be very, very simple to present not to a scientific forum, but to the U.N. for it to the mother ship, to the General Assembly, to diplomats, to policy and political leaders, and show them what science is.
00;05;21;24 - 00;05;43;04
And to give you a practical example, our first meeting was on biobanking. And you know, the main attention, wasn't it? What's biobanking? You see, that's exactly what we want. The want the question we wanted them to ask. And from Matt and that first mission, I think there's about 18 people in the room and we had about four or five diplomats last year at the Science summit.
00;05;43;06 - 00;06;07;02
We had approximately 60,000 participants. We had just under 400 sessions and we had 1600 speakers. So we've come a long way. And that really now is it's it's it's established. But we want to keep promoting. We want to keep science in the eye of the U.N. and we want to ensure that the future recognizes the contribution of science.
00;06;07;05 - 00;06;27;29
That's quite a journey. I think you did just about everything except science. Are you sure you weren't in the circus as well? Yeah, well, it's it's, you know, it's all true, you know, So, yeah, it's it's put a lot of it. Last 20 years has been on primarily on science. Yeah. Well in the intro I mentioned the United Nations Sustainable Development Goals or SDGs.
00;06;27;29 - 00;06;54;00
And our listeners are pretty savvy. They probably know about those, but I'm not savvy. So what are SDGs and how do they speak to global health and humanity in the in the in the mid nineties the the United Nations. And when I say the United Nations, I mean many of the United Nations constituent entities and agencies obviously were very concerned about what we generally call global challenges.
00;06;54;00 - 00;07;18;29
And in the area of health and other forms of well-being, the environment, climate, food security and safety and so on and so forth. And that led to a consensus that there needed to be, quote unquote, you know, how's this for a cliche? We have to do something. So that we have to do something resulted in the Millennium Development Goals, which were, as you can imagine, launched on the year 2000.
00;07;19;02 - 00;07;44;01
And they set forward these goals to to address challenges. And that that 50 years went by pretty quickly. And that then led on to a similar mechanism where you identify a challenge, you define a response to it, and then you allocate specific targets within that and get everyone to sign up to that and off you go now.
00;07;44;03 - 00;08;12;18
So that then that broad approach was repeated for the United Nations SDGs, the Sustainable Development Goals, of which there are 17. And they cover the headlines that you'd imagine between poverty reduction, hunger reduction, improved health, a life below water, life on land, addressing obviously biodiversity, climate and many other areas. And then we're in the middle of these now.
00;08;12;21 - 00;08;45;10
But already the world is turning its attention to the post SDG agenda. And this is where this probably where we are now. The United Nations is organizing the summit of the future September 2024, and that I suppose you could characterize that meeting rather I do as a a banging of heads together because there is a sense of crisis, there is a sense the SDGs are not being achieved, that progress towards the attainment of the SDGs is insufficient.
00;08;45;12 - 00;09;07;19
It is exclusive. It excludes many constituencies, many countries, and again, I won't enumerate them here, but I just present that as as the scenario. So there's now a lot of momentum behind what we know. What do we do next? Why old humble viewers? I don't think it's going to be a if you like, a goals oriented process. I think that's too simplistic.
00;09;07;19 - 00;09;41;01
The world. I think as we found out, is much, much more complex. And I think the issue of inclusion and equity are issues that are present in a way that they were not when the Millennium Development Goals and the Sustainable Development Goals were designed 30 and 50 years ago, respectively. And I think this equity dimension is going to give a far stronger voice to less developed nations.
00;09;41;01 - 00;10;07;05
And just on the back of an envelope calculation, I think if you take the OECD countries and change, you've probably got 30 nations that we could call a developed. And then I suppose the big questions that what about everybody else? And that is becoming a very stark consideration, which was not there. And this needs to be addressed in terms of inclusion and equity to a much, much greater extent than is currently the case.
00;10;07;05 - 00;10;37;01
And arguably then will lead to a more successful approach to whatever succeeds the SDGs, the SDGs. I'm interested in the mechanics behind that because I'm just kind of reading between the lines of what you're saying and it's like for this thing to have true accountability and for these goals to have any teeth at all. There does need to be a someone accountable, be a very good grasp of who the participants are going to be and some form of deadline.
00;10;37;04 - 00;11;01;19
Absolutely correct. Mike And that that was that the plan A the problem with that in in in in a word is it doesn't really work you've so many moving parts you've so many constituencies that it's you know, having this set table of goals and table of targets and allocating milestones know simply doesn't work. Now, why doesn't it work?
00;11;01;21 - 00;11;29;07
I believe in my view it is that many less developed nations don't have the wherewithal to achieve these SDGs. One needs investment, one needs skills, one needs training, one needs cooperation, one is finance. I mean, these are all requirements to make change it, particularly in the area of or particularly in every area. But if you look at health, if you look at energy transformation, if you look at digital transformation, they don't happen without moolah, without money.
00;11;29;14 - 00;11;48;22
So the question is, well, where's I coming from? The answer, I'm afraid, is it's not. And that leaves a lot of they again, when I say lesser developed nations, I mean that is the majority that's 150 nations on the on the on the on a rough calculation. And they're not they don't feel involved. They don't feel they're taken seriously in terms of support for the investment.
00;11;48;24 - 00;12;13;12
And I think they're looking looking at the developed world and they're saying, well, okay, you benefited from carbonized development then and now we're supposed to do on carbonized development and how is that going to work for us? And there's no answer to that. So I think it's extremely complex. And as you say, trying to build consensus around this is extremely difficult because any move forward does require political consensus as very, very hard to get these days.
00;12;13;12 - 00;12;30;16
I mean, you can you can look at Ukraine, you can look at you can look at the Sahel, you can look at many parts of the world where consensus are at a political level. It's very difficult, if not impossible. And then you factor into that, well, how do you then adopt action plans? How do you adopt roadmaps? Again, extremely difficult.
00;12;30;16 - 00;12;54;14
So I in my view, the the SDGs have come a bit unstuck because of the inability of developed nations to provide the necessary wherewithal, including funding. And therefore, of course, the other side of that coin is the inability of of many, many nations to advance those objectives, to achieve the goals that have been set out to reach those targets.
00;12;54;14 - 00;13;32;09
And that simply is not happening. And on SDG eight in the High-Level Policy Forum in July of this year and the the process of reporting on SDH was abandoned for reasons which I think are quite obvious, and no one had anything to report. So I point to that specifically. And also I was with a number of African nation ambassadors for dinner in Brussels two weeks ago, and they pointed out that they've stopped wearing their SDG lapel pins, you see.
00;13;32;11 - 00;13;56;13
And there's two reasons for that. One is in protest at the slow progress towards the SDGs, and secondly, because of, as they see it, their exclusion from the decision making process associated with the SDGs, which, as you can imagine, has a, you know, an annual review mechanism and and and all that sort of stuff. They feel excluded from that.
00;13;56;13 - 00;14;27;04
And my own view is they are for the reasons I've I think I've mentioned or alluded to and this brings this this promotes exclusion and inequity. And again, to repeat this, this wasn't in fashion 50 years ago to the extent that it is today. Now, it is a very, very strong policy and political force. And the institutions, the multilateral institutions that take leadership on these issues now have to find ways to to address that and to build inclusion in a very, very significant and meaningful way.
00;14;27;04 - 00;14;50;08
It's not just the family photo opportunities. It's making sure that these communities, that the stakeholders feel they're involved and they are involved. They're seeing the benefits. And I suppose to that extent, it's it's you know, it's politics as usual. Boy, those those challenges are just huge. It's it's quite an undertaking to to pursue those. But I guess that's what also makes it exciting as well.
00;14;50;10 - 00;15;11;10
Since this show is called Research and Action, we do talk a lot about the need to knock down barriers and support research, but research has several stages from basic all the way through clinical. What is especially important about supporting basic research and getting that right? What are those benefits? I suppose so. Simply put, you know, that's where it all starts.
00;15;11;10 - 00;15;45;05
And when we talk about basic research, we talk about basic research, but I would also call it pre competitive research. So that's a start for, you know, is everybody's friends and everybody is collaborating before they before they apply for a patent or before they discover discover something they can monetize or exploit or innovation in whichever way. And I think a very important aspect of this is the fact that it's by and large government funded, and this gives it a very important dimension, not to mention is seeding the potential for innovation.
00;15;45;07 - 00;16;08;28
And I often reflect that if you if you the government plays a huge role in science and technology. And now I don't have the details in front of me, but, you know, as far as I understand it, about a Tesla Enterprise wouldn't be where it is today without a small business loan from the US government. And of course, Mr. Gates was a beneficiary of government contracts at a very early stage in the development of Microsoft.
00;16;08;28 - 00;16;30;01
So just to point there to the importance of government funding across the board with respect to the government investment in science and technology in the pre competitive space, there's a clear recognition that without a synchrotron or without the government investing in synchrotron or large scale science facilities, then I think we're not going to have stakeholders who can build those.
00;16;30;03 - 00;16;52;12
So it simply simply won't happen. Many, many outcomes I think are evident in terms of the investment and in science and technology. You know, basically we have an advance in knowledge. Basic research seeks to understand the fundamental principles underlying various phenomena. And I think the curiosity driven research around this then leads to much innovation. But of course you don't know that at the beginning.
00;16;52;12 - 00;17;10;28
So I think there has to be a very strong political commitment to Blue skies research. And again, I stress the word political committee because it is a policy decision for a government, any government to invest in pretty competitive research, in science, capacity building, which is predominantly pre competitive and on in there in basic science. So I think that's that's hugely important.
00;17;10;28 - 00;17;34;11
Just to point to the policy dimension, I think that then leads to various innovations and that that that is applying. So you see a very clear narrative between basic research, innovation and applied research. Many groundbreaking innovations and technological advancements have emerged from the discoveries made in basic research. And I think this needs to be spelt out very often when a policymaker gets up in the morning.
00;17;34;18 - 00;17;56;18
That can be a complicated narrative. You know what I want to be getting from this? Why spend vast sums of money on basic research, blah, blah, blah? But I think when you look at the evidence, I think then the case is is compelling. But of course, that needs to be understood continuously, primarily by policymakers. And it does bring long term benefits, The outcomes of basic research might not lead to immediate benefits or applications.
00;17;56;18 - 00;18;25;27
However, these insights often lay the groundwork for future breakthroughs, which could and very often do have significant societal, economic or technological impacts over time. Problem solving is another reason to fund and do basic research educational value. Basic research plays a critical role in educating the next generation or generations, indeed, of scientists, researchers and thinkers. It provides a training ground for students to learn research methodologies, critical thinking and analytical skills.
00;18;26;00 - 00;18;52;06
And these values have multiple applications, multiple applications. And then we have cross-disciplinary insights. I think this is self evident. Basic research often leads to unexpected connections between different fields of study. These interdisciplinary insights can spark collaborations and innovations that otherwise wouldn't come to the fore. Intellectual curiosity, I think, needs also to be highlighted. Then we have the benefits coming from scientific advancement.
00;18;52;10 - 00;19;26;18
So I think Mike, there are many, many, many benefits in that. And I'd just like to point to really one example of basic research. You may not be a follower of radio astronomy or you might be about South Africa won a global competition to build the square kilometer Array telescope, the SKA, and that was a global competition in 2011 against the UK, against Chile, China, Brazil and Canada.
00;19;26;18 - 00;19;50;25
I believe there may be one or two other countries there as South Africa won the right to host and to build the UK and it is now doing that. It's probably a 30 year project. But here you have an example of of an African nation competing to build a hugely complex scientific instrument in the middle of the Karoo desert.
00;19;50;25 - 00;20;30;21
Now why do that? Many reasons to do it. But one of the compelling reasons that I learned from exposure to the project is the enormous commitment that the South African government and now, of course, to have partner countries, including Australia, that huge commitment they have made to education and training the next generation through the scale. And you will see in the system you'll see that many US multinationals, the Dell Corporation, IBM, Microsoft have very strong project association and collaboration with the UK and South Africa.
00;20;30;24 - 00;21;00;04
When the Economist wrote about the UK in 2016, I believe it was, they said this is the world's largest science project. And I think, you know, just it's worth reflecting on that. And this has enormous, enormous future potential. It has existing benefits to the scientific community and of course it is a huge flagship idea that provides a lightning rod for scientific collaboration across Africa and across the world.
00;21;00;11 - 00;21;26;13
At a very practical level, it brings many scientists to visit the facility to work with African and South African collaborators. So this is an ongoing benefit. I think a wonderful example of what our research infrastructure is, what basic science is, and why it should be funded. Yeah, what you just described is an enormous success story. But, you know, candidly, my optimism is challenged because so much of this does rely on government participation.
00;21;26;19 - 00;21;54;08
Yet it feels like as long as money and politics is in the picture, those are the anchors that can weigh things down. And against that backdrop is the science summit. So how did the science summit become a reality and was there any resistance to it or did anybody think this wasn't a good idea or not worth doing? The as far as I've learned, I mean, the response has been universally very, very positive, extremely positive.
00;21;54;11 - 00;22;26;03
And that's because the science summit is designed aimed to advance a greater awareness of the contribution of science to the SDGs. Now, how do you do that? You do that by bringing folk together. And those folk are not just the scientists. I mean, we're not organizing an ecology conference, we're not organizing a radio astronomy conference, we're organizing a science engagement process with U.N. leadership.
00;22;26;06 - 00;22;54;09
And more than that, we are showing how science needs to be inclusive. So to that end, we have a very strong narrative around inclusion. We have a very strong narrative around development, finance for scientific education, for science, performance and investment in science. And through doing that, we are education policymakers. We are engaging with policy makers. And I need to stress this invariably is it is a process.
00;22;54;16 - 00;23;15;28
But at the end of the day, policymakers that I have engaged with at many levels in Africa, Europe and the United States, they want to make the world a better place. I don't think there's any any doubt about that at very often in that quest, they are very remote from the outputs of science for the evidence that is there that shows that science delivers.
00;23;15;28 - 00;23;38;28
Of course, it's in the system. But very often the political system of political decision making is very human. It's a very natural process. It's not always empirical. And I think as you know, and possibly in in the Western world, we see that policy making is becoming more political with a small P. So it's into that environment that we are going and showing how science makes a difference.
00;23;39;05 - 00;24;08;26
Practically. We're showing how science delivers on the SDGs, we're showing how science delivers on the future challenges. And with reference to a very important aspect, we're also highlighting the the importance of enabling access to data now, and this is you'll probably be familiar with the European Union's General Data Protection Regulation, and there are other regulatory regimes in in the United States and Canada, Japan and Brazil and and elsewhere.
00;24;08;28 - 00;24;33;19
And now we are looking at the evolution of regulation concerning artificial intelligence. Now, these regulatory processes as one outcome have impacts on access to data and the use of data for scientific purposes. There is no global regulator, there's no global policymaker. How do we address a global coordination on these issues? And that's something we want to raise within the context of Science Summit to ensure that science is data enabled.
00;24;33;21 - 00;25;00;25
When we talk about science capacity building, essentially we are talking about improving the flow of data, access to data, use of data from machine learning and AI and other purposes, and extending that capability globally. And when that can happen, then you will see dramatically improved outcomes in terms of health research at the environment, biodiversity, energy and many, many other areas.
00;25;00;29 - 00;25;44;06
But we're not there yet. That very much is in the future. So we're trying to align the debate around the objective of creating these new innovations with the need for aligning energy policy, energy technology and other information technology around alignment on regulations. That's huge, huge importance. So we see that. We see the opportunity after the United Nations General Assembly to talk to governments, to talk to political leaders, to talk to Balsillie was to talk to diplomats, to talk to regulators, to talk to bureaucrats and show them what this is, how this matters, and very importantly, how they can include optimized policies to support science in future policies at the bloc level, at nation level.
00;25;44;06 - 00;26;13;20
And we have many, many meetings bringing forward scientists to show what they do, what's necessary in terms of government regulation and support to enable. So we're talking about creating the enabling policy and regular Tory environment for more and better science. And funnily enough, we don't say that's more that's about more money. We don't feel that. We don't think that what there is, is more opportunity and a great need for alignment at government and policy level.
00;26;13;23 - 00;26;39;06
And if every country in the world goes it alone in terms of creating regulation and creating policies, then we're looking at extreme fragmentation. There is much, much untapped potential for governments to work together, and that's one reason we're very happy to be working with Oracle, because, you know, from there, you know, as a company and, you know, forgive me if this is too simplistic, but they, they they create these machines that can communicate data.
00;26;39;06 - 00;27;07;29
And this is a this is a vital and vital a vital need globally. And how they do that and future, I think, will point to many, many future opportunities, which is a very important consideration, because with the science summit and at the level of the U.N., there's there's a huge recognition of the need to work with industry players and the importance of working with industry to deliver innovations, because it's not going to be a university center in it.
00;27;07;29 - 00;27;33;27
With the greatest respect to Cork University in Ireland, they're not going to be making the mess that's going to come through a company. So and industry. So this collaboration opportunity between academia, between governments and industry, I think is ripe for transformation, I think has enormous potential to address global challenges. So can you give us kind of a feel for what kind of speakers and sessions can be expected at the summit?
00;27;34;04 - 00;28;02;24
Yes, Michael, we've got a very inclusive approach to the summit, so we're covering a lot of things, but I suppose I would accept that we have a bias towards health on the health research. On the 13th of September, we have an all day plenary on on One Health, which is a perspective that brings together planet people and animal health into a, if you like, a one world view.
00;28;02;27 - 00;28;26;10
We have a lot of amazing speakers from the five continents who will be coming to that meeting. And what we want to do then is this is relatively rare. It's a relatively new area. By that I mean it's a relatively new or a policymaking. So where want to advance policymaking in this area? We want to also promote interdisciplinary research and show how research matters across these three areas because they cannot be addressed in isolation.
00;28;26;12 - 00;28;56;06
And we'd argue at the moment, by and large, that they are. If you look at national funding systems and national priorities and all the rest of it, they look at animal health or they look at human health or they look at biodiversity. But looking at all three I think is vital. That's our that's our flagship session on Wednesday the 13th on the 14th, Thursday the 14th, we're going to focus on on pandemic preparedness and we're going to bring together the leadership from the National Research Foundation in South Africa, from the African Union Commission, from the European Union.
00;28;56;06 - 00;29;33;16
Delighted to have Irene North steps. The director for the People Directorate in Brussels is coming to join us. For three days. We have Professor Cortes at Lucca from the Medical University of Graz, who leads many European Union research initiatives. But he was the main instigator of the European Union's biobanking research infrastructure, of biobanking, of molecular resources. We should infrastructure, which does pretty much as it says on the can, and we're looking to create a UN version of that, if you like, And look at how this capacity for biobanking is going to contribute.
00;29;33;16 - 00;29;57;01
So and pandemic burden, it's very, very important that we also have President Biden's science adviser, Dr. Francis Collins, former director of the and I and the in the United States, Then we will also have representatives from Dr. Sao Victor. So from the U.S. Academy for Medicine, National Academy for Medicine. He'll be presenting the US approach to pandemic preparedness, which is called 100 days Mission.
00;29;57;06 - 00;30;22;17
What you Need to Do in the first hundred Days. We're very excited about that and very, very much looking forward to using that as a template for a global approach. And while there's been a lot of focus on global strategies, which we obviously very much support, we want to take that global strategy approach to the level of action in terms of what capacity is needed, where's that capacity needed, How can the capacity be delivered?
00;30;22;19 - 00;31;09;02
So very much looking forward to pandemic preparedness as a highlight of the summit. Then on Friday, Friday the 15th of September would have a one day plenary on genomics capacity building with a focus on Africa. But the approach will be global, But bring it forward. Will How does the capacity work for pandemic? Sorry for genomics and has been led by global industry in terms of Illumina and it's been led again by data experts, and that really looks at a future for genomics capacity building in Africa, without which we are going to be or Africa is going to be extremely hampered in the development of medicine and related therapies.
00;31;09;04 - 00;31;37;12
So there are three of the sessions. We also have the Obama Foundation having a meeting on the on the 17th of September. We're going to bring philanthropic organizations together, are for lunch on the 15th. We are going to have a number of sessions around the Amazon with the Brazilian Fapesp, the Rio National Research Agency, and they'll be looking at the future of Amazon from the perspective of collaborative research and development and science.
00;31;37;15 - 00;32;06;00
We will be working with a number of legal experts with the law firm Ropes and Gray, who will bring together experts to identify scenarios for an enabling regulatory environment for genomics that's going to take place on the afternoon of the 16th. We are going to have a number of focus days. The government of of government of Ethiopia will be joining us and they'll be presenting how the Ethiopian government presents or approaches the SDGs.
00;32;06;00 - 00;32;27;18
From the point of view of enabling science. We have a similar approach from the government of Ghana. We will have the nice people from Mongolia, the government of Mongolia. They will be presenting a regional approach from the roof of the world, and we would have the same from Nepal, from India, from Japan, from Brazil and many other nations.
00;32;27;23 - 00;32;58;22
And that national approach is very, very important because again, we want to highlight the need for synergies, highlight the similarity between national approaches and then how they can be brought together and benefit from one another. We will also have a presentation from the editor of Nature, Magdalena Skipper at They'll be presenting a what they call a storytelling evening, and that's that's designed to inform and show how science careers evolve.
00;32;58;28 - 00;33;27;05
So so the community can get an understanding of of how that has worked in a number of individuals so very much at look at looking forward to that. I think that personal aspect is is very, very important. And we will be having a number of sessions with with investors how they are approaching investing in science and technology, how that investment can be better aligned between governments, industry, not for profits, philanthropy.
00;33;27;05 - 00;33;50;18
And we're feeling we're seeing that a lot of these organizations have similar objectives. So there's enormous potential to see how they can be more aligned, work together for common objectives and thereby increase possible benefits and outputs. So very much look forward to dose those discussions. In terms of our principal outputs, what we want to do really is three levels.
00;33;50;18 - 00;34;12;01
First is we want to increase participation and collaboration. So we want to bring people together. And one of the main outputs of the science summit last year, researchers discovered each other. They went away and they started collaborating. That wouldn't have happened if they hadn't met at the science. So that's one level. Second level is what our agenda is.
00;34;12;04 - 00;34;44;27
So the United Nations will convene the summit of the future in 2024. So the question we're asking everybody is what should the science agenda for that meeting look like? And we want to compile it. And with the 400 odd sessions we're running, we want to work with them and see how can they contribute to that, What priorities can they put forward and how do they look in terms of a specific objective which the United Nations can support in terms of energy attainment or the post SDG agenda?
00;34;44;29 - 00;35;22;06
And the third element we want to advance is better policy making, make better policies. We will have tennis knocked and Dennis is the chair of the Inter-Parliamentary Union Science Committee. The Inter-Parliamentary Union is a global organization and represents 138 parliaments around the world. This dialog is hugely, hugely important. So we're going to be working with Denis to see how his members so those legislators in those 140 odd countries can incorporate better global ideas into policymaking at a local level.
00;35;22;06 - 00;35;52;29
And I'm talking about I'm talking about Nepal, I'm talking about Ghana, I'm talking about Kenya, I'm talking about many, many countries. And then what we what we hope that that will achieve is real sustained change. And as we move towards the end of this decade, that's going to be hugely, hugely demanding. But I think if we build this global momentum and we drive this cooperation and instill a sense of cooperation among scientists globally, and also we say that, you know, scientists in fact, are policy policymakers.
00;35;52;29 - 00;36;10;12
I don't see this divide between policymakers and scientists. I think scientists have a huge amount to contribute to policymaking. So, in fact, they're the policymakers. They know a lot about health, They know a lot about what policies are needed to deliver better health. And we want to give them a voice. Well, as I mentioned, Oracle will be speaking and participating at the summit.
00;36;10;12 - 00;36;37;01
And you touched on it a little bit. But when you think about the role for industry players, especially technology giants like Oracle and what's needed to pursue the SDGs, we've talked on the show a good bit about the concept of open science and increasing access to scientific data. It feels like big advances in global health can't happen if those developing or lower middle income countries are kept at arm's length from data.
00;36;37;04 - 00;37;00;02
Absolutely, Mike. Absolutely. Very, very well said. And as I've outlined, is that one of the main impediments potentially to this is regulation by advanced nations, which impacts on less developed nations. So I think an industry has a huge role to play in that because, you know, industry and providing the wherewithal to to advance this data exchange. So we very much look to industry leadership.
00;37;00;02 - 00;37;16;20
And I think Oracle is going to be very instrumental there in showing and leading the way in terms of how data is enabled and how data systems can allow access to data use of data, and of course the use of data for machine learning. And I think that's something we need to learn a lot about, particularly in developing nations.
00;37;16;23 - 00;37;35;25
I also think that the United Nations Global Sustainability Report, the latest version of which is available in draft, and I think the final version will be published at the end of this month. Points to a huge role for for industry. My own view is that I think industry need to be much more at the table at this U.N. table.
00;37;35;25 - 00;37;56;24
I'm delighted to see that Oracle is joining us in this quest, because I think we need to build a narrative and I think it'll be for industry are going to be a very credible partner in terms of telling governments what is necessary, what's needed in terms of creating the space for data to do what data needs. And again, in particular in the countries that are going to be challenged in their quest for access to data.
00;37;56;27 - 00;38;33;03
And that presumes that they have the capacity to have the infrastructure. Many don't, but they're going to need to have that and the industry going to be critical in delivering that. And I think that's that's terribly, terribly clear. So that role for industry in delivering, I think, spans the optimization of policy, the optimization of regulation, the deployment of technology, the maintenance and sustainability of that technology, and of course for the advancement of that technology into different areas in its application, particularly in ICT application, in the areas health and energy and the environment, biodiversity, climate and so forth.
00;38;33;06 - 00;38;55;25
And I think this is something that provides a gives me a lot of optimism in future. And I think also almost we're looking at a, if you like, a post, arguably a post regulatory model where where technology will allow us to define the the remit of Data Act access. I don't think we're there yet, but I think this is this is possibly in future.
00;38;55;27 - 00;39;16;01
And again, Oracle and the colleagues from Oracle will be engaging in a number of discussions on the regulatory side, on the technical side, on the access to data side that's going to help the communities understand not necessarily the solution, but at least define the questions. I think define the questions. Then we have a much greater opportunity in obtaining the answers.
00;39;16;03 - 00;39;39;17
Well, also in my intro, I mentioned that you are founder and managing director of ISC Intelligence and Science. Tell us about that endeavor. What does that do? Well, that that mainly is devoted towards building body types, capacity and advising governments on science. Capacity Building that many faces is based around scientific infrastructures. And of course they come in in many, many flavors.
00;39;39;22 - 00;39;59;29
But ours really is around the design of research infrastructures that that tends to be quite a long, competitive, drawn out, complicated process. Of course, for any funding, there is a there is a competitive process. This takes a a number a number of years, very often for an award, then a subsequent number of years for a design phase to be completed.
00;40;00;05 - 00;40;21;02
Before then you move into construction and operation. Our primary focus is on the design phase and we've done that in in Africa. We do it in India, in in North America, Latin America. And one of our main reasons for focusing on this area is because it means the capacity is there to to allow science to do what it does.
00;40;21;02 - 00;40;46;01
I've mentioned the case of the SKA and in Africa there are many others. But I would say hitherto there's been a lot of differentiation between science capacity. And of course this is this is quite understandable. But I think increasingly in future that capacity will be effectively one big data machine. It won't matter what flavor of science you're doing, you're going to be dipping into a common data reserves.
00;40;46;01 - 00;41;23;05
Now, there's some caveats around that, such as a a synchrotron, for example, or a light source. I think these are, as you can imagine, specific unique instruments. But we're looking forward very much to have the director of the Office of Science in the United States, Dr. Esmond Barrett, talk to us about how this can work on a global level and what are the challenges and how the US experience in building these science infrastructures and capacities can then help many, many other countries to to advance towards not net, not necessary do the same, but at least be on a path to access such capacity.
00;41;23;05 - 00;41;52;08
So ESI has been very, very involved in that and also involved in the regulatory aspects of the impact of updated regulation on science is something we're very exercised about. If we feel that the scientific community historically, by which I mean maybe over the last 15 years have been very slow to understand the implications of regulation of science, but equally the regulatory bodies at national level, equally have been very slow to understand the impacts of science because their primary concerns are not science.
00;41;52;13 - 00;42;23;27
The primary concerns are as they see them is the protection of individual data, etc., etc., etc. and that's very worthy and noble. But then once you pull the thread, you see that that has aspects and implications for scientific endeavor. So we're working in that interface, ensuring or trying to ensure or trying to increase respective awareness and visibility. And now this is has a very sharp focus in the advent of a EIA, the Artificial Intelligence Act in the European Union, which will be defining for reasons we mentioned earlier.
00;42;23;27 - 00;42;43;12
Also, we are very active in that space and we're very particularly active and, and how this seen, how this impacts on less developed nations. Well, Declan, again, we appreciate you being on the show today. If people wanted to learn more about the science Summit or ISC intelligence and science, how can they do that? Main ways. The website for the Science Summit is Science Summit.
00;42;43;15 - 00;45;13;24
It is sciencesummitunga.com the company website is ISC intelligence dot com and then you'll find the usual links to Twitter and all the rest there. Very good. We've got it. And if you listen are are interested in how Oracle can simplify and accelerate your own scientific research. Just take a look at Oracle dot com slash research and see what you think and of course join us again next time for research and action.
00;00;50;13 - 00;01;24;04
Her research looks at how we can enhance human information processing capabilities by designing adaptive interfaces based on rigorous models using machine learning and computer vision algorithms. With over 100 peer reviewed publications. Professor Ostadabbas has received recognition and awards from government agencies like the National Science Foundation, the Department of Defense and several private industries. In 2022, she received an NSF career award to use AI for early detection of autism, and she's working on that with Oracle for Research.
00;01;24;04 - 00;01;43;26
Dr. Ostadabbas, thank you so much for being with us today. Thanks for having me. I'm excited to be here and feel free to call me Sarah. Well, listeners, get ready because we're going to get all into computer vision, machine learning, augmented cognition and wherever else I can get nosy about. But first, let's hear about you, Sarah, and your background.
00;01;43;26 - 00;02;12;08
Your passion for technology and physics kind of started back in childhood, right? Yes, that's correct. Actually, physics was my favorite subject in middle school and high school. I was so passionate about it that I even went through the whole volume of Fundamentals of Physics by David Halliday and Robert Resnick in I believe it was in 10th year of my high school, and I was seriously considering to pursue the continuous PhD in physics even before graduating from high school.
00;02;12;10 - 00;02;39;09
And alongside my love for physics, I was always also fascinated by technology, especially computers and programing. I started coding in a language called Basic, which some of your audience may not even heard about that. Why I was in middle school and loved it. Data Analytics capabilities of computer and how computers are giving advanced processing power to human no matter where they are.
00;02;39;11 - 00;03;12;14
I was still living in Iran at the time and experiencing technological advances at that time, such as Internet and cell phone, and they were all very much interesting. And fast forward, all of this led me to pursue a natural combination of my interests, which was an electrical and computer engineering degree with a double majoring in biomedical engineering. And now when I look back, it's actually heartwarming to see one that one seemed to be diverse.
00;03;12;14 - 00;03;41;17
Interesting collection of interests now have shaped my academic journey so far. Was it unusual for someone, you know, at your age, at that early age of middle school, to be coding and thinking about technology and physics and looking that far into the future? I was actually going to date if school, middle school and high school at that time was designed for for math and science.
00;03;41;17 - 00;04;06;00
So no, I had a lot of of my classmates going and exploring different science topics. So it wasn't unusual. I mean, it was unusual when I was taking these heavy books to my gathering at parties, at my family, but not at the school. So I'm glad. And it was 200 of us, 200 girls at and now all of us are all around the world.
00;04;06;06 - 00;04;28;02
Most of us have PhDs. And yeah, it wasn't unusual, but it, it was something that I cherish. Yeah, it's great that you had a school that focused on things like that. So let's kick things off with your NSF CAREER Award focused on developing machine learning algorithms towards the early detection of autism. Tell me if I get this wrong.
00;04;28;02 - 00;04;53;08
But this is about using computer vision to predict autism a lot earlier in children. And what does what does that research involve and what does Oracle for Research have to do with it? You're certainly right. As I mentioned, my academic background revolves around electrical and computer engineering, focusing on data processing. And these data sources can be signals, images and videos.
00;04;53;11 - 00;05;21;06
How might a specific focus a work on computer vision began when I joined Northeastern University as an assistant professor in 2016. As you may know and have heard of over the past decade, deep learning models have been driving advancements in many AI topics, including computer vision. But these algorithms often require a large amount of training data. They are very data hungry.
00;05;21;08 - 00;05;48;24
So my National Science Foundation CAREER Award aims to leverage this advancement in computer vision for a specific health related domain that suffera from limited data. And I'm in particularly focusing on detecting autism in infant even before the first birthday. And this is true processing videos that is collected from them when they are doing daily activities, which is not a lot of things that they do.
00;05;49;01 - 00;06;16;13
They are sleeping, playing or eating. And as I mentioned, my algorithm, they are designed to be data deficient because I'm working on the area that the there are not a lot of data due to this privacy and security reason, but adapting these complex networks, these complex neural networks which are which are building blocks of deep learning necessitates powerful computing resources.
00;06;16;20 - 00;06;44;25
And that's where our collaboration with Oracle become highly valuable, allows me to make this model adapted to this specific application. So you have videos, video cameras, monitoring the kids and kind of like an in the wild get capturing of data. And then the computing power is needed to crunch all that video and that pulls out certain patterns that reveal autism earlier.
00;06;44;25 - 00;07;07;14
Is that how it works? Yeah. I mean, you can say that you put that on the simpler words. Yes, exactly. I'm a simple man. No, no, no. I'm just it's a good I mean, it's a good, good way to describe that. Yes, that's correct. So what we do, we actually leverage these computer vision techniques and contactless video processing algorithm to predict autism, as I mentioned, from daily activities.
00;07;07;19 - 00;07;35;17
And these are daily activities captured by commercial video recording messages. Imagine like a baby monitor or even parent's cell phone cameras. Every parent's love to record videos from the day of their child. So they focus on this specific developmental sign. How will that that relates to motor function, which means that relates to infants posture, muscle tone, body symmetry, and they balance and range of movement.
00;07;35;18 - 00;08;04;05
So these are specific markers that actually has been shown to be early visible warning signs of more developmental disorders such as autism. And they appear actually interestingly, long before the core feature of autism that you may have heard of and these are actually very known, such as social or communication difficulties as well as repetitive behavior. So we are focusing on these early signs.
00;08;04;08 - 00;08;29;11
However, currently the standard approach to monitor this motor function is through visits to child doctor, pediatrician and how is it, unfortunately, over half of these visits are missed. You could imagine often due to the lack of transportation, for parents, it's hard to take time off from work and also lack of child care for other other kids set at home.
00;08;29;13 - 00;09;12;29
So half of these visits are missed and a lot of this early sign has been overlooked. So to address this in equitable access to actually to clinical assessment and a lot of practical constraints, we are trying to to make a home based a I guided in monitoring tools that can track early motor function development very unobtrusively, like just a video that is watching like a baby monitor is rolling and then be the process this video on the back end and track this specific developmental sign and hopefully be we help for the early detection of autism.
00;09;13;02 - 00;09;40;15
I want to also point the fact that it's actually important, very important and crucial to have timely detection in the autism case, because early intervention, it's actually shown that is most effective before the age of four. Yet the average age of autism diagnosis is still around four and a half. So we are hoping to make a clear detection tools better intervention outcome.
00;09;40;18 - 00;10;00;06
It's really interesting to me that body symmetry is a hallmark of development. I guess my question is why would that be and how is Body Cemetery being addressed in your research? That's a very good question. So we are as I mentioned, a motor development is very important. If early signs offer any visible sign of something that may not working out right.
00;10;00;09 - 00;10;32;14
So one interesting aspects of motor function that has been identified as an indicator of neurodevelopmental health is body symmetry. You can imagine that symmetrical movements and posture are crucial for supporting independent movements such as sitting, crawling and walking, especially infant. Then an infant is typically developing movement posture. Actually, you start asymmetric and then gradually they become more symmetrical as our sensorimotor coordination develops.
00;10;32;16 - 00;11;05;06
And during the first year of life, infants could go through the various milestones, such as days rolling over, sitting up, standing so little by little watching, and all of these movement progressed from less symmetric to more symmetric movement and then also study, they have been looking at the infant movement. They have a map showing that the position is symmetry in their movement can be indication of disorders like autism.
00;11;05;09 - 00;11;28;09
However, if we want to have motor functional function assessment in infant, especially body symmetry in larger scale for a long period of time, our for health care provider is going to be very expensive. I mean, somehow impossible and very challenging because imagine if you have 10 hours of videos, how long does it take for you to watch that?
00;11;28;09 - 00;11;54;10
10 hours. I mean, it's going to take 10 hours. But what we want to do, we want to have these computer vision tools apply on these videos to automatically evaluate them all to a function and is start having something in home that people can use and start escorting to one of the mutual developmental indicators, escorting them the symmetry.
00;11;54;12 - 00;12;23;06
So the idea is that we are actually using infant pose estimation algorithms that we have already developed in the lab to assess postural asymmetry based on differences in joint angle between opposing the arms, between the left side and right side. So the effect the the difference is more than 45 degrees, which has been suggested by Esposito in this study in 2009, in the we can call it asymmetric.
00;12;23;12 - 00;12;50;15
We have also come up with our own measure, which is a data learned based assessment on using Bayesian assets to collect aggregation that we could actually come up with two different angles. But how that these are all allows us to do to process the beat you automatically. And then the video is called the whole movement of the infants based based on all of this processing symmetric or asymmetry.
00;12;50;15 - 00;13;12;01
And then physicians can look at that and see that it is something alarming or not. And then as the process of the science and research goes on, well, I've talked to enough researchers to know that recruiting is usually a challenge for any experiment. But with this, the target population is children like babies. How did you manage to get your patient population?
00;13;12;01 - 00;13;39;15
Were there any privacy, access or ethical concerns? It's a very good question and also absolutely an important matter. When recruiting for our experiment, we noticed that the challenge of targeting infants subject under the age of one, parents are already overworked, sleep deprived, and imagine asking them to to be part of yet another task. So it's very hard, however, to be able to overcome this this problem.
00;13;39;18 - 00;14;16;20
We leverage the fact that many parents already are using baby monitoring systems, so they just want to wash them. I mean, a lot of these baby monitors, even the one that they call smart, they don't do anything. It's just a trigger. If the mat the baby's crying or they are moving. So we are aiming to develop this normal system that not only allow the parents to observe the child, but also offers this long term monitoring capability to track the child's developmental process and provides alert if some abnormalities are detected.
00;14;16;26 - 00;14;38;14
So this may be a good incentive for for parents to take part in our study. And as one of the points that you mention about the privacy and ethical concern, we have taken several measures to make sure to address these concerns. We are collaborating with health care professional that they are more familiar with to dealing with the human subject.
00;14;38;17 - 00;15;15;14
And also we are working closely with a Northeastern Institutional Review board known as IAB to make sure our data collection protocol has strict security and privacy standard. We make sure that the parents that they are participating in our study are fully informed about the purpose of the research. And also we get they consent to to use some some part of these data for public use and public release for scientific and technological advancement, because a lot of them these days, how to win is shared in other a study can be built on top of that.
00;15;15;14 - 00;15;37;19
So but we make sure that parents are that the parents that they are part of this study, they are they are aware, fully aware of that. And I want to emphasize that our priority is to preserve the privacy and confidentiality of them, the participant to out the whole process, although they are looking and working on very important and impactful research.
00;15;37;19 - 00;16;05;12
QUESTION But this is also very important at the top of our list. Yes, security and privacy data for data that is important. Is that why a tech concern like Oracle Cloud that obsesses over things like privacy and security kind of speeds up the research? That's very good. Good point that you brought up. That's true. As I mentioned, security and privacy of the data, especially in our field based on the sensitive nature of data that we are collecting, is important.
00;16;05;16 - 00;16;50;21
We are working with them with personal health related information. So we required some sort of robust measure to to protect confidentiality and prevent unauthorized access. And working alongside part industry partners like Oracle ensures that we are actually having a huge safeguard on our sensitive information. The team that I am working with, Oracle has this huge expertise in data management and security practices, and this allows us to then when we are storing, processing and analyzing data in a in a protected environment, we can focus on our research objective while having a partner that gives us confidence in the security and privacy of the data that they are handling.
00;16;50;21 - 00;17;22;04
So it's a very useful and necessary collaboration. So your lab Augmented Cognition Laboratory or the A.C. Lab works with Computer Vision and machine learning. How did that lab come to be and what exactly is augmented cognition? This is actually brings back many fond memories for me, I think. Tell you the story behind the name, Why I was interested in physics, computers, math, and even literature.
00;17;22;04 - 00;17;53;11
I mean, this is specific. Interest by itself can be another podcast session, but not now. I always had a vision of becoming a university professor and leading my own research lab. I remember clearly that I wasn't seen earlier for my Ph.D. when I started to look at look for names for my future lab to reflect the into intersection of engineering inspired artificial intelligence because I was farming, doing school and data analytics.
00;17;53;18 - 00;18;28;25
But also I wanted to emphasize the positive impact of A.I. in human life rather than replacing them. So I came up with the name Augmented Cognition. Augmented Cognition. I actually represent the core idea that I have about enhancing human information processing capability through the design of adaptive interfaces guided by A.I. algorithm, especially machine learning and computer vision. This is specific definition is actually opening of my my web page when I started at my my position at Northeastern University.
00;18;28;28 - 00;18;59;00
This also highlights my focus on utilizing these advanced tools to augment human ability, especially in the data processing domain. Imagine what I'm doing here as part of my NSF CAREER and what I want to to give physician parents the power of processing hours and hours of data and then let them to extract the information that is needed to to make sure to make the informed decisions.
00;18;59;02 - 00;19;23;13
I often have this phrase that at the ACLab we use artificial intelligence or AI to do human intelligence amplification or IEEE. So I do more Iot and A.I.. Your work relies a lot on machine learning and computer vision as tools to generate truly augmented intelligence solutions. How do you leverage the recent advancement of AI in your work?
00;19;23;13 - 00;20;02;06
Because you've probably been watching it for years, but for most of the public, this A.I. thing came on like a tidal wave. So how does that get applied to computer vision? That's true. I mean, I it's the main wave, and I believe in my my opinion that the main a wave and also success is started from with the introduction of deep learning in 2012 2015 and the actually expand the recent advancement in AI to tackle challenges in understanding and predicting human behaviors from vision sources.
00;20;02;06 - 00;20;43;22
As I said, images or videos, I am focused my my work focus on representation learning in visual perception problems such as object detection, tracking and action recognition and using all of these these tools, we want to estimate the physical, physiological or even emotional states of the individual under study. So to be able to do a robust estimation, the algorithms that we are developing at the Sea Lab utilizes this concept called Pose, which is a low dimensional embedding that captures the essential information in the state of the human that we are monitoring.
00;20;43;28 - 00;21;10;14
For example, body pose, facial pose. You could imagine that you could from that to you can get body symmetry, you can get the emotional feeling of the the human. So help me that I want to emphasize the fact that many of these human data focus application that I work on belong to this small data domain. But the data collection and labeling are limited or restricted, such as healthcare application or even military application.
00;21;10;21 - 00;21;42;26
So to address the data limitation, my algorithm also integrate explicit domain knowledge into the learning process through the use of a generative AI model. We actually built our genitive AI model that this model, they are all data efficient machine learning while incorporating valuable insight from domain experts. So this allows us to to use less data. But on the other hand, we have all of these backing from from the experts that allows us to to make our model work.
00;21;43;04 - 00;22;18;24
This means collaborating with professionals from various fields such as physicians, psychologists, even physicians and neuroscientists are very much important and ensures the practical relevance of many of the models that we are developing in the lab. I definitely see use cases for improving health care and data analysis and augmentation. But for the clinical space, are you a let's go for it person when it comes to AI or more of a cautious person and there is a responsible way to apply, I think that your question comes from all of these debates happening.
00;22;18;24 - 00;22;43;25
Is AI for good or for bad? I mean, what we do, to be honest as a researcher working at the intersection of AI and health, I have been trying to keep a balanced perspective on this overall impact of AI. I am an optimistic optimist when it comes to the potential benefit of AI for health care, particularly for the data analysis and intelligence augmentation.
00;22;43;25 - 00;23;05;06
As the name of my lab, we then come back. I believe that A.I. has the potential to change the healthcare and improve diagnosis, personalized treatment, enhancing patient care, and expanding access to care, as I mentioned. I mean, you can actually make an air power system at your home and get the monitoring and the diagnosis that that you need.
00;23;05;08 - 00;23;35;10
And it can help clinician to make more accurate and timely decision leading to better outcomes for patient health. There is not that I'm just only say is the best and now we don't need to to think about other aspects. I also approach the use of AI in the clinical space, especially with caution. We have to be concerned and to address this concern related to privacy, security and ethical use.
00;23;35;12 - 00;24;02;29
We have to be transparent and accountable and ensure that a AI system are fair, unbiased and trustworthy. These are useful for on on human subject. So proper validation and rigorous testing are necessary to make sure these models are reliable and robust. Also, it's very essential to involve health care professionals, patient and other a stakeholder in the development process.
00;24;03;05 - 00;24;29;20
It cannot be inside AI sitting the lab and come up with something as okay, this is perfect. Let's so let's put that in every baby monitor around the world. We have to make sure the system is safe. A specific needs in inside the health care domain. So in one sentence, I believe that with responsible development and implementation, AI has the potential to significantly improve improved health care outcome.
00;24;29;22 - 00;24;59;11
And I'm hoping this balance will that of you, especially in the clinical setting, allows us to to work more to make better and stronger and more robust AI model while addressing the concern and challenges that comes with its use in the clinical space. Well, I know based on what you said, and because I cheated and researched you before you came on the show, that you you believe that AI, as long as it's good, should be able to augment our capabilities.
00;24;59;11 - 00;25;24;04
And again, you're saying not replace human capability, but augment capabilities. So as you mentioned, the average age of detection for autism is about four and a half years olds. How much and you mentioned about one year old, that's how much sooner than that you think the research could detect autism. And if you do detect it that much earlier, then what Can we actually improve developmental growth?
00;25;24;06 - 00;25;54;17
So before I proceed, I want to make it clear that I don't have any formal academic training in the health care domain. Power through my extensive collaboration and engagement, I have come to understanding the significance of the early detection in neurodevelopmental conditions such as autism, and also how timely intervention can improve the developmental outcome. So as you mention and that's right, the current average age of autism detection is around four and a half years.
00;25;54;20 - 00;26;27;02
But through our research, we want to aim to significantly reduces this age and we are hoping to make it on the age of one because we are able to detect this specific neurodevelopmental model signs unobtrusively, automatically and long term using our computer vision algorithm. And let's remember that the fact that the brain exhibits its highest level of neural plasticity during the first year of life.
00;26;27;04 - 00;27;14;09
So intervening during this sensitive window can have profound impact on long term. So the sooner that we can catch some of these not neurodevelopmental disorder, then the rehabilitation can start. And also intervention can be much more accurate. Also detecting a system that can track and quantify infant development aside from autism can can be used to detect and test other hypotheses related to a motor function hypothesis that based on my collaboration with other health care professionals related to this, a liberal policy congenital tool to coalesce list out that all of this stuff that has some motor representations, but they are not catch early.
00;27;14;09 - 00;27;43;09
You know, because infants are at home. Parents are especially new. Babies have a lot of work so they they missed a sign and then the number of visit is very limited if not missed. So by advancing the age of detection and enabling early intervention, I am not only hoping to have the individual outcome, but also the whole idea is studying other and testing other hypotheses in their developmental science.
00;27;43;09 - 00;28;12;17
So hopefully that would be a tool that empower researcher, physician parents in the field to study these motor related developmental condition much earlier and less expensive and much more on up to the CV. Well, research does need data for exploration and reproducibility, but a lack of data sharing in the research community is kind of a hot topic. There are several people that just doesn't want our collective knowledge to collect.
00;28;12;20 - 00;28;48;13
So why is data sharing vital to advancing science and getting to new discoveries and treatments? For sure, I'm not among those group that they don't share. I think I believe the data sharing plays a very important role in advancing scientific research. So essential for reproducibility, transparency and collaboration. So by sharing data research, it can not only validate what you have done, reproduce that, but also they can build upon your finding and start building new and new discoveries.
00;28;48;15 - 00;29;14;03
So rather than everybody start from scratch. So sitting on your data and not sharing that, it's I don't see that is a scientific manner. This is very fundamental. We do, we do actually share the data on both the data and code in our lab, in the computer science and engineering field is is known that people share data. They could, but in the medical domain, this data is very protected.
00;29;14;06 - 00;29;44;06
And it's I understand all of their privacy consent. But in our data collection procedure, we make sure that we inform at the participant about the value of data sharing. So we get they consent to share these data is pieces of the video that they are collecting. And then I am hoping that collectively we can add best knowledge, at least address complex challenges related to data specific types of a question that we are addressing.
00;29;44;06 - 00;30;16;28
And ultimately we want to improve human health and well-being well-being and enhance the quality of life for everybody. Do you think some of that reluctance has to do with concerns about intellectual property and researchers thinking about, you know, the marketability of what they're doing? Absolutely. Absolutely. That's the case. But I have a counter argument for that. So this is not 2000 years ago that we we come up with an idea and write it down and then buried so nobody can find it after after us.
00;30;17;00 - 00;30;40;22
So I think by sharing with the acknowledgment of that there the research and who came up with that is important. But if we keep this strain of sharing thoughts, sharing ideas, sharing data, which data nowadays holds a lot of intelligence insight inside that, then we can actually build and everybody get into the training of the is Discovery new discovery.
00;30;40;29 - 00;31;13;07
So if we want to keep that it's possible and then in industry because now the line between industry and academy is not as the strict as before because there are a lot of collaboration happen which we're very much I admire. But yeah, we have to to make sure to acknowledge both sides, industry and academics, to acknowledge their contribution, but then share the data and see and be happy on the growth, be happy about advancing the knowledge and the complex problem cannot be solved if we just keep it to ourselves.
00;31;13;09 - 00;31;41;09
Well, our audience of researchers is pretty bright. So is there anything else you'd kind of like them to know or for them to think about that we haven't touched on yet? Just something that you wish people paid a little bit more attention to. Oh, thanks for asking. Yes, I think that this in this podcast you talk about my research related to the use of AI in computer vision for for autism.
00;31;41;11 - 00;32;07;08
A study, as I said, that I don't have any any health care background. However, in my my lab doesn't only work on the autism patients, we are actually interested in developing computer vision and machine learning solution for a wide range of application dealing with the small data problem. The data, it's the the bread and butter of us because the intelligence, especially in the era of deep learning, it's all hidden in the data.
00;32;07;10 - 00;32;34;07
So I work on the rehabilitation, animal monitoring, even autonomous driving scenarios that is hard to collect. Data is expensive, is dangerous to collect data or is impossible. Sometimes, for example, it's very hard to to collect data from animal in this specific pose or conditions. So that's one thing that's enabling these advancements, especially advancement in computation and machine learning in this small little domain is important.
00;32;34;09 - 00;33;05;00
So rather than to do not be afraid or shy, if you think that, okay, this specific application needs a lot of detail, we don't have that. So let's not use let's abandon all of these advancement that we have because we don't have a lot of data. No, it's possible. And in our lab we are working on that to enable these advancement in the domain that rather than having millions and millions of sample, you have only 100 samples, you have only 20 samples of that in Central and all that.
00;33;05;02 - 00;33;28;04
So in my lab we are looking at the problem time to size. First we want to see that if we can make our machine work with less amount of data as I mentioned earlier, how we can do that, we should actually make research a space for the parameters of the model, make it more constrained by bringing some outside domain knowledge inside the model.
00;33;28;07 - 00;33;47;08
So rather than be say that, look, I don't want to hear anybody else's idea. I just want to look at the data and see what's happening. We only take them. They are data driven models. We are putting in some understanding of about the physics, about this specific phenomenal behind that, about the specific types of movement that we are looking for into the model.
00;33;47;13 - 00;34;15;21
So to make the model work with a less amount of data. On the other hand, we we were thinking about this in digital expanded this data is called synthetic data generation. So we are looking at a lot of simulators, even game engines, to see that if we can use them and make an avatar of infant, for example, fall from the bit better than looking at videos or waiting for infant fall of the bit, we actually see that picture can be simulated.
00;34;15;21 - 00;34;35;10
These data can be simulated driving in a very low trouble stability environment rather than asking actually a driver to go to do that. So these are also use of their simulators and synthetic data generation. So we expand the data as much as we can in the synthetic domain. And also we make our model to work with less amount of data.
00;34;35;16 - 00;34;55;27
So hopefully in future we are not abandoning this specific application and the use of AI in there because we don't have data. And if our audience does want to learn more about you or your research or the lab, is there any way they can do that or get in touch with you? Yes. My email, I'm actually very fast and responding to email.
00;34;56;00 - 00;35;41;19
You can find my email at my web page. And also you can find me a LinkedIn, send me a message there we we share our news in different platform but yeah the best way contacting me send me an email we do have them also even high schooler at our school right now that I'm talking with you Mike I have three high schooler they are collecting data from an avatar in fact in completely virtual world and they are just we are we want to use that to train our model to detect how intense to reach and grasp.
00;35;41;21 - 00;38;06;24
Gosh, that's great. So, Sarah, thank you so much for being on the show with us today. And to help people find you, I'm just going to spell your last name for them. It's Ostadabbas. So that's the way you can look up Sarah. And if you are interested in how Oracle can simplify and accelerate your research, check out Oracle dot com slash research and join us next time on Research in Action.
00;00;57;20 - 00;01;26;08
She's currently a research advocate at Oracle for Research. And we're going to learn what those research advocates do and get into a lot more. Thanks for being with us, Christine. Thank you so much, Mike. I can't wait to dive into this. Oh, yeah. I'm looking forward to it as well. And I am going to ping you with questions about clinical research, epidemiology, pharmacoepidemiology as I keep tripping over that word, probably where is what kind of shape health care is in.
00;01;26;09 - 00;01;48;12
We're going to talk about that and some other stuff. But first, what got you as a person interested in this line of work? Kind of give us a little history lesson on Christine. You know, growing up, I lived in rural upstate New York, so I lived right outside of Rochester, right up on Lake Ontario, in a really small town of Albion, New York.
00;01;48;14 - 00;02;17;09
And, you know, there really wasn't a lot there in terms of researchers and health care access, to be quite frank. And, you know, I was diagnosed with type one diabetes at a really young age. I was diagnosed at eight and it I had a couple options When I got diagnosed, I could either face it head on or I could kind of sorrow in getting diagnosed and, you know, kind of letting it take over my life.
00;02;17;09 - 00;03;05;17
And I chose truly to jump two feet in. And I was so interested in being kind of up to date with all of the newest, latest, greatest technology and research updates. And I would find myself as a young kid trying to Google once Google became available, what certain words meant and really kind of educating myself about it. But quickly, growing up in a small town, not having that research access and really not having access to health care providers that even necessarily were familiar with that technology, my parents got me connected with the University of Rochester, and that's where I had a lot of my care growing up.
00;03;05;20 - 00;03;34;21
And really got to to learn and grow as as a kid alongside some of the brightest scientists in the field and it truly inspired where I wanted to go and was so excited when I entered college at the University of Rochester and really getting to work more hands on than you would as an eight year old kid and really just fell in love with the field.
00;03;34;21 - 00;04;06;24
And so I didn't know what epidemiology was even entering college and quickly kind of figured it out. During my studies. You know, like a lot of kids at the University of Rochester, you kind of go in premed, everyone's going to med school. And unfortunately I got rejected many times. And so when I got my MPH, I really fell in love with that and really getting the opportunity to dive into AP research.
00;04;06;24 - 00;04;59;01
And I did a little bit of that at the New York State Department of Health, but really got to spread my wings at the University of Rochester in the Department of Neurosurgery, really exploring health outcomes for patients and really understanding how do we make patients first in research and it kind of set me on this journey. So this past year, I got accepted into UMC Chapel Hill's PHARMACOEPIDEMIOLOGY program, where I get the opportunity to start to understand how pharmacy or pharmacology so all of the treatments that patients are receiving impact their care in certainly looking for ways to continue to always drive patient care and continuing to accelerate new discoveries.
00;04;59;03 - 00;05;23;28
And I absolutely love it and love that. Oracle's giving me the opportunity to do both things, work full time, and also be a full time PhD student. Yeah, I think that's kind of common. I've known several friends from high school who, you know, their path into medicine was a result of something that they experienced themselves, whether it was getting put back together after a car wreck or a disease that they have.
00;05;24;01 - 00;05;50;24
How does your personal health journey with Type one diabetes influence the way you approach research and your job at Oracle now? Because it is kind of a different lens than someone who's just coming at it. Purely academic, purely scientific. Yeah, I think I kind of have to wear both hats to be totally honest, but I think the way that I approach clinical research is really with patients in mind.
00;05;50;29 - 00;06;32;17
Patients have so much knowledge and experience that they can kind of engage in that research process and really understanding how to combine the patient perspective with the traditional research perspective has really been super rewarding and really engaging and allows me to bring my experience as a patient and certainly as a patient advocate forward. And now with Oracle get diving headfirst into the health care space, it really allows me to kind of bring a bit of that perspective to our researchers as well.
00;06;32;19 - 00;07;05;27
And always talking about the new discoveries that they're doing. But how can we relate it back to improving patient care and accelerating discoveries, understanding really how digital health can also revolutionize the way that we've been doing that versus I was that kid that would always bring Excel sheets to doctor's appointments. But I think, you know, I think digital health really is the opportunity to combine new technologies with accelerating the way that we're doing research, which I'm really excited about.
00;07;06;04 - 00;07;30;22
Well, you were talking about how when you were younger, you were making yourself an expert in your condition and probably, you know, seeking answers rather aggressively. Were you happy with the degree to which you were being listened to or did you just keep running up against a brick wall? MM That's a really good question. I have to say I was so lucky as a kid.
00;07;30;26 - 00;08;05;09
My physician was or I should say my nurse practitioner was a type one diabetic herself, which honestly gave me a completely new perspective on life and on the trajectory of the disease. To have somebody who's treating your condition, who is super busy as all of our advanced care practitioners and our MDs are so busy all the time, to see her living a life like that truly impacted where I wanted to go.
00;08;05;13 - 00;08;41;06
Going forward, I will say there were times where I felt like it would not just with my diabetes care, but, you know, with health care in general where you're experiencing something or feeling something and you're like, just listen. And I think that's really where being able to have that patient interaction and research is going to be really critical to understand the unique nuances of health things present in individuals because everybody is different, which I think is really going to help accelerate discovery a lot a lot more as well.
00;08;41;09 - 00;09;11;01
Well, what exactly is a research advocate, especially as it relates to being one for a company like Oracle? Why? Why is that important to advancing research? I think each one of us take on a slightly different role, but really the research advocate is to work alongside researchers to help them navigate huge corporations. And, you know, a lot of us are used to navigating the academic setting because that's what we're familiar with.
00;09;11;01 - 00;09;45;22
That's what we've experienced. But when you throw in a huge company like Oracle, you kind of get a little overwhelmed. And so as a research advocates role, I can I've got the research experience and have navigated the academic setting, but I also have the experience navigating industry through Oracle and so it's really helping the researchers translate what they're doing for their research and how that translate in the academic or nonprofit setting to the industry setting and helping them.
00;09;45;22 - 00;10;14;15
If there is projects that I can help with, we do everything from digital humanities to quantum physics and everything in between. And I am certainly not an expert in everything, but in the health care and the clinical research in the epidemiology space. If there are research areas that I can really work alongside researchers and help them accelerate what they're doing, that's really my role with Oracle for Research.
00;10;14;17 - 00;10;49;10
What attracted me to Oracle for Research was that ability to have that collegiate experience and also provide that researcher to researcher experience as well. A lot of times you get assigned somebody that may not have a research experience or may have heard the word research, but really hadn't lived it with their career. And so I was so excited to be able to kind of bridge that gap, especially coming from academia into Oracle, which was a bit of a learning curve for me.
00;10;49;10 - 00;11;16;23
But to really help, help the researchers get what they want done for their projects and be able to help make really impactful changes to their given fields. But even though you have plenty of laurels to rest on, like you said, you're at the same time getting your PhD and Pharmacoepidemiology at USC. Not an easy thing to do. What is that and what kind of research are you doing?
00;11;16;23 - 00;11;47;04
And and how is that? How does that help us get toward something we can bring the public health as a whole? And so if any of you ask that I or Mike, I feel like I talk to my my parents, they're like, what is it that you do? My dad's just like, I don't know. She's in school. So Pharmacoepidemiology is really the the marrying of pharmacology and pharmaceutical science with epidemiology.
00;11;47;04 - 00;12;31;23
So looking at how patient treatment impacts their overall health outcomes. And so really with that, I've been excited to explore different types of therapy is that are already available on the market to really look at how can we use or repurpose drugs for treating rare diseases and in my focus has been in brain tumors. And so not only with UNC-Chapel Hill and doing all of my PhD work, but I've really been able to dive in with a lab in focus on my own research, looking at how do we improve patient outcomes with brain tumors.
00;12;31;26 - 00;13;09;07
And we do that through a whole host of different tools. Some of it is real world data, and that could be real world data from registries like the Medicare SEER Registry, as an example, where we look at brain tumors or any sort of cancers, and then also be able to take a look at prevalence, meaning the number of cases in total of a certain cancer or looking at incidence, the number of new cases of a certain type of cancer or utilizing other electronic health record data.
00;13;09;08 - 00;13;36;17
So look at continuum of care for health and then also doing firsthand collection of data through clinical trials or clinical research studies that are initiated either by industry or by clinicians. And so really the field of clinical research is is huge. My PhD touches a little bit on that when we take a look at just the treatment side of it.
00;13;36;20 - 00;14;26;05
But my hope ultimately coming out of this Ph.D., I guess what I dream to do is really be able to marry some of my genomic experience using genomic data to also drive precision medicine with our pharmacology, to really be able to start to make an impactful transition for patient care. And my specialty and my focus has really been brain tumors to date, but certainly really interested in the rare disease in oncology space because I think there's a lot of a lot more work that we can do to be able to continue to spread awareness about these different types of cancers, but also a ton of headway to really improve patient care.
00;14;26;07 - 00;14;52;04
Yeah, and you touched on the fact that, you know, the health care overall seems to be driving toward more personalized approaches to treating people. We are all individuals, like you said, Lord knows I like to think I'm special. I don't know. But there are so many environmental and biological variables in the research equation. The kind of research you're talking about sounds incredibly complex to me.
00;14;52;04 - 00;15;20;07
So what epidemiologists have to deal with in terms of procedure and ethics as they do research and try to get something usable out there for the public. That's a loaded question. So with epidemiology, there's a whole host of things to look at. You know, growing up in undergrad and certainly in my graduate studies, the focus has really been on the bio psycho social model, really looking at all effects that could impact a person's health.
00;15;20;07 - 00;16;02;13
So as you touched on environmental, biological, psychological effects, mental health, all of these really contribute to an overall person's wellness. And so epidemiologists have to look across a multitude of different factors to really understand the certain disease that they're studying. I can tell you in the brain tumor space, we've looked at across a multitude of factors, including environmental, including pharmaceutical, including biological, including mental health, to really understand where we can make the biggest impacts.
00;16;02;15 - 00;16;40;14
And then thinking about the ethics associated with research, everything has to be done in certain there's all sorts of procedures that you have to follow. But thinking about our clinical research and our clinical trial data, where we're collecting real world data from patients, it's incredibly important to make sure that the patients are in agreement with sharing their data with the researchers and really understand what the study is looking at and what the benefits or maybe no benefits may be for their particular care.
00;16;40;16 - 00;17;08;02
And so I think, you know, having those procedures in place ensure that the patients are protected, which is truly key. But it certainly is something that I think all of us really strive to hold ourselves accountable for is making sure that patients are front and center as they are truly the ones that are contributing this data. And in allowing us to do the work that we're doing.
00;17;08;05 - 00;17;35;01
Well, I do want to ask about clinical trials because modern medicine means, I assume, collaboration across a range of medical professionals. So how does an epidemiologist work, supplement or partner in clinical trials? What does that interplay usually look like? You know, I've been so fortunate in my career to have supportive physicians and clinicians to work alongside with, but I am not a medical professional.
00;17;35;01 - 00;18;05;19
I don't have my my M.D., I don't have my R.N., I don't have my my degree and physician assistant. So I don't have the firsthand knowledge of treating patients. And so really, epidemiologists are in that supportive role to help drive research. But allowing us to have that interaction with clinicians is key to be able to make sure that the questions we're asking are relevant to patient care.
00;18;05;21 - 00;18;41;05
And what we're finding also is relevant to patient care, because really that's ultimately what we're all trying to do is is improve patient care. So depending on the setting that you're in depends on what your team may look like. But I can say that a lot of times as part of my research teams, we have a physician or some sort of clinician on our team alongside an epidemiologist, a biostatistician who is far better at doing statistical analysis than me.
00;18;41;07 - 00;19;18;06
Sometimes computer scientists who may be helping with the coding, although I do a lot of my own statistical programing myself, but sometimes we'll have the luxury of having a computer scientist on there and then obviously having an IRP that oversees it. An IRP is an institutional review board that makes sure the decisions that we're making in terms of the design of the study and how we're conducting a study is done in an incredibly ethical manner and meets all of the standards that we should.
00;19;18;09 - 00;19;39;22
And so having that oversight is also really helpful to make sure that, again, patients are front and center and we're we're doing the best science we can for regular listeners. I know I keep bringing up Amy Docs or Marcus on the show. She's a Pulitzer Prize winning journalist from the Wall Street Journal and she was a guest. We talked about her book, We, The Scientists.
00;19;39;28 - 00;20;07;16
But it's such a compelling look at patients, scientists, doctor collaboration and how that citizen science is being used in the fight against rare diseases. It's a truly new way of thinking that still honors scientific rigor. What are your thoughts on citizen science and is it gaining traction? I mean, we talked a little bit about it earlier about patients being listened to more, but this kind of kicks it up a notch.
00;20;07;19 - 00;21;01;02
Yeah, her book was fantastic and certainly very insightful of how citizens science should be done in the health care space. In this day and age, all of us have all sorts of devices that are collecting data about all of our lives. I know I'm wearing an Apple Watch and I'm sure many of our listeners are as well. And what I think is interesting is, you know, several years ago, before citizen science in health care really became a thing in the diabetes landscape, folks were using technology to continuously record their glucose readings to be able to get more of a handle on avoiding hypoglycemia, meaning high blood sugar or hypoglycemia, meaning a low blood sugar level
00;21;01;04 - 00;21;45;00
to really help improve their overall care and improve their health outcomes. And so thinking of citizen science, it makes sense to make that leap from what a lot of folks are already doing by tracking their steps or tracking their EKG monitors, tracking their blood glucose level, etc. to be able to incorporate that into that holistic picture of what their daily lives look like from a care standpoint, it certainly helps give physicians a clearer picture of their life, of what they're doing in their day to day life, but also be able to provide more, more personalized care.
00;21;45;03 - 00;22;20;13
But in the research space, it gives you a multitude of data points that otherwise wouldn't have been able to be collected without a huge burden on the patient. And so one of the things I think we have to consider with citizen science is how do we make citizen science approachable for everybody that wants to engage, to engage, and then also be able to allow patients an easy time to find those engagements if they can.
00;22;20;15 - 00;22;48;14
And so when I was at Wake Forest Baptist Health, one of the interesting studies that they did in partnership with Oracle was the Community Research Partnership for COVID 19. And so it really provided patients who, during the heart of the COVID pandemic, maybe at home, working from home to collect data and let us know how they're feeling about everything.
00;22;48;14 - 00;23;12;15
So how are they feeling about the Thanksgiving holiday? How are they feeling about seeing people? When it came time for that Thanksgiving holiday, what were their daily symptoms? Did they receive a vaccine? If they didn't, Why? If they were comfortable sharing that, to be able to understand how do we start delivering care that fits a multitude of different needs?
00;23;12;18 - 00;23;45;18
We were so fortunate with that study to have thousands of patients that were so diligently collecting those pieces of data or sharing those pieces of data with us on a daily basis for over two years. And so seeing that sort of project really starts to open up your mind to what else can we do in the rare disease space in particular, I think that patients are so eager to be able to make advances.
00;23;45;20 - 00;24;19;20
But also if you take a look at traditional data sets that we may use to do analysis for rare diseases, the data is so small that it makes it really difficult to make meaningful discoveries. And so by having patients that are eager to engage, that are advocating on behalf of themselves, or a lot of times others that they're caring for, it provides a whole new perspective that as a researcher I may not have even considered.
00;24;19;23 - 00;24;53;22
And so I think it's really exciting to see how do we start to bridge that gap between patients and scientists. I think we've done a start with that. I think the Robert Wood Johnson Foundation has started to do some of this or other phenomenal grant organizations that have bridged the gap between traditional research grants by having a focus with patient advocates on those particular grants committees or their project committees to really start to bring that in.
00;24;53;22 - 00;25;32;04
And we're starting to see health care bridged the gap as well by creating patient advocacy groups and patient support groups to be able to do that. But I think, again, digital technology is really making that difference and providing apps that can provide that support in a positive manner to patients wide and far. So you may not have your next door neighbor who may be in the same boat, but you can log on to your phone and have somebody who at a couple clicks of a button that may be able to be there to support and really create those those communities for years.
00;25;32;04 - 00;26;02;06
I can say in the diabetes space, we certainly have done that successfully. But being able to bring that to research I think is really making a difference, but also making an option for treatments to truly develop an unapproachable manner for patients. Because if I have to tell you, you've got to do these 35 steps to get to what what would improve your health, you'd probably look at me and go, When do I have time to do all of that?
00;26;02;06 - 00;26;33;06
And so really taking that into consideration and having that first hand patient knowledge is truly going to be key, I think, for continuing to improve our health overall. Well, it is actually technology that's enabling these types of new interactions, especially cloud technologies. You went through a lot of the main benefits of digital health. I know I'm going to floor everybody with this statement, but technology has its drawbacks too, so we can't lose sight of ethics, safety, efficacy.
00;26;33;06 - 00;27;19;01
And I think people still see health care as an in-person human engage. But so in what ways can or does digital supplement that human touch without replacing it? What's the right balance? I think technology has the ability to bridge the gap between inpatient care in not inpatient care, especially in situations where obtaining inpatient care is incredibly difficult. Growing up in a rural, underserved community, my parents would take half a day or full days off of work to take me to Rochester for care, and I was so fortunate that my parents had the ability to do that with their jobs.
00;27;19;04 - 00;27;57;28
But not everybody does. And so really having an opportunity to provide high touch care in a digital setting allows for folks to get access to care that they may not have. It also allows for huge improvements in care. I know in Rochester, for instance, they've got a mobile stroke unit that was having the ability for paramedics to connect with neurologists in the field to be able to reduce the door to needle time with stroke patients, which is critical because time is brain.
00;27;58;01 - 00;28;22;00
And so instead of having to get carted from your house to the emergency room and then do diagnostics to determine if you're having a stroke, that could all be done in the field. And so, yes, there needs to be oversight. Yes, there needs to be some some sort of standards. And yes, there needs to be ethical reviewing of this technology.
00;28;22;00 - 00;28;57;08
But the huge advancements that technology is is truly making for folks is is phenomenal in certainly making health care a bit more approachable. I've always struggled with the whole concept, especially coming from a middle class, underserved health care, almost health care desert in some aspects. It's so nice to be able to make that connection for patients that may have that specialty care offer without having to take hours or days off of work to get it.
00;28;57;13 - 00;29;28;28
And so being able to connect physicians to physicians or patients to physicians outside of their typical catchment area, I think is what's driving improvements in health overall as well. Well, every research, discipline and project is unique, but aren't there some commonalities when it comes to okay, pretty much everybody can use a technology like this. What are some of the biggest technology roadblocks and benefits that you see today's researchers dealing with?
00;29;29;01 - 00;29;58;25
MM I think one of the biggest blocks for research is truly getting access to high powered computing resources that they that they now need because we're collecting data and more and more and more data, it's important to have high powered computing resources to analyze it. I often joke with with researchers when I first started out, I remember getting what we called big data back then.
00;29;58;27 - 00;30;33;17
You know, it was a couple million lines of rows and my poor little laptop that was probably, you know, five years old just could not handle that. But those few million rows now are few billion rows and so it's important to have those high power computing resources to truly be able to analyze the data effectively and efficiently. And that's what I've loved about my role is really being told to give out those resources to help researchers at least break down that barrier.
00;30;33;19 - 00;31;13;05
I think some of the other barriers from a patient perspective is the multitude of different apps that are out there, the multitude of different like telehealth platforms, you name it, you know, we've got it. And how many times have we had to say to somebody, unmute yourself on Zoom as an example, over the last three years? And so I think one of the things that we've kind of got to start putting our heads around is how do we create a fully immersive research platform and what does that look like for patients, I think is I can't even tell you if you told me today, I have to download these five apps and then do this
00;31;13;05 - 00;31;31;08
and that, you're going to lose me. And I'm in the in the field, right? So think about our everyday patients that you're having to say download these five apps and click this and log stuff here. It would be nice if it was all at one click of a button, and I think we're probably not far off from that.
00;31;31;10 - 00;31;58;16
I would say I hope that we're all thinking about that in the same way, but I think that's truly going to make at least getting access as a patient to participating in research a bit more accessible, especially in the technology space. And then for researchers thinking about how can we accelerate the collaboration beyond our typical walls of our institution is also going to be key.
00;31;58;16 - 00;32;21;17
And I think technology getting away from having to share data sets on prem to being able to put things in cloud is really the wave of the future to allow researchers from around the world to collaborate, to really drive change together as well. Well, I know Oracle's been thinking a lot about research data. Like you said, it's a ton of data already.
00;32;21;17 - 00;32;41;27
It's only going to grow exponentially. That's great. We can do a lot with that data, but there is the complexity of it and regulations. So how do you see the data landscape for health care and clinical research? Are we more than we can handle or are we at just right? Hmm. I don't think we're at more than we can handle.
00;32;42;00 - 00;33;29;14
I think what's going to be really key and I think Oracle is certainly becoming a leader in this space is really to connect with the industry standards in working together with researchers to define what those standards should be as we continue to accelerate more and more data growth. I think that with all of our wearables and with all of the multitude of ways that citizens science projects can can continue to grow, we've got a lot of data, but there's also a lot more that we can collect and a lot more that we can continue to grow both both as researchers and as as patients and research participants.
00;33;29;17 - 00;33;58;11
And so I think together with patients and with industry standards and with ethics review boards, everybody can come together as to really define what those standards should look like, both within our own countries as well as internationally, so that we can all start to really make progress together. I can say, I think in the research space and this is one of the things I love the most is, you know, research doesn't really happen in a box.
00;33;58;13 - 00;34;35;19
You certainly can sit in for all room and talk to nobody and you make some small progress. But I think really benefit to research is truly through those collaborations. And so I think as industry continues to dive into this and we get new industry partners like Oracle for the cloud, you know, having them be able to be front and center in helping to learn about what needs to be done in the data space to ensure we're keeping patient data secure is in mind is incredibly important.
00;34;35;19 - 00;35;05;28
And so I, I think we can see that through some of Oracle for research is partnerships with like the Research Data Alliance as an example of really wanting to extend working groups to figure out how do we best treat genomic data, which is something that the industry is just starting to get into. Genomic data is a whole host of tons of data, but truly something that standards haven't been fully developed yet for that.
00;35;05;28 - 00;35;30;23
And Oracle's leading the charge with the research data alliance at trying to define what those standards could and should be. And I think that's going to be where we need to continue to go in the future. So closed data and open data are different things. Thus the two different names, Discovery Research thrives on open data for Explorer and reproducibility.
00;35;30;23 - 00;35;59;28
But for whatever list of reasons, data sharing in the research community is still kind of limited. Why is data sharing important for things like aligning with you? Say the fair principles, all these new NIH policies that are coming out? Yeah, so let me kind of define pain in the health care space or what allows data to be open and what allows data to be closed, because I think that's really important for listeners.
00;36;00;05 - 00;36;46;00
So open data is totally de-identified data. And what I mean by that is in the United States, we've got a principle called Hippo, and there's a, I believe, 20 some odd identifiers that include names, date of birth addresses, dates of service, etc. that can be used to identify patients. And with that, because we want to keep patient identity incredibly secure, because we don't want to share personal data when data is shared in an open space, all sorts of identifiers are stripped from the data so that you cannot track a patient back.
00;36;46;00 - 00;37;13;00
So I can't look at a data set and go, Yeah, that's me, you know. And so that is one data set includes data that is some in-between of that. And that's really defined by an individual institution or organization of what they feel their standard should be. And there's use cases for both for open data and for close data.
00;37;13;02 - 00;37;38;26
Open data is so important for reproducibility because I should be able to take a data set that Mike, you've ran an analysis on and be able to use it to repeat it so that I can say, Yep. Mike, you're your results are right and I'm going to take this algorithm and I'm going to now apply it to a new data set, and it should function the same way.
00;37;38;28 - 00;38;08;02
And that's where I think NIH is really getting at, is to be able to ensure that the research that's being done is done in an open manner so that folks can truly be able to collaborate and grow from what folks have already done. You can continue to accelerate that forward. Closed data is also super important depending on what the researcher is, is trying to determine.
00;38;08;05 - 00;38;35;15
So for instance, if we want to look at a health exposure in a given area, we may need to use closed data to look at a patient's zip code or a patient's census track to really hone in on environmental factors, for instance, in a given vicinity to be able to determine what do we need to do from a public health intervention to reduce that particular exposure.
00;38;35;18 - 00;39;18;08
So really, depending on the institution, will define what may be in that closed data set for that particular research question. We would have to have zip code, but that's something that we probably don't want to share as part of open data. So I think that's where the nuances are going to be. I think we have a lot to figure out in terms of what those standards are for open and closed and how we can come together as researchers and industry to be able to make continue make data open and accessible to people, but also keeping security and patients rights and wants protected as well.
00;39;18;08 - 00;39;41;21
And so I think that's something that Oracle is certainly exploring. And I know a multitude of folks are also exploring. And I think NIH, by putting in these new principles, are truly is truly taking a step in this direction as well. And it'll be interesting to see how we can kind of grow from there over the next couple of years.
00;39;41;24 - 00;40;02;27
Well, I can't let you go without asking about A.I. and the use of these large language models For all the accompanying caution and fear they do show promise you've probably been thinking about, okay, what does this mean for scientific research? What excites you about AI and what makes you a little nervous? Well, I'll start with what makes me nervous.
00;40;02;27 - 00;40;32;20
I think I can do a lot of things and I think we've seen I do a lot of things. And I think one of the the thing that makes me the most nervous about AI is some of the assumptions that can be inherently baked into AI models that are unintentional consequences of a particular model that may have folks come to the wrong conclusion about a certain disease or a certain entity.
00;40;32;22 - 00;41;15;10
But I certainly think AI has a whole of use cases in the health care space that can truly start to add a little new element of precision medicine to given patient care. We've got researchers that are using AI to improve image detection in colonoscopy, in MRI's, to really start to take some of the nuances which radiologists do an incredible job, but to be able to give them another tool to help them as they're reviewing more and more MRI's and all sorts of radiography in a given day.
00;41;15;13 - 00;41;43;16
And so I think AI is going to have a new set of tools for us to be able to do that from the research space. I am excited about AI being able to provide a standardized way of, for instance, analyzing tumor volumes on based on MRI's and looking at time series progression, some tumor volumes to be able to understand how a particular treatment is improving or not.
00;41;43;18 - 00;42;07;27
Particular tumor growth. So I think there's a whole host of of use cases, but I think we all need to be a little cautious and certainly look into the nuances of particular models and algorithms before we we kind of jump to fit in to make sure that we're not inadvertently making assumptions that may not be great for research overall.
00;42;07;29 - 00;42;33;02
Well, Christine, thanks so much for taking the time to be with us. Really good stuff. And what we've talked about is write down our listeners, Ali, But if they want to learn more about what you're doing or get in touch with you, can they do that? Absolutely. So folks can certainly reach out to me on LinkedIn. I’m Christine Pittman Ballard on LinkedIn, and I look forward to connecting with you all as well.
00;42;33;04 - 00;44;44;10
Very good. Well, if you are interested in how Oracle can simplify and accelerate your research, check out Oracle dot com slash research and join us again next time on Research in Action.
