Dr. Karim Galil: Welcome to the Patientless Podcast. We discuss the good, the bad, and the ugly about real world data and AI in clinical research. This is your host, Karim Galil, Co-Founder and CEO of Mendel AI.
I invite key thought leaders across the broad spectrum of believers and dissenters of AI to share their experiences with actual AI and real world data initiatives.
Dr. Karim Galil: Hi everyone, on today’s show we have a very special guest. The first time I heard the concept of patientless trials was actually during one of our interactions. We were introduced to the digital team at Novartis, and I was basically explaining what products we were building, and then this guy goes “oh, you guys are building patientless trials”. That’s a very interesting term. It kind of inspired us and also significantly helped us in shaping our strategy. There are very few people who I’ve met in my life, that I can recall, that have inspired me in 30 minutes and he is definitely one of them. After getting his PhD from Harvard University, he started his career at McKinsey, helping Fortune 500 companies shape their R&D strategy. He was also a fellow at the Howard Hughes Medical Institute. After his time at McKinsey, he entered the CRO world and was the Chief of Staff to the Chief Medical Officer at PPD. Then right after that, he joined Novartis and his main objective there was to assist them in figuring out their business strategy when it comes to digital transformation. An entrepreneur-at-heart, he co-founded BIOME at Novartis. Which is very interesting because you don’t get to see a lot of people holding the title Co-Founder in the pharma world and especially in big pharma. Our guest today is Jacob LaPorte. Jacob, welcome to our show!
Jacob LaPorte, Ph.D.: Yeah, thanks so much. It's great to be here. And it's great to hear that I might've said something that ultimately inspired you, so that's really kind of, you.
Dr. Karim Galil: Yeah. If you’ve ever been to a trade show, when you see our booth, we usually have patientless trials, a big bolded display. So, just as a disclaimer, we haven’t paid Jacob anything for the rights of using the term patientless trials. But Jacob, again, thank you so much for being on today’s show. I believe that today we’re going to have a mind-blowing discussion because Jacob is an individual who is not only able to have the foresight for innovation for what can happen in the next 20 years, but also be pragmatic enough to understand what we can and cannot accomplish today. Maybe before we get started, why don’t you let us know a bit more about BIOME? What is BIOME?
Jacob LaPorte, Ph.D.: Yeah, sure. Well, in the simplest metaphor that we use, we kind of see it as an on-ramp for helping great external partners work with Novartis teams to co-create novel digital solutions. So just like in everyday life, it would be very hard for a car to get on the highway from a stop sign, no matter what car you drive. Similarly for external partners, that we work with at Novartis, it's very hard for them to get up and running and fit into the traffic flow of our highway without a better on-ramp system. So the BIOME is really looking at those classic challenges that exists at the interface of a big pharma company with the external digital health and tech ecosystems. And it's asking the question: how can we build tools and processes and approaches that help Novartis teams find the right partner in this very complex ecosystem? And when they do find the right partner, how can they kind of start to work together in a better way to more effectively co-create digital solutions. So that's really what we're all about.
Dr. Karim Galil: Is it a safe assumption to say that you guys are like the pharma version of a Silicon Valley incubator/accelerator?
Jacob LaPorte, Ph.D.: It's funny that you say that because I think a lot of people want to draw that analogy to something that they're used to, but we don't have a program in place that is very similar to an incubator or an accelerator as you would say, in a classical sense. What we do do is we help external partners really kind of onboard into Novartis and translate the technology that they've developed into a context and in an environment for Novartis, which ultimately helps them scale their solution into a major pharmaceutical company. So we have things, for instance, that do, really look to support our entrepreneurs and our external partners. We do augment them with internal expertise that we have, where relevant, we are thinking about tools that help them develop their solutions. Like one thing that we're kind of developing right now is called a data sandbox where if we have data in Novartis that could be useful, we might be able to anonymize that data or create synthetic datasets and upload them to an environment to allow these external partners to operate on that data and maybe evolve their algorithms. So we're thinking a lot about how to support our partners in developing these digital solutions.
Dr. Karim Galil: Are they free to work with other Pharma companies if they are part of the BIOME project?
Jacob LaPorte, Ph.D.: Yeah, a hundred percent. This is a pure open innovation model. We think that it's actually frankly advantageous for our partners to work with other companies because it really helps, you know, spread frankly, the risk, and also spread the opportunities to scale a particular solution. So we don't necessarily see ourselves as the natural owner of a lot of these solutions, but we'd rather use them to augment our ability to get, our medicines to patients, faster, more effectively to extend and improve their lives. So yeah, it's a complete open innovation system.
Dr. Karim Galil: You guys started around two years ago, and now have a multitude of success stories. One of which that I find very intriguing happened overseas in Ghana. Would you like to talk about that?
Jacob LaPorte, Ph.D.: Yeah, I think it's a great illustration of what the biome process can do. so I think what you're referring to is a story, we came out with actually a couple of weeks ago where we were working with our global health organization at Novartis, which already has an ongoing initiative around increasing the access to medicines in Sub-saharan Africa for sickle cell disease patients. But one of the classic problems of course, with that disease is the loss of follow up that you experience in healthcare populations that are distributed and may not have the best healthcare infrastructure, because you need to diagnose sickle cell right now with a blood laboratory study right? So what happens is you can get out into the population and collect their blood samples, but once you run the laboratory test or diagnostic, you've often lost that patient to follow up. So they actually don't get the diagnosis and the medicines that they need. So one of the things that the biome did to help out this initiative is we started looking for, diagnostics that could be delivered at point of care to help cut down that problem of loss to followup. And we wound up finding an amazing company in Portland, Oregon called Hemex Health. Which has a fantastic, cheap, portable, point of care diagnostic for sickle cell disease and malaria. And so this is an amazing story where a technology being developed in Portland, Oregon was able to plug into an initiative in sub Saharan Africa, and now we're starting to see some of that exciting fruit of that labor happen where we're being able to diagnose patients and get them on medicines, lifesaving medicines, a lot faster for that. So, this is an example where, you know, the biome also thinks about how to support its partners like I was mentioning before. So Hemex Health, had a great technology. We knew that it works, but it wasn't actually approved on the market in Ghana to actually be incorporate into this initiative. So we actually had, augmented their team with regulatory support from Novartis and I'd love to thank my associates that really stepped in, in a big way to help out in regulatory. And we were able to accelerate their approval onto the Ghana market, and therefore incorporate them in to this initiative. So it's a fantastic story about how we can more quickly translate these new technologies into our existing business to basically improve and extend patients' lives. It's really touching because when you think about sickle cell disease it has such a large burden, particularly in Sub-saharan Africa. Many of the patients that have sickle cell don't live beyond their sixth birthday and it's just amazing to think that we might be able to have the technology here that gets these patients on medications faster and therefore, you know, really extend and improve their quality of life. So it's a great story.
Dr. Karim Galil: It is a great story, and it’s a great example of the kind of corporate responsibility that big pharma should take on by breaking these borders. To add, you guys are doing a great job. I like the vision that you have now that has transformed the company from being a “pharma” company, to becoming more of a “pharma and data” company So you guys are leading that digital transformation in the Pharma industry. Talking about digital transformation, Jake, how would you define “Patientless Trials”? It's definitely one of those loosely-defined terms, and many people tend to think that “Patientless Trials” are against the patient. You had made the argument that the best thing we can do for patients are “Patientless Trials”, so how would you define a “Patientless Trial”?
Jacob LaPorte, Ph.D.: Defining is always so difficult. Right? To me, I guess I would say that, patientless trials is a subsegment of clinical trial simulation. So really what we're trying to do is simulate an outcome of a clinical trial using existing data. So therefore to reduce the need of actually using patients in a study to test the medicine and to determine whether it's safe or effective, which is the best tool that we have today, right? But if you think about it, what clinical trials are doing is they're testing these medicines on patients. So if we could somehow understand the outcomes using existing data and simulations, without putting these patients into trials, I would argue that's one of the more patient centric approaches to clinical trials that one could one could imagine. Right? So what you're doing is essentially, you know, giving them medicines without involving them in trials that you know, are already safe and effective. I mean, I can't imagine a better approach really.
Dr. Karim Galil: So you’ve mentioned this very interesting differentiation that I’d like to touch on which is essentially: data and simulations. We’ve chatted a lot about organoids and you have this very interesting article on Linkedin touching on “Organ-on-a-Chip”. So we’re all here talking about real world data, which is one part of it, but you’re also talking about the next level. Could you possibly explain to us what you mean by “data”, what you mean by “simulations”, and also what is an “Organ-on-a-Chip”?
Jacob LaPorte, Ph.D.: Yeah. So, so yeah, I wrote an article on LinkedIn called why humanless trials could be the pharmaceutical industry's nirvana. Right? And I published it quite some time ago. So it's nice that you, kind of referred back to that. And so what I was looking at that article was, you know, the concept of humanless or patientless trials. And as we just talked about, to me, that really means simulating, a trial outcome, you know, with existing data, without requiring patients to be in a trial. And so where are we at today? The key here is that you need to develop an accurate simulation of a trial. And with the advent of machine learning methodologies in AI, we're getting to the point where we can create very sophisticated models or simulations of complex systems. But you need to have good data to train these models. That's often the part that people leave out. Right? We often talk about AI and the power of AI and machine learning, and it can seemingly do all these amazing things. But you need to have the structure data and the right data, to actually train these models and make sure that what they're predicting is something that's accurate and representative of a complex system. So the issue, and what I believe is one of the grand challenges that we need to solve in order to unlock the power of AI in healthcare right now, is the fact that a lot of these healthcare data is very fragmented. It's all over the place in different systems. There's a lot lack of data standards. There's no universal ontology that helps you knit these data sets together. And so you don't we'll have a very complete picture of a person's health over time, let alone a population's health over time. So when you start to talk about simulating trials and outcomes, you really need to have that very nuanced picture of how people's health evolves over time based on various different stimuli. And so what you were talking about with organ-on-a-chip so what I was thinking about in the article was how does one approach this from a different angle? And so I was sort of asking the question, what if you could bypass that very large and sticky problem of trying to knit together all these datasets and instead generate very well structured and very representative data from technologies like organ-on-a-chip? These are generally called micro-physiological systems. So they're, I don't know if you've ever seen it, but they're, you know, organs on a chip are microfluidic systems that generally when they're honed in can do a great job of emulating, you know, the organs and their functionality. And then there's organoids, which are like 3D biology, which are growing, you know, different types of cells together in a way that emulates the physiology of an organ. And so the question is, can you start to interrogate those systems and generate data? And by the way, you can start to make this genetically diverse, right? So you can start to think of populations of organs-on-a-chip or patients-on-a-chip and so then can you use that data to them and create or train these machine learning algorithms that will better simulate potentially the outcomes of trials? So it's a long ways away. And I think, you know, some people be listening to this podcast and automatically say, well, how do you determine or how do you correct for environmental effects which we know are so significant to health, right? Or outcomes? Or health outcomes? And I would say, well, you know, at the very least, if you start to think about these genetically diverse populations on chips, you can start to at least get to more sophisticated hypotheses around subpopulations and inclusion/exclusion criteria, and design more effective trials. But then over time, as we start to learn a little bit more about the connections of environmental effects with health outcomes, you can actually start to weave that data in as well and actually get to a lot better simulations. So that's sort of what I was thinking. Yeah.
Dr. Karim Galil: So you’re basically talking about augmenting existing traditional randomized controlled trials with outside source data, rather than replacing it. I believe that a lot of the pushback we get about the concept of patientless trials is the preconceived notion that it's “randomized OR patientless” as opposed to, what you have explained, “randomized AND patientless”. It’s very interesting because what you are talking about is both randomized and patientless trials augmenting one another by supplementing data, is that correct?
Jacob LaPorte, Ph.D.: That's exactly right. I mean, at least in the near term, that's really the only way I see it, I don't think it's realistic to think at this point, we could truly replace randomized controlled trials with simulations. But that being said, you can start to think about how you hone your hypotheses around clinical trial design really getting into, you know, better subpopulation analysis or even categorization upfront. So I think these two things can be used in combination to be much more effective. Yeah.
Dr. Karim Galil: So, we all know the stats: $2.7 billion dollars and five to ten years to develop a drug. Do you think this kind of approach can accelerate this process and what would that change look like? Are we talking about going from $2.7B to $2.5B or are we talking about a significant decrease in both cost and time for drug development?
Jacob LaPorte, Ph.D.: Yeah, I think in terms of the impact, I think it will probably evolve over time, given, you know, our capabilities and the sophistication by which we're able to establish these models and simulations. Right. So right now we're already starting to see an impact. Right? I would argue that there are some elements of patientless trials already being adapted in the industry, right? We talk a lot about virtual control arms and we're starting to see them being effectively deployed a lot into oncology trials right now. So obviously there's already an impact where you don't need to stand up an entire control arm of a study giving them an existing treatment where you more or less should already know the outcomes right. So we're starting to see that impact. As we, get better at simulating , for instance, get better at maybe kind of using these simulations to design better subpopulation, categorizations and get more targeted on trials. I think you can start to see a pretty significant impact. I don't think we're talking about an incremental 10% here. I think you could really move the needle on and bend the cost time curve of drug development fairly significantly. I mean, you can imagine what, if you get to a point right where you're using these organs-on-a-chip to really get rid of some of these things that still occur in trials, which is like toxicity issues, which, you know, if you can model that out better through simulations and just stop doing some of these trials that aren't going to work out anyway, because the animal models aren't telling us the right answer, that could have a significant impact on the portfolio. So I'm pretty optimistic going forward that this type of approach will start to have more and more of an impact on the way we develop drugs.
Dr. Karim Galil: So Organs-on-Chips, are these science fiction? Or have you seen some projects already out there that have at least developed a proof on concept?
Jacob LaPorte, Ph.D.: But they actually exist. I've seen them in real life. So a part of my journey into humanless trials was actually meeting some of the folks that work on organs-on-a-chip. So I'm thinking in particular about this company called Emulate Bio and there's some great folks that I've been talking to there for multiple years now. There's a lot of other companies by the way, but I'm just more familiar with Emulate and their products and they really do create these microfluidic systems that's based off of a lot of work that that team did at the Wyss Institute at Harvard. Showing that these microfluidic systems can in fact emulate human physiology, thus the name. And so they have products like lung on a chip and various things. I think liver on a chip, which I think do in a lot of instances, do a lot better job of predicting things than say the traditional animal models in those diseases.
Dr. Karim Galil: We need a lung chip for those vaccines that we’re all trying to chase for COVID. Speaking about COVID-19, do you think that COVID-19 made the concept of patientless trials more of a “painkiller” as opposed to a “vitamin”? Or are we still at the “vitamin” stage?
Jacob LaPorte, Ph.D.: I'm going to go with a vitamin, I guess. Because I think that, you know, painkiller suggest to me that you're sort of masking the problem by treating the symptom. And I think that COVID-19 has opened up the gateways for us to more rapidly experiment with technologies and trials, thereby increasing the likelihood of finding solutions that are going to have a meaningful impact on the future paradigm of development. So I'm actually. Although, you know, COVID-19 is first and foremost, a human tragedy, some of the challenges that it's posed to the industry and the healthcare system at large, I think we're going to see these reverberating effects of the sort of technology experimentation that we're doing today going forward. So I'd like to think of it, even though this is a terrible experience. I'd like to think of it more as a vitamin and really treating some of the root cause effects of the challenges that we've historically faced.
Dr. Karim Galil: You touched on that a bit when you mentioned that machine learning is great, but it is only as great as the data we are feeding it. In healthcare there are a lot of issues with data, you’ve touched on some of them, but I’d like to hear some of the data problems that you’re currently recognizing and also any kind of advice or framework that you could give to help others evaluate the data they are feeding into their models?
Jacob LaPorte, Ph.D.: Yeah. Sure. So whenever we start to talk about healthcare data and the power of machine learning and potentially leading to a future of more personalized medicine or more targeted medicines, we always tend to reflect on what we've been able to do with genomic sequencing and proteomics and in fact, we've come a long way. Like I remember starting out as a scientist, I was actually a molecular biologist and it was right around the time that the human genome project was going on and I just reflect on how far we've come from that to now be doing whole genomic sequencing for roughly around that thousand dollar mark that we all said it would take to kind of really come into the mainstream. So it's just amazing. But what we often overlook is that it's extremely important to also collect nuanced data longitudinally on healthcare outcomes of people over time so that we can relate the genomic sequences and the proteomic signatures to those health care outcomes, because I think that's part of the challenges. We don't exactly know what those sequences mean right now in terms of health outcomes and people's susceptibility to disease or the response to medicines. So one of the biggest gaps that I see in this space is really not having that longitudinal healthcare outcomes data. That even exists in the first place. And when it does exist, often exists in various different places and in different EMR systems that don't necessarily talk to each other. And then even when they do talk to each other, there might not be a standard ontology that you can really use to relate these different healthcare data together. So I think that's kind of one of the biggest challenges, going forward, right, is that healthcare outcomes piece, that longitudinal piece. And then being able to relate that back to genomic sequences, proteomic signatures and what that means for health. And what that means to be able to predict health going forward for populations where younger populations, where you do know their genomic sequence, you know, their proteomic signatures and then start to predict what is the likelihood that they're going to have a disease? Or what is the likelihood that they're going to be able to respond to a medication in a meaningful way?
Dr. Karim Galil: The industry, in general, is very familiar with the concept of claims data, the concept of structured data that is used for billing. How good is the claims data? Is it representative of a patient journey, or do you believe that we have to go back to the “old school” unstructured doctor notes, pathology reports, and faxes? Is there one good answer here or does it really depend on the therapeutic area?
Jacob LaPorte, Ph.D.: Yeah. I mean, look, claims data is great for certain things. So I don't want to come across as dismissing the power of claims data, and what that could mean to even stitch that together in a more meaningful way. But, I think when you start really talking about, you know, these more sophisticated simulations and prediction models around, you know, disease, etiology, disease progression, right? And you're trying to predict disease progression or medication response, you're going to need a much better understanding of how a person's, you know, intrinsic makeup like their omics, relates to their healthcare outcomes and how environmental impacts also affect that. And so I think you're going to need this nuanced data that you know, to some extent sits in physician notes today, but I think to a large extent isn't, in a lot of societies, being collected holistically, right? Because what you think about our healthcare experience in the U.S. ,what's going on, well, most people see a physician once a year, unless you start to really have a problem. And then you're seeing a physician more frequently and you're getting more data collected about you. But even that data tends to be fragmented through images that are stored in one database that don't talk to the physician, you know, really relate to the physician's notes on another database. So I think we've got to fix that and we got to get much more nuanced picture of a person's health over time to really make these more sophisticated predictions.
Dr. Karim Galil: There are hundreds of vendors out there promising machine learning and AI, Mendel is actually one of them. There are also hundreds of vendors offering data assets with everyone claiming they have the best technology, best data, the most longitudinal, the most comprehensive. With so many companies like these trying to sign or work with a top pharma company like Novartis, how efficient is your gatekeeping strategy in assessing a vendor’s value? Every week we hear about a new startup, a new VC funding, or a new company with a very creative solution and I’m just curious to understand how you differentiate between promising projects and ones that might need a bit more work.
Jacob LaPorte, Ph.D.: Well, first of all, I'm always inspired by the tremendous entrepreneurs out there really trying to take on, these, health care issues in new and different ways. And so I think what we're seeing is just a product of so many opportunities out there really be getting a number of different startups and others kind of approaching solving these problems in new ways. And I would never, you know, never want to get rid of that right at all. I think the question we're asking ourselves at Novartis is how do we make it a better experience for those partners to kind of interact with us and make sure that the right people in Novartis are talking to the right people in the external ecosystem and that they have the right support system together to really create a novel digital solution. That's more of the kind of question that we're asking ourselves. Sometimes, within that, we'll present different tests to an external partner to really make sure that they have the right fit for the specific problem that we're trying to solve, because they might have a great technology, but it just might not be the right fit for what we're looking to do. So we want to definitely put that in place to make sure again that the right conversations are happening at the right times and that no one feels like this is a waste of anyone's time. So we've been thinking a lot about that at Novartis. And so one of the things that we're thinking about is how do we actually meet people virtually right off the bat. And we're creating this product called the digital brain. Where, we'll be able to have people, anywhere in the world that are creating their next big idea and their next company be able to upload their profile into this system. You might even, for instance, be able to sign a one-click NDA and start to get access to some of the problems that we're trying to solve. We might be able to ask you some questions online and kind of filter whether or not that problem is relevant for you and therefore, route you more directly to the right person in the company to talk to. And we just think that's going to be a lot better experience for everybody involved. So that's something we're actively working on today.
Dr. Karim Galil: Wow. I would assume that a one-click NDA in the pharma world must be harder than creating an Organ-on-a-Chip. A one-click NDA is pretty hard, with the process sometimes taking thirty days today just to get the paperwork done! So the option for a founder to just click a button and get access to what kind of problems they need to start working on, that is very impressive.
Jacob LaPorte, Ph.D.: Yeah as you indicated, it's definitely a journey, right? This isn't easy to get to the next level of becoming a pharmaceutical company powered by data and digital. So, we're obviously approaching this from multiple different dimensions, and one of them, as you sort of alluded to, is culture and talent. So we've been able to really ramp up the talent is in our company that really has an expertise in data and digital. We've hired some phenomenal people from some of the best institutions and companies out there. And that's just fantastic to see. And as a result, we're also kind of changing the culture as well and you know, frankly, the biome is one element of that, right? How do we kind of fluidly interact and co-create with this powerful, external ecosystem of digital health and tech companies? Answering that question is going to be really important to our culture and how we transform. So we've been thinking a lot about that, and I think you'll start to see some really meaningful evolution as a result of those efforts.
Dr. Karim Galil: It’s very interesting that you’ve touched on talent. My co-founder comes from outside of healthcare. Once he finished his PhD, all of his job offers were from top tech companies such as Amazon and Google. Something he always reminds me about are the challenges an individual with a CS background faces when coming into an industry like healthcare. For starters, there is a lot of domain experience within healthcare that is not easy to just “pick up” right off the bat. Another challenge is the lack of centralized data that many healthcare companies provide to AI scientists, as opposed to Google or Amazon having 20 years of aggregated and cleaned data making it easier to gain insights from data. What I would like to know is how you guys are convincing individuals from outside healthcare, that have the technical expertise to endure these challenges, to come work for a pharma company? How are you competing against big tech companies like Google?
Jacob LaPorte, Ph.D.: Yeah, absolutely. I think it comes down to a couple of things in my mind. So on the point of how do we attract talent? I mean, one thing is that, as you mentioned, these are hard problems. And I think a lot of people want to solve hard problems, right? So, I mean, I know that's what sort of attracted me into science and has driven me through a lot of my career is looking at how do I make an impact? And usually when you make an impact, it's not solving an easy problem, right? Otherwise it would have been solved. So I think that's one element. I think the other element is really looking at. How can you influence and improve, health? Right. I mean, it's a very important problem that touches a lot of people. And so I think what we're seeing is a lot of these folks that are passionate, yes, they have a technical background, they're great, you know, data scientists, but they want to make an impact in healthcare because they know that this is fundamentally a very important thing for our society. So that combination of having a hard problem that at the end of the day is going to create meaningful societal value, I think is half of the value proposition. The other half is at least for Novartis is our global scale. So if you want to make an impact, come to Novartis, because once you solve that problem in one area, we're going to look at how we replicate that across the world and really touch. I mean, we're operating in over 90 countries and really interacting with all those health systems. So, I think that also attracts a lot of people to our company as well. But as you mentioned, I mean, there's a lot of people starting to ramp up, their talent polls in this area as well. A lot of healthcare companies and Google and Facebook and all those folks are trying to get more and more into the healthcare space. So we're not taking anything for granted at Novartis. We're continuing to think about how do we sharpen our value proposition for people. How do we continue to tackle these very challenging healthcare problems that we know will inspire others to come and join us in our mission?
Dr. Karim Galil: You guys now have a dedicated AI team within Novartis’s organization, right?
Jacob LaPorte, Ph.D.: Yeah, that's right. I mean, at some point in time, you know, it was very clear to us that AI, although it's like a technology paradigm, but we knew that that was going to be really fundamental to a lot of things that we're trying to do. So therefore we're very interested in creating that backbone in that platform. That will allow us to deploy that technology paradigm into various different solutions more effectively. So yeah, we do have a dedicated organization.
Dr. Karim Galil: Reading your blog posts and talking to you, I feel like one of the individuals who really inspired you is David Eddy. You had mentioned him in one of your blog posts, would you like to talk about Eddy? He really built an amazing team. Actually, now at Mendel, we are looking for people that work at Archimedes because these individuals were on the forefront of the whole concept of patientless trials when it was not popular, which I believe to be very courageous and brave back at the time.
Jacob LaPorte, Ph.D.: Yeah. I mean, I don't really know David that well, to be honest, but the little bit I know of his work, it's just fascinating. Right? I mean, this is a guy that, you know, just has so many different talents. He is a physician, but yet also a scientist that really, you know, also did some amazing, incredible mathematics. And, you know, I think a lot of people look at him as one of the fathers of clinical trial simulations. He developed this program called Archimedes which was able to reproduce a large outcome study that the, I believe the national healthcare system in the UK had run around one of the statins. And he was able to kind of reproduce that you using these clinical trial simulations. And I think that led to this whole discipline around clinical trial simulations being created more or less. And it has been one of the major influencers of us even talking about the reality of humanless and patientless trials in the future. So I always get a kick out of people that just, they seem to have so many different talents and they're able to pull them together to create new and fascinating solutions. So, you know, David, if you ever hear this podcast, hats off to you, I mean, you just had an amazing career.
Dr. Karim Galil: So how do you think 2025? How is it going to look like in regards to clinical research?
Jacob LaPorte, Ph.D.: Yeah. So I think, I think the probability that some institution or some company will ultimately create a competitive advantage in some part of the value chain and it will force others to catch up quickly as a result. So, therefore, I actually think 2025 is going to look a lot more tech enabled than probably what I expected at the beginning of the year as a result of this pandemic. And so I think what you'll find is different people are going to innovate in different parts of the value chain, but it's going to force everyone to rise to that new expectation. And so I think it's going to catalyze a faster result.
Dr. Karim Galil: My last question, if you can zoom call any living person today, who would it be and why?
Jacob LaPorte, Ph.D.: I guess it would probably be Ray Kurzweil. All right. And for people that don't know, Ray, I certainly don't so I guess that's why I wish I would be able to zoom call him. But, again, another amazing scientist. Was one of the foundational scientists around voice recognition using AI to do that at MIT. He's now I believe still is head of engineering at Google, but beyond that he's written several popular science books around a concept called singularity, right. And the whole concept of singularity, I might butcher this a bit, so you'll have to forgive me Ray, but it's a point in time at which we're going to be able to create human like knowledge with AI. So essentially really passing the Turing test in all earnestly, and at that point, AI is going to be able to create all these fascinating technologies and it's anyone's guess as to how the future will evolve from there. But I remember reading that one of his books when I was traveling for an extended period of time after I left my consulting role at McKinsey and company. And that really inspired me. It seems, it seems cheesy, but it really did inspire me to go down this journey of thinking about how to digitize the R&D engine of the pharmaceutical industry. Which has led me, you know, it ultimately led me to Novartis, it ultimately led me to think about patientless trials, it led me to my current role, which I absolutely love doing. So he's had a tremendous influence on my career and probably doesn't even know it.
Dr. Karim Galil: Ray, if you can hear this podcast, please zoom call Jake. His book was actually translated into nine languages and it's one of the best selling books on Amazon so great choice, Jake. I'm just wondering if you’re going to talk to him or to his AI version? This is a guy who may have an AI version of himself. People that smart they can build a “Ray-On-Chip”, if there is a need. Hey Jake, thank you so much for taking the time to do this podcast. As always, it's been very inspiring. I’m sure our audience is going to find this to be really, really cool. Everyone, reach out to Jake on Linkedin, read his blog posts. They’re very inspiring and interesting. But again, thank you, stay safe, and hope to have you on our show another time.
Jacob LaPorte, Ph.D.: Yeah, no, absolutely. My pleasure. I'd love to come back and thanks so much for having me. I really appreciate it.
Delays in clinical trial enrollment and difficulties enrolling representative samples continue to vex sponsors, sites, and patient populations. Here we investigated use of an artificial intelligence-powered technology, Mendel.ai, as a means of overcoming bottlenecks and potential biases associated with standard patient prescreening processes in an oncology setting.
The application of Artificial Intelligence (AI) in healthcare has been revolutionary, especially with the recent advancements in transformer-based Large Language Models (LLMs). However, the task of understanding unstructured electronic medical records remains a challenge given the nature of the records (e.g., disorganization, inconsistency, and redundancy) and the inability of LLMs to derive reasoning paradigms that allow for comprehensive understanding of medical variables. In this work, we examine the power of coupling symbolic reasoning with language modeling toward improved understanding of unstructured clinical texts. We show that such a combination improves the extraction of several medical variables from unstructured records. In addition, we show that the state-of-the-art commercially-free LLMs enjoy retrieval capabilities comparable to those provided by their commercial counterparts. Finally, we elaborate on the need for LLM steering through the application of symbolic reasoning as the exclusive use of LLMs results in the lowest performance.
We’ve changed our look. Our goal remains the same: make medicine objective. The new site highlights the way our proprietary AI enables organizations to achieve quality and scale when structuring unstructured data. It comes down supercharging your clinical abstraction. We’ve validated that our human in the loop abstraction approach can support a machine that understands medical context like a physician. In our own experiments, the number of variables needing correction decreased by 40%. High quality abstraction = high quality data for cohort selection, real-world evidence, and registries.
The customer, a key player in the genomics space, had a strategic initiative to build a clinic genomic database to support their life sciences customers.
One clinical trial organization was using manual chart review and was looking to reduce the time it takes to find eligible patients.
From the Desk of the AI Team
Organizations that use patient data for internal or external research need to take steps to prevent the exposure of PHI to those who are not authorized to view it. They do this by redacting specific categories of identifiers from every patient document. Once the identifiers are masked, the risk profile of these datasets is significantly reduced. But how do you ensure that redaction engines are working to the highest accuracy?
The Mendel team is still buzzing from our week-long retreat in Cairo. The theme of the retreat was “coming together” and it was the first time the American and other remote employees were united with their Egyptian counterparts. Although there were many adventures–missing flights, seeing the pyramids, haggling at Khan el-Khalili–the highlight of the trip was collaborating together, as one global organization.
Competence via comprehension
Artificial intelligence (AI) is playing an increasingly important role in the healthcare industry. But to fully leverage the potential of AI, it must be equipped with clinical reasoning skills - the ability to truly comprehend clinical data, or in other words, to read it as a doctor would. When it comes to data processing tools, only a tool capable of clinical reasoning can effectively process unstructured clinical data.
Sailu Challapalli, our Chief Product Officer, spoke at a recent Harvard Business School Healthcare panel. The event brought together different healthcare and AI experts to discuss large language models and their impact.
Manually abstracting patient data at scale is an herculean task for humans alone. It is slow, expensive, difficult, and requires extreme precision and accuracy. Organizations have to choose between breadth and depth when it comes to making data useful for decision making. Because of these challenges, the Mendel team created Carbon. Carbon is an easy to use workspace that allows clinical abstraction teams to efficiently curate high quality clinical datasets at scale. The foundation of Carbon is Mendel’s AI. Carbon pulls directly from Mendel’s AI platform to give abstractors a headstart in identifying relevant data elements within a patient’s chart.
Within the real world evidence space, the generally accepted process for creating a regulatory grade data set is to have two human abstractors work with the same set of documents and bring in a third reviewer to adjudicate the differences. These datasets also serve a second purpose - as a reference standard against which the performance of human abstractors can be measured. Although this remains the industry standard, it is expensive, time consuming and difficult to scale.
From the Desk of the AI Team
AI projects have created tangible results for a wide range of industries. Despite the innovation, it is important to remember that AI is not a magic wand that will solve every problem in every industry with a single wave.