The Latest Dose

Oracle Life Sciences Vice President of Global Innovation, Kathy Vandebelt, interviews industry experts and leaders on pressing topics in the Life Sciences industry.

Ep. 43: Running regulatory and clinical operations in an AI world 09/13/2023

Ep. 43: Running regulatory and clinical operations in an AI world Artificially intelligent tools are revolutionizing nearly every stage of the drug discovery process, offering substantial potential to reshape the speed and economics of the industry. As the drug discovery and preclinical stages speed up and potentially produce more drugs to test in the clinical trial phase, how do clinical researchers prepare and respond to these challenging opportunities? In this episode, Toban Zolman, Chief Executive Officer at Kivo will share his thoughts on how AI-enabled successes in drug discovery will affect clinical operations and regulatory operations. We will discuss how advancements in technology and data analysis are reshaping the way we conduct clinical research. -------------------------------------------------------- Episode Transcript: 00;00;00;00 - 00;00;40;25 Hi, everyone, and welcome to the Latest Dose, the podcast that explores the depth of innovation and human compassion in clinical research. I'm your host, Katherine Vandebelt, global vice president of Clinical Innovation at Oracle Health Sciences. Traditionally drug discovery is a notoriously time consuming and expensive process. A host of artificial intelligence tools, AI, are said to be revolutionizing nearly every stage of the drug discovery process, offering substantial potential to reshape the speed and economics of the industry. 00;00;41;02 - 00;01;11;13 According to the Boston Consulting Group, as of March 2022, “ biotech companies are using an AI first approach had more than 150 small molecule drugs in discovery and more than 15 already in clinical trials”. Once the drug discovery and preclinical stages speed up and potentially produce more drugs to test in the clinical trial phase, how do we prepare and respond to this exciting new and challenging opportunity? 00;01;11;15 - 00;01;42;17 Today, our guest will share his thoughts on how AI enabled successes in drug discovery will affect clinical operations and regulatory operations. We will discuss how advancements in technology and data analysis are reshaping the way we conduct clinical research. Joining me today is Toban Zolman, Chief Executive Officer of Kivo. Toban has 20 years of experience in regulatory and clinical operations, drafting some of the first guidelines for electronic submission at Image Solutions. 00;01;42;19 - 00;02;09;06 Toban has consulted with 45 of the top 50 pharma companies in the world. After working in regulatory, Toban ran product teams for several tech companies. Toban has been at the forefront of multiple tech revolutions, such as cloud computing and the Internet of Things. Toban thinks the time has come for clinical trial management to level up. Toban, it is great to speak with you today. 00;02;09;06 - 00;02;45;17 Welcome to the Latest Dose. Yeah, thank you. Great to speak with you as well. In the intro I mentioned that you believe the time has come for clinical trial management to level up. What do you mean by that? Well, let me give you some context maybe on where that comment is coming from. So, I spent a chunk of my career helping tier one pharma transition to electronic submissions and kind of the promise of electronic submissions was improved process, improved visibility, faster review times by regulatory agencies. 00;02;45;19 - 00;03;22;16 And the way that we went about that as an industry, you know, 15 to 20 years ago, was really to take this new challenge, process challenge, of managing a ten X increase in the amount of documents going back and forth to a regulatory agency and controlling that incredibly tightly. And so literally, you know, I spent years and in windowless conference rooms with committees trying to figure out how to manage every aspect of increasingly complex process. 00;03;22;18 - 00;04;03;13 And honestly, it was soul crushing. So, I left the industry and spent over a decade working in other industries that were kind of on the edge of major transformations. E-commerce, social, cloud, IoT, and eventually circled back to life sciences. And I think the thing that struck me the most as I came back into life sciences and started to talk to clinical and regulatory leaders who were dealing with all of these advancements in how clinical trials operate, as this was kind of the same song, new verse. 00;04;03;16 - 00;04;32;03 The pace of clinical trials was accelerating. The complexity of tools was increasing. And the number of assets that they were having to manage that resulted from those advancements was also increasing. And the approach to managing all of that was to just have leaders in life sciences, you know, these pharma companies just literally tighten their grip on the process even more. 00;04;32;05 - 00;05;14;08 And that's just not a model that works, and it's not a model that any other industry has embraced. And so, really, I think what we've really focused on, at Kivo, is helping companies loosen their control a little bit, not control of process, but really trying to manage everything in a monolithic top down approach and instead move to more nimble, more decentralized, more collaborative processes to manage this massive increase in the amount of activity that's happening in the clinical pipeline. 00;05;14;10 - 00;05;41;16 Well, welcome back to the life sciences. So, you mentioned how these individuals are sort of holding on to the existing process. So, in preparation for this episode, I read a number of articles and they continued to talk about how pharmaceutical industry resists adopting digital tools, the need for them to change their strategic priorities, and also evolving the work place culture, perhaps in some of the ways you just mentioned. 00;05;41;18 - 00;06;08;08 What are your thoughts about these statements now that you're back? This is true? Are you seeing something else? What do you mean by that? Yeah, great question. So, yeah, I think you're correct in kind of meta level trends. Life sciences and especially folks that work in operations, whether that's clin ops, reg ops, etc., that are a very risk averse group of people and for good reason. 00;06;08;12 - 00;06;37;11 I'm not throwing shade on anyone. The nature of those jobs and their remit within the drug development process is fundamentally to be risk adverse, and that's what helps create safety in drugs. With that said, you know, Kivo is focused pretty much exclusively on working with emerging life science companies. And so, the vast majority of our customers do not have a drug in market yet. 00;06;37;11 - 00;07;14;04 They have active clinical pipelines, but they are new companies, new in life science terms. Many are 15 years old. But I think they are hitting growth inflection points really in a post pandemic world. And that's been super fascinating to be involved in because I think these smaller companies that are growing rapidly and hitting inflection points post-pandemic are really leaning into decentralized teams and maybe not even by choice. 00;07;14;04 - 00;07;48;02 It's just the nature of how you scale a company now. But they're leaning into that workplace culture of small, decentralized teams, relying heavily on partners; whether that's CROs, contract medical writers, reg affairs shops, whatever it is. And they are figuring out how to scale organizationally, to scale technologically, and scale as well, their clinical trial process in that landscape. 00;07;48;04 - 00;08;21;19 And so, the conversations we have with leaders in those companies who are really building the organization from the ground up, differ significantly from the conversations we have with companies that reached a scale point, you know, a decade ago or even pre pre-pandemic. Where the workplace culture was centered around in-person, everyone working in the same office sort of a culture. 00;08;21;21 - 00;08;51;24 And so, the industry is risk adverse. Ops folks are risk adverse. The customers we work with that are most successful are the ones that are baking into their corporate culture from the ground up, a more nimble, decentralized approach to managing this influx of data. So that makes sense to me about companies that are coming into the market a lot around the post pandemic and getting more decentralized. 00;08;51;27 - 00;09;14;12 But there, I still think there's a disparity that I'd love to get your thoughts on. So, we talk about AI, the promise, the culture, but we also see that we've had cloud around for more than 20 years. But there are some people that say in some articles that say that 50% of clinical trials are still utilizing paper processes somewhere in it. 00;09;14;15 - 00;09;41;23 So how do we deal with this disparity? How do these large companies deal with this? What are your thoughts on what they need to do? I think our experience aligns to that as well. Even with smaller companies, you know, half of our customers have some sort of paper element that they are navigating. I would frame the conversation about AI and cloud, this way. 00;09;41;26 - 00;10;22;18 Cloud and life sciences is very different than cloud in other industries. The majority of the incumbents, software vendors, especially that are offering part 11 compliant solutions software, that's used deep in the regulated process are they may be cloud based, but this is technology that was created before the iPhone was invented. And so, the paradigm in which a lot of these platforms use is not fundamentally changed from software and processes that were developed in the nineties and early 2000. 00;10;22;20 - 00;11;08;15 AI as a layer on top of that, creates so much acceleration, increase data process challenges, that those two are never going to play well together. So, I think what you are starting to see in the industry is kind of, it's almost like, you know, looking at geology where you've got three strata of incompatible technology. You've got paper on top of that, you have SAS based cloud centric solutions that fundamentally aren't very cloud like and then you have AI swirling on top of all of that. 00;11;08;18 - 00;11;45;16 And those three things are very difficult to stitch together, especially if a company is attempting to take, you know, for lack of a better framing, a monolithic view of how to control that process. And so, I think if you look at organizations outside of life sciences that have adapted and grown quickly, there are some commonalities in how they approach new technology and apply that technology in the organization. 00;11;45;18 - 00;12;24;05 Amazon is a great company that comes to mind and in terms of how they approach this. So, Amazon, obviously massive in scale, but I think what the way that they run that company is, you know, following the two pizza rule where there's no team that can't be fed with two pizzas at lunch. And that team manages all aspects of a project or product and has effectively total autonomy to drive features, process, etc... 00;12;24;07 - 00;13;20;07 And within life sciences, it's possible to take a two pizza mentality, especially as AI help accelerate the pace at which net new assets are spun out that may be completely discrete from other products in the company's pipeline may be different. Really, I think what we've seen that's been successful with companies that have grown rapidly at Kivo, is not to try and scale the organization in proportion to the pipeline or in proportion to the amount of assets being created. But rather to create fairly tightly constrained in terms of remit teams that have high degrees of autonomy and authority. 00;13;20;10 - 00;13;51;07 And those roll up into, you know, ultimate decision makers on clinical and regulatory, but have the ability operationally to adapt and dictate their own workflows. And that may sound scary to some folks, especially coming from a paper world where it was possible to have a pharma company with 50,000 people and everyone does the same process. 00;13;51;09 - 00;14;23;12 That's just not super practical these days. And so, picking tools and defining process that enables teams to be autonomous and nimble is really the only way to proportionately scale an organization to keep up with the tsunami of advancements being driven by cloud and AI. In our last episode, prior to this one, we actually did… had a conversation about creating drugs at the speed of AI. 00;14;23;12 - 00;14;48;06 And so, you talked about the increased input that's coming into these organizations. You talked about the two pizza teams; you talked about utilizing technology. So, this is a big change for pharms. I've been in pharma for many years, is a big change. So not having people do the work but really doing the work differently. So, what the implications of this and what advice do you give folks to scale? 00;14;48;09 - 00;15;20;14 First off, it's been fascinating to kind of be on both sides of this, right? Helping companies really codify a process in the early 2000 around how to manage, how to transform the entire organization from paper to electronic. Obviously, there's still some holdouts in the process there, but really, that was a transformational change and now seen another transformation of industry, which is, you know, cloud and AI. 00;15;20;14 - 00;16;09;26 Really driving further up the pipeline changes in how assets are developed and more rapidly finding promising new drugs. So, I think customers that we are working with that are managing this transition effectively are really doing two things. The first thing is they're taking what I kind of call a or they are running guardrail management. Which means for their organization from the top down, they are defining the guardrails in terms of process and technology that they want individual groups to follow. 00;16;09;28 - 00;16;44;27 They are not dictating every step, every workflow that has to happen with every team. But rather creating a North Star that everyone is working towards, defining policies, and operating procedures that define the parameters in which individuals and departments have authority and autonomy to work within. But generally, giving those teams the discretion to identify the most effective way to work. 00;16;44;29 - 00;17;25;09 Because let's be real about that. The speed at which things happen in clinical pipelines today, is faster than what a typical company could author, approve, and train a SOP. So, by the time you get your, you know, massive global process defined and implemented, enough tech has changed, enough insights have been drawn out of the data, that it no longer makes sense. 00;17;25;12 - 00;18;18;22 And so, defining guardrails, defining guardrails for groups, and then letting them operate within those ...while still staying compliant, still meeting the goals of the company is kind of the key. A chief medical officer or a VP doesn't necessarily have the operational insights to be that prescriptive anymore. So, I think that guardrail based management is super effective. We have a customer that in the past, maybe sixteen months, has gone from something like 2 to 15 assets that they're managing with a very small number of employees, and they have not scaled their organization. 00;18;18;24 - 00;18;51;13 You know, they have not doubled, and then doubled, and then doubled again in terms of headcount. They've maybe grown 30%. But that growth has been really centered and focused around those asset classes where individual groups have the ability to kind of figure that out on their own, within budget and some general guardrails. And they're one of the fastest moving life sciences organizations I've worked with as a result of that. 00;18;51;16 - 00;19;42;05 So, I think, you know, those are kind of key lessons that we are seeing is changing that top down mentality. The second trend that I would point to, is really taking a similar view of technology. And, you know, at Kivo, we see this from a document management or a process management perspective because that's the software we build. But this really is true throughout the entire stack and especially on the tools that are used on the AI side, the machine learning side. Either, you know, workbench tools to try and find insights into pharmaceuticals or tools to speed up and better analyze data on the clinical side. 00;19;42;05 - 00;20;20;09 Throughout that stack, I think teams that have the ability to select, implement, and iterate on those tools in a rapid fashion, probably goes without saying, but those are the ones that seem to be adapting and increasing their pipelines the fastest. And with modern cloud tools, with APIs, you know, less reliance on centralized IT, It's possible for a very small company to go very quickly and do all of that in a really pretty controlled way. 00;20;20;14 - 00;20;53;00 But it takes really thinking through the tools and thinking through the process in a way that is not nearly as top down and prescriptive as it may have been a decade ago. So, thinking through those two suggestions you have around the guardrails and also how to handle technology advancement with clinical operations and regulatory operations, are they prepared for this big change? 00;20;53;03 - 00;21;20;16 I get the examples you've given with companies that are starting and growing and so forth, but what sort of investments or what improvements or what have they experienced was going from paper to digital in the early 2000s enough to prepare them? What else have you seen prepare them for the change? So, I think what I would say is it is a mixed bag and that's not a way to dodge the question. 00;21;20;16 - 00;22;00;25 But the amount of deviation that we see across organizations is significant. There are, I think there are, individuals in the industry who get it and really are embracing these trends as a way to accelerate development. And see that there is a path to do that, while preserving the safety of the drug development process. 00;22;00;28 - 00;22;51;27 And I think that there are others, some of whom have, you know, legitimate perspectives but that are very much underprepared for the sea change that is happening with these tools. And are continuing to frame everything, not just on a, you know, all the way back to paper. Much of what I think happened in the trends transition from paper to electronic is that electronic processes were still fundamentally rooted in how you operated in paper. 00;22;51;29 - 00;23;56;03 They were more efficient. But literally the constructs in software UI, the steps in the process, all of that still kind of came back to underlying philosophies around where document sat in what file cabinet and what that file cabinet represented; whether that was draft documents or approved documents or, you know, things of that sort. And so, the entire paradigm of managing electronic data is still fundamentally anchored in a paper view of the world. And organizations and software that I think have gone beyond that, have been able to create much more nimble processes and are probably better prepared for the AI tsunami. Organizations and individuals that are still managing electronic data in a paper paradigm are in for a world of hurt. 00;23;56;03 - 00;24;31;03 And I think, that's probably the most common sort of trigger insight. I'm not sure what tell; this probably the best way to frame it; when we're talking to a life science company for the first time and they're asking questions about, you know, how they solve specific problems - - if it's anchored in references to file cabinets. You know, we have one set of responses. 00;24;31;05 - 00;25;03;00 If it's anchored in in terms of decentralized teams and collaboration and process management, it's a different set... /episode/index/show/thelatestdose/id/28022277

Ep. 42: Creating drugs at the speed of AI 08/03/2023

Ep. 42: Creating drugs at the speed of AI Artificial intelligence (AI) is one of the most discussed technologies across all industries. Life science professionals working in the pharmaceutical industry strive to improve people’s lives tackling incredibly complex diseases. Drug development is often perceived as slow. As the pharma industry looks to improve the drug development process AI promises nothing less than a revolution. Can AI help speed up the drug development process? Identify new drug molecules that have so far eluded scientists? Will AI–designed medicines be safe for people? Have the desired effect on the disease? Meet the rigorous regulatory standards to actually be approved for human use? In this episode, Andreas Busch, Ph.D., Chief Innovation Officer at Absci will answer these questions and shares the value generative-AI is providing drug development today. -------------------------------------------------------- Episode Transcript: 00;00;00;00 - 00;00;31;24 Hi, everyone, and welcome to the Latest Dose, the podcast that explores the depth of innovation and human compassion in clinical research. I'm your host, Katherine Vandebelt, global vice president of Clinical Innovation at Oracle Health Sciences. Artificial Intelligence, AI, is one of the most popular technologies on the planet, and I find it referenced in most, if not all, industries. 00;00;31;26 - 00;00;59;16 Those of us working in the pharmaceutical industry strive to improve people's lives. Can AI help scientists develop better medicines faster? Human bodies are incredibly complex. Drug development is slow. Since I've been engaged in drug development, many people, teams, organizations, and companies have been working tirelessly to improve the drug development process, the promise, is nothing more than a revolution for the pharmaceutical industry. 00;00;59;19 - 00;01;26;21 The March 8th, 2023 Politico article states “nearly 270 companies are working in AI driven drug discovery”. Let's start learning more about AI driven drug discovery and discuss if or when the promise of AI will be realized. Can AI help speed up the drug development process? Identify new drug molecules that have so far eluded scientists? 00;01;26;23 - 00;02;02;02 Can AI-designed medicines, be safe for people? Have the desire effect on the disease? Meet the rigorous regulatory standards to actually be approved for human use? You know, many of these questions can be answered today with my guest, Andreas Busch, Ph.D. Chief Information Officer at Absci. Andreas brings substantial R&D expertise to Absci’s leadership, a world renowned leader in drug discovery and has led R&D efforts for some of the globe's top pharma companies, including Sanofi, Bayer, and Shire. 00;02;02;05 - 00;02;37;05 Andreas’ leadership has resulted in over ten commercial drugs starting from bench to FDA approval, with several more in late stage clinical development. Andreas holds the title of Extraordinary Professor of Pharmacology at the Johann Wolfgang Goethe University in Frankfurt, Germany, where he also received his Ph.D. in pharmacology. Andreas loves, real football a.k.a soccer, enjoys riding his motorcycle through Alps and playing with his beloved dogs Zorro. 00;02;37;07 - 00;03;04;28 Welcome, Andreas. Thank you for making the time to speak with me today. Hey, it's a pleasure talking to you Katherine. So, Andreas I have been taught that artificial intelligence, referred to as AI, are computer intelligence programs that can handle real-time problems and help organizations and everyday people achieve their goal. And AI is obviously a topic of discussion these days and getting way more attention with the release of the articles around ChatGPT. 00;03;04;28 - 00;03;33;22 Today I'd like to focus our discussion on generative AI, but I thought it would be helpful if you could share with me what's important for me to actually know about this type of AI. I'm glad to talk about it. I guess ChatGPT was certainly a breakthrough in AI and the use of AI for a general population and everybody knows now what AI can do through a GPT. 00;03;33;26 - 00;04;07;07 And if you look at generative AI, what we're trying to accomplish simply is to have artificial intelligence supporting us, creating drugs. And as you know, with ChatGPT, you have to give ChatGPT the right prompt in order to get ChatGPT to do the job for you. And this is similar with our generative AI. We need to give the prompt, which is we need to give our models the target, the mechanism we want to work on. 00;04;07;10 - 00;04;43;12 And then the model produces for us, in our case for Absci, a de novo designed antibody. So that's fascinating. How long have you been developing this approach with these prompts and these programs and actually been using this at your organization? I mean, Absci is actually a company which started as a cell line development company and realized then that for AI to be very productive, you need a ton of data and you need a ton of very consistent, high quality data. 00;04;43;14 - 00;05;14;24 So, these two things have to come together, you know, improvement of AI models, but feeding the AI models with plenty of data. So, the models can get better and better. And we've started really implementing AI for our E.coli expression systems for antibody a bit more than two years ago. And the progress we saw in our generative AI approaches were really very significant, very fast. 00;05;14;26 - 00;05;57;16 Already a year ago we were at a stage that we could optimize existing antibodies, so we basically gave the model the information of … look here is a known antibody, …. can you optimize it for affinity, … can you optimize it for immunogenicity and so forth. And we managed to do that. And just half a year ago, for the first time, give the model the information of the structure of a protein that we wanted to address, to produce for us a binding sequence completely de novo or without any idea of an antibody structure before. I think there was …. really for us …. the breakthrough. 00;05;57;19 - 00;06;28;16 And that is something which we have meanwhile even further progressed in the last half year. We extended this approach to more than one binding regions and we are ready now in a situation to address three of the binding regions of an antibody. And we are very, very optimistic that this progress is going to be extremely meaningful and helpful and what we believe disruptive in biologics research in the future. 00;06;28;18 - 00;06;49;01 So, this is exciting and extremely fascinating. So, I'm going to go to a statement you made about the data. So, can we talk a little bit about that? So where do these sources of data come from? What types of volume are you talking about? And I guess more importantly, as somebody who has worked with data for many, many years, 00;06;49;01 - 00;07;11;00 and one of the things that people will often ask about is ….should you use that data? Is that data appropriate? Is it reliable? Some people use the word quality. So, in order to achieve these impressive results, can you tell us a little bit about, more about, the data that's being used? Where does it come from and all those things? 00;07;11;03 - 00;07;36;13 Sure. To make it clear, what we're doing is, once we know the structure of a mechanism we want to address, let's assume whatever a membrane protein like a G protein coupled receptor, whatever you name it, we identify the region to which we want our antibody to bind and we give this information in the structure of this region to the model. 00;07;36;14 - 00;08;08;25 The model then delivers to us a number of model hits. Artificial intelligence generated hits. Information about what the model thinks the binder should look like. And what we do then, and that's the very straightforward answer to your question of the quality, is we generate those hits in the laboratory, we express the genes relevant for those binding regions in our expression system. 00;08;08;27 - 00;08;42;06 That's a microbial expression system, E coli. And then we simply have a test available called the Ace assay, in which we then validate what is indeed the binding affinity of those calculated binder. So that gives us then immediately an experimental validation of the AI suggestions and of the AI results. And therefore, we feel very, very comfortable that of course the quality of our predictions is very high as we validate them right afterwards. 00;08;42;08 - 00;09;25;10 Not only that, we validate them, but we can then again also use the information of those data to further improve the model. You ask, how many data do we generate? Well, the nice thing about E coli is that it replicates very, very fast and we can express huge libraries. The libraries again are the genes suggested by the model, and we can express easily your libraries of 500,000 or 1 million binding regions and as a consequence can measure 2-3 million of individual binders in a week or two. 00;09;25;10 - 00;09;57;08 And we can of course, also then see how well those binders are expressed in the cells and can measure up to a billion data points and protein interactions per week. Okay. So, I have to ask, if you didn't have the generative AI and the capabilities that you've just talked about, how long would it take for a human to do this without these additional tools and capabilities? 00;09;57;10 - 00;10;28;20 I think the really exciting piece about what I'm describing to you is that the model not only spits out a binder of a certain quality, but it spits out, already something which we can in a multidimensional way, optimize. So, if you go back to a traditional way of how to generate an antibody, which would be through mouse immunization or rapid immunization or what is called a phage display, you also can get a binder. 00;10;28;20 - 00;11;08;21 However, that binder comes without any potential optimization you would want to see. For example, you know, you get a binder. But you cannot influence in this traditional way the affinity, you cannot influence the solubility, the immunogenicity, and so forth. All of those parameters are very, very important for an antibody. Our model can spit that out, and I think that is a breakthrough, especially if you consider this is indeed a rounded up, optimized candidate. 00;11;08;23 - 00;11;33;05 This is not just, you know, a first antibody, which then can take over years, years really to get finely optimized. So again, going back to revolutionizing this and actually making it very different. So, but this is so different than what some people are familiar with or what they've been educated. Absolutely. They've done in the past. Are you familiar, 00;11;33;10 - 00;12;02;11 you probably are, but I'll just check; are you familiar with the technology adoption curve where they use the terms innovator, early adopter, early majority, late majority and lagger. Sure. Yeah, that's what's kind of coming to my mind. This is so different than what scientists have been doing in the past. I guess how broadly is this currently being used or where do you see the industry right now with this way of working? 00;12;02;11 - 00;12;25;02 Are we in still the innovator stage? early adopter? or am I a bit behind and we're actually in the majority stage? so can you talk us through that, please? That would be great. Yeah, I think we certainly consider our approach at the forefront of biologics research right now. And our focus is, of course, entirely on generation of antibodies. 00;12;25;04 - 00;12;55;11 That is our focus and I think it really needs this focus to make the progress which we are having right now. But how in the context of in general, R&D of biopharmaceuticals, there are many, many more aspects which AI can address what we are doing with large molecules, with antibodies other companies are doing with small molecules, with the chemicals. 00;12;55;13 - 00;13;24;12 Then you can of course, beside the generation of drugs, discuss options of AI to identify the right mechanisms. Because of course you always need to start in a disease with the right mechanism to address. Otherwise wonderful antibodies or wonderful small molecules are not really worth a lot if you're working on the wrong mechanism or target. 00;13;24;15 - 00;14;05;27 So, I think when it comes to generation of antibodies, we are at the forefront. We certainly want to extend our knowledge in the future to other biologics beyond antibodies. But there are other approaches of AI which of course are also very productive and they all really did grow over the last couple of years based on the existence and availability of vast amount of data. 00;14;05;29 - 00;14;39;26 So how much, how expensive is this? So, we've talked about how it works. We talked about how it's going to save significant time, what it needs to run. Totally appreciate your focus area in antibodies and so forth and other companies are doing other things but how expensive it this is? Is it really cheap? Is it moderate? And I'm not necessarily asking you to tell us the price, but what sort of investment, I guess, or what sort of expense should companies think about as they get engaged in this type of work? 00;14;39;29 - 00;15;03;00 Yeah, I think that we should try probably to look at the end game. What is the end game, really. I mean, our goal clearly is , once we know a target, at the click of a button we will have the information of how the optimized antibody looks like. The consequence, and of course, the click of a button does not cost a lot of money. 00;15;03;00 - 00;15;45;18 As you can imagine, you're doing that every day yourself. But as you can imagine, the traditional path is a very, very different one. The path it takes from a target to a traditional antibody really means tons of lab work… it means tons of iterative processes… it involves many, many people, consumables and so forth. Until you indeed have an antibody in hands which you then start producing first in vitro and in vivo data later on, those data will still be needed. 00;15;45;18 - 00;16;18;06 So, you will need of course, once you have the antibody spit out of the model to characterize the antibody in the relevant disease models. But until then, of course, I would say the cost saving and the time saving are enormous. My assumption is right now, if you look at benchmark and the industry, the cost to come from a target to a candidate antibody is somewhere in the range of $5 - $10 million. 00;16;18;08 - 00;16;46;13 And you can imagine that a click of a button is certainly going to be faster and cheaper. I think McKinsey actually coined this phrase, pilot purgatory, which means that organizations are hesitant to take on new ways of working. They see better ways, they see exciting ways, but because they don't necessarily understand them or they're not that familiar, they require a lot of change in their organization, they’re hesitant. 00;16;46;17 - 00;17;14;10 And so often, we pilot things or I have piloted things or my company has piloted things in my past. And then what I notice across the industry, this slow adoption can kill very valuable innovation because we're constantly piloting them. Do you see those concerns with what you're talking about, or how do you recommend that we prevent that or escape it in this particular situation? 00;17;14;10 - 00;17;54;29 Because it looks so, so promising. I think like every breakthrough technology, there will be the winners and fast adopters and there will be the slow adopters. Listen, I've been R&D head in pharmaceutical industry for over 20 years. I was R&D at Bayer and I was R&D at Shire and I've certainly dealt with a lot of associates, you know, which were skeptic of new technologies and like you heard from the McKinsey reports, not readily available always in a situation to adopt technological breakthroughs. 00;17;55;02 - 00;18;42;06 Having said that, once the breakthrough is obvious, that's the latest moment. Then you can get on board and everybody knows that at the end there is going to be, if really the promise comes through, which I just described to you, that there is no way that you could say, okay, let's wait. And I think this is going to go much, much faster than a number of other breakthroughs in the past, I think, not just the entire world's population got prepared to apply AI to ChatGPT, but the industry is really eager to apply AI along the entire value chain of R&D and even beyond that, onto marketing aspects of drugs. 00;18;42;06 - 00;19;25;26 So I have to say, yes, there always is a chance of resistance, of adoption of technologies in R&D organizations, but I am completely convinced that once our approach has been validated on a couple of targets, that will be the case in my assumption is within the next year, it is going to be a must without very little alternatives for industries to adopt it because it brings them into the situation to be faster, to come up with molecules which have a higher probability of success based on a multi parameter optimized profile. 00;19;25;28 - 00;19;57;13 And the two things together; being faster & being better optimized, gives you a competitive advantage, which you cannot, cannot give up. Do AI designed medicine, meet the rigorous regulatory standards that are being used to get drugs approved to humans? So, it sounds like this might be changing the data package, it might be changing how we actually might need to talk to regulators. 00;19;57;16 - 00;20;37;06 Am I understanding this correctly or what is it I need to understand with regards to regulatory requirements? Actually, we should distinguish between what I expect over the next 5 to 10 years versus in the more distant future. What we will deliver will undergo exactly the same regulatory processes as all drugs, no matter how they are delivered, no matter whether they come from traditional small molecule approaches or traditional biologic approaches, the regulatory process will be exactly the same. 00;20;37;06 - 00;21;08;04 The regulatory process will be… you need to show in phase one, phase two and phase three clinical trials that the compounds are safe and efficacious in patients. You will go through before you go to the clinic through extensive pre IND activities to get to that stage. Those regulatory aspects will not be different from generative AI generated drugs versus the drugs coming from traditional pathways. 00;21;08;06 - 00;21;42;27 The only difference I can see immediately versus the future , I can see that based on the chance that we should be able to predict with AI a much better profile. And already also if we go into systems biology, get more information about potential side effects, mechanism based and so forth, the probability of success to get through those regulatory processes is going to be increased. 00;21;43;01 - 00;22;18;13 That is the one aspect in the long term off course, I do see that regulators want to understand what really is the productivity also of AI methods in clinical development. They want to see how valid my predictions were of, you know, development aspects based on AI information and I can see that AI will have a significant impact in the future also on regulatory processes. 00;22;18;16 - 00;22;43;20 Again, I know this sounds repetitive, but that's so exciting to me. Being working in this industry so long to see these types of changes is it's just very, very inspirational. As well as I'm getting older, hopefully... /episode/index/show/thelatestdose/id/27649254

Ep. 41: CancerX: Reducing incidence, burden, and disparities in cancer care 06/28/2023

Ep. 41: CancerX: Reducing incidence, burden, and disparities in cancer care Cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020. President Biden has reignited the Cancer Moonshot initiative and set a new national goal: “if we work together, we can cut the death rate from cancer by at least 50% over the next 25 years and improve the experience of people and their families living with and surviving cancer”. “To achieve [the cancer moonshot goals], we must amplify digital innovation,” stated Dr. Catharine Young, Assistant Director of Cancer Moonshot Engagement and Policy, White House Office of Science and Technology. CancerX, an initiative to rapidly accelerate the pace of cancer innovation in the U.S., will harness the power of innovation to reduce the burden of cancer for all people. Oracle is excited and honored to join Cancer Moonshot's new CancerX public-private partnership. In this episode Jennifer Goldsack, Chief Executive Officer at Digital Medicine Society (DiMe), Santosh Mohan, Vice President, Digital at Moffitt Cancer Center with Moffitt Cancer Center, and Stephen Konya, Senior Advisor to the Deputy National Coordinator, and Innovation Portfolio Lead for the Office of the National Coordinator for Health IT (ONC) will share more about Cancer Moonshot, CancerX and the importance of digital innovation to achieve the goals. -------------------------------------------------------- Episode Transcript: 00;00;00;00 - 00;00;34;26 Hi, everyone, and welcome to the latest dose, the podcast that explores the depth of innovation and human compassion in clinical research. I'm your host, Katherine Vandebelt, global vice president of Clinical Innovation at Oracle Health Sciences. Cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020, President Biden has reignited the Cancer Moonshot and set a new national goal. 00;00;34;29 - 00;00;56;27 If we work together, we can cut the death rate from cancer by at least 50% over the next 25 years and improve the experience of people and their families living with and surviving cancer. In response to the White House Cancer Moonshot, CancerX is formed, an initiative to rapidly accelerate the pace of cancer innovation in the United States. 00;00;57;00 - 00;01;26;12 CancerX will harness the power of innovation to reduce the burden of cancer for all people. Oracle is excited and honored to join Cancer's Moonshot New CancerX Public Private Partnership. Here with me today to share more about these inspirational initiatives, our Jennifer Goldsack, Santosh Mohan, and Stephen Konya. Jennifer, Jen, Goldsack is the CEO of the Digital Medicine Society, also known as DIME. 00;01;26;15 - 00;01;56;08 Jen's research focuses on applied approaches to the safe, effective, and equitable use of digital technologies to improve health, health care and health research. Jen is a member of the roundtable on Genetics and Precision Health at the National Academies of Science, Engineering and Medicine. Jen serves on the World Economic Forum Global Leadership Council on Mental Health. Previously, Jen spent several years developing and implementing projects with Clinical Trials Transformation Initiative, also known as CTTI. 00;01;56;10 - 00;02;26;08 This is a public private partnership co-founded by Duke University and the FDA. Jen conducted research at the hospital of the University of Pennsylvania, helped launch the Value Institute, a pragmatic research and innovation center embedded in the large academic medical center in Delaware. Jen earned her master's degree in chemistry from the University of Oxford, England, her master's in history and sociology of medicine from the University of Pennsylvania and her MBA from George Washington University. 00;02;26;10 - 00;03;04;24 Jen is a retired athlete, formerly a Pan American Games champion, Olympian, and world champion silver medalist. Santosh Mohan, vice president of digital at Moffitt Cancer Center, is also with us today. Santosh brings more than 15 years of digital health and health information technology experience to this role. Previously, he served as the managing director of the Innovation Hub at Brigham and Women's Hospital, where he led digital transformation through the use, development, evaluation and commercialization of digital health applications. 00;03;04;27 - 00;03;34;27 Throughout his career, Santosh has worked to leverage data and analytics to create and design new programs and digital abilities, with a strong focus on emerging technology to advance care and improve the clinician and patient experience. Santosh holds a master’s degree in clinical informatics from Duke University’s Fuqua School of Business and a bachelor’s degree in bioinformatics from Vellore Institute of Technology in India. 00;03;34;29 - 00;04;11;01 Santosh is a certified professional in healthcare information and Management Systems, a member of American Medical Informatics Association, a senior member and fellow of the Healthcare Information and Management Systems Society, known also as HIMMS. You will also hear from Stephen Konya, the senior advisor to the Deputy National Coordinator and the Innovation Portfolio Lead for the Office of the National Coordinator for Health I.T., also known as ONC, which is part of the U.S. Department of Health and Human Services, HHS. 00;04;11;03 - 00;05;04;06 Stephen is shaping the agency's long term strategy. The primary liaison to the White House Office of Science and Technology Policy. The primary liaison to the external health care startup and investor community. Stephen leads the Digital Health Innovation Workgroup under the Federal Health I.T. Coordinating Council, an interagency collaboration community comprised of innovation representatives from 40 other federal agencies. Previously, Stephen has led several key ONC projects, including the HHS Pandemic X Innovation Accelerator, the National Health I.T. Playbook, the Agency Patient Engagement Playbook for Providers, the Smart App Gallery, the FHIR at Scale Task Force, also known as FAST, and is a founding co-chair of the Together.Health Collaborative Effort. Prior to his position with the federal government, 00;05;04;07 - 00;05;35;07 Stephen served the state of Illinois in a variety of key positions and diverse responsibilities. Stephen holds a BBA in finance and international business from Loyola, University of Chicago, is fellow and mentor of the Mid-American Regional Public Health Leadership Institute Program at the University of Illinois-Chicago School of Public Health. Welcome, Jen, Santosh and Stephen to the Latest Dose and thank you so much for making time to speak with me today. 00;05;35;10 - 00;06;01;03 When I hear the word cancer, it elicits fear and anxiety, at least in me. So, researching the cancer trends does not provide me with much comfort. According to the World Health Organization, cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020. Or stated another way, nearly one in six deaths. It appears that the medical community's understanding of cancer is growing, 00;06;01;06 - 00;06;27;21 yet the death rate remains so high. What do we need to do differently? Thanks, Katherine. Cancer is out every day at Moffitt. We come face to face with this terrible, very difficult disease. Every single day. But we also see the courage of our patients fighting it. And that really inspires us to bring hope to every patient we serve and deliver to them some of the best outcomes. 00;06;27;22 - 00;06;52;26 Up to four times the national average. Now, cancer deaths in the US are actually falling, but they're not falling fast enough so the death rate needs to decline by a more rapid percentage to reach the Moonshot goal of reducing cancer deaths by 50% in the next 25 years. It's very clear that we need a multifaceted approach to tackle this complex issue. 00;06;52;29 - 00;07;21;18 First and foremost, prevention and early detection must be at the forefront, emphasizing lifestyle and behavior changes, like adopting a healthy diet, regular exercise, smoking cessation. All of these can significantly reduce cancer risks. Equally important is promoting awareness about the importance of regular screenings and recognizing early signs and symptoms. And we know that screening rates have declined for all cancers since the pandemic started. 00;07;21;18 - 00;07;48;28 So, we will likely soon start seeing cancers presenting at more advanced stages requiring longer and more complex treatment, as well as decreasing positive outcomes. This means that we need to move engagement upstream and increase those screening rates. And this is where digital channels can help. We've been at the forefront of prevention and screening for years now, and really the reignited 00;07;48;28 - 00;08;13;16 Moonshot has been an opportunity for us to accelerate these efforts around re-energizing the community to prioritize cancer screenings. Early interventions can make a world of difference, but prevention and early detection are just the beginning, and they require a lot of behavioral change within our society. And while we advance that, we should also recognize that cancer will continue to occur. 00;08;13;18 - 00;08;44;06 So, we need to change the trajectory of cancer mortality, not just the incidence with therapeutic advancements, including immunotherapy and especially CAR-T. And therefore, we need to continue investing in cancer research and innovation. And collaboration is really key in this space. Collaboration among academia, with the industry, research institutions and entrepreneurs, it's really vital to expedite progress in this space. 00;08;44;08 - 00;09;11;11 But progress also means nothing if it is not accessible to everyone. And so, ensuring affordable and accessible cancer care is a must. So again, this is another space where organizations must work together to bridge that gap and provide quality care to all individuals regardless of their socioeconomic background. I feel we also need to take a very patient centered approach, that is crucial. 00;09;11;13 - 00;09;36;20 Cancer care should encompass more than just medical treatment; which is supported emotionally, it should provide symptom management and certainly address financial toxicity. So, these are all very, very important things that we need to do, to decline, to help that that decline faster, at a faster pace. And we really do need a collaborative effort from everyone. 00;09;36;23 - 00;10;06;05 Beating cancer truly demands delivery and collaboration, but also bold innovation. And this is where I feel CancerX is creating a dynamic ecosystem where people can come together, organizations can come together, ideas can flourish, expertise and resources can be shared, and innovative solutions can rapidly be developed and equitably deployed to really prevent and cure cancer in this fight. 00;10;06;07 - 00;10;30;02 Well, thank you for sharing all of that. That's extremely motivating. You mentioned the Moonshot and so I believe the White House Cancer Moonshot has been reignited. So, what I know, you mentioned a couple of things that the Moonshot focusing on, but is it possible that you could actually walk us through what the mission is currently or what it is globally around all of that? 00;10;30;02 - 00;11;02;14 So, Stephen, is that something you can talk us through? Yes, Katherine, thank you so much for the question because it is important to know how we got to where we are today. So, you know, it was actually in 2006 when the Cancer Moonshot was first launched. And like any Moonshot, you know, dating back to President Kennedy's Moonshot, is all about really how do we refocus everyone's attention, prioritize our resources, and like Santosh mentioned, collaborate together to try to tackle this massive challenge that we're still faced with today. 00;11;02;16 - 00;11;28;11 So, in 2016, when they launched the Cancer Moonshot, then Vice President Biden was put in charge of it. And it was a very personal story to him and something that he was very passionate about. Fast forward to now President Joseph Biden. The president has now reignited that Cancer Moonshot to build upon what was originally launched in 2015. The idea is really around two things at this point. 00;11;28;13 - 00;11;53;17 Number one, as Santosh just mentioned, we need to reduce the death rate for cancer by 50% in the next 25 years. That is the huge Herculean task that we have before us. And while, though it seems like it's a very difficult task, I think it is very realistic. As Santosh mentioned, the current death rate is going in the right direction at a rate about 2.3%. 00;11;53;19 - 00;12;26;20 But we need to drop that to 2.7. Recently, the National Cancer Institute released a study in a report that said we could accelerate that death rate to dropping it at 2.7% in order to achieve that target of a 50% reduction in death rates by in 25 years. So that's the number one thing we're focused on. And that's going to take everybody working together, not just, you know, industry, who's already begun to answer the call of the Moonshot organizations like Moffitt and DIME working Together and many others throughout the country all collaborating. 00;12;26;23 - 00;12;48;17 And then on the government side, you know, we're not alone. We need or we need to also work together across federal agencies and do it in partnership with organizations like Moffitt and DIME and others. And that's really where CancerX is just becoming a vehicle for that collaboration. We will get into that a little bit. The amount of response that we've seen so far has been amazing. 00;12;48;19 - 00;13;21;20 But, but, again, it's really about reducing that death rate. The second key area of the Moonshot that we're also focused on, that the president has mentioned as part of this reignited Moonshot is all around how can we help patients and their families and their caregivers navigate the complexities and the challenges of a cancer diagnosis, and going through that treatment and essentially, you know, dealing with all the challenges that come along with that, including things like financial toxicity, what they believe Jen's going to cover in a little bit here. 00;13;21;23 - 00;13;46;07 But, but the idea that it's not just about developing new drugs or new diagnostics, but really, we also need digital tools and other solutions that can help manage the complexities of cancer care and helping those families navigate and go through that with at least disruption as possible. We know that often the concerns around the complexity of the care and the cost of the care can lead to people avoiding getting screened. 00;13;46;14 - 00;14;13;28 They don't want to know if they have something because they're afraid of getting that bad outcome of hearing that they do have cancer because now they've got to deal with it financially or emotionally or other things. So, if we can figure out a best way to make that more, more friendly, and easier to try to minimize the amount of adverse impact it has and to try to reduce some of that fear and anxiety around what it's like to go through a diagnosis of treatment of cancer. 00;14;14;00 - 00;14;47;11 I think that could also severely help us in getting, you know, encouraging more patients to do go get screened early and then to help them navigate that. Well, those are very lofty and inspiring goals to decrease the death rate and to also make it significantly easier for people to move through any diagnosis, whether it's cancer or others, it's very stressful, very daunting, and takes usually a community to deal with that. 00;14;47;11 - 00;15;12;10 So that is awesome! And thank you for your leadership and thank you for your commitment to bring these changes to the United States. So, when I was reading about the Moonshot and when I was reading about you mentioned CancerX Stephan, Dr. Katherine Young, I believe she's the assistant director of Cancer Moonshot Engagement and policy, and she works at the White House Office of Science and Technology Policy. 00;15;12;13 - 00;15;38;05 She shared in March this year, so 2023, “ To achieve the Cancer Moonshot goals, we must amplify digital innovation, which is the mission of our newly formed CancerX”. So, you mentioned that Jen can talk a bit about CancerX. How did CancerX come about? And will you share more about the mission of CancerX? 00;15;38;08 - 00;16;02;26 Yeah. Happy to, Katherine. The CancerX was announced by the White House on the one year anniversary of the reignited Moonshot. So, February 2023, was that one year anniversary and it was announced as a public private partnership with the goal of harnessing the power of innovation to support and drive towards the achievement of the reignited Cancer Moonshot goals. 00;16;02;29 - 00;16;41;08 So, February was that announcement. We are very proud between the Digital Medicine Society and Moffitt Cancer Center to have a history of pre-competitive, multi-stakeholder set of research and implementation at the intersection of innovation and oncology. And so, we were privileged to have the opportunity to host CancerX alongside our colleagues at the Federal Government with Stephen Konya as lead from ONC, but also from the office of the Assistant Secretary for Health and the White House of course. 00;16;41;09 - 00;17;14;03 The mission is we have come together, to state it is, to unite to diverse and inclusive community of stakeholders, to rapidly develop and equitably deploy innovative solutions that can prevent and cure cancer. That mission is wholly in support of the Moonshot goals that Santosh and Stephen describe so well. And we are incredibly proud to have just announced over 90 members, including Oracle, Katherine, who has come to the table, raised their hand, and said they want to work with us. 00;17;14;03 - 00;17;52;15 They want to join the charge to harness the power of innovation, to reduce the burden of cancer for all people. So that's a little bit about the history and also our exceptional partners who share this vision. So, wow, 90 members already! That is fantastic! That's I think it reflects the decision making from our colleagues at the White House to structure CancerX as a public private partnership, bringing the very best of government and expertise and capacity together with cutting edge research and sort of clinical knowledge from the private sector. 00;17;52;17 - 00;18;13;05 This is how we are going to achieve the goals of the Moonshot. This is how we are going to harness the power of innovation. And I think it speaks to the industry's commitment as well as government's commitment, that we have this kind of engagement right out of the gates. So, I read that CancerX is using a three pronged approach to generate this impact. 00;18;13;08 - 00;18;40;09 So, is there important scope of these three prongs that are important for people to understand? And what will this mean for physicians? And you've already talked very clearly about the importance of patients. So, what does this mean for physicians and patients? Maybe I'll give a quick overview and then ask Stephen to talk more about the vision for the accelerator and Santosh to pick up the implications for sort of clinicians as these are his partners every day over at Moffitt. 00;18;40;11 - 00;19;07;25 So, you're exactly right, Katherine. When we when we were thinking about as a team how we would structure CancerX, we wanted to do several things. One, we wanted to create a structure that provided a truly big tent environment for stakeholders from across industry, academia,... /episode/index/show/thelatestdose/id/27312846

Ep. 40: CancerX: Breaking down the barriers to digital innovation use in oncology 05/30/2023

Ep. 39: Recruiting and retaining experienced principal investigators 04/25/2023

Ep. 38: Psychological aspects of clinical trials 03/29/2023

Ep. 37: Digital transformation: empowering or encumbering for research sites? 02/28/2023

Ep. 36: Digital predictions for clinical research in 2023 01/25/2023

Ep. 35: Converting to a patient-centric care model 12/07/2022

Ep. 34: Unmasking safety signals 11/15/2022

Ep. 33: A leap forward in safety 10/25/2022

Ep. 32: Expanding our knowledge by embracing real-world evidence 09/29/2022

Ep. 31: Advancing patient informed consent 08/30/2022

Ep. 30: Collaborating to scale the use of health sensor data 07/28/2022

Ep. 29: Debunking the myths of clinical trials; leveraging the realities 06/28/2022

Ep. 28: Celebrating clinical trial volunteers 05/18/2022

Ep. 27: Using RWE to deliver better treatments for patients 04/25/2022

Ep. 26: Facilitate clinical research in Europe 03/28/2022

Ep. 25: Raising awareness for unknown illnesses 02/23/2022

Ep. 24: Reimagining clinical research as new capabilities come to market 01/26/2022

Ep. 23: Finding strength in hardship – the story of Joey’s Little Angels 12/09/2021

Ep. 23: Finding strength in hardship – the story of Joey’s Little Angels When Nicole and James Angiolino’s son Joey was diagnosed with the rare disease Hurler’s Syndrome at seven months old, they did everything they could to save his life. They left their eldest son Nicholas, their family, friends, and teaching careers behind and headed to Duke University Hospital in North Carolina to get him the care he needed. James and Nicole remained unwavering in their fight for Joey, living in the hospital to ensure that Joey got every treatment option available. Their motto became Motivation, Perseverance, and Strength (MPS), mirroring the acronym for the medical term Mucopolysaccharidosis, from which Joey suffered. But after a tremendous battle, sadly, Joey passed away on July 16, 2010, at just 15 months old in Nicole’s arms. Instead of crumbling under the grief of such an immense loss, they vowed to maintain the positivity and strength that Joey demonstrated to them throughout his journey. While it was extremely difficult, they marched on in honor of their son, and decided to help other families going through similar, challenging circumstances. In episode 23 of The Latest Dose, Nicole and James Angiolino join us to talk about Joey’s Little Angels, the non-profit they founded in honor of Joey’s life and determination. With their last name Angiolino meaning “little angel” in Italian, Joey’s Little Angels’ mission is to provide financial and emotional support to families with children facing rare and difficult diseases. To date, the non-profit has donated over $100,000 to the Duke Pediatric Bone Marrow Transplant Family Support Program. During the holiday season, Joey’s Little Angels organizes an annual toy drive with the support of volunteers and community leaders. Together, they have gathered 30,000+ toys nationwide for hospitalized children and their siblings. In the past, they’ve made pivotal donations to Duke University Hospital, Children's Hospital of Philadelphia, RWJ New Brunswick, Capital Health, Boston Children's, Rutgers Cancer Institute, St. Christopher's Philadelphia, Cincinnati Children's Hospital, and many more. Their efforts in this space have made a tremendous difference in the lives of families with children undergoing medical treatment. /episode/index/show/thelatestdose/id/21436370