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: http://traffic.libsyn.com/thelatestdose/The_Latest_Dose-S01_E43.mp3 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 wh
