Ep. 43: Running regulatory and clinical operations in an AI world

The Latest Dose

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:

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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.

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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?

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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...

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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.

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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.

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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.

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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?

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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?

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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.

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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.

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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.

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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.

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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.

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If it's anchored in in terms of decentralized teams and collaboration and process management, it's a different set of responses. And so, I think what you're really seeing in the industry right now is, a… hate to use the term paradigm… but a paradigm shift and really a transformation in how work gets done and how companies think about what that is.

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We have a close partner at Kiva who talks about how the product of pharma companies is documents, not pharmaceuticals. And I think for the majority of individuals within a pharma company, that's totally true. That is what they do day in and day out, is prepare documents that represent some portion of the narrative of their clinical trial and ensure that those get teed up to a regulatory body for approval.

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I think in a new world view, while that narrative element and communicating everything to regulatory agencies through documents is true, organizations that can shift that thinking and really understand that they're developing pharmaceuticals, biologics, whatever they are working on. And that exists in the context of an ongoing, evolving process are the ones that are being are the ones that are ultimately going to be able to adapt to this and be prepared for the transition most effectively.

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That's great. So, I agree with you that I still come across individuals that are working with technology based on a paper originated, a paper fundamental process. So, my question to those individuals that you're familiar with or collaborate with or discussed with…. through the COVID-19 pandemic, we had to change the way we were working.

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And in many cases, it pushed us to do things very differently than we had to do in the past. And to be very creative, yet safe and produce quality. So, do you feel that these trends actually helped people to move towards where they should be going, or do you think it's more of the same? I actually do think that that COVID was a game changer.

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And I mean, I think that's true in a lot of industries, but certainly in life sciences, I think it affected two things in a fundamental way. One was business process had to move to a decentralized approach. And the second is so many clinical trials were affected by COVID, in terms of, you know, if you had a clinical trial in progress when COVID hit, well, doctors can't be in the same room as their patients for every single visit, or there has to be social distancing.

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So all of the sorts of data collection methods changed and that forced many, most clinical trials, to switch up protocols, update documentation and frankly created a highly dynamic environment on the clinical side where changes to protocols for clinical trials changed, you know, on a weekly, daily basis and change from one country to another in a way that was way more aggressive and dynamic than I think most individuals had had ever dealt with before.

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And that frankly was a forcing function to be more adaptive, to leverage a different class of tools, a different class of partner, and I think forever changed the operational aspects of how clinical trials are run. And so, I think the adoption of AI and the willingness to transition to more decentralized models, in terms of process and technology, was accelerated by a decade or more due to COVID because it just it changed the baseline of what is acceptable in terms of how dynamic a process can or should be… to move drug development forward while still maintaining safety.

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So, I think more than AI, more than cloud, COVID was probably the biggest accelerant in the drug development process in the last two decades at least. So now that we have your advice around guardrails, decentralize teams, leveraging the cloud, leveraging AI, take the learnings from COVID-19, right, like embrace them, take the learning, and keep going. Is there any other improvement or change that the regulatory operations and clinical operations teams need to take in account that we haven't touched on yet?

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There's really kind of three aspects, from a meta level to kind of any sort of transition, really in industry, but specifically in life sciences, that you can anchor against. So, people, process, and technology. On the people side, I think redefining who makes up a team is a critical part of this. And life sciences, this is a pendulum in life sciences where, you know, business process outsourcing becomes supercritical.

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Organizations move to that to restructure cost, etc. The pendulum swings back the other way. They bring those roles in-house. When I talk about people, there is a different trend happening now, and it's not just business process outsourcing and small companies work with CROs. It is really a fundamental rethink of how organizations scale and how teams are built. And it is not where you have employees of the sponsor and employees of a partner and there is a wall where documents are lobbied over from one organization to another.

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But really what we are seeing is a much more fluid arrangement between sponsors and partners. Where partners; and they could be sizable organizations, they could be independent contractors; but they are bringing specific domain expertise into the team and are a core part of that team and process for whatever duration makes sense.

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Could be six months, could be six years. But companies that seem to be doing exceptionally well are ones that really are looking for the most efficient way to bring the right domain expertise into a team. And it doesn't matter, necessarily, at least in the short run, where that person sits, if they're a sponsor employee, or a partner. And we can almost see at Kivo, you know, companies that are moving very quickly and, you know, our internal stories about them are, man, did you see Acme Pharma, they are cruising!

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If you look at their users in our system, you see a high intermix of email addresses that belong to the sponsor and email addresses that belong to partners. Because they are shoulder to shoulder, elbow to elbow, working through the challenges of bringing a drug to market as a team, everyone bringing kind of their best expertise to the table.

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So, I think, you know, rethinking where that domain expertise comes from and how you scale a team internally versus through partners is a key piece of that. On the process side, I won't repeat what I've said previously, but I think, you know, a guardrail based approach to process development versus a prescriptive approach seems to be what is really helping successful companies drive innovation faster.

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And finally, on the technology side, this is drug development is no longer a monolithic process. Even within a single company. Individual teams are going to be using individual tools. You'll use tools for short periods of the process and then wind those down, especially with AI.

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And so, I think the tools that you do pick to be anchor points in your process, need to be nimble, need to be highly configurable so that you can adapt them over time. And frankly you need to be able to have a change management process that can go incredibly fast so that you can adapt those systems to your people and process as they change because they're going to be highly dynamic. So, all three of those areas have to be able to scale and change quickly and all adapt around each other.

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The days of mapping out a process in a conference room, paying a vendor to implement that process in software and then revisiting that in five years is no longer a model that makes sense. So, people, process, and technology, that's the key. They all got to work together in a dynamic fashion. Thank you for that great advice. So, as we come to the close about this episode and you've shared some excellent, great content for our listeners to think about, I really want to ask you this question.

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What do I; what do the listeners do; what do we need to start doing today in order to embrace this new way of working; this new reality to really capitalize on this opportunity of a faster drug development coming at us? Share your thoughts with us…what we could do tomorrow after we've listened to you. So, I think there's a couple ,a couple levels to think about this, from a kind of a top level, taking one step back from the day to day and not necessarily focusing on what do I need to do next, but instead thinking about like what is the ultimate objective in my role, in the trial, whatever, and what actions actually move the needle the most to achieve that, I think is important.

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As someone who talks to customers every single day and gets asked for, you know, for features; told a process is changing, you know, whatever it is. What I have found to be really effective in understanding that is asking why, you know, why …. why do you want that feature?

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Why are you making that process change? And so, the five why’s, you know asking why five times, to get to the root reason why something is; it is turns out to be really effective. Because a lot of …. a lot of decisions, a lot of process, it turns out the original reason why you are doing that as has been so mutated by process, by committee, by interpersonal challenges, political challenges within the company, that if you really stripped down what is the end goal and why are we making changes to get to that?

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It turns out you can often short circuit a whole lot of added complexity and instead strip away complexity to really get to the, you know, the core challenge. So, you know, that's kind of a big idea. But in terms of what people can do today or tomorrow, I really think it is less about focusing on explicit deliverables, even though that's how everybody gets measured for their job. And instead, really thinking about overall outcomes.

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And I don't mean to sound like a process consultant when I say that. But fundamentally that's what drug development is about, is producing positive outcomes for patients that are safe and effective and reframing process based on the outcomes of a process; not based on individual deliverables, not based on a description and composition document was produced, but instead, you know, the ultimate goal is not the document, but it's getting engagement with the regulatory authority faster or moving a clinical trial forward faster.

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Those sorts of reframing day to day tasks in a way that is measurable and visible across an organization turn out to really be key. And then the conversations seem to be less about horse trading on deadlines and trying to figure out, you know, who's responsible for a certain deliverable and really more about how do you align … how do you align what you're working on with other contributors, employees, partners, etc. to really drive the ultimate outcome. Which for clinical it could be getting into a clinical trial, completing a clinical trial. On the regulatory side could be getting a submission to a regulatory agency or approved by a regulatory agency.

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So, I think understanding what the ultimate outcome is, less than the deliverable and then you just figure out how to get the deliverables marshaled through; turns out to be a positive way to reframe things and think about things at an organizational level. Well, Toban, as we prepare and respond to this exciting new and challenging opportunity, I am very grateful and I'm sure my audience is grateful for you sharing your experience and advice on how to embrace this change, prepare for the bottleneck.

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And I really appreciate your time with us today. Thank you so much. Thank you. I enjoyed it. Thank you for listening to The Latest Dose, the podcast that explores the depths of innovation and human compassion in clinical research. After learning from more than 60 guests over 40 episodes, the Latest Dose will be taking a break. I encourage listeners, both new and old, to go back and enjoy the fabulous content shared by our thought leaders. Thank you for all you do to bring new medical inventions to the market so family, friends, colleagues, and I can live longer and healthier lives. Thank you for listening and subscribing to the Latest Dose! Till we connect with you again, stay well! Good bye.