Jim Reilly, Vice President, R&D Strategy, Veeva Systems, Discusses AI and New Technology in Clinical Trials


In an interview with ACT editor Andy Studna, Jim Reilly, VP, R&D Strategy, Veeva Systems, discusses ways stakeholders can select the best solutions available in clinical trials.

ACT: As we transition into this new year, there’s a lot of talk about AI, technology, multiple data sources, etc. in clinical trials. It seems like there’s a lot going on there, so how can stakeholders effectively choose which solutions may be best for their needs and trials?

Reilly: At any point in time, there's always going to be a hype cycle going on because that's how we are as people. We latch onto something that's new and exciting. I think right now we're in that cycle with artificial intelligence, and it's not to diminish the idea that the hype can't sometimes be real. It's just a way of saying that sometimes we get ahead of ourselves. I think when you look at clinical trials, with the emphasis now being put on automation and artificial intelligence, I think many people are pulling back from that. The most important thing is making sure you have your foundation, or your house in order. I think that means you got the process and good organization. In technology, you need to make sure that you've done the hard work that shows the core things running your business are working well for you. We've noticed that there is this movement towards snapping back a bit from the hype related things such as decentralized trials and artificial intelligence and saying "Before I get there, focus here." A few years back, I heard a CEO say "Artificial intelligence yes, but first, data." That's a way of saying we need to make sure the foundation is set, and that's where I'm seeing a lot of investment and a lot of dialogue happening.

Coming back to the promise and potential of artificial intelligence, it is real. But, I think it's real in pockets. There are practical applications and there are impractical applications. I think we will see a day when protocol is largely created through intelligence rather than written exclusively by a human. We're already seeing some great movement there with things like scheduled assessments being standardized, being something that can automatically included in a protocol written from that. I think we're heading in that direction.

ACT: How can biopharmas migrate ongoing studies from legacy EDC systems to more modern applications?

Reilly: When you look at it, legacy EDC systems have been on the market for 20+ years and it had very little innovation. They didn't have to innovate because they had been the leaders. That means when it comes to making it easy to transmit their data elsewhere, there hasn't been a lot of investment there. Further, there's not really an industry standard for what EDC data should look like. The idea of the data being portable from one EDC to the next is a foreign concept. When a sponsor has assessed moving to another EDC and asks if they can migrate their in-flight studies over, often times they've realized how difficult it is because there's two different data models at play. Also, it's very risky because they're dealing with live clinical data. Now we're seeing a point where the appetite is growing because people in data management yearning for innovation. They're yearning for a better way to collect and manage their data than a way that some of these providers have done. They want to make the switch, but in order to do so, they need a cutoff of the existing study flow and that means migrating some ongoing studies. The way you can do that is with really good tooling. It's something that we have proven out here at Veeva. We've built really good tooling to allow for the exchange of data from one EDC to the next and its not just the collected patient data, but it's also all the rules and the checks. That all has to come for the ride and we've proven we can do that. We've actually issued a press release just this past week where a top 20 biopharma has done just that. These aren't small trials. For instance, one of them had over 7,000 patients and more than 500 research sites.

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