Register Now: Beyond EDC: Designing a Connected Data Strategy for Predictive Clinical Development
Commentary|Videos|April 23, 2025

Understanding the True Meaning of Risk in Clinical Trial Operations

In this video interview, Rob Jones, product manager, TMF practice area, Pharmalex, highlights recent ICH guidelines and how risk should be an area of focus as trial designs continue to get more complex.

In a recent video interview with Applied Clinical Trials, Rob Jones, product manager, TMF practice area, Pharmalex, discussed challenges in the trial master file (TMF) space, highlighting outdated regulations such as ICH E6(R3). When it comes to innovation and compliance in the space, he stressed the importance of a risk-based approach, focusing on critical records and leveraging artificial intelligence for big data analysis. Moving forward, Jones believes the TMF community's collaborative nature can be viewed as a strength in navigating regulatory changes and technological advancements.

ACT: What should leaders in the TMF space be focused on the most in navigating the increased demand for quality and compliance?

Jones: I mentioned, not only have we got these new regulations, but things are changing at a rate that we've never seen before, we can't keep up with. There was a time back in the 2010s where it felt like there was new things happening all the time with, again, bringing in a real foundational level smartphones and tablets and suddenly, internet everywhere, but we're even past that now, it's moving even faster. I think the thing is, if you look at things like ICH E8(R1) that was released the other year, that was almost like a foundation for ICH E6(R3), and it really talked about quality by design. It really talked about thinking about the holistic and bigger picture. They talk in there that if you're thinking about going to certain countries, don't wait until further on down the line, just incorporate those countries into the trials you're doing. That means that trials are going to become more complicated. Complication brings cost, and fast and complicated bring risk. There's no getting around that. Risk is a big, big thing, but again, ICH E8(R1) really built in the idea of quality by design and risk being a big part of that. ICH E6(R3) also talks a lot about risk. I think the thing that we should be focusing on from a leadership point of view, is really understanding that word, risk-based has been a buzzword for as long as I've been around in the scene. We've talked about risk-based, we've talked about these different approaches, but I think it's time for us to really think what that means. Risk inherently means the potential of something bad happening. It's our job to understand that and do what we can to minimize it. In the past, terms like fit-for-purpose, good enough, they're almost dirty words. They were sins. “Good enough isn't enough.” Actually, it is now, that is enough.


Related to this article

Where the Industry Is Headed as Clinical, Operational, and Real-World Data Connect
In this video interview, Rajneesh Patil, global head of AI, digital, and innovation in clinical development at Syneos Health, describes the industry’s trajectory toward a unified intelligence orchestration environment—and explains why the future isn’t one model that knows everything, but a trusted system that brings the right evidence to the right human decision.
© putilov_denis - © putilov_denis - stock.adobe.com
Real-time clinical trials require standardized digital data capture at the point of care through scaled eSource-EDC-EHR integration, but progress stalls because competing standards lack regulatory enforcement and one-off system integrations cannot scale beyond single studies without industry-wide alignment and legal clarity on data ownership.
What Expanding AI Ecosystems Mean for Sponsor Complexity and Oversight
In this video interview, Rajneesh Patil, global head of AI, digital, and innovation in clinical development at Syneos Health, argues that CROs must act as orchestrators rather than aggregators—and that the real test of any AI ecosystem is whether it reduces sponsor coordination burden or just hands it back to them.