Real-Time Monitoring and Smarter Site Allocation as Efficiency Drivers

In a recent video interview with Applied Clinical Trials, Angela Zubel, chief development officer, Debiopharm, described 2026 as an implementation year for AI and advanced analytics in drug development. She explained that many technologies had moved beyond pilot testing and were ready for broader adoption across clinical operations. Zubel highlighted opportunities to shorten timelines, reduce costs, and improve oversight through real-time monitoring, AI-supported site selection, and predictive analytics for compound prioritization. While acknowledging ongoing limitations in predictive modeling—particularly in oncology—she emphasized the importance of organizational openness to innovation. Sponsors that proactively standardized data, adopted practical AI tools, and experimented responsibly, she noted, were already seeing measurable gains in efficiency and competitiveness.

Editor's note: This transcript is a lightly edited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

ACT: From a clinical trials perspective, where do you see the most meaningful changes in how studies are designed or executed next year?

Zubel: From a clinical trial perspective, I think we’ll see meaningful changes in timelines, cost, and resource allocation if we properly implement the tools that already exist.

For example, site selection, patient allocation, and online monitoring can now happen in real time. Instead of waiting two or three months for data packages, monitoring and data checks can be done continuously.

Another major opportunity is addressing open sites that are not recruiting. Keeping those sites open is a significant cost factor. If we can identify non-performing sites quickly and shift resources to sites more likely to recruit—potentially based on AI recommendations—that would be a major achievement. We lose a lot of time and money maintaining sites that never enroll patients.