Introducing the "PharmTech AI Pulse Check" Expert Video Series

In the debut episode of PharmTech's new biweekly video series, “PharmTech AI Pulse Check,” host Chris Cole, associate editorial director, PharmTech, sits down with three expert co-hosts—Richard Jaenisch, Open Biopharma Research and Training Institute, Florin Muraru, independent regulatory affairs advisor, and Gourav Pandey, R&D quality lead, Takeda—to unpack the AI developments and questions that matter most to people building, manufacturing, and delivering therapies for patients.

The conversation opens with a pointed question: is manufacturing and quality documentation developing its own version of "AI slop," the fluent-but-empty content already flagged widely on social platforms? All three experts agree that the risk is real, particularly around deviation reports, where generative AI's ease of use can produce polished but substance-free documentation. Jaenisch warns that slop is "almost unavoidable" and represents a real cost when reviewers spend time on content that ultimately proves useless, or worse, reaches a regulator undetected. Muraru reframes the issue as fundamentally a human problem, not a model problem, and raises a deeper concern: AI-generated volume can outpace human review capacity, shifting the real bottleneck to the person signing off. The panel also discusses a "second-degree slop" framework, distinguishing between AI directly producing flawed documents and humans trusting AI-generated advice without further verification, citing a recent FDA warning letter as an early real-world example. The panel converges on a shared fix: redesigning source documents so AI has clear, deterministic rules to follow, reducing ambiguity at the root rather than relying solely on downstream review.

The discussion shifts to multi-agent AI systems, prompted by a recent case in which AI agents secretly built a private communication channel after being shut down. Drawing parallels to pharma's existing segregation-of-duties principles, the panel debates where human sign-off gates should sit as multi-agent systems move toward the shop floor. Muraru argues that agent-to-agent reasoning is acceptable, but any GxP-relevant action, like releasing a batch, requires human decision making, and that inter-agent exchanges must be captured as auditable records. Pandey stresses that data integrity principles already governing handwritten notes apply equally to AI outputs, and that traceable, accountable human sign-off remains non-negotiable regardless of how much groundwork AI does.

The conversation turns to document intelligence, sparked by a newly open-sourced model capable of parsing entire multi-page documents—including scanned records, handwritten logbooks, and historical batch data—in a single pass. The panel sees major potential for mining decades of previously unsearchable pharma records, including failed experiments and terminated programs, but cautions that the moment this information feeds a regulatory submission or GxP decision, the system requires formal validation, and OCR errors could become data integrity issues.

The episode continues with a discussion of Merck and Moderna's Interpath 001 trial, which uses individualized mRNA-based cancer therapy tailored to each patient's tumor mutations. The panelists emphasize a critical distinction: this relies on precision machine learning, not the generative AI most people associate with chatbots, an important nuance as public conversation increasingly conflates the two.

Wrapping up with a rapid-fire round, each panelist names pharma manufacturing's biggest AI governance gap: Jaenisch points to inadequate workforce training, Muraru highlights the absence of standards for validating models after retraining and drift, and Pandey argues the industry over-focuses on the AI model itself rather than the surrounding pipeline and source documentation.