PharmTech AI Pulse Check: INTerpath-001, Precision ML, Pharma's Governance Gaps

When Merck and Moderna's INTerpath-001 trial of an individualized mRNA-based cancer therapy hit its endpoints, many observers credited AI with the breakthrough, a framing PharmTech AI Pulse Check panelists argue obscures what the technology actually does and where pharma's real governance gaps lie.

Richard Jaenisch, senior director of education, outreach, and digital experience at Open Biopharma, notes that people interpret new technology through the frame of reference they already have, which leads many to assume the tool behind a cancer therapy is the same chatbot they use every day. The work behind an individualized neoantigen therapy relies predominantly on precision machine learning models, an older and narrowly focused category of AI that, in this case, evaluates how a patient's tumor mutations fit together and which patients are likely to be good candidates. Jaenisch has watched online forums declare that AI has cured cancer, a conclusion he rejects even as he calls the results a promising sign for the mRNA platform. He adds that the approach will require new training for clinical trial teams and may perform differently outside selective trial populations.

Florin Muraru, an independent regulatory advisor specializing in EU and US regulatory strategy and AI governance, describes the technology as a targeted prediction tool rather than a general system, and not a new one. What is changing, he argues, is the shift from AI finding drugs to AI personalizing treatment for each patient. From a regulatory standpoint, Muraru says that shift is easier to defend: because the tool does not continuously develop on its own the way newer AI models do, it can be locked and presented to health authorities as a validated system within the Module 3 CMC section of a submission. Long term, he expects more AI tools to move toward true personalization of medicine.

Gourav Pandey, R&D quality lead at Takeda, compares each patient's tumor mutations to a unique fingerprint or password; the model's job is to identify that fingerprint and link it to mutations likely to provoke an immune response. Unlike a chatbot, he points out, this kind of model can be checked in both directions by comparing what it predicted with how the patient actually responded.

Asked to name the single biggest AI governance gap in pharma manufacturing, the panelists offer three distinct answers. Jaenisch points to training, observing that many professionals who claim AI expertise cannot define retrieval-augmented generation, or RAG, and that most corporate AI training so far covers basic cyber hygiene rather than how to produce genuinely useful work. "If we do not have adequate training, well, we have inadequate results," he notes, warning that without the right instruction, bad habits get reinforced and low-quality output multiplies.

Muraru argues that industry and regulators know how to validate a model at submission but have not agreed on an evidence standard showing a model still behaves as intended after retraining; the space between validation and drift, he says, is where governance is thinnest. Pandey takes a different angle. "The model is the least interesting part; the pipeline is the most important part," he argues. Too much attention goes to validating models and curating golden datasets, he explains, rather than asking whether the AI is accurate and answering the question it is meant to answer. Citing EU GMP Annex 22 and its distinction between deterministic and probabilistic models, he urges companies to make sure source documents clearly define what AI should do and where human judgment begins.

For manufacturing and quality leaders, the next phase of AI governance will hinge less on the models themselves than on training, post-retraining evidence, and the pipelines around them.