Manufacturing Intelligence: Why NAMs May Not Impact Development Pipelines

Richard Jaenisch and Chris Cole dig into whether New Approach Methodologies (NAMs) actually shorten the drug development pipeline, or simply redistribute where the burden of proof falls, in the latest clip from PharmTech's Manufacturing Intelligence series. The conversation centers on a scenario with real regulatory teeth: an in silico model powered by generative AI that hallucinates, and what happens when that hallucination becomes part of the official record.

Watch the full episode, “What FDA's NAM Rule Means for CMC Teams”!

Jaenisch pushes back on the assumption that FDA's posture toward NAMs changes the underlying evidentiary bar. Pointing to CAR T trials as a case in which cell and gene therapy still demanded full scrutiny, he argues the agency's language about not lowering standards should be taken at face value. “It does not appear to change the burden of proof, or evidence, or any of the other pieces that have to come with all of these things,” he notes, framing the FDA Modernization Act as giving the agency statutory cover rather than a mandate to relax rigor.

Where the real variability lives, Jaenisch explains, is in the NAM itself. An organoid-on-a-chip introduces its own complications—physical components like microfluidic tubing that can enter the system and require separate accounting—while an in silico model carries the distinct risk of a generative-AI hallucination resetting a program's timeline entirely. He contrasts this with animal models, which despite their limitations offer systemic visibility: a mouse has kidneys, lungs, and other organs that can reveal off-target effects that an isolated organoid simply can't surface. That tradeoff, he suggests, is why companies may end up running NAM and animal-model submissions in parallel rather than treating NAMs as a wholesale replacement.

The discussion turns to incentive structures, particularly around patents and rare disease development. Jaenisch connects the context-of-use requirement embedded in NAM qualification to how drug patent portfolios are typically built, noting that large companies may find multiple applications and pull-throughs stack more efficiently under this framework. For rare disease programs, where conventional testing pathways may not exist at all, he sees genuine promise: NAMs could offer a route to generate evidence where no other practical option is available and potentially work in conjunction with shortened clinical trial timelines.

Hedging emerges as a recurring theme. Jaenisch predicts some sponsors will submit both NAM-derived and animal-model data simultaneously, accepting added cost in exchange for a richer combined dataset, one that could, in turn, strengthen the design of subsequent trials. “You very well might have a very quick move to commercialization that we have never seen,” he says, while cautioning that the industry's conservative instincts make it unlikely to abandon traditional models outright in the near term.

Jaenisch is candid about the uncertainty ahead, repeatedly noting that the real test of whether NAMs deliver on speed will only become clear over the next one to two years as companies begin submitting combined data packages and FDA responds in practice rather than policy language. The episode leaves manufacturing and quality teams with a central tension to watch: NAMs may not lower the regulatory bar, but how sponsors choose to pair them with legacy methods could reshape both cost structures and the pace of commercialization for cell and gene therapies and beyond.