Written by: Madhu Nag, PhD, Chief Scientific Officer at InSphero
For those of us working on New Approach Methodologies (NAMs) and human-relevant models, the question is increasingly not simply whether these technologies can generate valuable evidence, but what is needed to build the regulatory confidence required to use that evidence in decision-making. As initiatives such as the FDA’s roadmap for reducing animal testing through greater use of NAMs move the conversation from scientific potential toward implementation, questions around validation, standardization, context of use, and regulatory acceptance are becoming increasingly important.
Every September, the Global Coalition for Regulatory Science Research convenes the agencies that decide what evidence counts. This year it met in Beijing, co-hosted by China’s National Medical Products Administration and its National Institutes for Drug Control, under the theme Emerging Technologies and Intelligent Regulation.
I want to describe who was in the room before I say anything about what was said, because in this case the attendance list is the lynch pin.
The room: regulators, scientists and industry
The summit opened with the Director General of the NIDC and Tucker Patterson, Director of the FDA’s National Center for Toxicological Research, who chairs the coalition. The opening plenary was not a survey of the field. It was four agencies stating where they are going: an Associate Commissioner from the FDA, the European Food Safety Authority, and Emmanuel Cormier, who leads the Regulatory Science and Innovation Task Force at the European Medicines Agency.
Across the two days, the podium was held by the FDA, the EMA, EFSA, Health Canada, Swissmedic, Germany’s Federal Institute for Risk Assessment, Japan’s National Institute of Health Sciences, China’s National Center for Food Safety Risk Assessment, and the Luxembourg Institute of Science and Technology. This is not a conference where industry presents and regulators listen. It is a conference where regulators work through their own open questions in front of each other, and a small number of outside voices are invited in.
What changed for NAMs in regulatory science this year
Last year’s summit, in Lausanne, had no session dedicated to microphysiological systems. New approach methodologies surfaced mainly through the artificial intelligence track, as one thread among several.
This year, the second day opened with a half-day devoted entirely to MPS and NAMs for next-generation regulatory science, co-chaired by EFSA and NIDC. Germany’s BfR presented on organ-on-chip in regulatory science. Japan’s NIHS presented a gut-liver-on-chip first-pass metabolism model and the work underway toward an OECD test guideline. Japan’s MPS consortium presented on standardization. LIST presented from Luxembourg.
Regulatory bodies do not allocate half a day to technology they are still deciding whether to take seriously. They allocate it when the question has moved from , this is interesting to what evidence would make this usable. That shift, inside twelve months, is the single most important thing I brought home.
InSphero’s role in the NAMs regulatory conversation
On the first afternoon, the summit held its panel discussion: Are NAMs Ready for Regulatory Science? It was moderated by Supriya Sharma, Acting Head of Health Canada, and Weida Tong of FDA/NCTR. The panel included Emmanuel Cormier of the EMA, Steven Musser of the FDA, and me, representing InSphero.
Why industry representation mattered on the panel
The next morning I gave a talk in the MPS session titled “Co-Creating Confidence: Building the NAMs Evidence Base With Regulators, Not For Them”. Every other speaker in that session came from a national institute. InSphero was the only commercial MPS platform provider on the program.
I say this plainly because it means something specific, and it is easy to hear it as a vanity claim. It is not that we were noticed. It is that when regulators sat down to work out whether this field is ready, they wanted a platform provider at the table while they worked it out. That is a different kind of invitation, and it carries a different kind of obligation.
The argument I made about confidence in NAMs
The instinct in our industry is to build the evidence package first and bring it to the agency afterward, finished. That sequence produces beautiful data that answers questions nobody asked.
The alternative is slower and better. As the regulatory discussion around human-relevant safety science increasingly turns toward context of use, validation, and transparent performance data, the way evidence is generated becomes as important as the model itself. Define the context of use with the reviewer before the study runs, not after. Agree in advance what would falsify the model, not only what would support it. Document reproducibility as a property of the method rather than of the laboratory, so the result survives leaving our hands. Publish the failures alongside the successes, because a qualification file with no negative results is not a strong file, it is an incomplete one.
That is what with regulators, not for them means in practice. It is not a posture of deference. It is a recognition that confidence in a method is co-authored, and that a company holding the platform and an agency holding the standard are each in possession of half the problem.
What comes next for NAMs and regulatory acceptance
Japan is pushing an organ-on-chip model toward an OECD guideline. At the same time, initiatives such as the NIH Standardized Organoid Modeling (SOM) Institute are putting greater emphasis on reproducibility, benchmarking, and standardization as prerequisites for regulatory readiness. The EMA and the FDA are also strengthening the regulatory science frameworks needed to evaluate this class of evidence.
My biggest takeaway for NAMs and regulatory acceptance
The challenge for NAMs is increasingly not demonstrating that advanced models can generate sophisticated biology, but building the shared evidence, standards, and regulatory confidence needed to use those models consistently in decision-making.
