SAN DIEGO, CA / ACCESS Newswire / November 5, 2026 / Following the U.S. Food and Drug Administration's (FDA) finalized rule permitting non-animal methods for drug safety testing, VivoSim Labs Executive Chairman Keith Murphy called on the agency to take the next step and make a determination that some non-animal methods have reached a well-qualified state that already makes them supportive of the agency's mandates. Murphy is arguing that the evidence, and the FDA's own past logic applied to animal models, already support relying on some human-model results to protect or best serve patients enrolling in clinical studies, giving human-relevant data the same regulatory urgency for certain use cases that it has long given animal studies.

The FDA's September 21 rule replaced the term "animal tests" with "nonclinical tests" across its regulatory framework, formally opening pathways for New Approach Methodologies (NAMs), including human cell-based tests, organ-on-chip systems, and computational models. Although the move to update the regulatory framework to allow more NAM use is meaningful and welcome, Murphy suggests the FDA must now move on expeditiously to identify use cases where non-animal models have sufficient credibility to be expected by guideline to be filed in an IND. The process to do this should not be a years-long qualification program, but immediate proactive review from FDA as an active review. Areas and use cases that the FDA determines are at the right development point can then be included in guidance, where an expectation that they be provided is laid out, though an allowance for equivalent data to accomplish the same end can always be accommodated, just as such an allowance is now made for animal studies.

For example, Murphy notes that complex primary 3D cell models for liver toxicity testing are an area where data now exist from several platforms offered by different providers that show successful assessment of liver toxicity missed by traditional methods (including exposure in animals). Several platforms have shown qualification data that includes basic liver metabolic function, proper response to known toxicants, and detection of frank cytotoxicity for molecules that were not detected as causing liver toxicity in animals. Those assessing the systems should also understand that primary cell-based platforms have a lower bar to success. Assessments correctly require a high bar for engineering solutions on cell lines, but the challenge for a primary cell model is lower: retain enough of a useful in vivo signature during the course of an experiment to be relevant, and that's been demonstrated to be done well in many contexts.

"Several platforms have shown broadly normal liver function using liver cells, alongside clear detection of classic missed liver toxicity examples that were not detected in animals and few false positives." Murphy noted. "For some of those cases, animal models were later backfitted to see a signal but can hardly be expected to be predictive for novel chemical entities. In contrast, 3D primary cell liver models essentially well represent liver biology by so many measures that there's no logic for ignoring a red flag seen in one. They are built to represent the liver, and when tested in that baseline mode, they reveal liver toxicity for compounds that were previously not detected by other methods. Such a set of results is entirely consistent with readiness for regular utility in assisting the FDA in its safety mandate, rather than testing those compounds in humans before screening in such a system. There's a strong case for FDA pushing to see such data (or equivalent data assuring a higher level of safety) before approving any IND."

"The safety mandate the FDA upholds is only met by creation of such an expectation, and we should not tolerate waiting years for these systems to be considered qualified before taking this step. A negative result in such a system, if seen, should be treated no less seriously by FDA than a negative result in an animal study showing a safety concern. These models can catch true toxicity, and FDA immediately moving to assess if they should now be an expectation could immediately reduce the number of humans exposed to liver toxic compounds in human clinical trials. The failure rate of drugs due to surprising liver toxicity is still high enough that we should seek solutions urgently, and in the past few years, we continue to see compounds being pulled back that show up in our models as very clearly toxic."

VivoSim's liver tissue models are already capable of flagging drug candidates likely to cause liver toxicity, the leading cause of drug failures and market withdrawals, but Murphy stressed that VivoSim's platform is not the only one he would consider qualified, and that it was in part the breadth of the available options that confirm each other's results that make the case compelling.

Because the agency has historically treated certain signals from animal studies as informative, rather than requiring years of additional validation before the data can inform a decision, Murphy's position is that the FDA should treat negative safety signals generated by human-relevant models that show reasonable qualification data in parallel with the same weight and urgency, without requiring a multi-year qualification process first.

"The FDA's role is not solely to be responsible to ethics in the sense of reducing unneeded animal use," said Murphy. "It's also to be ethical in human experimentation. Given the ability that some platforms already have demonstrated to catch liver toxicity beyond what traditional methods detected," said Murphy, "I truly believe that not requiring them or similar data is no longer ethically justifiable."

Murphy emphasized that while the 3D liver toxicity testing example is clear to him, the call is for FDA to review more broadly to seek solutions that may be supportive of its goals, and not wait for sponsors to qualify them if FDA's mandates benefit from their use. With the new rule opening a regulatory pathway for methods like VivoSim's, Murphy said the next step is for the agency not solely to allow alternative data, but to move quickly to the next logical step: identifying areas and use cases where acting today on NAM data with the same urgency that it has always applied to animal data would better serve patients and the FDA mandate for safety and efficacy.

About VivoSim Labs

VivoSim Labs is a biomedical technology company developing human tissue models - including liver and intestinal systems - that predict how drug candidates will perform in patients before they reach clinical trials. VivoSim's platform is used to identify liver toxicity risk in drug candidates and is expanding into efficacy modeling for diseases including fatty liver disease and Crohn's disease. Learn more at vivosim.ai.

Here is the media contact info:
Company Name: VivoSim
Contact Name: Keith Murphy
Contact Email: kemurph@viscientbio.com
City / State: San Diego, CA

SOURCE: VivoSim