Wall Street Is Wary Of NAMs — And For Good Reason: The Evidentiary Shift

By Zaher Nahle, Ph.D., MPA

The last major shift in drug evidentiary standards triggered the reassessment of more than 3,400 drugs. As human-relevant methods increasingly reshape the evidentiary landscape, another consequential transition may be underway.

In 1962, Congress changed the rules of medicine. The Kefauver–Harris Drug Amendments introduced what now seems like an obvious requirement: before a new drug could be marketed in the United States, its manufacturer had to provide substantial evidence that it actually worked. But thousands of drugs were already on the market, approved under an earlier evidentiary regime that required evidence of safety without the same demonstration of effectiveness now demanded by law.¹

The United States did something extraordinary: it went back and looked. FDA enlisted the National Academy of Sciences/National Research Council to evaluate the effectiveness of drugs approved between 1938 and 1962. What followed became the Drug Efficacy Study Implementation (DESI), one of the largest retrospective examinations of marketed medicines in American regulatory history. DESI ultimately encompassed more than 3,000 products and more than 16,000 therapeutic claims. By 1984, FDA had completed final action on 3,443 products: 2,225 were found effective, 1,051 were found not effective, and another 167 remained pending.¹

The drugs had not suddenly changed. What changed was the standard of evidence. Once that standard changed, what regulators, physicians, manufacturers — and ultimately the market — could legitimately claim to know about a substantial portion of the pharmaceutical portfolio changed with it.

That distinction is important because an evidentiary standard does more than determine which box a sponsor must check. It helps determine which observations carry regulatory weight, which uncertainties are tolerated, what constitutes sufficient support for a decision, and, ultimately, what claims can legitimately be made about a medicine. A change in evidentiary standards can therefore reach beyond the mechanics of drug development. It can change the information on which regulatory, clinical, and economic decisions are based.

More than six decades later, another consequential shift in drug evidence is underway, driven in part by the emergence of new approach methodologies (NAMs) — a broad category encompassing human-derived cellular systems, organoids, microphysiological systems, advanced in vitro assays, computational models, and other approaches increasingly being incorporated into drug development and regulatory science.^2–5 The circumstances are very different from 1962, but the underlying question deserves attention: what happens when the methods used to generate evidence begin to change the evidence itself?

From Permission To Practice

The FDA Modernization Act 2.0 marked an important inflection point by broadening the statutory conception of nonclinical testing beyond conventional animal studies.² But changing a statute and changing an evidentiary system are very different things. Regulatory systems accumulate guidance, validation requirements, reviewer expectations, infrastructure, and institutional practice over decades. I have previously examined this transition through the lens of path dependency and, more recently, the progression from FDA Modernization Act 2.0 to FDA Modernization Act 3.0 and the challenge of translating legislative reform into regulatory practice.^6,7 The central point bears repeating here: permission is not implementation.

The developments since 2025, however, suggest that implementation is gathering momentum. In April 2025, FDA announced a road map for reducing, refining, and potentially replacing animal testing in drug development, initially emphasizing monoclonal antibodies while anticipating expansion to other biologics and new chemical entities. The agency encouraged sponsors to submit NAM data and highlighted approaches including computational models, human cell systems, organoids, and other advanced in vitro methods.³

Implementation accelerated in 2026. In March, FDA issued draft guidance providing a validation framework and general recommendations for NAMs and encouraged their inclusion in regulatory submissions, particularly when they improve the predictivity of nonclinical studies for clinical safety.⁴ In April, the agency reported progress on weight-of-evidence approaches, qualification infrastructure, computational tools, and a searchable database clarifying circumstances in which alternative approaches may be acceptable.⁵ In May, FDA issued draft oncology guidance describing streamlined approaches intended to eliminate or reduce unnecessary animal studies while providing safety information equivalent or superior to animal studies.⁸

Taken together, these developments point toward an important change in emphasis. The regulatory question is gradually moving beyond whether a traditional animal study was performed toward a more consequential question: which evidence best predicts what will happen in humans?

That is a much more demanding standard. It shifts attention from methodological convention toward predictive performance. And once prediction becomes the central question, longevity alone cannot determine the value of a model. New methods must demonstrate that they are reliable for their intended context of use, but legacy methods must also remain open to interrogation. The scientific objective is not to declare one category of model inherently superior to another; it is to identify the evidence that most reliably informs the human decision at hand.

A Bridge Rather Than A Cliff

The transition will not, and should not, be a binary switch from animals to NAMs. Traditional methods have accumulated decades of regulatory experience, while NAMs vary substantially in maturity, validation, and context of use. A sophisticated organoid is not automatically superior because it contains human cells, just as an animal experiment is not automatically informative because it has historically been required. The relevant question is which method, or combination of methods, provides reliable evidence for the decision at hand — and ultimately how well that evidence corresponds to human biology and outcomes.

At the same time, an indefinite “complement-only” model presents a different problem. If validated NAMs merely become additional requirements layered onto legacy animal studies, rather than replacing them where they provide equal or better information, modernization risks adding complexity without changing the underlying evidentiary architecture — a concern I have raised previously.⁹ That concern should be distinguished, however, from describing what a transitional evidentiary system may look like in practice.

In appropriate contexts, a streamlined program combining evidence from one animal species with a validated NAM could emerge as an interim bridge between the old evidentiary system and the new one.^4,8,10 Such a framework need not make permanent complementarity the objective. Nor should coexistence itself be mistaken for the desired endpoint. Rather, it creates a period in which evidence generated by legacy and emerging methods may coexist while the predictive performance of each can increasingly be judged against the benchmark that ultimately matters: what happens in humans.

This distinction is critical. Parallel evidence can be informative without making the animal model the reference standard against which a NAM must prove itself. Agreement between the two may be reassuring, but agreement alone does not establish which method is more predictive. A NAM that faithfully reproduces an animal finding that itself fails to predict a human outcome has not necessarily improved the evidentiary system. Conversely, disagreement with an animal result should not automatically be interpreted as failure of the NAM.

Indeed, disagreement may be more informative. When a NAM and a conventional model point in different directions, subsequent human evidence can help reveal which better anticipated the human outcome. The purpose of overlap, therefore, should not be to validate NAMs by their ability to reproduce animal findings, but to accumulate evidence about the relative predictive value of both approaches against human biology and clinical experience. In that sense, the transitional period can do more than accommodate new technology: it can generate evidence about the evidentiary system itself.

The bridge also provides a hedge against moving too abruptly from a familiar system to one whose methods are still uneven in maturity. Regulators retain an established evidentiary anchor while experience accumulates around newer approaches; sponsors gain time to adapt development programs; and developers of NAM platforms gain opportunities to demonstrate performance in defined contexts of use. Where a NAM consistently adds little, that will become apparent. Where it consistently predicts human outcomes better, the rationale for maintaining redundant legacy requirements becomes progressively harder to defend.

But there is another, less obvious hedge, and one with particular relevance to Wall Street. A transitional framework in which NAMs coexist with streamlined legacy evidence creates continuity between the old and emerging evidentiary systems rather than a clean break between them. That continuity could make the transition easier for markets to absorb and potentially reduce pressure for the kind of wholesale retrospective reassessment that becomes more conceivable when one evidentiary regime is clearly displaced by another.

This is one reason today’s transition may look very different from DESI. The 1962 amendments created an identifiable change in the legal standard governing effectiveness, leaving a large preexisting portfolio that had entered the market under a different requirement. A gradual NAM transition may instead blur the boundary between evidentiary generations. Retaining some legacy evidence during the transition could allow the pharmaceutical portfolio to migrate toward a new standard rather than suddenly finding itself on the other side of one. The bridge, in other words, can hedge in both directions: against embracing immature methods too quickly and against creating an abrupt evidentiary discontinuity that immediately calls the legacy portfolio into question.

From Evidence To Investment

For Wall Street, this distinction matters. A gradual transition gives regulators time to assess emerging methods, sponsors time to adapt, and investors time to understand which forms of evidence are actually proving more predictive. But the transition also creates something potentially more valuable than regulatory flexibility: an extended period in which different approaches generate evidence around many of the same biological and development questions.

That period should not be understood as a contest in which NAMs are scored according to how closely they reproduce animal findings. The more consequential comparison is prospective: what did each approach predict, and what subsequently happened in humans? Over time, such comparisons can reveal where a model is informative, where it is redundant, where its domain of applicability ends, and where another approach captures human biology more accurately.

This turns the transition into a kind of natural experiment in regulatory science. That is also the point at which a scientific question becomes an economic one. If one evidentiary approach begins to identify toxicities another missed, rescue compounds another rejected, distinguish responders another obscured, or redefine the biological boundaries of an indication, the implications extend beyond laboratory methodology. They reach the assumptions on which development decisions, probabilities of success, addressable markets, and pharmaceutical valuations are built.

The most consequential evidence generated during this transition may therefore come not from where old and new methods agree, but where they disagree — and which one ultimately proves more predictive of what happens in humans. That is where an evidentiary transition becomes an investment question. And it raises the question at the center of part 2 of this article series: what happens when better evidence is turned not only toward tomorrow’s pipeline but toward yesterday’s drugs?

References

1. U.S. Food and Drug Administration. A Brief History of the Center for Drug Evaluation and Research. FDA History.
2. FDA Modernization Act 2.0. Consolidated Appropriations Act, 2023, Pub. L. No. 117-328, §3209. Enacted December 29, 2022.
3. U.S. Food and Drug Administration. FDA Announces Plan to Phase Out Animal Testing Requirement for Monoclonal Antibodies and Other Drugs. FDA Press Announcement. April 10, 2025.
4. U.S. Food and Drug Administration, Center for Drug Evaluation and Research. General Considerations for the Use of New Approach Methodologies in Drug Development: Draft Guidance for Industry. March 2026; issued March 18, 2026. Docket No. FDA-2025-D-6131.
5. U.S. Food and Drug Administration. FDA Achieves Year 1 Goals in Reducing Animal Testing in Drug Development. FDA Press Announcement. April 20, 2026.
6. Nahle Z. Path dependency and the rescuing of the biomedical research enterprise. Frontiers in Medical Technology. 2026;7:1683835. Published January 12, 2026. doi:10.3389/fmedt.2025.1683835.
7. Nahle Z. From Path Dependency To Demonstrable Progress: FDA Modernization Act 3.0. Drug Discovery Online. July 23, 2026.
8. U.S. Food and Drug Administration, Oncology Center of Excellence. Oncology Pharmaceuticals: Streamlined Nonclinical Safety Studies for Biologics and Conjugated Products: Draft Guidance. May 2026. Docket No. FDA-2026-D-2839.
9. Nahle Z. Making NAMs a “Complement” to animal experimentation is the Trojan Horse of the animal-industrial complex—A scheme to nullify the principles of the 3Rs and void the quest to “replace” animals for good! NAM Journal. 2025;1:100030. Published June 6, 2025. doi:10.1016/j.namjnl.2025.100030.
10. U.S. Food and Drug Administration, Center for Drug Evaluation and Research. CDER Streamlined Nonclinical Studies and Acceptable New Approach Methodologies (NAMs). 2026.

About The Author

Zaher Nahle, Ph.D., MPA, is a science policy expert, biomedical scientist, and senior advisor whose work has helped shape reforms at the U.S. Food and Drug Administration and National Institutes of Health. He serves as senior scientific advisor to the Center for a Humane Economy and is the founder of The Ivyctory Group, a market research and life sciences advisory firm. Nahle has held executive leadership positions at biomedical and medical research organizations, including as CEO, chief scientific officer, and vice president for research. Earlier in his career, he led independent research programs and served as a founding investigator of an NIH-funded data management and coordinating center. He is published in leading scientific journals, including Nature, and received competitive grants and distinctions from organizations, such as the American Heart Association, Qatar Foundation, and the U.S. Department of Defense, as well as an American Cancer Society Scholar Award designation. He earned a Ph.D. in physiology and biophysics through the Stony Brook University–Cold Spring Harbor Laboratory joint program, a certificate in public policy and management from Harvard Kennedy School, and an MPA from Harvard University, where he was a Mason Fellow.