Comment from Zwirchitz Gabrielle

I strongly support the FDA’s continued efforts to reduce redundant and unnecessary animal studies while advancing more efficient and human-relevant approaches to nonclinical safety assessment. The FDA should continue assisting sponsors in implementing streamlined strategies for drug development while actively promoting adoption of New Approach Methodologies (NAMs) and other emerging technologies. Continued support of programs such as the Innovative Science and Technology Approaches for New Drugs (ISTAND) initiative will be essential for encouraging qualification and implementation of these approaches.¹ Successful adoption of these technologies requires more than their availability; it also depends on incentives, harmonized standards, and coordinated stakeholder engagement. Similar challenges have been identified in FAIR (Findable, Accessible, Interoperable, and Reusable) biomedical data practices, where limited incentives, inconsistent standards, and fragmented infrastructure have hindered widespread adoption.² Improving access to existing clinical and nonclinical datasets is critical, as it can maximize the value of research contributions, reduce unnecessary and wasteful duplication, and improve the efficiency of drug development. Although the FDA has established expectations for data sharing, barriers remain regarding accessibility, interoperability, and reuse of available datasets. I encourage the FDA to continue strengthening data-sharing practices and collaborate with organizations such as NIH, CDC, BARDA, and other stakeholders to develop coordinated solutions. Dedicated funding mechanisms or expanded budget support for data stewardship could enable institutions to recruit FAIR data experts, improve metadata curation, strengthen data infrastructure, and provide training necessary for effective data sharing and reuse.²,³ Establishing meaningful incentives and recognizing data sharing as a valuable scientific contribution will be essential to overcoming persistent barriers to FAIR implementation. Improved data accessibility, standardization, and coordination would directly enhance sponsors’ ability to develop successful Weight of Evidence packages by enabling better-informed decisions and reducing unnecessary repeat studies. As Hughes et al. emphasized, effective FAIR data implementation requires coordinated action across researchers, institutions, funding agencies, repositories, and publishers rather than reliance on policy requirements alone.² Recent scientific advances further demonstrate the limitations of relying solely on animal models as predictors of human biology. A study published in Nature Genetics used spatial transcriptomic approaches to examine erythroid development in mice and humans and identified important species-specific differences in erythroid niche organization and regulatory mechanisms.⁴ These findings reinforce that differences in biology, pathophysiology, pharmacokinetics, and toxicological responses can limit the ability of animal models to fully predict human outcomes. While animal studies have historically contributed to biomedical research, they should not remain the sole benchmark for evaluating safety and efficacy when more predictive, human-relevant technologies are available. I respectfully request that the FDA continue advancing the development, validation, and implementation of NAMs and other 21st-century technologies within its regulatory authority. Continued leadership in this area will accelerate the transition toward more predictive, scientifically rigorous approaches that improve human relevance while reducing unnecessary reliance on animal testing. References 1.U.S. Food and Drug Administration. Innovative Science and Technology Approaches for New Drugs (ISTAND) Pilot Program. FDA; 2020. https://www.fda.gov/drugs/drug-development-tool-ddt-qualification-programs/innovative-science-and-technology-approaches-new-drugs-istand-pilot-program 2.Hughes LD, Tsueng G, DiGiovanna J, et al.; NIAID Systems Biology Data Dissemination Working Group. Addressing barriers in FAIR data practices for biomedical data. Sci Data. 2023;10(1):98. doi:10.1038/s41597-023-01969-8. 3.National Institutes of Health. NIH Policy for Data Management and Sharing. NIH; 2023. https://grants.nih.gov/policy/sharing.htm 4.Han X, Ren K, Wang P, et al. Spatial transcriptomic analyses highlight distinct erythroid niches in mice and humans. Nat Genet. 2026;58:1620-1631. doi:10.1038/s41588-026-02671-2.