Introduction
On April 10, 2025, the U.S. Food and Drug Administration (FDA) announced a landmark initiative to phase out animal testing in drug development, signaling a major shift toward human-relevant, science-driven alternatives. This initiative formalizes the agency’s long-building effort to incorporate New Approach Methodologies (NAMs) into regulatory review and follows the release of draft guidance in January 2025 on using artificial intelligence (AI) to support regulatory decisions.[1] In parallel, the National Institutes of Health (NIH) announced the creation of a new internal office to prioritize human-based research[2] and, weeks later, announced an end to funding for animal-only studies.[3] Collectively, these developments signal a coordinated policy shift by HHS agencies that will reshape not only scientific workflows but also the strategic IP landscape. As human-relevant technologies increasingly underpin safety and efficacy claims, innovators face new opportunities – and risks – in patenting platforms, data models, and trial designs that meet evolving regulatory standards.
Regulatory and Legislative Context
The FDA Modernization Act 2.0, enacted in December 2022, amended the Federal Food, Drug, and Cosmetic (FD&C) Act to eliminate the requirement for animal testing prior to clinical trials and authorized the use of validated, scientifically sound alternatives.[4] The Act, passed with bipartisan support, reflected growing consensus that non-animal models can outperform animal testing, particularly in early development.
Since then, FDA centers such as the Center for Drug Evaluation and Research (CDER) and Center for Biologics Evaluation and Research (CBER) have expanded their internal capabilities to evaluate NAM data. The April 2025 roadmap, issued under Commissioner Martin Makary, introduces a pilot program and outlines a phased strategy to reduce reliance on animal studies.[5] NIH’s simultaneous announcement established the Office of Research, Innovation, and Application (ORIVA) to coordinate research funding, training, and infrastructure to support non-animal technologies across NIH’s broad research portfolio.[6]
Key Elements of the FDA’s Plan
The FDA’s pilot program invites selected developers of monoclonal antibodies and similar biologics to submit safety data that exclude animal testing. CDER and CBER will jointly administer the pilot, which will help inform future guidance on incorporating NAMs into regulatory submissions.[7] The pilot, scheduled to launch within 12 months, will evaluate:
- AI-based computational toxicology and pharmacokinetics
- High-throughput in vitro assays using human cell lines
- Organoids and organ-on-a-chip systems
- Human microdosing data and real-world evidence
Although not mandatory, the FDA is encouraging sponsors to submit alternative data when scientifically justified. While the pilot focuses initially on antibodies, the agency stated this is “the beginning of a broader effort” that will eventually extend to additional biologics and small molecules. Its stated long-term goal is to make animal studies “the exception rather than the norm” for preclinical safety testing within 3–5 years.[8] This shift reflects both ethical and scientific considerations. Studies cited by the FDA show that most drugs passing animal studies fail in human trials due to unanticipated safety or efficacy issues.[9] NAMs may offer more predictive, efficient, and cost-effective models.
While some stakeholders remain cautious, many biotech and platform companies have publicly embraced the shift, viewing the FDA’s new framework as both scientifically valid, and commercial enabling. Organ-on-chip innovator Emulate publicly endorsed the FDA’s April 2025 roadmap, highlighting the agency’s recognition of validated microphysiological systems as viable alternatives to animal models. The company emphasized that this regulatory shift reinforces the utility of its platform and supports broader adoption across pharmaceutical R&D pipelines.[10] As the rollout continues, it will be imperative to monitor industry adaptation and response.
NIH Confirms Strategic Pivot to Human-Based Research
While the FDA’s roadmap modernizes regulatory review, the NIH’s new ORIVA office will drive structural change across biomedical research. Announced on April 29, 2025, ORIVA will coordinate efforts to validate and scale non-animal research technologies.[11] The NIH also intends to expand training, mitigate reviewer bias, and integrate evaluation criteria for NAMs into its grant-making processes.[12] The office will train reviewers to better assess the scientific merit and translational potential of NAM-based proposals. Together, the FDA and NIH initiatives signal a unified HHS vision: regulatory reform alongside systemic changes in federally funded research.
At a July 2025 workshop co-hosted by the FDA and NIH, NIH officials confirmed that future grant opportunities will no longer permit proposals that rely exclusively on animal studies.[13] Instead, all new NIH-funded research must at minimum incorporate consideration of NAMs including computer modeling, artificial intelligence, and organ-on-chip platforms. While the guidance stops short of a full mandate, the NIH’s formal policy shift clearly aligns with the broader federal transition away from animal-based testing, and towards next-generation models, including AI.
AI, In Silico Models, and Regulatory Acceptance
In addition to the NAMs described above, the FDA is actively integrating AI elsewhere into its regulatory framework. In January 2025, the agency released draft guidance introducing a risk-based approach for evaluating AI tools based on their influence on decisions and associated risks to patients.[14] Sponsors must disclose model architecture, training data, and governance protocols, especially for high-impact applications. Notably, the FDA has already accepted certain in silico platforms, such as the UVA/Padova Type 1 Diabetes Simulator, in regulatory submissions.[15] The agency has also deployed internal AI tools like “Elsa” to streamline reviews – demonstrating that AI-driven platforms are not merely permissible, but increasingly central to regulatory science.
Implications for Patent and IP Strategy
As AI tools become foundational to NAM-driven drug development, patent strategy must evolve to address novel risks. One key concern is the emergence of AI-generated prior art. Machine learning models deployed in target identification, compound screening, or toxicology prediction may produce outputs that enter the public domain via academic publication, data repositories, or regulatory submissions. These outputs, even if unintentionally shared, could later be cited to challenge novelty or render claims obvious.[16]
To mitigate this, early-stage innovators may consider front-loading patent filings for novel algorithms, data processing workflows, and NAM-integrated systems before widespread deployment or disclosure. Claims that capture specific implementations – such as model validation pipelines, bias mitigation protocols, or multi-modal integration of biospecimen data – may be particularly valuable.
Further complexity arises around patent eligibility. The USPTO continues to scrutinize AI-related inventions under the Alice/Mayo framework, often rejecting abstract model claims absent a clear technical improvement. However, NAM systems that demonstrate real-world utility – for instance, by stratifying patients or altering trial protocols – may satisfy eligibility requirements when claim language emphasizes technical architecture and regulatory relevance.[17]
Meanwhile, the FDA’s draft guidance encourages methodological transparency for AI used in regulatory submissions, including documentation of model design, data provenance, and performance characteristics. While this enhances regulatory trust, it also narrows the path for trade secret protection and underscores the importance of compartmentalized IP strategies. Where possible, sponsors should implement modular documentation systems that isolate proprietary elements from disclosed content – and consider filing claims to tools that enable such separation.[18]
Conclusion
The 2025 initiatives from the FDA and NIH mark a fundamental shift in drug development and biomedical research. NAMs and AI are no longer on the periphery – they are integral to regulatory science. Sponsors should act now to engage with regulators, participate in pilot programs, and secure innovation through thoughtful IP and data governance strategies.
[1] From Algorithms to Approvals: Navigating AI in Drug and Biological Product Regulation (Biosimilars IP, Feb. 2025).
[2] NIH, NIH News Release, NIH to prioritize human-based research technologies (Apr. 29, 2025), https://www.nih.gov/news-events/news-releases/nih-prioritize-human-based-research-technologies.
[3] Brian Buntz, NIH announces end to funding for animal-only studies, Drug Discovery & Development (July 7, 2025), https://www.drugdiscoverytrends.com/nih-announces-end-to-funding-for-animal-only-studies/.
[4] FDA Modernization Act 2.0, Pub. L. No. 117-328, § 3209 (2022).
[5] U.S. Food and Drug Administration. (2025, April 10). FDA Announces Initiative to Modernize Drug Development Through Alternative Testing Methods. Retrieved from fda.gov.
[6] NIH, NIH News Release, NIH to prioritize human-based research technologies (Apr. 29, 2025), https://www.nih.gov/news-events/news-releases/nih-prioritize-human-based-research-technologies.
[7] U.S. Food and Drug Administration. (2025, April 10). FDA Announces Initiative to Modernize Drug Development Through Alternative Testing Methods. Retrieved from fda.gov.
[8] Roadmap to Reducing Animal Testing in Preclinical Safety Studies; available at https://www.fda.gov/media/186092/download?attachment.
[9] Dowden, H., & Munro, J. (2019). Trends in clinical success rates and therapeutic focus. Nature Reviews Drug Discovery, 18(7), 495-496.
[10] Emulate Applauds FDA’s Roadmap to Reduce Animal Testing and Embrace Organ‑Chip Technologies. Emulate, April 2025, https://emulatebio.com/press/emulate-applauds-fdas-roadmap-to-reduce-animal-testing-and-embrace-organ-chip-technologies/.
[11] NIH, NIH News Release, NIH to prioritize human-based research technologies (Apr. 29, 2025), https://www.nih.gov/news-events/news-releases/nih-prioritize-human-based-research-technologies
[12] Id.
[13] Brian Buntz, NIH announces end to funding for animal-only studies, Drug Discovery & Development (July 7, 2025), https://www.drugdiscoverytrends.com/nih-announces-end-to-funding-for-animal-only-studies/.
[14] From Algorithms to Approvals: Navigating AI in Drug and Biological Product Regulation (Biosimilars IP, Feb. 2025).
[15] Cobelli C, Kovatchev B. Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility. J Diabetes Sci Technol. 2023;17(6):1493-1505.
[16] See, e.g., Dennis Crouch, “Discerning Signal from Noise: Navigating the Flood of AI‑Generated Prior Art,” Patently‑O, April 30, 2024, https://patentlyo.com/patent/2024/04/discerning-navigating-generated.html.
[17] Alison Frankel, “Navigating Patent Eligibility for AI Inventions After USPTO’s Guidance Update,” Reuters Legal, Oct. 8, 2024, https://www.reuters.com/legal/legalindustry/navigating-patent-eligibility-ai-inventions-after-usptos-ai-guidance-update-2024-10-08.
[18] Darren Smyth, “IP Implications of FDA Guidance for Use of AI in Drug Development,” IPKat, June 2025, https://ipkitten.blogspot.com/2025/06/ip-implications-of-fda-guidance-for-use.html
