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Animal models remain foundational to biomedical research, having contributed to the development of vaccines, drugs, and surgical innovations. But their continued use faces growing scrutiny due to ethical concerns, species-specific physiological differences that limit the predictive power of animal models for human disease, and high costs. 

Notably, a May 2025 GAO report signals a shift from exploratory research toward standardized regulatory adoption of Non-Animal Models (NAMs). For innovators, these developments raise not only scientific and regulatory questions but also complex issues of intellectual property and data stewardship.

Background

The removed the statutory mandate for animal testing in new drug approvals, allowing sponsors to submit NAM-based data.  The Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) aims to reduce animal testing by 2025 and eliminate all mammalian testing by 2035. The FDA has also launched initiatives under . We have previously discussed the FDA’s guidance on the use of NAMs in the article entitled “Navigating FDA’s Proposed Guidance on AI and Non-Animal Models: Safeguarding Innovation in Drug Development.” 

Regulators are increasingly acknowledging the limits of animal testing and actively encouraging alternatives when seeking new drug approvals. NAMs are rapidly gaining scientific credibility and regulatory traction as alternatives to animal studies. The growing policy momentum culminated in the GAO report discussed below, which outlines concrete mechanisms to scale NAMs from promise to practice.

The GAO Report. In May 2025, the U.S. Government Accountability Office (GAO) released an assessment of NAMs entitled “Human Organ-on-a-Chip: Technologies Offer Benefits Over Animal Testing but Challenges Limit Wider Adoption ().”&�Բ�����; The report evaluates the current state, challenges, and policy options for developing organ-on-a-chip (OOC) NAMs for drug testing. It concludes that while OOCs can complement and partially replace animal testing, they are not yet sufficiently validated to serve as full replacements. 

The Limits of Animal Testing

The failure of up to 90% of drug candidates that perform well in animals but fail in human trials underscores the need for human-relevant systems. Replacing or supplementing animal testing with NAMs such as OOCs can improve accuracy, efficiency, and trust in translational science. See  et al., Replacing Animal Testing with Stem Cell-Organoids: Advantages and Limitations, Stem Cell Rev Rep, (Aug. 2024).

The Promise of Human-Based Platforms

Stem-cell–derived systems are at the forefront of non-animal testing. Organoids and OOCs based on stem cells offer human-specific, physiologically dynamic models that more accurately replicate tissue-level responses. Some of the most recent advances include:

• Neuroscience: Dr. Kathuria’s team recently developed multi-region brain organoids (MRBOs) integrating multiple brain regions and endothelial systems, maintaining “distinct transcriptional profiles that closely correlate with human fetal brain regional identity.” et al., Multi-Region Brain Organoids Integrating Cerebral, Mid-Hindbrain, and Endothelial Systems, Advanced Science (July 2025).
• Toxicology: Dr. Ivan Rusyn’s team demonstrated that microphysiological systems (MPS) can integrate cross-species hepatocyte studies, controlled drug exposures, and longitudinal biomarker profiling to improve preclinical safety evaluation. et al., Comparative Analysis of Species-Specific Hepatocyte Function and Drug Effects in a Liver Microphysiological System PhysioMimix LC12 and 96-Well Plates, ACS Pharm. & Transl. Science (October 2025).
• Standardization: Large-scale efforts such as the Human Endoderm-derived Organoid Cell Atlas (HEOCA) are providing unified single-cell datasets to support reproducibility and regulatory confidence. et al., An integrated transcriptomic cell atlas of human endoderm-derived organoids, Nature (May 2025). 

It’s hard not to admire how far these systems have come since the first lung-on-a-chip appeared in 2010. These advances illustrate the type of evidence and models regulators could soon expect in submissions of preclinical data.

Persistent Challenges Slowing Adoption of NAMs Identified in the GAO Report

Despite accelerating progress, systemic barriers continue to limit widespread  implementation of NAMs. The GAO report makes clear that the obstacles to NAM adoption are both technical and structural, and include:

1. Limited availability of high-quality, diverse human cells.
2. High resource demands and need for specialized expertise.
3. Lack of technology-specific standards.
4. Insufficient validation studies and performance benchmarks.
5. Limited data sharing due to intellectual-property concerns.
6. Persistent regulatory uncertainty.

Addressing these obstacles will require coordinated funding, public-private partnerships, standard developments, and clear regulatory pathways.

Six Needed Policy Changes Identified by the GAO Report

1. Expand access to diverse, high-quality human cells through national biobanks and standardized procurement.
2. Fund benchmark and validation studies to quantify predictive accuracy in defined contexts of use.
3. Create precompetitive data-sharing frameworks that balance collaboration with IP protection.
4. Provide clear regulatory guidance clarifying when and how OOC data may replace animal data.
5. Support standards development through collaborations with National Institute of Standards & Technology, International Organization for Standardization, and Organization for Economic Co-operation and Development.
6. Monitor progress under the status quo while improving cross-agency coordination.

Collectively, these recommendations aim to enhance reproducibility, reliability, and regulatory confidence in the data, thereby advancing broader acceptance of NAMs like OOCs in drug development. For legal and scientific teams, they outline the next compliance landscape built around data provenance, validation, and transparency. These factors are of increasing importance given the focus on study design, data availability, efficacy, safety, bias assessments, adverse events, device recalls, and risk classification of FDA-cleared Artificial Intelligence/Machine Learning (AI/ML) devices.¹

Innovation and Intellectual Property Implications

The GAO report emphasizes that benchmarks, validation, and data sharing are national priorities. Consequently, the complexities surrounding intellectual property (IP) rights and effective data stewardship are becoming increasingly prominent in discussions about technology and innovation. 

The OOC technologies present significant opportunities for patentable advancements, ranging from assay standardization to AI-driven data integration and the development of multi-organ systems. Innovators must increasingly decide whether to rely on patent protection or maintain proprietary control over data and software infrastructure. Companies should also consider how emerging transparency requirements could erode exclusivity. Aligning IP protection and data governance early in development allows firms to balance openness with competitive advantage and engendering long-term trust in their offerings.

Key Takeaways 

The GAO report underscores that NAMs and OOCs are becoming integral to how we model human biology and evaluate safety. As regulators formalize standards for NAM validation, how should companies balance openness with IP protection, and structure access and licensing models around shared data?

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