Executive Summary

In a transformative shift, the U.S. Food and Drug Administration (FDA) announced plans in April 2025 to phase out animal testing requirements for monoclonal antibodies and other drugs, favoring more human-relevant methods. This move aims to enhance drug safety, reduce research and development costs, and accelerate the availability of treatments.

Simultaneously, the National Institutes of Health (NIH) declared a strategic pivot towards prioritizing human-based research technologies, including organoids, tissue chips, computational models, and real-world data analytics, to better mimic human physiological and pathological conditions.

This dual initiative marks a significant departure from traditional animal models, signaling a new era in biomedical research and drug development.

What’s Changing and Why It Matters

Historically, animal models have been central to drug development. However, they often fail to accurately predict human responses, leading to high attrition rates in clinical trials. The FDA's new roadmap emphasizes the adoption of New Approach Methodologies (NAMs), such as AI-based computational models and human-derived organoid systems, to improve predictive accuracy and reduce reliance on animal testing.

The NIH's initiative complements this by expanding the development and use of cutting-edge, non-animal models to address long-standing translational challenges in biomedical research.

What Counts as a “Human-Relevant” Model

The FDA and NIH are endorsing several innovative approaches, including:

• Organoids and Tissue Chips

  Miniaturized, lab-grown human organs that replicate key aspects of human physiology.

• Computational Models

  AI-driven simulations that predict drug toxicity and efficacy.

• Real-World Data Analytics

  Utilizing patient data to inform drug development and safety assessments.

These methodologies aim to provide more accurate, efficient, and ethical alternatives to animal testing, with the FDA encouraging their inclusion in investigational new drug applications.

What This Means for Pharma, Biotech, and Healthcare AI

For companies developing next-generation therapies, this shift presents both opportunities and challenges:

• Accelerated Development

  Human-relevant models can streamline preclinical testing, reducing time and costs.

• Regulatory Alignment

  Adopting NAMs aligns with emerging FDA and NIH guidelines, potentially facilitating smoother approval processes.

• Increased Scrutiny

  As reliance on AI and computational models grows, so does the need for transparency, validation, and risk assessment.

Healthcare AI developers, in particular, must ensure their models are robust, explainable, and compliant with regulatory expectations.

Why This Change Makes AI Even More Central

The integration of AI into drug development is no longer optional—it's becoming essential. The FDA's draft guidance outlines a risk-based framework for evaluating AI models used in regulatory decision-making, emphasizing the importance of model credibility and context of use .

AI applications in this context include:

• Predictive Modeling

  Assessing drug safety and efficacy through simulations.

• Data Integration

  Combining diverse datasets to inform clinical trial design and patient selection.

• Post-Market Surveillance

  Monitoring real-world outcomes to detect adverse events and inform ongoing safety assessments.

As AI becomes more embedded in regulatory processes, developers must prioritize transparency, validation, and compliance.

Why This Matters for AI Validation Infrastructure

At Gesund.ai, we recognize that the shift towards human-relevant models and AI integration necessitates robust validation infrastructure. Our platform supports:

• Bias and Subgroup Performance Evaluations

  Ensuring AI models perform equitably across diverse populations.

• Audit Trails and Explainability Dashboards

  Providing transparency into model decision-making processes.

• Lifecycle-Aware Versioning

  Tracking model changes over time to maintain compliance and performance.

By aligning with emerging regulatory frameworks, we help developers build trust and reduce risk in AI-driven drug development.

What Forward-Looking Teams Should Do Now

To stay ahead in this evolving landscape, organizations should:

• Evaluate Current Models

  Assess the predictive accuracy and relevance of existing animal models.

• Invest in Human-Relevant Alternatives

  Adopt organoids, tissue chips, and computational models to enhance preclinical testing.

• Strengthen AI Validation

  Implement rigorous validation protocols to ensure AI models meet regulatory standards.

• Engage with Regulatory Guidance

  Stay informed about FDA and NIH initiatives to align development strategies accordingly.

Embracing these steps will position organizations to lead in a future where ethical, efficient, and human-centric drug development is the norm.

How Gesund.ai Supports the Transition

Gesund.ai provides the tools and infrastructure necessary to navigate this paradigm shift:

• Comprehensive Validation

  Assessing AI models for accuracy, bias, and compliance.

• Regulatory Alignment

  Ensuring development processes meet FDA and NIH guidelines.

• Lifecycle Management

  Tracking model performance and changes over time.

By partnering with us, organizations can confidently transition to human-relevant models and AI-driven methodologies, fostering innovation while maintaining regulatory compliance.

📍The end of animal testing marks not just a moral victory—but a scientific upgrade.

Let’s build the infrastructure to support it.

→ Learn how Gesund.ai helps life sciences companies validate and deploy AI-driven models with confidence: https://gesund.ai/meeting-request

Bibliography

• U.S. Food and Drug Administration. (2025, April 10). FDA announces plan to phase out animal testing requirement for monoclonal antibodies and other drugs. Retrieved from https://www.fda.gov/news-events/press-announcements/fda-announces-plan-phase-out-animal-testing-requirement-monoclonal-antibodies-and-other-drugs

• National Institutes of Health. (2025, April 30). NIH to prioritize human-based research technologies. Retrieved from https://www.nih.gov/news-events/news-releases/nih-prioritize-human-based-research-technologies

• U.S. Food and Drug Administration. (2025, January). Considerations for the use of artificial intelligence to support regulatory decision-making for drug and biological products. Retrieved from https://www.fda.gov/media/186092/download

• Animal-Free Science Advocacy. (2025, April). FDA unveils landmark roadmap to replace animal testing in preclinical safety studies. Retrieved from https://animalfreescienceadvocacy.org.au

• BioSpace. (2025, May). FDA and NIH accelerate shift away from animal research as experts warn of limitations. Retrieved from https://www.biospace.com

• National Institutes of Health. (n.d.). Alternatives to animals in research. Grants.nih.gov. Retrieved from https://grants.nih.gov/policy-and-compliance/policy-topics/air/alternatives

• Office of Animal Care and Use, NIH. (n.d.). New approach methodologies (NAMs). Retrieved from https://oacu.oir.nih.gov/new-approach-methodologies