The promise of animal testing alternatives at the FDA and NIH
Animal testing has long played a central role in drug development, but animal studies often fail to accurately predict how safe or effective a drug will be in humans. They also increase the cost and time it takes to bring new treatments to patients. To address these issues, the FDA and NIH are focusing on new approaches that rely on advanced computer modeling, super intelligence, and laboratory techniques using human cells or organ-like systems.
A New England Journal of Medicine AI 2025 Policy Corner article by FDA and NIH scientists highlights scientific and regulatory momentum toward shifting from traditional animal models to these innovative human-based alternatives (Scheufen Tieghi et al. 2025). These methods have the potential to improve the safety and effectiveness of drugs while reducing costs and accelerating the discovery process. Several real-world examples show how alternative testing methods are already being used in regulatory decisions, providing evidence that these approaches are both feasible and effective. Successfully integrating these new methods into drug approval processes requires continued public awareness, collaboration with the pharmaceutical industry, and increased transparency in research. Mapping out milestones and addressing challenges, such as validating the accuracy of these methods and updating regulatory policies, will be critical for the widespread adoption of alternatives. By prioritizing these predictive technologies, regulatory agencies aim to modernize drug development, making it faster, safer, and more cost-effective, ultimately improving patient access to life-changing treatments.