Researchers Identify Opportunities, Limits for Animal Testing Alternatives
As FDA moves to reduce animal testing requirements for certain drugs, researchers say emerging technologies involving human organoids, organ-on-a-chip systems and artificial intelligence could replace some traditional animal studies, although significant scientific and regulatory limitations remain.
In a Perspective published 11/2 in The Innovation, researchers from Zhejiang University and Hangzhou City University examined the capabilities and limitations of new approach methodologies (NAMs) following the agency’s 4/2025 announcement that it would phase out mandatory animal testing for certain therapeutics, including monoclonal antibodies.
The authors cited evidence that human-based models may improve predictions of drug toxicity. One liver-on-a-chip system identified drugs associated with liver injury with 87% accuracy while correctly identifying all nontoxic compounds evaluated. An AI model reportedly predicted results from nine conventional animal tests with more than 80% agreement.
However, the researchers cautioned that organoids and organ-on-a-chip systems cannot adequately reproduce complex whole-body immune responses, including cytokine storms. AI models also remain constrained by the quality and scope of their training data.
The authors said high-dose animal toxicology studies remain important for establishing safe starting doses in first-in-human trials. They also identified differences in regulatory validation requirements among FDA, the European Medicines Agency and regulators in Japan and China as potential obstacles to broader adoption.
Rather than advocating immediate elimination of animal studies, the researchers recommended a phased transition incorporating validated NAMs alongside conventional testing.
They highlighted FDA's Innovative Science and Technology Approaches for New Drugs (ISTAND) program as one potential pathway for qualifying emerging technologies for regulatory use.
The researchers concluded that broader adoption will depend on demonstrating that alternative methods can reliably support drug development and regulatory decisions.