The U.S. Food and Drug Administration said on Thursday it plans to replace animal testing in the development of monoclonal antibody therapies and other drugs with "human-relevant methods," including the use of AI-based models.

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The new approach would help improve drug safety, lower research and development costs and drug prices, the agency said.

"This initiative marks a paradigm shift in drug evaluation and holds promise to accelerate cures and meaningful treatments for Americans while reducing animal use," said FDA Commissioner Martin Makary.

The FDA's animal testing requirement will be "reduced, refined, or potentially replaced" with so-called New Approach Methodologies, or NAMs data, which include the use of AI-based models to predict a drug's behavior as well as side effects and testing on human organ-like structures made in a laboratory.

"There is currently no full replacement for animal models in biomedical research and drug development," the National Association for Biomedical Research (NABR) said in a statement.

While AI promises to speed up many aspects of research, it largely depends on pulling from existing data, said NABR President Matthew Bailey.

"The unknown variables could present the biggest risk to patients. So, it will be interesting to see where AI is validated as a replacement to a whole biological organism and where it isn't," Bailey said.

The FDA said it will begin implementation of the new approach immediately, encouraging NAMs data instead of animal data for human trial applications.

The companies that submit strong safety data from non-animal tests may receive a streamlined review, which would incentivize investment in modern testing platforms, the regulator said.

The agency plans to launch a pilot program, over the next year allowing select antibody-based drug developers to use a non-animal-based testing strategy. The findings from an accompanying pilot study would then be used to decide broader policy changes and guidance updates, which are expected to be rolled out in phases.