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Tackling the challenges of new approach methods for predicting drug effects from model systems

The passage of the FDA Modernization Act 2.0 in 2022 has provided additional impetus to develop new approach methods for predicting the effects of drug candidates in humans from models such as microphysiological systems based on human-derived induced pluripotent stem cells. Here, we highlight progress in the field and strategies to address various challenges, including the application of artificial intelligence tools.
  1. Paul D. Pang

    1. Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA; Greenstone Biosciences, Palo Alto, CA, USA. *These authors contributed equally.

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  2. Syed Mukhtar Ahmed

    1. Greenstone Biosciences, Palo Alto, CA, USA. *These authors contributed equally.

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  3. Masataka Nishiga

    1. Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA.

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  4. Norman L. Stockbridge

    1. US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, USA.

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  5. Joseph C. Wu

    1. Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA; Greenstone Biosciences, Palo Alto, CA, USA.

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doi: https://doi.org/10.1038/d41573-024-00081-9

Acknowledgements

J.C.W. has received funding from NIH grants related to the topic of this article, including R01HL163680, R01HL141371, and P01HL163680. P.D.P. has received NIH grant R21TR004938 and CIRM grants TRAN4-14124 and DISC2-14133. S.M.A. has received NIH grant R44HL170756.

References

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Competing Interests

J.C.W. is a co-founder of Greenstone Biosciences. The other authors declare no competing interests.

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