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A generative adversarial network model alternative to animal studies for clinical pathology assessment

Author

Listed:
  • Xi Chen

    (National Center for Toxicological Research, Food and Drug Administration)

  • Ruth Roberts

    (ApconiX Ltd, Alderley Park
    University of Birmingham, Edgbaston)

  • Zhichao Liu

    (National Center for Toxicological Research, Food and Drug Administration
    Currently working at Integrative Toxicology, Nonclinical Drug Safety, Boehringer Ingelheim Pharmaceuticals, Inc.)

  • Weida Tong

    (National Center for Toxicological Research, Food and Drug Administration)

Abstract

Animal studies are unavoidable in evaluating chemical and drug safety. Generative Adversarial Networks (GANs) can generate synthetic animal data by learning from the legacy animal study results, thus may serve as an alternative approach to assess untested chemicals. AnimalGAN, a GAN method to simulate 38 rat clinical pathology measures, was developed with significant robustness even for the drugs that vary significantly from these used during training, both in terms of chemical structure, drug class, and the year of FDA approval. AnimalGAN showed comparable results in hepatotoxicity assessment as using the real animal data and outperformed 12 conventional quantitative structure-activity relationship approaches. Using AnimalGAN, a virtual experiment of 100,000 rats ranked hepatotoxicity of three structurally similar drugs in a similar trend that has been observed in human population. AnimalGAN represented a significant step with artificial intelligence towards the global effort in replacement, reduction, and refinement (3Rs) of animal use.

Suggested Citation

  • Xi Chen & Ruth Roberts & Zhichao Liu & Weida Tong, 2023. "A generative adversarial network model alternative to animal studies for clinical pathology assessment," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42933-9
    DOI: 10.1038/s41467-023-42933-9
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    References listed on IDEAS

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    1. Richard Van Noorden, 2018. "Software beats animal tests at predicting toxicity of chemicals," Nature, Nature, vol. 559(7713), pages 163-163, July.
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