AnimalGAN Initiative
Initiative to develop virtual animal models to simulate animal study results using AI.
Objective: To develop virtual animal models using generative AI (GenAI) methodologies to generate synthetic toxicology data, reducing the reliance on animal testing.
Introduction: Testing data from animal models provide evidence for the safety evaluation of chemicals. These data have been an essential component in regulating drug, food, and chemical safety by regulatory agencies around the world. As a result, a wealth of animal data is available from the public domain and other sources. As the toxicology community and regulatory agencies move towards a reduction, refinement, and replacement (3Rs principle) of animal studies, we are exploring GenAI architecture such as generative adversarial networks (GAN) and diffusion models to learn from existing animal studies to generate synthetic animal testing results to minimize our reliance on animal studies for untested compounds.
Approaches: We are developing a GAN method that can generate animal-study data which otherwise would have to be obtained with actual animal studies. In a pilot study, we demonstrated that synthetic data from both ToxGAN and AnimalGAN for toxicogenomics, hematology, and clinical chemistry has the potential to be used in toxicity assessments, mechanistic studies, and biomarker development similar to actual experimental data.
Potential Impact: Conventional animal studies can be expensive, time-consuming, labor-intensive, and raise ethical concerns. AnimalGAN is an AI-based suite to generate specific animal-study datasets for new and untested chemicals by learning from legacy animal-study data. This suite could supplement animal studies by screening new and untested chemicals to guide and refine subsequent animal experiments in risk assessment and safety evaluation.
References
| Year | Title | Authors | Full Citation |
|---|---|---|---|
| 2023 | A Generative Adversarial Network Model Alternative to Animal Studies for Clinical Pathology Assessment. | Chen X., Liu Z., and Tong W. | A Generative Adversarial Network Model Alternative to Animal Studies for Clinical Pathology Assessment. Chen X., Liu Z., and Tong W. Nature Communications. 2023, 14, 7141. doi: 10.1038/s41467-023-42933-9. |
| 2022 | Tox-GAN: An Artificial Intelligence Approach Alternative to Animal Studies—A Case Study With Toxicogenomics. | Chen X., Roberts R., Tong W. et al. | Tox-GAN: An Artificial Intelligence Approach Alternative to Animal Studies—A Case Study With Toxicogenomics. Chen X., Roberts R., Tong W. et al. Toxicological Sciences. 2022,186:242-259. |