@inbook{bibcite_9346,
  keywords = {Animalgan, Artificial intelligence, Data science, Drug, generative adversarial networks, Machine Learning, ToxGAN, Transorgan},
  author = {Xi Chen and Ting Li and Weida Tong and Minjun Chen},
  editor = {Catharine E. Krebs and Gail A. Van Norman},
  title = {Chapter 7 - The application of generative adversarial networks (GANs) as new approach methods (NAMs) toward nonanimal testing},
  abstract = {Animal models have historically played a critical role in biomedical research and regulatory submissions. However, there is a growing movement toward nonanimal approaches driven by ethical concerns, cost-effectiveness, and limitations in predicting human outcomes. This chapter explores the transformative potential of artificial intelligence (AI) applications, particularly generative adversarial networks (GANs), in advancing nonanimal testing methodologies. It discusses how GANs can leverage existing data from traditional animal models, offering an alternative to animal studies as future avenues for integrating AI into the risk and safety assessment of drugs and chemicals. The chapter elucidates the principles of GANs with examples in toxicology through three case studies: ToxGAN, AnimalGAN, and TransOrGAN. In addition, it discusses the existing challenges and potential trajectories for leveraging this technology to enhance preclinical safety studies.},
  year = {2027},
  journal = {Moving Toward Nonanimal Approaches in Medical Research and Testing},
  pages = {105-119},
  month = {2027-01-01},
  publisher = {Academic Press},
  isbn = {978-0-443-26514-3},
  url = {https://www.sciencedirect.com/science/article/pii/B9780443265143000199},
}