TY - ECHAP
KW - Animalgan
KW - Artificial intelligence
KW - Data science
KW - Drug
KW - generative adversarial networks
KW - Machine Learning
KW - ToxGAN
KW - Transorgan
AU - Xi Chen
AU - Ting Li
AU - Weida Tong
AU - Minjun Chen
AU - Catharine E. Krebs
AU - Gail A. Van Norman
AB - 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.
BT - Moving Toward Nonanimal Approaches in Medical Research and Testing
DA - 2027-01-01
N2 - 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.
PB - Academic Press
PY - 2027
SN - 978-0-443-26514-3
SP - 105
EP - 119
T2 - Moving Toward Nonanimal Approaches in Medical Research and Testing
TI - Chapter 7 - The application of generative adversarial networks (GANs) as new approach methods (NAMs) toward nonanimal testing
UR - https://www.sciencedirect.com/science/article/pii/B9780443265143000199
Y2 - 2026-10-05
ER -