TY - ECHAP
KW - 3Rs
KW - Artificial intelligence
KW - Machine Learning
KW - new approach methods
KW - regulatory science
AU - Thomas Hartung
AU - Thomas Luechtefeld
AU - Alexandra Maertens
AU - Catharine E. Krebs
AU - Gail A. Van Norman
AB - The rapid advancement of artificial intelligence (AI) is transforming science. We explore the role of AI in advancing nonanimal approaches for chemical safety assessment and drug discovery. We discuss the limitations of animal models and then examine how AI can serve as a key enabler for nonanimal methods, leveraging the growing availability of big data, natural language processing for literature mining, machine learning for predictive modeling, and explainable AI for model interpretation. The synergies between AI and other innovative technologies, such as microphysiological systems, adverse outcome pathways, and integrated testing strategies, are highlighted for creating human-relevant, predictive toxicology. We also address the challenges and future directions of AI, including data quality and bias issues, validation and regulatory acceptance, the need for responsible and human-centered AI, and the importance of multidisciplinary collaboration. We conclude with a forward-looking perspective on the transformative potential of AI in toxicology and pharmacology, envisioning AI as a toxicology copilot, the development of tox chatbots, and the acceleration of drug discovery. Ultimately, we call for embracing these new technologies while upholding scientific rigor and ethical principles, and for a future integration of AI with nonanimal approaches that revolutionize chemical safety assessment and drug discovery and reduce reliance on animal testing.
BT - Moving Toward Nonanimal Approaches in Medical Research and Testing
DA - 2027-01-01
N2 - The rapid advancement of artificial intelligence (AI) is transforming science. We explore the role of AI in advancing nonanimal approaches for chemical safety assessment and drug discovery. We discuss the limitations of animal models and then examine how AI can serve as a key enabler for nonanimal methods, leveraging the growing availability of big data, natural language processing for literature mining, machine learning for predictive modeling, and explainable AI for model interpretation. The synergies between AI and other innovative technologies, such as microphysiological systems, adverse outcome pathways, and integrated testing strategies, are highlighted for creating human-relevant, predictive toxicology. We also address the challenges and future directions of AI, including data quality and bias issues, validation and regulatory acceptance, the need for responsible and human-centered AI, and the importance of multidisciplinary collaboration. We conclude with a forward-looking perspective on the transformative potential of AI in toxicology and pharmacology, envisioning AI as a toxicology copilot, the development of tox chatbots, and the acceleration of drug discovery. Ultimately, we call for embracing these new technologies while upholding scientific rigor and ethical principles, and for a future integration of AI with nonanimal approaches that revolutionize chemical safety assessment and drug discovery and reduce reliance on animal testing.
PB - Academic Press
PY - 2027
SN - 978-0-443-26514-3
SP - 143
EP - 157
T2 - Moving Toward Nonanimal Approaches in Medical Research and Testing
TI - Chapter 9 - The role of artificial intelligence in overcoming animal testing
UR - https://www.sciencedirect.com/science/article/pii/B9780443265143000072
Y2 - 2026-10-05
ER -