Predictive Roles of Artificial Intelligence in Pre-Clinical Studies: An Overview

Abstract

Animal testing in preclinical drug development has long raised scientific and ethical concerns. Artificial intelligence (AI), through its deep learning (DL) and machine learning (ML) sub-fields causing a paradigm shift in pre-clinical research by offering the potential to partially or fully reduce reliance on animal experimentation and to provide more human-relevant alternatives. This review examines the emerging roles of AI in preclinical drug development process which includes drug screening; pharmacodynamic and pharmacokinetic prediction; toxicity and carcinogenicity assessment; organ-on-chip technology, digital twins, virtual animal models, regulatory compliance, and autonomous AI agents while critically addressing the challenges and limitations that accompany this transition.