01810nas a2200253   4500000000100000008004100001260001500042653001800057653001900075653002800094653001800122653001800140653003600158653002100194653001800215100001700233700002200250245012000272856007200392300001100464490000700475520106000482022001401542       2025                            d  c2025-05-0110a3Rs principle10aanimal studies10aArtificial intelligence10adeep learning10aDigital Twins10agenerative adversarial networks10aMachine Learning10aorgan-on-chip1 aAmit Gangwal1 aAntonio Lavecchia00aArtificial intelligence in preclinical research: enhancing digital twins and organ-on-chip to reduce animal testing  uhttps://www.sciencedirect.com/science/article/pii/S135964462500073X  a1043600 v303 aArtificial intelligence (AI) is reshaping preclinical drug research offering innovative alternatives to traditional animal testing. Advanced techniques, including machine learning (ML), deep learning (DL), AI-powered digital twins (DTs), and AI-enhanced organ-on-a-chip (OoC) platforms, enable precise simulations of complex biological systems. AI plays a critical role in overcoming the limitations of DTs and OoC, improving their predictive power and scalability. These technologies facilitate early-stage, reliable evaluations of drug safety and efficacy, addressing ethical concerns, reducing costs, and accelerating drug development while adhering to the 3Rs principle (Replace, Reduce, Refine). By integrating AI with these advanced models, preclinical research can achieve greater accuracy and efficiency in drug discovery. This review examines the transformative impact of AI in preclinical research, highlighting its advancements, challenges, and the critical steps needed to establish AI as a cornerstone of ethical and efficient drug discovery.  a1359-6446