TY - JOUR
KW - 3Rs principle
KW - animal studies
KW - Artificial intelligence
KW - deep learning
KW - Digital Twins
KW - generative adversarial networks
KW - Machine Learning
KW - organ-on-chip
AU - Amit Gangwal
AU - Antonio Lavecchia
AB - Artificial 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.
BT - Drug Discovery Today
DA - 2025-05-01
DO - 10.1016/j.drudis.2025.104360
IS - 5
N2 - Artificial 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.
PY - 2025
EP - 104360
ST - Artificial intelligence in preclinical research
T2 - Drug Discovery Today
TI - Artificial intelligence in preclinical research: enhancing digital twins and organ-on-chip to reduce animal testing
UR - https://www.sciencedirect.com/science/article/pii/S135964462500073X
VL - 30
Y2 - 2025-09-03
SN - 1359-6446
ER -