Every drug that reaches a human clinical trial first passes through a gauntlet of preclinical safety tests designed to catch cardiac, neurological, and respiratory dangers. The accuracy and efficiency of those tests now stands to be fundamentally reshaped by artificial intelligence — a shift that could meaningfully reduce both drug development failures and patient harm from adverse effects that slip through traditional screening.
This comprehensive review maps how AI and machine learning are being integrated into the core battery of safety pharmacology assessments. On the cardiovascular side, neural network architectures are being applied to proarrhythmic risk profiling within the ICH S7B and Comprehensive in vitro Proarrhythmia Assay (CiPA) regulatory frameworks — areas where false-negative predictions have historically contributed to costly late-stage drug withdrawals. Seizure liability is being assessed through AI-enhanced analysis of microelectrode array signals, while respiratory function data from whole-body plethysmography is being processed with ML algorithms to extract subtle patterns human analysts may miss. Beyond organ-system endpoints, AI applications extend to multi-endpoint toxicity prediction and digital pathology, with federated learning consortia enabling cross-institutional model training without sharing proprietary compound data. A 2024 survey of 89 Safety Pharmacology Society members found 57% had not yet deployed AI tools, though 44% of non-users planned adoption within twelve months.
The pharmaceutical industry's relationship with AI safety tools is at an inflection point, not yet mainstream but clearly past early-adopter status. The federated learning angle deserves particular attention: it offers a structural solution to the industry's persistent data-siloing problem, potentially enabling far more robust predictive models than any single organization could train alone. Key limitations remain — most AI models in this space are trained on narrow compound libraries, raising generalizability concerns for novel chemical scaffolds. The survey sample of 89 respondents also limits how broadly conclusions can be extrapolated. Still, for health-conscious adults, the practical implication is meaningful: better preclinical screening tools upstream translate into safer drugs downstream.