AI-Driven Adverse Event Prediction in Clinical Trials

Abstract

Adverse events (AEs) remain the leading cause of clinical trial failure and post-market drug withdrawal, with approximately 30% of drug candidates failing in Phase III due to unexpected safety signals and an estimated 4.5% of approved drugs receiving safety-related label changes within five years of market authorisation. Traditional pharmacovigilance relies on reactive signal detection from spontaneous reporting, but artificial intelligence offers the potential to predict adverse events before they occur by learning patterns from molecular structure, preclinical data, patient characteristics, and early clinical signals. This study presents the AI-Driven Adverse Event Prediction Framework (AIAEPF), evaluating five prediction approaches -- chemical structure-based toxicity prediction, preclinical-to-clinical translation models, patient-level risk stratification, NLP-based clinical narrative mining, and multi-modal ensemble integration -- across four prediction tasks: organ-specific toxicity forecasting, serious adverse event (SAE) risk scoring, drug-drug interaction AE prediction, and time-to-AE-onset modelling. Our Adverse Event Prediction Score (AEPS) integrates prediction accuracy, lead time, specificity, clinical actionability, and generalisability across therapeutic areas. Multi-modal ensemble integration achieved the highest AEPS (0.926) through gradient-boosted fusion of molecular, preclinical, clinical, and textual features that predicted SAEs with AUROC 0.91 a median of 28 days before clinical manifestation, while NLP-based narrative mining achieved the highest lead time (42 days) by detecting linguistic patterns in physician notes that preceded formal AE reporting.