An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results
This work addresses safety risk assessment in pharmaceutical research by providing a new benchmark for ADE prediction, though it is incremental as it builds on existing methods with a novel dataset.
The authors tackled the problem of predicting adverse drug events (ADEs) in clinical trials by introducing CT-ADE, a dataset with 168,984 drug-ADE pairs, and found that large language models using contextual information achieved an F1-score of 56%, outperforming structure-only models by 21%-38%.
Adverse drug events (ADEs) are a major safety issue in clinical trials. Thus, predicting ADEs is key to developing safer medications and enhancing patient outcomes. To support this effort, we introduce CT-ADE, a dataset for multilabel ADE prediction in monopharmacy treatments. CT-ADE encompasses 2,497 drugs and 168,984 drug-ADE pairs from clinical trial results, annotated using the MedDRA ontology. Unlike existing resources, CT-ADE integrates treatment and target population data, enabling comparative analyses under varying conditions, such as dosage, administration route, and demographics. In addition, CT-ADE systematically collects all ADEs in the study population, including positive and negative cases. To provide a baseline for ADE prediction performance using the CT-ADE dataset, we conducted analyses using large language models (LLMs). The best LLM achieved an F1-score of 56%, with models incorporating treatment and patient information outperforming by 21%-38% those relying solely on the chemical structure. These findings underscore the importance of contextual information in ADE prediction and establish CT-ADE as a robust resource for safety risk assessment in pharmaceutical research and development.