MERIT: Mechanism driven model predicts drug outcomes and nominates indications for failed drugs
Abstract
Drug development depends on efficacy and safety, but many trial-outcome prediction models incorporate trial design, prior development history or compound identity, enabling compound memorization and inflating apparent performance. We developed MEchanism-Resolved Inference of Trial outcomes (MERIT), a model that predicts trial outcomes from molecular and disease features without using information on similar-compound success. MERIT integrates the disease and drug of interest with large-scale drug-protein, protein-metabolite and immune interaction maps to link a drug’s intended and potential off-target effects to tissue-specific efficacy and safety. Across 753 small-molecule drugs and 3,133 trials, MERIT achieved a best-in-class overall AUROC of 0.770 (0.765 for efficacy and 0.784 for safety). MERIT also recovered the eventual approved indications for 83% of failed drugs. Finally, we registered locked, outcome-blind predictions for 55 drug-indication pairs in ongoing Phase III trials, establishing a prospective evaluation cohort.
How strong is the evidence?
Evidence Hub reads this as a cohort study. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence.
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