Evidence-constrained mechanistic synthesis for drug discovery

Abstract

Mechanistic drug-development programmes often have more biological evidence than they can safely quantify. We developed evidence-constrained mechanistic synthesis (ECMS), a framework that classifies what information each finding contains and converts only that information into restrictions on a family of mechanistic hypotheses. Evidence shifts the frequency of supported events in a reproducible ensemble rather than being converted into unsupported coefficients or probabilities of biological truth. In a chronic spontaneous urticaria (CSU) implementation, a representative, non-exhaustive corpus of 114 atomic findings from 53 sources and 13 public data resources compiled 18 relation/context constraints and a frozen 4,096-hypothesis ensemble. Regimen evaluation was formulated as continuous multi-node target matching: researchers specify desired changes and importance coefficients for modeled nodes, while package-declared controls vary continuously. A deterministic Sobol-to-block-refinement search, validated on all 4,096 hypotheses, reduced target-matching loss by 27.3% relative to the best of 44 deterministic anchors under a prespecified heuristic demonstration profile; changing the objective profile changed the selected control vector without changing the evidence ensemble. A complementary D-only reference analysis localized decision-relevant uncertainty around the mast-cell-to-disease relation, illustrating that mechanistic prioritization depends on the declared objective. ECMS is intended for the pre-calibration stage of drug development: it makes heterogeneous literature computable while keeping evidence, uncertainty and decision preferences distinct.