Drug repurposing promises to shorten parts of therapeutic development by redirecting existing compounds toward new disease indications, yet computationally attractive candidates often receive inconsistent support across transcriptomic signatures, molecular networks, genetics, phenotypic assays, observational data, and prior clinical studies. The unresolved problem is not simply insufficient prediction accuracy. It is the absence of a disciplined method for interpreting evidence sources that differ in biological scale, causal proximity, directionality, context, uncertainty, and translational authority. This article develops a proposed therapeutic evidence-synthesis model in which repurposing is treated as evidence reconciliation rather than score aggregation. The model distinguishes disease-state representation, target-mechanism plausibility, phenotypic response, human causal evidence, prior clinical outcomes, and context transfer as separate evidentiary layers. It further proposes that candidate prioritization should depend on compatibility among these layers, explicit accounting for contradictory and negative findings, and staged decision gates that prevent molecular plausibility from being mistaken for therapeutic readiness. The synthesis emphasizes that supportive evidence can converge without being equivalent, while apparent disagreement may reflect differences in cell type, dose, timing, population, endpoint, disease stage, modality, or mechanism. The proposed contribution is conceptual and requires retrospective and prospective evaluation. It cannot establish efficacy, safety, regulatory acceptability, or deployment readiness for any candidate. Its principal implication is that repurposing systems should expose why evidence agrees or conflicts, how close each signal lies to the intended therapeutic mechanism, and which uncertainties must be resolved before a hypothesis advances toward experimental or clinical testing.