Translational pharmaceutical research is commonly organized around experiments that are optimized locally: targets are selected for biological plausibility, models for mechanistic sophistication, doses for acceptable exposure, biomarkers for measurable change, and early trials for interpretable signals. Yet a technically strong result at one layer has limited developmental value when the inference connecting it to the next layer is weak. This Perspective argues that translational performance should therefore be evaluated at the level of the evidence chain rather than the individual experiment. Target evidence, mechanistic models, exposure, target engagement, biomarkers, preclinical-to-human bridging, and early clinical findings are treated as distinct but conditionally dependent evidentiary layers. Their relationships are neither automatically additive nor interchangeable. A proposed evidence-chain view emphasizes concordance, transfer boundaries, competing explanations, and the localization of weak links. It also reframes discordant human findings as potentially informative evidence about which assumption failed rather than as indiscriminate falsification of all preceding work. The resulting approach prioritizes experiments according to their capacity to resolve consequential uncertainty across connected decisions. Evidence-chain optimization does not imply that every layer requires maximal certainty. Instead, it requires that confidence, validation effort, and experimental investment be proportionate to the influence of each link on downstream development decisions.
IJPRAS
