Controlled, responsive, and long-acting drug-delivery systems are commonly evaluated through cumulative release curves, release-rate constants, burst fractions, or apparent duration of liberation. These measurements are indispensable for formulation characterization, yet they do not independently establish local exposure, systemic pharmacokinetics, biological response, therapeutic durability, safety, or clinical usefulness. This article develops an original drug-release theory to address the unresolved tendency to treat release performance as a surrogate for therapeutic performance. The proposed Release-to-Performance Coupling Theory organizes delivery-system behavior into linked but non-equivalent domains: assay-conditioned drug liberation, evolving depot state, material transformation, tissue transport, local and systemic exposure, biological response, and context-specific therapeutic performance. It further identifies physiological context and uncertainty as cross-cutting modifiers of every transition. The theory distinguishes analytical validity from biological relevance, correlation from mechanism, simulated exposure from prospective usefulness, and sustained drug presence from sustained therapeutic benefit. It also explains why formulations with similar cumulative release profiles may produce different exposure and response trajectories, whereas formulations with dissimilar in-vitro profiles may occasionally converge in vivo through compensating transport or disposition processes. The proposed construct is intended to guide mechanistic evaluation, model development, evidence integration, and formulation design rather than to function as a validated predictive model or regulatory decision rule. Its applicability is conditional on delivery platform, administration site, drug properties, disease state, biological trigger, and intended decision. Reframing release as one component of a multiscale performance chain may improve the design of biorelevant tests, computational models, responsive systems, and translation strategies while reducing unsupported claims based on cumulative release curves alone.