Drug Action Has an Address: Spatially Resolved Computational Pharmacology within Heterogeneous Tissue Microenvironments Download PDF

Pharmacological action is conventionally summarized through systemic exposure, tissue-average concentration, target expression, or aggregate response, yet each of these measures can obscure the local conditions that determine whether a drug reaches, engages, and alters a biologically relevant tissue state. This article develops a proposed Spatially Resolved Computational Pharmacology Architecture for representing drug action as an address-dependent process within heterogeneous microenvironments. The approach integrates coordinate-resolved exposure, transport, target accessibility, cell identity, tissue state, target engagement, response, resistance, toxicity, uncertainty, and whole-body coupling while preserving the evidentiary distinctions among measured, inferred, and simulated quantities. The synthesis argues that no single performance metric can establish spatial pharmacological adequacy because local action depends on the interaction of delivery, binding, cellular composition, structural barriers, adaptive dynamics, and temporal context. The central contribution is an architecture that assigns explicit data contracts, uncertainty boundaries, and validation obligations to each component, thereby preventing spatial maps, omics-derived states, and mechanistic simulations from being treated as interchangeable evidence. The article further distinguishes spatial association from mechanism, local exposure from pharmacologically available concentration, target expression from engagement, and benchmark performance from prospective pharmaceutical usefulness. Important limitations include incomplete tissue sampling, cross-platform registration error, uncertain scale translation, sparse longitudinal measurements, parameter non-identifiability, and restricted evidence outside intensively studied disease settings. The proposed architecture is therefore not a validated predictive system or clinical decision tool. Its value lies in organizing testable questions, guiding multimodal evidence integration, and defining the conditions under which localized and systemic therapies may be evaluated with greater spatial and mechanistic discipline.