Exposure–response analysis is often interpreted as though exposure, pharmacodynamic responsiveness, and disease state were stable coordinates. That assumption becomes fragile when drug exposure changes during treatment, effects lag behind plasma concentrations, responsiveness adapts, disease progresses, or treatment alters the processes that determine subsequent pharmacokinetics and outcome. This Perspective argues that the clinically relevant exposure–response relationship is therefore conditional on temporal state and history rather than being a fixed mapping between a concentration summary and an endpoint. Evidence from therapeutic antibodies, turnover and effect-compartment models, tolerance and tachyphylaxis analyses, disease-progression models, and longitudinal pharmacometrics is used to distinguish forward drug effects from feedback-compatible associations and model-dependent interpretations. We propose a state-conditioned longitudinal exposure–response framework that separates measured exposure, latent pharmacodynamic state, adaptive responsiveness, disease trajectory, and clinical outcome processes. The framework is intended as an interpretive discipline, not a validated universal model. Its value depends on identifiability, prospective or external validation, appropriate temporal sampling, and decision-relevant biomarkers. Dose optimization should therefore rely on static exposure targets only when stationarity is credible; when it is not, treatment decisions should be conditioned on validated evolving states.
IJPRAS
