Precision pharmacotherapy is often framed as the selection of an individualized dose from pretreatment characteristics. That framing is incomplete because treatment itself generates new information. Measured concentrations, early pharmacodynamic responses, toxicity signals, evolving organ function, inflammatory state, adherence, and other time-varying features can make an initially reasonable PKPD prediction progressively less representative of the patient. This Perspective argues that precision dosing should therefore be treated as a sequential inference problem rather than a one-time calculation. Prior population PKPD knowledge remains essential, but it should function as an auditable prior whose transportability and structural assumptions can be challenged by post-dose observations. Early concentration and response data should update different components of uncertainty, while treatment-affected covariates require special handling because they may be predictors, mediators, consequences, or markers rather than independent causal drivers. We propose a dynamic PKPD updating logic that separates prior-model uncertainty, patient-specific parameter uncertainty, observation uncertainty, and evolving clinical-state uncertainty before dose adaptation. The framework does not assume that more frequent updating is always beneficial. Its clinical value depends on observability, response lag, model adequacy, decision consequences, and prospective validation of the complete data-to-dose workflow.
IJPRAS
