Clinical pharmacokinetic–pharmacodynamic models support increasingly individualized predictions of exposure, response, and dose requirements, yet uncertainty in those predictions is often summarized without distinguishing its origin. This can obscure a consequential methodological difference: uncertainty about whether the model adequately represents the system is not equivalent to variability among patients represented within that model. Structural misspecification, uncertain parameter estimates, between-patient variability, within-patient change, residual error, incomplete observations, and software implementation can all widen or distort individual predictions, but they imply different corrective actions. This Methodological Perspective argues for explicit separation of these sources before prediction error is interpreted as biological heterogeneity. Structural uncertainty should prompt model comparison, external evaluation, and challenge of assumptions; parameter uncertainty should be propagated; patient variability should remain conditional on the selected structure and measured covariates; and residual variability should undergo attribution rather than being treated automatically as irreducible noise. The proposed decomposition is intended as an inference and qualification architecture rather than a new statistical estimator. Its purpose is to make individual PKPD predictions more interpretable by showing what is known about the patient, what is uncertain about the model, what remains unresolved in the observations, and which uncertainty matters for the clinical decision at hand.
IJPRAS
