Designing Individualized Dosing Regimens under Pharmacokinetic Variability, Incomplete Patient Information, Uncertain Exposure–Response Relationships, and Competing Safety Objectives

Individualized dosing is frequently presented as a problem of estimating an appropriate dose from patient covariates, drug concentrations, and population pharmacokinetic or pharmacodynamic models. This framing is incomplete when patient information is missing or delayed, exposure–response relationships are uncertain, efficacy and toxicity objectives conflict, and treatment must be revised as the patient’s condition changes. This article develops an original decision-theoretic framework for organizing individualized dose selection under these conditions. The proposed architecture represents available observations, missingness, measurement quality, adherence information, and time-varying characteristics as an evolving patient-evidence state rather than a fixed covariate profile. Candidate regimens are evaluated through predictive distributions for exposure, efficacy-related response, toxicity-related response, and clinically relevant outcomes. Safety constraints are considered separately from preference-sensitive benefit–harm comparisons so that a predicted efficacy advantage cannot automatically compensate for an unacceptable probability of serious harm. Additional monitoring is treated as valuable only when the information is expected to alter the decision sufficiently to justify its burden, delay, and cost. Sequential dose adaptation incorporates predefined continuation, modification, stopping, abstention, and escalation rules. Decision-focused simulation is proposed to examine calibration, unsafe regimen selection, constraint violations, decision regret, inappropriate abstention, model misspecification, and performance under changing patient states before prospective clinical evaluation. Clinical governance requires transparent model scope, traceable data, version control, equity assessment, documented human override, and clear accountability. The framework is an original conceptual synthesis rather than a validated dosing method. Its use remains conditional on drug-specific evidence, defensible safety boundaries, externally evaluated predictive performance, prospective patient-outcome validation, and implementation studies within the intended clinical context.