Counterfactual Dosing with Causal Digital Twins under Patient Heterogeneity, Disease Evolution, and Time-Varying Treatment Response Download PDF

Individualized dosing is commonly framed as the selection of a dose that best predicts exposure, biomarker response, efficacy, or toxicity for a particular patient. That framing is incomplete when treatment decisions alter subsequent physiology, disease state, adherence, monitoring, and future treatment allocation. In such settings, the clinically relevant question is not merely what outcome is likely under the observed dose, but how the same patient might evolve under alternative feasible dose histories. This article proposes a causal dose twin: a conceptual treatment-simulation model that combines longitudinal causal structure, treatment-history representation, mechanistic pharmacokinetic–pharmacodynamic and disease models, sequential patient-state updating, and safety-constrained counterfactual comparison. The proposed architecture distinguishes prediction from causal estimation, observed response from treatment effect, prescribed dose from realized exposure, and model confidence from decision-relevant uncertainty. It treats patient heterogeneity as dynamic rather than fixed and represents disease progression, adherence, organ-function change, co-interventions, and monitoring as components that can alter both future response and future treatment. Counterfactual dose strategies are therefore evaluated as trajectories rather than isolated actions. The article further argues that validation must be matched to context of use and must include causal identification, state reconstruction, pharmacological adequacy, uncertainty calibration, transportability, safety behavior, and governance. The causal dose twin is presented as an original methodological synthesis, not as an empirically validated dosing system. Its usefulness is conditional on defensible causal assumptions, adequate longitudinal data, drug- and disease-specific mechanistic knowledge, clinically meaningful constraints, and accountable human authorization. The framework provides a research structure for moving individualized dosing beyond descriptive personalization toward bounded comparison of alternative treatment histories.