Drug dosing is commonly initiated from population evidence and refined through covariates, therapeutic drug monitoring, pharmacokinetic–pharmacodynamic models, or clinician judgment. These approaches remain essential, but they do not necessarily provide a unified representation of an individual whose physiology, disease state, treatment adherence, drug exposure, therapeutic response, and toxicity risk change concurrently. This article proposes the Dose Twin, a patient-linked and time-indexed computational pharmacology decision model for reconstructing actual treatment, estimating evolving pharmacological and disease states, and comparing bounded counterfactual dose regimens. The construct integrates population priors with longitudinal patient observations while preserving explicit distinctions among measured variables, latent states, mechanistic assumptions, learned components, uncertainty, and clinical decisions. Its proposed architecture contains a treatment-history reconstruction layer, dynamic physiology and disease representations, a pharmacokinetic–pharmacodynamic or quantitative systems pharmacology core, an adherence model, a sequential updating mechanism, a counterfactual dose evaluator, safety and uncertainty gates, and a clinician authorization layer. The central contribution is not a new optimization metric or autonomous dosing algorithm, but an organizing decision model that treats individualized dosing as repeated inference under changing and incompletely observed conditions. It also prevents apparent treatment failure from being attributed automatically to altered pharmacology when missed doses, delayed administration, disease progression, measurement error, or model misspecification remain plausible explanations. The Dose Twin is presented as a conceptual and methodological contribution requiring context-specific technical verification, pharmacological validation, prospective clinical evaluation, workflow assessment, and governance. It does not establish individual causal effects, clinical benefit, regulatory acceptability, or deployment readiness. Its value lies in defining what must be represented, updated, tested, bounded, and communicated before longitudinal treatment simulation can responsibly influence model-informed precision dosing.