The Pharmacogenomic Digital Twin for Counterfactual Therapy Selection under Genotype, Phenoconversion, Polypharmacy, Disease Evolution, and Changing Treatment Goals

Pharmacogenomic decisions are commonly anchored to inherited variation, yet the functional consequences of genotype may change as medications, inflammation, organ function, disease severity, adherence, and treatment priorities evolve. This article proposes a Pharmacogenomic Digital Twin for Counterfactual Therapy Selection as an original, non-empirical architecture for representing these interacting determinants over time. The proposed twin separates relatively stable genomic information from time-varying functional phenotype, medication exposure, disease state, clinical observations, and treatment goals. It combines semantic harmonization, genotype-to-phenotype interpretation, phenoconversion assessment, pharmacokinetic and pharmacodynamic state estimation, counterfactual treatment simulation, constrained benefit–risk comparison, uncertainty characterization, and governed clinical updating. Competing therapeutic objectives are represented explicitly rather than collapsed into a single universal utility, allowing efficacy, safety, treatment burden, urgency, and patient preferences to influence conditional comparisons while preserving non-negotiable safety constraints. New concentrations, biomarkers, adverse effects, medication changes, and disease observations may update the estimated patient state, but population-model modification remains subject to separate change control. The architecture produces conditional treatment trajectories, uncertainty statements, requests for additional information, or abstention rather than autonomous prescriptions. Its original contribution is the integration of pharmacogenomic interpretation with longitudinal state updating and counterfactual comparison within one traceable decision framework. Validation would require component verification, temporal and external validation, subgroup calibration, causal assessment, workflow testing, prospective silent-mode evaluation, and postimplementation monitoring. Principal boundaries include incomplete haplotype inference, phenoconversion uncertainty, unobserved adherence, model misspecification, confounding, limited transportability, changing patient preferences, automation bias, and inequitable data or service access. The proposed architecture therefore establishes a testable research framework, not a validated clinical system.