Digital Twins of Natural Product Pharmacology: Modeling Compound Exposure, Target Engagement, Pathway Response, Safety Risk, and Therapeutic Outcomes

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Natural product pharmacology presents a distinctive modeling challenge because botanical materials and derived preparations may contain multiple constituents with variable composition, uncertain bioavailability, overlapping targets, pathway-level effects, and context-dependent safety liabilities. This article proposes a conceptual digital twin framework that connects authenticated natural product identity with compound exposure, target engagement, pathway response, safety-risk interpretation, and bounded therapeutic outcome simulation. The framework begins with botanical provenance, phytochemical composition, chemical identity, and evidence quality before incorporating absorption, distribution, metabolism, excretion, physiologically based pharmacokinetic logic where supported, population variability, and pharmacokinetic–pharmacodynamic relationships. Target engagement is represented through graded confidence derived from binding, interaction, proteomic, functional, and attainable-exposure evidence rather than computational prediction alone. Pathway-response modeling integrates network and systems pharmacology with disease or phenotype context, while safety assessment incorporates ADMET evidence, toxicity findings, herb–drug interaction mechanisms, and pharmacovigilance signals. Virtual patient and virtual population logic may support scenario analysis and research prioritization, but simulated therapeutic outcomes remain hypotheses rather than demonstrations of efficacy, safety, clinical utility, or treatment benefit. Uncertainty quantification, expert review, internal and external validation planning, experimental confirmation, feedback updating, and a no-clinical-claim boundary are therefore treated as integral architectural components. The principal contribution is a staged and evidence-bounded digital twin architecture designed to strengthen mechanistic hypothesis generation and translational planning in natural product research without replacing experimental pharmacology, toxicology, clinical investigation, or accountable professional judgment.