Precision Phytopharmacology: Patient Stratification, Molecular Mechanisms, Exposure Modeling, and Response Prediction for Natural Product-Based Therapeutics

Download PDF

Natural product-based therapeutics present distinctive precision-pharmacology challenges because botanical identity, phytochemical composition, biological mechanisms, systemic exposure, patient characteristics, concomitant medications, and safety liabilities may vary simultaneously. This article proposes a precision phytopharmacology framework for organizing these sources of variability without implying that natural products can presently be personalized through unvalidated biomarkers or computational predictions. The framework begins with authenticated natural product identity and reproducible phytochemical characterization, followed by structured definition of patient context and development of testable stratification hypotheses. Molecular evidence is organized across candidate biomarkers, targets, pathways, bioactivity findings, network relationships, and mechanistic plausibility. Exposure modeling integrates bioavailability, absorption, distribution, metabolism, excretion, constituent-specific pharmacokinetics, physiologically based pharmacokinetic logic, and provisional exposure–response relationships where adequate evidence exists. Response and safety predictions are treated as research outputs requiring uncertainty estimation, calibration, external validation, and assessment of herb–drug interaction risk. Personalization is therefore restricted to hypothesis generation, research prioritization, validation planning, and identification of evidence gaps. It does not constitute individualized dosing, therapeutic selection, clinical decision support, or proof of clinical utility. The principal contribution is an integrated, safety-aware architecture that connects natural product quality, patient heterogeneity, molecular mechanisms, exposure variability, response hypotheses, expert review, and explicit treatment-claim boundaries within a single translational research framework.