Computational Safety Intelligence for Herbal Medicines: Predicting Toxicity, Herb–Drug Interactions, Dose Sensitivity, and Patient-Specific Risk

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Herbal medicines are widely used in clinical and community settings, yet their safety assessment remains challenging because herbal products may differ in botanical identity, preparation method, chemical composition, exposure profile, and patterns of concurrent use with conventional medicines. This original safety framework article proposes a computational safety intelligence approach for herbal medicines that integrates product identity, chemical characterization, toxicity prediction, herb–drug interaction mapping, dose and exposure interpretation, patient-specific vulnerability, evidence grading, validation planning, pharmacovigilance feedback, and clinical or regulatory decision boundaries. The framework distinguishes predicted toxicity alerts from experimentally observed toxicity, clinical adverse events, pharmacovigilance signals, mechanistic plausibility, exposure relevance, and regulatory actionability. Toxicity prediction is positioned as a prioritization tool for identifying toxicophore alerts, ADMET concerns, organ toxicity hypotheses, and validation needs. Herb–drug interaction assessment is framed through pharmacokinetic mechanisms, pharmacodynamic overlap, transporter and CYP involvement, polypharmacy context, and exposure plausibility. Dose sensitivity and patient-specific risk are treated as interpretation filters rather than universal risk thresholds. The proposed framework contributes a structured safety-intelligence model for supporting clinicians, toxicologists, pharmacologists, regulators, and researchers while emphasizing that computational predictions require cautious interpretation, rigorous validation, and qualified professional judgment.