Herb–Drug Interaction Prediction in the AI Era: Pharmacokinetic Mechanisms, Pharmacodynamic Overlap, Patient Risk, and Clinical Safeguards

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Herb–drug interactions represent a clinically important safety challenge because herbal product composition, patient disclosure, comedication patterns, pharmacological mechanisms, and evidence quality vary substantially across real-world use. This article develops an original AI-era risk prediction framework for herb–drug interactions that integrates pharmacokinetic mechanisms, pharmacodynamic overlap, patient-specific vulnerability, evidence confidence, uncertainty, and clinical safeguards. The framework separates predicted interaction signals from mechanistic plausibility, exposure relevance, patient-level risk, validation needs, and clinical decision boundaries. Pharmacokinetic interpretation focuses on CYP enzyme modulation, transporter effects, Phase II metabolism where supported, absorption and elimination considerations, exposure plausibility, therapeutic index, and dose-context caution. Pharmacodynamic interpretation addresses additive toxicity, opposing pharmacological effects, bleeding-related concern, sedation or central nervous system overlap, cardiovascular or QT-relevant concern, glycemic effects, blood-pressure effects, and organ vulnerability. Patient-risk interpretation incorporates older age, pregnancy and lactation where relevant, hepatic and renal function, comorbidities, polypharmacy, pharmacogenomic considerations where supported, self-medication disclosure, adherence context, and narrow therapeutic index medicines. The proposed framework positions AI as a tool for risk prioritization, evidence integration, literature triage, uncertainty communication, and safeguard planning. It does not present AI-assisted prediction as a substitute for clinician, pharmacist, toxicologist, pharmacologist, or regulatory judgment, and it does not provide patient-specific medical advice.