Pharmacokinetic–Pharmacodynamic Network Modeling of Herb–Drug Interactions and Combination Safety - Fanlong Dong; Wayne L. Allen (International Journal of Clinical and Translational Medicine, 2026)
The concurrent use of herbal medicines and conventional pharmaceuticals has expanded globally, but systematic assessment of herb–drug interactions remains constrained by fragmented pharmacological data, heterogeneous product compositions, and limited mechanistic integration across biological scales. This article presents a system-level analysis of pharmacokinetic–pharmacodynamic network modeling as a framework for anticipating herb–drug interactions and evaluating combination safety. Rather than isolating molecular targets, the perspective treats herb–drug interactions as emergent properties of coupled absorption, distribution, metabolism, excretion, and target-response networks. The discussion examines architectural choices in constructing multilayer networks that integrate chemical constituents, membrane transporters, metabolic enzymes, regulatory pathways, and clinical phenotypes. It emphasizes structural trade-offs between mechanistic depth and computational tractability, between data-driven inference and mechanistic identifiability, and between population-level risk estimation and individualized safety prediction. Because herb–drug interaction models depend on heterogeneous data sources, the article analyzes data infrastructure requirements, metadata standards, provenance tracking, and semantic interoperability. Governance and validation challenges are discussed in relation to regulatory guidance, explainability, auditability, and post-market surveillance. Fairness and equity concerns arising from biased training data, population-specific metabolic variation, and uneven representation in herbal medicine knowledge bases are also addressed. Deployment pathways are considered within clinical decision support, pharmacovigilance, and adaptive learning systems. The article argues that robust, sustainable, and equitable network models require coordinated investment in open data, causal inference methods, model stewardship, and regulatory science. The resulting framework can inform safer polypharmacy involving complex herbal products while supporting evidence-based policy.