Pharmacokinetic interactions between natural products (NPs) and conventional medications (prescription and nonprescription) are a longstanding but understudied problem in contemporary pharmacotherapy. Consequently, there are no established methods for selecting and prioritizing commercially available NPs to evaluate as precipitants of NP-drug interactions (NPDIs). As such, NPDI discovery remains largely a retrospective, bedside-to-bench process. This Recommended Approach, developed by the Center of Excellence for Natural Product Drug Interaction Research (NaPDI Center), describes a systematic method for selecting NPs to evaluate as precipitants of potential clinically significant pharmacokinetic NPDIs. Guided information-gathering tools were used to score, rank, and triage NPs from an initial list of 47 candidates. Triaging was based on the presence and/or absence of an NPDI identified in a clinical study (≥20% or <20% change in the object drug area under the concentration vs. time curve, respectively), as well as mechanistic and descriptive in vitro and clinical data. A qualitative decision-making tool, termed the fulcrum model, was developed and applied to 11 high-priority NPs for rigorous study of NPDI risk. Application of this approach produced a final list of five high-priority NPs, four of which are currently under investigation by the NaPDI Center.

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Quantitative prediction of herb–drug interaction risk remains challenging. A quantitative framework to assess a potential interaction was used to evaluate a mechanism not previously tested in humans. The semipurified milk thistle product, silibinin, was selected as an exemplar herbal product inhibitor of raloxifene intestinal glucuronidation. Physiologically based pharmacokinetic (PBPK) model simulations of the silibinin–raloxifene interaction predicted up to 30% increases in raloxifene area under the curve (AUC0‐inf) and maximal concentration (Cmax). Model‐informed clinical evaluation of the silibinin–raloxifene interaction indicated minimal clinical interaction liability, with observed geometric mean raloxifene AUC0‐inf and Cmax ratios lying within the predefined no effect range (0.75–1.33). Further refinement of PBPK modeling and simulation approaches will enhance confidence in predictions and facilitate generalizability to additional herb–drug combinations. This quantitative framework can be used to develop guidances to evaluate potential herb–drug interactions prospectively, providing evidenced‐based information about the risk or safety of these interactions.