Physiologically Based Pharmacokinetic Modeling Framework for Quantitative Prediction of an Herb–Drug Interaction

Abstract

Herb–drug interaction predictions remain challenging. Physiologically based pharmacokinetic (PBPK) modeling was used to improve prediction accuracy of potential herb–drug interactions using the semipurified milk thistle preparation, silibinin, as an exemplar herbal product. Interactions between silibinin constituents and the probe substrates warfarin (CYP2C9) and midazolam (CYP3A) were simulated. A low silibinin dose (160 mg/day × 14 days) was predicted to increase midazolam area under the curve (AUC) by 1%, which was corroborated with external data; a higher dose (1,650 mg/day × 7 days) was predicted to increase midazolam and (S)-warfarin AUC by 5% and 4%, respectively. A proof-of-concept clinical study confirmed minimal interaction between high-dose silibinin and both midazolam and (S)-warfarin (9 and 13% increase in AUC, respectively). Unexpectedly, (R)-warfarin AUC decreased (by 15%), but this is unlikely to be clinically important. Application of this PBPK modeling framework to other herb–drug interactions could facilitate development of guidelines for quantitative prediction of clinically relevant interactions.

Identification of Diet-Derived Constituents as Potent Inhibitors of Intestinal Glucuronidation

“…This framework centers on the evaluation of isolated constituents to ascertain the relative contribution of each constituent to the mixture, as well as to identify marker constituents that can be used to predict the likelihood and magnitude of these interactions. As an extension of previous studies focused on cytochrome P450-mediated inhibition (Brantley et al, 2010(Brantley et al, , 2013(Brantley et al, , 2014bKim et al, 2011), this framework was applied to 13 isolated constituents and 2 extracts as inhibitors of glucuronidation with a focus on gut-relevant enzymes.…”

“…Although inhibition potency toward 4-MU glucuronidation may not be predictive of the effects on enteric glucuronidation of clinically relevant substrates (Dong et al, 2012;Chengcheng et al, 2013), these dietary substances could be evaluated further as inhibitors of the glucuronidation of such substrates, including ezetimibe, MPA, and raloxifene, using a similar systematic approach. Results would prioritize for advanced modeling and simulation techniques that integrate in vitro inhibitory potency (K i ) with available clinical pharmacokinetic data to provide quantitative predictions of dietary substance-drug interaction risk (Brantley et al, 2014b). Modeling and simulation can be used to prioritize for clinical evaluation and to guide the design of clinical interaction studies, with the ultimate goal of providing conclusive evidence about the risk or safety of certain dietary substance-drug combinations.…”

“…Potent inhibition of enteric UGTs by these constituents/extracts at clinically achievable intestinal concentrations warrants their further evaluation as potential perpetrators of clinically relevant dietary substance-drug interactions via dynamic pharmacokinetic modeling and simulation techniques (http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatory Information/Guidances/ucm292362.pdf; Won et al, 2012;Brantley et al, 2014b).…”

“…Silibinin, a semipurified milk thistle extract composed of a 1:1 mixture of silybin A and silybin B , was selected previously as a model herbal product perpetrator of dietary substance-drug interactions (Brantley et al, 2013(Brantley et al, , 2014b. Microsomal intrinsic clearances of silybin A and silybin B were determined to assess the impact of inhibitor depletion on the recovery of apparent IC 50 using pooled HIMs and HLMs.…”

The potential of silymarin for the treatment of hepatic disorders

“…These observations combined with the high systemic silibinin concentrations suggested a potential warfarin-milk thistle interaction [43]. However, a proof-of-concept clinical study showed only minimal interaction between a high-dose of silibinin in combination with both midazolam (CYP3A and (S)-warfarin 9 and 13 % increase in AUC, respectively) [44].…”