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Physiologically based pharmacokinetic modeling framework for quantitative prediction of an herb-drug interaction.

Brantley SJ, Gufford BT, Dua R, Fediuk DJ, Graf TN, Scarlett YV, Frederick KS, Fisher MB, Oberlies NH, Paine MF - CPT Pharmacometrics Syst Pharmacol (2014)

Bottom Line: 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.Pharmacol. (2014) 3, e107; doi:10.1038/psp.2013.69; advance online publication 26 March 2014.

View Article: PubMed Central - PubMed

Affiliation: Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

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.CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e107; doi:10.1038/psp.2013.69; advance online publication 26 March 2014.

No MeSH data available.


Base physiologically based pharmacokinetic model structure. Model structure wasmodified from the literature.30 Organ weightsand blood flows were obtained from the International Commission on RadiologicalProtection.31 Following oraladministration, drug transfer from dosing compartment to intestine is driven by the oralabsorption rate constant (ka). Drug clearance (Cl) is mediated bymetabolic processes in the intestine and liver. The pancreas and spleen were combinedinto a hybrid “organ” designated as PSP.
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fig4: Base physiologically based pharmacokinetic model structure. Model structure wasmodified from the literature.30 Organ weightsand blood flows were obtained from the International Commission on RadiologicalProtection.31 Following oraladministration, drug transfer from dosing compartment to intestine is driven by the oralabsorption rate constant (ka). Drug clearance (Cl) is mediated bymetabolic processes in the intestine and liver. The pancreas and spleen were combinedinto a hybrid “organ” designated as PSP.

Mentions: PBPK model development. The base model structure was adapted from theliterature30 (Figure4), incorporating physiologic parameters obtained from the InternationalCommission on Radiological Protection.31Warfarin partition coefficients (Kps)32 and binding parameters33were obtained from the literature (Table 3);absorption rate constants (kas) and clearance parameters wereobtained by fitting the PBPK model to previously reported plasma concentration–timeprofiles.20 The reversible inhibitionconstant (Ki) of (R)-warfarin toward CYP2C9 activity wasobtained from the literature.34 MidazolamKps and ka were obtained from theliterature30,35; intestinal and hepatic clearance parameters were extrapolatedfrom in vitro data16 asdescribed36,37 (Table 3). Silybin A and silybinB Kps were predicted from physicochemicalproperties38 using GastroPlus (version8.0; Simulations Plus, Lancaster, CA). Silibinin binding parameters were obtained fromthe literature39; clearance parameters weregenerated by fitting the PBPK model to plasma concentration–time data from hepatitisC patients receiving silymarin26 (Table 3). Silybin A and silybin B mechanism-based(KI, kinact) and reversible inhibition kineticparameters were obtained from the literature.15,16 Mechanism-based inhibition ofCYP2C9 was not considered based on a previous publication showing no IC50 shiftusing (S)-warfarin as the probe substrate.15


Physiologically based pharmacokinetic modeling framework for quantitative prediction of an herb-drug interaction.

Brantley SJ, Gufford BT, Dua R, Fediuk DJ, Graf TN, Scarlett YV, Frederick KS, Fisher MB, Oberlies NH, Paine MF - CPT Pharmacometrics Syst Pharmacol (2014)

Base physiologically based pharmacokinetic model structure. Model structure wasmodified from the literature.30 Organ weightsand blood flows were obtained from the International Commission on RadiologicalProtection.31 Following oraladministration, drug transfer from dosing compartment to intestine is driven by the oralabsorption rate constant (ka). Drug clearance (Cl) is mediated bymetabolic processes in the intestine and liver. The pancreas and spleen were combinedinto a hybrid “organ” designated as PSP.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4042458&req=5

fig4: Base physiologically based pharmacokinetic model structure. Model structure wasmodified from the literature.30 Organ weightsand blood flows were obtained from the International Commission on RadiologicalProtection.31 Following oraladministration, drug transfer from dosing compartment to intestine is driven by the oralabsorption rate constant (ka). Drug clearance (Cl) is mediated bymetabolic processes in the intestine and liver. The pancreas and spleen were combinedinto a hybrid “organ” designated as PSP.
Mentions: PBPK model development. The base model structure was adapted from theliterature30 (Figure4), incorporating physiologic parameters obtained from the InternationalCommission on Radiological Protection.31Warfarin partition coefficients (Kps)32 and binding parameters33were obtained from the literature (Table 3);absorption rate constants (kas) and clearance parameters wereobtained by fitting the PBPK model to previously reported plasma concentration–timeprofiles.20 The reversible inhibitionconstant (Ki) of (R)-warfarin toward CYP2C9 activity wasobtained from the literature.34 MidazolamKps and ka were obtained from theliterature30,35; intestinal and hepatic clearance parameters were extrapolatedfrom in vitro data16 asdescribed36,37 (Table 3). Silybin A and silybinB Kps were predicted from physicochemicalproperties38 using GastroPlus (version8.0; Simulations Plus, Lancaster, CA). Silibinin binding parameters were obtained fromthe literature39; clearance parameters weregenerated by fitting the PBPK model to plasma concentration–time data from hepatitisC patients receiving silymarin26 (Table 3). Silybin A and silybin B mechanism-based(KI, kinact) and reversible inhibition kineticparameters were obtained from the literature.15,16 Mechanism-based inhibition ofCYP2C9 was not considered based on a previous publication showing no IC50 shiftusing (S)-warfarin as the probe substrate.15

Bottom Line: 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.Pharmacol. (2014) 3, e107; doi:10.1038/psp.2013.69; advance online publication 26 March 2014.

View Article: PubMed Central - PubMed

Affiliation: Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

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.CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e107; doi:10.1038/psp.2013.69; advance online publication 26 March 2014.

No MeSH data available.