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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.


Geometric mean concentration–time profile of (a) warfarin, (b)midazolam, and (c) silibinin in 12 healthy volunteers following a 10 mgoral dose of warfarin or 5 mg oral dose of midazolam given alone (open symbols)or following a 7-day treatment with silibinin (solid symbols). Lines in a andb denote physiologically based pharmacokinetic (PBPK) model simulations of theconcentration–time profiles when the probe substrates were given alone (black) orwith silibinin (green). Blue and orange lines in c denote PBPK model simulationsof the concentration–time profiles of silybin A and silybin B, respectively.Symbols and error bars denote observed geometric means and upper limits of the 90%confidence interval, respectively.
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fig2: Geometric mean concentration–time profile of (a) warfarin, (b)midazolam, and (c) silibinin in 12 healthy volunteers following a 10 mgoral dose of warfarin or 5 mg oral dose of midazolam given alone (open symbols)or following a 7-day treatment with silibinin (solid symbols). Lines in a andb denote physiologically based pharmacokinetic (PBPK) model simulations of theconcentration–time profiles when the probe substrates were given alone (black) orwith silibinin (green). Blue and orange lines in c denote PBPK model simulationsof the concentration–time profiles of silybin A and silybin B, respectively.Symbols and error bars denote observed geometric means and upper limits of the 90%confidence interval, respectively.

Mentions: Simulations of the silibinin–warfarin interaction with a higher dose of silibinin(1,650 mg/day, or 720 mg silybin A plus 930 mg silybin B/day;see below), assuming reversible CYP2C9 inhibition only, predicted negligible changes(<5%) in all pharmacokinetic outcomes (Figure2a; Table 2). Simulations of thehigh-dose silibinin–midazolam interaction assuming reversible CYP3A inhibitionpredicted no change in midazolam t1/2 and ≤5% increase in bothCmax and AUC (Figure 2b;Table 2). Simulations assuming mechanism-basedCYP3A inhibition predicted a 2-, 5-, and 1.5-fold increase in Cmax,AUC, and t1/2, respectively (Table2).


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)

Geometric mean concentration–time profile of (a) warfarin, (b)midazolam, and (c) silibinin in 12 healthy volunteers following a 10 mgoral dose of warfarin or 5 mg oral dose of midazolam given alone (open symbols)or following a 7-day treatment with silibinin (solid symbols). Lines in a andb denote physiologically based pharmacokinetic (PBPK) model simulations of theconcentration–time profiles when the probe substrates were given alone (black) orwith silibinin (green). Blue and orange lines in c denote PBPK model simulationsof the concentration–time profiles of silybin A and silybin B, respectively.Symbols and error bars denote observed geometric means and upper limits of the 90%confidence interval, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Geometric mean concentration–time profile of (a) warfarin, (b)midazolam, and (c) silibinin in 12 healthy volunteers following a 10 mgoral dose of warfarin or 5 mg oral dose of midazolam given alone (open symbols)or following a 7-day treatment with silibinin (solid symbols). Lines in a andb denote physiologically based pharmacokinetic (PBPK) model simulations of theconcentration–time profiles when the probe substrates were given alone (black) orwith silibinin (green). Blue and orange lines in c denote PBPK model simulationsof the concentration–time profiles of silybin A and silybin B, respectively.Symbols and error bars denote observed geometric means and upper limits of the 90%confidence interval, respectively.
Mentions: Simulations of the silibinin–warfarin interaction with a higher dose of silibinin(1,650 mg/day, or 720 mg silybin A plus 930 mg silybin B/day;see below), assuming reversible CYP2C9 inhibition only, predicted negligible changes(<5%) in all pharmacokinetic outcomes (Figure2a; Table 2). Simulations of thehigh-dose silibinin–midazolam interaction assuming reversible CYP3A inhibitionpredicted no change in midazolam t1/2 and ≤5% increase in bothCmax and AUC (Figure 2b;Table 2). Simulations assuming mechanism-basedCYP3A inhibition predicted a 2-, 5-, and 1.5-fold increase in Cmax,AUC, and t1/2, respectively (Table2).

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.