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HERBAL MEDICINE |
From the Faculty of Pharmacy, the University of Sydney, Australia (Dr Jiang, Dr Blair, Dr McLachlan); the Department of Medicine, University of Chicago, Chicago, Illinois (Dr Jiang); the Centre for Education and Research on Ageing, Concord Repatriation General Hospital, Concord, Australia (Dr McLachlan); and Sanofi-Aventis, Sydney, Australia (Dr Blair).
Address for reprints: Professor Andrew J. McLachlan, PhD, Faculty of Pharmacy, Building A15, the University of Sydney, NSW 2006, Australia; e-mail: andrewm{at}pharm.usyd.edu.au.
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONCONCLUSIONACKNOWLEDGEMENTSREFERENCES |
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Key Words: Warfarin • anticoagulants • herb-drug interactions • St John's wort • Asian ginseng • ginkgo • ginger • population pharmacokinetic-pharmacodynamic modeling
St John's wort, Asian ginseng, ginkgo, and ginger are widely used herbal medicines that are often taken with conventional medicines.4,5 Systematic investigation of possible pharmacokinetic and pharmacodynamic (PKPD) herb-drug interactions is needed to understand the herb-drug interaction mechanisms and to help guide the quality use of herbal and conventional medicines. Mills et al6 recently conducted a systematic review of clinical trials examining interactions of St John's wort with conventional drugs, which highlighted the awareness of potentially serious herb-drug interactions and the need for well-designed trials to provide rigorous information to inform clinical decision making. However, the focus of most herb-drug interaction studies has been on the impact of herbs or their constituents on drug metabolism without assessment of the possible independent effect on pharmacodynamics. The effects of herbal medicines on warfarin have been studied by a number of researchers,7-10 and a number of mechanisms have been proposed. However, the possibility that herbal medicines might have a direct effect on the pharmacodynamics of warfarin has not been systematically investigated. In the case of warfarin, theoretical considerations suggest that herbs like St John's wort may potentially affect the synthesis or catabolism of clotting factors or Ginkgo biloba might have an independent effect on the blood coagulation system.5,11
Pharmacokinetic-pharmacodynamic models have proven to be useful tools to elucidate and predict the pharmacologic behavior of medicines12-15 and offer possible insights into the mechanisms of drug-drug interactions.11 Warfarin PK-PD models have been extensively studied16 and have been reviewed by Holford.17 A population PK-PD modeling approach allows investigation of the effect of herbal medicines on the PK-PD behavior of warfarin that presents advantages for understanding the mechanisms involved in herb-drug interactions.
The aims of the present study were to investigate the PK-PD mechanisms of herb-drug interactions with warfarin using population PK-PD modeling and to evaluate the clinical implications of herbwarfarin interactions using a simulation approach.
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METHODS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONACKNOWLEDGEMENTSREFERENCES |
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Population PK-PD Model Development and Selection
The effect of the 4 herbal medicines on warfarin pharmacokinetics and anticoagulant response was investigated with the use of concentrations of S-warfarin and prothrombin complex activity (PCA) derived from subject INR.16 International normalized ratios for prothrombin time data were converted to PCA% according to equation 1,20 and the pharmacodynamic data were fitted to the indirect pharmacodynamic model and linked warfarin dose regimen and S-warfarin concentrations to PCA% using equation 2, which is modified from Pitsiu et al20 and Sharma and Jusko.15
 | (1) |
 | (2) |
where a = 80.65 and b = 0.18 are constants determined using serial dilutions of normal plasma, CS is the S-warfarin concentration, t
,PCA, is the half-life of the PCA, C50,S is the concentration of S-warfarin that produces 50% inhibition of PCA, PCA0 is the PCA in the absence of warfarin, and 
is the shape factor in the concentration-response relationship.
Because the anticoagulant effect of racemic warfarin is predominately contributed by S-warfarin, R-warfarin was not considered in the combined PK-PD modeling analysis.16 Total plasma concentration of S-warfarin was used in this PK-PD population modeling approach, because coadministration of these 4 herbal medicines were previously shown not to affect unbound fraction of S-warfarin.7,18
A nonlinear mixed effect modeling approach was employed to analyze the PK-PD data and to predict PK-PD data. The first-order estimation method implemented in NONMEM version 5 level 1.1 (Globomax, Hanover, Md)21 was used for all tests during model development, whereas the first-order conditional estimation (FOCE) with interaction method was used to refine the final model and provide definitive parameter estimates. Wings for NONMEM22 was used to aid model building and conduct bootstrapping. A 3-step modeling building approach was used. In the first step, a basic structural model using first-order absorption and elimination was evaluated without incorporating any covariates except body weight. Allometric scaling23 was employed for inclusion of body weight as a covariate on apparent clearance (CL/F), apparent volume of distribution in the central compartment (VC/F), clearance between the central and peripheral compartments (Q/F) and apparent volume of distribution for the peripheral compartment (VP/F) in the base model. Pharmacokinetic-pharmacodynamic data were simultaneously fitted to identify the structural part of the model. In the second step, the possible impact of covariates including age, height, ethnicity, and pretreatment with herbal medicines on CL/F and VC/F were systematically evaluated using a stepwise inclusion and backward elimination strategy.24 Categorical covariates, including ethnicity and pretreatment with herbal medicines, were modeled using indicator variables.21 Continuous covariates, including age, body weight, and height, were centered to their respective median values, and their influence on model parameters were evaluated using multiplicative models.21 The effect of coadministration of the 4 herbal medicines on the pharmacodynamic parameters including t,PCA, C50,S, and PCA0 was systematically investigated. In the third step, the full model was refined to establish the final model by removing covariates that had a less significant effect on the PK-PD parameters during backward elimination.
In the structural model development, a decrease in the NONMEM-generated objective function value (OFV) 
6.64 units (P 
.01, df = 1) was used as an inclusion criterion. For the covariate search process, a reduction of the OFV 3.84 units (P .05, df = 1) for forward inclusion and a reduction of OFV 6.64 units (P .01, df = 1) for backward elimination was employed. Furthermore, physiological relevance, decrease in the between-subject variability, and improvement in the diagnostic plots were also important considerations in the model-development process. Graphical displays for model building were based on individual goodness-of-fit plots, goodness-of-fit plots of predicted values versus observed values, and visual predictive check. The visual predictive check was conducted by simulating data for 100 "clinical trials" based on the same study design and using the estimated population parameters; the 5th, 50th, and 95th percentiles were reported. In this study a greater than 20% difference in PK-PD parameters between control and treatment with herbal medicine group was considered to be of clinical significance7,18 and likely to reflect the need for a change in warfarin dose.
The S-warfarin concentration-time data were described by a 2-compartment pharmacokinetic model with first-order absorption and elimination. The full PK-PD model contained the following 8 parameters: absorption rate constant (ka), CL/F, VC/F, Q/F, VP/F, t,PCA, PCA0, and C50,S. The value of PCA0 is assumed to be equal to maximum effect of warfarin (Emax). Despite the availability of relatively frequent sampling concentration-time data, preliminary studies found that reliable estimates of ka for S-warfarin could not be obtained, so this parameter was fixed at 3.15 h-1. Warfarin was administered orally in these 2 studies; hence, the absolute bioavailability (F) could not be independently estimated, and the typical values of CL/F, VC/F, Q/F, and VP/F were reported.
Pharmacokinetic-pharmacodynamic parameters were assumed to be lognormally distributed. Individual values of ka, CL/F, VC/F, Q/F, VP/F, t,PCA, C50,S, and PCA0 were modeled as their respective population mean multiplied by the exponentials of their individual random effect as their between-subject variability. Between-occasion variability was estimated for CL/F and VC/F in the crossover study design. An additive residual variability was modeled to describe the observations of PCA%, whereas a combined residual variability was used to describe the observations of S-warfarin concentration.
Simulations and Evaluation
One hundred trials with 12 subjects per trial were simulated using the NONMEM-derived model population PK-PD parameters for treatment with warfarin only, warfarin with St John's wort, and warfarin with Asian ginseng, respectively. The typical S-warfarin concentration-time and INR-time profiles were visualized to examine the effect of herbal medicines on the pharmacokinetics and pharmacodynamics of warfarin using a dose regimen of 7.5 mg warfarin daily for 7 days. An analysis of 500 bootstrapping runs from the final model using the first-order method was used to obtain 95% confidence intervals (CIs) for selected model parameters.
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RESULTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONACKNOWLEDGEMENTSREFERENCES |
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OFV = -158.361). The inclusion of a lag-time in the absorption phase for S-warfarin in the pharmacokinetic model had little improvement on model fitting and thus was not included in the final model.
The typical values of PK-PD parameters including ka, CL/F, VC/F, VP/F, Q/F, t,PCA, C50,S, PCA0, and their corresponding between-subject variability, between-occasion variability, and residual error estimated from the final population combined PK-PD model for S-warfarin are shown in Table I. During the model-building process, the variance for ka was estimated and then fixed to the estimated value to achieve a successful covariance. Moderate variability was found in CL/F (between-subject variability, 21%; between-occasion variability, 10%), VC/F (between-subject variability, 14%; between-occasion variability, 13%), t,PCA (between-subject variability, 7%), whereas high variability was found in ka (between-subject variability, 59%) and C50,S (between-subject variability, 59%). Considering the possible influence of body weight, inclusion of covariate effects from demographic characteristics including height, ethnicity, and age on CL/F and VC/F of S-warfarin did not significantly improve the performance of the PK-PD model of S-warfarin. Figures 1, 2, 3 and 4 show the performance of the final combined population PK-PD model with respect to the S-warfarin concentration and PCA% data. These figures indicate that the final model provided a good description of the PK-PD data for warfarin.
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Interactions With Herbal Medicines
Inclusion of St John's wort treatment as a covariate in the model resulted in a significant reduction of the OFV (OFV = -33.233) and accounted for 5% variability in CL/F of S-warfarin. Consideration of the effect of Asian ginseng treatment in the model also caused a significant reduction of the objective function value (OFV = -17.857) but accounted for only 2% variability in the CL/F for S-warfarin. Compared to the control group, coadministration of St John's wort accounted for a 39% increase in CL/F of S-warfarin, whereas Asian ginseng accounted for a 14% increase in CL/F of S-warfarin. Treatment with St John's wort and Asian ginseng were included as significant covariates on the CL/F of S-warfarin in the final model. Allometric scaling was employed to describe the effect of body weight on the CL/F and VC/F of S-warfarin. The final covariate relationships are described below:
 |
where CLS-warfarin/F and VC,S-warfarin/F are typical values for S-warfarin, SJW = 1 during St John's wort treatment and 0 otherwise; GS = 1 during Asian ginseng treatment and 0 otherwise; WT represents body weight in kilograms. The value of 70 kg is included as the typical body weight.
The inclusion of coadministration with ginkgo or ginger as covariates in the model had little effect on CL/F and VC/F of S-warfarin. Coadministration of these 4 herbal medicines with warfarin was not found to significantly influence the pharmacodynamic parameters of PCA0, t,PCA, and C50,S in the population combined PK-PD model.
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONACKNOWLEDGEMENTSREFERENCES |
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The population mean values of CL/F, VC/F, VP/F, Q/F, t,PCA, PCA0, and C50,S for warfarin have been estimated for these 24 healthy male subjects using population PK-PD modeling. The population mean values of CL/F of S-warfarin (191 mL/h), VC/F of S-warfarin (6180 mL), C50,S (393 ng/mL), and t,PCA (18 hours) reported in this study are in close agreement with previous values reported in the literature.16,20 An analysis of 500 bootstrapping runs from the final model using the first-order method found that 90% of runs successfully minimized. Robust 95% CIs were obtained for model parameters including the effect of St John's wort (95% CI, 1.22-1.45), ginseng (95% CI, 1.10-1.26), ginkgo (95% CI, 0.96-1.09), and ginger (95% CI, 0.85-1.08) on S-warfarin CL/F. These data and other diagnostics presented in this study support the robust nature of the model and the final estimates.
Drug interactions with warfarin can be caused by various mechanisms, which are mainly classified as pharmacokinetic interactions and pharmacodynamic interactions.13 For example, rifampicin25 has been reported to induce the CL/F of warfarin, whereas voriconazole26 has been demonstrated to inhibit the CL/F of warfarin. On the other hand, paracetamol27,28 appears to have a direct interaction with clotting factor kinetics and causes a pharmacodynamic interaction with warfarin. This PK-PD modeling approach employed in this study allows the identification of possible pharmacokinetic and pharmacodynamic mechanisms involved in the herb-drug interactions with warfarin. Because the anticoagulant effect of racemic warfarin is predominantly contributed by S-warfarin, the pharmacokinetics of R-warfarin were not investigated in this study. S-warfarin is a substrate of CYP2C929 and highly protein bound. In our previous studies, we found that the unbound fraction of warfarin was not changed by the treatment of herbal medicines.7,18 The results of the present study support the finding that St John's wort induces the metabolism of S-warfarin by induction of the metabolic activity of CYP2C9.30 The present findings using population PK-PD modeling are in agreement with the results from the noncompartmental approach7 and other reports in the literature.6 An increasing number of clinical studies have investigated the interactions between St John's wort and conventional medicines. Mills et al6 recently conducted a systematic review of St John's wort interaction studies that involved a critical assessment of the methodological quality of these clinical trials. The nature of the study design, the use of appropriate control subjects, the use of a placebo, justification of the sample size, and assessment of the quality of the herbal medicine products containing St John's wort were identified as critical factors and evaluated across different published clinical trials involving St John's wort interaction. Mills et al6 suggest that the interpretation of the findings of herb-drug interaction studies should account for possible differences in clinical trial quality. Nevertheless, the authors concluded that clinicians and patients should be aware of possible decreases in the systemic "bioavailability" of conventional drugs when taken concomitantly with St John's wort.
This study found that coadministration of Asian ginseng appeared to induce the CL/F of S-warfarin. The ratio (and SE) of CL/F of S-warfarin during warfarin with Asian ginseng treatment to warfarin was 1.14 ± 0.04 and represents a statistically significant difference, but the clinical significance of this observation is less clear (<20%). A recent herb-drug interaction clinical trial by Yuan et al8 studied the effect of American ginseng (Panax quinquefolius) on warfarin response. American and Asian ginseng (Panax ginseng) come from different species but contain similar constituent ginsenosides.31 These researchers concluded that American ginseng reduced the effects of warfarin. The study by Yuan et al8 did meet some of the criteria deemed necessary for a herb-drug interaction clinical trial outlined by Mills et al6 and Coxeter et al32 in that the researchers employed a randomized, double-blind trial and analyzed the quality of the herbal medicine product employed in the study. However, the study used a parallel treatment design whereby 12 subjects were allocated to the treatment arm (receiving 1 g American ginseng) and 8 subjects were randomized to the placebo group. The power of the study was not addressed, and the variability in the primary end point (ie, the difference in the area under the concentration-time curve [AUC] of INR between American ginseng and placebo) was very wide, with a mean difference of -0.43 (-1.00 to -0.09). Furthermore, Yuan et al8 attempted to characterize the effect of American ginseng on warfarin pharmacokinetics but did not use adequately timed blood samples to measure AUC or a stereoselective assay to quantitate warfarin enantiomer concentrations. Unfortunately, these limitations mean that the mechanism of the interaction between American ginseng and warfarin cannot be evaluated and the significance of the results is difficult to assess.
Coadministration of Ginkgo biloba has been widely suspected to interact with warfarin in numerous review articles33,34 based mainly on extensive in vitro data5,35 and the theoretical potential for a pharmacodynamic interaction. The in vitro data have been supported by Kudolo et al36 who found a significant decrease in platelet aggregation in healthy subjects and type 2 diabetic patients taking a Ginkgo biloba extract.36 However, controlled in vivo studies using EGb761, which is the most widely investigated standardized extract of Ginkgo biloba, have found no significant independent effect on platelet function and coagulation37 or additive effects on coagulation parameters in patients receiving warfarin.9 The discrepancy between the large body of in vitro data and the lack of an interaction based on in vivo studies is likely to be a consequence of the pharmacokinetics of the constituents of ginkgo.38,39 This is further complicated by the lack of equivalence observed between different herbal medicine products containing ginkgo.36 Further study about ginkgo-warfarin interactions in patients is needed to clarify the clinical relevance of this interaction in patients receiving warfarin.
The present analysis reports the between-occasion variability of warfarin pharmacokinetic parameters with data collected using a crossover study design. The relatively small between-occasion variability in CL/F (CV% = 10%) and VC/F (CV% = 13 %) were found in the crossover study design, which supports the lack of a carry-over effect in these studies. This study also estimated the between-subject variability for warfarin ka, CL/F, VC/F, VP/F, Q/F, PCA0, C50,S, and t,PCA using the population PK-PD modeling approach. The between-subject variability (expressed as %CV) was relatively low for CL/F (21%) and VC/F (14 %) of S-warfarin but relatively large for the pharmacodynamic parameter C50,S (59 %). Intrinsic factors might account for some of the observed between-subject variability observed in these parameters including genetic polymorphism in drug-metabolizing enzymes and the clotting system, and variability in vitamin K concentrations. Numerous studies have demonstrated that polymorphisms in CYP2C9 have a significant impact on the metabolism of S-warfarin. Two known allelic variants CYP2C9*2 and CYP2C9*3 display impaired hydroxylation of S-warfarin compared to wild-type CPY2C9*1.40 Furthermore, Shikata et al41 reported that polymorphisms occur in factors II, VII, IX, and X; proteins S and C; and -glutamyl carboxylase, which accounts for 50% of the variability in dose. Further studies in a larger population are needed to identify the significance of the polymorphisms in CYP2C9 and clotting factors as covariates in the PK-PD model and how these might have an impact on the severity and significance of drug interactions with warfarin.
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CONCLUSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONACKNOWLEDGEMENTSREFERENCES |
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ACKNOWLEDGEMENTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONACKNOWLEDGEMENTSREFERENCES |
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Finanacial disclosure: This project was supported by the National Health and Medical Research Council (NHMRC).
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REFERENCES |
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