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DRUG INTERACTIONS |
From Clinical Pharmacology (Dr Zhou, Dr Mayer) and Clinical R&D (Dr Wajdula, Dr Fatenejad), Wyeth Research, Collegeville, Pennsylvania. Part of the results in this manuscript was presented at the annual meeting of the American College of Rheumatology (ACR), Orlando, Florida, October 2003.
Address for reprints: Honghui Zhou, PhD, FCP, Clinical Pharmacology, Wyeth Research, 500 Arcola Road, Collegeville, PA 19426.
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Key Words: Methotrexate • etanercept • NONMEM • drug interaction
) in the pathogenesis of RA, and inhibition of TNF activity has been shown to reduce or ameliorate arthritic symptoms and joint injury in animal models and in clinical trials. TNF is a naturally occurring cytokine that is thought to play a central role in the pathogenesis of rheumatoid arthritis.5 Etanercept is a fully human, soluble, dimeric, fusion protein consisting of 2 copies of the extracellular ligand-binding portion of humanized TNF p75 receptor linked to the constant (Fc) portion of human IgG1. Etanercept binds to TNF and lymphotoxin (LT) with high affinity and has proven highly efficacious in animal models and human rheumatic disease.5,6 Etanercept is slowly absorbed from the site of subcutaneous injection. The absolute bioavailability of etanercept was 58% in healthy subjects who received subcutaneous etanercept.7 It is also slowly cleared from the body, with an elimination half-life (t1/2) of 70 to 100 hours and a mean body clearance of 0.066 L/h in patients with RA.8 In children with polyarticular-course juvenile RA after < 18 weeks of treatment with subcutaneous doses of 0.4 mg/kg twice weekly, the clearance of etanercept may be slightly reduced in children ages 4 to 8 years.9 No clinically relevant interactions between etanercept and warfarin10 or between etanercept and digoxin11 were observed in healthy subjects.
The pharmacokinetics of methotrexate have been well described in patients with RA.12 Methotrexate is widely recognized to be one of the most effective oral drugs in current use for the treatment of RA. At a weekly dose of 15 mg, methotrexate is well absorbed, with a mean bioavailability of approximately 60%.13 The plasma half-life is generally between 3 and 10 hours but can vary greatly among patients. Renal elimination is the primary route of excretion for methotrexate. Greater than 80% of the oral dose is excreted in the urine. Renal excretion is by glomerular secretion and active tubular transport.14 Impaired renal transport or concurrent medication with drugs that also undergo tubular secretion can increase serum methotrexate concentrations. Methotrexate is not metabolized by cytochrome P450-dependent pathways but undergoes hepatic and extrahepatic metabolism to polyglutamated forms via folylpolyglutamate synthetase. A small amount of methotrexate is metabolized to 7-hydroxymethotrexate via aldehyde oxidase15 and/or xanthine oxidase.16 Conversely, methotrexate is not an inhibitor of CYP3A4 activity in human liver microsomes.17
Methotrexate is frequently used with anti-TNF biologics in the treatment of RA. It has been shown that the concurrent administration of methotrexate could decrease the clearance of 2 monoclonal antibodies specific for TNF.18-21 However, the mechanisms for the decreased clearance of both infliximab (REMICADE®) and adalimumab (HUMIRA®) with concurrent methotrexate administration are not known. The effect of the concurrent administration of methotrexate on the pharmacokinetics of etanercept has not been formally assessed in the same clinical study. The combination of etanercept and methotrexate in this study was found to be significantly better in reducing disease activity, improving functional disability, and retarding radiographic progression compared with methotrexate or etanercept alone.22 To optimize the combination therapy of etanercept and methotrexate, it is crucial to understand if the concomitant administration of methotrexate can alter the pharmacokinetics of etanercept.
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METHODS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Population Pharmacokinetic Model Building
Serum etanercept concentrations were analyzed for samples obtained at week 24 from 50 randomly selected patients each in the etanercept-alone and the combination groups. The sample size of 50 per group was determined based on the power calculation assuming parallel design and percent intersubject variability in etanercept pharmacokinetics. To detect a 25% difference ( = 0.05) in etanercept systemic exposures between treatments with 80% power, a total of 88 patients (44 per treatment) would be needed. Blood samples for etanercept serum pharmacokinetics were drawn at baseline and the week 24 visit. The dose of etanercept was administered at various times in relation to the time of blood sample collection and could vary from several hours up to 4 days. The actual sampling time for 2 of the 50 patients in the etanercept-alone group was not recorded, and their concentration data were not included for the pharmacokinetic assessment. The demographics of the 48 patients with RA in the etanercept-alone group and the 50 patients with RA in combination group are summarized in Table I.
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As shown in Table I, the patients in the etanercept-alone group had demographic characteristics comparable with the patients in the combination group. The median and maximal dose of oral methotrexate taken was 20 mg.
Given the variable sample collection times elapsed from the last dose at week 24, the serum concentrations reflected the range of steady-state trough and peak etanercept serum concentrations. To assess the effect of concomitant methotrexate administration on the pharmacokinetics of etanercept, appropriate pharmacokinetic parameters (steady-state trough concentration and clearance) of etanercept in the patients with RA in both etanercept-treated groups were estimated.
Initially, a population pharmacokinetic modeling using the computer program NONMEM (Double Precision, Version V) was attempted on the etanercept serum concentration-time data in the patients with RA in the current study. Given the sparse nature of the concentration data, only a 1-compartment linear model was tested, but as anticipated, the current data did not support reasonable pharmacokinetic parameter estimation for etanercept. Therefore, to obtain reliable individual pharmacokinetic parameter estimates for etanercept in the present study, etanercept data obtained from prior clinical studies were used to build a robust population structural pharmacokinetic model. The model was then used to obtain Bayesian-estimated individual parameters for etanercept in the patients with RA in the current study. To achieve this goal, the analysis was performed in a stepwise fashion.
Base Model Development
A base population pharmacokinetic model was built based on the combined database for etanercept serum concentration-time data from 53 healthy subjects and 212 patients with RA receiving different dose regimens via 2 different administration routes (subcutaneous and intravenous) in 10 prior clinical studies. A total of 3181 previously obtained etanercept serum concentrations were included for population model building and validation. Table II lists the demographic and subject characteristics for the subjects included in the population pharmacokinetic database.
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We compared 1-, 2-, and 3-compartment pharmacokinetic models without any covariates. The optimal structural model was selected at this stage.
Covariate Model Development
The apparent influence of subject covariates on etanercept disposition was analyzed by use of the individual Bayesian pharmacokinetic parameter estimates. Continuous (body weight, age) and categorical (sex, race) variables were plotted against the individual pharmacokinetic parameters and examined visually. Covariates selected by this screening were subsequently incorporated one by one into the population model and evaluated by their influence on the objective function (OF).
The optimal pharmacokinetic model was chosen based on goodness of fit, population-predicted concentrations versus observed concentrations, decreases in intersubject variances, and the value of the OF. The goodness of fit of each NONMEM run was assessed by the examination of scatterplots of predicted versus measured etanercept concentrations and weighted residuals. The OF was used for hypothesis testing to discriminate among alternative hierarchical models. The influence of a certain covariate was considered significant only if the difference in the OF between 2 hierarchical models was greater than 11 (P < .001, 1 degree of freedom), which is more rigorous than that recommended in the guidance for population pharmacokinetics issued by the Food and Drug Administration (FDA) in 1999.
The influence of demographic covariates on the pharmacokinetics of etanercept was explored with the use of locally weighted regression smoothing (loess regression) and by conventional linear regression, respectively, to test for nonlinear and linear trends in continuous covariates. A 2-tailed unpaired Student t test was used to assess dichotomous covariates. After a thorough evaluation of the potential covariates in the population pharmacokinetic model, all significant covariates were included, and a final covariate population pharmacokinetic model was established. The predictive performance of the final model was examined by both data-splitting and bootstrap validation approaches.
Assessment of Potential Drug Interaction
Bayesian-derived individual pharmacokinetic parameters in patients with RA in the current study were then computed based on the final covariate population pharmacokinetic model. The effect of the concurrent administration of methotrexate on the pharmacokinetics of etanercept was evaluated by comparing the individual Bayesian-predicted pharmacokinetic parameters (CL, steady-state trough concentration) in patients receiving a combination of methotrexate and etanercept with those in patients receiving etanercept alone. Statistical bioequivalence criteria (90% confidence interval [CI] method) were also used to test the potential effect of the concomitant administration of methotrexate on the pharmacokinetics of etanercept.
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RESULTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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OF = -940; P < .0001), whereas analysis on the basis of a 3-compartment model did not improve the OF any further. Therefore, the 2-compartment model was selected for further model building. The results for the base 2-compartment population model indicated that etanercept has slow absorption (typical ka = 0.0436 1/h; percent coefficient of variation [CV%] = 9.2%) and slow clearance (typical CL = 0.0625 L/h; CV% = 7.8%) after subcutaneous etanercept administration. The volumes of distribution in the central compartment (typical Vc = 6.65 L; CV% = 9.6%) and in the peripheral compartment (typical Vp = 2.44 L; CV% = 14.8%) suggested that etanercept's distribution in the human body is mostly intravascular. An additive plus proportional model best described the intraindividual residual variability, values of which were 49.0% (CV% = 4.2%) plus 3.4 ng/mL (CV% = 27.4%).
Covariate Model Development
Covariate effects were thoroughly investigated, and an optimum population pharmacokinetic model for etanercept was selected that comprised the following covariate effects:
Age on clearance. Age (< 17 years) was found to be an important covariate on the clearance (CL) of etanercept. A positive correlation between age and clearance was observed when age was younger than 17 years. However, the dependence of CL on age was no longer apparent when age was 17 years and older.
Body weight on clearance. A positive correlation was observed for the dependence of CL on body weight when body weight was less than 60 kg.
Race on volume of distribution in the central compartment (Vc). Race was also identified as an important covariate on Vc. White subjects had a larger Vc than nonwhite subjects. Nevertheless, when Vc was corrected by the respective body weight, the difference in Vc between white and nonwhite subjects became less apparent.
RA status on absorption rate constant (ka). The presence of RA was also found to have a significant impact on ka and therefore was also incorporated into the final model. It was included to account for the difference in ka between the patients with RA and healthy subjects, although its clinical relevance is considered limited.
Validation of the final model. Two validation methods were used to examine the predictive performance of the final model: data splitting and bootstrapping.
The NONMEM data set, based on the 10 prior clinical studies, was randomly split into 2 data sets: an index data set and a validation data set. The index data set consisted of about two thirds of the original data set, whereas the validation data set was one third of the original data set. The index-derived typical population pharmacokinetic parameters were fixed and used to estimate the Bayesian individual CL, Vc, ka, and F in the validation data set. Small bias (median model prediction error: 4.5% for CL, 2.4% for Vc, -1.0% for ka, and 6.8% for F) and good precision (median of the absolute model prediction error: 7.4% for CL, 8.6% for Vc, 6.5% for ka, and 28.6% for F) were observed for the validation data set.
In addition to the data-splitting approach, a bootstrap approach was also used to further validate the developed model. The bootstrapping method is a validation tool for estimating generalization error based on a "resampling" technique. The final model was fitted to the 1000 bootstrapped samples to evaluate its stability and performance. The typical value of the parameter estimates of the final model all fell within the 5th and 95th percentiles of the respective ones by bootstrapping, indicating that the predictive performance of the final population pharmacokinetic model of etanercept was sufficient (Table III).
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Goodness of Fit of the Final Model
Upon visual inspection (Figure 1), no significant bias was observed, which suggested that the model was appropriately developed. At low concentrations, the model tended to slightly overestimate etanercept concentrations. One possible reason for the overprediction could be due to the less accurate sampling and/or dosing time recording because many of those samples were collected more than 120 days after the last etanercept administration. Another possible reason for the overprediction could be due to the imperfect first-order absorption23 assumed in the current analysis.
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Assessment of Potential Drug Interaction
In total, 98 patients with RA had etanercept concentrations measurement after the etanercept dose at week 24 (1 concentration/patient) in the current study. Forty-eight received etanercept alone, and 50 received a combination of etanercept and methotrexate. The sparse nature of the data collected in the current study precluded the direct estimation of the Bayesian pharmacokinetic parameters based on the fixed population mean parameters in the final model. The data in the current study therefore were combined with the NONMEM data set used for the development and validation of the final model. The Bayesian-estimated steady-state trough etanercept serum concentrations and clearance values were subsequently estimated using the post hoc option based on the final model. Representative plots (patient 408 receiving etanercept alone and patient 1344 receiving a combination of etanercept and methotrexate) of Bayesian-predicted and observed concentrations versus time are illustrated (Figure 2).
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The Bayesian-predicted mean steady-state trough etanercept concentration (corrected by body weight) in patients with RA receiving etanercept alone was 2005 ng/mL (CV% = 29.8%), which was slightly different from that (2135 ng/mL, CV% = 26.9%) in patients receiving etanercept in combination with methotrexate. Similarly, the Bayesian-predicted mean etanercept clearance (corrected by body weight) in patients receiving etanercept alone (0.071 L/h) was similar to that (0.072 L/h) in patients receiving etanercept in combination with methotrexate (Table IV).
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Statistical bioequivalence criteria (90% CI method) as well as an unpaired t test were also used to examine any potential effect of the coadministration of methotrexate on the pharmacokinetics of etanercept, as shown in Table IV. All parameters fell within the standard bioequivalence range (80%-125%). The results of the unpaired t test were also indicative of the lack of effect of methotrexate on the pharmacokinetics of etanercept.
In summary, etanercept pharmacokinetics were not altered by the concurrent administration of methotrexate in patients with RA.
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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In the current TEMPO study comparing the combination of etanercept plus methotrexate versus etanercept or methotrexate alone, a single blood sample was collected from each patient with RA during routine clinical care at week 24. The Bayesian-estimated clearance of etanercept in patients receiving etanercept monotherapy (mean = 0.070 L/h) was similar to that following a combination of etanercept plus methotrexate (mean = 0.067 L/h). The effect of the methotrexate weekly dose on the Bayesian-estimated individual etanercept clearance is depicted in Figure 3. As expected, no trend was evident between the methotrexate dose administered and etanercept clearance. There is no overlap in elimination pathways between etanercept and methotrexate; therefore, no mechanistic effect of the concurrent administration of methotrexate on the pharmacokinetics of etanercept is expected. The current study results confirm our hypothesis. It is interesting to note that methotrexate can decrease clearance of 2 monoclonal antibodies against TNF, infliximab18 (a chimeric IgG1 monoclonal antibody for RA), and adalimumab19-21 (a recombinant human IgG1 monoclonal antibody for the treatment of RA). However, the underlying mechanisms for those interactions appear to be unknown.
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Lee et al24 developed a 1-compartment population pharmacokinetic model for etanercept based solely on subcutaneous data.24 During our model development, however, a 2-compartment population model was superior to a 1-compartment model in characterizing the pharmacokinetics of etanercept.25 This discrepancy is not unanticipated because our analysis was based on the combined intravenous and subcutaneous data. Sometimes, when absorption extends over a long period of time for a drug exhibiting biexponential elimination, its concentration-time profile may visually present as a 1-compartment model because the distribution part of the curve is actually masked by the absorption portion.26 The population mean estimates for both CL/F (0.115 L/h, F = 0.626) and ka (0.028 1/h) in the current study were comparable to Lee et al's study—that is, 0.117 L/h (females) for CL/F and 0.033 1/h, respectively.
Several methods have been used to estimate the relative clearance for detecting potential drug-drug interactions in a routine clinical care setting. The multiple trough screen method and the multiple peak screen method have been used to estimate the population mean relative drug clearance and to detect a potential drug-drug interaction.27-31 The mixed-effect modeling method can detect clinically relevant drug interactions and produce information similar to that found in traditional pharmacokinetic crossover study designs.32,33 Similar to the sampling scheme adopted in the routine therapeutic drug monitoring setting, blood sampling for etanercept serum concentration in the current study was allowed anytime after the dose at week 24. The widely variable and inconsistent sampling time from the last dose (as depicted in Figure 4) between the 2 treatments (etanercept alone and etanercept plus methotrexate) did not simply yield either steady-state trough or peak concentrations, which can be directly used to detect the potential drug-drug interaction between methotrexate and etanercept. The mean elapsed time for sampling was 70.8 hours and 59.3 hours for patients receiving etanercept alone or a combination of etanercept plus methotrexate, respectively. In view of that, a nonlinear mixed-effect modeling approach was used in the current study. The previously obtained etanercept concentration data from 265 subjects (53 healthy subjects and 212 patients with RA) along with their demographic characteristics were used to develop a validated population covariate pharmacokinetic model for etanercept. The developed model was then used to estimate the Bayesian individual parameters for both etanercept-treated groups.
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The bootstrapping resampling method, considered to be a powerful validation technique, and the data-splitting method showed that the final population pharmacokinetic model had adequate predictive performance.34 The values of the parameter estimates obtained by bootstrap were in good accordance with the parameter estimates of the final model. The typical population value of clearance (0.072 L/h) obtained in the current population analysis was also in good agreement with the clearance value reported in the literature.5
In conclusion, a valid population pharmacokinetic model was developed to describe the pharmacokinetics of etanercept, which were not altered by the concurrent administration of methotrexate in patients with RA. Thus, no etanercept dose adjustment is needed for patients taking methotrexate.
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ACKNOWLEDGEMENTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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FOOTNOTES |
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Submitted for publication April 1, 2004; Revised version accepted June 8, 2004.
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