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PHARMACOKINETICS |
From the Department of Pharmacokinetic and Pharmacodynamic Sciences, Genentech, Inc, South San Francisco, California (C. M. Ng, R. Bruno, D. Combs) and the Department of Clinical Pharmacology, Hoffmann-LaRoche, Inc, Nutley, New Jersey (B. Davies).
Address for reprints: Chee M. Ng, PharmD, PhD, Department of Pharmacokinetic and Pharmacodynamic Sciences, Genentech, Inc, 1 DNA Way, South San Francisco, CA 94080-4990.
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ABSTRACT |
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 TOP ABSTRACT MATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIXREFERENCES |
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monoclonal antibody being investigated for the treatment of rheumatoid arthritis. The purpose of this study was to develop a population pharmacokinetic model in rheumatoid arthritis patients. In addition, the final pharmacokinetic model was used to assess the variability in drug exposure (AUC0-
) for fixed versus body surface area-based dosing. A total of 102 patients were included in this population pharmacokinetic analysis. A 2-compartment pharmacokinetic model described the data reasonably well. Body surface area and gender were the most significant covariates for both CL and Vc. Body surface area alone only explained about 19.7% of the total interindividual variability of CL. In a simulation study, body surface area-based dosing normalized drug exposure over a wide range of body surface area but did not seem to improve the predictability of rituximab AUC0- in rheumatoid arthritis patients. Therefore, no rationale for body surface area-based dosing for rituximab in rheumatoid arthritis patients was found.
Key Words: Rituximab • rheumatoid arthritis • population pharmacokinetic model
Although rituximab is widely used in the treatment of NHL, detailed pharmacokinetic analysis of rituximab in this patient population is limited. When rituximab was administered at a dose of 375 mg/m2 once weekly for 4 weeks in a pivotal clinical study of NHL patients, median values for peak concentrations (Cmax) were 239.1 mg/L and 460.7 mg/L for the first and fourth infusions, respectively.4 Rituximab was detectable in the serum of patients at 3 months posttreatment, with median concentrations of 20.2 mg/L.4 Further detailed analysis suggested that there was a 2.7-fold increase in mean terminal half-life from the first rituximab infusion to the fourth infusion. The mean terminal half-life was 3.2 days and 8.6 days for the first and fourth rituximab infusions, respectively. The mean clearance values decreased from 0.038 to 0.009 L/h for the first and fourth rituximab infusions. The changes in terminal half-life and clearance from the first to fourth rituximab infusions were possibly due to the elimination of circulating CD20-positive B cells after the initial infusion of rituximab.4,5
An open-label study in 5 rheumatoid arthritis (RA) patients provided first evidence for the potential therapeutic use of rituximab in RA.6 A randomized controlled trial was then conducted to determine the safety and efficacy of rituximab in a total of 160 subjects with RA. Rituximab was administered either as monotherapy or in combination with methotrexate or cyclophosphamide in RA patients who had failed prior disease-modifying antirheumatoid drug (DMARD) therapy and had an inadequate clinical response to methotrexate.7 At 24 weeks, the proportion of patients who had at least a 50% improvement in disease symptoms was substantially greater in all the rituximab regimens than in the methotrexate (control) group. All the rituximab groups had a significantly higher proportion of patients who had at least a 20% improvement in disease symptoms than in the control group. Furthermore, more than 80% of patients treated with rituximab had a moderate or good response according to the criteria of the European League Against Rheumatism (EULAR response), as compared with 50% of patients in the control group.7 The results from subsequent independent open-label studies reinforce the findings from the original observation and the double-blind randomized study that rituximab treatment produced clinical benefit in RA patients.8,9 Although the efficacy of rituximab was documented in these studies, the population pharmacokinetics of rituximab in RA patients have never been reported in the literature.
The main objectives of this study were to estimate typical population parameters and interindividual variability for rituximab in RA patients and to assess the effects of subject characteristics and other covariates on rituximab pharmacokinetic parameters. A secondary objective was to determine and compare the rituximab exposures after fixed and body surface area (BSA)-based dosing based on the final developed population pharmacokinetic model.
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MATERIALS AND METHODS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIXREFERENCES |
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10 mg weekly). For the first dose on day 1, rituximab was administrated intravenously over approximately 255 minutes to all subjects, and blood samples were collected for pharmacokinetic (PK) analysis at predose, 3 hours, at the end of infusion, and 6 and 48 hours following the start of the infusion. The second rituximab dose was administered intravenously over approximately 195 minutes to all subjects on day 15. Blood samples were collected for PK analysis at predose, 3 hours, at the end of infusion, and 6, 48, 336, 1088, 2352, and 3696 hours following the start of the infusion. All patients received 100 mg IV methylprednisolone on days 1, 3, 15, and 17. On days 2 and 4 to 7, patients received 60 mg oral prednisolone and, on days 8 to 14, 30 mg oral prednisolone. Table I lists the patient covariates that were evaluated and their values. Seven covariates were considered in the analysis, including both continuous (age [AGE], body surface area [BSA], height [HT], baseline B cells [BCF], and weight [WT]) and categorical (treatment groups [GRP] and sex [SEX]) variables. Body surface area was calculated by the Du Bois and Du Bois equation.10 A validated enzyme-linked immunosorbent assay (ELISA) was used to determine the serum concentration of rituximab. In brief, an affinity-purified polyclonal goat antirituximab antibody was used as the capturing reagent, and goat antibody to mouse IgG F(ab')2 conjugated to horseradish peroxidase was used as the detection reagent. Diluted samples were quantitated with the standard curve prepared with rituximab standard.11 The lower limit of quantification for rituximab was 0.001 mg/mL in serum.
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Population Pharmacokinetic Analysis
NONMEM software (Version V, Level 1.1) with NMTRAN and PREDPP and the Compaq Visual Fortran compiler (Version 6.5) were used for population nonlinear mixed-effect modeling.12 Two different basic structural models, a 1- and 2-compartmental linear PK model with IV infusion, were fit to rituximab concentration-time data. The first-order conditional estimation (FOCE) method with 
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interaction was used throughout the model-building procedure. Interindividual variability for the pharmacokinetic parameters was modeled using the following exponential error model:
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A multiplicative covariate regression model was implemented as follows:
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where ip denotes the proportional difference between the "true" parameters (Pi) of the ith individual patients and the typical value (
) in the population, adjusted for values of covariates equal to those of this individual patient. The ip is the random effect with mean zero and variance 
2.The 
X, D, and C are the regression coefficients to be estimated for continuous (eg, WT), dichotomous (eg, SEX), or categorical (eg, GRP) covariates. Continuous variables were centered around their median (med(X)) values, thus allowing 1 to represent the clearance estimate for the typical patient with median covariates. Dichotomous covariates were coded 0 or 1 (eg, SEX = 0 for female, SEX = 1 for male). For categorical covariate GRP, IND is an indicator variable that has a value of 1 when patients received rituximab in combination with either cyclophosphamide or methotrexate (otherwise, IND = 0 for rituximab monotherapy), and C represents the covariate effect (when IND = 1).
The residual variability was modeled as a proportional-additive error model: Cpij = 
pij (1 + ij, prop) + ij, add where Cpij and pij are the jth measured and model-predicted concentration, respectively, for the ith individual, and ij, prop and ij, add denote the proportional and additive residual intraindividual random errors distributed with zero means and variances 
and 
.
Covariate models were built by the stepwise selection procedure using a 2-step approach. First, a model for biological covariates (ie, sex, baseline B cells, BSA, weight, and height) was built. The effect of each biological covariate was examined by adding 1 covariate at a time to the base model. The biological covariates that resulted in the greatest statistically significant decrease in the value of the objective function were added to the base model, and the entire procedure was repeated stepwise until all significant covariates were included in the base model to form the full model (forward selection). Then the biological covariates from the full model were removed one by one. Covariates whose removal resulted in a statistically significant increase in the objective function were retained in the model (backward elimination). However, if correlated covariates with similar pharmacological meaning (ie, weight and BSA) were in the model, the least influential covariate was removed from the model. This model was designated as the final model with biological covariates (final BC). After the covariate effect of the biological covariates had been accounted for in the final BC model, the covariate effect of dose group (GRP) was tested to yield the final model. This 2-step approach was taken to minimize confusion between any changes in pharmacokinetic parameters related to biological covariates and any changes resulting from interaction with combination therapy.
Comparison of alternative structural models and construction of the covariate model for rituximab were based on the typical goodness-of-fit diagnostic plots and likelihood ratio test. When comparing alternative hierarchical models, the differences in the value of the objective function are approximately chi-square distributed with n degrees of freedom (n is the difference in the number or parameters between the full and the reduced model). This approximation has been shown to be reliable for the FOCE-INTERACTION estimation method.13 Differences in objective function of greater than 7.9 for 1 degree of freedom, corresponding to a significance level of P < .005, were used to discriminate 2 hierarchical models. This stringent criterion was used because of the multiple comparisons inherent in the stepwise selection procedure.
The percentage of interindividual variance (% variance) explained by the covariate(s) in the regression model for a given PK parameter (eg, CL) was computed as follows:
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Model Evaluation
A bootstrap resampling technique was used to evaluate the stability of the final model and estimate confidence intervals of the parameters. This model evaluation technique consists of repeatedly fitting the model to bootstrap replicates of the data set using the bootstrap option in the software package Wings for NONMEM (N. Holford, Version 404, June 2003, Auckland, New Zealand), and parameter estimates for each of the replicate data sets were obtained. The results from 500 successful runs were obtained, and the mean and 2.5th and 97.5th percentiles (denoting the 95% confidence interval) for the population parameters were determined and compared with the estimates of the original data.
Rituximab Exposures After Body Surface Area-Based and Fixed Dosing in Rheumatoid Arthritis Patients
The final population pharmacokinetic model was used to estimate rituximab exposures after BSA-based and fixed dosing in RA patients. Rituximab exposure after dosing was calculated according to the following formula: AUC0- = Dose/Clearance. Using the clearance predicted by the final population model with covariates, the percent difference between expected AUC0- in patients with extreme BSA values (5th and 95th percentiles) from the patients with median values after BSA-based and fixed dosing was compared. In addition, clearance values of 1000 subjects were simulated using the final model with a data set obtained by bootstrapping (with replacement) the original PK data set. The population variability of AUC0- after different dosing regimens was then assessed.
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RESULTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIXREFERENCES |
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The fit of the 2-compartment model to the data was markedly better than that of the 1-compartment model based on the change of objective function (
= -1242) and diagnostic plots. The NONMEM subroutine ADVAN3 TRANS1 was used for the 2-compartment model fitting. The estimated CL and Vc values were then used to yield the basic PK parameter of the elimination rate constant from the central compartment (K) needed for the subroutine using the following equation: K = CL/Vc. The removal of interindividual variability terms () of K12 and K21* from the 2-compartment models did not result in a statistically significant increase ( < 7.88) in the objective function; therefore, only CL and Vc were retained in the final best structural model. The effect of the presence of a covariance term between CL and Vc on model performance was assessed. Incorporation of a covariance term between CL and Vc improved the fit significantly ( = -58). However, CL, Vc, and CL-Vc were poorly estimated (coefficient of variation [CV] > 100%); the estimated correlation between CL and Vc was not large (rCL-Vc = 0.71); and parameter estimates were not influenced (data not shown). Therefore, the covariance term was not retained for covariate effect model building.
Final Model With Covariates
In the final model, BSA and gender were the significant covariates to explain interindividual variability for the Vc and CL of rituximab. No significant differences in CL and Vc were observed between patients treated with rituximab as a single agent and patients treated with rituximab in combination with either cyclophosphamide or methotrexate. The final model was therefore as follows:
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The parameter estimates of the final model are summarized in Table II. The typical interdepartmental clearance (Q) and volume of distribution at peripheral tissues (Vp) were 655.6 mL/d and 3642.2 mL, respectively, based on calculations from the following equations: Q = K12 x Vc and Vp = Q/K21. Interindividual variability for CL and Vc in the final model, calculated as the square root of interindividual variance (2) and expressed as %CV, are 28.2% and 12.3%, respectively, compared to 34.2% and 16.2% for the base model without covariates. The covariate effect of BSA and SEX in the final model therefore explained about 32% of the interindividual variance for CL and 42% of the interindividual variance for Vc. Coefficient BSA_Vc was 0.73 for BSA on Vc, indicating a less than proportional increase in log (Vc) with BSA. After adjusting for BSA, men still had a larger Vc (16.5%) than women. Coefficient BSA_CL was 1.02 for BSA on CL, indicating a proportional increase in log (CL) with BSA. After adjusting for BSA, gender remained a significant effect, with 38.7% faster CL in men than women. The dependency of CL on BSA and SEX with the base model is accounted for in the final model, as shown in Figure 1. Incorporation of covariance terms between CL and Vc did not affect the parameter estimates for the covariate effects, and the correlation between CL and Vc was not strong (rCL-Vc = 0.56) (data not shown). Therefore, the covariance term was not included in the final model.
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The estimate of the proportional term for the variance model was 19%. The estimate of the additive residual variance component was quite small (5.4 x 10-4 mg/mL) and below the limit of quantitation of the rituximab assay at 0.001 mg/mL. However, the additive error term was included in the final model because removal of this term in the final model resulted in a statistically significant increase ( = 27; P < .005) in the objective function.
For the final model with covariates, predicted versus observed rituximab concentrations and weighted residuals versus predicted concentration plots are shown in Figures 2 and 3, respectively. There was some bias for the model prediction at the high concentrations. Model misspecification may be one of the potential reasons for the bias observed for the model prediction at the high concentrations. A 2-compartment model with both linear and nonlinear elimination kinetics was fit to the data. The model exhibited convergence difficulties and did not perform significantly better than the basic 2-compartment model with only linear elimination PK ( = 0.00; df =2; P > .005). The estimates of the parameters (Vmax and Km) for the nonlinear elimination pathway were very small (Vmax = 2.18 x 10-6 mg•day and Km = 1.07 x 10-5 mg). In addition, similar bias was observed in the diagnostic plots (data not shown). These results suggested that the bias observed is not due to nonlinearity of rituximab pharmacokinetics. An attempt to improve the fit with a 3-compartment linear PK model was unsuccessful because the model was unstable and exhibited convergence difficulties. The proposed dosing scheme of rituximab was a complicated infusion regimen with 9 sequential escalation steps to avoid infusion-related toxicity. Initially, the rituximab was infused intravenously over 30 minutes with an infusion rate of 50 mg/h. Then the infusion rate was increased incrementally up to the maximum infusion rate of 400 mg/h. The total infusion time was 255 minutes. However, only the starting time and the time for the end of infusion were recorded in the study. No information about the duration, amount delivered, and infusion rate on each infusion step was documented. Therefore, we made an assumption that the rituximab was administered as a continuous intravenous infusion with a single infusion rate. This assumption of using a single continuous infusion rate instead of multiple rates for multiple infusion steps may be one of the reasons for the bias observed for the model prediction at the high concentrations.
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Model Evaluation
From the original data set, 600 replicate data sets were generated and used for the evaluation of the stability of the final model. NONMEM failed to achieve a successful minimization step in only 95 (15.8%) replicated data sets, suggesting that the model was relatively stable. The result from the first 500 of the successful runs is shown in Table II. The mean population parameter estimates obtained from the bootstrap procedure were similar to the parameter estimates of the original data set, indicating that the developed model was stable. Furthermore, the 95% confidence intervals for the fixed-effect parameters were narrow and indicated good precision.
Rituximab Exposures After Body Surface Area-Based and Fixed Dosing in Rheumatoid Arthritis Patients
Rituximab exposures after BSA-based and fixed dosing in RA were examined because BSA was the most important continuous covariate to explain interindividual variability for the CL and Vc of rituximab. In the RA clinical studies, rituximab was administered 1000 mg intravenously on days 1 and 15, with the total administered dose of 2000 mg. Therefore, rituximab AUC0- values after a fixed dose of 2000 mg were examined. Predicted rituximab AUC0- values after a fixed dose of 2000 mg for theoretical subjects according to the final model are shown in Table III. After a fixed 2000-mg dose of rituximab, the change in rituximab AUC0- in women with extreme BSA was up to 16% and 24% of the typical values for female and male subjects, respectively. Similarly, rituximab AUC0- values after a BSA-based dose of 1163 mg/m2 for theoretical subjects according to the final model were also examined. A BSA-based dose of 1163 mg/m2 was used because under this BSA-based dosing regimen, subjects with the median BSA (1.72 m2) would receive a 2000-mg dose. As expected, exposure does not depend on BSA for both women and men after a BSA-based dose of rituximab (Table III). To investigate the population variability of rituximab AUC0- after the fixed dose and the BSA-based dose, the AUC0- of 1000 subjects was determined using the clearance values simulated from the final model. As expected, the dependency of AUC0- on BSA decreased with BSA-based dosing (Figure 4). However, the distributions of predicted rituximab AUC0- after a fixed dose or BSA-based dose were very similar (Figures 4, 5), and population variability of AUC0- was only slightly reduced (16.5%) with BSA-based dosing. Body surface area-based dosing reduced the population variability (variance) of AUC0- only moderately (16.5%) from 7.03 to 5.87. The %CV of AUC0- was 34.3% and 31.4% for the fixed dose and the BSA-based dose, respectively. The ratio of the 95th percentile to the 5th percentile rituximab AUC0- was 3.19 and 2.83 for the fixed dose and BSA-based dose, respectively.
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The same simulated data set was used to determine the rituximab exposure after a fixed dose and the BSA-based dose for patient populations with extreme BSA (ie, BSA 
10th and 90th percentiles). The median percent change in rituximab AUC0- from the population median value for a patient population less than or equal to the 10th percentile BSA was 30.8% (range, -45.8 to 161.1) and 12.1% (range, -53.6 to 114.5) for a fixed dose and a BSA-based dose, respectively. The median percent rituximab AUC0- change from the population median value for a patient population greater than or equal to the 90th percentile BSA was -18.8% (range, -60.0 to 54.6) and -2.1% (range, -52.0 to 82.6) for the fixed dose and BSA-based dose, respectively. The population variability of AUC0- after a fixed dose and BSA-based dose was similar in these patient populations.
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DISCUSSION |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIXREFERENCES |
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and t1/2ß of rituximab were 2.4 and 19.7 days, respectively. The pharmacokinetics of natural immunoglobulin G after IV administration in humans are well known. The decline in the antibody activity following IV administration is generally biphasic.14,15 After IV administration, initial plasma IgG concentrations are consistent with a distribution volume of 45 mL/kg, which is equal to plasma volume. Then there is an initial period of a rapid fall in plasma IgG concentrations, representing both elimination of the molecules and distribution of the immunoglobulin from the intravascular to the extravascular compartment. During the subsequent period (terminal phase), the antibody concentrations decline slowly, with a half-life of 18 to 23 days, representing predominantly elimination of the antibody molecule.14 Therefore, at clinical doses, the pharmacokinetics of rituximab in RA patients are similar to those of a typical native IgG, with an initial volume of distribution equal to the plasma volume, a rapid distribution phase, and a slower terminal phase of elimination.
In NHL patients, rituximab shows a 4-fold decrease in clearance from the first to fourth infusions,4,16 possibly due to the elimination of circulating CD20-positive B cells (which play a role in the elimination of rituximab antibody) after the initial infusions of rituximab.4,5,16 An attempt to model 2 different clearance values for the first and second rituximab infusions in the base model failed to improve the fit significantly ( = 1; df = 1; P > .05). One possible reason is that the baseline CD20-positive B cells in the non-NHL patients were much lower than those observed in NHL patients, and the B cells did not contribute significantly to the clearance of the rituximab in RA patients.17,18 Therefore, a 2-compartment linear PK model with a single clearance value was selected as the base model for covariate model building.
The covariate effects in the final model explained about 32% of the interindividual variance for CL and 42% of the interindividual variance for Vc. Body surface area and gender were the most significant covariates to explain interindividual variability for both CL and Vc. No differences in CL and Vc were observed between patients treated with rituximab as a single agent and patients treated with rituximab in combination with either cyclophosphamide or methotrexate, suggesting that cyclophosphamide and methotrexate did not affect the pharmacokinetics of rituximab in RA patients. Baseline B cell values did not affect the CL and Vc of rituximab, confirming that the specific B cell-mediated mechanisms seen in the NHL patients did not play an important role in rituximab elimination and distribution in RA patients for the reason mentioned previously.
The effect of BSA on CL suggested that rituximab may be dosed based on BSA as it is in oncology indications. However, the covariate effect of BSA alone in the model only explained about 19.7% of the total interindividual variability of CL. This suggests that although BSA is a predictor of CL, the effect of BSA on CL and AUC0- may be measurable but not highly contributory. Results obtained from the analysis using the final population PK model supported this observation. BSA-adjusted dosing would normalize drug exposure over a wide range of BSA, but fixed dosing results in only modest differences in exposure (<25%) for subjects with extreme BSA. In addition, the simulation study suggested that although the dependency of AUC0- on BSA decreased with BSA-adjusted dosing, BSA-based dosing population variability of AUC0- only slightly reduced with BSA-based dosing. Similar results were obtained from the subgroup analysis in a patient population with extreme BSA (ie, BSA 10th and 90th percentiles). Hence, it is concluded that rituximab CL and AUC0- after dosing in RA patients are related to BSA. However, the relationship is relatively weak, and BSA alone explains only a small percentage of the interindividual variability in CL. Therefore, BSA-adjusted dosing does not seem to improve the predictability of rituximab CL and AUC0- in RA patients.
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APPENDIX |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIXREFERENCES |
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* K12 and K21 represented distribution rate constants from central to peripheral, and from peripheral to central compartment, respectively. 
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REFERENCES |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIXREFERENCES |
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