Pharmacometrics
Population Pharmacokinetic Modeling of Quetiapine After Administration of Seroquel and Seroquel XR Formulations to Western and Chinese Patients With Schizophrenia, Schizoaffective Disorder, or Bipolar Disorder
The Journal of Clinical Pharmacology 2015, 55(11) 1248–1255 © 2015, The American College of Clinical Pharmacology DOI: 10.1002/jcph.544
Diansong Zhou, PhD, Khanh H. Bui, PhD, Jianguo Li, PhD, and Nidal Al-Huniti, PhD
Abstract A population model describing quetiapine pharmacokinetics (PK) in Western and Chinese patients following oral administration of immediate-release (IR) and extended-release (XR) formulations was developed using plasma concentrations in 127 patients from 5 studies with quetiapine IR and/or XR in Western patients and 1 study with quetiapine XR in Chinese patients. A 1-compartmental model with first-order absorption and first-order elimination adequately described the quetiapine PK. The typical apparent volume of distribution and elimination rate constant of quetiapine were 574 L and 0.12 h1, respectively. The estimated population absorption rate constants were 1.46 and 0.10 h1 for quetiapine IR and XR, respectively. Covariate analysis revealed that race was not a significant covariate influencing the PK of quetiapine. Simulation conducted with the final quetiapine population PK model predicted that the administration of a 200-mg twice-daily dose of quetiapine IR in Chinese patients would achieve a steady-state AUC (AUCss) standard deviation of 3087 1480 ng h/mL, which is in close agreement with the reported value (3538 1728 ng h/mL). The model also predicted that once-daily administration of 300 mg quetiapine IR or XR would achieve similar exposure in terms of AUCss in Chinese patients.
Keywords Quetiapine, Western, Chinese
Quetiapine, a dibenzothiazepine derivative, is an atypical antipsychotic drug with efficacy demonstrated in clinical trials for the treatment of schizophrenia, bipolar disorder, major depressive disorder, and generalized anxiety disorder.1 Quetiapine has a short half-life (7 hours), and twice-daily administration is recommended for the immediate-release formulation (Seroquel).2 In addition, an extended-release formulation (Seroquel XR) has been developed for once-daily use. Quetiapine XR is approved in the United States and the European Union for the treatment of schizophrenia, for the acute treatment of manic or mixed episodes associated with bipolar disorder (bipolar I disorder in the United States), for the acute treatment of depressive episodes associated with bipolar disorder, as an adjunct in the maintenance treatment of bipolar disorder (bipolar I disorder in the United States) and as an add-on treatment for major depressive disorder in patients with a suboptimal response to antidepressants.1,3 The pharmacokinetics of quetiapine have been extensively studied in Western populations, both in normal healthy volunteers and in patients with selected psychotic disorders.3–8 Quetiapine is rapidly absorbed after oral administration of quetiapine immediate release (IR), and food has minimal effects on quetiapine absorption.3 The apparent oral clearance of quetiapine ranges from 55 to
87 L/h in schizophrenic patients, and the oral clearance declines with age. Quetiapine is predominantly metabolized by CYP3A, and the apparent oral clearance of quetiapine did not change in patients with renal impairment.3 A crossover study in patients comparing pharmacokinetic profiles of quetiapine XR and quetiapine IR indicated that the 2 formulations were bioequivalent in terms of AUC,6 but the maximum plasma concentration at steady state was approximately 13% lower for quetiapine XR than for quetiapine IR. The apparent oral clearance of quetiapine in Chinese patients has been shown to be 59 to 78 L/h,9,10 suggesting similar pharmacokinetic characteristics in Western and Chinese patients. Only 1 population pharmacokinetic analysis has been reported for quetiapine
Quantitative Clinical Pharmacology, AstraZeneca LP, Waltham, MA, USA Submitted for publication 5 February 2015; accepted 10 May 2015. Corresponding Author: Diansong Zhou, PhD, Quantitative Clinical Pharmacology, AstraZeneca LP, 35 Gatehouse Dr.,Waltham, MA 02451 Email: [email protected]
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IR, in which first-order absorption and elimination in combination with 1-compartment model was applied.11 The objectives of the current analysis were to develop a population pharmacokinetics (PK) model describing the quetiapine pharmacokinetics in Western and Chinese patients, following oral administration of quetiapine IR and/or XR formulations, and to predict quetiapine exposure after administration of the same daily dose of quetiapine IR and XR in Chinese patients.
Methods Data Assembly The quetiapine population PK analysis data set consisted of data from 127 patients collected from 5 phase 1 studies with quetiapine IR and/or XR formulations in Western patients (studies D1441C00130,4 D1444C00001, D1444C00003, 5077IL/0097,6 and 5077IL/0118) and a single phase 1 study in Chinese patients dosed with quetiapine XR formulation (D1444C0000710). Only PK data obtained from patients in the fasted state were used in this analysis. Study design information, including patient populations and dose regimens, can be found in Table 1. Quetiapine (IR or XR) was administered orally in all studies, with titration up to a targetdailydose.Intensiveplasmasampleswerecollectedin all studies. The concentrations of quetiapine in plasma were determined by validated liquid chromatography–tandem mass spectrometric detection methods, which have been reported previously.4,6,10 All patients provided written informed consent. The protocols were approved by the institutional review
board, and the studies were conducted in accordance with the ethical principles of the Declaration of Helsinki and were consistent with the International Conference on Harmonization and Good Clinical Practice. Software The software packages Nonlinear Mixed Effects Modeling (NONMEM),12 version 7.2 (ICON, Ellicott City, Maryland), and Perl-speaks-NONMEM (PsN)13 (Uppsala University, Uppsala, Sweden) were used for modeling and simulation. R, version 2.15.2 (R Foundation for Statistical Computing, Vienna, Austria), was used for data preparation, graphical analysis, model diagnostics, and statistical summaries. XPOSE4 (Jonsson & Karlsson, Missouri) and PsN were used for model diagnostics and bootstrapping. The first-order conditional estimation with interaction between interindividual and residual random effects (FOCEI) method in NONMEM was employed for all model runs. Base Model Development Initially, exploratory graphs of individual plasma quetiapine levels were constructed by study and by formulation to qualitatively explore the suitability of different base PK models. One- and 2-compartmental models with different absorption assumptions were tested. The between-patient variability model was described as: ui ¼ uTV expðhi Þ where ui represents the value of the PK parameter u for the ith subject and uTV is the population mean of parameter u
Table 1. Brief Summary of Quetiapine Clinical Studies Study D1441C00130
D1444C00001
D1444C00003
5077IL/0097
5077IL/0118
D1444C00007
Brief Description of Study A study to characterize the steady-state pharmacokinetics and safety and tolerability of quetiapine in adults with selected psychotic disorders A phase 1, randomized, open-label, 5-treatment, 5-period, 4-sequence crossover study to compare the pharmacokinetics of 4 sustained-release formulations and the immediate release formulation of quetiapine in adults with schizophrenia, schizoaffective disorder, or bipolar disorder A study to compare the pharmacokinetics of 50 and 300 mg quetiapine XR administered following a light meal and in the fasted state in adult volunteers and adults with schizophrenia, schizoaffective disorder1, or bipolar disorder A trial to compare the steady-state pharmacokinetics of quetiapine in men and women with selected psychotic disorders following the administration of quetiapine XR or quetiapine IR Steady-state dose unit proportionality, and food effect study using commercial scale quetiapine XR in patients with schizophrenia or schizoaffective disorder A randomized, open-label trial to evaluate the pharmacokinetics of quetiapine XR 300, 600, and 800 mg in Chinese schizophrenic patients
Dosing Regimen
Number of Subjects
200 and 400 mg quetiapine IR
26
400 mg of quetiapine IR and 50 and 400 mg quetiapine XR
14
50 mg and 300 mg quetiapine XR
13
150 mg of quetiapine IR twice a day or 300 mg quetiapine XR once daily
24
50, 200, 300, and 400 mg quetiapine XR and 300 mg of quetiapine IR
10
300, 600, and 800 mg quetiapine XR
40
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1250 in the structural model. The deviation of u from the mean uTV can be approximated with hi, which is in a normal distribution, with a mean of 0 and a variance of v2 (ie, hi N[0, v2]). Different residual error models were evaluated, including exponential, additive, proportional, and a combined additive and proportional models. The combined additive and proportional residual model was defined as: ^ ij ð1 þ epij Þ þ eaij C ij ¼ C where Cij is the jth measured concentration in the ith ^ ij is the jth model-predicted concentration in individual; C the ith individual; epij and eaij are proportional and additive residual random errors, respectively, for individual i and measurement j and are each assumed to be independently normally distributed (ie, epij N[0, s12] and eaij N[0, s22]). Model development and selection were driven by the data and were based on various goodness-of-fit indicators,14 including comparisons based on the minimum objective function value (OFV; equal to -2 log likelihood), successful minimization and, if possible, completion of covariance steps in NONMEM, and various goodness-of-fit criteria: visual inspection of diagnostic plots (eg, observed versus predicted concentrations, weighted residual versus predicted concentration or time, correlations of between-individual random effects), precision of parameter estimates, and plausibility of parameter estimates. Covariate Model Development Covariate effect modeling started from the previously developed parsimonious base model by a procedure to include the covariate effects on the population mean PK parameters, with a statistical hypothesis test of significance and clinical significance. A stepwise regression approach was used for the covariate modeling, building with forward selection and backward elimination of subject covariates. First, the effect of each covariate was examined univariately by adding 1 covariate at a time to the base model. The covariates that resulted in the greatest statistically significant decrease in the value of the objective function were added to the base model, then the entire procedure was repeated stepwise until all significant covariates were included. Once all covariate relationships for the PK parameters had been defined from the forward selection step, backward elimination of the covariates one by one was performed to see if a covariate should be deleted from the full model or retained. The model for each relevant parameter covariate relationship was prepared and tested using stepwise covariate model (SCM) approach implemented in PsN. Stepwise forward or backward comparisons, based on the likelihood ratio test and a pre-specified a level, have
been made across nested multivariate models, each expressing different covariate-parameter combinations. According to the likelihood ratio test, the difference in -2 log likelihood from nested models is assumed to be asymptotically x2 distributed with degrees of freedom (df) equal to the difference in number of model parameters. Significance of covariate effect was determined at a ¼ 0.1 (or 3.84 of change in NONMEM OFV with df ¼ 1) at the forward selection step and at a ¼ 0.001 (or 10.8 of change in NONMEM OFV with df ¼ 1) at the backward elimination step. Baseline demographic characteristics including weight, age, race, and sex were tested on quetiapine absorption, distribution, and elimination parameters. Given the involvement of the liver in quetiapine metabolism, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and bilirubin concentrations (as measurements of liver function) were also evaluated as covariates for quetiapine elimination. Creatinine clearance as a measure of patient renal function was only reported in 2 of 6 studies; therefore, it was not evaluated as covariate in the current analysis. The relationship between continuous covariates and the typical value of PK parameters was primarily modeled using power models, whereas the relationship between categorical covariates and the typical value of PK parameters was modeled as a constant proportional model. Model Evaluation The final full covariate model of quetiapine was evaluated using stratified nonparametric bootstrap15 and visual predictive checks (VPCs).16 For the nonparametric bootstrap procedure, 1000 replicate bootstrap data sets were obtained by random resampling using subject as the sampling unit, with replacement from the original data set, and were fitted with the same model to obtain parameter estimates for each replicate. Empirical 95% confidence intervals were constructed by obtaining the 2.5th and 97.5th percentiles of the resulting parameter distributions for those bootstrap runs with successful convergence. The final model parameter estimates were compared with the bootstrap median parameter estimates to evaluate the final model performance. The predictive performance of the final model was assessed with VPC. Simulation of 1000 new data sets was carried out for each drug using the final model with the estimated fixed- and random-effects model parameters. The concentration–time profiles were plotted for the 50th percentile and the 5th and 95th percentiles (presenting the 90% prediction interval) of the simulated data and overlaid with observed data. Model Applications The final model for quetiapine with estimated fixed- and random-effects parameters was also applied to simulate
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the exposure of quetiapine IR to mimic a quetiapine study conducted in Chinese patients.9 Briefly, 1000 patients were titrated with quetiapine IR and reached a 200-mg twice-daily dose on day 4. On day 8, blood samples were collected up to 24 hours after the 200-mg dose in the morning. The concentration–time profiles were plotted for the mean and the 2.5th and 97.5th percentiles (presenting the 95% prediction interval) of the simulated data and overlaid with the reported data, which were digitalized from the published report.9 The mean AUC and Cmax values at steady state were also summarized and compared with reported values. Finally, the final model for quetiapine with estimated fixed- and random-effects parameters was applied to simulate the relative exposure in Chinese patients after administration of the quetiapine IR and XR formulations. Briefly, quetiapine concentration–time courses for 1000 patients were simulated for 300 mg quetiapine IR once daily or 300 mg quetiapine XR once daily for 8 days. The predicted geometric mean AUC and Cmax values were summarized and compared with each other and the reported values.
Results The baseline characteristics for patients in the final PK data set are summarized in Table 2. For the127 patients included in the final population PK modeling, the average age was 39.8 years, with a range from 18 to 65 years; the average body weight was 80 kg, with a range from 45 to 124.5 kg. The data set had 87 male and 40 female patients. White (36), black (31), and Chinese (40) are the 3 main races in the data set, with 20 patients of other ethnicities including Hispanic, Asian, and others. The population PK data set contained 4364 quetiapine plasma concentrations. There were 4 patients from study 5077IL/0097 missing an ALT value and 2 patients from study D1444C00001
missing ALT, AST, and bilirubin concentrations. Population median values were substituted for these missing laboratory values. Base Model A 1-compartment disposition model with first-order elimination and first-order absorption provided the best fit for the quetiapine IR and XR plasma concentration– time profiles. Adding a peripheral compartment to the model did not decrease objective function value or improve model diagnostic plots. The quetiapine data also supported different absorption rate constants (Ka) for IR and XR formulations. Interindividual variability was estimated for all structural pharmacokinetic model parameters, and the covariance between eliminate rate constant (Ke) and volume of distribution (V) was supported by the quetiapine concentration data. Different residual error models were tested, and the combined proportional and additive residual error model was found to best describe the data. Diagnostic plots for the base model showed adequate fit to the data, with no apparent trends of residuals over time or model predictions (figure not shown). The base model structural parameter and random variance estimates are presented in Table 3. Covariate Model A covariate model-building approach was applied, with a forward selection and backward elimination method via the SCM approach. Baseline demographic characteristics, including weight, age, race, and sex were explored on Ke, V, and Ka. The ALT, AST, and bilirubin concentrations were also explored as covariates for Ke. Race was coded as a 4-category covariate in the analysis: white (36), black (31), Chinese (40), and others (20). The effect of age on the Ke of quetiapine was modeled as a power model normalized at 42 years, with a population mean exponent of -0.47, indicating the
Table 2. Summary of Population Demographic Characteristics by Study for Continuous and Categorical Variables Study n Age (y) Median (range) Weight (kg) Median (range) Sex (n) Male Female Race (n) White Black Hispanic Chinese Asian Others
D1441C00130
D1444C00001
D1444C00003
D1444C00007
5077IL/0097
5077IL/0118
All
26
14
13
40
24
10
127
39.5 (18–45)
40.5 (18–45)
30 (23–44)
41 (19–55)
45 (18–62)
50.5 (42–65)
42 (18–65)
89.1 (59–109)
80.2 (59.1–114)
90 (60–114)
65 (45–89)
90.5 (58.5–124.5)
89.3 (64.9–102)
77.5 (45–124.5)
18 8
14 0
11 2
20 20
15 9
9 1
87 40
10 12 0 0 3 1
0 12 1 0 0 1
12 0 0 0 0 1
0 0 0 40 0 0
12 6 6 0 0 0
2 1 7 0 0 0
36 31 14 40 3 3
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Table 3. Parameter Estimates of the Final Quetiapine Population PK Covariate Model Nonparametric Bootstrap Parameter Typical PK parameters u1: TVKe (/h) u2: TVKe (age/42)u2 u3: TV (L) u4: TKa, XR (/h) u5: TKa, IR (/h) Random BSV v1,1: BKe v2,2: BV v1,2: (BKe, BV) v3,3: BKa,XR v4,4: BKa,IR RVa u6: PRV (%) u7: ARV (SD) u8: PRV (%) u9: ARV (SD)
Description
Base Model Estimate
Final Model Estimate
RSE (%)
Median
95%CI
Typical Ke Age effect on Ke Typical V Typical Ka for SEROQUEL XR Typical Ka for SEROQUEL
0.12 – 580.6 0.098 1.47
0.12 0.47 573.7 0.10 1.46
5.1 25.3 5.5 28.6 11.1
0.12 0.48 571.5 0.09 1.48
(0.11–0.13) (0.72 to 0.24) (509.8–642.7) (0.05–0.16) (1.17–1.85)
Interindividual Interindividual Covariance of Interindividual Interindividual
0.41 0.47 0.46 1.5 0.74
0.4 0.47 0.5 1.5 0.75
19 20 34 21 32
0.39 0.46 0.52 1.49 0.73
(0.32–0.46) (0.39–0.54) (1.18 to 0.18) (1.19–1.87) (0.47–0.95)
0.38 1.02 0.48 1.46
0.38 1.01 0.48 1.51
3.4 19.7 3.6 35.9
0.38 1.02 0.48 1.56
variance of variance of Ke and V variance of variance of
Ke V Ka, XR Ka, XR
Proportional error of RV for XR Additive error of RV for XR Proportional error of RV for IR Additive error of RV for IR
(0.36, (0.65, (0.45, (0.77,
0.41) 1.83) 0.51) 2.63)
a Residual variability was parameterized by the fixed-effect parameter (u). Interindividual variance was expressed as CV, covariance was expressed as correlation, residual variance was expressed as %CV for proportional terms and SD (ng/mL) for additive terms, RSE was expressed as 100 (standard error of the estimate/ point estimate). Median and 95%CI were estimated from nonparametric bootstrap estimates from1000 patients. BSV, between-subject variability; CI, confidence interval; CV, coefficient of variation; h, hour; Ka, absorption rate; Ke, elimination rate; RSE, relative standard error; RV, residual variability; SD, standard deviation; V, volume of distribution.
clearance of quetiapine decreases with age. This was also the only covariate identified in the covariate search. The inclusion of this covariate effect on quetiapine PK resulted in an 18.55-point decrease in object function value compared with the base model. Other covariates including weight, race, and sex had no significant effect on Ke, V, or Ka. Laboratory observations, including ALT, AST, and bilirubin concentration, as a measure of liver function had no significant effect on Ke. Final estimates of the parameters, together with relative standard errors, are presented in Table 3. Goodness-of-fit plots (Figure 1), although they showed that concentrations above 1000 ng/mL were underpredicted, indicated a good fit of the model for the majority of the data. Those data were primarily Cmax values from the 400-mg IR dose, and were not well predicted with the first-order absorption model because of the rapid absorption for the IR formulation. The distribution of the conditional weighted residuals was unbiased with respect to time or population predictions. Model Evaluation The median values of parameters and 95% confidence intervals obtained from the converged bootstrap runs for quetiapine are presented in Table 3. The median values of parameters were in close agreement with the population estimates in the final models, suggesting that the NONMEM parameter estimates of the model were unbiased. Results from the dose-normalized VPC analysis stratified by formulation type with the final parameter
estimates in pharmacokinetic model of quetiapine are shown in Figure 2. Overall, the VPC analysis suggests the models can predict the distribution of observed quetiapine concentrations for both IR and XR formulations. The calculated median (based on 1000 simulated data sets) represented the trend of the observed data. In addition, the majority of the observed concentrations were within the 95% prediction interval, indicating that the predicted variability did not exceed the observed variability. Clinical Applications Simulation was conducted using the final quetiapine population PK model, and the simulated quetiapine concentration–time profiles were compared with those observed from reported clinical study.9 Figure 3 shows the simulated steady-state concentration profile of Chinese patients treated with 200 mg quetiapine IR twice daily overlaid with the observed mean concentration, indicating that the model can adequately predict the quetiapine concentration profile in Chinese patients after administration of quetiapine IR. The simulated mean steady-state area under the plasma concentration–time curve over the 12-hour dosing interval (ie, AUCss) standard deviation (SD) was 3087 1480 ng h/mL, which is in close agreement with the reported value (3538 1728 ng h/mL).9 The simulations suggested that the final population PK model was adequate to predict the quetiapine concentration profile in Chinese populations.
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Figure 1. Basic goodness-of-fit graphs for the final quetiapine population PK model. Note: The solid line is the line of identity or horizontal line, and the dashed line is the loess smooth line. (A) Observed versus population predicted quetiapine concentrations for final covariate model. (B) Observed versus individual predicted quetiapine concentrations for final covariate model. (C) Absolute individual weighted residual error (iWRES) versus individual predicted quetiapine concentrations. (D) Conditional weighted residual error (CWRES) versus time. Solid line, line of identity or horizontal line; dashed line: lowess smooth line.
Figure 2. Visual predictive check graphs for IR formulation (left) and XR formulation (right) based on the final quetiapine population PK model. The solid red line represents the median observed plasma concentration, and the red shaded area represents a simulation-based 95% confidence interval for the median. The observed 5th and 95th percentiles are presented with dashed red lines, and the 95% confidence intervals for the corresponding model-predicted percentiles are shown as blue-shaded areas. The observed plasma concentration values are represented by blue circles.
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Figure 3. Simulated concentration–time profiles (1000 patients) for Chinese patients taking 200 mg quetiapine IR twice daily. Note: The dashed line represents the mean simulated plasma concentration, and the shaded area represents a simulation-based 95% confidence interval. The black dot and error bar represent the reported mean and standard deviation by Li et al.9
Simulations were also conducted using the final quetiapine population PK model to predict the quetiapine exposure in Chinese patients after treatment with 300 mg quetiapine IR once daily or 300 mg quetiapine XR once daily. The predicted mean steady-state AUC SD after administration of 300 mg quetiapine XR was 4791 2262 ng h/mL, which is in close agreement with the observed value (5315 1798 ng h/mL) in study D1444C00007. The predicted mean steady-state AUC SD after administration of 300 mg quetiapine IR is 4770 2288 ng h/mL. The predicted steady-state geometric mean ratio of AUC for the 2 formulations (XR/IR) in Chinese patients was 1.01 with a 90% confidence interval of 0.97–1.04. The predicted steady-state geometric mean ratio of Cmax for the 2 formulations (XR/IR) in Chinese patients was 0.68 with a 90% confidence interval of 0.65–0.70. The results indicate that the IR formulation would provide similar exposure in terms of AUC relative to the XR formulation in Chinese patients. As expected, the Cmax with the IR formulation was higher than that predicted from dosing with the XR formulation. Race was not a factor influencing the PK of quetiapine, and a 1-year increase in age would result in a 4% decrease in the oral clearance of quetiapine for both formulations.
Discussion The quetiapine population PK model has been developed using data from 6 phase 1 clinical studies conducted in Western and Chinese patients. A 1-compartmental structure model with first-order absorption and first-order
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elimination adequately described the quetiapine IR and XR concentration–time course. The modeled typical volume of distribution of quetiapine was 574 L, which is within the reported ranges of 513 to 710 L.3,7 The modeled typical elimination rate constant of quetiapine was 0.12 h1, indicating an overall apparent clearance of 69 L/h for quetiapine. The apparent clearance was also within the range of the reported values of 55 to 87 L/ h.3,7,9–11 The only pharmacokinetic characteristic that differs between IR and XR formulations is the absorption rate. Although most extended-release formulations exhibit a zero-order dissolution release profile, the in vitro dissolution release profile of quetiapine XR formulation was first order (data in house), and hence a first-order absorption model was used to for quetiapine XR. The typical absorption rates were estimated to be 1.46 and 0.10 h1 for the quetiapine IR and XR formulations, respectively. The unexplained betweensubject random variability (%CV) in the final model was moderate for most PK parameters, except the absorption rate for the XR formulation, which exhibited large variability, estimated to be about 150%. The effect of age on the elimination rate of quetiapine was the only covariate identified in the covariate search, which is also consistent with those reported previously.3 Previous analysis also showed that the clearance appeared to be reduced by about 20% to 40% in elderly patients.3 In addition, in a pharmacokinetic study comparing elderly patients with younger adults, a 30%–50% reduction in apparent oral clearance was observed.17 As quetiapine is mainly metabolized by CYP3A, the decrease in clearance could be attributed to a decline in hepatic CYP3A content in elderly patients.3 Other covariates including body weight, race, and sex had no significant effect on key quetiapine PK parameters: absorption rate, volume of distribution, and elimination rate. The results indicated quetiapine PK was similar between men and women or between Western and Chinese patients. Because quetiapine is eliminated predominantly by metabolism via CYP3A, a cytochrome P450 enzyme not influenced by ethnicity, there is no mechanistic reason to expect a race difference in the PK of quetiapine.18 In the clinical development program, both quetiapine IR and XR PK have been extensively evaluated in Western populations, and bioequivalence between IR and XR formulations has also been demonstrated in Western populations. However, the only PK study in Chinese patients was conducted with quetiapine XR, so the PK of quetiapine IR has not been specifically evaluated in Chinese patients by AstraZeneca. The main objective of the current analysis was to explore if quetiapine IR and XR would have similar exposure in Chinese patients given the same daily doses. The model evaluation by bootstrap and visual predictive check indicated that the final model provided a reliable description of the observed
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clinical data and was suitable for simulation. The simulation conducted with the final quetiapine population PK model reasonably predicted quetiapine exposure after administration of quetiapine IR in Chinese patients, reported by external investigators,9 demonstrating that the final population PK model is reliable and predictive. The final model predicted that the 300-mg once-daily quetiapine XR formulation would provide similar exposure relative to the 300-mg once-daily IR formulation in Chinese patients. The predicted steady-state AUC geometric mean ratio of the 2 formulations (XR/IR) in Chinese patients wass 1.01, with a 90% confidence interval of 0.97–1.04. In the current analysis, the population PK modeling was successfully applied to integrate the wealth of information available internally for quetiapine in Western and Chinese populations, as well as published information in Chinese to predict the PK of quetiapine IR in Chinese patients and to estimate the relative bioavailability between IR and XR formulation in Chinese patients. In conclusion, the quetiapine population PK model developed adequately described the clinical data observed in Western and Chinese patients. The PK of quetiapine XR and IR was only affected by age (on elimination rate) but not by body weight, race, or sex. The model suggested that the once-daily administration of 300 mg quetiapine XR would provide similar exposure relative to the oncedaily administration of 300 mg quetiapine IR in Chinese patients. Declaration of Conflicting Interests This study was sponsored by AstraZeneca Pharmaceuticals. All authors were employees of AstraZeneca at the time of conducting this analysis.
Literature Cited 1. AstraZeneca. Seroquel1 (quetiapine fumarate) tablets/US prescribing information. http://www1.astrazeneca-us.com/pi/seroquel.pdf. Accessed July 23, 2012. 2. AstraZeneca. Seroquel XR (quetiapine fumarate) extended-release tablets/US prescribing information. http://www1.astrazeneca-us. com/pi/seroquelxr.pdf. Accessed July 23, 2012. 3. DeVane CL, Nemeroff CB. Clinical pharmacokinetics of quetiapine: an atypical antipsychotic. Clin Pharmacokinet. 2001;40: 509–522.
1255 4. Winter HR, Earley WR, Hamer-Maansson JE, Davis PC, Smith MA. Steady state pharmacokinetic, safety and tolerability profiles of quetiapinen, Norquetiapine and other quetiapine metabolites in pediatric and adult patients with psychotic disorders. J Child Adolesc Psychopharmacol. 2008;18:81–98. 5. Grimm SW, Richtand NM, Winter HR, Stams KR, Reele SB. Effects of cytochrome P450 3A modulators ketoconazole and carbamazepine on quetiapine pharmacokinetics. Br J Clin Pharmacol. 2005;61:58–69. 6. Figueroa C, Brecher M, Hamer-Maansson JE, Winter H. Pharmacokinetic profile of extended release quetiapine fumarate compared with quetiapine immediate release. Prog Neurophyschopharmacol Biol Psychiatry. 2009;33:199–204. 7. Mauri MC, Volonteri LS, Colasanti A, Fiorentini A, De Gaspari IF, Bareggi SR. Clinical pharmacokinetics of atypical antipsychotics: a critical review of the relationship between plasma concentrations and clinical response. Clin Pharmacokinet. 2007;46:359–388. 8. Bui K, Earley W, Nyberg S. Pharmacokinetic profile of the extended-release formulation of quetiapine fumarate (quetiapine XR): clinical implications. Curr Med Res Opin. 2013;29:813–825. 9. Li KY, Li X, Cheng ZN, Peng WX, Zhang BK, Li HD. Multiple dose pharmacokinetics of quetiapine and some of its metabolites in Chinese suffering from schizophrenia. Acta Pharmacol Sin. 2004;25:390–394. 10. Li Q, Su YA, Liu Y, et al. Pharmacokinetics and tolerability of extended-release quetiapine fumarate in Han Chinese patients with schizophrenia. Clin Pharmacokinet. 2014;53:455–465. 11. Kimko HC, Reele SS, Holford NH, Peck CC. Prediction of the outcome of a phase 3 clinical trial of an antischizophrenic agent (quetiapine fumarate) by simulation with a population pharmacokinetic and pharmacodynamic model. Clin Pharmacol Ther. 2000;68:568–577. 12. Beal S, Sheiner LB, Boeckmann A, Bauer, RJ, NONMEM User’s Guides. Ellicott City, Maryland: Icon Development Solutions; 2009. 13. Lindbom L, Ribbing J, Jonsson EN. Perl-speaks-NONMEM (PsN)— a Perl module for NONMEM related programming. Comput Methods Programs Biomed. 2004;75:85–94. 14. Ette EI, Williams P, Kim Y, Lane J, Liu M, Capparelli EV. Model appropriateness and population pharmacokinetics modeling. J Clin Pharm. 2003;43:610–623. 15. Ette EI. Stability and performance of a population pharmacokinetic model. J. Clin Pharmacol. 1997;37:486–495. 16. Post TM, Freijer JI, Ploeger BA, Danhof M. Extensions to the visual predictive check to facilitate model performance evaluation. J Pharmacokinet Pharmacodyn. 2008;35:185–202. 17. Jaskiw GE, Thyrum PT, Fuller MA, Arvanitis LA, Yeh C. Pharmacokinetics of quetiapine in elderly patients with selected psychotic disorders. Clin Pharmacokinet. 2004;43:1025–1035. 18. Myrand SP, Sekiguchi K, Man MZ, et al. Pharmacokinetics/ genotype associations for major cytochrome P450 enzymes in native and first- and third-generation Japanese populations: comparison with Korean, Chinese, and Caucasian populations. Clin Pharmacol Ther. 2008;84:347–361.
Population Pharmacokinetic Modeling of Quetiapine After Administration of Seroquel and Seroquel XR Formulations to Western and Chinese Patients With Schizophrenia, Schizoaffective Disorder, or Bipolar Disorder
The Journal of Clinical Pharmacology 2015, 55(11) 1248–1255 © 2015, The American College of Clinical Pharmacology DOI: 10.1002/jcph.544
Diansong Zhou, PhD, Khanh H. Bui, PhD, Jianguo Li, PhD, and Nidal Al-Huniti, PhD
Abstract A population model describing quetiapine pharmacokinetics (PK) in Western and Chinese patients following oral administration of immediate-release (IR) and extended-release (XR) formulations was developed using plasma concentrations in 127 patients from 5 studies with quetiapine IR and/or XR in Western patients and 1 study with quetiapine XR in Chinese patients. A 1-compartmental model with first-order absorption and first-order elimination adequately described the quetiapine PK. The typical apparent volume of distribution and elimination rate constant of quetiapine were 574 L and 0.12 h1, respectively. The estimated population absorption rate constants were 1.46 and 0.10 h1 for quetiapine IR and XR, respectively. Covariate analysis revealed that race was not a significant covariate influencing the PK of quetiapine. Simulation conducted with the final quetiapine population PK model predicted that the administration of a 200-mg twice-daily dose of quetiapine IR in Chinese patients would achieve a steady-state AUC (AUCss) standard deviation of 3087 1480 ng h/mL, which is in close agreement with the reported value (3538 1728 ng h/mL). The model also predicted that once-daily administration of 300 mg quetiapine IR or XR would achieve similar exposure in terms of AUCss in Chinese patients.
Keywords Quetiapine, Western, Chinese
Quetiapine, a dibenzothiazepine derivative, is an atypical antipsychotic drug with efficacy demonstrated in clinical trials for the treatment of schizophrenia, bipolar disorder, major depressive disorder, and generalized anxiety disorder.1 Quetiapine has a short half-life (7 hours), and twice-daily administration is recommended for the immediate-release formulation (Seroquel).2 In addition, an extended-release formulation (Seroquel XR) has been developed for once-daily use. Quetiapine XR is approved in the United States and the European Union for the treatment of schizophrenia, for the acute treatment of manic or mixed episodes associated with bipolar disorder (bipolar I disorder in the United States), for the acute treatment of depressive episodes associated with bipolar disorder, as an adjunct in the maintenance treatment of bipolar disorder (bipolar I disorder in the United States) and as an add-on treatment for major depressive disorder in patients with a suboptimal response to antidepressants.1,3 The pharmacokinetics of quetiapine have been extensively studied in Western populations, both in normal healthy volunteers and in patients with selected psychotic disorders.3–8 Quetiapine is rapidly absorbed after oral administration of quetiapine immediate release (IR), and food has minimal effects on quetiapine absorption.3 The apparent oral clearance of quetiapine ranges from 55 to
87 L/h in schizophrenic patients, and the oral clearance declines with age. Quetiapine is predominantly metabolized by CYP3A, and the apparent oral clearance of quetiapine did not change in patients with renal impairment.3 A crossover study in patients comparing pharmacokinetic profiles of quetiapine XR and quetiapine IR indicated that the 2 formulations were bioequivalent in terms of AUC,6 but the maximum plasma concentration at steady state was approximately 13% lower for quetiapine XR than for quetiapine IR. The apparent oral clearance of quetiapine in Chinese patients has been shown to be 59 to 78 L/h,9,10 suggesting similar pharmacokinetic characteristics in Western and Chinese patients. Only 1 population pharmacokinetic analysis has been reported for quetiapine
Quantitative Clinical Pharmacology, AstraZeneca LP, Waltham, MA, USA Submitted for publication 5 February 2015; accepted 10 May 2015. Corresponding Author: Diansong Zhou, PhD, Quantitative Clinical Pharmacology, AstraZeneca LP, 35 Gatehouse Dr.,Waltham, MA 02451 Email: [email protected]
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IR, in which first-order absorption and elimination in combination with 1-compartment model was applied.11 The objectives of the current analysis were to develop a population pharmacokinetics (PK) model describing the quetiapine pharmacokinetics in Western and Chinese patients, following oral administration of quetiapine IR and/or XR formulations, and to predict quetiapine exposure after administration of the same daily dose of quetiapine IR and XR in Chinese patients.
Methods Data Assembly The quetiapine population PK analysis data set consisted of data from 127 patients collected from 5 phase 1 studies with quetiapine IR and/or XR formulations in Western patients (studies D1441C00130,4 D1444C00001, D1444C00003, 5077IL/0097,6 and 5077IL/0118) and a single phase 1 study in Chinese patients dosed with quetiapine XR formulation (D1444C0000710). Only PK data obtained from patients in the fasted state were used in this analysis. Study design information, including patient populations and dose regimens, can be found in Table 1. Quetiapine (IR or XR) was administered orally in all studies, with titration up to a targetdailydose.Intensiveplasmasampleswerecollectedin all studies. The concentrations of quetiapine in plasma were determined by validated liquid chromatography–tandem mass spectrometric detection methods, which have been reported previously.4,6,10 All patients provided written informed consent. The protocols were approved by the institutional review
board, and the studies were conducted in accordance with the ethical principles of the Declaration of Helsinki and were consistent with the International Conference on Harmonization and Good Clinical Practice. Software The software packages Nonlinear Mixed Effects Modeling (NONMEM),12 version 7.2 (ICON, Ellicott City, Maryland), and Perl-speaks-NONMEM (PsN)13 (Uppsala University, Uppsala, Sweden) were used for modeling and simulation. R, version 2.15.2 (R Foundation for Statistical Computing, Vienna, Austria), was used for data preparation, graphical analysis, model diagnostics, and statistical summaries. XPOSE4 (Jonsson & Karlsson, Missouri) and PsN were used for model diagnostics and bootstrapping. The first-order conditional estimation with interaction between interindividual and residual random effects (FOCEI) method in NONMEM was employed for all model runs. Base Model Development Initially, exploratory graphs of individual plasma quetiapine levels were constructed by study and by formulation to qualitatively explore the suitability of different base PK models. One- and 2-compartmental models with different absorption assumptions were tested. The between-patient variability model was described as: ui ¼ uTV expðhi Þ where ui represents the value of the PK parameter u for the ith subject and uTV is the population mean of parameter u
Table 1. Brief Summary of Quetiapine Clinical Studies Study D1441C00130
D1444C00001
D1444C00003
5077IL/0097
5077IL/0118
D1444C00007
Brief Description of Study A study to characterize the steady-state pharmacokinetics and safety and tolerability of quetiapine in adults with selected psychotic disorders A phase 1, randomized, open-label, 5-treatment, 5-period, 4-sequence crossover study to compare the pharmacokinetics of 4 sustained-release formulations and the immediate release formulation of quetiapine in adults with schizophrenia, schizoaffective disorder, or bipolar disorder A study to compare the pharmacokinetics of 50 and 300 mg quetiapine XR administered following a light meal and in the fasted state in adult volunteers and adults with schizophrenia, schizoaffective disorder1, or bipolar disorder A trial to compare the steady-state pharmacokinetics of quetiapine in men and women with selected psychotic disorders following the administration of quetiapine XR or quetiapine IR Steady-state dose unit proportionality, and food effect study using commercial scale quetiapine XR in patients with schizophrenia or schizoaffective disorder A randomized, open-label trial to evaluate the pharmacokinetics of quetiapine XR 300, 600, and 800 mg in Chinese schizophrenic patients
Dosing Regimen
Number of Subjects
200 and 400 mg quetiapine IR
26
400 mg of quetiapine IR and 50 and 400 mg quetiapine XR
14
50 mg and 300 mg quetiapine XR
13
150 mg of quetiapine IR twice a day or 300 mg quetiapine XR once daily
24
50, 200, 300, and 400 mg quetiapine XR and 300 mg of quetiapine IR
10
300, 600, and 800 mg quetiapine XR
40
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1250 in the structural model. The deviation of u from the mean uTV can be approximated with hi, which is in a normal distribution, with a mean of 0 and a variance of v2 (ie, hi N[0, v2]). Different residual error models were evaluated, including exponential, additive, proportional, and a combined additive and proportional models. The combined additive and proportional residual model was defined as: ^ ij ð1 þ epij Þ þ eaij C ij ¼ C where Cij is the jth measured concentration in the ith ^ ij is the jth model-predicted concentration in individual; C the ith individual; epij and eaij are proportional and additive residual random errors, respectively, for individual i and measurement j and are each assumed to be independently normally distributed (ie, epij N[0, s12] and eaij N[0, s22]). Model development and selection were driven by the data and were based on various goodness-of-fit indicators,14 including comparisons based on the minimum objective function value (OFV; equal to -2 log likelihood), successful minimization and, if possible, completion of covariance steps in NONMEM, and various goodness-of-fit criteria: visual inspection of diagnostic plots (eg, observed versus predicted concentrations, weighted residual versus predicted concentration or time, correlations of between-individual random effects), precision of parameter estimates, and plausibility of parameter estimates. Covariate Model Development Covariate effect modeling started from the previously developed parsimonious base model by a procedure to include the covariate effects on the population mean PK parameters, with a statistical hypothesis test of significance and clinical significance. A stepwise regression approach was used for the covariate modeling, building with forward selection and backward elimination of subject covariates. First, the effect of each covariate was examined univariately by adding 1 covariate at a time to the base model. The covariates that resulted in the greatest statistically significant decrease in the value of the objective function were added to the base model, then the entire procedure was repeated stepwise until all significant covariates were included. Once all covariate relationships for the PK parameters had been defined from the forward selection step, backward elimination of the covariates one by one was performed to see if a covariate should be deleted from the full model or retained. The model for each relevant parameter covariate relationship was prepared and tested using stepwise covariate model (SCM) approach implemented in PsN. Stepwise forward or backward comparisons, based on the likelihood ratio test and a pre-specified a level, have
been made across nested multivariate models, each expressing different covariate-parameter combinations. According to the likelihood ratio test, the difference in -2 log likelihood from nested models is assumed to be asymptotically x2 distributed with degrees of freedom (df) equal to the difference in number of model parameters. Significance of covariate effect was determined at a ¼ 0.1 (or 3.84 of change in NONMEM OFV with df ¼ 1) at the forward selection step and at a ¼ 0.001 (or 10.8 of change in NONMEM OFV with df ¼ 1) at the backward elimination step. Baseline demographic characteristics including weight, age, race, and sex were tested on quetiapine absorption, distribution, and elimination parameters. Given the involvement of the liver in quetiapine metabolism, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and bilirubin concentrations (as measurements of liver function) were also evaluated as covariates for quetiapine elimination. Creatinine clearance as a measure of patient renal function was only reported in 2 of 6 studies; therefore, it was not evaluated as covariate in the current analysis. The relationship between continuous covariates and the typical value of PK parameters was primarily modeled using power models, whereas the relationship between categorical covariates and the typical value of PK parameters was modeled as a constant proportional model. Model Evaluation The final full covariate model of quetiapine was evaluated using stratified nonparametric bootstrap15 and visual predictive checks (VPCs).16 For the nonparametric bootstrap procedure, 1000 replicate bootstrap data sets were obtained by random resampling using subject as the sampling unit, with replacement from the original data set, and were fitted with the same model to obtain parameter estimates for each replicate. Empirical 95% confidence intervals were constructed by obtaining the 2.5th and 97.5th percentiles of the resulting parameter distributions for those bootstrap runs with successful convergence. The final model parameter estimates were compared with the bootstrap median parameter estimates to evaluate the final model performance. The predictive performance of the final model was assessed with VPC. Simulation of 1000 new data sets was carried out for each drug using the final model with the estimated fixed- and random-effects model parameters. The concentration–time profiles were plotted for the 50th percentile and the 5th and 95th percentiles (presenting the 90% prediction interval) of the simulated data and overlaid with observed data. Model Applications The final model for quetiapine with estimated fixed- and random-effects parameters was also applied to simulate
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the exposure of quetiapine IR to mimic a quetiapine study conducted in Chinese patients.9 Briefly, 1000 patients were titrated with quetiapine IR and reached a 200-mg twice-daily dose on day 4. On day 8, blood samples were collected up to 24 hours after the 200-mg dose in the morning. The concentration–time profiles were plotted for the mean and the 2.5th and 97.5th percentiles (presenting the 95% prediction interval) of the simulated data and overlaid with the reported data, which were digitalized from the published report.9 The mean AUC and Cmax values at steady state were also summarized and compared with reported values. Finally, the final model for quetiapine with estimated fixed- and random-effects parameters was applied to simulate the relative exposure in Chinese patients after administration of the quetiapine IR and XR formulations. Briefly, quetiapine concentration–time courses for 1000 patients were simulated for 300 mg quetiapine IR once daily or 300 mg quetiapine XR once daily for 8 days. The predicted geometric mean AUC and Cmax values were summarized and compared with each other and the reported values.
Results The baseline characteristics for patients in the final PK data set are summarized in Table 2. For the127 patients included in the final population PK modeling, the average age was 39.8 years, with a range from 18 to 65 years; the average body weight was 80 kg, with a range from 45 to 124.5 kg. The data set had 87 male and 40 female patients. White (36), black (31), and Chinese (40) are the 3 main races in the data set, with 20 patients of other ethnicities including Hispanic, Asian, and others. The population PK data set contained 4364 quetiapine plasma concentrations. There were 4 patients from study 5077IL/0097 missing an ALT value and 2 patients from study D1444C00001
missing ALT, AST, and bilirubin concentrations. Population median values were substituted for these missing laboratory values. Base Model A 1-compartment disposition model with first-order elimination and first-order absorption provided the best fit for the quetiapine IR and XR plasma concentration– time profiles. Adding a peripheral compartment to the model did not decrease objective function value or improve model diagnostic plots. The quetiapine data also supported different absorption rate constants (Ka) for IR and XR formulations. Interindividual variability was estimated for all structural pharmacokinetic model parameters, and the covariance between eliminate rate constant (Ke) and volume of distribution (V) was supported by the quetiapine concentration data. Different residual error models were tested, and the combined proportional and additive residual error model was found to best describe the data. Diagnostic plots for the base model showed adequate fit to the data, with no apparent trends of residuals over time or model predictions (figure not shown). The base model structural parameter and random variance estimates are presented in Table 3. Covariate Model A covariate model-building approach was applied, with a forward selection and backward elimination method via the SCM approach. Baseline demographic characteristics, including weight, age, race, and sex were explored on Ke, V, and Ka. The ALT, AST, and bilirubin concentrations were also explored as covariates for Ke. Race was coded as a 4-category covariate in the analysis: white (36), black (31), Chinese (40), and others (20). The effect of age on the Ke of quetiapine was modeled as a power model normalized at 42 years, with a population mean exponent of -0.47, indicating the
Table 2. Summary of Population Demographic Characteristics by Study for Continuous and Categorical Variables Study n Age (y) Median (range) Weight (kg) Median (range) Sex (n) Male Female Race (n) White Black Hispanic Chinese Asian Others
D1441C00130
D1444C00001
D1444C00003
D1444C00007
5077IL/0097
5077IL/0118
All
26
14
13
40
24
10
127
39.5 (18–45)
40.5 (18–45)
30 (23–44)
41 (19–55)
45 (18–62)
50.5 (42–65)
42 (18–65)
89.1 (59–109)
80.2 (59.1–114)
90 (60–114)
65 (45–89)
90.5 (58.5–124.5)
89.3 (64.9–102)
77.5 (45–124.5)
18 8
14 0
11 2
20 20
15 9
9 1
87 40
10 12 0 0 3 1
0 12 1 0 0 1
12 0 0 0 0 1
0 0 0 40 0 0
12 6 6 0 0 0
2 1 7 0 0 0
36 31 14 40 3 3
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Table 3. Parameter Estimates of the Final Quetiapine Population PK Covariate Model Nonparametric Bootstrap Parameter Typical PK parameters u1: TVKe (/h) u2: TVKe (age/42)u2 u3: TV (L) u4: TKa, XR (/h) u5: TKa, IR (/h) Random BSV v1,1: BKe v2,2: BV v1,2: (BKe, BV) v3,3: BKa,XR v4,4: BKa,IR RVa u6: PRV (%) u7: ARV (SD) u8: PRV (%) u9: ARV (SD)
Description
Base Model Estimate
Final Model Estimate
RSE (%)
Median
95%CI
Typical Ke Age effect on Ke Typical V Typical Ka for SEROQUEL XR Typical Ka for SEROQUEL
0.12 – 580.6 0.098 1.47
0.12 0.47 573.7 0.10 1.46
5.1 25.3 5.5 28.6 11.1
0.12 0.48 571.5 0.09 1.48
(0.11–0.13) (0.72 to 0.24) (509.8–642.7) (0.05–0.16) (1.17–1.85)
Interindividual Interindividual Covariance of Interindividual Interindividual
0.41 0.47 0.46 1.5 0.74
0.4 0.47 0.5 1.5 0.75
19 20 34 21 32
0.39 0.46 0.52 1.49 0.73
(0.32–0.46) (0.39–0.54) (1.18 to 0.18) (1.19–1.87) (0.47–0.95)
0.38 1.02 0.48 1.46
0.38 1.01 0.48 1.51
3.4 19.7 3.6 35.9
0.38 1.02 0.48 1.56
variance of variance of Ke and V variance of variance of
Ke V Ka, XR Ka, XR
Proportional error of RV for XR Additive error of RV for XR Proportional error of RV for IR Additive error of RV for IR
(0.36, (0.65, (0.45, (0.77,
0.41) 1.83) 0.51) 2.63)
a Residual variability was parameterized by the fixed-effect parameter (u). Interindividual variance was expressed as CV, covariance was expressed as correlation, residual variance was expressed as %CV for proportional terms and SD (ng/mL) for additive terms, RSE was expressed as 100 (standard error of the estimate/ point estimate). Median and 95%CI were estimated from nonparametric bootstrap estimates from1000 patients. BSV, between-subject variability; CI, confidence interval; CV, coefficient of variation; h, hour; Ka, absorption rate; Ke, elimination rate; RSE, relative standard error; RV, residual variability; SD, standard deviation; V, volume of distribution.
clearance of quetiapine decreases with age. This was also the only covariate identified in the covariate search. The inclusion of this covariate effect on quetiapine PK resulted in an 18.55-point decrease in object function value compared with the base model. Other covariates including weight, race, and sex had no significant effect on Ke, V, or Ka. Laboratory observations, including ALT, AST, and bilirubin concentration, as a measure of liver function had no significant effect on Ke. Final estimates of the parameters, together with relative standard errors, are presented in Table 3. Goodness-of-fit plots (Figure 1), although they showed that concentrations above 1000 ng/mL were underpredicted, indicated a good fit of the model for the majority of the data. Those data were primarily Cmax values from the 400-mg IR dose, and were not well predicted with the first-order absorption model because of the rapid absorption for the IR formulation. The distribution of the conditional weighted residuals was unbiased with respect to time or population predictions. Model Evaluation The median values of parameters and 95% confidence intervals obtained from the converged bootstrap runs for quetiapine are presented in Table 3. The median values of parameters were in close agreement with the population estimates in the final models, suggesting that the NONMEM parameter estimates of the model were unbiased. Results from the dose-normalized VPC analysis stratified by formulation type with the final parameter
estimates in pharmacokinetic model of quetiapine are shown in Figure 2. Overall, the VPC analysis suggests the models can predict the distribution of observed quetiapine concentrations for both IR and XR formulations. The calculated median (based on 1000 simulated data sets) represented the trend of the observed data. In addition, the majority of the observed concentrations were within the 95% prediction interval, indicating that the predicted variability did not exceed the observed variability. Clinical Applications Simulation was conducted using the final quetiapine population PK model, and the simulated quetiapine concentration–time profiles were compared with those observed from reported clinical study.9 Figure 3 shows the simulated steady-state concentration profile of Chinese patients treated with 200 mg quetiapine IR twice daily overlaid with the observed mean concentration, indicating that the model can adequately predict the quetiapine concentration profile in Chinese patients after administration of quetiapine IR. The simulated mean steady-state area under the plasma concentration–time curve over the 12-hour dosing interval (ie, AUCss) standard deviation (SD) was 3087 1480 ng h/mL, which is in close agreement with the reported value (3538 1728 ng h/mL).9 The simulations suggested that the final population PK model was adequate to predict the quetiapine concentration profile in Chinese populations.
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Figure 1. Basic goodness-of-fit graphs for the final quetiapine population PK model. Note: The solid line is the line of identity or horizontal line, and the dashed line is the loess smooth line. (A) Observed versus population predicted quetiapine concentrations for final covariate model. (B) Observed versus individual predicted quetiapine concentrations for final covariate model. (C) Absolute individual weighted residual error (iWRES) versus individual predicted quetiapine concentrations. (D) Conditional weighted residual error (CWRES) versus time. Solid line, line of identity or horizontal line; dashed line: lowess smooth line.
Figure 2. Visual predictive check graphs for IR formulation (left) and XR formulation (right) based on the final quetiapine population PK model. The solid red line represents the median observed plasma concentration, and the red shaded area represents a simulation-based 95% confidence interval for the median. The observed 5th and 95th percentiles are presented with dashed red lines, and the 95% confidence intervals for the corresponding model-predicted percentiles are shown as blue-shaded areas. The observed plasma concentration values are represented by blue circles.
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Figure 3. Simulated concentration–time profiles (1000 patients) for Chinese patients taking 200 mg quetiapine IR twice daily. Note: The dashed line represents the mean simulated plasma concentration, and the shaded area represents a simulation-based 95% confidence interval. The black dot and error bar represent the reported mean and standard deviation by Li et al.9
Simulations were also conducted using the final quetiapine population PK model to predict the quetiapine exposure in Chinese patients after treatment with 300 mg quetiapine IR once daily or 300 mg quetiapine XR once daily. The predicted mean steady-state AUC SD after administration of 300 mg quetiapine XR was 4791 2262 ng h/mL, which is in close agreement with the observed value (5315 1798 ng h/mL) in study D1444C00007. The predicted mean steady-state AUC SD after administration of 300 mg quetiapine IR is 4770 2288 ng h/mL. The predicted steady-state geometric mean ratio of AUC for the 2 formulations (XR/IR) in Chinese patients was 1.01 with a 90% confidence interval of 0.97–1.04. The predicted steady-state geometric mean ratio of Cmax for the 2 formulations (XR/IR) in Chinese patients was 0.68 with a 90% confidence interval of 0.65–0.70. The results indicate that the IR formulation would provide similar exposure in terms of AUC relative to the XR formulation in Chinese patients. As expected, the Cmax with the IR formulation was higher than that predicted from dosing with the XR formulation. Race was not a factor influencing the PK of quetiapine, and a 1-year increase in age would result in a 4% decrease in the oral clearance of quetiapine for both formulations.
Discussion The quetiapine population PK model has been developed using data from 6 phase 1 clinical studies conducted in Western and Chinese patients. A 1-compartmental structure model with first-order absorption and first-order
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elimination adequately described the quetiapine IR and XR concentration–time course. The modeled typical volume of distribution of quetiapine was 574 L, which is within the reported ranges of 513 to 710 L.3,7 The modeled typical elimination rate constant of quetiapine was 0.12 h1, indicating an overall apparent clearance of 69 L/h for quetiapine. The apparent clearance was also within the range of the reported values of 55 to 87 L/ h.3,7,9–11 The only pharmacokinetic characteristic that differs between IR and XR formulations is the absorption rate. Although most extended-release formulations exhibit a zero-order dissolution release profile, the in vitro dissolution release profile of quetiapine XR formulation was first order (data in house), and hence a first-order absorption model was used to for quetiapine XR. The typical absorption rates were estimated to be 1.46 and 0.10 h1 for the quetiapine IR and XR formulations, respectively. The unexplained betweensubject random variability (%CV) in the final model was moderate for most PK parameters, except the absorption rate for the XR formulation, which exhibited large variability, estimated to be about 150%. The effect of age on the elimination rate of quetiapine was the only covariate identified in the covariate search, which is also consistent with those reported previously.3 Previous analysis also showed that the clearance appeared to be reduced by about 20% to 40% in elderly patients.3 In addition, in a pharmacokinetic study comparing elderly patients with younger adults, a 30%–50% reduction in apparent oral clearance was observed.17 As quetiapine is mainly metabolized by CYP3A, the decrease in clearance could be attributed to a decline in hepatic CYP3A content in elderly patients.3 Other covariates including body weight, race, and sex had no significant effect on key quetiapine PK parameters: absorption rate, volume of distribution, and elimination rate. The results indicated quetiapine PK was similar between men and women or between Western and Chinese patients. Because quetiapine is eliminated predominantly by metabolism via CYP3A, a cytochrome P450 enzyme not influenced by ethnicity, there is no mechanistic reason to expect a race difference in the PK of quetiapine.18 In the clinical development program, both quetiapine IR and XR PK have been extensively evaluated in Western populations, and bioequivalence between IR and XR formulations has also been demonstrated in Western populations. However, the only PK study in Chinese patients was conducted with quetiapine XR, so the PK of quetiapine IR has not been specifically evaluated in Chinese patients by AstraZeneca. The main objective of the current analysis was to explore if quetiapine IR and XR would have similar exposure in Chinese patients given the same daily doses. The model evaluation by bootstrap and visual predictive check indicated that the final model provided a reliable description of the observed
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clinical data and was suitable for simulation. The simulation conducted with the final quetiapine population PK model reasonably predicted quetiapine exposure after administration of quetiapine IR in Chinese patients, reported by external investigators,9 demonstrating that the final population PK model is reliable and predictive. The final model predicted that the 300-mg once-daily quetiapine XR formulation would provide similar exposure relative to the 300-mg once-daily IR formulation in Chinese patients. The predicted steady-state AUC geometric mean ratio of the 2 formulations (XR/IR) in Chinese patients wass 1.01, with a 90% confidence interval of 0.97–1.04. In the current analysis, the population PK modeling was successfully applied to integrate the wealth of information available internally for quetiapine in Western and Chinese populations, as well as published information in Chinese to predict the PK of quetiapine IR in Chinese patients and to estimate the relative bioavailability between IR and XR formulation in Chinese patients. In conclusion, the quetiapine population PK model developed adequately described the clinical data observed in Western and Chinese patients. The PK of quetiapine XR and IR was only affected by age (on elimination rate) but not by body weight, race, or sex. The model suggested that the once-daily administration of 300 mg quetiapine XR would provide similar exposure relative to the oncedaily administration of 300 mg quetiapine IR in Chinese patients. Declaration of Conflicting Interests This study was sponsored by AstraZeneca Pharmaceuticals. All authors were employees of AstraZeneca at the time of conducting this analysis.
Literature Cited 1. AstraZeneca. Seroquel1 (quetiapine fumarate) tablets/US prescribing information. http://www1.astrazeneca-us.com/pi/seroquel.pdf. Accessed July 23, 2012. 2. AstraZeneca. Seroquel XR (quetiapine fumarate) extended-release tablets/US prescribing information. http://www1.astrazeneca-us. com/pi/seroquelxr.pdf. Accessed July 23, 2012. 3. DeVane CL, Nemeroff CB. Clinical pharmacokinetics of quetiapine: an atypical antipsychotic. Clin Pharmacokinet. 2001;40: 509–522.
1255 4. Winter HR, Earley WR, Hamer-Maansson JE, Davis PC, Smith MA. Steady state pharmacokinetic, safety and tolerability profiles of quetiapinen, Norquetiapine and other quetiapine metabolites in pediatric and adult patients with psychotic disorders. J Child Adolesc Psychopharmacol. 2008;18:81–98. 5. Grimm SW, Richtand NM, Winter HR, Stams KR, Reele SB. Effects of cytochrome P450 3A modulators ketoconazole and carbamazepine on quetiapine pharmacokinetics. Br J Clin Pharmacol. 2005;61:58–69. 6. Figueroa C, Brecher M, Hamer-Maansson JE, Winter H. Pharmacokinetic profile of extended release quetiapine fumarate compared with quetiapine immediate release. Prog Neurophyschopharmacol Biol Psychiatry. 2009;33:199–204. 7. Mauri MC, Volonteri LS, Colasanti A, Fiorentini A, De Gaspari IF, Bareggi SR. Clinical pharmacokinetics of atypical antipsychotics: a critical review of the relationship between plasma concentrations and clinical response. Clin Pharmacokinet. 2007;46:359–388. 8. Bui K, Earley W, Nyberg S. Pharmacokinetic profile of the extended-release formulation of quetiapine fumarate (quetiapine XR): clinical implications. Curr Med Res Opin. 2013;29:813–825. 9. Li KY, Li X, Cheng ZN, Peng WX, Zhang BK, Li HD. Multiple dose pharmacokinetics of quetiapine and some of its metabolites in Chinese suffering from schizophrenia. Acta Pharmacol Sin. 2004;25:390–394. 10. Li Q, Su YA, Liu Y, et al. Pharmacokinetics and tolerability of extended-release quetiapine fumarate in Han Chinese patients with schizophrenia. Clin Pharmacokinet. 2014;53:455–465. 11. Kimko HC, Reele SS, Holford NH, Peck CC. Prediction of the outcome of a phase 3 clinical trial of an antischizophrenic agent (quetiapine fumarate) by simulation with a population pharmacokinetic and pharmacodynamic model. Clin Pharmacol Ther. 2000;68:568–577. 12. Beal S, Sheiner LB, Boeckmann A, Bauer, RJ, NONMEM User’s Guides. Ellicott City, Maryland: Icon Development Solutions; 2009. 13. Lindbom L, Ribbing J, Jonsson EN. Perl-speaks-NONMEM (PsN)— a Perl module for NONMEM related programming. Comput Methods Programs Biomed. 2004;75:85–94. 14. Ette EI, Williams P, Kim Y, Lane J, Liu M, Capparelli EV. Model appropriateness and population pharmacokinetics modeling. J Clin Pharm. 2003;43:610–623. 15. Ette EI. Stability and performance of a population pharmacokinetic model. J. Clin Pharmacol. 1997;37:486–495. 16. Post TM, Freijer JI, Ploeger BA, Danhof M. Extensions to the visual predictive check to facilitate model performance evaluation. J Pharmacokinet Pharmacodyn. 2008;35:185–202. 17. Jaskiw GE, Thyrum PT, Fuller MA, Arvanitis LA, Yeh C. Pharmacokinetics of quetiapine in elderly patients with selected psychotic disorders. Clin Pharmacokinet. 2004;43:1025–1035. 18. Myrand SP, Sekiguchi K, Man MZ, et al. Pharmacokinetics/ genotype associations for major cytochrome P450 enzymes in native and first- and third-generation Japanese populations: comparison with Korean, Chinese, and Caucasian populations. Clin Pharmacol Ther. 2008;84:347–361.
