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PHARMACOKINETICS |
From the Department of Pharmaceutical Sciences (Dr Bigos, Dr Coley, Dr Bies) and Department of Psychiatry (Dr Pollock, Ms Kirshner, Dr Bies), University of Pittsburgh, Pittsburgh, Pennsylvania; Rotman Research Institute, University of Toronto, Toronto, Ontario (Dr Pollock); Psychiatry Research, University of Iowa, Carver College of Medicine, Iowa City, Iowa (Dr Miller); Psychiatry and Biobehavioral Sciences, University of California, Los Angeles (Dr Marder, Dr Aravagiri); and Keck School of Medicine, University of Southern California, Los Angeles (Dr Schneider).
Address for correspondence: Robert R. Bies, PharmD, PhD, University of Pittsburgh, School of Pharmacy, 805 Salk Hall, 3501 Terrace St, Pittsburgh, PA 15261; e-mail: rrb47{at}pitt.edu.
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Key Words: Pharmacokinetics • olanzapine • antipsychotic • schizophrenia • Alzheimer's disease • clinical antipsychotic trials of intervention effectiveness • CATIE
The CATIE trials reported overall high rates of discontinuation due to lack of efficacy and/or intolerable side effects for all antipsychotics. One reason for the high rates of discontinuation may relate to the wide variability in the pharmacokinetics of these drugs, which often results in differences in the pharmacodynamics, both in the response to a drug and the incidence of adverse effects. For example, if a patient clears a drug faster than average, he or she will experience lower drug levels and may not respond as well at the same dose. Conversely, if a patient clears the drug slower than average, he or she will have higher drug levels and may be at a higher risk of experiencing adverse effects. Therefore, to limit the variability in response to a drug, it is often necessary to limit or control for the variability in the pharmacokinetics. This is particularly important in an older population such as patients with Alzheimer's disease, where the variability is often greater.3
Several studies have shown a correlation between plasma concentrations of olanzapine and clinical response.4-9 Perry and colleagues6 determined that a minimum effective concentration of 9 ng/mL at 24 hours was a therapeutic marker significantly increasing the likelihood of a patient with schizophrenia responding to olanzapine. This was confirmed by 2 studies that showed that 23 to 25 ng/mL at 12 to 13.5 hours correlated with treatment response.5,8 Mauri and colleagues9 found a curvilinear relationship between plasma olanzapine concentration and clinical response, with clinical efficacy being approximately associated with a plasma concentration range of 20 to 50 ng/mL. In addition, Lane and colleagues7 found that a plasma olanzapine concentration of 36 ng/mL predicted treatment response of depressive symptoms in patients with schizophrenia.
Population pharmacokinetic methodologies provide a means of estimating the magnitude of drug exposure in a large number of patients in a minimally invasive way, using sparse sampling.10 These methodologies also allow one to identify factors that contribute to variability in drug exposure as well as detect potential pharmacokinetic drug interactions.10 Limited data on the pharmacokinetics of olanzapine have been published.11-14 Most studies were conducted in a small number of subjects, and other studies elude to population analyses but do not report actual values or the magnitude of effect of the contributors to variability.11-14 Some studies have shown that sex and smoking may affect the clearance of olanzapine, which results in variability in exposure.11,14
The CATIE trials afforded a unique opportunity to study a large number of subjects treated with antipsychotics. This ancillary study to the CATIE trials aimed to capture the magnitude and variability of concentration exposure of antipsychotics using mixed-effects population pharmacokinetic methodologies. This allowed us, for the first time, to evaluate the population pharmacokinetics of olanzapine, as well as identify factors that contribute to variability in exposure, in large populations of patients with schizophrenia and Alzheimer's disease.
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METHODS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Analytical Procedures
Plasma levels of olanzapine were determined using liquid chromatography tandem mass spectrometry (LC/MS/MS).17 Briefly, 0.5 mL of plasma was alkalized with 0.5 mL of saturated aqueous solution of sodium carbonate and extracted by a liquid-liquid extraction method (15% methylene chloride in pentane). The organic extract was dried and reconstituted in mobile phase, and an aliquot was injected into the LC/MS/MS system. The compounds were separated on a phenyl-hexyl (5-µ 50 x 4.6 mm) column by isocratic elution using a mobile phase containing aqueous 78 µM ammonium acetate, methanol, and acetonitrile (5:45:50). The analytes were ionized in the mass spectrometer in a TurboIon source with positive ion atmospheric pressure electrospray ionization and detected with multiple-reaction monitoring modes. The ion transitions monitored were m/z 313
256 for olanzapine and m/z 327270 for the internal standard (LY 170222). These transition ions were selected based on predominant fragmentation pathways of olanzapine and internal standard and their intensity, as observed in their product ion mass spectra. The olanzapine standard was linear over the range of 0.1 to 100 ng/mL when 0.5 mL plasma was used for the analysis (r2 > 0.999). The intra- and interassay variations were less than 15% for the spiked standard curve and quality control samples. The variations for the long-term patient quality control samples were <10%.
Population Pharmacokinetic Analysis
Nonlinear mixed-effects modeling was used for the population pharmacokinetic analysis using NONMEM (Version 5, Level 1.1; GloboMax, Ellicott City, Maryland).18,19 The population pharmacokinetic analysis included the development of a structural base model, which defines the pharmacokinetic parameters and describes the plasma concentration-time profile for olanzapine. Olanzapine dose, time of dose, time of blood draw, and olanzapine concentration were entered into the model for each sample for each subject. One- and 2-compartment models were evaluated using NONMEM20,21 with the subroutine ADVAN2 TRANS2 (1-compartment model) and ADVAN4 TRANS4 (2-compartment model). Pharmacokinetic parameters, including apparent oral clearance (CL) and volume of distribution (Vd), as well as interindividual (between-subject) and intraindividual (within-subject) variability, were estimated. The interindividual variability (IIV) model describes the unexplained random variability in individual values of structural model parameters. It was assumed that the IIV of the pharmacokinetic parameters was log-normally distributed. The relationship between a parameter (P) and its variance could therefore be expressed as follows:
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P denoted the difference between Pj and PTV, independently, which was identically distributed with a mean of 0 and variance of 
.
The residual variability, which was composed of but not limited to intraindividual variability, experimental errors, process noise, and/or model misspecifications, was modeled using additive, proportional, and combined error structures as described as follows:
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was the corresponding model prediction, and 
ij (or 
) was a normally distributed random error with a mean of 0 and a variance of 
2. The population analysis used here calculates both "population" characteristics (eg, the population average CL and Vd for the group analyzed) as well as individual specific CL and Vd that are conditioned on the population characteristics and estimated using a Bayesian approach. The first-order conditional estimation method (FOCE) with interaction was used for both the base and the final models.
The initial base model provides the basis for exploring subpopulations based on whether those individual specific characteristics or covariates can explain and reduce the population variance estimated in the group. The final model was then developed by testing the effects of subject-specific covariates on pharmacokinetic parameter estimates. Continuous covariates (eg, age, height, weight) and discrete covariates (eg, sex, race, smoking status) were introduced into each parameter in a stepwise fashion.
The following example shows the effect of a continuous covariate on CL:
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TVCL was the typical value for the population; i was the random effect representing the difference of the ith subject from the population mean. The random effects of between-subject variability were assumed log-normally distributed, with a mean of 0 and standard deviation of 
. Cov was the continuous covariate that was affecting CL, and MedCov was the median Cov.
The following example shows the effect of a discrete covariate (ie, sex) on CL:
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CL because the numeric value for (2-female) = 0, resulting in a 0 multiplier for the covariate effect. For male subjects, the Sex term was added to the population estimate of CL to modify it. Covariates for inclusion in the model were first identified using a graphical method comparing the individual Bayesian estimates for that parameter with the covariate. The stepwise incorporation of the covariates on a particular parameter is analogous to a multiple regression on the particular parameter (ie, the incorporation of an effect on clearance in a stepwise fashion). The approach provides for what would be returned from a classical multiple linear regression of the post hoc values but has the significant advantage of preserving the data structure and uncertainty when evaluating the covariate effect. A combination of covariates was included only if there was a significant additional reduction in the objective function value indicating a substantially improved goodness of fit. Concomitant medications that had an incidence of approximately 1% or greater were also individually tested as discrete covariates to identify potential pharmacokinetic drug interactions with olanzapine.
Statistical Analyses
The developed models were evaluated using both statistical and graphical methods. The likelihood ratio test was used to discriminate between alternative models. The likelihood ratio test is based on the property that the ratio of the NONMEM objective function values (-2 log-likelihood) is asymptotically chisquare distributed. The objective function value is the sum of squared deviations between the predictions and the observations. An objective function decrease of 3.84 units was considered significant (chi-square, df = 1, P < .05). Likewise, a covariate was retained in the model if it decreased the objective function value by 3.84 units. Multiple covariates were included into the model only if their combination resulted in an additional 3.84-unit decrease in objective function. Covariate influence on interindividual variability and goodness of fit was also examined. Standard errors for all parameters were obtained using the covariance option in NONMEM. The modeling approach is to determine the most parsimonious model that adequately describes the data.
Postprocessing of NONMEM outputs were performed using Prism (Version 4.03; GraphPad Software, Inc, San Diego).22 Linear regression was performed to determine the magnitude of contribution to the variability of clearance for significant covariates. Unpaired t tests were done for each significant covariate. Analysis of variance (ANOVA) was used to compare clearance for each of the race categories, and Bonferroni's multiple comparison test was used to correct for multiple comparisons. Data are reported as mean ± SD; P values <.05 were considered significant. All plots were generated using Prism; horizontal lines represent the median for each data set. Data in the plots were estimated using the final model (eg, lowest objective function), which included the covariates.
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RESULTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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The population pharmacokinetic model adequately described the olanzapine pharmacokinetics in this population of patients with Alzheimer's disease and schizophrenia. A 1-compartment pharmacokinetic model with additive error best described the data. Pharmacokinetic parameters are summarized in Table II. The population mean clearance and volume of distribution were 16.1 L/h and 2150 L, respectively. The absorption constant Ka was fixed at 0.5 h-1 based on previous literature reports.11
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Elimination of olanzapine varied nearly 10-fold (range, 6.66-67.96 L/h). Smoking status, sex, and race accounted for 26%, 12%, and 7% of the variability, respectively (P < .0001 for each parameter; Table III). Smokers cleared olanzapine 55% faster than non/past smokers (P < .0001, unpaired t test; Figure 1). Men cleared olanzapine 38% faster than women (P < .0001, unpaired t test; Figure 2). Patients who identified themselves as black or African American cleared olanzapine 26% faster than other races (ANOVA overall P < .05; Figure 3). Olanzapine clearance was significantly higher in black/African American patients than in white patients (ANOVA mean difference 6.141, P < .001) and Asian patients (mean difference 7.738, P < .05) and was also higher than in American Indian patients and those who identified with 2 or more races (mean differences 4.514 and 7.995, respectively, P > .05), although these did not reach significance likely due to small sample sizes. Figure 4 illustrates the combined effect of sex, race, and smoking status by comparing black/African American men who smoke with non-black/African American women who do not smoke (35.70 L/h ± 10.70 vs 16.70 L/h ± 4.662, P < .0001, unpaired t test). Hispanic ethnicity did not have an effect on olanzapine clearance.
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Pharmacokinetic model statistics are summarized in Table IV. Each covariate (sex, smoking, and race) independently contributed to a better model fit, as reflected by a decrease in objective function of 3.84 points, which is statistically equivalent to a P value equal to .05. When the covariates were added in a stepwise fashion, the model continued to improve. The best model fit included sex, smoking, and race as contributors to variability in olanzapine clearance.
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The Alzheimer's disease and schizophrenia populations were also analyzed separately. Differences in clearance between these groups were explained entirely by differences in sex and smoking status between these groups and not by age or any other factor. Age, height, and weight did not have an effect on olanzapine clearance. None of the 41 concomitant medications tested had an effect on olanzapine clearance. None of the covariates (including concomitant medications) had an effect on the volume of distribution of olanzapine.
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Sex differences in pharmacokinetics have been reported for many psychotropic medications.27,28 Differences in olanzapine clearance due to sex have been reported.11,14,29,30 In this study, men cleared olanzapine 38% faster than women. Estrogen is a known inhibitor of CYP1A2, which could explain the slower olanzapine clearance found in women.31 Other possible mechanisms include sex differences in blood flow and liver size, as well as differences in expression of metabolizing enzymes and transporters.27,28 The difference in olanzapine clearance between men and women is not a result of differences in body weight, given that body weight was not a significant covariate.
This is the first study to find racial differences in olanzapine clearance. Patients who identified themselves as black or African American cleared olanzapine faster than patients of other races. There are many possible factors underlying these racial differences, potentially arising from both the sociologic and possible biologic realms.32 One possible explanation for racial differences in pharmacokinetics is the known genotypic differences in metabolizing enzymes. With respect to metabolizing enzyme differences, more than 80 allelic variants have been identified for the CYP2D6 gene among different racial populations, which results in variable enzymatic activity.33 Feng and colleagues34,35 found that race was a significant predictor of paroxetine clearance but was no longer significant when the CYP2D6 genotype was incorporated in the model, which suggests that race is acting as a surrogate for CYP2D6 genotype. The average paroxetine clearance was 27.4 L/h for the African American population compared with only 21.9 L/h for the Caucasian population, which may relate to the 3-fold higher frequency of the *4 null allele in Caucasians compared with African Americans.36 There have also been polymorphisms found in the CYP1A2 gene,37 but there are limited data on the effects of CYP1A2 genetics on drug metabolism.
It may also be that there are differences in adherence or drug intake rates across the different racial populations. Some research has found that African Americans have poorer treatment adherence to antipsychotic therapy.38 A consistently lower intake of the drug would result in an increased estimate of clearance due to lower drug concentrations. In addition, if patients were inconsistently taking the medication, this would contribute to variability in apparent elimination across occasions. Due to the nature of the illnesses, patients with schizophrenia and Alzheimer's disease are often noncompliant. Therefore, some variability in clearance may be due to noncompliance.
There is greater genetic diversity in African populations than in either European or Asian populations, which leads to considerable heterogeneity in African populations.39 Other potential reasons for the racial differences found in this study may arise, as the race covariate is collected primarily as a social category, not a biological category.40 Due to the multifactorial nature of race, this finding requires further study. Therefore, conclusions regarding dosage should not be made on the basis of race.
In summary, sex, race, and smoking status affect olanzapine clearance and therefore affect drug exposure. This 10-fold difference in clearance may account for some of the variability in response to olanzapine, in terms of efficacy as well as the frequency of side effects. Therefore, an insufficient or undesirable response to olanzapine might be explained by a patient's sex, race, or smoking status. It is important for treating physicians to be aware of the potential impact of smoking on olanzapine clearance. If a patient quits smoking and experiences increased side effects, his or her dose of olanzapine may have to be reduced by about one third (given that smoking increases clearance by more than 50%). Conversely, patients may experience a relapse when restarting smoking due to insufficient olanzapine concentrations. Contrary to the label recommendations, our data suggest that smoking may well have a clinical impact, and therefore dosing adjustments may be warranted. Although we also found significant differences due to sex and race, we believe that the underlying reasons for their potential contributions to affecting olanzapine clearance are worthy of further exploration and that the combined effect of smoking, sex, and race is substantial and therefore should be taken into consideration when deciding an appropriate dose.
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ACKNOWLEDGEMENTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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REFERENCES |
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