Population pharmacokinetics of clozapine and its primary metabolite norclozapine in Chinese patients with schizophrenia

Semi-physiological Pharmacokinetic Model of Clozapine and Norclozapine in Healthy, Non-smoking Volunteers: The Impact of Race and Genetics

BACKGROUND AND OBJECTIVES: Clozapine is the medication of choice for treatment-resistant schizophrenia. However, it has a complex metabolism and unexplained interindividual variability. The current work aims to develop a pharmacokinetic model of clozapine and norclozapine in non-smokers and assess the impact of demographic and genetic predictors.

METHODS

Healthy volunteers were recruited in a population pharmacokinetic study. Blood samples were collected at 30 min and 1, 2, 3, 5 and 8 h following a single flat dose of clozapine (12.5 mg). The clozapine and norclozapine concentrations were measured via high-performance liquid chromatography-ultraviolet method. A semi-physiological pharmacokinetic model of clozapine and norclozapine was developed using nonlinear mixed-effects modeling. Clinical and genetic predictors were evaluated, including CYP1A2 (rs762551) and ABCB1 (rs2032582), using restriction fragment length polymorphism.

RESULTS

A total of 270 samples were collected from 33 participants. The data were best described using a two-compartment model for clozapine and a two-compartment model for norclozapine with first-order absorption and elimination and pre-systemic metabolism. The estimated (relative standard error) clearance of clozapine and norclozapine were 27 L h-1 (31.5 %) and 19.6 L h-1 (30%), respectively. Clozapine clearance was lower in sub-Saharan Africans (n = 4) and higher in Caucasians (n = 9) than Asians (n = 20). Participants with CYP1A2 (rs762551) (n = 18) and ABCB1 (rs2032582) (n = 12) mutant alleles had lower clozapine clearance in the univariate analysis.

CONCLUSIONS

This is the first study to develop a semi-physiological pharmacokinetic model of clozapine and norclozapine accounting for the pre-systemic metabolism. Asians required lower doses of clozapine as compared with Caucasians, while clozapine pharmacokinetics in sub-Saharan Africans should be further investigated in larger trials.

Exploring clozapine pharmacokinetics in Tunisian schizophrenic patients: A population-based modelling approach investigating the impact of genetic and non-genetic variables

Clozapine is characterized by a large within- and between-patient variability in its pharmacokinetics, attributed to non-genetic and genetic factors. A cross-sectional analysis of clozapine trough concentration (Clz C0) issued from Tunisian schizophrenic patients was collected and analysed using a nonparametric modelling approach. We assessed the impact of demographic covariates (age, weight and sex), patient's habits (smoking status, alcohol and caffeine intake) and the genetic factors (CYP1A2*1C, CYP1A2*1F and CYP2C19*2 polymorphisms) on each pharmacokinetic parameter. An external validation of this pharmacokinetic model using an independent data set was performed. Fit goodness between observed- and individual-predicted data was evaluated using the mean prediction error (% MPE), the mean absolute prediction error (% MAPE) as a measure of bias, and the root mean squared error (% RMSE) as a measure of precision. Sixty-three CLz C0 values issued from 51 schizophrenic patients were assessed in this study and divided into building and validation groups. CYP1A2*1F polymorphism and smoking status were the only covariates significantly associated with clozapine clearance. Precision parameters were as follows: 1.02%, 0.95% and 22.4%, respectively, for % MPE, % MAPE and % RMSE. We developed and validated an accurate pharmacokinetic model able to predict Clz C0 in Tunisian schizophrenic patients using the two parameters CYP1A2*1F polymorphism and smoking.

Chromatographic Methods for the Analysis of the Antipsychotic Drug Clozapine and Its Major Metabolites: A Review

Clozapine (CLZ), a second-generation antipsychotic, can effectively reduce schizophrenia, bipolar disorder and major depression symptoms. This review provides an overview of all reported chromatographic methods (62 references) for the quantification of CLZ and its two main metabolites, norclozapine and clozapine N-oxide in pharmaceutical formulations, biological matrices and environmental samples.

Validation of Population Pharmacokinetic Models for Clozapine Dosage Prediction

BACKGROUND

Clozapine is unique in its capacity to ameliorate severe schizophrenia but at high risk of toxicity. A relationship between blood concentration and clinical response and evidence for concentration-response relationships to some adverse effects justify therapeutic drug monitoring of clozapine. However, the relationship between drug dose and blood concentration is quite variable. This variability is, in part, due to inductive and inhibitory interactions varying the activity of cytochrome P450 1A2 (CYP1A2), the principal pathway for clozapine elimination. Several population pharmacokinetic models have been presented to facilitate dose selection and to identify poor adherence in individual patients. These models have faced little testing for validity in independent populations or even for persisting validity in the source population.

METHODS

Therefore, we collected a large population of clozapine-treated patients (127 patients, 1048 timed plasma concentrations) in whom dosing and covariate information could be obtained with high certainty. A population pharmacokinetic model was constructed with data collected in the first 6 weeks from study enrolment (448 plasma concentrations), to estimate covariate influences and to allow alignment with previously published models. The model was tested for its performance in predicting the concentrations observed at later time intervals up to 5 years. The predictive performances of 6 published clozapine population models were then assessed in the entire population.

RESULTS

The population pharmacokinetic model based on the first 6 weeks identified significant influences of sex, smoking, and cotreatment with fluvoxamine on clozapine clearance. The model built from the first 6 weeks had acceptable predictive performance in the same patient population up to the first 26 weeks using individual parameters, with a median predictive error (PE) of -0.1% to -15.9% and median absolute PE of 22.9%-27.1%. Predictive performance fell progressively with time after 26 weeks. Bayesian addition of plasma concentration observations within each prediction period improved individual predictions. Three additional observations extended acceptable predictive performance into the second 6 months of therapy. When the published models were tested with the entire data set, median PE ranged from -8% to +35% with a median absolute PE of >39% in all models. Thus, none of the tested models was successful in external validation. Bayesian addition of single patient observations improved individual predictions from all models but still without achieving acceptable performances.

CONCLUSIONS

We conclude that the relationship between covariates and blood clozapine concentrations differs between populations and that relationships are not stable over time within a population. Current population models for clozapine are not capturing influential covariates.

A population pharmacokinetic model to guide clozapine dose selection, based on age, sex, ethnicity, body weight and smoking status

AIMS

Guidance on clozapine dosing in treatment-resistant schizophrenia is based largely on data from White young adult males. This study aimed to investigate the pharmacokinetic profiles of clozapine and N-desmethylclozapine (norclozapine) across the age range, accounting for sex, ethnicity, smoking status and body weight.

METHODS

A population pharmacokinetic model, implemented in Monolix, that linked plasma clozapine and norclozapine via a metabolic rate constant, was used to analyse data from a clozapine therapeutic drug monitoring service, 1993-2017.

RESULTS

There were 17 787 measurements from 5960 patients (4315 male) aged 18-86 years. The estimated clozapine plasma clearance was reduced from 20.2 to 12.0 L h-1 between 20 and 80 years. Model-based dose predictions to attain a predose plasma clozapine concentration of 0.35 mg L-1 was 275 (90% prediction interval 125, 625) mg day-1 in nonsmoking, White males weighing 70 kg and aged 40 years. The corresponding predicted dose was increased by 30% in smokers, decreased by 18% in females, and was 10% higher and 14% lower in otherwise analogous Afro-Caribbean and Asian patients, respectively. Overall, the predicted dose decreased by 56% between 20 and 80 years.

CONCLUSION

The large sample size and wide age range of the patients studied allowed precise estimation of dose requirements to attain predose clozapine concentration of 0.35 mg L-1 . The analysis was, however, limited by the absence of data on clinical outcome and future studies are required to determine optimal predose concentrations specifically in those aged over 65 years.

Association of clozapine and norclozapine levels with patient and therapy characteristics-focus on interaction with valproic acid

PURPOSE

The goal of the study was to examine clozapine (CLZ) and norclozapine (NCLZ) therapeutic drug monitoring (TDM) data and associated sources of pharmacokinetic variability, particularly the impact of valproic acid (VPA) use.

METHODS

This study included 126 patients with psychiatric disorders on mono- or co-therapy with CLZ. Patients' data during routine TDM were collected retrospectively from clinical records. The descriptive and statistical analysis was computed using IBM SPSS Statistics software (version 22, NY, USA). Multiple linear regression, based on the last observations, was used to assess correlation between demographic characteristics, life habits and co-therapy with dose-corrected serum levels (C/D) of CLZ and NCLZ, as well as CLZ/NCLZ.

RESULTS

A total of 295 CLZ concentrations were measured in 126 patients, with a mean of 275.5 ± 174.4 µg/L, while 124 NCLZ concentrations were determined in 74 patients, with a mean of 194.6 ± 149.8 µg/L. A statistically significant effect on ln-transformed CLZ C/D was confirmed for sex and smoking, whereas sex, smoking and VPA therapy were associated with ln-transformed NCLZ C/D. According to the final models, lower values of NCLZ C/D for about 45.9% can be expected in patients receiving VPA. Concomitant use of VPA was the only factor detected to contribute in CLZ/NCLZ variability.

CONCLUSION

The results of this study may help clinicians interpret TDM data and optimize CLZ dosing regimens, especially in patients concomitantly treated with VPA. Our results show that VPA primarily decreases NCLZ levels, while alteration of the parent drug is not statistically significant.

Joint population pharmacokinetic modeling of venlafaxine and O-desmethyl venlafaxine in healthy volunteers and patients to evaluate the impact of morbidity and concomitant medication

Introduction: Venlafaxine (VEN) is a widely used dual selective serotonin/noradrenaline reuptake inhibitor indicated for depression and anxiety. It undergoes first-pass metabolism to its active metabolite, O-desmethyl venlafaxine (ODV). The aim of the present study was to develop a joint population pharmacokinetic (PPK) model to characterize their pharmacokinetic characters simultaneously. Methods: Plasma concentrations with demographic and clinical data were derived from a bioequivalence study in 24 healthy subjects and a naturalistic TDM setting containing 127 psychiatric patients. A parent-metabolite PPK modeling was performed with NONMEM software using a non-linear mixed effect modeling approach. Goodness of fit plots and normalized prediction distribution error method were used for model validation. Results and conclusion: Concentrations of VEN and ODV were well described with a one-compartment model incorporating first-pass metabolism. The first-pass metabolism was modeled as a first-order conversion. The morbid state and concomitant amisulpride were identified as two significant covariates affecting the clearance of VEN and ODV, which may account for some of the variations in exposure. This model may contribute to the precision medication in clinical practice and may inspire other drugs with pre-system metabolism.

Towards Precision Dosing of Clozapine in Schizophrenia: External Evaluation of Population Pharmacokinetic Models and Bayesian Forecasting

BACKGROUND

Therapeutic drug monitoring and treatment optimization of clozapine are recommended, owing to its narrow therapeutic range and pharmacokinetic (PK) variability. This study aims to assess the clinical applicability of published population PK models by testing their predictive performance in an external data set and to determine the effectiveness of Bayesian forecasting (BF) for clozapine treatment optimization.

METHODS

Available models of clozapine were identified, and their predictive performance was determined using an external data set (53 patients, 151 samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated. The final concentration was reestimated for all patients using covariates or previously observed concentrations.

RESULTS

The 7 included models presented limited predictive performance. Only 1 model met the acceptability criteria (median PE ≤ ±20% and median absolute PE ≤30%). There was no difference between the data used for building the models (therapeutic drug monitoring or PK study) or the number of compartments in the models. A tendency for higher inaccuracy at low concentrations during treatment initiation was observed. Heterogeneities were observed in the predictive performances between the subpopulations, especially in terms of smoking status and sex. For the models included, BF significantly improved their predictive performance.

CONCLUSIONS

Our study showed that upon external evaluation, clozapine models provide limited predictive performance, especially in subpopulations such as nonsmokers. From the perspective of model-informed prediction dosing, model predictability should be improved using updating or metamodeling methods. Moreover, BF substantially improved model predictability and could be used for clozapine treatment optimization.

Physiologically-based pharmacokinetic model for clozapine in Korean patients with schizophrenia

Clozapine has been used as a treatment of schizophrenia. Despite its large interindividual variability, few reports addressed the physiologically-based pharmacokinetic modeling and simulation (PBPK M&S) of clozapine in patients. This study aimed to develop a PBPK M&S of clozapine in Korean patients with schizophrenia. PBPK modeling for clozapine was constructed using a population-based PBPK platform, the SimCYP^® Simulator (V19; Certara, Sheffield, UK). The PBPK model was developed by optimizing the physiological parameters of the built-in population and compound libraries in the SimCYP^® Simulator. The model verification was performed with the predicted/observed ratio for pharmacokinetic parameters and visual predictive checks (VPCs) plot. Simulations were performed to predict toxicities according to dosing regimens. From published data, 230 virtual trials were simulated for each dosing regimen. The predicted/observed ratio for the area under the curve and peak plasma concentration was calculated to be from 0.78 to 1.34. The observation profiles were within the 5th and 95th percentile range with no serious model misspecification through the VPC plot. A significant impact on age and gender was found for clozapine clearance. The simulation results suggested that 150 mg twice a day and 150 mg three times a day of clozapine have toxicity concerns. In conclusion, a PBPK model was developed and reasonable parameters were made from the data of Korean patients with schizophrenia. The provided model might be used to predict the pharmacokinetics of clozapine and assist dose adjustment in clinical settings.

A Machine Learning Model to Predict Risperidone Active Moiety Concentration Based on Initial Therapeutic Drug Monitoring

Risperidone is an efficacious second-generation antipsychotic (SGA) to treat a wide spectrum of psychiatric diseases, whereas its active moiety (risperidone and 9-hydroxyrisperidone) concentration without a therapeutic reference range may increase the risk of adverse drug reactions. We aimed to establish a prediction model of risperidone active moiety concentration in the next therapeutic drug monitoring (TDM) based on the initial TDM information using machine learning methods. A total of 983 patients treated with risperidone between May 2017 and May 2018 in Beijing Anding Hospital were collected as the data set. Sixteen predictors (the initial TDM value, dosage, age, WBC, PLT, BUN, weight, BMI, prolactin, ALT, MECT, Cr, AST, Ccr, TDM interval, and RBC) were screened from 26 variables through univariate analysis (p < 0.05) and XGBoost (importance score >0). Ten algorithms (XGBoost, LightGBM, CatBoost, AdaBoost, Random Forest, support vector machine, lasso regression, ridge regression, linear regression, and k-nearest neighbor) compared the model performance, and ultimately, XGBoost was chosen to establish the prediction model. A cohort of 210 patients treated with risperidone between March 1, 2019, and May 31, 2019, in Beijing Anding Hospital was used to validate the model. Finally, the prediction model was evaluated, obtaining R ² (0.512 in test cohort; 0.374 in validation cohort), MAE (10.97 in test cohort; 12.07 in validation cohort), MSE (198.55 in test cohort; 324.15 in validation cohort), RMSE (14.09 in test cohort; 18.00 in validation cohort), and accuracy of the predicted TDM within ±30% of the actual TDM (54.82% in test cohort; 60.95% in validation cohort). The prediction model has promising performance to facilitate rational risperidone regimen on an individualized level and provide reference for other antipsychotic drugs' risk prediction.

Pharmacokinetic changes of clozapine and norclozapine in a rat model of non-alcoholic fatty liver disease induced by orotic acid

Impaired in vitro oxidation of clozapine has been reported in steatotic rat liver due to downregulation of cytochrome P450 (CYP) 1A. Pharmacokinetic changes of clozapine and its major metabolite, norclozapine, were evaluated in a rat model of non-alcoholic fatty liver disease (NAFLD) induced by orotic acid. Significantly slower in vitro CL_int for formation of norclozapine from clozapine was observed in NAFLD rats than in control rats as a result of the reduced protein expression and metabolic activity of CYP1A1/2. However, systemic exposures to clozapine in NAFLD rats were comparable to those in controls after intravenous (4 mg/kg) and oral (10 mg/kg) administration of clozapine. Of note, the AUC of the norclozapine and AUC_norclozapine/AUC_clozapine ratio following intravenous and oral administration of clozapine rather increased significantly in NAFLD rats, as a result of the slowed subsequent metabolism of norclozapine via CYP1A1/2. Steady-state brain concentrations of both clozapine and norclozapine were significantly higher in NAFLD rats than those in control rats following intravenous infusion of clozapine. Increased systemic exposure to norclozapine and elevated brain concentrations of clozapine and norclozapine observed in NAFLD rats imply that further studies are warranted on the pharmacotherapy of clozapine in patients with pre-existing or drug-induced hepatic steatosis.

The relationship between serum clozapine concentrations and hematological parameters by a validated mass spectrometric method

OBJECTIVE

Clozapine is one of the most effective drugs for resistant schizophrenia, but its severe metabolic and hematological side effects limit the use of clozapine. It has been reported that clozapine blood concentrations should be maintained between 350-600 ng/mL. Our aim was to develop a determination method for clozapine and its main metabolites norclozapine and clozapine-N-oxide, to perform validation studies and to investigate the change of various biochemical parameters in patients using clozapine.

METHODS

A liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed and validated for clozapine measurement. Thus, blood samples were collected from 38 patients with schizophrenia and 32 healthy volunteers. Biochemical and hematological parameters were measured by Beckman-Coulter AU 5800 (Beckman Coulter, Brea, USA) and Beckman Coulter LH 780 analyzer (Beckman Coulter, Miami, FL, USA), respectively. Hormone levels were analyzed using Cobas 6000 analyzer (Roche Diagnostics, Germany).

RESULTS

The LCMS/MS method was linear between 1.22-2500 ng/mL (r² = 0.9971) for clozapine. The retention times of clozapine, norclozapine and clozapine-N-oxide were 0.92, 0.89 and 0.95, respectively. Blood glucose (GLU) (p = 0.025), low density lipoprotein (LDL-cholesterol) (p = 0.015), triglyseride (TG) (p = 0.042) and total cholesterol (TC) (p = 0.024) levels were higher; hemoglobin (HGB) (0.015), mean corpuscular hemoglobin (MCH) (0.036), red blood cell count (RBC) (0.020), neutrophil (NEU) (0.034), and platelet (PLT) (P = 0.005) levels were lower in the clozapine group.

CONCLUSIONS

This LC-MS/MS method was rapid, simple, cost-effective and suitable for the routine clozapine monitoring. Furthermore, norclozapine and clozapine-N-oxide were also determined. Monitoring of metabolic and hematological parameters with clozapine levels is very important. However, the limitations of the study were that the method was not validated for norclozapine and clozapine-N-oxide, so the validation parameters were not evaluated for these two metabolites.

Understanding variability in the pharmacokinetics of atypical antipsychotics - focus on clozapine, olanzapine and aripiprazole population models

Antipsychotic medicines are widely used for the management of psychotic symptoms regardless of the underlying diagnosis. Most atypical antipsychotics undergo extensive metabolism prior to excretion. Various factors may influence their pharmacokinetics, particularly elimination, leading to highly variable drug concentrations between individual patients following the same dosing regimen. Population pharmacokinetic approach, based on nonlinear mixed effects modeling, is a useful tool to identify covariates explaining pharmacokinetic variability, as well as to characterize and distinguish unexplained residual and between-subject (interindividual) variability. In addition, this approach allows the use of both sparsely and intensively sampled data. In this paper, we reviewed the pharmacokinetic characteristics of clozapine, olanzapine and aripiprazole, focusing on a population modeling approach. In particular, models based on a nonlinear mixed effects approach performed by NONMEM^® software in order to identify and quantify sources of pharmacokinetic variability are presented. Population models were identified through systematic searches of PubMed and sixteen studies were selected. Some of the factors identified that significantly contribute to variability in elimination among clozapine, olanzapine, and aripiprazole are demographic characteristics, body weight, genetic polymorphism, smoking and in some cases drug interactions. Scientific research based on pharmacometric modeling is useful to further characterize sources of variability and their combined effect.

Population Pharmacokinetics of Clozapine: A Systematic Review

BACKGROUND AND OBJECTIVE

Clozapine is a second-generation antipsychotic drug that is considered the most effective treatment for refractory schizophrenia. Several clozapine population pharmacokinetic models have been introduced in the last decades. Thus, a systematic review was performed (i) to compare published pharmacokinetics models and (ii) to summarize and explore identified covariates influencing the clozapine pharmacokinetics models.

METHODS

A search of publications for population pharmacokinetic analyses of clozapine either in healthy volunteers or patients from inception to April 2019 was conducted in PubMed and SCOPUS databases. Reviews, methodology articles, in vitro and animal studies, and noncompartmental analysis were excluded.

RESULTS

Twelve studies were included in this review. Clozapine pharmacokinetics was described as one-compartment with first-order absorption and elimination in most of the studies. Significant interindividual variations of clozapine pharmacokinetic parameters were found in most of the included studies. Age, sex, smoking status, and cytochrome P450 1A2 were found to be the most common identified covariates affecting these parameters. External validation was only performed in one study to determine the predictive performance of the models.

CONCLUSIONS

Large pharmacokinetic variability remains despite the inclusion of several covariates. This can be improved by including other potential factors such as genetic polymorphisms, metabolic factors, and significant drug-drug interactions in a well-designed population pharmacokinetic model in the future, taking into account the incorporation of larger sample size and more stringent sampling strategy. External validation should also be performed to the previously published models to compare their predictive performances.

Quantitative efficacy of three antipsychotic drugs for schizophrenia based on a real-world study in China

Atypical antipsychotics exert remarkable long-term efficacy on the personal and social functions of schizophrenic patients. However, quantitative information on the social function of schizophrenic patients treated with atypical antipsychotics is scarce in the current clinical guidelines. In this study, we established pharmacodynamic models to quantify the time–efficacy relationship of three antipsychotic drugs based on the data from a real-world study conducted in China. A total of 373 schizophrenic patients who received antipsychotic monotherapy with olanzapine (n = 144), risperidone (n = 160), or aripiprazole (n = 69) were selected from a three-year prospective, multicenter study. The follow-up times were 13, 26, 52, 78, 104, 130, and 156 weeks after baseline. A time–efficacy model was developed with nonlinear mixed effect method based on changes in Personal and Social Performance (PSP) score compared with the baseline level. Crucial pharmacodynamic parameters, including maximum efficacy and drug onset time, were used to distinguish the efficacy of the three drugs. We quantified the time course of PSP improvement in patients after treatment with these three antipsychotics: olanzapine, risperidone, and aripiprazole reached an E_max value of 80.3%, 68.2%, and 23.9% at weeks 56.7, 29.2, and 36.8, respectively. General psychotic symptoms, onset frequency, and illness course were identified as significant factors affecting the efficacy of these drugs. The newly constructed models provide an evidence of the benefit of long-term maintenance therapy with atypical antipsychotics in individualized schizophrenia treatment in China.

Population Pharmacokinetics of Clozapine and Norclozapine and Switchability Assessment between Brands in Uruguayan Patients with Schizophrenia

Clozapine (CZP) is an atypical antipsychotic agent commonly used in the treatment of schizophrenia. It is metabolized primarily by CYP1A2 enzyme, yielding a pharmacologically active metabolite, norclozapine (NCZP). Significant intra- and interindividual pharmacokinetic (PK) variability for CZP and NCZP has been observed in routine therapeutic drug monitoring. So the goal of this study was to evaluate the magnitude and variability of concentration exposure to CZP and its active metabolite NCZP on pharmacokinetic parameters in Uruguayan patients with schizophrenia with a focus on covariates such as cigarette smoking, age, sex, caffeine consumption, brands available of CZP, and comedication using population PK (PPK) modeling methodologies. Patients with a diagnosis of schizophrenia treated with brand-name CZP (Leponex®) for more than a year were included in the study. Then these patients were switched to the similar brand of CZP (Luverina®). Morning predose blood samples for determination of CZP and NCZP using a HPLC system equipped with a UV detector were withdrawn on both occasions at steady state and under the same comedication. Ninety-eight patients, 22 women and 76 men, took part in the study. Mean ± standard deviation for CZP and NCZP concentration was 421 ± 262 ng/mL and 275 ± 180 ng/mL, respectively. After covariate evaluation, only smoking status remained significant in CZP apparent clearance, inducing a mean increment of 32% but with no clinical impact. The results obtained with the two brands of CZP should ensure comparable efficacy and tolerability with the clinical use of either product. Smoking was significantly associated with a lower exposure to CZP due to higher clearance. The results obtained with the two brands commercialized in our country hint a bioequivalence scenario in the clinical setting.

Physiologically Based Modeling Approach to Predict Dopamine D2 Receptor Occupancy of Antipsychotics in Brain: Translation From Rat to Human

Receptor occupancy (RO) is a translational biomarker for assessing drug efficacy and safety. We aimed to apply a physiologically based pharmacokinetic (PBPK) modeling approach to predict the brain dopamine D2 RO time profiles of antipsychotics. Clozapine and risperidone were modeled together with their active metabolites, norclozapine and paliperidone, First, in PK‐Sim a rat PBPK model was developed and optimized using literature plasma PK data. Then, blood‐brain barrier parameters including the expression and efflux transport kinetics of P‐glycoprotein were optimized using literature microdialysis data on brain extracellular fluid (brainECF), which were further adapted when translating the rat PBPK model into the human PBPK model. Based on the simulated drug and metabolite concentrations in brainECF, drug‐D2 receptor binding kinetics (association and dissociation rates) were incorporated in MoBi to predict RO. From an extensive literature search, 32 plasma PK data sets (16 from rat and 16 from human studies) and 23 striatum RO data sets (13 from rat and 10 from human studies) were prepared and compared with the model predictions. The rat PBPK‐RO model adequately predicted the plasma concentrations of the parent drugs and metabolites and the RO levels. The human PBPK‐RO model also captured the plasma PK and RO levels despite the large interindividual and interstudy variability, although it tended to underestimate the plasma concentrations and RO measured at late time points after risperidone dosing. The developed human PBPK‐RO model was successfully applied to predict the plasma PK and RO changes observed after risperidone dose reduction in a clinical trial in schizophrenic patients.

Population Pharmacokinetics and Dosing Simulations of Ceftazidime in Chinese Neonates

An accurate dosage determination is required in neonates when antibiotics are used. The adult data cannot be simply extrapolated to the pediatric population due to significant individual differences. We aimed to identify factors impacting ceftazidime exposure in neonates and to provide drug dosing guidance to clinicians. Forty-three neonates aged less than 60 days with proven or suspected infections were enrolled in this study. After intravenous administration, blood samples were collected, and plasma ceftazidime concentration was determined using a HPLC method. Pharmacokinetic data were fitted using a nonlinear mixed-effects model approach. One-compartmental model could nicely characterize the ceftazidime in vivo behavior. The covariate test found that the postmenstrual age (day) was strongly associated with systemic drug clearance (L/h), and the effect of body weight (kg) was identified as the covariate on distribution volume (L). Compared with the base model, the addition of covariates improved the goodness-of-fit of the final model. Model validation (bootstrap, visual predictive check, and prediction-corrected visual predictive check) suggested a robust and reliable pharmacokinetic model was developed. Personalized dosage regimens were provided based on model simulations. The intravenous dose should be adjusted according to postmenstrual age, body weight, and minimum inhibitory concentration.

Cigarette smoking has a differential effect on the plasma level of clozapine in Taiwanese schizophrenic patients associated with the CYP1A2 gene -163A/C single nucleotide polymorphism

OBJECTIVE

The efficacy of clozapine clearance has been shown to be associated with smoking and genetic polymorphism of CYP1A2. This study aims to investigate the effect of smoking on the plasma level of clozapine in Taiwanese schizophrenic patients and its relevance to the CYP1A2 gene -163A/C single nucleotide polymorphism.

MATERIALS AND METHODS

A total of 143 hospitalized schizophrenic patients who had received clozapine therapy for at least 14 days were enrolled in this study. The trough plasma concentration of clozapine was measured with LC/MS/MS. The -163A/C variant in the CYP1A2 gene was identified by DNA sequencing and restriction fragment length polymorphism analysis. The effect of smoking on the clozapine level was examined by multiple linear regression analysis and its relation to the -163A/C variant of the CYP1A2 gene was analyzed using a general linear model with Bonferroni correction.

RESULTS

Patients with smoking habits showed a significantly lower plasma level of clozapine than those without smoking habits (P=0.022) and the difference in clozapine levels between smokers and nonsmokers appeared to be significant in the individuals carrying the homozygous -163A allele (P=0.02). It was also found that nonsmokers carrying the -163A allele tended to have higher plasma levels of clozapine. This tendency was not found in the individuals with smoking habits.

CONCLUSION

Cigarette smoking has a significant impact on the plasma level of clozapine in Taiwanese schizophrenic patients carrying the homozygous -163A allele in the CYP1A2 gene. Cigarette smoking may increase the clearance of clozapine in these patients.

Optimal sampling of antipsychotic medicines: a pharmacometric approach for clinical practice

AIM

To determine optimal sampling strategies to allow the calculation of clinical pharmacokinetic parameters for selected antipsychotic medicines using a pharmacometric approach.

METHODS

This study utilized previous population pharmacokinetic parameters of the antipsychotic medicines aripiprazole, clozapine, olanzapine, perphenazine, quetiapine, risperidone (including 9-OH risperidone) and ziprasidone. d-optimality was utilized to identify time points which accurately predicted the pharmacokinetic parameters (and expected error) of each drug at steady-state. A standard two stage population approach (STS) with MAP-Bayesian estimation was used to compare area under the concentration-time curves (AUC) generated from sparse optimal time points and rich extensive data. Monte Carlo Simulation (MCS) was used to simulate 1000 patients with population variability in pharmacokinetic parameters. Forward stepwise regression analysis was used to determine the most predictive time points of the AUC for each drug at steady-state.

RESULTS

Three optimal sampling times were identified for each antipsychotic medicine. For aripiprazole, clozapine, olanzapine, perphenazine, risperidone, 9-OH risperidone, quetiapine and ziprasidone the CV% of the apparent clearance using optimal sampling strategies were 19.5, 8.6, 9.5, 13.5, 12.9, 10.0, 16.0 and 10.7, respectively. Using the MCS and linear regression approach to predict AUC, the recommended sampling windows were 16.5-17.5 h, 10-11 h, 23-24 h, 19-20 h, 16.5-17.5 h, 22.5-23.5 h, 5-6 h and 5.5-6.5 h, respectively.

CONCLUSION

This analysis provides important sampling information for future population pharmacokinetic studies and clinical studies investigating the pharmacokinetics of antipsychotic medicines.

Population pharmacokinetic/pharmacodynamic model of clozapine for characterizing the relationship between accumulated exposure and PANSS scores in patients with schizophrenia

BACKGROUND

The aim of this study was to characterize the relationship between accumulated exposure of clozapine and changes in Positive and Negative Syndrome Scale (PANSS) score in Chinese patients with schizophrenia by pharmacokinetic/pharmacodynamic (PK/PD) modeling.

METHODS

Sparse clozapine PK data and PANSS scores were collected from 2 clinical studies of Chinese inpatients with schizophrenia. Two other rich PK data sets were included for more accurate assessment of clozapine PK characteristics. The relationship between clozapine-accumulated exposure and PANSS score was investigated using linear, log-linear, E(max), and sigmoid models, and each model was evaluated using visual predictive condition and normalized prediction distribution error methods. Simulations based on the final PK/PD model were preformed to investigate the effect of clozapine on PANSS scores under different dose regimens.

RESULTS

A total of 1391 blood clozapine concentrations from 198 subjects (180 patients and 18 healthy volunteers) and 576 PANSS scores from 137 patients were included for PK and PK/PD analysis. A first-order 2-compartment PK model with covariates gender and smoking status influencing systemic clearance adequately described the PK profile of clozapine. The decrease in total PANSS score during treatment was best characterized using cumulated clozapine area under the curve (AUC) data in the E(max) model. The maximum decrease in PANSS during clozapine treatment (Emax) was 55.4%, and the cumulated AUC(50) (cAUC(50)) required to attain half of E(max) was 296 mg·L(-1)·h(-1)·d(-1). The simulations demonstrated that the accelerated dose titration and constant dose regimens achieved a similar maximum drug response but with a slower relief of symptoms in dose titration regimen.

CONCLUSIONS

The PK/PD model can describe the clinical response as measured by decreasing PANSS score during treatment and may be useful for optimizing the dose regimen for individual patients.

Meta-analysis: the effects of smoking on the disposition of two commonly used antipsychotic agents, olanzapine and clozapine

OBJECTIVE

To clarify the effects of smoking on the disposition of two commonly used antipsychotics, olanzapine and clozapine, and to create standards to adjust the doses of these drugs in clinical practice based on the smoking status.

DESIGN

A meta-analysis was conducted by searching MEDLINE, Scopus and the Cochrane Library for relevant prospective and retrospective studies.

INCLUDED STUDIES

We included the studies that investigated the effects of smoking on the concentration to dose (C/D) ratio of olanzapine or clozapine.

PRIMARY OUTCOME MEASURE

The weighted mean difference was calculated using a DerSimonian-Laird random effects model, along with 95% CI.

RESULTS

Seven association studies, comprising 1094 patients (652 smokers and 442 non-smokers) with schizophrenia or other psychiatric disorders, were included in the meta-analysis of olanzapine. The C/D ratio was significantly lower in smokers than in non-smokers (p<0.00001), and the mean difference was -0.75 (ng/mL)/(mg/day) (95% CI -0.89 to -0.61). Therefore, it was estimated that if 10 and 20 mg/day of olanzapine would be administered to smokers, about 7 and 14 mg/day, respectively, should be administered to non-smokers in order to obtain the equivalent olanzapine concentration. Four association studies of clozapine were included in the meta-analysis of clozapine, comprising 196 patients (120 smokers and 76 non-smokers) with schizophrenia or other psychiatric disorders. The C/D ratio was significantly lower in smokers than in non-smokers (p<0.00001), and the mean difference was -1.11 (ng/mL)/(mg/day) (95% CI -1.53 to -0.70). Therefore, it was estimated that if 200 and 400 mg/day of clozapine would be administered to smokers, about 100 and 200 mg/day, respectively, should be administered to non-smokers.

CONCLUSIONS

We suggest that the doses of olanzapine and clozapine should be reduced by 30% and 50%, respectively, in non-smokers compared with smokers in order to obtain an equivalent olanzapine or clozapine concentration.