Population pharmacokinetic analysis for risperidone using highly sparse sampling measurements from the CATIE study

Impact of CYP2D6*2, CYP2D6*35, rs5758550, and related haplotypes on risperidone clearance in vivo

PURPOSE

The CYP2D6 gene exhibits significant polymorphism, contributing to variability in responses to drugs metabolized by CYP2D6. While CYP2D6*2 and CYP2D6*35 are presently designated as alleles encoding normal metabolism, this classification is based on moderate level evidence. Additionally, the role of the formerly called "enhancer" single nucleotide polymorphism (SNP) rs5758550 is unclear. In this study, the impacts of CYP2D6*2, CYP2D6*35 and rs5758550 on CYP2D6 activity were investigated using risperidone clearance as CYP2D6 activity marker.

METHODS

A joint parent-metabolite population pharmacokinetic model was used to describe 1,565 serum concentration measurements of risperidone and 9-hydroxyrisperidone in 512 subjects. Risperidone population clearance was modeled as the sum of a CYP2D6-independent clearance term and the partial clearances contributed from each individually expressed CYP2D6 allele or haplotype. In addition to the well-characterized CYP2D6 alleles (*3-*6, *9, *10 and *41), *2, *35 and two haplotypes assigned as CYP2D6*2-rs5758550G and CYP2D6*2-rs5758550A were evaluated.

RESULTS

Each evaluated CYP2D6 allele was associated with significantly lower risperidone clearance than the reference normal function allele CYP2D6*1 (p < 0.001). Further, rs5758550 differentiated the effect of CYP2D6*2 (p = 0.005). The haplotype-specific clearances for CYP2D6*2-rs5758550A, CYP2D6*2-rs5758550G and CYP2D6*35 were estimated to 30%, 66% and 57%, respectively, relative to the clearance for CYP2D6*1. Notably, rs5758550 is in high linkage disequilibrium (R2 > 0.85) with at least 24 other SNPs and cannot be assigned as a functional SNP.

CONCLUSION

CYP2D6*2 and CYP2D6*35 encode reduced risperidone clearance, and the extent of reduction for CYP2D6*2 is differentiated by rs5758550. Genotyping of these haplotypes might improve the precision of genotype-guided prediction of CYP2D6-mediated clearance.

Unraveling the Influence of Age, IQ, Education, and Negative Symptoms on Neurocognitive Performance in Schizophrenia: A Conditional Inference Tree Analysis

INTRODUCTION

The complex nature of neurocognitive impairment in schizophrenia has been discussed in light of the mixed effects of antipsychotic drugs, psychotic symptoms, dopamine D2 receptor blockade, and intelligence quotient (IQ). These factors have not been thoroughly examined before.

METHODS

This study conducted a comprehensive re-analysis of the CATIE data using machine learning techniques, in particular Conditional Inference Tree (CTREE) analysis, to investigate associations between neurocognitive functions and moderating factors such as estimated trough dopamine D2 receptor blockade with risperidone, olanzapine, or ziprasidone, Positive and Negative Syndrome Scale (PANSS), and baseline IQ in 573 patients with schizophrenia.

RESULTS

The study reveals that IQ, age, and education consistently emerge as significant predictors across all neurocognitive domains. Furthermore, higher severity of PANSS-negative symptoms was associated with lower cognitive performance scores in several domains. CTREE analysis, in combination with a genetic algorithm approach, has been identified as particularly insightful for illustrating complex interactions between variables. Lower neurocognitive function was associated with factors such as age>52 years, IQ<94/95,<12/13 education years, and more pronounced negative symptoms (score<26).

CONCLUSIONS

These findings emphasize the multifaceted nature of neurocognitive functioning in patients with schizophrenia, with the PANSS-negative score being an important predictor. This gives rise to a role in addressing negative symptoms as a therapeutic objective for enhancing cognitive impairments in these patients. Further research must examine nonlinear relationships among various moderating factors identified in this work, especially the role of D2 occupancy.

Evidence for Therapeutic Drug Monitoring of Atypical Antipsychotics

Second-generation antipsychotics (SGAs), also known as atypical antipsychotics, are a newer class of antipsychotic drugs used to treat schizophrenia, bipolar disorder, and related psychiatric conditions. The plasma concentration of antipsychotic drugs is a valid measure of the drug at its primary target structure in the brain, and therefore determines the efficacy and safety of these drugs. However, despite the well-known high variability in pharmacokinetics of these substances, psychiatric medication is usually administered in uniform dosage schedules. Therapeutic drug monitoring (TDM), as the specific method that can help personalised medicine in dose adjustment according to the characteristics of the individual patient, minimizing the risk of toxicity, monitoring adherence, and increasing cost-effectiveness in the treatment, thus seems to be an elegant tool to solve this problem. Non-response to therapeutic doses, uncertain adherence to medication, suboptimal tolerability, or pharmacokinetic drug-drug interactions are typical indications for TDM of SGAs. This review aims to summarize an overview of the current knowledge and evidence of the possibilities to tailor the dosage of selected SGAs using TDM, including the necessary pharmacokinetic parameters for personalised pharmacotherapy.

Pathogenesis and Personalized Interventions for Pharmacological Treatment-Resistant Neuropsychiatric Symptoms in Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common form of dementia, with cognitive impairment as a core symptom. Neuropsychiatric symptoms (NPSs) also occur as non-cognitive symptoms during the disease course, worsening the prognosis. Recent treatment guidelines for NPSs have recommended non-pharmacological treatments as the first line of therapy, followed by pharmacological treatments. However, pharmacological treatment for urgent NPSs can be difficult because of a lack of efficacy or an intolerance, requiring multiple changes in psychotropic prescriptions. One biological factor that might be partly responsible for this difficulty is structural deterioration in elderly people with dementia, which may cause a functional vulnerability affecting the pharmacological response. Other causative factors might include awkward psychosocial interpersonal relations between patients and their caregiver, resulting in distressful vicious circles. Overlapping NPS sub-symptoms can also blur the prioritization of targeted symptoms. Furthermore, consistent neurocognitive reductions cause a primary apathy state and a secondary distorted ideation or perception of present objects, leading to reactions that cannot be treated pharmacologically. The present review defines treatment-resistant NPSs in AD; it may be necessary and helpful for clinicians to discuss the pathogenesis and comprehensive solutions based on three major hypothetical pathophysiological viewpoints: (1) biology, (2) psychosociology, and (3) neurocognition.

Comorbidities and the right dose: antipsychotics

INTRODUCTION

The effects of antipsychotic drugs are dose-dependent, which is particularly true for their efficacy, each antipsychotic having a specific dose-response curve. This may justify individualizing doses for these agents.

AREAS COVERED

We review the pharmacokinetic profiles of seven oral antipsychotics: haloperidol, risperidone, olanzapine, clozapine, quetiapine, ziprasidone, and aripiprazole. Their main indications are psychotic and affective disorders. They are prescribed in a very large population which may have comorbidities. Hence, we analyze the impact of the latter on the pharmacokinetic profiles of these antipsychotics, focusing on renal and hepatic impairment. Reviews and clinical trials were discussed based on a systematic literature search (PubMed) ranging from 1995 to 2022.

EXPERT OPINION

Factors liable to impact antipsychotic dosage are numerous and their subsequent effects often hard to predict, due to multi-level interactions and compensatory phenomena. In clinical practice, physicians must be aware of these potential effects, but base their decisions on monitoring antipsychotic plasma levels.

External Evaluation of Risperidone Population Pharmacokinetic Models Using Opportunistic Pediatric Data

Risperidone is approved to treat schizophrenia in adolescents and autistic disorder and bipolar mania in children and adolescents. It is also used off-label in younger children for various psychiatric disorders. Several population pharmacokinetic models of risperidone and 9-OH-risperidone have been published. The objectives of this study were to assess whether opportunistically collected pediatric data can be used to evaluate risperidone population pharmacokinetic models externally and to identify a robust model for precision dosing in children. A total of 103 concentrations of risperidone and 112 concentrations of 9-OH-risperidone, collected from 62 pediatric patients (0.16–16.8 years of age), were used in the present study. The predictive performance of five published population pharmacokinetic models (four joint parent-metabolite models and one parent only) was assessed for accuracy and precision of the predictions using statistical criteria, goodness of fit plots, prediction-corrected visual predictive checks (pcVPCs), and normalized prediction distribution errors (NPDEs). The tested models produced similarly precise predictions (Root Mean Square Error [RMSE]) ranging from 0.021 to 0.027 nmol/ml for risperidone and 0.053–0.065 nmol/ml for 9-OH-risperidone). However, one of the models (a one-compartment mixture model with clearance estimated for three subpopulations) developed with a rich dataset presented fewer biases (Mean Percent Error [MPE, %] of 1.0% vs. 101.4, 146.9, 260.4, and 292.4%) for risperidone. In contrast, a model developed with fewer data and a more similar population to the one used for the external evaluation presented fewer biases for 9-OH-risperidone (MPE: 17% vs. 69.9, 47.8, and 82.9%). None of the models evaluated seemed to be generalizable to the population used in this analysis. All the models had a modest predictive performance, potentially suggesting that sources of inter-individual variability were not entirely captured and that opportunistic data from a highly heterogeneous population are likely not the most appropriate data to evaluate risperidone models externally.

Evaluation of the estimation and classification performance of NONMEM when applying mixture model for drug clearance

Maximum likelihood estimation of parameters involving mixture model is known to have significant and specific patterns of errors. Population pharmacokinetic (PopPK) modeling using NONMEM is no exception. A few relevant studies on estimation and classification performance were done, but a comprehensive study was not yet available. The current study aims to evaluate performance and likelihood ratio test (LRT)‐based true covariate detection rate when fitting a bimodal mixture of drug clearance (CL) in NONMEM. A large number of PopPK datasets with various settings were simulated and then estimated. The estimates were compared to the simulated values and summarized. The separation between the CL distributions of the two subpopulations is systematically overestimated. The major factor associated with the performance is the change in the minimum objective function value after removing the mixture component (dOFV). Other significant factors include estimated disparity index (DI), estimated mixing proportion, and number of subjects in the dataset. Small dOFV and large estimated DI are associated with the worst performance. Omitting a true mixture resulted in reduced true covariate detection rates. It is recommended that on top of routinely generated standard errors and model diagnostics, dOFV, and other factors when necessary, should be taken into account for the evaluation of performance when fitting mixture model using NONMEM. In addition, when fitting mixture model for CL is intended, the mixture component should be introduced prior to LRT‐based covariate model development for CL.

Review of Pharmacokinetics and Pharmacogenetics in Atypical Long-Acting Injectable Antipsychotics

Over the last two decades, pharmacogenetics and pharmacokinetics have been increasingly used in clinical practice in Psychiatry due to the high variability regarding response and side effects of antipsychotic drugs. Specifically, long-acting injectable (LAI) antipsychotics have different pharmacokinetic profile than oral formulations due to their sustained release characteristics. In addition, most of these drugs are metabolized by CYP2D6, whose interindividual genetic variability results in different metabolizer status and, consequently, into different plasma concentrations of the drugs. In this context, there is consistent evidence which supports the use of therapeutic drug monitoring (TDM) along with pharmacogenetic tests to improve safety and efficacy of antipsychotic pharmacotherapy. This comprehensive review aims to compile all the available pharmacokinetic and pharmacogenetic data regarding the three major LAI atypical antipsychotics: risperidone, paliperidone and aripiprazole. On the one hand, CYP2D6 metabolizer status influences the pharmacokinetics of LAI aripiprazole, but this relation remains a matter of debate for LAI risperidone and LAI paliperidone. On the other hand, developed population pharmacokinetic (popPK) models showed the influence of body weight or administration site on the pharmacokinetics of these LAI antipsychotics. The combination of pharmacogenetics and pharmacokinetics (including popPK models) leads to a personalized antipsychotic therapy. In this sense, the optimization of these treatments improves the benefit–risk balance and, consequently, patients’ quality of life.

Modelling Age-Related Changes in the Pharmacokinetics of Risperidone and 9-Hydroxyrisperidone in Different CYP2D6 Phenotypes Using a Physiologically Based Pharmacokinetic Approach

PURPOSE

Dose-optimization strategies for risperidone are gaining in importance, especially in the elderly. Based on the genetic polymorphism of cytochrome P 450 (CYP) 2D6 genetically and age-related changes cause differences in the pharmacokinetics of risperidone and 9-hydroxyrisperidone. The goal of the study was to develop physiologically based pharmacokinetic (PBPK) models for the elderly aged 65+ years. Additionally, CYP2D6 phenotyping using metabolic ratio were applied and different pharmacokinetic parameter for different age classes predicted.

METHODS

Plasma concentrations of risperidone and 9-hydroxyrisperidone were used to phenotype 17 geriatric inpatients treated under naturalistic conditions. For this purpose, PBPK models were developed to examine age-related changes in the pharmacokinetics between CYP2D6 extensive metabolizer, intermediate metabolizer, poor metabolizer, (PM) and ultra-rapid metabolizer.

RESULTS

PBPK-based metabolic ratio was able to predict different CYP2D6 phenotypes during steady-state. One inpatient was identified as a potential PM, showing a metabolic ratio of 3.39. About 88.2% of all predicted plasma concentrations of the inpatients were within the 2-fold error range. Overall, age-related changes of the pharmacokinetics in the elderly were mainly observed in Cmax and AUC. Comparing a population of young adults with the oldest-old, Cmax of risperidone increased with 24-44% and for 9-hydroxyrisperidone with 35-37%.

CONCLUSIONS

Metabolic ratio combined with PBPK modelling can provide a powerful tool to identify potential CYP2D6 PM during therapeutic drug monitoring. Based on genetic, anatomical and physiological changes during aging, PBPK models ultimately support decision-making regarding dose-optimization strategies to ensure the best therapy for each patient over the age of 65 years.

Population Pharmacokinetic Modelling of Pyrazinamide and Pyrazinoic Acid in Patients with Multi-Drug Resistant Tuberculosis

BACKGROUND AND OBJECTIVES

Pyrazinamide, a drug used in the regimen for the treatment of drug-sensitive tuberculosis, is also used for the treatment of multidrug-resistant tuberculosis (MDR-TB). We aimed to describe the population pharmacokinetics of pyrazinamide and its major metabolite, pyrazinoic acid, in patients with MDR-TB and characterise the effects of demographic variables.

METHODS

This was a non-randomised clinical study involving 51 adult patients admitted for the intensive phase of MDR-TB treatment. Blood samples were collected at pre-dose and at 0.5, 1, 1.5, 2, 3, 4, 8, 16 and 24 h after drug administration. Plasma concentrations of pyrazinamide and pyrazinoic acid were analysed using a validated LC-MS/MS method. Nonlinear mixed-effects modelling using Monolix 2018R1 software was employed to estimate population pharmacokinetic parameters.

RESULTS

A one-compartment pharmacokinetic model with transit compartment absorption process and first-order elimination best described the pyrazinamide and pyrazinoic acid concentration-time data. The estimated population pharmacokinetic parameters were 0.7 h, 3.38 h^-1, 57.1 l, 4.37 L/h and 10.5 L/h for mean transit time, absorption rate constant, apparent distribution volume for pyrazinamide, and apparent clearance for pyrazinamide and pyrazinoic acid (CLm/F), respectively. These parameters were not affected by patient age, HIV status or sex. The parameter variability in CLm/F was the highest (83.5%), while the rest of the parameters ranged from 16.2 to 58%.

CONCLUSIONS

The developed population pharmacokinetic model adequately described the disposition of pyrazinamide and pyrazinoic acid and can be useful for dose determination of pyrazinamide in patients with MDR-TB.

Linking Tumor Growth Dynamics to Survival in Ipilimumab-Treated Patients With Advanced Melanoma Using Mixture Tumor Growth Dynamic Modeling

Early tumor assessments have been widely used to predict overall survival (OS), with potential application to dose selection and early go/no‐go decisions. Most published tumor dynamic models assume a uniform pattern of tumor growth dynamics (TGDs). We developed a mixture TGD model to characterize different patterns of longitudinal tumor sizes. Data from 688 patients with advanced melanoma who received ipilimumab 3 or 10 mg/kg every 3 weeks in a phase III study (NCT01515189) were used in a TGD‐OS analysis. The mixture model described TGD profiles using three subpopulations (no‐growth, intermediate, and fast). The TGD model showed a positive exposure/dose‐response (i.e., a higher proportion of patients in no/intermediate growth subpopulations and a lower tumor growth rate with ipilimumab 10 mg/kg relative to the 3 mg/kg dose). Finally, the mixture TGD model‐based measures of tumor response provided better predictions of OS compared with the nonmixture model.

A systematic review of the effects of CYP2D6 phenotypes on risperidone treatment in children and adolescents

The second generation antipsychotic drug risperidone is widely used in the field of child and adolescent psychiatry to treat conditions associated with disruptive behavior, aggression and irritability, such as autism spectrum disorders. While risperidone can provide symptomatic relief for many patients, there is considerable individual variability in the therapeutic response and side-effect profile of the medication. One well established biological factor that contributes to these individual differences is genetic variation in the cytochrome P450 enzyme 2D6. The 2D6 enzyme metabolizes risperidone and therefore affects drug levels and dosing. In the present review, we summarize the current literature on 2D6 variants and their effects on risperidone responses, specifically in children and adolescents. Relevant articles were identified through systematic review, and after irrelevant articles were discarded, ten studies were included in the review. Most prospective studies were well controlled, but often did not have a large enough sample size to make robust statements about rarer variants, including those categorized as ultra-rapid and poor metabolizers. Individual studies demonstrated a role for different genetic variants in risperidone drug efficacy, pharmacokinetics, hyperprolactinemia, weight gain, extrapyramidal symptoms and drug-drug interactions. Where studies overlapped in measurements, there was typically a consensus between results. These findings indicate that the value of 2D6 genotyping in the youth population treated with risperidone requires further study, in particular with the less common variants.

Exposure-response analysis after subcutaneous administration of RBP-7000, a once-a-month long-acting Atrigel formulation of risperidone

AIMS

A new, long-acting, subcutaneous (SC) formulation of risperidone (RBP-7000) has been developed for the treatment of schizophrenia to address issues of non-adherence associated with oral risperidone treatment. The objective of this work was to establish an exposure-response relationship between total active moiety (AM) plasma exposure (risperidone + 9-hydroxy-risperidone) and Positive and Negative Syndrome Scale (PANSS) or Clinical Global Impression severity (CGI-S) scores using data from a registration trial.

METHODS

This was a Phase 3 randomized, double-blind, placebo-controlled, multicenter study in 354 patients to evaluate the efficacy, safety and tolerability of RBP-7000 (90 mg and 120 mg). Non-linear mixed effects modelling was used to develop an integrated population pharmacokinetic/pharmacodynamic (PK/PD) model that included a joint PK model for risperidone and 9-hydroxy-risperidone with placebo and drug-effect models to establish the relation between total AM exposure and PANSS or CGI-S scores.

RESULTS

CYP2D6 poor and intermediate metabolizers had lower formation rates of 9-hydroxy-risperidone (94% and 76% lower, respectively) compared to the extensive CYP2D6 metabolizers. The maximum placebo-corrected relative decrease in PANSS score from baseline following RBP-7000 treatment was 5.4%, half of which could be achieved at plasma concentrations of 4.6 ng ml^-1 of the total AM. A proportional odds model for the CGI-S score related the total AM plasma concentration to the probability of improving/worsening scores over time.

CONCLUSIONS

Exposure-response analysis was established between total AM concentrations and PANSS and CGI-S scores, with good precision in parameter estimates. CYP2D6 phenotype on risperidone metabolism was the only identified covariate.

Dopamine D2/3 Receptor Occupancy Following Dose Reduction Is Predictable With Minimal Plasma Antipsychotic Concentrations: An Open-Label Clinical Trial

BACKGROUND

Population pharmacokinetics can predict antipsychotic blood concentrations at a given time point prior to a dosage change. Those predicted blood concentrations could be used to estimate the corresponding dopamine D2/3 receptors (D2/3R) occupancy by antipsychotics based on the tight relationship between blood and brain pharmacokinetics. However, this 2-step prediction has never been tested.

METHODS

Two blood samples were collected at separate time points from 32 clinically stable outpatients with schizophrenia (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition; mean ± SD age: 60.1 ± 7.3 years) to measure plasma concentrations of olanzapine or risperidone at baseline. Then, subjects underwent a dose reduction of olanzapine or risperidone and completed a [(11)C]-raclopride positron emission tomography scan to measure D2/3R occupancy in the putamen. The plasma concentration at the time of the scan was predicted with the 2 samples based on population pharmacokinetic model, using NONMEM. D2/3R occupancy was then estimated by incorporating the predicted plasma concentration in a hyperbole saturation model. The predicted occupancy was compared to the observed value.

RESULTS

The mean (95% CI) prediction errors for the prediction of D2/3R occupancy were -1.76% (-5.11 to 1.58) for olanzapine and 0.64% (-6.18 to 7.46) for risperidone. The observed and predicted D2/3R occupancy levels were highly correlated (r = 0.67, P = .001 for olanzapine; r = 0.67, P = .02 for risperidone).

CONCLUSIONS

D2/3R occupancy levels can be predicted from blood drug concentrations collected prior to dosage change. Although this 2-step model is subject to a small degree of error, it could be used to select oral doses aimed at achieving optimal D2/3R occupancy on an individual basis.

A population approach to characterise amisulpride pharmacokinetics in older people and Alzheimer's disease

INTRODUCTION

Current prescribing guidelines for the antipsychotic amisulpride are based largely on pharmacokinetic (PK) studies in young adults, and there is a relative absence of data on older patients, who are at greatest risk of developing adverse events.

METHODS

This study aimed to develop a population PK model for amisulpride specifically in older people, by combining data from a richly sampled phase 1, single (50 mg) dose study in healthy older people (n = 20, 65-79 years), with a clinical dataset obtained during off label, low-dose (25-75 mg daily) amisulpride prescribing in older people with Alzheimer's disease (AD) (n = 25, 69-92 years), as part of an observational study.

RESULTS

After introducing a scaling factor based on body weight, age accounted for 20 % of the inter-individual variability in drug clearance (CL), resulting in a 54 % difference in CL between those aged 65 and those aged 85 years, and higher blood concentrations in older patients.

DISCUSSION

These findings argue for the consideration of age and weight-based dose stratification to optimise amisulpride prescribing in older people, particularly in those aged 85 years and above.

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 modeling of the Qishe pill in three major traditional Chinese medicine-defined constitutional types of healthy Chinese subjects: study protocol for a randomized controlled trial

BACKGROUND

High incidences of neck pain morbidity are challenging in various situations for populations based on their demographic, physiological and pathological characteristics. Chinese proprietary herbal medicines, as Complementary and Alternative Medicine (CAM) products, are usually developed from well-established and long-standing recipes formulated as tablets or capsules. However, good quantification and strict standardization are still needed for implementation of individualized therapies. The Qishe pill was developed and has been used clinically since 2009. The Qishe pill's personalized medicine should be documented and administered to various patients according to the ancient TCM system, a classification of personalized constitution types, established to determine predisposition and prognosis to diseases as well as therapy and life-style administration. Therefore, we describe the population pharmacokinetic profile of the Qishe pill and compare its metabolic rate in the three major constitution types (Qi-Deficiency, Yin-Deficiency and Blood-Stasis) to address major challenges to individualized standardized TCM.

METHODS/DESIGN

Healthy subjects (N = 108) selected based on constitutional types will be assessed, and standardized pharmacokinetic protocol will be used for assessing demographic, physiological, and pathological data. Laboratory biomarkers will be evaluated and blood samples collected for pharmacokinetics(PK) analysis and second-generation gene sequencing. In single-dose administrations, subjects in each constitutional type cohort (N = 36) will be randomly divided into three groups to receive different Qishe pill doses (3.75, 7.5 and 15 grams). Multiomics, including next generation sequencing, metabolomics, and proteomics, will complement the Qishe pill's multilevel assessment, with cytochrome P450 genes as targets. In a comparison with the general population, a systematic population pharmacokinetic (PopPK) model for the Qishe pill will be established and verified.

TRIAL REGISTRATION

This study is registered at ClinicalTrials.gov, NCT02294448 .15 November 2014.

Tardive dyskinesia in relation to estimated dopamine D2 receptor occupancy in patients with schizophrenia: analysis of the CATIE data

OBJECTIVE

The objective of this study was to evaluate the relationship between antipsychotic-induced tardive dyskinesia (TD) and estimated dopamine D2 receptor occupancy levels in patients with schizophrenia, using the dataset from the Clinical Antipsychotic Trials in Intervention Effectiveness (CATIE).

METHODS

The dataset from 218 subjects (risperidone, N=78; olanzapine, N=100; ziprasidone, N=40) who presented with a score of zero on the Abnormal Involuntary Movement Scale (AIMS) at baseline in Phase 1 of the CATIE study, and remained for ≥6months, was used. Peak and trough dopamine D2 receptor occupancy levels on the day of the AIMS assessment at the endpoint were estimated from plasma antipsychotic concentrations, using population pharmacokinetic analysis and our D2 prediction model. The estimated dopamine D2 receptor occupancy levels were compared between patients who presented an AIMS score of ≥2 at endpoint and those with a score of zero, using the Mann-Whitney U test.

RESULTS

Estimated dopamine D2 receptor occupancy levels at trough were significantly higher in subjects who developed involuntary movements (N=23) than those who did not (N=195) (71.7±14.4% vs. 64.3±19.3%, p<0.05) while no significant difference was found in the estimated peak D2 receptor occupancy between them (75.4±8.7% vs. 72.1±9.9%, p=0.07). When the analyses were separately conducted for the three drugs, there were no significant differences in estimated peak or trough D2 occupancy although the values were consistently numerically higher among those developing involuntary movements.

CONCLUSION

Greater dopamine D2 receptor blockade with antipsychotics at trough might increase the risk of tardive involuntary movements although this finding needs to be replicated in larger trials.

Population pharmacokinetic modeling of risperidone and 9-hydroxyrisperidone to estimate CYP2D6 subpopulations in children and adolescents

AIM

The study aims were to characterize risperidone and (±)-9-hydroxyrisperidone pharmacokinetic (PK) variability in children and adolescents and to evaluate covariate effects on PK parameters.

METHODS

Steady-state samples were drawn at predose, 1, 2, 4, and 7 hours postdose; cytochrome P450 2D6 (CYP2D6) genotypes were available for 28 subjects. A nonlinear mixed-effects model (NONMEM) modeled the PKs of risperidone and (±)-9-hydroxyrisperidone; covariates included age, weight, sex, and CYP2D6 phenotype. The model included 497 observations [risperidone (n = 163), (+) and (-)-9-hydroxyrisperidone (n = 334)] from 45 subjects aged 3-18.3 (mean 9.6 ± 3.7) years, weighing 16.8-110 (43 ± 20.2) kg.

RESULTS

A 1-compartment mixture model described risperidone and (±)-9-hydroxyrisperidone clearances for 3 CYP2D6 metabolizer subpopulations: extensive, intermediate, and poor. Weight significantly affected (±)-9-hydroxyrisperidone clearance. Clearance estimates in the mixture model were poor metabolizer 9.38 L/h, intermediate metabolizer 29.2 L/h, and extensive metabolizer 37.4 L/h.

CONCLUSION

Active moiety [risperidone plus (±)-9-hydroxyrisperidone] PK variability and the covariate effects were better explained with the addition of metabolite PK parameters. This model may aid the development of individualized risperidone dosing regimens in children and adolescents.

Dopamine D2 receptor occupancy and cognition in schizophrenia: analysis of the CATIE data

INTRODUCTION

Antipsychotic drugs exert antipsychotic effects by blocking dopamine D2 receptors in the treatment of schizophrenia. However, effects of D2 receptor blockade on neurocognitive function still remain to be elucidated. The objective of this analysis was to evaluate impacts of estimated dopamine D2 receptor occupancy with antipsychotic drugs on several domains of neurocognitive function in patients with schizophrenia in the Clinical Antipsychotic Trials in Intervention Effectiveness (CATIE) trial.

METHODS

The dataset from the CATIE trial was used in the present analysis. Data were extracted from 410 subjects who were treated with risperidone, olanzapine, or ziprasidone, received assessments for neurocognitive functions (verbal memory, vigilance, processing speed, reasoning, and working memory) and psychopathology, and provided plasma samples for the measurement of plasma antipsychotic concentrations. D2 receptor occupancy levels on the day of neurocognitive assessment were estimated from plasma antipsychotic concentrations, using population pharmacokinetic analysis and our recently developed model. A multivariate general linear model was used to examine effects of clinical and demographic characteristics, including estimated D2 occupancy levels, on neurocognitive functions.

RESULTS

D2 occupancy levels showed significant associations with the vigilance and the summary scores. Neurocognitive functions, including vigilance, were especially impaired in subjects who showed D2 receptor occupancy level of >77%.

DISCUSSION

These findings suggest a nonlinear relationship between prescribed antipsychotic doses and overall neurocognitive function and vigilance. This study shows that D2 occupancy above approximately 80% not only increases the risk for extrapyramidal side effects as consistently reported in the literature but also increases the risk for cognitive impairment.

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

AIM

To develop a combined population pharmacokinetic model (PPK) to assess the magnitude and variability of exposure to both clozapine and its primary metabolite norclozapine in Chinese patients with refractory schizophrenia via sparse sampling with a focus on the effects of covariates on the pharmacokinetic parameters.

METHODS

Relevant patient concentration data (eg, demographic data, medication history, dosage regimen, time of last dose, sampling time, concentrations of clozapine and norclozapine, etc) were collected using a standardized data collection form. The demographic characteristics of the patients, including sex, age, weight, body surface area, smoking status, and information on concomitant medications as well as biochemical and hematological test results were recorded. Persons who had smoked 5 or more cigarettes per day within the last week were defined as smokers. The concentrations of clozapine and norclozapine were measured using a HPLC system equipped with a UV detector. PPK analysis was performed using NONMEM. Age, weight, sex, and smoking status were evaluated as main covariates. The model was internally validated using normalized prediction distribution errors.

RESULTS

A total of 809 clozapine concentration data sets and 808 norclozapine concentration data sets from 162 inpatients (74 males, 88 females) at multiple mental health sites in China were included. The one-compartment pharmacokinetic model with mixture error could best describe the concentration-time profiles of clozapine and norclozapine. The population-predicted clearance of clozapine and norclozapine in female nonsmokers were 21.9 and 32.7 L/h, respectively. The population-predicted volumes of distribution for clozapine and norclozapine were 526 and 624 L, respectively. Smoking was significantly associated with increases in the clearance (clozapine by 45%; norclozapine by 54.3%). The clearance was significantly greater in males than in females (clozapine by 20.8%; norclozapine by 24.2%). The clearance of clozapine and norclozapine did not differ significantly between Chinese patients and American patients.

CONCLUSION

Smoking and male were significantly associated with a lower exposure to clozapine and norclozapine due to higher clearance. This model can be used in individualized drug dosing and therapeutic drug monitoring.

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.

Pharmacogenetics of antipsychotic treatment response and side effects

Antipsychotic drugs are particularly interesting in pharmacogenetic studies as they are associated with a large interindividual variability in terms of response and side effects and, therefore, frequently need to be discontinued, requiring switches to other antipsychotics. Any information that allows the prediction of outcome to a given antipsychotic in a particular patient will, therefore, be of great help for the clinician to minimize time and find the right drug for the right patient, thus optimizing response and minimizing side effects. This will also have a substantial impact on compliance and doctor-patient relationships. Moreover, antipsychotic drug treatments are often required for life-long treatment and are also frequently prescribed to the more 'vulnerable' populations: children, adolescents and the elderly. This article focuses on some important studies performed with candidate gene variants associated with antipsychotic response. In addition, important findings in pharmacogenetic studies of antipsychotic-induced side effects will be briefly summarized, such as antipsychotic treatment induced tardive dyskinesia and weight gain.

Population pharmacokinetics of perphenazine in schizophrenia patients from CATIE: impact of race and smoking

The goal of the study was to characterize population pharmacokinetics (PPK) for perphenazine in patients with schizophrenia from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE). Patients (n = 156) received 8 to 32 mg of perphenazine daily for 14 to 600 days for a total of 421 plasma concentrations measurements. Nonlinear mixed-effects modeling was used to determine PPK characteristics of perphenazine. One- and 2-compartment models with various random effect implementations and mixture distributions were evaluated. Objective function values and goodness-of-fit plots were used as model selection criteria. Age, weight, sex, race, smoking, and concomitant medications were evaluated as covariates. A 1-compartment linear model with proportional error best described the data. The population mean clearance and volume of distribution for perphenazine were 483 L/h and 18 200 L, respectively. Race and smoking status had significant impacts on perphenazine clearance estimates. In addition, the estimated population mean clearance was 48% higher in nonsmoking African Americans than in nonsmoking other races (512 L/h vs 346 L/h). Active smokers eliminated perphenazine 159 L/h faster than nonsmokers in each race. Clearances for smoking African Americans versus smokers in other races were 671 L/h versus 505 L/h, respectively.