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

Exploratory focused pharmacogenetic testing reveals novel markers associated with risperidone pharmacokinetics in Saudi children with autism

Background: Autism spectrum disorders (ASDs) encompass a broad range of phenotypes characterized by diverse neurological alterations. Genomic studies have revealed considerable overlap between the molecular mechanisms implicated in the etiology of ASD and genes involved in the pharmacokinetic (PK) and pharmacodynamic (PD) pathways of antipsychotic drugs employed in ASD management. Given the conflicting data originating from candidate PK or PD gene association studies in diverse ethnogeographic ASD populations, dosage individualization based on "actionable" pharmacogenetic (PGx) markers has limited application in clinical practice. Additionally, off-label use of different antipsychotics is an ongoing practice, which is justified given the shortage of approved cures, despite the lack of satisfactory evidence for its safety according to precision medicine. This exploratory study aimed to identify PGx markers predictive of risperidone (RIS) exposure in autistic Saudi children. Methods: This prospective cohort study enrolled 89 Saudi children with ASD treated with RIS-based antipsychotic therapy. Plasma levels of RIS and 9-OH-RIS were measured using a liquid chromatography-tandem mass spectrometry system. To enable focused exploratory testing, genotyping was performed with the Axiom PharmacoFocus Array, which included a collection of probe sets targeting PK/PD genes. A total of 720 PGx markers were included in the association analysis. Results: A total of 27 PGx variants were found to have a prominent impact on various RIS PK parameters; most were not located within the genes involved in the classical RIS PK pathway. Specifically, 8 markers in 7 genes were identified as the PGx markers with the strongest impact on RIS levels (p < 0.01). Four PGx variants in 3 genes were strongly associated with 9-OH-RIS levels, while 5 markers in 5 different genes explained the interindividual variability in the total active moiety. Notably, 6 CYP2D6 variants exhibited strong linkage disequilibrium; however, they significantly influenced only the metabolic ratio and had no considerable effects on the individual estimates of RIS, 9-OH-RIS, or the total active moiety. After correction for multiple testing, rs78998153 in UGT2B17 (which is highly expressed in the brain) remained the most significant PGx marker positively adjusting the metabolic ratio. For the first time, certain human leukocyte antigen (HLA) markers were found to enhance various RIS exposure parameters, which reinforces the gut-brain axis theory of ASD etiology and its suggested inflammatory impacts on drug bioavailability through modulation of the brain, gastrointestinal tract and/or hepatic expression of metabolizing enzymes and transporters. Conclusion: Our hypothesis-generating approach identified a broad spectrum of PGx markers that interactively influence RIS exposure in ASD children, which indicated the need for further validation in population PK modeling studies to define polygenic scores for antipsychotic efficacy and safety, which could facilitate personalized therapeutic decision-making in this complex neurodevelopmental condition.

Clinical pharmacokinetics of antipsychotics in pediatric populations: a scoping review focusing on dosing regimen

INTRODUCTION

Achieving optimal clinical responses and minimizing side effects through precision dosing of antipsychotics in children and adolescents with psychiatric disorders remains a challenge. Identifying patient characteristics (covariates) that affect pharmacokinetics can inform more effective dosing strategies and ultimately improve patient outcomes. This review aims to provide greater insight into the impact of covariates on the clinical pharmacokinetics of antipsychotics in pediatric populations.

AREAS COVERED

A comprehensive literature search was conducted, and the main findings regarding the effects of the covariates on the pharmacokinetics of antipsychotics in children and adolescents are presented.

EXPERT OPINION

Our study highlights significant covariates, including age, sex, weight, CYP2D6 phenotype, co-medication, and smoking habits, which affect the pharmacokinetics of antipsychotics. However, the findings were generally limited by the small sample sizes of naturalistic, open-label, observational studies, and the homogeneous subgroups. Dosing based on weight and preemptive genotyping could prove beneficial for optimizing the dosing regimen in pediatric populations. Future research is needed to refine dosing recommendations and establish therapeutic reference ranges critical for precision dosing and Therapeutic Drug Monitoring (TDM). The integration of individual patient characteristics with TDM can further optimize the efficacy and safety of antipsychotics for each patient.

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.

Pharmacogenomic Studies in Intellectual Disabilities and Autism Spectrum Disorder: A Systematic Review

BACKGROUND

Individuals with intellectual disability (ID) and autism spectrum disorder (ASD) often receive psychotropic medications such as antipsychotics and antidepressants to treat aberrant behaviors and mood symptoms, frequently resulting in polypharmacy and drug-related adverse effects. Pharmacogenomic (PGx) studies with ASD and/or ID (ASD/ID) have been scarce despite the promise of optimizing treatment outcomes. We reviewed the literature on PGx studies with antipsychotics and antidepressants (e.g., treatment response and adverse effects) in ASD/ID.

METHODS

We performed a systematic review using MEDLINE, Embase, and PsycINFO, including peer-reviewed original articles in English referring to PGx in the treatment of ASD/ID in any age groups (e.g., treatment response and adverse effects).

RESULTS

A total of 28 PGx studies using mostly candidate gene approaches were identified across age groups. Notably, only 3 studies included adults with ASD/ID while the other 25 studies focused specifically on children/adolescents with ASD/ID. Twelve studies primarily investigated treatment response, of which 5 and 6 studies included patients treated with antipsychotics and antidepressants, respectively. Most interesting results for response were reported for 2 sets of candidate gene studies, namely: (1) The DRD3 Ser9Gly (rs6280) polymorphism was examined in patients treated with risperidone in 3 studies, 2 of which reported an association with risperidone treatment response and (2) the SLC6A4 5-HTTLPR polymorphism and treatment response to antidepressants which was investigated in 4 studies, 3 of which reported significant associations. In regard to side effects, 9 of 15 studies focused on hyperprolactinemia in patients treated with risperidone. Among them, 7 and 5 studies examined the impact of CYP2D6 and DRD2 Taq1A polymorphisms, respectively, yielding mostly negative study findings.

CONCLUSIONS

There is limited data available on PGx in individuals with ASD/ID and in particular in adults. Given the potential for PGx testing in improving treatment outcomes, additional PGx studies for psychotropic treatment in ASD/ID across age groups are warranted.

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.

Population Pharmacokinetic Method to Predict Within-Subject Variability Using Single-Period Clinical Data

Sample sizes for single-period clinical trials, including pharmacokinetic studies, are statistically determined by within-subject variability (WSV). However, it is difficult to determine WSV without replicate-designed clinical trial data, and statisticians typically estimate optimal sample sizes using total variability, not WSV. We have developed an efficient population-based method to predict WSV accurately with single-period clinical trial data and demonstrate method performance with eperisone. We simulated 1000 virtual pharmacokinetic clinical trial datasets based on single-period and dense sampling studies, with various study sizes and levels of WSV and interindividual variabilities (IIVs). The estimated residual variability (RV) resulting from population pharmacokinetic methods were compared with WSV values. In addition, 3 × 3 bioequivalence results of eperisone were used to evaluate method performance with a real clinical dataset. With WSV of 40% or less, regardless of IIV magnitude, RV was well approximated by WSV for sample sizes greater than 18 subjects. RV was underestimated at WSV of 50% or greater, even with datasets having low IIV and numerous subjects. Using the eperisone dataset, RV was 44% to 48%, close to the true value of 50%. In conclusion, the estimated RV accurately predicted WSV in single-period studies, validating this method for sample size estimation in clinical trials.

CYP2D6 and Antipsychotic Treatment Outcomes in Children and Youth: A Systematic Review

Objective: To systematically review the impact of CYP2D6 genetic variation on antipsychotic pharmacokinetics, efficacy, and adverse drug reactions among children and youth. Method: The published literature was systematically searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations and critically evaluated using standardized tools and consensus criteria. Results: A total of 20 eligible studies comprising 1078 children and youth were evaluated. The included studies were of fair to moderate quality and included mostly males, individuals of European ancestry, and those treated with risperidone. CYP2D6 poor metabolizers (PMs) were consistently shown to have increased concentrations of risperidone relative to normal metabolizers (NMs). PMs were also consistently shown to have a greater propensity to experience antipsychotic (primarily risperidone) associated adverse drug reactions relative to NMs. However, robust evidence for an association between CYP2D6 and efficacy was less apparent. Conclusion and Clinical Significance: The current knowledge base suggests that CYP2D6 genetic variation has an appreciable impact on antipsychotic pharmacokinetics and the propensity for adverse drug reactions, particularly among children receiving risperidone treatment. However, several limitations with the current literature (e.g., sample sizes, study design, sample heterogeneity) should be addressed in future studies. Assuming that future studies support the link between CYP2D6 genetic variation and antipsychotic outcomes, we would anticipate an increase in the implementation of CYP2D6-guided antipsychotic drug selection and dose optimization in child and adolescent psychiatric services.

Dose-Corrected Serum Concentrations and Metabolite to Parent Compound Ratios of Venlafaxine and Risperidone from Childhood to Old Age

OBJECTIVE

Comparative pharmacokinetic data about the antidepressant venlafaxine (VEN) and the antipsychotic drug risperidone (RIS) over the lifespan and especially in children and adolescents is lacking. This is the first cross-sectional study that aimed to investigate differences in dose-corrected serum concentrations (CDs) and metabolite to parent compound ratios (MPRs) of VEN and RIS across the lifespan.

METHODS

Patients treated with VEN and RIS at the University Hospital of Würzburg, Germany were included in the study. Serum level determinations were performed during clinical routine care. Patients with CYP2D6 influencing co-medication were excluded from analyses.

RESULTS

In 953 patients (12-93 years) treated with VEN and 552 patients (7-92 years) treated with RIS, children/adolescents (<18 years) showed 11% and 19%, and 44% and 42% lower CDs of the active moieties (CDs_AM) of VEN and RIS than adults and elderly (≥60 years) (Kruskal-Wallis tests; p ≤ 0.001). However, when CDs were normalized to body weight, a different pattern emerged. Gender differences, with higher CDs_AM in females were present in adults and elderlies but not in children/adolescents. No gender- or age-dependent difference in MPRs was found; however, 80% of MPRs of RIS in children/adolescents were below the range of "normal" CYP2D6 function for adults.

CONCLUSIONS

We suggest a higher clearance as a reason for lower CDs_AM of VEN and RIS in children/adolescents compared to adults/elderlies. Metabolism of VEN or RIS by CYP2D6, characterized by MPRs, was not associated with age. However, MPRs of RIS were lower in children/adolescents, possibly due to a higher renal clearance of 9-OH-risperidone.

Risperidone plasma concentrations are associated with side effects and effectiveness in children and adolescents with autism spectrum disorder

AIM

Risperidone is the most commonly prescribed antipsychotic drug to children and adolescents worldwide, but it is associated with serious side effects, including weight gain. This study assessed the relationship of risperidone and 9-hydroxyrisperidone trough concentrations, maximum concentrations and 24-hour area under the curves (AUCs) with body mass index (BMI) z-scores in children and adolescents with autism spectrum disorder (ASD) and behavioural problems. Secondary outcomes were metabolic, endocrine, extrapyramidal and cardiac side effects and effectiveness.

METHODS

Forty-two children and adolescents (32 males) aged 6-18 years were included in a 24-week prospective observational trial. Drug plasma concentrations, side effects and effectiveness were measured at several time points during follow-up. Relevant pharmacokinetic covariates, including medication adherence and CYP2D6, CYP3A4, CYP3A5 and P-glycoprotein (ABCB1) genotypes, were measured. Nonlinear mixed-effects modelling (NONMEM®) was used for a population pharmacokinetic analysis with 205 risperidone and 205 9-hydroxyrisperidone concentrations. Subsequently, model-based trough concentrations, maximum concentrations and 24-hour AUCs were analysed to predict outcomes using generalized and linear mixed-effects models.

RESULTS

A risperidone two-compartment model combined with a 9-hydroxyrisperidone one-compartment model best described the measured concentrations. Of all the pharmacokinetic parameters, higher risperidone sum trough concentrations best predicted higher BMI z-scores during follow-up (P < .001). Higher sum trough concentrations also predicted more sedation (P < .05), higher prolactin levels (P < .001) and more effectiveness measured with Aberrant Behavior Checklist irritability score (P < .01).

CONCLUSION

Our results indicate a therapeutic window exists, which suggests that therapeutic drug monitoring of risperidone might increase safety and effectiveness in children and adolescents with ASD and behavioural problems.

Dose-Related Concentrations of Neuroactive/Psychoactive Drugs Expected in Blood of Children and Adolescents

PURPOSE

Therapeutic drug monitoring is highly recommended for children and adolescents treated with neurotropic/psychotropic drugs. For interpretation of therapeutic drug monitoring results, drug concentrations (C/D) expected in a "normal" population are helpful to identify pharmacokinetic abnormalities or nonadherence. Using dose-related concentration (DRC) factors obtained from pharmacokinetic data, C/D ranges expected under steady state can be easily calculated by multiplication of DRC by the daily dose. DRC factors, however, are defined only for adults so far. Therefore, it was the aim of this study to estimate DRC factors for children and adolescents and compare them with those of adults.

METHODS

To obtain pharmacokinetic data (apparent total clearance of drugs from plasma after oral administration, elimination half-life, area under the curve, and minimum serum drug concentration) from children and adolescents treated with psychotropic drugs, a systematic review of published literature was performed, and the pharmaceutical companies that market these drugs were contacted. Available information was used for the calculation of DRC factors.

RESULTS

Fourteen of 26 drugs had similar DRC factors to those reported for adults; 8 and 4 had higher and lower factors, respectively. The antidepressants citalopram, clomipramine, fluvoxamine, and imipramine and the antipsychotics haloperidol and olanzapine showed higher DRC factors than those calculated for adults. The DRC factors of amphetamine and methylphenidate were higher in children (6-12 years) but not in adolescents (13-17 years). On the contrary, the antipsychotic quetiapine and the mood-stabilizing antiepileptics lamotrigine, oxcarbazepine, and topiramate showed lower DRC factors than those calculated for adults.

CONCLUSIONS

It was concluded that concentrations of neuroactive/psychoactive drugs to be expected in blood for a given dose may differ between adults and children or adolescents, most probably owing to age-dependent differences in the elimination of these drugs.

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.

Clinical Pharmacogenetics of Cytochrome P450-Associated Drugs in Children

Cytochrome P450 (CYP) enzymes are commonly involved in drug metabolism, and genetic variation in the genes encoding CYPs are associated with variable drug response. While genotype-guided therapy has been clinically implemented in adults, these associations are less well established for pediatric patients. In order to understand the frequency of pediatric exposures to drugs with known CYP interactions, we compiled all actionable drug-CYP interactions with a high level of evidence using Clinical Pharmacogenomic Implementation Consortium (CPIC) data and surveyed 10 years of electronic health records (EHR) data for the number of children exposed to CYP-associated drugs. Subsequently, we performed a focused literature review for drugs commonly used in pediatrics, defined as more than 5000 pediatric patients exposed in the decade-long EHR cohort. There were 48 drug-CYP interactions with a high level of evidence in the CPIC database. Of those, only 10 drugs were commonly used in children (ondansetron, oxycodone, codeine, omeprazole, lansoprazole, sertraline, amitriptyline, citalopram, escitalopram, and risperidone). For these drugs, reports of the drug-CYP interaction in cohorts including children were sparse. There are adequate data for implementation of genotype-guided therapy for children for three of the 10 commonly used drugs (codeine, omeprazole and lansoprazole). For the majority of commonly used drugs with known CYP interactions, more data are required to support pharmacogenomic implementation in children.

Influences of CYP2D6*10 polymorphisms on the pharmacokinetics of iloperidone and its metabolites in Chinese patients with schizophrenia: a population pharmacokinetic analysis

AIM

Iloperidone is an atypical antipsychotic drug that is mainly metabolized by CYP2D6, CYP3A4, and cytosolic enzymes. Previous studies show that extensive and poor metabolizers of CYP2D6 exhibit different plasma concentrations of iloperidone and its metabolites. The aim of this study was to develop a parent-metabolite population pharmacokinetic (PPK) model to quantify the effects of CYP2D6^*10 allele on the pharmacokinetics of iloperidone and its metabolites in Chinese schizophrenia patients.

METHODS

Seventy Chinese schizophrenia patients were enrolled, from whom limited blood samples were collected on d 15 (0 h) and d 28 (0, 4 and 12 h after drug administration). The plasma concentrations of iloperidone and its metabolites M₁ (P-88) and M₂ (P-95) were simultaneously detected using a validated HPLC-MS assay. CYP2D6^*10 (rs1065852) genotyping was performed. A PPK model was developed based on data from the patients using the NONMEM software (version 7.2). A one-compartment model with first-order absorption and elimination was used to describe the pharmacokinetic data related to iloperidone and its metabolites.

RESULTS

Patients with the CYP2D6^*10 T/T genotype had significantly higher concentrations of iloperidone and M₁, and lower concentrations of M₂ than the patients with C/C or C/T genotypes. The CYP2D6^*10 genotype affected the elimination constants for transformation of iloperidone to the metabolites M₁ (K₂₃) and M₂ (K₂₄). The K₂₃ value of the patients with T/T genotype was 1.34-fold as great as that of the patients with C/C or C/T genotype. The K₂₄ value of the patients with C/T and T/T genotypes was 0.693- and 0.492-fold, respectively, as low as that of the patients with C/C genotype.

CONCLUSION

CYP2D6^*10 mutations affect the pharmacokinetics of iloperidone and its metabolites in Chinese schizophrenia patients, suggesting that the clinical doses of iloperidone for patients with CYP2D6^*10 mutations need to be optimized.

Prediction of paraquat exposure and toxicity in clinically ill poisoned patients: a model based approach

AIMS

Paraquat poisoning is a medical problem in many parts of Asia and the Pacific. The mortality rate is extremely high as there is no effective treatment. We analyzed data collected during an ongoing cohort study on self-poisoning and from a randomized controlled trial assessing the efficacy of immunosuppressive therapy in hospitalized paraquat-intoxicated patients. The aim of this analysis was to characterize the toxicokinetics and toxicodynamics of paraquat in this population.

METHODS

A non-linear mixed effects approach was used to perform a toxicokinetic/toxicodynamic population analysis in a cohort of 78 patients.

RESULTS

The paraquat plasma concentrations were best fitted by a two compartment toxicokinetic structural model with first order absorption and first order elimination. Changes in renal function were used for the assessment of paraquat toxicodynamics. The estimates of toxicokinetic parameters for the apparent clearance, the apparent volume of distribution and elimination half-life were 1.17 l h(-1) , 2.4 l kg(-1) and 87 h, respectively. Renal function, namely creatinine clearance, was the most significant covariate to explain between patient variability in paraquat clearance.This model suggested that a reduction in paraquat clearance occurred within 24 to 48 h after poison ingestion, and afterwards the clearance was constant over time. The model estimated that a paraquat concentration of 429 μg l(-1) caused 50% of maximum renal toxicity. The immunosuppressive therapy tested during this study was associated with only 8% improvement of renal function.

CONCLUSION

The developed models may be useful as prognostic tools to predict patient outcome based on patient characteristics on admission and to assess drug effectiveness during antidote drug development.

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.

Clinical trial simulation to evaluate population pharmacokinetics and food effect: capturing abiraterone and nilotinib exposures

The objectives of this study were to determine (1) the accuracy with which individual patient level exposure can be determined and (2) whether a known food effect can be identified in a trial simulation of a typical population pharmacokinetic trial. Clinical trial simulations were undertaken using NONMEM VII to assess a typical oncology pharmacokinetic trial design. Nine virtual trials for each compound were performed for combinations of different levels of between-occasion variability, number of patients in the trial, and magnitude of a food covariate on oral clearance. Less than 5% and 20% bias and precision were obtained in individual clearance estimated for both abiraterone and nilotinib using this design. This design resulted in biased and imprecise population clearance estimates for abiraterone. The between-occasion variability in most trials was captured with less than 30% of percent bias and precision. The food effect was detectable as a statistically significant covariate on oral clearance for abiraterone and nilotinib with percent bias and precision of the food covariate less than 20%. These results demonstrate that clinical trial simulation can be used to explore the ability of specific trial designs to evaluate the power to identify individual and population level exposures, covariate, and variability effects.

Use of a glyphosate-based herbicide-induced nephrotoxicity model to investigate a panel of kidney injury biomarkers

Accidental or intentional ingestion of glyphosate surfactant-based herbicides, like Roundup(®), leads to nephrotoxicity as well as death. In this study, a panel of kidney injury biomarkers was evaluated in terms of suitability to detect acute kidney injury and dysfunction. The Roundup(®) intoxication model involved oral administration of glyphosate to rats at dose levels of 250, 500, 1200 and 2500 mg/kg. Urinary and plasma biomarker patterns were investigated at 8, 24 and 48 h after dosing. Biomarkers were quantified by absolute concentration; by normalising to urine creatinine; and by calculating the excretion rate. The diagnostic performances of each method in predicting of acute kidney injury were compared. By Receiver Operating Characteristic (ROC) analysis of the selected biomarkers, only urinary kidney injury molecule-1 (KIM-1) best predicted histological changes at 8h (best cut-off point>0.00029 μg/ml). Plasma creatinine performed better than other biomarkers at 24 h (best cut-off point>0.21 mg/dl). Urinary KIM-1 was the best early biomarker of kidney injury in this glyphosate-induced nephrotoxicity model.

Considering CYP1A2 phenotype and genotype for optimizing the dose of olanzapine in the management of schizophrenia

INTRODUCTION

Schizophrenia, a mental disorder, is a debilitating condition which typically strikes young people in their early 20's. Antipsychotic medications are widely prescribed for the treatment of schizophrenia however a balancing act is necessary to provide the correct dose to each patient. It is suggested that a large number of patients discontinue antipsychotic pharmacotherapy because the treatments provided do not always reduce the positive symptoms of the disease, while many have adverse effects on the patients. This implies that neither the incorrect drug nor the optimal dosage for that patient is achieved.

AREAS COVERED

The current review investigates variability in response to olanzapine with a specific focus on the common intrinsic and extrinsic factors that influence both olanzapine and CYP1A2 activity. Furthermore, the authors discuss the utilization of phenotyping and genotyping of CYP1A2 and their potential utility in clinical practice for olanzapine dosing regimens. The authors also consider the potential of pharmacometrics compared to pharmacogenomics as a tool to personalize medicine.

EXPERT OPINION

Careful consideration must be given to the impact of a genetic variant on the disposition of a drug prior to implementing genetic 'tests' to determine response. CYP1A2 phenotypic assessment can yield important information regarding the disposition of olanzapine; however, it relies on the accuracy of the metric and the minimal impact of other metabolic pathways. The application of pharmacometrics provides an effective method to establish covariates that significantly influence olanzapine disposition which can incorporate phenotype and/or genotype.

Summary of the National Institute of Child Health and Human Development-best pharmaceuticals for Children Act Pediatric Formulation Initiatives Workshop-Pediatric Biopharmaceutics Classification System Working Group

BACKGROUND

The Biopharmaceutics Classification System (BCS) allows compounds to be classified based on their in vitro solubility and intestinal permeability. The BCS has found widespread use in the pharmaceutical community to be an enabling guide for the rational selection of compounds, formulation for clinical advancement, and generic biowaivers. The Pediatric Biopharmaceutics Classification System (PBCS) Working Group was convened to consider the possibility of developing an analogous pediatric-based classification system. Because there are distinct developmental differences that can alter intestinal contents, volumes, permeability, and potentially biorelevant solubilities at different ages, the PBCS Working Group focused on identifying age-specific issues that need to be considered in establishing a flexible, yet rigorous PBCS.

OBJECTIVE

We summarized the findings of the PBCS Working Group and provided insights into considerations required for the development of a PBCS.

METHODS

Through several meetings conducted both at The Eunice Kennedy Shriver National Institute of Child Health, Human Development-US Pediatric Formulation Initiative Workshop (November 2011) and via teleconferences, the PBCS Working Group considered several high-level questions that were raised to frame the classification system. In addition, the PBCS Working Group identified a number of knowledge gaps that need to be addressed to develop a rigorous PBCS.

RESULTS

It was determined that for a PBCS to be truly meaningful, it needs to be broken down into several different age groups that account for developmental changes in intestinal permeability, luminal contents, and gastrointestinal (GI) transit. Several critical knowledge gaps were identified, including (1) a lack of fully understanding the ontogeny of drug metabolizing enzymes and transporters along the GI tract, in the liver, and in the kidney; (2) an incomplete understanding of age-based changes in the GI, liver, and kidney physiology; (3) a clear need to better understand age-based intestinal permeability and fraction absorbed required to develop the PBCS; (4) a clear need for the development and organization of pediatric tissue biobanks to serve as a source for ontogenic research; and (5) a lack of literature published in age-based pediatric pharmacokinetics to build physiologically- and population-based pharmacokinetic (PBPK) databases.

CONCLUSIONS

To begin the process of establishing a PBPK model, 10 pediatric therapeutic agents were selected (based on their adult BCS classifications). These agents should be targeted for additional research in the future. The PBCS Working Group also identified several areas where greater emphasis on research was needed to enable the development of a PBCS.