Background and ObjectivesAripiprazole is an atypical antipsychotic drug that is metabolized by cytochrome P450 (CYP) 2D6 and CYP3A4, which mainly form its active metabolite dehydro-aripiprazole. Because of the genetic polymorphism of CYP2D6, plasma concentrations are highly variable between different phenotypes. In this study, phenotype-related physiologically based pharmacokinetic models were developed and evaluated to suggest phenotype-guided dose adjustments. Methods Physiologically based pharmacokinetic models for single dose (oral and orodispersible formulation), multiple dose, and steady-state condition were built using trial data from genotyped healthy volunteers. Based on evaluated models, dose adjustments were simulated to compensate for genetically caused differences. Results Physiologically based pharmacokinetic models were able to accurately predict the pharmacokinetics of aripiprazole and dehydro-aripiprazole according to CYP2D6 phenotypes, illustrated by a minimal bias and a good precision. For single-dose administration, 92.5% (oral formulation) and 79.3% (orodispersible formulation) of the plasma concentrations of aripiprazole were within the 1.25-fold error range. In addition, physiologically based pharmacokinetic steady-state simulations demonstrate that the daily dose for poor metabolizer should be adjusted, resulting in a maximum recommended dose of 10 mg, but no adjustment is necessary for intermediate and ultra-rapid metabolizers. Conclusions In clinical practice, CYP2D6 genotyping in combination with therapeutic drug monitoring should be considered to personalize aripiprazole dosing, especially in CYP2D6 poor metabolizers, to ensure therapy effectiveness and safety.

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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 9hydroxyrisperidone 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 r is p e r id on e i n c r e a s e d w it h 2 4-44 % a n d fo r 9hydroxyrisperidone 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.