Drug design, development and therapy · 2025
Development and Validation of a Population Pharmacokinetics Model of Perampanel for Pediatric Epilepsy Patients for Optimized Dosing.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Yu Lingyan, Mao Fengqian, Chen Shunan, Liu Jieqiong, Xiao Jiayu, Chen Meng, Luo Huan, Yu Zhenwei, Dai Haibin
Аннотация
BACKGROUND: Perampanel exhibits substantial interindividual variability, and pharmacokinetic data in pediatric patients are scarce. The aim of this study was to develop a population pharmacokinetic (PPK) model to optimize the dosing of perampanel in children with epilepsy.
METHODS: The PPK model was developed via a nonlinear mixed-effects modeling approach, utilizing a dataset comprising 454 plasma concentrations of perampanel obtained from 151 pediatric patients with epilepsy, 120 (79.5%) of whom were aged < 12 years. Goodness-of-fit plots and bootstrap analysis were employed to evaluate the final model. Monte Carlo simulations were utilized to suggest perampanel dosing strategies using a reference plasma concentration range of 100-1000 ng/mL.
RESULTS: In the final PPK models of perampanel, linear centralized age, coadministration of oxcarbazepine (OXC), carbamazepine (CBZ), and valproic acid (VPA) were covariates of clearance (CL/F), and log-transformed body weight was a covariate of the apparent distribution volume (V). The CL/F was estimated via the formula CL/F=0.177*((age+10)/8.8)1.31*1.51OXC*0.745VPA*1.88CBZ. The relative standard errors (RSEs) for each fixed effect parameter were 15.2%, 14.2%, 12.0%, 7.92%, and 16.3%, respectively. The V was estimated via the formula V=227*LGBW with an RSE of 14.1%. The model demonstrated good robustness according to goodness-of-fit plots and bootstrap analysis. The simulation analysis resulted in a dosing regimen stratified by covariates.
CONCLUSION: A reliable perampanel PPK model for pediatric patients was successfully developed. This result could be helpful for dosing optimization in pediatric patients receiving perampanel, especially those aged under 12 years.
METHODS: The PPK model was developed via a nonlinear mixed-effects modeling approach, utilizing a dataset comprising 454 plasma concentrations of perampanel obtained from 151 pediatric patients with epilepsy, 120 (79.5%) of whom were aged < 12 years. Goodness-of-fit plots and bootstrap analysis were employed to evaluate the final model. Monte Carlo simulations were utilized to suggest perampanel dosing strategies using a reference plasma concentration range of 100-1000 ng/mL.
RESULTS: In the final PPK models of perampanel, linear centralized age, coadministration of oxcarbazepine (OXC), carbamazepine (CBZ), and valproic acid (VPA) were covariates of clearance (CL/F), and log-transformed body weight was a covariate of the apparent distribution volume (V). The CL/F was estimated via the formula CL/F=0.177*((age+10)/8.8)1.31*1.51OXC*0.745VPA*1.88CBZ. The relative standard errors (RSEs) for each fixed effect parameter were 15.2%, 14.2%, 12.0%, 7.92%, and 16.3%, respectively. The V was estimated via the formula V=227*LGBW with an RSE of 14.1%. The model demonstrated good robustness according to goodness-of-fit plots and bootstrap analysis. The simulation analysis resulted in a dosing regimen stratified by covariates.
CONCLUSION: A reliable perampanel PPK model for pediatric patients was successfully developed. This result could be helpful for dosing optimization in pediatric patients receiving perampanel, especially those aged under 12 years.