Development and Validation of a Population Pharmacokinetics Model of Perampanel for Pediatric Epilepsy Patients for Optimized Dosing

Lingyan Yu,1,2 Fengqian Mao,3 Shunan Chen,3 Jieqiong Liu,2,4 Jiayu Xiao,1 Meng Chen,1 Huan Luo,1 Zhenwei Yu,2,4 Haibin Dai1,2 1Department of Pharmacy, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, People’s Republic of China; 2Research Center for Clinical Pharmacy, Zhejiang University, Hangzhou, 310058, People’s Republic of China; 3School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China; 4Department of Pharmacy, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People’s Republic of ChinaCorrespondence: Zhenwei Yu, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3rd East Qingchun Road, Hangzhou, People’s Republic of China, Email [email protected] Haibin Dai, Second Affiliated Hospital, Zhejiang University School of Medicine, 88th Jiefang Road, Hangzhou, People’s Republic of China, Email [email protected]: 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&ast;((age+10)/8.8)1.31&ast;1.51OXC&ast;0.745VPA&ast;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&ast;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.Keywords: perampanel, epilepsy, NONMEM, therapeutic drug monitoring, population pharmacokinetic model