Yue Li, Hong-Li Guo, Lin Fan, Jie Wang, Ya-Hui Hu, Yuan-Yuan Zhang, Jin-Chun Qiu, Jing Chen, Chun-Feng Wu, Gang Zhang, Xiao-Peng Lu, Feng Chen
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引用次数: 0
Abstract
Personalized precision dosing remains an unmet clinical need. This study used population pharmacokinetic (PopPK) modeling to evaluate transitioning lacosamide (LCM) in children with epilepsy from body weight (BW)-based (mg/kg) to simplified BW-band or fixed-dose (mg) regimens. Real-world data from 190 patients were analyzed using nonlinear mixed-effects modeling program, comparing a BW-based model (Model I) and a genotype-guided model (Model II); the latter showed superior predictive performance. Monte Carlo simulations confirmed comparable LCM exposure across regimens, with >78% target attainment in external validation. A fixed 100 mg dose for patients ≥10 kg achieved equivalent exposure to BW-adjusted dosing, with consistent results in 1-4 years and obese patients. These findings enabled BW-band dosing as a clinically viable alternative to mg/kg regimens, while CYP2C19 genotyping further enhanced precision. This PopPK-based strategy simplifies LCM therapy without compromising efficacy, offering a practical approach to personalized epilepsy management in children.
NPJ Genomic MedicineBiochemistry, Genetics and Molecular Biology-Molecular Biology
CiteScore
9.40
自引率
1.90%
发文量
67
审稿时长
17 weeks
期刊介绍:
npj Genomic Medicine is an international, peer-reviewed journal dedicated to publishing the most important scientific advances in all aspects of genomics and its application in the practice of medicine.
The journal defines genomic medicine as "diagnosis, prognosis, prevention and/or treatment of disease and disorders of the mind and body, using approaches informed or enabled by knowledge of the genome and the molecules it encodes." Relevant and high-impact papers that encompass studies of individuals, families, or populations are considered for publication. An emphasis will include coupling detailed phenotype and genome sequencing information, both enabled by new technologies and informatics, to delineate the underlying aetiology of disease. Clinical recommendations and/or guidelines of how that data should be used in the clinical management of those patients in the study, and others, are also encouraged.