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External evaluation of the predictive performance of seven population pharmacokinetic models for phenobarbital in neonates.
Ryu, Sunae; Jung, Woo Jin; Jiao, Zheng; Chae, Jung-Woo; Yun, Hwi-Yeol.
Afiliación
  • Ryu S; College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea.
  • Jung WJ; National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju, Republic of Korea.
  • Jiao Z; College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea.
  • Chae JW; Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, P.R. China.
  • Yun HY; College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea.
Br J Clin Pharmacol ; 87(10): 3878-3889, 2021 10.
Article en En | MEDLINE | ID: mdl-33638184
AIM: Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external evaluation of the predictive performance in published pharmacokinetic models. METHODS: Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control was used for external evaluation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external evaluation. The incorporation of allometric scaling for body size and maturation factors into the published models was also tested for prediction improvement. RESULTS: A total of 79 serum concentrations from 28 subjects were included in the external dataset. Seven population pharmacokinetic studies of PB were identified as relevant in the literature search and included for our evaluation. The model by Voller et al showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al and Marsot et al) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, with improvement as observed in the model of Vucicevic et al. CONCLUSIONS: The predictive performance of published pharmacokinetic models of PB was diverse. Bayesian forecasting and incorporation of both size and maturation factors could improve the predictability of the models for neonates.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fenobarbital / Modelos Biológicos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans / Infant / Newborn Idioma: En Revista: Br J Clin Pharmacol Año: 2021 Tipo del documento: Article Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fenobarbital / Modelos Biológicos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans / Infant / Newborn Idioma: En Revista: Br J Clin Pharmacol Año: 2021 Tipo del documento: Article Pais de publicación: Reino Unido