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Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy.
Ponthier, Laure; Franck, Bénédicte; Autmizguine, Julie; Labriffe, Marc; Ovetchkine, Philippe; Marquet, Pierre; Åsberg, Anders; Woillard, Jean-Baptiste.
Afiliação
  • Ponthier L; Pharmacology and Transplantation, INSERM U1248, Université de Limoges, Limoges, France.
  • Franck B; Department of Pediatrics, University Hospital of Limoges, Limoges, France.
  • Autmizguine J; Department of Clinical and Biological Pharmacology and Pharmacovigilance, Clinical Investigation Center CIC-P 1414, Rennes, France.
  • Labriffe M; Department of Pharmacology and Physiology, Université de Montréal, Montreal, Quebec, Canada.
  • Ovetchkine P; Research Center, Center Hospitalier Universitaire Sainte-Justine, Montreal, Quebec, Canada.
  • Marquet P; Department of Pediatrics, Center Hospitalier Universitaire Sainte-Justine, Montreal, Quebec, Canada.
  • Åsberg A; Pharmacology and Transplantation, INSERM U1248, Université de Limoges, Limoges, France.
  • Woillard J-B; Department of Pharmacology, Toxicology and Pharmacovigilance, University Hospital of Limoges, Limoges, France.
Antimicrob Agents Chemother ; 68(10): e0086024, 2024 Oct 08.
Article em En | MEDLINE | ID: mdl-39194260
ABSTRACT
Intravenous ganciclovir and oral valganciclovir display significant variability in ganciclovir pharmacokinetics, particularly in children. Therapeutic drug monitoring currently relies on the area under the concentration-time (AUC). Machine-learning (ML) algorithms represent an interesting alternative to Maximum-a-Posteriori Bayesian-estimators for AUC estimation. The goal of our study was to develop and validate an ML-based limited sampling strategy (LSS) approach to determine ganciclovir AUC0-24 after administration of either intravenous ganciclovir or oral valganciclovir in children. Pharmacokinetic parameters from four published population pharmacokinetic models, in addition to the World Health Organization growth curve for children, were used in the mrgsolve R package to simulate 10,800 pharmacokinetic profiles of children. Different ML algorithms were trained to predict AUC0-24 based on different combinations of two or three samples. Performances were evaluated in a simulated test set and in an external data set of real patients. The best estimation performances in the test set were obtained with the Xgboost algorithm using a 2 and 6 hours post dose LSS for oral valganciclovir (relative mean prediction error [rMPE] = 0.4% and relative root mean square error [rRMSE] = 5.7%) and 0 and 2 hours post dose LSS for intravenous ganciclovir (rMPE = 0.9% and rRMSE = 12.4%). In the external data set, the performance based on these two sample LSS was acceptable rMPE = 0.2% and rRMSE = 16.5% for valganciclovir and rMPE = -9.7% and rRMSE = 17.2% for intravenous ganciclovir. The Xgboost algorithm developed resulted in a clinically relevant individual estimation using only two blood samples. This will improve the implementation of AUC-targeted ganciclovir therapeutic drug monitoring in children.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Antivirais / Ganciclovir / Monitoramento de Medicamentos / Área Sob a Curva / Aprendizado de Máquina / Valganciclovir Limite: Adolescent / Child / Child, preschool / Female / Humans / Infant / Male Idioma: En Revista: Antimicrob Agents Chemother Ano de publicação: 2024 Tipo de documento: Article País de afiliação: França

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Antivirais / Ganciclovir / Monitoramento de Medicamentos / Área Sob a Curva / Aprendizado de Máquina / Valganciclovir Limite: Adolescent / Child / Child, preschool / Female / Humans / Infant / Male Idioma: En Revista: Antimicrob Agents Chemother Ano de publicação: 2024 Tipo de documento: Article País de afiliação: França