Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction.

Autor: Tang BH; Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China., Guan Z; Centre for Human Drug Research, Leiden, The Netherlands.; Leiden University Medical Center, Leiden, The Netherlands., Allegaert K; Department of Development and Regeneration, KU Leuven, Leuven, Belgium.; Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium., Wu YE; Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China., Manolis E; Modelling and Simulation Working Party, European Medicines Agency, Amsterdam, The Netherlands., Leroux S; Department of Pediatrics, CHU de Rennes, Rennes, France., Yao BF; Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China., Shi HY; Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China., Li X; Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China., Huang X; Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China.; Clinical Research Center, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China., Wang WQ; Clinical Research Center, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China., Shen AD; Key Laboratory of Major Diseases in Children and National Key Discipline of Pediatrics (Capital Medical University), Ministry of Education, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, Beijing, People's Republic of China., Wang XL; Clinical Research Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, People's Republic of China., Wang TY; Clinical Research Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, People's Republic of China., Kou C; Department of Neonatology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, People's Republic of China., Xu HY; Department of Pediatrics, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China., Zhou Y; Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China., Zheng Y; Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China., Hao GX; Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China., Xu BP; Department of Respiratory Diseases, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, People's Republic of China., Thomson AH; Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK., Capparelli EV; Pediatric Pharmacology and Drug Discovery, University of California, San Diego, CA, USA., Biran V; Neonatal Intensive Care Unit, Hospital Robert Debre, Paris, France., Simon N; Aix Marseille Univ, APHM, INSERM, IRD, SESSTIM, Hop Sainte Marguerite, Service de Pharmacologie Clinique, CAP-TV, Marseille, France., Meibohm B; Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, Memphis, TN, USA., Lo YL; Department of Pharmacy, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.; School of Pharmacy, International Medical University, Kuala Lumpur, Malaysia., Marques R; Department of Pharmacy Services, La Fe Hospital, Valencia, Spain., Peris JE; Department of Pharmacy and Pharmaceutical Technology, University of Valencia, Valencia, Spain., Lutsar I; Institute of Medical Microbiology, University of Tartu, Tartu, Estonia., Saito J; Department of Pharmacy, National Children's Hospital National Center for Child Health and Development, Tokyo, Japan., Burggraaf J; Centre for Human Drug Research, Leiden, The Netherlands.; Leiden University Medical Center, Leiden, The Netherlands., Jacqz-Aigrain E; Department of Pediatric Pharmacology and Pharmacogenetics, Hospital Robert Debre, APHP, Paris, France.; Clinical Investigation Center CIC1426, Hoŝpital Robert Debre, Paris, France.; University Paris Diderot, Sorbonne Paris Cite, Paris, France., van den Anker J; Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA.; Departments of Pediatrics, Pharmacology and Physiology, Genomics and Precision Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC, USA.; Department of Paediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland., Zhao W; Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China. zhao4wei2@hotmail.com.; Modelling and Simulation Working Party, European Medicines Agency, Amsterdam, The Netherlands. zhao4wei2@hotmail.com.; Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China. zhao4wei2@hotmail.com.; Clinical Research Center, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China. zhao4wei2@hotmail.com.
Jazyk: angličtina
Zdroj: Clinical pharmacokinetics [Clin Pharmacokinet] 2021 Nov; Vol. 60 (11), pp. 1435-1448. Date of Electronic Publication: 2021 May 27.
DOI: 10.1007/s40262-021-01033-x
Abstrakt: Background: Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data.
Objective: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.
Methods: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.
Results: The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.
Conclusion: A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.
(© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
Databáze: MEDLINE
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