Drug clearance in neonates: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction
Document Type
Article
Publication Date
11-1-2021
Abstract
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.
Keywords
Dosing optimization, Cross-validation, Big Data, Young, Regression
Divisions
fac_med
Funders
National Science and Technology Major Projects for 'Major New Drugs Innovation and Development' [2017ZX09304029-001] [2017ZX09304029-002],Young Taishan Scholars Program of Shandong Province,Qilu Young Scholars Program of Shandong University,Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University [FCYY201715],National Natural Science Foundation of China (NSFC) [81803433]
Publication Title
Clinical Pharmacokinetics
Volume
60
Issue
11
Publisher
Adis
Publisher Location
5 THE WAREHOUSE WAY, NORTHCOTE 0627, AUCKLAND, NEW ZEALAND