Drug clearance in neonates : a combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction

Tang, Bo Hao and Guan, Zheng and Allegaert, Karel and Wu, Yue-E. and Manolis, Efthymios and Leroux, Stephanie and Yao, Bu-Fan and Shi, Hai-Yan and Li, Xiao and Huang, Xin and Wang, Wen-Qi and Shen, A.-Dong and Wang, Xiao-Ling and Wang, Tian-You and Kou, Chen and Xu, Hai-Yan and Zhou, Yue and Zheng, Yi and Hao, Guo-Xiang and Xu, Bao-Ping and Thomson, Alison H. and Capparelli, Edmund V. and Biran, Valerie and Simon, Nicolas and Meibohm, Bernd and Lo, Yoke-Lin and Marques, Remedios and Peris, Jose-Esteban and Lutsar, Irja and Saito, Jumpei and Burggraaf, Jacobus and Jacqz-Aigrain, Evelyne and van den Anker, John and Zhao, Wei (2021) Drug clearance in neonates : a combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction. Clinical Pharmacokinetics, 60 (11). pp. 1435-1448. ISSN 0312-5963 (https://doi.org/10.1007/s40262-021-01033-x)

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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.

ORCID iDs

Tang, Bo Hao, Guan, Zheng, Allegaert, Karel, Wu, Yue-E., Manolis, Efthymios, Leroux, Stephanie, Yao, Bu-Fan, Shi, Hai-Yan, Li, Xiao, Huang, Xin, Wang, Wen-Qi, Shen, A.-Dong, Wang, Xiao-Ling, Wang, Tian-You, Kou, Chen, Xu, Hai-Yan, Zhou, Yue, Zheng, Yi, Hao, Guo-Xiang, Xu, Bao-Ping, Thomson, Alison H. ORCID logoORCID: https://orcid.org/0000-0002-2354-6116, Capparelli, Edmund V., Biran, Valerie, Simon, Nicolas, Meibohm, Bernd, Lo, Yoke-Lin, Marques, Remedios, Peris, Jose-Esteban, Lutsar, Irja, Saito, Jumpei, Burggraaf, Jacobus, Jacqz-Aigrain, Evelyne, van den Anker, John and Zhao, Wei;
CORE (COnnecting REpositories)