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

Authors

Bo-Hao Tang
Zheng Guan
Karel Allegaert
Yue-E Wu
Efthymios Manolis
Stephanie Leroux
Bu-Fan Yao
Hai-Yan Shi
Xiao Li
Xin Huang
Wen-Qi Wang
A-Dong Shen
Xiao-Ling Wang
Tian-You Wang
Chen Kou
Hai-Yan Xu
Yue Zhou
Yi Zheng
Guo-Xiang Hao
Bao-Ping Xu
Alison H Thomson
Edmund V Capparelli
Valerie Biran
Nicolas Simon
Bernd Meibohm
Yoke-Lin Lo
Remedios Marques
Jose-Esteban Peris
Irja Lutsar
Jumpei Saito
Jacobus Burggraaf
Evelyne Jacqz-Aigrain
John van den Anker, George Washington University
Wei Zhao

Document Type

Journal Article

Publication Date

11-1-2021

Journal

Clinical Pharmacokinetics

Volume

60

Issue

11

Inclusive Pages

1435-1448

DOI

10.1007/s40262-021-01033-x

Keywords

Drug Elimination Routes; Humans; Infant, Newborn; Machine Learning; Metabolic Clearance Rate; Models, Biological; Vancomycin

Comments

Epub 2021 May 27

APA Citation

Tang, B., Guan, Z., Allegaert, K., Wu, Y., Manolis, E., Leroux, S., Yao, B., Shi, H., Li, X., Huang, X., Wang, W., Shen, A., Wang, X., Wang, T., Kou, C., Xu, H., Zhou, Y., Zheng, Y., Hao, G., Xu, B., Thomson, A., Capparelli, E., Biran, V., Simon, N., Meibohm, B., Lo, Y., Marques, R., Peris, J., Lutsar, I., Saito, J., Burggraaf, J., Jacqz-Aigrain, E., van den Anker, J., & Zhao, W. (2021). Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction.. Clinical Pharmacokinetics, 60 (11). http://dx.doi.org/10.1007/s40262-021-01033-x

Peer Reviewed

1