Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction.
Bo-Hao TangZheng GuanKarel AllegaertYue-E WuEfthymios ManolisStephanie LerouxBu-Fan YaoHai-Yan ShiXiao LiXin HuangWen-Qi WangA-Dong ShenXiao-Ling WangTian-You WangChen KouHai-Yan XuYue ZhouYi ZhengGuo-Xiang HaoBao-Ping XuAlison H ThomsonEdmund V CapparelliValerie BiranNicolas SimonBernd MeibohmYoke-Lin LoRemedios MarquesJose-Esteban PerisIrja LutsarJumpei SaitoJacobus BurggraafEvelyne Jacqz-AigrainJohn van den AnkerWei ZhaoPublished in: Clinical pharmacokinetics (2021)
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.