Population pharmacokinetic model-guided machine learning for predicting the clearance of aripiprazole in children with tic disorders
OBJECTIVE To establish a machine learning model under the guidance of population pharmacokinetic(PPK)to predict the clearance of aripiprazole(ARI)and dehydroaripiprazole(DARI)in children of tic disorders(TD).METHODS Drug concentrations and clinical data of ARI and DARI in 81 TD children aged 4.8-17.3 years were collected.Clearance rate of ARI/DARI was calculated by PPK model plus maximum posterior Bayesian method,the importance of covariates ranked by random forest(RF)algorithm and mapping relationship between physiological and genetic characteristics and ARI/DARI clearance rate established by three machine learning algorithms of RF,decision tree regression(DTR)and neural network(NNET).Prediction error of the model was calculated and visual test method utilized for evaluating the prediction performance of machine learning algo-rithms.RESULTS RF results indicated that importance order of covariates affecting ARI clearance was CYP2D6 metabolic type＞body weight＞body surface area＞height＞age＞serum creatinine concentration;order of importance of covariates affecting DARI clearance was body surface area＞height＞body weight＞age＞serum creatinine concentration.NNET algo-rithm was employed for predicting ARI clearance with the lowest mean absolute prediction error and average prediction error squared and linear regression determination coefficient between predicted value and reference value of clearance was the largest.Prediction performance of DTR,RF and NNET algorithms for predicting DARI clearance was comparable.Mean relative predic-tion error and median relative prediction error of three machine learning algorithms for predicting ARI/DARI clearance were both ≤20％.CONCLUSION In this study,three kinds of machine learning models have been established under the guidance of PPK.It may a priori predict the clearance of ARI/DARI and provide rationales for clinical precision medicine in TD children.
NETL
NSTL
万方数据
