Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients.
Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the "derivation cohort" to develop dose-prediction algorithm, while the remaining 20% constituted the "validation cohort" to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.
Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, P. R. China.|Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078, Hunan, P. R. China.
Jie
J
Tang
Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, P. R. China.|Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078, Hunan, P. R. China.
Rong
R
Liu
Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, P. R. China.|Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078, Hunan, P. R. China.
Yue-Li
YL
Zhang
Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, P. R. China.|Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078, Hunan, P. R. China.
Mou-Ze
MZ
Liu
Peking University Third Hospital, Beijing, 100191, P. R. China.
Yong-Fang
YF
Hu
Research Center of Chinese Health Ministry of Transplantation Medicine Engineering and Technology, Third Affiliated Hospital, Central South University, Changsha, 410013, Hunan, P. R. China.
Ming-Jie
MJ
Shao
Research Center of Chinese Health Ministry of Transplantation Medicine Engineering and Technology, Third Affiliated Hospital, Central South University, Changsha, 410013, Hunan, P. R. China.
Li-Jun
LJ
Zhu
Department of Clinical Pharmacology, Wuhan General Hospital of Guangzhou Command, Wuhan, 430070, Hubei, P. R. China.
Hua-Wen
HW
Xin
Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, P. R. China.
Gui-Wen
GW
Feng
Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, P. R. China.
Wen-Jun
WJ
Shang
School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, 450001, Henan, P. R. China.
Xiang-Guang
XG
Meng
School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, 450001, Henan, P. R. China.
Li-Rong
LR
Zhang
Research Center of Chinese Health Ministry of Transplantation Medicine Engineering and Technology, Third Affiliated Hospital, Central South University, Changsha, 410013, Hunan, P. R. China.
Ying-Zi
YZ
Ming
Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, P. R. China.|Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078, Hunan, P. R. China.
Wei
W
Zhang
D000328:Adult; D001499:Bayes Theorem; D015331:Cohort Studies; D054796:Drug Dosage Calculations; D005260:Female; D006801:Humans; D007166:Immunosuppressive Agents / Q000627:therapeutic use*; D016030:Kidney Transplantation; D016014:Linear Models; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D057285:Precision Medicine; D051436:Renal Insufficiency, Chronic / Q000276:immunology* / Q000473:pathology / Q000601:surgery; D016559:Tacrolimus / Q000627:therapeutic use*; D066027:Transplant Recipients
D007166:Immunosuppressive Agents; D016559:Tacrolimus