In Expert review of clinical pharmacology
BACKGROUND : Cyclosporin A is a calcineurin inhibitor which has a narrow therapeutic window and high interindividual variability. Various population pharmacokinetic models have been reported; however, professional software and technical personnel were needed and the variables of the models were limited. Therefore, the aim of this study was to establish a model based on machine learning to predict CsA trough concentrations in Chinese allo-HSCT patients.
METHODS : A total of 7874 cases of CsA therapeutic drug monitoring data from 2069 allo-HSCT patients were retrospectively included. Sequential forward selection was used to select variable subsets, and eight different algorithms were applied to established the prediction model.
RESULTS : XGBoost exhibited the highest prediction ability. Except for the variables that were identified by previous studies, some rarely reported variables were found, such as norethindrone, WBC, PAB and hCRP. The prediction accuracy within ±30% of the actual trough concentration was above 0.80, and the predictive ability of the models was demonstrated to be effective in external validation.
CONCLUSION : In this study, models based on machine learning technology were established to predict CsA levels 3-4 days in advance during the early inpatient phase after HSCT. And a new perspective for CsA clinical application is provided.
Song Lin, Huang Chen-Rong, Pan Shi-Zheng, Zhu Jian-Guo, Cheng Zong-Qi, Yu Xun, Xue Ling, Xia Fan, Zhang Jin-Yuan, Wu De-Pei, Miao Li-Yan
2022-Nov-14
XGBoost, blood concentration prediction, cyclosporine, hematopoietic stem cell transplantation, machine learning
