Prediction of Tacrolimus Dose/Weight-Adjusted Trough Concentration in Pediatric Refractory Nephrotic Syndrome: A Machine Learning Approach.

PURPOSE

Tacrolimus (TAC) is a first-line immunosuppressant for patients with refractory nephrotic syndrome (NS). However, there is a high inter-patient variability of TAC pharmacokinetics, thus therapeutic drug monitoring (TDM) is required. In this study, we aimed to employ machine learning algorithms to investigate the impact of clinical and genetic variables on the TAC dose/weight-adjusted trough concentration (C/D) in Chinese children with refractory NS, and then develop and validate the TAC C/D prediction models.

PATIENTS AND METHODS

The association of 82 clinical variables and 244 single nucleotide polymorphisms (SNPs) with TAC C/D in the third month since TAC treatment was examined in 171 children with refractory NS. Extremely randomized trees (ET), gradient boosting decision tree (GBDT), random forest (RF), extreme gradient boosting (XGBoost), and Lasso regression were carried out to establish and validate prediction models, respectively. The best prediction models were validated on a cohort of 30 refractory NS patients.

RESULTS

GBDT algorithm performed best in the whole group (R=0.444, MSE=591.032, MAE=20.782, MedAE=18.980) and CYP3A5 nonexpresser group (R=0.264, MSE=477.948, MAE=18.119, MedAE=18.771), while ET algorithm performed best in the CYP3A5 expresser group (R=0.380, MSE=1839.459, MAE=31.257, MedAE=19.399). These prediction models included 3 clinical variables (ALB0, AGE0, and gender) and 10 SNPs ( rs3745859, rs56113315, 4 rs62121818, rs4553808, rs776746, rs12722489, rs1128880, rs7946115, rs2239781, and rs4821478).

CONCLUSION

The association between the clinical and genetic variables and TAC C/D was described, and three TAC C/D prediction models integrating clinical and genetic variables were developed and validated using machine learning, which may support individualized TAC dosing.