Predicting Blood Concentration of Tacrolimus in Patients With Autoimmune Diseases Using Machine Learning Techniques Based on Real-World Evidence

Abstract

Tacrolimus is a widely used immunosuppressive drug in patients with autoimmune diseases. It has a narrow therapeutic window, thus requiring therapeutic drug monitoring (TDM) to guide the clinical regimen. This study included 193 cases of tacrolimus TDM data in patients with autoimmune diseases at Southern Medical University Nanfang Hospital from June 7, 2018, to December 31, 2020. The study identified nine important variables for tacrolimus concentration using sequential forward selection, including height, tacrolimus daily dose, other immunosuppressants, low-density lipoprotein cholesterol, mean corpuscular volume, mean corpuscular hemoglobin, white blood cell count, direct bilirubin, and hematocrit. The prediction abilities of 14 models based on regression analysis or machine learning algorithms were compared. Ultimately, a prediction model of tacrolimus concentration was established through eXtreme Gradient Boosting (XGBoost) algorithm with the best predictive ability (R2 = 0.54, mean absolute error = 0.25, and root mean square error = 0.33). Then, SHapley Additive exPlanations was used to visually interpret the variable’s impacts on tacrolimus concentration. In conclusion, the XGBoost model for predicting blood concentration of tacrolimus on the basis of real-world evidence has good predictive performance, providing guidance for the adjustment of regimen in clinical practice.

Machine learning advances the integration of covariates in population pharmacokinetic models: Valproic acid as an example

“…Compared with the initially proposed XGBoost model, the reduced performance of our simplified XGBoost model indicated the important influences of these features, particularly the blood sampling time and ALB, on the model output. Nevertheless, a 60.00% IR of the simplified optimum XGBoost model on our external dataset suggested its good forecasting performance, considering the prediction accuracy of the predicted TDM within ±30% of the actual TDM in many similar studies that utilized XGBoost models, ranging from 40% to 75% (Huang et al, 2021b;Guo et al, 2021;Zheng et al, 2021;Ma et al, 2022). Based on the simplified optimum XGBoost model, we designed an easy-to-use web application by using only CYP2C19 genotypes and some noninvasive clinical parameters as an MIPD tool for personalized dosing adjustments.…”

“…For example, a previous study by us proved the feasibility of ML algorithms for predicting the dose-adjusted concentrations of lamotrigine for personalized dose adjustment ( Zhu et al, 2021a ). Although a lot of related work has been conducted to directly predict drug concentration or drug dose using ML-based strategies ( Jovanović et al, 2015 ; Huang et al, 2021a ; Zheng et al, 2021 ), the integration of model-informed and data-driven approaches is critical ( Kluwe et al, 2021 ).…”

Surgical Methods and Social Factors Are Associated With Long-Term Survival in Follicular Thyroid Carcinoma: Construction and Validation of a Prognostic Model Based on Machine Learning Algorithms

“…In addition, the research conducted by Zheng et al. ( 47 ) also demonstrated that the XGBoost model based on real-world evidence had good predictive performance in predicting the blood concentration of tacrolimus, which could provide guidance for the adjustment of the plan in clinical practice. Five commonly used ML algorithms were used to rank FTC’s risk factors in importance.…”

An interpretable stacking ensemble learning framework based on multi-dimensional data for real-time prediction of drug concentration: The example of olanzapine

“…Unlike previous studies that used only homogeneous ensembles (e.g., XGBoost) or simple weighted average ensembles for ML-assisted TDM ( Zhu et al, 2021a ; Guo et al, 2021 ; Hsu et al, 2021 ; Huang et al, 2021 ; Zheng et al, 2021 ; Lee et al, 2022 ), our is the first study, to the best of our knowledge, to explore the real-time estimation of drug concentrations by using a stacking ensemble framework as an MIPD tool. Our work here shows that stacking a heterogeneous ensemble, overall, is superior to homogeneous ensemble-based methods (e.g., bagging and XGBoost models) on several comparisons of model performance on the TDM-OLZ dataset.…”