A New Algorithm Optimized for Initial Dose Settings of Vancomycin Using Machine Learning

Abstract

This study aimed to construct an optimal algorithm for initial dose settings of vancomycin (VCM) using machine learning (ML) with decision tree (DT) analysis. Patients who were administered intravenous VCM and underwent therapeutic drug monitoring (TDM) at the Hokkaido University Hospital were enrolled. The study period was November 2011 to March 2019. In total, 654 patients were included in the study. Patients were divided into two groups, training (patients who received VCM from November 2011 to December 2017; n 496) and testing (patients who received VCM from January 2018 to March 2019; n 158) groups. For the training group, DT analysis of the classification and regression tree algorithm was performed to construct an algorithm (called DT algorithm) for the initial dose settings of VCM. For the testing group, the rates of attaining the VCM therapeutic range (trough value 10-15 and 10-20 mg/L) with the DT algorithm and three conventional dose-setting methods were compared for model evaluation. The DT algorithm was constructed to be used for patients with estimated glomerular filtration rate ≥50 mL/min and body weight ≥40 kg. As a result, the recommended daily doses ranged from 20.0 to 58.1 mg/kg. In model evaluation, the DT algorithm obtained the highest rates of attaining the VCM therapeutic range compared to conventional dose-setting methods. Therefore, our DT algorithm can be applied to clinical practice. In addition, ML is useful for setting drug doses.

Personalized drug concentration predictions with machine learning: an exploratory study

“…Imai in a study on vancomycin dosing using machine learning, found that machine learning is useful in drug dose setting. 13 Wenki You in a study on algorithmic approach to personalized drug concentration predictions has presented various machine learning algorithms to solve the problems in drug concentration predictions. The researcher found that RANSAC algorithm produces a more reasonable concentration curve.…”

A hybrid machine learning/pharmacokinetic approach outperforms maximum a posteriori Bayesian estimation by selectively flattening model priors

“…Applications of ML to MIPD to date have found that ML models are often able to accurately estimate past drug exposure 24,25 , predict future drug exposure 26-28 or select doses [29][30][31][32] . However, the improvement in accuracy from these earlier approaches comes at the expense of pharmacological interpretability and the ability to simulate patient response to alternative dosing regimens 24,33,34 .…”

Mycophenolic Acid Exposure Prediction Using Machine Learning

“…[6][7][8][9][10][11] Machine learning (ML) is widely used in numerous applications, including pharmacology (PubMed Entry "Machine learning" & "Pharmacology" = 600 in 2018, 846 in 2019) especially in structure/activity predictions 12,13 or drug discovery 14 but only a few applications to predict drug exposure, PK parameters, or optimal dose exist. [15][16][17][18][19] Recently, we successfully applied a ML approach for tacrolimus AUC estimation that yielded better performance in terms of relative bias or imprecision vs. reference trapezoidal rule AUC than MAP-BE, even with only two samples. 19 We used extreme gradient boosting (Xgboost R package) where simple regression trees are iteratively built by finding among all the input variables, split values that minimize the prediction error.…”