A Machine Learning Approach to Predict Interdose Vancomycin Exposure

Introduction Estimation of vancomycin area under the curve (AUC) is challenging in the case of discontinuous administration. Machine learning approaches are increasingly used and can be an alternative to population pharmacokinetic (POPPK) approaches for AUC estimation. The objectives were to train X...

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Bibliographic Details
Published in:	Pharmaceutical research Vol. 39; no. 4; pp. 721 - 731
Main Authors:	Bououda, Mehdi, Uster, David W., Sidorov, Egor, Labriffe, Marc, Marquet, Pierre, Wicha, Sebastian G., Woillard, Jean-Baptiste
Format:	Journal Article
Language:	English
Published:	New York Springer US 01-04-2022 Springer Springer Nature B.V American Association of Pharmaceutical Scientists
Subjects:	Algorithms Analysis Antibiotics Biochemistry Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Learning algorithms Life Sciences Machine learning Medical Law Methicillin Patient education Patients Pharmaceutical sciences Pharmacokinetics Pharmacology Pharmacology/Toxicology Pharmacy Research Paper Simulation methods Vancomycin model informed precision dosing vancomycin machine learning population pharmacokinetics simulations
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Summary:	Introduction Estimation of vancomycin area under the curve (AUC) is challenging in the case of discontinuous administration. Machine learning approaches are increasingly used and can be an alternative to population pharmacokinetic (POPPK) approaches for AUC estimation. The objectives were to train XGBoost algorithms based on simulations performed in a previous POPPK study to predict vancomycin AUC from early concentrations and a few features (i.e. patient information) and to evaluate them in a real-life external dataset in comparison to POPPK. Patients and Methods Six thousand simulations performed from 6 different POPPK models were split into training and test sets. XGBoost algorithms were trained to predict trapezoidal rule AUC a priori or based on 2, 4 or 6 samples and were evaluated by resampling in the training set and validated in the test set. Finally, the 2-sample algorithm was externally evaluated on 28 real patients and compared to a state-of-the-art POPPK model-based averaging approach. Results The trained algorithms showed excellent performances in the test set with relative mean prediction error (MPE)/ imprecision (RMSE) of the reference AUC = 3.3/18.9, 2.8/17.4, 1.3/13.7% for the 2, 4 and 6 samples algorithms respectively. Validation in real patient showed flexibility in sampling time post-treatment initiation and excellent performances MPE/RMSE<1.5/12% for the 2 samples algorithm in comparison to different POPPK approaches. Conclusions The Xgboost algorithm trained from simulation and evaluated in real patients allow accurate and precise prediction of vancomycin AUC. It can be used in combination with POPPK models to increase the confidence in AUC estimation.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0724-8741 1573-904X
DOI:	10.1007/s11095-022-03252-8