Machine learning approach for dosage individualization of azithromycin in children with community-acquired pneumonia

Document Type

Journal Article

Publication Date

4-3-2025

Journal

British journal of clinical pharmacology

DOI

10.1002/bcp.70050

Keywords

azithromycin; children; individual therapy; machine‐learning; pharmacometrics

Abstract

AIMS: The uncertainty about the efficacy and safety of currently used azithromycin dosing regimens in children warrants individualized therapy. The area under the plasma concentration-time curve over 24 h (AUC) of azithromycin correlates best with its effectiveness. The aim of this study was to evaluate the ability of machine learning (ML) to predict the AUC of azithromycin in children with community-acquired pneumonia. METHODS: Various ML algorithms were used to build ML models based on simulated pharmacokinetic profiles from a published population pharmacokinetic model. A priori-ML model predicted AUC using patients' characteristics and after the trough concentration (C) became available, a posteriori-ML model was built for improved prediction. Statistical methods and pharmacodynamic (PD) evaluation methods were used to evaluate the ML model's predictive accuracy in a real-world study. ML-optimized doses were evaluated by calculating the probability of PD target attainment in virtual trials compared with guideline-recommended doses. RESULTS: The AUC can be predicted by priori-ML model using the CatBoost algorithm with dosing regimen and two covariates as predictors (weight, alanine aminotransferase) before initial administration. A posteriori-ML model using CatBoost algorithm was built with adding C as a predictor. In real-world validation, the mean absolute prediction error of the priori-ML and posteriori-ML models was less than 30%. The accuracy (determining whether the PD target is met) of the priori-ML model was 76.3%, whereas that of the posteriori-ML model increased to 90.4%. CONCLUSIONS: ML models were established to predict the AUC of azithromycin successfully and could be used for individual dose adjustment in children before treatment and after obtaining C.

Department

Pediatrics

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