Estimation of Overall Cyclosporine Exposure Using Machine Learning.

Journal: Therapeutic drug monitoring
Published Date: Jul 23, 2025

Abstract

BACKGROUND: Cyclosporine (CsA), an immunosuppressant widely used in solid-organ transplantation, requires precise therapeutic drug monitoring to balance its efficacy and toxicity. The interdose area under the concentration-time curve (AUC0-12 h) is considered to be a superior metric of drug exposure compared with single concentration measurements but is, nevertheless, resource-intensive. Machine learning (ML) offers a novel approach for AUC prediction by leveraging patient-specific data without relying on traditional pharmacokinetic assumptions. This study intended to develop and evaluate XGBoost ML models for predicting CsA AUC0-12 h using either two or three blood concentrations and to compare their performance against maximum a posteriori Bayesian estimation (MAP-BE) based on population pharmacokinetic models.

Authors

  • Jean-Baptiste Woillard
    P&T, Unité Mixte de Recherche 1248 Université de Limoges, Institut National de la Santé et de la Recherche Médicale, Limoges, France.
  • Marc Labriffe
    University of Limoges, IPPRITT, Limoges, France.
  • Pierre Marquet
    University of Limoges, UMR 1248.

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