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Introduction <p>Valganciclovir, a prodrug of ganciclovir (GCV), is used to prevent cytomegalovirus infection after transplantation, with doses adjusted based on creatinine clearance (CrCL) to target GCV AUC0-24&#xa0;h of 40–60&#xa0;mg*h/L. This sometimes leads to overexposure or underexposure. This study aimed to train, test and validate machine learning (ML) algorithms for accurate GCV AUC0-24&#xa0;h estimation in solid organ transplantation.</p> Methods <p>We simulated patients for different dosing regimen (900&#xa0;mg/24&#xa0;h, 450&#xa0;mg/24&#xa0;h, 450&#xa0;mg/48&#xa0;h, 450&#xa0;mg/72&#xa0;h) using two literature population pharmacokinetic models, allocating 75% for training and 25% for testing. Simulations from two other literature models and real patients provided validation datasets. Three independent sets of ML algorithms were created for each regimen, incorporating CrCL and 2 or 3 concentrations. We evaluated their performance on testing and validation datasets and compared them with MAP-BE.</p> Results <p>XGBoost using 3 concentrations generated the most accurate predictions. In testing dataset, they exhibited a relative bias of -0.02 to 1.5% and a relative RMSE of 2.6 to 8.5%. In the validation dataset, a relative bias of 1.5 to 5.8% and 8.9 to 16.5%, and a relative RMSE of 8.5 to 9.6% and 10.7% to 19.7% were observed depending on the model used. XGBoost algorithms outperformed or matched MAP-BE, showing enhanced generalization and robustness in their estimates. When applied to real patients’ data, algorithms using 2 concentrations showed relative bias of 1.26% and relative RMSE of 12.68%.</p> Conclusions <p>XGBoost ML models accurately estimated GCV AUC0-24&#xa0;h from limited samples and CrCL, providing a strategy for optimized therapeutic drug monitoring.</p> Graphical Abstract <p></p>

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Estimation of Ganciclovir Exposure in Adults Transplant Patients by Machine Learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月28日

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  • Hamza Sayadi,
  • Yeleen Fromage,
  • Marc Labriffe,
  • Pierre-André Billat,
  • Cyrielle Codde,
  • Selim Arraki Zava,
  • Pierre Marquet,
  • Jean-Baptiste Woillard

摘要

Introduction

Valganciclovir, a prodrug of ganciclovir (GCV), is used to prevent cytomegalovirus infection after transplantation, with doses adjusted based on creatinine clearance (CrCL) to target GCV AUC0-24 h of 40–60 mg*h/L. This sometimes leads to overexposure or underexposure. This study aimed to train, test and validate machine learning (ML) algorithms for accurate GCV AUC0-24 h estimation in solid organ transplantation.

Methods

We simulated patients for different dosing regimen (900 mg/24 h, 450 mg/24 h, 450 mg/48 h, 450 mg/72 h) using two literature population pharmacokinetic models, allocating 75% for training and 25% for testing. Simulations from two other literature models and real patients provided validation datasets. Three independent sets of ML algorithms were created for each regimen, incorporating CrCL and 2 or 3 concentrations. We evaluated their performance on testing and validation datasets and compared them with MAP-BE.

Results

XGBoost using 3 concentrations generated the most accurate predictions. In testing dataset, they exhibited a relative bias of -0.02 to 1.5% and a relative RMSE of 2.6 to 8.5%. In the validation dataset, a relative bias of 1.5 to 5.8% and 8.9 to 16.5%, and a relative RMSE of 8.5 to 9.6% and 10.7% to 19.7% were observed depending on the model used. XGBoost algorithms outperformed or matched MAP-BE, showing enhanced generalization and robustness in their estimates. When applied to real patients’ data, algorithms using 2 concentrations showed relative bias of 1.26% and relative RMSE of 12.68%.

Conclusions

XGBoost ML models accurately estimated GCV AUC0-24 h from limited samples and CrCL, providing a strategy for optimized therapeutic drug monitoring.

Graphical Abstract