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ORIGINAL RESEARCH article

Front. Pharmacol.

Sec. Obstetric and Pediatric Pharmacology

Volume 16 - 2025 | doi: 10.3389/fphar.2025.1671652

Precision Dosing of Voriconazole in Immunocompromised Children Under 2 Years: Integrated Machine Learning and Population Pharmacokinetic Modeling

Provisionally accepted
  • 1Department of Clinical Pharmacy, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, China
  • 2Department of Pharmacy, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, China
  • 3Global Health Research Center, Duke Kunshan University, Kunshan, China, Kunshan, China
  • 4Department of Pharmacy, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
  • 5Department of Pharmacy, National Regional Medical Center, Binhai Campus of The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
  • 6Department of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, China
  • 7Department of Pharmacy, Kunshan Woman and Children’s Healthcare Hospital, Children’s Hospital of Fudan University Kunshan Branch, Kunshan, China

The final, formatted version of the article will be published soon.

Objective: This study aimed to develop an individualized dosing strategy for voriconazole (VRZ) in children under 2 years of age by integrating machine learning (ML) and population pharmacokinetic (PopPK) modeling. Methods: This retrospective observational study included 76 eligible pediatric patients for model development, analyzing their baseline characteristics and laboratory parameters. A population pharmacokinetic (PopPK) model using NONMEM® software was performed to assess the clearance (CL) and volume of distribution (V) of VRZ. The individual CL and V were included as input variables. The Boruta algorithm was employed for feature selection, after which six machine learning algorithms were applied. The models were evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²) to identify the optimal algorithm, which then underwent independent external validation. The selected final model was analyzed for interpretability using Shapley Additive Explanations (SHAP). Results: A total of 76 pediatric patients were enrolled for model development, consisting of 58 males (76.3%) and 18 females (23.7%), with a median age of 11 months and a median weight of 8.05 kg. We analyzed 110 therapeutic drug monitoring (TDM) samples of VRZ from these participants. A one-compartment model with first-order absorption and elimination described the population pharmacokinetics of VRZ. Population estimates for apparent clearance (CL/F) and volume of distribution (V/F) were 17.9 L/h (RSE, 10.8%) and 788 L (RSE, 15.4%), respectively. An XGBoost model accurately predicted voriconazole concentrations (R² = 0.81, RMSE =0.53) with a relative error of ±20% for most observations. In the external validation, the XGBoost model demonstrated an R2 of 0.75, RMSE of 0.14. SHAP analysis identified clearance, weight, and laboratory values as significant predictors. Conclusion: This study emphasized the importance of personalized treatment in utilizing VRZ for children under 24 months. The XGBoost model demonstrated potential in identifying an initial dose recommendation for VRZ.

Keywords: Voriconazole, Children, under 2 years, machine learning, SHAP analysis

Received: 23 Jul 2025; Accepted: 01 Sep 2025.

Copyright: © 2025 Shen, Hu, Xu, Zhou, Wu, Ge, Wang, Wang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Yi Wang, Department of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, China
Zhiping Li, Department of Clinical Pharmacy, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, China

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