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<p>Hemophilia A is a rare inherited bleeding disorder typically managed with coagulation factor VIII (FVIII) replacement therapy. Designing personalized prophylactic regimens requires accurate pharmacokinetic (PK) characterization; current population PK (popPK) and Bayesian approaches provide a principled framework for individualized dosing, but their routine clinical implementation may still be constrained by model specification requirements and practical considerations in data collection and analysis. Here we present a machine learning (ML) framework, incorporating state-of-the-art language models, to predict individual FVIII PK parameters in pediatric patients. Using minimal sampling and routinely collected clinical data, our approach achieves superior performance over the widely adopted WAPPS-Hemo platform in predicting in vivo recovery (IVR) and FVIII half-life. These findings highlight the potential of AI-driven methods to reduce patient burden while improving accuracy in individualized treatment planning for children with severe hemophilia A.</p>

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Machine learning estimation of FVIII pharmacokinetic parameters in Chinese children with severe Hemophilia A

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月27日

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  • Yuntian Wang,
  • Di Ai,
  • Shuo Wang,
  • Zekun Li,
  • Gang Li,
  • Yunqi Zhu,
  • Yuqi Zhong,
  • Wensheng Zhang,
  • Runhui Wu,
  • Zhenping Chen,
  • Yongqiang Tang

摘要

Hemophilia A is a rare inherited bleeding disorder typically managed with coagulation factor VIII (FVIII) replacement therapy. Designing personalized prophylactic regimens requires accurate pharmacokinetic (PK) characterization; current population PK (popPK) and Bayesian approaches provide a principled framework for individualized dosing, but their routine clinical implementation may still be constrained by model specification requirements and practical considerations in data collection and analysis. Here we present a machine learning (ML) framework, incorporating state-of-the-art language models, to predict individual FVIII PK parameters in pediatric patients. Using minimal sampling and routinely collected clinical data, our approach achieves superior performance over the widely adopted WAPPS-Hemo platform in predicting in vivo recovery (IVR) and FVIII half-life. These findings highlight the potential of AI-driven methods to reduce patient burden while improving accuracy in individualized treatment planning for children with severe hemophilia A.