在2岁以下的免疫受损儿童中精确剂量Voriconazole:集成机器学习和人口药理动力学建模
Li Shen1,2, Mengdi Hu1, Xiaoyong Xu1
1Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Frontiers in pharmacology
|October 1, 2025
概括
机器学习和人口药动力学建模优化了小孩子的伏利康纳剂量. 这种个性化的方法提高了两岁以下儿科患者的药物疗效和安全性.
科学领域:
- 药理学 药理学是指药理学的学科.
- 儿科医学 儿科医学
- 计算生物学 计算生物学
背景情况:
- 在两岁以下的儿童中,沃里康纳 (VRZ) 的剂量需要优化以改善治疗结果.
- 沃里康纳的药理动力学因个人变异而需要个性化剂量策略.
研究的目的:
- 开发一个个性化的剂量策略为小儿科患者两岁以下的voriconazole.
- 整合机器学习 (ML) 和人口药动力学 (PopPK) 建模,以获得精确的VRZ剂量建议.
主要方法:
- 追溯分析76名儿科患者的数据,包括基线特征和治疗药物监测样本.
- 使用NONMEM开发了一个PopPK模型,并应用了6个ML算法,包括Boruta特征选择.
- 使用MSE,RMSE,MAE和R2进行评估模型,然后进行外部验证和SHAP分析以查看可解释性.
主要成果:
- 一个XGBoost模型在预测voriconazole度方面取得了很高的准确性 (R2 = 0.81,RMSE = 0.53) 并在外部验证中表现良好 (R2 = 0.75).
- 确定了明显清除 (CL/F) 和分布量 (V/F) 的种群药动力学参数.
- SHAP分析确定了清除量,体重和实验室值作为VRZ度的关键预测指标.
结论:
- 对24个月以下的儿童来说,个性化沃里康纳治疗非常重要.
- 开发的XGBoost模型显示了在这个脆弱的儿科群体中指导初始伏利康纳剂量建议的巨大潜力.
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