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使用基于人口药理动力学模型的机器学习来估计肝功能障碍患者的线索利德暴露量

Ru Liao¹, Lihong Chen², Xiaoliang Cheng¹

¹Department of Pharmacy, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.

European journal of clinical pharmacology

|May 8, 2024

概括

这项研究开发了一种针对肝功能障碍的linezolid的种群药理动力学模型,使用机器学习来预测药物暴露,并确定毒性风险因素,以更好地管理患者.

科学领域:

药理动力学和药物新陈代谢
机器学习在临床药理学中的应用
传染病药物治疗传染病药物治疗

背景情况:

莱涅佐利德对于治疗感染至关重要,但在肝功能障碍中使用它需要小心剂量.
肝功能障碍可能会改变药物的药理动力学,可能会影响疗效和安全性.
预测线索利德暴露对于优化治疗和最大限度地减少不良事件至关重要.

研究的目的:

在肝功能受损的患者中描述linezolid的药理动力学.
开发对线化物暴露 (AUC0-24) 的预测模型.
为了识别linezolid诱导的血小板缺血的危险因素.

主要方法:

使用NONMEM.的人口药理动力学 (PPK) 建模.
蒙特卡洛模拟 (MCS) 用于生成虚拟患者数据.
机器学习 (ML) 模型,包括Xgboost,用于预测linezolid AUC0-24.
对血小板缺血的危险因素的分析.

主要成果:

一个PPK模型确定了3.83L/h的典型清除率和34.1L的分布量.
严重的肝功能障碍是影响莱涅佐利德清除量的显著共同变量.
与传统方法相比,Xgboost ML模型在预测线化物AUC0-24方面表现优异.
基线血小板计数,linezolid AUC0-24,并发性诺基诺使用被确定为血小板缺血的独立危险因素.

结论:

成功开发了一种强大的PPK模型用于肝功能障碍中的linezolid.
机器学习使用有限的数据有效估计了linezolid AUC0-24.
提出了一种用于确定linezolid毒性值的新方法,有助于临床决策.

关键词:

肝功能受损肝功能受损线索化物线索化物机器学习是机器学习.人口的药理动力学.血小板减少症 (Thrombocytopenia) 是一个严重的疾病.

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