机器学习方法在Isoniazid对结核病的剂量个性化
Bo-Hao Tang1,2, Xin-Fang Zhang2, Shu-Meng Fu2
1Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Clinical pharmacokinetics
|July 11, 2024
概括
机器学习模型可以准确预测异化的度,从而实现个性化剂量,提高结核病治疗的安全性和有效性. 这种方法优化了药物治疗方案,以获得更好的患者结果.
科学领域:
- 药量测量和机器学习
- 药物代谢和药理动力学
- 结核病治疗药物 结核病治疗药物
背景情况:
- 伊索尼亚兹是关键的第一线结核病药物,具有显著的个体间变异性.
- 优化异化治疗需要基于预测药物度的个性化剂量.
- 剂量后2小时的异化度 (C2h) 是安全性和有效性的关键指标.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测异化C2h.
- 在成年患者中建立基于ML的异亚的个性化剂量方案.
主要方法:
- 模拟的个人C2h数据集是使用四种种群体药理动力学 (PopPK) 模型生成的.
- 机器学习模型,包括CatBoost,在模拟数据上进行训练,并与现实数据进行验证.
- 虚拟试验比较了ML优化的异化剂量与PopPK模型优化的剂量.
主要成果:
- CatBoost算法展示了对异化C2h的优异预测性能.
- 该ML模型使用剂量方案,N-乙转移酶2 (NAT2) 基因型,体重和种族准确预测了C2h,达到93.4%的准确率.
- 与PopPK模型相比,ML优化剂量减少了45.7%的平均绝对预测误差,并增加了43.7%的目标实现概率.
结论:
- 开发的ML模型对异化C2h具有很高的预测性能.
- 这些ML模型可以有效地指导成人结核病患者个性化初始异化剂量.
- 该研究强调了ML在优化传染病药物治疗方面的潜力.
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