预测复杂的脏药物处理,使用基于生理学的药物动力学模型,由生物标志物估计的分泌清除和血流提供信息
Michael L Granda1,2, Weize Huang3,4, Catherine K Yeung2,5
1Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington, USA.
Clinical and translational science
|November 3, 2023
概括
这项研究引入了一种新的基于生理学的药理动力学 (PBPK) 模型,该模型使用单个生物标志物测量来比传统方法更准确地预测脏药物清除率. 这种方法通过结合管状分泌物和脏血流与GFR一起来增强个性化的药物剂量.
科学领域:
- 药理学 药理学是指药理学的学科.
- 腎臟病學 (nephrology) 是一種醫學.
- 生物标志物发现发现
背景情况:
- 目前根据功能调整的药物剂量仅依赖于估计的淋巴细胞过率 (GFR).
- 脏的药物处理涉及过,管状分泌和再吸收,仅GFR无法完全捕获这些.
- 基于生理学的药理动力学 (PBPK) 模型可以预测药物消除,但由于缺乏个人级别的功能测量而受到限制.
研究的目的:
- 调整和验证PBPK模型,用于预测药物清除,使用基于个体生物标志物的透和分泌清除估计.
- 通过结合管状分泌和脏血流的个别测量,提高脏药物清除预测的准确性.
主要方法:
- 开发了一种PBPK模型,其中包括GFR,氨酸清除 (用于有机离子转运器介导的分泌) 和异甲糖素清除 (用于脏血流).
- 给27名门诊患者注射了tenofovir和oseltamivir,并测量了他们的药物清除率.
- 与传统回归模型对比机械PBPK模型预测的准确性.
主要成果:
- 与回归模型 (平均误差为41.8mL/min) 相比,机械式PBPK模型在预测特诺福维尔清除 (平均误差为37.1mL/min) 中的准确性有所提高.
- 同样,PBPK模型改善了与回归模型 (平均误差为48.1 mL/min) 相比,奥塞塔米维尔碳酸盐清除预测 (平均误差为42.9 mL/min).
- 对管状分泌和脏血流的个性化估计提高了对诺福维尔和奥塞尔塔米维尔的PBPK模型的准确性.
结论:
- 个性化生物标志物为基础的功能测量显著改进了PBPK模型预测的药物清除.
- 这种方法有可能在患有不同功能的患者中提供更精确的个性化药物剂量.
- 经过验证的PBPK模型为优化基于个体脏生理学的药物治疗方案提供了一个有希望的工具.
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