介绍动态精细化模型用于预测肝脏清除和提取比率
Zhengyin Yan1, Li Ma1, Pasquale Carione1
1Department of Drug Metabolism and Pharmacokinetics, Genentech Inc., South San Francisco, CA, 94080, USA.
Journal of pharmaceutical sciences
|January 14, 2024
概括
使用动态自由分数 (fD) 的动态好模型 (dWSM) 提高了对传统模型 (WSM) 的肝清除预测,使用无结合分数 (fu). 该dWSM准确地解释了药物结合动力学,增强了对各种药物的预测.
科学领域:
- 药理动力学 药理动力学
- 药物新陈代谢 药物新陈代谢
- 生物制药科学 生物制药科学
背景情况:
- 传统的混合模型 (WSM) 使用未结合的药物分量 (fu) 来预测肝脏清除,假设即时的药物-蛋白结合平衡.
- 这种假设可能导致不准确,特别是对于高度结合的药物,可能低估了自由药物度.
研究的目的:
- 引入和验证一个包含动态自由分数 (fD) 的动态好模型 (dWSM),以更好地考虑药物-蛋白结合动力学.
- 为了比较dWSM与WSM在多种药物中对肝清除和肝提取率的预测准确度.
主要方法:
- 理论分析比较了fu和fD对内在空隙的影响.
- 在体外实验中使用重组CYP3A4与高度结合的米达佐拉姆和高度自由的维拉帕米尔.
- 使用36种不同药物的体外微体清除数据验证dWSM.
主要成果:
- 动态自由分数 (fD) 准确地纠正了对高度结合和高度自由药物的内在清除的白蛋白结合效应.
- 传统的未结合分数 (fu) 对密集结合的药物 (如米达佐拉姆) 进行过度校正.
- 与WSM相比,dWSM显著提高了人类肝脏清除和肝脏提取率的预测准确性.
结论:
- 使用fD的dWSM提供了更广泛适用和更准确的方法来预测肝脏清除,特别是对于具有不同蛋白质结合特性的药物.
- 药物血蛋白结合动力学对于准确的肝清除预测至关重要,dWSM解决了传统WSM的局限性.
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