基于生理学的药物动力学建模,以评估犯罪者和受害者的P450 2C诱导风险
Marina Slavsky1, Aniruddha Sunil Karve1, Niresh Hariparsad1
1Drug Metabolism and Pharmacokinetics, Oncology R&D (Research & Development), AstraZeneca, 35 Gatehouse Park Drive, Boston, MA 02451, USA.
基于生理学的药理动力学 (PBPK) 建模准确地预测了由CYP2C酶诱导介导的药物相互作用 (DDI). 这种方法改进了评估DDI风险的机械静态建模 (MSM),与临床结果有很好的相关性.
科学领域:
- 药理学
- 药物新陈代谢
- 毒理学
背景情况:
- 在药物开发中,由CYP2C酶诱导的药物相互作用 (DDI) 构成了重大挑战.
- 目前的临床前模型和有限的临床数据阻碍了对CYP2C基质的准确DDI风险评估.
研究的目的:
- 评估基于生理学的药理动力学 (PBPK) 建模用于评估基于CYP2C诱导的DDI.
- 将PBPK建模与机械静态建模 (MSM) 的预测性能进行比较.
主要方法:
- 使用全人肝细胞培养系统在体外量化CYP2C诱导.
- 试验室诱导参数被整合到PBPK模型中,以预测CYP2C基质的药理动力学 (PK).
- 将PBPK预测与MSM和临床DDI结果进行比较.
主要成果:
- 在DDI期间,PBPK模型显示CYP2C基质暴露的预测趋势较低.
- PBPK方法与观察到的临床DDI结果有很好的相关性.
- MSM准确预测了CYP3A4诱导的DDI,但缺乏CYP2C诱导的精度.
结论:
- PBPK建模是评估基于CYP2C诱导的DDI的MSM有价值的补充工具.
- 这项研究突出显示了PBPK模型在这种情况下的有用性.
- 通过PBPK建模可以改善CYP2C基质的DDI风险评估.
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