用PBPK建模来预测不同的CYP2C19基因型中的潘托普拉的药理动力学
Chang-Keun Cho1, Eunvin Ko1, Ju Yeon Mo1
1School of Pharmacy, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
Archives of pharmacal research
|December 27, 2023
概括
这项研究开发了一种基于生理学的药理动力学 (PBPK) 模型,以根据CYP2C19遗传变异预测潘托普拉的药物水平. 该模型准确地预测了跨不同代谢活动的潘托普拉的药理动力学,有助于个性化治疗.
科学领域:
- 药理学 药理学是指药理学的学科.
- 药物新陈代谢 药物新陈代谢
- 药物基因组学 药物基因组学
背景情况:
- 潘托普拉可以治疗GERD,EE和ZES.
- CYP2C19的遗传多态性影响了潘托普拉的药理动力学.
- 需要预测模型来进行个性化的潘托普拉治疗.
研究的目的:
- 为 pantoprazole 建立一个 PBPK 模型.
- 在不同的CYP2C19表型中预测潘托普拉的药理动力学.
- 根据临床数据验证模型.
主要方法:
- 收集的临床药物遗传学,物理化学和处置数据.
- 开发了一个包含CYP2C19变异的PBPK模型.
- 评估模型预测与观察到的血度和药理动力学参数 (AUC,Cmax) 相比.
主要成果:
- 该PBPK模型准确地预测了不同CYP2C19表型的潘托普拉血度-时间概况.
- 所有的AUC和Cmax折叠误差值都在两倍范围之内.
- 已建立的PBPK模型有效地根据CYP2C19基因型预测了潘托普拉的药理动力学.
结论:
- 考虑到CYP2C19遗传多态性,成功建立了潘托普拉的最小PBPK模型.
- 这种模型准确地预测了具有不同CYP2C19代谢活动的个体中的潘托普拉的药理动力学.
- 这些发现增强了对个性化潘托普拉药物治疗的理解.
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