使用LeiCNS-PK3.0生理学基础的药理动力学模型来预测小鼠的大脑细胞外液的药理动力学
Mohammed A A Saleh1, Berfin Gülave1, Olivia Campagne2
1Division of Systems Pharmacology and Pharmacy, Leiden Academic Center for Drug Research, Leiden University, Gorlaeus laboratorium, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.
Pharmaceutical research
|July 13, 2023
概括
这项研究使用了PBPK模型来预测小鼠大脑细胞外液 (ECF) 药理动力学 (PK). 对于大多数测试药物,LeiCNS-PK3.0模型合理地预测了大脑ECF PK,从而推进了小鼠对人类的PK转化.
科学领域:
- 药理动力学 药理动力学
- 翻译科学 翻译科学
- 计算生物学 计算生物学
背景情况:
- 血脑屏障 (BBB) 分割系数提供稳定状态的洞察力,但不是动态的药理动力学 (PK) 概况.
- 鼠标模型对于大脑PK研究至关重要,但直接转化到人类PK是由于生理差异而受到限制的.
- 基于生理学上的药理动力学 (PBPK) 模型有助于跨物种的PK数据翻译.
研究的目的:
- 通过使用LeiCNS-PK3.0 PBPK模型在小鼠中预测大脑细胞外液 (ECF) 药理动力学.
- 评估模型在预测大脑ECFPK特征中的准确性,以对小鼠的各种药物进行预测.
主要方法:
- 我们收集了10种药物的小鼠大脑生理学和PK的文献数据.
- 模拟了剂量依赖的血PK,并估计了BBB分区系数 (Kpuu,BBB).
- 估计的Kpuu,BBB值被输入到LeiCNS-PK3.0模型中,用于大脑ECF PK预测.
主要成果:
- 在10种药物中的7种中,LeiCNS-PK3.0模型充分预测了大脑ECF PK 配置文件.
- 对于7种药物,预测的误差是观察数据的两倍之内.
- 对于剩下的2种药物,预测的误差在5倍之内.
结论:
- 目前的老鼠LeiCNS-PK3.0 PBPK模型为大多数药物提供了对大脑ECF PK数据的合理预测.
- 这种模型代表了将老鼠大脑PK翻译为人类大脑PK的前进一步.
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