重新审视血脑屏障的透性:使用溶解-扩散模型预测内在的被动BBB透性
1Department of Computational Biology & Chemistry, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, Leipzig, 04318, Germany.
概括
这项研究证实,内在的被动血脑屏障 (BBB) 透性与Caco-2/MDCK试验可比. 溶解度-扩散模型 (SDM) 有效地预测了BBB的透性,为体外方法提供了有价值的替代方案.
科学领域:
- 药理动力学和药物输送方法
- 计算化学和分子建模计算化学和分子建模
背景情况:
- 通过血脑屏障 (BBB) 准确建模药物吸收对于预测药物的有效性和安全性至关重要.
- 被动透率是一个关键参数,但其准确的确定和预测仍然具有挑战性.
研究的目的:
- 测试内在的被动BBB透性 (P0,BBB) 是否相当于Caco-2或MDCK试验中的内在膜透性.
- 评估溶解度-扩散模型 (SDM) 对P0,BBB的预测能力.
- 评估分子大小对BBB透性预测的影响.
主要方法:
- 重新分析文献中的脑 perfusion 数据,重点关注膜有限的被动透.
- 与实验Caco-2/MDCK透度的内在被动透度 (P0) 数据的比较.
- 使用COSMOtherm和LSER方法应用溶解度-扩散模型 (SDM) 来估计分区系数.
主要成果:
- 84个膜限制P0值的数据集与Caco-2/MDCK透率 (RMSE = 0.86) 有很好的相关性.
- SDM预测显示了令人满意的性能 (RMSE = 1.73-2.29),小分子 (MW < 500 g/mol) 的准确性得到改善.
- 没有观察到分子尺寸切断效应;该模型对zwitterionic化合物也表现良好.
结论:
- 本质的被动BBB透性 (P0,BBB) 与Caco-2和MDCK测定测量的透性直接可比.
- 溶解度-扩散模型 (SDM) 是预测BBB透性的可靠工具.
- SDM有可能减少对BBB透性评估的资源密集的体外试验的需求.
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