Institute of Chemistry, University of Tartu, Ravila 14A, Tartu 50411, Estonia.
Institute of Chemistry, University of Tartu, Ravila 14A, Tartu 50411, Estonia.
Eur J Pharm Sci. 2018 Oct 15;123:429-440. doi: 10.1016/j.ejps.2018.07.014. Epub 2018 Jul 6.
The influence of pH on human intestinal absorption is frequently not considered in early drug discovery studies in the modelling and subsequent prediction of intestinal absorption for drug candidates. To bridge this gap, in this study, experimental membrane permeability data were measured for current and former drug substances with a parallel artificial membrane permeability assay (PAMPA) at different pH values (3, 5, 7.4 and 9). The presented data are in good agreement with human intestinal absorption, showing a clear influence of pH on the efficiency of intestinal absorption. For the measured data, simple and general quantitative structure-activity relationships (QSARs) were developed for each pH that makes it possible to predict the pH profiles for passive membrane permeability (i.e., a pH-permeability profile), and these predictions coincide well with the experimental data. QSARs are also proposed for the data series of highest and intrinsic membrane permeability. The molecular descriptors in the models were analysed and mechanistically related to the interaction pattern of permeability in membranes. In addition to the regression models, classification models are also proposed. All models were successfully validated and blind tested with external data. The models are available in the QsarDB repository (http://dx.doi.org/10.15152/QDB.203).
在药物发现早期的建模和随后对候选药物的肠吸收预测中，经常没有考虑 pH 对人体肠道吸收的影响。为了弥补这一差距，本研究在不同 pH 值（3、5、7.4 和 9）下使用平行人工膜渗透测定法（PAMPA）对当前和以前的药物物质进行了实验膜渗透性数据测量。所呈现的数据与人体肠道吸收非常吻合，表明 pH 对肠道吸收效率有明显影响。对于测量数据，针对每个 pH 值开发了简单而通用的定量构效关系（QSAR），从而可以预测被动膜渗透性的 pH 分布（即 pH-渗透性分布），并且这些预测与实验数据非常吻合。还针对最高和固有膜渗透性的数据集提出了 QSAR。对模型中的分子描述符进行了分析，并与膜中渗透性的相互作用模式进行了机理相关。除了回归模型外，还提出了分类模型。所有模型均成功进行了验证和盲测，并使用外部数据进行了测试。这些模型可在 QsarDB 存储库中获得（http://dx.doi.org/10.15152/QDB.203）。
