Institute of Chemistry , University of Tartu , Ravila 14A , Tartu 50411 , Estonia.
J Chem Inf Model. 2019 May 28;59(5):2442-2455. doi: 10.1021/acs.jcim.8b00833. Epub 2019 Mar 11.
Permeability is used to describe and evaluate the absorption of drug substances in the human gastrointestinal tract (GIT). Permeability is largely dependent on fluctuating pH that causes the ionization of drug substances and also influences regional absorption in the GIT. Therefore, classification models that characterize permeability at wide ranges of pH were derived in the current study. For this, drug substances were described with six data series that were measured with a parallel artificial membrane permeability assay (PAMPA), including a permeability profile at four pH values (3, 5, 7.4, and 9), and the highest and intrinsic membrane permeability. Logistic regression classification models were developed and compared by using two distinct sets of descriptors: (1) a hydrophobicity descriptor, the logarithm of the octanol-water partition (logP) or distribution (logD) coefficient and (2) theoretical molecular descriptors. In both cases, models have good classification and descriptive capabilities for the training set (accuracy: 0.76-0.91). Triple validation with three sets of drug substances shows good prediction capability for all models: validation set (accuracy: 0.73-0.91), external validation set (accuracy: 0.72-0.9), and the permeability classes of FDA reference drugs for the biopharmaceutical classification system (BCS) (accuracy: 0.72-0.88). The identification of BCS permeability classes was further improved with decision trees that consolidated predictions from models with each descriptor type. These decision trees have higher confidence and accuracy (0.91 for theoretical molecular descriptors and 0.81 for hydrophobicity descriptors) than the individual models in assigning drug substances into BCS permeability classes. A detailed analysis of classification models and related decision trees suggests that they are suitable for predicting classes of permeability for passively transported drug substances, including specifically within the BCS framework. All developed models are available at the QsarDB repository ( http://dx.doi.org/10.15152/QDB.206 ).
通透性用于描述和评估药物在人体胃肠道（GIT）中的吸收情况。通透性在很大程度上取决于不断变化的 pH 值，这会导致药物的离子化，并影响 GIT 中的区域吸收。因此，本研究中得出了描述宽 pH 范围内通透性的分类模型。为此，用平行人工膜渗透性测定法（PAMPA）测量的 6 个数据系列来描述药物，包括在 4 个 pH 值（3、5、7.4 和 9）下的渗透性曲线，以及最高和内在的膜通透性。开发了逻辑回归分类模型，并使用两个不同的描述符集进行了比较：（1）疏水性描述符，辛醇-水分配（logP）或分布（logD）系数的对数和（2）理论分子描述符。在这两种情况下，模型对训练集都具有良好的分类和描述能力（准确率：0.76-0.91）。用三组药物进行三重验证表明，所有模型都具有良好的预测能力：验证集（准确率：0.73-0.91）、外部验证集（准确率：0.72-0.9）和生物制药分类系统（BCS）的 FDA 参考药物的通透性分类（准确率：0.72-0.88）。使用合并了每种描述符类型模型预测结果的决策树，进一步提高了对 BCS 通透性分类的识别能力。这些决策树在将药物分配到 BCS 通透性分类中时，比单个模型具有更高的置信度和准确性（理论分子描述符的准确率为 0.91，疏水性描述符的准确率为 0.81）。对分类模型和相关决策树的详细分析表明，它们适合于预测被动转运药物的通透性分类，特别是在 BCS 框架内。所有开发的模型都可在 QsarDB 存储库（http://dx.doi.org/10.15152/QDB.206）中获得。
