Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China.
Mol Pharm. 2011 Jun 6;8(3):841-51. doi: 10.1021/mp100444g. Epub 2011 May 16.
Oral bioavailability is an essential parameter in drug screening cascades and a good indicator of the capability of the delivery of a given compound to the systemic circulation by oral administration. In the present work, we report a database of oral bioavailability of 1014 molecules determined in humans. A systematic examination of the relationships between various physicochemical properties and oral bioavailability were carried out to investigate the influence of these properties on oral bioavailability. A number of property-based rules for bioavailability classification were generated and evaluated. We found that no rule was an effective predictor for oral bioavailability because these simple rules cannot characterize the influence of important metabolic processes on bioavailability. Finally, the genetic function approximation (GFA) technique was employed to construct the multiple linear regression models for oral bioavailability using structural fingerprints as the basic parameters, together with several important molecular properties. The best model is able to predict human oral bioavailability with an r of 0.79, a q of 0.72, and a RMSE (root-mean-square error) of 22.30% of the compounds from the training set. The analysis of the descriptors chosen by GFA shows that the important structural fingerprints are primarily related to important intestinal absorption and well-known metabolic processes. The predictive power of the models was further evaluated using a separate test set of 80 compounds, and the consensus model can predict the oral bioavailability with r(test) = 0.71 and RMSE = 23.55% for the tested compounds. Since the necessary molecular properties and structural fingerprints can be calculated easily and quickly, the models we proposed here may help speed up the process of finding or designing compounds with improved oral bioavailability.
口服生物利用度是药物筛选级联中的一个重要参数,也是评估给定化合物通过口服途径递送至全身循环的能力的一个良好指标。在本工作中,我们报告了一个包含 1014 个人类测定的口服生物利用度的数据库。系统地研究了各种物理化学性质与口服生物利用度之间的关系,以研究这些性质对口服生物利用度的影响。生成并评估了一些基于性质的生物利用度分类规则。我们发现,没有一个规则是口服生物利用度的有效预测因子,因为这些简单的规则无法描述重要代谢过程对生物利用度的影响。最后,采用遗传函数逼近(GFA)技术,使用结构指纹作为基本参数,结合几个重要的分子性质,构建了口服生物利用度的多元线性回归模型。最佳模型能够以 r 为 0.79、q 为 0.72 和 RMSE(均方根误差)为 22.30%的化合物来预测口服生物利用度。通过 GFA 选择的描述符的分析表明,重要的结构指纹主要与重要的肠吸收和已知的代谢过程有关。使用 80 个化合物的独立测试集进一步评估了模型的预测能力,共识模型可以以 r(test) = 0.71 和 RMSE = 23.55%的精度预测测试化合物的口服生物利用度。由于所需的分子性质和结构指纹可以方便快捷地计算,因此我们提出的模型可能有助于加快寻找或设计具有改善口服生物利用度的化合物的过程。