当前位置：
X-MOL 学术
›
Chem. Res. Toxicol.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
In Silico Prediction of Human Renal Clearance of Compounds Using Quantitative Structure-Pharmacokinetic Relationship Models.
Chemical Research in Toxicology ( IF 3.7 ) Pub Date : 2020-01-28 , DOI: 10.1021/acs.chemrestox.9b00447 Jianhui Chen 1 , Hongbin Yang 1 , Lan Zhu 2 , Zengrui Wu 1 , Weihua Li 1 , Yun Tang 1 , Guixia Liu 1
Chemical Research in Toxicology ( IF 3.7 ) Pub Date : 2020-01-28 , DOI: 10.1021/acs.chemrestox.9b00447 Jianhui Chen 1 , Hongbin Yang 1 , Lan Zhu 2 , Zengrui Wu 1 , Weihua Li 1 , Yun Tang 1 , Guixia Liu 1
Affiliation
|
Renal clearance (CLr) plays an essential role in the elimination of drugs. In this study, 636 compounds were obtained from various sources to develop in silico models for the prediction of CLr. Stepwise multiple linear regression and random forest regression methods were employed to build global models and local models according to ionization state or net elimination pathways. The local models toward compounds undergoing different net elimination pathways showed good predictive power: the geometric mean fold error was close to 2, indicating the clearance of most compounds could be predicted within a 2-fold error range. Six classification methods were used to construct classification models. However, the performance of these classification models was less than satisfactory, and the mean accuracy of the top five models in test sets was 0.65. Moreover, qualitative analysis of physicochemical profiles between compounds undergoing different net elimination pathways revealed that compounds with higher lipophilicity tended to be reabsorbed more easily and showed lower CLr, while compounds with higher values of polar descriptors tended to secrete more easily and showed higher CLr.
中文翻译：
使用定量结构-药代动力学关系模型对化合物的人类肾脏清除率进行计算机预测。
肾脏清除率（CLr）在消除药物方面起着至关重要的作用。在这项研究中，从各种来源获得了636种化合物，以开发用于预测CLr的计算机模拟模型。根据电离状态或净消除途径，采用逐步多元线性回归和随机森林回归方法建立全局模型和局部模型。针对经历不同净消除途径的化合物的局部模型显示出良好的预测能力：几何平均折叠误差接近2，表明大多数化合物的清除率可在2倍误差范围内预测。使用六种分类方法构建分类模型。但是，这些分类模型的性能不尽人意，测试集中前五种模型的平均准确性为0.65。
更新日期：2020-01-29
中文翻译：
使用定量结构-药代动力学关系模型对化合物的人类肾脏清除率进行计算机预测。
肾脏清除率（CLr）在消除药物方面起着至关重要的作用。在这项研究中，从各种来源获得了636种化合物，以开发用于预测CLr的计算机模拟模型。根据电离状态或净消除途径，采用逐步多元线性回归和随机森林回归方法建立全局模型和局部模型。针对经历不同净消除途径的化合物的局部模型显示出良好的预测能力：几何平均折叠误差接近2，表明大多数化合物的清除率可在2倍误差范围内预测。使用六种分类方法构建分类模型。但是，这些分类模型的性能不尽人意，测试集中前五种模型的平均准确性为0.65。
京公网安备 11010802027423号