A novel chemometric method for the prediction of human oral bioavailability.
A novel chemometric method for the prediction of human oral bioavailability.
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DOI：
10.3390/ijms13066964
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发表时间：
2012
影响因子：
5.6
通讯作者：
Ling Y
中科院分区：
文献类型：
--
作者：
Xu X;Zhang W;Huang C;Li Y;Yu H;Wang Y;Duan J;Ling Y
口服药物在到达目标部位之前必须克服几个障碍。这些障碍在很大程度上取决于特定的膜运输系统和细胞内药物代谢酶。首次将限制药物口服生物利用度(OB)的主要防线P-糖蛋白(P-gp)和细胞色素P450引入到基于805种结构不同的药物和类药物分子的人体OB的QSAR建模中。使用线性(多元线性回归：MLR，偏最小二乘回归：PLS)和非线性(支持向量机回归(SVR))方法构建模型，并通过五次交叉验证和独立外部检验验证模型的预测性。支持向量机的性能略好于最大似然回归法和偏最小二乘法，测试集的决定系数(R2)为0.80，估计标准误差(SEE)为0.31。对于最大似然比和最小二乘法，它们的预测能力相对较弱，对SEE分别为0.40和0.31的训练集的预测能力分别为0.60和0.64。我们的研究表明，基于MLR、PLS和SVR的计算机模型在促进口服生物利用度预测方面具有良好的潜力，可以应用于未来的药物设计。
Orally administered drugs must overcome several barriers before reaching their target site. Such barriers depend largely upon specific membrane transport systems and intracellular drug-metabolizing enzymes. For the first time, the P-glycoprotein (P-gp) and cytochrome P450s, the main line of defense by limiting the oral bioavailability (OB) of drugs, were brought into construction of QSAR modeling for human OB based on 805 structurally diverse drug and drug-like molecules. The linear (multiple linear regression: MLR, and partial least squares regression: PLS) and nonlinear (support-vector machine regression: SVR) methods are used to construct the models with their predictivity verified with five-fold cross-validation and independent external tests. The performance of SVR is slightly better than that of MLR and PLS, as indicated by its determination coefficient (R2) of 0.80 and standard error of estimate (SEE) of 0.31 for test sets. For the MLR and PLS, they are relatively weak, showing prediction abilities of 0.60 and 0.64 for the training set with SEE of 0.40 and 0.31, respectively. Our study indicates that the MLR, PLS and SVR-based in silico models have good potential in facilitating the prediction of oral bioavailability and can be applied in future drug design.
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影响因子：
10.8
作者：
Benet, LZ;Wu, CY;Wacher, VJ
通讯作者：
Wacher, VJ
影响因子：
2.9
作者：
Guha, R;Serra, JR;Jurs, PC
通讯作者：
Jurs, PC
影响因子：
5.6
作者：
Hou, Tingjun;Wang, Junmei;Xu, Xiaojie
通讯作者：
Xu, Xiaojie
DOI：
10.1126/science.1168750
发表时间：
2009-03-27
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Aller SG;Yu J;Ward A;Weng Y;Chittaboina S;Zhuo R;Harrell PM;Trinh YT;Zhang Q;Urbatsch IL;Chang G
通讯作者：
Chang G
DOI：
10.2174/138620709788489082
发表时间：
2009-06-01
影响因子：
1.8
作者：
Hou, Tingjun;Li, Youyong;Wang, Junmei
通讯作者：
Wang, Junmei