Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches.
Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches.
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通过使用各种化学信息学方法，对药物的人类口服生物利用度的批判性评估。
DOI：
10.1007/s11095-013-1222-1
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发表时间：
2014-04
影响因子：
3.7
通讯作者：
Zhu, Hao
中科院分区：
文献类型：
--
作者：
Kim, Marlene T.;Sedykh, Alexander;Chakravarti, Suman K.;Saiakhov, Roustem D.;Zhu, Hao
口服生物利用度（%F）是决定新药在临床试验中命运的关键因素。传统上，%F是通过昂贵且耗时的实验测试来测量的.开发计算模型以在合成新药之前评估其%F将有益于药物发现过程。我们采用组合定量构效关系方法建立了几个计算%F模型。我们从公共来源收集了995种药物的%F数据集。在生成每个化合物的化学描述符后，我们使用随机森林，支持向量机，k近邻和CASE Ultra开发相关的QSAR模型。使用五重交叉验证对所得模型进行验证。%F值的外部预测性较差（R2=0.28，n=995，MAE=24），但通过过滤与MDR 1和MRP 2转运蛋白相互作用的可能性很高的不可靠预测，可以改善（R2=0.40，n=362，MAE=21）。此外，根据%F值对化合物进行分类（%F<50%为“低”，%F≥50%为“高”）并开发类别QSAR模型，外部准确度为76%。在这项研究中，我们开发了预测%F QSAR模型，可用于评估新的药物化合物，并整合药物转运蛋白相互作用的数据大大有利于产生的模型。
Oral bioavailability (%F) is a key factor that determines the fate of a new drug in clinical trials. Traditionally, %F is measured using costly and time -consuming experimental tests. Developing computational models to evaluate the %F of new drugs before they are synthesized would be beneficial in the drug discovery process. We employed Combinatorial Quantitative Structure-Activity Relationship approach to develop several computational %F models. We compiled a %F dataset of 995 drugs from public sources. After generating chemical descriptors for each compound, we used random forest, support vector machine, k nearest neighbor, and CASE Ultra to develop the relevant QSAR models. The resulting models were validated using five-fold cross-validation. The external predictivity of %F values was poor (R2=0.28, n=995, MAE=24), but was improved (R2=0.40, n=362, MAE=21) by filtering unreliable predictions that had a high probability of interacting with MDR1 and MRP2 transporters. Furthermore, classifying the compounds according to the %F values (%F<50% as “low”, %F≥50% as ‘high”) and developing category QSAR models resulted in an external accuracy of 76%. In this study, we developed predictive %F QSAR models that could be used to evaluate new drug compounds, and integrating drug-transporter interactions data greatly benefits the resulting models.
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影响因子：
4.5
作者：
Benet, Leslie Z.;Broccatelli, Fabio;Oprea, Tudor I.
通讯作者：
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影响因子：
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