The development and application of in silico models for drug induced liver injury.
The development and application of in silico models for drug induced liver injury.
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DOI：
10.1039/c7ra12957b
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发表时间：
2018-02-19
期刊：
影响因子：
3.9
通讯作者：
Zhao, Yong
中科院分区：
文献类型：
--
作者：
Li, Xiao;Chen, Yaojie;Song, Xinrui;Zhang, Yuan;Li, Huanhuan;Zhao, Yong
药物性肝损伤（DILI）是由药物、草药制剂或营养补充剂引起的，是患者和制药行业的主要问题。它是临床试验失败和FDA撤销批准的主要原因。在这项研究中，我们专注于基于结构多样的有机化学品对人体的化学DILI潜力的计算机估计。我们使用五种不同的机器学习方法和八种不同的特征约简方法开发了一系列二进制分类模型。该模型，支持向量机（SVM）和MACCS指纹开发的，最好的测试集和外部验证。在测试集上的预测准确率为80.39%，在外部验证集上的预测准确率为82.78%。我们提供了这个模型。用户可以自由预测分子的DILI潜力。此外，我们分析了12个关键的物理化学性质的分布之间的差异，DILI阳性和DILI阴性的化合物和20个特权的亚结构，负责DILI的Klekota-Roth指纹图谱确定。此外，由于中药（TCM）诱导的肝损伤也是毒性作用中的主要问题之一，我们使用本研究开发的MACCS_SVM模型评估了中药成分的DILI潜力。我们希望该模型和特权子结构可以成为化学DILI评价的有用补充工具。药物性肝损伤（DILI）是由药物、草药制剂或营养补充剂引起的，是患者和制药行业的主要问题。
Drug-induced liver injury (DILI), caused by drugs, herbal agents or nutritional supplements, is a major issue for patients and the pharmaceutical industry. It has been a leading cause of clinical trials failure and withdrawal of FDA approval. In this research, we focused on in silico estimation of chemical DILI potential on humans based on structurally diverse organic chemicals. We developed a series of binary classification models using five different machine learning methods and eight different feature reduction methods. The model, developed with the support vector machine (SVM) and the MACCS fingerprint, performed best both on the test set and external validation. It achieved a prediction accuracy of 80.39% on the test set and 82.78% on external validation. We made this model available at . The user can freely predict the DILI potential of molecules. Furthermore, we analyzed the difference of distributions of 12 key physical–chemical properties between DILI-positive and DILI-negative compounds and 20 privileged substructures responsible for DILI were identified from the Klekota–Roth fingerprint. Moreover, since traditional Chinese medicine (TCM)-induced liver injury is also one of the major concerns among the toxic effects, we evaluated the DILI potential of TCM ingredients using the MACCS_SVM model developed in this study. We hope the model and privileged substructures could be useful complementary tools for chemical DILI evaluation. Drug-induced liver injury (DILI), caused by drugs, herbal agents or nutritional supplements, is a major issue for patients and the pharmaceutical industry.
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影响因子：
3.6
作者：
Ivanov, Sergey;Semin, Maxim;Poroikov, Vladimir
通讯作者：
Poroikov, Vladimir
影响因子：
3.8
作者：
Chen, Minjun;Hong, Huixiao;Tong, Weida
通讯作者：
Tong, Weida
DOI：
10.1093/bioinformatics/btn479
发表时间：
2008-11-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Klekota J;Roth FP
通讯作者：
Roth FP
影响因子：
3
作者：
Kim E;Nam H
通讯作者：
Nam H
影响因子：
7.5
作者：
CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V