Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.
Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.
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DOI：
10.1021/tx900451r
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发表时间：
2010-04-19
影响因子：
4.1
通讯作者：
Tropsha, Alexander
中科院分区：
文献类型：
--
作者：
Rodgers, Amie D.;Zhu, Hao;Fourches, Denis;Rusyn, Ivan;Tropsha, Alexander
药物（AED）的不良反应仍然是药物开发和上市后停药的主要原因。虽然肝脏相关的AED是药物安全性的主要关注点，但很少有计算机模型用于预测候选药物的人类肝脏毒性。我们已经应用定量构效关系（QSAR）的方法来模拟肝脏抗癫痫药物。在这项研究中，我们的目的是建立一个定量构效关系模型，能够二进制分类（活性与非活性）的药物，肝脏抗癫痫药物的化学结构的基础上。为了建立QSAR模型，我们采用了FDA关于人类肝脏AED（血清肝酶活性升高）的自发报告数据库，其中包含约500种批准药物的数据。选择了约200种具有广泛临床数据覆盖范围、结构相似性和平衡（40/60）活性/非活性比率的化合物进行建模，并将其分为多个训练/测试和外部验证集。QSAR模型使用k近邻法开发，并使用外部数据集进行验证。在外部验证集中，开发了预测肝脏AED的高灵敏度（>73%）和特异性（>94%）模型。为了测试模型的适用性，三个化学数据库（世界药物索引，Prestwick化学图书馆和Biowisdom肝脏智能模块）进行了筛选，并在可能的情况下，通过比较基于模型的分类与公开文献中的断言来确定预测的有效性。基于来自FDA自发报告系统的数据的经验证的肝脏AED的QSAR模型可以用作AED的敏感和特异性预测因子，用于临床前筛选候选药物以用于人类的潜在肝毒性。
Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals both in development and post-marketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the Quantitative Structure Activity Relationship (QSAR) approach to model liver AEDs. In this study, we aimed to construct a QSAR model capable of binary classification (active vs. inactive) of drugs for liver AEDs based on chemical structure. To build QSAR models, we have employed an FDA spontaneous reporting database of human liver AEDs (elevations in activity of serum liver enzymes), which contains data on approximately 500 approved drugs. Approximately 200 compounds with wide clinical data coverage, structural similarity and balanced (40/60) active/inactive ratio were selected for modeling and divided into multiple training/test and external validation sets. QSAR models were developed using the k nearest neighbor method and validated using external datasets. Models with high sensitivity (>73%) and specificity (>94%) for prediction of liver AEDs in external validation sets were developed. To test applicability of the models, three chemical databases (World Drug Index, Prestwick Chemical Library, and Biowisdom Liver Intelligence Module) were screened in silico and the validity of predictions was determined, where possible, by comparing model-based classification with assertions in publicly available literature. Validated QSAR models of liver AEDs based on the data from the FDA spontaneous reporting system can be employed as sensitive and specific predictors of AEDs in pre-clinical screening of drug candidates for potential hepatotoxicity in humans.
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影响因子：
3.8
作者：
Jaeschke, H;Gores, GJ;Lemasters, JJ
通讯作者：
Lemasters, JJ
影响因子：
4.1
作者：
Kassahun, K;Pearson, PG;Baillie, TA
通讯作者：
Baillie, TA
DOI：
10.1002/qsar.19870060103
发表时间：
1987-03-01
期刊：
QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS
影响因子：
--
作者：
KIER, LB
通讯作者：
KIER, LB
DOI：
10.1002/qsar.19910100108
发表时间：
1991-03-01
期刊：
QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS
影响因子：
--
作者：
HALL, LH;MOHNEY, B;KIER, LB
通讯作者：
KIER, LB
影响因子：
4.1
作者：
Bolton, JL;Trush, MA;Monks, TJ
通讯作者：
Monks, TJ