Comparative evaluation of 11 in silico models for the prediction of small molecule mutagenicity: role of steric hindrance and electron-withdrawing groups

IF 2.8 4区 医学 Q2 TOXICOLOGY Toxicology Mechanisms and Methods Pub Date : 2017-01-02 DOI:10.1080/15376516.2016.1174761
Kevin A. Ford, Gregory A. Ryslik, Bryan K. Chan, Sock-Cheng Lewin-Koh, D. Almeida, Michael Stokes, Stephen Gomez
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引用次数: 7

Abstract

Abstract The goal of this investigation was to perform a comparative analysis on how accurately 11 routinely-used in silico programs correctly predicted the mutagenicity of test compounds that contained either bulky or electron-withdrawing substituents. To our knowledge this is the first study of its kind in the literature. Such substituents are common in many pharmaceutical agents so there is a significant need for reliable in silico programs to predict precisely whether they truly pose a risk for mutagenicity. The predictions from each program were compared to experimental data derived from the Ames II test, a rapid reverse mutagenicity assay with a high degree of agreement with the traditional Ames assay. Eleven in silico programs were evaluated and compared: Derek for Windows, Derek Nexus, Leadscope Model Applier (LSMA), LSMA featuring the in vitro microbial Escherichia coli–Salmonella typhimurium TA102 A-T Suite (LSMA+), TOPKAT, CAESAR, TEST, ChemSilico (±S9 suites), MC4PC and a novel DNA docking model. The presence of bulky or electron-withdrawing functional groups in the vicinity of a mutagenic toxicophore in the test compounds clearly affected the ability of each in silico model to predict non-mutagenicity correctly. This was because of an over reliance on the part of the programs to provide mutagenicity alerts when a particular toxicophore is present irrespective of the structural environment surrounding the toxicophore. From this investigation it can be concluded that these models provide a high degree of specificity (ranging from 71% to 100%) and are generally conservative in their predictions in terms of sensitivity (ranging from 5% t o 78%). These values are in general agreement with most other comparative studies in the literature. Interestingly, the DNA docking model was the most sensitive model evaluated, suggesting a potentially useful new mode of screening for mutagens. Another important finding was that the combination of a quantitative structure–activity relationship and an expert rules system appeared to offer little advantage in terms of sensitivity, despite of the requirement for such a screening paradigm under the ICH M7 regulatory guideline.
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11个预测小分子致突变性的计算机模型的比较评价:空间位阻和吸电子基团的作用
摘要本研究的目的是对计算机程序中常规使用的11种化合物如何准确地正确预测含有庞大或吸电子取代基的测试化合物的致突变性进行比较分析。据我们所知,这是文献中首次进行此类研究。这种取代基在许多药物中很常见,因此非常需要可靠的计算机程序来准确预测它们是否真的会带来致突变性的风险。将每个程序的预测与Ames II试验的实验数据进行比较,Ames II是一种与传统Ames试验高度一致的快速反向致突变性试验。对11个计算机程序进行了评估和比较:Derek for Windows、Derek Nexus、Leadscope Model Applier(LSMA)、以体外微生物大肠杆菌-鼠伤寒沙门氏菌TA102 A-T套件(LSMA+)为特征的LSMA、TOPKAT、CAESAR、TEST、ChemSilico(±S9套件)、MC4PC和一个新的DNA对接模型。试验化合物中致突变性毒团附近存在体积庞大或吸电子官能团,这明显影响了每个计算机模型正确预测非致突变性的能力。这是因为当存在特定的毒代时,无论毒代周围的结构环境如何,都过度依赖程序的一部分来提供致突变性警报。从这项研究可以得出结论,这些模型提供了高度的特异性(从71%到100%),并且在灵敏度方面的预测通常是保守的(从5%t到78%)。这些价值观与文献中的大多数其他比较研究大体一致。有趣的是,DNA对接模型是评估的最敏感的模型,这表明了一种潜在的有用的诱变剂筛选新模式。另一个重要发现是,尽管ICH M7监管指南要求使用这种筛选范式,但定量结构-活动关系和专家规则系统的结合在敏感性方面似乎没有什么优势。
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来源期刊
自引率
3.10%
发文量
66
期刊介绍: Toxicology Mechanisms and Methods is a peer-reviewed journal whose aim is twofold. Firstly, the journal contains original research on subjects dealing with the mechanisms by which foreign chemicals cause toxic tissue injury. Chemical substances of interest include industrial compounds, environmental pollutants, hazardous wastes, drugs, pesticides, and chemical warfare agents. The scope of the journal spans from molecular and cellular mechanisms of action to the consideration of mechanistic evidence in establishing regulatory policy. Secondly, the journal addresses aspects of the development, validation, and application of new and existing laboratory methods, techniques, and equipment. A variety of research methods are discussed, including: In vivo studies with standard and alternative species In vitro studies and alternative methodologies Molecular, biochemical, and cellular techniques Pharmacokinetics and pharmacodynamics Mathematical modeling and computer programs Forensic analyses Risk assessment Data collection and analysis.
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