Evaluation of TOPKAT, Toxtree, and Derek Nexus in Silico Models for Ocular Irritation and Development of a Knowledge-Based Framework To Improve the Prediction of Severe Irritation
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- 作者：Barun Bhhatarai ; Daniel M. Wilson ; Amanda K. Parks ; Edward W. Carney ; Pamela J. Spencer
- 刊名：Chemical Research in Toxicology
- 出版年：2016
- 出版时间：May 16, 2016
- 年：2016
- 卷：29
- 期：5
- 页码：810-822
- 全文大小：576K
- 年卷期：0
- ISSN：1520-5010
文摘
Assessment of ocular irritation is an essential component of any risk assessment. A number of (Q)SARs and expert systems have been developed and are described in the literature. Here, we focus on three in silico models (TOPKAT, BfR rulebase implemented in Toxtree, and Derek Nexus) and evaluate their performance using 1644 in-house and 123 European Centre for Toxicology and Ecotoxicology of Chemicals (ECETOC) compounds with existing in vivo ocular irritation classification data. Overall, the in silico models performed poorly. The best consensus predictions of severe ocular irritants were 52 and 65% for the in-house and ECETOC compounds, respectively. The prediction performance was improved by designing a knowledge-based chemical profiling framework that incorporated physicochemical properties and electrophilic reactivity mechanisms. The utility of the framework was assessed by applying it to the same test sets and three additional publicly available in vitro irritation data sets. The prediction of severe ocular irritants was improved to 73–77% if compounds were filtered on the basis of AlogP_MR (hydrophobicity with molar refractivity). The predictivity increased to 74–80% for compounds capable of preferentially undergoing hard electrophilic reactions, such as Schiff base formation and acylation. This research highlights the need for reliable ocular irritation models to be developed that take into account mechanisms of action and individual structural classes. It also demonstrates the value of profiling compounds with respect to their chemical reactivity and physicochemical properties that, in combination with existing models, results in better predictions for severe irritants.