Kar Supratik, Roy Kunal
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
Comput Biol Med. 2014 May;48:102-8. doi: 10.1016/j.compbiomed.2014.02.014. Epub 2014 Mar 2.
The eye irritation potential of chemicals has largely been evaluated using the Draize rabbit-eye test for a very long time. The Draize eye-irritation data on 38 compounds established by the European Center for Ecotoxicology and Toxicology of Chemicals (ECETOC) has been used in the present quantitative structure-activity relationship (QSAR) analysis in order to predict molar-adjusted eye scores (MES) and determine possible structural requisites and attributes that are primarily responsible for the eye irritation caused by the studied solutes. The developed model was rigorously validated internally as well as externally by applying principles of the Organization for Economic Cooperation and Development (OECD). The test for applicability domain was also carried out in order to check the reliability of the predictions. Important fragments contributing to higher MES values of the solutes were identified through critical analysis and interpretation of the developed model. Considering all the identified structural attributes, one can choose or design safe solutes with low eye irritant properties. The presented approach suggests a model for use in the context of virtual screening of relevant solute libraries. The developed QSAR model can be used to predict existing as well as future chemicals falling within the applicability domain of the model in order to reduce the use of animals.
长期以来，化学品的眼刺激潜力主要通过Draize兔眼试验进行评估。欧洲化学品生态毒理学中心（ECETOC）建立的38种化合物的Draize眼刺激数据已用于本定量构效关系（QSAR）分析，以预测摩尔调整眼评分（MES），并确定主要导致所研究溶质引起眼刺激的可能结构要求和属性。所开发的模型通过应用经济合作与发展组织（OECD）的原则进行了严格的内部和外部验证。还进行了适用域测试，以检查预测的可靠性。通过对所开发模型的批判性分析和解释，确定了导致溶质MES值较高的重要片段。考虑到所有确定的结构属性，可以选择或设计具有低眼刺激特性的安全溶质。所提出的方法为相关溶质库的虚拟筛选提供了一个模型。所开发的QSAR模型可用于预测属于该模型适用域内的现有及未来化学品，以减少动物的使用。
