Drug Theoretics and Cheminformatics Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India.
Drug Theoretics and Cheminformatics Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India.
J Hazard Mater. 2013 Jun 15;254-255:166-178. doi: 10.1016/j.jhazmat.2013.03.023. Epub 2013 Mar 25.
Ionic liquids have been judged much with respect to their wide applicability than their considerable harmful effects towards the living ecosystem which has been observed in many instances. Hence, toxicological introspection of these chemicals by the development of predictive mathematical models can be of good help. This study presents an attempt to develop predictive classification and regression models correlating the structurally derived chemical information of a group of 62 diverse ionic liquids with their toxicity towards Daphnia magna and their interpretation. We have principally used the extended topochemical atom (ETA) indices along with various topological non-ETA and thermodynamic parameters as independent variables. The developed quantitative models have been subjected to extensive statistical tests employing multiple validation strategies from which acceptable results have been reported. The best models obtained from classification and regression studies captured necessary structural information on lipophilicity, branching pattern, electronegativity and chain length of the cationic substituents for explaining ecotoxicity of ionic liquids towards D. magna. The derived information can be successfully used to design better ionic liquid analogues acquiring the qualities of a true eco-friendly green chemical.
离子液体因其广泛的适用性而备受关注，但其对生态系统的危害也不容忽视，在许多情况下都有观察到这种危害。因此，通过开发预测性数学模型对这些化学物质进行毒理学反思可能会有很大帮助。本研究旨在开发预测性分类和回归模型，将 62 种不同离子液体的结构衍生化学信息与它们对大型溞的毒性及其解释相关联。我们主要使用了扩展拓扑原子（ETA）指数以及各种拓扑非-ETA 和热力学参数作为自变量。所开发的定量模型经过了广泛的统计测试，采用了多种验证策略，报告了可接受的结果。从分类和回归研究中获得的最佳模型捕捉到了有关亲脂性、支化模式、电负性和阳离子取代基链长的必要结构信息，用于解释离子液体对 D. magna 的生态毒性。所得到的信息可成功用于设计更好的离子液体类似物，获得真正环保的绿色化学物质的特性。
