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首页> 外文期刊>Molecular informatics >Ecotoxicological Modeling, Ranking and Prioritization of Pharmaceuticals Using QSTR and i-QSTTR Approaches: Application of 2D and Fragment Based Descriptors
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Ecotoxicological Modeling, Ranking and Prioritization of Pharmaceuticals Using QSTR and i-QSTTR Approaches: Application of 2D and Fragment Based Descriptors

机译：使用QSTR和I-QSTTR的药物的生态毒理学建模，排名和优先化方法：应用2D和片段基于碎片的描述符

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摘要

There is a huge lack of experimental data on ecotoxicity of pharmaceuticals, while existing resources are insufficient to gather these data against all possible environmental endpoints. Computational tools such as quantitative structure-toxicity relationship (QSTR) can help us to a great extent to overcome this problem through filling of data gaps. In the current study, QSTR models have been developed for toxicity of 260 diverse pharmaceuticals on three different trophic level species namely algae, daphnia and fish, using partial least squares (PLS) regression approach with 2D descriptors selected through a genetic algorithm approach in order to study underlying chemical features responsible for the observed acute toxicity. The final obtained statistically reliable QSTR models were extensively validated following the OECD guidelines. Interspecies quantitative structure-toxicity-toxicity (QSTTR) models were also developed using genetic algorithm followed by multiple linear regression (GA-MLR) approach to check for the pattern of responses observed as we move across the hierarchy of genetics in different taxonomical class. The obtained interspecies models were finally utilized to fill the data gaps for 260 pharmaceuticals, where experimental data were missing for at least one of the endpoints. Finally, a prioritized list for 7106 existing drug like substances was prepared by predicting their acute toxicity using developed QSTR models.
机译：关于药品生态毒性的实验数据巨大缺乏实验数据，而现有资源不足以收集所有可能的环境终点的这些数据。数量结构毒性关系（QSTR）的计算工具可以通过填充数据间隙来帮助我们在很大程度上克服这个问题。在目前的研究中，QSTR模型已经为三种不同营养水平物种的260种不同药物的毒性开发了藻类，Daphnia和Fish，利用通过遗传算法方法选择的2D描述符的部分最小二乘（PLS）回归方法来实现研究潜在的化学特征，负责观察到的急性毒性。在经合组织指南之后，最终获得的统计上可靠的QSTR模型广泛验证。使用遗传算法以及多元线性回归（GA-MLR）方法的遗传算法也开发了数量的定量结构毒性毒性（QSTTR）模型，以检查所观察到不同分类课程的遗传学等级的响应模式。最终利用所获得的InterSpecies模型来填补260药物的数据差距，其中至少一个终点缺少实验数据。最后，通过使用发育QSTR模型预测其急性毒性来制备7106个现有药物的优先列表。

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