Models of quantitative relationship “Structure – activity” in performing preliminary toxicological assessment of chemicals

Ekaterina A. Guseva, N. Nikolayeva, A. Filin, Yulia V. Rasskazova, G. G. Onishchenko
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Abstract

Introduction. In vivo testing of a huge number of chemical compounds is difficult from an ethical point of view, time-consuming, depends on a large number of objects of animal origin and requires large material costs for conducting experiments. Therefore, there is a need for new thinking to optimize the conduct of toxicological studies. The purpose of this study is to substantiate the possibility of using structure-activity models in the framework of a preliminary assessment of chemicals toxicity. Materials and methods. The study included three groups of chemicals including organothiophosphates, triazoles, and carbamates. The calculation of descriptors based on SMILES, the construction and validation of regression models was carried out using the tools of the Scikit-learn Version 1.2.2 library in an interactive cloud environment working with the Google Colaboratory program code. Results. When comparing a number of models for predicting oral toxicity, it was revealed that a model based on decision trees has the best predictive ability for organothiophosphates and triazoles: 70.1% and 69.5% of cases of descriptor changes led to a change in the endpoint value, respectively; a model for predicting carbamate toxicity based on a random forest explains 53.1% of the observed variance common log (1/DL50). Limitations. The study is limited to the area of distribution of the obtained mathematical models. Conclusion. As the study showed, the constructed models can explain only some part of the studied effect, therefore, models based on the structure-activity relationship should be used exclusively for preliminary assessment of the toxicity of chemicals, as a screening tool.
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在对化学品进行初步毒理学评估时采用 "结构-活性 "定量关系模型
导言。从伦理角度来看,对大量化合物进行体内试验是一件困难的事情,不仅耗时,而且依赖于大量的动物源性实验对象,并需要大量的材料成本来进行实验。因此,我们需要新的思路来优化毒理学研究的开展。 本研究的目的是证实在化学品毒性初步评估框架内使用结构-活性模型的可能性。 材料和方法。研究包括三组化学品,包括有机硫代磷酸酯类、三唑类和氨基甲酸酯类。在使用谷歌实验室程序代码的交互式云环境中,使用 Scikit-learn 1.2.2 版库的工具计算了基于 SMILES 的描述符,并构建和验证了回归模型。 研究结果在对多种口服毒性预测模型进行比较后发现,基于决策树的模型对有机硫磷酸盐和三唑类化合物的预测能力最强:分别有70.1%和69.5%的描述符变化导致终点值发生变化;基于随机森林的氨基甲酸酯类毒性预测模型解释了53.1%的观测变异共同对数(1/DL50)。 局限性。本研究仅限于所获数学模型的分布区域。 结论。研究表明,所构建的模型只能解释部分研究效应,因此,基于结构-活性关系的模型应仅用于初步评估化学品的毒性,作为一种筛选工具。
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