Sholokhova Anastasia Yu, Grinevich Oksana I, Matyushin Dmitriy D, Buryak Aleksey K
A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31 Leninsky Prospect, Moscow, GSP-1, 119071, Russia.
A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31 Leninsky Prospect, Moscow, GSP-1, 119071, Russia.
Chemosphere. 2022 Nov;307(Pt 1):135764. doi: 10.1016/j.chemosphere.2022.135764. Epub 2022 Jul 18.
Unsymmetrical dimethylhydrazine (UDMH) is a toxic and environmentally hostile compound that was massively introduced to the environment during previous decades due to its use in the space and rocket industry. The compound forms multiple transformation products, and many of them are as dangerous as UDMH or even more dangerous. The danger includes, but is not limited to, acute toxicity, chronic health hazards, carcinogenicity, and environmental damage. UDMH transformation products are poorly investigated. In this work, the mixture formed by long storage of the waste that contained UDMH was studied. Even a preliminary screening of such a mixture is a complex task. It consists of dozens of compounds, and most of them are missing in chemical and spectral databases. The complete preparative separation of such a mixture is very laborious. We applied several methods of gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry, and several machine learning and chemoinformatics methods to make a preliminary but informative screening of the mixture. Machine learning allowed predicting retention indices and mass spectra of candidate structures. The combination of various ion sources and a comparison of the observed with the predicted spectra and retention was used to propose confident structures for 24 compounds. It was demonstrated that neither high-resolution mass spectrometry nor mass spectral library matching is enough to elucidate the structures of unknown UDMH transformation products. At the same time, the use of machine learning and a combination of methods significantly improves the identification power. Finally, machine learning was applied to estimate the acute toxicity of the discovered compounds. It was shown that many of them are comparable to or even more toxic than UDMH itself. Such an extremely wide and still underestimated variety of easily formed derivatives of UDMH can lead to a significant underestimation of the potential hazard of this compound.
不对称二甲基肼（UDMH）是一种有毒且对环境有害的化合物，由于其在航天和火箭工业中的应用，在过去几十年中大量进入环境。该化合物会形成多种转化产物，其中许多与UDMH一样危险甚至更危险。危险包括但不限于急性毒性、慢性健康危害、致癌性和环境破坏。对UDMH转化产物的研究较少。在这项工作中，研究了含有UDMH的废物长期储存形成的混合物。即使对这种混合物进行初步筛选也是一项复杂的任务。它由数十种化合物组成，其中大多数在化学和光谱数据库中都没有。对这种混合物进行完全的制备分离非常费力。我们应用了几种气相色谱-质谱联用和液相色谱-质谱联用方法，以及几种机器学习和化学信息学方法对该混合物进行初步但有信息量的筛选。机器学习可以预测候选结构的保留指数和质谱。结合各种离子源，并将观察到的光谱和保留情况与预测结果进行比较，从而为24种化合物提出可靠的结构。结果表明，高分辨率质谱和质谱图库匹配都不足以阐明未知的UDMH转化产物的结构。同时，机器学习和多种方法的结合显著提高了识别能力。最后，应用机器学习来估计所发现化合物的急性毒性。结果表明，其中许多化合物的毒性与UDMH本身相当甚至更高。UDMH这种极易形成的衍生物种类极其广泛且仍被低估，可能导致对该化合物潜在危害的严重低估。
