Predicting explosive properties of chemicals accounting for thermodynamic and kinetic factors

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology Pub Date : 2022-08-01 DOI:10.1016/j.comtox.2022.100230

Chanita Kuseva , Valentin Marinov , Todor Pavlov , Todor Petkov , Atanas Chapkanov , Detelina Dimitrova , Tobias Wombacher , Sarah Mullen-Hinkle , Wisdom Zhu , Michael Siebold , Ovanes Mekenyan

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引用次数: 2

Abstract

A novel modelling platform, the Merck Explosive Prioritisation Scheme, is introduced. It’s dependent on neither atom types nor the chemical class of the assessed molecule. The thermodynamic layer includes simulation of chemical decomposition with further estimation of the enthalpy of the decomposition and the volume of released gases. A new algorithm, the “Greedy” method, is used in calculating decomposition enthalpy. The heats of formation are estimated by quantum-chemical calculations. The enthalpy of decomposition and volume of released gases are used to predict the Power Index (PI) of the chemicals estimated as the ratio of the explosive power of the analysed substance towards the reference chemical picric acid. Based on regulatory defined thresholds for the enthalpy of explosion and volume of released gases, a threshold for the PI is defined. Chemicals are classified as “explosive” if their PI values are higher than the threshold. The performances of the algorithms in the thermodynamic layer showed good predictability. Given the nature of the Greedy algorithm, the thermodynamic model tends to slightly overpredict experimental power indices but never underestimates the explosive properties of chemicals. The kinetic layer estimates the explosive sensitivity by applying the COREPA model based on quantum-chemical and physicochemical parameters. The COmmon REactivity PAttern (COREPA) modelling system performs well for the impact sensitivity dataset. Given the limited number of chemicals used to derive the model, its current applicability domain is narrow.

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考虑热力学和动力学因素的化学品爆炸特性预测

介绍了一种新的建模平台，默克炸药优先排序方案。它既不依赖于原子类型，也不依赖于被评估分子的化学类别。热力学层包括化学分解的模拟，并进一步估计分解的焓和释放气体的体积。提出了一种新的分解焓计算算法——贪心法。生成热是通过量子化学计算来估计的。利用分解焓和释放气体的体积来预测化学物质的威力指数(PI)，该指数是被分析物质的爆炸威力与参比化学物质苦味酸的比值。根据规定的爆炸焓和释放气体体积的阈值，确定了PI的阈值。如果化学物质的PI值高于阈值，则被归类为“爆炸性”。该算法在热力学层的性能表现出良好的可预测性。考虑到贪心算法的性质，热力学模型倾向于略微高估实验功率指数，但从不低估化学品的爆炸特性。动力学层采用基于量子化学和物理化学参数的COREPA模型估计爆炸敏感性。共同反应模式(COREPA)建模系统在冲击敏感性数据集上表现良好。考虑到用于推导模型的化学物质数量有限，它目前的适用范围很窄。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Toxicology Computer Science-Computer Science Applications

CiteScore

5.50

自引率

0.00%

发文量

审稿时长

56 days

期刊介绍： Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs

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