Discriminant analysis of asbestiform and non-asbestiform amphibole particles and its implications for toxicological studies

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology Pub Date : 2022-08-01 DOI:10.1016/j.comtox.2022.100233

Ann G. Wylie , Andrey A. Korchevskiy , Drew R. Van Orden , Eric J. Chatfield

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引用次数: 9

Abstract

Context

Rock dusts often contain minerals called amphiboles. Elongate mineral particles produced by excavation, crushing, or grinding amphibole-containing rock can belong to different morphological groups, or habits: asbestiform or non-asbestiform. Some asbestiform particles are highly potent for causing mesothelioma, but non-asbestiform elongate structures have not been implicated in elevated cancer risk. Computational analysis and modelling of the dimensional characteristics of the elongate mineral particles is needed to develop efficient criteria for their differentiation, and also for determining the parameters driving their carcinogenic potential.

Objectives

To develop conceptual and quantitative models allowing reliable distinctions between asbestiform and non-asbestiform amphibole particles that are based on particle dimensions and are consistent with observed disease outcome following human exposure.

Methods

For modelling, the unique database including 56 datasets designated as dominantly asbestiform (67,876 amphibole particles), 37 designated as dominantly non-asbestiform (235,247 amphibole particles), and 12 as inhomogeneous or anomalous (35,277 amphibole particles) was utilized. The discriminant analysis was used to determine functions that separate elongate mineral particles by their habit based on length and width. Linear regression and cluster analysis were applied to determine the relationship between values of the selected discriminant function and relevant toxicological parameters.

Results

For particles longer than 5 µm, the function $Y = 2.99 \log_{10} L e n g t h - 5.82 \log_{10} W i d t h - 3.80$ was selected as the best discriminator of particles for their asbestiform and non-asbestiform habits, with a misclassification rate of about 15% total. The value of the discriminant function derived for each particle correlates with the particle’s calculated aerodynamic diameter (R = −0.859, p < 0.00001) and with its specific surface area (R = 0.857, p < 0.00001). The cluster analysis demonstrated that subdivision of particles by two groups according to their length and width closely reconstructs the pre-defined habits.

Conclusion

The proposed methodology of differentiating between asbestiform and non-asbestiform particles can be used for analytical, toxicological, and regulatory purposes.

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石棉和非石棉角孔颗粒的判别分析及其对毒理学研究的意义

岩石粉尘通常含有一种叫做角闪石的矿物质。通过挖掘、破碎或研磨含角闪石的岩石而产生的细长矿物颗粒可以属于不同的形态群或习性:石棉质或非石棉质。一些石棉颗粒对引起间皮瘤非常有效，但非石棉颗粒的细长结构与癌症风险升高无关。需要对细长矿物颗粒的尺寸特征进行计算分析和建模，以制定有效的区分标准，并确定驱动其致癌潜力的参数。目的建立概念和定量模型，根据颗粒尺寸可靠地区分石棉和非石棉角孔颗粒，并与人类接触后观察到的疾病结果相一致。方法利用独特的数据库进行建模，其中包括56个主要为石棉形式的数据集(67,876个角闪孔颗粒)，37个主要为非石棉形式的数据集(235,247个角闪孔颗粒)，以及12个不均匀或异常的数据集(35,277个角闪孔颗粒)。采用判别分析确定了根据长度和宽度的习惯分离细长矿物颗粒的函数。采用线性回归和聚类分析确定所选判别函数值与相关毒理学参数之间的关系。结果对于长度大于5µm的颗粒，选择Y=2.99log10Length-5.82log10Width-3.80函数作为颗粒的石棉和非石棉特征的最佳判别因子，总误分类率约为15%。每个粒子的判别函数值与计算得到的粒子气动直径相关(R = - 0.859, p <0.00001)，与比表面积(R = 0.857, p <0.00001)。聚类分析表明，根据粒子的长度和宽度将粒子分成两类，这与预先定义的习惯密切相关。结论所提出的区分石棉和非石棉颗粒的方法可用于分析、毒理学和监管目的。

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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