A multicomponent similarity approach to identify potential substances of very high concern

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology Pub Date : 2025-04-02 DOI:10.1016/j.comtox.2025.100343

Yordan Yordanov , Emiel Rorije , Jordi Minnema , Thimo Schotman , Willie J.G.M. Peijnenburg , Pim N.H. Wassenaar

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引用次数: 0

Abstract

The number of chemicals being placed on the market is increasing. As such, there is an increased need for screening and evaluation of chemical hazards and risks. Particularly, chemicals with intrinsic properties that are considered of very high concern are ideally identified and regulated before wide-spread use and exposure. The use of in silico tools can help to identify potential substances of very high concern (SVHCs).

Earlier, predictive models have been developed that identify potential SVHCs based on global structural similarity to known SVHCs. Here in this study, these read-across similarity models have been extended with other similarity modules, including toxicophore, biological and physicochemical similarity.

The newly developed SVHC similarity profiles do individually not outperform the existing global similarity model. However, combining these new modules in an extended similarity approach results in more comprehensive predictions and allows for improved interpretability and applicability to the broader chemical universe. As such, this new approach is thought to support model users in interpretation of the model-prediction, and can thereby contribute to better screening and prioritization of potential SVHCs.

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一种多组分相似度方法，用于识别高度关注的潜在物质

投放市场的化学品越来越多。因此，越来越需要筛选和评价化学品的危害和风险。特别是那些被认为具有高度关注的内在特性的化学品，在广泛使用和接触之前，最好能确定并加以管制。使用计算机工具可以帮助识别潜在的高度关注物质（svhc）。此前，已经开发出预测模型，根据与已知svhc的整体结构相似性来识别潜在的svhc。在本研究中，这些跨读相似性模型已经扩展到其他相似性模块，包括毒理学，生物和物理化学相似性。新开发的SVHC相似度曲线并不优于现有的全局相似度模型。然而，将这些新模块结合在一个扩展的相似方法中，可以得到更全面的预测，并允许改进的可解释性和更广泛的化学领域的适用性。因此，这种新方法被认为支持模型用户解释模型预测，从而有助于更好地筛选和优先考虑潜在的svhc。

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