In silico approaches using CASE Ultra and QSAR Toolbox for predicting genotoxicity and carcinogenicity on diverse groups of chemicals

IF 2.9 Q2 TOXICOLOGY

Computational Toxicology Pub Date : 2025-09-14 DOI:10.1016/j.comtox.2025.100380

Gowrav Adiga Perdur , Zabiullah AJ , Mohan Krishnappa , Kamil Jurowski , Varun Ahuja

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引用次数: 0

Abstract

Humans are daily exposed to a wide range of chemicals in their environment, many of which may exert harmful effects on health. Hence, knowledge of these chemicals for their genotoxicity and carcinogenicity potential is crucial for protecting human health. Genotoxicity, in particular, serves as an early indicator of carcinogenic risk. The assessment of both genotoxicity and carcinogenicity is vital for regulatory bodies and has led to the development of alternative non-animal testing methods. One such method is in silico approach, which relies on predictive software tools for faster, more cost-effective screening.

This paper examines two in silico tools, CASE Ultra 1.9.0.8 (MultiCASE, USA) and QSAR Toolbox 4.5 (OECD), to evaluate their ability to predict the genotoxicity and carcinogenicity of various chemicals. The in silico tools CASE Ultra, QSAR Toolbox, and its profilers demonstrated remarkable performance, with balanced accuracy rates of 80%, 85%, and 62%, for genotoxicity and 79%, 86% and 66% for carcinogenicity, respectively. These promising results underscore the potential of computational approaches in risk assessment, offering a valuable complement to traditional testing methods for evaluating the genotoxicity and carcinogenicity of chemicals. Such tools can play a crucial role in regulatory decision-making and public health protection.

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使用CASE Ultra和QSAR工具箱预测不同化学物质的遗传毒性和致癌性的计算机方法

人类每天在环境中接触到各种各样的化学物质，其中许多可能对健康产生有害影响。因此，了解这些化学品的遗传毒性和潜在致癌性对保护人类健康至关重要。遗传毒性尤其可作为致癌风险的早期指标。遗传毒性和致癌性的评估对监管机构至关重要，并导致了替代非动物试验方法的发展。其中一种方法是“计算机方法”，它依靠预测软件工具进行更快、更经济的筛查。本文研究了两种计算机工具CASE Ultra 1.9.0.8 （MultiCASE， USA）和QSAR Toolbox 4.5 (OECD)，以评估它们预测各种化学物质的遗传毒性和致癌性的能力。计算机工具CASE Ultra、QSAR Toolbox及其profiler表现出卓越的性能，在遗传毒性方面的平衡准确率分别为80%、85%和62%，在致癌性方面的平衡准确率分别为79%、86%和66%。这些有希望的结果强调了计算方法在风险评估中的潜力，为评估化学品的遗传毒性和致癌性的传统测试方法提供了有价值的补充。这些工具可在监管决策和公共卫生保护方面发挥关键作用。

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