Simplified toxicity assessment in pharmaceutical and pesticide mixtures: Leveraging interpretable structural parameters

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2024-04-24 DOI:10.1016/j.comtox.2024.100312

Mohammad Hossein Keshavarz, Zeinab Shirazi, Zeinab Davoodi

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Abstract

The potential toxicity arising from antibiotics and pesticides poses a significant risk to the preservation of groundwater. This study investigates the effects of binary mixtures of pharmaceuticals and pesticides by assessing their log EC₅₀, log EC₃₀, and log EC₁₀ values in relation to Vibrio fischeri bacteria. Based on a comprehensive dataset of 459 observations, this work identifies suitable simple descriptors. Rigorous statistical analysis confirms the models’ reliability, accuracy, precision, and favorable goodness-of-fit. Notably, the ratios of coefficient of determination (R²) for the novel models compared to the best comparative models exceed 1.0: 0.8618/0.8085 for log EC₅₀, 0.8856/0.8422 for log EC₃₀, and 0.8973/0.8556 for log EC₁₀. Additionally, the ratios of root mean square error (RMSE) for the new models relative to their counterparts are all below 1.0: 0.159/0.191 for log EC₅₀, 0.131/0.169 for log EC₃₀, and 0.182/0.215 for log EC₁₀.

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简化药物和农药混合物的毒性评估：利用可解释的结构参数

抗生素和杀虫剂的潜在毒性对地下水的保护构成了重大风险。本研究通过评估药物和杀虫剂二元混合物对鱼腥弧菌的对数 EC50、对数 EC30 和对数 EC10 值，研究了它们的影响。基于 459 个观测数据的综合数据集，这项研究确定了合适的简单描述因子。严格的统计分析证实了模型的可靠性、准确性、精确性和良好的拟合度。值得注意的是，与最佳比较模型相比，新型模型的判定系数（R2）之比超过了 1.0：对数 EC50 为 0.8618/0.8085，对数 EC30 为 0.8856/0.8422，对数 EC10 为 0.8973/0.8556。此外，新模型与同类模型的均方根误差（RMSE）之比都低于 1.0：对数 EC50 为 0.159/0.191，对数 EC30 为 0.131/0.169，对数 EC10 为 0.182/0.215。

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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