Exploring QSTR and q-RASTR modeling of agrochemical toxicity on cabbage for environmental safety and human health.

IF 5.8 3区 环境科学与生态学 0 ENVIRONMENTAL SCIENCES Environmental Science and Pollution Research Pub Date : 2025-02-11 DOI:10.1007/s11356-025-36033-y
Surbhi Jyoti, Anjali Murmu, Balaji Wamanrao Matore, Jagadish Singh, Partha Pratim Roy
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Abstract

Cabbage is a widely consumed vegetable in the human diet because of its low cost, broad availability and high nutritional value. The rising use of pesticides in food production creates a need to assess vegetable toxicity, which primarily results from residues in food products and environmental exposure. The study aims to offer exploration of vegetable toxicity in cabbage with the help of reliable QSTR and q-RASTR models. All the developed models were robust and predictive enough (Q2LOO = 0.7491-0.8164, Q2F1 = 0.5243-0.6253, Q2F2 = 0.513-0.617, MAEext = 0.495-0.690). Furthermore, the reliability and predictability of models were assessed and confirmed by applicability domain and prediction reliability indicator analysis. Additionally, different machine learning models were developed to making effective predictions and multiple linear regression (MLR) comparison. Consensus approach was advocated data gap filling for USEPA ECOTOX database compounds. The most and least toxic compounds from both MLR model predictions were prioritized and analyzed. Mechanistic interpretation highlighted the structural features or fragments responsible for the agrochemical toxicity and a safe approach for designing green chemicals minimizing the toxicity. This first reported study can be useful for toxicity profiling, data gap filling and designing safer and green agrochemical for minimizing vegetable toxicity, healthy human life and environmental safety.

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卷心菜是人类饮食中广泛食用的蔬菜,因为它成本低、供应广、营养价值高。随着农药在食品生产中使用量的不断增加,需要对蔬菜的毒性进行评估,这主要是由食品中的残留和环境暴露造成的。本研究旨在借助可靠的 QSTR 和 q-RASTR 模型,对卷心菜的蔬菜毒性进行探索。所有开发的模型都具有足够的稳健性和预测性(Q2LOO = 0.7491-0.8164, Q2F1 = 0.5243-0.6253, Q2F2 = 0.513-0.617, MAEext = 0.495-0.690)。此外,通过适用域和预测可靠性指标分析,评估和确认了模型的可靠性和可预测性。此外,还开发了不同的机器学习模型,以进行有效预测和多元线性回归(MLR)比较。针对美国环保署 ECOTOX 数据库中的化合物,提倡采用共识方法来填补数据缺口。对两种 MLR 模型预测中毒性最高和最低的化合物进行了优先排序和分析。机理解释突出了造成农用化学品毒性的结构特征或片段,以及设计毒性最小的绿色化学品的安全方法。这项首次报道的研究可用于毒性分析、填补数据空白和设计更安全的绿色农用化学品,以最大限度地减少蔬菜毒性,保障人类健康和环境安全。
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来源期刊
CiteScore
8.70
自引率
17.20%
发文量
6549
审稿时长
3.8 months
期刊介绍: Environmental Science and Pollution Research (ESPR) serves the international community in all areas of Environmental Science and related subjects with emphasis on chemical compounds. This includes: - Terrestrial Biology and Ecology - Aquatic Biology and Ecology - Atmospheric Chemistry - Environmental Microbiology/Biobased Energy Sources - Phytoremediation and Ecosystem Restoration - Environmental Analyses and Monitoring - Assessment of Risks and Interactions of Pollutants in the Environment - Conservation Biology and Sustainable Agriculture - Impact of Chemicals/Pollutants on Human and Animal Health It reports from a broad interdisciplinary outlook.
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