为水生生态系统提供更安全、更环保的化学品:化学物质对红鳉的长期和慢性水生毒性化学计量模型

IF 5.4 3区 材料科学 Q2 CHEMISTRY, PHYSICAL ACS Applied Energy Materials Pub Date : 2024-06-01 DOI:10.1016/j.aquatox.2024.106985
Ankur Kumar , Probir Kumar Ojha , Kunal Roy
{"title":"为水生生态系统提供更安全、更环保的化学品:化学物质对红鳉的长期和慢性水生毒性化学计量模型","authors":"Ankur Kumar ,&nbsp;Probir Kumar Ojha ,&nbsp;Kunal Roy","doi":"10.1016/j.aquatox.2024.106985","DOIUrl":null,"url":null,"abstract":"<div><p>In the modern era, chemicals and their products have been used everywhere like agriculture, healthcare, food, cosmetics, pharmaceuticals, household products, clothing industry, etc. These chemicals find their way to reach the aquatic ecosystem (directly/indirectly) and cause severe chronic and prolonged toxic effects to aquatic species which is also then translated to human beings. Prolonged and chronic toxicity data of many chemicals that are used daily is not available due to high experimentation testing costs, time investment, and the requirement of a large number of animal sacrifices. Thus, in silico approaches (e.g., QSAR (quantitative structure-activity relationship)) are the best alternative for chronic and prolonged toxicity predictions. The present work offers multi-endpoint (five endpoints: chronic_LOEC, prolonged_14D_LC<sub>50</sub>, prolonged_14D_NOEC, prolonged_21D_LC<sub>50</sub>, prolonged_21D_NOEC) QSAR models for addressing the prolonged and chronic aquatic toxicity of chemicals toward fish (<em>O. latipes)</em>. The statistical results (<span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> =0.738–0.869, <span><math><msubsup><mi>Q</mi><mrow><mi>LOO</mi></mrow><mn>2</mn></msubsup></math></span> =0.712–0.831, <span><math><msubsup><mi>Q</mi><mrow><mo>(</mo><mrow><mi>F</mi><mn>1</mn></mrow><mo>)</mo></mrow><mn>2</mn></msubsup></math></span> =0.618–0.731) of the developed models show that they were robust, reliable, reproducible, accurate, and predictive. Some of the features that are responsible for prolonged and chronic toxicity of chemicals towards <em>O. latipes</em> are as follows: the presence of substituted benzene, hydrophobicity, unsaturation, electronegativity, the presence of long-chain fragments, the presence of a greater number of atoms at conjugation, and the presence of halogen atoms. On the other hand, hydrophilicity and graph density descriptors retard the aquatic chronic and prolonged toxicity of chemicals toward <em>O. latipes</em>. The PPDB (pesticide properties database) and experimental and investigational classes of drugs from the DrugBank database were also screened using the developed model. Thus, these multi-endpoint models will be helpful for data-gap filling and provide a broad range of applicability. Therefore, this research will aid in the in silico QSAR (quantitative structure-activity relationship) prediction (non-animal testing) of the prolonged and chronic toxicity of untested and new toxic chemicals/drugs/pesticides, design and development of eco-friendly, novel, and safer chemicals, and help to protect the aquatic ecosystem from exposure to toxic and hazardous chemicals.</p></div>","PeriodicalId":4,"journal":{"name":"ACS Applied Energy Materials","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Safer and greener chemicals for the aquatic ecosystem: Chemometric modeling of the prolonged and chronic aquatic toxicity of chemicals on Oryzias latipes\",\"authors\":\"Ankur Kumar ,&nbsp;Probir Kumar Ojha ,&nbsp;Kunal Roy\",\"doi\":\"10.1016/j.aquatox.2024.106985\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In the modern era, chemicals and their products have been used everywhere like agriculture, healthcare, food, cosmetics, pharmaceuticals, household products, clothing industry, etc. These chemicals find their way to reach the aquatic ecosystem (directly/indirectly) and cause severe chronic and prolonged toxic effects to aquatic species which is also then translated to human beings. Prolonged and chronic toxicity data of many chemicals that are used daily is not available due to high experimentation testing costs, time investment, and the requirement of a large number of animal sacrifices. Thus, in silico approaches (e.g., QSAR (quantitative structure-activity relationship)) are the best alternative for chronic and prolonged toxicity predictions. The present work offers multi-endpoint (five endpoints: chronic_LOEC, prolonged_14D_LC<sub>50</sub>, prolonged_14D_NOEC, prolonged_21D_LC<sub>50</sub>, prolonged_21D_NOEC) QSAR models for addressing the prolonged and chronic aquatic toxicity of chemicals toward fish (<em>O. latipes)</em>. The statistical results (<span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> =0.738–0.869, <span><math><msubsup><mi>Q</mi><mrow><mi>LOO</mi></mrow><mn>2</mn></msubsup></math></span> =0.712–0.831, <span><math><msubsup><mi>Q</mi><mrow><mo>(</mo><mrow><mi>F</mi><mn>1</mn></mrow><mo>)</mo></mrow><mn>2</mn></msubsup></math></span> =0.618–0.731) of the developed models show that they were robust, reliable, reproducible, accurate, and predictive. Some of the features that are responsible for prolonged and chronic toxicity of chemicals towards <em>O. latipes</em> are as follows: the presence of substituted benzene, hydrophobicity, unsaturation, electronegativity, the presence of long-chain fragments, the presence of a greater number of atoms at conjugation, and the presence of halogen atoms. On the other hand, hydrophilicity and graph density descriptors retard the aquatic chronic and prolonged toxicity of chemicals toward <em>O. latipes</em>. The PPDB (pesticide properties database) and experimental and investigational classes of drugs from the DrugBank database were also screened using the developed model. Thus, these multi-endpoint models will be helpful for data-gap filling and provide a broad range of applicability. Therefore, this research will aid in the in silico QSAR (quantitative structure-activity relationship) prediction (non-animal testing) of the prolonged and chronic toxicity of untested and new toxic chemicals/drugs/pesticides, design and development of eco-friendly, novel, and safer chemicals, and help to protect the aquatic ecosystem from exposure to toxic and hazardous chemicals.</p></div>\",\"PeriodicalId\":4,\"journal\":{\"name\":\"ACS Applied Energy Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Energy Materials\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0166445X24001553\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Energy Materials","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0166445X24001553","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0

摘要

在现代社会,化学品及其产品的使用无处不在,如农业、医疗保健、食品、化妆品、药品、家居用品、服装业等。这些化学品(直接/间接)进入水生生态系统,对水生物种造成严重的慢性和长期毒性影响,进而影响人类。由于实验测试成本高、时间投入大以及需要牺牲大量动物,许多日常使用的化学品的长期和慢性毒性数据无法获得。因此,硅学方法(如 QSAR(定量结构-活性关系))是预测慢性和长期毒性的最佳选择。本研究提供了多端点(五个端点:chronic_LOEC、prolonged_14D_LC50、prolonged_14D_NOEC、prolonged_21D_LC50、prolonged_21D_NOEC)QSAR 模型,用于研究化学品对鱼(O. latipes)的长期和慢性水生毒性。所建立模型的统计结果(R2 =0.738-0.869,QLOO2 =0.712-0.831,Q(F1)2 =0.618-0.731)表明,这些模型是稳健、可靠、可重现、准确和具有预测性的。导致化学物质对扁虱产生长期和慢性毒性的一些特征如下:存在取代苯、疏水性、不饱和、电负性、存在长链片段、共轭原子数较多以及存在卤素原子。另一方面,亲水性和图密度描述符会降低化学品对长尾鲈的水生慢性和长期毒性。此外,还利用所开发的模型对 PPDB(农药特性数据库)以及 DrugBank 数据库中的实验类和研究类药物进行了筛选。因此,这些多端点模型将有助于填补数据空白,并提供广泛的适用性。因此,这项研究将有助于对未经测试的和新的有毒化学品/药物/农药的长期和慢性毒性进行硅学 QSAR(定量结构-活性关系)预测(非动物试验),设计和开发环保、新型和更安全的化学品,并帮助保护水生生态系统免受有毒和有害化学品的危害。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Safer and greener chemicals for the aquatic ecosystem: Chemometric modeling of the prolonged and chronic aquatic toxicity of chemicals on Oryzias latipes

In the modern era, chemicals and their products have been used everywhere like agriculture, healthcare, food, cosmetics, pharmaceuticals, household products, clothing industry, etc. These chemicals find their way to reach the aquatic ecosystem (directly/indirectly) and cause severe chronic and prolonged toxic effects to aquatic species which is also then translated to human beings. Prolonged and chronic toxicity data of many chemicals that are used daily is not available due to high experimentation testing costs, time investment, and the requirement of a large number of animal sacrifices. Thus, in silico approaches (e.g., QSAR (quantitative structure-activity relationship)) are the best alternative for chronic and prolonged toxicity predictions. The present work offers multi-endpoint (five endpoints: chronic_LOEC, prolonged_14D_LC50, prolonged_14D_NOEC, prolonged_21D_LC50, prolonged_21D_NOEC) QSAR models for addressing the prolonged and chronic aquatic toxicity of chemicals toward fish (O. latipes). The statistical results (R2 =0.738–0.869, QLOO2 =0.712–0.831, Q(F1)2 =0.618–0.731) of the developed models show that they were robust, reliable, reproducible, accurate, and predictive. Some of the features that are responsible for prolonged and chronic toxicity of chemicals towards O. latipes are as follows: the presence of substituted benzene, hydrophobicity, unsaturation, electronegativity, the presence of long-chain fragments, the presence of a greater number of atoms at conjugation, and the presence of halogen atoms. On the other hand, hydrophilicity and graph density descriptors retard the aquatic chronic and prolonged toxicity of chemicals toward O. latipes. The PPDB (pesticide properties database) and experimental and investigational classes of drugs from the DrugBank database were also screened using the developed model. Thus, these multi-endpoint models will be helpful for data-gap filling and provide a broad range of applicability. Therefore, this research will aid in the in silico QSAR (quantitative structure-activity relationship) prediction (non-animal testing) of the prolonged and chronic toxicity of untested and new toxic chemicals/drugs/pesticides, design and development of eco-friendly, novel, and safer chemicals, and help to protect the aquatic ecosystem from exposure to toxic and hazardous chemicals.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
ACS Applied Energy Materials
ACS Applied Energy Materials Materials Science-Materials Chemistry
CiteScore
10.30
自引率
6.20%
发文量
1368
期刊介绍: ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.
期刊最新文献
Red ginseng polysaccharide promotes ferroptosis in gastric cancer cells by inhibiting PI3K/Akt pathway through down-regulation of AQP3. Diagnostic value of 18F-PSMA-1007 PET/CT for predicting the pathological grade of prostate cancer. Correction. Wilms' tumor 1 -targeting cancer vaccine: Recent advancements and future perspectives. Toll-like receptor agonists as cancer vaccine adjuvants.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1