Equations for estimating binary mixture toxicity: Methyl-2-chloroacetoacetate-containing combinations

Q1 Environmental Science Toxicology Reports Pub Date : 2025-02-05 DOI:10.1016/j.toxrep.2025.101939
Douglas A. Dawson , T. Wayne Schultz
{"title":"Equations for estimating binary mixture toxicity: Methyl-2-chloroacetoacetate-containing combinations","authors":"Douglas A. Dawson ,&nbsp;T. Wayne Schultz","doi":"10.1016/j.toxrep.2025.101939","DOIUrl":null,"url":null,"abstract":"<div><div>Mixture toxicity was determined for 30 A+B combinations. Chemical A was the reactive soft electrophile methyl-2-chloroacetoacetate (M2CA), and chemical B was one of 30 reactive or non-reactive agents. Bioluminescence inhibition in <em>Allovibrio fischeri</em> was measured after 15-, 30-, and 45-minutes of exposure for A, B, and the mixture (MX) with EC<sub>x</sub> (i.e., EC<sub>25</sub>, EC<sub>50</sub>, and EC<sub>75</sub>) values being calculated. Concentration-response curves (CRCs) were developed for A and B at each exposure duration and used to create predicted CRCs for the concentration addition (CA) and independent action (IA) mixture toxicity models. Likewise, MX CRCs were generated and compared with model predictions, along with the calculation of additivity quotient (AQ) and independence quotient (IQ) values. Mixture toxicity vs. the models showed various combined effects, including toxicity that was slightly greater than IA and/or CA, consistency with CA, IA or both models, effects that were less toxic than expected for either model and antagonism. Simple linear regression analyses of time-dependent toxicity (TDT) data showed very strong correlations (r<sup>2</sup> ≥ 0.997) for B-TDT vs. the average TDT for A and B. Likewise, for both CA and IA, multiple linear regression analyses showed strong correlations (r<sup>2</sup> &gt; 0.960) between MX EC<sub>x</sub> and either CA EC<sub>x</sub> and AQ<sub>x</sub> or IA EC<sub>x</sub> and IQ<sub>x</sub> values at each exposure duration. The results show that analyses of binary mixture toxicity data produced linear relationships resulting in equations that can effectively predict such toxicity.</div></div>","PeriodicalId":23129,"journal":{"name":"Toxicology Reports","volume":"14 ","pages":"Article 101939"},"PeriodicalIF":0.0000,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Toxicology Reports","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214750025000575","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0

Abstract

Mixture toxicity was determined for 30 A+B combinations. Chemical A was the reactive soft electrophile methyl-2-chloroacetoacetate (M2CA), and chemical B was one of 30 reactive or non-reactive agents. Bioluminescence inhibition in Allovibrio fischeri was measured after 15-, 30-, and 45-minutes of exposure for A, B, and the mixture (MX) with ECx (i.e., EC25, EC50, and EC75) values being calculated. Concentration-response curves (CRCs) were developed for A and B at each exposure duration and used to create predicted CRCs for the concentration addition (CA) and independent action (IA) mixture toxicity models. Likewise, MX CRCs were generated and compared with model predictions, along with the calculation of additivity quotient (AQ) and independence quotient (IQ) values. Mixture toxicity vs. the models showed various combined effects, including toxicity that was slightly greater than IA and/or CA, consistency with CA, IA or both models, effects that were less toxic than expected for either model and antagonism. Simple linear regression analyses of time-dependent toxicity (TDT) data showed very strong correlations (r2 ≥ 0.997) for B-TDT vs. the average TDT for A and B. Likewise, for both CA and IA, multiple linear regression analyses showed strong correlations (r2 > 0.960) between MX ECx and either CA ECx and AQx or IA ECx and IQx values at each exposure duration. The results show that analyses of binary mixture toxicity data produced linear relationships resulting in equations that can effectively predict such toxicity.
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
求助全文
约1分钟内获得全文 去求助
来源期刊
Toxicology Reports
Toxicology Reports Environmental Science-Health, Toxicology and Mutagenesis
CiteScore
7.60
自引率
0.00%
发文量
228
审稿时长
11 weeks
期刊最新文献
Corrigendum to: “Co-delivery of methotrexate and berberine based on PAMAM dendrimers for targeting HeLa cancer cells” [Toxicol. Rep. Volume 13, December 2024, 101765] In silico, in vitro and in vivo toxicity assessment of the antitumoral peptide GK-1 Mini meta-analysis of anticholinesterase actions of atorvastatin, simvastatin and rosuvastatin, and in silico identification of their protein targets in Mus musculus Protective effects of Allium sativum essential oil against lead nitrate-induced cardiotoxicity: Modulation of lipid metabolism, nitric oxide dynamics, inflammatory mediators, and histological profiles in Swiss albino mice Development of therapeutic supplement using roasted-cashew-nut to protect cerebral vasoconstriction injury triggered by mixture of petroleum hydrocarbons in the hypothalamus and hippocampus of rat model
×
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