Equations for estimating binary mixture toxicity: Methyl-2-chloroacetoacetate-containing combinations
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 ex...
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Elsevier
2025-06-01
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| author | Douglas A. Dawson T. Wayne Schultz |
| author_facet | Douglas A. Dawson T. Wayne Schultz |
| author_sort | Douglas A. Dawson |
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| description | 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. |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-7e8a8f641c41430aafcf32afddcb1a182025-02-09T05:00:29ZengElsevierToxicology Reports2214-75002025-06-0114101939Equations for estimating binary mixture toxicity: Methyl-2-chloroacetoacetate-containing combinationsDouglas A. Dawson0T. Wayne Schultz1Department of Biological Sciences and Toxicology, Ashland University, Ashland, OH 44805, USA; Cooresponding author.College of Veterinary Medicine, University of Tennessee, Knoxville, TN 37996, USAMixture 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.http://www.sciencedirect.com/science/article/pii/S2214750025000575Concentration additionIndependent actionTime-dependent toxicityElectro(nucleophilic) reactivityMixture toxicity |
| spellingShingle | Douglas A. Dawson T. Wayne Schultz Equations for estimating binary mixture toxicity: Methyl-2-chloroacetoacetate-containing combinations Toxicology Reports Concentration addition Independent action Time-dependent toxicity Electro(nucleophilic) reactivity Mixture toxicity |
| title | Equations for estimating binary mixture toxicity: Methyl-2-chloroacetoacetate-containing combinations |
| title_full | Equations for estimating binary mixture toxicity: Methyl-2-chloroacetoacetate-containing combinations |
| title_fullStr | Equations for estimating binary mixture toxicity: Methyl-2-chloroacetoacetate-containing combinations |
| title_full_unstemmed | Equations for estimating binary mixture toxicity: Methyl-2-chloroacetoacetate-containing combinations |
| title_short | Equations for estimating binary mixture toxicity: Methyl-2-chloroacetoacetate-containing combinations |
| title_sort | equations for estimating binary mixture toxicity methyl 2 chloroacetoacetate containing combinations |
| topic | Concentration addition Independent action Time-dependent toxicity Electro(nucleophilic) reactivity Mixture toxicity |
| url | http://www.sciencedirect.com/science/article/pii/S2214750025000575 |
| work_keys_str_mv | AT douglasadawson equationsforestimatingbinarymixturetoxicitymethyl2chloroacetoacetatecontainingcombinations AT twayneschultz equationsforestimatingbinarymixturetoxicitymethyl2chloroacetoacetatecontainingcombinations |