Work

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

Year: 2025

Type: article

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 EC x (i.e., EC 25 , EC 50 , and EC 75 ) 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 2 ≥ 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 2 > 0.960) between MX EC x and either CA EC x and AQ x or IA EC x and IQ x 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. • Single chemical and mixture toxicity was assessed at three exposure durations for 30 A+B M2CA-containing combinations. • Combined effects varied from slight potentiation through additivity and independence to antagonism. • Linear regression analyses of time-dependent toxicity (TDT) data showed very strong correlations between B-TDT and average A+B TDT values. • Multiple linear regression analyses of toxicity data gave strong correlations for estimating binary mixture toxicity. • The equations may be used for predicting binary mixture toxicity in Microtox for other such combinations.

Source: Toxicology Reports

DOI: 10.1016/j.toxrep.2025.101939

Authors: Douglas A. Dawson, T. Wayne Schultz

Institutions: Ashland University, University of Tennessee at Knoxville

Language: English

Topic: Environmental Toxicology and Ecotoxicology

Subfield: Health, Toxicology and Mutagenesis

Field: Environmental Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,530