Concentration Addition, Independent Action, and QSAR for Chemical Mixture Toxicities of the Disinfection Byproducts of Haloacetic Acids on the Green Alga Raphidocelis subcapitata.

Abstract

The potential toxicity of haloacetic acids (HAAs), common disinfection byproducts (DBPs), has been widely studied, but their combined effects on freshwater green algae remain poorly understood. This study was conducted to investigate the toxicological interactions of HAA mixtures in the green alga Raphidocelis subcapitata and predict the DBP mixture toxicities based on concentration addition (CA), independent action (IA), and quantitative structure-activity relationship (QSAR) models. The acute toxicities of six HAA (iodoacetic acid [IAA], bromoacetic acid [BAA], chloroacetic acid [CAA], dichloroacetic acid [DCAA], trichloroacetic acid [TCAA], and tribromoacetic acid [TBAA]) and its 68 binary mixtures to the green algae were analyzed in 96-well microplates. Results reveal that The rank order of the toxicity of individual HAAs is CAA > IAA≈BAA > TCAA > DCAA > TBAA. With CA as the reference additive model, the mixture effects are synergetic in 47.1% and antagonistic in 25%, whereas the additive effects are only observed in 27.9% of the experiments. DCAA, IAA, and BAA are the main components that induce synergism, and CAA is the main component that causes antagonism. CA and IA prediction results indicate that the two models fail to accurately predict 72% mixture toxicity at an effective concentration level of 50%. QSAR models of the mixtures are established by statistically analyzing descriptors for the determination of the relationship between their chemical structures and the negative logarithm of 50% effective concentration. The additive mixture toxicities are accurately predicted by the QSAR model based on two parameters, namely, octanol/water partition coefficient and acid dissociation constant (pKa). The toxicities of synergetic mixtures can be interpreted with the total energy (ET ) and pKa of the mixtures. ET and dipole moment are the quantum descriptors that influence the antagonistic mixture toxicity. Therefore, in silico modeling may be a useful tool in predicting DBP mixture toxicities. This article is protected by copyright. All rights reserved.