Mathematical Modeling Approaches for Assessing the Joint Toxicity of Chemical Mixtures Based on Luminescent Bacteria: A Systematic Review

Abstract

Developments in industrial applications inevitably accelerate the discharge of enormous substances into the environment, whereas multi-component mixtures commonly cause joint toxicity which is distinct from the simple sum of independent effect. Thus, ecotoxicological assessment, by luminescent bioassays has recently brought increasing attention to overcome the environmental risks. Based on the above viewpoint, this review included a brief introduction to the occurrence and characteristics of toxic bioassay based on the luminescent bacteria. In order to assess the environmental risk of mixtures, a series of models for the prediction of the joint effect of multi-component mixtures have been summarized and discussed in-depth. Among them, Quantitative Structure-Activity Relationship (QSAR) method which was widely applied in silico has been described in detail. Furthermore, the reported potential mechanisms of joint toxicity on the luminescent bacteria were also overviewed, including the Trojan-horse type mechanism, funnel hypothesis, and fishing hypothesis. The future perspectives toward the development and application of toxicity assessment based on luminescent bacteria were proposed.

Acute and Chronic Toxicity of Binary Mixtures of Bisphenol A and Heavy Metals

“…The quantitative structure–activity relationship (QSAR) model, CA and IA models, and integrated CA and IA models such as TSP (two–stage prediction) have been used to predict the toxicity of mixtures of contaminants [ 56 ]. The CA model is usually used to predict the mixtures with the same MOA but IA with different MOA.…”

“…According to the different toxic modes of action, two conventional models are used to predict the joint effect of mixtures. One is independent action (IA, different mode of action, MOA), and the other is concentration addition (CA, similar to MOA) [ 56 ]. In this study, BPA and heavy metals indicate different toxic action modes, and both CA and IA were used to predict the mixture of toxicities.…”

A structure–activity approach towards the toxicity assessment of multicomponent metal oxide nanomaterials

“…The descriptors used to predict the MCNM toxic class were computed using the additive mixture approach. 47 Although some works have tested the use of descriptors computed using an independent action approach for the case of nanomaterial or chemical mixtures, 100,101 the additive mixture approach has been the main approach used to build predictive models for nanomaterial mixtures, 66,67,102 as well as for the case of MCNMs. [47][48][49][50]52 Nevertheless, the list of MCNMs has been more limited.…”

Application of the Luminescent luxCDABE Gene for the Rapid Screening of Antibacterial Substances Targeting Pseudomonas aeruginosa

“…At present, mathematical models are widely used in scientific research [32,[52][53][54]. For different research samples, choosing an appropriate model to analyze and obtain a highly reliable research model can provide a certain degree of theoretical guidance for practical production applications.…”