MRA Toolbox v. 1.0: a web-based toolbox for predicting mixture toxicity of chemical substances in chemical products

Abstract

The chemical risk assessment paradigm is shifting from “substance-based” to “product/mixture-based” and from “animal testing” to “alternative testing” under chemical regulations. Organisms and the environment may be exposed to mixtures rather than a single substance. Conducting toxicity tests for all possible combinations is impractical due to the enormous combinatorial complexity. This study highlights the development and application case studies of Mixture Risk Assessment Toolbox, a novel web-based platform that supports mixture risk assessment through the use of different prediction models and public databases. This integrated framework provides new functional values for assessors to easily screen and compare the toxicity of mixture products using different computational techniques and find strategic solutions to reduce the mixture toxicity in the product development process. The toolbox (https://www.mratoolbox.org) includes four additive toxicity models: two conventional (Concentration Addition; and Independent Action) and two advanced (Generalized Concentration Addition; and Quantitative Structure–Activity Relationship-based Two-Stage Prediction) models. We demonstrated the multiple functions of the toolbox using three cases: (i) how it can be used to calculate the mixture toxicity, (ii) those for which safety data sheet (SDS) only indicating representative toxicity values (EC50; and LC50), and (iii) those comprising chemicals with low toxic effects.

In Vitro Toxicity Screening of Fifty Complex Mixtures in HepG2 Cells

“…In response to this research need, several mixture toxicity prediction models have been developed, including the generalized concentration addition (GCA) model [ 10 ], the stepwise modelling combined CA and IA models [ 11 , 12 ], and the full logistic model (FLM) [ 13 ]. To support the mixture toxicity prediction, our research team has developed a web-based platform that conveniently estimates the mixture toxicity values based on various prediction models [ 14 ]. However, the computational prediction models still require improvement.…”

Predictive toxicology of chemical mixtures using proteome-wide thermal profiling and protein target properties

“…The new approach methodologies, such as in vitro methods, omics, quantitative structureactivity relationship, threshold of toxicological concern, read-across, toxicokinetic models, and in silico predictions, have been proposed as the emerging tools for assessing the hazard of mixtures [7][8][9] . Frameworks like the adverse outcome pathway (AOP) have also been developed to integrate knowledge from molecular, cellular, tissue, and organismal levels, aiding in the understanding of exposures to mixtures 10 .…”