Multiparameter Phenotypic Profiling in MCF-7 Cells for Assessing the Toxicity and Estrogenic Activity of Whole Environmental Water.

Multi-parameter phenotypic profiling of small molecules is a powerful approach to their toxicity assessment and identifying potential mechanisms of actions. The present study demonstrates the application of image-based multi-parameter phenotypic profiling in MCF-7 cells to assess the overall toxicity and estrogenic activity of whole environmental water. Phenotypic profiling of 30 reference compounds and their complex mixtures was evaluated to investigate the cellular morphological outcomes to targeted biological pathways. Overall toxicity and estrogenic activity of environmental water samples were then evaluated by phenotypic analysis comparing with conventional bioassays and chemical analysis by multivariate analysis. The phenotypic analysis for reference compounds demonstrated that size and structure of cells related to biological processes like cell growth, death, and communication. The phenotypic alteration and nuclei intensity were selected as potential biomarkers to evaluate overall toxicity and estrogenic activities, respectively. The phenotypic profiles were associated with the chemical structure profiles in environmental water samples. Since the phenotypic parameters revealed multiple toxicity endpoints, it could provide more information that is relevant to assessing the toxicity of environmental water samples in compare with conventional bioassays. In conclusion, the image-based multi-parameters phenotypic analysis with MCF-7 cells provides a rapid and information-rich tool for toxicity evaluation and identification in whole water samples.

Generalized concentration addition approach for predicting mixture toxicity.

A new mathematical model for analyzing data and predicting the effect of mixtures of toxic substances is presented as a generalized form of the concentration addition model. The proposed method, the generalized concentration addition (GCA) model, can be applied to mixtures with arbitrary strengths of interactions (synergistic or antagonistic). It requires mixture effect data for least 1 exposure concentration of the mixture in which fractions of all components and concentration-response functions for each component are known. The GCA model evaluates the interaction between components by introducing a novel response function, which is independent of the response functions for each individual components, to describe the effect of addition between different components. The GCA method was applied to published mixture toxicity data, and it was found to fit the mixture effect better than both the concentration addition model and the independent action model, the implication being that the proposed approach is widely applicable. Environ Toxicol Chem 2017;36:265-275. © 2016 SETAC.