Репозиторий Университета

© 2019, The Author(s). Humans are exposed to multiple chemicals on a daily basis instead of to just a single chemical, yet the majority of existing toxicity data comes from single-chemical exposure. Multiple factors must be considered such as the route, concentration, duration, and the timing of exposure when determining toxicity to the organism. The need for adequate model systems (in vivo, in vitro, in silico and mathematical) is paramount for better understanding of chemical mixture toxicity. Currently, shortcomings plague each model system as investigators struggle to find the appropriate balance of rigor, reproducibility and appropriateness in mixture toxicity studies. Significant questions exist when comparing single-to mixture-chemical toxicity concerning additivity, synergism, potentiation, or antagonism. Dose/concentration relevance is a major consideration and should be subthreshold for better accuracy in toxicity assessment. Previous work was limited by the technology and methodology of the time, but recent advances have resulted in significant progress in the study of mixture toxicology. Novel technologies have added insight to data obtained from in vivo studies for predictive toxicity testing. These include new in vitro models: omics-related tools, organs-on-a-chip and 3D cell culture, and in silico methods. Taken together, all these modern methodologies improve the understanding of the multiple toxicity pathways associated with adverse outcomes (e.g., adverse outcome pathways), thus allowing investigators to better predict risks linked to exposure to chemical mixtures. As technology and knowledge advance, our ability to harness and integrate separate streams of evidence regarding outcomes associated with chemical mixture exposure improves. As many national and international organizations are currently stressing, studies on chemical mixture toxicity are of primary importance.

© 2019 Elsevier B.V. Human populations are chronically exposed to mixtures of toxic chemicals. Predicting the health effects of these mixtures require a large amount of information on the mode of action of their components. Xenobiotic metabolism by bacteria inhabiting the gastrointestinal tract has a major influence on human health. Our review aims to explore the literature for studies looking to characterize the different modes of action and outcomes of major chemical pollutants, and some components of cosmetics and food additives, on gut microbial communities in order to facilitate an estimation of their potential mixture effects. We identified good evidence that exposure to heavy metals, pesticides, nanoparticles, polycyclic aromatic hydrocarbons, dioxins, furans, polychlorinated biphenyls, and non-caloric artificial sweeteners affect the gut microbiome and which is associated with the development of metabolic, malignant, inflammatory, or immune diseases. Answering the question ‘Who is there?’ is not sufficient to define the mode of action of a toxicant in predictive modeling of mixture effects. Therefore, we recommend that new studies focus to simulate real-life exposure to diverse chemicals (toxicants, cosmetic/food additives), including as mixtures, and which combine metagenomics, metatranscriptomics and metabolomic analytical methods achieving in that way a comprehensive evaluation of effects on human health.