Abstract
Toxicity caused by chemical mixtures has emerged as a significant challenge for toxicologists and risk assessors. Information on individual chemicals' modes of action is an important part of the hazard identification step. In this study, an automatic text mining-based tool was employed as a method to identify the carcinogenic modes of action of pesticides frequently found in fruit on the Swedish market. The current available scientific literature on the 26 most common pesticides found in apples and oranges was evaluated. The literature was classified according to a taxonomy that specifies the main type of scientific evidence used for determining carcinogenic properties of chemicals. The publication profiles of many pesticides were similar, containing evidence for both genotoxic and non-genotoxic modes of action, including effects such as oxidative stress, chromosomal changes and cell proliferation. We also found that 18 of the 26 pesticides studied here had previously caused tumors in at least one animal species, findings which support the mode of action data. This study shows how a text-mining tool could be used to identify carcinogenic modes of action for a group of chemicals in large quantities of text. This strategy could support the risk assessment process of chemical mixtures.
Highlights
Chemical risk assessment of mixtures is an important but challenging task for toxicologists
Data shows that the majority of the tested apples and oranges contained several pesticide residues, 78 percent of the apple samples contained more than one pesticide and for orange samples this number was as high as 96 percent
In this study we used an automatic text mining-based tool for literature review and mode of action (MOA) analysis to evaluate 26 pesticides that were frequently found in apples and oranges on the Swedish market
Summary
Chemical risk assessment of mixtures is an important but challenging task for toxicologists. Mixture effects can be described as caused either by additivity or interactions (such as synergistic or antagonistic effects) of the individual compounds (Feron and Groten, 2002). Strategies based on these theories have been developed to predict the toxic effects of mixtures, and include e.g., dose addition, which assumes a common or similar toxicological mode of action (MOA) of the individual chemicals (US EPA, 2000; Teuschler, 2007). Chemicals with the same or similar MOA may suggest potential for additive effects when such compounds are combined (US EPA, 2000)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
