title: Evaluation of carcinogenic modes of action for pesticides in fruit on the Swedish market using a text-mining tool.
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.
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet Stockholm, Sweden ; Computer Laboratory, University of Cambridge Cambridge, UK.
authors.forename: Ilona
authors.initials: I
authors.lastname: Silins
authors.affiliation: Computer Laboratory, University of Cambridge Cambridge, UK.
authors.forename: Anna
authors.initials: A
authors.lastname: Korhonen
authors.affiliation: Institute of Environmental Medicine, Karolinska Institutet Stockholm, Sweden.
authors.forename: Ulla
authors.initials: U
authors.lastname: Stenius
keywords: chemical carcinogenesis; chemical mixtures; mode of action; pesticides; risk assessment; text-mining