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Evaluation of carcinogenic modes of action for pesticides in fruit on the Swedish market using a text-mining tool.

Silins I, Korhonen A, Stenius U - Front Pharmacol (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Environmental Medicine, Karolinska Institutet Stockholm, Sweden ; Computer Laboratory, University of Cambridge Cambridge, UK.

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.

No MeSH data available.


Related in: MedlinePlus

Schematic flow chart of the tool used for classifying abstracts on the 26 selected pesticides.
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Figure 1: Schematic flow chart of the tool used for classifying abstracts on the 26 selected pesticides.

Mentions: Table 1 shows the 15 most frequently detected pesticides/residues in apples and the 15 most frequently detected residues in oranges from the analysis of the Swedish National Food Agency (NFA) (Jansson et al., 2011). The total number of published PubMed abstracts concerning the 15 pesticides detected in oranges was higher compared to the apple pesticides (14 772 and 9 652 abstracts respectively). To conduct a MOA analysis of these pesticides we used the CRAB tool to analyze the literature. For each pesticide the tool classified the published abstracts automatically according to the MOA taxonomy. In Figure 1, a schematic flow chart of the classification of abstracts is shown. The tool identified 2 552 and 3 535 abstracts, respectively, as relevant for MOA classification and classified 18 337 as irrelevant (Figure 1). Thus, only 25 percent of the original PubMed collection was classified by the tool as relevant for cancer MOA classification and 75 percent of the retrieved articles were deemed by the tool not to be relevant for MOA, requiring no further examination. Based on the results from the MOA classification, the group of orange pesticides was in general studied more widely than the group of apple pesticides (Table 1). The range of abstracts showed that some pesticides were less studied (e.g., only 14 boscalid abstracts were relevant for MOA), while other pesticides were more well-studied (e.g., chlorpyrifos and malathion, 672 and 609 abstracts, respectively). The information about data gaps may also be important and could point to knowledge gaps that require more research.


Evaluation of carcinogenic modes of action for pesticides in fruit on the Swedish market using a text-mining tool.

Silins I, Korhonen A, Stenius U - Front Pharmacol (2014)

Schematic flow chart of the tool used for classifying abstracts on the 26 selected pesticides.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4066588&req=5

Figure 1: Schematic flow chart of the tool used for classifying abstracts on the 26 selected pesticides.
Mentions: Table 1 shows the 15 most frequently detected pesticides/residues in apples and the 15 most frequently detected residues in oranges from the analysis of the Swedish National Food Agency (NFA) (Jansson et al., 2011). The total number of published PubMed abstracts concerning the 15 pesticides detected in oranges was higher compared to the apple pesticides (14 772 and 9 652 abstracts respectively). To conduct a MOA analysis of these pesticides we used the CRAB tool to analyze the literature. For each pesticide the tool classified the published abstracts automatically according to the MOA taxonomy. In Figure 1, a schematic flow chart of the classification of abstracts is shown. The tool identified 2 552 and 3 535 abstracts, respectively, as relevant for MOA classification and classified 18 337 as irrelevant (Figure 1). Thus, only 25 percent of the original PubMed collection was classified by the tool as relevant for cancer MOA classification and 75 percent of the retrieved articles were deemed by the tool not to be relevant for MOA, requiring no further examination. Based on the results from the MOA classification, the group of orange pesticides was in general studied more widely than the group of apple pesticides (Table 1). The range of abstracts showed that some pesticides were less studied (e.g., only 14 boscalid abstracts were relevant for MOA), while other pesticides were more well-studied (e.g., chlorpyrifos and malathion, 672 and 609 abstracts, respectively). The information about data gaps may also be important and could point to knowledge gaps that require more research.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Environmental Medicine, Karolinska Institutet Stockholm, Sweden ; Computer Laboratory, University of Cambridge Cambridge, UK.

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.

No MeSH data available.


Related in: MedlinePlus