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

In vitro cytotoxicity and genotoxicity of single and combined pesticides used by Bolivian farmers

We previously showed that farmers in Bolivia are exposed to many pesticides, some at elevated levels, and that this was associated with increased risk of genetic damage. To improve the understanding of possible mixture effects, the cytotoxicity and genotoxicity of pesticides were studied in vitro using human liver HepG2 cells. The studied pesticides were 2,4-D, chlorpyrifos, cypermethrin, glyphosate, methamidophos, paraquat, profenofos, and tebuconazole. Three mixtures (U1, U2, and U3) were based on profiles of urinary pesticide metabolites and one mixture on the most frequently used pesticides (S1). The results showed that paraquat and methamidophos were the most cytotoxic pesticides (EC₅₀ ≤0.3 mM). Paraquat, chlorpyrifos, tebuconazole, and the U1, U2, and U3 mixtures, which contained a large proportion of either chlorpyrifos or tebuconazole, significantly increased intracellular ROS levels. Most pesticides activated DNA damage signaling through proteins Chk1 and H2AX. Strongest responses were elicited by paraquat, profenofos, chlorpyrifos, cypermethrin, and the S1 mixture, which contained 25% paraquat. Comet assay revealed significant increases of DNA damage in response to paraquat, cypermethrin, and U2 and S1 mixtures, which contained high levels of cypermethrin and paraquat, respectively. In summary, we showed that the tested pesticides, alone or in mixtures, in general induced oxidative stress and that most pesticides, and especially paraquat and cypermethrin, were genotoxic in HepG2 cells. We could also show that mixtures dominated by these two pesticides displayed a marked genotoxic potency, which agreed with our previous population studies.

Application of Text Mining in Risk Assessment of Chemical Mixtures: A Case Study of Polycyclic Aromatic Hydrocarbons (PAHs)

BACKGROUND

Cancer risk assessment of complex exposures, such as exposure to mixtures of polycyclic aromatic hydrocarbons (PAHs), is challenging due to the diverse biological activities of these compounds. With the help of text mining (TM), we have developed TM tools-the latest iteration of the Cancer Risk Assessment using Biomedical literature tool (CRAB3) and a Cancer Hallmarks Analytics Tool (CHAT)-that could be useful for automatic literature analyses in cancer risk assessment and research. Although CRAB3 analyses are based on carcinogenic modes of action (MOAs) and cover almost all the key characteristics of carcinogens, CHAT evaluates literature according to the hallmarks of cancer referring to the alterations in cellular behavior that characterize the cancer cell.

OBJECTIVES

The objective was to evaluate the usefulness of these tools to support cancer risk assessment by performing a case study of 22 European Union and U.S. Environmental Protection Agency priority PAHs and diesel exhaust and a case study of PAH interactions with silica.

METHODS

We analyzed PubMed literature, comprising 57,498 references concerning priority PAHs and complex PAH mixtures, using CRAB3 and CHAT.

RESULTS

CRAB3 analyses correctly identified similarities and differences in genotoxic and nongenotoxic MOAs of the 22 priority PAHs and grouped them according to their known carcinogenic potential. CHAT had the same capacity and complemented the CRAB output when comparing, for example, benzo[a]pyrene and dibenzo[a,l]pyrene. Both CRAB3 and CHAT analyses highlighted potentially interacting mechanisms within and across complex PAH mixtures and mechanisms of possible importance for interactions with silica.

CONCLUSION

These data suggest that our TM approach can be useful in the hazard identification of PAHs and mixtures including PAHs. The tools can assist in grouping chemicals and identifying similarities and differences in carcinogenic MOAs and their interactions. https://doi.org/10.1289/EHP6702.

Pesticide use and risk of Hodgkin lymphoma: results from the North American Pooled Project (NAPP)

PURPOSE

The purpose of this study was to investigate associations between pesticide exposures and risk of Hodgkin lymphoma (HL) using data from the North American Pooled Project (NAPP).

METHODS

Three population-based studies conducted in Kansas, Nebraska, and six Canadian provinces (HL = 507, Controls = 3886) were pooled to estimate odds ratios and 95% confidence intervals for single (never/ever) and multiple (0, 1, 2-4, ≥ 5) pesticides used, duration (years) and, for select pesticides, frequency (days/year) using adjusted logistic regression models. An age-stratified analysis (≤ 40/ > 40 years) was conducted when numbers were sufficient.

RESULTS

In an analysis of 26 individual pesticides, ever use of terbufos was significantly associated with HL (OR: 2.53, 95% CI 1.04-6.17). In age-stratified analyses, associations were stronger among those ≤ 40 years of age. No significant associations were noted among those > 40 years old; however, HL cases ≤ 40 were three times more likely to report ever using dimethoate (OR: 3.76 95% CI 1.02-33.84) and almost twice as likely to have ever used malathion (OR: 1.86 95% CI 1.00-3.47). Those ≤ 40 years of age reporting use of 5 + organophosphate insecticides had triple the odds of HL (OR: 3.00 95% CI 1.28-7.03). Longer duration of use of 2,4-D, ≥ 6 vs. 0 years, was associated with elevated odds of HL (OR: 2.59 95% CI 1.34-4.97).

CONCLUSION

In the NAPP, insecticide use may increase the risk of HL, but results are based on small numbers.

Quality control of goji (fruits of Lycium barbarum L. and L. chinense Mill.): A value chain analysis perspective

ETHNOPHARMACOLOGICAL RELEVANCE

Goji (fruits of Lycium barbarum L. and L. chinense Mill., Solanaceae) have been used as a traditional food and medicine for hundreds of years in Asian countries and are now consumed globally. Quality of herbal medicines is critical for safe use and has been shown to be affected by value chains.

AIM OF THE STUDY

Using a value chain (VC) framework, we aim at understanding the influence of different VC types on goji quality and revenue of stakeholders.

MATERIALS AND METHODS

Participant observation and semi-structured interviews were conducted during five months of fieldwork in the main production areas in China with a total of 65 stakeholders. Quality of goji, behaviour and financial performance of stakeholders was documented and analysed for different VCs.

RESULTS

Ten different types of VCs were identified. VCs with vertical integration and horizontal collaboration were found to have a more coherent quality control and better goji quality as well as improved stakeholders' financial performance. Vertical integration at different levels was found for independent farmer-based VCs, horizontal collaboration was found in the cooperative-based VCs. Full vertically integrated VCs were found in large-scale production.

CONCLUSIONS

Goji quality and stakeholders' revenues are linked with different types of VCs which mirror stakeholders' behaviour driven by target markets. Considering their positive influence on quality and revenues, well-developed vertically integrated value chains are likely to become more important in the near future.

Relationship between the Structure of Ionic Liquid and Its Enrichment Ability To Trace Fungicides from an Environmental Water Sample

To elucidate the relationship between the structure of ionic liquid (IL) and its enrichment ability to trace pesticides from an environmental water sample, a series of imidazole-based ILs were synthesized to extract four fungicides (boscalid, cyprodinil, fluazinam, and pyrimethanil) through an in situ ionic liquid dispersive liquid-liquid microextraction method. The results showed that aromatic heterocyclic monocation ionic liquids (MILs) had better extraction ability to fungicides than other three alicyclic heterocyclic MILs. Dication ionic liquids (DILs) with the four carbons at the side chain had better ability to extract fungicides than MILs, and DILs with a long bridge carbon chain had better recoveries of fungicides with low K_ow values. The proposed method showed high mean enrichment factors and high recoveries of the fungicides from real water samples. The rules of the relationship between the structure of IL and enrichment ability are instructive to the application of ILs in pretreatment of complex substances.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

Grouping chemicals for health risk assessment: A text mining-based case study of polychlorinated biphenyls (PCBs)

As many chemicals act as carcinogens, chemical health risk assessment is critically important. A notoriously time consuming process, risk assessment could be greatly supported by classifying chemicals with similar toxicological profiles so that they can be assessed in groups rather than individually. We have previously developed a text mining (TM)-based tool that can automatically identify the mode of action (MOA) of a carcinogen based on the scientific evidence in literature, and it can measure the MOA similarity between chemicals on the basis of their literature profiles (Korhonen et al., 2009, 2012). A new version of the tool (2.0) was recently released and here we apply this tool for the first time to investigate and identify meaningful groups of chemicals for risk assessment. We used published literature on polychlorinated biphenyls (PCBs)-persistent, widely spread toxic organic compounds comprising of 209 different congeners. Although chemically similar, these compounds are heterogeneous in terms of MOA. We show that our TM tool, when applied to 1648 PubMed abstracts, produces a MOA profile for a subgroup of dioxin-like PCBs (DL-PCBs) which differs clearly from that for the rest of PCBs. This suggests that the tool could be used to effectively identify homogenous groups of chemicals and, when integrated in real-life risk assessment, could help and significantly improve the efficiency of the process.