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

Papamokos, George; Silins, Ilona

doi:10.3389/fphar.2016.00284

Cited by 12 publications

(4 citation statements)

References 48 publications

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“…Existing in silico or in vitro methods to detect non-genotoxic carcinogen mode of actions are insufficiently accurate yet (Benigni et al, 2013 ; Papamokos and Silins, 2016 ). Consequently, when applying the current TTC concept, structures cannot reliably be identified as non-genotoxic carcinogens.…”

Section: Discussionmentioning

confidence: 99%

Threshold of Toxicological Concern—An Update for Non-Genotoxic Carcinogens

et al. 2021

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The Threshold of Toxicological Concern (TTC) concept can be applied to organic compounds with the known chemical structure to derive a threshold for exposure, below which a toxic effect on human health by the compound is not expected. The TTC concept distinguishes between carcinogens that may act as genotoxic and non-genotoxic compounds. A positive prediction of a genotoxic mode of action, either by structural alerts or experimental data, leads to the application of the threshold value for genotoxic compounds. Non-genotoxic substances are assigned to the TTC value of their respective Cramer class, even though it is recognized that they could test positive in a rodent cancer bioassay. This study investigated the applicability of the Cramer classes specifically to provide adequate protection for non-genotoxic carcinogens. For this purpose, benchmark dose levels based on tumor incidence were compared with no observed effect levels (NOELs) derived from non-, pre- or neoplastic lesions. One key aspect was the categorization of compounds as non-genotoxic carcinogens. The recently finished CEFIC LRI project B18 classified the carcinogens of the Carcinogenicity Potency DataBase (CPDB) as either non-genotoxic or genotoxic compounds based on experimental or in silico data. A detailed consistency check resulted in a dataset of 137 non-genotoxic organic compounds. For these 137 compounds, NOEL values were derived from high quality animal studies with oral exposure and chronic duration using well-known repositories, such as RepDose, ToxRef, and COSMOS DB. Further, an effective tumor dose (ETD10) was calculated and compared with the lower confidence limit on benchmark dose levels (BMDL10) derived by model averaging. Comparative analysis of NOEL/EDT10/BMDL10 values showed that potentially bioaccumulative compounds in humans, as well as steroids, which both belong to the exclusion categories, occur predominantly in the region of the fifth percentiles of the distributions. Excluding these 25 compounds resulted in significantly higher but comparable fifth percentile chronic NOEL and BMDL10 values, while the fifth percentile EDT10 value was slightly higher but not statistically significant. The comparison of the obtained distributions of NOELs with the existing Cramer classes and their derived TTC values supports the application of Cramer class thresholds to all non-genotoxic compounds, such as non-genotoxic carcinogens.

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Section: Discussionmentioning

confidence: 99%

Threshold of Toxicological Concern—An Update for Non-Genotoxic Carcinogens

et al. 2021

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“…We plan to upgrade the tools such that chemicals identified by the CRAB3 tool will be assigned a Chemical Abstracts Service number. For classification of compounds with limited amount of human or animal toxicity data, we also plan to integrate structure–activity relationship modeling into our TM tools, as suggested in our previous study ( Papamokos and Silins 2016 ).…”

Section: Discussionmentioning

confidence: 99%

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

Ali

Dreij

Baker

et al. 2021

Environ Health Perspect

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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.

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“…Mechanisms such as immunosuppression and hormonal receptor-mediated effects were involved. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens [71].…”

Section: Further Evolutionsmentioning

confidence: 99%

Testing the Mutagenicity Potential of Chemicals

Verheyen¹

2017

J Genet Genome Res

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All information for the proper development, functioning and reproduction of organisms is coded in the sequence of matched base-pairs of DNA. DNA mutations can result in harmful effects and play a role in genetic disorders and cancer. As mutations can arise through exposure to chemical substances, testing needs to be done on substances that humans and animals can be exposed to. This review focuses on the different testing strategies for risk assessment of chemicals for genotoxicity and carcinogenicity. This review is not meant to cover all testing methodologies, but rather to give an overview of the main methodologies that are used in a regulatory context. In silico and in vitro tools are playing an increasingly important role, in order to reduce in vivo animal testing. Especially in silico tools like (Q)SARs are promising as they are non-testing methods and thereby also reduce the costs and time that are needed to perform in vitro assays. (Q)SARs are not sufficiently reliable to be used as stand-alone tools, and should be associated with expert judgment or used in combination with other Weight of Evidence such as in vitro and in vivo data and data derived from other similar molecules (read-across). For some product (sub) categories, testing (in vitro/in vivo) will be unavoidable; however the future may bring reliable approaches for ensuring human safety.

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

Cited by 12 publications

References 48 publications

Threshold of Toxicological Concern—An Update for Non-Genotoxic Carcinogens

Threshold of Toxicological Concern—An Update for Non-Genotoxic Carcinogens

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

Testing the Mutagenicity Potential of Chemicals

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