Effects of Neonicotinoids on Promoter-Specific Expression and Activity of Aromatase: Implications for the Development of Hormone-Dependent Breast Cancer

Caron-Beaudoin, Élyse; Sanderson, J. Thomas

doi:10.14800/ccm.1216

Cited by 1 publication

(1 citation statement)

References 33 publications

(35 reference statements)

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“…A previous study from our institute used publicly available data and identified 296 chemicals that increased E2 (182), P4 (185) or both (71) from 2,012 chemicals tested in the HT-H295R assay as part of US EPA’s Tox21/ToxCast program 5 . This assay is internationally validated for regulatory contexts by the Organization for Economic Co-operation and Development (OECD) 11 , 12 and slight variations are also used for research purposes 13 – 18 . We have shown that chemicals known to cause mammary gland effects in vivo are enriched for increasing E2 and P4 steroidogenesis 5 , so exposure to these chemicals may also increase breast cancer risk.…”

Section: Introductionmentioning

confidence: 99%

Cheminformatics analysis of chemicals that increase estrogen and progesterone synthesis for a breast cancer hazard assessment

Borrel

Rudel

2022

Sci Rep

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Factors that increase estrogen or progesterone (P4) action are well-established as increasing breast cancer risk, and many first-line treatments to prevent breast cancer recurrence work by blocking estrogen synthesis or action. In previous work, using data from an in vitro steroidogenesis assay developed for the U.S. Environmental Protection Agency (EPA) ToxCast program, we identified 182 chemicals that increased estradiol (E2up) and 185 that increased progesterone (P4up) in human H295R adrenocortical carcinoma cells, an OECD validated assay for steroidogenesis. Chemicals known to induce mammary effects in vivo were very likely to increase E2 or P4 synthesis, further supporting the importance of these pathways for breast cancer. To identify additional chemical exposures that may increase breast cancer risk through E2 or P4 steroidogenesis, we developed a cheminformatics approach to identify structural features associated with these activities and to predict other E2 or P4 steroidogens from their chemical structures. First, we used molecular descriptors and physicochemical properties to cluster the 2,012 chemicals screened in the steroidogenesis assay using a self-organizing map (SOM). Structural features such as triazine, phenol, or more broadly benzene ramified with halide, amine or alcohol, are enriched for E2 or P4up chemicals. Among E2up chemicals, phenol and benzenone are found as significant substructures, along with nitrogen-containing biphenyls. For P4up chemicals, phenol and complex aromatic systems ramified with oxygen-based groups such as flavone or phenolphthalein are significant substructures. Chemicals that are active for both E2up and P4up are enriched with substructures such as dihydroxy phosphanedithione or are small chemicals that contain one benzene ramified with chlorine, alcohol, methyl or primary amine. These results are confirmed with a chemotype ToxPrint analysis. Then, we used machine learning and artificial intelligence algorithms to develop and validate predictive classification QSAR models for E2up and P4up chemicals. These models gave reasonable external prediction performances (balanced accuracy ~ 0.8 and Matthews Coefficient Correlation ~ 0.5) on an external validation. The QSAR models were enriched by adding a confidence score that considers the chemical applicability domain and a ToxPrint assessment of the chemical. This profiling and these models may be useful to direct future testing and risk assessments for chemicals related to breast cancer and other hormonally-mediated outcomes.

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