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

Abstract

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.

Application of the Key Characteristics Framework to Identify Potential Breast Carcinogens Using Publicly Available in Vivo , in Vitro , and in Silico Data

“… 73 to these predicted ER agonists would help identify additional ER-active chemicals beyond the 267 listed here. Similarly, we developed a quantitative structure-activity relationship (QSAR) model to predict chemicals that likely increase E2 or P4 steroidogenesis, 185 and in vitro testing of these chemicals could highlight additional chemicals that likely increase BC risk. Finally, in addition to chemicals not being tested for steroidogenic and ER activities, there are other endocrine pathways relevant to breast carcinogenesis (e.g., PR activation, prolactin signaling) that do not have reliable HT assays, so we were not able to include chemicals with these BC-relevant effects in this list.…”

“…In addition to filling data gaps in chemical screening, identifying BC-relevant chemicals and their KCs can support a range of future inquiries. For example, this study could be used to develop QSAR models for flagging structural features common to chemicals with different combinations of genotoxic, steroidogenic, and ER-agonistic activities, such as the one we have recently published for E2/P4 steroidogens, 185 supporting chemical read-across and predictive toxicology. Similarly, a study of genotoxic EDCs that did not induce mammary tumors in a bioassay could provide insights about chemical features that prevent the anticipated mammary tumors from developing.…”