SAR Studies to Assess the Risk of Breast Cancer Due to Environmental Estrogens

摘要

Environmental estrogens have been implicated as etiological agents for breast cancer. This study is aimed at identifying mechanistic information regarding the estrogenicity and carcinogenicity of environmental estrogens through structure-activity relationship modeling. Learning sets have been derived from bioassays designed to detect estrogenic chemicals. These databases include an in vitro estrogen competitive binding assay, an in vitro MCF-7 cell proliferation assay and a whole animal uterine weight increase assay. The models derived from these databases are considered in conjunction with models for rodent carcinogenicity and other toxicological phenomena to identify features that are common to estrogenicity and carcinogenicity. I have been able to initially demonstrate a link between a chemical's estrogenicity and its ability to induce adverse chromosomal effects. I have also been able to develop a series of computational model for estrogenic activity that are capable of identify chemicals with estrogenic activity. The utility of these models are two fold. First they will be able to identify environmental agents that posses estrogenic activity. Thus, we will be able scan inventories of environmental chemicals for the identification of potential environmental estrogens. These data could be used to prioritize chemicals for further testing, regulation, or study. Second, these models are useful for the development of selective estrogen receptor modulators as breast cancer chemotherapies. The models derived for rodent carcinogens allows for the early detection of potential carcinogenic agents in the development of these therapies. I have participated in and NIH breast cancer SPORE and will participate in an NIH Program Project based on my ability to use these models in the development of chemotherapies.