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Human CYP2A and Respiratory Tract Xenobiotic Toxicity

Principal Investigator: Xinxin Ding

Funding Mechanism: National Institutes of Health- Grant

ID number: 3 R01 CA092596-09S1

Award Date: 9/17/2012

Institution: Wadsworth Center

Animal models of cigarette smoke-induced cancer are important for evaluating new tobacco products that want to claim decreased toxicity and carcinogenicity, chemopreventive agent efficacy, and mechanisms of cigarette smoke-induced cancer. However, currently available animal models of smoke-induced lung cancer have significant downsides: studies require a large number of animals and a long exposure period, and the models tend to induce a relatively small number of tumors. The proposed studies will test a novel approach for increasing mouse model sensitivity to lung tumor formation in order to elucidate the signaling pathways that regulate smoke toxicant effects. The goal of this study is to clarify the role of the respiratory tract cytochrome P450 family of biotransformation enzymes in modulating cigarette smoke-induced lung tumor formation in mouse models. Investigators will use two novel transgenic mouse models. The first model will test whether hepatic P450 activity suppression leads to increased circulating and tissue levels of carcinogens and, consequently, increased lung DNA damage and tumor formation in mice chronically exposed to cigarette smoke; the ability to show increased tumorigenic responses in tumor bioassays would enable their use in testing new potential "reduced toxicity" tobacco products and lung tumor chemoprevention agents. The second model will test whether tumor formation depends on P450 enzyme bioactivation of tobacco carcinogens; results will provide further insight about whether lung tumors observed in smoke-exposed mouse models are due to direct tumor initiation by smoke (through genotoxic pathways that depend on P450-mediated bioactivation of procarcinogens), rather than spontaneous tumor promotion. These investigations may provide support for the mouse model’s predictive value regarding human lung cancer risk.

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