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Development of a Novel Quantitative Adverse Outcome Pathway Predictive Model for Lung Cancer
Citation:
Hill, T. AND R. Conolly. Development of a Novel Quantitative Adverse Outcome Pathway Predictive Model for Lung Cancer. SETAC Europe 28th Annual Meeting, Rome, Rome, ITALY, May 13 - 17, 2018.
Impact/Purpose:
Dr. Hill intends to present data and participate in the SETAC Europe 28th Annual Meeting “Responsible and Innovative Research for Environmental Quality” from May 13-17 in Rome, Italy. One of the major organizing topics is the advancement of AOP/qAOP in chemical decision making and regulatory activities. This topic is of great interest to the US EPA Chemical Safety for Sustainability (CSS) National Research Program 17.01 AOPDD and the focus of Dr. Hill’s current research at EPA under Dr. Rory Conolly. Dr. Hill will be presenting “Development of a Novel Quantitative Adverse Outcome Pathway Predictive Model for Lung Cancer” as part of his projects in support of CSS 17.01 AOPDD Tasks E/G and the QAPP “Development of Quantitative Outcome Models for Carcinogenicity.” The SETAC Europe 2018 Annual Meeting will provide an international venue for scientists from academia, business/industry and government to present, debate and disseminate the most recent scientific knowledge, developments and applications for reducing and regulating the use of chemicals, remediating soil, air and water and proposing more sustainable chemicals for responsible and innovative research. This opportunity to present these findings and network within this audience of peers will enhance Dr. Hill’s ability to conduct his assigned projects, and provide professional development and career development commensurate with the goals and objectives as outlined in his ORISE project description. In addition to dissemination of scientific findings at the international level, any networking and data sharing connections initiated by Dr. Hill will enhance the future collaborative potential and research opportunities for the AOPDD project, the CSS program, and other scientific or regulatory activities within the US EPA.
Description:
Traditional methods for carcinogenicity testing are resource-intensive, retrospective, and time consuming. An increasing testing burden has generated interest in the adverse outcome pathway (AOP) concept as a tool to evaluate chemical safety in a more efficient, rapid and effective manner that better directs resource utilization. A central premise of the AOP concept is that pathway progression from the molecular initiating event (MIE) implies a definable “response-response” (R-R) relationship exists between each key event (KE) that drives the pathway towards the adverse outcome. Computational description of these R-R relationships in a quantitative AOP (qAOP) enables dose-response consideration of probabilities and uncertainty, as well as flagging of special at-risk populations or sentinel species. The qAOP also provides a platform to utilize early genomic and in vitro data streams for rapid, less resource-intensive hazard prediction, as well as the dose response and exposure duration that informs the level of risk. This poster describes a novel AOP/qAOP for lung cancer in the mouse from the MIE of CYP2F2-specific formation of reactive metabolites, advancing through KE for protein/nucleic acid adducts, diminished CC10 capacity and hyperplasia of CC10 deficient Club cells, and culminating in the adverse outcome of mixed-cell tumor formation in the airway. The AOP is independent of route of exposure and grounded in overlapping mechanistic events for naphthalene, styrene, ethyl benzene, isoniazid and fluensulfone in the mouse. The qAOP modeling is supported by defined mechanistic relationships and quantitative data (PB-PK, dose-response and time-course) from archival data in peer-reviewed literature. Findings will include evaluation of data supporting the cancer qAOP, suitability for characterization of R-R relationships, and identification of data gaps or additional research as required. This approach supports international efforts on use of quantitative effect thresholds for adversity predictions and incorporation of novel data streams into the cancer risk assessment process. This abstract does not necessarily represent the views or policies of the U.S. EPA.