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PNNL-13501 - Pacific Northwest National Laboratory

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Mechanistic Modeling of Early Genotoxic Events for Chemicals and Radiation<br />

Study Control Number: PN00064/1471<br />

Robert D. Stewart<br />

Mechanistic models are needed to improve our understanding of, and ability to predict and mitigate, the human health<br />

effects of exposure to hazardous agents found at many DOE facilities, such as tritium, beryllium, and organic solvents.<br />

Our project will advance the state of the art in mechanistic modeling of early biophysical events involved in the<br />

development of cancer.<br />

Project Description<br />

This project has two main goals: 1) develop a scalable<br />

and extensible modeling framework to bridge the gap<br />

between dosimetry and early events involved in the<br />

pathogenesis of cancer, and 2) perform the initial work<br />

needed to demonstrate the feasibility of using detailed<br />

molecular and cellular models to predict dose-response<br />

effects at the organ and tissue levels.<br />

A calculational framework to integrate molecular and<br />

cellular dose-response models into three-dimensional<br />

tissue constructs has been developed. As a high-impact<br />

way to illustrate how such modeling tools might be used<br />

to study the effects of complicated workplace or<br />

environmental exposures, we used our modeling tools to<br />

examine ways that the temporal and spatial delivery of<br />

radiation to a tumor could be manipulated to improve<br />

treatment outcome.<br />

Introduction<br />

Genotoxic chemical agents and ionizing radiation create a<br />

multitude of different types of DNA damages. Regardless<br />

of the process or agent that initially creates the DNA<br />

damage, the same basic physiochemical and biochemical<br />

processes either correctly repair the damage or convert the<br />

damage into a lethal or nonlethal point mutation or<br />

chromosome aberration. Because of cellular adaptations<br />

in damage repair and effects associated with, for example,<br />

the mitotic cell cycle, the expected number of point<br />

mutations and chromosome aberrations created in a cell is<br />

a complicated and generally nonlinear function of a cell’s<br />

exposure history.<br />

Two fundamental questions that must be answered in<br />

order to quantitate the health risks associated with human<br />

exposure to man-made physical and chemical agents are:<br />

“How many and what kinds of abnormal genetic<br />

294 FY 2000 <strong>Laboratory</strong> Directed Research and Development Annual Report<br />

alterations are created in a cell per unit dose of the<br />

agent?” and “How many of these randomly created<br />

genetic alterations does it take to transform a normal cell<br />

into one capable of producing a cancer?” The central aim<br />

of this project is to help answer these questions by<br />

advancing the state of the art in mechanistic modeling of<br />

DNA damage formation, repair, and misrepair processes.<br />

Moreover, this project aims to better link these<br />

fundamental processes to cell transformation and killing<br />

effects in vitro and in vivo.<br />

Results and Accomplishments<br />

A major focus of the project this year has been to develop<br />

the scalable and extensible software tools needed to<br />

integrate molecular and cellular models into more realistic<br />

three-dimensional organ, tissue, and tumor models. We<br />

have also further refined our model calibration and testing<br />

strategy.<br />

From Molecules to Man: Three-Dimensional Tissue<br />

Modeling<br />

In our modeling approach, an organ, tissue, or tumor is<br />

first subdivided into a large number of smaller,<br />

rectangular tissue regions. Then, we use a molecular and<br />

cellular dose-response model to simulate the life history<br />

of a large number of cells (and their progeny) in each<br />

tissue region. Biophysical response quantities of interest<br />

such as the yield of lethal and nonlethal genetic<br />

alterations in critical subpopulations (stem cells) or in<br />

certain regions of the tissue can then be computed from<br />

the life histories of the individual cells. The main<br />

advantage of this ab initio approach is that the degree of<br />

realism in the tissue model is ultimately only limited by<br />

our computing capabilities, our understanding of intra-<br />

and intercellular biophysical processes, and the<br />

availability of appropriate experimental datasets for use as<br />

model inputs and in model testing.

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