Use of a State-Vector Model of Radiation Carcinogenesis to Integrate Information from in vitro, in vivo, Epidemiological and Physiological Studies

Crawford-Brown, Doug ; Serre, Marc

University of North Carolina at Chape Hill

关键词: Proliferation;    Cell Killing;    98 Nuclear Disarmament, Safeguards, And Physical Protection;    Ionizing Radiations;    In Vitro;   

DOI  :  10.2172/898312
RP-ID  :  DOEER63673-1
RP-ID  :  FG02-03ER63673
RP-ID  :  898312

美国|英语

来源: UNT Digital Library

【 摘 要 】

This project focused on extension of a generalized state-vector model developed by Crawford-Brown and Hofmann (1-4). The model incorporates phenomena such as DNA damage and repair, intercellular communication mechanisms, both spontaneous and radiation-induced cell death and cell division, to predict cellular transformation following exposure to ionizing radiation. Additionally, this model may be simulated over time periods that correspond to the temporal scale of biological mechanisms. The state-vector model has been shown to generally reproduce transformation frequency patterns for in vitro studies (2), but still significantly underpredicted in vivo cancer incidence data at the higher doses for high-LET radiations when biologically realistic rate constants for cell killing are included (1). Mebust et al. (1) claimed that one reason for this underprediction might be that the model's ability to fit the in vitro data is due in part to compensating errors that only reveal themselves when the more complex in vivo and epidemiological data are considered. This implies that the original in vitro model may be based on incomplete assumptions regarding the underlying biological mechanisms. The present research considered this explanation for the case of low LET radiation. An extension of the in vitro state-vector model was tested that includes additional biological mechanisms in order to improve model predictions with respect to dose-response data on in vitro oncogenic transformation of C3H10T1/2 mouse fibroblast cells exposed to acute doses of X-radiation (5). These data display a plateau of transformation frequency per surviving cell in the X-ray dose range of 0.1 to 1 Gy, with an increase in transformation frequency at higher acute doses. To reproduce these trends in the data, additional biological processes were formulated mathematically and incorporated into the existing model as parameters whose values could be adjusted and tested by an optimization method (genetic algorithm). The model extension presented here includes many of the same biological phenomena as the original state-vector model, though some mathematical representations of these mechanisms have been adjusted and new pathways in which these events occur have been included. In addition to these original processes, this state-vector model extension incorporates: (1) pre-irradiation background transformation, (2) the bystander effect on cell killing and (3) an explicit representation for compensatory proliferation. These experimentally recognized biological mechanisms are established as important processes within carcinogenesis and their ability to improve model predictions should be explored.