Alpha/beta ratio: A dose range dependence study.
OBJECTIVE
To investigate the dependence of the alpha/beta ratio determined from in vitro survival curves on the dose ranges.


METHODS
Detailed clonogenic cell survival experiments were used to determine the least squares estimators for the linear quadratic model for different dose ranges. The cell lines used were CHO AA8, a Chinese hamster fibroblast cell line; U-373 MG, a human glioblastoma cell line; and CP3 and DU-145, two human prostate carcinoma cell lines. The alpha, beta, and alpha/beta ratio behaviors, combined with a goodness-of-fit analysis and Monte Carlo simulation of the experiments, were assessed within different dose regions.


RESULTS
Including data from the low-dose region has a significant influence on the determination of the alpha, beta, and alpha/beta ratio from in vitro survival curve data. In this region, the values are poorly determined and have significant variability. The mid-dose region is characterized by more precise and stable values and is in agreement with the linear quadratic model. The high-dose region shows relatively small statistical error in the fitted parameters but the goodness-of-fit and Monte Carlo analyses showed poor quality fits.


CONCLUSIONS
The dependence of the fitted alpha and beta on the dose range has an impact on the alpha/beta ratio determined from the survival data. The low-dose region had a significant influence that could be a result of a strong linear, rather than quadratic, component, hypersensitivity, and adaptive responses. This dose dependence should be interpreted as a caution against using inadequate in vitro cell survival data for alpha/beta ratio determination.
Garcia
Lourdes M
LM
Department of Physics, Carleton University, Ottawa, ON, Canada. logarcia@ottawahospital.on.ca
Wilkins
David E
DE
Raaphorst
Gijsbert P
GP
D000818:Animals; D002460:Cell Line; D002470:Cell Survival; D019583:Dose Fractionation, Radiation; D004307:Dose-Response Relationship, Radiation; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D009010:Monte Carlo Method