Identifying /Evaluating Sources Of Variability In Rodent

Environmental Health Sciences

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Abstract

The goal of this project is to identify and evaluate factors that may influence variability in response in laboratory studies. For example, we investigated the impact of the new NTP-2000 diet on body weight, survival and tumor occurrence in F344 rats relative to the older NIH-07 diet. We found that survival was significantly (p<0.05) higher in groups fed the NTP-2000 diet compared to the corresponding groups fed the NIH-07 diet, irrespective of sex or housing conditions. Use of the NTP-2000 diet was also associated with a decreased incidence of pituitary gland tumors in both sexes and decreased incidences of adrenal pheochromocytoma and preputial gland tumors in males. The incidence and severity of nephropathy was also reduced in animals receiving the NTP-2000 diet, especially males. The decreased nephropathy severity and the decreased incidence of pituitary gland tumors are likely the major factors contributing to the improved survival of rats receiving the NTP-2000 diet compared to those given the NIH-07 diet. A secondary objective of this study was to compare tumor incidences in feeding study controls (which are group housed in hanging-drawer-type polycarbonate cages and receive the diet in mash form) and inhalation study controls (which are individually housed in stainless steel wire mesh cages and are fed pelleted feed). These two control groups differed significantly (p<0.01) in the incidence of a variety of tumors, suggesting that differences in animal care and housing protocols may impact tumor occurrence in F344 rats. We also evaluated a log linear extrapolation model that has been described in a series of articles as a procedure that can demonstrate a threshold and resolve the uncertainies associated with low dose cancer risk extrapolation. We pointed out the significant shortcomings of this model, namely that it (i) is a model that essentially forces a threshold; (ii) assumes the control response is known without error; (iii) ignores the uncertainty of the extrapolated threshold estimate; (iv) subjectively discards datapoints from the extrapolation, including potentially important tumor responses at low doses; (v) purportedly can unequivocally demonstrate thresholds even when all dosed groups show highly significant carcinogenic effects; (vi) purportedly can unequivocally demonstrate thresholds with as few as two doses; (vii) fails at some unknown point in the low dose region, possibly well above the purported threshold dose; and (viii) demonstrably can significantly under-estimate low dose risk. For these reasons, it would be a serious mistake for the scientific community to adopt this log linear extrapolation model for chemical carcinogenesis risk assessment.