Due to limited human exposure data, risk classification and the consequent regulation of exposure to potential carcinogens has conventionally relied mainly upon animal tests. However, several investigations have revealed animal carcinogenicity data to be lacking in human predictivity. To investigate the reasons for this, we surveyed 160 chemicals possessing animal but not human exposure data within the US Environmental Protection Agency chemicals database, but which had received human carcinogenicity assessments by 1 January 2004. We discovered the use of a wide variety of species, with rodents predominating, and of a wide variety of routes of administration, and that there were effects on a particularly wide variety of organ systems. The likely causes of the poor human predictivity of rodent carcinogenicity bioassays include: 1) the profound discordance of bioassay results between rodent species, strains and genders, and further, between rodents and human beings; 2) the variable, yet substantial, stresses caused by handling and restraint, and the stressful routes of administration common to carcinogenicity bioassays, and their effects on hormonal regulation, immune status and predisposition to carcinogenesis; 3) differences in rates of absorption and transport mechanisms between test routes of administration and other important human routes of exposure; 4) the considerable variability of organ systems in response to carcinogenic insults, both between and within species; and 5) the predisposition of chronic high dose bioassays toward false positive results, due to the overwhelming of physiological defences, and the unnatural elevation of cell division rates during ad libitum feeding studies. Such factors render profoundly difficult any attempts to accurately extrapolate human carcinogenic hazards from animal data.
Animal Carcinogenicity Studies: 2. Obstacles to Extrapolation of Data to HumansEXPERIMENTATION
Citation InformationKnight, A., Bailey, J., & Balcombe, J. (2006). Animal carcinogenicity studies: 2. Obstacles to Extrapolation of Data to Humans. ATLA-NOTTINGHAM-, 34(1), 29.