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New directions for predicting carcinogenesis
Author(s) -
Schwetz Bernard,
Gaylor David
Publication year - 1997
Publication title -
molecular carcinogenesis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.254
H-Index - 97
eISSN - 1098-2744
pISSN - 0899-1987
DOI - 10.1002/(sici)1098-2744(199711)20:3<275::aid-mc3>3.0.co;2-k
Subject(s) - biology , carcinogenesis , carcinogen , risk assessment , selection (genetic algorithm) , computational biology , toxicology , bioinformatics , computer science , cancer , genetics , machine learning , computer security
Carcinogenicity testing today normally includes conducting 2‐yr studies of rats and mice of both sexes and following widely accepted procedures for husbandry, selection of dose levels, pathology and toxicity observations, and statistical interpretation of tumor data. These studies are usually preceded by tests for genetic toxicity and subchronic toxicity studies to select dose levels for the 2‐yr studies. While these data are used for quantitative risk assessment, the mechanistic basis for effects is usually unknown, and such series of studies are very expensive and require five or more years to conduct. Alternate approaches are being developed that would provide more mechanistic information and perhaps would permit decisions to be made about carcinogenic potential without the need to conduct 2‐yr studies of rats and mice of both sexes. Decisions could be based on a profile of data rather than the result of one test. Regulatory acceptance of new approaches for carcinogenicity testing is critical to future progress in the field of carcinogenesis. Mol. Carcinog. 20:275–279, 1997. © 1997 Wiley‐Liss, Inc. This article is a US Government work and, as such, is in the public domain in the United States of America.