DNA-ADDUCTS - BIOLOGICAL MARKERS OF EXPOSURE AND POTENTIAL APPLICATIONS TO RISK ASSESSMENT

Authors
Citation
Dk. La et Ja. Swenberg, DNA-ADDUCTS - BIOLOGICAL MARKERS OF EXPOSURE AND POTENTIAL APPLICATIONS TO RISK ASSESSMENT, Mutation research. Reviews in genetic toxicology, 365(1-3), 1996, pp. 129-146
Citations number
122
Categorie Soggetti
Genetics & Heredity",Toxicology
ISSN journal
01651110
Volume
365
Issue
1-3
Year of publication
1996
Pages
129 - 146
Database
ISI
SICI code
0165-1110(1996)365:1-3<129:D-BMOE>2.0.ZU;2-J
Abstract
DNA adducts have been investigated extensively during the past decade, This research has been advanced, in part, by the development of ultra sensitive analytical methods, such as P-32-postlabeling and mass spect rometry, that enable detection of DNA adducts at concentrations as low as one adduct per 10(9) to 10(10) normal nucleotides. Studies of muta tions in activated oncogenes such as ras, inactivated tumor suppressor genes such as p53, and surrogate genes such as hprt provide linkage b etween DNA adducts and carcinogenesis. The measurement of DNA adducts, or molecular dosimetry, has important applications for cancer risk as sessment, Cancer risk assessment currently involves estimating the pro bable effects of carcinogens in humans based on results of animal bioa ssays. Estimates of risk are then derived from mathematical models tha t fit data of tumor incidence at the high animal exposures and extrapo late to probable human exposures that may be orders of magnitude lower , Molecular dosimetry could extend the observable range of mechanistic data several orders of magnitude lower than can be achieved in carcin ogenesis bioassays. This measurement also compensates automatically fo r individual and species differences in toxicokinetic factors, as well as any nonlinearities that affect the quantitative relationships betw een exposure and molecular dose. As a result, molecular dosimetry can provide a basis for conducting high- to low-dose, route-to-route, and interspecies extrapolations. The incorporation of such data into risk assessment promises to reduce uncertainties and produce more accurate estimates of risk compared to current methods.