Predictive Models for Carcinogenicity and Mutagenicity: Frameworks, State-of-the-Art, and Perspectives

被引:80
作者
Benfenati, E. [1 ]
Benigni, R. [2 ]
DeMarini, D. M. [3 ]
Helma, C. [4 ]
Kirkland, D. [5 ]
Martin, T. M. [6 ]
Mazzatorta, P. [7 ]
Ouedraogo-Arras, G. [8 ]
Richard, A. M. [9 ]
Schilter, B. [7 ]
Schoonen, W. G. E. J. [10 ]
Snyder, R. D. [11 ]
Yang, C. [12 ]
机构
[1] Ist Ric Farmacol Mario Negri, Lab Environm & Chem Toxicol, I-20156 Milan, Italy
[2] Ist Super Sanita, Environm & Hlth Dept, I-00161 Rome, Italy
[3] US EPA, Div Environm Carcinogenesis, Res Triangle Pk, NC 27711 USA
[4] Silico Toxicol, Basel, Switzerland
[5] Covance Labs Ltd, Harrogate, England
[6] US EPA, Sustainable Technol Div, Natl Risk Management Res Lab, Cincinnati, OH 45268 USA
[7] Nestle Res Ctr, Qual & Safety Dept, CH-1000 Lausanne, Switzerland
[8] LOreal, Safety Res Dept, Aulnay Sous Bois, France
[9] US EPA, Natl Ctr Computat Toxicol, Res Triangle Pk, NC 27711 USA
[10] Schering Plough Res Inst, Oss, Netherlands
[11] Schering Plough Res Inst, Summit, NJ USA
[12] US FDA, Ctr Food Safety & Appl Nutr, College Pk, MD USA
关键词
Carcinogenicity; mutagenicity; QSAR; in silico; predictive methods; FOLLOW-UP; SACCHAROMYCES-CEREVISIAE; TOXICITY DATABASES; GENOTOXICITY ASSAY; CANCER MORTALITY; AROMATIC-AMINES; QSAR MODELS; TOXICOLOGY; CHEMICALS; REPAIR;
D O I
10.1080/10590500902885593
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include Vitotox, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-throughput assays combined with innovative data-mining and in silico methods. Various initiatives in this regard have begun, including CAESAR, OSIRIS, CHEMOMENTUM, CHEMPREDICT, OpenTox, EPAA, and ToxCast. In silico methods can be used for priority setting, mechanistic studies, and to estimate potency. Ultimately, such efforts should lead to improvements in application of in silico methods for predicting carcinogenicity to assist industry and regulators and to enhance protection of public health.
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页码:57 / 90
页数:34
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