Literature DB >> 16386343

An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: I. Identification of carcinogens using surrogate endpoints.

Edwin J Matthews1, Naomi L Kruhlak, Michael C Cimino, R Daniel Benz, Joseph F Contrera.   

Abstract

A retrospective analysis of standard genetic toxicity (genetox) tests, reproductive and developmental toxicity (reprotox) studies, and rodent carcinogenicity bioassays (rcbioassay) was performed to identify the genetox and reprotox endpoints whose results best correlate with rcbioassay observations. A database of 7205 chemicals with genetox (n = 4961), reprotox (n = 2173), and rcbioassay (n = 1442) toxicity data was constructed; 1112 of the chemicals have both genetox and rcbioassay data and 721 chemicals have both reprotox and rcbioassay data. This study differed from previous studies by using conservative weight of evidence criteria to classify chemical carcinogens, data from 63 genetox and reprotox toxicological endpoints, and a new statistical parameter of correlation indicator (CI, the average of specificity and positive predictivity) to identify good surrogate endpoints for predicting carcinogenicity. Among 63 endpoints, results revealed that carcinogenicity was well correlated with certain tests for gene mutation (n = 8), in vivo clastogenicity (n = 2), unscheduled DNA synthesis assay (n = 1), and reprotox (n = 3). The current FDA regulatory battery of four genetox tests used to predict carcinogenicity includes two tests with good correlation (gene mutation in Salmonella and in vivo micronucleus) and two tests with poor correlation (mouse lymphoma gene mutation and in vitro chromosome aberrations) by our criteria.

Entities:  

Mesh:

Substances:

Year:  2005        PMID: 16386343     DOI: 10.1016/j.yrtph.2005.11.003

Source DB:  PubMed          Journal:  Regul Toxicol Pharmacol        ISSN: 0273-2300            Impact factor:   3.271


  15 in total

1.  Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors.

Authors:  Albert R Cunningham; C Alex Carrasquer; Shahid Qamar; Jon M Maguire; Suzanne L Cunningham; John O Trent
Journal:  Carcinogenesis       Date:  2012-06-07       Impact factor: 4.944

2.  Navigating through the minefield of read-across tools: A review of in silico tools for grouping.

Authors:  Patlewicz Grace; Helman George; Pradeep Prachi; Shah Imran
Journal:  Comput Toxicol       Date:  2017-08

Review 3.  Genetic toxicology in the 21st century: reflections and future directions.

Authors:  Brinda Mahadevan; Ronald D Snyder; Michael D Waters; R Daniel Benz; Raymond A Kemper; Raymond R Tice; Ann M Richard
Journal:  Environ Mol Mutagen       Date:  2011-04-28       Impact factor: 3.216

4.  Using VEGAHUB Within a Weight-of-Evidence Strategy.

Authors:  Serena Manganelli; Alessio Gamba; Erika Colombo; Emilio Benfenati
Journal:  Methods Mol Biol       Date:  2022

5.  In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs.

Authors:  Raymond R Tice; Arianna Bassan; Alexander Amberg; Lennart T Anger; Marc A Beal; Phillip Bellion; Romualdo Benigni; Jeffrey Birmingham; Alessandro Brigo; Frank Bringezu; Lidia Ceriani; Ian Crooks; Kevin Cross; Rosalie Elespuru; David M Faulkner; Marie C Fortin; Paul Fowler; Markus Frericks; Helga H J Gerets; Gloria D Jahnke; David R Jones; Naomi L Kruhlak; Elena Lo Piparo; Juan Lopez-Belmonte; Amarjit Luniwal; Alice Luu; Federica Madia; Serena Manganelli; Balasubramanian Manickam; Jordi Mestres; Amy L Mihalchik-Burhans; Louise Neilson; Arun Pandiri; Manuela Pavan; Cynthia V Rider; John P Rooney; Alejandra Trejo-Martin; Karen H Watanabe-Sailor; Angela T White; David Woolley; Glenn J Myatt
Journal:  Comput Toxicol       Date:  2021-09-23

6.  A Systematic Review of Carcinogenic Outcomes and Potential Mechanisms from Exposure to 2,4-D and MCPA in the Environment.

Authors:  Katherine von Stackelberg
Journal:  J Toxicol       Date:  2013-02-26

7.  Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses.

Authors:  Curran Landry; Marlene T Kim; Naomi L Kruhlak; Kevin P Cross; Roustem Saiakhov; Suman Chakravarti; Lidiya Stavitskaya
Journal:  Regul Toxicol Pharmacol       Date:  2019-10-03       Impact factor: 3.271

8.  An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential.

Authors:  Patrick McCarren; Clayton Springer; Lewis Whitehead
Journal:  J Cheminform       Date:  2011-11-22       Impact factor: 5.514

Review 9.  The biomechanisms of metal and metal-oxide nanoparticles' interactions with cells.

Authors:  Sondra S Teske; Corrella S Detweiler
Journal:  Int J Environ Res Public Health       Date:  2015-01-22       Impact factor: 3.390

10.  Comet assay in reconstructed 3D human epidermal skin models--investigation of intra- and inter-laboratory reproducibility with coded chemicals.

Authors:  Astrid A Reus; Kerstin Reisinger; Thomas R Downs; Gregory J Carr; Andreas Zeller; Raffaella Corvi; Cyrille A M Krul; Stefan Pfuhler
Journal:  Mutagenesis       Date:  2013-11       Impact factor: 3.000

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.