Literature DB >> 18977375

Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals I. Alternative toxicity measures as an estimator of carcinogenic potency.

Raghuraman Venkatapathy1, Ching Yi Wang, Robert Mark Bruce, Chandrika Moudgal.   

Abstract

Determining the carcinogenicity and carcinogenic potency of new chemicals is both a labor-intensive and time-consuming process. In order to expedite the screening process, there is a need to identify alternative toxicity measures that may be used as surrogates for carcinogenic potency. Alternative toxicity measures for carcinogenic potency currently being used in the literature include lethal dose (dose that kills 50% of a study population [LD(50)]), lowest-observed-adverse-effect-level (LOAEL) and maximum tolerated dose (MTD). The purpose of this study was to investigate the correlation between tumor dose (TD(50)) and three alternative toxicity measures as an estimator of carcinogenic potency. A second aim of this study was to develop a Classification and Regression Tree (CART) between TD(50) and estimated/experimental predictor variables to predict the carcinogenic potency of new chemicals. Rat TD(50)s of 590 structurally diverse chemicals were obtained from the Cancer Potency Database, and the three alternative toxicity measures considered in this study were estimated using TOPKAT, a toxicity estimation software. Though poor correlations were obtained between carcinogenic potency and the three alternative toxicity (both experimental and TOPKAT) measures for the CPDB chemicals, a CART developed using experimental data with no missing values as predictor variables provided reasonable estimates of TD(50) for nine chemicals that were part of an external validation set. However, if experimental values for the three alternative measures, mutagenicity and logP are not available in the literature, then either the CART developed using missing experimental values or estimated values may be used for making a prediction.

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Year:  2008        PMID: 18977375     DOI: 10.1016/j.taap.2008.09.028

Source DB:  PubMed          Journal:  Toxicol Appl Pharmacol        ISSN: 0041-008X            Impact factor:   4.219


  7 in total

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Authors:  Kazutoshi Tanabe; Bono Lučić; Dragan Amić; Takio Kurita; Mikio Kaihara; Natsuo Onodera; Takahiro Suzuki
Journal:  Mol Divers       Date:  2010-02-26       Impact factor: 2.943

Review 2.  Joint toxicity of alkoxyethanol mixtures: contribution of in silico applications.

Authors:  H R Pohl; P Ruiz; F Scinicariello; M M Mumtaz
Journal:  Regul Toxicol Pharmacol       Date:  2012-06-28       Impact factor: 3.271

3.  Development, validation, and use of quantitative structure-activity relationship models of 5-hydroxytryptamine (2B) receptor ligands to identify novel receptor binders and putative valvulopathic compounds among common drugs.

Authors:  Rima Hajjo; Christopher M Grulke; Alexander Golbraikh; Vincent Setola; Xi-Ping Huang; Bryan L Roth; Alexander Tropsha
Journal:  J Med Chem       Date:  2010-11-11       Impact factor: 7.446

4.  Perspectives on Non-Animal Alternatives for Assessing Sensitization Potential in Allergic Contact Dermatitis.

Authors:  Nripen S Sharma; Rohit Jindal; Bhaskar Mitra; Serom Lee; Lulu Li; Tim J Maguire; Rene Schloss; Martin L Yarmush
Journal:  Cell Mol Bioeng       Date:  2012-03       Impact factor: 2.321

5.  An in silico approach for evaluating a fraction-based, risk assessment method for total petroleum hydrocarbon mixtures.

Authors:  Nina Ching Y Wang; Glenn E Rice; Linda K Teuschler; Joan Colman; Raymond S H Yang
Journal:  J Toxicol       Date:  2012-02-08

6.  Identification, isolation, structural characterization, in silico toxicity prediction and in vitro cytotoxicity assay of simeprevir acidic and oxidative degradation products.

Authors:  Rasha M Ahmed; Marwa A A Fayed; Mohammed F El-Behairy; Inas A Abdallah
Journal:  RSC Adv       Date:  2020-11-24       Impact factor: 4.036

7.  Prediction of acute mammalian toxicity using QSAR methods: a case study of sulfur mustard and its breakdown products.

Authors:  Patricia Ruiz; Gino Begluitti; Terry Tincher; John Wheeler; Moiz Mumtaz
Journal:  Molecules       Date:  2012-07-27       Impact factor: 4.411

  7 in total

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