Literature DB >> 12896862

Use of QSARs in international decision-making frameworks to predict health effects of chemical substances.

Mark T D Cronin1, Joanna S Jaworska, John D Walker, Michael H I Comber, Christopher D Watts, Andrew P Worth.   

Abstract

This article is a review of the use of quantitative (and qualitative) structure-activity relationships (QSARs and SARs) by regulatory agencies and authorities to predict acute toxicity, mutagenicity, carcinogenicity, and other health effects. A number of SAR and QSAR applications, by regulatory agencies and authorities, are reviewed. These include the use of simple QSAR analyses, as well as the use of multivariate QSARs, and a number of different expert system approaches.

Entities:  

Mesh：

Substances：

Year:  2003        PMID： 12896862      PMCID： PMC1241622          DOI： 10.1289/ehp.5760

Source DB:  PubMed          Journal:  Environ Health Perspect        ISSN： 0091-6765            Impact factor:   9.031


  27 in total

1.  Evaluation of the TOPKAT system for predicting the carcinogenicity of chemicals.

Authors:  M J Prival
Journal:  Environ Mol Mutagen       Date:  2001       Impact factor: 3.216

Review 2.  Quantitative structure-permeability relationships (QSPRs) for percutaneous absorption.

Authors:  G P Moss; J C Dearden; H Patel; M T D Cronin
Journal:  Toxicol In Vitro       Date:  2002-06       Impact factor: 3.500

3.  A random walk model of skin permeation.

Authors:  H Frederick Frasch
Journal:  Risk Anal       Date:  2002-04       Impact factor: 4.000

4.  Development of a decision support system for the introduction of alternative methods into local irritancy/corrosivity testing strategies. Creation of fundamental rules for a decision support system.

Authors:  I Gerner; S Zinke; G Graetschel; E Schlede
Journal:  Altern Lab Anim       Date:  2000 Sep-Oct       Impact factor: 1.303

5.  AI and SAR approaches for predicting chemical carcinogenicity: survey and status report.

Authors:  A M Richardt; R Benigni
Journal:  SAR QSAR Environ Res       Date:  2002-03       Impact factor: 3.000

6.  Prediction of eye irritation from organic chemicals using membrane-interaction QSAR analysis.

Authors:  A Kulkarni; A J Hopfinger; R Osborne; L H Bruner; E D Thompson
Journal:  Toxicol Sci       Date:  2001-02       Impact factor: 4.849

7.  Prediction of rodent carcinogenicity of aromatic amines: a quantitative structure-activity relationships model.

Authors:  R Franke; A Gruska; A Giuliani; R Benigni
Journal:  Carcinogenesis       Date:  2001-09       Impact factor: 4.944

8.  Applications of computational toxicology methods at the Agency for Toxic Substances and Disease Registry.

Authors:  Hisham A el-Masri; Moiz M Mumtaz; Gangadhar Choudhary; William Cibulas; Christopher T De Rosa
Journal:  Int J Hyg Environ Health       Date:  2002-03       Impact factor: 5.840

9.  Extended quantitative structure-activity relationships for 80 aromatic and heterocyclic amines: structural, electronic, and hydropathic factors affecting mutagenic potency.

Authors:  F T Hatch; M G Knize; M E Colvin
Journal:  Environ Mol Mutagen       Date:  2001       Impact factor: 3.216

10.  A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software.

Authors:  E J Matthews; J F Contrera
Journal:  Regul Toxicol Pharmacol       Date:  1998-12       Impact factor: 3.271
View more
  24 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.  A multiparametric organ toxicity predictor for drug discovery.

Authors:  Chirag N Patel; Sivakumar Prasanth Kumar; Rakesh M Rawal; Daxesh P Patel; Frank J Gonzalez; Himanshu A Pandya
Journal:  Toxicol Mech Methods       Date:  2019-10-29       Impact factor: 2.987

3.  Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses.

Authors:  Natalja Fjodorova; Marjan Vračko; Marjan Tušar; Aneta Jezierska; Marjana Novič; Ralph Kühne; Gerrit Schüürmann
Journal:  Mol Divers       Date:  2009-08-15       Impact factor: 2.943

Review 4.  Big-data and machine learning to revamp computational toxicology and its use in risk assessment.

Authors:  Thomas Luechtefeld; Craig Rowlands; Thomas Hartung
Journal:  Toxicol Res (Camb)       Date:  2018-05-01       Impact factor: 3.524

5.  Identifying and designing chemicals with minimal acute aquatic toxicity.

Authors:  Jakub Kostal; Adelina Voutchkova-Kostal; Paul T Anastas; Julie Beth Zimmerman
Journal:  Proc Natl Acad Sci U S A       Date:  2014-03-17       Impact factor: 11.205

6.  Rapid experimental measurements of physicochemical properties to inform models and testing.

Authors:  Chantel I Nicolas; Kamel Mansouri; Katherine A Phillips; Christopher M Grulke; Ann M Richard; Antony J Williams; James Rabinowitz; Kristin K Isaacs; Alice Yau; John F Wambaugh
Journal:  Sci Total Environ       Date:  2018-05-02       Impact factor: 7.963

7.  Mammary carcinogen-protein binding potentials: novel and biologically relevant structure-activity relationship model descriptors.

Authors:  A R Cunningham; S Qamar; C A Carrasquer; P A Holt; J M Maguire; S L Cunningham; J O Trent
Journal:  SAR QSAR Environ Res       Date:  2010-07       Impact factor: 3.000

8.  Extracting Relevant Information from FDA Drug Files to Create a Structurally Diverse Drug Database Using KnowItAll®

Authors:  Malcolm J D'Souza; Fumie Koyoshi
Journal:  Pharm Rev       Date:  2009-05-08

Review 9.  Physiologically based pharmacokinetic models: integration of in silico approaches with micro cell culture analogues.

Authors:  A Chen; M L Yarmush; T Maguire
Journal:  Curr Drug Metab       Date:  2012-07       Impact factor: 3.731

Review 10.  The interplay between QSAR/QSPR studies and partial order ranking and formal concept analyses.

Authors:  Lars Carlsen
Journal:  Int J Mol Sci       Date:  2009-04-17       Impact factor: 6.208
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.