Read-across of 90-day rat oral repeated-dose toxicity: A case study for selected 2-alkyl-1-alkanols-Reference-Cited by-同舟云学术

Read-across of 90-day rat oral repeated-dose toxicity: A case study for selected 2-alkyl-1-alkanols

Published:2017-05 Issue: Volume:2 Page:28-38
ISSN:2468-1113
Container-title:Computational Toxicology
language:en
Short-container-title:Computational Toxicology

Author:

Schultz Terry W.,Przybylak Katarzyna R.,Richarz Andrea-Nicole,Mellor Claire L.,Bradbury Steven P.,Cronin Mark T.D.

Funder

Seventh Framework Programme

FP7

Publisher

Elsevier BV

Subject

Health, Toxicology and Mutagenesis,Computer Science Applications,Toxicology

Reference73 articles.

1. Read-across of 90-day rat oral repeated-dose toxicity: a case study for selected β-olefinic alcohols;Przybylak;Comput. Toxicol.,2017

2. Read-across of 90-day rat oral repeated-dose toxicity: a case study for selected n-alkanols;Schultz;Comput. Toxicol.,2017

3. Regulation (EC) No 1223/2009 of the European Parliament and of the Council of 30 November 2009 on cosmetic products, replacing Directive 76/768/EC;Off. J. Eur. Union,2009

4. Chemical Toxicity Prediction: Category Formation and Read-Across Applications,2013

5. Read-across for hazard assessment: the ugly duckling in growing up;Teubner;ATLA,2015

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