It's difficult, but important, to make negative predictions-Reference-Cited by-同舟云学术

It's difficult, but important, to make negative predictions

Published:2016-04 Issue: Volume:76 Page:79-86
ISSN:0273-2300
Container-title:Regulatory Toxicology and Pharmacology
language:en
Short-container-title:Regulatory Toxicology and Pharmacology

Author:

Williams Richard V.,Amberg Alexander,Brigo Alessandro,Coquin Laurence,Giddings Amanda,Glowienke Susanne,Greene Nigel,Jolly Robert,Kemper Ray,O'Leary-Steele Catherine,Parenty Alexis,Spirkl Hans-Peter,Stalford Susanne A.,Weiner Sandy K.,Wichard Joerg

Publisher

Elsevier BV

Subject

Toxicology,General Medicine

Reference27 articles.

1. Inter-laboratory evaluation of the bioluminescent Salmonella reverse mutation assay using 10 model chemicals;Ackerman;Mutagenesis,2009

2. Defining molecular initiating events in the adverse outcome pathway framework for risk assessment;Allen;Chem. Res. Toxicol.,2014

3. Advancing the 3Rs in regulatory toxicology – carcinogenicity testing: scope for harmonisation and advancing the 3Rs in regulated sectors of the European Union;Annys;Regul. Toxicol. Pharmacol.,2014

4. Establishing best practise in the application of expert review of mutagenicity under ICH M7;Barber;Regul. Toxicol. Pharmacol.,2015

5. Pharmaceutical toxicology: designing studies to reduce animal use, while maximizing human translation;Chapman;Regul. Toxicol. Pharmacol.,2013

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