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Toward Good Read-Across Practice (GRAP) guidance

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Nicholas Ball 1, Mark T. D. Cronin 2, Jie Shen 3, Karen Blackburn 4, Ewan D. Booth 5, Mounir Bouhifd 6, Elizabeth Donley 7, Laura Egnash 7, Charles Hastings 8, Daland R. Juberg 1, Andre Kleensang 6, Nicole Kleinstreuer 9, E. Dinant Kroese 10, Adam C. Lee 11, Thomas Luechtefeld 6, Alexandra Maertens 6, Sue Marty 1, Jorge M. Naciff 4, Jessica Palmer 7, David Pamies 6, Mike Penman 12, Andrea-Nicole Richarz 2, Daniel P. Russo 13, Sharon B. Stuard 4, Grace Patlewicz 14, Bennard van Ravenzwaay 10, Shengde Wu 4, Hao Zhu 13 and Thomas Hartung 6,15
1 The Dow Chemical Company, Midland, MI, USA and Dow AgroSciences, Indianapolis, IN, USA
2 School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
3 Research Institute for Fragrance Materials, Inc., Woodcliff Lake, NJ, USA
4 The Procter and Gamble Co., Cincinnati, OH, USA
5 Syngenta Ltd, Jealott’s Hill International Research Centre, Bracknell, Berkshire, UK
6 Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
7 Stemina Biomarker Discovery Inc., Madison, WI, USA
8 BASF SE, Ludwigshafen am Rhein, Germany and Research Triangle Park, NC, USA
9 National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
10 Risk Analysis for Products in Development, TNO Zeist, Zeist, The Netherlands
11 DuPont Haskell Global Centers for Health and Environmental Sciences, Newark, NJ, USA
12 Penman Consulting, Brussels, Belgium
13 Department of Chemistry and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
14 US EPA/ORD, National Center for Computational Toxicology, Research Triangle Park, NC, USA
15 University of Konstanz, CAAT-Europe, Konstanz, Germany

Summary
 

Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislation such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and indi­viduals. In order to facilitate the effective use of read-across, this document presents the state of the art, summarizes insights learned from reviewing ECHA published decisions regarding the relative successes/pitfalls surrounding read-across under REACH, and compiles the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHA’s published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data, and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015.

 

Keywords: computational toxicology, chemical similarity, read-across, hazard assessment, uncertainty

 

 

ALTEX 33(2), 149-166

doi: 10.14573/altex.1601251


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