title: Supporting read-across using biological data.
abstract: Read-across, i.e. filling toxicological data gaps by relating to similar chemicals, for which test data are available, is usually done based on chemical similarity. Besides structure and physico-chemical properties, however, biological similarity based on biological data adds extra strength to this process. In the context of developing Good Read-Across Practice guidance, a number of case studies were evaluated to demonstrate the use of biological data to enrich read-across. In the simplest case, chemically similar substances also show similar test results in relevant in vitro assays. This is a well-established method for the read-across of e.g. genotoxicity assays. Larger datasets of biological and toxicological properties of hundreds and thousands of substances become increasingly available enabling big data approaches in read-across studies. Several case studies using various big data sources are described in this paper. An example is given for the US EPA's ToxCast dataset allowing read-across for high quality uterotrophic assays for estrogenic endocrine disruption. Similarly, an example for REACH registration data enhancing read-across for acute toxicity studies is given. A different approach is taken using omics data to establish biological similarity: Examples are given for stem cell models in vitro and short-term repeated dose studies in rats in vivo to support read-across and category formation. These preliminary biological data-driven read-across studies highlight the road to the new generation of read-across approaches that can be applied in chemical safety assessment.
authors: Department of Chemistry and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA.
authors: Hao
authors: H
authors: Zhu
authors: Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA.
authors: Mounir
authors: M
authors: Bouhifd
authors: Stemina Biomarker Discovery Inc., Madison, WI, USA.
authors: Elizabeth
authors: E
authors: Donley
authors: Stemina Biomarker Discovery Inc., Madison, WI, USA.
authors: Laura
authors: L
authors: Egnash
authors: National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA.
authors: Nicole
authors: N
authors: Kleinstreuer
authors: Risk Analysis for Products in Development, TNO Zeist, The Netherlands.
authors: E Dinant
authors: ED
authors: Kroese
authors: US FDA, NCTR, Little Rock, Arkansas, USA.
authors: Zhichao
authors: Z
authors: Liu
authors: Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA.
authors: Thomas
authors: T
authors: Luechtefeld
authors: Stemina Biomarker Discovery Inc., Madison, WI, USA.
authors: Jessica
authors: J
authors: Palmer
authors: Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA.
authors: David
authors: D
authors: Pamies
authors: Research Institute for Fragrance Materials, Inc. Woodcliff Lake, New Jersey, USA.
authors: Jie
authors: J
authors: Shen
authors: BASF Aktiengesellschaft, Experimental Toxicology and Ecology, Ludwigshafen, Germany.
authors: Volker
authors: V
authors: Strauss
authors: Procter & Gamble, Cincinnati, OH, USA.
authors: Shengde
authors: S
authors: Wu
authors: Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA.|University of Konstanz, CAAT-Europe, Konstanz, Germany.
authors: Thomas
authors: T
authors: Hartung
mesh_terms: D000826:Animal Testing Alternatives; D000818:Animals; D001681:Biological Assay / Q000379:methods*; D062067:Chemical Safety / Q000379:methods*; D057225:Data Mining; D016208:Databases, Factual; D015386:Hazardous Substances / Q000737:chemistry* / Q000633:toxicity*; D057166:High-Throughput Screening Assays; D015394:Molecular Structure; D051381:Rats; D013329:Structure-Activity Relationship
chemical_list: D015386:Hazardous Substances
keywords: big data; biological similarity; read-across; safety assessment