Development of new COSMOS oRepeatDose and non-cancer Threshold of Toxicological Concern (TTC) databases to support alternative testing methods for cosmetics related chemicals

Yang, Chihae; Massimo, Ambrosio; Arvidson, Kirk; Barlow, Sue; Boobis, Alan R.; Checheva, Maria; Cronin, Mark T.D.; Felter, Susan P.; Fioravanzo, Elena; Hollnagel, Heli M.; Hristozov, Dimitar; Jacobs, Kristi L. Muldoon; Keller, Detlef; Mostrag-Szylchtying, A.; Nelms, Mark; Rathman, James F.; Richarz, Andrea; Tsakovska, Ivanka; Vidry, Stéphane; Vitcheva, Vessela; Worth, Andrew

doi:10.1016/j.toxlet.2013.05.082

Cited by 5 publications

(2 citation statements)

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“…Under the SEURAT-1 initiative, there were five research projects and one coordinating project funded and extensive data curation/management work was involved ( Kohonen et al 2013 ). For example, one of these projects, the COSMOS project, was dedicated to the development of freely available tools and workflows to predict the safety of cosmetic ingredients to humans ( Yang et al, 2013 ). In the released COSMOS database web portal 7 , there are over 5,500 unique cosmetic-relevant compounds with their respective in vivo toxicity data.…”

Section: The State Of the Art Of Read-across Using Biological Datamentioning

confidence: 99%

Supporting read-across using biological data

Zhu

2016

ALTEX

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Summary 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.

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Section: The State Of the Art Of Read-across Using Biological Datamentioning

confidence: 99%

Supporting read-across using biological data

Zhu

2016

ALTEX

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“…A new non-cancer TTC database for cosmeticsrelated chemicals has been compiled by augmenting the COS-MOS database with substances from the Munro dataset found in the Cosmetics Inventory. The resulting TTC database contains over 580 chemical structures with NOAEL (Yang et al, 2013). The consortium stated in several presentations that the chemical space of the new TTC database has been compared with existing TTC databases to demonstrate that the coverage is suitable for the assessment of cosmetics products.…”

Section: Ttc For Cosmetics and Other Consumer Productsmentioning

confidence: 99%