TY - JOUR
T1 - Supporting read-across using biological data
AU - Zhu, Hao
AU - Bouhifd, Mounir
AU - Donley, Elizabeth
AU - Egnash, Laura
AU - Kleinstreuer, Nicole
AU - Kroese, E. Dinant
AU - Liu, Zhichao
AU - Luechtefeld, Thomas
AU - Palmer, Jessica
AU - Pamies, David
AU - Shen, Jie
AU - Strauss, Volker
AU - Wu, Shengde
AU - Hartung, Thomas
N1 - Funding Information:
T.L. was supported by NIEHS training grant (T32 ES007141). Support from the EU Horizon 2020 project EUToxRisk is gratefully appreciated.
PY - 2016
Y1 - 2016
N2 - 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, biological similarity based on biological data adds extra strength to this process. 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 are becoming available, enabling big data approaches in read-across studies. In the context of developing Good Read-Across Practice guidance, a number of case studies using various big data sources were evaluated to assess the contribution of biological data to enriching read-across. An example is given for the US EPA's ToxCast dataset which allows read-across for high quality uterotrophic assays for estrogenic endocrine disruption. Similarly, an example is given for REACH registration data that enhances read-across for acute toxicity studies. A different approach is taken using omics data to establish biological similarity: Examples are given for in vitro stem cell models and short-term in vivo repeated dose studies in rats used to support read-across and category formation. These preliminary biological data-driven read-across studies show the way towards the generation of new read-across approaches that can inform chemical safety assessment.
AB - 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, biological similarity based on biological data adds extra strength to this process. 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 are becoming available, enabling big data approaches in read-across studies. In the context of developing Good Read-Across Practice guidance, a number of case studies using various big data sources were evaluated to assess the contribution of biological data to enriching read-across. An example is given for the US EPA's ToxCast dataset which allows read-across for high quality uterotrophic assays for estrogenic endocrine disruption. Similarly, an example is given for REACH registration data that enhances read-across for acute toxicity studies. A different approach is taken using omics data to establish biological similarity: Examples are given for in vitro stem cell models and short-term in vivo repeated dose studies in rats used to support read-across and category formation. These preliminary biological data-driven read-across studies show the way towards the generation of new read-across approaches that can inform chemical safety assessment.
KW - Big data
KW - Biological similarity
KW - Read-across
KW - Safety assessment
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U2 - 10.14573/altex.1601252
DO - 10.14573/altex.1601252
M3 - Article
C2 - 26863516
AN - SCOPUS:84962920734
SN - 1868-596X
VL - 33
SP - 167
EP - 182
JO - ALTEX : Alternativen zu Tierexperimenten
JF - ALTEX : Alternativen zu Tierexperimenten
IS - 2
ER -