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Generalized Read-Across (GenRA) prediction using chemical and biological information (BOSC)
Citation:
Shah, I., J. Lin, R. Judson, R. Thomas, AND G. Patlewicz. Generalized Read-Across (GenRA) prediction using chemical and biological information (BOSC). Presented at BOSC CSS Meeting, RTP, NC, November 16 - 18, 2016. https://doi.org/10.23645/epacomptox.5179369
Impact/Purpose:
Poster presented at the BOSC CSS meeting in RTP, NC
Description:
Read-across is a popular data gap filling technique within category and analogue approaches for regulatory purposes. Acceptance of read-across remains a challenge with several efforts underway for identifying and addressing uncertainties. To date, these approaches have been qualitative in nature. Here, an algorithmic approach to facilitate read-across using ToxCast in vitro bioactivity data in conjunction with chemical descriptor information to predict in vivo outcomes in guideline (and guideline-like) testing studies from ToxRefDB is demonstrated. The read-across prediction for a given chemical is based on the similarity weighted endpoint outcomes of its nearest neighbors, calculated using in vitro bioactivity and chemical structure descriptors, called Generalized Read-across (GenRA). GenRA is a first step in systemizing read-across by providing performance metrics and enabling the scientific confidence of a prediction to be objectively assessed.
URLs/Downloads:
DOI: Generalized Read-Across (GenRA) prediction using chemical and biological information (BOSC)
CSS BOSC_GENRA POSTERV2_031116.PDF (PDF, NA pp, 698.528 KB, about PDF)