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Systematic Approaches to Biological/Chemical Read-Across for Hazard Identification
Citation:
Patlewicz, G., G. Helman, AND I. Shah. Systematic Approaches to Biological/Chemical Read-Across for Hazard Identification. Environmental Mutagenesis and Genomics Society (EMGS), Raleigh,NC, September 09 - 13, 2017.
Impact/Purpose:
Invited presentation at Environmental Mutagenesis and Genomics Society (EMGS) annual meeting
Description:
Read-across is a well-established data gap filling technique used within chemical category and analogue approaches for regulatory purposes. The category/analogue workflow comprises a number of steps starting from decision context, data gap analysis through to analogue identification, analogue evaluation, data gap filling and uncertainty assessment. Critical steps in the workflow are analogue identification and analogue evaluation. Source analogues are commonly identified on the basis of their structural similarity whereas analogue evaluation relies upon characterising bioavailability, reactivity, metabolic, toxicokinetic, mechanistic and toxicological similarity. Here we describe a generalised read-across (GenRA) approach that uses mechanistic information from high throughput screening bioactivity data (e.g., ToxCast) for read-across predictions. Using GenRA, the read-across prediction is a similarity weighted activity of nearest neighbours characterised by chemical and bioactivity descriptors. The approach has been recently implemented into a web-based software tool to enable objective, reproducible, and transparent read-across predictions. We present several examples where GenRA has been practically applied to identify source analogues, evaluate their validity, and make read-across predictions for specific chemicals of interest. The views expressed are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.
URLs/Downloads:
EMGS_2017_ABSTRACT_JC_CLEAN.PDF (PDF, NA pp, 45.748 KB, about PDF)PATLEWICZ_EMGS_080917.PDF (PDF, NA pp, 1415.666 KB, about PDF)