U.S. Environmental Protection Agency Office of Research and Development EPA www.epa.gov Building a compendium of expert driven read-across cases to facilitate an analysis of the contribution that different similarity contexts play in read-across G Patlewicz1*, Amanda Ross2, HC Bledsoe2, Janielle Vidal2, Louis Groff1, Brett Hagan1,3, Imran Shah1 1 CCTE, U.S. EPA, RTP, NC, USA, 2ICF Corporation, Reston, VA, USA,3ORAU, Oak Ridge, TN, USA BACKGROUND STRUCTURAL SIMILARITY EVALUATION Grace Patlewicz l Patlewicz.grace@epa.gov l ORCID: 0000-0003-3863-9689 l 919-541-1540 CONCLUSIONS Disclaimer: The views expressed are those of the authors and do not necessarily reflect the views or policies of the US EPA. • 82 Read-across examples cases were compiled from the three main sources. • There were 22 unique decision contexts when aggregated by NAMs, technical guidance or regulatory purposes. • Of the 82 examples, 68 captured regulatory purposes, the remainder were relatively evenly split between efforts to improve guidance or evaluate the utility of NAMs to substantiate read-across justifications. METABOLIC SIMILARITY EVALUATION CLASSYFIRE METHODS The 82 read-across example cases were characterised by 497 individual substances of which 468 could be mapped to a discrete organic structure by way of SMILES To gain a perspective of the chemical diversity across these 468 substances, the chemistry ontology ClassyFire was used to assign structures into their respective chemical class. Some ~63% of the substances were members of either the “Benzene and substituted derivatives” class (25%), the “Fatty Acyls” class (14%), the “Organooxygen compounds” class (12%) or the “Carboxylic acids and derivatives” class (12%). A t-SNE plot based on Morgan chemical fingerprints shows the chemical landscape and is colour coded using a subset of the most populist chemical subclasses. Across the 82 examples, there were 77 different evidence streams characterising the basis for identifying and evaluating the source analogues in each case. The first component of the evidence stream characterising the primary means of identifying candidate source analogues was a structural one in 72 cases, and metabolism in 4 cases. There were 13 different approaches by which analogues were identified. The barplot highlights the main tools and approaches. The OECD Toolbox, DSSTox (within the EPA CompTox Chemicals Dashboard), the NIH’s structure searching tool within ChemIDPlus or some combination of these tools were most common in terms of identifying structural analogues. However by far the most common means of identifying analogues was to look for common scaffolds based on functional groups. • The pairwise Jaccard similarities were computed within each read-across example to explore how structurally similar the target and source analogues were amongst themselves. • The median of the distribution of median values for each case study was determined to be 0.34. (see Fig a) • There was a large degree of variation in pairwise similarities within each case study (See Fig b) Read-across is a data-gap filling technique utilised to predict the toxicity of a target chemical using data from similar analogues. Read-across is predominantly performed as part of an expert-driven assessment which can impeded broad acceptance. Data-driven approaches such as Generalised Read-Across (GenRA) offer scope to generate reproducible read-across predictions where uncertainties and performance are quantified. A key issue is how to reconcile an expert-driven approach with a data driven approach both in terms of how analogues are identified and evaluated as well as how the read-across prediction is derived. An important component of analogue identification and selection is in understanding the contribution that different similarity contexts play, i.e. does structural similarity play a larger role in analogue selection compared with metabolism similarity. This study aimed to explore some of these conside