Computational approaches have recently gained popularity in the field of read-across to automatically fill data-gaps for untested chemicals. Previously, we developed the generalized read-across (GenRA) tool, which utilizes <i>in vitro</i> bioactivity data in conjunction with chemical descriptor information to derive local validity domains for selection of existing legacy data to qualitatively predict hazard based on study data summarized in US’s EPA ToxRefDB v1.0. Following the success in qualitatively predicting hazard by study type and target organ or system, a clear need surfaced: quantitative predictions of the potency of possible hazard based on analogous chemicals. To address this need, we modified the GenRA workflow to make quantitative predictions for point of departure (POD) values obtained from ToxRefDB v2.0. To evaluate GenRA predictions, we aggregated oral Lowest Observed Adverse Effect Levels (LOAEL) for 1,049 chemicals by endpoint category, namely effects grouped as: systemic, developmental, reproductive, and/or cholinesterase-related. The mean LOAEL values for each chemical were converted to log10 molar equivalents. For defining chemicals with minimum similarity to the target chemical, Morgan chemical fingerprints with a minimum Jaccard similarity threshold of 0.5 and a maximum of 10 nearest neighbors were used. The predicted systemic, developmental, reproductive, and cholinesterase LOAEL had R<sup>2</sup> (and RMSE) values of 0.38 (1.34), 0.22 (0.78), 0.14 (0.93), and 0.25 (0.7), respectively. These findings suggest that the GenRA workflow can be updated to include quantitative predictions, and that due to the relatively limited chemical set and hazard data available, it may be helpful to provide the user with the range of possible POD values for a given endpoint category for use in screening-level assessments.

Case study in quantitative GenRA predictions using repeated dose toxicity studies from ToxRefDB v2.0

2019 Spring National Meeting

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