Abstract
Generalized Read-Across (GenRA) is a data driven approach which makes read-across predictions on the basis of a similarity weighted activity of source analogues (nearest neighbors). GenRA has been described in more detail in the literature (Shah et al., 2016; Helman et al., 2018). Here we present its implementation within the EPA's CompTox Chemicals Dashboard to provide public access to a GenRA module structured as a read-across workflow. GenRA assists researchers in identifying source analogues, evaluating their validity and making predictions of in vivo toxicity effects for a target substance. Predictions are presented as binary outcomes reflecting presence or absence of toxicity together with quantitative measures of uncertainty. The approach allows users to identify analogues in different ways, quickly assess the availability of relevant in vivo data for those analogues and visualize these in a data matrix to evaluate the consistency and concordance of the available experimental data for those analogues before making a GenRA prediction. Predictions can be exported into a tab-separated value (TSV) or Excel file for additional review and analysis (e.g., doses of analogues associated with production of toxic effects). GenRA offers a new capability of making reproducible read-across predictions in an easy-to use-interface.
Highlights
Given the thousands of data-poor or toxicologically uncharacterized chemicals in commerce, read-across has proved to be a convenient and efficient data gap filling technique that can be used within analogue and category approaches for many different regulatory purposes
The approach is a generalization of the Chemical Biological Read-Across (CBRA) approach published by Low et al (2013)
The Generalized Read-across (GenRA) framework has been implemented using a classical three tier architecture, which is seamlessly embedded in the EPA CompTox Chemicals Dashboard, and includes: 1) a web-based presentation tier; 2) an application tier based on representational
Summary
Given the thousands of data-poor or toxicologically uncharacterized chemicals in commerce, read-across has proved to be a convenient and efficient data gap filling technique that can be used within analogue and category approaches for many different regulatory purposes. We present the web-based implementation of Generalized Read-across (GenRA), a data-driven approach that makes reproducible read-across predictions of toxicity outcomes from in vivo studies (Shah et al, 2016). The presentation layer components in GenRA perform their specific tasks by obtaining information about chemicals, analogues, bioactivity and toxicity from the application tier.
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