Abstract
Read-across (RAX) is a popular data-gap filling technique that uses category and analogue approaches to predict toxicological endpoints for a target. Despite its increasing relevance, RAX relies on human expert judgement and lacks a reproducible and automated protocol. It also only relies on structural similarity for identifying the analogues, while other aspects are often neglected. In this paper, we propose an automated procedure for the selection of analogues for data gap-filling. Analogues were identified with a decision algorithm that integrates three similarity metrics, each considering different toxicologically relevant aspects (i.e., structural, biological and metabolic similarity). Structural filters based on the presence of maximum common substructures (MCS) and common functional groups were applied to narrow the chemical space for the analogues search. The procedure has been implemented as a workflow in KNIME and is freely available. The workflow provides informative tabular and graphical outputs to support toxicologists and risk assessors in drawing conclusion based on the RAX approach. The procedure has been validated for its predictive power on two datasets related to high-tier in vivo toxicological endpoints, i.e. human hepatotoxicity and drug-induced liver injury (DILI). The validation results gave good accuracy values (i.e., up to 0.79 for the binary hepatotoxicity classification and up to 0.67 for the three-class DILI classification) that were higher than those returned by RAX based on the sole use of structural similarity. Results confirmed the suitability of the procedure as a source of data to support regulatory decision-making.
Highlights
In the last 15 years, regulations in the field of chemical safety assessment have changed in that toxicological information for a large number of chemicals needs to be gathered prior to manufacture or import into the EU (EC, 2006)
We present a novel automated workflow for analogue(s) selection for RAX based on a weight of evidence (WoE) approach that systematically computes and combines three similarity metrics between target and potential analogue(s)
3.1 Overall RAX strategy output It should be kept in mind that this workflow is not primarily designed for batch calculation on large datasets, and one cannot expect to reach prediction accuracies at the same level of other methodologies (e.g., quantitative structure-activity relationships (QSARs)) tailored for predicting large databases
Summary
In the last 15 years, regulations in the field of chemical safety assessment have changed in that toxicological information for a large number of chemicals needs to be gathered prior to manufacture or import into the EU (EC, 2006). The EU Registration, Evaluation, Authorisation and restriction of CHemicals (REACH) calls for the use of non-testing approaches to be used in the assessment of chemical substances while vertebrate animal testing should be seen as a last resort (ECHA, 2014). RAX is a data gap-filling technique used to predict unknown toxicological endpoints for a chemical substance (target) by using the same endpoint information from one or more chemicals that are highly similar to the target (analogue(s)) (Patlewicz et al, 2013; Pradeep et al, 2017; OECD, 2014; ECHA, 2008). The first step of RAX consists in identifying potential analogue(s) that may serve to fill the target’s toxicological data gaps. This can be done using quantitative metrics to evaluate the similarity between the target and potential analogue(s). The presence of functional group(s) (e.g., aldehyde, epoxide, ester, specific metal ion) shared with the target, common constituents or chemical classes, similar carbon range numbers or the likelihood of common precursors and/or breakdown products are typical considerations when evaluating
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