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Read-across is an alternative or supplementary method to the use of test results (including test results from tests employing laboratory animals) for chemical hazard assessment. Its use by both the industrial and the regulatory communities has increased markedly over the past decade. A seminal issue for the regulatory acceptance of read-across predictions is reducing uncertainty in the prediction by evaluating similarity among the chemicals participating in the read-across. To assist in demonstrating and documenting this similarity, a “category consistency” functionality has been added to the OECD QSAR Toolbox. This module (semi-) automatically collects and displays information key to establishing physicochemical, structural and mechanistic similarities, as well as any experimental data associated with the endpoint under consideration. The Toolbox user can then employ this suite of information to draw a conclusion on the consistency of the recorded similarity measurements of the category members. The report writer provides documentation of the Toolbox information supporting the conclusion. The implementation of “category consistency” functionality is illustrated with a case example.

Toxicological understanding and computational possibilities progress every year. The OECD QSAR Toolbox, an in silico system for predicting toxicity of chemicals, is regularly updated with up to data knowledge and functionalities. This expansion has made the Toolbox increasingly complex and difficult to use for non-experts. Recent activities addressed this issue and enhanced the user-friendliness of the system. In a streamlining exercise, we developed automated and standardized workflows for acute aquatic toxicity and skin sensitization. These endpoints have been chosen based on understanding of the mechanisms of action, availability of experimental data and regulatory relevance. The workflows are designed to produce rational and reliable predictions, or no prediction if acceptance criteria are not met. The automated workflow is a predefined algorithm that generates a prediction without further interaction with the user after activation. The standardised workflow uses a similar algorithm but leaves to the user the key choices among a selection of suitable options. In the present work, we described in detail the algorithms for both the automated and standardized workflows. Using examples, the rationale behind each step of the two processes is explained.

Both industry and regulatory agencies use structure-activity relationships and read-across as toxicity assessment methods alternative to animal experimentation. A prerequisite for acceptance of the outcome of such in silico predictions is establishing the mechanistic similarity (i.e. a common mode of (toxicological) action) among the target substance(s) and the source substance(s). To assist in assessing this similarity, an “Alert performance” functionality has been added to the OECD QSAR Toolbox Version 4.0. This functionality is designed to provide information for evaluating the mechanistic and/or structural similarity among the analogues forming a category. This “Alert performance” informs the user on whether the identified alert(s) in the target substance or its metabolites is linked to consistent effects for the endpoint of interest for other substances triggering the same alert and for which experimental data are available. In this respect, calculation of alert performance shows the predictivity of a given alert to the selected endpoint. The predictivity of alerts is context-dependent and is affected by the structure of target chemical, targeted endpoint, selected databases, data usage and data scale. The use of alerts with high performances will lead to the formation of categories of chemicals showing similar toxicological effects, thus reducing the uncertainty when filling data gaps. This paper illustrates the workflow of the alert performance module and addresses several questions that could arise during its applications.

Metabolism is a critical aspect of toxicokinetics. It is crucial to take it into account when evaluating similarity among the chemicals participating in a read-across prediction. Lack of considerations on metabolism represents a major source of uncertainty in a read-across justification. To better assist users in applying metabolic considerations during data gap filling, the metabolism/transformations functionality in the OECD QSAR Toolbox has been upgraded in version 4. Analyses of metabolic similarity are specific to a toxic endpoint and dependent on data availability. By using optimized features, the metabolic simulators available in Toolbox can predict the generation of possible metabolites and take them into account for read-across. In the traditional downstream approach, metabolic information in the Toolbox is used to identify the formation of (a)biotic products of known toxicity and read-across their toxicity to the parent chemical. By a stepwise protocol the user may explore knowledge embedded in the Toolbox to identify source analogues that have the same pattern of metabolic activation as the target. Furthermore, following the upstream approach, precursors of the chemicals could be also searched in the Toolbox databases. Five scenarios, highlighting the applications of the metabolism in chemical assessment within the Toolbox, are illustrated.