A KNIME Workflow to Assist the Analogue Identification for Read-Across, Applied to Aromatase Activity

The reduction and replacement of in vivo tests have become crucial in terms of resources and animal benefits. The read-across approach reduces the number of substances to be tested, exploiting existing experimental data to predict the properties of untested substances. Currently, several tools have...

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Bibliographic Details
Published in:Molecules (Basel, Switzerland) Switzerland), 2023-02, Vol.28 (4), p.1832
Main Authors: Caballero Alfonso, Ana Yisel, Chayawan, Chayawan, Gadaleta, Domenico, Roncaglioni, Alessandra, Benfenati, Emilio
Format: Article
Language:English
Subjects:
Aromatase
Biocompatibility
Biomedical materials
case study
Computer applications
Datasets
human aromatase
Identification
In vivo methods and tests
integrated similarity
Metabolism
Organic chemicals
read-across
Risk assessment
Short tandem repeats
Similarity
Toxicity
Workflow
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Summary:The reduction and replacement of in vivo tests have become crucial in terms of resources and animal benefits. The read-across approach reduces the number of substances to be tested, exploiting existing experimental data to predict the properties of untested substances. Currently, several tools have been developed to perform read-across, but other approaches, such as computational workflows, can offer a more flexible and less prescriptive approach. In this paper, we are introducing a workflow to support analogue identification for read-across. The implementation of the workflow was performed using a database of azole chemicals with in vitro toxicity data for human aromatase enzymes. The workflow identified analogues based on three similarities: structural similarity (StrS), metabolic similarity (MtS), and mechanistic similarity (McS). Our results showed how multiple similarity metrics can be combined within a read-across assessment. The use of the similarity based on metabolism and toxicological mechanism improved the predictions in particular for sensitivity. Beyond the results predicting a large population of substances, practical examples illustrate the advantages of the proposed approach.
ISSN:1420-3049
1420-3049
DOI:10.3390/molecules28041832