Dataset Information

Development of a CSRML version of the Analog Identification Methodology (AIM) fragments and their evaluation within the Generalised Read-Across (GenRA) approach.

ABSTRACT: The Analog Identification Methodology (AIM) was developed over 20 years ago to identify analogues to support read-across at the US Environmental Protection Agency. However, the current public version of the standalone tool, released in 2012, is no longer usable on Windows operating systems supported by Microsoft. Additionally, the structural logic for analogue selection is based on older, customised Simplified molecular-input-line-entry system (SMILES)-type features that are incompatible with modern cheminformatics tools. Given these limitations, a case study was undertaken to explore a more transparent, extensible method of implementing the AIM fragments using Chemical Subgraphs and Reactions Mark-up Language (CSRML). A CSRML file was developed to codify the original AIM fragments, and the extent to which AIM fragments were faithfully replicated was assessed using the AIM Database. The overall mean performance of the CSRML-AIM across all fragments in terms of sensitivity, specificity, and Jaccard similarity was 89.5%, 99.9%, and 82.2%, respectively. Comparing the AIM fragments with public ToxPrints using a large set of ~25,000 substances of regulatory interest to EPA found them to be dissimilar, with an average maximum Jaccard score of 0.24 for AIM and 0.29 for ToxPrint fingerprints. Both fragment sets were then used as inputs in the automated read-across approach, Generalised Read-Across (GenRA), to evaluate the quality of fit in predicting rat acute oral toxicity LD₅₀ values with the coefficient of determination (R²) and root mean squared error (RMSE). The performance of AIM fragments was R²=0.434 and RMSE=0.663 whereas that of ToxPrints was R²=0.477 and RMSE=0.638. A bootstrap resampling using 100 iterations found the mean and the 95^th confidence interval of R² to be 0.349 [0.319, 0.379] for AIM fragments and 0.377 [0.338, 0.412] for ToxPrints. Although AIM and ToxPrints performed similarly in predicting LD_50, they differed in their performance at a local level, revealing that their features can offer complementary insights.

SUBMITTER: Adams M

PROVIDER: S-EPMC9888031 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Development of a CSRML version of the Analog Identification Methodology (AIM) fragments and their evaluation within the Generalised Read-Across (GenRA) approach.

Adams Matthew M Hidle Hannah H Chang Daniel D Richard Ann M AM Williams Antony J AJ Shah Imran I Patlewicz Grace G

Computational toxicology (Amsterdam, Netherlands) 20230201

The Analog Identification Methodology (AIM) was developed over 20 years ago to identify analogues to support read-across at the US Environmental Protection Agency. However, the current public version of the standalone tool, released in 2012, is no longer usable on Windows operating systems supported by Microsoft. Additionally, the structural logic for analogue selection is based on older, customised Simplified molecular-input-line-entry system (SMILES)-type features that are incompatible with mo ...[more]

PMID: 36733411

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Project description:Read-across is a useful data gap filling technique used within category and analogue approaches in regulatory hazard and risk assessment. Recently we developed an algorithmic, approach called Generalised Read-Across (GenRA) (Shah et al., 2016) which makes read-across predictions of toxicity effects using a similarity weighted average of source analogues characterised by their chemical and/or bioactivity descriptors. A default GenRA approach (termed baseline GenRA) relies on identifying 10 source analogues relative to a target substance that are structurally similar based on Morgan chemical fingerprints and computing an activity score to estimate presence or absence of in vivo toxicity. This current study investigated the impact that similarity in bioavailability plays in altering the local neighbourhood of source analogues as well as read-across performance relative to baseline GenRA using physicochemical property information as a surrogate for bioavailability. Two approaches were evaluated: 1) a filtering approach which restricted structurally related analogues based on their physicochemical properties; and 2) a search expansion approach which included additional analogues based on a combined structural and physicochemical similarity index. Filtering minimally improved performance, and was very dependent on the similarity threshold selected. The search expansion approach performed at least as well as the baseline GenRA, and showed up to a 9% improvement in read-across performance for at least 10 of the 50 organs considered. We summarise the overall impact that physicochemical information plays on GenRA performance, illustrate the improvement for a specific case study substance and describe how to select the most appropriate physicochemical similarity threshold to achieve optimal read-across performance depending on the toxicity effect and chemical of interest. The analyses show that physicochemical property information does result in a modest (up to 9% increase) improvement in structural based read-across predictions.

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Dataset Information

Development of a CSRML version of the Analog Identification Methodology (AIM) fragments and their evaluation within the Generalised Read-Across (GenRA) approach.

Publications

Development of a CSRML version of the Analog Identification Methodology (AIM) fragments and their evaluation within the Generalised Read-Across (GenRA) approach.

Similar Datasets

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