Assessing metabolic similarity for read-across predictions

Yordanova, Darina; Schultz, T.W.; Kuseva, Chanita; Mekenyan, Ovanes G.

doi:10.1016/j.comtox.2021.100160

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…8 Another approach for assessing similarity in metabolism controls the propagation of in silico predicted metabolites with metabolic data and expert judgment and involves a side-by-side comparison of metabolic maps for SOI/analogue pairs of structures. 11 Next steps for this work involve the development of a database to house the fingerprints for each compound considered. Metabolism is the most labor-intensive similarity metric involving some expert judgment and literature review to select the most relevant biotransformation keys.…”

Section: Discussionmentioning

confidence: 99%

“…Calculation of such a metabolism similarity score is not new; however, some implementations rely on in silico prediction of pathways which often select all possible transformations for the structural features present, thus diluting the biological relevance of the attribute . Another approach for assessing similarity in metabolism controls the propagation of in silico predicted metabolites with metabolic data and expert judgment and involves a side-by-side comparison of metabolic maps for SOI/analogue pairs of structures …”

Section: Discussionmentioning

confidence: 99%

“…Boyce et al computed metabolic similarity using pathway transformations predicted by Meteor for 3,4-toluenediamine and potential analogues for defining a fingerprint with comparisons performed by calculating a Jaccard distance . Yordanova et al considered metabolic similarity by comparing side-by-side arrays of metabolites for a target/analogue pair of structures for identification of common metabolic pathways, common metabolites, as well as consideration of the formation of common reactive metabolites . Yang et al defined “analogue quality” as the geometric mean of similarity in structure and molecular/physical chemical properties for a target/analogue pair and show that this metric may be used to derive a range of NOAEL (No Observed Adverse Effect Level) values for the target based on those values determined for analogues deemed to be high quality .…”

Section: Introductionmentioning

confidence: 99%

“… 10 Yordanova et al considered metabolic similarity by comparing side-by-side arrays of metabolites for a target/analogue pair of structures for identification of common metabolic pathways, common metabolites, as well as consideration of the formation of common reactive metabolites. 11 Yang et al defined “analogue quality” as the geometric mean of similarity in structure and molecular/physical chemical properties for a target/analogue pair and show that this metric may be used to derive a range of NOAEL (No Observed Adverse Effect Level) values for the target based on those values determined for analogues deemed to be high quality. 12 Finally, “biological similarity” may be calculated by comparing the output from various HTS (high-throughput screening) assays that may include profiles of gene expression, assays of enzymatic activity, or measurements of cytotoxicity.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Quantifying Analogue Suitability for SAR-Based Read-Across Toxicological Assessment

Lester

Byrd

Shobair

et al. 2023

Chem. Res. Toxicol.

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Structure activity relationship (SAR)-based read-across often is an integral part of toxicological safety assessment, and justification of the prediction presents the most challenging aspect of the approach. It has been established that structural consideration alone is inadequate for selecting analogues and justifying their use, and biological relevance must be incorporated. Here we introduce an approach for considering biological and toxicological related features quantitatively to compute a similarity score that is concordant with suitability for a read-across prediction for systemic toxicity. Fingerprint keys for comparing metabolism, reactivity, and physical chemical properties are presented and used to compare these attributes for 14 case study chemicals each with a list of potential analogues. Within each case study, the sum of these nonstructural similarity scores is consistent with suitability for read-across established using an approach based on expert judgment. Machine learning is applied to determine the contributions from each of the similarity attributes revealing their importance for each structure class. This approach is used to quantify and communicate the differences between a target and a potential analogue as well as rank analogue quality when more than one is relevant. A numerical score with easily interpreted fingerprints increases transparency and consistency among experts, facilitates implementation by others, and ultimately increases chances for regulatory acceptance.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantifying Analogue Suitability for SAR-Based Read-Across Toxicological Assessment

Lester

Byrd

Shobair

et al. 2023

Chem. Res. Toxicol.

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show abstract

“…QSAR Toolbox predicts the structures of metabolites. At the same time, TIMES, which can be docked into OECD QSAR Toolbox, can both identify metabolites and simulate metabolic maps that could be compared for similarity based on common transformations, common metabolites and common reactivity pattern when used for the read-across approach (Yordanova et al, 2021 (Bhatia et al, 2015).…”

Section: Initial Considerations For the Toxicity Data Requirementsmentioning

confidence: 99%

Public consultation on the draft scientific guidance on the data required for the risk assessment of flavourings to be used in or on foods

2022

EFSA Supporting Publications

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This technical report presents the outcome of the public consultation carried out by the European Food Safety Authority (EFSA) to receive input from all interested parties on the draft scientific guidance for the preparation of applications on flavourings to be used in or on foods. The guidance document was prepared by the EFSA Panel on Food Additives and Flavourings (FAF), supported by the Working Group on Guidance Update on Flavourings, and endorsed for public consultation at the 29th plenary meeting of the FAF Panel, held on 30 March – 1 April 2022. The public consultation for this document was open from 25 April until 19 June 2022. On 25 May 2022, EFSA also organised a technical hearing with interested parties with the aim to present the content of the draft guidance document and to collect preliminary comments and input on its clarity and completeness ahead of the closing date of the public consultation. During the public consultation EFSA received written comments from 4 different interested parties. EFSA and its FAF Panel wish to thank all stakeholders for their contributions. The present report contains the comments received and explains the way they have been considered for the finalisation of the guidance on flavourings. The guidance was adopted at the FAF Panel plenary meeting on 8‐10 November and published in the EFSA Journal.

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Criteria for quantitative assessment of metabolic similarity between chemicals. II. Application to human health endpoints

Kuseva

Yordanova

Ivanova

et al. 2021

Computational Toxicology

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Assessing metabolic similarity for read-across predictions

Cited by 8 publications

References 28 publications

Quantifying Analogue Suitability for SAR-Based Read-Across Toxicological Assessment

Quantifying Analogue Suitability for SAR-Based Read-Across Toxicological Assessment

Public consultation on the draft scientific guidance on the data required for the risk assessment of flavourings to be used in or on foods

Criteria for quantitative assessment of metabolic similarity between chemicals. II. Application to human health endpoints

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