Development of a read-across workflow for skin irritation and corrosion predictions

Abe, Asaki; Sezaki, T; Kinoshita, K.

doi:10.1080/1062936x.2019.1595136

Cited by 5 publications

(9 citation statements)

References 1 publication

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“…For OPERA, a lower LogWS (logarithm of water solubility) and a higher MP (melting point) contributed to a negative prediction in both tasks, showing consistency with the reportedly important properties. 16,18,38 As for attention weights learned by Attentive FP, following the same way as its original paper, 27 we first used min-max normalization to rescale the attention weights within a graph to the range of 0 to 1. Next, the normalized attention weight of each node, which was derived from the first graph-level attention layer, was visualized to provide an interpretation based on the atomic contributions.…”

Section: ■ Resultsmentioning

confidence: 99%

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AttentiveSkin: To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods

Huang,

Lou,

Wang

et al. 2024

Chem. Res. Toxicol.

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Skin Corrosion/Irritation (Corr./Irrit.) has long been a health hazard in the Globally Harmonized System (GHS). Several in silico models have been built to predict Skin Corr./Irrit. as an alternative to the increasingly restricted animal testing. However, current studies are limited by data amount/quality and model availability.To address these issues, we compiled a traceable consensus GHS data set comprising 731 Corr., 1283 Irrit., and 1205 negative (Neg.) samples from 6 governmental databases and 2 external data sets. Then, a series of binary classifiers were developed with five machine learning (ML) algorithms and six molecular representations. For 10fold cross-validation, the best Corr. vs Neg. classifier achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 97.1%, while the best Irrit. vs Neg. classifier achieved an AUC of 84.7%. Compared with existing in silico tools on external validation, our Attentive FP classifiers showed the highest metrics on Corr. vs Neg. and the second highest accuracy on Irrit. vs Neg. The SHapley Additive exPlanation approach was further applied to figure out important molecular features, and the attention weights were visualized to perform interpretable prediction. Structural alerts associated with Skin Corr./Irrit. were also identified. The interpretable Attentive FP classifiers were integrated into the software AttentiveSkin at https://github.com/ BeeBeeWong/AttentiveSkin. The conventional ML classifiers are also provided on our platform admetSAR at http://lmmd.ecust. edu.cn/admetsar2/. Considering the data deficiency and the limited model availability of Skin Corr./Irrit., we believe that our data set and models could facilitate chemical safety assessment and relevant studies.

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Section: ■ Resultsmentioning

confidence: 99%

“…OPERA was selected because of its ability to calculate reportedly important properties like water solubility and melting point. 16,18,38 We also trained Attentive FP on molecular graph, which is a state-of-the-art GNN architecture designed for molecular tasks, 28 to provide the first GNN model for Skin Corr./Irrit.…”

Section: ■ Resultsmentioning

confidence: 99%

AttentiveSkin: To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods

Huang,

Lou,

Wang

et al. 2024

Chem. Res. Toxicol.

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“…Computational evaluation of structural alerts was conducted with OECD QSAR Toolbox 4.2 , and VEGA , as shown in Table . These alerts predict properties associated with the toxic effects of chemicals, such as DNA and protein covalent binding and are based on functional groups known to undergo these reactions, either directly or upon biotransformation to a reactive metabolite.…”

Section: Methods For Clustering a Chemical Inventorymentioning

confidence: 99%

“… 10 The endpoints of interest include both systemic toxicity, such as in vivo genotoxicity and developmental-reproductive toxicity, and local toxicity, such as skin sensitization or respiratory toxicity. 11 All of these approaches use Tanimoto or other structural similarity scores 12 as a rudimentary basis to identify similar chemicals calculated from either SMILES or fingerprints. 13 The main drawback of clustering an inventory based on such scores is that the substructural features, which can affect toxicodynamic and toxicokinetic properties, are not weighted according to their impact on the toxicity.…”

Section: Introductionmentioning

confidence: 99%

“…Various methods for clustering inventories and searching read-across analogues have been described previously. − These approaches employed methods ranging from simple quantitative structure–activity relationships (QSAR) to a complex machine learning techniques on big data . The endpoints of interest include both systemic toxicity, such as in vivo genotoxicity and developmental-reproductive toxicity, and local toxicity, such as skin sensitization or respiratory toxicity . All of these approaches use Tanimoto or other structural similarity scores as a rudimentary basis to identify similar chemicals calculated from either SMILES or fingerprints .…”

Section: Introductionmentioning

confidence: 99%

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Clustering a Chemical Inventory for Safety Assessment of Fragrance Ingredients: Identifying Read-Across Analogs to Address Data Gaps

Date

O’Brien

Botelho

et al. 2020

Chem. Res. Toxicol.

381

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A valuable approach to chemical safety assessment is the use of read-across chemicals to provide safety data to support the assessment of structurally similar chemicals. An inventory of over 6000 discrete organic chemicals used as fragrance materials in consumer products has been clustered into chemical class-based groups for efficient search of read-across sources. We developed a robust, tiered system for chemical classification based on (1) organic functional group, (2) structural similarity and reactivity features of the hydrocarbon skeletons, (3) predicted or experimentally verified Phase I and Phase II metabolism, and (4) expert pruning to consider these variables in the context of specific toxicity end points. The systematic combination of these data yielded clusters, which may be visualized as a top-down hierarchical clustering tree. In this tree, chemical classes are formed at the highest level according to organic functional groups. Each subsequent subcluster stemming from classes in this hierarchy of the cluster is a chemical cluster defined by common organic functional groups and close similarity in the hydrocarbon skeleton. By examining the available experimental data for a toxicological endpoint within each cluster, users can better identify potential read-across chemicals to support safety assessments.

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From Pipeline to Plant Protection Products: Using New Approach Methodologies (NAMs) in Agrochemical Safety Assessment

Henriquez,

Badwaik,

Bianchi

et al. 2024

J. Agric. Food Chem.

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The human population will be approximately 9.7 billion by 2050, and food security has been identified as one of the key issues facing the global population. Agrochemicals are an important tool available to farmers that enable high crop yields and continued access to healthy foods, but the average new agrochemical active ingredient takes more than ten years, 350 million dollars, and 20,000 animals to develop and register. The time, monetary, and animal costs incentivize the use of New Approach Methodologies (NAMs) in early-stage screening to prioritize chemical candidates. This review outlines NAMs that are currently available or can be adapted for use in early-stage screening agrochemical programs. It covers new in vitro screens that are on the horizon in key areas of regulatory concern. Overall, early-stage screening with NAMs enables the prioritization of development for agrochemicals without human and environmental health concerns through a more directed, agile, and iterative development program before animal-based regulatory testing is even considered.

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Development of a read-across workflow for skin irritation and corrosion predictions

Cited by 5 publications

References 1 publication

AttentiveSkin: To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods

AttentiveSkin: To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods

Clustering a Chemical Inventory for Safety Assessment of Fragrance Ingredients: Identifying Read-Across Analogs to Address Data Gaps

From Pipeline to Plant Protection Products: Using New Approach Methodologies (NAMs) in Agrochemical Safety Assessment

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