Skin sensitization quantitative QSAR models based on mechanistic structural alerts

Chayawan,; Selvestrel, Gianluca; Baderna, Diego; Toma, Cosimo; Alfonso, Ana Yisel Caballero; Gamba, Alessio; Benfenati, Emilio

doi:10.1016/j.tox.2022.153111

Cited by 4 publications

(4 citation statements)

References 57 publications

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“…DC activation (KE3) was assessed by the THP-1 activation assay (modified version of the h-CLAT), which combines measurements of cell surface markers CD54 and CD86 alongside the inflammatory cytokine IL-8—and additionally by an in silico structural profiler associated with the h-CLAT. A pair of statistical QSAR models, trained upon LLNA data, provided linkage to T-cell proliferation (KE4) (Gądarowska et al 2022 ; Chayawan et al 2022 ).…”

Section: Discussionmentioning

confidence: 99%

The evaluation of skin sensitization potential of the UVCB substance diisopentyl phthalate by in silico and in vitro methods

de Souza,

Iulini,

Galbiati

et al. 2024

Arch Toxicol

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Diisopentyl phthalate (DiPeP) is primarily used as a plasticizer or additive within the production of polyvinyl chloride (PVC), and has many additional industrial applications. Its metabolites were recently found in urinary samples of pregnant women; thus, this substance is of concern as relates to human exposure. Depending upon the nature of the alcohol used in its synthesis, DiPeP may exist either as a mixture consisting of several branched positional isomers, or as a single defined structure. This article investigates the skin sensitization potential and immunomodulatory effects of DiPeP CAS No. 84777-06-0, which is currently marketed and classified as a UVCB substance, by in silico and in vitro methods. Our findings showed an immunomodulatory effect for DiPeP in LPS-induced THP-1 activation assay (increased CD54 expression). In silico predictions using QSAR TOOLBOX 4.5, ToxTree, and VEGA did not identify DiPeP, in the form of a discrete compound, as a skin sensitizer. The keratinocyte activation (Key Event 2 (KE2) of the adverse outcome pathway (AOP) for skin sensitization) was evaluated by two different test methods (HaCaT assay and RHE assay), and results were discordant. While the HaCaT assay showed that DiPeP can activate keratinocytes (increased levels of IL-6, IL-8, IL-1α, and ILA gene expression), in the RHE assay, DiPeP slightly increased IL-6 release. Although inconclusive for KE2, the role of DiPeP in KE3 (dendritic cell activation) was demonstrated by the increased levels of CD54 and IL-8 and TNF-α in THP-1 cells (THP-1 activation assay). Altogether, findings were inconclusive regarding the skin sensitization potential of the UVCB DiPeP—disagreeing with the results of DiPeP in the form of discrete compound (skin sensitizer by the LLNA assay). Additional studies are needed to elucidate the differences between DiPeP isomer forms, and to better understand the applicability domains of non-animal methods in identifying skin sensitization hazards of UVCB substances.

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Section: Discussionmentioning

confidence: 99%

The evaluation of skin sensitization potential of the UVCB substance diisopentyl phthalate by in silico and in vitro methods

de Souza,

Iulini,

Galbiati

et al. 2024

Arch Toxicol

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“…The developed PLS q-RASAR model superseded the corresponding PLS QSAR model both in terms of internal and external validation metric values. Most of the previous works 68,69 have performed QSAR analysis based on chemicals that share the same mechanism of skin sensitization, and thus, lack diversity in their datasets. Moreover, the number of compounds required to train and validate their models was also quite low.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the training set validation statistics were much poorer as compared to our PLS q-RASAR model. Chayawan et al 68 also performed a QSAR analysis to predict the skin sensitization potential. Similar to the works of Dearden et al , 60 they have developed various QSAR models for different classes of compounds that are expected to induce skin sensitization using various reaction mechanisms.…”

Section: Interpretation Of the Different Pls Plotsmentioning

confidence: 99%

Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals

Banerjee,

Roy

2023

Environ. Sci.: Processes Impacts

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“…In computational toxicology, structural alerts represent molecular substructures responsible for molecular initiating events (MIE) in various toxicological phenomena . They are commonly incorporated into in silico systems for predictive (Q)SAR analysis or for grouping analogues in read across methods.…”

Section: Introductionmentioning

confidence: 99%

Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments

Chakravarti

2023

Chem. Res. Toxicol.

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Structural alerts are molecular substructures assumed to be associated with molecular initiating events in various toxic effects and an integral part of in silico toxicology. However, alerts derived using the knowledge of human experts often suffer from a lack of predictivity, specificity, and satisfactory coverage. In this work, we present a method to build hybrid QSAR models by combining expert knowledge-based alerts and statistically mined molecular fragments. Our objective was to find out if the combination is better than the individual systems. Lasso regularization-based variable selection was applied on combined sets of knowledge-based alerts and molecular fragments, but the variable elimination was only allowed to happen on the molecular fragments. We tested the concept on three toxicity end points, i.e., skin sensitization, acute Daphnia toxicity, and Ames mutagenicity, which covered both classification and regression problems. Results showed the predictive performance of such hybrid models is, indeed, better than the models based solely on expert alerts or statistically mined fragments alone. The method also enables the discovery of activating and mitigating/deactivating features for toxicity alerts and the identification of new alerts, thereby reducing false positive and false negative outcomes commonly associated with generic alerts and alerts with poor coverage, respectively.

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Skin sensitization quantitative QSAR models based on mechanistic structural alerts

Cited by 4 publications

References 57 publications

The evaluation of skin sensitization potential of the UVCB substance diisopentyl phthalate by in silico and in vitro methods

The evaluation of skin sensitization potential of the UVCB substance diisopentyl phthalate by in silico and in vitro methods

Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals

Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments

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