SkinSensDB: a curated database for skin sensitization assays

Lin, Ying; Wang, Shan Shan; Shih, C. C.; Lin, Yi; Tung, Chun Wei

doi:10.1186/s13321-017-0194-2

Cited by 20 publications

(25 citation statements)

References 33 publications

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“…The SkinSensDB platform (Wang et al 2017;Tung et al 2018) not only provides data relevant to skin sensitization (LLNA, human, DPRA/PPRA, KeratinoSens TM /LuSens, and h-CLAT) but also includes functionality for the prediction of the skin sensitization potential of compounds based on the integration of these experimental data. For compounds of interest, values for missing experimental data are derived by a read-across approach.…”

Section: Hybrid In Silico Modelsmentioning

confidence: 99%

Computational approaches for skin sensitization prediction

Wilm

Kühnl

Kirchmair

2018

Critical Reviews in Toxicology

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Drugs, cosmetics, preservatives, fragrances, pesticides, metals, and other chemicals can cause skin sensitization. The ability to predict the skin sensitization potential and potency of substances is therefore of enormous importance to a host of different industries, to customers' and workers' safety. Animal experiments have been the preferred testing method for most risk assessment and regulatory purposes but considerable efforts to replace them with non-animal models and in silico models are ongoing. This review provides a comprehensive overview of the computational approaches and models that have been developed for skin sensitization prediction over the last 10 years. The scope and limitations of rule-based approaches, read-across, linear and nonlinear (quantitative) structure-activity relationship ((Q)SAR) modeling, hybrid or combined approaches, and models integrating computational methods with experimental results are discussed followed by examples of relevant models. Emphasis is placed on models that are accessible to the scientific community, and on model validation. A dedicated section reports on comparative performance assessments of various approaches and models. The review also provides a concise overview of relevant data sources on skin sensitization.

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Section: Hybrid In Silico Modelsmentioning

confidence: 99%

Computational approaches for skin sensitization prediction

Wilm

Kühnl

Kirchmair

2018

Critical Reviews in Toxicology

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“…Dermal contact to skin sensitizing substances (contact allergens) may result in contact allergy (sensitization) or an allergic contact dermatitis (ACD). 10 Repeated contact with a potent sensitizer may be sufficient to induce sensitization (contact allergy), and the re-exposure of a sensitized individual to that contact allergen can then result in an allergic reaction. For skin sensitization, skin rash, redness and edema are typical responses (adverse outcome).…”

Section: Discussionmentioning

confidence: 99%

The Evaluation of Potential Skin Sensitization and Collagen Synthesis of Rats Receiving Proteoglycan Serum

Chuenwattana

Chatthanawaree²,

Bunman³

et al. 2018

bkkmedj

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The aim of this study is to evaluate potential skin sensitization and collagen synthesis on the skin of rats receiving PG serum extracted from fish (Trichopoduspectoralis) in vivo model. Abstract OBJECTIVE:The aim of this study was to evaluate potential skin sensitization and collagen synthesis of rats receiving proteoglycan (PG) serum. MATERIAL AND METHODS: Eighteen rats were randomly divided into 3 groups equally and assigned to receive 1 ml of serum base (control), 1% PG and 2% PG applied to skin. Histological analysis was evaluated for potential skin sensitization and collagen synthesis on days 7, 14, 21and 28. RESULTS: Histological analysis showed that 1% PG and 2% PG accelerated collagen synthesis. The evaluation of potential skin sensitization, on day 7, 14, 21 and 28 of rat applied with 1% PG and 2% PG were not significantly different when compared to the control group. CONCLUSION: This study demonstrated that PG serum accelerated collagen synthesis and low potential skin sensitization.

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“…Some online skin sensitization data sources, which have collected the data and structural alerts, can be used to build predictive models and are listed in Table 6 . Of various skin sensitization databases, SkinSensDB , which has collected the animal and non-animal tests and contains 710 unique chemicals with 2,078, 467, 1,323, and 1,060 assay values for peptide reactivity (DPRA), keratinocyte activation (KeratinoSen™), dendritic cell activation (h-CLAT), and T-cell activation (LLNA-EC3), respectively ( Tung et al, 2019 ; Wang et al, 2017 ), is freely accessible. The non-confidential substance data, which have been submitted to European chemicals agency (ECHA) under the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation, are publicly available and free of charge.…”

Section: In Silico Modelsmentioning

confidence: 99%

In silico Prediction of Skin Sensitization: Quo vadis?

Weng²,

Leong

2021

Front. Pharmacol.

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Skin direct contact with chemical or physical substances is predisposed to allergic contact dermatitis (ACD), producing various allergic reactions, namely rash, blister, or itchy, in the contacted skin area. ACD can be triggered by various extremely complicated adverse outcome pathways (AOPs) remains to be causal for biosafety warrant. As such, commercial products such as ointments or cosmetics can fulfill the topically safe requirements in animal and non-animal models including allergy. Europe, nevertheless, has banned animal tests for the safety evaluations of cosmetic ingredients since 2013, followed by other countries. A variety of non-animal in vitro tests addressing different key events of the AOP, the direct peptide reactivity assay (DPRA), KeratinoSens™, LuSens and human cell line activation test h-CLAT and U-SENS™ have been developed and were adopted in OECD test guideline to identify the skin sensitizers. Other methods, such as the SENS-IS are not yet fully validated and regulatorily accepted. A broad spectrum of in silico models, alternatively, to predict skin sensitization have emerged based on various animal and non-animal data using assorted modeling schemes. In this article, we extensively summarize a number of skin sensitization predictive models that can be used in the biopharmaceutics and cosmeceuticals industries as well as their future perspectives, and the underlined challenges are also discussed.

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SkinSensDB: a curated database for skin sensitization assays

Cited by 20 publications

References 33 publications

Computational approaches for skin sensitization prediction

Computational approaches for skin sensitization prediction

The Evaluation of Potential Skin Sensitization and Collagen Synthesis of Rats Receiving Proteoglycan Serum

In silico Prediction of Skin Sensitization: Quo vadis?

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