Open Access

SkinSensDB: a curated database for skin sensitization assays

Journal of Cheminformatics20179:5

https://doi.org/10.1186/s13321-017-0194-2

Received: 22 October 2016

Accepted: 23 January 2017

Published: 31 January 2017

Abstract

Skin sensitization is an important toxicological endpoint for chemical hazard determination and safety assessment. Prediction of chemical skin sensitizer had traditionally relied on data from rodent models. The development of the adverse outcome pathway (AOP) and associated alternative in vitro assays have reshaped the assessment of skin sensitizers. The integration of multiple assays as key events in the AOP has been shown to have improved prediction performance. Current computational models to predict skin sensitization mainly based on in vivo assays without incorporating alternative in vitro assays. However, there are few freely available databases integrating both the in vivo and the in vitro skin sensitization assays for development of AOP-based skin sensitization prediction models. To facilitate the development of AOP-based prediction models, a skin sensitization database named SkinSensDB has been constructed by curating data from published AOP-related assays. In addition to providing datasets for developing computational models, SkinSensDB is equipped with browsing and search tools which enable the assessment of new compounds for their skin sensitization potentials based on data from structurally similar compounds. SkinSensDB is publicly available at http://cwtung.kmu.edu.tw/skinsensdb.

Background

Skin sensitization associated with allergic contact dermatitis (ACD) is the second most common occupational illness accounting for 10–15% of all occupational disease worldwide [1]. The disease not only impairs the quality of life for the patients but also results in high costs in healthcare systems and economy [2]. Skin sensitization is thereby an important toxicological endpoint in chemical safety assessment and a focus in regulatory decision making. Chemical sensitizers, which may be detergents, preservatives, or fragrances in household and personal care products or active ingredients, impurities from synthetic process and industrial materials in the pharmaceutical products, act as haptens binding to protein molecules. These chemically modified proteins may trigger T cell-mediated immune reactions and lead to ACD [3].

Traditionally, guinea pig maximization test (GPMT) and Buehler assay (BA) are utilized as predictive animal models for the identification of skin sensitizers and are widely accepted by regulatory authorities due to their reliable detection of potential human contact allergens. Nevertheless, these protocols are relatively long and complex with some limitations [4]. The murine local lymph node assay (LLNA) has been established as an alternative animal model to traditional guinea pig methods to provide important animal welfare benefits and has been successfully validated and incorporated into a regulatory guideline described as Organization for Economic Cooperation and Development (OECD) Test Guideline Number 429 [5]. The assay measures the proliferation of lymphocytes during the induction phase of skin sensitization and provides EC3 values (effective concentration for a stimulation index of threefold in lymphocyte proliferation compared to vehicle controls) to make comparisons of the relative potency of different chemical sensitizers. The animal tests have been recently banned for cosmetic ingredients in 2013 by the European Union. There is a strong need for the development of alternative testing methods for identifying skin sensitizers [6, 7].

Many quantitative structure–activity relationship (QSAR) methods have been proposed to predict skin sensitizers based on descriptors of chemical structures [818]. All of the QSAR models were developed for a single type of sensitization endpoints mostly from either LLNA or GPMT. Although reasonably good prediction performance was obtained from the QSAR models, the prediction gave no insights into the detailed mechanism of actions. Recently, skin sensitization has been formulated as an adverse outcome pathway (AOP) to address the complex nature of sensitization [19]. Four key events including protein binding, keratinocyte activation, dendritic cell activation and T cell activation were clearly defined. Several alternative methods have been developed according to the key events such as Direct Peptide Reactivity Assay (DPRA) and Peroxidase Peptide Reactivity Assay (PPRA) [20, 21] for quantification of chemical peptide reactivity, KeratinoSens [22, 23] and LuSens [24] for keratinocyte activation by determining the activation of antioxidant response element (ARE) reporter genes, and h-CLAT [25, 26] for activation of dendritic cells by measuring the increased level of CD54 and CD86 maturation markers, respectively. The integration of the key events of the AOP for predicting skin sensitizers showed improved prediction performance over single models [2730]. In addition, the data from individual steps of the sensitization progress can be interpreted and evaluated by experts for hazard determination and risk assessment.

To facilitate the development of AOP-based computational prediction methods, a novel curated database named SkinSensDB was constructed by manual curation of published literature. A total of 710 unique chemicals were curated into SkinSensDB with corresponding reactivities of 2078, 467, 1323 and 1060 assay values for peptide reactivity, keratinocyte activation, dendritic cell activation, and T-cell activation, respectively. Search tools have been implemented with exact, similarity, and substructure search functionality. The SkinSensDB is expected to be a useful database supporting the development of AOP-based computational models for predicting skin sensitizers.

Construction and content

The SkinSensDB database was implemented using MongoDB version 3.0.7. The web interface and search functions were implemented using PHP, Python, HTML and JavaScript programming languages and frameworks of AngularJS version 1.4.6 (https://angularjs.org/) and Angular Material version 1.1.1 (https://material.angularjs.org/latest/). The SkinSensDB website is publicly available at http://cwtung.kmu.edu.tw/skinsensdb.

Database content

SkinSensDB database consists of chemical information and four types of well-developed assays associated with the four events of the AOP for skin sensitization reported by OECD [19]. For each chemical, its basic structure and physicochemical property information was extracted from PubChem Compound database using PUG REST functions from PubChem [31]. Chemical-related information provided at SkinSensDB included the CAS number, IUPAC name, INCHI, INCHIKEY, formula, SMILES, hydrogen-bond acceptor, hydrogen-bond donor, molecular weight and topological polar surface area (TPSA). In addition, external links to PubChem database and SDF files for 2D and 3D structures were available for further exploration of chemical-specific information and development of QSAR models, respectively.

For protein binding ability of chemicals, results of peptide reactivity assays of DPRA/PPRA were collected. For each chemical, its corresponding curated information included source literature, assay types, markers, peptide concentrations, chemical concentrations, the addition of horseradish peroxidase (HRP) enzymes and the percentage of peptide depletion with standard deviations. Peptide depletion assays with and without enzymatic activation using HRP enzymes are so called PPRA and DPRA, respectively. The keratinocyte activation ability of chemicals was represented by data from KeratinoSens and LuSens assays. Curated information for keratinocyte activation included the maximum fold induction of luciferase activity (Imax), concentrations for the n-fold induction of luciferase (EC1.5, EC2, and EC3) and cytotoxicity (IC50). For the activation potential of dendritic cells, information related to h-CLAT was curated including CD54 and CD86 markers, the induction criteria for the markers (EC type), the effective concentrations for the markers (EC), relative fluorescence intensity (RFI), and the concentrations that produced 75% cell viability (CV75). The LLNA assays representing the T-cell activation ability of chemicals were collected from NICEATM LLNA database [32]. LLNA-associated data consisted of vehicles, effective concentration for threefold induction of draining lymph node cell proliferation, the result of LLNA, evaluated concentrations and corresponding stimulation index (SI).

Utility and discussion

SkinSensDB is a curated database for AOP-associated skin sensitization assays aiming to provide an easily accessible resource for the development of AOP-based computational models. Currently, there are 710 unique chemicals with 4928 assay values of AOP-related skin sensitization assays from the literature. A typical record of SkinSensDB is shown in Fig. 1 where basic information of chemicals and associated skin sensitization assays can be found. The full record can be exported as an Excel file via the download button. Both the 2D and 3D structure files are downloadable for each chemical. With the integration of chemical structure and physicochemical property information, it is expected to be useful for developing AOP-based QSAR models. Users who wish to contribute their data to this database can submit their data using an Excel file provided at SkinSensDB.
Fig. 1

An illustrated record of SkinSensDB. For simplicity, only one row for each assay is included in this figure. The section of Basic Information shows the chemical structure and physicochemical properties with links to PubChem database and structure files in SDF format. Four sections comprise assay results corresponding to the four key events of adverse outcome pathway for skin sensitization

For the exploration of chemicals and corresponding skin sensitization assays, a browse tool providing searchable summarized information of the four assays has been implemented. The criteria for defining the summarized information were shown in Table 1. The data table can be easily browsed by sorting and filtering on specific columns. Functions for batch queries and exporting the summarized information were also implemented.
Table 1

The classification criteria of positive and negative responses to a chemical

Assay type

Criteria

Classification

DPRA/PPRA

Peptide depletion ≤ 6.38%

Negative

Peptide depletion > 6.38%

Positive

KeratinoSens/LuSens

EC1.5 ≥ 1000 µM

Negative

EC1.5 < 1000 µM

Positive

h-CLAT

Neither CD86 EC150 nor CD54 EC200 was determined

Negative

CD86 EC150 ≤ CV75 or CD54 EC200 ≤ CV75

Positive

LLNA

SI < 3

Negative

SI ≥ 3

Positive

EC effective concentration, CV75 75% cell viability, SI stimulation index

Based on the assumption that structurally similar chemicals could have similar bioactivities, three types of tools for exact, similarity and substructure searches have been implemented to facilitate the search of structurally similar chemicals. Figure 2 shows the user interface of search functions. For the input of chemical structure for search, users can either draw a structure in the JSME [33] editor or enter a SMILES text that can be automatically converted into chemical structures. The search functions were implemented based on RDKit [34], an open-source cheminformatics library. The similarity search was based on Tanimoto similarity between topological fingerprints of two chemicals. In addition to the summarized information, the similarity score between 0 and 1 will be available in the search results as shown in Fig. 3.
Fig. 2

The search functions. Users can search the SkinSensDB database using functions of exact, substructure and similarity searches with a user-supplied chemical structure by either drawing a chemical structure or converting from a SMILES string

Fig. 3

An illustrated example of the similarity search in SkinSensDB. A similarity score between query and target chemicals is available and sortable in the second column. All the other columns are the same as the browse tool in SkinSensDB consisting of name, CAS number, PubChem CID, summarized results for the four key events

In addition, a file summarizing essential information for developing QSAR models is available at the SkinSensDB website. The summarized file consists of fields of the chemical name, CAS number, PubChem CID, the highest peptide depletion of DPRA/PPRA, the lowest EC1.5 value of KeratinoSens/LuSens, the lowest EC and CV75 values of h-CLAT, and the EC3 value of LLNA for all chemicals in SkinSensDB. The structure files can be easily downloaded from PubChem database using the CIDs and transformed into descriptors using softwares such as PaDEL-Descriptor [35]. QSAR models can be subsequently constructed to study the relationship between structure descriptors and response values from assays of four key events.

Conclusions

SkinSensDB is a web-based resource providing useful information of chemical structures, physicochemical properties and experimental data from both alternative in vitro and in vivo skin sensitization assays. Browse and search tools were implemented to facilitate the exploration of skin sensitization data. The integration of chemical structures, physicochemical properties, and experimental results from these AOP-related assays could be helpful for the development of an AOP-based prediction system integrating all models corresponding to the four major events.

Declarations

Authors’ contributions

CWT conceived the project. CCW, YCL, CS, YHL and CWT participated in data collection and curation. SSW and CWT developed the database and web interface. CCW, YCL and CWT wrote the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Availability and requirements

SkinSensDB is freely available at http://cwtung.kmu.edu.tw/skinsensdb without restrictions for academic use. The website has been tested with browsers of latest versions including Opera, Internet Explorer Edge, Firefox and Google Chrome.

Funding

This work was supported by National Health Research Institutes of Taiwan (NHRI-105A1-PDCO-0316164), Ministry of Science and Technology of Taiwan (MOST104-2221-E-037-001-MY3, MOST104-2320-B-037-032-MY2), Kaohsiung Medical University Research Foundation (KMU-M105005), NSYSU-KMU Joint Research Project (NSYSUKMU105-I007) and Research Center for Environmental Medicine (KMU-TP104A25).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
School of Pharmacy, Kaohsiung Medical University
(2)
PhD Program in Toxicology, Kaohsiung Medical University
(3)
National Institute of Environmental Health Sciences, National Health Research Institutes
(4)
Institute of Environmental Engineering, National Sun Yat-sen University
(5)
Research Center for Environmental Medicine, Kaohsiung Medical University

References

  1. Basketter DA, White IR, McFadden JP, Kimber I (2015) Skin sensitization: implications for integration of clinical data into hazard identification and risk assessment. Hum Exp Toxicol 34:1222–1230. doi:https://doi.org/10.1177/0960327115601760 View ArticleGoogle Scholar
  2. Peiser M, Tralau T, Heidler J et al (2012) Allergic contact dermatitis: epidemiology, molecular mechanisms, in vitro methods and regulatory aspects. Current knowledge assembled at an international workshop at BfR, Germany. Cell Mol life Sci CMLS 69:763–781. doi:https://doi.org/10.1007/s00018-011-0846-8 View ArticleGoogle Scholar
  3. Karlberg A-T, Bergström MA, Börje A et al (2008) Allergic contact dermatitis–formation, structural requirements, and reactivity of skin sensitizers. Chem Res Toxicol 21:53–69. doi:https://doi.org/10.1021/tx7002239 View ArticleGoogle Scholar
  4. Basketter DA, Scholes EW (1992) Comparison of the local lymph node assay with the guinea-pig maximization test for the detection of a range of contact allergens. Food Chem Toxicol Int J Publ Br Ind Biol Res Assoc 30:65–69. doi:https://doi.org/10.1016/0278-6915(92)90138-B View ArticleGoogle Scholar
  5. Kimber I, Hilton J, Dearman RJ et al (1995) An international evaluation of the murine local lymph node assay and comparison of modified procedures. Toxicology 103:63–73View ArticleGoogle Scholar
  6. Mehling A, Eriksson T, Eltze T et al (2012) Non-animal test methods for predicting skin sensitization potentials. Arch Toxicol 86:1273–1295. doi:https://doi.org/10.1007/s00204-012-0867-6 View ArticleGoogle Scholar
  7. Vandebriel RJ, van Loveren H (2010) Non-animal sensitization testing: state-of-the-art. Crit Rev Toxicol 40:389–404. doi:https://doi.org/10.3109/10408440903524262 View ArticleGoogle Scholar
  8. Li Y, Tseng YJ, Pan D et al (2007) 4D-fingerprint categorical QSAR models for skin sensitization based on the classification of local lymph node assay measures. Chem Res Toxicol 20:114–128. doi:https://doi.org/10.1021/tx6002535 View ArticleGoogle Scholar
  9. Ren Y, Liu H, Xue C et al (2006) Classification study of skin sensitizers based on support vector machine and linear discriminant analysis. Anal Chim Acta 572:272–282. doi:https://doi.org/10.1016/j.aca.2006.05.027 View ArticleGoogle Scholar
  10. Liu J, Kern PS, Gerberick GF et al (2008) Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors. J Comput Mol Des 22:345–366. doi:https://doi.org/10.1007/s10822-008-9190-y View ArticleGoogle Scholar
  11. Li Y, Pan D, Liu J et al (2007) Categorical QSAR models for skin sensitization based upon local lymph node assay classification measures part 2: 4D-fingerprint three-state and two-2-state logistic regression models. Toxicol Sci Off J Soc Toxicol 99:532–544. doi:https://doi.org/10.1093/toxsci/kfm185 View ArticleGoogle Scholar
  12. Golla S, Madihally S, Robinson RL, Gasem KAM (2009) Quantitative structure-property relationship modeling of skin sensitization: a quantitative prediction. Toxicol Vitr Int J Publ Assoc with BIBRA 23:454–465. doi:https://doi.org/10.1016/j.tiv.2008.12.025 View ArticleGoogle Scholar
  13. Yuan H, Huang J, Cao C (2009) Prediction of skin sensitization with a particle swarm optimized support vector machine. Int J Mol Sci 10:3237–3254. doi:https://doi.org/10.3390/ijms10073237 View ArticleGoogle Scholar
  14. Nandy A, Kar S, Roy K (2013) Development and validation of regression-based QSAR models for quantification of contributions of molecular fragments to skin sensitization potency of diverse organic chemicals. SAR QSAR Environ Res 24:1009–1023. doi:https://doi.org/10.1080/1062936X.2013.821422 View ArticleGoogle Scholar
  15. Alves VM, Muratov E, Fourches D et al (2015) Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicol Appl Pharmacol 284:262–272. doi:https://doi.org/10.1016/j.taap.2014.12.014 View ArticleGoogle Scholar
  16. Dearden JC, Hewitt M, Roberts DW et al (2015) Mechanism-based QSAR modeling of skin sensitization. Chem Res Toxicol 28:1975–1986. doi:https://doi.org/10.1021/acs.chemrestox.5b00197 View ArticleGoogle Scholar
  17. Sarath Kumar KL, Tangadpalliwar SR, Desai A et al (2016) Integrated computational solution for predicting skin sensitization potential of molecules. PLoS ONE. doi:https://doi.org/10.1371/journal.pone.0155419 Google Scholar
  18. Li S, Fedorowicz A, Singh H, Soderholm SC (2005) Application of the random forest method in studies of local lymph node assay based skin sensitization data. J Chem Inf Model 45:952–964. doi:https://doi.org/10.1002/chin.200539199 View ArticleGoogle Scholar
  19. OECD (2012) The adverse outcome pathway for skin sensitisation initiated by covalent binding to proteins. Part 2: Use of the AOP to Develop Chemical Categories and Integrated Assessment and Testing Approaches. Series on Testing and Assessment. No. 168. ENV/JM/MONO(2012)/PART2Google Scholar
  20. Gerberick GF, Troutman JA, Foertsch LM et al (2009) Investigation of peptide reactivity of pro-hapten skin sensitizers using a peroxidase-peroxide oxidation system. Toxicol Sci Off J Soc Toxicol 112:164–174. doi:https://doi.org/10.1093/toxsci/kfp192 View ArticleGoogle Scholar
  21. Gerberick GF, Vassallo JD, Bailey RE et al (2004) Development of a peptide reactivity assay for screening contact allergens. Toxicol Sci Off J Soc Toxicol 81:332–343. doi:https://doi.org/10.1093/toxsci/kfh213 View ArticleGoogle Scholar
  22. Emter R, Ellis G, Natsch A (2010) Performance of a novel keratinocyte-based reporter cell line to screen skin sensitizers in vitro. Toxicol Appl Pharmacol 245:281–290. doi:https://doi.org/10.1016/j.taap.2010.03.009 View ArticleGoogle Scholar
  23. Natsch A, Emter R (2008) Skin sensitizers induce antioxidant response element dependent genes: application to the in vitro testing of the sensitization potential of chemicals. Toxicol Sci Off J Soc Toxicol 102:110–119. doi:https://doi.org/10.1093/toxsci/kfm259 View ArticleGoogle Scholar
  24. Bauch C, Kolle SN, Ramirez T et al (2012) Putting the parts together: combining in vitro methods to test for skin sensitizing potentials. Regul Toxicol Pharmacol RTP 63:489–504. doi:https://doi.org/10.1016/j.yrtph.2012.05.013 View ArticleGoogle Scholar
  25. Sakaguchi H, Ashikaga T, Miyazawa M et al (2006) Development of an in vitro skin sensitization test using human cell lines; human Cell Line Activation Test (h-CLAT). II. An inter-laboratory study of the h-CLAT. Toxicol Vitr Int J Publ Assoc with BIBRA 20:774–784. doi:https://doi.org/10.1016/j.tiv.2005.10.014 View ArticleGoogle Scholar
  26. Ashikaga T, Hoya M, Itagaki H et al (2002) Evaluation of CD86 expression and MHC class II molecule internalization in THP-1 human monocyte cells as predictive endpoints for contact sensitizers. Toxicol Vitr Int J Publ Assoc with BIBRA 16:711–716. doi:https://doi.org/10.1016/S0887-2333(02)00060-7 View ArticleGoogle Scholar
  27. Patlewicz G, Kuseva C, Kesova A et al (2014) Towards AOP application–implementation of an integrated approach to testing and assessment (IATA) into a pipeline tool for skin sensitization. Regul Toxicol Pharmacol 69:529–545. doi:https://doi.org/10.1016/j.yrtph.2014.06.001 View ArticleGoogle Scholar
  28. Pirone JR, Smith M, Kleinstreuer NC et al (2014) Open source software implementation of an integrated testing strategy for skin sensitization potency based on a Bayesian network. ALTEX 31:336–340. doi:https://doi.org/10.14573/altex.1310151 View ArticleGoogle Scholar
  29. van der Veen JW, Rorije E, Emter R et al (2014) Evaluating the performance of integrated approaches for hazard identification of skin sensitizing chemicals. Regul Toxicol Pharmacol 69:371–379. doi:https://doi.org/10.1016/j.yrtph.2014.04.018 View ArticleGoogle Scholar
  30. Strickland J, Zang Q, Kleinstreuer N et al (2016) Integrated decision strategies for skin sensitization hazard. J Appl Toxicol 36(9):1150–1162. doi:https://doi.org/10.1002/jat.3281 View ArticleGoogle Scholar
  31. Kim S, Thiessen PA, Bolton EE, Bryant SH (2015) PUG-SOAP and PUG-REST: web services for programmatic access to chemical information in PubChem. Nucleic Acids Res 43:W605–W611. doi:https://doi.org/10.1093/nar/gkv396 View ArticleGoogle Scholar
  32. NICEATM LLNA database (2013) http://ntp.niehs.nih.gov/pubhealth/evalatm/test-method-evaluations/immunotoxicity/nonanimal/index.html#NICEATM-Murine-Local-Lymph-Node-Assay-LLNA-Database
  33. Bienfait B, Ertl P (2013) JSME: a free molecule editor in JavaScript. J Cheminf. doi:https://doi.org/10.1186/1758-2946-5-24 Google Scholar
  34. RDKit: Open-source cheminformatics. http://www.rdkit.org/
  35. Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474. doi:https://doi.org/10.1002/jcc.21707 View ArticleGoogle Scholar

Copyright

© The Author(s) 2017