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Evaluation of the GARD Assay in a Blind Cosmetics Europe Study.

1 Introduction

Chemical hypersensitivity is a disease state induced by the human immune system in response to chemical sensitizers, which most frequently gives rise to the clinical symptoms of allergic contact dermatitis (ACD). The molecular and cellular mechanisms of sensitization have been reviewed extensively (Ainscough et al., 2013; Martin, 2015; Martin et al., 2011). Briefly, sensitization involves skin penetration of the sensitizing agent with a subsequent haptenization of endogenous proteins. Protein-hapten complexes are taken up by resident dendritic cells (DCs), which upon maturation migrate to local lymph nodes where antigen presentation to naive T cells occurs. This results in the induction of an immunologic memory towards the specific sensitizer. Upon repeated exposure, a sensitized individual will suffer from ACD-associated symptoms following the elicitation of specific Th1 and cytotoxic [CD8.sup.+] T-cells.

A link has been made between the prevalence of ACD and the increased exposure of the population to the abundance of chemical sensitizers in consumer products (Lunder and Kansky, 2000; Nguyen et al., 2008). In order to limit hazardous effects of chemicals, risk assessments aim at safeguarding humans and the environment by eliminating and mitigating risks of exposure. The European REACH (EU, 2006) legislation requires all manufactured substances to undergo safety testing in order to identify, e.g., chemical sensitizers. Historically, such tests have been conducted in guinea pig (Magnusson and Kligman, 1969) and murine (Basketter et al., 2002) models. Mainly, the murine Local Lymph Node Assay (LLNA) continues to be used today. However, the use of animals for testing cosmetic ingredients has been banned in the EU since 2013 (EU, 2009), and the REACH legislation urges other industries to use animal testing only as a last resort when no relevant alternative testing methods exists, thereby clearly stating an intent to comply with the 3R principles (Russel and Burch, 1959).

As a consequence, the field of predictive toxicology has recently seen a surge in the development of novel non-animal assays for the assessment of chemical sensitization potential. The Direct Peptide Reactivity Assay (DPRA) (Gerberick et al., 2004), KeratinoSens[TM] (Natsch, 2010) and the human Cell Line Activation Test (h-CLAT) (Ashikaga et al., 2006) have been validated by the European Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) and have recently been accepted by the OECD as test guidelines, which demonstrates that these tests are adequately reproducible and transferable (DPRA, OECD TG 442C; Keratinosens, OECD TG 442D; h-CLAT, OECD TG 442E).

However, none of the aforementioned assays are thought to fully cover the complexity of the skin sensitization process as stand-alone tests. Rather, it is widely proposed that assessment of hazard and/or risk should be carried out using integrated testing strategies (ITS), also referred to as integrated approaches to testing and assessment (IATA) (Jaworska and Hoffmann, 2010; Hartung et al., 2013; Rovida et al., 2015; Ezendam et al., 2016). However, the overall predictive performance of an ITS will invariably depend on the predictivity of its assay constituents. In addition, being based on a single or a few biomarkers, current methods provide only limited predictive information, as well as sometimes overlapping mechanistic information. Thus, when designing an ITS, tests with high predictive performance and information content, covering one or more of the key events of the adverse outcome pathway (AOP) (OECD, 2012), would clearly be an advantageous option (Lindstedt and Borrebaeck, 2011).

The Genomic Allergen Rapid Detection (GARD) assay is a cell-based in vitro assay for assessment of chemical sensitizers (Johansson et al., 2011). The readout of the assay is based on differentially regulated transcriptional changes of selected genomic biomarkers, referred to as the GARD prediction signature (GPS), induced in a myeloid dendritic cell-like cell line in response to chemical stimulation. GARD has been shown to be functional and able to accurately predict sensitizing chemicals in blind evaluations (Johansson et al., 2014) and exhibits high predictive performance in comparison with in vitro counterparts (Johansson and Lindstedt, 2014). Following a thorough evaluation of technological platforms (Forreryd et al., 2014), the assay was recently adapted to a medium-to-high throughput format in order to meet industrial and regulatory demands of reliability, resource effectiveness and sample capacity (Forreryd et al., 2016). Furthermore, an adaptation of GARD using identical cellular protocols but a different biomarker signature to differentially classify respiratory sensitizers from a set of skin sensitizers and non-sensitizers has been demonstrated (Forreryd et al., 2015). This illustrates the unparalleled flexibility of applications of genomics-based platforms, which is due to the massive amount of information that multivariate readouts deliver.

In an attempt to evaluate the performance of currently validated assays, as well as selected assays that are currently in the validation process or are being considered for validation, the Cosmetics Europe Skin Tolerance Task Force (CE STTF) recently published a comparative study in which a limited set of chemicals were classified as sensitizers or non-sensitizers (Reisinger et al., 2015). Based on this study, the best-performing assays, among them GARD, were selected for a second evaluation phase comprising a larger number of blinded chemicals with human and LLNA data. Here, we report the predictive performance of GARD on this Cosmetics Europe dataset as well as an updated overall predictive accuracy of the assay, calculated using strictly independent sets of test chemicals.

2 Materials and methods

Chemicals and datasets

A dataset for model training, consisting of 40 different cell stimulations in biological triplicates, was defined previously and the respective dataset details are described elsewhere (Johansson et al., 2011; Forreryd et al., 2016). In this study, a total of 73 chemicals (see Tab. 1 for details) were assayed blindly using the above-mentioned training data set. All chemicals were provided by the CE STTF, which also kept the code for the blinded chemicals. All chemicals were stored according to the suppliers' recommendations. In addition to the blinded chemicals of the test set, a set of non-blind benchmark controls (see Tab. S1 at doi: 10.14573/altex. 1701121s for details) were included. The purpose of the benchmark controls was to calibrate the prediction model to the current batch of cells, as described (Forreryd et al., 2016). All chemicals used as benchmark controls were purchased from Sigma Aldrich (St. Louis, MO, USA) and were stored according to the manufacturer's instructions.

Cell maintenance, chemical stimulations, phenotypic analysis and total RNA isolation

All GARD protocols for cell maintenance, cellular stimulation with chemicals, required phenotypical quality control of cells prior to chemical stimulation, and isolation of total RNA have been described previously (Johansson et al., 2013, 2011; Forreryd et al., 2016) and were followed without deviation in this study. The myeloid cell line used in this study was derived from MUTZ-3 (DSMZ, Braunschweig, Germany) and is available via SenzaGen AB (SenzaGen AB, Lund, Sweden). All cellular stimulations were performed in biological triplicates, using separate cell batches for each replicate. Following chemical stimulation, cells were harvested and lysed with TRizol reagent (Thermo Scientific, Waltham, MA), and stored at -20[degrees]C until RNA extraction. Total RNA was isolated from lysed samples using Direct-zol[TM] RNA MiniPrep column purification kit (Zymo Research, Irvine, CA, USA) according to protocols provided by the manufacturer. Total RNA concentrations and RNA integrity were assessed using the Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA). Total RNA was stored at -80[degrees]C until NanoString nCounter analysis.

Gene expression analysis using NanoString technology The design of a custom NanoString CodeSet, corresponding to the GARD prediction signature (GPS), was described recently (Forreryd et al., 2016). All NanoString-associated protocols for gene expression analysis were performed according to instructions by the manufacturer. In short, the custom CodeSet was hybridized with 100 ng total RNA (5 [micro]l at 20 ng/[micro]l) and incubated at 65[degrees]C for 24 h. Hybridized samples were processed in the NanoString GEN2 nCounter Prep Station 5s, using the High Sensitivity protocol, and analyzed in the NanoString Digital Analyzer 5s for digital quantification of each transcript of the GPS, using maximal resolution (555 fields of view). All required equipment, CodeSet and master kit reagents were obtained from NanoString Technologies (NanoString Technologies, Seattle, WA, USA).

Data pre-processing, normalization and analysis Raw nCounter gene expression data was imported into the R statistical environment (R Development Core Team, 2014), in which all downstream analysis was performed. Data was normalized using a counts per total counts (CPTC) algorithm, which reports normalized values for any given gene of the GPS as the ratio of digital counts for the specific gene and the total counts of all measured genes within that sample. Generation of prediction calls for each sample (sensitizer/non-sensitizer) was performed as described previously. Briefly, a support vector machine (SVM) (Cortes and Vapnik, 1995) was trained on the training dataset and used to generate decision values (DVs) for each sample of the benchmark control dataset and test dataset, respectively. The predictive performance of the model was evaluated on the benchmark control dataset using the additional R package ROCR (Sing et al., 2005). Observations of the receiver operating characteristic (ROC) (Lasko et al., 2005) allowed the identification of the prediction model cutoff that achieves the highest accuracy of predictions of the benchmark control dataset, which was subsequently subtracted from all DVs generated from samples of the test dataset. Thus, final predictions were performed on calibrated DVs (cDVs). A specific chemical used for stimulation was classified as a sensitizer if the mean cDV from biological triplicates was greater than zero. The predictive performance of the model's classifications of the test dataset was assessed using Cooper statistics (Cooper et al., 1979).

3 Results

3.1 GARD classifications of the blinded CE-reference panel of chemicals

A set of blinded chemicals was classified as sensitizers or non-sensitizers by the GARD assay using established protocols. GARD predictions of the chemicals used in this study are presented in Table 1. Calculations of various predictive performance parameters based on Cooper statistics are presented in Table 2. For the purpose of binary predictions, a composite reference was defined to classify a sensitizer as a compound that is categorized as having a human potency (HP) (Basketter et al., 2014) of 1-4, or being categorized as HP 5, if it is also predicted as a sensitizer by the LLNA. Consequently, compounds categorized as HP 5, predicted as non-sensitizers by the LLNA, are here defined as non-sensitizers, together with all compounds of HP 6. This binary classification system perfectly correlates with the Global Harmonization System (GHS) / Classification for Labelling and Packaging (CLP) classifications. By this definition, based on the current data, the accuracy, specificity and sensitivity of GARD, is 83%, 56% and 93%, respectively. Comparing GARD predictions strictly with either HP or LLNA, the concordance was estimated to be 81% and 76%, respectively. The mean magnitudes of the cDVs are visualized in box-and-whisker plots in Figure 1, grouped according to their sensitizing potency as defined by the GHS/ CLP system. The observed differences in mean cDVs indicate that the GARD predictions correlate with potency classifications.

3.2 Accumulated GARD performance parameters across historical datasets

In order to relate the current results to previously published figures of predictive performance, an update of accumulated Cooper statistics for independent GARD assessments across various datasets is presented in Table 3. Combining datasets from a total of 127 chemicals, the accuracy of GARD was calculated to be 86%.

4 Discussion

In the last decade, substantial efforts have been made to develop and validate alternative non-animal assays for the assessment of chemical sensitizers in order to meet changing regulatory and industrial demands. The current leading opinion is that no single assay is likely to provide sufficient information for accurate safety assessment of chemicals as a stand-alone test. This notion is supported by the data generated by currently validated tests and the subsequent recommendations given by EURL ECVAM (EC, 2013, 2014, 2015). For this reason, it is of great importance to continuously compare and evaluate novel and already established test methods using coherent reference chemical panels in order to prioritize assays that display superior functionality and predictivity when designing IATAs, or in the quest for stand-alone tests.

In this report, we present novel data regarding the functionality and predictive performance of GARD, generated in a blind study performed in association with the CE STTF. In this independent dataset, GARD accurately classified 83% out of a total of 72 chemicals for skin sensitization hazard. Adding this figure to previously published data from independent evaluation studies, GARD displays an accumulated accuracy of 86%, based on the classification of a total of 127 chemicals.

It is appropriate at this point to consider the gold standard of sensitization assessment, i.e., the reference against which such performance estimations are calculated. In this report, comparisons have been made with both LLNA classifications and human potency (HP), as defined by Basketter et al. (2014). The concordance of GARD with these metrics was 76% and 81%, respectively. Of note, the concordance between LLNA and HP within the same data is 78%, clearly demonstrating that perfect correlation with either metric is mutually exclusive. In particular, HP category 5 includes numerous compounds that have historically been classified as both sensitizers (e.g., hexyl cinnamic aldehyde) and non-sensitizers (e.g., isopropanol). For this reason, a composite reference was proposed for binary classifications, in which a sensitizer was defined to include HP categories 1-4, together with chemicals of the HP category 5, for which the LLNA classification was positive. Still, looking at the present data, we find that GARD misclassifications are overrepresented in HP category 5. Considering only chemicals assigned within the HP categories 1-4, GARD accurately predicts 91% as sensitizers, while the corresponding accuracy within category 5 is 80%. Based on the reasoning above, it is logical to assume that the annotations provided as a reference may include errors based on flawed conclusions, as discussed (Basketter et al., 2014).

On a chemical by chemical basis, false GARD classifications were obtained for thioglycerol, benzoyl peroxide, penicillin G, hexyl salicylate (false negatives, HP category 1-4), hydrocortisone, methyl salicylate, triethanolamine, propyl paraben (false positives, HP category 5) and tocopherol, diethyl phthalate, diethyl toluamide and Tween 80 (false positives, HP category 6).

For the false negatives, the obvious common denominator is that a majority fails to induce any cytotoxic effect in the present cellular system. It should be noted, however, that inducing cytotoxic effects is not a requirement for the successful assessment of a sensitizer. Indeed, numerous examples of correctly classified sensitizers that do not induce cytotoxicity are available within this dataset. Correspondingly, toxic effects are not exclusively induced by sensitizers. It has previously been observed that non-toxic compounds are overrepresented among false negatives (Johansson et al., 2014). Furthermore, the connection between toxic or irritating effects and induction of sensitization has previously been discussed by other authors (Nukada et al., 2011). Thus, the overrepresentation of misclassifications among non-toxic sensitizers is a problem shared with many cell-based assays. For false negatives that do induce cytotoxicity, no apparent explanation is available at this point.

The false positives among HP category 5 are, as discussed above, likely related to the ambiguous annotations provided by current gold standards. Indeed, the fact that they are listed within HP category 5 separates them from true non-sensitizers, at least by one metric, suggesting that observed LLNA classifications are non-concordant with the effects observed in the clinic. As an example, clinical cases of sensitization towards hydrocortisone are indeed not infrequent (Burden and Beck, 1992). Thus, the correctness of calling such substances non-sensitizers, and thereby concluding that GARD produces misclassifications, is certainly controversial.

Finally, false positives within HP category 6 include tocopherol, which is classified as a moderate sensitizer by the LLNA. Furthermore, diethyl phthalate and diethyl toluamide are both frequently classified as positives in cell-based assays (Ashikaga et al., 2010; Piroird et al., 2015), while Tween 80 is consistently classified as a sensitizer in numerous assays (Emter et al., 2013; Piroird et al., 2015; Ramirez et al., 2014). Indeed, the sensitizing capacity of Tween 80 has been closely examined and confirmed to be evident both before and after oxidation (Bergh et al., 1997). Consequently, the inherent difficulty of accurately assessing these compounds should rather be regarded as general. Naturally, these aspects were a contributing factor to including such compounds in the blinded dataset used in this study, likely skewing the estimated specificity within the dataset towards lower figures compared to what would be expected in broader chemical domains.

During GARD development, it was observed that the relative magnitude of the GARD decision values correlates with sensitizing potency (Johansson et al., 2011), a hypothesis that has been maintained since. In light of the above discussed ambiguities regarding sensitizing potency, as estimated by current gold standards, GARD development towards potency assessment focuses on the distinction between strong and weak sensitizers in accordance with the GHS/CLP classification system. In Figure 1, the cDVs of the test substances are grouped according to this system. From the current data it is clear that the hypothesis based on earlier observations prevails, since strong sensitizers (1A) on average generate higher DVs compared to weak sensitizers (1B). Furthermore, it is evident that the cytotoxicity of a chemical is also related to its sensitizing potency. In current GARD protocols, cytotoxic compounds are used at concentrations that maintain 90% relative cell viability. From Figure 1, it is evident that strong sensitizers (1A) are on average assayed at lower concentrations compared to weak sensitizers (1B), due to their higher levels of cytotoxic effects. While the GARD platform indeed holds information regarding sensitizing potency, there is an overlap between the different categories, which presently hampers its utilization for accurate potency assessment. However, the harnessing of accurate potency information is currently being refined for accurate sub-categorization (manuscript in preparation).

In conclusion, we here report data of GARD performance on an extended, blinded set of chemicals. Taken together, GARD is consistently functional across datasets, with a predictive accuracy of 83% in this Cosmetics Europe dataset and average predictive accuracy of 86% in a combined dataset of 127 chemicals for skin sensitization hazard.


Received January 12, 2017; Accepted February 14, 2017; Epub February 17, 2017;


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Conflict of interest

The authors are employed or collaborate with SenzaGen, a company which commercializes the GARD test.


The authors would like to thank the Cosmetics Europe Skin Tolerance Task Force for providing the chemicals of the test data set and ensuring the blind integrity of the study.

Correspondence to

Henrik Johansson, PhD

SenzaGen AB

Medicon Village

Scheelevagen 2

22381 Lund, Sweden

Phone: +46 704 492724


Henrik Johansson (1), Robin Gradin (1), Andy Forreryd (2), Maria Agemark (1), Kathrin Zeller (2), Angelica Johansson (1), Olivia Larne (1), Erwin van Vliet (3), Carl Borrebaeck (2) andMalin Lindstedt (2)

(1) SenzaGen AB, Lund, Sweden; (2) Dept. of Immunotechnology, Lund University, Lund, Sweden; (3) Cosmetics Europe The Personal Care Association, Brussels, Belgium

Caption: Fig. 1: GARD predictions correlate with potency classifications

Box-and-whisker plots of mean GARD cDVs, grouped by sensitizing potency as defined by the GHS/CLP classification system. Only test substances for which such classifications are available are included, see Table 1 (n chemicals = 52). The color of each data point is mapped to the GARD input concentration ([micro]M) used for that test substance.
Tab. 1: Dataset details and test results

Chemical identifiers                         References

Substance ID                    CAS          LLNA       HP   GHS/CLP


1,4-phenylenediamine            106-50-3     strong     1    1A
Tetrachlorosalicylanilide       1154-59-2    extreme    1    --
Dimethyl fumarate               624-49-7     strong     1    --
2-aminophenol                   95-55-6      strong     2    1A
2-Nitro-1,4-phenylenediamine    5307-14-2    moderate   2    1A
Formaldehyde (act.. 37%)        50-00-0      strong     2    1A
Glutaraldehyde (act. 50%)       111-30-8     extreme    2    1A
Methyl heptine carbonate        111-12-6     strong     2    1A
Propyl gallate                  121-79-9     strong     2    1A
Toluene diamine sulphate        615-50-9     strong     2    --
Glyoxal (act. 40%)              107-22-2     strong     2    1A
Isoeugenol                      97-54-1      moderate   2    1A
1,2-Benzisothiazolin-3-one      2634-33-5    moderate   2    --
3-dimethylaminopropylamine      109-55-7     moderate   2    --
Thioglycerol                    96-27-5      moderate   2    --
Lyral                           31906-04-4   weak       2    1B
Chlorpromazine                  50-53-3      moderate   3    1A
Benzoyl peroxide                94-36-0      extreme    3    --
Bisphenol A-diglycidyl ether    1675-54-3    moderate   3    1A
Ethylene diamine                107-15-3     moderate   3    1B
Glyceryl monothioglycolate      30618-84-9   moderate   3    1B
Farnesol                        4602-84-0    moderate   3    --
Abietic acid                    514-10-3     weak       3    1B
Butyl glycidyl ether            2426-08-6    weak       3    1B
Cinnamic alcohol                104-54-1     weak       3    1B
Citral                          5392-40-5    moderate   3    1B
Eugenol                         97-53-0      weak       3    1B
Imidazolidinyl urea             39236-46-9   weak       3    1B
Penicillin G                    61-33-6      weak       3    --
5-methyl-2,3-hexanedione        13706-86-0   weak       3    --
Coumarin                        91-64-5      NS         3    --
Hexyl salicylate                6259-76-3    strong     4    1A
Iodopropynyl butylcarbamate     55406-53-6   strong     4    1A
Neomycin sulphate               1405-10-3    NS         4    --
Resorcinol                      108-46-3     moderate   4    1B
Amylcinnamyl alcohol            101-85-9     NS         4    1B
Aniline                         62-53-3      weak       4    1B
Benzocaine                      94-09-7      NS         4    1B
Geraniol                        106-24-1     weak       4    1B
Lillial                         80-54-6      weak       4    1B
Linalool                        78-70-6      weak       4    1B
Amyl cinnamic aldehyde          122-40-7     weak       4    --
Carvone                         6485-40-1    weak       4    --
Kanamycin                       70560-51-9   NS         4    --
Anethole                        104-46-1     moderate   5    1B
Anisyl alcohol                  105-13-5     moderate   5    1B
Benzyl salicylate               118-58-1     moderate   5    --
Limonene                        5989-27-5    weak       5    1B
Hexyl cinnamic aldehyde         101-86-0     weak       5    1B
Benzyl benzoate                 120-51-4     weak       5    1B
Citronellol                     106-22-9     weak       5    1B
Diethanolamine                  111-42-2     weak       5    1B
Pentachlorophenol               87-86-5      weak       5    1B
Pyridine                        110-86-1     weak       5    1B


Hydrocortisone                  50-23-7      NS         5    no cat.
Isopropanol                     67-63-0      NS         5    no cat.
Methyl salicylate               119-36-8     NS         5    no cat.
Phenoxyethanol                  122-99-6     NS         5    no cat.
Propylene glycol                57-55-6      NS         5    no cat.
Triethanolamine                 102-71-6     NS         5    --
4-aminobenzoic acid             150-13-0     NS         5    no cat.
Benzaldehyde                    100-52-7     NS         5    no cat.
Propyl paraben                  94-13-3      NS         5    --
Vanillin                        121-33-5     NS         5    no cat.
Dextran                         9004-54-0    NS         6    no cat.
Glycerol/Glycerin               56-81-5      NS         6    no cat.
Octanoic acid                   124-07-2     NS         6    no cat.
Phenol                          108-95-2     NS         6    no cat.
Tocopherol                      59-02-9      moderate   6    --
Diethyl phthalate               84-66-2      NS         6    no cat.
Diethyl toluamide               134-62-3     NS         6    --
Tween 80                        9005-65-6    NS         6    no cat.

Chemical identifiers            Assay parameters

Substance ID                    vehicle   c.max   c.rv90   c.input


1,4-phenylenediamine            DMSO      500     70       70
Tetrachlorosalicylanilide       DMSO      500     20       20
Dimethyl fumarate               DMSO      500     --       90
2-aminophenol                   DMSO      500     80       80
2-Nitro-1,4-phenylenediamine    DMSO      500     200      200
Formaldehyde (act.. 37%)        DMSO      500     260      260
Glutaraldehyde (act. 50%)       DMSO      500     100      100
Methyl heptine carbonate        DMSO      500     50       50
Propyl gallate                  DMSO      500     100      100
Toluene diamine sulphate        DMSO      500     100      100
Glyoxal (act. 40%)              DMSO      500     --       500
Isoeugenol                      DMSO      500     500      500
1,2-Benzisothiazolin-3-one      DMSO      500     12.5     12.5
3-dimethylaminopropylamine      DMSO      500     --       500
Thioglycerol                    DMSO      500     --       500
Lyral                           DMSO      400     200      200
Chlorpromazine                  DMSO      100     10       10
Benzoyl peroxide                DMSO      500     85       85
Bisphenol A-diglycidyl ether    DMSO      200     50       50
Ethylene diamine                DMSO      500     --       500
Glyceryl monothioglycolate      DMSO      500     200      200
Farnesol                        DMSO      500     --       500
Abietic acid                    DMSO      200     --       200
Butyl glycidyl ether            DMSO      500     480      480
Cinnamic alcohol                DMSO      500     --       500
Citral                          DMSO      500     80       80
Eugenol                         DMSO      500     400      400
Imidazolidinyl urea             dH2O      500     50       50
Penicillin G                    DMSO      500     --       500
5-methyl-2,3-hexanedione        DMSO      500     --       500
Coumarin                        DMSO      500     --       500
Hexyl salicylate                DMSO      500     120      120
Iodopropynyl butylcarbamate     DMSO      500     10       10
Neomycin sulphate               dH2O      500     --       500
Resorcinol                      dH2O      500     --       500
Amylcinnamyl alcohol            DMSO      500     260      260
Aniline                         DMSO      500     --       500
Benzocaine                      DMSO      500     --       500
Geraniol                        DMSO      500     --       500
Lillial                         DMSO      500     160      160
Linalool                        DMSO      500     --       500
Amyl cinnamic aldehyde          DMSO      500     110      110
Carvone                         DMSO      500     --       500
Kanamycin                       dH2O      200     --       200
Anethole                        DMSO      500     --       500
Anisyl alcohol                  DMSO      500     --       500
Benzyl salicylate               DMSO      500     200      200
Limonene                        DMSO      500     --       500
Hexyl cinnamic aldehyde         DMSO      500     100      100
Benzyl benzoate                 DMSO      500     500      500
Citronellol                     DMSO      500     --       500
Diethanolamine                  DMSO      500     --       500
Pentachlorophenol               DMSO      200     150      150
Pyridine                        DMSO      500     --       500


Hydrocortisone                  DMSO      500     --       500
Isopropanol                     DMSO      500     --       500
Methyl salicylate               DMSO      500     --       500
Phenoxyethanol                  DMSO      500     --       500
Propylene glycol                DMSO      500     --       500
Triethanolamine                 DMSO      500     --       500
4-aminobenzoic acid             DMSO      500     --       500
Benzaldehyde                    DMSO      500     --       500
Propyl paraben                  DMSO      500     --       500
Vanillin                        DMSO      500     --       500
Dextran                         DMSO      40      --       40
Glycerol/Glycerin               DMSO      500     --       500
Octanoic acid                   DMSO      500     --       500
Phenol                          DMSO      500     --       500
Tocopherol                      DMSO      100     --       100
Diethyl phthalate               DMSO      500     --       500
Diethyl toluamide               DMSO      500     --       500
Tween 80                        DMSO      500     13       13

Chemical identifiers                        GARD output

Substance ID                    cDV ([+ or -]SD)      Prediction


1,4-phenylenediamine            5.8    [+ or -] 0.6   S
Tetrachlorosalicylanilide       3.3    [+ or -] 0.2   S
Dimethyl fumarate               5.9    [+ or -] 0.4   S
2-aminophenol                   6.1    [+ or -] 1.6   S
2-Nitro-1,4-phenylenediamine    3.6    [+ or -] 0.5   S
Formaldehyde (act.. 37%)        1.2    [+ or -] 0.5   S
Glutaraldehyde (act. 50%)       2.8    [+ or -] 2.2   S
Methyl heptine carbonate        0.2    [+ or -] 0.8   S
Propyl gallate                  6.7    [+ or -] 1.9   S
Toluene diamine sulphate        1.9    [+ or -] 1     S
Glyoxal (act. 40%)              0.8    [+ or -] 1.1   S
Isoeugenol                      6      [+ or -] 0.4   S
1,2-Benzisothiazolin-3-one      1.6    [+ or -] 0.7   S
3-dimethylaminopropylamine      0.3    [+ or -] 0.2   S
Thioglycerol                    -0.8   [+ or -] 0.5   NS
Lyral                           2.9    [+ or -] 0.6   S
Chlorpromazine                  1.9    [+ or -] 0.9   S
Benzoyl peroxide                -1.1   [+ or -] 0.4   NS
Bisphenol A-diglycidyl ether    3.4    [+ or -] 1.9   S
Ethylene diamine                1.4    [+ or -] 2.5   S
Glyceryl monothioglycolate      1.2    [+ or -] 1.5   S
Farnesol                        2.1    [+ or -] 0.9   S
Abietic acid                    1.3    [+ or -] 1.3   S
Butyl glycidyl ether            3.6    [+ or -] 2.1   S
Cinnamic alcohol                8.8    [+ or -] 1     S
Citral                          5.8    [+ or -] 0.7   S
Eugenol                         2.6    [+ or -] 0.4   S
Imidazolidinyl urea             1.5    [+ or -] 2.4   S
Penicillin G                    -0.8   [+ or -] 0.9   NS
5-methyl-2,3-hexanedione        3.1    [+ or -] 0.9   S
Coumarin                        0.3    [+ or -] 0.8   S
Hexyl salicylate                -0.9   [+ or -] 0.1   NS
Iodopropynyl butylcarbamate     0.7    [+ or -] 1.5   S
Neomycin sulphate               0.7    [+ or -] 2     S
Resorcinol                      2      [+ or -] 1.1   S
Amylcinnamyl alcohol            2.1    [+ or -] 1.3   S
Aniline                         0.4    [+ or -] 2.2   S
Benzocaine                      0.8    [+ or -] 1.7   S
Geraniol                        2.4    [+ or -] 1.7   S
Lillial                         1.7    [+ or -] 0.5   S
Linalool                        0.6    [+ or -] 0.8   S
Amyl cinnamic aldehyde          5.3    [+ or -] 1.2   S
Carvone                         2.3    [+ or -] 0.7   S
Kanamycin                       0.2    [+ or -] 1.2   S
Anethole                        2.3    [+ or -] 1.5   S
Anisyl alcohol                  0.1    [+ or -] 1.5   S
Benzyl salicylate               0.6    [+ or -] 1.4   S
Limonene                        0      [+ or -] 0.4   S
Hexyl cinnamic aldehyde         1.1    [+ or -] 0.8   S
Benzyl benzoate                 2.3    [+ or -] 1.8   S
Citronellol                     1.8    [+ or -] 0.7   S
Diethanolamine                  0.5    [+ or -] 0     S
Pentachlorophenol               3.1    [+ or -] 0.8   S
Pyridine                        0.4    [+ or -] 0.2   S


Hydrocortisone                  5.9    [+ or -] 0.1   S
Isopropanol                     -0.9   [+ or -] 0.8   NS
Methyl salicylate               0.2    [+ or -] 2.4   S
Phenoxyethanol                  -0.3   [+ or -] 1.3   NS
Propylene glycol                -1.3   [+ or -] 0.7   NS
Triethanolamine                 0.2    [+ or -] 2.4   S
4-aminobenzoic acid             -1.4   [+ or -] 0.8   NS
Benzaldehyde                    0      [+ or -] 1.2   NS
Propyl paraben                  5.3    [+ or -] 0.1   S
Vanillin                        -2.4   [+ or -] 0.9   NS
Dextran                         -1     [+ or -] 0.5   NS
Glycerol/Glycerin               -0.5   [+ or -] 0.8   NS
Octanoic acid                   -0.2   [+ or -] 1.1   NS
Phenol                          -0.3   [+ or -] 1.9   NS
Tocopherol                      0.7    [+ or -] 1.7   S
Diethyl phthalate               1.9    [+ or -] 1     S
Diethyl toluamide               1.3    [+ or -] 0.4   S
Tween 80                        1.9    [+ or -] 1.4   S

LLNA, Local Lymph Node Assay (as listed in the CE STTF
database); HP, human potency (as listed in Basketter et
al., 2014); GHS/CLP, Global Harmonization System /
Classification for Labelling and Packaging (as listed in
Piroird et al., 2015); c.max, maximum concentration of
titration range [micro]M); rv90, in-well concentration
yielding 90% relative viability ([micro]M); c.input,
concentration used for cell stimulation, derived from
c.max and c.rv90 [micro]M); c.DV, calibrated decision value;
NS, non-sensitizer; S, sensitizer. For details, see
Johansson et al., 2013.

Tab. 2: Cooper statistics of current data

Characteristic    LLNA   HP   Composite

Accuracy (%)      76     81   83
Sensitivity (%)   90     84   93
Specificity (%)   45     50   56

LLNA, Local Lymph Node Assay; HP, human potency

Tab. 3: Accumulated predictive performance

Dataset               Sensitivity     Specificity

GARD in-house         89%   (17/19)   86%   (6/7)
Technology transfer   94%   (16/17)   83%   (10/12)
  and method
Current study         93%   (50/54)   56%   (10/18)
Accumulated           92%   (83/90)   70%   (26/37)

Dataset               Accuracy          Source

GARD in-house         88%   (23/26)     Johansson, 2014
Technology transfer   90%   (26/29)     Forreryd, 2016
  and method
Current study         83%   (60/72)     --
Accumulated           86%   (109/127)   --
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Article Details
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Author:Johansson, Henrik; Gradin, Robin; Forreryd, Andy; Agemark, Maria; Zeller, Kathrin; Johansson, Angeli
Publication:ALTEX: Alternatives to Animal Experimentation
Article Type:Report
Geographic Code:4E
Date:Sep 22, 2017
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