Binary test battery with KeratinoSens™ and h-CLAT as part of a bottom-up approach for skin sensitization hazard prediction

Skin sensitization is one of the key safety endpoints for chemicals applied directly to the skin. Several integrated testing strategies (ITS) using multiple non-animal test methods have been developed to accurately evaluate the sensitizing potential of chemicals, but there is no regulatory-accepted ITS to classify a chemical as a non-sensitizer. In this study, the predictive performance of a binary test battery with KeratinoSens™ and h-CLAT compared to the local lymph node assay (LLNA) and human data was examined using comprehensive dataset of 203 chemicals. When two negative results indicate a non-sensitizer, the binary test battery provided sensitivity of 93.4% or 94.4% compared with the LLNA or human data. Taking into account the predictive limitations (i.e. high log Kow, pre-/pro-haptens and acyl transfer agents (or amine-reactive)), the binary test battery had extremely high sensitivity comparable to that of the 3 out of 3 ITS where three negative results of the DPRA, KeratinoSens™ and h-CLAT indicate a non-sensitizer. Therefore, the data from KeratinoSens™ or h-CLAT may provide partly redundant information on the molecular initiating event derived from DPRA. Taken together, the binary test battery of KeratinoSens™ and h-CLAT could be used as part of a bottom-up approach for skin sensitization hazard prediction.     

Identifying and characterizing chemical skin sensitizers without animal testing: Colipa’s research and method development program

The sensitizing potential of chemicals is usually identified and characterized using one of the available animal test methods, such as the mouse local lymph node assay. Due to the increasing public and political concerns regarding the use of animals for the screening of new chemicals, the Colipa Skin Tolerance Task Force collaborates with and/or funds research groups to increase and apply our understanding of the events occurring during the acquisition of skin sensitization. Knowledge gained from this research is used to support the development and evaluation of novel alternative approaches for the identification and characterization of skin sensitizing chemicals. At present one in chemico (direct peptide reactivity assay (DPRA)) and two in vitro test methods (cell based assays (MUSST and h-CLAT)) have been evaluated within Colipa inter-laboratory ring trials and accepted by the European Centre for the Validation of Alternative Methods (ECVAM) for pre-validation. Data from all three test methods will be used to support the development of testing strategy approaches for skin sensitizer potency prediction. The replacement of the need for animal testing for skin sensitization risk assessment is viewed as ultimately achievable and the next couple of years should set the timeline for this milestone.     

Peptide reactivity associated with skin sensitization: The QSAR Toolbox and TIMES compared to the DPRA

The molecular initiating event (MIE) of skin sensitization is the binding of a hapten to dermal proteins. This can be assessed using the in chemico direct peptide reactivity assay (DPRA) or in silico tools such as the QSAR Toolbox and TIMES SS. In this study, the suitability of these methods was analyzed by comparing their results to in vivo sensitization data of LLNA and human studies.Compared to human data, 84% of non-sensitizers and sensitizers yielded consistent results in the DPRA. In silico tools resulted in ‘no alert’ for 83%–100% of the non-sensitizers, but alerted only 55%–61% of the sensitizers. The inclusion of biotic and abiotic transformation simulations yielded more alerts for sensitizers, but simultaneously dropped the number of non-alerted non-sensitizers. In contrast to the DPRA, in silico tools were more consistent with results of the LLNA than human data. Interestingly, the new “DPRA profilers” (QSAR Toolbox) provided unsatisfactory results.Additionally, the results were combined in the ‘2 out of 3’ prediction model with in vitro data derived from LuSens and h-CLAT. Using DPRA results, the model identified 90% of human sensitizers and non-sensitizers; using in silico results (including abiotic and biotic activations) instead of DPRA results led to a comparable high predictivity.     

Prediction of skin sensitization potency of chemicals by human Cell Line Activation Test (h-CLAT) and an attempt at classifying skin sensitization potency

The human Cell Line Activation Test (h-CLAT), an in vitro skin sensitization test, is based on the augmentation of CD86 and CD54 expression in THP-1 cells following exposure to chemicals. The h-CLAT was found to be capable of determining the hazard of skin sensitization. In contrast, the local lymph node assay (LLNA), widely used as a stand-alone method in Europe and US, identifies the same hazard, but also classifies the potency by using the estimated concentration of SI = 3 (EC3). In this study, several values calculated from the h-CLAT data were evaluated for its correlation to the LLNA EC3 determination. A statistically significant correlation was observed between h-CLAT concentration providing a cell viability of 75% (CV75), h-CLAT estimated concentration of RFI = 150 for CD86 (EC150), and for CD54 (EC200) with LLNA’s EC3. From EC150 and EC200, a minimum induction threshold (MIT) was determined as the smaller of either EC150 or EC200. MIT showed a correlation with EC3 (R = 0.638). Also, MIT had an approximate 80% accuracy for sub-categories of the globally harmonized system (GHS) when a tentative threshold of 13 μg/mL was used. From these data, the h-CLAT values may be one of the useful tools to predict the allergic potency of chemicals.     

Skin sensitization risk assessment model using artificial neural network analysis of data from multiple in vitro assays

The sensitizing potential of chemicals is usually identified and characterized using in vivo methods such as the murine local lymph node assay (LLNA). Due to regulatory constraints and ethical concerns, alternatives to animal testing are needed to predict skin sensitization potential of chemicals. For this purpose, combined evaluation using multiple in vitro and in silico parameters that reflect different aspects of the sensitization process seems promising.We previously reported that LLNA thresholds could be well predicted by using an artificial neural network (ANN) model, designated iSENS ver.1 (integrating in vitro sensitization tests version 1), to analyze data obtained from two in vitro tests: the human Cell Line Activation Test (h-CLAT) and the SH test. Here, we present a more advanced ANN model, iSENS ver.2, which additionally utilizes the results of antioxidant response element (ARE) assay and the octanol–water partition coefficient (Log P, reflecting lipid solubility and skin absorption). We found a good correlation between predicted LLNA thresholds calculated by iSENS ver.2 and reported values. The predictive performance of iSENS ver.2 was superior to that of iSENS ver.1. We conclude that ANN analysis of data from multiple in vitro assays is a useful approach for risk assessment of chemicals for skin sensitization.     

Prediction of skin sensitization potency using machine learning approaches

The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non‐sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non‐animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens™ assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave‐one‐out cross‐validation. A one‐tiered strategy modeled all three categories of response together while a two‐tiered strategy modeled sensitizer/non‐sensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two‐tiered model using the support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one‐tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60 of 87 substances correctly categorized). These results suggest that computational models using non‐animal methods may provide valuable information for assessing skin sensitization potency. Copyright © 2017 John Wiley & Sons, Ltd.     

Can currently available non-animal methods detect pre and pro-haptens relevant for skin sensitization?

Predictive testing to characterize substances for their skin sensitization potential has historically been based on animal tests such as the Local Lymph Node Assay (LLNA). In recent years, regulations in the cosmetics and chemicals sectors have provided strong impetus to develop non-animal alternatives. Three test methods have undergone OECD validation: the direct peptide reactivity assay (DPRA), the KeratinoSens™ and the human Cell Line Activation Test (h-CLAT). Whilst these methods perform relatively well in predicting LLNA results, a concern raised is their ability to predict chemicals that need activation to be sensitizing (pre- or pro-haptens). This current study reviewed an EURL ECVAM dataset of 127 substances for which information was available in the LLNA and three non-animal test methods. Twenty eight of the sensitizers needed to be activated, with the majority being pre-haptens. These were correctly identified by 1 or more of the test methods. Six substances were categorized exclusively as pro-haptens, but were correctly identified by at least one of the cell-based assays. The analysis here showed that skin metabolism was not likely to be a major consideration for assessing sensitization potential and that sensitizers requiring activation could be identified correctly using one or more of the current non-animal methods.     

Evaluation of skin sensitization induced by four ionic liquids

Ionic liquids (ILs) are synthetic solvents used as replacements for volatile organic solvents. Human exposure occurs through dermal or oral routes. In rodents, several ILs were reported to induce dermal toxicity, irritation, and sensitization. Due to the potential for occupational exposure, and industrial use as nonvolatile solvents, 1-ethyl-3-methylimidazolium chloride (EMIM, 6.25% to 50% v/v), 1-butyl-3-methylimidazolium chloride (BMIM, 3.12% to 12.5% v/v), 1-butyl-1-methylpyrrolidinium chloride (BMPY, 0.825% to 6.25% v/v), and N-butylpyridinium chloride (NBuPY, 0.825% to 12.5% v/v) were nominated to the National Toxicology Program and evaluated for skin sensitization. The test compound was applied to the ears of female BALB/c mice daily for 3 days in a primary irritancy (IRR)/local lymph node assay (LLNA). Sensitization was assessed in vitro in the direct peptide reactivity assay (DPRA), KeratinoSens™ assay, and human cell line activation test (h-CLAT). In the LLNA, the butylated ILs, BMIM, and BMPY were more potent than NBuPY (butylated) or EMIM (ethylated), which was neither an irritant nor a sensitizer. NBuPY induced skin irritation in vivo at ≥3.12% (p ≤ 0.01), and sensitization in vitro in the KeratinoSens™ assay and h-CLAT, but was negative for sensitization in vivo and in the DPRA. Although SI3 was not achieved, dermal treatment with 12.5% BMIM or 6.25% BMPY increased (p ≤ 0.01) lymph node cell proliferation in the LLNA. In vitro, BMIM was positive for sensitization in the h-CLAT, and BMPY was positive in the h-CLAT and KeratinoSens™ assay; both were negative in the DPRA. Integrated data analyses, weighted toward in vivo data, suggested that BMIM and BMPY may induce weak to mild sensitization.     

Non-animal methods to predict skin sensitization (I): the Cosmetics Europe database*

Cosmetics Europe, the European Trade Association for the cosmetics and personal care industry, is conducting a multi-phase program to develop regulatory accepted, animal-free testing strategies enabling the cosmetics industry to conduct safety assessments. Based on a systematic evaluation of test methods for skin sensitization, five non-animal test methods (DPRA (Direct Peptide Reactivity Assay), KeratinoSens^TM, h-CLAT (human cell line activation test), U-SENS^TM, SENS-IS) were selected for inclusion in a comprehensive database of 128 substances. Existing data were compiled and completed with newly generated data, the latter amounting to one-third of all data. The database was complemented with human and local lymph node assay (LLNA) reference data, physicochemical properties and use categories, and thoroughly curated. Focused on the availability of human data, the substance selection resulted nevertheless resulted in a high diversity of chemistries in terms of physico-chemical property ranges and use categories. Predictivities of skin sensitization potential and potency, where applicable, were calculated for the LLNA as compared to human data and for the individual test methods compared to both human and LLNA reference data. In addition, various aspects of applicability of the test methods were analyzed. Due to its high level of curation, comprehensiveness, and completeness, we propose our database as a point of reference for the evaluation and development of testing strategies, as done for example in the associated work of Kleinstreuer et al. We encourage the community to use it to meet the challenge of conducting skin sensitization safety assessment without generating new animal data.     

Accounting for data variability,a key factor in in vivo/in vitro relationships: application to the skin sensitization potency (in vivo LLNA versus in vitro DPRA) example

When searching for alternative methods to animal testing, confidently rescaling an in vitro result to the corresponding in vivo classification is still a challenging problem. Although one of the most important factors affecting good correlation is sample characteristics, they are very rarely integrated into correlation studies. Usually, in these studies, it is implicitly assumed that both compared values are error‐free numbers, which they are not. In this work, we propose a general methodology to analyze and integrate data variability and thus confidence estimation when rescaling from one test to another. The methodology is demonstrated through the case study of rescaling the in vitro Direct Peptide Reactivity Assay (DPRA) reactivity to the in vivo Local Lymph Node Assay (LLNA) skin sensitization potency classifications. In a first step, a comprehensive statistical analysis evaluating the reliability and variability of LLNA and DPRA as such was done. These results allowed us to link the concept of gray zones and confidence probability, which in turn represents a new perspective for a more precise knowledge of the classification of chemicals within their in vivo OR in vitro test. Next, the novelty and practical value of our methodology introducing variability into the threshold optimization between the in vitro AND in vivo test resides in the fact that it attributes a confidence probability to the predicted classification. The methodology, classification and screening approach presented in this study are not restricted to skin sensitization only. They could be helpful also for fate, toxicity and health hazard assessment where plenty of in vitro and in chemico assays and/or QSARs models are available. Copyright © 2016 John Wiley & Sons, Ltd.     

Applicability of amino acid derivative reactivity assay (4 mM) for the prediction of skin sensitization by combining multiple alternative methods to evaluate key events

The amino acid derivative reactivity assay (ADRA) is an alternative method for evaluating key event 1 (KE-1) in the skin sensitization mechanism included in OECD TG442C (OECD, 2021). Recently, we found that ADRA with a 4-mM test chemical solution had a higher accuracy than the original ADRA (1 mM). However, ADRA (4 mM) has yet to be evaluated using integrated approaches to testing and assessment (IATA), a combination of alternative methods for evaluating KE. In this study, the sensitization potency of three defined approaches (DAs) using ADRA (4 mM) as KE-1 was predicted and compared with those of two additional ADRAs or direct peptide reactivity assay (DPRA): (i) “2 out of 3” approach, (ii) “3 out of 3” approach, and (iii) integrated testing strategy (ITS). In the hazard identification of chemical sensitizers, the accuracy of human data and local lymph node assay (LLNA) remained almost unchanged among the three approaches evaluated. Potency classifications for sensitization were predicted with the LLNA and human data sets using ITS. The potency classifications for the sensitization potency prediction accuracy of LLNA data using any alternative method were almost unchanged, at approximately 70%, and those with ITS were not significantly different. When ITS was performed using DPRA, the prediction accuracy was approximately 73% for human data, which was similar to that of the LLNA data; however, the accuracy tended to increase for all ADRA methods. In particular, when ITS was performed using ADRA (4 mM), the prediction accuracy was approximately 78%, which proved to be a practical level.     

Integrated decision strategies for skin sensitization hazard

One of the top priorities of the Interagency Coordinating Committee for the Validation of Alternative Methods (ICCVAM) is the identification and evaluation of non‐animal alternatives for skin sensitization testing. Although skin sensitization is a complex process, the key biological events of the process have been well characterized in an adverse outcome pathway (AOP) proposed by the Organisation for Economic Co‐operation and Development (OECD). Accordingly, ICCVAM is working to develop integrated decision strategies based on the AOP using in vitro, in chemico and in silico information. Data were compiled for 120 substances tested in the murine local lymph node assay (LLNA), direct peptide reactivity assay (DPRA), human cell line activation test (h‐CLAT) and KeratinoSens assay. Data for six physicochemical properties, which may affect skin penetration, were also collected, and skin sensitization read‐across predictions were performed using OECD QSAR Toolbox. All data were combined into a variety of potential integrated decision strategies to predict LLNA outcomes using a training set of 94 substances and an external test set of 26 substances. Fifty‐four models were built using multiple combinations of machine learning approaches and predictor variables. The seven models with the highest accuracy (89–96% for the test set and 96–99% for the training set) for predicting LLNA outcomes used a support vector machine (SVM) approach with different combinations of predictor variables. The performance statistics of the SVM models were higher than any of the non‐animal tests alone and higher than simple test battery approaches using these methods. These data suggest that computational approaches are promising tools to effectively integrate data sources to identify potential skin sensitizers without animal testing. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.     

A dataset on 145 chemicals tested in alternative assays for skin sensitization undergoing prevalidation

Skin sensitization is a key endpoint for cosmetic ingredients, with a forthcoming ban for animal testing in Europe. Four alternative tests have so far been submitted to ECVAM prevalidation: (i) MUSST and (ii) h‐Clat assess surface markers on dendritic cell lines, (iii) the direct peptide reactivity assay (DPRA) measures reactivity with model peptides and (iv) the KeratinoSens^TM assay which is based on detection of Nrf2‐induced luciferase. It is anticipated that only an integrated testing strategy (ITS) based on a battery of tests might give a full replacement providing also a sensitization potency assessment, but this concept should be tested with a data‐driven analysis. Here we report a database on 145 chemicals reporting the quantitative endpoints measured in a U937‐ test, the DPRA and KeratinoSens^TM . It can serve to develop data‐driven ITS approaches as we show in a parallel paper and provides a view as to the current ability to predict with in vitro tests as we are entering 2013. It may also serve as reference database when benchmarking new molecules with in vitro based read‐across and find use as a reference database when evaluating new tests. The tests and combinations thereof were evaluated for predictivity, and overall a similar predictivity was found as before on three‐fold smaller datasets. Analysis of the dose–response parameters of the individual tests indicates a correlation to sensitization potency. Detailed analysis of chemicals false‐negative and false‐positive in two tests helped to define limitations in the tests but also in the database derived from animal studies. Copyright © 2013 John Wiley & Sons, Ltd.     

A new in vitro method for identifying chemical sensitizers combining peptide binding with ARE/EpRE-mediated gene expression in human skin cells

Allergic contact dermatitis (ACD) is a significant safety concern for developers of cosmetic, personal care, chemical, pharmaceutical, and medical device products. The guinea pig maximization test (GMPT) and the murine local lymph node assay (LLNA) are accepted methods for determining chemical sensitization. Recent legislative initiatives in Europe require the development of new in vitro alternatives to animal tests for chemical sensitization. The aim of this project was to develop an in vitro screening method that uses a human skin cell line (HaCaT), chemical reactivity, and gene expression profiling to identify positive and negative responses, to place chemicals into potency categories of extreme/strong (ES), moderate (M), weak (W), and nonsensitizers (N), and to provide an estimate of corresponding LLNA values. The method and processing algorithm were developed from a training set of 39 chemicals possessing a wide range of sensitization potencies. Three cationic metals, chromium (Cr), nickel (Ni), and silver (Ag), were also evaluated in this model. Chemical reactivity was determined by measuring glutathione (GSH) depletion in a cell free matrix. Three signaling pathways (Keap1/Nrf?2/ARE/EpRE, ARNT/AhR/XRE, and Nrf1/MTF/MRE) that are known to be activated by sensitizing agents were monitored by measuring the relative abundance of 11 genes whose expression is controlled by one of these 3 pathways. Final exposure concentrations were based on toxicity and solubility. A range-finding experiment was conducted with each compound to determine cytotoxicity and solubility. Six exposure concentrations (0.1 to 2,500?μM) and an exposure time of 24 hours were used in the final experiments. Glutathione depletion alone did not provide the accuracy necessary to differentiate potency categories. However, chemical reactivity combined with gene expression profiles significantly improved the in vitro predictions. A predicted toxicity index (PTI) was determined for each test chemical. A comparison of LLNA values with PTI values revealed an inverse relationship. The large variation in LLNA data for compounds in the same potency category makes direct extrapolation from PTI to LLNA difficult. To challenge the system, 58 additional compounds were submitted in a blinded manner. Compounds placed into ES and M categories were considered positive, whereas compounds classified as W or N were considered negative. Accuracy was approximately 84%, with a sensitivity of 81% and a specificity of 92%. The model correctly identified 2 of 3 cationic metals as positive. In conclusion, the method described here demonstrates a valuable in vitro method for identifying chemicals and metals that induce skin sensitization.     

Two tiered approaches combining alternative test methods and minimizing the use of reconstructed human cornea-like epithelium tests for the evaluation of eye irritation potency of test chemicals

In order to overcome the limitations of single in vitro eye irritation tests, Integrated Approaches to Testing Assessment strategies have been suggested for evaluating eye irritation. This study developed two tiered approaches combining alternative test methods. They were designed in consideration of the solubility property of test chemicals and to use the RhCE tests at final steps. The tiered approach A is composed of the STE, BCOP, HET-CAM or RhCE tests, whereas the tiered approach B is designed to perform simultaneously two in vitro test methods at the first stage and the RhCE test at the final stage. The predictive capacity of the two tiered approaches was estimated using 47 chemicals. The accuracy, sensitivity, and specificity value of the tiered approach A were 95.7% (45/47), 100% (34/34), and 84.6% (11/13), respectively, whereas those of the tiered approach B were 95.7% (45/47), 97.1% (33/34), and 92.3% (12/13), respectively. The approach A and B were considered to be available methods for distinguishing test chemicals of Category 1 (all 73.3%) and No Category (84.6% and 92.3%), respectively. Especially, the approach B was considered as an efficient method as the Bottom-Up approach, because it predicted correctly test chemicals classified as No Category.     

An in vitro human skin test for assessing sensitization potential

Sensitization to chemicals resulting in an allergy is an important health issue. The current gold‐standard method for identification and characterization of skin‐sensitizing chemicals was the mouse local lymph node assay (LLNA). However, for a number of reasons there has been an increasing imperative to develop alternative approaches to hazard identification that do not require the use of animals. Here we describe a human in‐vitro skin explant test for identification of sensitization hazards and the assessment of relative skin sensitizing potency. This method measures histological damage in human skin as a readout of the immune response induced by the test material. Using this approach we have measured responses to 44 chemicals including skin sensitizers, pre/pro‐haptens, respiratory sensitizers, non‐sensitizing chemicals (including skin‐irritants) and previously misclassified compounds. Based on comparisons with the LLNA, the skin explant test gave 95% specificity, 95% sensitivity, 95% concordance with a correlation coefficient of 0.9. The same specificity and sensitivity were achieved for comparison of results with published human sensitization data with a correlation coefficient of 0.91. The test also successfully identified nickel sulphate as a human skin sensitizer, which was misclassified as negative in the LLNA. In addition, sensitizers and non‐sensitizers identified as positive or negative by the skin explant test have induced high/low T cell proliferation and IFNγ production, respectively. Collectively, the data suggests the human in‐vitro skin explant test could provide the basis for a novel approach for characterization of the sensitizing activity as a first step in the risk assessment process. Copyright © 2015 John Wiley & Sons, Ltd.     

Applicability of a keratinocyte gene signature to predict skin sensitizing potential

There is a need to replace animal tests for the identification of skin sensitizers and currently many alternative assays are being developed that have very promising results. In this study a gene signature capable of very accurate identification of sensitizers was established in the HaCaT human keratinocyte cell line. This signature was evaluated in a separate study using six chemicals that are either local lymph node (LLNA) false-positive or false-negative chemicals in addition to nine sensitizers and four non-sensitizers. Similar studies do not apply these more difficult to classify chemicals, which show the true potential for human predictions of an assay. Although the gene signature has improved prediction accuracy compared to the LLNA, the misclassified compounds were comparable between the two assays. Gene profiling also showed a sensitizer specific response of the Nrf2-keap1 and Toll-like receptor signaling pathways. After exposure to non-sensitizing chemicals that induce either of the pathways the signature misclassified all Nrf2-inducers, while the Toll-like receptor ligands were correctly classified. In conclusion, we confirm that keratinocyte based prediction assays may provide essential information on the properties of compounds. Furthermore, chemical selection is critical for assessment of the performance of in vitro alternative assays.