Use of human predictive patch test (HPPT) data for the classification of skin sensitization hazard and potency.

Since the 1940s, patch tests in healthy volunteers (Human Predictive Patch Tests, HPPTs) have been used to identify chemicals that cause skin sensitization in humans. Recently, we reported the results of a major curation effort to support the development of OECD Guideline 497 on Defined Approaches (DAs) for skin sensitization (OECD in Guideline No. 497: Defined Approaches on Skin Sensitisation, 2021a. https://doi.org/10.1787/b92879a4-en ). In the course of this work, we compiled and published a database of 2277 HPPT results for 1366 unique test substances (Strickland et al. in Arch Toxicol 97:2825-2837, 2023. https://doi.org/10.1007/s00204-023-03530-3 ). Here we report a detailed analysis of the value of HPPT data for classification of chemicals as skin sensitizers under the United Nations' Globally Harmonized System of Classification and Labelling of Chemicals (GHS). As a result, we propose the dose per skin area (DSA) used for classification by the GHS to be replaced by or complemented with a dose descriptor that may better reflect sensitization incidence [e.g., the DSA causing induction of sensitization in one individual (DSA1+) or the DSA leading to an incidence of induction in 5% of the tested individuals (DSA05)]. We also propose standardized concepts and workflows for assessing individual HPPT results, for integrating multiple HPPT results and for using them in concert with Local Lymph Node Assay (LLNA) data in a weight of evidence (WoE) assessment. Overall, our findings show that HPPT results are often not sufficient for deriving unambiguous classifications on their own. However, where they are, the resulting classifications are reliable and reproducible and can be integrated well with those from other skin sensitization data, such as the LLNA.

In Vitro and In Vivo Methods for Analysis of Nanoparticles' Potential to Induce Delayed-Type Hypersensitivity Reactions.

Delayed-type hypersensitivity (DTH) reactions are among the common reasons for drug withdrawal from clinical use during the post-marketing stage. Several in vivo methods have been developed to test DTH responses in animal models. They include the local lymph node assay (LLNA) and local lymph node proliferation assay (LLNP). While LLNA is instrumental in testing topically administered formulations (e.g., creams), the LLNP was proven to be predictive of drug-mediated DTH in response to small molecule pharmaceuticals. Global efforts in reducing the use of research animals lead to the development of in vitro models to predict test-materials' mediated DTH. Two such models include the analysis of surface marker expression in human cell lines THP-1 and U-937. These tests are known as the human cell line activation test (hCLAT) and myeloid U937 skin sensitization test (MUSST or U-SENS), respectively. Here we describe experimental procedures for all these methods, discuss their in vitro-in vivo correlation, and suggest a strategy for applying these tests to analyze engineered nanomaterials and nanotechnology-formulated drug products.

Use of guinea pig data to obtain starting points for skin sensitisation risk assessment - A commentary.

The increasing drive to understand the likelihood of skin sensitisation from plant protection products (PPPs) in workers and the general public has resulted in recent initiatives to establish a quantitative risk assessment (QRA) methodology applicable to these products and their exposure scenarios. The effective evaluation of skin sensitising substances requires not only the identification of that toxicological hazard, but also determination of relative sensitising potency. Typically, this has been achieved by interpretation of local lymph node assay (LLNA) dose response data, delivering what is known as the EC3 value. This permitted regulatory division of skin sensitisers into defined potency sub-categories, but more importantly enabled derivation of a no expected sensitisation induction level (NESIL) as the point of departure for QRA. However, for many existing substances there is no LLNA data, only older guinea pig results exist. To avoid additional (in vivo) testing, an approach has been outlined to employ guinea pig data and existing regulatory guidelines on the determination of potency sub-categorisation to provide a guinea pig based NESIL. The approach adopts a conservative extrapolation from LLNA NESIL benchmarks to deliver points of departure as the basis for the type of QRA process already in successful use by other industries.

A novel three-dimensional Nrf2 reporter epidermis model for skin sensitization assessment.

Skin sensitization assessment has progressed from the use of animal models towards the application of New Approach Methodologies (NAMs). Several skin sensitization NAMs are accepted for regulatory use, but a majority relies on submerged in vitro cell cultures that limit their applicability domain, posing challenges for testing hydrophobic chemicals and mixtures. A newly developed three-dimensional (3D) Nrf2 reporter epidermis model for skin sensitization assessment is reported. This NAM may help to overcome these limitations. The NAM combines the in vivo-like biology and exposure conditions of 3D epidermis models with the reliability, convenience, and cost-effectiveness of secreted reporter gene technology. The Keap1-Nrf2-ARE pathway was chosen as the reporter gene read-out, as it is induced by most skin sensitizers and already adopted in OECD Test guideline 442D. Immortalized human primary keratinocytes (Ker-CT) were stably transfected with the pIGB-Nrf2-SEAP vector to construct a Nrf2 reporter cell line. Ker-CT Nrf2 reporter cells showed negligible basal expression of the Secreted Embryonic Alkaline Phosphatase (SEAP) reporter, which was induced 13.5-fold by exposure to the skin sensitizer cinnamic aldehyde (CA). Co-exposure to CA and the Nrf2 inhibitor glucocorticoid clobetasol propionate significantly suppressed the CA-induced SEAP expression, confirming dependance of the SEAP expression on Nrf2 activation. Using air-liquid interface and animal constituent free culture conditions, the Ker-CT Nrf2 reporter cells differentiated to stratified 3D epidermis models with an in vivo-like skin architecture and functional skin barrier. Evaluation of a Ker-CT Nrf2 reporter cell-based 2D assay by testing 10 conventional reference chemicals showed a predictive accuracy for skin sensitization potential of 80% and 70% compared to LLNA and human data in two independent laboratories and a high intra- and interlaboratory reproducibility. Moreover, the 3D epidermis models predicted 3 sensitizing and 2 non-sensitizing reference chemicals correctly in a first proof-of-concept study. Further investigations foresee the testing of additional chemicals, including hydrophobic compounds and mixtures to confirm the potential of the 3D epidermis models to broaden the applicability domain for NAM-based skin sensitization assessment.

Development of an in silico evaluation system that quantitatively predicts skin sensitization using OECD Guideline No. 497 ITSv2 defined approach for skin sensitization classification.

The Integrated Testing Strategy version 2 (ITSv2) Defined Approach, which is a reliable skin sensitization hazard and multi-step risk assessment method, does not support quantitative risk assessment such as local lymph node assay EC3 values. In this study, we developed a high-performance in silico evaluation system that quantitatively predicts the EC3 values of chemical substances by combining the ITSv2 Defined Approach for hazard identification (ITSv2 HI) with machine learning models. This system uses in chemico/in vitro test data, molecular descriptors, and distance information based on read-across concepts as explanatory variables. The system achieves an R2 value of 0.617 on external-validation data. Substances misclassified in ITSv2 HI are considered to have properties that do not match the correspondence between tests expressing the adverse outcome pathway assumed in the ITSv2 Defined Approach and skin sensitization. Therefore, ITSv2 HI is assumed to be correct within the applicability domains of this system. When using only substances within the applicability domains to reconstruct CatBoost models, the R2 value reached 0.824 on the external-validation data, representing an improvement in system performance. The results demonstrate the utility of explanatory variables that reflect the read-across concept and the advantages of integrating multiple prediction methods.

The BMDC model, a performant cell-based test to assess the sensitizing potential and potency of chemicals including pre/pro-haptens.

BACKGROUND: Chemical-induced allergies at workplace represent a significant occupational health issue. These substances must be properly identified as sensitizers. In previous studies, an original model using mouse bone marrow-derived dendritic cells (BMDC) was developed for this purpose. OBJECTIVES: The aim of this study was to evaluate the predictive capacity of the BMDC model with a large panel of sensitizers (including pre- and pro-haptens) and non-sensitizers. METHODS: The readout from the BMDC model is based on expression levels of six phenotypic markers measured by flow cytometry. RESULTS: The results indicate that 29 of the 37 non-sensitizers, and 81 of the 86 sensitizers were correctly classified compared to the Local Lymph Node Assay (LLNA). Statistical analysis revealed the BMDC model to have a sensitivity of 94%, a specificity of 78%, and an accuracy of 89%. The EC2 (Effective Concentration) values calculated with this model allow sensitizers to be categorized into four classes: extreme, strong, moderate and weak. CONCLUSIONS: These excellent predictive performances show that the BMDC model discriminates between sensitizers and non-sensitizers with outstanding precision equal to or better than existing validated alternative models. Moreover, this model allows to predict sensitization potency of chemicals. The BMDC test could therefore be proposed as an additional tool to assess the sensitizing potential and potency of chemicals.

Reconstructed human epidermis-based testing strategy of skin sensitization potential and potency classification using epidermal sensitization assay and in silico data.

The hazards and potency of skin sensitizers are traditionally determined using animal tests such as the local lymph node assay (LLNA); however, significant progress has been made in the development of non-animal test methods addressing the first three mechanistic key events of adverse outcome pathway in skin sensitization. We developed the epidermal sensitization assay (EpiSensA), which is a reconstructed human epidermis-based assay, by measuring four genes related to critical keratinocyte responses during skin sensitization. Four in vitro skin sensitization test methods (EpiSensA, direct peptide reactivity assay [DPRA], KeratinoSens™, and human cell line activation test [h-CLAT]) were systematically evaluated using 136 chemicals including lipophilic chemicals and pre/pro-haptens, which may be related to assay-specific limitations. The constructed database included existing and newly generated data. The EpiSensA showed a broader applicability domain and predicted the hazards with 82.4% and 78.8% accuracy than LLNA and human data. The EpiSensA could detect 76 out of 88 sensitizers at lower concentrations than the LLNA, indicating that the EpiSensA has higher sensitivity for the detection of minor sensitizing constituents. These results confirmed the potential use of the EpiSensA in evaluating a mixture of unknown compositions that can be evaluated by animal tests. To combine different information sources, the reconstructed human epidermis-based testing strategy (RTS) was developed based on weighted multiple information from the EpiSensA and TImes MEtabolism Simulator platform for predicting Skin Sensitization (TIMES-SS; RTSv1) or Organization for Economic Cooperation and Development (OECD) QSAR Toolbox automated workflow (RTSv2). The predictivities of the hazards and Globally Harmonized System (GHS) subcategories were equal to or better than the defined approaches (2 out of 3, integrated testing strategy [ITS]v1, and ITSv2) adopted as OECD Guideline 497.

Challenges integrating skin sensitization data for assessment of difficult to test substances.

Difficult to test substances, including poorly soluble, mildly irritating, or UVCBs (unknown or variable composition complex reaction products or biological materials), producing weak or borderline in vivo results, face additional challenges in in vitro assays that often necessitate data integration in a weight of evidence (WOE) approach to inform skin sensitization potential. Here we present several case studies on difficult to test substances and highlight the utility of the toxicological priority index (ToxPi) as a data visualization tool to compare skin sensitization biological activity. The case study test substances represent two poorly soluble substances, tetrakis (2-ethylbutyl) orthosilicate and decyl palmitate, and two UVCB substances, alkylated anisole and hydrazinecarboximidamide, 2-[(2-hydroxyphenyl)methylene]-, reaction products with 2 undecanone. Data from key events within the skin sensitization adverse outcome pathway were gathered from publicly available sources or specifically generated. Incorporating the data for these case study test substances as well as data on chemicals of a known sensitization class (sensitizer, irritating non-sensitizer, and non-sensitizer) into ToxPi produced biological activity profiles which were grouped using unsupervised hierarchical clustering. Three of the case study test substances concluded to lack skin sensitization potential by traditional WOE produced biological activity profiles most consistent with non-sensi­tizing substances, whereas the prediction was less definitive for a substance considered positive by traditional WOE. Visualizing the data using bioactivity profiles can provide further support for WOE conclusions in certain circumstances but is unlikely to replace WOE as a stand-alone prediction due to limitations of the method including the impact of missing data points.

Non-animal test methods to detect chemicals that cause skin allergies are accepted alternatives to animal testing for this purpose. However, some chemicals are difficult to test using these methods, e.g., substances that cause skin irritation, are not water soluble or are mixtures of different compo­nents. We compiled existing and new data on how four such chemicals activate key elements of the biological pathway leading to allergic skin reactions and compared the resulting patterns with respective patterns of many chemicals confirmed to cause skin allergy, skin irritation or neither. The patterns were visualized and analyzed with a computer software tool. The tool confirmed that three substances were non-sensitizers but did not confirm that the fourth substance was a skin sensitizer as predicted by the standard assessment. This approach, which incorporates all available data types into the assessment of difficult to test chemicals, may further reduce unnecessary animal testing.

Electrophilic Reactivity of Sulfated Alcohols in the Context of Skin Sensitization.

The surfactant sodium lauryl sulfate (SLS), although consistently positive in the murine local lymph node assay (LLNA) for skin sensitization, shows no evidence of being a human sensitizer and is often described as a false positive, lacking structural alerts for sensitization. However, there is evidence of the cinnamyl sulfate anion being the metabolite responsible for the sensitization potential of cinnamyl alcohol to humans and in animal tests. Here, manufacturing chemistry data and physical organic chemistry principles are applied to confirm that SLS is not reactive enough to sensitize, whereas sensitization to cinnamyl alcohol via cinnamyl sulfate is plausible. Sensitization data for several other primary alcohols, including geraniol, farnesol, and possibly hydrocortisone, are also consistent with this mechanism. It seems possible that biosulfation may play a wider role than has previously been recognized in skin sensitization.

Skin sensitization data for methyl esters of sulphonic acids-Corrections to published databases required.

BACKGROUND: Several methyl esters of sulphonic acids are listed in murine local lymph node assay (LLNA) databases, with dose-response data and EC3 values. However, some of these entries are questionable-in one case the chemical tested is not the chemical named in the databases and in others the EC3 value has been derived by extrapolation from data that do not meet the applicability criteria for the approved extrapolation method. OBJECTIVES: To consider how LLNA data came to be attributed to the wrong chemical and to address the inappropriate extrapolated EC3 values. METHODS: Dose-response data for methyl hexadec-3-enesulphonate (wrongly named as methyl hexadec-1-enesulphonate), two other methyl sulphonates and hexadec-1-ene-1,3-sultone are re-evaluated using the single dose probit extrapolation method (SDPEM). The different reaction chemistry profiles of methyl hexadec-3-enesulphonate and methyl hexadec-1-enesulphonate are discussed. RESULTS: Extrapolated EC3 values for hexadec-1-ene-1,3-sultone are the same by both methods but for the methyl sulphonates the differences are substantial. CONCLUSIONS: Current databases should be corrected and further analysed to identify other cases where EC3 values are likely to be unreliable due to inappropriate estimation by extrapolation.

Prediction of skin sensitization using machine learning.

As global awareness of animal welfare spreads, the development of alternative animal test models is increasingly necessary. The purpose of this study was to develop a practical machine-learning model for skin sensitization using three physicochemical properties of the chemicals: surface tension, melting point, and molecular weight. In this study, a total of 482 chemicals with local lymph node assay results were collected, and 297 datasets with 6 physico-chemical properties were used to develop Random Forest (RF) model for skin sensitization. The developed model was validated with 45 fragrance allergens announced by European Commission. The validation results showed that RF achieved better or similar classification performance with f1-scores of 54% for penal, 82% for ternary, and 96% for binary compared with Support Vector Machine (SVM) (penal, 41%; ternary, 81%; binary, 93%), QSARs (ChemTunes, 72% for ternary; OECD Toolbox, 89% for binary), and a linear model (Kim et al., 2020) (41% for penal), and we recommend the ternary classification based on Global Harmonized System providing more detailed and precise information. In the further study, the proposed model results were experimentally validated with the Direct Peptide Reactivity Assay (DPRA, OECD TG 442C approved model), and the results showed a similar tendency. We anticipate that this study will help to easily and quickly screen chemical sensitization hazards.

Skin sensitisation prediction using read-across, an illustrative next generation risk assessment (NGRA) case study for vanillin.

Skin sensitisation is a key adverse human health effect to be addressed in the safety assessment of cosmetic ingredients. Regulatory demands and scientific progress have led to the development of a Next Generation Risk Assessment (NGRA) framework, relying on the use of New Approach Methodologies (NAM) Defined Approaches (DA) and read-across instead of generating animal data. This case study illustrates the application of read-across for the prediction of the skin sensitisation potential of vanillin at the hypothetical use concentration of 0.5% in a shower gel and face cream. A three-step process was applied to select the most suitable analogues based on their protein reactivity, structural characteristics, physicochemical properties, skin metabolism profile and availability of skin sensitisation data. The applied read-across approach predicted a weak skin sensitiser potential for vanillin corresponding with a Local Lymph Node Assay EC3 value of 10%. Based on this EC3 value a point of departure of 2500 µg/cm2 was derived, resulting in an acceptable exposure level (AEL) of 25 µg/cm2. Because the consumer exposure levels (CEL) for the face cream (13.5 µg/cm2) and shower gel (0.05 µg/cm2) scenarios were lower than the AEL, the NGRA concluded both uses as safe.

Assessing the risk of induction of skin sensitization to plant protection products: A quantitative approach.

Exposure to skin sensitizers is common and regulated in many industry sectors. For cosmetics, a risk-based approach has been implemented, focused on preventing the induction of sensitization. First, a No Expected Sensitization Induction Level (NESIL) is derived, then modified by Sensitization Assessment Factors (SAFs) to derive an Acceptable Exposure Level (AEL). The AEL is used in risk assessment, being compared with an estimated exposure dose, specific to the exposure scenario. Since in Europe there is increased concern regarding exposure towards potentially sensitizing pesticides via spray drift, we explore how existing practice can be modified to allow Quantitative Risk Assessment (QRA) of pesticides for bystanders and residents. NESIL derivation by the Local Lymph Node Assay (LLNA), the globally required in vivo assay for this endpoint, is reviewed alongside consideration of appropriate SAFs. Using a case study, the principle that the NESIL in µg/cm2 can be derived by multiplying LLNA EC3% figure by a factor of 250 is adopted. The NESIL is then reduced by an overall SAF of 25 to establish an exposure level below which there is minimal bystander and resident risk. Whilst this paper focuses on European risk assessment and management, the approach is generic and universally applicable.

Specificity of the local lymph node assay (LLNA) for skin sensitisation.

The local lymph node assay (LLNA) has provided a large dataset against which performance of non-animal approaches for prediction of skin sensitisation potential and potency can be assessed. However, a recent comparison of LLNA results with human data has argued that LLNA specificity is low, with many human non-sensitisers, particularly hydrophobic chemicals, being false positives. It has been suggested that such putative false positives result from hydrophobic chemicals causing cytotoxicity, which induces irritancy, in turn driving non-specific lymphocyte proliferation. This paper finds that the apparent reduced specificity of the LLNA largely reflects differences in definitions of the boundaries between weak skin sensitisers and non-sensitisers. A small number of LLNA false positives may be due to lymphocyte proliferation without skin sensitisation, but most alleged 'false' positives are in fact very weak sensitisers predictable from structure-activity considerations. The evidence does not support the hypothesis for hydrophobicity-induced false positives. Moreover, the mechanistic basis is untenable. Sound LLNA data, appropriately interpreted, remain a good measure of sensitisation potency, applicable across a wide hydrophilicity-hydrophobicity range. The standard data interpretation protocol enables detection of very low levels of sensitisation, irrespective of regulatory significance, but there is scope to interpret the data to give focus on regulatory significance.

Integrated skin sensitization assessment based on OECD methods (III): Adding human data to the assessment.

Skin sensitizer potency assessment based on new approach methodologies is key to deriving a point of departure (PoD) for risk assessment. Regression models to predict a PoD based on OECD-validated in vitro tests and trained on local lymph node assay (LLNA) data were previously presented, and results from human tests were recently compiled. To integrate both data sources, the Reference Chemical Potency List (RCPL), which provides potency values (PV) for 33 chemicals integrating LLNA and human data in a structured weight-of-evidence approach, was developed. When calculating regression models vs PV or LLNA data, different weights for the input parameters were noted. As the RCPL is based on too few chemicals to train robust statistical models, the list of human data was extended to a larger set of PV (n = 139) with associated in vitro data. This database was used to retrain the regression models and to compare regression models trained vs (i) LLNA, (ii) PV or (iii) human DSA04 values. Using the PV as a target, predictive models of similar predictivity to the LLNA-based models were obtained, which mainly differ in a lesser weight of cytotoxicity and a higher weight of cell activation and reactivity parameters. Analysis of the human DSA04 dataset indicates a similar pattern but also shows that the human dataset is too small and biased to be a key dataset for potency prediction. Hence, an enlarged set of PV values appears to be a complementary tool to train predictive models next to an LLNA-only database.

Incorporating integrated testing strategy (ITSv1) defined approach into read-across (RAx) in predicting skin sensitization potency: ITSv1-based RAx.

Recently, due to regulatory and ethical demands, new approach methodologies (NAMs), defined approaches (DAs), and read-across (RAx) have been used in the risk assessment of skin sensitization. Integrated testing strategy (ITS)v1 DA, adopted in OECD Guideline No. 497, can be used for skin sensitization potency categorization. However, ITSv1 DA alone is not used for further refinement of the potency prediction based on EC3 (the estimated concentration that produces a stimulation index of 3 in murine local lymph node assay) values. Moreover, there is no explicit approach to incorporating NAM/DA data into RAx to fill the data gap of EC3 values with high confidence. This study developed a strategy incorporating ITSv1 DA into RAx to predict skin sensitization potency: ITSv1-based RAx. To examine the reliability of this novel strategy, a case study with lilial, a fragrance material, was performed. Based on ITSv1-based RAx, the skin sensitization potency of lilial was determined by extrapolating the EC3 value of 9.5% for the suitable analogue bourgeonal, which was close to the historical EC3 value of 8.6%. The result suggested that the strategy can refine the prediction of EC3 values with high confidence and be useful for the risk assessment of skin sensitization.

Estimating uncertainty in LLNA EC3 data and its impact on regulatory classifications.

The murine Local Lymph Node Assay (LLNA) is a test that produces numerical results (EC3 values) quantifying the sensitization potency of chemicals. These results are broadly used in toxicology and serve as a basis for various classifications, which determine subsequent regulatory decisions. The continuing interest in LLNA data and the diminished likelihood of new experimental EC3 data being generated sparked this investigation of uncertainty. Instead of using the Gaussian distribution as a default choice for assessing variability in a data set, two strictly positive distributions were proposed and their performance over the available experimental EC3 values was tested. In the application stage, how the uncertainty in EC3 values affects the possible classifications was analyzed, and the percentage of the chemicals receiving ambiguous classification was determined. It was shown that this percentage is high, which increases the risk of improper classification. Two approaches were suggested in regulatory practice to address the uncertainty in the EC3 data: the approaches based on "grey zones" and the classification distribution. If a chemical cannot be classified unambiguously, the latter appears to be an acceptable means to assess the level of sensitization potency of chemicals and helps provide better regulatory decisions.

Retrospective quantitative risk assessment of skin sensitization caused by an antimicrobial agent used in a consumer product: 2,3,5,6-Tetrachloro-4-(methylsulfonyl) pyridine.

BACKGROUND: Serious cases of allergic contact dermatitis (ACD) caused by exposure to 3,5,6-tetrachloro-4-(methylsulfonyl)pyridine (TCMSP; CAS no. 13108-52-6) used as an antimicrobial agent for desk mats have been reported in Japan. OBJECTIVE: A quantitative risk assessment (QRA) of TCMSP contained in desk mats was performed retrospectively. MATERIALS AND METHODS: A local lymph node assay (LLNA): BrdU-ELISA was conducted to determine a reliable EC1.6 value for TCMSP. The acceptable exposure level (AEL) for TCMSP was derived from the EC1.6 value by applying sensitization assessment factors (SAFs). The exposure level was estimated based on the assumption referring to the use conditions of thiabendazole in the same purpose. Then, the estimated exposure level was compared with the AEL to evaluate the skin sensitization risk. RESULTS: The AEL was calculated as 0.00458 µg/cm2 based on the EC1.6 value (0.011%, 2.75 µg/cm2 ) by applying a composite SAF of 600. The estimated TCMSP exposure level from the desk mat was 500 times greater than the AEL, indicating a serious skin sensitization risk. CONCLUSIONS: Assessments of skin sensitization potencies of chemicals to be used in consumer products are strongly recommended, and QRAs should be performed at the pre-marketing stage to avoid the skin sensitization risk in consumers.