Direct Peptide Reactivity Assay Research Articles

An integrated In-Silico-In-Chemico-In-Vitro (iSiCiV) Approach to identify biomarkers to predict the skin sensitisation potential of phytochemicals

BackgroundSkin sensitisation is one of the important regulatory endpoints used to determine whether a given test chemical can elicit an allergic response in susceptible individuals. The in-vitro approaches for predicting skin sensitisation potential for chemicals have been well-developed and widely accepted. Continuous efforts are being made to identify novel solutions to overcome the limitations of the existing approaches. PurposeThe herbal preparations used in dermal therapeutics are frequently found to elicit skin sensitisation, requiring caution. The herbal preparations tend to contain more than one phytochemical. The expected active ingredient may not possess skin sensitisation. However, the other phytochemical traces could pose synergistic and potentiating effects towards enhanced/suppressed skin sensitisation. In this regard, isolation, purification, yield, and in-vitro / in-vivo evaluation of skin sensitisation for all the traces would be time, cost, and animal-consuming. Hence, the regulatory agencies appreciate using computational tools to substantiate in-vitro methods. The regulators have also endorsed using in-silico approaches like quantitative structure-activity relationships (QSAR) and read-across. These approaches are limited to structure-based predictions and in combination with system-level approaches, which comprehensively facilitate a better understanding of complex biological systems. Study designIn the present study, 603 phytochemicals were screened in-silico using OECD QSAR Toolbox. The limitations of the QSAR Toolbox in predicting skin sensitisers were identified. MethodThe systems biology-based approach to complement the QSAR-based prediction was developed, and 41 biomarkers were identified as a determinant of skin sensitisation. The molecules attributed to these biomarkers were predicted as skin sensitisers. ResultsAs a result, 31 phytochemicals were predicted as skin sensitisers. Acrolein, eugenol, formaldehyde, and glycerol were selected for experimental validation of the in-silico predictions using direct peptide reactivity assay (DPRA), KeratinoSens™, and h-CLAT assays representing each key event in skin sensitisation. Further, identified biomarkers were validated in THP-1 and KeratinoSens cell lines using qRT-PCR. Among all genes, IL-8 and CCL4 were upregulated by 6.4 and 5.6 folds, respectively, in THP-1, while AKR1C2 and AKR1C3 were upregulated by 7.8 and 11.1 folds, respectively, in KeratinoSens. IL-8 level was analysed by ELISA and found to be in line with qRT-PCR results. ConclusionThe study proposes a possible strategy based on the “iSiCiV” approach to enhance the prediction of the dermal sensitisation potential of a test compound. The approach offers a robust platform and highlights an opportunity to shift the paradigm of skin sensitisation assay with a better understanding of toxicology mechanisms.

Predicting points of departure and potency categories for fragrance ingredients by integrating OECD in vitro models

Continuous potency assessment is crucial for conducting quantitative risk assessment (QRA) of sensitizers. Quantitative regression models, based on in vitro methods, have been developed to calculate points of departure for use in skin sensitization QRA. These models calculate a point of departure as a predicted value for Local Lymph Node Assay (LLNA) EC3 or potency value (PV), integrating data from the kinetic Direct Peptide Reactivity Assay (kDPRA), KeratinoSens (KS) assay, and human Cell Line Activation Test (h-CLAT). The goal of this study was to determine how in vitro predicted EC3s and PVs compare to published reference data. In vitro data were combined in point of departure regression models to predict EC3s and PVs. These points of departure were then grouped into sensitization potency categories, such as extreme, strong, moderate, weak, very weak, or non-sensitizer, as previously described. Trends in potency distribution and high concordance between predicted EC3 and PV categories and published potency categories were observed. Furthermore, the median absolute fold-misprediction ranged between 1.8 and 2.5 for models predicting EC3 and between 1.7 and 3.4 for those predicting PVs. These regression models are a promising animal alternative for determining sensitization quantitative potency for fragrance ingredients, thereby facilitating QRA.

The dimer effect: A refinement approach towards skin sensitization assessment in-chemico using Amino acid Derivative Reactivity Assay.

Skin sensitization is a key endpoint for safety assessment, especially for cosmetics and personal care products. The adverse outcome pathway for skin sensitization and the chemical and biological events driving the induction of human skin sensitization are now well understood. Several non-animal test methods have been developed to predict sensitizer potential by measuring the impact of chemical sensitizers on these key events. In this work, we have focused on Key Event 1 (the molecular initiating step), which is based on formation of a covalent adduct between skin sensitizers and endogenous proteins and/or peptides in the skin. There exists three in-chemico assays approved by the Organization for Economic Co-operation and Development-(1) Direct Peptide Reactivity Assay (DPRA), (2) Amino Acid Derivative Reactivity Assay (ADRA), and (3) Kinetic Direct Peptide Reactivity Assay (kDPRA) to quantify peptide/amino acid derivative depletion after incubation with test chemicals. However, overestimated depletion of the cysteine-based peptide/amino acid derivatives is known in such assays because of the dimerization of the thiol group. In this present work, we report the synthesis and structural confirmation of the dimer of N-(2-[1-naphthyl]acetyl)-L-cysteine (NAC) from the ADRA assay to allow simultaneous determination of (a) peptide depletion by quantifying NAC monomer and (b) peptide dimerization by quantifying NAC dimer thereby eliminating the overestimation. We present a case study with three chemicals to demonstrate the importance of this approach. Thus, this simultaneous assay gives a more informed view of the peptide reactivity of chemicals to better identify skin sensitizers.

Use of in vitro methods combined with in silico analysis to identify potential skin sensitizers in the Tox21 10K compound library.

Introduction: Skin sensitization, which leads to allergic contact dermatitis, is a key toxicological endpoint with high occupational and consumer prevalence. This study optimized several in vitro assays listed in OECD skin sensitization test guidelines for use on a quantitative high-throughput screening (qHTS) platform and performed in silico model predictions to assess the skin sensitization potential of prioritized compounds from the Tox21 10K compound library. Methods: First, we screened the entire Tox21 10K compound library using a qHTS KeratinoSensTM (KS) assay and built a quantitative structure-activity relationship (QSAR) model based on the KS results. From the qHTS KS screening results, we prioritized 288 compounds to cover a wide range of structural chemotypes and tested them in the solid phase extraction-tandem mass spectrometry (SPE-MS/MS) direct peptide reactivity assay (DPRA), IL-8 homogeneous time-resolved fluorescence (HTRF) assay, CD86 and CD54 surface expression in THP1 cells, and predicted in silico sensitization potential using the OECD QSAR Toolbox (v4.5). Results: Interpreting tiered qHTS datasets using a defined approach showed the effectiveness and efficiency of in vitro methods. We selected structural chemotypes to present this diverse chemical collection and to explore previously unidentified structural contributions to sensitization potential. Discussion: Here, we provide a skin sensitization dataset of unprecedented size, along with associated tools, and analysis designed to support chemical assessments.

Sveobuhvatan pregled testa direktne reaktivnosti peptida u ispitivanju preosetljivosti kože - primenjivost, ograničenja i razmatranja

Skin sensitization is a crucial endpoint in the safety assessment of chemicals, with the Direct Peptide Reactivity Assay (DPRA) emerging as a valuable in chemico method for evaluating a substance's sensitization potential. This review delves into the principles, applicability, and limitations of the DPRA within the context of the Adverse Outcome Pathway (AOP) framework for skin sensitization. We examine the DPRA'srole in addressing the molecular initiating event of skin sensitization, its integration into Integrated Approaches to Testing and Assessment (IATA), and its performance in predicting sensitizers. The review also highlights the challenges in testing certain categories of chemicals and the importance of considering the DPRA's results alongside other complementary methods. By providing a comprehensive overview of the DPRA, this review aims to inform researchers, regulators, and clinicians about its utility and limitations in the context of skin sensitization testing.

Low-cost quantum mechanical descriptors for data efficient skin sensitization QSAR models

Quantitative Structure Activity Relationship modelling methodologies need to incorporate relevant mechanistic information to have high predictive performance and validity. Electrophilic reactivity is a common mechanistic feature of skin sensitization endpoints which could be concisely characterized with electronic descriptors which is key to enabling the modelling of small datasets in this domain. However, quantum mechanical methodologies have previously featured high computational costs which would exclude the use of large datasets. Consequently, we investigate the use of electronic descriptors calculated using the Hartree Fock with 3 corrections (Hf-3c) method, a low-cost ab initio methodology that has higher chemical accuracy than previous semiempirical methodologies for modelling in vitro skin sensitization assay outcomes. We also model the Ames assay as a surrogate for determining skin sensitization outcomes. The quantum chemical descriptors calculated using the Hf-3c method with conductor-like polarizable continuum model (CPCM) implicit solvation found improved QSAR model performance for the in vitro Ames (n = 6049, 0.770 AUC), KeratinoSens (n = 164, 0.763 AUC), and Direct Peptide Reactivity Assay (n = 122, 0.750 AUC) datasets, with their combination producing high predictive performance for unseen in vivo Local Lymph Node Assay (n = 86, 0.789 AUC) and Human Repeated Insult Patch Test (n = 86, 0.791 AUC) assay toxicant outcomes.

Potency classification of isothiazolinone compounds based on defined approaches of skin sensitization in OECD GL 497.

Regulatory decisions for skin sensitization are now based on adverse outcome pathway (AOP) and integrated approaches to testing and assessment (IATA). Based on these, Organisation for Economic Co-operation and Development (OECD) guidelines on defined approaches for skin sensitization were adopted with a fixed data interpretation procedure (DIP). In the guidelines, "Defined Approaches" (DA) on skin sensitization uses the results from multiple information sources of in chemico, in vitro, and in silico data to achieve an equivalent predictive capacity as those of the animal tests. In this review, we evaluated the skin sensitization of eleven isothiazolinone compounds including 4,5-Dichloro-2-octyl-3(2H)-isothiazolone (DCOIT), 2-n-Octyl-4-isothiazolin-3-one (OIT), 2-Methyl-4-isothiazolin-3-one (MIT), 1,2-Benzisothiazolin-3-one (BIT), 1,2-Benzisothiazolin-3-one, 2-butyl (BBIT), 5-Chloro-2-methyl-4-isothiazolin-3-one (CMIT), 2-methyl-4,5-trimethylene-4-isothiazolin-3-one (MTMIT), 2-methyl-1,2-benzothiazol-3-one (MBIT), 2-methyl-1,2-benzothiazole-3-thione (MBIT-S), 1,2-benzisothiazolin-3-one, 2-methyl-, 1,1-dioxide (BBIT-O), and a mixture of CMIT/MIT. Data from direct peptide reactivity assay (DPRA), human cell line activation (h-CLAT) test, and quantitative structure activity relationship (QSAR) Toolbox were evaluated and were applied to the DIP to derive a prediction of hazard identification and a potency classification. Among the evaluated chemicals, six isothiazolinone compounds were classified to be UN GHS 1A, one compound to be UN GHS 1, and four compounds could not be classified due to lack of data. The results of sensitizer chemicals were found to coincide well with those of in vivo test.

Anti-Aging Potential of a Novel Ingredient Derived from Sugarcane Straw Extract (SSE).

Natural and sustainable anti-aging ingredients have gained attention from the cosmetic industry. This study evaluated the anti-aging potential of a sugarcane straw extract-based (SSE) cosmetic ingredient. First, cytotoxicity tests were assessed in keratinocytes and fibroblast cell lines, and sensitization was carried out through the direct peptide reactivity assay. Subsequently, various anti-aging properties were investigated, including inhibiting skin aging-related enzymes, promoting elastin and hyaluronic acid synthesis, and anti-pollution activity. Finally, a permeability assay using a synthetic membrane resembling skin was conducted. The results demonstrated that the SSE ingredient effectively inhibited elastase (55%), collagenase (25%), and tyrosinase (47%) while promoting hyaluronic acid production at non-cytotoxic and low-sensitizer concentrations. Moreover, it reduced the inflammatory response provoked by urban pollution, as evidenced by decreased levels of IL1-α and IL-6. However, it was observed that the phenolic compounds predominantly reached the skin's surface, indicating a limited ability to penetrate deeper layers of the skin. Therefore, it can be concluded that the SSE ingredient holds anti-aging properties, albeit with limited penetration into deeper skin layers. Further research and formulation advancements are needed to optimize the ingredient's ability to reach and exert its effects in deeper skin layers.

Investigating sensitization activity of azobenzene disperse dyes via the Direct Peptide Reactivity Assay (DPRA)

Azobenzene disperse dyes are the fastest-growing category of commercial dyestuffs and have been found in indoor house dust and in children’s polyester apparel. Azobenzene disperse dyes are implicated as potentially allergenic; however, little experimental data is available on allergenicity of these dyes. Here, we examine the binding of azobenzene disperse dyes to nucleophilic peptide residues as a proxy for their potential reactivity as electrophilic allergenic sensitizers. The Direct Peptide Reactivity Assay (DPRA) was utilized via both a spectrophotometric method and a high-performance liquid chromatography (HPLC) method. We tested dyes purified from commercial dyestuffs as well as several known transformation products. All dyes were found to react with nucleophilic peptides in a dose-dependent manner with pseudo-first order kinetics (rate constants as high as 0.04 h−1). Rates of binding reactivity were also found to correlate to electrophilic properties of dyes as measured by Hammett constants and electrophilicity indices. Reactivities of polyester shirt extracts were also tested for DPRA activity and the shirt extracts with high measured abundances of azobenzene disperse dyes were observed to induce greater peptide reactivity. Results suggest that azobenzene disperse dyes may function as immune sensitizers, and that clothing containing these dyes may pose risks for skin sensitization.

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.

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.

Development of an in chemico high-throughput screening method for the identification of skin sensitization potential

It is well established that chemical-peptide conjugation represents the molecular initiating event (MIE) in skin sensitization. This MIE has been successfully exploited in the development of in chemico peptide reactivity assays, with the Direct Peptide Reactivity Assay (DPRA) being validated as a screening tool for skin sensitization hazard as well as an OECD test guideline. This test relies on the use of a high-performance liquid chromatography/ultraviolet detection method to quantify chemical-peptide conjugation through measurement of the depletion of two synthetic peptides containing lysine or cysteine residues, which is labor-intensive and time-consuming. To improve assay throughput, sensitivity, and accuracy, we have developed a spectrophotometric assay for skin sensitization potential based on MIE measurement—the ProtReact assay. ProtReact is also a cheaper, faster, simpler, and more accessible alternative for the DPRA, giving comparable results. A set of 106 chemicals was tested with ProtReact and the peptide depletion values compared with those reported for the DPRA. The predictive capacity of both assays was evaluated with human reference data. ProtReact and DPRA assays show similar predictive capacities for hazard identification (75% and 74%, respectively), although ProtReact showed a higher specificity (86% versus 74%, respectively) and lower sensitivity (69% versus 73%). Overall, the results show that ProtReact assay described here represents an efficient, economic, and accurate assay for the prediction of skin sensitization potential of chemical haptens.

Applicability of the DPRA on mixture testing: challenges and opportunities

The in chemico direct peptide reactivity assay (DPRA) is validated to assess protein reactivity of chemical compounds, relating to the molecular initiating event of skin sensitization induction. According to OECD TG 442C, the DPRA is technically applicable to test multi-constituent substances and mixtures of known composition, even though limited experimental data are publicly available. First, we assessed the DPRA’s predictive capability for individual substances, but at concentrations other than the recommended 100 mM, i.e., based on the LLNA EC3 concentration (Experiment A). Next, the applicability of the DPRA to test unknown mixtures was assessed (Experiment B). Here, the complexity of unknown mixtures was reduced to mixtures containing either two known skin sensitizers with varying potencies, or a combination of a skin sensitizer with a non-skin sensitizer, or multiple non-sensitizers. Experiments A and B revealed that one extremely potent sensitizer (oxazolone) was incorrectly classified as a non-sensitizer when tested at its low EC3 concentration of 0.4 mM instead of the suggested molar excess conditions of 100 mM (Experiments A). For binary mixtures tested in experiments B, the DPRA was able to distinguish all skin sensitizers and the strongest skin sensitizer in the mixture was determinant for the overall peptide depletion of a sensitizer. In conclusion, we confirmed that the DPRA test method can be used efficiently for well-known characterized mixtures. However, when deviating from the recommended testing concentration of 100 mM, caution should be taken in case of negative results, limiting the DPRA’s applicability for mixtures of unknown composition.

Defining the reactivity of nanoparticles to peptides through direct peptide reactivity assay (DPRA) using a high pressure liquid chromatography system with a diode array detector.

The possibility of inducing skin sensitization reactions following exposure to various chemicals can lead to skin diseases, and the evaluation of skin sensitivity to such substances is very important. However, as animal tests for skin sensitization are prohibited, the OECD Test Guideline 442C was designated as part of an alternative testing method. Therefore, in this study, the reactivity of cysteine and lysine peptides to nanoparticle substrates was identified through HPLC-DAD analysis according to the skin sensitization animal replacement test method specified in the OECD Test Guideline 442C. In this study, all criteria for skin sensitization experiments specified in OECD Test Guideline 442C were satisfied. As a result of analyzing the disappearance rates of cysteine and lysine peptides for the five types of nanoparticle substrates (TiO2, CeO2, Co3O4, NiO, and Fe2O3) using the established analytical method, all were identified as positive. Therefore, our findings suggest that basic data from this technique can contribute to skin sensitization studies by providing the depletion percentage of cysteine and lysine peptides for nanoparticle materials that have not yet been tested for skin sensitization.

Investigation into Propolis Components Responsible for Inducing Skin Allergy: Air Oxidation of Caffeic Acid and Its Esters Contribute to Hapten Formation.

Propolis is a resin-like material produced by bees from the buds of poplar and cone-bearing trees and is used in beehive construction. Propolis is a common additive in various biocosmetics and health-related products, despite the fact that it is a well-known cause of contact allergy. Caffeic acid and its esters have been the primary suspects behind the sensitization potency of propolis-induced contact allergy. However, the chemical structures of the protein adducts formed between these haptens and skin proteins during the process of skin sensitization remain unknown. In this study, the reactivity of three main contact allergens found in propolis, namely, caffeic acid (CA), caffeic acid 1,1-dimethylallyl ester (CAAE), and caffeic acid phenethyl ester (CAPE), was investigated. These compounds were initially subjected to the kinetic direct peptide reactivity assay to categorize the sensitization potency of CA, CAAE, and CAPE, but the data obtained was deemed too unreliable to confidently classify their skin sensitization potential based on this assay alone. To further investigate the chemistry involved in generating possible skin allergy-inducing protein adducts, model peptide reactions with CA, CAAE, and CAPE were conducted and analyzed via liquid chromatography-high-resolution mass spectrometry. Reactions between CA, CAAE, and CAPE and a cysteine-containing peptide in the presence of oxygen, both in closed and open systems, were monitored at specific time points. These studies revealed the formation of two different adducts, one corresponding to thiol addition to the α,β-unsaturated carbonyl region of the caffeic structure and the second corresponding to thiol addition to the catechol, after air oxidation to o-quinone. Observation of these peptide adducts classifies these compounds as prehaptens. Interestingly, no adduct formation was observed when the same reactions were performed under oxygen-free conditions, highlighting the importance of air oxidation processes in CA, CAAE, and CAPE adduct formation. Additionally, through NMR analysis, we found that thiol addition occurs at the C-2 position in the aromatic ring of the CA derivatives. Our results emphasize the importance of air oxidation in the sensitization potency of propolis and shed light on the chemical structures of the resultant haptens which could trigger allergic reactions in vivo.

Off to a good start? Review of the predictivity of reactivity methods modelling the molecular initiating event of skin sensitization.

The assessment of skin sensitizing properties of chemicals has moved away from animal methods to new approach methodologies (NAM), guided by qualitative mechanistic understanding operationalized in an adverse outcome pathway (AOP). As with any AOP, the molecular initiating event (MIE) of covalent binding of a chemical to skin proteins is particularly important. This MIE has been modelled by several test methods by measuring the reaction of a test chemical with model peptides in chemico. To better understand the similarities and differences, a data repository with publicly available data for the direct peptide reactivity assay (DPRA), amino acid derivative reactivity assay (ADRA) and kinetic DPRA (kDPRA), as well as the peroxidase peptide reactivity assay (PPRA) was assembled. The repository comprises 260 chemicals with animal and human reference data, data on four relevant physicochemical properties, and between 161 to 242 test chemical results per test method. First, an overview of the experimental conditions of the four test methods was compiled allowing to readily compare them. Second, data analyses demonstrated that the test methods’ predictivity was consistently reduced for poorly watersoluble chemicals and that the DPRA and ADRA can be used interchangeably. It also revealed new categorization thresholds for the DPRA and ADRA that are potentially relevant for strategic uses. In summary, a detailed assessment of reactivity test methods is provided, highlighting their potential and limitations. The results presented are intended to stimulate scientific discussion around test methods modelling the MIE of the skin sensitization AOP.

Application of Defined Approaches to Assess Skin Sensitization Potency of Isothiazolinone Compounds.

Isothiazolinones (ITs) are widely used as antimicrobial preservatives in cosmetics and as additives for preservation of consumer and industrial products to control bacteria, fungi, and algae. Although they are effective biocides, they have the potential to produce skin irritation and sensitization, which poses a human health hazard. In this project, we evaluated nonanimal defined approaches (DAs) for skin sensitization that can provide point-of-departure estimates for use in quantitative risk assessment for ITs. The skin sensitization potential of six ITs was evaluated using three internationally harmonized nonanimal test methods: the direct peptide reactivity assay, KeratinoSens™, and the human cell line activation test. Results from these test methods were then applied to two versions of the Shiseido Artificial Neural Network DA. Sensitization hazard or potency predictions were compared with those of the in vivo murine local lymph node assay (LLNA). The nonanimal methods produced skin sensitization hazard and potency classifications concordant with those of the LLNA. EC3 values (the estimated concentration needed to produce a stimulation index of three, the threshold positive response) generated by the DAs had less variability than LLNA EC3 values, and confidence limits from the DAs overlapped those of the LLNA EC3 for most substances. The application of in silico models to in chemico and in vitro skin sensitization data is a promising data integration procedure for DAs to support hazard and potency classification and quantitative risk assessment.

Assessment of commercial polymers with and without reactive groups using amino acid derivative reactivity assay based on both molar concentration approach and gravimetric approach.

The amino acid derivative reactivity assay (ADRA), an alternative method for testing skin sensitization, has been established based on the molar concentration approach. However, the additional development of gravimetric concentration and fluorescence detection methods has expanded its range of application to mixtures, which cannot be evaluated using the conventional testing method, the direct peptide reactivity assay (DPRA). Although polymers are generally treated as mixtures, there have been no reports of actual polymer evaluations using alternative methods owing to their insolubility. Therefore, in this study, we evaluated skin sensitization potential of polymers, which is difficult to predict, using ADRA. As polymers have molecular weights ranging from several thousand to more than several tens of thousand Daltons, they are unlikely to cause skin sensitization due to their extremely low penetration into the skin, according to the 500-Da rule. However, if highly reactive functional groups remain at the ends or side chains of polymers, relatively low-molecular-weight polymer components may penetrate the skin to cause sensitization. Polymers can be roughly classified into three major types based on the features of their constituent monomers; we investigated the sensitization capacity of each type of polymer. Polymers with alert sensitization structures at their ends were classified as skin sensitizers, whereas those with no residual reactive groups were classified as nonsensitizers. Although polymers with a glycidyl group need to be evaluated carefully, we concluded that ADRA (0.5mg/ml) is generally sufficient for polymer hazard assessment.

A development of a graph-based ensemble machine learning model for skin sensitization hazard and potency assessment.

Many defined approaches (DAs) for skin sensitization assessment based on the adverse outcome pathway (AOP) have been developed to replace animal testing because the European Union has banned animal testing for cosmetic ingredients. Several DAs have demonstrated that machine learning models are beneficial. In this study, we have developed an ensemble prediction model utilizing the graph convolutional network (GCN) and machine learning approach to assess skin sensitization. The model integrates in silico parameters and data from alternatives to animal testing of well-defined AOP to improve DA predictivity. Multiple ensemble models were created using the probability produced by the GCN with six physicochemical properties, direct peptide reactivity assay, KeratinoSens™, and human cell line activation test (h-CLAT), using a multilayer perceptron approach. Models were evaluated by predicting the testing set's human hazard class and three potency classes (strong, weak, and non-sensitizer). When the GCN feature was used, 11 models out of 16 candidates showed the same or improved accuracy in the testing set. The ensemble model with the feature set of GCN, KeratinoSens™, and h-CLAT produced the best results with an accuracy of 88% for assessing human hazards. The best three-class potency model was created with the feature set of GCN and all three assays, resulting in 64% accuracy. These results from the ensemble approach indicate that the addition of the GCN feature could provide an improved predictivity of skin sensitization hazard and potency assessment.

Decision making in next generation risk assessment for skin allergy: Using historical clinical experience to benchmark risk

Our aim is to develop and apply next generation approaches to skin allergy risk assessment that do not require new animal test data and better quantify uncertainties. Quantitative risk assessment for skin sensitisation uses safety assessment factors to extrapolate from the point of departure to an acceptable human exposure level. It is currently unclear whether these safety assessment factors are appropriate when using non-animal test data to derive a point-of departure.Our skin allergy risk assessment model Defined Approach uses Bayesian statistics to infer a human-relevant metric of sensitiser potency with explicit quantification of uncertainty, using any combination of human repeat insult patch test, local lymph node assay, direct peptide reactivity assay, KeratinoSens™, h-CLAT or U-SENS™ data. Here we describe the incorporation of benchmark exposures pertaining to use of consumer products with clinical data supporting a high/low risk categorisation for skin sensitisation. Margins-of-exposure (potency estimate to consumer exposure level ratio) are regressed against the benchmark risk classifications, enabling derivation of a risk metric defined as the probability that an exposure is low risk. This approach circumvents the use of safety assessment factors and provides a simple and transparent mechanism whereby clinical experience can directly feed-back into risk assessment decisions.