Alternative Integrated Testing for Skin Sensitization: Assuring Consumer Safety

Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA

Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.

Expansion of the Cosmetics Europe skin sensitisation database with new substances and PPRA data

The assessment of skin sensitisation is a key requirement in all regulated sectors, with the European Union's regulation of cosmetic ingredients being most challenging, since it requires quantitative skin sensitisation assessment based on new approach methodologies (NAMs). To address this challenge, an in-depth and harmonised understanding of NAMs is fundamental to inform the assessment. Therefore, we compiled a database of NAMs, and in vivo (human and local lymph node assay) reference data. Here, we expanded this database with 41 substances highly relevant for cosmetic industry. These structurally different substances were tested in six NAMs (Direct Peptide Reactivity Assay, KeratinoSens™, human Cell Line Activation Test, U-SENS™, SENS-IS, Peroxidase Peptide Reactivity Assay). Our analysis revealed that the substances could be tested without technical limitations, but were generally overpredicted when compared to reference results. Reasons for this reduced predictivity were explored through pairwise NAM comparisons and association of overprediction with hydrophobicity. We conclude that more detailed understanding of how NAMs apply to a wider range of substances is needed. This would support a flexible and informed choice of NAMs to be optimally applied in the context of a next generation risk assessment framework, ultimately contributing to the characterisation and reduction of uncertainty.

Determination of Chemical Irritation Potential Using a Defined Gene Signature Set on Tissue-Engineered Human Skin Equivalents

There are no physical or visual manifestations that define skin sensitivity or irritation; a subjective diagnosis is made on the basis of the evaluation of clinical presentations, including burning, prickling, erythema, and itching. Adverse skin reaction in response to topically applied products is common and can limit the use of dermatological or cosmetic products. The purpose of this study was to evaluate the use of human skin equivalents based on immortalized skin keratinocytes and evaluate the potential of a 22-gene panel in combination with multivariate analysis to discriminate between chemicals known to act as irritants and those that do not. Test compounds were applied topically to full-thickness human skin equivalent or human ex vivo skin and gene signatures determined for known irritants and nonirritants. Principle component analysis showed the discriminatory potential of the 22-gene panel. Linear discrimination analysis, performed to further refine the gene set for a more high-throughput analysis, identified a putative seven-gene panel (IL-6, PTGS2, ATF3, TRPV3, MAP3K8, HMGB2, and matrix metalloproteinase gene MMP-3) that could distinguish potential irritants from nonirritants. These data offer promise as an in vitro prediction tool, although analysis of a large chemical test set is required to further evaluate the system.

Assessment of the skin sensitization potential of fragrance ingredients using the U-SENS™ assay

The U-SENS™ assay was developed to address the third key event of the skin sensitization adverse outcome pathway (AOP) and is described in OECD test guideline 442E, Annex II. A dataset of 68 fragrance ingredients comprised of 7 non-sensitizers and 61 sensitizers was tested in the U-SENS™ assay. The potential for fragrance ingredients to activate dendritic cells, measured by U-SENS™, was compared to the sensitization potential determined by weight of evidence (WoE) from historical data. Of the non-sensitizers, 4 induced CD86 cell surface marker ≥1.5-fold while 3 did not. Of the sensitizers, 50 were predicted to be positive in U-SENS™, while the remaining 11 were negative. Positive and negative predictive values (PPV and NPV) of U-SENS™ were 93% and 21%, respectively. No specific chemical property evaluated could account for misclassified ingredients. Assessment of parent and metabolite protein binding alerts in silico suggests that parent chemical metabolism may play a role in CD86 activation in U-SENS™. Combining the U-SENS™ assay in a "2 out of 3" defined approach with the direct peptide reactivity assay (DPRA) and KeratinoSens™ predicted sensitization hazard with PPV and NPV of 97% and 24%, respectively. Combining complementary in silico and in vitro methods to the U-SENS™ assay should be integrated to define the hazard classification of fragrance ingredients, since a single NAM cannot replace animal-based methods.

In silico Prediction of Skin Sensitization: Quo vadis?

Skin direct contact with chemical or physical substances is predisposed to allergic contact dermatitis (ACD), producing various allergic reactions, namely rash, blister, or itchy, in the contacted skin area. ACD can be triggered by various extremely complicated adverse outcome pathways (AOPs) remains to be causal for biosafety warrant. As such, commercial products such as ointments or cosmetics can fulfill the topically safe requirements in animal and non-animal models including allergy. Europe, nevertheless, has banned animal tests for the safety evaluations of cosmetic ingredients since 2013, followed by other countries. A variety of non-animal in vitro tests addressing different key events of the AOP, the direct peptide reactivity assay (DPRA), KeratinoSens™, LuSens and human cell line activation test h-CLAT and U-SENS™ have been developed and were adopted in OECD test guideline to identify the skin sensitizers. Other methods, such as the SENS-IS are not yet fully validated and regulatorily accepted. A broad spectrum of in silico models, alternatively, to predict skin sensitization have emerged based on various animal and non-animal data using assorted modeling schemes. In this article, we extensively summarize a number of skin sensitization predictive models that can be used in the biopharmaceutics and cosmeceuticals industries as well as their future perspectives, and the underlined challenges are also discussed.

Next generation risk assessment for skin sensitisation: A case study with propyl paraben

Skin sensitisation is a key adverse health effect to be addressed in the safety assessment of cosmetic ingredients. Regulatory demands have urged the development of Next Generation Risk Assessment (NGRA) using New Approach Methodologies (NAM) and Defined Approaches (DA) instead of animal models. An illustrative NGRA case study shall demonstrate if the use of propyl paraben at 0.2% in a face cream was safe for consumers. A sequential stacking tier testing DA based on NAM data predicted propyl paraben to be a non-sensitiser, while some NAM input data showed positive results. To increase confidence, structurally related parabens were considered, which revealed NAM and DA hazard predictions similar to those of propyl paraben, non-sensitiser classifications in animal models and very rare cases of human skin allergy. Based on a weight of evidence it was decided that propyl paraben should be considered a non-sensitiser leading to a favourable NGRA conclusion, in line with traditional risk assessment. Examination of an ab initio NGRA based on NAM and metabolism data resulted in a more conservative weak sensitiser consideration as point of departure, which still led to a favourable conclusion.

Beyond adverse outcome pathways: making toxicity predictions from event networks, SAR models, data and knowledge

Adverse outcome pathways have shown themselves to be useful ways of understanding and expressing knowledge about sequences of events that lead to adverse outcomes (AOs) such as toxicity. In this paper we use the building blocks of adverse outcome pathways-namely key events (KEs) and key event relationships-to construct networks which can be used to make predictions of the likelihood of AOs. The networks of KEs are augmented by data from and knowledge about assays as well as by structure activity relationship predictions linking chemical classes to the observation of KEs. These inputs are combined within a reasoning framework to produce an information-rich display of the relevant knowledge and data and predictions of AOs both in the abstract case and for individual chemicals. Illustrative examples are given for skin sensitization, reprotoxicity and non-genotoxic carcinogenicity.

Prediction of the skin sensitization potential of polyhexamethylene guanidine and triclosan and mixtures of these compounds with the excipient propylene glycol through the human Cell Line Activation Test

Household products often contain an antimicrobial agent such as biocides, polyhexamethylene guanidine (PHMG), triclosan (TCS), and propylene glycol (PG) as an excipient to dissolve the active ingredients. The skin sensitization (SS) potentials of each of these substances or mixtures of PHMG or TCS with PG have not been investigated through in vitro alternative test methods. The in vitro alternative assay called human Cell Line Activation Test (h-CLAT) served to address these issues. The h-CLAT assay was conducted in accordance with OECD TG 442E. On three independent runs, all the three substances were predicted to be sensitizers according to the SS positivity with relative fluorescence intensity of CD86 ≥ 150% and/or CD54 ≥ 200% at any tested concentrations. Mixtures of PHMG or TCS with PG at ratios of 9:1, 4:1, or 1:4 weight/volume were all positive in terms of SS potential. Since humans can be occupationally or environmentally exposed to mixtures of excipients with active ingredients of biocides, the present study may give insights into further investigations of the SS potentials of various chemical mixtures.

Towards a tiered test strategy for plant protection products to address mixture toxicity by alternative approaches in human health assessment

Plant protection products (PPPs) consist of pesticide active substances and co-formulants. Generally, active substance effects are assumed to dominate in PPP toxicity. Nevertheless, co-formulants may well affect the toxicity of PPPs via toxicodynamic and toxicokinetic interaction. To account for potential mixture effects and improve PPP data requirements for application in risk assessment, a tiered test strategy is proposed. The strategy is based on a comparison of PPP and active substance toxicity, which enables the prioritisation of PPPs for further testing, adaptation of the toxicological threshold value or removal of toxic co-formulants from the PPP. Moreover, it focuses on the integrative assessment of existing information and newly generated data using alternative test methods. The proposed strategy will improve PPP toxicological assessment by accounting for mixture toxicity, providing a set of regulatory options for risk assessment and the necessary data for hazard assessment. The predictivity of alternative methods for PPPs will improve by evaluation of their reliability and uncertainty. © 2020 The Authors. Pest Management Science published by JohnWiley & Sons Ltd on behalf of Society of Chemical Industry.

Read-across can increase confidence in the Next Generation Risk Assessment for skin sensitisation: A case study with resorcinol

Historically skin sensitisation risk assessment for cosmetic ingredients was based on animal models, however regulatory demands have led to Next Generation Risk Assessment (NGRA), using data from New Approach Methodologies (NAM) and Defined Approaches (DA). This case study was meant to investigate if the use of resorcinol at 0.2% in a face cream was safe and a maximum use concentration could be defined. The NAM data and DA predictions could not provide sufficient confidence to determine a point of departure (POD). Therefore, the application of read-across was explored to increase the level of confidence. Analogue searches in various tools and databases using "mode of action" and "chemical structural features" retrieved 535 analogues. After refinement by excluding analogues without a defined structure, similar reactivity profile and skin sensitisation data, 39 analogues remained. A final selection was made based on three approaches: expert judgment, chemical similarity or Local Lymph Node Assay data (LLNA). All read-across approaches supported a moderate potency. A POD derived from the LLNA EC3 of 3.6% was determined leading to a favourable NGRA conclusion and a maximum use concentration of 0.36%. This was supported by a traditional risk assessment based on the available animal data for resorcinol.

Skin sensitisation testing in practice: Applying a stacking meta model to cosmetic ingredients

Recently, several non-animal approaches contributing to the identification of skin sensitisation hazard have been introduced. Their validation and acceptance has largely been directed towards regulatory classification. Considering the driving force for replacement of in vivo tests centred on cosmetics, it is reasonable to ask how well the new approaches perform in this respect. In the present study, 219 substances, largely cosmetic raw materials (including dyes, preservatives and fragrances), have been evaluated in our Defined Approach integrating a stacking meta model (version 5), incorporating the individual outcomes of 3 in vitro validated methods (Direct Peptide Reactivity Assay, Keratinosens™, U-SENS™), 2 in silico tools (TIMES SS, TOXTREE) and physicochemical parameters (volatility, pH). Stacking meta model outcomes were compared with existing local lymph node assay (LLNA) data. Non-sensitisers comprised 68/219; 86 were weak/moderate and 65 were stronger sensitisers. The model version revision demonstrate the gain to discriminate sensitizers to non-sensitiser when the in silico TIMES model is incorporated as input parameter. The 85% to 91% accuracy for the cosmetics categories, indicates the stacking meta model offers value for the next generation risk assessment framework. These results pinpoint the power of the stacking meta model relying on a confidence based on the probability given in any individual prediction.

Prediction of the skin sensitization potential of polyhexamethylene guanidine phosphate, oligo(2-(2-ethoxy)ethoxyethyl) guanidinium chloride, triclosan, and mixtures of these compounds with the excipient propylene glycol through the local lymph node assay: BrdU-FCM

The guanidine family of antimicrobial agents, which includes polyhexamethylene guanidine phosphate (PHMG) and oligo(2-(2-ethoxy)ethoxyethyl) guanidinium chloride (PGH), and chlorophenol biocidal chemicals such as 2,4,4′-trichloro-2′-hydroxydiphenyl ether (triclosan) are used in various occupational and environmental biocidal applications. The excipient propylene glycol (PG) is used to dissolve the active ingredients. The skin sensitization (SS) potential of these substances has not been systemically investigated and is still debated. Moreover, mixtures of PHMG, PGH, or triclosan with PG have not been evaluated for SS potency. An in vivo assay known as the local lymph node assay: 5-bromo-2-deoxyuridine-flow cytometry method (LLNA: BrdU-FCM) was recently adopted as an alternative testing method and was used to address these issues. Via the LLNA: BrdU-FCM, PHMG, PGH, and triclosan were predicted to be sensitizers, while PG was predicted to be a nonsensitizer. In addition, d-limonene, which is used as a flavoring in various consumer products, was also predicted to be a sensitizer, although no unanimous conclusion has been reached regarding its SS potential. Mixtures of PHMG, PGH, triclosan, or d-limonene with PG at ratios of 9:1, 4:1, and 1:4 (w/w) were all positive in terms of SS potential, indicating that the PG excipient does not influence the SS predictions of these chemicals. Since humans can be occupationally and environmentally exposed to mixtures of excipients with active ingredients, the present study may give insight into further investigations of the SS potentials of various chemical mixtures.

A defined approach for predicting skin sensitisation hazard and potency based on the guided integration of in silico, in chemico and in vitro data using exclusion criteria

A decision tree-based defined approach (DA) has been designed using exclusion criteria based on applicability domain knowledge of in chemico/in vitro information sources covering key events 1-3 in the skin sensitisation adverse outcome pathway and an in silico tool predicting the adverse outcome (Derek Nexus). The hypothesis is that using exclusion criteria to de-prioritise less applicable assays and/or in silico outcomes produces a rational, transparent, and reliable DA for the prediction of skin sensitisation potential. Five exclusion criteria have been established: Derek Nexus reasoning level, Derek Nexus negative prediction, metabolism, lipophilicity, and lysine-reactivity. These are used to prioritise the most suitable information sources for a given chemical and results from which are used in a '2 out of 3' approach to provide a prediction of hazard. A potency category (and corresponding GHS classification) is then assigned using a k-Nearest Neighbours model containing human and LLNA data. The DA correctly identified the hazard (sensitiser/non-sensitiser) for 85% and 86% of a dataset with reference LLNA and human data. The correct potency category was identified for 59% and 68% of chemicals, and the GHS classification accurately predicted for 73% and 76% with reference LLNA and human data, respectively.

Non-animal methods to predict skin sensitization (II): an assessment of defined approaches *

Skin sensitization is a toxicity endpoint of widespread concern, for which the mechanistic understanding and concurrent necessity for non-animal testing approaches have evolved to a critical juncture, with many available options for predicting sensitization without using animals. Cosmetics Europe and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods collaborated to analyze the performance of multiple non-animal data integration approaches for the skin sensitization safety assessment of cosmetics ingredients. The Cosmetics Europe Skin Tolerance Task Force (STTF) collected and generated data on 128 substances in multiple in vitro and in chemico skin sensitization assays selected based on a systematic assessment by the STTF. These assays, together with certain in silico predictions, are key components of various non-animal testing strategies that have been submitted to the Organization for Economic Cooperation and Development as case studies for skin sensitization. Curated murine local lymph node assay (LLNA) and human skin sensitization data were used to evaluate the performance of six defined approaches, comprising eight non-animal testing strategies, for both hazard and potency characterization. Defined approaches examined included consensus methods, artificial neural networks, support vector machine models, Bayesian networks, and decision trees, most of which were reproduced using open source software tools. Multiple non-animal testing strategies incorporating in vitro, in chemico, and in silico inputs demonstrated equivalent or superior performance to the LLNA when compared to both animal and human data for skin sensitization.