Mechanism-Based QSAR Modeling of Skin Sensitization

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.

Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals

Environmental chemicals and contaminants cause a wide array of harmful implications to terrestrial and aquatic life which ranges from skin sensitization to acute oral toxicity. The current study aims to assess the quantitative skin sensitization potential of a large set of industrial and environmental chemicals acting through different mechanisms using the novel quantitative Read-Across Structure-Activity Relationship (q-RASAR) approach. Based on the identified important set of structural and physicochemical features, Read-Across-based hyperparameters were optimized using the training set compounds followed by the calculation of similarity and error-based RASAR descriptors. Data fusion, further feature selection, and removal of prediction confidence outliers were performed to generate a partial least squares (PLS) q-RASAR model, followed by the application of various Machine Learning (ML) tools to check the quality of predictions. The PLS model was found to be the best among different models. A simple user-friendly Java-based software tool was developed based on the PLS model, which efficiently predicts the toxicity value(s) of query compound(s) along with their status of Applicability Domain (AD) in terms of leverage values. This model has been developed using structurally diverse compounds and is expected to predict efficiently and quantitatively the skin sensitization potential of environmental chemicals to estimate their occupational and health hazards.

Prediction-Inspired Intelligent Training for the Development of Classification Read-across Structure-Activity Relationship (c-RASAR) Models for Organic Skin Sensitizers: Assessment of Classification Error Rate from Novel Similarity Coefficients

The advancements in the field of cheminformatics have led to a reduction in animal testing to estimate the activity, property, and toxicity of query chemicals. Read-across structure-activity relationship (RASAR) is an emerging concept that utilizes various similarity functions derived from chemical information to develop highly predictive models. Unlike quantitative structure-activity relationship (QSAR) models, RASAR descriptors of a query compound are computed from its close congeners instead of the compound itself, thus targeting predictions in the model training phase. The objective of the present study is not to propose new QSAR models for skin sensitization but to demonstrate the enhancement in the quality of predictions of the skin-sensitizing potential of organic compounds by developing classification-based RASAR (c-RASAR) models. A diverse, previously curated data set was collected from the literature for which 2D descriptors were computed. The extracted essential features were then used to develop a classification-based linear discriminant analysis (LDA) QSAR model. Furthermore, from the read-across-based predictions, RASAR descriptors were calculated using the basic settings of the hyperparameters for the Laplacian Kernel-based optimum similarity measure. After feature selection, an LDA c-RASAR model was developed, which superseded the prediction quality of the LDA-QSAR model. Various other combinations of RASAR descriptors were also taken to develop additional c-RASAR models, all showing better prediction quality than the LDA QSAR model while using a lower number of descriptors. Various other machine learning c-RASAR models were also developed for comparison purposes. In this work, we have proposed and analyzed three new similarity metrics: gm_class, sm1, and sm2. The first one is an indicator variable used to generate a simple univariate c-RASAR model with good prediction ability, while the remaining two are similarity indices used to analyze possible activity cliffs in the training and test sets and are believed to play an important role in the modelability analysis of data sets.

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 (0.5 mg/ml) is generally sufficient for polymer hazard assessment.

“In Litero” Screening: Retrospective Evaluation of Clinical Evidence to Establish a Reference List of Human Chemical Respiratory Sensitizers

Despite decades of investigation, test methods to identify respiratory sensitizers remain an unmet regulatory need. In order to support the evaluation of New Approach Methodologies in development, we sought to establish a reference set of low molecular weight respiratory sensitizers based on case reports of occupational asthma. In this context, we have developed an “in litero” approach to identify cases of low molecular weight chemical exposures leading to respiratory sensitization in clinical literature. We utilized the EPA-developed Abstract Sifter literature review tool to maximize the retrieval of publications relevant to respiratory effects in humans for each chemical in a list of chemicals suspected of inducing respiratory sensitization. The literature retrieved for each of these candidate chemicals was sifted to identify relevant case reports and studies, and then evaluated by applying defined selection criteria. Clinical diagnostic criteria were defined around exposure history, respiratory effects, and specific immune response to conclusively demonstrate occupational asthma as a result of sensitization, rather than irritation. This approach successfully identified 28 chemicals that can be considered as human respiratory sensitizers and used to evaluate the performance of NAMs as part of a weight of evidence approach to identify novel respiratory sensitizers. Further, these results have immediate implications for the development and refinement of predictive tools to distinguish between skin and respiratory sensitizers. A comparison of the protein binding mechanisms of our identified “in litero” clinical respiratory sensitizers shows that acylation is a prevalent protein binding mechanism, in contrast to Michael addition and Schiff base formation common to skin sensitizers. Overall, this approach provides an exemplary method to evaluate and apply human data as part of the weight of evidence when establishing reference chemical lists.

Validation of Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) approaches as alternatives to skin sensitization risk assessment

The aim of this study was conducted to validate the physicochemical properties of a total of 362 chemicals [305 skin sensitizers (212 in the previous study + 93 additional new chemicals), 57 non-skin sensitizers (38 in the previous study + 19 additional new chemicals)] for skin sensitization risk assessment using quantitative structure-activity relationship (QSAR)/quantitative structure-property relationship (QSPR) approaches. The average melting point (MP), surface tension (ST), and density (DS) of the 305 skin sensitizers and 57 non-sensitizers were used to determine the cutoff values distinguishing positive and negative sensitization, and correlation coefficients were employed to derive effective 3-fold concentration (EC3 (%)) values. QSAR models were also utilized to assess skin sensitization. The sensitivity, specificity, and accuracy were 80, 15, and 70%, respectively, for the Toxtree QSAR model; 88, 46, and 81%, respectively, for Vega; and 56, 61, and 56%, respectively, for Danish EPA QSAR. Surprisingly, the sensitivity, specificity, and accuracy were 60, 80, and 64%, respectively, when MP, ST, and DS (MP+ST+DS) were used in this study. Further, MP+ST+DS exhibited a sensitivity of 77%, specificity 57%, and accuracy 73% when the derived EC3 values were classified into local lymph node assay (LLNA) skin sensitizer and non-sensitizer categories. Thus, MP, ST, and DS may prove useful in predicting EC3 values as not only an alternative approach to animal testing but also for skin sensitization risk assessment.

Insights into the molecular properties underlying antibacterial activity of prenylated (iso)flavonoids against MRSA

High resistance towards traditional antibiotics has urged the development of new, natural therapeutics against methicillin-resistant Staphylococcus aureus (MRSA). Prenylated (iso)flavonoids, present mainly in the Fabaceae, can serve as promising candidates. Herein, the anti-MRSA properties of 23 prenylated (iso)flavonoids were assessed in-vitro. The di-prenylated (iso)flavonoids, glabrol (flavanone) and 6,8-diprenyl genistein (isoflavone), together with the mono-prenylated, 4'-O-methyl glabridin (isoflavan), were the most active anti-MRSA compounds (Minimum Inhibitory Concentrations (MIC) ≤ 10 µg/mL, 30 µM). The in-house activity data was complemented with literature data to yield an extended, curated dataset of 67 molecules for the development of robust in-silico prediction models. A QSAR model having a good fit (R²_adj 0.61), low average prediction errors and a good predictive power (Q²) for the training (4% and Q²_LOO 0.57, respectively) and the test set (5% and Q²_test 0.75, respectively) was obtained. Furthermore, the model predicted well the activity of an external validation set (on average 5% prediction errors), as well as the level of activity (low, moderate, high) of prenylated (iso)flavonoids against other Gram-positive bacteria. For the first time, the importance of formal charge, besides hydrophobic volume and hydrogen-bonding, in the anti-MRSA activity was highlighted, thereby suggesting potentially different modes of action of the different prenylated (iso)flavonoids.

The unreliability of the reliability criteria in the estimation of QSAR for skin sensitivity: A pun or a reliable law?

Some new products, which include common personal-care products, drugs, household items, can be hazardous in aspect personal care products/cosmetics and their ingredients (i.e. the above can effect human skin). International organizations (e.g. the Organisation for Economic Co-operation and Development-OECD) recommend evaluating individual ingredients when assessing the safety of personal care or cosmetic products. Thus, checking up that "popular at the market" substances are non-toxic, do not penetrate into or through normal or compromised human skin, and therefore, pose no risk to human health is an essential element of modern toxicology. The development of reliable models of toxicological endpoints is a tool to carry out the above checking up via quantitative structure-activity relationships (QSARs). The reliability of the QSAR is the current task of mathematical statistics. Recently, the index of ideality of correlation (IIC) and correlation intensity index (CII) were suggested as criteria of predictive potential (i.e. reliability) of QSAR-models. Here, the abilities of these criteria were studied for the case of building up models for skin sensitivity (LLNA, local lymph node assay). Computational experiments have confirmed that the IIC demonstrates an obvious ability to improve the predictive potential of models of skin sensitization. The applying of the CII for the case of skin sensitization also improves the quality of the model. However, the best models for skin sensitization were observed if the above-mentioned criteria are applied jointly (n = 268; R² = 0.60; RMSE = 0.63).

The activity of methacrylate esters in skin sensitisation test methods II. A review of complementary and additional analyses

Allergic contact dermatitis is an important occupational health issue, and there is a need to identify accurately those chemicals that have the potential to induce skin sensitisation. Hazard identification was performed initially using animal (guinea pig and mouse) models. More recently, as a result of the drive towards non-animal methods, alternative in vitro and in silico approaches have been developed. Some of these new in vitro methods have been formally validated and have been assigned OECD Test Guideline status. The performance of some of these recently developed in vitro methods, and of 2 quantitative structure-activity relationships (QSAR) approaches, with a series of methacrylate esters has been reviewed and reported previously. In this article that first review has been extended further with additional data and complementary analyses. Results obtained using in vitro methods (Direct Peptide Reactivity Assay, DPRA; ARE-Nrf2 luciferase test methods, KeratinoSens and LuSens; Epidermal Sensitisation Assay, EpiSensA; human Cell Line Activation Test, h-CLAT, and the myeloid U937 Skin Sensitisation test, U-SENS), and 2 QSAR approaches (DEREK™-nexus and TIMES-SS), with 11 methacrylate esters and methacrylic acid are reported here, and compared with existing data from the guinea pig maximisation test and the local lymph node assay. With this series of chemicals it was found that some in vitro tests (DPRA and ARE-Nrf2 luciferase) performed well in comparison with animal test results and available human skin sensitisation data. Other in vitro tests (EpiSensA and h-CLAT) proved rather more problematic. Results with DEREK™-nexus and TIMES-SS failed to reflect accurately the skin sensitisation potential of the methacrylate esters. The implications for assessment of skin sensitising activity are discussed.

Pesticides, cosmetics, drugs: identical and opposite influences of various molecular features as measures of endpoints similarity and dissimilarity

The similarity is an important category in natural sciences. A measure of similarity for a group of various biochemical endpoints is suggested. The list of examined endpoints contains (1) toxicity of pesticides towards rainbow trout; (2) human skin sensitization; (3) mutagenicity; (4) toxicity of psychotropic drugs; and (5) anti HIV activity. Further applying and evolution of the suggested approach is discussed. In particular, the conception of the similarity (dissimilarity) of endpoints can play the role of a "useful bridge" between quantitative structure property/activity relationships (QSPRs/QSARs) and read-across technique.

Idealization of correlations between optimal simplified molecular input-line entry system-based descriptors and skin sensitization

The Index of Ideality of Correlation (IIC) is a new criterion of the predictive potential for quantitative structure-property/activity relationships. The value of the IIC is a mathematical function sensitive to the value of the correlation coefficient and dispersion (expressed via mean absolute error). The IIC has been applied to develop QSAR models for skin sensitization achieving good predictive potential. The 'ideal correlation' is based on elementary fragments of simplified molecular input-line entry system (SMILES) and on the taking into account of the total numbers of nitrogen, oxygen, sulphur and phosphorus in the molecule.

Skin and respiratory chemical allergy: confluence and divergence in a hybrid adverse outcome pathway

Sensitisation of the respiratory tract to chemicals resulting in respiratory allergy and allergic asthma is an important occupational health problem, and presents toxicologists with no shortage of challenges. A major issue is that there are no validated or, even widely recognised, methods available for the identification and characterisation of chemical respiratory allergens, or for distinguishing respiratory allergens from contact allergens. The first objective here has been review what is known (and what is not known) of the mechanisms through which chemicals induce sensitisation of the respiratory tract, and to use this information to construct a hybrid Adverse Outcome Pathway (AOP) that combines consideration of both skin and respiratory sensitisation. The intention then has been to use the construction of this hybrid AOP to identify areas of commonality/confluence, and areas of departure/divergence, between skin sensitisation and sensitisation of the respiratory tract. The hybrid AOP not only provides a mechanistic understanding of how the processes of skin and respiratory sensitisation differ, buy also a means of identifying areas of uncertainty about chemical respiratory allergy that benefit from a further investment in research.

SkinSensDB: a curated database for skin sensitization assays

Skin sensitization is an important toxicological endpoint for chemical hazard determination and safety assessment. Prediction of chemical skin sensitizer had traditionally relied on data from rodent models. The development of the adverse outcome pathway (AOP) and associated alternative in vitro assays have reshaped the assessment of skin sensitizers. The integration of multiple assays as key events in the AOP has been shown to have improved prediction performance. Current computational models to predict skin sensitization mainly based on in vivo assays without incorporating alternative in vitro assays. However, there are few freely available databases integrating both the in vivo and the in vitro skin sensitization assays for development of AOP-based skin sensitization prediction models. To facilitate the development of AOP-based prediction models, a skin sensitization database named SkinSensDB has been constructed by curating data from published AOP-related assays. In addition to providing datasets for developing computational models, SkinSensDB is equipped with browsing and search tools which enable the assessment of new compounds for their skin sensitization potentials based on data from structurally similar compounds. SkinSensDB is publicly available at http://cwtung.kmu.edu.tw/skinsensdb.

QSAR models of human data can enrich or replace LLNA testing for human skin sensitization

Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential.

Anti-Inflammatory and Antioxidant Effects of Repeated Exposure to Cruciferous Allyl Nitrile in Sensitizer-Induced Ear Edema in Mice

Background

Skin sensitizers induce allergic reactions through the induction of reactive oxygen species. Allyl nitrile from cruciferous vegetables has been reported to induce antioxidants and phase II detoxification enzymes in various tissues. We assessed the effects of repeated exposure to allyl nitrile on sensitizer-induced allergic reactions.

Material/Methods

Mice were dosed with allyl nitrile (0–200 μmol/kg), and then received a dermal application of 1 of 3 sensitizers on the left ear or 1 of 2 vehicles on the right ear. Quantitative assessment of edema was carried out by measuring the difference in weight between the portions taken from the right and left ears. We tested enzymes (superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), and thiobarbituric acid reactive substances (TBARS) in ears.

Results

Repeated exposure to allyl nitrile reduced edemas induced by glutaraldehyde and by 2, 4-dinitrochlorobenzene (DNCB), but not by formaldehyde. The repeated exposure decreased levels of TBARS, a marker of oxidative stress, induced by glutaraldehyde and by DNCB, but not by formaldehyde. Allyl nitrile elevated SOD levels for the 3 sensitizers, and CAT levels for formaldehyde and DNCB. Allyl nitrile also increased GPx levels for formaldehyde and DNCB, but not for glutaraldehyde. The reduced edemas were associated with changes in oxidative stress levels and antioxidant enzymes.

Conclusions

Repeated exposure to allyl nitrile reduced allergic reactions induced by glutaraldehyde and by DNCB, but not by formaldehyde. This reduction was associated with changes in ROS levels and antioxidant enzyme activities.