Identification of mechanisms of toxic action for skin sensitisation using a SMARTS pattern based approach

The evaluation of skin sensitization potential of the UVCB substance diisopentyl phthalate by in silico and in vitro methods

Diisopentyl phthalate (DiPeP) is primarily used as a plasticizer or additive within the production of polyvinyl chloride (PVC), and has many additional industrial applications. Its metabolites were recently found in urinary samples of pregnant women; thus, this substance is of concern as relates to human exposure. Depending upon the nature of the alcohol used in its synthesis, DiPeP may exist either as a mixture consisting of several branched positional isomers, or as a single defined structure. This article investigates the skin sensitization potential and immunomodulatory effects of DiPeP CAS No. 84777-06-0, which is currently marketed and classified as a UVCB substance, by in silico and in vitro methods. Our findings showed an immunomodulatory effect for DiPeP in LPS-induced THP-1 activation assay (increased CD54 expression). In silico predictions using QSAR TOOLBOX 4.5, ToxTree, and VEGA did not identify DiPeP, in the form of a discrete compound, as a skin sensitizer. The keratinocyte activation (Key Event 2 (KE2) of the adverse outcome pathway (AOP) for skin sensitization) was evaluated by two different test methods (HaCaT assay and RHE assay), and results were discordant. While the HaCaT assay showed that DiPeP can activate keratinocytes (increased levels of IL-6, IL-8, IL-1α, and ILA gene expression), in the RHE assay, DiPeP slightly increased IL-6 release. Although inconclusive for KE2, the role of DiPeP in KE3 (dendritic cell activation) was demonstrated by the increased levels of CD54 and IL-8 and TNF-α in THP-1 cells (THP-1 activation assay). Altogether, findings were inconclusive regarding the skin sensitization potential of the UVCB DiPeP-disagreeing with the results of DiPeP in the form of discrete compound (skin sensitizer by the LLNA assay). Additional studies are needed to elucidate the differences between DiPeP isomer forms, and to better understand the applicability domains of non-animal methods in identifying skin sensitization hazards of UVCB substances.

In Silico Prediction of Skin Sensitization for Compounds via Flexible Evidence Combination Based on Machine Learning and Dempster-Shafer Theory

Skin sensitization is increasingly becoming a significant concern in the development of drugs and cosmetics due to consumer safety and occupational health problems. In silico methods have emerged as alternatives to traditional in vivo animal testing due to ethical and economic considerations. In this study, machine learning methods were used to build quantitative structure-activity relationship (QSAR) models on five skin sensitization data sets (GPMT, LLNA, DPRA, KeratinoSens, and h-CLAT), achieving effective predictive accuracies (correct classification rates of 0.688-0.764 on test sets). To address the complex mechanisms of human skin sensitization, the Dempster-Shafer theory was applied to merge multiple QSAR models, resulting in an evidence-based integrated decision model. Various evidence combinations and combination rules were explored, with the self-defined Q3 rule showing superior balance. The combination of evidence such as GPMT and KeratinoSens and h-CLAT achieved a correct classification rate (CCR) of 0.880 and coverage of 0.893 while maintaining the competitiveness of other combinations. Additionally, the Shapley additive explanations (SHAP) method was used to interpret important features and substructures related to skin sensitization. A comparative analysis of an external human test set demonstrated the superior performance of the proposed method. Finally, to enhance accessibility, the workflow was implemented into a user-friendly software named HSkinSensDS.

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.

Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments

Structural alerts are molecular substructures assumed to be associated with molecular initiating events in various toxic effects and an integral part of in silico toxicology. However, alerts derived using the knowledge of human experts often suffer from a lack of predictivity, specificity, and satisfactory coverage. In this work, we present a method to build hybrid QSAR models by combining expert knowledge-based alerts and statistically mined molecular fragments. Our objective was to find out if the combination is better than the individual systems. Lasso regularization-based variable selection was applied on combined sets of knowledge-based alerts and molecular fragments, but the variable elimination was only allowed to happen on the molecular fragments. We tested the concept on three toxicity end points, i.e., skin sensitization, acute Daphnia toxicity, and Ames mutagenicity, which covered both classification and regression problems. Results showed the predictive performance of such hybrid models is, indeed, better than the models based solely on expert alerts or statistically mined fragments alone. The method also enables the discovery of activating and mitigating/deactivating features for toxicity alerts and the identification of new alerts, thereby reducing false positive and false negative outcomes commonly associated with generic alerts and alerts with poor coverage, respectively.

An integrated in silico-in vitro investigation to assess the skin sensitization potential of 4-Octylphenol

One of the major challenges in chemical toxicity testing is the possibility to protect human health against adverse effects with non-animal methods. In this paper, 4-Octylphenol (OP) was tested for skin sensitization and immunomodulatory effects using an integrated in silico-in vitro test approach. In silico tools (QSAR TOOLBOX 4.5, ToxTree and VEGA) were used together with several in vitro tests including HaCaT cells (quantification of IL-6; IL-8; IL-1α and IL-18 by ELISA and expression of genes TNF, IL1A, IL6 and IL8 by RT- qPCR), RHE model (quantification of IL-6; IL-8; IL-1α and IL-18 by ELISA) and THP-1 activation assay (CD86/CD54 expression and IL-8 release). Additionally, the immunomodulatory effect of OP was investigated using lncRNAs MALAT1 and NEAT1 expression and LPS-induced THP-1 activation (CD86/CD54 expression and IL-8 release). The in silico tools predicted OP as a sensitizer. In vitro tests are also concordant with the in silico prediction. OP increased IL-6 expression (HaCaT cells); IL-18 and IL-8 expressions (RHE model). An irritant potential was also shown by a great expression of IL-1α (RHE model); and increased expression of CD54 marker and IL-8 in THP-1 cells. Immunomodulatory effects of OP were demonstrated by the downregulation of NEAT1, MALAT1 (epigenetic markers), IL6 and IL8; and an increase in LPS-induced CD54 and IL-8 expressions. Overall, results indicate that OP is a skin sensitizer, being positive in three key events of the AOP for skin sensitization, also showing immunomodulatory effects.

The Good, The Bad, and The Perplexing: Structural Alerts and Read-Across for Predicting Skin Sensitization Using Human Data

In our earlier work (Golden et al., 2021), we showed 70-80% accuracies for several skin sensitization computational tools using human data. Here, we expanded the data set using the NICEATM human skin sensitization database to create a final data set of 1355 discrete chemicals (largely negative, ∼70%). Using this expanded data set, we analyzed model performance and evaluated mispredictions using Toxtree (v 3.1.0), OECD QSAR Toolbox (v 4.5), VEGA's (1.2.0 BETA) CAESAR (v 2.1.7), and a k-nearest-neighbor (kNN) classification approach. We show that the accuracy on this data set was lower than previous estimates, with balanced accuracies being 63% and 65% for Toxtree and OECD QSAR Toolbox, respectively, 46% for VEGA, and 59% for a kNN approach, with the lower accuracy likely due to the higher percentage of nonsensitizing chemicals. Two hundred eighty seven chemicals were mispredicted by both Toxtree and OECD QSAR Toolbox, which was approximately 20% of the entire data set, and 84% of these were false positives. The absence or presence of metabolic simulation in OECD QSAR Toolbox made no overall difference. While Toxtree is known for overpredicting, 60% of the chemicals in the data set had no alert for skin sensitization, and a substantial number of these chemicals were in fact sensitizers, pointing to sensitization mechanisms not recognized by Toxtree. Interestingly, we observed that chemicals with more than one Toxtree alert were more likely to be nonsensitizers. Finally, a kNN approach tended to mispredict different chemicals than either OECD QSAR Toolbox or Toxtree, suggesting that there was additional information to be garnered from a kNN approach. Overall, the results demonstrate that while there is merit in structural alerts as well as QSAR or read-across approaches (perhaps even more so in their combination), additional improvement will require a more nuanced understanding of mechanisms of skin sensitization.

Skin Sensitization Testing: The Ascendancy of Non-Animal Methods

A century ago, toxicology was an empirical science identifying substance hazards in surrogate mammalian models. Over several decades, these models improved, evolved to reduce animal usage, and recently have begun the process of dispensing with animals entirely. However, despite good hazard identification, the translation of hazards into adequately assessed risks to human health often has presented challenges. Unfortunately, many skin sensitizers known to produce contact allergy in humans, despite being readily identified as such in the predictive assays, continue to cause this adverse health effect. Increasing the rigour of hazard identification is inappropriate. Regulatory action has only proven effective via complete bans of individual substances. Since the problem applies to a broad range of substances and industry categories, and since generic banning of skin sensitizers would be an economic catastrophe, the solution is surprisingly simple—they should be subject to rigorous safety assessment, with the risks thereby managed accordingly. The ascendancy of non-animal methods in skin sensitization is giving unparalleled opportunities in which toxicologists, risk assessors, and regulators can work in concert to achieve a better outcome for the protection of human health than has been delivered by the in vivo methods and associated regulations that they are replacing.

Next generation risk assessment for skin allergy: Decision making using new approach methodologies

Our aim is to develop and apply next generation approaches to skin allergy risk assessment (SARA) that do not require new animal test data and better quantify uncertainties. Significant progress has been made in the development of New Approach Methodologies (NAMs), non-animal test methods, for assessment of skin sensitisation and there is now focus on their application to derive potency information for use in Next Generation Risk Assessment (NGRA). The SARA model utilises a Bayesian statistical approach to infer a human-relevant metric of sensitiser potency and a measure of risk associated with a given consumer exposure based upon any combination of human repeat insult patch test, local lymph node, direct peptide reactivity assay, KeratinoSens™, h-CLAT or U-SENS™ data. Here we have applied the SARA model within our weight of evidence NGRA framework for skin allergy to three case study materials in four consumer products. Highlighting how to structure the risk assessment, apply NAMs to derive a point of departure and conclude on consumer safety risk. NGRA based upon NAMs were, for these exposures, at least as protective as the historical risk assessment approaches. Through such case studies we are building our confidence in using NAMs for skin allergy risk assessment.

Prediction of the Neurotoxic Potential of Chemicals Based on Modelling of Molecular Initiating Events Upstream of the Adverse Outcome Pathways of (Developmental) Neurotoxicity

Developmental and adult/ageing neurotoxicity is an area needing alternative methods for chemical risk assessment. The formulation of a strategy to screen large numbers of chemicals is highly relevant due to potential exposure to compounds that may have long-term adverse health consequences on the nervous system, leading to neurodegeneration. Adverse Outcome Pathways (AOPs) provide information on relevant molecular initiating events (MIEs) and key events (KEs) that could inform the development of computational alternatives for these complex effects. We propose a screening method integrating multiple Quantitative Structure–Activity Relationship (QSAR) models. The MIEs of existing AOP networks of developmental and adult/ageing neurotoxicity were modelled to predict neurotoxicity. Random Forests were used to model each MIE. Predictions returned by single models were integrated and evaluated for their capability to predict neurotoxicity. Specifically, MIE predictions were used within various types of classifiers and compared with other reference standards (chemical descriptors and structural fingerprints) to benchmark their predictive capability. Overall, classifiers based on MIE predictions returned predictive performances comparable to those based on chemical descriptors and structural fingerprints. The integrated computational approach described here will be beneficial for large-scale screening and prioritisation of chemicals as a function of their potential to cause long-term neurotoxic effects.

In Silico Models for Skin Sensitization and Irritation

The assessment of skin irritation, and in particular of skin sensitization, has undergone an evolution process over the last years, pushing forward to new heights of quality and innovation. Public and commercial in silico tools have been developed for skin sensitization and irritation, introducing the possibility to simplify the evaluation process and the development of topical products within the dogma of the computational methods, representing the new doctrine in the field of risk assessment.The possibility of using in silico methods is particularly appealing and advantageous due to their high speed and low-cost results.The most widespread and popular topical products are represented by cosmetics. The European Regulation 1223/2009 on cosmetic products represents a paradigm shift for the safety assessment of cosmetics transitioning from a classical toxicological approach based on animal testing, towards a completely novel strategy, where the use of animals for toxicity testing is completely banned. In this context sustainable alternatives to animal testing need to be developed, especially for skin sensitization and irritation, two critical and fundamental endpoints for the assessment of cosmetics.The Quantitative Risk Assessment (QRA) methodology and the risk assessment using New Approach Methodologies (NAM) represent new frontiers to further improve the risk assessment process for these endpoints, in particular for skin sensitization.In this chapter we present an overview of the already existing models for skin sensitization and irritation. Some examples are presented here to illustrate tools and platforms used for the evaluation of chemicals.

Evaluation of toxicological aspects of three new benzoxazole compounds with sunscreen photophysical properties using in silico and in vitro methods

Sunscreening chemicals protect against damage caused by sunlight most absorbing UVA or UVB radiations. In this sense, 2-(2'-hydroxyphenyl)benzoxazole derivatives with amino substituents in the 4' and 5' positions have an outstandingly high Sun Protection Factor and adequate photostability, but their toxicity is not yet known. This study aimed to evaluate the toxicity of three synthetic 2-(2'-hydroxyphenyl)benzoxazole derivatives for their possible application as sunscreens. In silico tools were used in order to assess potential risks regarding mutagenic, carcinogenic, and skin sensitizing potential. Bioassays were performed in L929 cells to assess cytotoxicity in MTT assay and genotoxic activities in the Comet assay and micronucleus test. Also, the Salmonella/microsome assay was performed to evaluate gene mutations. The in silico predictions indicate a low risk of mutagenicity and carcinogenicity of the compounds while the skin sensitizing potential was low or inconclusive. The 2-(4'-amino-2'-hydroxyphenyl)benzoxazol compound was the most cytotoxic and genotoxic among the compounds evaluated in L929 cells, but none induced mutations in the Salmonella/microsome assay. The amino substituted at the 4' position of the phenyl ring appears to have greater toxicological risks than substituents at the 5' position of 2-(phenyl)benzoxazole. The findings warrant further studies of these compounds in cosmetic formulations.

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.

Comparison of the predictive nature of the Genomic Allergen Rapid Detection (GARD) assay with mammalian assays in determining the skin sensitisation potential of agrochemical active ingredients

Alternatives to mammalian testing are highly desirable to predict the skin sensitisation potential of agrochemical active ingredients (AI). The GARD assay, a stimulated, dendritic cell-like, cell line measuring genomic signatures, was evaluated using twelve AIs (seven sensitisers and five non-sensitisers) and the results compared with historical results from guinea pig or local lymph node assay (LLNA) studies. Initial GARD results suggested 11/12 AIs were sensitisers and six concurred with mammalian data. Conformal predictions changed one AI to a non-sensitiser. An AI identified as non-sensitising in the GARD assay was considered a potent sensitiser in the LLNA. In total 7/12 GARD results corresponded with mammalian data. AI chemistries might not be comparable to the GARD training set in terms of applicability domains. Whilst the GARD assay can replace mammalian tests for skin sensitisation evaluation for compounds including cosmetic ingredients, further work in agrochemical chemistries is needed for this assay to be a viable replacement to animal testing. The work conducted here is, however, considered exploratory research and the methodology needs further development to be validated for agrochemicals. Mammalian and other alternative assays for regulatory safety assessments of AIs must provide confidence to assign the appropriate classification for human health protection.

Scopy: an integrated negative design python library for desirable HTS/VS database design

BACKGROUND

High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery.

RESULTS

In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram.

CONCLUSION

Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

Skin sensitization in silico protocol

The assessment of skin sensitization has evolved over the past few years to include in vitro assessments of key events along the adverse outcome pathway and opportunistically capitalize on the strengths of in silico methods to support a weight of evidence assessment without conducting a test in animals. While in silico methods vary greatly in their purpose and format; there is a need to standardize the underlying principles on which such models are developed and to make transparent the implications for the uncertainty in the overall assessment. In this contribution, the relationship between skin sensitization relevant effects, mechanisms, and endpoints are built into a hazard assessment framework. Based on the relevance of the mechanisms and effects as well as the strengths and limitations of the experimental systems used to identify them, rules and principles are defined for deriving skin sensitization in silico assessments. Further, the assignments of reliability and confidence scores that reflect the overall strength of the assessment are discussed. This skin sensitization protocol supports the implementation and acceptance of in silico approaches for the prediction of skin sensitization.

Application of the hard and soft, acids and bases (HSAB) theory as a method to predict cumulative neurotoxicity

Xenobiotic electrophiles can form covalent adducts that may impair protein function, damage DNA, and may lead a range of adverse effects. Cumulative neurotoxicity is one adverse effect that has been linked to covalent protein binding as a Molecular Initiating Event (MIE). This paper describes a mechanistic in silico chemical screening approach for neurotoxicity based on Hard and Soft Acids and Bases (HSAB) theory. We evaluated the applicability of HSAB-based electrophilicity screening protocol for neurotoxicity on 19 positive and 19 negative reference chemicals. These reference chemicals were identified from the literature, using available information on mechanisms of neurotoxicity whenever possible. In silico screening was based on structural alerts for protein binding motifs and electrophilicity index in the range of known neurotoxicants. The approach demonstrated both a high positive prediction rate (82-90 %) and specificity (90 %). The overall sensitivity was relatively lower (47 %). However, when predicting the toxicity of chemicals known or suspected of acting via non-specific adduct formation mechanism, the HSAB approach identified 7/8 (sensitivity 88 %) of positive control chemicals correctly. Consequently, the HSAB-based screening is a promising approach of identifying possible neurotoxins with adduct formation molecular initiating events. While the approach must be expanded over time to capture a wider range of MIEs involved in neurotoxicity, the mechanistic nature of the screen allows users to flag chemicals for possible adduct formation MIEs. Thus, the HSAB based toxicity screening is a promising strategy for toxicity assessment and chemical prioritization in neurotoxicology and other health endpoints that involve adduct formation.

RIFM fragrance ingredient safety assessment, isobutyl alcohol, CAS Registry Number 78-83-1

The existing information supports the use of this material as described in this safety assessment. Isobutyl alcohol was evaluated for genotoxicity, repeated dose toxicity, reproductive toxicity, local respiratory toxicity, phototoxicity/photoallergenicity, skin sensitization, and environmental safety. Data show that isobutyl alcohol is not genotoxic. Data on isobutyl alcohol provide a calculated MOE >100 for the repeated dose toxicity and reproductive toxicity endpoints. Data from read-across material isoamyl alcohol (CAS # 123-51-3) show that there are no safety concerns for isobutyl alcohol for skin sensitization under the current declared levels of use. The phototoxicity/photoallergenicity endpoints were evaluated based on UV spectra; isobutyl alcohol is not expected to be phototoxic/photoallergenic. The local respiratory toxicity endpoint was evaluated using the TTC for a Cramer Class I material and the exposure to isobutyl alcohol is below the TTC (1.4 mg/day). The environmental endpoints were evaluated; isobutyl alcohol was found not to be PBT as per the IFRA Environmental Standards, and its risk quotients, based on its current volume of use in Europe and North America (i.e., PEC/PNEC), are <1.

Comparing and contrasting the coverage of publicly available structural alerts for protein binding

The molecular initiating event for many mechanisms of toxicological action comprise the reactive, covalent binding between an exogenous electrophile and an endogenous nucleophile. The target sites for electrophiles are typically peptides, proteins, enzymes or DNA. Of these, the formation of covalent adducts with proteins and DNA are perhaps the most established as they are most closely associated with skin sensitisation and genotoxicity endpoints. As such, being able to identify electrophilic features within a chemical structure provides a starting point to characterise its reactivity profile. There are a number of software tools that have been developed to help identify structural features indicative of electrophilic reactive potential to address various purposes, including: 1) to facilitate category formation for read-across of toxicity effects such as skin sensitisation potential, as well as 2) to profile substances to identify potential confounding factors to rationalise their activity in high-throughput screening (HTS) assays. Here, three such schemes that have been published in the literature as collections of SMARTS patterns and their associated chemical-biological reaction domains have been compared. The goals are 1) to better understand their scope and coverage, and 2) to assess their performance relative to a published skin sensitisation dataset where manual annotations to assign likely mechanistic domains based on expert judgement were already available. The 3 schemes were then applied to the Tox21 library and the consensus outcome was reported to highlight the proportion of chemicals likely to exhibit a reactivity response, specific to a mechanistic reaction domain, but non-specific with respect to target-tissue based activity. ToxPrint fingerprints were computed and activity enrichments computed to compare the structural features identified for the skin sensitisation dataset and Tox21 chemicals for each 'consensus' reaction domain. Enriched ToxPrints were also used to identify ToxCast assays potentially informative for reactivity.

Cytotoxicity and Pro-inflammatory Properties of Aliphatic Alpha, Beta-unsaturated Acid and Ester Monomers in RAW264.7 Cells and Their Chemical Reactivity

BACKGROUND/AIM

α,β-Unsaturated ester monomers such as methyl methacrylates (MMA), 2-hydroxyethyl methacrylates (2-HEMA), ethyleneglycol dimethacrylate (EGDMA) and triethyleneglycol dimetacrylate (TEGDMA) have been widely used in dentistry as dental materials. The present study was designed to clarify the proinflammatory activity of monomers.

MATERIALS AND METHODS

The cytotoxicity of the monomers and their effects on the expression of cyclooxygenase-2 (Cox2), nitric oxide synthase 2 (Nos2) and heme oxygenase 1 (Ho-1) mRNAs in RAW264.7 cells were determined using a cell counting kit and real-time reverse transcriptase-polymerase chain reaction, respectively.

RESULTS

The cytotoxicity declined in the order n-butyl acrylate (nBA) > acrylic acid > TEGDMA > EGDMA > methacrylic acid ≈ 2-HEMA > lauryl methacrylate > nBMA > MMA. nBA and EGDMA at 1 mM up-regulated the expression of Cox2 mRNA. In contrast, 1 mM nBA and 10 mM 2-HEMA up-regulated the expression of Nos2 mRNA. Up-regulation of Ho-1 mRNA expression was found for 0.1 mM nBA, 1 mM EGDMA and 2 mM TEGDMA. The electrophilicity, ω was calculated on the basis of the density function theory BLYP/6-31G*.

CONCLUSION

nBA and EGDMA with high ω values exerted potent pro-inflammatory activities. nBA, EGDMA and TEGDMA upregulated Ho-1 gene expression. Ho-1 gene activation of monomers may promote resistance of chemical carcinogenesis in biological systems.

In vitro assessment of mechanistic events induced by structurally related chemical rubber sensitizers

Allergic contact dermatitis (ACD) is one of the most common forms of immunotoxicity, and increased understanding of how chemicals trigger these adverse reactions is needed in order to treat or design testing strategies to identify and subsequently avoid exposure to such substances. In this study, we investigated the cellular response induced by rubber chemicals in a dendritic cell (DC) model, focusing on the structurally similar chemicals diethylthiocarbamylbenzothiazole sulfide and dimethylthiocarbamylbenzothiazole sulfide, with regard to regulation of microRNA, and messenger RNA expression. Only a few miRNAs were found to be commonly regulated by both rubber chemicals, among them miR1973, while the overall miRNA expression profiles were diverse. Similarly, out of approximately 500 differentially regulated transcripts for each chemical, about 60% overlapped, while remaining were unique. The pathways predicted to be enriched in the cell model by stimulation with the rubber chemicals were linked to immunological events, relevant in the context of ACD. These results suggest that small structural differences can trigger specific activation of the immune system in response to chemicals. The here presented mechanistic data can be valuable in explaining the immunotoxicological events in DC activation after exposure to skin sensitizing chemicals, and can contribute to understanding, preventing and treating ACD.

Prediction of the skin sensitising potential and potency of compounds via mechanism-based binary and ternary classification models

Skin sensitisation, one of the most frequent forms of human immune toxicity, is authenticated to be a significant endpoint in the field of drug discovery and cosmetics. Due to the drawbacks of traditional animal testing methods, in silico methods have advanced to study skin sensitisation. In this study, mechanism-based binary and ternary classification models were constructed with a comprehensive data set. 1007 compounds were collected to develop five series of local and global models based on mechanisms. In each series, compounds were classified into five groups according to EC3 values, and applied as training sets, test sets and external validation sets. For each of the five series, 81 binary classification models and 81 ternary classification models were acquired via 9 molecular fingerprints and 9 machine learning methods using a novel KNIME workflow. Meanwhile, the applicability domains for the best 10 models were figured out to certify the rationality of prediction effect. In addition, 8 toxic substructures probably causing skin sensitisation were identified to speculate whether a compound is a skin sensitiser. The mechanism-based prediction models and the toxic substructures can be applied to predict the skin sensitising potential and potency of compounds.

Cytotoxicity and Pro-/Anti-inflammatory Properties of Cinnamates, Acrylates and Methacrylates Against RAW264.7 Cells

BACKGROUND/AIM

Periodontitis is a chronic inflammatory disease linked to various systemic age-related conditions. It is known that α,β-unsaturated carbonyl compounds such as dietary cinnamates (β-phenyl acrylates) and related (meth)acrylates can have various positive and negative health effects, including cytotoxicity, allergic activity, pro-and anti-inflammatory activity, and anticancer activity. To clarify the anti-inflammatory properties of α,β-unsaturated carbonyl compounds without a phenolic group in the context of periodontal tissue inflammation and alveolar bone loss, we investigated the cytotoxicity and up-regulatory/down-regulatory effect of three trans-cinnamates (trans-cinnamic acid, methyl cinnamate, trans-cinnamaldehyde), two acrylates (ethyl acrylate, 2-hydroxyethyl acrylate), and three methacrylates (methyl methacrylate, 2-hydroxyethyl methacrylate, and triethyleneglycol dimethacrylate) using RAW264.7 cells.

MATERIALS AND METHODS

Cytotoxicity was determined using a cell counting kit (CCK-8) and mRNA expression was determined using real-time reverse transcriptase-polymerase chain reaction (RT-PCR). Pro-inflammatory and anti-inflammatory properties were assessed in terms of expression of mRNAs for cyclo-oxygenase-2 (Cox2), nitric oxide synthase 2 (Nos2), tumor necrosis factor-alpha (Tnfa) and heme oxygenase 1 (Ho1).

RESULTS

The most cytotoxic compound was 2-hydroxyethyl acrylate, followed by ethyl acrylate and cinnamaldehyde (50% lethal cytotoxic concentration, LC₅₀=0.2-0.5 mM). Cox2 mRNA expression was up-regulated by cinnamaldehyde and 2-hydroxyethyl acrylate, particularly by the former. In contrast, the up-regulatory effect on Nos2 mRNA expression was in the order: cinnamaldehyde >> ethyl acrylate ≈ triethyleneglycol dimethacrylate >> methyl methacrylate ≈ methyl cinnamate. On the other hand, cinnamic acid and 2-hydroxyethyl methacrylate had no effect on gene expression. The two acrylates, but not cinnamates and methacrylates, up-regulated the expression of Ho1 mRNA at a non-cytotoxic concentration of 0.1 mM. Expression of Cox2, Nos2 and Tnfa mRNAs induced by Porphyromonas gingivalis lipopolysaccharide was greatly suppressed by cinnamaldehyde, methyl cinnamate and the two acrylates at 0.1 mM (p<0.05), and slightly, but significantly suppressed by cinnamic acid and methacrylates at 0.1-1 mM (p<0.05).

CONCLUSION

Cinnamaldehyde and acrylates exhibited both anti-inflammatory and pro-inflammatory properties, possibly due to their marked ability to act as Michael reaction acceptors, as estimated from the beta-carbon ¹³C-nuclear magnetic resonance spectra. Methyl cinnamate exhibited potent anti-inflammatory activity with less cytotoxicity and pro-inflammatory activity, suggesting that this compound may be useful for treatment of periodontal disease and related systemic diseases.

Application of IATA - A case study in evaluating the global and local performance of a Bayesian network model for skin sensitization

The information characterizing key events in an Adverse Outcome Pathway (AOP) can be generated from in silico, in chemico, in vitro and in vivo approaches. Integration of this information and interpretation for decision making are known as integrated approaches to testing and assessment (IATA). One such IATA was published by Jaworska et al., which describes a Bayesian network model known as ITS-2. The current work evaluated the performance of ITS-2 using a stratified cross-validation approach. We also characterized the impact of replacing the most significant component of the network, output from the expert system TIMES-SS, with structural alert information from the OECD Toolbox and Toxtree. Lack of structural alerts or TIMES-SS predictions yielded a sensitization potential prediction of 79%. If the TIMES-SS prediction was replaced by a structural alert indicator, the network predictivity increased up to 87%. The original network's predictivity was 89%. The local applicability domain of the original ITS-2 network was also evaluated using reaction mechanistic domains to understand what types of chemicals ITS-2 was able to make the best predictions for. We found that the original network was successful at predicting which chemicals would be sensitizers, but not at predicting their potency.

The History and Development of Quantitative Structure-Activity Relationships (QSARs)

It is widely accepted that modern QSAR began in the early 1960s. However, as long ago as 1816 scientists were making predictions about physical and chemical properties. The first investigations into the correlation of biological activities with physicochemical properties such as molecular weight and aqueous solubility began in 1841, almost 60 years before the important work of Overton and Meyer linking aquatic toxicity to lipid-water partitioning. Throughout the 20th century QSAR progressed, though there were many lean years. In 1962 came the seminal work of Corwin Hansch and co-workers, which stimulated a huge interest in the prediction of biological activities. Initially that interest lay largely within medicinal chemistry and drug design, but in the 1970s and 1980s, with increasing ecotoxicological concerns, QSAR modelling of environmental toxicities began to grow, especially once regulatory authorities became involved. Since then QSAR has continued to expand, with over 1400 publications annually from 2011 onwards.

State of the art in non-animal approaches for skin sensitization testing: from individual test methods towards testing strategies

The hazard assessment of skin sensitizers relies mainly on animal testing, but much progress is made in the development, validation and regulatory acceptance and implementation of non-animal predictive approaches. In this review, we provide an update on the available computational tools and animal-free test methods for the prediction of skin sensitization hazard. These individual test methods address mostly one mechanistic step of the process of skin sensitization induction. The adverse outcome pathway (AOP) for skin sensitization describes the key events (KEs) that lead to skin sensitization. In our review, we have clustered the available test methods according to the KE they inform: the molecular initiating event (MIE/KE1)-protein binding, KE2-keratinocyte activation, KE3-dendritic cell activation and KE4-T cell activation and proliferation. In recent years, most progress has been made in the development and validation of in vitro assays that address KE2 and KE3. No standardized in vitro assays for T cell activation are available; thus, KE4 cannot be measured in vitro. Three non-animal test methods, addressing either the MIE, KE2 or KE3, are accepted as OECD test guidelines, and this has accelerated the development of integrated or defined approaches for testing and assessment (e.g. testing strategies). The majority of these approaches are mechanism-based, since they combine results from multiple test methods and/or computational tools that address different KEs of the AOP to estimate skin sensitization potential and sometimes potency. Other approaches are based on statistical tools. Until now, eleven different testing strategies have been published, the majority using the same individual information sources. Our review shows that some of the defined approaches to testing and assessment are able to accurately predict skin sensitization hazard, sometimes even more accurate than the currently used animal test. A few defined approaches are developed to provide an estimate of the potency sub-category of a skin sensitizer as well, but these approaches need further independent evaluation with a new dataset of chemicals. To conclude, this update shows that the field of non-animal approaches for skin sensitization has evolved greatly in recent years and that it is possible to predict skin sensitization hazard without animal testing.

Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules

Introduction

Skin sensitization forms a major toxicological endpoint for dermatology and cosmetic products. Recent ban on animal testing for cosmetics demands for alternative methods. We developed an integrated computational solution (SkinSense) that offers a robust solution and addresses the limitations of existing computational tools i.e. high false positive rate and/or limited coverage.

Results

The key components of our solution include: QSAR models selected from a combinatorial set, similarity information and literature-derived sub-structure patterns of known skin protein reactive groups. Its prediction performance on a challenge set of molecules showed accuracy = 75.32%, CCR = 74.36%, sensitivity = 70.00% and specificity = 78.72%, which is better than several existing tools including VEGA (accuracy = 45.00% and CCR = 54.17% with ‘High’ reliability scoring), DEREK (accuracy = 72.73% and CCR = 71.44%) and TOPKAT (accuracy = 60.00% and CCR = 61.67%). Although, TIMES-SS showed higher predictive power (accuracy = 90.00% and CCR = 92.86%), the coverage was very low (only 10 out of 77 molecules were predicted reliably).

Conclusions

Owing to improved prediction performance and coverage, our solution can serve as a useful expert system towards Integrated Approaches to Testing and Assessment for skin sensitization. It would be invaluable to cosmetic/ dermatology industry for pre-screening their molecules, and reducing time, cost and animal testing.

Bioalerts: a python library for the derivation of structural alerts from bioactivity and toxicity data sets

BACKGROUND

Assessing compound toxicity at early stages of the drug discovery process is a crucial task to dismiss drug candidates likely to fail in clinical trials. Screening drug candidates against structural alerts, i.e. chemical fragments associated to a toxicological response prior or after being metabolized (bioactivation), has proved a valuable approach for this task. During the last decades, diverse algorithms have been proposed for the automatic derivation of structural alerts from categorical toxicity data sets.

RESULTS AND CONCLUSIONS

Here, the python library bioalerts is presented, which comprises functionalities for the automatic derivation of structural alerts from categorical (dichotomous), e.g. toxic/non-toxic, and continuous bioactivity data sets, e.g. [Formula: see text] or [Formula: see text] values. The library bioalerts relies on the RDKit implementation of the circular Morgan fingerprint algorithm to compute chemical substructures, which are derived by considering radial atom neighbourhoods of increasing bond radius. In addition to the derivation of structural alerts, bioalerts provides functionalities for the calculation of unhashed (keyed) Morgan fingerprints, which can be used in predictive bioactivity modelling with the advantage of allowing for a chemically meaningful deconvolution of the chemical space. Finally, bioalerts provides functionalities for the easy visualization of the derived structural alerts.

Analysis of publically available skin sensitization data from REACH registrations 2008-2014

The public data on skin sensitization from REACH registrations already included 19,111 studies on skin sensitization in December 2014, making it the largest repository of such data so far (1,470 substances with mouse LLNA, 2,787 with GPMT, 762 with both in vivo and in vitro and 139 with only in vitro data). 21% were classified as sensitizers. The extracted skin sensitization data was analyzed to identify relationships in skin sensitization guidelines, visualize structural relationships of sensitizers, and build models to predict sensitization.

A chemical with molecular weight > 500 Da is generally considered non-sensitizing owing to low bioavailability, but 49 sensitizing chemicals with a molecular weight > 500 Da were found.

A chemical similarity map was produced using PubChem’s 2D Tanimoto similarity metric and Gephi force layout visualization. Nine clusters of chemicals were identified by Blondel’s module recognition algorithm revealing wide module-dependent variation.

Approximately 31% of mapped chemicals are Michael’s acceptors but alone this does not imply skin sensitization. A simple sensitization model using molecular weight and five ToxTree structural alerts showed a balanced accuracy of 65.8% (specificity 80.4%, sensitivity 51.4%), demonstrating that structural alerts have information value.

A simple variant of k-nearest neighbors outperformed the ToxTree approach even at 75% similarity threshold (82% balanced accuracy at 0.95 threshold). At higher thresholds, the balanced accuracy increased. Lower similarity thresholds decrease sensitivity faster than specificity.

This analysis scopes the landscape of chemical skin sensitization, demonstrating the value of large public datasets for health hazard prediction.

Eugenol and Its Role in Chronic Diseases

The active components in cloves are eugenol and isoeugenol. Eugenol has recently become a focus of interest because of its potential role in alleviating and preventing chronic diseases such as cancer, inflammatory reactions, and other conditions. The radical-scavenging and anti-inflammatory activities of eugenol have been shown to modulate chronic diseases in vitro and in vivo, but in humans, the therapeutic use of eugenol still remains to be explored. Based on a review of the recent literature, the antioxidant, anti-proliferative, and anti-inflammatory activities of eugenol and its related compounds are discussed in relation to experimentally determined antioxidant activity (stoichiometric factor n and inhibition rate constant) and theoretical parameters [phenolic O-H bond dissociation enthalpy (BDE), ionization potential (IP according to Koopman's theorem), and electrophilicity (ω)], calculated using a density functional theory method. Dimers of eugenol and its related compounds showed large antioxidant activities and high ω values and also exerted efficient anti-inflammatory activities. Eugenol appears to possess multiple antioxidant activities (dimerization, recycling, and chelating effect) in one molecule, thus having the potential to alleviate and prevent chronic diseases.

In silico prediction of prostaglandin D2 synthase inhibitors from herbal constituents for the treatment of hair loss

ETHNOPHARMACOLOGICAL RELEVANCE

Many herbal topical formulations have been marketed worldwide to prevent hair loss or promote hair growth. Certain in vivo studies have shown promising results among them; however, the effectiveness of their bioactive constituents remains unknown.

AIM OF THE STUDY

Recently, prostaglandin D2 (PGD2) inhibition has been discovered as a pharmacological mechanism for treating androgenic alopecia (AGA). This present study was aimed to identify prostaglandin D2 synthase (PTGDS) inhibitors in traditional Chinese medicines (TCMs) for treating AGA.

MATERIALS AND METHODS

In this study, 389 constituents of 12 selected herbs were docked into 6 different crystal structures of PTGDS. The accuracy of the docking methods was successfully validated with experimental data from the ZINC In Man (Zim) database using receiver operating characteristic (ROC) studies. Seven essential drug properties were predicted for topical formulation: skin permeability, sensitisation, irritation, corrosion, mutagenicity, tumorigenicity and reproductive effects.

RESULTS

Many constituents of the twelve herbs were found to have more advanced binding energies than the experimentally proved PTGDS inhibitors, but many of them were indicative of at least one type of skin adverse reactions, and exhibited poor skin permeability.

CONCLUSIONS

Overall, ricinoleic acid, acteoside, amentoflavone, quercetin-3-O-rutinoside and hinokiflavone were predicted to be PTGDS inhibitors with good pharmacokinetic properties and minimal adverse skin reactions. These compounds have the highest potential for further in vitro and in vivo investigation with the aim of developing safe and high-efficacy hair loss treatment.

Mechanism-Based QSAR Modeling of Skin Sensitization

Many chemicals can induce skin sensitization, and there is a pressing need for non-animal methods to give a quantitative indication of potency. Using two large published data sets of skin sensitizers, we have allocated each sensitizing chemical to one of 10 mechanistic categories and then developed good QSAR models for the seven categories that have a sufficient number of chemicals to allow modeling. Both internal and external validation checks showed that each model had good predictivity.

Development of an in Silico Profiler for Mitochondrial Toxicity

This study outlines the analysis of mitochondrial toxicity for a variety of pharmaceutical drugs extracted from Zhang et al. ((2009) Toxicol. In Vitro, 23, 134-140). These chemicals were grouped into categories based upon structural similarity. Subsequently, mechanistic analysis was undertaken for each category to identify the molecular initiating event driving mitochondrial toxicity. The mechanistic information elucidated during the analysis enabled mechanism-based structural alerts to be developed and combined together to form an in silico profiler. This profiler is envisaged to be used to develop chemical categories based upon similar mechanisms as part of the adverse outcome pathway paradigm. Additionally, the profiler could be utilized in screening large data sets in order to identify chemicals with the potential to induce mitochondrial toxicity.

Development of an in silico profiler for respiratory sensitisation

In this article, we outline work that led the QSAR and Molecular Modelling Group at Liverpool John Moores University to be jointly awarded the 2013 Lush Science Prize. Our research focuses around the development of in silico profilers for category formation within the Adverse Outcome Pathway paradigm. The development of a well-defined chemical category allows toxicity to be predicted via read-across. This is the central approach used by the OECD QSAR Toolbox. The specific work for which we were awarded the Lush Prize was for the development of such an in silico profiler for respiratory sensitisation. The profiler was developed by an analysis of the mechanistic chemistry associated with covalent bond formation in the lung. The data analysed were collated from clinical reports of occupational asthma in humans. The impact of the development of in silico profilers on the Three Rs is also discussed.

Use of artificial neural networks in the QSAR prediction of physicochemical properties and toxicities for REACH legislation

With the introduction of the REACH legislation in the European Union, there is a requirement for property and toxicity data on chemicals produced in or imported into the EU at levels of 1 tonne/year or more. This has meant an increase in the in silico prediction of such data. One of the chief predictive approaches is QSAR (quantitative structure-activity relationships), which is widely used in many fields. A QSAR approach that is increasingly being used is that of artificial neural networks (ANNs), and this chapter discusses its application to the range of physicochemical properties and toxicities required by REACH. ANNs generally outperform the main QSAR approach of multiple linear regression (MLR), although other approaches such as support vector machines sometimes outperform ANNs. Most ANN QSARs reported to date comply with only two of the five OECD Guidelines for the Validation of (Q)SARs.

Modeling skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol compounds with quantum mechanistic properties

Background

Advanced structure-activity relationship (SAR) modeling can be used as an alternative tool for identification of skin sensitizers and in improvement of the medical diagnosis and more effective practical measures to reduce the causative chemical exposures. It can also circumvent ethical concern of using animals in toxicological tests, and reduce time and cost. Compounds with aniline or phenol moieties represent two large classes of frequently skin sensitizing chemicals but exhibiting very variable, and difficult to predict, potency. The mechanisms of action are not well-understood.

Methods

A group of mechanistically hard-to-be-classified aniline and phenol chemicals were collected. An in silico model was established by statistical analysis of quantum descriptors for the determination of the relationship between their chemical structures and skin sensitization potential. The sensitization mechanisms were investigated based on the features of the established model. Then the model was utilized to analyze a subset of FDA approved drugs containing aniline and/or phenol groups for prediction of their skin sensitization potential.

Results and discussion

A linear discriminant model using the energy of the highest occupied molecular orbital (ϵ_HOMO) as the descriptor yielded high prediction accuracy. The contribution of ϵ_HOMO as a major determinant may suggest that autoxidation or free radical binding could be involved. The model was further applied to predict allergic potential of a subset of FDA approved drugs containing aniline and/or phenol moiety. The predictions imply that similar mechanisms (autoxidation or free radical binding) may also play a role in the skin sensitization caused by these drugs.

Conclusions

An accurate and simple quantum mechanistic model has been developed to predict the skin sensitization potential of mechanistically hard-to-be-classified aniline and phenol chemicals. The model could be useful for the skin sensitization potential predictions of a subset of FDA approved drugs.

Electronic supplementary material

The online version of this article (doi:10.1186/2050-6511-15-76) contains supplementary material, which is available to authorized users.

Towards AOP application--implementation of an integrated approach to testing and assessment (IATA) into a pipeline tool for skin sensitization

Since the OECD published the Adverse Outcome Pathway (AOP) for skin sensitization, many efforts have focused on how to integrate and interpret nonstandard information generated for key events in a manner that can be practically useful for decision making. These types of frameworks are known as Integrated Approaches to Testing and Assessment (IATA). Here we have outlined an IATA for skin sensitization which focuses on existing information including non testing approaches such as QSAR and read-across. The IATA was implemented into a pipeline tool using OASIS technology to provide a means of systematically collating and compiling relevant information which could be used in an assessment of skin sensitization potential. A test set of 100 substances with available skin sensitization information was profiled using the pipeline IATA. In silico and in chemico profiling information alone was able to correctly predict skin sensitization potential, with a preliminary accuracy of 73.85%. Information from other relevant endpoints (e.g., Ames mutagenicity) was found to improve the accuracy (to 87.6%) when coupled with a reaction chemistry mechanistic understanding. This pipeline platform could be useful in the assessment of skin sensitization potential and marks a step change in how non testing approaches can be practically applied.

Evaluating the performance of integrated approaches for hazard identification of skin sensitizing chemicals

The currently available animal-free methods for the detection of skin sensitizing potential of chemicals seem promising. However, no single method is able to comprehensively represent the complexity of the processes involved in skin sensitization. To ensure a mechanistic basis and cover the complexity, multiple methods should be integrated into a testing strategy, in accordance with the adverse outcome pathway that describes all key events in skin sensitization. Although current majority voting testing strategies have proven effective, the performance of individual methods is not taken into account. To that end, we designed a tiered strategy based on complementary characteristics of the included methods, and compared it to a majority voting approach. This tiered testing strategy was able to correctly identify all 41 chemicals tested. In terms of total number of experiments required, the tiered testing strategy requires less experiments compared to the majority voting approach. On the other hand, this tiered strategy is more complex due the number of different alternative methods required, and predicted costs are similar for both strategies. Both the tiered and majority voting strategies provide a mechanistic basis for skin sensitization testing, but the strategy most suitable for regulatory decision-making remains to be determined.

In Silico Prediction of Tyrosinase and Adenylyl Cyclase Inhibitors from Natural Compounds

Although many herbal medicines are effective in the treatment of hyperpigmentation, the potency of different constituents remains unknown. In this work, more than 20,000 herbal ingredients from 453 herbs were docked into the crystal structures of adenylyl cyclase and a human homology tyrosinase model using Surflex-Dock. These two enzymes are responsible for melanin production and inhibition of them may attain a skin-whitening effect superior to currently available agents. The essential drug properties for topical formulation of the herbal ingredients, including skin permeability, sensitization, irritation, corrosive and carcinogenic properties were predicted by Dermwin, Skin Sensitization Alerts (SSA), Skin Irritation Corrosion Rules Estimation Tool (SICRET) and Benigni/Bossa rulebase module of Toxtree. Moreover, similarity ensemble and pharmacophore mapping approaches were used to forecast other potential targets for these herbal compounds by the software, SEArch and PharmMapper. Overall, this study predicted seven compounds to have advanced drug-like properties over the well-known effective tyrosinase inhibitors, arbutin and kojic acid. These seven compounds have the highest potential for further in vitro and in vivo investigation with the aim of developing safe and high-efficacy skin-whitening agents.