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Purpose: Of the several selenized yeasts authorised for use as feed additives in the EU, Saccharomyces cerevisiae CNCM I-3060 inactivated' (Sel-Plex®), was the first to be approved for use, in 2006. The additive has a concentration of selenium between 2000 and 2400 mg/kg and a selenomethionine content greater than 63%. Previous toxicological and safety studies have shown Sel-Plex® to be safe for use for target animal species, consumers, users and the environment. A new formulation of Sel-Plex® was recently developed however, with a minimum selenium content of 3000 mg/kg. The increase in selenium in this product, Sel-Plex® 3000, presented the need to assess the risk for workers and users and to establish if there would be any eye and/or skin irritancy and skin sensitisation effects associated with the product. The purpose of this paper is to present the methodology and results of the user safety skin and eye studies performed on Sel-Plex® 3000. Materials & Methods: In vitro skin and eye models were used to assess skin and eye irritancy, while skin sensitisation was examined using an in vivo method. The acute eye irritation was evaluated using a Reconstructed human Cornea-like Epithelium (RhCE) model, which followed the OECD guideline 492. The skin irritation was assessed based on its ability to induce cell death in a commercial reconstructed human epidermis (RhE) model (EPISKIN™) according to the OECD Guideline No. 439. The skin sensitising potential was evaluated in the Guinea pig in line with OECD Guideline 406, and measured the extent and degree of skin reaction to a challenge exposure following previous topical exposure of a substance on the skin. Results: The skin and eye irritation test results showed that Sel-Plex® 3000 was a non-irritant in both cases. The skin sensitisation study showed that the additive did not generate a sensitisation response in the guinea pig and should not be considered a skin sensitiser. Conclusion: These results indicate that Sel-Plex® 3000 is safe to use for workers in an industrial setting when handling the product and the studies may be further used to support regulatory compliance in respective markets. [ABSTRACT FROM AUTHOR]

Oil body (OB), a subcellular organelle that stores oil in plant seeds, is considered a new transdermal drug delivery system. With the increasing understanding of the OB and its main protein (oleosin), numerous studies have been conducted on OB as "carrier" for the expression of exogenous proteins. In our previous study, oil body fused with aFGF (OLAF) was obtained using a plant oil body expression system that had been preliminarily proven to be effective in accelerating the healing of skin wounds. However, no dermal toxicological information on OLAF is available. To ensure the dermal safety of OLAF, a series of tests (the acute dermal toxicity test, 21-day repeat dermal toxicity test, dermal irritation test and skin sensitisation test) were conducted after optimising the extraction protocol of OLAF. To improve the extraction rate of OLAF, response surface methodology (RSM) was first employed to optimise the extraction conditions. Then, Wistar rats were exposed to OLAF (400 mg·kg−1 body weight) in two different ways (6 hours/time for 24 hours and 1 time/day for 21 days) to evaluate the acute dermal toxicity and 21-day repeated dermal toxicity of OLAF. In the acute dermal toxicity test, clinical observations were conducted to evaluate the toxicity, behaviour, and health of the animals for 14 consecutive days. Similarly, the clinical signs, body weight, haematological and biochemical parameters, histopathological changes and other indicators were also detected during the 21 days administration. For the dermal irritation test, single and multiple doses of OLAF (125 mg·kg−1 body weight) were administered to albino rabbits for 14 days (1 time/day). The irritation reaction on the skin of each albino rabbit was recorded and scored. Meanwhile, skin sensitisation to OLAF was conducted using guinea pigs for a period of 28 days. Suitable extraction conditions for OLAF (PBS concentration 0.01, pH of PBS 8.6, solid–liquid ratio 1:385 g·mL−1) were obtained using RSM. Under these conditions, the extraction rate and particle size of OLAF were 7.29% and 1290 nm, respectively. In the tests of acute dermal toxicity and 21-day repeated dermal toxicity, no mortality or significant differences were observed in terms of clinical signs, body weight, haematological parameters, biochemical parameters and anatomopathological analysis. With respect to the dermal irritation test and skin sensitisation test, no differences in erythema, oedema or other abnormalities were observed between treatment and control groups on gross and histopathological examinations. The results of this study suggest that OLAF does not cause obvious toxicity, skin sensitisation or irritation in animals. [ABSTRACT FROM AUTHOR]

Recently, alternatives to animal testing have been used to evaluate skin sensitisers in cosmetic products. However, testing is still complicated and expensive. To develop a simpler, cost-effective and more accurate evaluation method for the skin sensitising chemicals, we employed cell-based and RT-PCR-based assay. Representative sensitiser specific gene expression in THP-1 cells was analysed by microarray. Gene ontology (GO) analysis revealed that 26 genes induced by the sensitisers were associated with immune function. First, seven of the 26 genes were chosen arbitrarily as candidate markers for our sensitisation assay. Then, THP-1 cells were exposed to 13 reference chemicals with known sensitising potential, and real-time RT-PCR assays targeting the candidate marker genes were performed. Among them, six markers were able to properly evaluate the sensitisation potential by classifying the gene induction rates with appropriate criteria. Especially, the results of the assay using TREM1 and TNFRSF12A gene markers showed 100% sensitivity and specificity. An existing test method, h-CLAT, requires a flow cytometer and is complicated to operate. In contrast, our method is relatively simpler and more cost-effective. Therefore, our method is a promising one to evaluate sensitising chemicals. [ABSTRACT FROM AUTHOR]

Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives, such as expert systems software. Here we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge-based) and TIMES-SS (hybrid), and evaluated their performance using two large sets of animal data: one set of 1249 substances from eChemportal and a second set of 515 substances from NICEATM. A model was considered successful at predicting skin sensitisation potential if it had at least the same balanced accuracy as the LLNA and the GPMT had in predicting the other outcomes, which ranged from 79% to 86%. We found that the highest balanced accuracy of any of the expert systems evaluated was 65% when making global predictions. For substances within the domain of TIMES-SS, however, balanced accuracies for the two datasets were found to be 79% and 82%. In those cases where a chemical was within the TIMES-SS domain, the TIMES-SS skin sensitisation hazard prediction had the same confidence as the result from LLNA or GPMT. [ABSTRACT FROM AUTHOR]

The TImes MEtabolism Simulator platform for predicting Skin Sensitisation (TIMES-SS) is a hybrid expert system, first developed at Bourgas University using funding and data from a consortium of industry and regulators. TIMES-SS encodes structure–toxicity and structure–skin metabolism relationships through a number of transformations, some of which are underpinned by mechanistic 3D QSARs. The model estimates semi-quantitative skin sensitisation potency classes and has been developed with the aim of minimising animal testing, and also to be scientifically valid in accordance with the OECD principles for (Q)SAR validation. In 2007 an external validation exercise was undertaken to fully address these principles. In 2010, a new industry consortium was established to coordinate research efforts in three specific areas: refinement of abiotic reactions in the skin (namely autoxidation) in the skin, refinement of the manner in which chemical reactivity was captured in terms of structure–toxicity rules (inclusion of alert reliability parameters) and defining the domain based on the underlying experimental data (study of discrepancies between local lymph node assay Local Lymph Node Assay (LLNA) and Guinea Pig Maximisation Test (GPMT)). The present paper summarises the progress of these activities and explains how the insights derived have been translated into refinements, resulting in increased confidence and transparency in the robustness of the TIMES-SS predictions. [ABSTRACT FROM PUBLISHER]

With the advancement of technology and industrial revolution, skin sensitisation has emerged as a major environmental and occupational health hazard. A chemical may induce B-cell and T-cell infiltration from lymph nodes resulting in irritation of skin and carcinoma on prolonged use. To minimise the animal study and also to reduce time and expenditure, development ofin silicopredictive models has achieved a considerable attention over the last few decades. In this study, we have developed classification- and regression-based QSAR models for skin sensitisation potential of 67 diverse organic chemicals. The developed models strongly suggest the importance of the number of H atoms in a molecule inferring that the unsaturated compounds are more skin-sensitising agent than the saturated compounds. Other two descriptors,andnCb − , signify the importance of the lipophilic character of the molecules. Here, we have also screened the DrugBank database (http://www.drugbank.ca) containing a large number of compounds using our developed models in an attempt to identify molecules with skin sensitisation potential. [ABSTRACT FROM AUTHOR]

Skin sensitisation is a key endpoint under REACH as it is costly and its assessment currently has a high dependency on animal testing. In order to reduce both the cost and the numbers of animals tested, it is likely that (quantitative) structure-activity relationships ((Q)SAR) and read-across methods will be utilised as part of intelligent testing strategies. The majority of skin sensitisers elicit their effect via covalent bond formation with skin proteins. These reactions have been understood in terms of well defined nucleophilic-electrophilic reaction chemistry. Thus, a first step in (Q)SAR analysis is the assignment of a chemical's potential mechanism of action enabling it to be placed in an appropriate reactivity domain. The aim of this study was to design a series of SMARTS patterns capable of defining these reactivity domains. This was carried out using a large database of local lymph node assay (LLNA) results that had had potential mechanisms of action assigned to them using expert knowledge. A simple algorithm was written enabling the SMARTS patterns to be used to screen a database of SMILES strings. The SMARTS patterns were then evaluated using a second, smaller, test set of LLNA results which had also had potential mechanisms of action assigned by experts. The results showed that the SMARTS patterns provided an excellent method of identifying potential electrophilic mechanisms. The findings are supported, in part, by molecular orbital calculations which confirm assignment of reactive mechanism of action. The ability to define a chemical's potential reaction mechanism is likely to be of significant benefit to regulators and risk assessors as it enables category formation and subsequent read-across to be performed. [ABSTRACT FROM AUTHOR]

A novel descriptor selection scheme for Support Vector Machine (SVM) classification method has been proposed and its utility demonstrated using a skin sensitisation dataset as an example. A backward elimination procedure, guided by mean accuracy (the average of specificity and sensitivity) of a leave-one-out cross validation, is devised for the SVM. Subsets of descriptors were first selected using a sequential t-test filter or a Random Forest filter, before backward elimination was applied. Different kernels for SVM were compared using this descriptor selection scheme. The Radial Basis Function (RBF) kernel worked best when a sequential t-test filter was adopted. The highest mean accuracy, 84.9%, was obtained using SVM with 23 descriptors. The sensitivity and the specificity were as high as 93.1% and 76.6%, respectively. A linear kernel was found to be optimal when a Random Forest filter was used. The performance using 24 descriptors was comparable with a RBF kernel with a sequential t-test filter. As a comparison, Fisher's linear discriminant analysis (LDA) under the same descriptor selection scheme was carried out. SVM was shown to outperform the LDA. [ABSTRACT FROM AUTHOR]

Skin sensitisation potential is an endpoint that needs to be assessed within the framework of existing and forthcoming legislation. At present, skin sensitisation hazard is normally identified using in vivo test methods, the favoured approach being the local lymph node assay (LLNA). This method can also provide a measure of relative skin sensitising potency which is essential for assessing and managing human health risks. One potential alternative approach to skin sensitisation hazard identification is the use of (Quantitative) structure activity relationships ((Q)SARs) coupled with appropriate documentation and performance characteristics. This represents a major challenge. Current thinking is that (Q)SARs might best be employed as part of a battery of approaches that collectively provide information on skin sensitisation hazard. A number of (Q)SARs and expert systems have been developed and are described in the literature. Here we focus on three models (TOPKAT, Derek for Windows and TOPS-MODE), and evaluate their performance against a recently published dataset of 211 chemicals. The current strengths and limitations of one of these models is highlighted, together with modifications that could be made to improve its performance. Of the models/expert systems evaluated, none performed sufficiently well to act as a standalone tool for hazard identification. [ABSTRACT FROM AUTHOR]

As part of a European Chemicals Bureau contract relating to the evaluation of (Q)SARs for toxicological endpoints of regulatory importance, we have reviewed and analysed (Q)SARs for skin sensitisation. Here we consider some recently published global (Q)SAR approaches against the OECD principles and present re-analysis of the data. Our analyses indicate that "statistical" (Q)SARs which aim to be global in their applicability tend to be insufficiently robust mechanistically, leading to an unacceptably high failure rate. Our conclusions are that, for skin sensitisation, the mechanistic chemistry is very important and consequently the best non-animal approach currently applicable to predict skin sensitisation potential is with the help of an expert system. This would assign compounds into mechanistic applicability domains and apply mechanism-based (Q)SARs specific for those domains and, very importantly, recognise when a compound is outside its range of competence. In such situations, it would call for human expert input supported by experimental chemistry studies as necessary. [ABSTRACT FROM AUTHOR]

Skin sensitisation is a delayed hypersensitivity reaction caused by repeated exposure to common natural and synthetic chemical allergens. It is thought that small chemical sensitisers (haptens) are required to form a strong irreversible bond with a self protein/peptide and generate an immunogenic hapten-protein complex in order to be recognised by the immune system and stimulate T cell proliferation. The sensitisers are usually electrophilic chemicals that are directly reactive with proteins or reactive intermediates (metabolites) of chemically inert compounds (prohaptens). Sensitising chemicals are also capable of weak, non-covalent association with proteins and there is an ongoing debate about the role of weak interactions of chemicals and proteins in the chemistry of allergy. The non-covalent interactions are reversible and thus have a major impact on skin/epidermal bioavailability of chemical/reactive metabolites. We investigated the relationship between the relative level of non-covalent association to a model protein and their relative potencies as determined by the EC3 values in the murine local lymph node assay (LLNA) for a number of chemicals. Using human serum albumin as a model protein, we determined that no observable relationship exists between the two parameters for the chemicals tested. Therefore, at least for this model protein, non-covalent interactions appear not to be a key determinant of allergen potency. [ABSTRACT FROM AUTHOR]

The role of metabolism in prioritising chemicals according to their potential adverse health effects is extremely important given the fact that innocuous parents can be transformed into toxic metabolites. Our recent efforts in simulating metabolic activation of chemicals are reviewed in this work. The application of metabolic simulators to predict biodegradation (microbial degradation pathways), bioaccumulation (fish liver metabolism), skin sensitisation (skin metabolism), mutagenicity (rat liver S-9 metabolism) are discussed. The ability of OASIS approach to predict metabolism (toxicokinetics) and toxicity (toxicodynamics) of chemicals resulting from their metabolic activation in a single modelling platform is an important advantage of the method. It allows prioritisation of chemicals due to predicted toxicity of their metabolites. [ABSTRACT FROM AUTHOR]

In 2001, the European Commission published a policy statement ("White Paper") on future chemicals regulation and risk reduction that proposed the use of non-animal test systems and tailor-made testing approaches, including (Q)SARs, to reduce financial costs and the number of test animals employed. The authors have compiled a database containing data submitted within the EU chemicals notification procedure. From these data, (Q)SARs for the prediction of local irritation/corrosion and/or sensitisation potential were developed and published. These (Q)SARs, together with an expert system supporting their use, will be submitted for official validation and application within regulatory hazard assessment strategies. The main features are; • two sets of structural alerts for the prediction of skin sensitisation hazard classification as defined by the European risk phrase R43, comprising 15 rules for chemical substructures deemed to be sensitising by direct action with cells or proteins, and three rules for substructures acting indirectly, i.e., requiring biochemical transformation; • a decision support system (DSS) for the prediction of skin and/or eye lesion potential built from information extracted from our database. This DSS combines SARs defining reactive chemical substructures relevant for local lesions to be classified, and QSARs for the prediction of the absence of such a potential. The role of the BfR database, and (Q)SARs derived from it, in the use of current and future (EU) testing strategies for irritation and sensitisation is discussed. [ABSTRACT FROM AUTHOR]