Development and prevalidation of a list of structure-activity relationship rules to be used in expert systems for prediction of the skin-sensitising properties of chemicals

The new European Union (EU) chemicals policy, as described in the White Paper entitled Strategy for a Future Chemicals Policy, has identified a need for computer-based tools suitable for predicting the hazardous properties of chemicals. Two sets of structural alerts (fragments of chemical structure) for the prediction of skin sensitisation hazard classification ("R43, may cause sensitisation by skin contact") have been drawn up, based on sensitising chemicals from a regulatory database (containing data for the EU notification of new chemicals). These alerts comprise 15 rules for chemical structures deemed to be sensitising by direct action of the chemicals with cells or proteins within the skin, and three rules for substructures that act indirectly, i.e. requiring chemical or biochemical transformation. The predictivity rates of the rules were found to be good (positive predictivity, 88%; false-positive rate, 1%; specificity, 99%; negative predictivity, 74%; false-negative rate, 80%; sensitivity, 20%). Because of the confidential nature of the regulatory database, the rules are supported by examples of sensitising chemicals taken from the "Allergenliste" now held by the Federal Institute for Risk Assessment (BfR) and the DEREK for Windows expert system. The rules were prevalidated against data not used for their development. As a result of the prevalidation study, it is proposed that the two sets of structural alerts should be taken forward for formal validation, with a view to incorporating them into regulatory guidelines.     

Local lymph node assay (LLNA) for detection of sensitization capacity of chemicals

The local lymph node assay (LLNA) is a murine model developed to evaluate the skin sensitization potential of chemicals. The LLNA is an alternative approach to traditional guinea pig methods and in comparison provides important animal welfare benefits. The assay relies on measurement of events induced during the induction phase of skin sensitization, specifically lymphocyte proliferation in the draining lymph nodes which is a hallmark of a skin sensitization response. Since its introduction the LLNA has been the subject of extensive evaluation on a national and international scale, and has been successfully validated and incorporated worldwide into regulatory guidelines. Experience gained in recent years has demonstrated that adherence to published procedures and guidelines for the LLNA (e.g., with respect to dose and vehicle selection) is critical for the successful conduct and eventual interpretation of the data. In addition to providing a robust method for skin sensitization hazard identification, the LLNA has proven very useful in assessing the skin sensitizing potency of test chemicals, and this has provided invaluable information to risk assessors. The primary method to make comparisons of the relative potency of chemical sensitizers is to use linear interpolation to estimate the concentration of chemical required to induce a stimulation index of three relative to concurrent vehicle-treated controls (EC3). In certain situations where there are available less than optimal dose response data a log-linear extrapolation method can be used to estimate an EC3 value which can reduce significantly the need for repeat testing of chemicals. The LLNA, when conducted according to published guidelines, provides a robust method for skin sensitization testing that not only provides reliable hazard identification information but also data necessary for effective risk assessment and risk management.     

Evaluation of a rule base for identifying contact allergens by using a regulatory database: Comparison of data on chemicals notified in the European Union with "structural alerts" used in the DEREK expert system

To assess the suitability of the use of structural alerts to identify the skin-sensitising properties of chemicals, the 40 originally published structural alerts for the prediction of skin-sensitisation properties used by the DEREK system (which now contains 59 structural alerts), have been evaluated against a database developed in the German Federal Institute for Health Protection of Consumers and Veterinary Medicine (BgVV), which contains data submitted under the procedure for notifying new chemicals within the European Union. The evaluation of the 40 structural alerts used in DEREK revealed that eight of the 40 structural alerts for the prediction of skin-sensitising potential could be used without any further refinement. Ten structural alerts may need further specifications or refinements in order avoid false-positive predictions - proposals for refinement are discussed. Not enough substances were found within the BgVV database (containing data for more than 1000 substances) to evaluate ten of the DEREK substructures; hence, for these structural alerts, a judgement on their suitability for prediction of skin-sensitisation properties in expert systems is not possible. For 12 structural alerts, no comparative result could be obtained, because these rules did not "fire" for any of the examined chemicals. As a general result of the evaluation process, the approach of using structural alerts for the prediction of skin-sensitising properties of chemicals proved to be reliable. Proposals are given for a refinement of the structural alerts for prediction of contact allergy used in the DEREK system. In addition, advice and several preconditions are given, that apply when trying to teach a computer system to use structural alerts to predict toxicological properties.     

Cholesterol sensitises the transient receptor potential channel TRPV3 to lower temperatures and activator concentrations

TRPV3, a thermosensitive cation channel, is predominantly expressed in keratinocytes. It contributes to physiological processes such as thermosensation, nociception, and skin development. TRPV3 is polymodally regulated by chemical agonists, innocuous heat, intracellular acidification or by membrane depolarization. By manipulating the content of plasma membrane cholesterol, a key modulator of the physicochemical properties of biological membranes, we here addressed the question, how the lipid environment influences TRPV3. Cholesterol supplementation robustly potentiated TRPV3 channel activity by sensitising it to lower concentrations of chemical activators. In addition, the thermal activation of TRPV3 is significantly shifted to lower temperatures in cholesterol-enriched cells. The sensitising effect of cholesterol was not caused by an increased plasma membrane targeting of the channel. In HaCaT keratinocytes, which natively express TRPV3, a cholesterol-mediated sensitisation of TRPV3-like responses was reproduced. The cholesterol-dependent modulation of TRPV3 activity may provide a molecular mechanism to interpret its involvement in keratinocyte differentiation.     

LLNA：BrdU-ELISA 改良法的建立及其在化妆品安全性评价中的应用

目的：建立可快速检测化学物致敏性和刺激性的局部淋巴结试验（ LLNA：BrdU-ELISA）改良法,并对化妆品产品进行评价。方法3种化学物（2,4二硝基氯苯（ DNCB）、丁子香酚、己基肉桂醛）和3种化妆品产品作为受试物,雌性BALB／c小鼠连续染毒3天,测量小鼠耳缘厚度,第5天腹腔注射BrdU,第6天称耳廓重,分离颌下淋巴结称重并制取单细胞悬液,用ELISA试剂盒检测淋巴细胞增殖。结果 DNCB（1.0％）、己基肉桂醛（25％、50％）和3号粉底霜引起耳肿胀和耳廓重显著增加（P ＜0.05或P ＜0.01）,可能为刺激物,其他均无刺激性;3种化学物和3号粉底霜致敏检测阳性,其他均为阴性。结论结合小鼠耳肿胀和耳廓重的LLNA：BrdU-ELISA改良法可较好地评价化学物和化妆品产品的致敏性和刺激性,有望在化妆品安全性评价中发挥重要作用。     

A two-centre evaluation of the human organotypic skin explant culture model for screening contact allergens

Animal models are considered to be the "gold standard" for determining the potential contact allergenicity of low molecular weight chemicals. However, governmental regulations and ethical considerations limit the use of animals for such purposes. There is therefore a need for in vitro alternative models. The human organotypic skin explant culture (HOSEC) model is reported to be a promising alternative method for the predictive testing of contact allergens. The accelerated migration of Langerhans cells from the epidermis upon exposure to contact allergens is used to identify chemicals that are potentially capable of inducing a delayed-type hypersensitivity. In the study described in this paper, the model was further refined, and used, in two independent laboratories, to screen 23 low molecular weight compounds of known classification for their allergenicity. Each laboratory was able to accurately detect the contact allergens, despite small variations in the protocols used. However, the classification of dermal irritants, which have often been falsely classified as allergens, varied between the two laboratories. Despite the current limitations of the HOSEC model, the accuracy of the predictions made (sensitiser or non-sensitiser) compare favourably with classifications obtained with commonly used animal models. The HOSEC model has the potential to be developed further as an in vitro alternative to animal models for screening for contact allergens.     

Reduction,refinement and replacement: putting the immune system to work

Many chemicals are known to be, or have been implicated as, contact allergens, and allergic contact dermatitis is an important occupational and environmental health issue. It is the responsibility of toxicologists to identify those chemicals that have the potential to induce skin sensitisation, and to assess the conditions under which there will exist a risk to human health. This article describes progress that has been made in the development of new approaches to the toxicological evaluation of skin sensitisation, and the benefits to animal welfare that such developments have already produced, and are likely to produce in the future. In this context, the local lymph node assay is described with regard to hazard identification and risk assessment, and possible strategies for the development of in vitro approaches to safety assessment are discussed.     

Simple stochastic fingerprints towards mathematical modeling in biology and medicine. 3. ocular irritability classification model

MARCH-INSIDE methodology and a statistical classification method—linear discriminant analysis (LDA)—is proposed as an alternative method to the Draize eye irritation test. This methodology has been successfully applied to a set of 46 neutral organic chemicals, which have been defined as ocular irritant or nonirritant. The model allow to categorize correctly 37 out of 46 compounds, showing an accuracy of 80.46%. Specifically, this model demonstrates the existence of a good categorization average of 91.67 and 76.47% for irritant and nonirritant compounds, respectively. Validation of the model was carried out using two cross-validation tools: Leave-one-out (LOO) and leave-group-out (LGO), showing a global predictability of the model of 71.7 and 70%, respectively. The average of coincidence of the predictions between leave-one-out/leave-group-out studies and train set were 91.3% (42 out of 46 cases)/89.1% (41 out of 46 cases) proving the robustness of the model obtained. Ocular irritancy distribution diagram is carried out in order to determine the intervals of the property where the probability of finding an irritant compound is maximal relating to the choice of find a false nonirritant one. It seems that, until today, the present model may be the first predictive linear discriminant equation able to discriminate between eye irritant and nonirritant chemicals.     

Local lymph node assay: differentiating allergic and irritant responses using flow cytometry.

The murine local lymph node assay (LLNA) is a method for assessing the contact sensitization potential of chemicals. Based on events that occur during the induction phase of a contact sensitization response, the LLNA measures the in vivo proliferation of cells in the draining lymph nodes (DLNs) of mice following topical exposure to chemicals. In terms of predictive identification of important skin sensitizers, the LLNA has been shown to be at least as sensitive as, and much more reliable than, current guinea pig tests. However, proliferation has also been observed following treatment with some irritants. In an attempt to distinguish allergic from irritant-induced proliferation, flow cytometric techniques have been used to examine the phenotype of lymphocyte subsets in the DLNs as well as markers of T-lymphocyte activation and memory. Mice were treated on the ears for 3 consecutive days with allergens or irritants. The DLNs were harvested 72 h after the final treatment. Single-cell suspensions were prepared, counted, and stained for analysis of the percentages of T cells and B cells and T-cell expression of two adhesion molecules that have been associated with differentiating na?ve and activated/memory T cells, CD62L (L-selectin) and CD44 (H-cam). Increases in lymph node cellularity were observed in both allergen- and irritant-treated mice relative to na?ve and vehicle-treated animals. Mice treated with allergens showed a preferential increase in the percentage of B220(+) B cells compared with irritant-treated mice. Treatment with allergens, but not irritants, resulted in a selective increase in the percentages of CD4(+) and CD8(+) cells expressing the T-cell activation/memory phenotype CD62L(lo)CD44(hi). Taken together, flow cytometric analysis of cell phenotype and expression of T-cell activation/memory markers may provide important information for differentiating allergen- and irritant-induced proliferative responses in the DLNs of chemically treated mice.     

Chitosan: antimicrobial action upon staphylococci after impregnation onto cotton fabric

Background: High levels of viable Staphylococcus aureus, which are often found on inflamed skin surfaces, are usually associated with atopic dermatitis. Textiles, owing to their high specific surface area and intrinsic hydrophilicity, retain moisture while also providing excellent environmental conditions for microbial growth and proliferation. Recently, a number of chemicals have been added to textiles, so as to confer antimicrobial activity. Aims: To evaluate the antimicrobial action of chitosan upon selected skin staphylococci. Methods and Results: We isolated staphylococci from normal skin of 24 volunteers and studied their survival upon contact with chitosan‐impregnated cotton fabric. Low and high molecular weight chitosans were used at two concentrations; all four did effectively reduce the growth of some staphylococci (namely Staph. aureus), by up to 5 log cycles, thus unfolding a potential towards control and even prevention of related skin disorders. Conclusion: Our data suggest an effective, but selective antibacterial action of chitosans towards skin bacteria. Significance and Impact of the Study: The possibility to use a natural biopolymer incorporated in a textile to alleviate and even treat some of the symptoms associated with this skin condition may raise an alternative to existing medical treatments. The selectivity observed prevents full elimination of bacteria from the skin surface, which is an advantage.     

Prediction of molecular targets of cancer preventing flavonoid compounds using computational methods

Plant-based polyphenols (i.e., phytochemicals) have been used as treatments for human ailments for centuries. The mechanisms of action of these plant-derived compounds are now a major area of investigation. Thousands of phytochemicals have been isolated, and a large number of them have shown protective activities or effects in different disease models. Using conventional approaches to select the best single or group of best chemicals for studying the effectiveness in treating or preventing disease is extremely challenging. We have developed and used computational-based methodologies that provide efficient and inexpensive tools to gain further understanding of the anticancer and therapeutic effects exerted by phytochemicals. Computational methods involving virtual screening, shape and pharmacophore analysis and molecular docking have been used to select chemicals that target a particular protein or enzyme and to determine potential protein targets for well-characterized as well as for novel phytochemicals.     

Identifying natural compounds as multi-target-directed ligands against Alzheimer’s disease: an in silico approach

Alzheimer’s disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, β-secretase, monoamine oxidase B, glycogen synthase kinase 3β, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/. The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database (https://www.ibscreen.com/natural-compounds). Further, the natural compounds that were categorized as ‘actives’ in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.     

Formation of methyl radicals derived from cumene hydroperoxide in reconstructed human epidermis: an EPR spin trapping confirmation by using 13C-substitution

Dermal exposure to cumene hydroperoxide (CumOOH) during manufacturing processes is a toxicological issue for the industry. Its genotoxicity, mutagenic action, ability to promote skin tumour, capacity to induce epidermal hyperplasia, and aptitude to induce allergic and irritant skin contact dermatitis are well known. These toxic effects appear to be mediated through the activation to free radical species such as hydroxyl, alkoxyl, and alkyl radicals characterised basically by electron paramagnetic resonance (EPR) and spin-trapping (ST) techniques. To be a skin sensitiser CumOOH needs to covalently bind to skin proteins in the epidermis to form the antigenic entity triggering the immunotoxic reaction. Cleavage of the O–O bond allows formation of unstable CumO^?/CumOO^? radicals rearranging to longer half-life specific carbon-centred radicals R^? proposed to be at the origin of the antigen formation. Nevertheless, it is not still clear which R^? is precisely formed in the epidermis and thus involved in the sensitisation process. The aim of this work was to elucidate in conditions closer to real-life sensitisation which specific R^? are formed in a 3D reconstructed human epidermis (RHE) model by using ¹³C-substituted CumOOH at carbon positions precursors of potentially reactive radicals and EPR-ST. We demonstrated that most probably methyl radicals derived from β-scission of CumO^? radicals occur in RHE through a one-electron reductive pathway suggesting that these could be involved in the antigen formation inducing skin sensitisation. We also describe a coupling between nitroxide radicals and β position ¹³C atoms that could be of an added value to the very few examples existing for the coupling of radicals with ¹³C atoms.     

Discovery of novel inhibitors of Mycobacterium tuberculosis MurG: homology modelling,structure based pharmacophore,molecular docking,and molecular dynamics simulations

MurG (Rv2153c) is a key player in the biosynthesis of the peptidoglycan layer in Mycobacterium tuberculosis (Mtb). This work is an attempt to highlight the structural and functional relationship of Mtb MurG, the three-dimensional (3D) structure of protein was constructed by homology modelling using Discovery Studio 3.5 software. The quality and consistency of generated model was assessed by PROCHECK, ProSA and ERRAT. Later, the model was optimized by molecular dynamics (MD) simulations and the optimized model complex with substrate Uridine-diphosphate-N-acetylglucosamine (UD1) facilitated us to employ structure-based virtual screening approach to obtain new hits from Asinex database using energy-optimized pharmacophore modelling (e-pharmacophore). The pharmacophore model was validated using enrichment calculations, and finally, validated model was employed for high-throughput virtual screening and molecular docking to identify novel Mtb MurG inhibitors. This study led to the identification of 10 potential compounds with good fitness, docking score, which make important interactions with the protein active site. The 25 ns MD simulations of three potential lead compounds with protein confirmed that the structure was stable and make several non-bonding interactions with amino acids, such as Leu290, Met310 and Asn167. Hence, we concluded that the identified compounds may act as new leads for the design of Mtb MurG inhibitors.     

Is TRAIL the holy grail of cancer therapy?

Abstract  Inducing apoptosis has become an important approach in the development of new anti-cancer treatments. Tumour necrosis factor apoptosis inducing ligand (TRAIL) based therapies have emerged as one of the most promising examples of this as they selectively induce apoptosis in tumour cells. However, many primary tumours are inherently resistant to TRAIL-mediated apoptosis and require additional sensitisation. Here we review apoptotic and non-apoptotic TRAIL-signalling, and the therapeutic effects of TRAIL-based treatments both as monotherapy and in combination with sensitising agents. T. Newsom-Davis and S. Prieske contributed equally to this work.     

Development and validation of a robust QSAR model for prediction of carcinogenicity of drugs

Carcinogenicity is one of the toxicological endpoints causing the highest concern. Also, the standard bioassays in rodents used to assess the carcinogenic potential of chemicals and drugs are extremely long, costly and require the sacrifice of large numbers of animals. For these reasons, we have attempted development of a global quantitative structure-activity relationship (QSAR) model using a data set of 1464 compounds (the Galvez data set available from http://www.uv.es/-galvez/tablevi.pdf), including many marketed drugs for their carcinogenesis potential. Though experimental toxicity testing using animal models is unavoidable for new drug candidates at an advanced stage of drug development, yet the developed global QSAR model can in silico predict the carcinogenicity of new drug compounds to provide a tool for initial screening of new drug candidate molecules with reduced number of animal testing, money and time. Considering large number of data points with diverse structural features used for model development (n(training) = 732) and model validation (n(test) = 732), the model developed in this study has an encouraging statistical quality (leave-one-out Q2 = 0.731, R2pred = 0.716). Our developed model suggests that higher lipophilicity values and conjugated ring systems, thioketo and nitro groups contribute positively towards drug carcinogenicity. On the contrary, tertiary and secondary nitrogens, phenolic, enolic and carboxylic OH fragments and presence of three-membered rings reduce the carcinogenicity. Branching, size and shape are found to be crucial factors for drug-induced carcinogenicity. One may consider all these points to reduce carcinogenic potential of the molecules.