Skin Sensitisation (Q)SARs/Expert Systems: from Past, Present to Future

This review describes the state of the art of available (Q)SARs/expert systems for skin sensitisation and evaluates their utility for potential regulatory use. There is a strong mechanistic understanding with respect to skin sensitisation which has facilitated the development of different models. Most existing models fall into one of two main categories either they are local in nature, usually specific to a chemical class or reaction chemical mechanism or else they are global in form, derived empirically using statistical methods. Some of the published global QSARs available have been recently characterised and evaluated elsewhere in accordance with the OECD principles. An overview of expert systems capable of predicting skin sensitisation is also provided. Recently, a new perspective regarding the development of mechanistic skin sensitisation QSARs so-called Quantitative Mechanistic Modelling (QMM) has been proposed, where reactivity and hydrophobicity, are used as the key parameters in mathematically modelling skin sensitisation. Whilst hydrophobicity can be conveniently modelled using log P, the octanol-water partition coefficient; reactivity is less readily determined from chemical structure. Initiatives are in progress to generate reactivity data for reactions relevant to skin sensitisation but more resources are required to realise a comprehensive set of reactivity data. This is a fundamental and necessary requirement for the future assessment of skin sensitisation.JRC.I.3-Toxicology and chemical substance

Multivariate statistical process control based on principal component analysis: implementation of framework in R

The interest in multivariate statistical process control (MSPC) has increased as the industrial processes have become more complex. This paper presents an industrial process involving a plastic part in which, due to the number of correlated variables, the inversion of the covariance matrix becomes impossible, and the classical MSPC cannot be used to identify physical aspects that explain the causes of variation or to increase the knowledge about the process behaviour. In order to solve this problem, a Multivariate Statistical Process Control based on Principal Component Analysis (MSPC-PCA) approach was used and an R code was developed to implement it according some commercial software used for this purpose, namely the ProMV (c) 2016 from ProSensus, Inc. (www.prosensus.ca). Based on used dataset, it was possible to illustrate the principles of MSPC-PCA. This work intends to illustrate the implementation of MSPC-PCA in R step by step, to help the user community of R to be able to perform it.FCT - Fundação para a Ciência e a Tecnologia(UID/CEC/00319/2013

Electrophilic Chemistry Related to Skin Sensitization. Reaction Mechanistic Applicability Domain Classification for a Published Data Set of 106 Chemicals Tested in the Mouse Local Lymph Node Assay

This paper presents an overview of electrophilic reaction mechanisms relevant to skin sensitization, with reference to a published skin sensitization test data set for 106 chemicals. It is shown that there is a close correspondence in the way differences and similarities in skin sensitization potency of chemicals relate to differences and similarities in their physical organic chemistry. electrophilic reaction mechanistic chemistry. The 106 chemicals are classified into their reaction mechanistic applicability domains and reactivity-sensitization trends are analysed for each domain: the Michael acceptor and pro-Michael acceptor electrophile domain; the SNAr electrophile domain; the SN2 electrophile domain; the Schiff base electrophile domain; the acyl transfer electrophile domain and the non-electrophilic non-pro-electrophilic domain. The last of these domains should be populated mainly by non-sensitizers. Classification of 87 of the 106 compounds, using these domains, was straightforward. In most of the domains and sub-domains where there are sufficient compounds, clear trends can be seen, in conformity with the RAI (Relative Alkylation Index) model, between sensitization potential and reactivity/hydrophobicity. Of the remaining 19 compounds 7 are a-X-methyl-g-lactones, which on the basis of published organic chemistry studies and guinea pig sensitization data can be classed as pro-Michael acceptors by elimination of HX, but which are mostly negative in the LLNA, indicating a difference in bioactivation capabilities between mice and guinea pigs. The other 12 compounds, whose chemistry was not immediately obvious, were found after further analysis and literature research to fit into appropriate mechanistic domains which rationalise their skin sensitizing properties.JRC.I.3-Toxicology and chemical substance

Mechanistic applicability domains fro non-animal based toxicological endpoints. QSAR analysis of the Schiff base applicability domain for skin sensitization

Several recent (1999 onwards) publications on skin sensitisation to aldehydes and ketones which can sensitise by covalent binding to skin protein via Schiff base formation present QSARs based on the Taft sigma* parameter to model reactivity and log P to model hydrophobicity. Here all of the data are re-analysed together in a stepwise self-consistent way using the parameters log P and sum sigma*, the latter being the sum of sigma* values for the two groups R and R’ in RCOR’. A QSAR is derived: pEC3 = 1.12(±0.07)sum of sigma* + 0.42(±0.04) log P - 0.62(±0.13); n = 16 R2 = 0.952 R2adj = 0.945 s = 0.12 F = 129.6, based on mouse local lymph node assay (LLNA) data for 11 aliphatic aldehydes, one alpha-ketoester and four alpha,beta-diketones. In developing this QSAR, an initial regression equation for a training set of ten aldehydes was found to be well predictive predict a test set consisting of the other six compounds. The QSAR is found to be well predictive for LLNA data on a series of alpha,gamma-diketones and also correctly predicts the non-sensitising properties of simple dialkylketones. It is shown to meet all the criteria of the OECD principles for applicability within regulatory practice. In view of the structural diversity within the sets of compounds considered here, the present findings confirm the view that within the mechanistic applicability domain the differences in sensitisation potential are dependent solely on differences in chemical reactivity and partitioning.JRC.I.3-Toxicology and chemical substance

Chemical Reactivity Indices and Mechanism-Based Read-Across For Non-Animal Based Assessment of Skin Sensitisation Potential

The skin sensitisation potential of chemicals is currently assessed using in vivo methods where the murine local lymph node assay (LLNA) is typically the method of first choice. Current regulatory initiatives are driving the impetus for the use of in vitro/in silico alternative approaches to provide the relevant information needed for the effective assessment of skin sensitisation, for both hazard characterisation and risk assessment purposes. A chemical must undergo a number of steps for it to induce skin sensitisation but the main determining step is formation of a stable covalent association with carrier protein. The ability of a chemical to react covalently with carrier protein nucleophiles relates to both its electrophilic reactivity and its hydrophobicity. This paper focuses on quantitative indices of electrophilic reactivity with nucleophiles, in a chemical mechanism-ofaction context, and compares and contrasts the experimental approaches available to generate reactivity data that are suitable for mathematical modelling and making predictions of skin sensitisation potential, using new chemistry data correlated against existing in vivo bioassay data. As such, the paper goes on to describe an illustrative example of how quantitative kinetic measures of reactivity can be usefully and simply applied to perform mechanism-based read-across that enables hazard characterisation of skin sensitisation potential. An illustration of the types of quantitative mechanistic models that could be built using databases of kinetic measures of reactivity, hydrophobicity and existing in vivo bioassay data is also given. Copyright © 2007 John Wiley & Sons, Ltd.JRC.I.3-Consumer products safety and qualit

Global (Q)SARs for Skin Sensitisation - Assessment Against OECD Principles

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.JRC.I.3-Toxicology and chemical substance

Mechanistic Applicability Domain Classification Of A Local Lymph Node Assay Dataset For Skin Sensitisation

The goal of eliminating animal testing in the predictive identification of chemicals with the intrinsic ability to cause skin sensitization is an important target, the attainment of which has recently been brought into even sharper relief by the EU Cosmetics Directive and the requirements of REACH legislation. Development of alternative methods requires that the chemicals used to evaluate, and validate novel approaches comprise not only confirmed skin sensitizers and non-sensitizers, but also substances that span the full chemical mechanistic spectrum associated with skin sensitization. To this end, a recently published database of more than 200 chemicals tested in the mouse local lymph node assay (LLNA) has been examined in relation to various chemical reaction mechanistic domains known to be associated with sensitization. It is demonstrated here that the dataset does cover the main reaction mechanistic domains. In addition, it is shown that assignment to a reaction mechanistic domain is a critical first step in a strategic approach to understanding, ultimately on a quantitative basis, how chemical properties influence the potency of skin sensitizing chemicals. This understanding is necessary if reliable non-animal approaches, including (Quantitative) Structure-Activity Relationships (Q)SARs, read-across, and experimental chemistry based models, are to be developed.JRC.I.3-Toxicology and chemical substance

An Evaluation of Selected Global (Q)SARs/expert Systems for the Prediction of Skin Sensitisation Potential

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.JRC.I.3-Toxicology and chemical substance

TIMES-SS - A Mechanistic Evaluation of an External Validation Study Using Reaction Chemistry Principles

The TImes MEtabolism Simulator platform used for predicting Skin Sensitization (TIMES-SS) is a hybrid expert system that was developed at Bourgas University using funding and data from a Consortium comprising 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. Here we describe an external validation exercise that was recently carried out. As part of this exercise, data were generated for 40 new chemicals in the murine Local Lymph Node Assay (LLNA) and then compared with predictions made by TIMES-SS. The results were promising with an overall good concordance (83%) between experimental and predicted values. The LLNA results were evaluated with respect to reaction chemistry principles for sensitization. Additional testing on a further 4 chemicals was carried out to explore some of the findings in more detail. Improvements for TIMES-SS, where appropriate, were put forward together with proposals for further research work. TIMES-SS is a promising tool to aid in the evaluation of skin sensitization hazard under legislative programs such as REACH.JRC.I.3-Toxicology and chemical substance