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

Approaches for Grouping Chemicals into Categories

This chapter outlines the various methods by which chemical similarity can be defined to allow for chemical category formation. The focus is on three methods: simple analogues, definition of the chemistry associated with molecular initiating events and chemoinformatics. An outline of how each method is used in practice and how they have been developed into in silico tools is presented.

Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces

Retrieving molecules with specific structural features is a fundamental requirement of today's molecular database technologies. Estimates claim the chemical space relevant for drug discovery to be around 10⁶⁰ molecules. This figure is many orders of magnitude larger than the amount of molecules conventional databases retain today and will store in the future. An elegant description of such a large chemical space is provided by the concept of fragment spaces. A fragment space comprises fragments that are molecules with open valences and describes rules how to connect these fragments to products. Due to the combinatorial nature of fragment spaces, a complete enumeration of its products is intractable. We present an algorithm to search fragment spaces for generic chemical patterns as present in the SMARTS chemical pattern language. Our method allows specification of the chemical surrounding of an atom in a query and, therefore, enables a chemically intuitive search. During the search, the costly enumeration of products is avoided. The result is a fragment space that exactly describes all possible molecules that contain the user-defined pattern. We evaluated the algorithm in three different drug development use-cases and performed a large scale statistical analysis with 738 SMARTS patterns on three public available fragment spaces. Our results show the ability of the algorithm to explore the chemical space around known active molecules, to analyze fragment spaces for the presence of likely toxic molecules, and to identify complex macromolecular structures under additional structural constraints. By searching the fragment space in its nonenumerated form, spaces covering up to 10¹⁹ molecules can be examined in times ranging between 47 s and 19 min depending on the complexity of the query pattern.

Mutagenicity, carcinogenicity, and other end points

Aiming at understanding the structural and physical chemical basis of the biological activity of chemicals, the science of structure-activity relationships has seen dramatic progress in the last decades. Coarse-grain, qualitative approaches (e.g., the structural alerts), and fine-tuned quantitative structure-activity relationship models have been developed and used to predict the toxicological properties of untested chemicals. More recently, a number of approaches and concepts have been developed as support to, and corollary of, the structure-activity methods. These approaches (e.g., chemical relational databases, expert systems, software tools for manipulating the chemical information) have dramatically expanded the reach of the structure-activity work; at present, they are powerful and inescapable tools for computer chemists, toxicologists, and regulators. This chapter, after a general overview of traditional and well-known approaches, gives a detailed presentation of the latter more recent support tools freely available in the public domain.

QSAR and metabolic assessment tools in the assessment of genotoxicity

In this chapter, a range of computational tools for applying QSAR and grouping/read-across methods are described, and their integrated use in the computational assessment of genotoxicity is illustrated through the application of selected tools to two case-study compounds-2-amino-9H-pyrido[2,3-b]indole (AαC) and 2-aminoacetophenone (2-AAP). The first case study compound (AαC) is an environment pollutant and a food contaminant that can be formed during the cooking of protein-rich food. The second case study compound (2-AAP) is a naturally occurring compound in certain foods and also proposed for use as a flavoring agent. The overall aim is to describe and illustrate a possible way of combining different information sources and software tools for genotoxicity and metabolism prediction by means of a simple stepwise approach. The chapter is aimed at researchers and assessors who have a basic knowledge of computational toxicology and some familiarity with the practical use of computational tools. The emphasis is on how to evaluate the data generated by multiple tools, rather than the practical use of any specific tool.

The definition of the applicability domain relevant to skin sensitization for the aromatic nucleophilic substitution mechanism

This study outlines how a glutathione reactivity assay (so-called in chemico data) can be used to define the applicability domain for the nucleophilic aromatic substitution (S(N)Ar) reaction for benzenes. This reaction is one of the six mechanistic domains that have been shown to be important in toxicological endpoints in which the ability to bind covalently to a protein is a key molecular initiating event. This study has analysed the experimental data, allowing a clear and interpretable structure-activity relationship to be developed for the S(N)Ar domain. The applicability domain has resulted in a series of structural alerts. The definition of the applicability domain for the S(N)Ar reaction and the resulting structural alerts are likely to be beneficial in the development of computational tools for category formation and read-across. The study concludes with how this information can be used in the development of adverse outcome pathways.

ToxAlerts: a Web server of structural alerts for toxic chemicals and compounds with potential adverse reactions

The article presents a Web-based platform for collecting and storing toxicological structural alerts from literature and for virtual screening of chemical libraries to flag potentially toxic chemicals and compounds that can cause adverse side effects. An alert is uniquely identified by a SMARTS template, a toxicological endpoint, and a publication where the alert was described. Additionally, the system allows storing complementary information such as name, comments, and mechanism of action, as well as other data. Most importantly, the platform can be easily used for fast virtual screening of large chemical datasets, focused libraries, or newly designed compounds against the toxicological alerts, providing a detailed profile of the chemicals grouped by structural alerts and endpoints. Such a facility can be used for decision making regarding whether a compound should be tested experimentally, validated with available QSAR models, or eliminated from consideration altogether. The alert-based screening can also be helpful for an easier interpretation of more complex QSAR models. The system is publicly accessible and tightly integrated with the Online Chemical Modeling Environment (OCHEM, http://ochem.eu). The system is open and expandable: any registered OCHEM user can introduce new alerts, browse, edit alerts introduced by other users, and virtually screen his/her data sets against all or selected alerts. The user sets being passed through the structural alerts can be used at OCHEM for other typical tasks: exporting in a wide variety of formats, development of QSAR models, additional filtering by other criteria, etc. The database already contains almost 600 structural alerts for such endpoints as mutagenicity, carcinogenicity, skin sensitization, compounds that undergo metabolic activation, and compounds that form reactive metabolites and, thus, can cause adverse reactions. The ToxAlerts platform is accessible on the Web at http://ochem.eu/alerts, and it is constantly growing.

Strategies for the generation, validation and application of in silico ADMET models in lead generation and optimization

INTRODUCTION

The most desirable chemical starting point in drug discovery is a hit or lead with a good overall profile, and where there may be issues; a clear SAR strategy should be identifiable to minimize the issue. Filtering based on drug-likeness concepts are a first step, but more accurate theoretical methods are needed to i) estimate the biological profile of molecule in question and ii) based on the underlying structure-activity relationships used by the model, estimate whether it is likely that the molecule in question can be altered to remove these liabilities.

AREAS COVERED

In this paper, the authors discuss the generation of ADMET models and their practical use in decision making. They discuss the issues surrounding data collation, experimental errors, the model assessment and validation steps, as well as the different types of descriptors and statistical models that can be used. This is followed by a discussion on how the model accuracy will dictate when and where it can be used in the drug discovery process. The authors also discuss how models can be developed to more effectively enable multiple parameter optimization.

EXPERT OPINION

Models can be applied in lead generation and lead optimization steps to i) rank order a collection of hits, ii) prioritize the experimental assays needed for different hit series, iii) assess the likelihood of resolving a problem that might be present in a particular series in lead optimization and iv) screen a virtual library based on a hit or lead series to assess the impact of diverse structural changes on the predicted properties.

Systematic benchmark of substructure search in molecular graphs - From Ullmann to VF2

Background

Searching for substructures in molecules belongs to the most elementary tasks in cheminformatics and is nowadays part of virtually every cheminformatics software. The underlying algorithms, used over several decades, are designed for the application to general graphs. Applied on molecular graphs, little effort has been spend on characterizing their performance. Therefore, it is not clear how current substructure search algorithms behave on such special graphs. One of the main reasons why such an evaluation was not performed in the past was the absence of appropriate data sets.

Results

In this paper, we present a systematic evaluation of Ullmann’s and the VF2 subgraph isomorphism algorithms on molecular data. The benchmark set consists of a collection of 1235 SMARTS substructure expressions and selected molecules from the ZINC database. The benchmark evaluates substructures search times for complete database scans as well as individual substructure-molecule pairs. In detail, we focus on the influence of substructure formulation and size, the impact of molecule size, and the ability of both algorithms to be used on multiple cores.

Conclusions

The results show a clear superiority of the VF2 algorithm in all test scenarios. In general, both algorithms solve most instances in less than one millisecond, which we consider to be acceptable. Still, in direct comparison, the VF2 is most often several folds faster than Ullmann’s algorithm. Additionally, Ullmann’s algorithm shows a surprising number of run time outliers.

Investigation of the Skin Sensitizing Properties of 5 Osmolytic Prodrugs in a Weight-of-Evidence Assessment, Employing In Silico, In Vivo, and Read Across Analyses

The amino acid esters ethyl glycinate (EG), dl-α-tocopheryl-(mono-)betainate hydrochloride (TMB), dl-α-tocopheryl-(mono-)glycinate hydrochloride (TMG), dl-α-tocopheryl-(mono-)prolinate hydrochloride (TMP), and dl-α-tocopheryl-(mono-)sarcosinate hydrochloride (TMS) were previously shown to exert an osmoprotective function to human skin in vitro. Based on literature data, the parent compounds α-tocopherol (vitamin E) and the amino acids glycine, betaine (trimethylated glycine), proline, and sarcosine ( N-methylated glycine) are not considered to be sensitizers. To investigate skin sensitizing properties of the esters, EG, TMG, and TMP were tested in the Local Lymph Node Assay (LLNA). Remaining esters were assessed by read across analysis considering structural similarities and mechanistic aspects. The LLNA results were consistent with in silico outcomes from ToxTree 2.5.0 indicative for protein binding; EG was negative; TMG and TMP were positive. Since TMB and TMS showed structural similarities to TMG and TMP and were also positive in ToxTree, it was concluded that both TMB and TMS can also be expected to have a skin sensitizing potential and therefore animal testing was waived.

Simulation of chemical metabolism for fate and hazard assessment. V. Mammalian hazard assessment

Animals and humans are exposed to a wide array of xenobiotics and have developed complex enzymatic mechanisms to detoxify these chemicals. Detoxification pathways involve a number of biotransformations, such as oxidation, reduction, hydrolysis and conjugation reactions. The intermediate substances created during the detoxification process can be extremely toxic compared with the original toxins, hence metabolism should be accounted for when hazard effects of chemicals are assessed. Alternatively, metabolic transformations could detoxify chemicals that are toxic as parents. The aim of the present paper is to describe specificity of eukaryotic metabolism and its simulation and incorporation in models for predicting skin sensitization, mutagenicity, chromosomal aberration, micronuclei formation and estrogen receptor binding affinity implemented in the TIMES software platform. The current progress in model refinement, data used to parameterize models, logic of simulating metabolism, applicability domain and interpretation of predictions are discussed. Examples illustrating the model predictions are also provided.

Guiding principles for the implementation of non-animal safety assessment approaches for cosmetics: skin sensitisation

Characterisation of skin sensitisation potential is a key endpoint for the safety assessment of cosmetic ingredients especially when significant dermal exposure to an ingredient is expected. At present the mouse local lymph node assay (LLNA) remains the 'gold standard' test method for this purpose however non-animal test methods are under development that aim to replace the need for new animal test data. COLIPA (the European Cosmetics Association) funds an extensive programme of skin sensitisation research, method development and method evaluation and helped coordinate the early evaluation of the three test methods currently undergoing pre-validation. In May 2010, a COLIPA scientific meeting was held to analyse to what extent skin sensitisation safety assessments for cosmetic ingredients can be made in the absence of animal data. In order to propose guiding principles for the application and further development of non-animal safety assessment strategies it was evaluated how and when non-animal test methods, predictions based on physico-chemical properties (including in silico tools), threshold concepts and weight-of-evidence based hazard characterisation could be used to enable safety decisions. Generation and assessment of potency information from alternative tools which at present is predominantly derived from the LLNA is considered the future key research area.

Application of chemical reaction mechanistic domains to an ecotoxicity QSAR model, the KAshinhou Tool for Ecotoxicity (KATE)

The validity of chemical reaction mechanistic domains defined by skin sensitisation in the Quantitative Structure-Activity Relationship (QSAR) ecotoxicity system, KAshinhou Tools for Ecotoxicity (KATE), March 2009 version, has been assessed and an external validation of the current KATE system carried out. In the case of the fish end-point, the group of chemicals with substructures reactive to skin sensitisation always exhibited higher root mean square errors (RMSEs) than chemicals without reactive substructures under identical C- or log P-judgements in KATE. However, in the case of the Daphnia end-point this was not so, and the group of chemicals with reactive substructures did not always have higher RMSEs: the Schiff base mechanism did not function as a high error detector. In addition to the RMSE findings, the presence of outliers suggested that the KATE classification rules needs to be reconsidered, particularly for the amine group. Examination of the dependency of the organism on the toxic action of chemicals in fish and Daphnia revealed that some of the reactive substructures could be applied to the improvement of the KATE system. It was concluded that the reaction mechanistic domains of toxic action for skin sensitisation could provide useful complementary information in predicting acute aquatic ecotoxicity, especially at the fish end-point.

Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010

The 7th amendment to the EU Cosmetics Directive prohibits to put animal-tested cosmetics on the market in Europe after 2013. In that context, the European Commission invited stakeholder bodies (industry, non-governmental organisations, EU Member States, and the Commission's Scientific Committee on Consumer Safety) to identify scientific experts in five toxicological areas, i.e. toxicokinetics, repeated dose toxicity, carcinogenicity, skin sensitisation, and reproductive toxicity for which the Directive foresees that the 2013 deadline could be further extended in case alternative and validated methods would not be available in time. The selected experts were asked to analyse the status and prospects of alternative methods and to provide a scientifically sound estimate of the time necessary to achieve full replacement of animal testing. In summary, the experts confirmed that it will take at least another 7-9 years for the replacement of the current in vivo animal tests used for the safety assessment of cosmetic ingredients for skin sensitisation. However, the experts were also of the opinion that alternative methods may be able to give hazard information, i.e. to differentiate between sensitisers and non-sensitisers, ahead of 2017. This would, however, not provide the complete picture of what is a safe exposure because the relative potency of a sensitiser would not be known. For toxicokinetics, the timeframe was 5-7 years to develop the models still lacking to predict lung absorption and renal/biliary excretion, and even longer to integrate the methods to fully replace the animal toxicokinetic models. For the systemic toxicological endpoints of repeated dose toxicity, carcinogenicity and reproductive toxicity, the time horizon for full replacement could not be estimated.

The Use of a Chemistry-based Profiler for Covalent DNA Binding in the Development of Chemical Categories for Read-across for Genotoxicity

An important molecular initiating event for genotoxicity is the ability of a compound to bind covalently with DNA. However, not all compounds that can undergo covalent binding mechanisms will result in genotoxicity. One approach to solving this problem, when in silico prediction techniques are being used, is to develop tools that allow chemicals to be grouped into categories based on their ability to bind covalently to DNA. For this analysis to take place, compounds need to be placed within categories where the trend in toxicity can be explained by simple descriptors, such as hydrophobicity. However, this can occur only when the compounds within a category are structurally and mechanistically similar. Chemistry-based profilers have the ability to screen compounds and highlight those with similar structures to a target compound, and are thus likely to act via a similar mechanism of action. Here, examples are reported to highlight how structure-based profilers can be used to form categories and hence fill data gaps. The importance of developing a well-defined and robust category is discussed in terms of both mechanisms of action and structural similarity.

Characterisation, Evaluation and Possible Validation of In Silico Models for Toxicity: Determining if a Prediction is Valid

This chapter describes the process whereby a (Q)SAR may be described, evaluated and, where possible, validated. The emphasis here is not to develop models, but to characterise them according to the guidance supplied by the Organisation for Economic Co-operation and Development (OECD) and the European Chemicals Agency (EChA). The backbone to this process are the OECD Principles for the Validation of (Q)SARs. Three case studies illustrating how to approach the OECD Principles are supplied.

Adverse Outcome Pathways: A Way of Linking Chemical Structure to In Vivo Toxicological Hazards

The concept of adverse outcome pathways is presented here as an organising principle to aid assessment and formation of toxicologically meaningful categories for hazard endpoints, especially chronic health effects where different molecular initiating events and different key biological events lead to the same in vivo outcome. A toxicologically meaningful category can be thought of as a group of chemicals whose human health and/or environmental toxicological properties are likely to be similar or follow a regular pattern for a particular hazard. An adverse outcome pathway is a description of plausible causal linkages, which illustrates how the molecular initiating event, leads to the key biochemical, cellular, physiological, behavioural etc. responses, which characterise the biological cascade across the different levels of biological organisation. The concept of the adverse outcome pathway is discussed in the context of the more stringent mechanism of action approach used in pharmacology. The value of this concept is demonstrated with five examples, each with a different type of molecular initiating event. The pathways concept is also discussed in context of elaborate hazards where the in vivo effects may be cumulative or life stage dependent.

Biological Read-Across: Mechanistically-Based Species–Species and Endpoint–Endpoint Extrapolations

This chapter describes the development and use of relationships between toxicity data, for the same chemicals, derived from different species. These relationships provide simple models to predict toxicity of, potentially, a higher species from that of a lower species. Approaches to the formation of these models are described, notably inter-species relationships, quantitative structure-activity-activity relationships and prediction models. It is noted that the best extrapolations are for closely related species i.e. within taxa. In addition, forming groups or categories of compounds according to common mechanisms of toxic action improves the correlation for extrapolations from lower to higher species. A freely available software package, Web-ICE, is introduced as a suitable tool to apply these methods.

Developing the Applicability Domain of In Silico Models: Relevance, Importance and Methods

The past two decades has seen the rapid growth in the development and utilisation of computational technologies to predict the toxicity of chemicals. Most notably, widespread pressure to both reduce and replace current animal testing regimes has led to in silico modelling becoming a widely utilised tool in toxicological screening. Unfortunately, given that computational models are open to misuse, there has been, and still is, significant reluctance to accept them for regulatory use. In an effort to combat this, the validation of both model and predictions is now at the forefront of research, with the concept of applicability domain being central to the validation process.

In this chapter the applicability domain concept is defined and numerous methods for its characterisation are detailed and explored with the aid of a case study example. These approaches are shown to span from relatively simple descriptor-based methods to more complex approaches based upon structural similarity or mechanism of action. Given the wealth of differing approaches available and the different information each method yields about the model, a stepwise scheme which considers numerous methods is recommended. With appreciation of model architecture and subsequent utilisation, this chapter shows that a robust and multifaceted applicability domain can be generated. Once defined, the applicability domain serves as a critical screening stage ensuring that a model is fit-for-purpose and predictions are made with maximal confidence.

The Use of Frontier Molecular Orbital Calculations in Predictive Reactive Toxicology

The aim of this chapter is to outline the use of quantum chemical descriptors in the modelling of chemical reactivity relevant to the prediction of toxicity. Chemical reactivity is an important component of a number of toxicological endpoints that involve the formation of a covalent bond between a biological macromolecule and a chemical. The chapter highlights how, within well defined mechanistic domains, simple and interpretable descriptors such as the energy of the lowest unoccupied molecular orbital can aid in the understanding of frontier orbital controlled chemical reactions. In addition, an outline of the importance of frontier molecular orbital theory is presented. The chapter illustrates the considerations that must be taken into account if the results of such calculations are going to be of practical use in regulatory environments.

Global QSAR models of skin sensitisers for regulatory purposes

Background

The new European Regulation on chemical safety, REACH, (Registration, Evaluation, Authorisation and Restriction of CHemical substances), is in the process of being implemented. Many chemicals used in industry require additional testing to comply with the REACH regulations. At the same time EU member states are attempting to reduce the number of animals used in experiments under the 3 Rs policy, (refining, reducing, and replacing the use of animals in laboratory procedures). Computational techniques such as QSAR have the potential to offer an alternative for generating REACH data. The FP6 project CAESAR was aimed at developing QSAR models for 5 key toxicological endpoints of which skin sensitisation was one.

Results

This paper reports the development of two global QSAR models using two different computational approaches, which contribute to the hybrid model freely available online.

Conclusions

The QSAR models for assessing skin sensitisation have been developed and tested under stringent quality criteria to fulfil the principles laid down by the OECD. The final models, accessible from CAESAR website, offer a robust and reliable method of assessing skin sensitisation for regulatory use.

The use of Mechanisms and Modes of Toxic Action in Integrated Testing Strategies: The Report and Recommendations of a Workshop held as part of the European Union OSIRIS Integrated Project

This report on The Potential of Mode of Action (MoA) Information Derived from Non-testing and Screening Methodologies to Support Informed Hazard Assessment, resulted from a workshop organised within OSIRIS (Optimised Strategies for Risk Assessment of Industrial Chemicals through Integration of Non-test and Test Information), a project partly funded by the EU Commission within the Sixth Framework Programme. The workshop was held in Liverpool, UK, on 30 October 2008, with 35 attendees. The goal of the OSIRIS project is to develop integrated testing strategies (ITS) fit for use in the REACH system, that would enable a significant increase in the use of non-testing information for regulatory decision making, and thus minimise the need for animal testing. One way to improve the evaluation of chemicals may be through categorisation by way of mechanisms or modes of toxic action. Defining such groups can enhance read-across possibilities and priority settings for certain toxic modes or chemical structures responsible for these toxic modes. Overall, this may result in a reduction of in vivo testing on organisms, through combining available data on mode of action and a focus on the potentially most-toxic groups. In this report, the possibilities of a mechanistic approach to assist in and guide ITS are explored, and the differences between human health and environmental areas are summarised.

The In Chemico–In Silico Interface: Challenges for Integrating Experimental and Computational Chemistry to Identify Toxicity

A number of toxic effects are brought about by the covalent interaction between the toxicant and biological macromolecules. In chemico assays are available that attempt to identify reactive compounds. These approaches have been developed independently for pharmaceuticals and for other non-pharmaceutical compounds. The assays vary widely in terms of the macromolecule (typically a peptide) and the analytical technique utilised. For both sets of methods, there are great opportunities to capture in chemico information by using in silico methods to provide computational tools for screening purposes. In order to use these in chemico and in silico methods, integrated testing strategies are required for individual toxicity endpoints. The potential for the use of these approaches is described, and a number of recommendations to improve this extremely useful technique, in terms of implementing the Three Rs in toxicity testing, are presented.