The Development of a Fragment-Based in silico Profiler for the Prediction of Thiol Reactivity and Toxicity

Regulatory toxicology in the 21st century is faced with the challenge of having to replace its use of experimental animals in chemical risk assessment with alternative methods. This is due to the introduction of the REACH legislation and the seventh amendment to the cosmetics directive. Such alternative methods include the use of in vitro (cell culture/tissue etc.), in chemico (chemical experiments e.g. determination of reactivity) and in silico (computational) approaches. Importantly, it is envisaged that data from all these alternative sources will be required for the prediction of the animal-based endpoints used in regulatory toxicology. One of the key computational approaches used for data gap filling is category formation and read-across. When using this approach to assess the potential toxicity of a chemical, a chemical category is best defined based on a common molecular initiating event e.g. the formation of a covalent bond with biological nucleophile via the same chemical mechanism. The structural features that define a chemical’s membership of such a category can be encoded computationally as structural alerts, which in turn, can be grouped together to form an in silico profiler. The work discussed in this thesis addresses the key shortcoming of traditional in silico profilers, this being that current in silico profilers provided no information about the rate of covalent bond formation for chemicals containing the same structural alert but with different substituents. The research within this thesis addresses this problem through the introduction of a fragment-based approach to in silico profiler development. This fragment-based approach introduces the use of calculated activation energies determined through the use of quantum mechanics calculations which enable chemical reactivity to be predicted. Chapter 3 outlines the development of the approach for α,β-unsaturated aldehydes, ketones and esters which form covalent bonds through Michael addition. Chapter 4 extends the work outlined in Chapter 3 demonstrating how the fragment-based profiler can be used to predict both chemical reactivity and skin sensitisation and toxicity to Tetrahymena pyriformis. Finally, Chapter 5 extends the approach to chemicals capable of reacting with proteins via an SN2 mechanism demonstrating the approach can be applied to any mechanistic domain for which data exist. Overall, this thesis outlines an approach for the development of novel fragment-based in silico profilers capable of quantitatively predicting chemical reactivity and by extension toxicity. It is envisaged that the work outlined in this thesis will be of use primarily in regulatory toxicology, within such tools as the OECD QSAR toolbox

Development of a Fragment-Based in Silico Profiler for Michael Addition Thiol Reactivity

The Adverse Outcome Pathway (AOP) paradigm details the existing knowledge that links the initial interaction between a chemical and a biological system, termed the molecular initiating event (MIE), through a series of intermediate events, to an adverse effect. An important example of a well-defined MIE is the formation of a covalent bond between a biological nucleophile and an electrophilic compound. This particular MIE has been associated with various toxicological end points such as acute aquatic toxicity, skin sensitization, and respiratory sensitization. This study has investigated the calculated parameters that are required to predict the rate of chemical bond formation (reactivity) of a dataset of Michael acceptors. Reactivity of these compounds toward glutathione was predicted using a combination of a calculated activation energy value (Eact, calculated using density functional theory (DFT) calculation at the B3YLP/6-31G+(d) level of theory, and solvent-accessible surface area values (SAS) at the α carbon. To further develop the method, a fragment-based algorithm was developed enabling the reactivity to be predicted for Michael acceptors without the need to perform the time-consuming DFT calculations. Results showed the developed fragment method was successful in predicting the reactivity of the Michael acceptors excluding two sets of chemicals: volatile esters with an extended substituent at the β-carbon and chemicals containing a conjugated benzene ring as part of the polarizing group. Additionally the study also demonstrated the ease with which the approach can be extended to other chemical classes by the calculation of additional fragments and their associated Eact and SAS values. The resulting method is likely to be of use in regulatory toxicology tools where an understanding of covalent bond formation as a potential MIE is important within the AOP paradigm

Validation of a fragment-based profiler for thiol reactivity for the prediction of toxicity: skin sensitisation and tetrahymena pyriformis

This study outlines the use of a recently developed fragment-based thiol reactivity profiler for Michael acceptors to predict toxicity towards Tetrahymena pyriformis and skin sensitisation potency as determined in the Local Lymph Node Assay (LLNA). The results showed that the calculated reactivity parameter from the profiler, -log RC50(calc), was capable of predicting toxicity for both endpoints with excellent statistics. However, the study highlighted the importance of a well-defined applicability domain for each endpoint. In terms of Tetrahymena pyriformis this domain was defined in terms of how fast or slowly a given Michael acceptor reacts with thiol leading to two separate quantitative structure-activity models. The first, for fast reacting chemicals required only –Log RC50(calc) as a descriptor, whilst the second required the addition of a descriptor for hydrophobicity. Modelling of the LLNA required only a single descriptor, -log RC50(calc), enabling potency to be predicted. The applicability domain excluded chemicals capable of undergoing polymerisation and those that were predicted to be volatile. The modelling results for both endpoints, using the –log RC50(calc) value from the profiler, were in keeping with previously published studies that have utilised experimentally determined measurements of reactivity. This results demonstrate the output from the fragment-based thiol reactivity profiler can be used to develop quantitative structure-activity relationship models where reactivity towards thiol is a driver of toxicity

Using Read-Across to Build Physiologically-Based Kinetic Models: Part 2. Case Studies for atenolol and flumioxazin

Read-across, wherein information from a data-rich chemical is used to make a prediction for a similar chemical that lacks the relevant data, is increasingly being accepted as an alternative to animal testing. Identifying chemicals that can be considered as similar (analogues) is crucial to the process. Two resources have been developed previously to address the issue of analogue selection and facilitate physiologically-based kinetic (PBK) model development, using read-across. Chemical-specific PBK models, available in the literature, were collated to form a PBK model dataset (PMD) of over 7,500 models. A KNIME workflow was created to accompany this PMD that can aid the selection of appropriate chemical analogues from chemicals within this dataset (i.e. chemicals that are similar to a target of interest and are known to have an existing PBK model). Information from the PBK model for the source chemical can then be used in a read-across approach to inform the development of a new PBK model for the target. The application of these resources is tested here using two case studies (i) for the drug atenolol and (ii) for the plant protection product, flumioxazin. New PBK models were constructed for these two target chemicals using data obtained from source chemicals, identified by the workflow as being similar (analogues). In each case, the published PBK model for the source chemical was initially reproduced, as accurately as possible, before being adapted and used as a template for the target chemical. The performance of the new PBK models was assessed by comparing simulation outputs to existing data on key kinetic properties for the targets. The results demonstrate that a read-across approach can be successfully applied to develop new PBK models for data-poor chemicals, thus enabling their deployment during early-stage risk assessment. This assists prediction of internal exposure whilst reducing reliance on animal testing

Molecular Fingerprint-Derived Similarity Measures for Toxicological Read-Across: Recommendations for Optimal Use

Computational approaches are increasingly used to predict toxicity, in part due to pressures to find alternatives to animal testing. Read-across is the “new paradigm” which aims to predict toxicity by identifying similar, data rich, source compounds. This assumes that similar molecules tend to exhibit similar activities, i.e. molecular similarity is integral to read-across. Various molecular fingerprints and similarity measures may be used to calculate molecular similarity. This study investigated the value and concordance of the Tanimoto similarity values calculated using six widely used fingerprints within six toxicological datasets. There was considerable variability in the similarity values calculated from the various molecular fingerprints for diverse compounds, although they were reasonably concordant for homologous series acting via a common mechanism. The results suggest generic fingerprint-derived similarities are likely to be optimally predictive for local datasets, i.e. following sub-categorisation. Thus, for read-across, generic fingerprint-derived similarities are likely to be most predictive after chemicals are placed into categories (or groups), then similarity is calculated within those categories, rather than for a whole chemically diverse dataset

Construction of an In Silico Structural Profiling Tool Facilitating Mechanistically Grounded Classification of Aquatic Toxicants.

The performance of chemical safety assessment within the domain of environmental toxicology is often impeded by a shortfall of appropriate experimental data describing potential hazards across the many compounds in regular industrial use. In silico schemes for assigning aquatic-relevant modes or mechanisms of toxic action to substances, based solely on consideration of chemical structure, have seen widespread employment─including those of Verhaar, Russom, and later Bauer (MechoA). Recently, development of a further system was reported by Sapounidou, which, in common with MechoA, seeks to ground its classifications in understanding and appreciation of molecular initiating events. Until now, this Sapounidou scheme has not seen implementation as a tool for practical screening use. Accordingly, the primary purpose of this study was to create such a resource─in the form of a computational workflow. This exercise was facilitated through the formulation of 183 structural alerts/rules describing molecular features associated with narcosis, chemical reactivity, and specific mechanisms of action. Output was subsequently compared relative to that of the three aforementioned alternative systems to identify strengths and shortcomings as regards coverage of chemical space

A scheme to evaluate structural alerts to predict toxicity – Assessing confidence by characterising uncertainties

Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are an essential tool in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence. The criteria are based around the stated purpose, description of the chemistry, toxicology and mechanism, performance and coverage, as well as corroborating and supporting evidence of the alert. Alerts can be given a confidence assessment and score, enabling the identification of areas where more information may be beneficial. The scheme to evaluate structural alerts was placed in the context of various use cases for industrial and regulatory applications. The analysis of alerts, and consideration of the evaluation scheme, identifies the different characteristics an alert may have, such as being highly specific or generic. These characteristics may determine when an alert can be used for specific uses such as identification of analogues for read-across or hazard identification

Development of a fragment-based in silico profiler for SN2 thiol reactivity and its application in predicting toxicity of chemicals towards Tetrahymena pyriformis

This study outlines the development of a fragment-based in silico profiler for SN2 thiol reactivity. The chemical space of the profiler related to a dataset of glutathione reactivity data for SN2 chemicals acti- vated by the presence of an electron-withdrawing carbonyl group. The approach is in keeping with a recently developed fragment-based in silico profiler for Michael addition thiol reactivity and involved developing a database of structural alert-type fragments with associated activation energy values (ΔEReact-TS). These energy values were calculated using density functional theory with a B3YLP functional coupled to a 6-31G + (d) basis set. The results showed the fragment-based in silico profiler for SN2 reactivity was able to successfully predict glutathione reactivity and toxicity to Tetrahymena pyriformis. Overall, the results of this study extend the previous fragmentbased profiler development to the SN2 domain and further validate the approach. The study also highlights the ability of the fragment-based in silico profilers to predict toxicological potency where the formation of a covalent bond is the key molecular initiating event

Development of an Enhanced Mechanistically-Driven Mode of Action Classification Scheme for Adverse Effects in Environmental Species

This study developed a novel classification scheme to assign chemicals to a verifiable mechanism of (eco)toxicological action to allow for grouping, read-across and in silico model generation. The new classification scheme unifies and extends existing schemes and has at its heart, direct reference to molecular initiating events (MIEs) promoting adverse outcomes. The scheme is based on three broad domains of toxic action representing non-specific toxicity (e.g. narcosis), reactive mechanisms (e.g. electrophilicity and free radical action) and specific mechanisms (e.g. associated with enzyme inhibition). The scheme is organised at three further levels of detail beyond broad domains to separate out mechanistic group, specific mechanism and the MIEs responsible. Novelty in this approach comes from the reference to taxonomic diversity within the classification, transparency, quality of supporting evidence relating to MIEs and that it can be updated readily

Derivation, characterisation and analysis of an adverse outcome pathway network for human hepatotoxicity

Adverse outcome pathways (AOPs) and their networks are important tools for the development of mechanistically based non-animal testing approaches, such as in vitro and/or in silico assays, to assess toxicity induced by chemicals. In the present study, an AOP network connecting 14 linear AOPs related to human hepatotoxicity, currently available in the AOP-Wiki, was derived according to established criteria. The derived AOP network was characterised and analysed with regard to its structure and topological features. In-depth analysis of the AOP network showed that cell injury/death, oxidative stress, mitochondrial dysfunction and accumulation of fatty acids are the most highly connected and central key events. Consequently, these key events may be considered as the rational and mechanistically anchored basis for selecting, developing and/optimising in vitro and/or in silico assays to predict hepatotoxicity induced by chemicals in view of animal-free hazard identification