Summary of work ····························································································

From the outset, the work presented in this thesis has been focussed on the development of an in silico profiler that can be utilised to assist in the safety assessment of chemicals upon repeated exposure. This need has arisen due to the introduction of EU legislation, such as REACH and the 7th _{amendment to the Cosmetics Directive. As part of this legislation, more}

traditional in vivo toxicity testing cannot be used as part of the safety assessment for cosmetic products or their ingredients. Therefore, alternative techniques are required to ensure the continued safety of these products for consumers. Therefore, Chapter 1 introduced the broad area of in silico toxicology, with a focus on Adverse Outcome Pathways and category formation, and the impact the EU regulation has had on driving research in the area of in silico toxicology since its inception and implementation over the past decade.

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The availability, and accessibility, of relevant toxicological data, including observed adverse physiological effects, is a necessity prior to the development of any in silico models or profilers. There is a multitude of commercially, and freely, available databases that hold a wide variety of toxicological data. However, there is a need for a single, comprehensive, and freely available database containing repeat dose toxicity data associated with chemical structures. Chapter 2 described how novel repeat dose toxicity data were extracted from EU regulatory (SCC(NF)P/SCCS) reports and, subsequently, input into the ToxRefDB data entry tool in order to be uploaded (by other partners in COSMOS) into the COSMOS database. These data were harvested following a standard operating procedure, developed by colleagues on the COSMOS project. This SOP provided a consistent method by which each of the data harvesters were to extract, and input, the data. Information extracted from the reports included the NOAEL and/or LOAEL values and the histopathological findings observed whilst undertaking the experiment. The investigation performed at the end of Chapter 2 examined whether the results from 28 day repeat dose toxicity studies are protective of results from 90 day repeat dose studies held in the COSMOS database. The outcome of this investigation identified that for six of the nine chemicals (66%) the 28 day study was protective of the 90 study, i.e. if the toxicity value for the 28 day study was over 1000 mg/kg bw/day the toxicity value for the 90 day study was also over 1000 mg/kg bw/day. The percentage of those chemicals that were protective within this investigation was marginally under those found previously by the HSE and Taylor et al (2014). This may, however, be explainable by the variances in the total number of chemicals in the final datasets between the investigation performed in Chapter 2 and those carried out by the HSE and Taylor et al (2014). Therefore, the results from the analysis performed in this chapter support the findings by the HSE and Taylor et al (2014). This investigation, in conjunction with the previous work set out by the HSE and Taylor et al (2014), could have a major impact both financially, and in terms of animal usage, with regards to those chemicals manufactured (or imported) above 100 tonnes per year under REACH. In addition, this investigation demonstrated that having toxicological values, and histopathological findings,

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compiled within the same database enabled this type of analysis to be performed more easily than if multiple databases were used.

In Chapter 3 a chemoinformatics analysis was performed on a set of chemicals, from the scientific literature, with associated qualitative data pertaining to mitochondrial toxicity. This analysis was undertaken using the freely available data mining software, ChemoTyper, which contains the ToxPrint library of predefined structural fragments. The analysis performed in this chapter found two types of structural alerts could be identified utilising this software; 1) well-defined alerts that could be associated with a mechanistic hypothesis, 2) more diverse alerts for which it was not possible to hypothesise a mechanism for the entire category. Overall, a total of twenty alerts were developed. Of these alerts, it was possible to hypothesise a mechanism encompassing all chemicals identified by the ChemoTyper for two alerts. For the remaining alerts it was not possible to hypothesise a mechanism that encompassed all the chemicals identified as ‘toxic to mitochondria’ within the group. In addition, this chapter also outlined the use of these different types of structural alerts; with mechanism-based structural alerts being intended for category formation and read-across, whilst chemistry-based alerts could be used for the purposes of screening and prioritisation of an inventory. The inherent differences in the two types of alert make them suitable for different purposes. Mechanistic information associated with the mechanism- based alerts provides additional support to both the development of chemical categories and subsequent read-across predictions for toxicity. In comparison, chemistry-based alerts, whilst lacking mechanistic information, are associated with toxicity, therefore, they can be used to identify chemicals, within an inventory, for which further in vitro/in chemico testing may be appropriate.

The work in Chapter 4 focussed on utilising structural similarity and category formation to re-analyse the data set from Chapter 3. Overall, 35 chemicals in the data set were identified as belonging to categories containing mitochondrial toxicants: local anaesthetics, anti- anginal, and anti-arrhythmic (6 chemicals); anti-diabetic drugs (3 chemicals); non-steroidal

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anti-inflammatory drugs (3 chemicals); anthracycline antibiotics (4 chemicals); hypolipodemic drugs (3 chemicals); bile acids (6 chemicals); anti-histaminic, anti-psychotic and anti-emetic drugs (7 chemicals); and β-blockers (3 chemicals). A total of eight mechanism-based alerts were developed covering five initiating events; inhibition of the electron transport chain, alternative electron acceptance, initiation of the death receptor pathway, uncoupling of oxidative phosphorylation and induction of the membrane permeability transition. Additionally, the work carried out in Chapters 3 and 4 demonstrated that no one approach can be utilised to identify all possible structural alerts. Therefore, it is envisaged that these techniques will be used in combination to cover as large a chemical space as possible.

The toxicological information provided by the regulatory dossiers for 94 hair dye chemicals, published by the SCC(NF)P/SCCS, was utilised in Chapter 5 to develop mechanism-based structural alerts. These dossiers are a valuable, yet currently under-used, source of toxicological data. The analysis performed in Chapter 5 expanded on the work undertaken in Chapter 4 by identifying additional mechanism-based alerts associated with mitochondrial dysfunction. A total of four mechanism-based alerts were identified covering pro-quinones (37 chemicals), quinones (7 chemicals), meta-substituted benzenes (4 chemicals), and aromatic azo compounds (8 chemicals). Each of these alerts is associated with inducing mitochondrial toxicity via a single Molecular Initiating Event (MIE): inhibition of the electron transport chain. The alerts identified in Chapter 5 broadened the chemical space regarding those chemicals that have the potential to induce mitochondrial toxicity via inhibition of the electron transport chain. These alerts can be utilised to either screen an inventory for prioritisation or to develop chemical categories, from which read-across predictions could be made regarding to a chemical’s ability to initiate mitochondrial toxicity. In order to expand the use of this profiler for mitochondrial toxicity additional alerts are required for (alternative) MIEs to account for the toxic potential of the remaining chemicals. The work undertaken in Chapters 4 and 5 demonstrate the vital importance of the available

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literature in providing the mechanistic information necessary for developing mechanism- based structural alerts. Chapter 5 reiterates the use of mechanistic structural alerts for the purposes of grouping and profiling. Furthermore, the work carried out within this chapter highlights the usability and usefulness of the information contained within regulatory reports, such as those published by the SCC(NF)P/SCCS.

Upon development of structural alerts it is necessary to substantiate that each of the alerts correctly identifies chemicals with the potential to instigate an MIE. Other alternative techniques, such as in vitro and/or in chemico assays, can be used to verify the alerts developed are correct. However, it should be noted that whilst these alerts may be correct other factors, such as the internal concentration or metabolism, may mean the MIE is not induced in vivo. Finally, Chapter 6 illustrates the importance of using data generated by in vitro and in chemico assays to verify, and refine, structural alerts. These alternative techniques are important as they provide experimentally derived mechanistic information. In turn, this information can be utilised to verify the correct mechanism is associated with an alert, whilst also providing support for possible refinements. In Chapter 6 the structural alerts investigated relate to the protein binding alerts in the OECD QSAR Toolbox. However, a similar process could be undertaken to verify other structural alerts, such as those pertaining to mitochondrial toxicity identified in Chapters 3, 4, and 5.