Investigating the Role of Oxidative Stress in the Generation Of

Home , Fluorodeoxyuridylate

Investigating the Role of Oxidative Stress in the Generation of Plausibly Misleading Positive Results for in vitro Genotoxicity Generated by Polyphenolic Antioxidants

A THESIS SUBMITTED TO THE UNIVERSITY OF MANCHESTER FOR THE DEGREE OF PHD IN THE FACULTY OF LIFE SCIENCES CHRISTOPHER ADDINSELL 2014

1 Contents 1 Abstract ...... 11 2 Introduction ...... 14 2.1 Background ...... 14 2.2 Genetic Toxicology ...... 15 2.3 Genotoxicity assessment ...... 21 2.4 Oxidative stress ...... 31 2.5 Antioxidants ...... 35 2.6 Assessments of cytotoxicity ...... 36 2.7 Aims and objectives ...... 37 3 Materials & Methods ...... 38 3.1 Cell lines and culture ...... 38 3.2 Chemicals used in these studies ...... 39 3.3 The GADD45a-GFP (‘GreenScreen HC’) genotoxicity assay ...... 40 3.4 Population doubling assessment ...... 48 3.5 in silico interrogation of compounds using Derek Nexus™ ...... 53 3.6 Using dichlorofluorescin diacetate to detect intracellular ROS generation ...... 55 3.7 Detection of early stages of toxicity and mitochondrial membrane depolarisation using JC-1 ..... 59 3.8 Quantifying oxidative DNA damage using antibodies against 8-oxoguanine ...... 61 3.9 Statistical analysis ...... 62 4 Results I – Compound Choice ...... 65 4.1 Introduction ...... 65 4.2 Polyphenolic antioxidants ...... 67 4.3 Monophenolic antioxidants ...... 77 4.4 Non-phenolic antioxidants ...... 80 4.5 Oxidants ...... 83 4.6 Genotoxins ...... 85 4.7 Non-genotoxic, cytotoxic compounds ...... 90 4.8 Miscellaneous compounds ...... 92 4.9 Pro-genotoxins ...... 95 4.10 Discussion ...... 97 5 Results II: Most polyphenolic antioxidants produce positive results in the GADD45a-GFP genotoxicity assay ...... 100 5.1 Introduction ...... 100 5.2 The autofluorescent and light absorbing properties of propyl gallate, nordihydroguaiaretic acid and epigallocatechin gallate confound spectrophotometric data collection ...... 100 5.3 Results for GADD45a-GFP genotoxicity assay in the absence of S9 ...... 102 5.4 Results for GADD45a-GFP genotoxicity assay with S9 metabolic activation ...... 107 5.5 Discussion ...... 110 5.6 Summary ...... 113 6 Results III – Most polyphenolic antioxidants produce alerts of “Plausible” or higher for genotoxic endpoints using in silico tool, Derek Nexus™ ...... 114 6.1 Introduction ...... 114 6.2 Results ...... 114 6.3 Discussion ...... 134 6.4 Summary ...... 136 7 Results IV – Incubation in the presence of a lowered oxygen concentration does not reduce the genotoxicity of polyphenolic antioxidants in the GADD45a-GFP assay ...... 137 7.1 Introduction ...... 137 7.2 GADD45a-GFP Assays Incubated with 5% Oxygen...... 137 7.3 GADD45a-GFP Assays Incubated with 1% Oxygen...... 144 7.4 Discussion ...... 153 7.5 Summary ...... 154 8 Results V – Most polyphenolic antioxidants lead to the generation of intracellular reactive oxygen species in vitro ...... 155 8.1 Introduction ...... 155 8.2 Optimisation of the DCFH-DA assay protocol in TK6 Cells ...... 155 8.3 Calculation of thresholds for DCF fluorescence and PI permeability ...... 156 2 8.4 Assessment of 34 compounds in the DCFH-DA assay ...... 159 8.5 Propidium iodide cannot detect a high proportion of cells that have committed to a cytotoxic endpoint within a 4 hour timeframe ...... 164 8.6 Eight phenolic antioxidants reduce the level of ROS in cells treated with oxidant, TBHP ...... 166 8.7 Discussion ...... 168 8.8 Summary ...... 170 9 Results VI – Most polyphenolic antioxidants are able to depolarise the mitochondrial membrane 171 9.1 Introduction ...... 171 9.2 Optimising the protocol for TK6 cells ...... 171 9.3 Generation of a threshold for JC-1 assessment ...... 173 9.4 Results ...... 173 9.5 Discussion ...... 183 9.6 Summary ...... 185 10 Results VII – Cells exposed to 8 of 12 polyphenolic antioxidants showed a decrease in binding to a FITC-conjugated 8-oxoguanine antibody compared to an untreated control ...... 186 10.1 Introduction ...... 186 10.2 Choice of compound dose ...... 186 10.3 Optimisation of FITC conjugated 8-OG antibody binding assay ...... 187 10.4 Assessment of 8-OG antibody binding in cells exposed to polyphenolic antioxidants...... 189 10.5 Discussion ...... 197 10.6 Summary ...... 198 11 Conclusion and future perspective ...... 199 11.1 Conclusions ...... 199 11.2 Summary of conclusions ...... 201 11.3 Future perspective ...... 201 11.4 Future Work ...... 202 12 Bibliography ...... 204 13 Appendix ...... 225 13.1 Derek Nexus™ databases ...... 225

Word Count: 79711

3 Abbreviations

Abbreviation Meaning ATM Ataxia-telangiectasia mutated ATR Ataxia- & Rad3-related BHA Butylated hydroxyanisole BHMP 2,6-Di-tert-butyl-4-hydroxymethylphenol BHT Butylated hydroxytoluene CCRIS Chemical Carcinogenesis Research Information System CPDB Carcinogenic Potency Database DCP / 2,4-DCP 2,4-Dichlorophenol DMSO Dimethyl sulfoxide DNAPKcs DNA protein kinase catalytic subunit ECVAM European Centre for the Validation of Alternative Methods EGCG Epigallocatechin gallate EPA Environmental Protection Agency FDA Food and Drug Administration GADD45a Growth arrest & DNA damage Inducible protein 45a GS-AM GreenScreen assay medium IARC International Agency for Research on Cancer ICH International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use ISSSTY Istituto Superiore di Sanita - Salmonella typhimurium LEC Lowest effective concentration MMS Methyl methane sulfonate MPA Monophenolic acids NAC n-Acetylcysteine NDGA Nordihydroguaiaretic acid NIH National Institutes of Health NPA Non-phenolic antioxidant NQO / 4-NQO 3-Nitroquinoline oxide Nrf2 Nuclear transcription factor erythroid 2p45-related factor-2 NTP National Toxicology Program OECD Organisation for Economic Co-operation and Development PARP Poly ADP ribose polymerase PI Propidium iodide PIKK Phosphatidylinositol 3-kinase-related kinase PPA Polyphenolic acid RCV Relative cell viability (relative to the vehicle control) RDF Relative DCF fluorescence (relative to the vehicle control) REACH Registration, Evaluation, Authorisation and Restriction of Chemicals ROS Reactive oxygen species RSD Relative standard deviation TBHP tert-Butyl hydroperoxide TBHQ tert-Butylhydroquinone TBQ 2-tert-Butyl-1,4-benzoquinone WT1 Wilms tumor protein 1

4 Glossary

Term Meaning Aneugen A compound capable of changing the number of chromosomes in a cell Antioxidant A compound that protects cells from oxidative damage by reacting with oxidant species, reducing them Bandpass A filter that allows light within a certain range of wavelengths to pass through Clastogen A compound capable of breaking chromosomes FL1 The channel of the FACSCalibur flow cytometer that detects fluorescent light within 30nm of a wavelength of 530 nm via a bandpass filter FL2 The channel of the FACSCalibur flow cytometer that detects fluorescent light within 30nm of a wavelength of 530 nm via a bandpass filter FL3 The channel of the FACSCalibur flow cytometer that detects fluorescent light within 30nm of a wavelength of 670 nm via a longpass filter Genotoxin A compound that damages the genome of a cell ICH S2(R1) The ICH Guidance on Genotoxicity Testing and Data Interpretation for Pharmaceuticals Intended for Human Use Longpass A filter that allows light above a certain wavelength to pass through Misleading result Any result of an assay where the result of the assay differs from the accepted physiological effect Monophenolic A compound containing only one hydroxyl group bound to a phenyl ring Mutagen Any compound capable of altering the genetic sequence of DNA Oxidant A compound that accepts electrons within a redox reaction Oxidative stress A state of imbalance within a cell whereby oxidative damage happens faster than a cell is able to repair Phenolic A compound containing one or more hydroxyl group bound to a phenyl ring Polyphenolic A compound containing more than one hydroxyl group bound to a phenyl ring Pro-genotoxin A compound that can be metabolised to form a genotoxin Pro-oxidant A compound that generates reactive oxygen species Reactive oxygen A number of highly reactive compounds and radicals that are capable of causing species oxidative damage in cells Sensitivity The proportion of positive results successfully detected by an assay Specificity The proportion of negative results successfully detected by an assay

5 List of Figures

Figure 2:1 “GO” base excision repair pathway for 8-Oxoguanine ...... 20 Figure 2:2 Flow chart detailing steps taken in the assessment of genetic toxicity ...... 22 Figure 2:3 Stylised diagram showing the three endpoints of GADD45a-GFP assessment ...... 24 Figure 2:4 Plasmid map representing GADD45a-GFP reporter pEP-GF532 ...... 25 Figure 2:5 Structure of oxidised base 8-OG...... 34 Figure 3:1 Illustration of the assay plate layout for the GADD45a-GFP assay ...... 44 Figure 3:2 Illustration of the assay plate layout for the GADD45a-GFP assay with S9 ...... 46 Figure 3:3 DCFH-DA data processing template ...... 58 Figure 3:4 Stylised figure representing JC-1 measurement of mitochondrial membrane potential ...... 59 Figure 5:1 EGCG, NDGA and propyl gallate autofluorescence interferes with GADD45a -GFP assay data collection by microplate spectrophotometer ...... 101 Figure 5:2 Example data from assessment of cytotoxicity and genotoxicity of 4 compounds using the GADD45a-GFP reporter assay...... 104 Figure 5:3 Example data from assessment of cytotoxicity and genotoxicity of 4 compounds following S9 metabolic activation using the GADD45a -GFP reporter assay...... 108 Figure 6:1: The podophyllotoxin structure (left) and the alkyl aldehyde precursor (right) substructures highlighted within the structure of etoposide ...... 115 Figure 6:2: The nitro group highlighted within the structure of 4-Nitroquinoline 1-oxide ...... 118 Figure 6:3: Catechol substructure highlighted within the structure of apomorphine ...... 121 Figure 6:4: Flavonoid (left) and flavonol (right) substructure highlighted within the structure of quercetin...... 122 Figure 6:5: 4-hydroxystilbene substructure highlighted within the structure of resveratrol ...... 124 Figure 6:6: 4-Alkylether phenol substructure highlighted within the structure of BHA ...... 125 Figure 6:7: The hydroperoxide structure found within both tert-butyl hydroperoxide and hydrogen peroxide ...... 126 Figure 6:8: 2-Methyl-1,4-benzoquinone substructure highlighted within the structure of 2-tert-butyl-1,4- benzoquinone ...... 129 Figure 6:9: Both α,β-Unsaturated carbonyl groups highlighted within the structure of 2-tert-butyl-1,4- benzoquinone ...... 130 Figure 7:1 Charts comparing population doublings of GenM-C01 cells seeded at 1 × 106 cells/ml and incubated in the presence of 5% oxygen or in the presence of 20% oxygen over a period of 72 hours...... 140 Figure 7:2 Sample data comparing the results of testing polyphenols, EGCG and propyl gallate in the GADD45a-GFP incubated in the presence 5% oxygen with those incubated in the presence of 20% oxygen ...... 142 Figure 7:3 Sample data comparing the results of testing MMS and BHT in the GADD45a-GFP incubated in the presence 5% oxygen with those incubated in the presence of an unmodulated oxygen concentration 143 Figure 7:4 Bar chart comparing the percentage of cells incubated in the presence of differing concentrations of oxygen and differing cell seeding densities left unstained after exposure to PI giving an indication of cell viability within a cell population...... 147 Figure 7:5 Sample data comparing the results of testing polyphenols, EGCG and propyl gallate in the GADD45a-GFP incubated in the presence 1% oxygen with those incubated in the presence of 20% oxygen ...... 148 Figure 7:6 Sample data comparing the results of testing MMS and BHT in the GADD45a-GFP incubated in the presence 1% oxygen with those incubated in the presence of 20% oxygen ...... 149 Figure 8:1 Titration of DCFH-DA and positive control TBHP to optimise the DCFH-DA assessment protocol ...... 157 Figure 8:2 A time-course evaluation of compound exposure within the DCFH-DA assay ...... 158 Figure 8:3 Sample data from assessment of compounds using the DCFH-DA assay for intracellular ROS generation ...... 161 Figure 8:4 Cells treated with 4 test compounds for 4 hours show a reduction in cell division and cell viability during the following 48 hours...... 165 Figure 8:5 Eight phenolic antioxidants lower the level of intracellular ROS in TBHP-treated cells ...... 167 Figure 9:1 Titration of JC-1 dye to determine optimal concentration in the assessment of mitochondrial membrane disruption...... 174 Figure 9:2 FL2 channel compensation within the JC-1 assay ...... 175 Figure 9:3 Titration of Positive Control CCCP within the JC-1 Assessment...... 176 Figure 9:4 Time course assessment of JC-1 dye...... 177 Figure 9:5 Sample data of 6 compounds from JC-1 assessment...... 179

6 Figure 9:6 Sample flow cytometry data of 3 compounds and a vehicle control from JC-1 assessment ...... 180 Figure 9:7 Sample flow cytometry data of 3 compounds and a vehicle control from JC-1 assessment ...... 181 Figure 10:1 The addition of serum to PBS and the addition of ice cold ethanol to cells on a vortex reduced the number of “debris” events ...... 190 Figure 10:2 Histograms displaying the difference in antibody fluorescence between vehicle and positive control samples before the addition of an antibody “stop” step ...... 191 Figure 10:3 Histograms displaying the difference in antibody fluorescence between vehicle and positive control samples after the addition of an antibody “stop” step ...... 192 Figure 10:4 Results from experiments to determine the optimal concentration of positive pro-oxidant controls to be used for FITC-conjugated 8-OG-specific antibody binding assay...... 193 Figure 10:5 A scatter chart showing the fluorescence of cells treated with hydrogen peroxide for between 1 and 8 hours relative to vehicle control cells...... 194 Figure 10:6 Results from experiments to determine the optimal number of cells to use in samples to assess FITC-conjugated 8-OG-specific antibody binding...... 195 Figure 10:7: A chart showing the relative increase in FITC-conjugated 8-OG antibody binding in cells exposed to test compounds for 4 hours...... 196

7 List of Tables

Table 2:1 The bacterial strains commonly used in the Ames bacterial mutagenicity assay ...... 29 Table 3:1 Routine cell culture medium for TK6, GenM-C01 and GenM-T01 cell lines...... 38 6 Table 3:2 Passage dilutions used to achieve a cell concentration of ~1× 10 cells on the day of assessment . 39 Table 3:3 Composition of air in CO2 incubators with differing concentrations of oxygen compared to atmospheric air...... 39 Table 3:4 Compounds assessed as part of this investigation ...... 41 Table 3:5 Reasoning glossary (Derek Nexus™ v.3.0.1) ...... 55 Table 4:1 The CAS number, molecular weight and structure of apomorphine hydrochloride ...... 67 Table 4:2 The CAS number, molecular weight and structure of tert-butylhydroquinone ...... 67 Table 4:3 The CAS number, molecular weight and structure of dodecyl gallate ...... 68 Table 4:4 The CAS number, molecular weight and structure of epigallocatechin gallate ...... 69 Table 4:5 The CAS number, molecular weight and structure of nordihydroguaiaretic acid ...... 69 Table 4:6 The CAS number, molecular weight and structure of octyl gallate ...... 70 Table 4:7 The CAS number, molecular weight and structure of propyl gallate ...... 71 Table 4:8 The CAS number, molecular weight and structure of pyrogallol...... 72 Table 4:9 The CAS number, molecular weight and structure of quercetin ...... 73 Table 4:10 The CAS number, molecular weight and structure of resorcinol ...... 74 Table 4:11 The CAS number, molecular weight and structure of γ-resorcylic acid ...... 75 Table 4:12 The CAS number, molecular weight and structure of resveratrol ...... 75 Table 4:13 The CAS number, molecular weight and structure of butylated hydroxyanisole ...... 77 Table 4:14 The CAS number, molecular weight and structure of butylated hydroxytoluene ...... 78 Table 4:15 The CAS number, molecular weight and structure of 2,6-di-tert-butyl-4-hydroxymethylphenol .. 79 Table 4:16 The CAS number, molecular weight and structure of vanillic acid ...... 79 Table 4:17 The CAS number, molecular weight and structure of n-acetylcysteine ...... 80 Table 4:18 The CAS number, molecular weight and structure of L-ascorbic acid ...... 81 Table 4:19 The CAS number, molecular weight and structure of ethoxyquin ...... 82 Table 4:20 The CAS number, molecular weight and structure of tert-butyl hydroperoxide ...... 83 Table 4:21 The CAS number, molecular weight and structure of hydrogen peroxide ...... 83 Table 4:22 The CAS number, molecular weight and structure of potassium bromate ...... 84 Table 4:23 The CAS number, molecular weight and structure of bleomycin sulfate ...... 85 Table 4:24 The CAS number, molecular weight and structure of etoposide ...... 86 Table 4:25 The CAS number, molecular weight and structure of 5-fluorouracil ...... 87 Table 4:26 The CAS number, molecular weight and structure of methyl methanesulfonate ...... 87 Table 4:27 The CAS number, molecular weight and structure of 4-nitroquinoline-1-oxide ...... 88 Table 4:28 The CAS number, molecular weight and structure of vincristine sulfate ...... 89 Table 4:29 The CAS number, molecular weight and structure of 2,4-dichlorophenol ...... 90 Table 4:30 The CAS number, molecular weight and structure of phenformin hydrochloride ...... 91 Table 4:31 The CAS number, molecular weight and structure of 2-tert-butyl-1,4-benzoquinone ...... 92 Table 4:32 The CAS number, molecular weight and structure of carbonyl cyanide m-chlorophenyl hydrazine ...... 92 Table 4:33 The CAS number, molecular weight and structure of phenol ...... 93 Table 4:34 The CAS number, molecular weight and structure of staurosporine ...... 94 Table 4:35 The CAS number, molecular weight and structure of 2-acetylaminofluorene ...... 95 Table 4:36 The CAS number, molecular weight and structure of 6-aminochrysene...... 95 Table 4:37 The CAS number, molecular weight and structure of cyclophosphamide ...... 96 Table 4:38: An Index of the Compounds used in this Investigation ...... 98 Table 4:39: A summary of the reasons underlying the compounds chosen ...... 99 Table 5:1 Summary of results for the compounds tested in the GADD45a-GFP assay in the absence of S9 .105 Table 5:2 Continued summary of results for the compounds tested in the GADD45a-GFP assay in the absence of S9 ...... 106 Table 5:3 Summary of results for the compounds tested in the GADD45a-GFP assay S9 metabolic activation...... 109 Table 5:4 Table detailing the dose of five mechanistically diverse genotoxins necessary for a positive result in four different in vitro genotoxicity assays ...... 112 Table 6:1: The predictive performance of Derek Nexus™ alert 641 (in vitro chromosomal damage) for podophyllotoxins in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 116

8 Table 6:2: The predictive performance of Derek Nexus™ alert 578 (in vitro chromosomal damage) for 5- fluoropyrimadines in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 116 Table 6:3: The predictive performance of Derek Nexus™ alert 073 (carcinogenicity) for known alkylating agents in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 117 Table 6:4: The predictive performance of Derek Nexus™ alert 027 (mutagenicity) for known alkylating agents in data sets for mutagenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 117 Table 6:5: The predictive performance of Derek Nexus™ alert 027 (in vitro chromosomal damage) for known alkylating agents in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 118 Table 6:6: The predictive performance of Derek Nexus™ alert 755 (in vivo chromosomal damage) for alkyl sulfates and sulfonates in data sets for in vivo chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 118 Table 6:7: The predictive performance of Derek Nexus™ alert 105 (carcinogenicity) for aromatic nitro compounds in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 119 Table 6:8: The predictive performance of Derek Nexus™ alert 329 (in vitro chromosomal damage) for aromatic nitro compounds in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 119 Table 6:9: The predictive performance of Derek Nexus™ alert 329 (in vivo chromosomal damage) for aromatic nitro compounds in data sets for in vivo chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 119 Table 6:10: The predictive performance of Derek Nexus™ alert 329 (mutagenicity) for aromatic nitro compounds in data sets of results for the Ames test. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 120 Table 6:11: The predictive performance of Derek Nexus™ alert 303 (mutagenicity) for aromatic-N-oxides and N-hydroxy tautomers in data sets for mutagenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 120 Table 6:12: The predictive performance of Derek Nexus™ alert 581 (in vitro chromosomal damage) for vinca alkaloid compounds in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 120 Table 6:13: the predictive performance of Derek Nexus™ alert 625 (in vitro chromosomal damage) for compounds with a structural catechol moiety in data sets for in vitro chromosomal aberrations. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 122 Table 6:14: The predictive performance of Derek Nexus™ alert 251 (carcinogenicity) for compounds with a structural catechol moiety in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 122 Table 6:15: The predictive performance of Derek Nexus™ alert 515 (in vitro chromosomal damage) for compounds with a structural flavonoid moiety in data sets for in vitro chromosomal aberrations. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 123 Table 6:16: The predictive performance of Derek Nexus™ alert 203 (in vitro mutagenicity) for compounds with a structural flavonol moiety in data sets for in vitro chromosomal aberrations. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 123 Table 6:17: The predictive performance of Derek Nexus™ alert 252 (carcinogenicity) for compounds with a 4-alkylether phenol structure in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 125 Table 6:18: The predictive performance of Derek Nexus™ alert 358 (in vitro chromosomal damage) for hydroperoxide compounds in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 126 Table 6:19: The predictive performance of Derek Nexus™ alert 358 (mutagenicity) for hydroperoxide compounds in data sets of Ames test results. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 127 Table 6:20: The predictive performance of Derek Nexus™ alert 749 (carcinogenicity) for known oxidising agents in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 127

9 Table 6:21: The predictive performance of Derek Nexus™ alert 518 (in vitro chromosomal damage) for halogen oxyacid salts in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 127 Table 6:22: The predictive performance of Derek Nexus™ alert 116 (carcinogenicity) for polyhalogenated phenols in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 128 Table 6:23: The predictive performance of Derek Nexus™ alert 494 (in vitro chromosomal damage) for halophenols in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 128 Table 6:24: The predictive performance of Derek Nexus™ alert 751 (in vitro chromosomal damage) for 1,4- benzoquinone or para-quinone in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 130 Table 6:25: The predictive performance of Derek Nexus™ alert 751 (in vivo chromosomal damage) for 1,4- benzoquinone or para-quinone in data sets for in vivo chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 130 Table 6:26: The predictive performance of Derek Nexus™ alert 743 (carcinogenicity) for 743 in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™...... 131 Table 6:27: A summary of results of an in silico screen of polyphenolic antioxidants for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™ ...... 132 Table 6:28: A summary of results of an in silico screen of monophenolic antioxidants for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™ ...... 132 Table 6:29: A summary of results of an in silico screen of non-phenolic antioxidants for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™ ...... 132 Table 6:30: A summary of results of an in silico screen of oxidants for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™ ...... 133 Table 6:31: A summary of results of an in silico screen of known genotoxins for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™ ...... 133 Table 6:32: A summary of results of an in silico screen of cytotoxic, non-genotoxic for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™ ...... 133 Table 6:33: A summary of results of an in silico screen of miscellaneous compounds for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™ ...... 134 Table 7:1 Summary of results from assessment of 12 PPAs in the GADD45a-GFP assay, seeded at various cell densities and incubated in the presence of various concentrations of oxygen (1/3) ...... 150 Table 7:2 Summary of results from assessment of 10 monophenolic and non-phenolic antioxidants in the GADD45a-GFP assay, seeded at various cell densities and incubated in the presence of various concentrations of oxygen (2/3) ...... 151 Table 7:3 Summary of results from assessment of 6 genotoxic compounds and 2 non-genotoxic cytotoxic compounds in the GADD45a-GFP assay, seeded at various cell densities and incubated in the presence of various concentrations of oxygen (3/3) ...... 152 Table 8:1 Summary of results from the assessment of 34 compounds using the DCFH-DA assay (1/2) ...... 162 Table 8:2 Summary of results from the assessment of 34 compounds using the DCFH-DA assay (2/2) ...... 163 Table 9:1: A summary of results from experiments to assess the disruption of mitochondrial membrane potential within TK6 cells exposed to 34 different test compounds using mitochondrial dye, JC-1...... 182 Table 10:1 A summary of the dose chosen to assess the potential of compounds to increase the level of 8- OG...... 187 Table 10:2: The vehicle and positive controls assessed to determine the optimum concentrations of hydrogen peroxide and TBHP to be used as positive controls...... 188

10 1 Abstract Phenolic antioxidants reduce the effect of oxidative stress within cells. They are found in a various fruits, vegetables and as food additives to reduce spoilage. Consumption of antioxidants by humans has been linked with increased lifespan and reduced incidence of cancer and cardiovascular disorders (Cabrera et al. 2006; Kuriyama 2008). In cultured mammalian cells however, some of these phenolic antioxidants have been reported to generate reactive oxygen species (ROS), leading to chromosomal breakage (Long et al. 2007; Long & Halliwell 2001). It is clear then, that amongst this group of compounds, in vitro toxicological study is not a reliable prediction of human hazard. It is for this reason that the work described in this thesis was undertaken: the principal aim was to gain a better understanding of the reasons underlying this contradiction. It has been suggested that excessive ROS generated in vitro might be a result of the higher levels of oxygen (~20%) compared to (1-7%) in vivo: (Yusa et al. 1984; Turrens et al. 1982). With clearer understanding, new experimental approaches might be taken to highlight or reduce positive in vitro genotoxicity test results that might be considered misleading. A diverse set of test compounds was first chosen. It included polyphenolic (PPA), monophenolic (MPA) and non-phenolic antioxidants (NPA), in addition to mechanistically characterised oxidants, genotoxins and cytotoxic, non-genotoxins as controls. Genotoxicity was assessed in vitro using the GADD45a, GFP reporter assay and in silico using Derek Nexus™. Amongst the 19 antioxidants assessed, the 11 of 12 of PPAs, 0 of 4 MPAs and 1 of 3 NPAs (ethoxyquin) produced positive results in vitro and 8 of 12 PPAs generated alerts of at least plausible genotoxicity in silico. To discover whether these results were the result of cellular hyperoxia-promoted generation of physiologically irrelevant ROS in cells, genotoxicity was reassessed in the presence of 1 and 5% oxygen. This reduced oxygen exposure had no effect upon the qualitative result for any of the assessed compounds and a negligible effect upon the dose at which any positive result was produced. An assessment of the ability of antioxidants to generate potentially genotoxic ROS within cells was carried out using the intracellular fluorescent dye, dichlorofluorescin diacetate (DCFH-DA). 10 of 12 PPAs, 0 of 4 MPAs and 1 of 3 NPAs (ethoxyquin) were shown to increase the level of ROS within TK6 human lymphoblastoid cells within 4 hours of compound exposure. Within this same timeframe, the mitochondrial membranes in cells treated with 10 of 12 PPAs, 2 of 4 MPAs and 1 of 3 NPAs (ethoxyquin) were shown to become depolarised using JC-1 dye. It was unclear however, whether mitochondrial membrane depolarisation was a cause or a consequence of ROS generation within the cells. In order to assess whether the increase in intracellular ROS led to an increase in oxidised DNA within treated cells, 8-oxoguanine (8-OG) was quantified using a FITC conjugated anti8-OG antibody. This assessment revealed that levels of the oxidised base were only increased in cells exposed to two of the 12 PPAs (quercetin and resorcinol). The level of 8- OG detected was lower than the vehicle control for cells treated with 10 of the 15 antioxidants. One interpretation of this is that these agents induce the repair pathway for oxidative damage, which leads to a lower level of oxidised DNA bases in the genome. The results showed that while a large proportion of PPAs produce genotoxic results in vitro and lead to increased levels of ROS, the amount of oxidised DNA is not higher in treated cells. This would suggest the presence of a different mechanism for the observed genotoxicity.

11 Declaration

No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.

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12 Acknowledgements

Firstly I would like to thank my supervisor and mentor, Richard Walmsley. Your patience, guidance and support has been immeasurably valuable over the years. I would like to thank you for the opportunity to work in your lab, on a project that has through its highs and lows driven me to excel. Nick Billinton, my industrial supervisor, has always been there to offer guidance and help, even when he barely had time to draw breath. His support and his sense of humour have been greatly appreciated. Thank you to my advisor, Chris Grant for his advice and guidance throughout my studies. Thank you to the BBSRC and Gentronix Ltd for funding my research.

I would like to thank the members of Gentronix ltd past and present who have been so supportive. Your friendship has meant an enormous amount to me throughout my studies. Caroline Topham, Chris Hughes, Jodie Allsup, Chris Jagger, Paul Cahill, Louise Birrell, Adam Djouani, Heather Davies and Andrew Knight have been a pleasure to work with and I appreciate your help and advice a great deal. Special thanks go to Matt Tate, your support and guidance has been extraordinary and I hope that you know how much it has meant to me.

My family has been a bottomless well of support. Emma, Andrew, Mum and Dad, you have always been there for me, or at the end of a phone. You always made time for me when things seemed their hardest. It is unimaginable to have been able to do this without you all.

I also offer thanks to all of my friends throughout my studies. I will strive to be there for any and all of you, as you have been there for me throughout the last four years. I would like to specifically thank Giulia Veluscek, I could not have hoped for a truer friend, especially during my writing-up. There are simply too many people to name and too few superlatives in the English language to describe my gratitude, but rest assured that all of you have inspired me and made me laugh, even when I’ve felt like crying.

I would finally like to thank anyone who takes the time to read my work presented in this thesis.

13 2 Introduction 2.1 Background

It is important that the chemicals to which humans might be exposed, such as pharmaceuticals, herbicides, pesticides and food additives are safe. Toxicology is a field borne out of this need. Paracelsus, a 16th century Swiss physician and polymath is often seen as being the father of the field of toxicology and he once noted: “Die Dosis macht das Gift” - the dose makes the poison (Paracelsus 1538). That is to say that any chemical, however innocuous, might at a high enough dose cause harm or death. It is therefore often without reason to claim that a chemical is dangerous without first considering the dose of that chemical that one might come into contact with. This might seem to be common knowledge now, but it has not always been so. This observation established the discipline of toxicology and prompted further study of the chemicals and processes present in the food, medicines and chemicals that might cause humans harm, and of how such harm might be avoided. Toxicological research has and continues to lead toward a greater understanding of the diverse mechanisms by which compounds exert their toxicity and the doses necessary to bring about such toxic effects, from famously poisonous hydrogen cyanide to the seemingly harmless air we breathe.

The all-important element in the air we breathe, oxygen, presents an important case-in-point. Levels of oxygen that are too high or too low are both capable of causing a wide range of toxic effects to cells, molecules and processes in humans. This is true not only with human exposure to molecular oxygen but also as a part of various molecules and importantly, as any one of a variety of reactive oxygen species (ROS). Of the many molecules within humans vulnerable to oxidative damage following overexposure to oxidative reactions, DNA is possibly the most critical. Genetic toxicology is the field that aims to determine which chemicals, at what doses cause which type of damage to genetic information. This is because damage to DNA is widely accepted to be a very important contributing factor to the prevalence of cancer. As research in this field continues, the understanding of which chemicals, at which doses pose a risk, and how that risk is posed is reviewed and refined. Part of this refinement includes proper validation of the tools and methods that are used to determine whether compounds cause genetic damage, to ensure that they are as accurate and as informative as possible.

Polyphenolic antioxidants (PPAs) are found regularly in fruits and vegetables and are used frequently as food preservatives. They are widely considered as healthy and beneficial. This is in contrast to oxidative chemicals which can cause DNA damage and mutation, which can lead to the development of cancer. As a consequence, there has been a large amount of interest in discovering whether the consumption of antioxidants can provide protection from cancer: population studies have found no link between the consumption of these chemicals and an increased incidence of genetic damage or carcinogenicity. Their consumption, instead was linked with decreases in the incidence of cancer (Kuriyama 2008; C.-L. Sun et al. 2006). In this context it was perhaps surprising when it was discovered by Halliwell and co-workers that within in vitro mammalian genotoxicity assays (Section 4) several of these compounds produce positive results (Halliwell & Aruoma 1991; Long et al. 2007).

The research presented in this thesis was conceived to increase knowledge on the prevalence of genotoxicity in a broader collection of PPAs using the GADD45a-GFP ‘GreenScreen HC’ assay. This relatively new assay is able to detect all the mechanistic classes of genotoxic carcinogen: mutagens, pro-mutagens,

14 clastogens and aneugens (Section 2.2). Investigation of the underlying mechanism of these in vitro effects is essential to provide a better understanding of how relevant these positive results are to humans. It follows that it might be possible to design follow-up strategies that will enable an assessment of which results are physiologically relevant to humans, and which are misleading in vitro artefacts (results that differ between in vitro and in vivo observation).

This introductory chapter aims to provide the necessary information and context to understand the studies carried out as a part of this thesis and the reasons for their having been carried out.

2.2 Genetic Toxicology

The concern of genetic toxicology is specifically those toxins that damage genetic material. These agents are called genotoxins or genotoxicants and the damage they cause can occur directly to the DNA or arise through interference with the processes by which a cell repairs, replicates or segregates its DNA molecules. Those chemicals that cause a change in the sequence of DNA bases such as methyl methane sulfonate and 5-fluorouracil (Section 4.6) are mutagens. Chemicals such as etoposide (Section 4.6.2) that lead to the breakage of chromosomes or rearrange their contents are clastogens. Aneugens are chemicals that, like vincristine sulfate (Section 4.6.6), can lead to the mis-segregation of chromosomes during cell division leading to an unequal distribution of chromosomes within daughter cells.

2.2.1 Genotoxicity When genetic damage occurs to cells at a rate faster than it can be effectively repaired, the resulting accumulation of genetic damage can produce a variety of outcomes including potentially carcinogenesis, apoptotic cell death or necrosis. Within Hanahan and Weinberg’s two papers regarding the hallmarks of cancer (2000, 2011), genome instability and mutation is defined as one of the “enabling characteristics” of cancer along with tumour promoting inflammation. These “enabling characteristics” allow cells to undergo the necessary changes to present the other 8 defined hallmarks of cancer. These hallmarks include the ability of cells to sustain signals promoting cellular proliferation, the insensitivity to or evasion of growth suppression, the induction of angiogenesis to provide oxygen and nutrients to cells as well as the ability to resist cell death and replicate indefinitely. It follows that an increase in exposure to genotoxins increases the likelihood of tumours developing.

2.2.1.1 Direct Genotoxicity Direct acting genotoxins are those that react with or damage DNA or chromosomes leading to mutation or clastogenesis. If a compound intercalates with the DNA structure, forms adducts, causes single strand breakage, base damage or chromosomal breakage, then the compound is considered a direct acting genotoxin. An example of such a direct acting genotoxin is methyl methanesulfonate (MMS), an alkylating agent which methylates guanine and adenine bases, converting them to 7-methylguanine and 3- methyladenine respectively (Lawley & Shah 1972). These modifications cause the mismatch of bases during replication and also block the passage of the DNA replication enzyme complex along the DNA (Beranek 1990). While historically it was assumed that MMS also produced DNA double-strand breaks (DSBs), studies have shown that this is not the case in vivo and DSBs in vitro may have arisen through sample preparation (Lundin et al. 2005).

15 2.2.1.2 Indirect Genotoxicity Indirect genotoxins cause genetic damage by interfering with any cellular mechanism by which DNA or chromosomes are maintained and processed. These processes can involve spindle formation or the enzymes integral to replication or repair. Several chemicals including carbendazim, colchicine and vincristine sulfate are able to cause aneugenesis by binding to tubulin, interfering with microtubule function and preventing cells from exiting mitotic metaphase (Borisy & Taylor 1967; Jordan et al. 1991; Van Hummelen et al. 1995). Topoisomerase inhibitors such as etoposide prevent the enzyme correctly relaxing DNA supercoils. Etoposide inhibits the re-ligation activity of topoisomerase II following the induction of strand breaks, leading to clastogenesis (Long & Stringfellow 1988). A wide range of chemicals including peroxides and metal compounds are able to interfere with the balance of reducing or oxidising agents within cells (Joenje 1989; Imlay et al. 1988; Leonard et al. 2004). An increase in reactive oxygen species within cells, is able to lead to the oxidative damage of DNA (Section 2.4).

2.2.1.3 Progenotoxins An additional challenge in the development of in vitro tests was that some compounds only exert their genotoxic potential after metabolism. This metabolism typically, although not exclusively occurs in the liver. Such compounds are called progenotoxins and their products, genotoxic metabolites. Without the necessary conditions in an in vitro test, a progenotoxic compound may not be identified as potentially hazardous. As a consequence of this, in vitro testing protocols are used in which extracts such as the post- mitochondrial S9 liver fraction are included to better emulate an in vivo model by generating metabolites that would be created in the body. These S9 liver fractions are taken from rodents treated with a hepatotoxin such as Aroclor 1254 to increase the production of cytochrome P450s enzymes, integral to the metabolic activation of many compounds (Maron & Ames 1983; Jagger et al. 2009). Before this method had been developed, in vitro genotoxicity assays were ineffective at identifying known carcinogens such as benzo[a]pyrene (B[a]P). One of the metabolites of B[a]P is benzo[a]pyrene diol epoxide which binds covalently to DNA, forming a DNA adduct which intercalates into the base sequence. These molecules, bound to the DNA cause errors in replication by distorting the DNA’s double-helical structure thus preventing the replicative machinery from correctly copying the DNA base sequence (Volk et al. 2003).

2.2.1.4 Non-genotoxic carcinogenicity Although the initiating event in many tumours is DNA damage, there are also non-genotoxic carcinogens, which affect other cell processes that lead indirectly to the accumulation of DNA damage. The possibility of non-genotoxic carcinogenicity was not realised until relatively recently (Grasso & Hinton 1991; Clayson 1989). The pathways by which compounds exert carcinogenicity without a genotoxic mode of action can include the simple stimulation of cell division. DNA replication has very high fidelity, but every cell division adds to the risk of errors in DNA replication, which can lead to the accumulation of mutations. Increased cell division can arise through sustained exposure to physical injury or chemical toxicity, leading to inflammatory hyperplasia (Marks et al. 1995). Some compounds exert carcinogenesis by interfering with the cell cycle checkpoints, causing cells to bypass checkpoints where cell damage is ordinarily detected and repaired. This can lead to an accumulation of mistakes. An example of this is phenobarbital which brings about non-genotoxic carcinogenesis by attenuating the G1 checkpoint (Gonzales et al. 1998). Non- genotoxic carcinogenesis through epigenetic modification is an emerging field of study. It is known that

16 DNA methylation at CpG islands by methyl-transferases such as DNMT1 (CG-rich repetitive sequences) is responsible for the silencing of DNA (Takai & Jones 2002; Bird 2002). Chemicals that interfere with the action of DNA methyl-transferase enzymes can lead to changes in the epigenome (Bird 2002). Decitabine does this, inhibiting the activity of DNA methyl transferase by hypomethylating DNA (Stresemann & Lyko 2008). Changes can also be effected by modification of the post-translational modifications to the N- terminus of histones. The acetylation, methylation, ubiquitination, SUMOylation and phosphorylation of this exposed tail can alter the chromatin state of the DNA and activate or repress the expression of DNA wound around the histone (Kouzarides 2007). Therefore chemicals such as garcinol which is able to interfere with histone acetyltransferase, inhibiting the acetylation of the N-terminus of histones, repressing chromatin transcription (Balasubramanyam et al. 2004). These changes to the epigenome can lead to cancer by altering global transcription within cells (Sharma et al. 2010).

2.2.2 Cellular response to DNA damage Given that over a human lifespan, an estimated number of cell divisions in the order of 1017 occur and each cell division requires the replication of the entire genome, mistakes in DNA replication are not a rare occurrence (Brown 2011). To prevent these mutations from accumulating, cells pause their cell-cycle and use a wide range of mechanisms to repair DNA damage or prevent its transmission to daughter cells by undergoing cell-death (Surova & Zhivotovsky 2013). Briefly detailed below are the cellular responses to DNA damage.

2.2.2.1 Cell-cycle arrest Upon detecting DNA damage, a cell can arrest cell cycle at the next checkpoint (Jin & Levine 2001). The principle checkpoints during the cell cycle are during or at the end of the G1 phase, during S phase and at the end of G2 phase (Kastan & Bartek 2004). Most DNA damage happens when the DNA is exposed during S- phase (Bartek et al. 2004). DNA damage can, however, be caused at other points in the cell-cycle for example during M-phase following interference with spindle formation by vincristine sulfate (Gonzalez-Cid 1999). The mechanisms by which these checkpoints are activated is complicated and beyond the scope of this chapter however it is worth noting that DNA phosphatidylinositol 3-kinase-like kinases (PIKKs), ATM, ATR and DNAPKcs play key roles in the initiation of each of these checkpoints (Kastan & Lim 2000). ATM and ATR lead to the activation of CHK2 and CHK1 which in turn activate p53, a transcription factor involved in the initiation of apoptosis, cell-cycle arrest, cellular senescence and apoptosis, while inhibiting CDC25s (Craig et al. 2003; Shiloh 2003; Bartek et al. 2004; Donzelli & Draetta 2003). These checkpoints can be activated transiently, allowing DNA damage to be repaired or prolonged, arresting the cell cycle. They can alternatively be activated permanently, inducing the cell’s senescence (Campisi & d’Adda di Fagagna 2007; Bartek & Lukas 2001). p53 then plays an important role in activating DNA damage response pathways through GADD45a and regulating the cells’ commitment to an apoptotic pathway through BAX (Müller et al. 1997; Muller et al. 1998).

2.2.2.2 GADD45a Growth arrest and DNA-damage-inducible protein, GADD45a, is involved in the entry to cell-cycle arrest, DNA damage response and apoptosis (Fornace et al. 1989; Hollander & Fornace 2002; Hollander et al. 1999; Gupta et al. 2005). It is regulated through p53 and p53-independent pathways (Kastan et al. 1992; Carrier et

17 al. 1996). Regulation of its expression is governed by a promoter sequence in addition to a p53 response element and WT1 response element within the GADD45a promoter (Johnson et al. 2013). Expression of GADD45a increases following DNA damage, a mechanism that is exploited in the GADD45a-GFP reporter assay in the detection of genotoxicity (Hastwell et al. 2006) (Section 2.3.1.1). This expression, in the absence of genotoxic stress, is repressed by the presence of Myc on the WT1 binding site in the GADD45a promoter (Amundson et al. 1998). This has been shown to be true for both p53 dependent and independent pathways (Johnson et al. 2013). Furthermore expression of GADD45a mRNA has been shown to be increased in response to various other non-genotoxic cell-stresses – endoplasmic reticulum stress, induced by thapsigargin; nutrient deprivation induced by growing cells in leucine-free media; oxidative stress induced by arsenite exposure and proteasome inhibition, induced by MG132 (Jiang et al. 2007; Hollander & Fornace 2002; Zhan et al. 1994). The GADD45a protein however has been seen to accumulate only in response to oxidative stress and proteasome inhibition (Jiang et al. 2007). The increased expression of GADD45a-GFP in response to oxidative stress has been shown to be mediated by forkhead transcription factor (Tran et al. 2002; Furukawa-Hibi et al. 2002). The GADD45a-GFP reporter assay has shown that protein expression is robustly increased by all manner of genotoxic agents, aneugens, clastogens, mutagens and pro-genotoxins (Hastwell et al. 2006; Jagger et al. 2009; Birrell et al. 2010). Protein expression was also shown to not increase following exposure to non-genotoxic apoptotic agents (Topham et al. 2012).

2.2.2.3 DNA damage repair Base-excision repair (BER) is a DNA damage repair pathway able to restore altered bases, such as 8- oxoguanine (8-OG): (Section 2.4.4), abasic sites and single-strand DNA breaks (Jaruga & Dizdaroglu 1996; David et al. 2007). These lesions occur as a result of ROS, ionising radiation and DNA alkylation by agents such as MMS (Krokan et al. 2000). BER is the primary repair pathway to remove and repair 8-OG (David et al. 2007), this occurs through the “GO” repair pathway (Figure 2:1).

Nucleotide-excision repair (NER) occurs within cells to remove damaged or cross-linked DNA nucleotides and nucleotides bound to DNA adducts (Harper & Elledge 2007). These lesions can be caused by ROS, ultraviolet light, DNA crosslinking agents and compounds able to form bulky DNA adducts such as polycyclic aromatic hydrocarbons (Ciccia & Elledge 2010). Unrepaired, crosslinked DNA prevents the DNA from unwinding, halting replication and transcription. This can be caused by DNA crosslinking agents such as mitomycin C or diepoxybutane (Moldovan & D’Andrea 2009).

Proofreading is a mechanism of DNA damage repair carried out during replication by DNA polymerase. The enzyme rapidly checks the bases that have been inserted and if a mis-match occurs, the direction of the enzyme is reversed as the exo subunit of the enzyme carries out exonuclease activity in a 3’-5’ direction before the pol subunit continuing synthesis 5’-3’ (Kunkel 1988; Gill et al. 2011).

Translesion synthesis is a process by which the replicative machinery can bypass DNA lesions. When DNA polymerase stalls at a DNA lesion, it is removed, leaving the proliferating cell nuclear antigen (PCNA) clamp. The polymerase is replaced by a translesion DNA polymerase (Waters et al. 2009). These polymerases have a larger active site allowing the enzyme to bypass the lesion before being replaced by the replicative DNA polymerase (Prakash et al. 2005). Translesion DNA polymerases however, lack the 3’-5’ exonuclease activity of the replicative DNA polymerase. This leads to far more error prone replication in the translesion region

18 (Goodman 2002). Mismatch repair (MMR) is a DNA repair mechanism that removes and replaces DNA bases that are mismatched but not removed by DNA proofreading. MMR proteins are recruited to mismatched bases by PCNA where the MMR proteins excise the mismatched bases and DNA back bone before DNA polymerase δ carries out accurate DNA synthesis. DNA ligase then seals the nicks, completing the process (Kunkel & Erie 2005).

Recombinational repair occurs in cells to repair double strand breaks and cross-linking between the two strands. The two mechanisms responsible for this type of repair are homologous recombination (HR) and non-homologous end-joining (Takata et al. 1998).

If a sequence homologous to the damaged region exists elsewhere in the genome (e.g. within a sister chromatid), cells are able to carry out HR. DNA damage inducible protein, RAD51 forms a homo-oligomeric heptamer around damaged DNA allowing the homologous recombination factors to carry out repair (Shin et al. 2003). Double-strand DNA breaks can be repaired by double strand break repair (DSBR) or synthesis- dependant strand annealing (SDSA) (San Filippo et al. 2008). Within both mechanisms, the damaged DNA is resectioned to produce sticky ends (Sugiyama et al. 1997). One strand from the damaged DNA then invades the donor DNA allowing DNA synthesis to follow the homologous template (Baumann et al. 1996). Within the SDSA pathway, the donor and damaged DNA dissociate allowing the repair to be completed. DSBR, however, resolves this by the formation of a double Holliday junction with the homologous regions of the damaged and the donor region of DNA (San Filippo et al. 2008).

While HR-mediated DNA-damage repair is only able to occur during S or G2 phases of the cell cycle, NHEJ is able to occur during any stage of the cell-cycle (Lieber 2008). First, ATM recruits and phosphorylates γH2AX either side of a DSB (Derheimer & Kastan 2010). The Ku protein subunits then locate to γH2AX and bind to the DNA, flanking the DSB (Weterings & Chen 2008). DNAPKcs then bind to the Ku subunits, bridging the gap between the DNA ends allowing the DNA to be ligated (Lamarche et al. 2010). While NHEJ can correctly repair DSBs providing that there are complementary overhangs, it is often more error prone than HR (Shrivastav et al. 2008).

2.2.2.4 Cell-death Following the accumulation of genetic damage within a cell, several pathways exist to commit the cell to a cell-death pathway. These cell-death pathways can be necrotic, apoptotic or autophagic (Surova & Zhivotovsky 2013). The cell-death pathway that the cell enters varies depending upon the cell’s position in the cell-cycle, external signals and the severity of the genotoxic insult. Following depletion of ATP in the cell, PARP mediates the cell’s entry into a necrotic pathway (Virág et al. 1998; Liaudet et al. 2000). If the cell’s energy is not depleted, but the DNA damage is left unrepaired, cells enter into apoptosis mainly through a p53-mediated pathway (Oda et al. 2000). The entry of a cell into autophagy relies upon more complex, and less well defined conditions (Surova & Zhivotovsky 2013).

Figure 2:1 “GO” base excision repair pathway for 8-Oxoguanine 8-Oxoguanine (O) left unrepaired in the DNA leads to G:C and T:A transversions. BER glycosylase, hOGG1 is able to remove and replace 8-OG, and replace it with guanine. BER glycosylase, MutYH is able to remove and replace adenine bases paired with 8-OG, allowing hOGG1 to remove the 8-OG base and DNA polymerase to subsequently replace the residue. MTH1, not shown hydrolyses free 8-oxoguanine bases, removing them from the nucleotide pool (David et al. 2007).

20 2.3 Genotoxicity assessment

Once it was recognised that cancer can be brought about through exposure to genotoxins, genotoxicicity assessment became a regulatory requirement for novel xenobiotic substances (chemicals that enter an organism from a non-biological source) for example pesticides, food additives and pharmaceuticals. For brevity, the example used here is the safety assessment of pharmaceuticals. The in vitro assays are the first to be performed: they are not 100% accurate. Their sensitivity, i.e. the proportion of positive results that a test gives for carcinogens, and their specificity, i.e. the proportion of negative results that a test gives for non-carcinogens varies from assay to assay and also depending on the list of chemicals being examined (Kirkland et al. 2005; Walmsley 2010). It is however expedient to identify compounds which are likely to produce misleading positive results, work out why and in what way the anomalous result has been generated and to seek to prevent misleading conclusions from arising through similar means in the future. This will allow fewer, potentially valuable medicines to be discarded due to spurious safety concerns.

Whether these compounds are destined for foods, drugs, domestic or industrial chemicals, they must all must be assessed to ensure that they do not induce genetic damage at the levels that humans may be exposed to. The FDA (Food and Drug Administration) in the USA, REACH (Registration, Evaluation, Authorisation and restriction of Chemicals) in Europe, the ICH (International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use), the OECD (Organisation for Economic Co-operation and Development) and various other bodies set out guidelines for manufacturers to adhere to. These bodies might not always allow the most efficient screening procedures. There is a consensus that it is better to err on the side of caution with matters relating to human exposure to genotoxins.

Genotoxicity is of prime concern to companies involved in drug development. Their aim is to develop effective and safe compounds and to do so at a cost that allows them to see profit. In order to do this they need to be able to detect compounds that are capable of causing genetic damage. The earlier in development that this can be revealed, then the sooner they can be excluded from further development, allowing efforts to be focussed upon those compounds more likely to be safe at a level defined by regulatory bodies (unless genotoxicity is an intended effect of the drug). This should in turn also reduce the costs of drug development. Figure 2:2 shows a flow chart detailing the steps taken in the assessment of the genetic toxicity of test chemicals.

21 •Cheapest •No test chemical needed in silico assessment •Very high throughput •Informative •Limited accuracy

•Cheaper than regulatory assays Non-regulatory •Can be higher throughput in vitro •Can be more accurate/informative genotoxicity •Can be performed with smaller chemical volume assessment •Can offer mechanistic insight •Not industrially recognised as an empirical measure of safety

Derek

CASE Ultra™ Regulatory in •Widely recognised as a measure of safety vitro •Necessary for the release of chemical to market

Nexus™ genotoxicity •Expensive assessment γH2AX assessment γH2AX •Older assessments that have been superseded in many regards

GreenScreen® HC GreenScreen®

etc. •Provides insight into chemicals that are genotoxic in vivo through complex pathways that have yet to be genotoxicity simulated in vitro assessment •Very expensive

Micronucleus Test Micronucleus

•Necessary for the identification of non-

etc. in vivo genotoxic carcinogenicity carcinogenicity •Allows the identification of genotoxins that Ames assessment lead to cancer endpoint

in vivo vivo in •Most expensive

chromosomal aberrations chromosomal

etc.

in vivo in

Test Micronucleus

Figure 2:2 Flow chart detailing steps taken in the assessment of genetic toxicity The flow chart above details the various steps that can be taken in the assessment of a compounds genetic toxicity. Within the blue boxes are detailed the type of assessment. Below these are detailed two examples of this type of assessment except for in vivo carcinogenicity assessment for which only one example exists. To the right of each blue box in green are any advantages to this assessment over subsequent assessments. In red are any disadvantages of this type of assessment.

22 2.3.1 Non-regulatory genotoxicity assessment Due to the cost and ethical implications of in vivo genotoxicity assessment, the assessment of chemicals’ genotoxic potential in vitro is imperative. The assays detailed below were developed to predict whether a compound is capable of causing genetic damage to cells in vivo using mammalian or bacterial cells in culture.

Further to the regulatory assessment of genotoxicity detailed in Section 2.3.2, it is often necessary to carry out one or more non-regulatory assessments of genotoxicity or carcinogenicity. These assays can have various advantages over the regulatory assays:

 They can provide higher levels of sensitivity or specificity than the existing regulatory in vitro tests (e.g. GADD45a-GFP assay – Section 2.3.1.1)  They can include variant protocols of the regulatory assays that allow a higher throughput assessment, allowing more compounds to be assessed, quicker (e.g. Ames MPF, MicroFlow – Section 2.3.1.2)  They can provide more specific information regarding any potential genotoxic mode of action of a chemical (e.g. ToxTracker – Section 2.3.1.2)  in silico assays can provide instant feedback, indicating the genotoxic or carcinogenic potential of a compound structure without the chemical needing to be synthesised (e.g. Derek Nexus™, Multicase – Section 2.3.1.3)

These assessments are ordinarily carried out prior to the more expensive regulatory tests detailed in Section 2.3.2 to refine a longer list of candidate compounds preventing unnecessary testing of compounds likely to produce positive results in the regulatory assays. As such, any false positive result at this point can lead to a candidate compound being unduly excluded from further testing.

2.3.1.1 GADD45a-GFP reporter assay This non-regulatory in-vitro genotoxicity assay was developed by Gentronix Ltd. (UK) in 2005 (Hastwell et al. 2006). The test relies upon the increased expression of the GADD45a gene in response to genotoxic stress (Section 2.2.2.2), Figure 2:3. The assay makes use of two different strains of modified TK6 cells (p53 competent, human lymphoblastoid cell-line), a test strain and a control strain. The test strain was modified to express green fluorescent protein (GFP) when GADD45a is expressed, by adding a plasmid containing various regulatory and promoter sequences of GADD45a and a GFP gene (EGFP) (Figure 2:4). This means that while under genotoxic stress, the cells increase synthesis of GFP via the GADD45a regulatory sequences in the plasmid, and as a consequence become measurably more fluorescent. The control strain was similarly modified, however the copy of the plasmid with which it was transfected contained an EGFP gene from which 4 bases have been removed, leaving the codons out-of-frame. This version of the gene does not encode a functional GFP protein. It is through comparison of the fluorescence of exposed and unexposed test-strain cells that gives a semi-quantitative measure of genotoxicity that the compound may be involved in. The assay’s high sensitivity and specificity and its relatively high throughput methodology lend it a great deal of utility in early screening of compounds. As a proprietary technology however, there is resistance against its becoming a regulatory assay. The assay is however in the process of becoming an OECD approved method. 23 No Fluorescence Safe

PI Fluorescence Cytotoxic Test chemical 617 nm fluorescence added to TK6 Human Lymphoblastoid cells containing Genotoxic GADD45a-GFP GFP Fluorescence 509 nm fluorescence plasmid

Figure 2:3 Stylised diagram showing the three endpoints of GADD45a-GFP assessment The diagram above shows the three endpoints that can be reached after GenM-T01 human lymphoblastoid cells are exposed to a test chemical and assessed by flow cytometry. If the chemical is neither genotoxic nor cytotoxic then the cells will not fluoresce under excitation by 488 nm laser light indicating that the chemical is safe. Membranes of cells exposed to cytotoxic chemical will permeabilise, allowing PI to enter the cell and fluoresce 617 nm light under excitation by 488 nm light indicating that the chemical is cytotoxic. Cells exposed to a genotoxic compound will produce GFP using the GADD45a-GFP plasmid. These cells will then fluoresce 509 nm light under 488 nm excitation indicating that the chemical is genotoxic.

Figure 2:4 Plasmid map representing GADD45a-GFP reporter pEP-GF532 Shown in orange are the Epstein-Barr virus origin of replication (Ori P) and nuclear antigen gene (EBNA-1). In yellow, the hygromycin B resistance gene (Hygromycin R) ensures stable episomal replication in cells. Also shown are the sequence of EGFP (green), 2.4 kb of the GADD45a-promoter (red), GADD45a exons 3 and 4 (blue) and GADD45a intron3 (light blue). Also shown is the putative p53 binding motif in intron 3. The direction of arrows show the relative direction of transcription (Hastwell et al. 2006).

25 2.3.1.2 New in vitro genotoxicity assays Several new assessment techniques are currently in development in companies and research institutes around the world. Some of these assays seek to provide a clearer insight into the mechanism of genotoxicity, some seek to be able to predict a high-throughput of compounds allowing a broad screen of candidate compounds while others aim to provide robust genotoxic assessment, seeking to predict genotoxicity with greater sensitivity and specificity than the regulatory assays. Below are detailed some of the new assays that are being developed or validated.

γH2AX assessment By assessing the level of serine139-phosphorylated histone H2AX (γH2AX), an indication of the cellular response to DSBs can be provided (Bonner et al. 2008). This can be used to identify genotoxic activity. Measuring the level of γH2AX by flow cytometry using FITC-conjugated antibodies allows a high-throughput assessment of chemicals (Smart et al. 2011).

EpiDerm™ EpiDerm™ uses a 3D human skin model (EPI-200-MNA) to allow the detection of genotoxicity applied topically to the skin or suffused throughout. The measurement of DNA damage is carried out by the detection of micronuclei in cells following two compound doses during a 48 hour period. This model aims to provide a better indicator of the genotoxicity of cosmetics applied to the skin (Curren et al. 2006; Mun et al. 2009). This is vital as since 2009, the EU banned the use of in vivo studies for the genotoxicity of cosmetic products and in vitro studies where the compound is added to cell culture media can give a physiologically irrelevant indication of the genotoxicity of a topically applied chemical.

Higher throughput protocols of existing assays Adapted protocols of both the Ames test and MNT offer a much higher throughput of compounds, allowing faster, more inexpensive and less labour-intensive assessment of chemicals. The Ames microplate format (Ames MPF™: Xenometrix, Allschwil, Switzerland) assay depends upon carrying out the Ames test within 384-well microplates. This allows the detection of revertant colonies through spectrophotometry providing results comparable with those scored manually on agar plates (Flückiger-Isler & Kamber 2012). MicroFlow (Litron Laborotories, Rochester, NY, USA) allows the detection of in vitro or in vivo micronuclei using flow cytometry. Two nucleic acid dyes, ethidium monoazide and SYTOX green, stain dead cells and micronuclei respectively and fluoresce at different wavelengths. These are then quantified by flow cytometry to give an indication of cytotoxicity and genotoxicity (Bryce et al. 2008; Dertinger et al. 2011). This technique also allows the use of 96-well microplates, significantly increasing throughput and reducing labour-intensiveness (Bryce et al. 2013).

Reporter assays with mechanistic insight Various reporter assays have been developed to provide a clearer mechanistic insight than is available through conventional genotoxicity assessment. Luciferase-based reporter assays have been developed in HepG2 cells allowing an indication of the levels of RAD51C, Cystatin A, p53 and Nrf2 synthesis within cells (Schoonen et al. 2009; Westerink et al. 2010). Changes in cellular levels of RAD51 provide an indication of the cells’ response to DSBs (Section 2.2.2.3). Increased synthesis of Cystatin A indicates cells resistance to entering apoptosis. Though specifically induced by genotoxins, its role in the genotoxic mechanism is

26 unclear (Westerink et al. 2010). Changes in levels of p53 transcription factor (Section 2.2.2) provide an indication of cellular response to genotoxic stress. Not all mechanisms of DNA repair are p53-dependent and p53 is also involved in cell-cycle arrest and apoptosis, so while p53 may provide further insight into genotoxic mechanism, it alone is not an ideal measure of genotoxic stress response. Protein levels of Nrf2 (Section 2.4.3) provide allow the detection of the cells’ oxidative stress response, providing an indication of whether any detected DNA damage is through an oxidative mechanism.

ToxTracker™ (Toxys, Lieden, Netherlands) consists of six strains of mouse embryonic stem cells. Each strain contains a different reporter pathway to provide a holistic indication of the response of cells to toxic compounds. Genotoxic stress is detected in cells containing a Bscl2-GFP reporter to detect ATR/Chk1 DNA damage signalling and in cells containing an Rtkn-GFP reporter to detect NF-κB signalling. Oxidative stress is detected using Nrf2 dependent, Srxn1-GFP, and Nrf2 independent, Blvrb-GFP. Cellular stress through the p53 pathway is measured using a Btg2-GFP reporter and protein damage is detected using Ddit3-GFP (Hendriks et al. 2012; Hendriks et al. 2011; Hendriks et al. 2013). By using reporters for multiple stress pathways, ToxTracker is able to detect not only the genotoxic potential of a compound but also offer insight into the mechanism of its action.

2.3.1.3 in silico genotoxicity and carcinogenicity assessment In addition to in vitro assessment of genetic damage, recent years have seen the development of in silico methods for detection of genetic damage and carcinogenicity. Such in silico methods provide an important and relatively inexpensive method for predictive assessment of a candidate compound’s capacity to cause genetic damage or cancer. This type of predictive result is valuable, particularly in the early-stage development of pharmaceuticals. At this stage, a wide range of candidate compounds can be assessed, reducing the number of candidate compounds. The number is reduced by removing those compounds that are likely to pose a greater risk than others or to add further insight into a result generated by in vitro assessment.

Two types of in silico assessment exist; rule-based expert systems and statistical quantitative structure- activity relationship (QSAR) models.

Rule-based expert systems, such as Derek Nexus™, rely upon the curating of a set of rules by experts based upon observations of existing results from regulatory genotoxicity and carcinogenicity assessment in addition to research relating to genotoxic modes of action. Each rule is attributed to a compound structure or activity such as “halophenolic” structure or “pro-oxidant” activity respectively (Sanderson & Earnshaw 1991; Ridings et al. 1996; Greene et al. 1999). The test compound’s structure is compared against this list of rules and any rules that match the structure or known activity of the compound attributes an alert based upon this rule to the compound. A compound may have no alert attributed to it if it bears no structural or activity similarity any of the alerts indicating that the compound’s capacity to cause genetic damage is either none or unknown (Sutter et al. 2013). Further developments upon the software aim to add resolution to this difference highlighting which compound structures might be associated with negative results for genotoxicity and/or carcinogenicity and those which might not be associated with any existing regulatory genotoxicity/carcinogenicity data (Lhasa Ltd 2014). This knowledge-based approach offers the advantage of providing a curated set of supporting evidence for any alert generated.

27 Statistical QSAR systems rely far more heavily upon computation to provide results to predict a compound’s capacity to cause genetic damage. By modelling a compounds 3D structure and statistically assessing how likely the compound is to react with DNA or its associated machinery, QSAR systems can provide an indication of a chemical’s genotoxic potential. QSAR system, CASE Ultra™ (MultiCase, Cleveland, OH, USA), is able to predict a wide variety of toxicological endpoints, including bacterial mutagenicity, genotoxicity and carcinogenicity. CASE Ultra™ is founded upon data from existing compounds with well-established toxicological data and produces a model of positive and deactivating structural alerts. A test compound’s structure is compared against this model to provide insight into the compound’s activity (Saiakhov et al. 2013; Chakravarti et al. 2012). This statistical approach offers the advantage of offering a probabilistic outcome, indicating to some degree the likelihood of a certain activity. This output requires expert knowledge to interpret the results.

While both knowledge-based and statistical models of in silico toxicological prediction have their own inherent advantages and limitations, they are best used together to provide a clearer result (Sutter et al. 2013). This is highlighted within the recommendation of the ICH M7 draft guidelines regarding the assessment and control of DNA reactive impurities in pharmaceuticals (ICH 2014).

2.3.2 Regulatory in vitro genotoxicity assessment There are four regulatory in vitro assays for genotoxicity, one bacterial assay and three assays performed with mammalian cell lines. The Ames test is the regulatory bacterial assay and uses Salmonella typhimurium and Escherichia coli bacteria. The three mammalian cell assays are the mouse lymphoma assay (tk mutation), the micronucleus test and the chromosomal aberrations assay.

Option 1 of the current ICH S2(R1) guidelines require novel pharmaceutical compounds to undergo testing in a battery consisting of the following:

i) A bacterial mutation assay ii) An in vitro mammalian cytogenetic evaluation (chromosomal aberrations or micronucleus test) OR an in vitro mouse lymphoma mutation assay iii) An in vivo test for genetic damage – (chromosomal aberrations or micronuclei)

Alternatively, option 2 requires compounds to undergo:

i) A bacterial mutation assay ii) An in vivo test for genetic damage of two different tissues

The guidelines also require that novel pharmaceutical compounds should be tested up to a concentration of 5 mg/plate in bacterial cell assays and 1 mM or 0.5 mg/ml, whichever is lower, in mammalian cell assays (when not limited by cytotoxicity or solubility). OECD guidelines for genotoxicity assessment recommend testing to a top dose of 10 mM (OECD 1997a; OECD 1997b; OECD 1997e; OECD 2010) however growing evidence suggests that this dose is excessive (Kirkland et al. 2007; Kirkland et al. 2008; Kirkland & Fowler 2010; EFSA 2012; Parry et al. 2010). These studies found that reducing the top dose to 1 mM greatly reduced the number of false positive results produced in genotoxicity assays and that the carcinogens with positive results only at doses above 1 mM were likely carcinogenic by non-genotoxic means. In the non- pharmaceutical chemical sectors, the 10 mM top testing dose is still required (OECD 2013). 28 2.3.2.1 Descriptions of the regulatory in vitro genotoxicity assays

Ames Developed in 1973, the Ames bacterial mutation assay involves exposing various auxotrophic strains of Salmonella typhimurium and Escherichia coli bacteria to a compound and then plating them out on agar deficient in the nutrient for which they are auxotrophic (usually histidine or tryptophan). In order to grow on the medium, the bacteria must undergo reversion mutation to restore prototrophy and hence to permit growth and colony formation on the restricted medium. The number of “revertant” colonies compared to the number of colonies growing in the untreated control gives an indication of whether the compound is mutagenic or not (Ames et al. 1973; OECD 1997a). The bacterial strains commonly used within the assay are summarised in Table 2:1.

This method for detecting genotoxins has clear limitations. Bacterial cells are physiologically quite distinct from mammalian cells: they do not have nuclei or linear chromosomes and their mechanisms for DNA damage response and repair differ from those in mammals. The failure or the Ames test to produce positive results for increasing numbers of carcinogens soon led to the development of mammalian cell genotoxicity assays, in the belief that these would identify compounds missed by Ames. It was not anticipated at the time than some of those carcinogens may not in fact have had a genotoxic mode of action. It wasn’t until 1989 that non-genotoxic carcinogenicity was considered a possibility (Clayson 1989).

Strain Gene affected Reversion events S. typhimurium TA1535 hisG46 Base-pair substitution S. typhimurium TA1537 hisC3076 Frameshifts S. typhimurium TA97 hisD6610 Frameshifts S. typhimurium TA98 hisD3052 Frameshifts S. typhimurium TA100 hisG46 Base-pair substitution S. typhimurium TA102 hisG428 All transitions and transversions, small deletions E. coli WP2 trpE All transitions and transversions, small deletions Table 2:1 The bacterial strains commonly used in the Ames bacterial mutagenicity assay The table above summarises the bacterial strains commonly used, the gene mutation that renders the cells auxotrophic to histidine or tryptophan and the reversion events to which the mutations are sensitive (Gatehouse 2012).

Chromosomal Aberrations (CA) Test This test involves arresting the cell cycle of a population of cells in metaphase using an inhibitory protein that interferes with mitosis such as colchicine or colcemid. Chromosomal aberrations within the cells are then counted. These aberrations include clastogenicity (breakage of chromosomes), aneuploidy (too many or too few chromosomes) or rearrangement within chromosomes (translocations etc.) (OECD 1997b). This is a very difficult and labour-intensive assay to perform and very open to incorrect results through human error. Improvements have been made to the assay to provide clearer information on the presence and nature of chromosomal aberrations using fluorescent in situ hybridisation (FISH) (Beskid et al. 2007; Jesudasan et al. 1993). The popularity of this test is giving way to less laborious methods.

Micronucleus Test (MNT) The newest of the regulatory tests, MNT, is the main method replacing the chromosomal aberrations assay. The assay relies upon the quantitation of micronuclei within treated cells. When chromosomes break or 29 mis-segregate, nuclear membranes form around these chromosomes or chromosomal fragments. These small nuclear-membrane-bound envelopes are micronuclei. These micronuclei increase in number within cells with increasing levels of genetic damage. Within the regulatory assay, these micronuclei are then observed and counted by microscopy (OECD 2010). The average number of micronuclei per cell gives an indication of the prevalence of aneuploidy and clastogenicity within the population (Parry et al. 1996; Fenech 2000). This test is very sensitive but several reports suggest that this high sensitivity comes at the cost of the assay’s specificity (Kirkland et al. 2005). tk Mutation / Mouse Lymphoma Assay (MLA) Developed during the 1970s at much the same time as the Ames assay, the tk mutation assay was developed and first described by Clive et al. The assay is often referred to as the mouse lymphoma assay because of the cell strain most frequently used to perform the assay. The cells are mouse lymphoma cells heterozygous for the TK gene encoding the thymidine kinase enzyme. The assay detects the mutagenic properties of a test compound by assessing forward mutation rate. This is achieved by determining how many TK heterozygotes (TK+/-) have undergone mutations by assessing the number of colonies that grow while the TK+ allele is being selected against. In order to achieve this, the selection against wild type cells with TK+ is performed by growing cells in either a microplate or a soft agar plate in the presence of trifluorothymidine, a toxic compound that is incorporated into DNA by thymidine kinase, killing the cell. TK-/- cells, lacking the functional gene for thymidine kinase are therefore resistant to incorporating the TFT into their DNA and hence survive to form colonies (Clive & Spector 1975; OECD 1997e). The specificity of this test, has been shown to be poor. Test results from 105 non-carcinogens produced correct negative results for only 39% suggesting that the test may not be as effective a test as other options (Kirkland et al. 2005). It has subsequently been shown that one reason for the generation of so many false positive results is testing compounds at excessive concentrations. A recent review suggests that safety assessment based on MLA positive results generated above 1 mM should be regarded with caution (Moore et al. 2011).

2.3.3 in vivo genotoxicity The nature of the research presented in this thesis does not require a great amount of familiarity with in vivo methodology and so what follows is a very brief overview. The measurement of chromosomal aberrations or micronuclei within polychromatic erythrocytes and/or bone marrow of treated rodents (usually rats and mice) is used in the assessment of genotoxicity in vivo. This allows a far more physiologically relevant prediction of genotoxicity in humans. in vivo genotoxicity assessment does cost significantly more than in vitro methods. This, alongside the ethical implications of sacrificing the treated animals means that in vivo assessment is limited to compounds that have already been screened in vitro. The dosing regimen, rodent species and treatment time used vary depending upon the mechanism by which any genotoxicity is expected to occur (OECD 1997c; OECD 1997d; ICH 2011).

2.3.4 in vivo carcinogenicity To understand the research presented in this thesis, a great amount of familiarity with in vivo carcinogenicity assessment methodology is not required and so what follows is a very brief overview. Carcinogenicity is assessed using long-term studies, primarily carried out in rats or mice. A typical 24 month rodent study uses at least 50 animals of each sex. Animals are treated with at least 3 doses by various

30 routes of administration (gavage, dermal or inhalation as appropriate). These studies provide the clearest economical prediction of human carcinogenicity but due to the financial and labour costs, and the ethical concerns of the assessment, it is often only carried out following no indication of genotoxicity in vitro and in vivo assays (OECD 2009; ICH 2012).

2.3.5 Improvements to in vitro genotoxicity assays Improvements to assays, such as the development of in vitro assays with metabolic activation detailed above, can increase their sensitivity. Improvements to an assay’s specificity are equally important. Generation of positive results for genotoxicity can lead to the termination of development of novel pharmaceuticals (Bowes et al. 2012). If this positive result has misleadingly arisen due to an inaccuracy in the assay then a new potentially helpful drug has been needlessly lost.

One proposed mechanism for the generation of some misleading positive results in the in vitro genotoxicity assays is through irrelevant oxidative stress (Long et al. 2007). Oxidative DNA damage is a mode of action through which various genotoxins do indeed act in vivo, there are also oxidative compounds that are only positive in vitro. The work described in this report set out to test the hypothesis that some misleading genotoxicity results are produced due to increased in vitro oxidative stress potentially brought about through cells being exposed to an unnaturally high concentration of oxygen, with a consequent high background level of oxidative stress to contend with.

With the exception of skin, buccal, oesophageal and lung tissue, no tissue in the body is exposed regularly to oxygen of found in the air (~21%). In fact the normal oxygen pressure found in tissue within the human body lies within the ranges of 1-7% partial pressure (Kallinowski et al. 1990; Zuo & Clanton 2005; Csete 2005). The relatively high level of oxygen to which cells are ordinarily exposed to in culture (20%) has long been linked with the generation of reactive oxygen species in cells (Turrens et al. 1982; Yusa et al. 1984) however few steps have been taken to carry out genotoxicity assessment in the presence of more physiologically relevant conditions.

Section 2.4 further explains oxidative stress, the cellular defences against it and its role in genetic toxicity.

2.4 Oxidative stress

Oxidative stress is a scenario in which a cell’s defence to any one of a selection of oxygen-containing reactive radicals and molecules referred to as reactive oxygen species (ROS) is overwhelmed. The ROS can react with virtually any molecule in the cell including DNA and its replicative machinery. They can be generated by exposure to various drugs such as acetaminophen (Gerson et al. 1985). Ionising radiation and UV light can generate ROS by exciting electrons in oxygen containing molecules (Scandalios 1997). Metals, with their delocalised electron field are primarily toxic via oxidative mechanisms. ROS are also generated endogenously through many metabolic reactions. This ever-present danger requires cells to have a defence against these compounds. In some scenarios however these cellular defences (Section 2.4.3) are overwhelmed and damage is sustained. The following ions, radicals and molecules are the most prevalent reactive oxygen species.

31 2.4.1 Reactive oxygen species

1 Singlet Oxygen O2

Dioxygen (O2), the allotrope of oxygen most readily found in the troposphere (lower atmosphere) can be found in two different states. Most often it is found in its ground state, triplet oxygen. In this form the oxygen will not readily react with material unless a catalyst is present or a necessary level of heat makes a combustion reaction favourable. In its more excited state, singlet oxygen, reactions become far more favourable due to a change in electron spin in the π* orbitals of the oxygen atoms. This form of oxygen can occur as a consequence of various metabolic reactions along with other reactive oxygen species detailed below or as an effect of UV radiation exciting the molecules (Epe 1991).

Superoxide •O’2 Superoxide is an anion of dioxygen, an extra electron occupies the outer shell of one oxygen atom and the pi bond between the oxygen molecules is weakened making the molecule a highly reactive radical. It is generated regularly in mitochondria as a side effect of respiration. These radicals are prone to forming peroxyl radicals and the extremely reactive, short lived hydroxyl radicals by the following reaction.

’ - H2O + ∙O 2 → O2 + OH + ∙OH

(Rice-Evans et al. 1991)

Hydrogen Peroxide H 2O2 Hydrogen peroxide, like the superoxide anion is capable of directly oxidising biomolecules and also causing oxidation through the generation of hydroxyl radicals. In the presence of transition metal ions such as Fe2+, hydrogen peroxide undergoes an oxidative reaction to form hydroxyl ions and radicals as follows:

- - H2O2 + e → OH + ∙OH

(Rice-Evans et al. 1991)

Hydroxyl Radicals •OH Hydroxyl radicals are the most reactive of the ROS. After generation, hydroxyl radicals will largely react with the first molecule encountered (Rice-Evans et al. 1991). This reactivity means that in order to cause genetic damage, the hydroxyl radicals must be generated in the immediate vicinity of the DNA. Reactions involve extraction of hydrogen atoms or addition to double bonds:

(Rice-Evans et al. 1991)

Alkoxyl (RO•), Peroxyl (RO2•) & Hydroperoxyl (HO 2•) Alkoxyl, peroxyl and hydroperoxyl radicals all display lower levels of reactivity than the other ROS. Alkoxyl and peroxyl radicals can be formed by reactions of various other ROS with the alkyl group of organic compounds or by the loss of hydrogen atoms from the peroxyl group of organic hydroperoxides.

32 Hydroperoxyl radicals are formed by superoxide radicals in acid conditions (Rice-Evans et al. 1991; Halliwell & Gutteridge 2007).

2.4.2 The endogenous generation of reactive oxygen species The endogenous generation of reactive oxygen species is not a process exclusively associated with exposure to certain xenobiotics. The generation of reactive oxygen species and oxidative stress do not occur solely under extreme situations. Peroxisomes generate ROS in order to break down lipids. Phagocytes generate large amounts of ROS to kill phagocytosed bacteria. The majority of endogenously generated ROS however are generated as part of cellular respiration in the mitochondria. The mitochondria generate the ROS as a side effect of the mitochondrial electron transport chain. This process forms the backbone of all eukaryotic aerobic respiration and occurs at the interface of the intermembrane space and the matrix of the mitochondrion (Hatefi 1985). As the electron transport chain occurs, electrons “leak” from the respiratory complexes along the path. These electrons convert oxygen into a superoxide radical. These radicals are then broken down into hydrogen peroxide by superoxide dismutase (Turrens 1997; Turrens 2003).

2.4.3 Cellular defence to oxidative stress In order to defend cells against the damage caused by oxidative stress including lipid peroxidation, DNA damage and damage to all manner of proteins, the cells must neutralise the ROS present endogenously or outside of any controlled microenvironment such as the peroxisome where ROS function is entirely necessary (Clayson et al. 1994). To protect themselves, the cells have an arsenal of responses at their disposal. Defence can include reduction of radical oxygen species by electron donation from anti-oxidants such as glutathione, α-tocopherol (vitamin E) and ascorbic acid (vitamin C) (Scandalios 1997). Proteins such as transferrin that bind metal ions capable of generating ROS also play an important role as do proteins that directly catalyse the removal of ROS such as catalase, superoxide dismutase peroxidase and thiol-specific antioxidants (Halliwell & Gutteridge 2007). These antioxidant responses are regulated through various pathways. α-Tocopherol and ascorbic acid and other dietary antioxidants are obtained through food, while endogenous defences are upregulated through NF-κB and AP-1 (Valko et al. 2006). Nrf2 is an important transcription factor for the genes of various antioxidant response elements (Kensler et al. 2007; Reddy et al. 2007; Copple et al. 2008). These enzymes are regulated by inhibitory proteins that remain constitutively attached to the enzyme until oxidative conditions occur and cysteine rich regions of the inhibitors are oxidised. This then frees the enzyme to upregulate the oxidative response genes (Kensler et al. 2007; Xanthoudakis et al. 1992).

The Nrf2 pathway, involved in many of the different cellular responses to oxidative stress, is active within healthy cells and cells under low levels of oxidative stress, driven by p53 (Chen et al. 2012). Under greater levels of oxidative stress however, p53 promotes cell-death by stopping the upregulation of Nrf2 and instead induces pro-oxidant genes that then lead to cell death or apoptosis. Cells with mutant p53 do not undergo cell-death in response to oxidative stress, accumulating mutations within their genome caused by oxidative DNA damage (Chen et al. 2012).

2.4.4 Genetic damage resulting from oxidative stress A number of compounds have been shown to exhibit an oxidative, genotoxic mode of action. Examples include hydrogen peroxide, para-acetylaminophenol (paracetamol) and methyl viologen (Paraquat) (Das et 33 al. 2010; Gerson et al. 1985; Copple et al. 2008; Speit et al. 1998). UVA, UVB and X-ray radiation also cause oxidative stress in cells. Leading to DNA damage. Oxidative mechanisms of genotoxicity are diverse. They include the oxidation of nucleotides and nucleosides to a variety of oxidised forms. 8-OG (Figure 2:5) is the most abundant oxidised base (Halliwell & Aruoma 1991). These damaged bases can cause errors in replication or impedance of the replicative machinery. Replication errors often include single nucleotide polymorphisms, 8-oxo-dG, though an oxidised version of deoxyguanosine, can itself be substituted for any of the other three bases (Culp & Cho 1989).

2.4.5 Quantifying oxidative stress within cells Various means can be taken to quantify the levels of oxidative stress being experienced by cells in vitro. The level of ROS within cells can be quantified by a variety of intracellular dyes that undergo colourimetric or fluorimetric changes when oxidised by ROS. The most widely used of these dyes is dichlorofluorescin diacetate (DCFH-DA) although others exist. Some dyes such as hydroethidine become their fluorescent form only when oxidised by a specific ROS, in the case of hydroethidine, superoxide. DCFH-DA enters freely through the cell membrane before being hydrolysed within the cell preventing the dye from exiting the cell. This dichlorofluorescin dye can then be oxidised by hydroperoxides and organic peroxides to form the fluorescent dichlorofluorescein (Tsuchiya et al. 1994; Cathcart et al. 1983). This method is not without its limitations, the specificity of DCFH-DA and its tendency for autoxidation when used alongside horseradish peroxidase are both factors that must be borne in mind when using the dye (Bonini et al. 2006; Rota et al. 1999). Nonetheless, DCFH-DA remains amongst the most popular dyes for measuring in vitro ROS generation.

Oxidative stress can also be quantified by means of evaluating the cells’ response to oxidative stress. By quantifying the upregulation of oxidative stress-induced Nrf2, an indication of the level of oxidative stress being experienced by the cell can be measured (Schoonen et al. 2009).

Oxidative stress can also be measured by the endpoint of oxidative stress, oxidised proteins, oxidised lipids and most pertinent to the work presented here, oxidised DNA. Experiments to quantify oxidised DNA bases through different methods produce dramatically different results (Collins et al. 1997). in vivo oxidative DNA damage is most conveniently estimated by analysis of oxidised bases excreted in urine (Svoboda et al. 2006). While the results largely manage to correlate with the level of oxidative stress it is seen as being far too complex by some (Collins et al. 1997). Results in vitro have been seen to show around 1000 times higher levels of 8-OG. This could be due to the potential difference in oxidative stress levels brought about through in vitro tests being carried out in atmospheric oxygen concentrations.

Figure 2:5 Structure of oxidised base 8-OG The oxidised region is highlit.

34 2.5 Antioxidants

Antioxidants are chemicals capable of reducing or slowing the oxidative reactions and/or reactions involving dioxygen and ROS. They act through a wide variety of different pathways and are both found naturally and manufactured industrially. Antioxidants generated endogenously within organisms serve to protect cells from oxidative damage caused by endogenously produced ROS detailed in Section 2.4.2. Antioxidants, both naturally occurring and industrially manufactured are added to food, rubber, plastics, cosmetics and many more products to prevent spoilage or damage caused by oxidation. Antioxidants are also consumed within the diet of humans and these antioxidants such as vitamin C serve important purposes. Amongst these, it is thought that their antioxidant activity may serve to protect humans from a variety of diseases including cancer and heart disease. Those chemicals that act as endogenous or dietary antioxidants can be compounds capable of reacting directly with oxidant chemicals or precursors to such chemicals or by removing oxidative damage from a molecule (Halliwell & Gutteridge 2007).

Antioxidants within food currently hold a great cachet with various foods including blueberries, green tea and acai marketed as having a variety of health benefits (Gutteridge & Halliwell 2010). Various studies have linked ROS with aging, cardiovascular disease and cancer (Roy & Saha 2002; Leonard et al. 2004; Kukreja & Hess 1992). Dietary antioxidants have been linked with combating these diseases (Damianaki et al. 2000; Bustamante et al. 1998). Epidemiological studies have supported these observations, correlating the consumption of PPAs with a reduction in cancer and cardiovascular mortality (C.-L. Sun et al. 2006; Kuriyama 2008; Ames et al. 1993; Hollman et al. 1996) . More recently, attempts to replicate the longevity- promoting and anti-cancer effects using antioxidant supplementation within a clinical setting have produced far less promising results (Bjelakovic et al. 2013). It is likely that the dietary benefits of eating antioxidant-rich food is not due entirely to the antioxidant character of compounds within (Gutteridge & Halliwell 2010).

Previous attempts to rank different antioxidants led to a variety of methods being developed, each producing different results. The value of measuring and comparing the antioxidant capacity has fallen out of favour leading the US department of agriculture to drop the comparison of antioxidants using the Oral Radical Absorbance Capacity in 2010 due to “the values indicating antioxidant capacity have no relevance to the effects of specific bioactive compounds, including polyphenols on human health” (Haytowitz & Bhagwat 2012).

Phenolic antioxidants, found in fruits and vegetables, have also been seen to paradoxically generate ROS in cells in vitro. Studies from the Halliwell lab have shown that various phenolic antioxidants are able to generate ROS in cultured cells (Long et al. 2000; Long & Halliwell 2001; Aruoma & Murcia 1993; Halliwell 2008). Furthermore polyphenol epigallocatechin gallate (EGCG) was seen to be clastogenic and pro-oxidant in cultured Chinese hamster cells (Long et al. 2007). The level to which the compound produced hydrogen peroxide was seen to vary depending upon the choice of cell media. Iron-rich, Dulbecco’s modified eagle medium (DMEM) produced the greatest amount of hydrogen peroxide. DMEM’s high iron content in the form of ferric nitrate was linked with the high peroxide generation. This likely occurred through a Fenton reaction (Perron et al. 2008). An in depth assessment of the genotoxicity of EGCG found the compound to

35 produce negative results for bacterial mutagenicity and in vivo micronucleus in mouse bone marrow cells. EGCG was seen to cause clastogenicity in vitro in mouse lymphoma cells (Isbrucker et al. 2006).

Scientific opinion is shifting to recognise that where antioxidants were once recognised as good and oxidants as bad, a larger and more multifarious image is coming into focus. Antioxidants, n-acetyl cysteine and vitamin E have been observed to accelerate the progression of lung cancer in mice reducing their survival (Sayin et al. 2014). Nineteen antioxidant compounds were assessed within this thesis. They are detailed in Section 4 alongside their structure, use and existing genotoxicity and carcinogenicity assessment.

2.6 Assessments of cytotoxicity

Within in vitro assessments of genotoxicity, it is also necessary to provide some measure of the cytotoxicity being experienced by cells within the assay. By doing so, a clearer understanding can be made of results provided by any cell-based assay. This is due to the differing results that cells that are dead or committed towards an endpoint of cell-death may provide. Though more methods exist than can be detailed within the scope of this introduction, three methods shall be detailed below along with their advantages and limitations.

2.6.1 Optical measurement of relative cell density The cell density of a treated sample relative to that of a vehicle control can be used as a measurement of the cytotoxicity experienced by cells. Toxic compounds will cause cells to die or to slow or delay their cell cycle during treatment leading to a reduced cell number at the end of the treatment, provided that the measurement is made after sufficient time for viable cells to complete a full cell division cycle. This can be measured optically by transilluminating a sample and measuring the level of absorbance of a treated cell sample, subtracting the absorbance of a cell-free sample (to remove the factor of compound or media light absorbance) and dividing this figure by the same figure calculated for a vehicle control treated cell. Furthermore an observed underestimation of cytotoxicity by the optical measurement of relative suspension growth of TK6 cells has been noted (Tate et al. 2009).

2.6.2 Dye exclusion Dyes such as propidium iodide (PI) and trypan blue can enter cells with permeabilised or depolarised membranes. Cell membranes become depolarised and permeabilised during the process of cell-death and so the level to which cells become stained by the dye can act as an indicator of cell health. Trypan blue is visible within permeabilised cells as a dark blue stain whereas PI fluoresces orange-red light following excitation with blue light. This allows PI to be quantified using flow cytometry (Altman et al. 1999). PI is a versatile dye that does not require cells to have undergone division to allow the measurement of cell viability. The dye also allows cells that are committing to cell-death but not yet dead to be identified as through flow cytometry, partially stained cells can be identified.

2.6.3 Detection of mitochondrial membrane potential Mitochondrial membrane potential indicators such as JC-1 provide an indication of reduced electronegativity within mitochondria. Depolarisation of mitochondrial membranes can provide an early indication of a cell’s commitment to a cell-death endpoint (Smiley et al. 1991; Reers et al. 1991). 36 2.7 Aims and objectives

The aim of this investigation was to try and understand the underlying mechanism behind the prevalence of positive results for in vitro genotoxicity for phenolic antioxidant compounds. This research focussed on those compounds with negative results for carcinogenicity and/or no epidemiological link between their consumption and the incidence of cancer. By better understanding the mechanism responsible for these results, steps can be taken toward rationally highlighting compounds that may be producing positive results through a physiologically irrelevant mode of action. The potential of these compounds to cause genetic damage in vivo could then be further investigated.

These aims were achieved through the following objectives:

I. Selecting a diverse set of compounds including antioxidants of various phenolic moieties; polyphenolic, monophenolic and non-phenolic as well as a variety of compounds capable of inducing genotoxic, oxidative, apoptotic, mitotoxic and cytotoxic stress upon cells II. Assessing the genotoxic potential in vitro the selected compound set using the GADD45a-GFP genotoxicity assay III. Assessing the reasoning that may or may not support the results of the genotoxicity assessment through means of an in silico assessment using rule-based, expert in silico toxicology tool, Derek Nexus™ IV. Investigating the influence of external oxygen concentration during genotoxicity assessment of the set of compounds V. Quantifying the relative levels of intracellular reactive oxygen species that are generated by the selected compounds VI. Investigating the in vitro toxicity of these species, using three intracellular dyes, DCFH-DA, PI and JC-1 VII. Assessing the extent to which the chosen chemicals affect the amount of oxidative DNA damage generated with cells

37 3 Materials & Methods 3.1 Cell lines and culture

Three different strains of the TK6 lymphoblastoid cell line were used in this investigation. TK6 cells were obtained from the European collection of cell cultures, they are referred to in this work as TK6 cells to differentiate them from the other 2 strains. These cells were used in all assessments within this thesis with the exception of GADD45a-GFP assays. Reporter cell line GenM-T01 and the control cell line GenM-C01 described by Hastwell et al. (2006) (provided by Gentronix Ltd, Alderley Park, UK) were used in GADD45a- GFP studies within this investigation and are herein referred to as GenM-C01 and GenM-T01. The “test” cell line GenM-T01 is a stably transfected cell line containing a GADD45a-GFP plasmid that expresses increasing levels of GFP as GADD45a becomes up-regulated. The “control” cell line contains the same reporter construct but with the GFP out of frame leaving cells unable to produce GFP. Table 3:1 provides details the contents of cell culture medium used for the routine passaging of all cells in this investigation. Unless specifically mentioned, all RPMI used in this investigation was prepared in this manner.

Reagent Source Stock Final Volume (ml) Concentration Concentration RPMI 1640 + Lonza, Switzerland 500 Glutamax & 25 mM HEPES Heat-inactivated Life Technologies, 100% 10% 57 horse serum CA, USA Sodium pyruvate Life Technologies, 100 mM 1.8 mM 10.4 CA, USA Penicillin / Life Technologies, 5000 µg/ml 50 µg/ml 5.8 Streptomycin CA, USA Hygromycin B* PAA Laboratories 50 mg/ml 200 µg/ml 2.3 GmbH, Austria Table 3:1 Routine cell culture medium for TK6, GenM-C01 and GenM-T01 cell lines. *Only added to GenM-C01 and GenM-T01 cell culture medium.

3.1.1 Cell counting and passage All routine cell counting carried out during the passage of cells in this investigation was carried out using a Coulter Z1™ Particle Counter® (Beckman Coulter, Pasadena CA, USA) herein referred to as a Coulter counter. The device counts cells by measurement of electrical impedance as cells pass through a narrow aperture. The counter was equipped with a 100 µm aperture and calibrated to detect particles of diameter between 9.3 and 19.64 µm. For each cell count (unless otherwise specified), a volume of 0.5 ml of cells was extracted from a cell culture flask and diluted with 9.5 ml of ISOTON® (Beckman Coulter, Pasadena CA, USA). Three technical replicate measurements of the aliquot were made and the mean of the values was used as a measure of cell density.

Cell cultures were maintained at cell densities of between 2 × 105 and 1 × 106 cells/ml via passage between 2 and 4 times per week. Cells were maintained at a volume of 50 or 100 ml, depending on the volume required for experiments. The requisite number of cells were added to a new flask and pre-warmed fresh RPMI media was added to bring the cells to the required volume and concentration for passage (Table 3:2).

Cell cultures and any experiments were incubated in a Galaxy® 170S CO2 (New Brunswick, USA) incubator unless otherwise stated.

38 Passage Duration (Days) Starting cell concentration (cells/ml) 1 5 × 105 2 1.5 × 105 3 5 × 104 4 1.5 × 104 6 Table 3:2 Passage dilutions used to achieve a cell concentration of ~1× 10 cells on the day of assessment

3.1.2 Control of oxygen concentration In GADD45a-GFP expression studies using lowered oxygen concentration, the concentration of oxygen was controlled within a Galaxy 170R CO2 (New Brunswick, USA) incubator with O2 control by displacing oxygen using nitrogen. The increased concentration of nitrogen does not affect cells at normal atmospheric pressure. The concentration of oxygen was monitored in the incubator using an electrochemical oxygen sensor, calibrated by referencing the sensor to atmospheric oxygen concentration. The composition of the air during incubation in different oxygen concentration conditions are detailed below in Table 3:3.

Incubator: Air Atmospheric O₂ Incubator: 5% O₂ Incubator: 1% O₂ concentration Oxygen 20.9 19.9 5.00 1.00 Nitrogen 78.1 74.2 89.8 94.0 Carbon dioxide 0.0397 5.00 5.00 5.00 Other 0.937 0.890 0.223 0.0450

Table 3:3 Composition of air in CO2 incubators with differing concentrations of oxygen compared to atmospheric air.

3.2 Chemicals used in these studies

A diverse set of antioxidants were chosen from the countless millions of antioxidant chemicals that exist based upon a broad set of criteria to provide as broad a spectrum of insight as possible. The list of compounds needed to include polyphenolic, monophenolic and non-phenolic antioxidants to provide insight into the importance of the phenolic moiety in the generation of positive genotoxicity results in vitro. Chemicals that are found in food and generally recognised as safe needed to be included so that further evidence for misleading positive results could be investigated. In addition, chemicals that are not regarded as safe were chosen to provide evidence if any exists for the difference between legitimately genotoxic antioxidants and those with misleading positive results. Well studied antioxidants were chosen where possible, however when seeking structurally diverse antioxidants, this is not always possible. Further detail regarding the reasons for choosing the antioxidant test compounds can be found in Sections 4.2-4.4.

The chemicals assessed as part of this study were chosen following a specific methodology. Positive controls were chosen for genotoxic endpoints. To provide the most valuable insight, four compounds with diverse genotoxic mechanisms were chosen. These would need to include a clastogen, an aneugen, an adduct- forming mutagen and a nucleobase analogue mutagen. Two further genotoxins were chosen to add further insight to results generated by test compounds in this thesis. The reasoning underlying the choice of these compounds can be found in Section 4.6.

39 To provide insight into any oxidative stress that may cause certain results for test chemicals, three oxidative compounds were chosen these three chemicals had to include both direct acting oxidative agents and ROS- mediated oxidative agents.

Listed in Table 3:4 are the compounds tested as part of this investigation. All compounds were sourced at the highest available purity. These compounds were dissolved in 1% dimethyl sulfoxide (DMSO) (Sigma- Aldrich catalogue number 154938) within the assay medium of the assay in question.

3.2.1 Choice of top dose for assessment of compounds This investigation aims to investigate potential reasons for the generation of misleading positive results for genotoxicity generated by phenolic antioxidants. There is concern that testing the genotoxic potential of compounds at doses between 1 and 10 mM contributes to the generation of misleading positive results (Section 2.3). With this in mind, any positive result for a phenolic antioxidant at a dose between 1 and 10 mM would almost certainly be attributed to excessive dose. With this in mind compounds were not tested to doses above 1 mM. Compounds were tested to a top concentration of 1 mM or 0.5 mg/ml (whichever is lower), highest soluble concentration or the limit of cytotoxicity.

3.3 The GADD45a-GFP (‘GreenScreen HC’) genotoxicity assay

3.3.1 Setup of the GADD45a-GFP genotoxicity assay As detailed below, the GADD45a-GFP assay in the absence of S9 metabolic activation was set up following the protocol as detailed by Hastwell et al. (2006). The GADD45a-GFP assay in the presence of S9 metabolic activation was set up following the protocol detailed by Jagger et al. (2009).

3.3.1.1 Assay medium preparation The medium used in the GADD45a-GFP assay (GS-AM) is a proprietary, low-fluorescence medium produced and supplied by Gentronix Ltd. (Alderley Park, UK). It was stored refrigerated at 4°C, and heated to 37°C in a water bath prior to its use in the assay. The medium is supplied at 2x concentration and was diluted 1:1 on the microplate with 2% (v/v) aqueous DMSO.

3.3.1.2 Assay diluent preparation 49 ml of deionised water were added to 1 ml of 100% DMSO to create a solution of 2% (v/v) DMSO for use as diluent in the assay.

3.3.1.3 Compound preparation Compounds were prepared to a concentration twofold higher than their testing concentration and diluted 1:1 with cell suspension on the microplate during the microplate setup.

Solid test compounds were prepared on the day of their assessment and dissolved in 100% DMSO at a concentration 100 times higher than their final test concentration. These solutions were further diluted fifty-fold with deionised water to achieve a concentration of twofold their final test concentration in a 2% DMSO.

Compounds insoluble in DMSO were dissolved in water before adding DMSO to a concentration of 2%.

40 Liquid test compounds or those already in solution were diluted to achieve a concentration of twofold the final test concentration and 2% DMSO.

In the S9 variant of the assay, compounds were prepared at 2.5 fold the final test concentration to compensate for the increased dilution by “S9 mix” (Section 3.3.1.7).

Compound Name CAS Number Supplier 2-Acetylaminofluorene 53-96-3 Sigma-Aldrich n-Acetylcysteine 616-91-1 Sigma-Aldrich 6-Aminochrysene 2642-98-0 Sigma-Aldrich Apomorphine 41372-20-7 Sigma-Aldrich L-Ascorbic acid 50-81-7 Sigma-Aldrich Bleomycin 11056-06-7 Sigma-Aldrich 2-tert-Butyl-1,4-benzoquinone 3602-55-9 Sigma-Aldrich tert-Butyl hydroperoxide 75-91-2 Sigma-Aldrich Butylated hydroxyanisole 25013-16-5 Sigma-Aldrich Butylated hydroxytoluene 128-37-0 Sigma-Aldrich tert-Butylhydroquinone 1948-33-0 Sigma-Aldrich Carbonyl cyanide m-chlorophenyl hydrazone 555-60-2 Sigma-Aldrich Cyclophosphamide 50-18-0 Sigma-Aldrich 2,6-Di-tert-butyl-4-hydroxymethylphenol 88-26-6 Sigma-Aldrich 2,4-Dichlorophenol 120-83-2 Sigma-Aldrich Dodecyl gallate 1166-52-5 Sigma-Aldrich Epigallocatechin gallate 989-51-5 Cayman Chemicals Ethoxyquin 91-53-2 Sigma-Aldrich Etoposide 33419-42-0 Sigma-Aldrich 5-Fluorouracil 51-21-8 Sigma-Aldrich Hydrogen peroxide 7722-84-1 Sigma-Aldrich Methyl methanesulfonate 66-27-3 Sigma-Aldrich 4-Nitroquinoline oxide 56-57-5 Sigma-Aldrich Nordihydroguaiaretic acid 500-38-9 Sigma-Aldrich Octyl gallate 1034-01-1 Sigma-Aldrich Phenformin hydrochloride 834-28-6 Sigma-Aldrich Phenol 100-95-2 Sigma-Aldrich Potassium bromate 7758-01-2 Sigma-Aldrich Propyl gallate 121-79-9 Sigma-Aldrich Pyrogallol 87-66-1 Sigma-Aldrich Quercetin 117-39-5 Sigma-Aldrich Resorcinol 108-46-3 Sigma-Aldrich γ-Resorcylic acid 303-07-1 Sigma-Aldrich Resveratrol 501-36-0 Sigma-Aldrich Staurosporine 62996-74-1 Sigma-Aldrich Vanillic acid 121-34-6 Sigma-Aldrich Vincristine sulfate 2068-78-2 Sigma-Aldrich Table 3:4 Compounds assessed as part of this investigation

41 3.3.1.4 Preparation of positive control for genotoxicity Methyl methanesulfonate (MMS) was used as a positive control for genotoxicity and cytotoxicity in the GADD45a-GFP assay in the absence of S9. Two concentrations were used to demonstrate a dose-dependent genotoxic and cytotoxic effect, MMS high (452 µM) and MMS low (90.5 µM). They were prepared on the day of assessment at twofold their final test concentrations in 2% DMSO.

2 × MMS High

- 5 µl 100% MMS were added to 1.3 ml 2% DMSO = 45.2 mM MMS - 50 µl [45.2 mM MMS] were added to 2.45 ml 2% DMSO = 905 µM

2 × MMS Low

- 400 µl [twofold MMS high] were added to 1.6 ml 2% DMSO = 181 µM

Cyclophosphamide (CPA) was used as a positive control for genotoxicity and cytotoxicity in the GADD45a- GFP assay in the presence of S9. Two concentrations were used to demonstrate a dose-dependent genotoxic and cytotoxic effect, CPA high (95.8 µM) and CPA low (19.2 µM).

Both concentrations of the positive control were prepared on the day of assessment at 2.5-fold their test concentrations in 2% DMSO.

CPA High

- 10 mg of CPA were added to 1 ml 100% DMSO = 38.3 mM CPA - 100 µl [38.3 mM CPA] were added to 900 µl 2% DMSO = 3.83 mM CPA - 125 µl [3.83 mM CPA] were added to 1875 µl 2% DMSO = 239 µM (2.5-fold CPA high)

CPA Low

- 25 µl [2.5-fold CPA high] were added to 1975 µl 2% DMSO = 47.9 µM (2.5-fold CPA low)

3.3.1.5 Propidium iodide preparation for use in the estimation of cytotoxicity in the flow-cytometry measured GADD45a-GFP assay A solution of 10 µg/ml propidium iodide (PI, Sigma-Aldrich, Gillingham, UK) was prepared, in PBS and filter- sterilised into aliquots of 50 ml. 60 µl of the solution were added to each well of the microplate leaving a final concentration of 2.86 µg/ml.

PI enters the nucleus of cells with compromised cell membranes and intercalates with DNA. The fluorescent properties of PI then allow cells to be differentiated depending on the integrity of their cell membranes giving an indication of cytotoxicity. Cells with little or no PI staining were considered viable while those more permeable cells that exhibit more staining were considered dead.

3.3.1.6 GADD45a-GFP assay microplate setup for testing in the absence of S9 The prepared test and control compound solutions were added to a 96-well, clear, flat-bottomed, black walled microplates (Matrix, NH, USA [Cat. no. 4928]). Nine serial twofold dilutions of 4 test compounds were assessed on each plate. The plate layout is shown in Figure 3:1. The protocol, adapted from the GreenScreen HC protocol version 3.3 (Gentronix Ltd, Alderley Park, UK) is detailed below.

42 Prior to the assay setup, GS-AM and PBS were both added to a 37°C water bath. 2% DMSO was then added as follows:

o 75 µl to all wells in columns 2-10 o 75 µl to wells C12 and D12 o 150 µl to wells A12 and B12

Twofold solutions of test compound A were added:

o 75 µl to well E12 to serve as an optical control to detect compound autofluorescence or light absorbance that may impede accurate detection of GFP fluorescence o 150 µl to wells A1 and E1

Twofold solutions of test compound B were added:

o 75 µl to well F12 as an optical control o 150 µl to wells B1 and F1

Twofold solutions of test compound C were added:

o 75 µl to well G12 as an optical control o 150 µl to wells C1 and G1

Finally, twofold solutions of test compound D were added:

o 75 µl to well H12 as an optical control o 150 µl to wells D1 and H1

The compounds were serially diluted twofold by drawing 75 µl of fluid from each well in column 1, then adding and mixing to the wells of column 2. This was repeated 9 times across the plate before discarding 75 µl from the wells of column 9. 75 µl of MMS low were added to wells A11, B11, E11 and F11 to serve as positive controls for genotoxicity and cytotoxicity. 75 µl of MMS high were added to wells C11, D11, G11 and H11 to serve as positive controls for genotoxicity and cytotoxicity. 75 µl of GS-AM were added to wells C12, D12, E12, F12, G12 & H12. Cell cultures were taken from the incubator and re-suspended by aspiration with a 25 ml serological pipette. Cell densities for GenM-C01 and GenM-T01 cells were then estimated using a Coulter Counter® as detailed in Section 3.1.1. The following equation was used to provide the volume of cells needed to prepare a suspension of 2 × 106 cells/ml:

(2 × 106) × (푁 × 5) 푉 = C

o V defines the volume of cells needed to be taken from the cell culture flask o N defines the number of assay plates being prepared o C defines the concentration in cells/ml of the cells in the cell culture flask

If the assay microplate was being measured by microplate spectrophotometer; The assay microplate was shaken on a flat-bed orbital microplate shaker for 30 seconds, readings for absorbance and fluorescence were taken (see Section 3.3.2) and assay microplates were resealed using a new breathable membrane and incubated at 37°C, 5% CO2 in a humidified atmosphere for a further 24 hours. If the assay microplate was being measured by flow-cytometry, the microplate was left, undisturbed in the incubator for a further 24 hours. The assay microplate was shaken on a flat-bed orbital microplate shaker for 30 seconds before a second set of readings for absorbance and fluorescence were taken.

Figure 3:1 Illustration of the assay plate layout for the GADD45a-GFP assay

3.3.1.7 GADD45a-GFP assay microplate setup for testing in the presence of S9 The prepared test and control compound solutions were added to a 96-well clear, U-bottomed microplates (Matrix, NH, USA [Cat. no. 4911]). Nine serial 2.5-fold dilutions of 4 test compounds were assessed on each plate. The plate layout is shown in Figure 3:2. The protocol, adapted from the GreenScreen HC S9 protocol version 2.2 (Gentronix Ltd, Alderley Park, UK) is detailed below.

Before beginning the assay setup, a vial of S9 from Aroclor 1254-induced male SD rat liver in 0.15 M KCl (cat no. 11-101, MolTox, Inc., US) was placed on ice to defrost. GS-AM diluted 1:1 with deionised water and PBS were both added to a 37°C water bath.

44 2% DMSO diluent was added to the microplate as follows:

o 60 µl to wells in columns 2 to 10 o 75 µl to wells A12, B12, E12 & F12 o 15 µl to wells C12, D12, G12 & H12

120 µl of a 2.5-fold solution of test compound A were added to wells A1 and E1. 120 µl of a 2.5-fold solution of test compound B were added to wells B1 and F1. 120 µl of a 2.5-fold solution of test compound C were added to wells C1 and G1. 120 µl of a 2.5-fold solution of test compound D were added to wells D1 and H1.

The compounds were serially diluted twofold by drawing 60 µl of fluid from each well in column 1, then adding and mixing to the wells of column 2. This was repeated 9 times across the plate before discarding 60 µl from the wells of column 9. 60 µl of CPA low were added to wells A11, B11, E11 and F11 to serve as positive controls for genotoxicity and cytotoxicity. 60 µl of CPA high were added to wells C11, D11, G11 and H11 to serve as positive controls for genotoxicity and cytotoxicity. 60 µl of CPA high were added to wells C12, D12, G12 and H12 to serve as negative controls for genotoxicity and cytotoxicity in the absence of S9. 200 µl of the thawed S9 was added to 1.8 ml of S9 cofactor mix (Gentronix Ltd, Alderley Park, UK). 15 µl of S9 mix was added to all wells in columns 1 to 11. Cell cultures were taken from the incubator and re- suspended by aspiration with a 25 ml serological pipette. Cell densities for GenM-C01 and GenM-T01 cells were then estimated using a Coulter Counter® as detailed in Section 3.1.1. The following equation was used to provide the volume of cells needed to prepare a suspension of 2 × 106 cells/ml:

(2 × 106) × (푁 × 5) 푉 = C

The two volumes of cells were added to 50 ml centrifuge tubes and spun at 340 × g for 5 minutes. The supernatant was discarded from each tube and the pellet re-suspended in 10 ml of pre-warmed PBS. The tubes were then spun once more at 340 × g for 5 minutes. The supernatant was discarded from each tube and the pellet re-suspended in the required volume of pre-warmed GS-AM (N × 5 ml). 75 µl of the prepared suspension of GenM-C01 cells were added to all wells in rows A to D and columns 1 to 12. 75 µl of the prepared suspension of GenM-T01 cells were added to all wells in rows E to H and columns 1 to 12. The assay microplate was then covered with a breathable membrane and shaken on a flat-bed orbital microplate shaker for 30 seconds. The assay microplate was then incubated at 37°C, 5% CO2 in a humidified atmosphere for 3 hours. Assay plates were spun at 340 × g for 5 minutes before 120 µl of supernatant were removed and discarded from each well. 120 µl of PBS were added to each well. The assay microplate was then covered with a breathable membrane and shaken on a flat-bed orbital microplate shaker for 30 seconds. 120 µl of the 1:1 diluted GS-AM were added to each well. The assay microplate was then covered with a breathable membrane and shaken on a flat-bed orbital microplate shaker for 30 seconds. The assay microplate was then incubated at 37°C, 5% CO2 in a humidified atmosphere for 45 hours. The breathable membrane was removed and 60 µl of PI were added to each well. Flow-cytometric readings for GFP and PI fluorescence were then taken (see Section 3.3.2.3). 45

Figure 3:2 Illustration of the assay plate layout for the GADD45a-GFP assay with S9

3.3.2 Data collection of the GADD45a-GFP genotoxicity assay Fluorescence and absorbance data from the GADD45a-GFP genotoxicity assay (minus S9) were collected using a spectrophotometric plate reader (Ultra 384, Tecan, Switzerland), following the protocol detailed by Hastwell et al. (2006). Where compounds autofluorescence interfered with the detection of GFP fluorescence, measurements were carried out using the fluorescence light polarisation method detailed by Knight (2002): GFP is a very large (32.7 kD protein) molecule and emits polarised light when illuminated by polarised laser light, in contrast to the non-polarised fluorescence emitted by the generally much smaller test compounds. Light absorbing compounds interfere with the measurement of cell density, and where this was identified (in wells only containing test compound), Data were collected and processed using the fluorescence light polarisation method (Section 3.3.2.2). For reasons explained in Section 5.2, data from all subsequent GADD45a-GFP assessment and within Section 7 were collected using the flow cytometric methods (Section 3.3.2.3).

3.3.2.1 Spectrophotometric data collection in the GADD45a-GFP genotoxicity assay in the absence of S9 After the 48 hour exposure time, the microplate was shaken on a flat-bed orbital microplate shaker for 30 seconds to re-suspend cells. The sealing membrane was then removed and the plate placed in a Tecan Ultra-384 multimode microplate spectrophotometer (Tecan, Switzerland). Optical absorbance was measured by illuminating each well with light at a wavelength of 620 nm, 4 times in a circular 2 × 2 pattern. Using a standard fluorescein isothiocyanate [FITC] filter pair. Fluorescence data were collected using excitation with light filtered to a wavelength of 485 nm 4 times in the same circular pattern used to measure absorbance. Fluorescent light of a wavelength of 535 nm was measured for each excitation. The instrument optimised gain individually for each microplate tested using the fluorescence of the MMS high,

46 positive control well (G11) as a reference. Data were collected into a Microsoft Excel spreadsheet and were processed as detailed in Section 3.4.4.1.

3.3.2.2 Fluorescence light polarised, spectrophotometric measurement of the GADD45a-GFP genotoxicity assays for autofluorescent compounds When assessing autofluorescent test compounds, the compound fluorescence can interfere with the measurement of GFP brightness. To discriminate between GFP fluorescence and autofluorescent test compounds, an assessment of polarised fluorescence was made. The same parameters used for spectrophotometric assessment of optical absorbance were used detailed in Section 3.3.2.1. In addition to this, a measurement of parallel and perpendicular fluorescent light was made for each well. The parameters of this measurement were the same as those detailed in Section 3.3.2.1. These data were processed as is detailed in Section 3.4.4.1.

3.3.2.3 Flow cytometric data collection for highly autofluorescent compounds, and compounds tested in the presence of S9 and where specified The sealing membrane was removed from the microplate. PI was added to the plate as detailed in Section 3.3.1.5. The microplate was then placed into the high throughput sampler of a FACSCalibur flow cytometer (Becton Dickinson, San Jose, USA). Samples were extracted individually from each well and 10,000 events were recorded per sample. Cells were illuminated using a low-power air-cooled 15 mW blue (488nm) argon laser and fluorescence was detected by two fluorescence channels, FL-1 and FL-3. FL-1 detects GFP fluorescence that passes through a bandpass filter with a 30nm range centred at 530 nm. FL-3 detects PI fluorescence that passes through a 670 nm longpass filter. Data acquisition and analysis were carried out concurrently using CellQuest Pro software (Becton Dickinson, San Jose, USA). The same acquisition-analysis template was used for all microplates. This template carried out the following steps during acquisition and analysis.

- The template produces a bivariate scatter plot of the forward scatter height (FSC-H) against the side scatter height (SSC-H) of each event from a well. This chart differentiates events by their cell morphology. Within this chart a region of low FSC-H and low SSC-H was defined as debris and excluded from further analysis - The template produces a bivariate scatter plot of FL3 height (FL3-H) against SSC-H for all events defined as [NOT debris]. This chart differentiates cells by the level of PI staining. Those cells with compromised membranes were highly stained and were defined as dead cells. Those events defined as dead cells were used in the calculation of relative population survival (RPS) but not in the measurement of GFP-fluorescence. - The template produces a bivariate scatter plot of FL1 height (FL1-H) against SSC-H for all events defined as [NOT dead cells]. This plot differentiates cells by the level of GFP fluorescence. An arithmetic mean of the value from the FL1 channel of events within this chart was used in the data processing template as a measure of the cells’ response to genotoxicity. - Data from wells that produced less than 10,000 events that were [NOT dead cells] were excluded from further data analysis

47 Data were generated for each well containing cells. In the presence of S9, all 96 wells contain cells. However in microplates assessing genotoxicity in the absence of S9, wells in column 12 were cell-free controls acting as optical controls when measuring assay plates by spectrophotometry, and these were omitted from flow cytometry acquisition.

These data were output as a comma-separated values (.csv) file and processed as detailed in Section 3.4.4.3 for assays carried out with S9 and Section 3.4.4.4 for assays carried out in the absence of S9.

3.4 Population doubling assessment

Two methods of quantifying population doubling are presented in this thesis. The first was carried out in 24-well plates to provide a sufficient of cells to quantify their cell density and viability. Due to observed differences in population doubling within 24 and 96-well plates shown in Section 7.2.2. A second protocol, using 96 well plates was developed. The assessment was used to quantify the difference in population doubling of cells incubated in the presence of 20%, 5% and 1% oxygen.

3.4.1 24-well plate population doubling assessment For each assessment plate, GenM-C01 were counted as detailed in Section 3.1.1. Volumes containing 8 × 106, 4 × 106, 2 × 106 and 1 × 106 cells were then put in centrifuge tubes pre-warmed RPMI added to each tube to make a volume of 8 ml. Cells were pelleted by centrifugation (300 × g for 5 minutes). Cells were re- suspended in 8 ml of pre-warmed RPMI. Tubes contained 1 × 106 cells/ml, 500,000 cells/ml, 250,000 cells/ml and 125,000 cells/ml respectively. Cells were added to a 24-well clear, flat bottomed plate (Corning, NY, USA [Cat. no. 3526]) such that each well in row A contained 500 µl of 1 × 106 cells/ml, each well in row B contained 500 µl of 500,000 cells/ml etc. A breathable membrane was added and the plate was placed in a CO2 incubator (Galaxy® 170S 5% CO2 unmodulated oxygen/ Galaxy® 170R 5% CO2 5% O2/

Galaxy® 170R 5% CO2 1% O2). Cells in the remaining volume of each of the centrifuge tube were counted (Section 3.1.1) this provided a 0-hour cell density measurement. 400 µl from the volume remaining in each of the centrifuge tubes were added to a flow cytometer tube. This was assessed for cytotoxicity using the GADD45a-GFP template (Section 3.3.2.3). After 24 hours the plate was removed from the incubator and the breathable membrane removed and discarded. 400 µl of cells were extracted from each well in column 1 and added to 7.6 ml of ISOTON® (Beckman Coulter, Pasadena CA, USA). Cells were then counted (Section 3.1.1). 400 µl of cells were extracted from each well in column 2 and added to a flow cytometer tube. This was assessed for cytotoxicity using the GADD45a-GFP template (Section 3.3.2.3). A new breathable membrane was added and the plate was placed back in the CO2 incubator. The previous 4 steps were repeated after 48 and 72 hours.

3.4.2 96-well plate population doubling assessment For each assessment plate, GenM-C01 were counted as detailed in Section 3.1.1. Volumes containing 5 × 106, 2.5 × 106, 1.25 × 106and 625,000 cells were then put in centrifuge tubes pre-warmed RPMI added to each tube to make a volume of 5 ml. Cells were pelleted by centrifugation (300 × g for 5 minutes). Cells were re-suspended in 5 ml of pre-warmed RPMI. Tubes contained 1 × 106 cells/ml, 500,000 cells/ml, 250,000 cells/ml and 125,000 cells/ml respectively. Cells were added to a clear, 96-well, U-bottomed, microplate (Matrix, NH, USA [Cat. no. 4911]) such that each well in row A and B contained 150 µl of 1 × 106

48 cells/ml, each well in row C and D contained 150 µl of 500,000 cells/ml etc. A breathable membrane was added and the plate was placed in a CO2 incubator (Galaxy® 170S 5% CO2 unmodulated oxygen/ Galaxy®

170R 5% CO2 5% O2/ Galaxy® 170R 5% CO2 1% O2). Cells in the remaining volume of each of the centrifuge tube were counted (Section 3.1.1) this provided a 0-hour cell density measurement. 400 µl from the volume remaining in each of the centrifuge tube were added to a flow cytometer tube. This was assessed for cytotoxicity using the GADD45a-GFP template (Section 3.3.2.3). After 24 hours the plate was removed from the incubator and the breathable membrane removed and discarded. 4×100 µl of cells were extracted from 4 wells for each cell seeding density (A1-4/C1-4/E1-4/G1-4) and added to 7.6 ml of ISOTON® (Beckman Coulter, Pasadena CA, USA). Cells were then counted (Section 3.1.1). 4×100 µl of cells were extracted from 4 wells for each cell seeding density (B1-4/D1-4/F1-4/H1-4) and added to a flow cytometer tube. This was assessed for cytotoxicity using the GADD45a-GFP template (Section 3.3.2.3). A new breathable membrane was added and the plate was placed back in the CO2 incubator. The previous 4 steps were repeated after 48 and 72 hours.

3.4.3 Calculation of population doubling In order to calculate the number of times that cells in a population had divided. Population doubling was calculated as follows:

푛 푃퐷 = 퐿표푔2( ) 푛0

Where 푃퐷 is the number of population doublings, 푛 is the cell density in cells/ml and 푛0 is the 0-hour cell density in cells/ml.

3.4.4 Processing of GADD45a-GFP genotoxicity assay data Spectrophotometry data from GADD45a-GFP assays were processed following the method detailed by Hastwell et al. (2006). Fluorescence polarisation data were processed following the method of Knight et al. (2002) before continuing data analysis through the method detailed by Hastwell (2006). Data from GADD45a-GFP assays carried out with S9 and measured by flow cytometry were processed following the method detailed by Jagger et al. (2009). Data processing from GADD45a-GFP assays carried out in the absence of S9, but collected using flow cytometry was carried out following a method adapted from the work of Jagger et al. (2009).

3.4.4.1 Processing of fluorescence polarisation data Before it can be further processed, values for GFP fluorescence must be generated from the fluorescence polarisation data. The anisotropic fluorescence of the GFP is differentiated from isotropic background and autofluorescence by finding the difference between light that was fluoresced parallel to the plane of excitation and light that was fluoresced perpendicular to the plane of excitation. The difference between parallel and perpendicular fluorescence for each well was then transposed into the data processing template detailed in Section 3.4.4.2 in place of the well’s raw fluorescence data.

3.4.4.2 Processing spectrophotometry data from the GADD45a-GFP assay carried out in the absence of S9 The raw absorbance and raw fluorescence (or the difference between light that was fluoresced parallel to the plane of excitation and light that was fluoresced perpendicular to the plane of excitation for microplates

49 that have been measured using fluorescence polarisation) was processed using a Microsoft Excel data processing template provided by Gentronix Ltd (Alderley Park, UK). This template produces figures for relative cell density and GFP induction as detailed in Sections 3.4.4.2.1 and 3.4.4.2.2 respectively. The template also highlighted where data fell outside of acceptability criteria as detailed in Section 3.4.4.2.3.

3.4.4.2.1 Calculating relative cell density The relative cell density in each well was measured by the following calculation:

퐴푏푠표푟푏푎푛푐푒 표푓 푡ℎ푒 푡푒푠푡 푤푒푙푙 − 퐴푏푠표푟푏푎푛푐푒 표푓 푡ℎ푒 푚푒푑𝑖푎 푏푙푎푛푘 푅퐶퐷 = × 100 퐴푏푠표푟푏푎푛푐푒 표푓 푡ℎ푒 푢푛푡푟푒푎푡푒푑 푐표푛푡푟표푙 − 퐴푏푠표푟푏푎푛푐푒 표푓 푡ℎ푒 푚푒푑𝑖푎 푏푙푎푛푘

Media blank defines the mean measurement of cell free wells; C12 and D12

Untreated control defines the mean measurement of the four untreated control wells containing the same cell line as the test well.

3.4.4.2.2 Calculating GFP induction Raw fluorescence figures were normalised to generate a brightness value using the following calculation:

퐹푙푢표푟푒푠푐푒푛푐푒 표푓 푡ℎ푒 푡푒푠푡 푤푒푙푙 − 퐹푙푢표푟푒푠푐푒푛푐푒 표푓 푡ℎ푒 푚푒푑𝑖푎 푏푙푎푛푘 퐵푟𝑖푔ℎ푡푛푒푠푠 = 퐴푏푠표푟푏푎푛푐푒 표푓 푡ℎ푒 푡푒푠푡 푤푒푙푙 − 퐴푏푠표푟푏푎푛푐푒 표푓 푡ℎ푒 푚푒푑𝑖푎 푏푙푎푛푘

The brightness figure for wells containing the GenM-T01 test strain and the brightness of the corresponding well containing GenM-C01 control strain was used to provide a figure for GFP induction using the following calculation:

퐵푟𝑖푔ℎ푡푛푒푠푠 표푓 퐺푒푛푀푇01 푡푒푠푡 푤푒푙푙 − 퐵푟𝑖푔ℎ푡푛푒푠푠 표푓 퐺푒푛푀퐶01 푡푒푠푡 푤푒푙푙 퐺퐹푃 𝑖푛푑푢푐푡𝑖표푛 = 푀푒푎푛 푏푟𝑖푔ℎ푡푛푒푠푠 표푓 퐺푒푛푀푇01 푈퐶 − 푀푒푎푛 푏푟𝑖푔ℎ푡푛푒푠푠 표푓 퐺푒푛푀퐶01 푈퐶

UC defines the untreated control wells.

RCD and GFP induction were calculated for each well containing cells. Untreated control wells were given values of 100% RCD and 1× GFP induction.

3.4.4.2.3 Data acceptability criteria It was necessary that data collected fell within certain acceptability criteria to be considered a valid assessment of genotoxicity. The criteria for assays carried out in the absence of S9 are as follows:

- To assert that the assay had measured cytotoxicity accurately, the RCD in wells treated with MMS high must fall below 80% and below the RCD of wells treated with MMS low - To assert that the assay had measured genotoxicity accurately, the GFP induction in wells treated with MMS high must fall above 2× the untreated control and above the GFP induction of wells treated with MMS low

A failure to achieve either criterion leads to the exclusion of all data collected from the assay microplate. Furthermore, the following criterion needed to be achieved in order for GFP induction data from each individual dose of a test compound to be considered valid:

- The RCD within a well containing either GenM-C01 or GenM-T01 cells must be above 30%

50 This is because as detailed by Hastwell et al. (2006), two factors make the assessment of genotoxicity of samples below 30% RCD of little value. Firstly, variation in the measurement of absorbance using the spectrophotometer interferes with the accurate detection of cell density. Secondly, the cells within a sample of lower than 30% RCD were unlikely to have undergone an entire cell cycle, making detection of certain modes of genotoxic action impossible.

3.4.4.3 Processing flow cytometry data from the GADD45a-GFP assay carried out in the presence of S9 The comma-separated values file output as detailed in Section 3.3.2.3 was opened using Microsoft Excel and the entire content inserted into a Microsoft Excel S9 data processing template provided by Gentronix Ltd (Alderley Park, UK). This processing template carries out the following calculations:

3.4.4.3.1 Relative population survival (RPS) The percentage toxicity of each well on the 96-well microplate was calculated:

푛푢푚푏푒푟 표푓 푒푣푒푛푡푠 “푁푂푇 퐷푒푏푟𝑖푠 퐴푁퐷 푁푂푇 퐷푒푎푑 푐푒푙푙푠” 푃푒푟푐푒푛푡푎푔푒 푡표푥𝑖푐𝑖푡푦 = (1 − ) × 100 푛푢푚푏푒푟 표푓 푒푣푒푛푡푠 “푁푂푇 퐷푒푏푟𝑖푠”

The percentage toxicity for a test well was divided by that of the respective untreated control:

100 − 푃푒푟푐푒푛푡푎푔푒 푇표푥𝑖푐𝑖푡푦 표푓 푡푒푠푡 푤푒푙푙 푅푃푆 = × 100 100 − 푃푒푟푐푒푛푡푎푔푒 푡표푥𝑖푐𝑖푡푦 표푓 푟푒푙푒푣푎푛푡 푢푛푡푟푒푎푡푒푑 푐표푛푡푟표푙

The “Percentage toxicity of relevant untreated control” is a mean value from the 4 wells containing the same cell strain as the test well, exposed solely to diluent providing that each well met the event collection criteria detailed in Section 3.3.2.3.

3.4.4.3.2 GFP induction The brightness of test wells containing GenM-C01 and GenM-T01 cells were normalised to the average fluorescence of GenM-T01 untreated control wells to allow the relative brightness of the two strains to be compared. Brightness was calculated thusly:

퐹푙푢표푟푒푠푐푒푛푐푒 표푓 푡푒푠푡 푤푒푙푙 퐵푟𝑖푔ℎ푡푛푒푠푠 = × 100 퐴푣푒푟푎푔푒 푓푙푢표푟푒푠푐푒푛푐푒 표푓 퐺푒푛푀-푇01 푢푛푡푟푒푎푡푒푑 푐표푛푡푟표푙

The GFP induction values used for the assessment of genotoxicity were calculated by dividing the brightness of wells containing GenM-T01 cells by the brightness of those containing GenM-C01 cells using the following equation:

퐺푒푛푀-푇01 퐺퐹푃 𝑖푛푑푢푐푡𝑖표푛 퐵푟𝑖푔ℎ푡푛푒푠푠 표푓 퐺푒푛푀-푇01 푡푒푠푡 푤푒푙푙 − 퐵푟𝑖푔ℎ푡푛푒푠푠 표푓 푐표푟푟푒푠푝표푛푑𝑖푛푔 퐺푒푛푀-퐶01 푡푒푠푡 푤푒푙푙 = × 100 푀푒푎푛 푏푟𝑖푔ℎ푡푛푒푠푠 표푓 퐺푒푛푀-푇01 푈퐶 − 푀푒푎푛 푏푟𝑖푔ℎ푡푛푒푠푠 표푓 퐺푒푛푀-퐶01 푈퐶

Where UC defines the untreated control wells.

3.4.4.3.3 Data acceptability criteria The data acceptability criteria detailed by Jagger et al. (2009) needed to be met by each assay microplate in order to be considered a valid assessment of genotoxicity.

GADD45a-GFP assay data collected using flow cytometry pass through various acceptance criteria during the flow cytometer’s acquisition-analysis step detailed in Section 3.3.2.3. These criteria exclude irrelevant 51 events such as cell and other debris, and discriminate between dead and living cells. Any wells from which less than 10000 events were measured were also omitted from further analysis, because this indicates insufficient cell proliferation.

The further data acceptability criteria for assays carried out in the presence of S9 were as follows:

- To assert that the assay had measured cytotoxicity accurately, the RPS in wells treated with CPA high must fall below the RPS of wells treated with CPA low - To assert that the assay had measured genotoxicity accurately, the GFP induction in wells treated with CPA high must fall above 1.3× the untreated control and above the GFP induction of wells treated with CPA low

A failure to achieve either criterion was considered grounds to exclude all data measured from the assay microplate. Furthermore, the following criterion needed to be achieved in order for GFP induction data from each individual dose of a test compound to be considered valid:

- The RPS within a well containing either GenM-C01 or GenM-T01 cells must be above 20%

3.4.4.4 Processing flow cytometry data from the GADD45a-GFP assay carried out in the absence of S9 The comma-separated values file output as detailed in Section 3.3.2.3 was opened using Microsoft Excel and the entire content was inserted into a Microsoft Excel data processing template provided by Gentronix Ltd (Alderley Park, UK). The same calculations as detailed in Section 3.4.4.3 were carried out with the difference that brightness and percentage toxicity values were only generated for the wells in columns 1- 11. Wells in column 12 were cell-free controls acting as optical controls when measuring assay plates by spectrophotometry.

3.4.4.4.1 Data acceptability criteria The data acceptability criteria for GADD45a-GFP assay microplates assessing genotoxicity in the absence of S9 and measured by flow-cytometry must meet the following criteria.

GADD45a-GFP assays measured via flow cytometry pass through various data acceptance criteria during the flow cytometer’s acquisition-analysis step detailed in Section 3.3.2.3. These data acceptance criteria omitted events considered to be debris from further assessment and defined events representing dead cells. Any wells from which less than 10000 events were measured were also omitted from further analysis.

The further data acceptability criteria for assays carried out in the absence of S9 were as follows:

- To assert that the assay had measured cytotoxicity accurately, the RPS in wells treated with MMS high must fall below the RPS of wells treated with MMS low - To assert that the assay had measured genotoxicity accurately, the GFP induction in wells treated with MMS high must fall above 1.3× the untreated control and above the GFP induction of wells treated with MMS low

52 - The RPS within a well containing either GenM-C01 or GenM-T01 cells must be above 20%

3.4.5 Decision thresholds for GADD45a-GFP assays To determine if a dose of test compound was to be considered genotoxic or cytotoxic the observed level of cytotoxicity or genotoxicity must pass defined thresholds. These thresholds differ depending on the setup protocol and method of measurement. As defined by Hastwell et al. (2006), these thresholds were derived statistically as follows:

퐺푒푛표푡표푥𝑖푐𝑖푡푦 푡ℎ푟푒푠ℎ표푙푑 > 1 + (3 × 푠퐻푈퐶 푓푙푢표푟푒푠푐푒푛푐푒)

퐶푦푡표푡표푥𝑖푐𝑖푡푦 푡ℎ푟푒푠ℎ표푙푑 < 100% − (3 × 푠퐻푈퐶 푡표푥푖푐푖푡푦 푚푒푎푠푢푟푒푚푒푛푡 )

Where s defines the standard deviation from the mean, HUC defines the historical untreated control data and “toxicity measurement” defines the RPD for assays measured by spectrophotometry and RPS for assays measured by flow cytometry.

For assessment within the protocols defined by Hastwell et al. (2006) and Jagger et al. (2009), the thresholds defined in those respective papers were applied and are as follows:

 When measuring GADD45a-GFP assay microplates using spectrophotometry o Cytotoxicity threshold was 80% RPD o Genotoxicity threshold was 1.5× relative GFP induction  When measuring GADD45a-GFP assay microplates carried out in the presence of S9 using flow cytometry o Cytotoxicity threshold was 90% RPS o Genotoxicity threshold was 1.3× relative GFP induction

For any assessment stated to deviate in any way from the protocols defined in by Hastwell et al. (2006) and Jagger et al. (2009), new thresholds were generated. The new thresholds and their calculation are stated in the results chapters.

3.5 in silico interrogation of compounds using Derek Nexus™

Derek Nexus™ is an in silico tool developed by Lhasa ltd (Leeds, UK) for providing expert, knowledge-based prediction of various toxic endpoints based on a compound’s structure. These endpoints include carcinogenicity, chromosomal damage, mutagenicity and genotoxicity as well as skin sensitisation, teratogenicity, irritation, respiratory sensitisation and reproductive toxicity.

Structures of compounds were compared against a database of alerts. Each alert links a structural element present in the compound being assessed to a result found in the literature or from one of Lhasa ltd’s partners that has been compiled by an expert. These alerts were processed through decision trees to give an indication of probability related to a specific endpoint. The probability output provided by the software are shown in Section 3.5.3.1.

53 3.5.1 Derek Nexus™ version and knowledge base Results were generated using Derek Nexus version 3.0.1 with the knowledge base 2012 v.1. Results from other versions of the software of knowledge base may produce different results from those presented in this thesis.

3.5.2 Prediction setup All of the structures of compounds within the compound were uploaded to the Derek Nexus™ software using SMILES annotation. These structures were then analysed as part of a batch analysis. Alerts were restricted to those relating to bacterial and mammalian species specifically detailing predictions related to humans. The alerts were also restricted to the following specific endpoints:

 Carcinogenicity  Chromosome Damage  Genotoxicity  Mutagenicity

The software was set to perceive mixtures and tautomeric forms of the structures. The data were then processed into individual and batch reports.

3.5.3 Results structure Derek Nexus™ produces a report individually for each compound. This report contains:

 Any alerts for the endpoints tested  The reasoning underlying that result from a list shown in Table 3:5  The decision tree leading to the reasoning produced is provided  Any structural moiety related to the alert highlighted within the structure of the test compound  Comments written by an expert, bringing together knowledge from existing literature related to the alert detailing any example compounds  The predictivity of the alert for matching compounds within databases of results for in vitro or in vivo chromosomal damage assays, in vitro mutagenicity assays and in vivo carcinogenicity assays. These lists are detailed in Section 3.5.3.2  Any references related to the creation of the alert rule

3.5.3.1 Reasoning glossary Derek Nexus™ ranks the likelihood of an alert being true using a decision tree. The output of this decision tree is one of the words shown below in Table 3:5

3.5.3.2 Databases of results for calculating alert predictivity Derek Nexus™ provides the results of applying each alert rule to existing databases of results for in vitro chromosomal damage assays, Ames bacterial mutagenicity assays and in vivo carcinogenicity assays. This gives a figure indicating the predictive performance of the alert for each database. The details of these databases are presented in the appendix (Section 13).

54 Certain There is proof that the proposition is true. Probable There is at least one strong argument that the proposition is true and there are no arguments against it. Plausible The weight of evidence supports the proposition. Equivocal There is an equal weight of evidence for and against the proposition. Doubted The weight of evidence opposes the proposition. Improbable There is at least one strong argument that the proposition is false and there are no arguments that it is true. Impossible There is proof that the proposition is false. Open There is no evidence that supports or opposes the proposition. Contradicted There is proof that the proposition is both true and false. Table 3:5 Reasoning glossary (Derek Nexus™ v.3.0.1)

3.6 Using dichlorofluorescin diacetate to detect intracellular ROS generation

Dichlorofluorescin diacetate (DCFH-DA) is a chemical capable of being taken up by cells and hydrolysed, removing the two acetyl groups of the compound preventing it from leaving intact cells (Section 2.4.5). Whilst within the cell, the hydrolysed dichlorofluorescin is capable of becoming oxidised by hydroperoxyl or organic peroxyl chemicals and radicals. The oxidised form of dichlorofluorescin, dichlorofluorescein fluoresces at a wavelength of ~530 nm under excitation with 488 nm light. This transformation allows the quantitation of hydroperoxyl and organic peroxyl chemicals and radicals within cells.

3.6.1 DCFH-DA stock reagents DCFH-DA was obtained from Sigma-Aldrich (Cat. no. D6883) and stored frozen at 100 mM in DMSO. To prevent the repeated thawing and defrosting of the 100 mM DCFH-DA stock, a smaller and more dilute stock (20 mM, 100% DMSO) was prepared and stored in an aluminium foil-wrapped tube at 4°C. This stock contained enough to prepare 9 plates.

3.6.2 DCFH-DA optimisation To ensure the optimal performance of the DCFH-DA assay in detecting the generation of ROS by TK6 cells in RPMI cell culture medium, the following protocols were followed.

3.6.2.1 Determining the optimum concentration of the indicator dye, DCFH-DA DCFH-DA stock was thawed to room temperature. RPMI was pre-warmed in a 37°C water bath. TK6 cells were counted and a volume of 2 × 107 cells were collected by centrifugation (300 × g for 5 minutes). The supernatant was discarded and the cells in each tube were re-suspended in 18 ml of pre-warmed RPMI. 0.9 ml of cells were added to each of 7 FACSCalibur flow cytometer tubes (Becton Dickinson, San Jose, USA). The tubes were labelled “N1”-“N7” to be left untreated as negative controls. 18 µl of 50 mM TBHP in aqueous solution were added to the remaining cells such that the final concentration would be 100 µM TBHP. 0.9 ml of cells were added to each of 7 flow cytometer tubes. The tubes were labelled “P1”-“P7”. DCFH-DA was diluted to 250 µl volumes of concentrations: 50, 100, 200,400, 800 and 1600 µM in 10% DMSO. A further 250 µl volume of 10% DMSO were prepared as a vehicle control. 100 µl of the vehicle control were added to “N1” and “P1”. 100 µl of the lowest concentration of DCFH-DA were added to “N2” and “P2” etc. The cells were placed in an humidified incubator at 37.5°C with 5% CO2 for 4 hours. The contents of the tubes were gently shaken and data was collected by FACSCalibur flow cytometer using the template detailed in Section 3.6.6.

55 3.6.2.2 Determining the optimum compound exposure time DCFH-DA stock was thawed to room temperature. RPMI was pre-warmed in a 37°C water bath. TBHP was diluted to a concentration of 2 mM. TK6 cells were counted and a volume of 25 × 106 cells were collected by centrifugation (300 × g for 5 minutes). The supernatant was discarded and the cells re-suspended in 12.5 ml of pre-warmed RPMI. 1 ml of cells was added to each of 12 eppendorf tubes. 2 µl of DCFH-DA were added to the cells in tube 1 making a concentration of 40 µM, 2 × the intended final concentration. The cells were placed in an humidified incubator at 37.5°C with 5% CO2 for 30 minutes. During the cells’ incubation, 150 µl of TBHP were added to the first cell of each column on a clear, 96-well, U-bottomed, microplate (Matrix, NH, USA [Cat. no. 4911]). 75 µl of RPMI were added to each of the wells in columns 2-12. 75 µl of the samples in row 1 were extracted and then added to the wells of row 2. The wells were mixed. The previous 2 steps were repeated, diluting the test compounds down the plate in a serial twofold dilution until row G. 75 µl of the volume left within wells in row G were removed and discarded, leaving row H as a set of vehicle control wells. 2 µl of DCFH-DA were added to the cells in tube 2 making a concentration of 40 µM, 2 × the intended final concentration. Cells were removed from the incubator following a 30 minute incubation. In a class II isolator without illumination, 75 µl of cells from tube 1 were added to each well in column 1. A breathable membrane was added to the plate. The plate was placed in an humidified incubator at 37.5°C with 5% CO2 for 30 minutes. The previous 5 steps were repeated every 30 minutes adding cells to the next column of the microplate. Once each tube has been added, the microplate was incubated for a further 3 hours. The plate was gently shaken for 30 seconds. The plate was placed on the HTS unit of a FACSCalibur flow cytometer and examined using the template detailed in Section 3.6.6.

3.6.3 DCFH-DA microplate protocol The following protocol was adapted from the method provided by the supplier (Abcam 2012) with adaptations based upon optimisation experiments presented within Section 8. DCFH-DA stock was thawed to room temperature. RPMI was pre-warmed in a 37°C water bath. Test compounds solutions were prepared to a concentration of 2 × their intended highest tested concentration. TK6 cells were counted and a volume of 2 × 107 cells were collected by centrifugation (300 × g for 5 minutes). The supernatant was discarded and the cells re-suspended in 10 ml of pre-warmed RPMI. The centrifuge tube of cells was wrapped in aluminium foil. 20 µl of DCFH-DA were added to the cells making a concentration of 40 µM, 2 × the intended final concentration. The cells were placed in an humidified incubator at 37.5°C with 5% CO2 for 30 minutes. During the cells’ incubation, 150 µl of each of the seven test compounds were added to the first cell of its respective row on a clear, 96-well, U-bottomed, microplate (Matrix, NH, USA [Cat. no. 4911]). In row H, 150 µl of 2 mM TBHP were added. 75 µl of RPMI were added to each of the wells in columns 2-12. 75 µl of the samples in column 1 were extracted and then added to the wells of column 2. The wells were mixed. The previous 2 steps were repeated, diluting the test compounds across the plate in a serial twofold dilution until column 11. 75 µl of the volume left within column 11 were removed and discarded, leaving column 12 as a set of vehicle control wells. Cells were removed from the incubator following a 30 minute incubation. In a class II isolator without illumination, 75 µl of cells were added to each well. A breathable membrane was added to the plate. The plate was placed in an humidified incubator at 37.5°C with 5% CO2 for 4 hours. Following 4 hours of incubation, the plate was placed in a class II isolator without illumination. The breathable membrane was removed. 60 µl of PI were added to each well leaving a final concentration

56 of 2.86 µg/ml see Section 3.3.1.5. The plate was gently shaken for 30 seconds. The plate was placed on the HTS unit of a FACSCalibur flow cytometer and examined using the template detailed in Section 3.6.6.

3.6.4 An assessment of extended toxicity To assess the toxicity during the 48 hours following a 4 hour compound exposure, the following experiment was designed:

RPMI and PBS was pre-warmed in a 37°C water bath. Test compounds solutions were prepared to a concentration of 2 × their intended highest tested concentration. TK6 cells were counted and a volume of 5 × 107 cells were collected by centrifugation (300 × g for 5 minutes). The supernatant was discarded and the cells re-suspended in 25 ml of pre-warmed RPMI. 5 ml of cells were added to each of 5 centrifuge tubes. 5 ml of each test compound were added to a tube. The centrifuge tubes were placed in an humidified incubator at 37.5°C with 5% CO2 for 4 hours with their caps loosened. The caps of the centrifuge tubes were tightened and the cells were washed twice with PBS before resuspending them in RPMI (Centrifugation at 300 × g for 5 minutes). At 0, 24 and 48 hours, 500 µl were extracted from each tube and counted (Section 3.1.1). At each time point, 500 µl were also extracted, treated with 200 µl of PI and examined by flow cytometry using the template detailed in Section 3.6.6.

3.6.5 Antioxidant assessment To determine whether antioxidant compounds were able to reduce the level of ROS in cells treated with TBHP, the following experiment was designed:

An assay plate was set up following the protocol detailed in Section 3.6.3 with the following modifications. Cells added to rows A-G were all treated with 100 µl TBHP. Cells in row H acted as vehicle controls. Test compounds were added to wells B1-G1. Test compounds were diluted serially across the plate to column 12.

3.6.6 Collection of DCFH-DA assessment data using a flow cytometry A template was created within CellQuest Pro (Figure 3:3) to process data from the flow cytometer and output the data as a comma-separated values file for later interpretation using Microsoft Excel. Within the template a bivariate scatter plot of the forward scatter height (FSC-H) against the side scatter height (SSC-H) of each event from a well was produced. This chart differentiates events by their cell morphology. Within this chart a region of low FSC-H and low SSC-H was defined as debris and excluded from further analysis. A histogram of FL3-H was produced for all events NOT “debris”. A region was selected within the FL3-H histogram selecting cells unstained with PI and was denoted “viable cells”. A histogram of FL1-H was produced for all “viable cells” events. The output of the FL1-H histogram was a geometric mean value of events collected from a well. 10,000 NOT “debris” events were collected. The proportion of NOT “debris” events that were also “viable cells” was used as an indication of cell viability. The median value of the FL1-H histogram of “viable cells” was used as a measure of DCFH-DA fluorescence.

A B

Figure 3:3 DCFH-DA data processing template (A) A bivariate dot scatter plot of forward scatter height (FSC-H) against the side scatter height (SSC-H) differentiates events based upon their morphology. Events with FSC-H were defined as “Debris” and omitted from further assessment. (B) A Histogram of FL3-Height differentiates cells by their PI fluorescence. Highly stained cells were defined “Dead”, unstained cells were defined “Viable” (C) A histogram of FL1-Height for all NOT “Debris” events differentiates all cells by the level of DCF fluorescence (D) A histogram of FL1-Height for all NOT “Debris”, NOT “Dead” events differentiates all non-degraded cells by the level of DCF fluorescence (E) A histogram of FL1-Height for all “Viable” events differentiates all viable cells by the level of DCF fluorescence. The median value of this plot was used as the measure of DCF fluorescence

58 3.7 Detection of early stages of toxicity and mitochondrial membrane depolarisation using JC-1

The mitochondrial dye, JC-1 was chosen as a means to detect mitochondrial depolarisation. As detailed in Section 2.6.3 of the introduction, JC-1 freely enters the mitochondria of cells and forms J-aggregates within the electronegative interior of healthy and functioning mitochondria with normal membrane potentials. In mitochondria with dysfunctional membrane potentials, the lack of sufficient electronegativity prevents J- aggregates from forming leaving JC-1 as a monomer. If the membrane potential of a mitochondrion becomes dysfunctional after exposure to JC-1, J-aggregates within the mitochondrion will break down into JC-1’s monomeric form Figure 3:4.

JC-1’s aggregate form exhibits red fluorescence (~590 nm) under excitation by 488 nm light whereas its monomeric form exhibits green fluorescence (~529 nm) under excitation. The different fluorescence profiles of the aggregate and monomeric forms of JC-1 allow the ratio between the two forms to be quantified by measuring the red and green fluorescence using the FL2 and FL1 channel of a FACSCalibur flow cytometer. FL1 detects fluorescence that passes through a bandpass filter with a 30nm range centred at 530 nm. FL2 detects fluorescence that passes through a bandpass filter with a 40nm range centred at 585 nm. The mean of each of these two values can be taken from cells in one condition and the ratio between the two means (red:green) can be compared with the same values calculated from an untreated control. This red:green fluorescence ratio can be used to indicate the ratio of JC-1 aggregate:monomer and thus the ratio of healthy:depolarised mitochondria within cells.

JC-1 enters mitochondrion  Depolarised membrane  Polarised membrane  Compromised  Healthy mitochondrion mitochondrion  Electronegative interior  Loss of electronegativity  JC-1 forms red  J-aggregates become fluorescent J-aggregates green fluorescent JC-1 monomers Membrane disrupted

Figure 3:4 Stylised figure representing JC-1 measurement of mitochondrial membrane potential

59 3.7.1 JC-1 assessment protocol The following protocol was adapted from that used by Chun-Qi Li (2005) investigating mitochondrial membrane potential in TK6 cells.

Prior to beginning the protocol, RPMI was warmed in a water bath at 37°C and the stock solutions were thawed. JC-1 stock solution was diluted in PBS to a concentration of 4.9 µM (2.63% DMSO). CCCP stock solution was diluted in RPMI and DMSO such that the final concentration of CCCP was 400 µM and the concentration of DMSO was 2%. 3 test compounds were dissolved in DMSO and then diluted in RPMI to prepare solutions of 2× the intended highest tested concentration of the test compound and 2% DMSO. On a clear, U-bottomed, 96-well microplate (Matrix, NH, USA [Cat. no. 4911]) 150 µl of each test compound were added to wells A1, B1 and C1 respectively. 150 µl of CCCP were added to well D1. 75 µl of RPMI were added to wells in columns 2-12. 75 µl of the compounds and control from column 1 were extracted and added to the corresponding wells of column 2. These were mixed and the process repeated, diluting the compounds serially twofold across the microplate to column 11 leaving the wells in column 12 unchanged as vehicle controls. 75 µl from each of the wells in column 11 were taken and discarded. 5 × 106 TK6 cells were placed in a large centrifuge tube and cells underwent centrifugation at 300 × g for 5 minutes. The supernatant was removed and the cells were re-suspended with 5 ml fresh, warm RPMI. 75 µl of cells were added to each of the test wells. This left 150 µl in each well of 1 × 106 cells/ml exposed to 4 compounds including the positive control diluted twofold serially from column 1-11 and a column of 8 vehicle control samples in column 12. A breathable membrane was added to the plate and the plate was incubated for 3.5 hours in a humidified CO2 incubator at 37°C. 60 µl of the JC-1 PBS solution were added to each well and the plate was gently shaken. Another breathable membrane was added to the plate and the plate was returned to the incubator for 30 minutes. The microplate was then examined using the HTS unit of a FACSCalibur flow cytometer.

3.7.2 Analysis of JC-1 assessment using flow cytometry A template was created within CellQuest Pro to process data from the flow cytometer and output these data as a comma-separated values file for subsequent interpretation using Microsoft Excel. Within the template, a bivariate scatter plot of the forward scatter height (FSC-H) against the side scatter height (SSC- H) of each event from a well was produced. This chart differentiates events by their cell morphology. Within this chart a region of low FSC-H and low SSC-H was defined as debris and excluded from further analysis. Two histograms then displayed the FL1-H and FL2-H respectively and output the median values of the two histograms. These median values were then used to calculate the FL2:FL1 fluorescence ratio as an indicator of mitochondrial membrane potential within cells. A bivariate plot of FL2-H against FL1-H was also displayed providing useful visual feedback regarding whether the two wavelengths of fluorescence interfere with one another.

3.7.2.1 Compensation of FL2 channel to reduce crosstalk As detailed in Section 9.1.1, interfering crosstalk between measurements by the FL2 and FL1 channels of the flow cytometer were observed. Decisions were made manually regarding the level of compensation needed to reduce the crosstalk between the two channels. The steps involved in this are detailed in Section 9.

60 3.7.3 Kinetic measurement of JC-1 fluorescence by plate spectrophotometry To determine the optimal JC-1 exposure time, assay plates were set up following the protocol detailed in Section 3.7.1 with CCCP as a test compound. The plate was placed in a Tecan Ultra-384 multimode microplate spectrophotometer. Optical absorbance was measured by illuminating each well with light at a wavelength of 620 nm, 4 times in a circular 2 × 2 pattern. Using a standard fluorescein isothiocyanate [FITC] filter pair. Fluorescence data were collected using excitation with light filtered to a wavelength of 485 nm 4 times in the same circular pattern used to measure absorbance. Fluorescent light of a wavelength of 535 nm was measured for each excitation. Gain was optimised upon the first assessment using the fluorescence of the JC-1, positive control well (A1) as a reference and fixed thereafter. Following each measurement, a further measurement was made for a period of 3 hours.

3.8 Quantifying oxidative DNA damage using antibodies against 8-oxoguanine

The method used to assess oxidative DNA damage in vitro required the use of a FITC conjugated antibody for 8-OG, which was provided as part of the OxyDNA kit from Merck KGaA (Darmstadt, Germany). The procedure followed was adapted from the protocol provided with the kit and freely available on Merck’s website (Merck KGaA 2010). Any modifications to the protocol made to adapt the protocol to use with TK6 cells are detailed as part of the results chapter in Section 10.3.

3.8.1 Provided reagents The following reagents were provided by Merck KGaA (Darmstadt, Germany) as part of the OxyDNA kit:

 Wash Concentrate: 25× Tris-buffered Saline/TWEEN® 20 Detergent containing thimerosal  FITC-Conjugate Concentrate: Binding Protein conjugated to FITC, containing thimerosal

3.8.2 Reagent preparation For the fixation and permeabilisation of cells, 4% (w/v) paraformaldehyde solutions and 70% (v/v) ethanol solutions were prepared ahead of time. The 4% paraformaldehyde was prepared as follows:

8 g of paraformaldehyde was added to 40 ml of deionised water. The tube was heated to 70 °C in a sealed 50 ml centrifuge tube until the powder dissolved. The solution was then left to cool on ice for 20 minutes. After this time, 5 ml of 10× PBS solution were added to the tube within a ventilated cabinet. The volume of the solution was topped up to a total volume of 50 ml. The contents of the tube were sterile-filtered through a vacuum filter (Corning, NY, USA [Cat. no. 431475]). The final solution was stored in a sealed container at 4 °C.

The 70% ethanol solution was prepared by adding 35 ml of absolute ethanol to 15 ml of deionised water. This solution was stored at -20 °C.

The reagents provided were diluted on the day of assessment before carrying out the protocol using the following dilution scheme:

 Wash solution: 10 ml of wash concentrate were added to 240 ml sterile, deionised water  1× FITC-conjugate: 100 µl of FITC-Conjugate Concentrate were added to 900 µl of wash solution

61  1% paraformaldehyde: 12.5 ml 4% paraformaldehyde were added to 37.5 ml of sterile deionised water

3.8.3 8-oxoguanine protocol For each sample of 1 × 106 treated TK6 cells

The sample was spun in a centrifuge at 300 × g for 5 minutes. The supernatant was discarded and the pellet re-suspended in 5 ml of PBS. The sample was spun once more in a centrifuge at 300 × g for 5 minutes. The supernatant was discarded the pellet re-suspended in 5 ml of 1% paraformaldehyde. The sample was placed on ice for 1 hour in the dark. The sample was spun in a centrifuge at 300 × g for 5 minutes. The supernatant was discarded. 1 ml of ice cold 70% ethanol was added to the pellet. This sample was stored at -20 °C overnight. The sample was then spun in a centrifuge at 300 × g for 5 minutes. The supernatant was discarded and the pellet re-suspended in wash solution. The wash step was repeated to ensure no ethanol remained. The sample was then spun in a centrifuge at 300 × g for 5 minutes. 100 µl of 1× FITC-conjugate were added to the cell pellet. The sample was incubated in the dark for 60 minutes at room temperature. The sample was then spun in a centrifuge at 300 × g for 5 minutes. The supernatant was discarded and the pellet re-suspended in wash solution. Read fluorescence using a flow cytometer at an excitation wavelength of 495 nm and barrier filter of 515 nm.

3.9 Statistical analysis

Three methods of statistical analysis were used to determine the significance of results generated in the investigations presented in this thesis. Presented below are the methods used and the methods used to calculate them.

3.9.1 Standard deviation Standard deviation (푠) within a sample was measured using the “푛 − 1” method to reduce sample bias as follows:

∑(푥 − 푥̅)2 푠 = √ (푛 − 1)

Where 푥 is the sample, 푥̅ is the sample mean and 푛 is the sample number. The standard deviation, 푠, has the same units as the sample measurements.

3.9.2 Three-times standard deviation threshold Assuming that the distribution of results for control values follow a normal distribution, an assumption can be made that >95% of all values fall within 2 standard deviations of the mean result and >99% of all values fall within 3 standard deviations of the mean result. Any result that differs from the control mean value by more than 2 × 푠 can therefore be assumed to be significantly different from the control value and results that differ by more than 3 × 푠 can be assumed to be very significantly different.

62 3.9.3 Relative standard deviation / Coefficient of variance The relative standard deviation (RSD) otherwise referred to as the cofficient of variance (CV) is a measure of deviation within a sample relative to the sample mean. It is calculated as follows:

푠 푐̂ = 푣 푥̅

Where 푐̂푣 is the RSD value within the sample.

3.9.4 Threshold calculation By measuring the standard deviation within a sample of the values produced by all negative and vehicle control treatments and multiplying the result by 3, a threshold of significance can be produced. Any value generated that was greater than that of the vehicle control plus 3 times the standard deviation of the sample (3 × 푠) can be considered to be greater than that of the vehicle control by a highly significant degree. Likewise, any value generated that was lower than that of the vehicle control minus 3 × 푠 can be considered to be lower than that of the vehicle control by a highly significant degree.

3.9.5 Student’s t-Test The Student’s t-Test provides an indication of the difference between a limited set of results (n<30) generated in two different conditions that follow a normal distribution. The Student’s t-Test can be carried out in a variety of ways depending upon whether the distribution of samples was one or two-tailed, whether each result was paired with one from the other condition and whether the results from the two conditions have different sample sizes or differing levels of variance.

The result of a Student’s t-Test is a p-value, a probability that the null hypothesis can be rejected. Where a resulting p-value was lower than 0.05, a statistically significant difference between the two conditions can be assumed. Where said p-value was lower than 0.01, a highly statistically significant difference between the two conditions can be assumed.

For the purposes of the work presented in this thesis the following equation was used:

Homoscedastic two-sample Student’s t-Test for samples with equal sample sizes

For assessments aiming to determine a significant change between two samples of results where the level of variance was expected to be equal in both samples and the sample size from the two conditions was equal, the following equation was used to produce a p-value.

x̅1 − x̅2 t = 2 2 √s1 + s2 n

Where n is the sample size

2 2 s1 and s2 are the variance (standard deviation squared) of the two samples

And x̅1 and x̅2 are the means of the results of the two samples

t can be compared against a one-tailed or two tailed p-table (depending on whether a significant change was expected to be either an increase or a decrease, or either respectively) to give an estimate of the p- value of the given samples however a more exact figure was determined using the T.TEST() function of Microsoft Excel.

3.9.6 Correlation coefficient To compare correlation between two sets of ranked values, the Pearson correlation coefficient was used. This calculation, carried out upon ranked values provided a value of the Spearman rank correlation coefficient.

Values were ranked from highest to lowest. The correlation was then calculated using the following equation:

∑(푥 − 푥̅)(푦 − 푦̅) 푟 = √∑(푥 − 푥̅)2 ∑(푦 − 푦̅)2

Where 푥 and 푦 are the values from the two sets and 푥̅ and 푦̅ are the mean values of the two sets respectively.

The value, 푟 provides an indication of the strength of the correlation. If 푟 is close to or equal to -1, a strong negative correlation exists, if 푟 is close to or equal to 1, a strong positive correlation exists and if 푟 is close to or equal to 0, little or no correlation exists.

To provide a significance of the 푟 value, a 푝 value was calculated using a statistical calculator available at http://www.danielsoper.com/statcalc3/calc.aspx?id=44 .

64 4 Results I – Compound Choice 4.1 Introduction

In order to investigate the mechanism by which some PPAs generate misleading positive results in in vitro mammalian genotoxicity assessment, it was first necessary to identify compounds relevant to the study. These compounds were then assessed in a wide range of tests detailed in the results chapters of this thesis, in order to better understand the role that polyphenolic and other antioxidants play in producing positive results in the GADD45a-GFP assay and the possible role that oxygen concentration, reactive oxygen species (ROS) generation and DNA oxidation might play in the generation of these results.

This chapter is intended primarily as a reference resource. As such, a summary table of existing data is presented in Table 4:38 on page 98. A summary of the reasons for each compounds choice is presented in Table 4:39.

As detailed later in this chapter, positive genotoxicity data have been reported for several antioxidants in the GADD45a-GFP assay and other in vitro mammalian genotoxicity assays. Links between PPAs and pro- oxidant character have been suggested as being responsible for the genotoxicity of the compounds within in vitro assessments (Long et al. 2010; Long et al. 2000; Yoshino et al. 2002; Mazumdar et al. 2011). To assess whether pro-oxidant character is responsible for the positive genotoxicity results generated by PPAs and also those generated by certain monophenolic (MPA) and non-phenolic (NPA) antioxidants, a wide range of antioxidants were included in the compound list. Antioxidants make up the majority of the 37 chemicals chosen to be tested in this thesis (Table 4:38). They include 12 PPAs, 4 MPAs and 3 NPAs. In addition to these a chemically diverse range of additional compounds was selected to provide context and a clearer understanding of the results generated from the first (Table 4:38). These included: 6 genotoxins with diverse modes of action and 3 pro-genotoxins (positive genotoxic controls); 2 non-genotoxic cytotoxins (cytotoxic controls); 3 compounds with different mechanisms of intracellular oxidation; 1 electron decoupler (positive control for mitochondrial membrane disruption); Phenol, the isolated functional group of the phenolic antioxidants; 2-tert-Butyl-1,4-benzoquinone, a metabolite of polyphenol tert- butylhydroquinone (to better understand the action of the TBHQ metabolite without using S9 to metabolise the compound). The apoptogen, staurosporine was chosen as a positive control to elucidate the role that apoptosis might play in results generated by the test compounds.

This chapter presents the results of a literature search to identify suitable compounds to be tested in this investigation. The structure and use of any compound chosen is detailed and any existing limitations relating to its use, if any, in foodstuffs is listed.

Existing data for genotoxicity and carcinogenicity for the following assays were collected:

 Ames bacterial mutagenicity assay  in vitro chromosome aberrations assay  in vitro micronucleus assay  Mouse lymphoma assay  in vivo chromosome aberrations assay  in vivo micronucleus assay 65  in vivo rodent carcinogenicity assessment

Compound results generated by these assays were searched for in the following databases of regulatory genotoxicity and carcinogenicity data:

 Benchmark Data Set for in silico Prediction of Ames Mutagenicity (Hansen et al. 2009)  Brambilla Data Set for Carcinogenicity of Marketed Pharmaceuticals (Brambilla & Martelli 2009; Brambilla et al. 2012)  Chemical Carcinogenesis Research Information System (CCRIS) (NCI 2011)  ECVAM retrospective validation of in vitro micronucleus test (Corvi et al. 2008)  The Carcinogenic Potency Database (CPDB) (EPA 2008)  Istituto Superiore di Sanita database of in vitro Mutagenesis in Salmonella typhimurium (Ames test) (ISSSTY) (ISS 2011)  Evaluation of the ability of a battery of three in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens (Kirkland et al. 2005)  Mohr database for in vitro chromosome aberrations (Mohr et al. 2010)  The carcinogenicity and genotoxicity database of the National Toxicology Program (NTP 2013)

For compounds that had little or no data in the above datasets, a search of publicly accessible data was carried out using compound identifiers (compound name, CAS number, structure) and keywords regarding the genotoxicity and carcinogenicity assays detailed above. Six of the chosen compounds were found to have no existing publicly accessible data.

For those test compounds that are listed by the International Agency for Research on Cancer (IARC) monographs on the Evaluation of Carcinogenic Risks to Humans volumes 1-109, their group classification is detailed. The compound groups within the IARC monographs are as follows:

 Group 1 Carcinogenic to humans  Group 2A Probably carcinogenic to humans  Group 2B Possibly carcinogenic to humans  Group 3 Not classifiable as to its carcinogenicity to humans  Group 4 Probably not carcinogenic to humans

Compounds are arranged alphabetically within the following groups:

 Polyphenolic antioxidants  Monophenolic antioxidants  Non-phenolic antioxidants  Pro-oxidants  Genotoxins  Non-genotoxic cytotoxins  Miscellaneous compounds  Pro-genotoxins

66 4.2 Polyphenolic antioxidants

Twelve PPAs were chosen for this investigation. It was important that the chosen compounds were diverse. As such, the compounds detailed below have widely differing molecular weights, sources, uses and volumes of existing genotoxicity and carcinogenicity results.

Several test compounds with evidence of genotoxicity only in in vitro mammalian genotoxicity assessment (in vitro micronucleus, chromosome aberrations and mouse lymphoma assays) were chosen, as they represent the most likely candidates for an oxidative stress-related genotoxic mode of action. The physiological relevance of results generated by these compounds is specifically discussed in Section 2.5.

4.2.1 Apomorphine hydrochloride Name Apomorphine hydrochloride Apomorphine hydrochloride, Table 4:1, is a polyphenolic CAS Number 41372-20-7 synthetic opioid generated industrially by adding MW 322 morphine to hydrochloric acid (Royal Society of Chemistry Structure 2013). It is a powerful emetic and has been investigated as a therapeutic treatment for a wide variety of conditions including Parkinson’s (Frankel et al. 1990; Oxford

University Press 2006). Apomorphine hydrochloride Table 4:1 The CAS number, molecular weight oxidises readily, decomposing in contact with air or light, and structure of apomorphine hydrochloride turning green (Royal Society of Chemistry 2013).

The following results exist for mutagenicity:

 Positive results in the Ames test for bacterial mutagenicity inducing frame-shift mutations both with and without S9 metabolic activation. These was shown however to be dependent on the presence of oxygen allowing the chemical to autoxidise forming semiquinone and oxygen radicals (Suter & Matter-Jaeger 1984).

Apomorphine hydrochloride was chosen as a test compound due to evidence of the compound autoxidising to form ROS. It differs from many of the other polyphenols chosen in that it produces positive results in the Ames test.

4.2.2 tert-Butylhydroquinone Name tert-Butylhydroquinone tert-Butylhydroquinone (TBHQ), Table 4:2, is a food CAS Number 1948-33-0 MW 166 additive derived from hydroquinone. It is regularly added Structure to unsaturated oils and fats to prevent rancidification. TBHQ has the food additive designation E319 within the European Union (European Parliament & Council on food additives 2011).

TBHQ is a compound in the European Centre for the Table 4:2 The CAS number, molecular weight Validation of Alternative Methods (ECVAM) and structure of tert-butylhydroquinone recommended list for assessment of new genotoxicity tests. It is listed within the “Non-carcinogens that are negative or equivocal for genotoxicity in vivo”. The 67 compound has previously been assessed using the GADD45a-GFP within an assessment of compounds in the ECVAM recommended list for assessment of new genotoxicity tests producing a negative result for genotoxicity without S9 metabolic activation and a positive result for genotoxicity following S9 metabolic activation (Birrell et al. 2010). TBHQ also produced positive results in the Nrf2 reporter assay and the Srxn1- GFP reporter assay indicating an increased cellular response to oxidative stress. Negative results, however, were produced in the RAD51C-luc and p53-luc reporter assays (Westerink et al. 2010; Hendriks et al. 2011).

Early studies with regard to the genotoxicity and carcinogenicity of TBHQ produced conflicting results.

 Negative results in five-strain Ames test with and without S9 activation (van Esch 1986; ISS 2011; Imhoff & Hansen 2010; NTP 2013)  Positive results in the in vitro chromosome aberrations assay (NTP 2013)  Equivocal result in mouse lymphoma assay (van Ecsh 1986)  Negative results in in vivo micronucleus test carried out in male mouse bone marrow (van Esch 1986; NTP 2013)  Negative result in in vivo chromosome aberrations assay (NTP 2013)  Negative results for carcinogenesis in male and female rats (NTP 2013; EPA 2008)

TBHQ was chosen as a test compound because it is a widely used food additive with no link to carcinogenicity.

4.2.3 Dodecyl gallate Name Dodecyl gallate Dodecyl gallate, also known as lauryl gallate, Table 4:3, is CAS Number 1166-52-5 MW 338 a PPA. It bears the same gallate ester subunit as the other Structure alkyl gallates tested as part of this investigation, propyl gallate and octyl gallate.

Dodecyl gallate is used as an antioxidant food additive to prevent lipid rancidification in oils, spreads, pet food and Table 4:3 The CAS number, molecular weight cosmetics. It is recognised as safe for human consumption and structure of dodecyl gallate in the EU and given the designation E312 (European Parliament & Council on food additives 2011).

No publicly available data for carcinogenicity or genotoxicity assessment of dodecyl gallate were found.

Dodecyl gallate was chosen as a test compound due to its use as a food antioxidant and structural similarity to propyl gallate.

4.2.4 Epigallocatechin gallate Name Epigallocatechin gallate Epigallocatechin gallate (EGCG), Table 4:4, is a catechin CAS Number 989-51-5 MW 458 with a gallic acid subunit. The compound occurs in Structure several plants, most notably in green tea plant Camellia sinensis. In a cup of prepared green tea, the concentration of EGCG can be as high as 1.49 millimolar (Shao et al. 1995). The compound has been found to

have a half-life of 5-5.5 hours in human plasma and, Table 4:4 The CAS number, molecular weight and structure of epigallocatechin gallate unlike other tea catechins, EGCG is not excreted in the urine (Yang et al. 1998).

EGCG has been shown to inhibit tumour development and the possible use of EGCG as an anti-cancer drug is being widely investigated (Royal Society of Chemistry 2013).

A mixture of green tea gallates including 85% EGCG has been shown to produce negative results for in vivo carcinogenicity in male mice (EPA 2008). Negative results in the Ames test were produced in S. typhimurium strain TA 102 with and without S9 (ISS 2011). Further to these studies, an in depth study by Isbrucker et al. (Isbrucker et al. 2006) produced negative results in the mouse lymphoma assay, Ames test and in vivo micronucleus assay. Epidemiological evidence shows a link between green tea consumption and a lower rate of breast cancer (C.-L. Sun et al. 2006). EGCG was shown to produce hydrogen peroxide to a concentration of over 250 µM within 2 hours when EGCG was added to a concentration of 1 mM to Dulbecco’s modified Eagle cell culture medium (Long et al. 2000). This effect was further reproduced in various cell culture media including RPMI (with and without Hepes). This effect was also linked with cytotoxic and clastogenic activity (Long et al. 2007).

EGCG was chosen as a test compound due to the results presented by Long, linking the compound and polyphenols in the wider context to levels of cell peroxidation in vitro that differ from those observed physiologically. Of the chosen test compounds, it is the largest, with the highest number of phenol subunits. EGCG has also been consumed as a constituent of green tea by humans for thousands of years with no observed link with promoting cancer.

4.2.5 Nordihydroguaiaretic acid Name Nordihydroguaiaretic acid Nordihydroguaiaretic acid (NDGA), Table 4:5, is an CAS Number 500-38-9 MW 302 antioxidant compound extracted from the creosote Structure bush, Larrea tridentate. In addition to its chemical antioxidant behaviour, NDGA exhibits a biological antioxidant behaviour by inhibiting the lipoxygenase’s lipid peroxidation activity (Gowri et al. 2000). Table 4:5 The CAS number, molecular weight and structure of nordihydroguaiaretic acid Countering the antioxidant behaviour of NDGA, the chemical was shown to be a pro-oxidant in in vitro mammalian cell culture in Dulbecco’s modified Eagle’s medium (DMEM) by Sahu et al. (2006). In their 69 research, NDGA was shown to increase oxidative stress using the nitroblue tetrazolium method, it was also shown to increase lipid peroxidation via the thiobarbituric acid method. They also showed that NDGA caused double strand DNA breaks in a dose-dependent manner.

It was used as a food additive but its use was later banned by the FDA following reports of liver and kidney toxicity (Brent 1999; Smith et al. 1994; Food and Drug Administration 2013a). NDGA is used pharmaceutically under the name masoprocol as an antineoplastic (Royal Society of Chemistry 2013) and is now being investigated for many treatments including increased longevity and antiviral activity (Strong et al. 2008; Lü et al. 2010).

The following results exist for mutagenicity, genotoxicity and carcinogenicity:

 NDGA produced negative results in 5 strains of S. typhimurium in the Ames test both with and without S9 (ISS 2011; NCI 2011)  NDGA produced negative results in the mouse lymphoma assay in the presence of S9 (NCI 2011)  NDGA produced positive results in the mouse lymphoma assay in the absence of S9 (NCI 2011)

NDGA was chosen as a test compound as it is an antioxidant with evidence linking it to pro-oxidant behaviour in in vitro mammalian cell culture. Negative results in the Ames test and positive results in the mouse lymphoma assay, could indicate that NDGA is only genotoxic in in vitro mammalian cells.

4.2.6 Octyl gallate Name Octyl gallate Octyl gallate, Table 4:6, is a PPA. It bears the same CAS Number 1034-01-1 MW 282 gallate ester subunit as the other alkyl gallates tested as Structure part of this investigation, propyl gallate and dodecyl gallate.

Octyl gallate is used as an antioxidant food additive to prevent lipid rancidification in oils, spreads, pet food Table 4:6 The CAS number, molecular weight and structure of octyl gallate and cosmetics. It is recognised as safe for human consumption in the EU and given the designation E311 (European Parliament & Council on food additives 2011).

In the approach taken in this study, no publicly available data for carcinogenicity or genotoxicity assessment of octyl gallate were found.

Octyl gallate was chosen as a test compound due to its use as a food additive and its structural similarity to propyl gallate.

70 4.2.7 Propyl gallate Name Propyl gallate Propyl gallate, Table 4:7, is a PPA. It bears the same CAS Number 121-79-9 gallate ester subunit as the other alkyl gallates tested as MW 212 Structure part of this investigation, octyl gallate and dodecyl gallate.

Propyl gallate is used as an antioxidant food additive to prevent lipid rancidification in oils, spreads, pet food Table 4:7 The CAS number, molecular weight and cosmetics. It is recognised as safe for human and structure of propyl gallate consumption in the EU and given the designation E310 (European Parliament & Council on food additives 2011). The FDA classifies propyl gallate as generally recognised as safe (GRAS) (Food and Drug Administration 1973).

Propyl gallate was shown by Aruoma et al. (1993) to exhibit antioxidant protection against ROS and to be a pro-oxidant in the presence of iron, accelerating oxidative DNA damage by a ferric-bleomycin system.

Propyl gallate is a compound in the ECVAM recommended list for assessment of new genotoxicity tests. Propyl gallate is listed within the “supplementary list (prediction of in vitro genotoxicity tests less clear)”. The compound has previously been assessed using the GADD45a-GFP within an assessment of compounds in the ECVAM recommended list for assessment of new genotoxicity tests producing a positive result for genotoxicity in 2 out of 3 assessments without S9 metabolic activation and a positive result for genotoxicity following S9 metabolic activation (Birrell et al. 2010). Propyl gallate also produced positive results in the Nrf2-luc reporter assay indicating an increased cellular response to oxidative stress (Westerink et al. 2010). Positive results for genotoxicity were also produced in the RAD51C-luc and p53-luc reporter assays, ToxTracker™ and the flow cytometric γH2AX assay (Westerink et al. 2010; Hendriks et al. 2011; Smart et al. 2011).

The following results exist for mutagenicity, genotoxicity and carcinogenicity:

 Negative results in the Ames test using 5-strains of S. typhimurium and also E. coli with and without S9 activation (Hansen et al. 2009; ISS 2011; NTP 2013)  Equivocal result for the in vitro micronucleus assay (Kirkland 2005)  Positive result for in vitro Chromosomal aberrations in V79 cells in the absence of S9 (NCI 2011; Kirkland 2005; NTP 2013)  Positive result for MLA (Kirkland 2005; NTP 2013)  Equivocal result for the in vivo micronucleus assay carried out in cells extracted from the bone marrow of male mice (NTP 2013)  Positive results for in vivo chromosomal aberrations (NTP 2013)  Negative results for carcinogenicity assessment in male and female rats and mice (NTP 2013; EPA 2008)  Equivocal results for carcinogenicity assessment in male rats and mice caused by statistically insignificant increase in the incidence of lymphomas over historical results (NTP 2013)

71 Propyl gallate was chosen as a test compound due to its use as a food additive, its positive and equivocal genotoxicity results in mammalian cells and rodents and its negative results in the Ames test. Propyl gallate’s antioxidant and pro-oxidant characteristics and its results in vitro highlight a possible similarity to EGCG and resveratrol as being a possible misleading in vitro genotoxin however this is undermined by positive results for in vivo chromosomal aberrations.

4.2.8 Pyrogallol Name Pyrogallol CAS Number 87-66-1 Pyrogallol, Table 4:8, is a simple tri-phenol. The three MW 126 hydroxy groups are bound to the 1, 2 and 3 carbons of Structure the phenyl ring. Pyrogallol oxidises in contact with air or light turning a greyish colour (Royal Society of Chemistry 2013).

Pyrogallol is a powerful naturally occurring antioxidant used in dying hair and the treatment of psoriasis (Royal Table 4:8 The CAS number, molecular weight and structure of pyrogallol Society of Chemistry 2013). It occurs naturally in tea, various hardwoods and as a breakdown product of various tannins.

Human exposure to pyrogallol is usually through its use in dying hair, tea and contaminated groundwater supplies.

There is well-documented evidence of pyrogallol inducing hepatotoxicity by the generation of free radicals and this is linked with pyrogallol-induced changes in levels of cytochrome p450s and a drop in levels of antioxidant enzymes (Upadhyay et al. 2010).

The following results exist for mutagenicity, genotoxicity and carcinogenicity:

 Equivocal results for carcinogenicity assessment in male and female rats due to an increase in squamous cell papilloma at the site of application (NTP 2013)  Negative results for carcinogenicity assessment in male and female mice (NTP 2013)  Equivocal results for the in vivo micronucleus assay carried out using the peripheral blood of male mice (NTP 2013)  Negative results for the in vivo micronucleus assay carried out using the peripheral blood of female mice and the bone marrow of male mice (NTP 2013)  Positive results in the Ames test when tested in 5 strains of S. typhimurium and also E. coli with and without S9 activation (Hansen et al. 2009; NTP 2013; ISS 2011)  Positive results for the in vitro chromosomal aberrations test (NCI 2011)

Pyrogallol was chosen as a test compound due to its pro-oxidant and antioxidant behaviour. It was also chosen for its small molecular weight. This small size may allow the compound to behave differently to the other compounds. Pyrogallol’s positive result in the Ames test is indicative that mechanisms not present in the in vitro genotoxicity of test compounds such as resveratrol and EGCG may be at play and these

72 mechanisms may be the responsible factor in the hepatotoxicity and equivocal results for in vivo carcinogenicity and genotoxicity of pyrogallol.

4.2.9 Quercetin Name Quercetin Structurally, quercetin, Table 4:9, is a flavonol and a CAS Number 117-39-5 MW 302 flavonoid. It is an antioxidant that occurs naturally in a Structure wide range of food plants including capers, coriander and fennel (Bhagwat et al. 2011).

Quercetin is used as a capillary protectant (Royal Society of Chemistry 2013). It is also being researched as a treatment for a wide variety of illnesses including Table 4:9 The CAS number, molecular weight and structure of quercetin asthma and eczema. A study investigating human metabolism and bioavailability of dietary polyphenols found that quercetin concentration in the serum peaked 30 minutes after oral administration via juice or wine and returned to the baseline level after 4 hours. Between 2.9 and 7.0 % of the administered quercetin was excreted in the urine within 24 hours (Goldberg et al. 2003). The speed with which levels of serum levels of quercetin reduced and its low level in urine raise questions as to how much of an effect, dietary consumption of quercetin might have on disease.

In studies carried out upon rat H4IIE cells, low doses of quercetin (<50 µM) were shown to exert a cytoprotective effect upon cells treated with hydrogen peroxide and higher doses (>100 µM) were shown to have a pro-oxidant (thiobarbituric acid assay) and genotoxic (comet assay) effect upon cells (Wätjen et al. 2005).

The International Agency for Research on Cancer (IARC) lists quercetin as a Group 3 compound indicating that it is "not classifiable as to its carcinogenicity to humans" (IARC 1987; IARC 1983; IARC 1999).

The following results exist for mutagenicity, genotoxicity and carcinogenicity:

 Positive results for carcinogenicity assessment in male rats owing to an increase in kidney, bladder and intestinal tumours (Brambilla et al. 2012; EPA 2008; NTP 2013)  Negative results for carcinogenicity assessment in female rats and male and female mice and hamsters (Brambilla et al. 2012; EPA 2008)  Positive results for in vitro chromosomal aberrations (Kirkland 2005; NTP 2013)  Positive results in the mouse lymphoma assay (Kirkland 2005)  Positive results in the Ames test tested in 5 strains of S. typhimurium and also E. coli with and without S9 activation (Hansen et al. 2009; ISS 2011; NTP 2013)  Inconclusive results in the in vivo micronucleus test carried out upon the bone marrow cells of male rats and mice (NCI 2011; NTP 2013)

Quercetin was chosen as a test compound as it is an antioxidant, present in a wide variety of food with a high number of positive results in genotoxicity assessment. The positive result for carcinogenicity and equivocal result for in vivo genotoxicity highlight that physiologically relevant mechanisms of genotoxicity may be caused by quercetin that are not caused by test compounds such as resveratrol and EGCG.

73 4.2.10 Resorcinol Name Resorcinol Resorcinol, Table 4:10, is a simple diphenol antioxidant CAS Number 108-46-3 consisting of two hydroxyl groups bound to a phenyl MW 110 Structure ring at the 1 and 3 carbons. The compound oxidises in contact with light, air or iron becoming pink (Royal Society of Chemistry 2013).

The compound is found in various plants but is not found in high concentration in any foodstuffs. In 2010, the European Food Safety Authority Panel on Food Table 4:10 The CAS number, molecular weight and structure of resorcinol Additives reasoned that current evidence suggests that resorcinol is not genotoxic but is acutely toxic. Thusly they did not approve it for use as a food additive in shellfish due to evidence that exposure would exceed the acceptable daily intake (EFSA 2010). Resorcinol also produced positive results for in vitro mammalian genotoxicity within the γH2AX, flow cytometric assay and ToxTracker™ (Smart et al. 2011; Hendriks et al. 2011).

IARC lists resorcinol as a Group 3 compound indicating that it is "not classifiable as to its carcinogenicity to humans" (IARC 1987; IARC 1977; IARC 1990).

The following results exist for mutagenicity, genotoxicity and carcinogenicity:

 Negative results for carcinogenicity when tested in male and female rats and mice (EPA 2008; NTP 2013)  Negative result for Ames test in 7-strains of S. typhimurium with and without S9 (Hansen et al. 2009; ISS 2011; NTP 2013)  Positive results in the micronucleus test in vivo in male mouse bone marrow (NTP 2013)  Positive results in the in vitro micronucleus assay in human lymphocytes (Kirkland et al. 2005)  Positive results in the mouse lymphoma assay (Kirkland et al. 2005; NTP 2013)  Positive results in the in vitro chromosomal aberrations assay (Kirkland et al. 2005; NTP 2013)

In work published by Fowler et al. (2012) seeking the role of cell line choice in producing misleading positive results in the micronucleus assay, resorcinol was tested in the presence of S9. A significant increase in micronucleus induction was observed in V79, CHL and CHO cells, but not in human lymphocytes, TK6 or HepG2 cells.

Resorcinol, like pyrogallol was chosen for being a very low weight polyphenol and being an Ames negative compound with no evidence of carcinogenicity that produces a wide range of positive results in mammalian genotoxicity assessment.

74 4.2.11 γ-Resorcylic acid Name γ-Resorcylic acid gamma-Resorcylic acid, Table 4:11, is a CAS Number 303-07-1 MW 154 dihydroxybenzoic acid chosen for this investigation due Structure to having no existing genotoxicity or carcinogenicity data. The compound is a plant-derived polyphenol with two hydroxyl groups bound to a single phenyl ring at the 2 and 6-carbons with a carboxylic acid group bound to the 1-carbon between the two hydroxyl groups. This structure lends the structure a similarity to resorcinol Table 4:11 The CAS number, molecular weight (detailed in Section 4.2.10) and other hydroxybenzoic and structure of γ-resorcylic acid acids such as gentisic acid and salicylic acid.

The compound produces evidence of weak antioxidant activity in vitro using a DPPH∙ antiradical assay (Brand-Williams et al. 1995).

In the approach taken in this study, no publicly available data for carcinogenicity or genotoxicity assessment of γ-resorcylic acid were found.

γ-resorcylic acid was chosen as a test compound due to being a weak PPA. If the polyphenolic moiety is responsible for the generation of positive results in in vitro mammalian genotoxicity assessment then a compound with a weaker antioxidant character may be less prone to generating ROS.

4.2.12 Resveratrol Name Resveratrol CAS Number 501-36-0 Resveratrol, Table 4:12, is a polyphenolic, antioxidant MW 228 compound with a hydroxystilbene structure. This Structure structure includes two phenyl groups; one bound to a single hydroxyl group and the other bound to two.

Resveratrol occurs naturally in several plants including grapes as a response to fungal attack (Dai et al. 1995). In grapes, resveratrol is present primarily in the skin. As Table 4:12 The CAS number, molecular weight red wine is produced using ‘skin on’ fermentation and structure of resveratrol techniques, it contains far higher concentrations of resveratrol than ‘skinless’ white wine. The concentration of resveratrol in red wine varies between 0.987 and 25.5 µM (Gu et al. 1999). Resveratrol was also posited as the chemical responsible for the “French Paradox” whereby French and Greek populations with a diet high in fat were observed to suffer little heart disease (Kopp 1998).

A study investigating human metabolism and bioavailability of dietary polyphenols however found that resveratrol concentration in the serum peaked 30 minutes after oral administration via juice or wine and returned to the baseline level after 4 hours. Between 16.0 and 17.0 % of the administered resveratrol was excreted in the urine within 24 hours (Goldberg et al. 2003). The speed with which levels of serum levels of resveratrol reduced and its low levels in urine raise questions as to how much of an effect, dietary consumption of resveratrol might have on disease. 75 Despite resveratrol’s role in plants as an antioxidant, within mammalian cells in vitro, the compound has been observed to exert pro-oxidant effects (de la Lastra & Villegas 2007). Doses of resveratrol between 0.1 and 50 µM were shown to cause an increase in ROS production in GRX hepatic cells after 120 hours using intracellular dye, dichlorofluorescin diacetate (DCFH-DA). Only the highest dose (50 µM) was shown to elicit any cytotoxicity (Martins et al. 2014).

The possible effects of resveratrol have been researched broadly. Amongst other topics, its effect upon longevity, cancer and heart disease with mixed results (Baur & Sinclair 2006; Bhat et al. 2001).

Resveratrol produced the following results for genotoxicity:

 Negative results in the Ames test both in 5 strains of S. typhimurium with and without S9 activation (ISS 2011; NCI 2011)  Positive results for micronucleus assay in vitro in Chinese hamster lung (CHL) cells without S9 (Matsuoka et al. 2001)  Positive results for Chromosomal aberrations in vitro in CHL cells without S9 (Matsuoka et al. 2001)  Negative results were produced in the in vivo micronucleus assay conducted in peripheral blood cells of male and female rats and mice (NTP 2013)

Resveratrol also produced conflicting results, being shown to both promote (Sato et al. 2003) and inhibit (Bishayee & Dhir 2009) tumour development in rats.

Resveratrol was chosen as a test compound due to being an Ames negative compound with negative results for in vivo genotoxicity and positive results in in vitro mammalian genotoxicity assessment. Resveratrol’s observed antioxidant and pro-oxidant behaviour and its widespread consumption being linked with reduction in heart disease and cancer make this an ideal candidate compound for producing misleading positive results in in vitro mammalian genotoxicity assessment.

76 4.3 Monophenolic antioxidants

Alongside the PPAs assessed as part of this investigation, other antioxidants with only one single phenol group were tested. This provided a clearer understanding of whether or not positive results were restricted to polyphenols or to phenolic antioxidants in the wider context. If the phenolic nature of PPAs is responsible for any observed in vitro genotoxicity then it could be expected that antioxidant compounds with a single phenolic group might produce similar results. Below are detailed the structures, characteristics and uses of the four compounds chosen along with detailed results of any existing genotoxicity and carcinogenicity assessment.

4.3.1 Butylated hydroxyanisole Name Butylated hydroxyanisole CAS Number 25013-16-5 Butylated hydroxyanisole (BHA), Table 4:13, is a MW 180 phenolic antioxidant. With a tert-butyl phenol Structure structure, the compound bears structural similarity to tert-butylhydroquinone, BHT and BHMP.

BHA is used as an antioxidant preservative in food, cosmetics and pet food and cosmetics and petroleum products. During digestion, BHA is metabolised into Table 4:13 The CAS number, molecular weight TBHQ and despite evidence of acute toxicity it is and structure of butylated hydroxyanisole considered safe for human consumption with an acceptable daily intake (ADI) of 1 mg/kg and is given the designation E320 (EFSA Panel on Food Additives and Nutrient Sources added to food 2011).

IARC lists BHA as a Group 2B compound indicating that it is "possibly carcinogenic to humans" though it highlights that no carcinogenicity has been observed in Japanese house musk shrews which, like humans have no forestomach (IARC 1986; IARC 1987).

BHA produced the following results for carcinogenicity and genotoxicity:

 Negative results in the Ames test carried out in 5 strains of S. typhimurium and also E. coli with and without S9 activation (ISS 2011; Hansen et al. 2009; NTP 2013)  Positive results in the in vitro micronucleus assay in the presence of S9 (NCI 2011; Kirkland 2005)  Positive results for in vitro chromosome aberrations in CHL cells (Kirkland 2005)  Negative results in the in vivo micronucleus assay carried out in mouse and rat bone marrow (NCI 2011; NTP 2013)  Positive results for carcinogenicity assessment carried out in male mice and in male and female rats and hamsters increasing the development of forestomach carcinomas (NCI 2011; EPA 2008)  Negative results for carcinogenicity assessment carried out in female mice, male and female shrews and various fish species (NCI 2011; EPA 2008)

BHA was chosen as a test compound due to its longstanding use as a synthetic food antioxidant, its monophenolic structure. The structural similarity to polyphenolic test compound, TBHQ and the mixture of positive results for in vitro mammalian genotoxicity and negative results for in vivo genotoxicity make BHA

77 and ideal compound to assess whether MPAs and PPAs exert genotoxicity in mammalian cells in vitro through a similar mechanism.

4.3.2 Butylated hydroxytoluene Name Butylated hydroxytoluene Butylated hydroxytoluene (BHT), Table 4:14, is a CAS Number 128-37-0 MW 220 phenolic antioxidant. With a tert-butyl phenol structure, Structure the compound bears structural similarity to tert- butylhydroquinone, BHA and BHMP.

BHT is used as an antioxidant preservative in small amounts in food and in petroleum products. Despite evidence of acute toxicity it is considered safe for human consumption with an acceptable daily intake Table 4:14 The CAS number, molecular weight and structure of butylated hydroxytoluene (ADI) of 0.25 mg/kg and is given the designation E321 (EFSA Panel on Food Additives and Nutrient Sources added to food 2012).

IARC lists BHT as a Group 3 compound indicating that it is "not classifiable as to its carcinogenicity to humans" (IARC 1986; IARC 1987).

BHT produced the following results for carcinogenicity and genotoxicity:

 No evidence of carcinogenicity was observed in male and female rats or mice (EPA 2008; NTP 2013)  Negative results in the Ames test carried out in 5 strains of S. typhimurium and also E. coli with and without S9 activation (Hansen et al. 2009; NCI 2011; ISS 2011; NTP 2013)  Negative results for in vitro chromosome aberrations in CHO cells in the presence and absence of S9 activation (NCI 2011; Kirkland 2005; NTP 2013)  Positive results in the mouse lymphoma assay (Kirkland 2005; NTP 2013)*  Negative results in the in vivo micronucleus assay carried out in male mouse bone marrow (NTP 2013)  Negative results for in vivo chromosome aberrations (NTP 2013)

*Provided that only one positive result exists for genotoxicity for BHT and that positive result is from MLA studies carried out following the NTP protocol and as has been highlighted by the MLA workgroup of the International Workshop for Genotoxicity Tests (IWGT) the protocol followed by the NTP in their MLA assessment provides a high number of erroneous results (Moore et al. 2011). With this in mind, the MLA result detailed above may be of questionable value.

BHT, like BHA was chosen as a test compound due to its longstanding use as a synthetic food antioxidant, its monophenolic structure and the structural similarity to polyphenolic test compound, TBHQ. BHT has produced far fewer positive results for genotoxicity than BHA and shows no evidence of carcinogenicity in vivo. Any difference in results between BHA and BHT could provide insight into the role of oxidative stress induced genotoxicity in mammalian cells in vitro.

78 4.3.3 2,6-Di-tert-butyl-4-hydroxymethylphenol

2,6-Di-tert-butyl-4-hydroxymethylphenol (BHMP), Name 2,6-Di-tert-butyl-4- hydroxymethylphenol Table 4:15, is an alcohol metabolite of BHT. The CAS Number 88-26-6 compound displays antioxidant behaviour and has MW 236 historically been used as a food antioxidant under the Structure name IONOX 100.

At present, there are no published data for this compound in the Ames test, micronucleus assay, chromosomal damage assay or any other genotoxicity assay. in vivo carcinogenicity assessments carried out Table 4:15 The CAS number, molecular weight in male and female rats and mice showed no evidence and structure of 2,6-di-tert-butyl-4- hydroxymethylphenol of carcinogenicity (EPA 2008; Dacre 1970).

BHMP was chosen due to its structural similarity to tert-butyl phenols, TBHQ, BHA and BHT and its negative results for in vivo carcinogenicity.

4.3.4 Vanillic acid Name Vanillic acid Vanillic acid, Table 4:16, is a derivative of the CAS Number 121-34-6 MW 168 dihydroxybenzoic acid, protocatechuic acid whereby the Structure 3-hydroxyl group is methylated. It is also the oxidised form of phenolic antioxidant, vanillin. The compound structure consists of a phenyl ring with a carboxylic acid group bound to the 1-carbon, a methoxy group bound to the 3-carbon and a hydroxyl group bound to the 4- carbon. Table 4:16 The CAS number, molecular weight and structure of vanillic acid Vanillic acid has been shown to exhibit weak antioxidant behaviour in vitro (Brand-Williams et al. 1995). It is used in food as a flavouring and is naturally found in vanilla (Ranadive 1992).

Unlike structurally similar compounds, vanillic acid has been shown to inhibit iron autoxidation, a posited cause of PPA ROS generation (Chvátalová et al. 2008).

In the approach taken in this study, no publicly available data for carcinogenicity or genotoxicity assessment of vanillic acid were found.

Vanillic acid was chosen as a test compound due to being a food antioxidant, both naturally and as an additive. Vanillic acid, like γ-resorcylic acid has a phenolic acid structure however vanillic acids observed inhibition of transition metal autoxidation may lead to very different results between the two.

79 4.4 Non-phenolic antioxidants

Three diverse NPAs were tested alongside the phenolic antioxidants. These compounds were chosen to allow a comparison with results produced by polyphenolic and phenolic antioxidants. The antioxidants are distinct from one another; n-acetylcysteine is a biological antioxidant precursor used in the production of glutathione, l-ascorbic acid is a naturally occurring antioxidant and an essential nutrient for humans, ethoxyquin is an industrially produced antioxidant used in agriculture and historically in food. Although the investigation focussed on the possible pro-oxidant behaviour of PPAs, l-ascorbic acid has also been linked with pro-oxidant characteristics (Zhang & Omaye 2001). Below are detailed the structures, characteristics and uses of the compounds chosen along with detailed results of any existing genotoxicity and carcinogenicity assessment.

4.4.1 n-Acetylcysteine Name n-Acetylcysteine CAS Number 616-91-1 n-Acetylcysteine (NAC), Table 4:17, is a precursor in the MW 163 production of glutathione. The compound is Structure deacetylated to form the amino acid l-cysteine. L- Cysteine is often a rate limiting factor in the production of glutathione and the increase in l-cysteine therefore leads to an increase in physiological levels of Table 4:17 The CAS number, molecular weight glutathione (Sen 2001). and structure of n-acetylcysteine

NAC is used medically as a mucolytic agent and as a detoxifying agent in the treatment of paracetamol (acetaminophen) poisoning. Its capacity for detoxification is as a result of the increasing physiological glutathione levels (Oxford University Press 2006). n-Acetylcysteine produced the following results in tests for genotoxicity and carcinogenicity:

 Negative results in the Ames test carried out in 3 strains of S. typhimurium (ISS 2011)  Positive results in the Ames test carried out in 3 strains of S. typhimurium following S9 activation (Hansen et al. 2009)  Negative results for carcinogenicity carried out in male rats (EPA 2008)

NAC was chosen as a test compound as it is a precursor to glutathione and an easy way to increase intracellular levels of the important antioxidant. Although there is evidence of mutagenicity in salmonella following metabolism with rat S9, negative results for carcinogenicity in male rats and no evidence linking NAC’s therapeutic use in humans with an increased incidence of cancer brings the physiological relevance of this result into question. In experiments without S9 activation, any mutagenic activity of NAC will likely not be a factor.

80 4.4.2 L-Ascorbic acid Name L-Ascorbic acid Ascorbic acid, Table 4:18, is an antioxidant compound that CAS Number 50-81-7 MW 176 exists naturally in many fruits and vegetables. It is an Structure essential nutrient for humans and is referred to as vitamin C. It is also used as an antioxidant and preservative in food. Its use as an additive is approved for human consumption including infants by the EU and it is given the designation E301 (European Parliament & Council on Table 4:18 The CAS number, molecular food additives 2011). The FDA classifies ascorbic acid as weight and structure of L-ascorbic acid GRAS (Food and Drug Administration 1979a).

L-Ascorbic acid has been tested in a wide range of assays for genotoxicity as well as carcinogenicity and has produced positive results in the micronucleus assay (both in vitro and in vivo) and in in vivo chromosome aberrations assessment (Kirkland 2005; NTP 2013). It has also produced equivocal results in the mouse lymphoma assay and Ames test (NTP 2013). These results are considered misleading positive results and are attributed to ascorbic acid being tested at excessive doses prior to the amendment to the proscribed top- dose for genotoxicity assessment (ICH 2011; Fowler et al. 2012; Tweats et al. 2006). The in vitro micronucleus assay and mouse lymphoma assay produced positive and equivocal results respectively at 4.26 mM and 2.27 mM respectively, doses higher than the 1 mM top-dose now recommended by the ICH for human pharmaceutical use (NTP 2013; Miller et al. 1995; ICH 2011). The doses tested in the in vivo studies do however fall within current guidelines (NTP 2013).

Ascorbic acid produced the following results for carcinogenicity and genotoxicity:

 Negative results in the Ames test carried out in 5 strains of S. typhimurium and also E. coli with and without S9 activation (ISS 2011; NTP 2013)  Positive results in the in vitro micronucleus assay in CHO cells tested in the absence of S9 (Corvi et al. 2008; Miller et al. 1995)  Negative results in the in vitro micronucleus assay in CHO cells tested in the absence of S9 (NCI 2011)  Negative results for in vitro chromosome aberrations in CHO cells with and without S9 activation (Kirkland 2005; NTP 2013)  Equivocal results in the mouse lymphoma assay (Kirkland 2005; NTP 2013)  Positive results in the in vivo micronucleus assay carried out in the bone marrow of male mice (NTP 2013)  Positive results for in vivo chromosome aberrations (NTP 2013)  Negative results for carcinogenicity assessment carried out in male and female rats and mice (EPA 2008; NTP 2013)

Ascorbic acid was chosen as a test compound as it is probably the most consumed dietary antioxidant and despite mixed results both in vivo and in vitro for genotoxicity, dietary consumption by humans and other animals has not been linked with carcinogenicity and is likely physiologically irrelevant (Williams & Jeffrey

81 2000; Valko et al. 2006). Any results generated using ascorbic acid that mirror those results generated using PPAs may be the result of antioxidant behaviour independent of their phenolic structure.

4.4.3 Ethoxyquin Name Ethoxyquin CAS Number 91-53-2 Ethoxyquin, Table 4:19, is a salt of lactic acid developed by MW 217 Monsanto in the 1950s for use as a preservative in animal Structure feed to preserve fat soluble vitamins. It was previously recognised as safe for human consumption in the EU and given the designation E324 however concern over liver toxicity led to its use as an additive to human food being Table 4:19 The CAS number, molecular removed from the most recent regulations on food weight and structure of ethoxyquin additives (European Parliament & Council on food additives 2011). It is considered safe for use as a pet food additive and a preservative in spices in the USA (Food and Drug Administration 2013b).

Ethoxyquin was also registered for use as the active ingredient in the pesticide, Stop-Scald in 1965. Its use continues in the USA (Environmental Protection Agency 2004) but its use is no longer permitted in the EU due to concerns regarding the level of residue left on fruit after treatment (European Food Safety Authority 2013).

Ethoxyquin, like several of the chosen phenolic test compounds shows not only antioxidant, but also pro- oxidant characteristics. This pro-oxidant behaviour was linked with the observed genotoxicity of ethoxyquin within the comet assay in human, peripheral blood lymphocytes producing a significant, dose-dependent increase in percentage of DNA in comet tails (Skolimowski et al. 2010).

Ethoxyquin produced the following results for carcinogenicity and genotoxicity:

 Negative results in the Ames test with and without S9 activation (Hansen et al. 2009; ISS 2011; NTP 2013)  Negative results for in vitro chromosome aberrations (NTP 2013)  Negative results in the in vivo micronucleus assay carried out in the bone marrow of male mice (NTP 2013)  Negative results for carcinogenicity assessment carried out in male and female rats (EPA 2008)

Ethoxyquin was chosen as a test compound due to being an antioxidant compound. Due to its molecular weight and O-bound phenyl ring structure the compound has limited structural similarities to some of the chosen polyphenols. The compound also shows no evidence of genotoxicity or carcinogenicity in regulatory assays. The compound’s antioxidant/pro-oxidant duality and evidence of genotoxicity in the comet assay present a similar picture to the plausibly misleading positive genotoxicity results of test compounds such as EGCG and resveratrol without a phenolic moiety.

82 4.5 Oxidants

Three oxidant compounds were chosen. These compounds act as positive controls in the assessments carried out for intracellular reactive oxygen species generation and oxidised DNA antibody binding. These compounds were tested in this investigation to lend insight into the results produced by known pro- oxidants allowing comparison and contrast with the results of PPAs that are thought to be acting in a pro- oxidant manner. Below are detailed the structures, characteristics and uses of the compounds chosen along with detailed results of any existing genotoxicity and carcinogenicity assessment.

4.5.1 tert-Butyl hydroperoxide Name tert-Butyl hydroperoxide CAS Number 75-91-2 tert-Butyl hydroperoxide (TBHP), Table 4:20, is an ROS MW 90.1 generating organic compound. TBHP consists of a Structure tertiary butyl structure with a hydroperoxyl group bound to the core carbon atom. It is used industrially in the initiation of polymerisation reactions (Shahani & Indictor 1978). The O-O bond of TBHP readily undergoes homolytic fission, generating hydroxyl and hydroperoxyl Table 4:20 The CAS number, molecular weight radicals (Kennedy et al. 1992). and structure of tert-butyl hydroperoxide

TBHP produced the following results in tests for genotoxicity:

 Positive results in the Ames test carried out in 5 strains of S. typhimurium and also E. coli with and without S9 activation (NCI 2011; ISS 2011; Hansen et al. 2009; NTP 2013)  Positive results for in vitro chromosome aberrations (Mohr; NTP 2013)

TBHP was chosen as a positive control for oxidative stress due to its ability to continually generate hydroperoxyl and hydroxyl ROS without the thermodynamic instability of hydrogen peroxide.

4.5.2 Hydrogen peroxide Name Hydrogen peroxide CAS Number 7722-84-1 Hydrogen peroxide, Table 4:21, is amongst the most MW 34.0 commonly used pro-oxidant positive control. Hydrogen Structure peroxide is itself a ROS and is capable of generating the more reactive hydroperoxyl and hydroxyl radical ROS after reacting with transition metal ions or UV-light. It is readily generated in cells through a variety of cellular reactions including in mitochondria. In mitochondria hydrogen Table 4:21 The CAS number, molecular weight and structure of hydrogen peroxide peroxide is generated as a by-product of cytochrome oxidase catalysed reactions (Oxford University Press 2006).

Hydrogen peroxide is GRAS for use as a preservative and bleaching agent in food, fats and oils, wines and spirits, cotton and dry food packaging and for treating wine and cheese (Food and Drug Administration 1979b; International Food Information Service 2009).

83 IARC lists hydrogen peroxide as a Group 3 compound indicating that it is "not classifiable as to its carcinogenicity to humans" (IARC 1985; IARC 1990; IARC 1987).

Hydrogen peroxide produced the following results in tests for genotoxicity and carcinogenicity:

 Positive results in the Ames test carried out in 5 strains of S. typhimurium and also E. coli with and without S9 activation (ISS 2011)  Positive results in the in vitro chromosome aberrations assay carried out in several cell lines including CHO cells (Corvi et al. 2008)  Positive results in the in vitro micronucleus assay carried out in Chinese hamster lung cells and human lymphocytes (Corvi et al. 2008)  Positive results for carcinogenicity assessment carried out in female mice (EPA 2008)  Negative results for carcinogenicity assessment carried out in male mice (EPA 2008)

Hydrogen peroxide was chosen as a positive control for oxidative stress as it is possibly the most widely used positive control for reactive oxygen species generation. The simple structure of hydrogen peroxide means that unlike TBHP, it is only able to take part in redox reactions.

4.5.3 Potassium bromate Name Potassium bromate CAS Number 7758-01-2 Potassium bromate, Table 4:22, is a salt with a strong MW 167 oxidant character. It was previously used as a dough Structure conditioner but has largely fallen out of use in developed countries following concerns regarding human carcinogenicity (International Food Information Service 2009).

Table 4:22 The CAS number, molecular weight Potassium bromate has been shown to oxidise DNA and and structure of potassium bromate that the DNA damage profile of KBrO3 is different to that of hydrogen peroxide and other hydroxyl radical generators (Ballmaier & Epe 2006). Further study showed that the oxidative DNA damage generated by KBrO3 is quite unique and that the compound doesn’t react efficiently with cellular machinery therefore presenting low levels of cytotoxicity but is able to oxidatively react directly with DNA without generating ROS (Ballmaier & Epe 2006).

IARC lists potassium bromate as a Group 2B compound indicating that it is "possibly carcinogenic to humans" (IARC 1985; IARC 1987; IARC 1999).

Potassium bromate produced the following results in tests for genotoxicity and carcinogenicity:

 Positive results in the Ames test carried out in 5 strains of S. typhimurium with and without S9 activation (ISS 2011; NTP 2013)  Positive results in the in vitro micronucleus assay in CHL cells (Corvi et al. 2008; Kirkland 2005)  Positive results for in vitro chromosome aberrations in CHL cells (Corvi et al. 2008; Kirkland 2005)  Positive results in the in vivo micronucleus assay carried out in the bone marrow cells of male mice (NCI 2011)

84  Positive results for carcinogenicity assessment carried out in male and female rats, mice and hamsters (EPA 2008)

Potassium bromate was chosen as a positive control for oxidative stress as it is an oxidant compound that, unlike TBHP and hydrogen peroxide is able to cause oxidative damage to DNA without generating ROS.

4.6 Genotoxins

To act as genotoxic controls in the GADD45a-GFP assay, six compounds were chosen which were known to reproducibly produce positive results for genotoxicity in the GADD45a-GFP assay. Chosen compounds have differing modes of genotoxic action including aneugenesis, clastogenesis and mutagenesis. These various modes of action cause DNA damage that is repaired or responded to by different DNA damage response pathways. It was important to test these compounds when addressing the effect of changing the oxygen tension during the GADD45a-GFP assay to ensure that changes to assay conditions were not leading to different qualitative results for known genotoxic compounds thereby reducing the assay’s sensitivity.

4.6.1 Bleomycin sulfate Name Bleomycin sulfate CAS Number 11056-06-7 Bleomycin sulfate, Table 4:23, is a glycopeptide MW 1510 antibiotic produced by the bacteria Streptomyces Structure verticillus. It is an oxygen-dependant genotoxin and is capable of halting cell cycle in the G2 phase. It is used as an antineoplastic chemotherapeutic agent used in the treatment of Hodgkin's lymphoma (Oxford University Press 2006). Bleomycin is also used as an x- Table 4:23 The CAS number, molecular weight ray-mimetic agent experimentally (Scott & Zampetti- and structure of bleomycin sulfate Bosseler 1985).

The generation of ROS was previously believed to be the core mode-of-action of bleomycin compounds however their sequence-selective degradation of DNA appears to be the result of oxidative transformation by a metal∙bleomycin radical. The metal∙bleomycin radical is a product of an oxygen-dependent metal chelation step with redox-active metal ions including but not limited to Fe2+ or Cu+ (Hecht 1999).

IARC lists bleomycin sulfate as a Group 2B compound indicating that it is "possibly carcinogenic to humans" (IARC 1981; IARC 1987).

Bleomycin sulfate has produced the following results in genotoxicity and carcinogenicity assessment:

 Inconclusive results in the Ames test carried out in 3 strains of S. typhimurium and also E. coli (NCI 2011; ISS 2011)  Positive results in the in vitro micronucleus assay in several cell lines including V79 (Corvi 2008)  Positive results for in vitro chromosome aberrations in several cell lines (Corvi 2008)  Positive results in the in vivo micronucleus assay carried out in the peripheral blood cells of male mice (NCI 2011)  Positive results for carcinogenicity carried out in male and female rats (Brambilla et al. 2012)  Equivocal results for carcinogenicity assessment carried out mice (Brambilla et al. 2012)

85 Bleomycin sulfate was chosen as a test compound to investigate whether assessing genotoxicity with a lower oxygen concentration during the incubation step might lead to a different qualitative result for genotoxicity, plausibly reducing the sensitivity of the assessment to this known genotoxic-carcinogen.

4.6.2 Etoposide Name Etoposide CAS Number 33419-42-0 Etoposide, Table 4:24, is an inhibitor of DNA MW 589 topoisomerase II, an enzyme involved in the relaxation Structure of DNA supercoiling through the introduction of strand breaks. Etoposide prevents the enzyme from re- ligating the DNA ends after relaxing the DNA supercoil, leading to clastogenesis. It is also able to inhibit the transport of nucleosides in mammalian cells. Because of these mechanisms, etoposide is used as a powerful Table 4:24 The CAS number, molecular weight antitumour agent in the treatment of cancer (Mosesso and structure of etoposide et al. 1998; Oxford University Press 2006).

Etoposide is a compound in the ECVAM recommended list for assessment of new genotoxicity tests. Etoposide is listed within the “Ames-positive in vivo genotoxins”. The compound has previously been assessed using the GADD45a-GFP assay producing a positive result for genotoxicity without S9 metabolic activation and a negative result for genotoxicity following S9 metabolic activation (Birrell et al. 2010).

IARC originally listed etoposide as a Group 2A compound (IARC 2000), however in light of further understanding of the mechanisms by which etoposide can lead to DNA damage, the compound was reassessed as a Group 1 compound (IARC 2012). This indicates that etoposide is "carcinogenic to humans".

Etoposide produced the following results in tests for genotoxicity and carcinogenicity:

 Negative results in the Ames test carried out in 3 strains of S. typhimurium and also E. coli with and without S9 activation (NCI 2011; ISS 2011)  Positive results in the in vitro micronucleus assay in CHO cells with and without S9 activation and in TK6 cells (NCI 2011)  Positive results in the in vitro chromosome aberrations assay in CHL cells (NCI 2011)  Positive results in the in vivo micronucleus assay carried out in the bone marrow cells of male and female mice and the peripheral blood cells of male mice (NCI 2011)  Negative results for carcinogenicity assessment carried out in NF1 transgenic knockout mice (Brambilla et al. 2012)

Etoposide was chosen as a test compound to act as a model clastogen.

86 4.6.3 5-Fluorouracil Name 5-Fluorouracil 5-fluorouracil, Table 4:25, is a pyramidine analogue used CAS Number 51-21-8 MW 130 routinely as an anticancer drug used against fast Structure growing tumours (Oxford University Press 2006).

5-Fluorouracil acts as an analogue to uracil and is converted inside the cell into fluorodeoxyuridylate which in turn acts as a potent inhibitor of thymidylate synthase inhibiting DNA synthesis. Table 4:25 The CAS number, molecular weight and structure of 5-fluorouracil IARC lists 5-fluorouracil as a Group 3 compound indicating that it is "not classifiable as to its carcinogenicity to humans" (IARC 1981; IARC 1987).

5-Fluorouracil produced the following results in tests for genotoxicity and carcinogenicity:

 Negative results in the Ames test carried out in 5 strains of S. typhimurium and also E. coli with and without S9 activation (Hansen et al. 2009; NCI 2011; ISS 2011; NTP 2013)  Positive results in the in vitro micronucleus assay in several cell lines including CHL and V79 cells with and without S9 activation (Corvi et al. 2008; Kirkland 2005)  Positive results for in vitro chromosome aberrations in several cell lines including CHO and CHL cells (Corvi et al. 2008; Kirkland 2005)  Positive results in the mouse lymphoma assay (Kirkland 2005)  Positive results in the in vivo micronucleus assay carried out in the bone marrow cells of male and female mice and the peripheral blood cells of male mice (NCI 2011)  Positive results for carcinogenicity assessment carried out in male and female rats (Brambilla et al. 2012; EPA 2008)  Negative results for carcinogenicity assessment carried out in male and female mice (Brambilla et al. 2012; EPA 2008)

5-Fluorouracil was chosen as a test compound to act as a model mammalian mutagen.

4.6.4 Methyl methanesulfonate Name Methyl methanesulfonate CAS Number 66-27-3 Methyl methane sulfonate (MMS), Table 4:26, is a DNA MW 110 alkylating agent that methylates DNA nucleosides. These Structure adducts are thought to stall replication forks (Lundin et al. 2005).

MMS is regularly used experimentally as a positive control

for assessing mutagenicity, carcinogenicity and Table 4:26 The CAS number, molecular teratogenicity (Royal Society of Chemistry 2013). The DNA weight and structure of methyl methanesulfonate damage generated by MMS is repaired through the nucleotide excision repair pathway (Reagan et al. 1995).

MMS is a compound in the ECVAM recommended list for assessment of new genotoxicity tests. MMS is listed within the “Ames-positive in vivo genotoxins”. The compound has previously been assessed using the 87 GADD45a-GFP assay producing a positive result for genotoxicity with and without S9 metabolic (Birrell et al. 2010).

IARC lists MMS as a Group 2A compound indicating that it is "probably carcinogenic to humans" (IARC 1974; IARC 1987; IARC 1990).

MMS produced the following results in tests for genotoxicity and carcinogenicity:

 Positive results in the Ames test carried out in 3 strains of S. typhimurium and also E. coli with and without S9 activation (Hansen et al. 2009 ; NCI 2011; ISS 2011; NTP 2013)  Positive results in the in vitro micronucleus assay in several cell lines including CHL cells in the absence of S9 (Corvi et al. 2008; Kirkland 2005)  Positive results for in vitro chromosome aberrations several cell lines in the absence of S9 (Corvi et al. 2008; Kirkland 2005)  Positive results in the mouse lymphoma assay (Kirkland 2005)  Positive results in the in vivo micronucleus assay carried out in the bone marrow cells of female mice and the peripheral blood of male rats (NCI 2011)  Positive results for carcinogenicity assessment carried out in male rats and male and female mice (EPA 2008; NCI 2011)

MMS was chosen as a positive control for genotoxicity as it is the most widely used model mutagen and is able to reproducibly induce a clear response in the GADD45a-GFP assay.

4.6.5 4-Nitroquinoline -1-oxide Name 4-Nitroquinoline-1-oxide CAS Number 56-57-5 4-nitroquinoline-1-oxide (NQO), Table 4:27, is a genotoxic MW 190 quinolone derivative that induces DNA lesions. Structure NQO is used as a UV-mimetic agent (Oxford University Press 2006). It is capable of producing DNA adducts and also of oxidising DNA, increasing levels of 8-oxo-guanine

(Kohda et al. 1986). The DNA damage generated by NQO Table 4:27 The CAS number, molecular is repaired via the nucleotide excision repair pathway (Tao weight and structure of 4-nitroquinoline-1- et al. 1987). oxide

NQO produced the following results in tests for genotoxicity and carcinogenicity:

 Positive results in the Ames test carried out in 5 strains of S. typhimurium and also E. coli with and without S9 activation (NCI 2011; ISS 2011)  Positive results in the in vitro micronucleus assay carried out in L5178Y cells (Corvi et al. Kirkland 2005)  Positive results for in vitro chromosome aberrations carried out in various cell lines (Corvi et al. 2008; Kirkland 2005; NTP 2013)  Positive results in the mouse lymphoma assay (Kirkland 2005; NTP 2013)  Positive results for in vivo chromosome aberrations (NTP 2013)

88  Positive results for carcinogenicity assessment carried out in male and female rats and mice (NCI 2011)

NQO was chosen as a test compound to act as a model genotoxic carcinogen. NQO’s ability to oxidise DNA bases and its positive results for carcinogenicity in rodents make it important to ensure that any change to the concentration of oxygen present during the incubation step of the GADD45a-GFP assay does not lead to a qualitative change in the result produced by the assay.

4.6.6 Vincristine sulfate Name Vincristine sulfate CAS Number 2068-78-2 Vincristine sulfate, Table 4:28, is an alkaloid compound MW 923 extracted from Vinca rosea that acts as a spindle poison, Structure binding to spindle microtubules and arresting cells in mitotic metaphase. This leads to the mis-segregation of chromosomes within daughter cells. Vincristine is used as a medical treatment for cancer as an antineoplastic (Oxford University Press 2006).

Table 4:28 The CAS number, molecular weight IARC lists vincristine sulfate as a Group 3 compound and structure of vincristine sulfate indicating that it is "not classifiable as to its carcinogenicity to humans" (IARC 1981; IARC 1987).

Vincristine sulfate produced the following results in tests for genotoxicity and carcinogenicity:

 Negative results in the Ames test carried out in 3 strains of S. typhimurium and also E. coli with and without S9 activation (NCI 2011; NTP 2013)  Positive results in the in vitro micronucleus assay in several cell lines (Corvi et al. 2008)  Positive results for in vitro chromosome aberrations tested in CHL cells (Corvi et al. 2008)  Negative results in the in vivo micronucleus assay carried out in the bone marrow of male mice (NTP 2013)  Equivocal results for carcinogenicity assessment carried out in mice and rats (Brambilla et al. 2012)

Vincristine sulfate was chosen as a test compound as its ability to act as a spindle poison makes it a model aneugen.

89 4.7 Non-genotoxic, cytotoxic compounds

To act as non-genotoxic, cytotoxic controls in the GADD45a-GFP assay, two compounds were chosen which were known to reproducibly produce positive results for cytotoxicity and negative results for genotoxicity in the GADD45a-GFP assay. It was important to test these compounds when addressing the effect of changing the oxygen tension during the GADD45a-GFP assay to ensure that changes to assay conditions were not leading to different qualitative results for known cytotoxic, non-genotoxic compounds.

4.7.1 2,4-Dichlorophenol Name 2,4-Dichlorophenol 2,4-Dichlorophenol (DCP), Table 4:29, is a toxic CAS Number 120-83-2 MW 163 chlorinated phenol compound that is a degradation Structure product of triclosan (Latch et al. 2005).

DCP was chosen as a non-genotoxic, cytotoxic control compound due to its reproducible cytotoxic results and non-genotoxic results in the GADD45a-GFP assay.

Table 4:29 The CAS number, molecular DCP is a compound in the ECVAM recommended list for weight and structure of 2,4-dichlorophenol assessment of new genotoxicity tests. DCP is listed within the “supplementary list (prediction of in vitro genotoxicity tests less clear)”. The compound has previously been assessed using the GADD45a-GFP assay producing a negative result for genotoxicity without S9 metabolic activation and a positive result for genotoxicity following S9 metabolic activation (Birrell et al. 2010). This positive result was produced at a dose not tolerated in the protocol without S9 and is therefore possibly irrelevant.

DCP produced the following results in tests for genotoxicity and carcinogenicity:

 Negative results in the Ames test carried out in 5 strains of S. typhimurium (Hansen et al. 2009; ISS 2011)  Equivocal results in the Ames test (NTP 2013)  Negative results for in vitro chromosome aberrations (NTP 2013)  Positive results in the mouse lymphoma assay (NTP 2013)  Negative results for carcinogenicity assessment carried out in male and female rats and mice (EPA 2008; NTP 2013)

*Provided that only one positive result exists for genotoxicity for DCP and that positive result is from MLA studies carried out following the NTP protocol and as has been highlighted by the MLA workgroup of the International Workshop for Genotoxicity Tests (IWGT) the protocol followed by the NTP in their MLA assessment provides a high number of erroneous results (Moore et al. 2011). With this in mind, the MLA result detailed above may be of questionable value.

DCP was chosen as a test compound as it is able to reproducibly produce positive results for cytotoxicity and negative results for genotoxicity within the GADD45a-GFP assay. Assessment of the compound’s genotoxicity, incubated at lowered oxygen concentrations will indicate whether the assay is still able to reproducibly produce negative results for genotoxicity in the absence of S9.

90 4.7.2 Phenformin hydrochloride Name Phenformin hydrochloride Phenformin hydrochloride, Table 4:30, is an anti- CAS Number 834-28-6 MW 242 diabetic treatment that has since been superseded by Structure other drugs due to its tendency to cause fatal incidences of lactic acidosis (Brayfield 2014).

Table 4:30 The CAS number, molecular weight Phenformin hydrochloride was chosen as a non- and structure of phenformin hydrochloride genotoxic, cytotoxic control compound due to its reproducible cytotoxic results and non-genotoxic results in in vitro genotoxicity assessment including the GADD45a-GFP assay.

Phenformin hydrochloride is a compound in the ECVAM recommended list for assessment of new genotoxicity tests. Phenformin hydrochloride is listed within the “Non-carcinogens with no in vivo genotoxicity data”. The compound has previously been assessed using the GADD45a-GFP assay, producing a negative result for genotoxicity with and without S9 metabolic (Birrell et al. 2010).

Phenformin hydrochloride produced the following results in tests for genotoxicity and carcinogenicity:

 Negative results in the Ames test carried out in 3 strains of S. typhimurium with and without S9 activation (Hansen et al. 2009; ISS 2011; NTP 2013)  Negative results for in vitro chromosome aberrations (Kirkland 2005; NTP 2013)  Equivocal results in the mouse lymphoma assay (Kirkland 2005; NTP 2013)  Negative results for carcinogenicity assessment carried out in male and female rats and mice (EPA 2008; NTP 2013)

Phenformin hydrochloride was chosen as a test compound as it is able to reproducibly produce positive results for cytotoxicity and negative results for genotoxicity within the GADD45a-GFP assay. Assessment of the compound’s genotoxicity, incubated at lowered oxygen concentrations will indicate whether the assay is still able to reproducibly produce negative results for genotoxicity.

4.8 Miscellaneous compounds

Various other compounds were assessed as part of this investigation. The reason for choosing each compound is individually detailed.

4.8.1 2-tert-Butyl-1,4-benzoquinone Name 2-tert-Butyl-1,4- benzoquinone 2-tert-Butyl-1,4-benzoquinone (TBQ), Table 4:31, is the CAS Number 3602-55-9 most common metabolite of TBHQ. It is the compound MW 164 thought to be responsible for the positive results that Structure TBHQ produces for chromosomal aberrations and micronucleus assessment following metabolic activation with S9 (Gharavi et al. 2007).

TBQ produced the following results in tests for Table 4:31 The CAS number, molecular genotoxicity: weight and structure of 2-tert-butyl-1,4- benzoquinone  Positive results in the Ames test with and without S9 activation (Hansen et al. 2009)  Negative results in the Ames test tested in 3 strains of typhimurium with and without S9 activation (ISS 2011)

TBQ was chosen as a test compound to provide insight into the potential of the metabolite to generate ROS or oxidative DNA damage without having to metabolise TBHQ using S9.

4.8.2 Carbonyl cyanide m-chlorophenyl hydrazine

Carbonyl cyanide m-chlorophenyl hydrazine (CCCP), Name Carbonyl cyanide m- chlorophenyl hydrazine Table 4:32, is a chemical that powerfully disrupts the CAS Number 555-60-2 membrane potential of mitochondria (Oxford University MW 205 Press 2006). The ionophore, CCCP, as a lipid-soluble Structure weak acid passes through the membrane of mitochondria in a protonated form. Therein it becomes anionic and leaves the mitochondrion, short-circuiting

the charge gradient between the inside and outside of Table 4:32 The CAS number, molecular weight the mitochondrion (McQueen et al. 2010). and structure of carbonyl cyanide m- chlorophenyl hydrazine In the approach taken in this study, no publicly available data for carcinogenicity or genotoxicity assessment of CCCP were found.

CCCP was chosen as a positive control to be used in the JC-1 assessment of mitochondrial membrane potential. CCCP was also tested in the GADD45a-GFP assay and DCFH-DA assessment to give a broader understanding of the role that mitochondrial disruption may play in observed genotoxicity and ROS generation of the PPAs.

92 4.8.3 Phenol Name Phenol Structurally phenol, Table 4:33, consists of a phenyl CAS Number 108-95-2 MW 94.1 group with a single hydroxyl group. The chemical is not Structure an antioxidant and is primarily used industrially in the production of organic polymers and as a precursor for various pharmaceuticals including aspirin.

Phenol's previous household use as carbolic soap fell Table 4:33 The CAS number, molecular weight out of favour later in the 20th century due to its toxicity and structure of phenol and the availability of safer alternatives. It is still used as disinfectant and medically is used as a topical anaesthetic, antiseptic and antipruritic agent (Royal Society of Chemistry 2013).

Phenol is known to cause a wide variety of toxic effects in humans ranging in severity from dermatitis to lung oedema and coma. There is no observed link between phenol and human carcinogenicity. IARC lists phenol as a Group 3 compound indicating that it is "not classifiable as to its carcinogenicity to humans".

Phenol produced the following results in tests for genotoxicity and carcinogenicity:

 Negative results in the Ames test carried out in 5 strains of S. typhimurium and also E. coli with and without S9 activation (Hansen et al. 2009; NCI 2011; ISS 2011; NTP 2013)  Positive results for in vitro micronucleus assay conducted in CHL and CHO cells with and without S9 activation (Corvi et al. 2008; Kirkland 2005)  Positive results for in vitro chromosome aberrations in CHO cells in the presence of S9 (Corvi et al. 2008; Kirkland 2005; NTP 2013)  Equivocal results for in vitro chromosome aberrations in CHO cells tested in the absence of S9 (Corvi et al. 2008)  Positive results in the mouse lymphoma assay (Kirkland 2005; NTP 2013)  Positive results in the in vivo micronucleus assay carried out in bone marrow cells of male mice (NCI 2011; NTP 2013)  Positive results for in vivo chromosome aberrations (NTP 2013)  Negative results for carcinogenicity assessment carried out in male and female rats and mice (EPA 2008; NTP 2013)

Phenol was chosen as a test compound to add insight into whether the phenol structure, independent of any antioxidant behaviour could behave similarly to the chosen phenolic antioxidants.

93 4.8.4 Staurosporine Name Staurosporine Staurosporine, Table 4:34, is an antibiotic compound CAS Number 62996-74-1 produced by Streptomyces and is an inhibitor of protein MW 467 Structure kinase C (Oxford University Press 2006). It is a potent apoptosis inducer, thought to be a consequence of caspase-3 activation (Chae et al. 2000).

Staurosporine has previously been tested in the

GADD45a-GFP assay and showed no indication of Table 4:34 The CAS number, molecular genotoxic potential (Topham et al. 2012). weight and structure of staurosporine

Staurosporine was chosen to allow a clearer understanding of apoptosis induction in the breakdown of mitochondria. Allowing a comparison of the results produced by staurosporine and those produced by PPAs, shedding light on whether an apoptotic pathway to mitochondrial membrane disruption is plausible.

94 4.9 Pro-genotoxins

Three pro-genotoxins were chosen to be assessed alongside phenolic antioxidant compounds in the GADD45a-GFP assessment with S9 metabolic activation. Each of the three compounds is metabolised in the presence of S9 to form genotoxic products. These genotoxic products are genotoxic through diverse mechanisms.

4.9.1 2-Acetylaminofluorene Name 2-Acetylaminofluorene CAS Number 53-96-3 2-Acetylaminofluorene (AAF), Table 4:35, is a potent pro- Structure genotoxin that is a substrate for cytochrome P450 monooxygenases. These enzymes convert AAF into N- hydroxy-2-acetylaminofluorene. This intermediate is then converted by cellular N-acetyltransferase into N-acetyl-N- Table 4:35 The CAS number, molecular acetoxyaminofluorene which is then capable of reacting weight and structure of 2- directly with DNA forming adducts (Heflich & Neft 1994). acetylaminofluorene

AAF produced the following results in tests for genotoxicity and carcinogenicity:

 Positive results in the Ames test following S9 activation (Hansen et al. 2009; ISS 2011)  Positive results in the in vitro micronucleus assay (Kirkland 2005)  Positive results in the in vitro chromosome aberrations assay (Kirkland 2005)  Positive results in the mouse lymphoma assay (Kirkland 2005; NTP 2013)  Positive results for carcinogenicity when tested in male and female mice rats and hamsters (EPA 2008)

AAF was chosen as a test compound for assessment in the GADD45a-GFP assay with S9 metabolic activation to act as a model pro-genotoxin.

4.9.2 6-Aminochrysene Name 6-Aminochrysene 6-Aminochrysene (6-AC), Table 4:36, is a less well CAS Number 2642-98-0 Structure characterised pro-genotoxin than the other two tested in this investigation. 6-AC is known to be a substrate of cytochrome P450s and produce positive results in the

Ames test following metabolic activation by S9 exposure Table 4:36 The CAS number, molecular (Yamazaki et al. 1993). It is believed that following this weight and structure of 6-aminochrysene metabolism, highly reactive nitrenium ions are formed that are responsible for a direct attack upon DNA bases (Marczylo & Ioannides 1994).

6-Aminochrysene produced the following results in tests for genotoxicity:

 Positive results in the Ames test following S9 activation (Hansen et al. 2009; ISS 2011)

6-AC was chosen as a test compound for assessment in the GADD45a-GFP assay with S9 metabolic activation to act as a model pro-genotoxin.

95 4.9.3 Cyclophosphamide Name Cyclophosphamide Cyclophosphamide (CPA), Table 4:37, is a pro-genotoxic CAS Number 50-18-0 agent that is metabolised by cytochrome P450s in vivo or Structure in S9 treated in vitro tests into 4- hydroxycyclophosphamide and aldophosphamide. These

are then converted to phosphoramine mustard and acrolein (Oxford University Press 2006). Phosphoramine Table 4:37 The CAS number, molecular weight and structure of cyclophosphamide mustard then causes DNA crosslinks between guanine residues (Hall & Tilby 1992).

CPA is used as a chemotherapeutic agent in the treatment of haematological malignancies (Hall & Tilby 1992).

CPA is a compound in the ECVAM recommended list for assessment of new genotoxicity tests. CPA is listed within the “Ames-positive in vivo genotoxins”. The compound has previously been assessed using the GADD45a-GFP assay producing a negative result for genotoxicity without S9 metabolic activation and a positive result for genotoxicity following S9 metabolic activation (Birrell et al. 2010).

IARC lists cyclophosphamide as a Group 1 compound indicating that it is "carcinogenic to humans".

Cyclophosphamide produced the following results in tests for genotoxicity and carcinogenicity:

 Positive results in the Ames test following S9 activation (Hansen et al. 2009; ISS 2011)  Positive results in the in vitro micronucleus assay (Kirkland 2005)  Positive results in the in vitro chromosome aberrations assay (Kirkland 2005)  Positive results in the mouse lymphoma assay (Kirkland 2005; NTP 2013)  Positive results in the in vivo micronucleus assay carried out in the bone marrow cells of male mice and rats and in the peripheral blood cells of male mice (NCI 2011; NTP 2013)  Positive results for carcinogenicity when tested in male and female mice and rats (EPA 2008)

CPA was chosen as a test compound for assessment in the GADD45a-GFP assay with S9 metabolic activation to act as a model pro-genotoxin.

96 4.10 Discussion

This literature search was carried out in order to find a diverse selection of compounds to test the hypotheses posed by this thesis presented in Section 2.7. A broad sample of polyphenolic, monophenolic and non-phenolic antioxidants needed to be selected from the essentially uncountable number that exist naturally or could be manufactured. These chosen compounds needed to have diverse size, structure and phenolic character. The compounds would ideally also have existing results for genotoxicity and carcinogenicity within in vitro and in vivo studies.

The antioxidants chosen vary in size between the smallest polyphenol, resorcinol (MW - 110) and the relatively large polyphenol, EGCG (MW - 458). They also include 16 phenolic antioxidants with differing phenolic moieties, from those with one phenol group, such as BHA, to EGCG with 8 hydroxyl groups bound to three phenyl rings. The structural groups into which the phenols fall are also diverse, including simple phenols such as resorcinol; phenolic acid esters such as propyl gallate; tert-butyl phenols such as BHA; a flavonoid, quercetin; and a hydroxystilbene, resveratrol. It is expected that by choosing a diverse range of phenolic antioxidants, an understanding of the role that compound size, structure and phenolic character of antioxidants contributes to the results of their assessment in this thesis. This allows the conclusions drawn by the research presented in this thesis to be viewed in the wider context of the countless thousands of phenolic antioxidants that exist. Thirteen of the 16 chosen phenolic antioxidants have previously been used as food additives or are found naturally in food, but prior to this study, there has not been a systematic assessment of genotoxicity using a single test, to allow ranking of potency within the group. This makes it imperative to assess the entire list of compounds presented above in a single mammalian in vitro genotoxicity assay. Three non-phenolic antioxidants were also chosen to be assessed. The results generated by these chemicals should help highlight how phenolic antioxidants mechanistically differ from NPAs, which are not linked with the generation of misleading positive results for genotoxicity. Positive control compounds for genotoxicity, non-genotoxic cytotoxicity, pro-oxidant DNA damage, apoptosis induction and mitochondrial depolarisation were also chosen.

By doing this, it will allow direct comparison of the results for the various phenolic antioxidants with one- another and with pro-oxidant and genotoxic chemicals.

There is no existing literature that has sought to bring together such broad data regarding the genotoxicity and carcinogenicity assessment of phenolic antioxidants. In hindsight however, the chosen test compounds might not have been the most suitable. Ideally, antioxidant compounds with a greater deal of results in existing genotoxicity and carcinogenicity assessment would have been chosen. By choosing better characterised test compounds, greater insight could have been gained on the reasons underlying the results in later chapters within this thesis. Using the insight gained through the research carried out during this project, a better selection of test compounds could have been selected. By only choosing phenolic antioxidants that have existing results within bacterial mutagenicity studies, at least one in vitro mammalian genotoxicity studies and at least one in vivo genotoxicity or carcinogenicity study.

In the following Results chapter, the genotoxicity of the selected compounds will be evaluated using the GADD45a-GFP assay. The following five results chapters report investigations into the underlying mechanism by which PPAs might produce positive results when tested using in vitro mammalian genotoxicity assays. 97 in vitro in vivo

Ames MNT CA MLA MNT CA Carc. Page

Compound

Group Compound name Apomorphine hydrochloride P 67

tert-Butylhydroquinone N P E N N N 67 Dodecyl gallate 68

Epigallocatechin gallate N N N N 69 Nordihydroguaiaretic acid N P 69

Polyphenolic Octyl gallate 70

antioxidants Propyl gallate N E P P E P N,E 71 Pyrogallol P P E N,E 72 Quercetin P P P I P,N 73 Resorcinol N P P P P N 74 γ-Resorcylic acid 75

Resveratrol N P P N 75

Butylated hydroxyanisole N P P N P,N 77 Butylated hydroxytoluene N N P N N N 78 Monophenolic 2,6-Di-tert-butyl-4- antioxidants N 79 hydroxymethylphenol Vanillic acid 79

n-Acetylcysteine N,P N 80 Non-phenolic L-Ascorbic acid N P,N N E P P N 81 antioxidants Ethoxyquin N N N N 82 tert-Butyl hydroperoxide P P 83

Oxidants Hydrogen peroxide P P P P,N 83 Potassium bromate P P P P P 84 Bleomycin sulfate I P P P P, E 85 Etoposide N P P P P N 86 5-Fluorouracil N P P P P P,N 87 Genotoxins Methyl methanesulfonate P P P P P P 87 4-Nitroquinoline-1-oxide P P P P P P 88 Vincristine sulfate N P P N E 89 Non-genotoxic, 2,4-Dichlorophenol N, E N P N 90 cytotoxic compounds Phenformin hydrochloride N P N E N 91 2-tert-Butyl-1,4- P,N 92 benzoquinone Carbonyl cyanide m- Miscellaneous 92 compounds chlorophenyl hydrazone Phenol N P, E P P P P N 93 Staurosporine 94 2-Acetylaminofluorene P P P P P P 95 Pro-genotoxins 6-Aminochrysene P 95

Cyclophosphamide P P P P P P 96 Table 4:38: An Index of the Compounds used in this Investigation The table above shows the compounds investigated within this thesis, the group into which the compound was placed, the page on which a summary of the literature search for each compound and a summary of the results from the existing assessment of the genotoxicity or carcinogenicity of each compound. P denotes a positive result; N denotes a negative result; E denotes an equivocal result; I denotes an inconclusive result; where two results are presented, two different results were produced under differing conditions or in different investigations. For further details of each result, please consult the section relating to the specific compound. 98 Compound Group Compound name Reasons for compound choice Apomorphine hydrochloride Autoxidant, positive Ames data tert-Butylhydroquinone PMP, widely studied, found in food, no links to carc., Dodecyl gallate Food antioxidant, similar to propyl gallate Epigallocatechin gallate PMP, widely studied, found in green tea Nordihydroguaiaretic acid Autoxidant, negative Ames data Polyphenolic Octyl gallate Food antioxidant, similar to propyl gallate antioxidants Propyl gallate PMP, widely studied, food additive Pyrogallol Autoxidant, positive Ames data, toxic Quercetin PMP, widely studied, found in fruit and vegetables Resorcinol PMP, widely studied, simple structure γ-Resorcylic acid Simple phenolic acid structure Resveratrol PMP, Widely studied, found in wine Butylated hydroxyanisole Widely studied food antioxidant, similarity to TBHQ Butylated hydroxytoluene Food antiox., Similarity to TBHQ, no evidence of carc. Monophenolic 2,6-Di-tert-butyl-4- antioxidants Similarity to TBHQ, no evidence of carc. hydroxymethylphenol Vanillic acid Found in food, GRAS, inhibits autoxidation n-Acetylcysteine Glutathione precursor Non-phenolic L-Ascorbic acid Widely studied food antioxidant, GRAS antioxidants Ethoxyquin Structural similarity to phenolic antioxidants tert-Butyl hydroperoxide Stable ROS-mediated oxidant Oxidants Hydrogen peroxide Widely used ROS-mediated oxidant Potassium bromate Direct-acting oxidative genotoxin Bleomycin sulfate Oxygen-dependent genotoxin Etoposide Clastogen 5-Fluorouracil Base analogue mutagen Genotoxins Methyl methanesulfonate Adduct-forming mutagen 4-Nitroquinoline-1-oxide Mutagen known to generate oxidised DNA bases Vincristine sulfate Aneugen Non-genotoxic, 2,4-Dichlorophenol Non-genotoxic cytotoxin, oxidative cytotoxic compounds Phenformin hydrochloride Non-genotoxic cytotoxin, non-oxidative 2-tert-Butyl-1,4- Metabolic product of TBHQ benzoquinone Carbonyl cyanide m- Miscellaneous Mitochondrial membrane depolarising agent compounds chlorophenyl hydrazone Phenol Simplest phenol moiety, no antioxidant activity Staurosporine Apoptogen 2-Acetylaminofluorene Progenotoxic clastogen Pro-genotoxins 6-Aminochrysene Progenotoxic direct mutagen Cyclophosphamide Progenotoxic indirect mutagen Table 4:39: A summary of the reasons underlying the compounds chosen The table above shows the compounds investigated within this thesis, summarised next to each chemical are the reason(s) for their choice. PMP denotes plausibly misleading positive results for in vitro mammalian genotoxicity assessment.

99 5 Results II: Most polyphenolic antioxidants produce positive results in the GADD45a-GFP genotoxicity assay 5.1 Introduction

This chapter presents results from an assessment of genotoxicity using the GADD45a-GFP assay, in a collection of 37 compounds including a high proportion of PPAs. All compounds detailed in Section 4, except for three pro-genotoxic controls, were tested in the GADD45a-GFP assay without S9 activation (methodology described in Section 3.3.1.6). A sub-group of 18 compounds, including the three progenotoxic controls, were tested in the GADD45a-GFP assay with S9 activation (methodology described in Section 3.3.1.7).

This is the first time that such a group of compounds has been tested with the same assay using the same protocols, and hence provides a better understanding of the prevalence and potency of positive genotoxicity results amongst them. Most have only limited data from one or more different genotoxicity assays in several different cell-lines (Section 4).

5.2 The autofluorescent and light absorbing properties of propyl gallate, nordihydroguaiaretic acid and epigallocatechin gallate confound spectrophotometric data collection

Data from the GADD45a-GFP assay are usually collected using a microplate spectrophotometer (See Section 3.4.4.2). In this method, the 535 nm fluorescence emission produced by 485 nm excitation, normalised to cell density measured by visible light absorbance (“brightness”), is used to detect dose-dependent increases in expression of GADD45a and, by implication, genotoxicity in the test sample. A dose dependant decrease in cell density provides a measure of toxicity.

Propyl gallate, NDGA and EGCG were all autofluorescent and light-absorbing to a degree that confounded assay interpretation using the spectrophotometric method. Results for the three compounds are shown in Figure 5:1A-C. As a consequence, these compounds were re-assessed using fluorescence polarisation optics to reduce the interference caused by the autofluorescence. This was not however effective in allowing the discrimination of the GFP signal from autofluorescence in the cell-free compound wells containing NDGA or EGCG below the threshold of 2× the fluorescence of the compound-free, cell-free well. The data for these two compounds were therefore not interpretable (Figure 5:1D-E). Measuring the fluorescence of cells treated with propyl gallate using fluorescence polarisation optics did reduce the compound fluorescence to a sufficient degree to allow the detection of the GFP fluorescence Figure 5:1F. As a consequence, a second approach, flow cytometry, was used. There are two reasons why this can be a better approach: data are collected from individual cells rather than cells support medium; cells are diluted in the sheath buffer during analysis and this dilutes the interfering signal from the compound in the growth medium.

Collecting data from GADD45a-GFP assessment of the 3 compounds using a FACS Calibur flow cytometer prevented the compounds’ light absorption and autofluorescence in the media from interfering with interpretation of the data allowing a clear assessment of cytotoxicity and genotoxicity to be made. Results for the three compounds are shown in Figure 5:1G-I.

100

Figure 5:1 EGCG, NDGA and propyl gallate autofluorescence interferes with GADD45a -GFP assay data collection by microplate spectrophotometer . Panels A, B and C show the GFP brightness of GenM-C01 cells ( ) and GenM-T01 cells ( ) treated with EGCG, NDGA and propyl gallate respectively. Fluorescence measurements were made by plate spectrophotometer without fluorescence polarisation. All three compounds show a dose dependent increase in brightness in the control (GenM-C01) cells indicating compound fluorescence. This interferes with the measurement of GFP brightness in the test (GenM-T01) cells. Panels D, E and F show the GFP brightness of cells treated with the same compounds measured by plate spectrophotometer with fluorescence polarisation. The polarisation does not remove the compound fluorescence observed in cells treated with EGCG and NDGA (D & E). A reduction in compound fluorescence is seen however seen in control cells treated with propyl gallate (F) allowing the measurement of GFP brightness of the test cells. Panels G, H and I show the measurement of GFP induction by flow cytometer. Collecting assay data by flow cytometry lead to a far lower level of autofluorescence allowing an interpretation of the results.

101 5.3 Results for GADD45a-GFP genotoxicity assay in the absence of S9

The results of assessing 34 compounds (Section 4) in the GADD45a-GFP assay without S9 are presented below. These results are summarised in Table 5:1 and Table 5:2. Sample graphical data for four of the tested compound are presented in Figure 5:2. During assessment, 3 assay plates produced results that fell outside of the data acceptability criteria detailed in Sections 3.4.4.2.3 and 3.4.4.3.3, the results were discarded.

5.3.1 6 of 6 genotoxic control compounds tested in the GADD45a-GFP assay produce positive results for genotoxicity All 6 of the tested genotoxins produced positive results for genotoxicity in the GADD45a-GFP assay. The oxygen-dependent genotoxin, bleomycin sulfate produced a positive result for genotoxicity with an LEC of 1.32 µM and a cytotoxic LEC of 2.64 µM. Topoisomerase inhibitor, etoposide, produced a genotoxic and cytotoxic LEC of 0.1 µM. Pyrimidine analogue 5-fluorouracil produced a genotoxic and cytotoxic LEC 4.69 µM. Alkylating agent, MMS, produced genotoxic and cytotoxic results with LECs of 113 µM and 225 µM respectively. The result for MMS is graphically displayed in Figure 5:2A. NQO produced genotoxic and cytotoxic results with LECs of 0.16 µM and 0.33 µM respectively. Spindle poison, vincristine sulfate, produced genotoxicity and cytotoxicity with an LEC of 3.81 nM.

5.3.2 2 of 2 non-genotoxic, cytotoxic compounds tested in the GADD45a-GFP assay produced negative results for genotoxicity and positive results for cytotoxicity. Both 2,4-dichlorophenol and phenformin hydrochloride were both toxic within the tested range, but neither was genotoxic up to the top doses of 1 mM and of 300 µM (limited by cytotoxicity) respectively.

5.3.3 10 of 12 polyphenolic antioxidants tested in GADD45a-GFP assay produce positive results for genotoxicity Ten of the twelve compounds produced positive results for genotoxicity. The two compounds that did not show a measurable increase in GADD45a expression were gamma-resorcylic acid and tert- butylhydroquinone. The latter of the two compounds exceeded the acceptable limit of cytotoxicity within the GADD45a-GFP assay with an LEC of 100 µM. High levels of cytotoxicity limited the top dose tested to 200 µM. gamma-Resorcylic acid was not cytotoxic at any of the tested concentrations. The positive results for genotoxicity for EGCG and propyl gallate are displayed graphically in Figure 5:2B and Figure 5:2C.

Following incubation, wells containing high concentrations of apomorphine hydrochloride, EGCG, pyrogallol and resorcinol were coloured green, red, green and light red respectively. These wells were not coloured prior to testing, indicating that a change had occurred during the assay. These colour changes may be indicative of the compounds having oxidised. Note is made of coloured oxidation products within Section 4.

102 5.3.4 4 of 4 monophenolic antioxidants tested in the GADD45a-GFP assay produce negative results for genotoxicity The 4 MPAs tested produced only negative genotoxicity data. BHA and BHT were cytotoxic, and data were rejected above 400 µM and 100 µM respectively due to excessive cytotoxicity (data for BHT are shown graphically in Figure 5:2D).

5.3.5 2 of 3 non-phenolic antioxidants tested in the GADD45a-GFP assay produce negative results for genotoxicity Ascorbic acid and n-acetylcysteine were neither cytotoxic nor genotoxic. Ethoxyquin produced positive genotoxic and cytotoxic results with a LEC of 78.1 µM.

5.3.6 3 of 3 oxidants tested in the GADD45a-GFP assay produce positive results for genotoxicity tert-Butyl hydroperoxide (TBHP), hydrogen peroxide and potassium bromate produced positive results for genotoxicity in the GADD45a-GFP assay. The three compounds produced these results with LECs of 275 µM (TBHP) and 125 µM (hydrogen peroxide and potassium bromate). The compounds were cytotoxic at doses above 138 µM (TBHP), 125 µM (hydrogen peroxide) and 1000 µM (potassium bromate).

5.3.7 GADD45a-GFP assay results for 4 miscellaneous compounds The TBHQ metabolite, TBQ produced positive results for genotoxicity and cytotoxicity with LECs of 7.81 µM. The Mitochondrial membrane disruptor, carbonyl cyanide m-chlorophenyl hydrazine, produced genotoxic and cytotoxic results with an LEC of 7.81 µM. The apoptogen, staurosporine, produced a positive result for cytotoxicity at 0.64 µM but a negative result for genotoxicity up to the cytotoxicity-limited top dose of 20.4 µM. Phenol produced positive results for genotoxicity and cytotoxicity at doses above 500 and 1000 µM respectively.

103

Figure 5:2 Example data from assessment of cytotoxicity and genotoxicity of 4 compounds using the GADD45a-GFP reporter assay. Cell survival and GFP induction relative to that of an untreated control displayed as line graphs after 48 hour exposure to: (A) MMS, (B) EGCG, (C) propyl gallate and (D) BHT respectively. Any dose where the relative cell survival falls below a threshold of 80% is considered cytotoxic and any dose where the relative GFP induction rises above 1.3x is considered genotoxic. MMS (A), an alkylating agent produces a cytotoxic result with a lowest effective concentration (LEC) of 225 µM, and a genotoxic LEC of 113 µM. Both PPAs, EGCG (B) and propyl gallate (C) cause a reduction in cell survival below the threshold and an increase in GFP induction above the threshold indicating both compounds to be cytotoxic and genotoxic within the assay. BHT (D) an MPA causes a reduction in cell survival at 100 µM but produces no increase in GFP induction indicating a negative result for genotoxicity. Error bars represent the standard deviation within 3 biological replicate experiments.

104

Limitation of Top Test Test Top Limitation of

Concentration (µM) Concentration

Genotoxicity Result Genotoxicity

Cytotoxicity Result Cytotoxicity

Compound Name Compound

GADD45a GADD45a

Concentration

Highest Test Highest

LEC (µM) LEC (µM) LEC

- -

GFP GFP GFP

PPA Apomorphine hydrochloride 100 Toxicity Positive 15.4 Positive 10.3 tert-Butylhydroquinone 100 Toxicity Positive 200 Negative Dodecyl gallate 100 Toxicity Positive 3.13 Positive 6.25 Epigallocatechin gallate 19.5 Toxicity Positive 2.44 Positive 2.44

Nordihydroguaiaretic acid 19.5 Toxicity Positive 0.61 Positive 0.61

Octyl gallate 100 Toxicity Positive 12.5 Positive 12.5 Propyl gallate 313 Toxicity Positive 4.89 Positive 4.89 Pyrogallol 313 Toxicity Positive 92.6 Positive 61.7 Quercetin 500 Solubility Positive 31.3 Positive 15.6 Resorcinol 1000 Molarity Positive 1000 Positive 500 γ-Resorcylic acid 1000 Molarity Negative Negative Resveratrol 500 Toxicity Positive 62.5 Positive 15.6 MPA Butylated hydroxyanisole 400 Toxicity Positive 400 Negative Butylated hydroxytoluene 100 Toxicity Positive 100 Negative 2,6-Di-tert-butyl-4- 1000 Molarity Negative Negative hydroxymethylphenol Vanillic acid 1000 Molarity Negative Negative NPA n-Acetylcysteine 1000 Molarity Negative Negative L-Ascorbic acid 1000 Molarity Negative Negative Ethoxyquin 1000 Molarity Positive 78.1 Positive 78.1 Oxidants TBHP 1000 Molarity Positive 138 Positive 275 Hydrogen peroxide 1000 Molarity Positive 125 Positive 125 Potassium bromate 1000 Molarity Positive 1000 Positive 125 Table 5:1 Summary of results for the compounds tested in the GADD45a-GFP assay in the absence of S9

105

Limitation of Top Test Test Top Limitation of

Concentration (µM) Concentration

Genotoxicity Result Genotoxicity

Cytotoxicity Result Cytotoxicity

Compound Name Compound

GADD45a GADD45a

Concentration

Highest Test Highest

LEC (µM) LEC (µM) LEC

- -

GFP GFP GFP

Genotoxins Bleomycin 5.30 Toxicity Positive 2.64 Positive 1.32 Etoposide 0.4 Toxicity Positive 0.1 Positive 0.1 5-Fluorouracil 150 Toxicity Positive 4.69 Positive 4.69 Methyl 450 Toxicity Positive 225 Positive 113 methanesulfonate 4-NQO 5.2 Toxicity Positive 0.16 Positive 0.33 Vincristine sulfate 4 Toxicity Positive 0.00381 Positive 0.00381 Cytotoxic, 2,4-Dichlorophenol 1000 Molarity Positive 500 Negative non- Phenformin 300 Toxicity Positive 18.44 Negative genotoxins hydrochloride Other 2-tert-Butyl-1,4- 50 Toxicity Positive 7.81 Positive 7.81 benzoquinone CCCP 250 Toxicity Positive 7.81 Positive 7.81 Phenol 1000 Molarity Positive 1000 Positive 500 Staurosporine 20.4 Toxicity Positive 0.64 Negative Table 5:2 Continued summary of results for the compounds tested in the GADD45a -GFP assay in the absence of S9

106 5.4 Results for GADD45a-GFP genotoxicity assay with S9 metabolic activation

A limited group of 3 pro-genotoxins, 9 PPAs, 4 MPAs with structural similarity to TBHQ and 2 NPAs were tested in the GADD45a-GFP assay with S9 metabolic activation. All antioxidants that produced negative results for genotoxicity in the absence of S9 were assessed to show if metabolic activation created positive genotoxicity results for any of these compounds. The following results are summarised in Table 5:3.

5.4.1 Three pro-genotoxins produced positive results for genotoxicity in the GADD45a- GFP assay with S9 metabolic activation 2-Acetylaminofluorene produced positive results for genotoxicity and cytotoxicity at LECs of 358 µM. 6- Aminochrysene produced positive results for genotoxicity and cytotoxicity at LECs of 16.4 µM. Cyclophosphamide produced positive results for genotoxicity and cytotoxicity at LECs of 24.0 and 47.9 µM respectively (shown in Figure 5:3A).

5.4.2 Only one compound that produced negative results for genotoxicity in the GADD45a-GFP assay without S9 produced a positive result with S9 metabolic activation tert-Butylhydroquinone produced a positive result for genotoxicity in GADD45a-GFP assay when tested using the S9 metabolic activation protocol. The LEC was 1 mM for both cytotoxicity and genotoxicity (shown in Figure 5:3B). PPA; gamma-resorcylic acid, MPAs; BHA, BHT, BHMP and vanillic acid and NPA, ascorbic acid all produced negative results for genotoxicity in the GADD45a-GFP assay following S9 metabolic activation. Of these, only BHA produced a positive result for cytotoxicity at an LEC of 1 mM. Results for BHT are shown in Figure 5:3D.

5.4.3 Eight compounds that produced positive results for genotoxicity in the GADD45a- GFP assay without S9 produced varied results with S9 metabolic activation EGCG, octyl gallate, propyl gallate, and ethoxyquin all produced positive results for genotoxicity in the absence of S9 but following S9 metabolic activation produced equivocal results for genotoxicity with one positive result and two negative results from the triplicate repeats. All of these equivocal genotoxicity results were at LECs of 1 mM except for that of octyl gallate which was at an LEC of 500 µM. EGCG produced an equivocal result, again with one positive and two negative results for cytotoxicity at an LEC of 1 mM. Octyl gallate produced a positive result for cytotoxicity at an LEC of 1 mM and propyl gallate and ethoxyquin did not cross the threshold for cytotoxicity at any of the tested doses.

Resorcinol produced a negative result for genotoxicity and cytotoxicity following S9 metabolic activation. The compound was tested to the top dose of 1 mM.

Both dodecyl gallate and NDGA produced positive results for genotoxicity and cytotoxicity with higher LECs than observed without S9 activation. Both genotoxicity and cytotoxicity LECs were 62.5 µM for dodecyl gallate and 39.1 µM for NDGA. Results for dodecyl gallate are shown in Figure 5:3C.

Quercetin produced a positive result for genotoxicity following S9 activation with a lower LEC (1.95 µM) than observed without S9 activation (15.6 µM). The positive result for cytotoxicity was produced at an LEC of 62.5 compared to an LEC of 31.3 observed without S9 activation. 107

Figure 5:3 Example data from assessment of cytotoxicity and genotoxicity of 4 compounds following S9 metabolic activation using the GADD45a -GFP reporter assay. Cell survival and GFP induction relative to that of an untreated control displayed as line graphs after 48 hour exposure to: (A) cyclophosphamide, (B) tert-butylhydroquinone, (C) dodecyl gallate and (D) BHT respectively. Any dose where the relative cell survival falls below a threshold of 80% is considered cytotoxic and any dose where the relative GFP induction rises above 1.3x is considered genotoxic. Cyclophosphamide (A), a progenotoxic agent produces a cytotoxic result with a lowest effective concentration (LEC) of 47.9 µM, and a genotoxic LEC of 24.0 µM. Both PPAs, tert-butylhydroquinone (B) and dodecyl gallate (C) cause a reduction in cell survival below the threshold and an increase in GFP induction above the threshold indicating both compounds to be cytotoxic and genotoxic within the assay. BHT (D) an MPA causes neither a reduction in relative population survival nor an increase in relative GFP induction beyond the defined threshold at any dose up to the highest soluble dose of 500 µM. Error bars represent the standard deviation within 3 biological replicate experiments.

108 Limitation of Top Te Top Limitation of

Concentration (µM) Concentration

Genotoxicity Result Genotoxicity

Cytotoxicity Result Cytotoxicity

Compound Name Compound

GADD45a GADD45a

Concentration

Highest Test Highest

LEC (µM) LEC (µM) LEC

- -

GFP GFP GFP

PPA tert-Butylhydroquinone 1000 Molarity Positive 1000 Positive 1000 Dodecyl gallate 500 Toxicity Positive 62.5 Positive 62.5 Epigallocatechin gallate 1000 Molarity Equivocal* 1000 Equivocal* 1000 Nordihydroguaiaretic 313 Toxicity Positive 39.1 Positive 39.1 acid Octyl gallate 1000 Toxicity Positive 1000 Equivocal* 500 Propyl gallate 1000 Toxicity Negative Equivocal* 1000 Quercetin 500 Solubility Positive 62.5 Positive 1.95 Resorcinol 1000 Molarity Negative Negative γ-Resorcylic acid 1000 Molarity Negative Negative MPA Butylated 1000 Toxicity Positive 1000 Negative hydroxyanisole Butylated 500 Solubility Negative Negative hydroxytoluene 2,6-Di-tert-butyl-4- 1000 Molarity Negative Negative hydroxymethylphenol Vanillic acid 1000 Molarity Negative Negative NPA L-Ascorbic acid 1000 Molarity Negative Negative Ethoxyquin 1000 Molarity Negative Equivocal* 1000 Pro- 2-Acetylaminofluorene 717 Solubility Positive 358 Positive 358 genotoxins 6-Aminochrysene 131 Toxicity Positive 16.4 Positive 16.4 Cyclophosphamide 95.8 Toxicity Positive 47.9 Positive 24.0 Table 5:3 Summary of results for the compounds tested in the GADD45a-GFP assay S9 metabolic activation. *Equivocal result consists of 1 positive result and 2 negative results

109 5.5 Discussion

This chapter presented the results of experiments to determine the extent of genotoxic liability within a wide range of phenolic antioxidants. A survey of published literature had previously revealed that 9 of the 16 had positive results from at least one of the current battery of OECD guideline in vitro mammalian genotoxicity assays (Section 4).

During initial assessments of genotoxicity using the GADD45a-GFP assay, three of the PPAs (NDGA, EGCG, propyl gallate) were found to be fluorescent at the excitation/emission wavelengths used in GFP data collection. This autofluorescence interfered with the measurement of GFP fluorescence, making it impossible to discriminate between GFP induction and the autofluorescent test compound. The use of polarised light fluorescence spectroscopy was only effective in discriminating GFP from propyl gallate fluorescence. This most likely reflects the structures of the compounds. Both NDGA and EGCG have 2 chiral centres while propyl gallate has none. This means that both EGCG and NDGA can exist in any of 4 enantiomers, isomers that differ only around a chiral centre. The EGCG supplied by Sigma was enantiomerically pure (only one enantiomer is present in the sample). The enantiomeric purity of the NDGA from the same supplier was not specified. Although no mention is made of the enantiomeric purity of the NDGA, it is possible that it is manufactured in a process that leaves an unequal proportion of the different enantiomers. An unequal distribution of enantiomers or an enantiomerically pure sample could be able to fluoresce polarised light under excitation (Weber & Teale 1957). This polarised fluorescence would explain why collection of data using polarised light fluorescence spectroscopy failed to discriminate between the fluorescence of GFP and the fluorescence of the test compounds.

Ten of the 16 phenolic antioxidants produced positive GADD45a-GFP assay data without S9. All 10 of these positive compounds were polyphenolic compounds. Amongst the 6 producing negative results, 2 were PPAs and 4 were MPAs. This indicates that a very high proportion of PPAs have the potential to cause genetic damage in vitro. While only phenolic antioxidants with more than one phenolic group generated positive results in the GADD45a-GFP assay, it should be noted that the MPAs BHA and BHT have previously been reported to produce positive results in other mammalian assays (MNT & CA – BHA, MLA – BHT) (Section 4.3). Another noteworthy observation from the assessment of compounds in the GADD45a-GFP assay is that the NPA ethoxyquin produced a positive result for genotoxicity. Ethoxyquin has previously produced negative results in the CA assay in vitro, as well as in the Ames test, MNT in vivo and in vivo carcinogenicity (male and female rats) (Section 4.4.3). Ethoxyquin has, however, produced positive results in the comet assay in human cells through a purportedly pro-oxidant mode of action (Skolimowski et al. 2010). This plausible pro-oxidant activity, coupled with negative results in vivo and in bacteria suggests that positive results in the GADD45a-GFP assay and the comet assay for ethoxyquin are misleading indications of potential cancer hazard.

The doses at which PPAs generated positive results for genotoxicity in the GADD45a-GFP assay vary from compound to compound. In the assay without S9, only NDGA had an LEC of between 0.1 and 1 µM. Propyl gallate, dodecyl gallate and EGCG had LECs between 1 and 10 µM. Five compounds (Apomorphine HCl, octyl gallate, pyrogallol, quercetin and resveratrol) had LECs of between 10 and 100 µM. Only resorcinol had an LEC between 100 and 1000 µM. When the correlation between the genotoxic LEC against the compound

110 -5 size, a significant negative correlation is seen for all compounds tested (rs=-0.754; p=1.58×10 ). Similarly, a significant correlation is seen within results for PPAs (rs=-0.709; p=0.0108). This correlation between compound size and potency is likely due to an increased number of reactive groups per molecule within larger compounds (Struck et al. 2008). The correlation between the genotoxic LEC of PPAs and the number of phenol groups is not statistically significant (rs=-0.506; p=0.0678). From this, there is no evidence that the number of phenol groups and their genotoxic potency are linked.

TBHQ did not produce a positive within the GADD45a-GFP assay without S9. It had, however, previously been shown to produce positive results following metabolic activation using S9 (Birrell et al. 2010). In light of this, phenolic antioxidants were then assessed in the GADD45a-GFP assay with S9. Of the 6 phenolic antioxidants that produced negative results in the GADD45a-GFP assay without S9, only one compound, TBHQ produced a positive result. Given that TBHQ was only able to produce positive results for genotoxicity following S9 metabolic activation TBHQ may be genotoxic through a different mechanism to the other PPAs. The other phenolic antioxidants (γ resorcylic acid and the four MPAs) did not produce positive results, either with or without S9 at any of the tested doses. The highest tested doses of BHA and BHT were limited by toxicity in the assay without S9 and by their toxicity and solubility respectively in the assay with S9. The highest tested doses of vanillic acid, γ resorcylic acid and BHMP were limited to 1 mM by the ICH S2(R1) guidelines. These guidelines were intended for use in the safety assessment of pharmaceutical compounds and it is possible that the compounds could have produced positive results if tested to higher concentrations. Regardless, no trend towards an increase in GFP induction was observed for any of the compounds.

It would be very difficult and time consuming to develop an S9 metabolic activation method for each of the assessments presented in this thesis. With this in mind and considering that only one compound exclusively produces positive results for genotoxicity following S9 metabolic activation, a decision was made to use the TBHQ metabolite, TBQ, in further assessments in lieu of carrying out S9 metabolic activation.

Unfortunately, given the GADD45a-GFP assay’s non-regulatory status, the results presented in this study may not be seen as a concern to many scientists within the field. The time constraints of this study however precluded the testing of the compound list in other assays. Testing the chemicals in the regulatory in vitro mammalian assays would however lend further credence to any findings as they are assays that all scientists in the field of genotoxicity assessment are familiar with.

Concerns have been raised that the regulatory in vitro mammalian genotoxicity assays, though sensitive suffer from reduced specificity (Kirkland et al. 2005; Moore et al. 2011). Given that the detection of plausibly misleading positive results is a cause for concern, it was a wise choice to use an assay that has been shown to be both highly sensitive and specific in the detection of genotoxins. The GADD45a-GFP assay has been shown to be highly sensitive and specific (Hastwell et al. 2006; Hastwell et al. 2009; Jagger et al. 2009; Birrell et al. 2010).

Due to the mechanism and protocol of different genotoxicity assays, very different doses of the same chemicals may be needed to produce a positive result. For people unfamiliar with the GADD45a-GFP assay, it can be difficult to contextualise a result within the broader scope of in vitro genotoxicity assessment.

111 Below in Table 5:4 are detailed the doses of five different genotoxins necessary to produce a positive result in the GADD45a-GFP assay and the regulatory mammalian in vitro genotoxicity assays.

GADD45a-GFP MLA MNT CA

Bleomycin 1.32 μM N/A 35.2 μM a 35.2 μM a

Etoposide 0.100 μM 2.55 μM a 8.50 μM a 2.00 μM a

5-Fluorouracil 4.69 μM 61.5 μM a 96.1 μM a 77.0 μM b

Methyl 113 μM 109 μM a 455 μM a 200 μM b methanesulfonate Vincristine sulfate 0.00381 μM N/A 1.08 μM a 1.20 μM b Table 5:4 Table detailing the dose of five mechanistically diverse genotoxins necessary for a positive result in four different in vitro genotoxicity assays a: (NCI 2011) b: (Ishidate et al. 1988) All MNT and CA results were generated in CHO or V79 cell lines

As shown in Table 5:4, the GADD45a-GFP assay is able to produce a positive result for the five genotoxins at lower doses than the MLA, MNT and CA in vitro mammalian genotoxicity assays with only one exception. The results presented in the table are not exhaustive though and other studies using differing cell types may have produced different results. This may mean that for some of the test chemicals such as resorcinol, that required a high dose to produce a positive result in the GADD45a-GFP assay may not produce positive results if tested in other genotoxicity assays at doses below 1 mM.

The results presented in Chapter 4 show that no study has previously tested a broad set of phenolic antioxidants in one single in vitro mammalian genotoxicity assay with one single cell line and the same protocol. This means that the results presented in this chapter represent an important and necessary foundation to any study exploring the reasons underlying the generation of plausibly misleading results in in vitro mammalian genotoxicity assessment.

The GADD45a-GFP assay doesn’t however provide a wealth of insight into the mechanisms that may underlie the results presented within this chapter. To better understand any mechanisms that may be important in the results presented later in this thesis it is important to seek further mechanistic insight. In light of this, the following chapter seeks to shed further understanding on the observed genotoxicity of PPAs by means of an in silico knowledge-based assessment of the compounds’ structures. The subsequent results chapters investigate the degree to which the genotoxicity of PPAs is reliant upon the presence of oxygen or the generation of oxygen radicals.

112 5.6 Summary

 The compound set was tested using the GADD45a-GFP assay  Propyl gallate, NDGA and EGCG all produced levels of autofluorescence and light absorption, that impaired data interpretation using spectrophotometry, necessitating the analysing GADD45a-GFP assays of test compounds using flow cytometry.  When tested in the GADD45a-GFP assay without S9 metabolic activation: o 10 of 12 PPAs tested produced positive results for genotoxicity o 0 of 4 MPAs tested produced positive results for genotoxicity o All of the oxidant compounds and known genotoxins tested produced positive results for genotoxicity  When tested in the GADD45a-GFP assay with S9 metabolic activation: o 4 of 9 PPAs tested produced positive results for genotoxicity including TBHQ which produced a negative result without S9 o 0 of 4 MPAs tested produced positive results for genotoxicity o All of the known pro-genotoxins tested produced positive results for genotoxicity

113 6 Results III – Most polyphenolic antioxidants produce alerts of “Plausible” or higher for genotoxic endpoints using in silico tool, Derek Nexus™ 6.1 Introduction

In order to gain further insight into the mechanism of genotoxicity within the selected polyphenolic compounds, they were assessed using an in silico screen for genotoxicity and carcinogenicity: Derek Nexus™ (Leeds, UK: see 3.5). It was developed as a hazard assessment tool for early stage screening of novel compounds. The software uses a curated set of structural alerts generated by experts using references from peer-reviewed literature and Lhasa’s industrial partners.

The 34 compounds described in chapter 4 were assessed using Derek Nexus™ in order to identify structural alerts related to genotoxicity, chromosomal aberrations, mutagenicity and carcinogenicity endpoints. For each compound, the software compares the chemical structure against a database of known structural alerts for each chosen endpoint. Each alert consists of a reasoning from “Impossible” to “Certain” (A list of these terms can be found along with their definitions in the materials and methods in Section 3.5.3.1), the endpoint to which the alert refers, a number denoting the alerts position within the database and the relevant structure or compound characteristic.

E.g. Plausible – Chromosome damage in vitro – 625 – Catechol

For each alert, Derek Nexus™ produces the following outputs:

 A diagram showing the alerting structure within the structure of the compound tested  Expert, knowledge-based comments supporting the alert referring to example compounds  The positive predictivity of the alert when applied to existing databases of relevant assay results (Section 13.1)  Example compounds used in the generation of the alert with referenced results for relevant assay results  A decision tree detailing the reasoning, followed by the software, to reach the given result

The information supporting the generation of each alert provided in this results chapter is paraphrased from the expert, knowledge-based comments provided by Derek Nexus™.

The positive predictivity of each alert is calculated by Derek Nexus™ by searching databases of existing genotoxicity, mutagenicity and carcinogenicity results. These data are tabulated for each alert produced. A list of the databases searched is found in Section 13.1.

6.2 Results

6.2.1 Known genotoxins Six of the thirty-four compounds screened using Derek Nexus were well-known genotoxins. Summary data are presented in Table 6:31. These genotoxins were tested to display the diverse range of alerts generated for compounds with well-reported mechanisms of action and existing data in regulatory assays and the

114 GADD45a-GFP assay. The results also allow cross-referencing with the results generated in other assessments in this investigation.

All of the assessed genotoxins, apart from bleomycin sulfate, generated alerts for in vitro chromosomal damage, in vitro genotoxicity, in vitro mutagenesis or human carcinogenesis. Etoposide produced a “plausible” alert for in vitro chromosomal damage, on account of the compound being a podophyllotoxin analogue. Etoposide produced “plausible” alerts for in vitro chromosomal damage, in vitro genotoxicity and in vitro mutagenesis, due to an alkyl aldehyde precursor moiety. 5-Fluorouracil produced a “probable” alert for in vitro chromosomal damage due to being a 5-fluoropyramidine. MMS produced “probable” alerts for in vitro and in vivo chromosomal damage and a “plausible” alert for in vitro mutagenesis, accounted for by MMS being a recognised DNA alkylating agent. MMS also produced a “probable” alert for human carcinogenesis, due to being an alkyl sulfonate. NQO produced “equivocal” alerts for in vitro and in vivo chromosomal damage and “plausible” alerts for human carcinogenicity and in vitro mutagenicity, due to NQO being an aromatic nitro compound. Another “plausible” alert was produced for in vitro mutagenicity for NQO as a result of being an aromatic N-oxide. Vincristine sulfate produced a “plausible” alert for in vitro chromosomal damage related to the compound being a vinca alkaloid.

Alerts are detailed below on a compound-by-compound basis.

6.2.1.1 Bleomycin sulfate Bleomycin produced no alerts for any of the endpoints assessed.

6.2.1.2 Etoposide

Figure 6:1: The podophyllotoxin structure (left) and the alkyl aldehyde precursor (right) substructures highlighted within the structure of etoposide

Etoposide produced a “plausible” alert for in vitro chromosomal damage on account of the compound being a containing structural similarities with podophyllotoxin as shown on the left of Figure 6:1. Both examples provided, podophyllotoxin and teniposide produced positive results in human cell in vitro chromosomal aberrations assays (FDA 2011). Teniposide was the only compound in any of the databases (FDA CFSAN) to structurally match the alert. Mention is made to positive result in in vitro chromosomal aberrations and micronucleus assays by etoposide (Mosesso et al. 1998; Parry et al. 2002). It is mentioned that whereas podophyllotoxin produces micronuclei by aneugenic mechanism, etoposide does so through a clastogenic mechanism (Parry et al. 2002). The clastogenesis is likely promoted by etoposide and teniposide stabilising and thereby inhibiting the action of DNA-topoisomerase II. Podophyllotoxin and its analogues are detailed 115 as having literature supporting a mode of action linked to their structure. Inhibiting DNA-topoisomerase II in a reaction involving the 4-hydroxy group on the top-leftmost phenyl group shown in Figure 6:1 (Long 1992; Long & Stringfellow 1988). Predictivity of the alert in existing datasets for chromosomal damage are listed in Table 6:1.

Etoposide also produced “plausible” alerts for in vitro chromosomal damage, in vitro genotoxicity and mutagenesis due to an alkyl aldehyde precursor group shown on the right of Figure 6:1. This structure could plausibly lead to etoposide producing chromosomal damage and mutagenesis. Derek Nexus™ does not make mention of a mechanism related to this structure but does give examples of positive results in in vivo and in vitro chromosomal aberrations assays as well as mammalian gene mutation assays caused by alkyl aldehydes, such as acetaldehyde and isobutyraldehyde and positive in vitro chromosomal aberrations results for alkyl aldehyde precursor, dimethoxane. A compound structurally similar to etoposide (Brambilla et al. 1989; Sofuni 1998; NTP 1989; NTP 1983b; NTP 1983a).

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results Sofuni data set 4 2 50% FDA CFSAN data set 31 18 58% Mohr data set 5 4 80% Snyder data set 6 0 0% CGX data set 7 5 71% Vitic database 6 5 83% Table 6:1: The predictive performance of Derek Nexus™ alert 641 (in vitro chromosomal damage) for podophyllotoxins in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

6.2.1.3 5-Fluorouracil A “probable” alert for in vitro chromosomal damage for 5-fluorouracil was produced by Derek Nexus™, due to the well documented existing literature highlighting the activity of 5-fluorouracil and other 5- fluoropyramidines. The risk of 5-fluorouracil and other 5-fluoropyramidines is due to their ability to act as analogues for endogenous nucleotides. This allows the chemical to inhibit the enzyme thymidylate synthetase, blocking the production of thymidine monophosphate. This activity is made use of in 5- fluorouracil’s role as an antineoplastic drug (Papamichael 1999; Bono & Twelves 2001). Table 6:2 shows the predictivity of this alert within databases for chromosomal damage.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results Sofuni data set 2 2 100% FDA CFSAN data set 3 3 100% Mohr data set 2 2 100% CGX data set 1 1 100% Table 6:2: The predictive performance of Derek Nexus™ alert 578 (in vitro chromosomal damage) for 5- fluoropyrimadines in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

116 6.2.1.4 Methyl methanesulfonate MMS produced “probable” alerts for human carcinogenicity in vitro chromosomal damage and in vitro mutagenicity due to being an alkylating agent and a “probable” alert for in vivo chromosomal damage, due to being an alkyl sulfonate. All alerts received a “probable” reasoning, due to existing positive results for MMS in in vivo micronucleus assay (Tsuyoshi et al. 1989), in vivo chromosomal aberrations assay (Frei & Venitt 1975), in vitro chromosomal aberrations assay (Sofuni 1998) and being classified as an IARC group 2A carcinogen. The software provides details of the genotoxic mechanism of action of MMS alkylating nucleophilic groups such as those in DNA (Beranek 1990). Presented below are data showing the predictivity of the alert for alkylating agents in databases for carcinogenicity, Table 6:3; in vitro mutagenicity, Table 6:4; and in vitro chromosomal aberrations assay, Table 6:5. Data showing the predictivity of the alert for alkyl sulfates and sulfonates in databases for in vivo chromosomal aberrations and micronucleus test are shown in Table 6:6.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results

CPDB data set 81 61 75% ToxRefDB data set 11 6 55% ISSCAN data set 76 62 82% Snyder data set 8 7 88% CRD-AGES pesticide data set 1 0 0% Brambilla data set 18 17 94% Table 6:3: The predictive performance of Derek Nexus™ alert 073 (carcinogenicity) for known alkylating agents in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results

CGX data set 46 38 83% Vitic database 87 67 77% Snyder data set 9 2 22% Proprietary data set 1 75 38 51% Proprietary data set 2 6 3 50% FDA CFSAN data set 439 346 79% Benchmark data set 371 279 75% Table 6:4: The predictive performance of Derek Nexus™ alert 027 (mutagenicity) for known alkylating agents in data sets for mutagenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

117 Data set Alerting compounds Alerting compounds Positive predictivity producing positive results

Sofuni data set 23 20 87% FDA CFSAN data set 89 72 81% Mohr data set 37 26 70% Snyder data set 5 4 80% CGX data set 29 23 79% Vitic database 23 17 74% Table 6:5: The predictive performance of Derek Nexus™ alert 027 (in vitro chromosomal damage) for known alkylating agents in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results

MMS data set 3 3 100% FDA CFSAN data set 4 4 100% FDA CFSAN data set 7 7 100% Table 6:6: The predictive performance of Derek Nexus™ alert 755 (in vivo chromosomal damage) for alkyl sulfates and sulfonates in data sets for in vivo chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

6.2.1.5 4-Nitroquinoline 1-oxide

Figure 6:2: The nitro group highlighted within the structure of 4-Nitroquinoline 1-oxide

NQO produced “plausible” alerts for carcinogenicity and in vitro mutagenicity on account of being an aromatic nitro compound and being an aromatic-N-oxide. “Equivocal” alerts were produced for both in vitro and in vivo chromosomal damage related to the aromatic nitro structure of NQO. The nitro group linked with these alerts is shown in Figure 6:2.

Aromatic nitro compounds are linked with causing carcinogenicity, following metabolism, to become aromatic hydroxylamine intermediates (Takahashi et al. 1978; Holder 1999; Haack et al. 2001). An alternate suggested mechanism is through the generation of ROS leading to oxidative DNA damage (Kovacic & Jacintho 2001; Holder 1999; Ohkuma & Kawanishi 1999). It is also mentioned that compounds with nitro groups perpendicular to the ring structure as seen in Figure 6:2 in NQO are often linked with “a drastic reduction in mutagenicity and tumorigenicity” (Holder 1999).

Mutagenesis is observed in the Ames test for many nitro compounds, most notably in TA98 and TA100 in the absence of S9. The highlighted mechanism is that aromatic nitro compounds being reduced by nitroreductase enzymes followed by O-esterification. This esterified product may then give rise to a

118 nitrenium ion, which binds to nucleophiles such as DNA. Several factors are listed as limiting the degree of mutagenicity, meaning that mutagenicity is not observed in all aromatic nitro compounds (Benigni et al. 1994; Debnath et al. 1992; Debnath et al. 1991).

The reasoning underlying the “Equivocal” value of the alert for both in vitro and in vivo chromosomal damage is reported as being due to mixed results in in vitro chromosomal aberrations assessment, an inconsistent requirement for S9 metabolism to produce a positive result and a clear mutagenic mode of action (McGregor & Brown 1988).

Below is summarised the predictivity of the alert for aromatic nitro compounds within databases for carcinogenicity, Table 6:7; in vitro chromosomal aberrations assay, Table 6:8; in vivo chromosomal aberrations assay and micronucleus assay, Table 6:9 and in vitro mutagenicity, Table 6:10. The predictivity of the alert for aromatic-N-oxides and N-hydroxy tautomers compounds within databases for in vitro mutagenicity is presented in Table 6:11.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results

CPDB data set 102 72 71% ToxRefDB data set 18 13 72% ISSCAN data set 85 67 79% Snyder data set 10 7 70% CRD-AGES pesticide data set 6 0 0% Brambilla data set 7 6 86% Table 6:7: The predictive performance of Derek Nexus™ alert 105 (carcinogenicity) for aromatic nitro compounds in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results Sofuni data set 21 18 86% FDA CFSAN data set 118 77 65% Mohr data set 45 29 64% Snyder data set 8 3 38% CGX data set 36 25 69% Vitic database 33 23 70% Table 6:8: The predictive performance of Derek Nexus™ alert 329 (in vitro chromosomal damage) for aromatic nitro compounds in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results MMS data set 16 4 25% FDA CFSAN data set 12 4 33% FDA CFSAN data set 41 16 39% Table 6:9: The predictive performance of Derek Nexus™ alert 329 (in vivo chromosomal damage) for aromatic nitro compounds in data sets for in vivo chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™. 119

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results CGX data set 64 61 95% Vitic database 134 111 83% Snyder data set 13 7 54% Proprietary data set 1 52 33 63% Proprietary data set 2 5 3 60% FDA CFSAN data set 1009 857 85% Benchmark data set 905 779 86% Table 6:10: The predictive performance of Derek Nexus™ alert 329 (mutagenicity) for aromatic nitro compounds in data sets of results for the Ames test. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results

CGX data set 2 2 100% Vitic database 2 2 100% Proprietary data set 1 1 1 100% FDA CFSAN data set 42 32 76% Benchmark data set 24 15 63% Table 6:11: The predictive performance of Derek Nexus™ alert 303 (mutagenicity) for aromatic-N-oxides and N-hydroxy tautomers in data sets for mutagenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

6.2.1.6 Vincristine sulfate Vincristine sulfate produced a “plausible” alert for in vitro chromosomal damage on account of being a vinca alkaloid. Vincristine sulfate is considered as an example vinca alkaloid with a positive result in the in vitro chromosomal aberrations assay (Gonzalez-Cid 1999) by the software. However the two compounds are not recognised as the same by the software. This is due to an inability of Derek Nexus™ to recognise compounds that it considers mixtures. By manually following the reasoning provided by Derek Nexus™ vincristine sulfate could be considered as producing a “probable” alert for in vitro chromosomal damage.

The comments produced regarding the alert refer to the aneugenic activity of vinca alkaloids (Antoccia et al. 1991; Gonzalez-Cid 1999). This aneugenesis is caused by disruption of microtubules (Downing 2000; Jordan et al. 1991). Below, in Table 6:12, is detailed the predictivity of this alert within databases for in vitro chromosome aberration data.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results

Sofuni data set 1 1 100% FDA CFSAN data set 2 2 100% Snyder data set 1 1 100% Table 6:12: The predictive performance of Derek Nexus™ alert 581 (in vitro chromosomal damage) for vinca alkaloid compounds in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

120 6.2.2 Polyphenolic antioxidants Of the 34 compounds assessed, 12 were antioxidants with polyphenolic structures. Table 6:27 provides a summary of the predictive alerts produced for all 12 compounds, along with CAS numbers, alert names and values. Ten out of these twelve compounds produced alerts for in vitro chromosomal damage and/or carcinogenicity. Two compounds, tert-butylhydroquinone and gamma-resorcylic acid produced no alerts for any of the endpoints assessed.

6.2.2.1 Alert 625 (in vitro chromosomal damage) and alert 251 (carcinogenicity) – Catechol

Figure 6:3: Catechol substructure highlighted within the structure of apomorphine

Alerts for in vitro chromosomal damage due to a catechol moiety, example shown in Figure 6:3, were produced for 8 of the 10 alerting compounds: apomorphine; NDGA; pyrogallol; quercetin and the 4 gallate compounds (see Table 6:27). These alerts highlighted a “plausible” reasoning for in vitro chromosomal damage for all but propyl gallate and quercetin. For these two compounds a reasoning of “probable” in vitro chromosomal damage was produced. The catechol moiety contained within the structure of both pyrogallol and propyl gallate also produced “equivocal” alerts for carcinogenicity.

The alert for chromosomal damage associated with the catechol substructure is due to existing data for chromosomal aberrations in Chinese hamster ovary (CHO) cells in vitro. Existing in vitro chromosomal damage results for catechol and compounds with catechol subunits such as propyl gallate and levodopa suggest that it is plausible that similar structures may also cause in vitro chromosomal damage within other mammalian cells. Both propyl gallate and quercetin have existing positive results in the chromosomal aberrations test and are considered “probable” to cause in vitro chromosomal damage in other mammalian cells by Derek Nexus™.

The supporting information regarding the alert for catechol substructures highlights a potential oxidative mechanism of genotoxicity. This mechanism may rely upon their potential to oxidise quinones and semiquinones (Kalyanaraman et al. 1985). Evidence that catechols have been seen to induce oxidative damage through a copper ion mediated redox reaction are also cited (Oikawa et al. 2001; Morin et al. 1998). Table 6:13 shows the predictive performance of alert 625 for in vitro chromosomal damage within data sets for in vitro chromosomal aberrations.

121 Data set Alerting compounds Alerting compounds Positive predictivity producing positive results Sofuni data set 9 8 89% FDA CFSAN data set 19 15 79% Mohr data set 12 8 67% Snyder data set 4 2 50% CGX data set 4 4 100% Vitic database 3 2 67% Table 6:13: the predictive performance of Derek Nexus™ alert 625 (in vitro chromosomal damage) for compounds with a structural catechol moiety in data sets for in vitro chromosomal aberrations. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

Due to both pyrogallol and propyl gallate being relatively small molecules (126 and 212 grams/mole respectively), they produced the alert 251 for carcinogenicity. This alert is founded upon similarly small catechols; catechol and caffeic acid both being IARC group 2B carcinogens (IARC 1993; IARC 1999). Certain small catechols have been observed to induce papillomas and carcinomas in the forestomach of rats (Asakawa et al. 2007; Kawabe et al. 1994). The alert is deemed equivocal by the software due to several factors contrary to human carcinogenicity: the compounds are Ames negative, humans do not have a forestomach and there is an absence of any other evidence of human carcinogenicity. Table 6:14 shows the predictive performance of alert 251 for carcinogenicity within data sets for carcinogenicity.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results CPDB data set 9 4 44% ISSCAN data set 4 4 100% Snyder data set 3 0 0% Brambilla data set 1 0 0% Table 6:14: The predictive performance of Derek Nexus™ alert 251 (carcinogenicity) for compounds with a structural catechol moiety in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

6.2.2.2 Alert 515 (in vitro chromosomal damage) – Flavonoid and alert 203 (mutagenicity) – Flavonol

Figure 6:4: Flavonoid (left) and flavonol (right) substructure highlighted within the structure of quercetin.

In addition to the alert for “probable” chromosomal damage produced in regard to the catechol structure of quercetin, the flavonoid structure produced another alert for “probable” chromosomal damage in vitro in relation to being flavonoid (see Table 6:27). Quercetin, is also a flavonol, and this led to the generation of

122 an alert for “plausible” mutagenicity. The structure of both moieties is highlighted within the structure diagram shown in Figure 6:4.

Various flavonoid compounds including quercetin have been shown to produce positive results in in vitro chromosomal aberrations assays in the absence of S9. The software reasons that it would therefore be probable that quercetin would induce chromosomal aberrations in other mammalian cells in vitro. Derek Nexus™ refers to 4 possible modes of action for flavonoids to cause chromosomal damage:

 Formation of reactive oxygen species,  Oxidation of the B ring to a quinone or semiquinone,  DNA intercalation  Inhibition, or poisoning, of DNA topoisomerase II (Duarte Silva et al. 2000; Snyder & Gillies 2002)  Studies suggest that the mechanism involved may depend on the flavonoid, and each may have a role to play (Duarte Silva et al. 2000; Snyder & Gillies 2002)

It is also noted that while polyphenolic compounds (quercetin and rhamnetin) were detected to be mutagenic, the structurally similar monophenols (galangin and kaempferol) were not mutagenic without S9 activation (Brown & Dietrich 1979; Macgregor & Jurd 1978; Sugimura et al. 1977). Table 6:15 shows the predicitive performance of alert 515 for in vitro chromosomal damage demonstrates the following predictive performance within data sets for in vitro chromosomal aberrations. Table 6:16 shows the predictive performance of alert 203 for mutagenicity within data sets of Ames data.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results Sofuni data set 1 1 100% FDA CFSAN data set 2 2 100% Mohr data set 1 1 100% CGX data set 1 1 100% Vitic database 1 1 100% Table 6:15: The predictive performance of Derek Nexus™ alert 515 (in vitro chromosomal damage) for compounds with a structural flavonoid moiety in data sets for in vitro chromosomal aberrations. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results CGX data set 1 1 100% Vitic database 1 1 100% FDA CFSAN data set 17 14 82% Benchmark data set 8 3 38% Table 6:16: The predictive performance of Derek Nexus™ alert 203 (in vitro mutagenicity) for compounds with a structural flavonol moiety in data sets for in vitro chromosomal aberrations. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

123 6.2.2.3 Alert 582 (in vitro chromosomal damage) – 4-hydroxystilbene

Figure 6:5: 4-hydroxystilbene substructure highlighted within the structure of resveratrol

Of the 12 PPAs screened, only resveratrol produced an alert for “probable” chromosomal damage in vitro due to the compound’s 4-hydroxystilbene structure (see Figure 6:5).

The alert for chromosomal damage is produced due to existing positive in vitro data for chromosomal damage and micronucleus test for resveratrol (Matsuoka et al. 2001) and structurally similar 4- hydroxystilbenes: 4-hydroxy-trans-stilbene and 3,4'-dihydroxy-trans-stilbene (Matsuoka et al. 2002). Posited modes of action include the inhibition of ribonucleotide reductase, a key enzyme in deoxyribonucleotide synthesis. Other modes of action that have been observed with resveratrol include scavenging tyrosine radicals from enzyme active sites (Fontecave et al. 1998; Matsuoka et al. 2004) or the inhibition of DNA polymerase (C. Sun et al. 2006).

No compounds in the Sofuni, FDA CFSAN, Mohr, Snyder, CGX or Vitic data sets for in vitro chromosomal damage contained the 4-hydroxystilbene moiety at the time of this analysis, making it impossible to ascertain the positive predictivity of this alert.

6.2.2.4 Alert 162 (carcinogenicity) – Extrapolation from thyroid toxicity Both resorcinol and resveratrol produced equivocal alerts for carcinogenicity due to an extrapolation from alerts for thyroid toxicity. There were also “equivocal” alerts (248: thyroid toxicity) for both resorcinol and resveratrol. The alert relates to the resorcinol moiety contained in both structures, which is associated with anti-thyroid effects including hypothyroidism and goitre. This alert causes the extrapolated alert 162 to be produced, due to a link between thyroid toxicity and non-genotoxic tumour development in the thyroid and pituitary glands. It is noted that humans are far less susceptible to this effect than rodents and so the software reasons that there is equivocal evidence for human carcinogenicity.

6.2.3 Monophenolic antioxidants Four of the thirty-four compounds screened using Derek Nexus were classified as ‘monophenolic’ compounds (containing a single phenolic group). Summary data for the 4 are presented in Table 6:28. Of these 4, only BHA was alerting: equivocal evidence of human carcinogenicity due to the 4-alkylether phenol moiety (shown in Figure 6:6).

124 6.2.3.1 Alert 252 (Carcinogenicity) 4-Alkylether phenol

Figure 6:6: 4-Alkylether phenol substructure highlighted within the structure of BHA

This alert was generated based upon evidence of 4-methoxyphenol and BHA causing the formation of forestomach cancers in rodents. Reference is made to BHA being classified by IARC as a class 2B carcinogen (IARC 1986). The report makes mention of the research of Clayson et al. (1991) suggesting that quinoid intermediates of BHA cause oxidative stress, damaging DNA and interfering with protein binding. Through loss of the methyl group, the compound may form a para-quinone, which has been shown to be more damaging than ortho-quinones.

Derek Nexus™ produced an “equivocal” alert due to evidence suggesting no role in human carcinogenicity for BHA. This evidence includes humans not having forestomachs, the observed locus of cancers in the rodent studies. An epidemiological study by Botterweck et al (2000) found no significant link between ordinary dietary consumption of small amounts of BHA and stomach cancer. A review by Williams et al. (Williams et al. 1999) is also referenced as finding no significant link and casting doubt on the possibility of BHA as a human carcinogen. Table 6:17 shows the predictive performance of alert 252 for carcinogenicity within data sets of in vivo carcinogenicity study results.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results CPDB data set 6 3 50% ISSCAN data set 3 2 67% Snyder data set 1 1 100% Brambilla data set 2 1 50% Table 6:17: The predictive performance of Derek Nexus™ alert 252 (carcinogenicity) for compounds with a 4-alkylether phenol structure in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

6.2.4 Non-phenolic antioxidants Of the thirty-four compounds tested, three compounds were antioxidant compounds with no phenolic moiety, and produced no alerts for any of the tested endpoints using Derek Nexus™.

6.2.5 Oxidants Of the thirty-four tested compounds, three compounds were classified as oxidants and all produced alerts. Summary data are presented for all three in Table 6:30.

Both hydrogen peroxide and tert-butyl hydroperoxide produced alerts for in vitro chromosomal damage with “probable” reasoning, and alerts for mutagenicity with “probable” and “plausible” reasoning

125 respectively due to their hydroperoxide structures. The structure of potassium bromate produced an alert for “plausible” in vitro chromosomal damage, on account of being a halogen oxyacid salt and all three compounds produced alerts for carcinogenesis, owing to being oxidising agents. Hydrogen peroxide received a “probable” alert while the other two received “plausible” alerts.

6.2.5.1 Alert 358 (in vitro chromosomal damage, Mutagenicity) – Hydroperoxide

Figure 6:7: The hydroperoxide structure found within both tert-butyl hydroperoxide and hydrogen peroxide

The hydroperoxide structure shown in Figure 6:7 is responsible for Derek Nexus™ producing “probable” alerts for both tert-butyl hydroperoxide and hydrogen peroxide for in vitro chromosomal damage and also “plausible” and “probable” alerts for in vitro mutagenicity respectively.

The “probable” reasoning underlying the alert for in vitro chromosomal damage is due to both compounds having produced positive results in in vitro chromosomal aberrations assays in CHO cells (Rueff et al. 1993; Sofuni 1998; Ochi 1989). The mechanism of DNA damage has been widely studied and is thought to be due to homolytic fission of the O-O bond creating reactive oxygen species (ROS) (Rueff et al. 1993; Ochi 1989; Ochi & Cerutti 1989; Dillon et al. 1998). Derek Nexus™ refers to the reviews of the genotoxic and carcinogenic potential of peroxides by Lai et al. (1996) and Watts (1985). The positive predictivity of the rule for compounds with hydroperoxide structures in databases of results for in vitro chromosomal damage is summarised in Table 6:18.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results

Sofuni data set 1 1 100% FDA CFSAN data set 3 3 100% Mohr data set 2 2 100% CGX data set 1 1 100% Vitic database 2 2 100% Table 6:18: The predictive performance of Derek Nexus™ alert 358 (in vitro chromosomal damage) for hydroperoxide compounds in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

Hydrogen peroxide and alkyl hydroperoxides, structurally similar to tert-butyl hydroperoxide, have produced positive results in Ames assays consistently in TA100 and TA102 strains of Salmonella typhimurium (Ball et al. 1992; Dillon et al. 1998; Haworth et al. 1983; Levin et al. 1982; Mortelmans et al. 1986; Stock et al. 1998; Zeiger et al. 1988). The mode of action of mutagenicity is also widely thought to be due to the production of ROS (Lai et al. 1996; Watts 1985). Results from compounds with hydroperoxide structures in databases of results for the Ames bacterial mutagenicity assay are summarised in Table 6:19.

126 Data set Alerting compounds Alerting compounds Positive predictivity producing positive results CGX data set 1 1 100% Vitic database 4 4 100% FDA CFSAN data set 13 12 92% Benchmark data set 12 12 100% Table 6:19: The predictive performance of Derek Nexus™ alert 358 (mutagenicity) for hydroperoxide compounds in data sets of Ames test results. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

6.2.5.2 Alert 749 (Carcinogenicity) – Oxidising agent Hydrogen peroxide is classified by IARC as a group 3 carcinogen and produced positive results in a mouse in vivo carcinogenicity assay (Ito et al. 1981). Hydrogen peroxide and other oxidising agents are believed to likely lead to cancer through genotoxic mechanisms based on the production of ROS, which then react with lipid and protein peroxidation and oxidising DNA nucleosides (Klaunig et al. 2011; Valavanidis et al. 2009). Having produced positive in vivo carcinogenicity data, the software deems hydrogen peroxide to be a “probable” carcinogen and in lieu of data for tert-butyl hydroperoxide or potassium bromate, Derek Nexus™ reasons that the compounds’ ability to act as oxidising agents makes them “plausible” carcinogens. Results from structurally similar oxidising agents in databases of results for in vivo carcinogenicity assay are summarised below in table Table 6:20.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results

CPDB data set 7 5 71% ToxRefDB data set 1 1 100% ISSCAN data set 3 3 100% Snyder data set 1 1 100% Table 6:20: The predictive performance of Derek Nexus™ alert 749 (carcinogenicity) for known oxidising agents in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

6.2.5.3 Alert 518 (in vitro chromosomal damage) – Halogen oxyacid salt Potassium bromate produced an alert for “plausible” in vitro chromosomal damage in Derek Nexus™ due to being a halogen oxyacid salt. Various halogen oxyacid salts have produced positive results in in vitro chromosomal aberrations assays, including potassium bromate (Sofuni 1998). The mechanism of action is unknown, but it is known that potassium bromate can cause oxidative damage to DNA in a glutathione- mediated reaction (Ballmaier & Epe 1995; Parsons 2000). Results from halogen oxyacid salts in databases of results for in vitro chromosomal damage are summarised in Table 6:21.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results Sofuni data set 4 4 100% Mohr data set 2 2 100% Table 6:21: The predictive performance of Derek Nexus™ alert 518 (in vitro chromosomal damage) for halogen oxyacid salts in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™. 127

6.2.6 Non-genotoxic cytotoxic compounds Of the thirty-four tested compounds, two compounds were known non-genotoxic, cytotoxic agents often used as controls in the GADD45a-GFP assay.

Of the two cytotoxic, non-genotoxic compounds tested, 2,4-dichlorophenol (DCP) produced a “plausible” alert for chromosomal damage in vitro due to being a halophenol and an “equivocal” alert for carcinogenesis due to being a polyhalogenated phenol. These alerts are summarised in Table 6:32.

6.2.6.1 2,4-Dichlorophenol The “equivocal” result for carcinogenesis for the polyhalogenated phenol, DCP, is qualified by a mixture of positive and negative carcinogenicity results for polyhalogenated phenols (NTP 1978; NTP 1985; NTP 1987). Though little is known about the mechanism of carcinogenesis for polyhalogenated phenols, it is thought to occur through a non-genotoxic mechanism. A proposal is referred to that renal toxicity through interaction with alpha-2-mu-globulin may be responsible; a mechanism that is believed to not be relevant in humans. Predictivity of this alert within databases for carcinogenicity assessment is detailed in Table 6:22.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results CPDB data set 41 19 46% ToxRefDB data set 31 21 68% ISSCAN data set 34 16 47% Snyder data set 10 5 50% CRD-AGES pesticide data set 12 7 58% Brambilla data set 9 3 33% Table 6:22: The predictive performance of Derek Nexus™ alert 116 (carcinogenicity) for polyhalogenated phenols in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

The “plausible” alert for in vitro chromosomal damage for DCP is produced, due to evidence that chlorophenols may uncouple oxidative phosphorylation (Matsumura 1972), leading to chromosomal damage (Hilliard et al. 1998). This alert was produced primarily on data for three compounds donated by a Lhasa Limited member. These compounds all give a positive response in the in vitro chromosome aberration test, but are inactive in the Ames test. Predictivity of this alert within databases for in vitro chromosomal aberration assessment is detailed in Table 6:23.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results Sofuni data set 9 9 100% FDA CFSAN data set 11 8 73% Mohr data set 6 0 0% CGX data set 2 1 50% Vitic database 5 3 60% Table 6:23: The predictive performance of Derek Nexus™ alert 494 (in vitro chromosomal damage) for halophenols in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

128 6.2.7 Miscellaneous compounds Of the thirty-four compounds tested, four compounds are considered miscellaneous. None of the compounds are related to one another and each was tested to offer a cross-reference with other assays presented in this investigation.

Of these four compounds, Derek Nexus™ only produced alerts for TBHQ metabolite, TBQ. The alerts produced were a “plausible” alert for carcinogenicity due to an α,β-Unsaturated carbonyl group and a “probable” and “equivocal” alert for in vitro and in vivo chromosomal damage respectively, due to the compound being a 1,4 benzoquinone. These alerts are summarised in Table 6:33.

6.2.7.1 Alert 751 (in vitro chromosomal damage) – 1,4-Benzoquinone

Figure 6:8: 2-Methyl-1,4-benzoquinone substructure highlighted within the structure of 2-tert-butyl-1,4- benzoquinone

The 1,4-benzoquinone or para-quinone substructure shown in Figure 6:8 causes Derek Nexus™ to produce an “probable” alert for in vitro chromosomal damage for TBQ. The example compound provided to detail this alert is TBQ owing to the compound producing positive results in in vitro chromosomal aberrations and micronucleus assays in CHO and mouse cells respectively. Several compounds with similar structures including 1,4 benzoquinone and 1,4-naphthaquinone also produced positive results in in vitro chromosomal aberrations and micronucleus assays. The “equivocal” alert for in vivo chromosomal damage was produced on account of data from assays for chromosomal aberrations or micronuclei in vivo with some compounds (plumbagin, 1,4-benzoquinone and lapachol) causing positive results (SivaKumar et al. 2005; Ciranni et al. n.d.; Maistro et al. 2010) and others including 2-hydroxy-1,4-naphthoquinone, producing negative results (Kirkland & Marzin 2003).

Several modes of action are suggested for the chromosomal damage caused by compounds with para- quinone substructures. These include electrophilic reaction with nucleophilic cellular components via a Michael-type addition (Monks et al. 1992), production of producing reactive radical anions through enzymatic or non-enzymatic redox cycling (Monks et al. 1992). Both of these can lead to reactions with DNA bases by either forming adducts or through oxidative reactions. There is evidence of some quinones also acting as topoisomerase inhibitors in mammalian cells in vitro. The data presented in Table 6:24 details the results in databases of mammalian in vitro chromosomal aberrations and micronucleus assays for compounds matching this structural alert. The data in Table 6:25 shows predictivity figures of the alert within databases for in vivo chromosomal aberrations and micronucleus assays:

129 Data set Alerting compounds Alerting compounds Positive predictivity producing positive results Sofuni data set 2 1 50% FDA CFSAN data set 6 4 67% Mohr data set 4 3 75% CGX data set 2 2 100% Vitic database 1 1 100% Table 6:24: The predictive performance of Derek Nexus™ alert 751 (in vitro chromosomal damage) for 1,4- benzoquinone or para-quinone in data sets for in vitro chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results MMS data set 3 3 100% FDA CFSAN data set 2 2 100% FDA CFSAN data set 7 3 43% Table 6:25: The predictive performance of Derek Nexus™ alert 751 (in vivo chromosomal damage) for 1,4- benzoquinone or para-quinone in data sets for in vivo chromosomal damage. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

6.2.7.2 Alert 743 (Carcinogenicity) – α,β-Unsaturated aldehyde, ketone or imine

Figure 6:9: Both α,β-Unsaturated carbonyl groups highlighted within the structure of 2-tert-butyl-1,4- benzoquinone

This alert for “plausible” human carcinogenicity is produced by Derek Nexus™ in relation to the α,β- unsaturated carbonyl groups within the structure of TBQ, detailed in Figure 6:9. This is due to evidence that several compounds with this substructure, cause cancers in rodents. The examples provided by Derek Nexus™ are crotonaldehyde (Chung et al. 1986), 2,4-hexadienal (NTP 2003) and sodium malonaldehyde (NTP 1988), which produced tumours in the liver, thyroid, kidney, forestomach and bile duct. Malonaldehyde is listed as a group 3 Carcinogen by IARC.

130 The predicted mode of action of carcinogenesis is direct nucleophilic attack of DNA bases via Michael-type conjugate additions with nucleophiles. α,β-Unsaturated aldehydes and ketones have been shown to be carcinogenic in rodents (Patai & Rappoport 1964; Budiawan & Eder 2000; Schuler & Eder 1999). The data shown in Table 6:26 detail the results in databases of in vivo carcinogenicity assays for compounds matching this structural alert:

Data set Alerting compounds Alerting compounds Positive predictivity producing positive results CPDB data set 16 9 56% ISSCAN data set 13 9 69% Table 6:26: The predictive performance of Derek Nexus™ alert 743 (carcinogenicity) for 743 in data sets for carcinogenicity. Some compounds may be present in more than one database, these results cannot be separated using Derek Nexus™.

Of the miscellaneous other compounds, none produced any alert of “equivocal” or higher for any of the assessed endpoints.

6.2.8 An in silico screen using Derek Nexus™ produces a high Prevalence of Alerts of at Least “Plausible” reasoning for in vitro chromosomal damage for polyphenolic antioxidants and no alerts for “Plausible” or higher reasoning of human carcinogenicity Table 6:27 to Table 6:33 summarise the results of the in silico screen of the compound set used in this investigation for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert knowledge based predictive toxicity software Derek Nexus™. Listed are the compound names beside their CAS numbers. The alert value produced by Derek Nexus™ is shown as the reasoning “probable” “plausible” or “equivocal”, followed by the endpoint chromosomal damage (CD)(in vitro unless otherwise stated), in vitro genotoxicity (Gen.), carcinogenicity (Carc.) or mutagenicity (Mut.). The Derek Nexus™ alert name is then listed. This consists of a rule reference number and a name. This name relates to the structural moiety, compound characteristic or the reason for the alert.

131 Polyphenolic antioxidants Compound name Derek Nexus™ Derek Nexus™ alert name alert value Apomorphine Plausible CD 625 – Catechol tert-Butylhydroquinone None Dodecyl gallate Plausible CD 625 – Catechol Epigallocatechin gallate Plausible CD 625 – Catechol Nordihydroguaiaretic acid Plausible CD 625 – Catechol Octyl gallate Plausible CD 625 – Catechol Propyl gallate Probable CD 625 – Catechol Equivocal Carc. 251 – Catechol Pyrogallol Plausible CD 625 – Catechol Equivocal Carc. 251 – Catechol Quercetin Probable CD 625 – Catechol Probable CD 515 – Flavonoid Plausible Mut. 203 – Flavonol Resorcinol Equivocal Carc. 162 – Extrapolation from thyroid toxicity γ-Resorcylic acid None Resveratrol Probable CD 582 4-Hydroxystilbene Equivocal Carc. 162 – Extrapolation from thyroid toxicity Table 6:27: A summary of results of an in silico screen of polyphenolic antioxidants for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™

Monophenolic antioxidants Compound name Derek Nexus™ Derek Nexus™ alert name alert value Butylated hydroxyanisole Equivocal Carc. 252 4-Alkylether phenol Butylated hydroxytoluene None 2,6-Di-tert-butyl-4- None hydroxymethylphenol Vanillic acid None Table 6:28: A summary of results of an in silico screen of monophenolic antioxidants for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™

Non-phenolic antioxidants Compound name Derek Nexus™ Derek Nexus™ alert name alert value n-Acetylcysteine None L-Ascorbic acid None Ethoxyquin None Table 6:29: A summary of results of an in silico screen of non-phenolic antioxidants for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™

132 Oxidants Compound name Derek Nexus™ Derek Nexus™ alert name alert value TBHP Probable CD 358 Hydroperoxide Plausible Carc. 749 Oxidising agent Plausible Mut. 358 Hydroperoxide Hydrogen peroxide Probable CD 358 Hydroperoxide Probable Carc. 749 Oxidising agent Probable Mut. 358 Hydroperoxide Potassium bromate Plausible CD 518 Halogen oxyacid salt Plausible Carc. 749 Oxidising agent Table 6:30: A summary of results of an in silico screen of oxidants for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™

Known genotoxins Compound name Derek Nexus™ Derek Nexus™ alert name alert value Bleomycin sulfate None Etoposide Plausible CD 641 Podophyllotoxin 306 Alkyl aldehyde Plausible Gen. 306 Alkyl aldehyde Plausible Mut. 306 Alkyl aldehyde 5-Fluorouracil Probable CD 578 5-Fluoropyramidine Methyl methanesulfonate Probable CD 027 Alkylating agent Probable CD* 755 Alkylating agent Probable Carc. 073 Alkyl sulphonate Plausible Mut. 027 Alkylating agent 4-Nitroquinoline 1-oxide Equivocal CD 329 Aromatic nitro compound Equivocal CD* 329 Aromatic nitro compound Plausible Carc. 105 Aromatic nitro compound Plausible Mut. 329 Aromatic nitro compound 303 Aromatic N-Oxide or N-hydroxy tautomer Vincristine sulfate Plausible CD 581 Vinca alkaloid Table 6:31: A summary of results of an in silico screen of known genotoxins for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™ * Methyl methanesulfonate and 4-Nitroquinoline 1-oxide produced alerts of “probable” and “equivocal” reasoning for chromosomal damage in vivo

Non-genotoxic cytotoxic compounds Compound name Derek Nexus™ Derek Nexus™ alert name alert value 2,4-Dichlorophenol Plausible CD 494 Halophenol Equivocal Carc. 116 Polyhalogenated aromatic Phenformin hydrochloride None Table 6:32: A summary of results of an in silico screen of cytotoxic, non-genotoxic for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™

133 Miscellaneous compounds Compound name Derek Nexus™ Derek Nexus™ alert name alert value 2-tert-Butyl-1,4-benzoquinone Probable CD 751 1,4-Benzoquinone Equivocal CD* 751 1,4-Benzoquinone Plausible Carc. 743 α,β-Unsaturated aldehyde, ketone or imine CCCP None Phenol None Staurosporine None Table 6:33: A summary of results of an in silico screen of miscellaneous compounds for predictive in vitro chromosomal damage, carcinogenicity, genotoxicity and mutagenicity using expert, knowledge based predictive toxicity software Derek Nexus™ *2-tert-Butyl-1,4-benzoquinone produced an equivocal alert for in vivo chromosomal damage

6.3 Discussion

This chapter sought to provide further insight into the genotoxic mode of action of the chemicals detailed in Section 4. In Section 5, 11 of 12 PPAs were shown to produce positive results for genotoxicity in the GADD45a-GFP assay. Six of these 12 compounds have published carcinogenicity studies, and only quercetin produced a positive result (Section 4.2.9). Furthermore 9 of these 12 compounds are present either naturally, or have been used as food additives with no observed link to incidence of cancer (Section 4.2-3). It is therefore important to gather as much mechanistic information supporting the observed in vitro specific effects of the compounds. An in silico screen using Derek Nexus™ provided a rapid and effective means to bring together mechanistic data supporting the genotoxicity of the test compounds based upon their structure.

It was found that 10 of the 12 PPAs produce equivocal, plausible or probable alerts for chromosomal damage or carcinogenicity. These ten compounds are the same that produced positive results in the GADD45a-GFP assay. Although TBHQ produced no alerts, its metabolite, TBQ produced a probable alert for chromosomal damage and a plausible alert for carcinogenicity. It should however be noted that the equivocal alert for carcinogenicity generated for resorcinol is based upon a non-genotoxic mechanism that does not provide insight into the mechanism of resorcinol’s observed genotoxicity.

The evidence supporting the alerts for catechol and flavonoid structural moieties both cited oxidative stress as potential modes of action. This supports the hypothesis that PPAs may be genotoxic through a ROS- mediated mechanism. This mechanism may of course be compounded in vitro by the presence of a surplus of oxygen. This hyperoxic condition has been shown to produce ROS within cells (Turrens et al. 1982; Yusa et al. 1984). The clastogenic effects of extreme hyperoxia (80-90% O2) have been shown to be exacerbated by the presence of many antioxidants including NAC, ascorbic acid and ethoxyquin (Gille et al. 1991).

Derek Nexus™ produces predictivity data where possible of a structural alert within existing databases of genotoxicity and carcinogenicity results. These provide useful insight into the value that can be ascribed to an alert. The alert for the catechol substructure successfully predicted positive results for in vitro chromosomal damage for 76% (39/51) of compounds (This does not account for compounds that appear in multiple databases, a more detailed breakdown of the predictivity is shown in Table 6:13). The predictivity of the alert within databases for carcinogenicity was a lower, 47% (8/17) (Table 6:14). This predictivity value

134 is the lowest of any of the alerts produced within this investigation. This may indicate that although compounds containing catechol structures may often cause positive results for in vitro chromosomal damage, these structures are less prone to causing carcinogenicity within rodent carcinogenicity assessment.

As an evaluation of Derek Nexus™ for its predictivity of results within the GADD45a-GFP assay, the in silico tool performs as follows:

 Of 23 compounds that produced positive results within the GADD45a-GFP assay without S9 o Derek Nexus produced an alert of equivocal, plausible or probable for one or more endpoints for 19 of the compounds (82.6%) o Derek Nexus produced an alert of plausible for one or more endpoints for 14 of the compounds (60.9%) o Derek Nexus produced an alert of probable for one or more endpoints for 8 of the compounds (34.8%) o Derek Nexus produced an alert of plausible or probable for in vitro chromosomal damage or mutagenicity for 17 of the compounds (73.9%)  Of 11 compounds that produced negative results within the GADD45a-GFP assay without S9 o Derek Nexus produced no alert of equivocal, plausible or probable for any endpoint for 9 of the compounds (81.8%) o Derek Nexus produced no alert of plausible or probable for any endpoint for 10 of the compounds (90.9%)

For the purposes of the following values, only alerts of plausible or greater are considered a positive result for Derek Nexus™. Considering that the GADD45a-GFP assay is a measure of genotoxicity in vitro, only alerts regarding chromosomal damage in vitro or mutagenicity are considered for the following predictivity values.

Derek Nexus was seen to predict results of the GADD45a-GFP assay with a sensitivity of 73.9% and a selectivity of 90.9% within the compound set tested.

The key advantage to using an in silico approach such as the one presented here is the speed with which one can screen a large number of compounds compared with in vitro methods. Within more industrial settings this advantage is even more pronounced. This, coupled with the low cost-per-compound and lack of need for a physical chemical sample allows a wide array of candidate compounds to be screened quickly and cheaply. Within the scope of this study, the main use of this tool was to quickly provide mechanistic insight. The information produced for well documented compounds was similar to the data and information found during the literature screen in Chapter 4 although the results presented in this chapter were produced much faster comparatively. For compounds with limited data in the literature, Derek Nexus™ allowed information to be found based on chemical structural similarities that would have be very difficult or impossible to carry out without the software.

The mechanistic insight provided for chemicals without existing data however is purely predictive and although useful doesn’t replace the need for experimental data. In an industrial setting however, where

135 time and cost are of great concern when screening very large sets of compounds, these predictive results could be used to preclude compounds from further testing. Given that this study aims to elucidate the reasons underlying positive results, removing any of these test compounds from further testing would be counterproductive.

Using an expert knowledge system as opposed to a QSAR system offers many advantages. The expert knowledgebase provides key mechanistic insight into the reasons underlying positive results and point towards research that further elucidates these results. Also, given that many of the PPAs are thought to be active through similar mechanisms, QSAR methods would likely have given results highlighting the same risk factors across all of the PPAs. One advantage that a QSAR approach could offer is the ability of the platform to provide a quantitative value to the predicted activity of any reactive moiety within a test compound.

The following chapter investigates the degree to which a surplus of oxygen within cells contributes to the genotoxicity of PPAs and ethoxyquin in the GADD45a-GFP assay.

6.4 Summary

 10 of 12 PPAs produced alerts for chromosomal damage, mutagenicity and/or carcinogenicity  8 PPAs produced alerts for chromosomal damage due to their catechol moiety  tert-Butylhydroquinone and gamma resorcylic acid did not produce alerts for chromosomal damage, mutagenicity or carcinogenicity  Quercetin produced alerts for in vitro chromosomal damage and mutagenicity due to its flavonol and flavonoid structure  Resveratrol produced an alert for in vitro chromosomal damage due to its 4-hydroxystilbene structure  Resveratrol and resorcinol produced “equivocal” alerts for carcinogenicity, reasoned by an extrapolation from a “plausible” alert for thyroid toxicity  Of the MPAs, only BHA produced an alert: o An “equivocal” alert for carcinogenicity related to its 4-alkylether phenol moiety  Of the NPAs, only TBQ produced alerts: o “Probable” in vitro chromosomal damage o “Equivocal” in vitro chromosomal damage o “Plausible carcinogenesis related to its α,β-Unsaturated carbonyl group  All 3 oxidants produced alerts of at “plausible” or “probable” for in vitro chromosomal damage and carcinogenesis both hydroperoxyl compounds also produced alerts for mutagenesis.  All known genotoxins produced alerts of “plausible” or “probable” in at least one of the tested endpoints except bleomycin sulfate.  2,4-Dichlorophenol produced an “equivocal” alert for carcinogenicity and a “plausible” result for in vitro chromosomal damage

136 7 Results IV – Incubation in the presence of a lowered oxygen concentration does not reduce the genotoxicity of polyphenolic antioxidants in the GADD45a-GFP assay 7.1 Introduction

This chapter presents the results of an investigation into the effects of lowered oxygen exposure on the outcome of GADD45a-GFP genotoxicity assays for PPAs.

Published studies have suggested a link between oxidative stress and the observed in vitro genotoxicity of certain PPAs that are not in vivo genotoxins. It is plausible that the difference in oxygen tension between the two conditions accounts for these different results. The partial pressure of oxygen present in the atmosphere within an ordinary CO2 incubator is far higher than that observed in the tissue of humans as is detailed in Section 2.3.5.

Using an incubator capable of displacing oxygen with nitrogen, compounds were tested for their ability to induce genotoxicity in the GADD45a-GFP assay incubated in the presence of lowered concentrations of gaseous oxygen, initially at 5% and then further lowered to 1%.

7.2 GADD45a-GFP Assays Incubated with 5% Oxygen

Given the importance of molecular oxygen in a wide array of cellular processes, altering the exposure level might be expected to alter cells’ behaviour. For example, incubating cells in the presence of a lower concentration of oxygen (5%) than would ordinarily be present (20%) in vitro may alter the rate of cell division if oxygen tension becomes a rate limiting factor. Given the importance of cell division in the effect of many genotoxic agents, it is important that the compound exposure time in standard GADD45a assay is sufficient for the population of cells within the vehicle control to undergo at least one population doubling. This allows cells to pass through all phases of the cell cycle and any genotoxic mode-of-action specific to a certain phase of cell cycle has had the opportunity to occur. Therefore, it was important at the outset of these studies to confirm that GenM-C01 and GenM-T01 cells cultured in the presence of 5% oxygen would also undergo at least one population doubling within a 48 hour timespan. See Sections 7.2.1, 7.2.2 & Figure 7:1.

Following optimisation of growth conditions, 30 compounds were assessed in the GADD45a-GFP assay in lowered oxygen. For each compound assessment, two microplates were set up side-by-side before being incubated for 48 hours with one assay plate incubated in a CO2 incubator with humidified, atmospheric air supplemented by 5% CO2. The other assay plate was incubated in a CO2 incubator programmed to maintain humidified air at concentrations of 5% O2 and 5% CO2 using nitrogen gas to displace the excess oxygen present, and supplemental CO2.

7.2.1 A reduced oxygen atmosphere does not reduce the population doubling rate of GenM-C01 cells in 24 well microplates To ascertain whether GenM-C01 cells, incubated in the presence of 5% oxygen, divide at a different rate to those incubated with atmospheric levels of oxygen, an experiment was carried out to determine the number of population doublings cells in the two conditions undergo during a period of 72 hours. To provide 137 a large enough volume to count using an automated cell counter (400 µl), the experiment was carried out in 24-well microplates. Zero-hour cell counts were made using an automated cell counter (Section 3.4). One of the microplates was then placed in a CO2 incubator with unaltered oxygen concentration and the other in a

CO2 incubator with a maintained concentration of 5% O2. Further cell counts were made after 24, 48 and 72 hours of incubation. These cell counts were used to calculate a value of relative population doublings (Section 3.4.3).

As shown in panel A of Figure 7:1, the rate of population doubling was similar in both conditions for cell cultures seeded at 1 × 106 cells/ml. Cells cultured in the presence of an unmodulated (20%) oxygen concentration underwent 1.39 population doublings within 48 hours while those incubated in the presence of 5% oxygen underwent 1.31 population doublings. The two figures fell within 1 standard deviation (calculated from the results of 3 biological replicate experiments) of one another indicating no significant difference in the rate of population doubling. The same was true for cell cultures seeded at 500,000, 250,000 and 125,000 cells/ml. At each time point the number of population doublings of samples incubated in the presence of 5% oxygen were within 1 standard deviation of those incubated in the presence of 20% oxygen and vice versa indicating no significant difference in the rate of population doubling between the two conditions for samples seeded at any of the tested cell-seeding densities.

7.2.2 Cells in GADD45a-GFP assays performed in a 5% oxygen atmosphere have reduced levels of viability than those incubated under normal conditions Duplicate GADD45a-GFP assay plates were set up for three compounds: MMS, NQO and phenformin hydrochloride along with a solvent control. One plate was incubated in a standard CO2 incubator (20% oxygen) and the second in a CO2 incubator with reduced (5%) oxygen. This was repeated on three different days with freshly prepared reagents. Data were collected by flow cytometry as is detailed in Section 3.3.2.3. The genotoxicity results generated in both conditions are very similar and produce the same qualitative result when using the standard thresholds (<80% relative population survival for cytotoxicity; >1.3 × the relative GFP induction of unexposed controls. However a difference (panel D, Figure 7:1) in the level of cell viability was observed and a homoscedastic, two-tailed student’s T-test of the viability of cells in each well to those in the corresponding well for each plate, showed a significant difference in cell viability (p=7.45×10- 3).

7.2.3 GenM-C01 & GenM-T01 cells grown in 96 well microplates with reduced (5%) oxygen have lower growth rates than cells grown in 24 well plates with reduced oxygen The population doubling rates of cells cultured in 24-well microplates are similar in both 5% oxygen and unaltered oxygen conditions. Cell viability in the 96-well microplate layout of the GADD45a-GFP assay significantly differed between the two conditions. It was therefore clear that a difference existed between culturing cells in 96-well and 24-well plates in the presence of 5% oxygen. This may be a consequence of the lower surface area to volume ratio (3.8 cm-1 in 24-well plates, 2.5 cm-1 in 96-well plates) and the greater depth of the sample that limits oxygen diffusion. To test this hypothesis, a second population doubling assay was carried out using 96-well plates (Section 3.4.2). Wells were seeded at four different seeding densities (125,000, 250,000, 500,000 and 1 × 106 cells/ml) and cultured for 72 hours. At 0, 24, 48 and 72 138 hour time points, samples were counted using an automated cell counter. As the volume needed to carry out an automated cell count was greater than the volume of a well within a 96-well plate, 4 × 100 µl were taken from 4 wells seeded with the same cell density. PI solution was used to determine cell viability. As shown in the panel B of Figure 7:1, the rate of population doubling visibly plateaued after 24 hours in samples seeded with 1 × 106 cells/ml incubated in the presence of 5% oxygen but did not plateau in those samples seeded at the same density and incubated in the presence of 20% oxygen. As shown in Figure 7:4, the percentage of cells remaining unstained after exposure to PI at the 48 hour time point was highly significantly lower in samples incubated in the presence of 5% oxygen than in those samples incubated in the presence of 20% oxygen (determined using homoscedastic, 2-tailed Student’s T-Test. p=3.22×10-5). The same was not true in samples seeded at a concentration of 500,000 cells/ml, where no plateauing of the rate of cell doubling was observed. Also, both the number of cell doublings and the percentage of unstained cells following PI exposure in samples seeded at a concentration of 500,000 cells/ml showed no significant difference between the two conditions at the 48 hour time point (determined using homoscedastic, 2-tailed Student’s T-Test. p=0.212 and p=0.0621 respectively).

7.2.4 GADD45a-GFP assays incubated with 5% oxygen, seeded at 500,000 cells/ml produce the same qualitative results as those incubated without oxygen control Thirty compounds were tested in the GADD45a-GFP assay (Section 3.3.1.6) seeded at 500,000 cells/ml, which equates to a seeding density of half that used in the standard protocol. Assay plates were incubated both in a CO2 incubator without oxygen-control and in a CO2 incubator maintained at an oxygen concentration of 5%. The results of the assays carried out in both oxygen concentrations were compared to one another to ascertain whether lowering the concentration of oxygen present in the incubator during the 48 hour incubation of assay plates led to a difference in the result of the GADD45a-GFP assay.

Sample data presented in Figure 7:2 and Figure 7:3 show a comparison of the results of assessing polyphenols, EGCG and propyl gallate as well as genotoxic control, MMS and MPA, BHT. The qualitative results (positive/negative) for genotoxicity and cytotoxicity of all 4 compounds are the same in both conditions. The genotoxicity LEC for EGCG was twofold lower in the assay carried out in the presence of 5% oxygen, the genotoxicity LEC for propyl gallate was twofold lower in the assay carried out in 20% oxygen and the genotoxicity LEC for MMS remained unchanged between the two conditions.

All compound results are summarised in Table 7:1, Table 7:2 and Table 7:3 alongside the results from Section 5.3. The cell viability within vehicle controls of all of the conditions assessed are compared in Figure 7:4 showing that the cell viability in those assay plates incubated in the presence of 5% oxygen do not significantly differ from those incubated in the presence of 20% oxygen.

139

Figure 7:1 Charts comparing population doublings of GenM-C01 cells seeded at 1 × 106 cells/ml and incubated in the presence of 5% oxygen or in the presence of 20% oxygen over a period of 72 hours. Cell population doubling assays were carried out in 24 well plates with well volumes of 500 µl (A) and in 96 well plates with well volumes of 150 µl (B & C). The first assessment (A) shows a similar rate of population doubling between the cells cultured in 20% (blue) and 5% (red) oxygen achieving 1.39 and 1.31 population doublings respectively after 48 hours. Shown in Panel D is the difference between relative population survival of vehicle control cells within GADD45a-GFP assay plates incubated in the presence of 20% (blue) and 5% oxygen (red). The significance of the difference between the two values was calculated using a homoscedastic, two-tailed Student’s T-Test. A p-value of p=7.45×10-3 was produced indicating a very significant difference in the relative population survival between the two conditions. The population doubling assessment was repeated in 96-well plates (B & C) with a smaller surface area to volume ratio. The population doubling of cells seeded at a density of 1 × 106 cells/ml (B) and incubated in the presence of 5% oxygen to plateau after 24 hours. These cells undergo only 0.700 population doublings in 48 hours, insufficient for use in the GADD45a-GFP assay. Cells seeded at a density of 1 × 106 cells/ml (C) and incubated in the presence of 5% oxygen continue to divide over the duration of the experiment. These cells undergo 1.36 population doublings in 48 hours, sufficient for use in the GADD45a-GFP assay. Error bars represent the standard deviation between 3 biological replicate experiments.

140 7.2.4.1 The variability of results generated in assay plates seeded at 500,000 cells/ml falls within the range observed in the standard protocol of the GADD45a-GFP assay The thresholds for genotoxicity and cytotoxicity in the GADD45a-GFP assay were defined as a change of greater than 3× the standard deviation within vehicle controls and results for non-genotoxic compounds (Section 3.9.4). For the standard protocol of GADD45a-GFP measured by flow cytometry, the threshold defined for cytotoxicity is a reduction in cell viability within a sample relative to the vehicle control to below 80% and the threshold for genotoxicity is an increase of greater than 1.3× the GFP fluorescence of a sample relative to the vehicle control. The choice of these thresholds is detailed by Jagger et al. (2009).

The relative standard deviation within cell viability measurements of vehicle control wells and samples of non-genotoxic compounds was 1.15% in plates incubated with 20% oxygen and 4.49% in plates cultured in the presence of 5% oxygen. Since a drop below 100% viability of 3 times both of these figures would still fall within the previously established threshold of 80% relative viability, the threshold was kept unchanged.

The standard deviation within GFP fluorescence measurements of vehicle control wells and samples of non- genotoxic compounds was 9.27% in plates incubated without a modulated level of oxygen present and 8.85% in plates cultured in the presence of 5% oxygen. Since an increase above the vehicle control fluorescence of 3 times both of these figures would still fall within the previously established threshold of 1.3× the relative GFP fluorescence then the threshold was kept unchanged.

7.2.4.2 Incubating GADD45a-GFP assays in the presence of 5% oxygen did not qualitatively change in results Each of the 30 compounds assessed, as shown in Table 7:1, Table 7:2 and Table 7:3, produced results with the same call of “positive” or “negative” in the GADD45a-GFP assays carried out following the standard protocol, and in assays with a seeding density of 500,000 cells/ml, both those incubated in the presence of 5% oxygen and those incubated without a modulated oxygen concentration. Each qualitative result for genotoxicity and cytotoxicity was also the same as had been observed in the standard assay (Section 5.3).

7.2.4.3 Lowering the cell seeding density of the GADD45a-GFP assay caused over a twofold change in genotoxicity LEC for 16 of the 30 compounds tested. 14 out of the 30 compounds tested in the GADD45a-GFP assay with a seeding density of 500,000 cells/ml produced positive results for genotoxicity with an LEC over twofold lower to that observed in assays carried out with a seeding density of 1 × 106 cells/ml. A further two produced positive results for genotoxicity with an LEC over twofold lower. A decrease in the LEC for cytotoxicity of over twofold was also observed for 14 of the 30 compounds tested and an increase in the LEC for cytotoxicity of over twofold was also observed for 5 of the 30 compounds. This change is likely due to an increased number of molecules of the test compound per each cell in assays carried out with a lower cell seeding density. For a summary of the results, see Table 7:1, Table 7:2 and Table 7:3.

141

Figure 7:2 Sample data comparing the results of testing polyphenols, EGCG and propyl gallate in the GADD45a-GFP incubated in the presence 5% oxygen with those incubated in the presence of 20% oxygen Shown above are the results of GADD45a-GFP assessments of EGCG (A & B) and propyl gallate (C & D) seeded with 500,000 cells/ml and incubated in the presence of 5% oxygen (A & C) and in the presence of an unmodulated (20%) oxygen concentration (B & D). The relative population survival (RPS) is shown in black and is measured on the left y-axis. This gives an indication of the cytotoxicity of a compound dose, any dose which causes the RPS to drop below the cytotoxicity threshold (blue dashed line) is considered cytotoxic. The relative GFP induction ratio is shown in green and is measured on the right y-axis. This gives an indication of the genotoxicity of a compound dose, any dose which causes the GFP induction ratio to rise above the genotoxicity threshold (red dashed line) is considered genotoxic. The threshold of cytotoxicity in both conditions is 80 % relative to the vehicle control and the threshold of genotoxicity in both conditions is 1.3 × the GFP induction ratio of the vehicle control. Error bars represent the standard deviation between the results of 3 biological replicate experiments. Both compound produce results with the same qualitative call (positive/negative) for cytotoxicity and genotoxicity in both of the tested conditions. The lowest genotoxic dose of EGCG was found to be 2.44 µM in 5% O2 and 4.88 µM in the presence 20% oxygen. The lowest cytotoxic dose of EGCG was found to be 0.305 µM in 5% O2 and 9.75 µM in the presence of a 20% oxygen. The lowest genotoxic dose of propyl gallate was found to be 9.8 µM in 5% O2 and 4.89 µM in the presence of 20% oxygen. The lowest cytotoxic dose of propyl gallate was found to be 39.1 µM in 5% O2 and 9.8 µM in the presence of 20% oxygen.

142

Figure 7:3 Sample data comparing the results of testing MMS and BHT in the GADD45a-GFP incubated in the presence 5% oxygen with those incubated in the presence of an unmodulated oxygen concentration Shown above are the results of GADD45a-GFP assessments of MMS (A & B) and BHT (C & D) seeded with 500,000 cells/ml and incubated in the presence of 5% oxygen (A & C) and in the presence of 20% oxygen (B & D). The relative population survival (RPS) is shown in black and is measured on the left y-axis. This gives an indication of the cytotoxicity of a compound dose, any dose which causes the RPS to drop below the cytotoxicity threshold (blue dashed line) is considered cytotoxic. The relative GFP induction ratio is shown in green and is measured on the right y-axis. This gives an indication of the genotoxicity of a compound dose, any dose which causes the GFP induction ratio to rise above the genotoxicity threshold (red dashed line) is considered genotoxic. The threshold of cytotoxicity in both conditions is 80 % relative to the vehicle control and the threshold of genotoxicity in both conditions is 1.3 × the GFP induction ratio of the vehicle control. Error bars represent the standard deviation between the results of 3 biological replicate experiments. Both compound produce results with the same qualitative call (positive/negative) for cytotoxicity and genotoxicity in both of the tested conditions. The lowest genotoxic dose of MMS was found to be 14.1 µM in both conditions. The lowest cytotoxic dose of MMS was found to be 56.3 µM in both conditions. BHT was not found to be genotoxic under either condition. The lowest cytotoxic dose of BHT was found to be 50 µM in 5% O2 and 100 µM in the presence of 20% oxygen.

143 7.2.4.4 Lowering the level of oxygen present during incubation of the GADD45a-GFP assay to 5% did not cause a greater than twofold change in genotoxicity LEC for any of the 30 compounds tested 6 out of the 20 compounds that produced positive results in the GADD45a-GFP assay with a seeding density of 500,000 cells/ml produced positive results for genotoxicity with an LEC twofold higher and 4 compounds produced positive results for genotoxicity with an LEC twofold lower in assays incubated in the presence of 5% oxygen. No compound produced a genotoxic result with an LEC over twofold different to that observed in assays incubated in the presence of 20% oxygen. For a summary of the results, see Table 7:1, Table 7:2 and Table 7:3.

7.3 GADD45a-GFP Assays Incubated with 1% Oxygen

Since no qualitative change in result in the GADD45a-GFP assay was observed by incubating assay plates in the presence 5% oxygen, further experiments were carried out in the presence of 1% oxygen following optimisation of cell seeding density. 30 compounds were then assessed in the GADD45a-GFP assay.

7.3.1 250,000 cells/ml is an optimal cell seeding density for carrying out GADD45a-GFP assay in the presence of 1% oxygen A repeat of the growth assessment detailed in Section 7.2.3 was carried out, this time comparing cell doubling rates within 96-well microplates incubated in the presence of 1% oxygen and atmospheric oxygen concentrations. The same range of cell seeding densities were chosen as for 5% oxygen. Cell doubling rates were observed to plateau earlier in wells seeded with 1 × 106 and 500,000 cells/ml within microplates incubated in the presence of 1% oxygen. Wells seeded with 250,000 cells/ml doubled on average 1.55 times after 48 hours of incubation in the presence of 1% oxygen and 1.58 times after 48 hours of incubation without a modulated oxygen concentration. These average figures were taken as the average value from 3 replicate experiments carried out on different days with freshly prepared reagents.

7.3.2 GADD45a-GFP assays incubated with 1% oxygen, seeded at 250,000 cells/ml produce the same qualitative results for genotoxicity as those incubated without oxygen control Thirty compounds were tested in the GADD45a-GFP assay (Section 3.3.1.6) seeded at 250,000 cells/ml, one quarter of the cell density used within the standard protocol. Assay microplates were incubated in a CO2 incubator without oxygen-control and also in a CO2 incubator maintained at an oxygen concentration of only 1%. These data were then compared to each other to investigate whether incubating assay plates in the presence of 1 % oxygen led to a difference in the result of the GADD45a-GFP assay.

Sample data presented in Figure 7:5 and Figure 7:6 show a comparison of the results of assessing polyphenols, EGCG and propyl gallate as well as genotoxic control MMS and MPA, BHT. The qualitative results (positive/negative) for genotoxicity and cytotoxicity of all 4 compounds are the same in both conditions. The genotoxicity LEC for EGCG was twofold lower in the assay carried out in the presence of 1% oxygen, the genotoxicity LEC for propyl gallate and MMS remained unchanged between the two conditions.

144 7:4 showing that the rate of cell viability in those assay plates incubated in the presence of 1% oxygen do not significantly differ from those incubated in the presence of 20% oxygen.

7.3.2.1 The variability of results generated in assay plates seeded at 250,000 cells/ml exceeds that observed in the standard protocol of the GADD45a-GFP assay As detailed in Section 7.2.4.1, thresholds for cytotoxicity and genotoxicity needed to be established as being greater than 3× the standard deviation observed in data from vehicle control wells and non-genotoxic compounds.

The relative standard deviation within cell viability measurements of vehicle control wells and samples of non-genotoxic compounds was 0.97% in plates incubated without a modulated level of oxygen present and 12.7% in plates cultured in the presence of 1% oxygen. Since a drop below 100% viability of 3 times both of the relative standard deviation observed within data from plates incubated in the presence of 20% oxygen (97.1) would still fall within the previously established threshold of 80% relative viability then the threshold was kept unchanged. For assays carried out in the presence of 1% oxygen, a drop below the relative population survival of the vehicle control samples (100%) of 3 times the relative standard deviation (3 × 12.7%) produced a value of 61.9%. In light of this, the threshold of cytotoxicity for plates incubated in the presence of 1% oxygen however was changed to 60%

The standard deviation within GFP fluorescence measurements of vehicle control wells and samples of non- genotoxic compounds was 10.4% in plates incubated without a modulated level of oxygen present and 11.7% in plates cultured in the presence of 1% oxygen. An increase above the vehicle control fluorescence (100%) of 3 times both of these figures (31.2% and 35.1%) falls outside of the previously established threshold of 30% higher relative GFP fluorescence than the vehicle control. Because of this, the threshold for both conditions was changed to a 35% increase (1.35×) in GFP fluorescence relative to the vehicle control.

7.3.2.2 Incubating GADD45a-GFP assays in the presence of 1% oxygen led to no qualitative change in genotoxicity result for the GADD45a-GFP assay. No difference was seen in the qualitative result for genotoxicity either between the plates seeded at 250,000 cells/ml compared to previous results or between the assays incubated in the presence of the different oxygen concentrations. MPAs, BHA and BHMP both produced positive results for cytotoxicity in the assay when incubated in the presence of an unmodulated concentration of oxygen but produced negative results for cytotoxicity in assays incubated in the presence of 1% oxygen. For a summary of the results, see Table 7:1, Table 7:2 and Table 7:3.

7.3.2.3 Lowering the cell seeding density of the GADD45a-GFP assay caused over a twofold change in genotoxicity LEC for 15 of the 30 compounds tested. 13 out of the 30 compounds tested in the GADD45a-GFP assay with a seeding density of 250,000 cells/ml produced positive results for genotoxicity with an LEC over twofold lower to that observed in assays carried out with a seeding density of 1 × 106 cells/ml and two produced positive results for genotoxicity with an LEC over twofold lower. A decrease in the LEC for cytotoxicity of over twofold was also observed for 14 of the 30 compounds tested and an increase in the LEC for cytotoxicity of over twofold was also observed for 4 of the 30 compounds. For a summary of the results, see Table 7:1, Table 7:2 and Table 7:3. 145 7.3.2.4 Lowering the level of oxygen present during incubation of the GADD45a-GFP assay to 1% did not cause a greater than twofold change in genotoxicity LEC for any of the 30 compounds tested 8 out of the 20 compounds that produced positive results in the GADD45a-GFP assay with a seeding density of 250,000 cells/ml produced positive results for genotoxicity with an LEC twofold higher and 3 compounds produced positive results for genotoxicity with an LEC twofold lower in assays incubated in the presence of 1% oxygen. No compound produced a genotoxic result with an LEC over twofold different to that observed in assays incubated in the presence of 20% oxygen. For a summary of the results, see Table 7:1, Table 7:2 and Table 7:3.

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Figure 7:4 Bar chart comparing the percentage of cells incubated in the presence of differing concentrations of oxygen and differing cell seeding densities left unstained after exposure to PI giving an indication of cell viability within a cell population. Data are from cells in vehicle control wells of GADD45a-GFP assay microplates. Cells cultured in the presence of 5% oxygen, seeded at a cell density of 1 × 106 cells/ml show a highly significant decrease in the percentage of unstained cells compared with cells seeded at the same density and incubated in the presence of 20% oxygen (** p=1.31×10-5 - determined using a homoscedastic, two-tailed Student’s T-Test). The same is true for cells cultured in the presence of 1% oxygen, seeded at a cell density of 250,000 cells/ml (p=2.45×10-4). The decrease in cell staining observed in cells seeded at a cell density of 500,000 cells/ml in the presence of 5% oxygen relative to cells seeded at the same density and incubated in the presence of 20% oxygen was not statistically significant (p=0.0574). The error bars represent the standard deviation between 3 biological replicate experiments.

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Figure 7:5 Sample data comparing the results of testing polyphenols, EGCG and propyl gallate in the GADD45a-GFP incubated in the presence 1% oxygen with those incubated in the presence of 20% oxygen Shown above are the results of GADD45a-GFP assessments of EGCG (A & B) and propyl gallate (C & D) seeded with 250,000 cells/ml and incubated in the presence of 1% oxygen (A & C) and in the presence of 20% oxygen (B & D). The relative population survival (RPS) is shown in black and is measured on the left y- axis. This gives an indication of the cytotoxicity of a compound dose, any dose which causes the RPS to drop below the cytotoxicity threshold (blue dashed line) is considered cytotoxic. The relative GFP induction ratio is shown in green and is measured on the right y-axis. This gives an indication of the genotoxicity of a compound dose, any dose which causes the GFP induction ratio to rise above the genotoxicity threshold (red dashed line) is considered genotoxic. The threshold of cytotoxicity in assays incubated in the presence of 1% oxygen is 60 % relative to the vehicle control and is 80% in assays incubated in the presence of 20% oxygen. The threshold of genotoxicity in both conditions is 1.35 × the GFP induction ratio of the vehicle control. Error bars represent the standard deviation between the results of 3 biological replicate experiments. Both compound produce results with the same qualitative call (positive/negative) for cytotoxicity and genotoxicity in both of the tested conditions. The lowest genotoxic dose of EGCG was found to be 2.44 µM in 1% O2 and 4.88 µM in the presence of 20% oxygen. The lowest cytotoxic dose of EGCG was found to be 2.44 µM in 1% O2 and 9.75 µM in the presence of 20% oxygen. The lowest genotoxic dose of propyl gallate was found to be 4.89 µM in both conditions. The lowest cytotoxic dose of propyl gallate was found to be 9.78 µM in both conditions.

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Figure 7:6 Sample data comparing the results of testing MMS and BHT in the GADD45a-GFP incubated in the presence 1% oxygen with those incubated in the presence of 20% oxygen Shown above are the results of GADD45a-GFP assessments of MMS (A & B) and BHT (C & D) seeded with 250,000 cells/ml and incubated in the presence of 1% oxygen (A & C) and in the presence of 20% oxygen (B & D). The relative population survival (RPS) is shown in black and is measured on the left y-axis. This gives an indication of the cytotoxicity of a compound dose, any dose which causes the RPS to drop below the cytotoxicity threshold (blue dashed line) is considered cytotoxic. The relative GFP induction ratio is shown in green and is measured on the right y-axis. This gives an indication of the genotoxicity of a compound dose, any dose which causes the GFP induction ratio to rise above the genotoxicity threshold (red dashed line) is considered genotoxic. The threshold of cytotoxicity in assays incubated in the presence of 1% oxygen is 60 % relative to the vehicle control and is 80% in assays incubated in the presence of 20% oxygen. The threshold of genotoxicity in both conditions is 1.35 × the GFP induction ratio of the vehicle control. Error bars represent the standard deviation between the results of 3 biological replicate experiments. Both compound produce results with the same qualitative call (positive/negative) for cytotoxicity and genotoxicity in both of the tested conditions. The lowest genotoxic dose of MMS was found to be 28.1 in both conditions. The lowest cytotoxic dose of MMS was found to be 28.1 µM in both conditions. BHT did not produce a positive result for genotoxicity under either condition. The lowest cytotoxic dose of BHT was found to be 200 µM in both conditions.

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Genotoxicity Cytotoxicity Cell seeding Cell seeding density 1 × 106 500,000 500,000 250,000 250,000 density 1 × 106 500,000 500,000 250,000 250,000 Oxygen Oxygen Concentration NC NC 5% NC 1% Concentration NC NC 5% NC 1% Compound Result LEC - μM LEC - LEC - LEC - LEC - Result LEC - μM LEC - LEC - LEC - LEC - μM μM μM μM μM μM μM μM Apomorphine Positive 62.5 15.6 15.6 31.3 31.3 Positive 62.5 62.5 62.5 31.3 15.6 hydrochloride tert-Butylhydroquinone Negative Positive 50 12.5 12.5 12.5 12.5

Dodecyl gallate Positive 3.13 0.78 1.56 0.78 1.56 Positive 3.13 0.78 1.56 0.78 1.56

Epigallocatechin gallate Positive 2.44 4.89 2.44 4.89 2.44 Positive 2.44 9.75 0.313 9.75 2.44

Nordihydroguaiaretic acid Positive 4.88 1.22 2.44 1.22 2.44 Positive 4.88 2.44 2.44 2.44 2.44

Octyl gallate Positive 6.25 1.56 3.13 1.56 3.13 Positive 6.25 1.56 1.56 1.56 1.56

Propyl gallate Positive 9.78 9.78 4.89 4.89 4.89 Positive 9.78 9.78 39.1 9.78 4.89

Pyrogallol Positive 61.7 78.1 78.1 78.1 78.1 Positive 92.6 78.1 39.6 78.1 39.6

Quercetin Positive 15.6 7.85 7.85 7.85 7.85 Positive 31.3 7.85 7.85 7.85 7.85

Resorcinol Positive 313 250 125 250 125 Positive 1250 1000 1000 1000 1000

γ-Resorcylic acid Negative Negative

Resveratrol Positive 15.6 7.81 7.81 7.81 7.81 Positive 7.81 7.81 7.81 7.81 7.81 Table 7:1 Summary of results from assessment of 12 PPAs in the GADD45a-GFP assay, seeded at various cell densities and incubated in the presence of various concentrations of oxygen (1/3) 150 NC refers to “No Change” and denotes that assessments in said column were incubated in the presence of an unmodulated concentration of oxygen.

Genotoxicity Cytotoxicity Cell seeding Cell seeding density 1 × 106 500,000 500,000 250,000 250,000 density 1 × 106 500,000 500,000 250,000 250,000 Oxygen Oxygen Concentration NC NC 5% NC 1% Concentration NC NC 5% NC 1% Compound Result LEC - μM LEC - LEC - LEC - LEC - Result LEC - μM LEC - LEC - LEC - LEC - μM μM μM μM μM μM μM μM Butylated hydroxyanisole Negative Equivocal 400 200 200 200

Butylated hydroxytoluene Negative Positive 100 100 50 200 200 2,6-Di-tert-butyl-4- Negative Equivocal 500 1000 500 hydroxymethylphenol Vanillic acid Negative Negative

n-Acetylcysteine Negative Negative

L-Ascorbic acid Negative Negative

Ethoxyquin Positive 156 80 160 80 160 Positive 156 80 80 80 80

tert-Butyl hydroperoxide Positive 275 275 138 275 138 Positive 138 138 138 138 138

Hydrogen peroxide Positive 125 62.5 62.5 62.5 62.5 Positive 125 125 62.5 125 62.5

Potassium bromate Positive 156 125 125 125 125 Positive 1250 1000 1000 1000 1000 Table 7:2 Summary of results from assessment of 10 monophenolic and non-phenolic antioxidants in the GADD45a-GFP assay, seeded at various cell densities and incubated in the presence of various concentrations of oxygen (2/3) NC refers to “No Change” and denotes that assessments in said column were incubated in the presence of an unmodulated concentration of oxygen. Equivocal results for cytotoxicity of BHT and BHMP refer to positive results for cytotoxicity in certain conditions and negative results in other conditions. Conditions where a LEC is specified are conditions that produced a positive result.

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Genotoxicity Cytotoxicity Cell seeding Cell seeding density 1 × 106 500,000 500,000 250,000 250,000 density 1 × 106 500,000 500,000 250,000 250,000 Oxygen Oxygen Concentration NC NC 5% NC 1% Concentration NC NC 5% NC 1% Compound Result LEC - μM LEC - LEC - LEC - LEC - Result LEC - μM LEC - LEC - LEC - LEC - μM μM μM μM μM μM μM μM Bleomycin sulfate Positive 0.331 0.331 0.66 0.331 0.66 Positive 2.64 2.64 1.32 2.64 1.32

Etoposide Positive 0.125 0.125 0.250 0.125 0.250 Positive 0.125 1.13 0.25 1.13 0.25

5-Fluorouracil Positive 4.69 0.78 0.78 0.78 0.78 Positive 37.5 3.13 3.13 3.13 3.13

Methyl methanesulfonate Positive 113 14.1 14.1 28.1 28.1 Positive 225 56.3 56.3 28.1 28.1

4-Nitroquinoline-1-oxide Positive 0.65 0.813 0.813 0.813 0.813 Positive 0.65 3.25 3.25 3.25 3.25

Vincristine sulfate Positive 0.00381 0.00381 0.00381 0.00381 0.00762 Positive 0.00381 0.00381 0.00762 0.00381 0.00762

2,4-Dichlorophenol Negative Positive 490 490 490 490 980

Phenformin hydrochloride Negative Positive 18.4 18.4 18.4 18.4 18.4 Table 7:3 Summary of results from assessment of 6 genotoxic compounds and 2 non-genotoxic cytotoxic compounds in the GADD45a-GFP assay, seeded at various cell densities and incubated in the presence of various concentrations of oxygen (3/3) NC refers to “No Change” and denotes that assessments in said column were incubated in the presence of an unmodulated concentration of oxygen.

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7.4 Discussion

This chapter presented the results of assessing the genotoxicity of a diverse set of compounds, including several phenolic antioxidants in the GADD45a-GFP assay in the presence of 5% and 1% oxygen. Many of the PPAs tested were shown previously to produce positive results for genotoxicity within the GADD45a-GFP assay (Section 5). It was hypothesised that many of these positive results were not physiologically relevant as several of the compounds have produced negative results for genotoxicity and carcinogenicity in vivo (Section 4). Oxidative stress was highlighted as a plausible mechanism for the genotoxicity of PPAs in vitro (Section 2.5) (Long et al. 2000; Long & Halliwell 2001; Aruoma & Murcia 1993; Halliwell 2008) and this mechanism was supported by the information supporting the in silico alerts for in vitro chromosomal damage and mutagenicity produced by Derek Nexus™ (Section 6). The oxygen tension within in vitro assessment is far higher than is present physiologically in most tissues (Section 2.3.5). Hyperoxia has been shown to increase the generation of ROS within cells (Yusa et al. 1984; Turrens et al. 1982). It is therefore plausible that the level of oxygen present during the GADD45a-GFP assay (20%) is contributing to physiologically irrelevant positive results that occur through a ROS-mediated pathway.

Assessing compounds in the presence of 5% and 1% oxygen had no effect upon the overall genotoxicity result for any tested compound compared to the results generated in the presence of 20% oxygen. The lower oxygen concentration also had negligible effect upon the potency of any of the compounds.

In order for cells to divide at least once in the presence of a lower oxygen concentration, the cell seeding densities needed to be reduced. This need to reduce the cell seeding density indicates that the oxygen concentration present became a limiting factor in the cells’ division. For oxygen to become a rate limiting factor in cell division, it can be assumed that cells are not exposed to a surplus of oxygen. If cells are still able to produce positive results for PPAs when oxygen is not present in surplus, then hyperoxia is not contributing to the prevalence of positive results for the compounds.

Cells incubated in the presence of 1% oxygen had a very significantly lower level of viability in vehicle controls. This was observed regardless of the cell seeding density and was likely due to the cells experiencing hypoxia. Even in these conditions, the GADD45a-GFP assay produced the same qualitative result for each of the test compounds. This indicates that the GADD45a mediated DNA damage response pathway is still functional within cells experiencing hypoxia.

The limited effect of changed oxygen tension upon the cultured cells could be an artefact of their being an immortalised cell line. During their immortalisation, it is likely that the cells were exposed to atmospheric oxygen tension meaning that those cells most able to live in the presence of this higher level of oxygen were able to survive and further spread their genes. This, over time, will have led to a selection pressure for cells that live in the presence of far higher levels of oxygen than primary cells (Kondoh et al. 2005). If a similar genotoxicity assessment to the one presented here were to be carried out using primary cells, or cells immortalised in the presence of physiological oxygen concentrations, the results may have been very different. Nonetheless, given that the purpose of this study is to find factors contributing to the generation of seemingly physiologically irrelevant positive results in cell lines such as GenM-T01 which are cultured in atmospheric oxygen concentrations, the results of such an assessment would likely be irrelevant.

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It is clear from the data presented in this chapter that whether oxygen levels are lowered to physiologically normoxic levels (5%) or hypoxic levels (1%), the incidence of positive results for phenolic antioxidants remains unchanged. The phenolic antioxidants are believed to exert their genotoxic effect in vitro by generating ROS in cells (Long et al. 2010). It was posited that the high concentration of oxygen present during assessment may have been a contributing factor to the generation of physiologically irrelevant levels of ROS in vitro (Halliwell 2003). The concentration of oxygen present during chemical assessment has little or no affect upon the genotoxicity of these compounds. Before further investigating the contributing factors to these compounds’ genotoxicity, it would be prudent to first ensure that the compounds do generate ROS within the cells.

The following results chapter investigates the potential of PPAs and ethoxyquin to increase the level of ROS within cells, a mechanism suspected to contribute to their observed genotoxicity in vitro.

7.5 Summary

 500,000 cells/ml was found to be the optimal cell seeding density to compare the results of GADD45a-GFP assays incubated in the presence of 5% oxygen  Lowering the cell seeding density of the GADD45a-GFP assay to 500,000 cells/ml produced no qualitative change in genotoxicity result for any of the 30 compounds tested  Lowering the oxygen concentration present during incubation of the GADD45a-GFP assay to 5% produced no qualitative change in genotoxicity result for any of the 30 compounds tested  250,000 cells/ml was found to be the optimal cell seeding density to compare the results of GADD45a-GFP assays incubated in the presence of 1% oxygen  Lowering the cell seeding density of the GADD45a-GFP assay to 250,000 cells/ml produced no qualitative change in genotoxicity result for any of the 30 compounds tested  Lowering the oxygen concentration present during incubation of the GADD45a-GFP assay to 1% produced no qualitative change in genotoxicity result for any of the 30 compounds

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8 Results V – Most polyphenolic antioxidants lead to the generation of intracellular reactive oxygen species in vitro 8.1 Introduction

Although the effect of lowering the concentration of oxygen present during the incubation of GADD45a-GFP assessment of PPAs did not change their result, the link between the generation of misleading positive results in in vitro mammalian genotoxicity assays by certain PPAs and their generation of reactive oxygen species (ROS), still required further investigation.

This chapter describes experiments conceived to determine whether PPAs generated ROS in vitro. To do this, the intracellular dye, dichlorofluorescin diacetate (DCFH-DA) was used as a fluorimetric indicator of the intracellular generation of ROS. The generation of its oxidised product, dichlorofluorescein (DCF), was measured as an increase in fluorescence at 530 nm (excitation 488 nm) using flow cytometry. An increase in DCF fluorescence in treated cells, relative to the vehicle control, indicates an increase in ROS within treated cells.

There is no reason to suppose that the plasmid in either GenM-C01 or GenM-T01 cells is involved in the observed genotoxic action of PPAs. To further examine the mechanisms underlying this genotoxicity, TK6 cells were used. These cells are identical to those used in the GADD45a-GFP assay except for their lack of the GADD45a-GFP plasmid. TK6 cells, like GenM-C01 and GenM-T01 cells are GADD45a competent.

8.2 Optimisation of the DCFH-DA assay protocol in TK6 Cells

Prior to carrying out experiments to investigate test compounds, steps were taken to optimise the various parameters of the DCFH-DA assay for use with TK6 cells cultured in RPMI within 96-well microplates. Below, are the results of experiments designed to find the optimal concentration of the positive control, tert-butyl hydroperoxide (TBHP), the concentration of the dye DCFH-DA and the compound exposure time. The final protocol is detailed in Section 3.6.

8.2.1 Determining the optimum concentration of the indicator dye, DCFH-DA Cells were first dosed with the dye, DCFH-DA over a twofold dilution series from 5 - 160 µM (Section 3.6.2.1). These cells were then exposed to 100 µM TBHP (or vehicle control) for four hours, before measuring the DCF fluorescence of the cells (detailed in Section 3.6.6). As shown in panel A of Figure 8:1, the DCF fluorescence in vehicle control cells increased proportionally to the increasing dose of DCFH-DA. The DCF fluorescence in the TBHP treated cells increased at a greater rate between the doses of 5 and 40 µM than between 40 and 160 µM. The greatest level of DCF fluorescence in the positive control relative to the negative control was observed in cells treated with 20 µM DCFH-DA, and hence this concentration was used in subsequent experiments.

8.2.2 Determining the optimum concentration for the positive control (TBHP) TK6 cells were treated with 20 µM DCFH-DA or left untreated. Cells treated with DCFH-DA were exposed to concentrations of TBHP increasing twofold from 7.81 - 500 µM, as well as to a vehicle control. DCFH-DA-free cells were exposed to the top dose of TBHP and the vehicle control (M&M Section 3.6.3). Cells were exposed for 4 hours before measuring DCF fluorescence. As is shown in panel B of Figure 8:1, the DCF 155 fluorescence of DCFH-DA treated cells increased continuously between the doses of 31.2 and 500 µM. There was an increase in the standard deviation between the measurements taken within the three biological replicates of the experiment with the increasing dose of TBHP. Those cells treated with 250 µM and 500 µM TBHP produced levels of fluorescence 2.53 × and 4.59 × higher (respectively) than observed in the vehicle control. The relative standard deviation of DCF fluorescence of the samples treated with 500 µM TBHP (20.0%) was almost twice as high as that of the samples treated with 250 µM (10.1%) and so 250 µM TBHP was chosen to be used as the positive control for all DCFH-DA assays. Results from any assay plate on which the DCF fluorescence of the positive control sample fell above or below 2 × the standard deviation amongst all recorded results for the same dose were disregarded and the experiment repeated.

8.2.3 Determining the optimum compound exposure time To determine the optimal compound exposure time to be used within the DCFH-DA assay, cells were first treated with DCFH-DA and then exposed to a two-fold dilution series of TBHP from 7.81 - 1000 µM (Section 3.6.2.2). Every 30 minutes for 5½ hours, cells were added to a new row on the microplate. After 5½ hours, the microplate was incubated for a further 3 hours before measuring the DCF fluorescence as detailed in Section 3.6.6. Figure 8:2 shows the DCF fluorescence of cells exposed to 250 µM TBHP relative to the vehicle control against the increasing compound exposure time. Cells exposed to compounds for 8½ hours show the greatest increase in relative DCF fluorescence (RDF) (13.8 × ± 13.3%). Cells exposed to compounds for 4 hours showed a lower increase in RDF. However they showed the lowest level of relative standard deviation (6.92 × ± 5.25 %) between the three biological replicate experiments. 4 hours was therefore chosen as the compound exposure time for the assay.

8.3 Calculation of thresholds for DCF fluorescence and PI permeability

Below in Section 8.4 are detailed the results of assessing 34 compounds using the DCFH-DA assay. To compare the change in RDF or relative cell viability (RCV) caused by the different compounds, thresholds were calculated. As is detailed in Section 3.9.4 a threshold of 3 × the relative standard deviation (RSD) within the DCF fluorescence of the vehicle control for each of the assay plates tested. The RSD for the DCF fluorescence was 20.2%. This meant that an increase above 161% of the DCF fluorescence of the vehicle control within the plate would be indicative of an increase in intracellular ROS. The threshold for RDF was therefore set at 1.61 ×. The same steps were taken to calculate a threshold for an indicative drop in cell viability measured using PI. The RSD value for cell viability within the vehicle controls of all tested plates was 0.802%. This meant that a drop in the cell viability of 2.41% relative to the vehicle control on an assay plate was indicative of a drop in RCV. The threshold for RCV was therefore set at 97.6%.

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Figure 8:1 Titration of DCFH-DA and positive control TBHP to optimise the DCFH-DA assessment protocol Error bars in the above charts show the standard deviation between the results of three biological replicate experiments. Panel A shows the DCF fluorescence in arbitrary units in vehicle control treated cells (grey ) and cells treated with 100 µM TBHP (black ). Cells were treated with a dilution series of DCFH-DA at concentrations incrementing twofold between 5 and 160 µM. The DCF fluorescence of vehicle control treated cells increases steadily with increasing DCFH-DA dose while the dose-dependent increase in DCF fluorescence of cells treated with TBHP diminishes at doses higher than 40 µM DCFH-DA. The greatest relative difference (9.94 ×) in DCF fluorescence between vehicle and positive control cells is observed at 20 µM DCFH-DA. Panel B shows the DCF fluorescence of cells with 20 µM DCFH-DA (green ) and without (red ) treated with a twofold dilution range of TBHP. The DCF fluorescence increased continuously with the increasing dose of TBHP from 31.2 to 500 µM as did the standard deviation of the fluorescence between the replicate experiments.

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Figure 8:2 A time-course evaluation of compound exposure within the DCFH-DA assay Panel A shows the DCF fluorescence of cells exposed to 250 µM TBHP relative to a vehicle treated control against the compound exposure time. Cells exposed to TBHP for 8.5 hours show the greatest relative DCF fluorescence. Panel B shows the relative standard deviation (RSD) for the DCF fluorescence measurement at each time point. The RDF of samples exposed for 3 and 3.5 hours are higher than any other exposure time, the RDF of the 3 hour sample (133%) continues beyond the scale of the graph.

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8.4 Assessment of 34 compounds in the DCFH-DA assay

Using the DCFH-DA protocol detailed in Section 3.6 including the modifications detailed in Section 8.2 and the thresholds defined in Section 8.3, 34 compounds were assessed. A summary of the results is presented in Table 8:1 and Table 8:2 and a sample of the results for 6 compounds; TBHP, EGCG, propyl gallate, gamma-resorcylic acid, BHT and vanillic acid are presented graphically in Figure 8:3. The results of the compounds are described below in further detail.

8.4.1 ROS generators, TBHP and hydrogen peroxide, cause an indicative increase in RDF whilst direct-acting oxidant, potassium bromate, does not The two oxidant compounds known to induce oxidative damage through an ROS-based mode of action, TBHP and hydrogen peroxide, both increased the RDF within cells. Indicative increases in RDF for the two compounds were observed in cells treated with higher than 125 and 500 µM concentrations of the compounds respectively. This indicates that the DCFH-DA assay is capable of detecting an increase in ROS within cells. Direct acting oxidant, potassium bromate, however, did not lead to an indicative increase in RDF at any tested dose. This indicates that the DCFH-DA assay is specific to ROS and is insensitive to direct- acting oxidants. The result for TBHP is shown graphically within panel A of Figure 8:3. The results of all oxidant compounds are summarised in Table 8:1 and Table 8:2.

8.4.2 10 of 12 polyphenolic antioxidants cause an indicative increase in RDF Of the 12 PPAs tested in the DCFH-DA assay, 10 caused an increase in RDF of greater than 1.61 times the vehicle control indicating an increase in ROS within the treated cells. Presented in Panels B, C and D of Figure 8:3 are the results for the polyphenols EGCG, propyl gallate and gamma resorcylic acid respectively. All three compounds produced an indicative increase in DCF fluorescence. EGCG produced an indicative increase in RDF with a LEC of 31.3 µM, showing an indicative decrease in RCV at a LEC of 1 mM. Propyl gallate produced an indicative increase in RDF with a LEC of 1 mM and did not produce an indicative decrease in RCV. gamma-Resorcylic acid, a compound that produced a negative result for genotoxicity within the GADD45a-GFP assay, produced an indicative increase in RDF in 2 of 3 biological replicate experiments. As the mean value of RDF for 500 and 1000 µM from the 3 replicates rose above the threshold, the compound was considered to have increased the RDF with a LEC of 500 µM. It should be noted that apomorphine hydrochloride and NDGA produced the greatest increase in RDF of 34 tested compounds including positive control TBHP. The two PPAs that did not cause an indicative increase in RDF were tert-butylhydroquinone and resorcinol. The TBHQ metabolite, TBQ, did cause an indicative increase in RDF with a LEC of 125 µM indicating that once metabolised, TBHQ may be capable of increasing levels of ROS in treated cells. The results of all PPAs are summarised in Table 8:1.

8.4.3 None of the 4 monophenolic antioxidants cause an indicative increase in RDF Of the 4 MPAs tested in the DCFH-DA assay, none caused an increase in RDF of greater than 1.61 times the vehicle control indicating that they did not increase in ROS within the treated cells. Presented in Panels E and F of Figure 8:3 are the results for the MPAs, BHT and vanillic acid respectively. BHT caused an indicative decrease in RCV in cells treated with a concentration of 125 µM and greater. In 1 of the 3 biological replicates, the RDF of cells treated with 1 mM BHT rose above the threshold. However this increase was not

159 dose-dependent and was not seen in the other replicates. Therefore this result was considered equivocal. It should be noted that only 14.0 % of the cells within the dose that displayed an indicative increase in RDF were “viable cells”. Cells treated with vanillic acid showed no indicative increase in RDF or indicative decrease in RCV indicating that vanillic acid did not increase the level of ROS within cells. However a reproducible decrease in RDF was observed indicating that the antioxidant was capable of reducing the level of ROS within cells below that of the vehicle control. This decrease was greater than 3 RSD below that of the vehicle control at doses above 250 µM. Vanillic acid was the only compound tested seen to reduce the RDF within the DCFH-DA assay below the threshold, at a dose that did not decrease RCV below the threshold. A further investigation of the antioxidant potential of 8 phenolic antioxidants in cells treated with ROS generator, TBHP, is presented in Section 8.6. The results of all MPAs are summarised in Table 8:1.

8.4.4 1 of 3 non-phenolic antioxidants cause an indicative increase in RDF Of the 3 NPAs tested, only ethoxyquin increased the RDF of cells above the threshold. The RDF was increased above the threshold at doses above 62.5 µM and the RCV fell below the threshold at doses above 500 µM. This indicates that ethoxyquin is able to increase the level of ROS within cells treated with a dose 8 times lower than the dose needed to cause an indicative reduction in the cells viability. This may indicate that PI is not a sensitive measure of cytotoxicity within the timeframe of the DCFH-DA assay (4 hours). Section 8.5 presents the results of experiments investigating if 4 compounds including ethoxyquin are capable of causing a drop in cell viability during the 48 hours following a 4 hour compound exposure. The results of all non- phenolic antioxidant compounds are summarised in Table 8:1Table 8:2.

8.4.5 4 of 8 genotoxic and non-genotoxic cytotoxic compounds cause an indicative increase in RDF Of the 6 genotoxic compounds and 2 cytotoxic non-genotoxic compounds assessed in the DCFH-DA assay, NQO, vincristine sulfate, DCP and phenformin hydrochloride all produced indicative increases in RDF in the DCFH-DA assay. Etoposide showed a dose dependent increase towards the threshold and in 1 of 3 biological replicate experiments crossed the threshold and so its result was deemed equivocal. The highest tested concentration of etoposide was limited to 425 µM due to limited compound availability, so it is reasonable to assume that at a higher dose, etoposide could reproducibly produce an indicative increase in RDF. The highest test concentration of bleomycin sulfate was also limited, due to limited compound availability and may be able to produce an indicative increase in RDF at a higher concentration. The results are summarised in Table 8:1Table 8:2.

8.4.6 2 of 4 miscellaneous compounds cause an indicative increase in RDF As is detailed in Section 8.4.2, TBHQ metabolite caused an indicative increase in RDF at a dose of 125 µM. Mitochondrial membrane decoupler, carbonyl cyanide m-chlorophenyl hydrazone, caused an indicative increase in RDF at doses above 31.3 µM and lowered the RCV of cells treated with 125 µM or higher doses of the compound. Neither phenol nor the apoptogen, staurosporine caused an indicative increase in RDF in treated cells. The results are summarised in Table 8:1 and Table 8:2.

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Figure 8:3 Sample data from assessment of compounds using the DCFH-DA assay for intracellular ROS generation Presented above is a sample of 6 of the results from the assessment of 34 compounds using the DCFH-DA assessment within TK6 cells. The x-axis represents the compound concentration. The left y-axis represents the relative cell viability (RCV) of the treatment as a percentage of that measured for the vehicle control and is represented by the red line ( ). The right y-axis represents the DCF-fluorescence relative to the vehicle control (RDF) and is represented by the green line ( ). The error bars show the standard deviation measured between three biological replicate experiments. The green dashed line ( ) represents the threshold of RDF. Any RDF measurement above this threshold represents an indicative increase and thereby an increase in intracellular ROS generation. The black dashed line ( ) represents the threshold for RCV. Any RCV value that falls below this value represents an indicative drop in RCV. Positive control, TBHP (A) produced an indicative increase in RDF at all doses above 125 µM, reaching a peak RDF of 12.95× at a dose of 1000 µM. TBHP produced an indicative decrease in RCV at doses above 250 µM. PPA, EGCG (B) produced an indicative increase in RDF at all doses above 31 µM except for the dose of 1000 µM and an indicative decrease in RCV 1000 µM. PPA, propyl gallate (C) produced an indicative increase in RDF at 1000 µM and showed no indicative decrease in cell viability. PPA, gamma-resorcylic acid (D) produced an indicative increase in RDF at 1000 µM (1 of the 3 replicate experiments showed no indicative increase in RDF) and produced no indicative decrease in cell viability. MPA, BHA (E) produced no indicative increase in RDF and no dose-dependent increase although one of the three replicate experiments showed an indicative increase in RDF at 1000 µM, it was not sufficient to raise the mean above the threshold. BHA produced an indicative decrease in RCV at doses above 250 µM. MPA, vanillic acid, (F) produced no indicative increase in RDF or decrease in RCV. There is however an observed dose dependent decrease in RDF. At doses above 250 µM, this decrease is greater than 3× the standard deviation of the untreated vehicle control samples across all tested plates, indicating an indicative decrease in RDF and thereby the level of intracellular ROS

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Highest Dose Cytotoxicity DCF Fluorescence Group Compound Name µM Reasoning Result LEC Result LEC Peak Positive control tert-Butyl hydroperoxide 1000 Molarity Positive 250 Positive 125 8.44 Polyphenolic Apomorphine hydrochloride 1000 Molarity Positive 250 Positive 31.3 9.77 antioxidants tert-Butylhydroquinone 1000 Molarity Positive 1000 Negative 1.00 Dodecyl gallate 1000 Molarity Positive 250 Positive 500 1.66 Epigallocatechin gallate 1000 Molarity Positive 1000 Positive 31.3 1.98 Nordihydroguaiaretic acid 1000 Molarity Positive 250 Positive 125 8.45 Octyl gallate 1000 Molarity Positive 500 Positive 250 1.71 Propyl gallate 1000 Molarity Negative Positive 1000 1.97 Pyrogallol 1000 Molarity Negative Positive 62.5 1.82 Quercetin 500 Solubility Positive 1000 Positive 31.3 1.83 Resorcinol 1000 Molarity Negative Negative 1.05 γ-Resorcylic acid 1000 Molarity Negative Positive 1000 2.18 Resveratrol 1000 Molarity Positive 1000 Positive 1000 2.22 Monophenolic Butylated hydroxyanisole 1000 Molarity Positive 1000 Negative 1.00 antioxidants Butylated hydroxytoluene 1000 Molarity Positive 500 Equivocal 1.55 2,6-Di-tert-butyl-4-hydroxymethylphenol 1000 Molarity Positive 500 Negative 1.38 Vanillic acid 1000 Molarity Negative Negative 1.04 Table 8:1 Summary of results from the assessment of 34 compounds using the DCFH-DA assay (1/2) The summary table to the left shows the 17 of the 34 compounds split into 3 groups, the highest concentration tested (µM) and the reason for choosing that concentration, the result for cytotoxicity measured by PI exclusion and the result of DCFH-DA assessment. For both measures of cytotoxicity and DCF fluorescence, the lowest effective concentration (LEC) to produce an indicative decrease in cell viability or increase in DCF fluorescence relative to the vehicle control treated sample is shown. For the DCF fluorescence result, the highest increase relative to the vehicle control treated sample summarised as the result’s “peak”.

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Highest Dose Cytotoxicity DCF Fluorescence Group Compound Name µM Reasoning Result LEC Result LEC Peak Non-phenolic n-Acetylcysteine 1000 Molarity Negative Negative 1.05 antioxidants L-Ascorbic acid 1000 Molarity Negative Negative 1.00 Ethoxyquin 1000 Molarity Positive 500 Positive 62.5 5.77 Oxidant compounds Hydrogen peroxide 1000 Molarity Negative Positive 500 3.61 Potassium bromate 1000 Molarity Negative Negative 1.27 Genotoxins Bleomycin sulfate 6.61 Stock Negative Negative 1.00 Etoposide 425 Stock Negative Equivocal 1.72 5-Fluorouracil 1000 Molarity Negative Negative 1.04 Methyl methanesulfonate 1000 Molarity Negative Negative 1.03 4-Nitroquinoline-1-oxide 1000 Molarity Negative Positive 31.3 1.71 Vincristine sulfate 10.8 Stock Negative Positive 10.8 1.66 Non-genotoxic cytotoxic 2,4-Dichlorophenol 1000 Molarity Positive 1000 Positive 1000 3.46 compounds Phenformin hydrochloride 1000 Molarity Negative Positive 1000 1.56 Miscellaneous 2-tert-Butyl-1,4-benzoquinone 1000 Molarity Positive 15.7 Positive 125 7.55 Carbonyl cyanide m-chlorophenyl hydrazone 1000 Molarity Positive 125 Positive 31.3 3.38 Phenol 1000 Molarity Positive 500 Negative 1.35 Staurosporine 1000 Molarity Negative Negative 1.04 Table 8:2 Summary of results from the assessment of 34 compounds using the DCFH-DA assay (2/2) The summary table to the left shows the 17 of the 34 compounds split into 3 groups, the highest concentration tested (µM) and the reason for choosing that concentration, the result for cytotoxicity measured by PI exclusion and the result of DCFH-DA assessment. For both measures of cytotoxicity and DCF fluorescence, the lowest effective concentration (LEC) to produce an indicative decrease in cell viability or increase in DCF fluorescence relative to the vehicle control treated sample is shown. For the DCF fluorescence result, the highest increase relative to the vehicle control treated sample is summarised as the result’s “peak”.

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8.5 Propidium iodide cannot detect a high proportion of cells that have committed to a cytotoxic endpoint within a 4 hour timeframe

Of the 19 compounds that caused an indicative increase in DCF fluorescence in the DCFH-DA assessment, only 12 of the compounds caused an indicative drop in cell viability. For 5 of 12 compounds that did cause an indicative drop in cell viability, the lowest dose at which the drop in viability crossed the threshold was over 4 times greater than the lowest dose to increase the DCF fluorescence above the threshold. Given the cytotoxicity of intracellular ROS, such a disparity indicated that PI may have not been detecting cells committed to a cytotoxic endpoint. The protracted compound exposure time of the DCFH-DA assay (4 hours) compared to that of the GADD45a-GFP assay (48 hours) may be the influencing factor to the lack of sensitivity observed. Cells exposed to a cytotoxic test compound for 4 hours may not have undergone enough damage to commit to a cytotoxic endpoint. Alternatively, cells may be committed to a cytotoxic endpoint but may have not yet permeabilised their cell membrane allowing PI to enter the cell. To investigate this possibility, a subset of 4 compounds that caused an indicative increase in DCF fluorescence was chosen for an assessment of longer-term cytotoxicity following a 4 hour compound exposure (Section 3.6.4). TBHP caused an indicative increase in DCF fluorescence at a dose (125 µM) 2-times lower than it caused an indicative drop in cell viability (250 µM). Ethoxyquin caused an indicative increase in DCF fluorescence at a dose (62.5 µM) 8-times lower than it caused an indicative drop in cell viability (500 µM). NQO caused an indicative increase in DCF fluorescence at a dose of 31.3 µM but did not produce an indicative decrease in cell viability. Dodecyl gallate however, caused an indicative increase in DCF fluorescence at a dose (500 µM) 2-times higher than it caused an indicative drop in cell viability (250 µM). Cells were exposed to these four compounds at the lowest concentration responsible an indicative decrease in cell viability (except for NQO which was tested at 5.2 µM due to its observed cytotoxicity and genotoxicity following a 3 hour-treatment with 5.2 µM concentration of the compound (Jagger et al. 2009) for 4 hours before being washed twice with PBS and re-suspended in RPMI. Cell count and PI permeability were measured at 0, 24 and 48 hours after the cells were washed. Presented in Figure 8:4 are the results of this experiment. Panel A, shows that while cells exposed to the vehicle control underwent (2.73) population doublings, cells exposed to all 4 compounds underwent between (-0.679) and (0.450) population doublings over the 48 hours following compound exposure. Panel B shows that while the percentage of vehicle control treated cells not stained with PI stayed above 96% for the duration of the 48 hours, the cells treated with the 4 compounds showed a continued increase in the proportion of PI stained cells. This indicates an accumulation of dead and degraded cells. It also indicates that within the 4 hour time frame of the DCFH- DA assay, PI may not be sufficiently sensitive to cells’ commitment to a cytotoxic endpoint and cytotoxicity within the assay would be best measured, either with an extended cytotoxicity study as presented here or using a different measure of cytotoxicity. Section 9 details the investigation of JC-1, an indicator of mitochondrial depolarisation, as an alternative method of cytotoxicity assessment.

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Figure 8:4 Cells treated with 4 test compounds for 4 hours show a reduction in cell division and cell viability during the following 48 hours. Cells were treated with 4 test compounds for 4 hours before being washed and re-suspended in RPMI. 0, 24 and 48 hours after being washed of the compounds, the cell populations are counted and the percentage of cells permeable to PI is quantified. All error bars represent the standard deviation within the results of 3 biological replicate experiments. Panel A shows the number of cells in thousands of cells/ml on a logarithmic scale. While cells treated with the vehicle control (indigo) continue to increase in cell number over the 48 hour period, cells treated with 5.2 µM NQO (yellow) and 250 µM TBHP (blue) show a continued reduction in number. Cells treated with 250 µM dodecyl gallate (grey) and 500 µM ethoxyquin (orange) show an increase in cell number several times lower than that of the vehicle control. Panel B shows the percentage of the cells that remained unstained with PI following the addition of PI cells to samples of cells at each time point. Unstained cells were defined as “viable”. While greater than 96% of vehicle control treated cells were viable at 0, 24 and 48 hours, cells treated with all 4 test compounds showed a continued reduction in the percentage of viable cells indicating that cells treated with the compounds were undergoing cell death over the 48 hours following compound exposure.

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8.6 Eight phenolic antioxidants reduce the level of ROS in cells treated with oxidant, TBHP

Broad dose ranges of eight phenolic antioxidants: octyl, dodecyl and propyl gallates, EGCG, NDGA, TBHQ, BHA and BHT were added to cells treated with 100 µM TBHP for 4 hours following the protocol of the DCFH- DA assay established in Section 3.6.5. The level of DCF fluorescence of the vehicle control and the positive control (treated only with 100 µM TBHP) were measured and scaled to place the fluorescence of the vehicle control at 0 and the fluorescence of the positive control at 100. The level of DCF-fluorescence measured for each dose of the 8 compounds was also scaled relative to the controls allowing the levels of DCF fluorescence to be easily compared. The results of the experiment, presented in Figure 8:5 show that all 8 of the antioxidants were able to reduce the level of DCF fluorescence of cells below that of the positive control, indicating that the antioxidants are capable of reducing the TBHP-generated ROS within the cells. 3 of the antioxidants further lowered the level of DCF fluorescence below that of the negative control, indicating that the antioxidants may be capable of reducing all of the TBHP-generated ROS in addition to endogenous ROS.

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Figure 8:5 Eight phenolic antioxidants lower the level of intracellular ROS in TBHP-treated cells Panels A-D show the DCF fluorescence of cells treated with octyl gallate (A), TBHQ (B), NDGA (C) and dodecyl gallate (D) in tandem with 100 µM of oxidant, TBHP. This fluorescence is represented in blue. Fluorescence of the cells is shown on an axis, scaled to show the fluorescence relative to the vehicle control (0) in black and the positive control cells treated only with TBHP (100) in green. Cells treated with all 4 antioxidants show a dose dependant drop in the level of DCF fluorescence below that of the positive control, indicating that the antioxidants are reducing the level of ROS within the cells. Cells treated with greater than 125 µM TBHQ in tandem with 100 µM TBHP show a lower level of DCF fluorescence than is observed in the vehicle control, indicating that TBHQ may be reducing more ROS than the TBHP is generating in cells. Cells treated with greater than 125 µM NDGA show a steep increase in DCF fluorescence and cells treated with greater than 250 µM NDGA display greater fluorescence than that of the positive control, indicating that at higher doses of NDGA, ROS are generated by both TBHP and NDGA faster than NDGA can reduce them. Panel E shows the lowest level to which 8 phenolic antioxidants lowered the DCF-fluorescence of TBHP- treated cells below the positive control and the dose at which this trough was reached. All 8 antioxidants tested lowered the DCF fluorescence below that observed in the positive control indicating that the antioxidants reduce the level of ROS within TBHP-treated cells. 3 of the compounds lowered the DCF fluorescence below that of the negative control indicating that the antioxidants may be able to reduce not only the TBHP generated ROS, but also the endogenous ROS within cells. The experiment was carried out only once. 167

8.7 Discussion

This chapter presented the results of an assessment of the potential of 34 compounds, including 16 phenolic antioxidants, to cause an increase in ROS within TK6 cells. This assessment was carried out using the DCFH-DA assay. The assay showed a clear increase in DCF-fluorescence in cells treated with the ROS generators TBHP and hydrogen peroxide, but not in those treated with the direct-acting oxidant, potassium bromate. This indicates that within TK6 cells in RPMI media, the DCH-DA is sufficiently sensitive to be oxidised by intracellular ROS and sufficiently specific to not be oxidised by direct-acting oxidants.

Positive results for intracellular ROS generation were produced in cells treated with all of the 12 PPAs tested except for resorcinol and TBHQ. The TBHQ metabolite, TBQ produced a positive result indicating that TBHQ can be metabolised to form a pro-oxidant compound. These results match the findings of the GADD45a-GFP assay (Section 5.3) with two exceptions: Resorcinol produced a positive result within the GADD45a-GFP assay but a negative result in the DCFH-DA assay. γ-Resorcylic acid produced a negative result within the GADD45a-GFP assay but a positive result in the DCFH-DA assay. The results for the other 10 compounds does support the hypothesis that the PPAs may be genotoxic through a pro-oxidant, ROS- mediated pathway. The results for the 3 of the 4 MPAs were negative while the other (BHT) was equivocal, showing an indicative increase in DCF fluorescence in only one of three biological replicates. These results support the hypothesis that, given the negative results in the GADD45a-GFP assay for all 4 compounds (Section 5.3), the MPAs are either not pro-oxidant or pro-oxidant to a lesser degree than the PPAs. Ethoxyquin, an antioxidant compound that has been seen to produce similar misleading positive results for genotoxicity in vitro including the GADD45a-GFP assay, also produced a positive result in the DCFH-DA assay. This suggests that the compound’s genotoxicity in in vitro assays is the result of a pro-oxidant ROS- mediated mode of action. The mitochondrial membrane depolarising agent, CCCP, produced a positive result within the DCFH-DA assay indicating that by decoupling the mitochondrial membrane potential, ROS are generated within the cell. These ROS are likely components of the electron-transport chain leaking into the cytosol.

Despite being able to produce positive results for ROS generation within the DCFH-DA assay, the ten PPAs and ethoxyquin are also known antioxidants (Section 4). It was therefore expected that within cells with raised levels of ROS, the antioxidant compounds would be capable of scavenging the ROS within the cells. To that end, an experiment was carried out (Section 8.6) to assess the potential of a subset of eight antioxidant compounds to reduce the level of DCF fluorescence in cells treated with 100 µM TBHP. These results indicated that the antioxidants, regardless of their results in the standard DCFH-DA assay, were capable of reducing the level of ROS in TBHP-dosed cells. NDGA, like the other antioxidants, produced a dose-dependent, antioxidant effect upon cells treated with up to 62.5 µM NDGA. At doses above 62.5 µM however, NDGA produced a dose-dependent, pro-oxidant effect upon the cells leading to a greater level of DCF fluorescence than those treated only with the positive control (Figure 8:5D). This would indicate that NDGA behaves in a markedly different manner at higher doses within cells than it does at lower doses. PPAs showing antioxidant behaviour at low doses and pro-oxidant behaviour at high doses has been previously reported in rat H4IIE cells (Wätjen et al. 2005).

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Other studies measuring ROS generation exist for some of the tested PPAs. Many of these studies do not test cells with a serial dilution of the chemical meaning no dose-response can be determined. Studies are also carried out in various cell-lines with various assay media meaning it can be difficult to draw any comparison between compounds from one study to the next. Comparing the results generated in this study with those in the literature can provide an indication of the relevance of this work in the wider scope of the existing literature. In this study, 31.3 μM EGCG was the lowest dose to produce an indicative increase in DCFH-DA fluorescence in TK6 cells. Previous studies have shown 100 μM EGCG to produce an increase in DCFH-DA fluorescence in HT-29 colon cancer cells (Hwang et al. 2007), 25 μM EGCG to produce an increase in DCFH-DA fluorescence in H1299 cells (Li et al. 2010) and 20 μM EGCG to produce an increase in DCFH-DA fluorescence in HL60 cells (Elbling et al. 2005). These similar doses suggest that the results produced in this study are comparable with the results of existing studies.

Although phenolic antioxidants have been shown to produce ROS through an iron-mediated Fenton reaction (Moran et al. 1997), it should be noted that the media used in this investigation (RPMI), unlike Dulbecco’s Modified Eagle Medium, does not contain freely available iron.

A shortcoming of the work presented in this chapter is that only DCFH-DA is used as a dye. DCFH-DA allows the detection of a broad but not specific range of ROS. Further work could be done to assess which specific ROS are being generated in cells using more specific dyes such as hydroethidine to detect superoxide and ferrous oxidation-xylenol orange assay to detect hydrogen peroxide. Unfortunately time-restrains prevented these from being carried out as part of this study.

The observation that only cells treated NQO did not produce a cytotoxic response detectable by PI at any dose up to and including 1 mM raised concern that PI was not a sensitive measure of cytotoxicity within the 4 hour timeframe of the DCFH-DA assay. In unpublished data from the Walmsley lab, NQO was shown to produce a positive result for cytotoxicity in the GADD45a-GFP assay after a 24 hour treatment at a dose of 1.13 µM. The great disparity between the cytotoxic dose of NQO following a 24 hour exposure and the highest tested, non-cytotoxic dose of NQO following a 4 hour exposure made further investigation imperative. In cells treated with NQO for 4 hours and then washed of the compound, 94.4% of cells remained unstained by PI indicating that the cells were viable. Over the following 48 hours, this level of viable cells dropped to 44.0% indicating that a 4 hour exposure to NQO was sufficient to commit over half of the cells to a cell-death pathway. This result, coupled with similar results for cells treated with antioxidants, ethoxyquin and dodecyl gallate, and oxidant, TBHP (Figure 8:4) made it evident that PI was unable to sensitively detect cells that had committed to cell-death following a 4 hour compound exposure. This made it necessary to investigate alternative methods of cytotoxicity assessment.

The next chapter details the results of an assessment of the mitochondrial membrane integrity of cells exposed to a diverse collection of 34 compounds for 4 hours using JC-1. This was carried out with the aim of providing a more sensitive measure of commitment to a cell-death pathway than PI following a short compound exposure.

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8.8 Summary

 The DCFH-DA assay was optimised for use with TK6 cells and the measurement of DCF fluorescence from cells within a 96-well plate format by flow cytometer o 20 µM DCFH-DA was found to create the greatest increase in DCF fluorescence between cells treated with 100 µM TBHP and vehicle treated controls o 250 µM of TBHP was found to reproducibly increase the RDF of cells and was chosen as a positive control concentration o A compound exposure time of 4 hours was found to produce the lowest level of RSD within RDF measurements of positive and vehicle control and was chosen as the compound exposure time to be used within the DCFH-DA assay  19 of 34 tested compounds caused an indicative increase in RDF in treated cells o ROS generators, TBHP increased RDF above the threshold in treated cells while direct acting oxidant, potassium bromate did not indicating that the DCFH-DA assay was specific to oxidation by ROS o 10 of 12 PPAs caused an indicative increase in RDF . Although PPA, TBHQ did not cause an indicative increase in RDF, however its metabolite, TBQ did o 0 of 4 MPAs caused an indicative increase in RDF o 1 of 3 NPAs (ethoxyquin) caused an indicative increase in RDF o 4 of 8 genotoxic and cytotoxic control compounds caused an indicative increase in RDF  PI detected may not be a suitable measure of cell viability within the timeframe of the DCFH-DA  5 PPAs that caused an indicative increase in RDF and 3 phenolic antioxidants that did not, were all able to lower the RDF of cells treated with 100 µM TBHP

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9 Results VI – Most polyphenolic antioxidants are able to depolarise the mitochondrial membrane 9.1 Introduction

Within the four hour timeframe of the DCFH-DA assay for intracellular ROS generation, there arose concern that PI was not sensitive enough to detect cells committed to a cytotoxic endpoint within the short timeframe. JC-1 was chosen as an alternative marker, as it allows for the detection of the disruption of mitochondrial membrane potential, which provides a measurement of an earlier commitment to a cytotoxic endpoint.

This chapter details the steps taken to optimise the JC-1 assay for use with TK6 cells within a 96-well microplate format. The chapter also presents data evaluating JC-1 as a measure of early-stage commitment to a cytotoxic endpoint.

9.2 Optimising the protocol for TK6 cells

It was important to optimise various parameters before assessing test compounds using the JC-1 dye. As JC- 1 was intended to be used as a toxicity measure within the DCFH-DA ROS assessment, the compound exposure time was fixed at 4 hours, the same as those found to be optimal for the DCFH-DA ROS assay in Section 8.2.3. This section describes the steps taken to optimise the concentration of JC-1, the JC-1 exposure time and the concentration of the positive control, carbonyl cyanide m-chlorophenyl hydrazone (CCCP).

9.2.1 Determining the optimal concentration of the dye, JC-1 and use of compensation to reduce crosstalk between FL1 and FL2 fluorescence measurements A titration of JC-1 dye against static exposures of 10 µM CCCP and vehicle controls was carried out following the protocol detailed in Section Error! Reference source not found.. The results of the JC-1 dilution series are presented in Figure 9:1. Crosstalk between the measurement of signal through the FL2 and FL1 channel of the flow cytometer was observed, therefore manual compensation was carried out to reduce this (Section 3.7.2.1). Compensating the signal from the FL2 channel by 16.4% against the signal from the FL1 channel was considered to provide the optimal reduction in crosstalk (Figure 9:2). This level of compensation was used in all subsequent assessments carried out in this chapter. Uncompensated results are presented in panel A of Figure 9:1 and results in which the FL2 channel results were compensated 16.4% against the FL1 channel are presented in panel B. The difference between the FL2:FL1 fluorescence ratio of treated and untreated samples clearly increases from doses of 0.5 µM to 2 µM in uncompensated results, however the results following compensation show a plateau and subsequent drop at doses of JC-1 higher than 1.4 µM. This plateau, coupled with increasing variability between data produced in the replicate experiments led to a decision being made that 1.4 µM was the most suitable concentration of JC-1 to use to carry out JC-1 assessment. This concentration was used in all subsequent assessments presented in this chapter.

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9.2.2 Determining the Optimal Concentration of the Positive Control, carbonyl cyanide m-chlorophenyl hydrazine Following the protocol detailed in Section 3.7.1 with the adjustments mentioned in Section 9.2.1, a dose range of CCCP was added to a 96-well plate with a highest dose of 200 µM diluted serially twofold across the plate to a lowest dose of 0.244 µM. 8 wells containing cells exposed to a vehicle control were also prepared. The FL2:FL1 fluorescence ratio relative to the vehicle control was calculated for each well. This experiment was carried out on three different days with freshly prepared reagents and the results are presented in Figure 9:3. A clear drop in the relative fluorescence ratio is observed with increasing dose until 12.5 µM. At doses higher than 12.5 µM, the fluorescence ratio increases before decreasing again for unclear reasons. This increase was reproducible across the 3 replicate experiments. The trough within the fluorescence ratio at 12.5 µM shows very little variability between replicate plates and so it would make a suitable vehicle control dose however due to the unexplained increase in fluorescence ratio at higher concentration and also the characteristic shape of the graph produced by the results for the dose range, it was decided that the entire dose range would serve as a positive control on each plate.

Any assay microplate where the FL2:FL1 fluorescence ratio of cells treated with 12.5 µM CCCP differed from the mean value of the same treatment sample from all assessed plates by a value greater than 2 × the standard deviation amongst all recorded results for the same dose were disregarded and the experiment repeated.

Despite 12.5 µM being a suitable dose to be used as a positive control for the assay as it showed a great difference in fluorescence ratio relative to the vehicle control and little variability between replicate experiments, the whole dose range of 0.244 µM to 200 µM was chosen to be set-up on each plate. The layout of the 96-well microplate protocol easily facilitated using the entire dose range of CCCP as a positive control and by using the whole dose range. Through this, far more effective troubleshooting could be carried out on plates which fell outside of acceptance criteria. If the dose producing the greatest decrease in fluorescence ratio were to be a dose higher or lower than 12.5 µM, it would indicate that a mistake could have been made in the number of cells added to the plate or the concentration of CCCP added to the plate. If the fluorescence ratio of all treated doses were to be higher or lower, it would indicate that a mistake could have been made in the concentration of JC-1 added to the plate or in the flow cytometer parameters used in the analysis of the plate.

9.2.3 Determining the Optimal JC-1 Exposure time To determine the optimal JC-1 exposure time an assay plate was set up as detailed in Section 3.7.1 and the 535 nm fluorescence from each well was measured multiple times across a timeframe of 3 hours using a kinetic assessment with a plate spectrophotometer (Section 3.7.3). The results are presented in Figure 9:4 as a scatter plot of time against raw 535 nm fluorescence. The results show that cells both treated with 12.5 µM of CCCP and those exposed to a vehicle control both increase in fluorescence rapidly during the first 22 minutes before plateauing allowing a clear discrepancy between CCCP-treated and untreated cells. After 45 minutes, the fluorescence level of both treated and untreated cells reduced and the difference in fluorescence between the two conditions reduced slightly. The decision to use a 30 minute exposure to JC-1 was made due to the highest fluorescence and the highest difference between the fluorescence of the two 172 conditions being observed 30 minutes after JC-1 exposure. The 30 minute time point also falls within the middle of the observable plateau in fluorescence indicating that slight deviations in the JC-1 exposure time would not lead to a great variability in the measured result.

9.3 Generation of a threshold for JC-1 assessment

A threshold indicative of a disruption of mitochondrial membranes was generated denoting a drop in the ratio of FL2:FL1 fluorescence of greater than three standard deviations of historical controls. This threshold was calculated as a drop of the FL2:FL1 fluorescence ratio of greater three times the standard deviation of the same ratio within vehicle control samples. Any dose of a compound that led to the FL2:FL1 fluorescence ratio of the treated cells dropping below the threshold value was responsible for an indicative drop in the ratio of FL2:FL1 fluorescence ratio. This drop indicated a change from the J-aggregate form of JC-1 within healthy mitochondria to the monomer form present in depolarised mitochondria within treated cells. Any dose that led to a fluorescence ratio below the threshold can therefore be considered to have disrupted the mitochondrial membrane potential within treated cells.

The FL2:FL1 ratio for vehicle controls from every compound assessment was collated and the relative standard deviation between these samples was found to be 10.9%. This figure multiplied by 3 is 32.7% and thus any compound dose responsible for lowering the ratio of FL2:FL1 fluorescence below 67.2% of the vehicle control well for that assessment was considered a positive dose for disruption of the mitochondrial membrane potential.

9.4 Results

A summary of the results from assessing 34 compounds using the JC-1 dye is presented below in Table 9:1. Of the 34 compounds assessed, 20 caused the ratio between mean FL2 and FL1 fluorescence measured within one or more tested doses to drop below the threshold of 67.2% of that of the vehicle control as detailed in Section 9.3. This drop is indicative of a change in the fluorescence of JC-1 within treated cells and therefore a disruption of mitochondrial membrane potential within cells. Sample data for the positive control compound, CCCP are presented in panel A of Figure 9:5. Panels B-F of Figure 9:5 present the sample data for PPAs, EGCG, propyl gallate and gamma-resorcylic acid; MPA, BHT and genotoxic, DNA alkylating agent MMS respectively. Flow cytometric data for the six compounds are also presented in Figure 9:6 and Figure 9:7. Within Table 9:1, results for compounds responsible for lowering the FL2:FL1 fluorescence ratio to below the threshold are referred to as “positive” whereas the results of compounds that did not lower the ratio below the threshold at any of the tested doses are referred to as “negative”.

To better compare the results of testing different compounds using JC-1, the lowest result is also recorded, this is referred to as the “trough”. The range into which these results was divided into quintiles. This allows compounds which cause the greatest drop in FL2:FL1 ratio to be differentiated from those that cause a smaller drop.

The positive control, JC-1 showed a clear drop in the FL2:FL1 fluorescence ratio. The dose of 1.95 µM was the lowest dose responsible for a drop in the fluorescence ratio below the defined threshold. The ratio between FL2 and FL1 fluorescence was at its lowest in samples treated with 7.81 µM where the ratio was 20.9% relative to the fluorescence ratio of the vehicle control sample. 173

Figure 9:1 Titration of JC-1 dye to determine optimal concentration in the assessment of mitochondrial membrane disruption. Panel A and B both show the relative ratio of fluorescence measured through the FL2 channel of a flow cytometer to that measured through the FL1 channel for samples exposed to 10 µM concentration of positive control, mitochondrial membrane decoupler, CCCP (blue line) and vehicle control samples (orange line). Following 3.5 hours of exposure, a linear dilution series of JC-1 was added to samples. Panel A shows values from data that have not been compensated to reduce interference between the two fluorescence channels. Panel B shows values from data following the FL2 channel being compensated 16.4%. At doses above 0.5 µM of JC-1 a clear difference between the fluorescence ratio of treated and untreated samples is observed, this difference increases in both compensated and uncompensated data before plateauing and then dropping at doses above 1.4 µM in the compensated data while continuing to increase in the uncompensated. Higher doses of JC-1 produce greater levels of variability in the results gathered between 3 independent replicates indicated by the increase in standard deviation and represented by error bars.

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A B

C D

Figure 9:2 FL2 channel compensation within the JC-1 assay Scatter plots showing the FL1-H value plotted against the FL2-H value. Plots A and B show the data collected prior to compensation and plots C and D show the data collected following the FL2 channel being compensated 16.4% against the FL1 channel. Panels A and C show the data collected from vehicle control cells and panels B and D show the data collected from 10 µM CCCP. The uncompensated data (A & B) show characteristic smear of results with fluorescence measured by the FL1 channel being proportional to the fluorescence measured by the FL2 channel. This is indicative of interference caused by overlapping fluorescence spectra being detected by both channels. Compensating the results gathered by the FL2 channel 16.4% against the FL1 channel was successful in removing the interference.

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Figure 9:3 Titration of Positive Control CCCP within the JC-1 Assessment. An experiment to determine the optimal concentration of mitochondrial membrane decoupler, CCCP for use in JC-1 assays was conducted. TK6 cells were treated with a twofold dilution range of CCCP for 3.5 hours before adding 1.4 µM JC-1 dye. The fluorescence of cells in each sample was measured via both the FL2 and FL1 channel of a flow cytometer. The ratio of these two values relative to that of the vehicle control are represented on the Y-axis. Error bars represent the standard deviation between three independent replicate experiments carried out on different days with freshly prepared reagents. A drop in the fluorescence ratio is indicative of a drop in the electronegativity of the interior of mitochondria and thus a disruption of the mitochondrial membrane. Cells treated with 12.5 µM show the greatest drop in fluorescence ratio and a low level of variability between the replicate experiments. As is detailed in Section 9.2.2, the results of any assay plate on which the FL2:FL1 ratio value was 0.109 higher than that of the mean value for the same dose of CCCP from all assessed plates would be rejected. By assessing the complete dilution range of CCCP on each assay plate, troubleshooting could be carried out to determine the reason for the control value falling outside of acceptance criteria. If the trough, observed on the chart above at a dose of 12.5 µM were to fall at a different dose on the x-axis, an error in the concentration of CCCP or the cell-seeding density could be considered the cause. If, however, the FL2:FL1 ratio of the trough were to be higher or lower than had been observed on other assessed plates, then an error in the concentration of JC-1 or the parameters of the flow cytometer used in the assessment of the assay plate could be considered the cause.

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Figure 9:4 Time course assessment of JC-1 dye. Scatter plot of 535 nm fluorescence (following excitation at 485 nm) against time assessed by plate spectrophotometry at 73 time points within a 3 hour window. Between 6 and 22 minutes a sharp increase in fluorescence is seen in wells containing cells treated with 12.5 µM CCCP (blue) and CCCP-untreated cells (orange). Before the 535 nm fluorescence of both conditions plateaus until 45 minutes before reducing steadily for the remainder of the time-course. The difference in 535 nm fluorescence between the two conditions drops continuously after 45 minutes. 30 minutes was chosen as the optimal exposure time for JC- 1.

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10 of the 12 tested PPAs produce positive results at doses below 1 mM. Each of these positive results produced a dose dependant decrease in FL2:FL1 fluorescence ratio over at least 3 successive doses and caused the fluorescence ratio to drop a visibly clear amount lower than the threshold. Apomorphine hydrochloride, dodecyl gallate, EGCG, octyl gallate and quercetin all produced positive results at doses lower than 100 micromolar. Neither resorcinol nor gamma-resorcylic acid produced an indicative decrease in the fluorescence ratio at any tested dose up to and including 1 millimolar.

Of the 4 MPAs tested, both BHA and BHT lowered the ratio of FL2:FL1 fluorescence below the defined threshold within cells treated with doses of 500 micromolar, with a dose dependant decrease in the fluorescence ratio over at least 3 successive doses. BHMP and vanillic acid did not produce results below the threshold in cells treated with any tested dose up to and including 1 millimolar.

Of the 3 miscellaneous antioxidants tested, ethoxyquin lowered the ratio of FL2:FL1 fluorescence below the threshold at a lowest dose of 500 micromolar within treated cells. Neither ascorbic acid nor n- acetylcysteine caused cells to produce a fluorescence ratio of less than the threshold in any dose tested dose up to and including 1 millimolar.

Of the 3 oxidant compounds assessed, only cells treated with hydrogen peroxide caused the fluorescence ratio to drop below the threshold for one single dose at the highest tested dose of 1 millimolar to 63.2% relative to the vehicle treated control sample. Potassium bromate and tert-butyl hydroperoxide did not cause the fluorescence ratio to drop below the threshold at any dose tested dose up to and including 1 millimolar.

6 genotoxins were assessed using JC-1. Of these 6 compounds, the limited availability of three of the compounds, etoposide, bleomycin sulfate and vincristine sulfate limited the highest concentration that the compounds could be tested. The genotoxic and cytotoxic potency of the chemicals would have also made handling higher concentrations of the compounds irresponsible. Only 1 of the 6 genotoxins tested, NQO caused cells to exhibit a fluorescence ratio crossing the threshold. The other 5 compounds, including the 3 with limited dose ranges failed to produce a fluorescence ratio lower than the threshold.

Of the two compounds regularly used as a cytotoxic, non-genotoxic control within the GADD45a-GFP assay that were assessed using JC-1, cells treated with one compound, 2,4-dichlorophenol displayed a fluorescence ratio that crossed under the threshold at doses above 250 µM. Cells treated with phenformin hydrochloride did not display a fluorescence ratio below the threshold at any of the treated doses up to and including 1 mM.

Of the three miscellaneous compounds from the list of compounds detailed in Section 4.8 assessed all three produced fluorescence ratios in treated cells that were lower than the threshold. Cells treated with phenol and TBHQ metabolite, TBQ displayed fluorescence ratios lower than the threshold at doses above 125 and 250 µM respectively. Cells treated with the apoptogen, staurosporine produced a fluorescence ratio of 66.4% that of the vehicle control, just barely crossing the threshold at the highest tested dose of 1 mM.

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Relative FL2:FL1 Relative FluorescenceRatio

Compound concentration - µM

Figure 9:5 Sample data of 6 compounds from JC-1 assessment. Data are presented showing the change in FL2:FL1 fluorescence ratio of cells treated with increasing doses of test compound and stained with mitochondrial dye JC-1 relative to the fluorescence ratio of the compound-untreated, JC-1 dyed control represented by the y-axis. Each graph shows the fluorescence ratio of cells in orange following a 4-hour exposure to a certain dose of a test compound represented by the x-axis. A threshold of 3 standard deviations of the fluorescence ratio of compound-untreated, JC-1 dyed control cells assessed within each of the JC-1 assessments carried out in this investigation is represented by a dashed line and is equal to 0.672. Any dose of a compound that causes the fluorescence ratio to fall below this threshold can be considered to have caused an indicative drop in the fluorescence ratio indicating a depolarisation of mitochondrial membranes within the treated cells. Panel A shows the positive control, mitochondrial membrane decoupler, CCCP causes cells treated with over 1.95 µM to display an indicative drop in the fluorescence ratio and the unexplained increase in fluorescence ratio at doses above 7.81 µM previously observed in Figure 9:3. Panel B shows PPA, EGCG causes cells treated with over 62.5 µM to display an indicative drop in the fluorescence ratio. Panel C shows PPA, propyl gallate causes cells treated with over 500 µM to display an indicative drop in the fluorescence ratio. Panel D shows PPA, gamma-resorcylic acid does not cause cells at any treated dose to display an indicative drop in the fluorescence ratio. Panel E shows MPA, BHT causes cells treated with over 500 µM to display an indicative

179 drop in the fluorescence ratio. Panel F shows DNA-alkylating agent, MMS does not cause cells at any treated dose to display an indicative drop in the fluorescence ratio. Error bars represent standard deviation between independent replicate experiments carried out on different days with freshly prepared reagents.

Compound Panels FL1-Median FL2-Median FL2:FL1 BHT – 1 mM A & B 137 2.76 0.0201 CCCP – 3.91 µM C & D 205 7.50 0.0366 EGCG – 1 mM E & F 56.2 1.00 0.0178 Vehicle control G & H 71.0 155 2.18 Figure 9:6 Sample flow cytometry data of 3 compounds and a vehicle control from JC-1 assessment Panels A,C,E and G show histograms representing fluorescence measured by the FL1 channel for four compounds detailed in the data table. Panels B, D, F and H show histograms representing fluorescence measured by the FL2 channel. Phenolic antioxidants, BHT and EGCG, and positive control CCCP all reduce the median value of fluorescence measured by the FL2 channel below that of the vehicle control indicating a reduction in J-aggregates. BHT and CCCP both increase the median value of fluorescence measured by the FL1 channel indicating an increase in JC-1 monomers. The FL2:FL1 ratio for all three test compounds are reduced indicating that mitochondrial membranes in treated cells have been depolarised.

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Compound Panels FL1-Median FL2-Median FL2:FL1 gamma-Resorcylic acid A & B 83.9 161 1.92 1 mM MMS – 1 mM C & D 82.8 126 1.52 Propyl gallate – 1 mM E & F 274 4.87 0.0178 Vehicle control G & H 71.0 155 2.18 Figure 9:7 Sample flow cytometry data of 3 compounds and a vehicle control from JC-1 assessment Panels A,C,E and G show histograms representing fluorescence measured by the FL1 channel for four compounds detailed in the data table. Panels B, D, F and H show histograms representing fluorescence measured by the FL2 channel. Phenolic antioxidant, propyl gallate reduces the median value of fluorescence measured by the FL2 channel below that of the vehicle control indicating a reduction in J-aggregates. Propyl gallate also increases the median value of fluorescence measured by the FL1 channel indicating an increase in JC-1 monomers. The FL2:FL1 ratio for all propyl gallate is reduced indicating that mitochondrial membranes in treated cells have been depolarised. Only a small change is observed in FL2 and FL1 values of cells exposed to genotoxin, MMS and phenolic antioxidant, gamma-resorcylic acid indicating that mitochondrial membrane polarity remains unchanged.

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Highest Dose Trough

Group Compound Name µM Reasoning Result LEC Value Quintile PC Carbonyl cyanide m- 1000 Molarity Positive 1.95 20.9% 1st chlorophenyl hydrazone PPA Apomorphine hydrochloride 1000 Molarity Positive 62.5 36.2% 2nd tert-Butylhydroquinone 1000 Molarity Positive 500 18.1% 1st Dodecyl gallate 1000 Molarity Positive 31.3 21.9% 1st Epigallocatechin gallate 1000 Molarity Positive 62.5 25.3% 1st Nordihydroguaiaretic acid 1000 Molarity Positive 125 18.1% 1st Octyl gallate 1000 Molarity Positive 31.3 21.5% 1st Propyl gallate 1000 Molarity Positive 250 17.6% 1st Pyrogallol 1000 Molarity Positive 1000 43.1% 2nd Quercetin 500 Solubility Positive 3.91 21.1% 1st Resorcinol 1000 Molarity Negative 93.8% 5th γ-Resorcylic acid 1000 Molarity Negative 99.6% 5th Resveratrol 1000 Molarity Positive 250 43.8% 2nd MPA Butylated hydroxyanisole 1000 Molarity Positive 500 25.0% 1st Butylated hydroxytoluene 1000 Molarity Positive 500 15.4% 1st 2,6-Di-tert-butyl-4- 1000 Molarity Negative 87.3% 5th hydroxymethylphenol Vanillic acid 1000 Molarity Negative 97.3% 5th NPA n-Acetylcysteine 1000 Molarity Negative 96.0% 5th L-Ascorbic acid 1000 Molarity Negative 83.1% 5th Ethoxyquin 1000 Molarity Positive 500 44.8% 2nd OX tert-Butyl hydroperoxide 1000 Molarity Negative 94.1% 5th Hydrogen peroxide 1000 Molarity Positive 1000 63.2% 4th Potassium bromate 1000 Molarity Negative 91.1% 5th GENO Bleomycin sulfate 6.61 Stock Negative 88.8% 5th Etoposide 425 Stock Negative 96.4% 5th 5-Fluorouracil 1000 Molarity Negative 71.9% 5th Methyl methanesulfonate 1000 Molarity Negative 95.8% 5th 4-Nitroquinoline-1-oxide 1000 Molarity Positive 15.6 53.9% 3rd Vincristine sulfate 10.8 Stock Negative 98.5% 5th CYTO 2,4-Dichlorophenol 1000 Molarity Positive 250 22.3% 1st Phenformin hydrochloride 1000 Molarity Negative 94.8% 5th MISC 2-tert-Butyl-1,4-benzoquinone 1000 Molarity Positive 250 16.9% 1st Phenol 1000 Molarity Positive 125 33.5% 2nd Staurosporine 1000 Molarity Positive 1000 66.4% 4th Table 9:1: A summary of results from experiments to assess the disruption of mitochondrial membrane potential within TK6 cells exposed to 34 different test compounds using mitochondrial dye, JC-1. The result of a compound which at any dose caused the value of the ratio of FL2:FL1 fluorescence of treated cells, measured by a flow cytometer, relative to that of the vehicle control to fall below an established threshold of 67.2%, three standard deviations lower than the fluorescence ratio of vehicle control samples from each assessment performed is denoted as “Positive”. Results for those compounds which do not cause the fluorescence ratio of treated cells to fall below the threshold at any tested dose are denoted as “Negative”. The lowest fluorescence ratio value of cells treated with any dose of a certain compound is referred to as the trough value and this quintile into which this value falls is displayed in the final column. Compound group definitions are as follows: PC=positive control, PPA=Polyphenolic antioxidants, MPA=Monophenolic antioxidants, NPA=Non-phenolic antioxidants, OX=Oxidant compounds, GENO=Genotoxic compounds, CYTO=Non-genotoxic, cytotoxic controls used in the GADD45a-GFP assay, MISC=miscellaneous compounds. 182

9.5 Discussion

JC-1 was chosen primarily as a method to detect cytotoxicity following short term (4 hour) compound exposure. This was due to a concern that PI was only able to detect cytotoxicity within cells treated with very high concentrations of test compounds known to be cytotoxic over longer periods of time. JC-1 is widely used as a means to detect oxidant-related cell death (Virág et al. 1998). This, coupled with its fast action made JC-1 a clear choice for an alternative to PI in the detection of cell-death.

Of the 34 compounds assessed both with JC-1 and with PI following 4 hours of compound exposure, 15 of the compounds produced positive results in both assessments. 5 compounds produced positive results only in the JC-1 assay. 2 compounds produced positive results only in the PI assessment.

12 of the compounds produced negative results in both assessments and 2 compounds produced negative results only in the JC-1 assay while 5 compounds produced negative results only in the PI assessment.

4 of the 34 compounds produced negative results for cytotoxicity when assessed using PI after 48 hours of compound exposure. Of these 4, 1 produced a positive result when assessed using PI following the shorter, 4 hour exposure and none produced a positive result when assessed using JC after 4 hours of compound exposure.

15 compounds produced positive results in the PI assessment after 4 and 48 hours of compound exposure and the JC-1 assay after 4 hours of compound exposure. Of these, the lowest doses to produce positive results in the JC-1 assay, compared to those observed in the PI assessment following 4 hours of compound exposure were:

 Lower for 12 compounds  Equal for 2 compounds  Higher for 1 compound

This could indicate that JC-1 provides a more sensitive measure of cytotoxicity following a 4 hour exposure. For 4 of the compounds, however, the lowest positive dose measured by JC-1 assessment is lower than the lowest positive dose measured by PI assessment after a 48 hour compound exposure for 4 compounds. This would indicate that cells exposed to doses of 4 compounds would produce positive results within the JC-1 assay after 4 hours of compound exposure but following a further 44 hours of compound exposure would not have shown an indicative decrease in cell viability.

From the data for each compound assessment in the JC-1 assay, each compound’s “trough” (the lowest value for the FL2:FL1 fluorescence ratio that cells treated with any dose of an assessed compound produced) was calculated. These results were then divided into quintiles. The “troughs” of 7 of the 12 PPAs fell within the 1st quintile indicating that they were within the lowest fifth of the range of results. The troughs of the other 3 PPAs that produced positive results in the assay fell within the 2nd quintile while the 2 PPAs that produced negative results fell within the 5th quintile. A summary of the trough for each compound assessed, alongside the quintile into which the result falls is presented in Table 9:1.

Though the measurement of mitochondrial membrane potential can provide a valuable indicator of cells’ commitment to a cell-death pathway. Cells can recover from mitochondrial membrane depolarisation so 183 long as not all mitochondria are affected (Galluzzi et al. 2012). Further investigation of whether the PPA- treated cells that display mitochondrial membrane depolarisation commit to a cell-death pathway would be valuable. Furthermore, it would be of interest to discover whether the cell-death pathway to which cells commit is a necrotic or apoptotic pathway. It has previously been shown that apoptosis alone is not sufficient to produce a positive result within the GADD45a-GFP assay (Topham et al. 2012). This would mean that if the PPAs are potentially causing cells to commit to apoptosis, a separate mechanism of genotoxicity must exist. A study of 50 monophenolic compounds including BHA and BHT showed that the compounds, while able to induce apoptosis in L1210 cells (Murine leukaemia cell line), were only able to do so at doses far higher than were cytotoxic (Selassie et al. 2005). This could potentially also be true for PPAs. If cells are committing to a necrotic pathway, the escape of the contents of the electron-transport chain could generate the ROS observed in the DCFH-DA assay. This was observed by CCCP producing a positive result within the DCFH-DA assay. Further mention of the potential for further investigation is made in Section 11.

The generation of superoxide has been shown to activate mitochondrial decoupling proteins (Echtay et al. 2002). If PPAs are leading to the generation of superoxide, this could be a key reason for the observed mitochondrial depolarisation in cells treated with PPAs.

Unfortunately, as JC-1 fluoresces in at a wavelength similar to DCFH-DA, the two dyes cannot be used in tandem. This prevents the use of flow cytometry to measure the fluorescence of viable cells as can be done using PI.

In work published by Elbling et al. (2011), it was shown that a 24 hour exposure of a 100 μM concentration of EGCG was able to raise the level of green fluorescence of monomeric JC-1 in treated HaCat keratinocytes maintained at a cell number of 350,000 cells/ml. This level of fluorescence was measured by flow cytometry to be 1.67 times higher than that observed in cells treated with a vehicle control. The level of fluorescence in cells treated with 250 μM H2O2 was 1.39 times greater than the vehicle control. Comparing these results to those presented in this study is difficult due to the different means of measuring the levels of monomeric JC-1. In this study, a 125 μM concentration of EGCG produced a FL2:FL1 fluorescence ratio 2.38 times lower than that of the vehicle control. A 250 μM concentration of H2O2 produced a FL2:FL1 fluorescence ratio 1.50 times lower than the vehicle control. Both of these figures are slightly greater than the comparable figures from Eibling et al.’s work. This could be due to the differences in cell type or the longer compound exposure time although it is more likely due to the difference in measuring levels of monomeric JC-1. It should also be noted that EIbling’s work showed a far greater difference in levels of monomeric JC-1 in samples containing a lower cell number. This difference is ascribed to cell-mediated clearance of H2O2 (Elbling et al. 2011).

The following chapter investigates whether the increase in intracellular ROS in cells treated with PPAs (Section 8) leads to an increase in oxidative DNA damage. An increase in oxidative DNA damage would indicate a clear mechanism for the observed genotoxicity in the GADD45a-GFP assay (Section 5).

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9.6 Summary

 Calibrations were made to optimise the protocol for JC-1 assessment  34 compounds were assessed using JC-1 o 20 of these 30 compounds caused a drop in the ratio of FL2:FL1 fluorescence below the threshold in treated cells indicating a disruption of mitochondrial membrane potential o 10 of 12 tested polyphenols caused a drop in the fluorescence ratio below the threshold in treated cells and the lowest observed fluorescence ratios of 7 of those compounds fell within the lowest quintile of results indicating that they had amongst the greatest effect on mitochondrial membrane potential of those compounds tested o Only 1 of 3 oxidant compounds tested caused a drop in the fluorescence ratio below the threshold in treated cells although only at the very highest tested dose and the lowest observed fluorescence ratio fell within the 4th quintile of results indicating that oxidant chemicals do not cause a great deal of, if any mitochondrial membrane dysfunction within the timeframe of assessment o Only 1 of 6 genotoxic compounds tested caused a drop in the fluorescence ratio below the threshold in treated cells although only at the very highest tested dose and the lowest observed fluorescence ratio fell within the 3rd quintile of results indicating that cells undergoing genotoxic stress do not experience a great deal of, if any mitochondrial membrane dysfunction though it must be noted that the highest test dose of 3 of the tested genotoxic compounds were limited o Apoptogen, staurosporine caused a drop in the fluorescence ratio below the threshold in treated cells although only at the very highest tested dose and the lowest observed fluorescence ratio fell within the 4th quintile of results indicating that within the timeframe tested, an apoptotic pathway may not lead to mitochondrial membrane dysfunction  JC-1 appeared suitable as a measure of cytotoxicity following a compound exposure of 4 hours however the strong response following exposure to polyphenolic compounds may indicate that more complex factors are responsible

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10 Results VII – Cells exposed to 8 of 12 polyphenolic antioxidants showed a decrease in binding to a FITC-conjugated 8-oxoguanine antibody compared to an untreated control 10.1 Introduction

This chapter presents the results from an investigation to assess levels of the oxidised DNA base, 8-OG within cells exposed to 12 PPAs using 8-OG specific antibodies conjugated to a FITC fluorophore.

The results presented for the DCFH-DA assessment in Section 8 indicated a clear link between PPA compounds and intracellular reactive oxygen species (ROS). To link the intracellular ROS generation with the increased GADD45a expression detailed in Section 5, it was important to show to what degree the intracellular ROS lead to the oxidative DNA damage.

8-OG was chosen as an indicator of oxidative DNA damage. This chapter presents the development of a protocol to detect levels of 8-OG in TK6 cells. 8-OG was detected using a FITC-conjugated 8-OG antibody provided as part of the OxyDNA kit from Merck KGaA (Darmstadt, Germany: Cat. no. 500095).

Levels of the bound FITC-conjugated antibody in cells was quantified by flow cytometry using a BD FACSCalibur by detection of emission of fluorescence in the 530 nm bandpass filtered FL:1 channel following excitation with a 488 nm blue laser.

To produce conditions comparable with those that produced the DCFH-DA assessment results presented in Section 8 the same compound exposure time of 4 hours was used.tert-Butyl hydroperoxide (TBHP) was used as a positive control in the DCFH-DA assessment and was used as a positive control again in the assessment of 8-OG antibody binding due to the stability of its radical product (NTP 2002). The use of hydrogen peroxide as a positive control was recommended by Merck KGaA and so also used as a positive control.

10.2 Choice of compound dose

Because of the volumes used, the 8-OG assessment could not be carried out within a 96-well format as the DCFH-DA assessment was. Also, due to the more complicated protocol of the 8-OG assessment it was not feasible to carry out an assessment of 8-OG antibody binding for the wide range of doses tested in the DCFH-DA assessment. Therefore, each test compound was assessed at one dose. The decisions in choosing the assessment doses were made based upon the results of the DCFH-DA assessment presented in Section 8.

It was important that the chosen dose was not overly toxic to the cell within the 4 hour treatment window and so any dose of a test compound that produced a result considered cytotoxic within the DCFH-DA assessment decision criteria was omitted from choice. Compound concentrations that did not produce a positive result for cytotoxicity through assessment with PI after a 4 hour treatment are referred to as “non- toxic” in the further compound choice criteria below.

PI was chosen as the cytotoxicity measure to inform the choice of dose in the 8-OG antibody assay. This was because PI gives an indication of cells that are already undergoing cell-death whereas JC-1 provides an early

186 indication of cells beginning to commit to a cell-death pathway. Given that the exposure time of the 8-OG assay is the same as the DCFH-DA assay, it was considered more important to exclude doses resulting in cell permeability within the same timeframe, rather than those doses indicating that a cell death pathway may have activated.

For compounds that produced positive results for intracellular ROS generation in the DCFH-DA assay, the “non-toxic” dose responsible for the greatest increase of DCFH-DA fluorescence was chosen.

For compounds that did not produce positive results for intracellular ROS generation in the DCFH-DA assay, the highest non-toxic dose tested was chosen. The doses tested are summarised in Table 10:1.

Compound Dose tested - µM Apomorphine hydrochloride 125 tert-Butylhydroquinone 500 Dodecyl gallate 125 Epigallocatechin gallate 500 Nordihydroguaiaretic acid 125 Octyl gallate 250 Propyl gallate 1000 Pyrogallol 1000 Quercetin 500 Resorcinol 1000 gamma-Resorcylic acid 1000 Resveratrol 500 Butylated hydroxytoluene 500 Ascorbic acid 1000 Ethoxyquin 250 2-tert-Butyl-1,4-benzoquinone 15.7 Carbonyl cyanide m-chlorophenyl hydrazone 62.5 Potassium bromate 1000 Hydrogen peroxide 500 tert-Butyl hydroperoxide 500 Table 10:1 A summary of the dose chosen to assess the potential of compounds to increase the level of 8- OG. The rationale underlying the choice of compound dose to test is detailed in Section 10.2.

10.3 Optimisation of FITC conjugated 8-OG antibody binding assay

It was important to refine the 8-OG protocol detailed in Section 3.8.3 for use with TK6 cells that had been exposed to a test compound for 4 hours, washed, fixed with paraformaldehyde and permeabilised with ice cold ethanol. The concentration of positive control was chosen using a titration of several concentrations. A time course assessment was used to ensure that a 4 hour compound exposure was suitable.

10.3.1 Cell shear stress within the cell permeabilisation step During early attempts to carry out the 8-OG antibody binding assessment, a large proportion of the cells being tested were lost during the permeabilisation step of the protocol detailed in Section 3.8.3. This led to difficulty in detecting more than 10,000 events on the flow cytometer. Examination by microscopy indicated that the cells were degraded due to shear stress. Two steps were taken which greatly reduced this issue. Firstly when 1 ml of ice-cold ethanol was added, the ethanol was added slowly and to the side of the centrifuge tube containing the sample pellet rather than to the pellet itself. Meanwhile, the sample was

187 mixed constantly using a vortex mixer. Secondly, following the sample being stored overnight in ethanol at - 20 °C, 5 ml of PBS supplemented with 1% (v/v) heat-inactivated donor horse serum were added, reducing shear stress during centrifugation. The addition of these two steps greatly reduced the number of events that were defined as “debris” (Figure 10:1).

10.3.2 Addition of an antibody stop step During early assessments of 8-OG antibody binding to cells exposed to the vehicle and positive controls, very little discrepancy was being observed in flow cytometry results for fluorescence through the FL-1 (530 nm bandpass filtered) channel (Figure 10:2). Examination of the cells using microscopy showed the fluorescence to be diffuse, spread throughout the cells and not localised to the nucleus. The antibody appeared to be persisting within the cytosol following the wash steps. To reduce this effect, 2 extra wash steps were added following the antibody binding step detailed in the protocol in Section 3.8.3. First the sample pellet was re-suspended in PBS supplemented with 1% (v/v) heat-inactivated donor horse serum to bind to unbound antibody and allow it to be washed away. The second wash step was with PBS to wash remaining serum and antibody away. The protocol then continued as detailed in Section 3.8.3 with the wash step, re-suspending the pellet in wash solution. The addition of these two wash steps greatly increased the disparity between the fluorescence of cells exposed to the vehicle and positive controls (Figure 10:3).

10.3.3 Optimisation of positive control concentration To determine the concentrations of hydrogen peroxide and TBHP that caused the greatest increase in 8-OG antibody binding, samples were prepared by exposing populations of 1×106 TK6 cells to the following conditions for 4 hours:

Compound Concentration - µM Vehicle control N/A Hydrogen peroxide 125 Hydrogen peroxide 250 Hydrogen peroxide 500 Hydrogen peroxide 1000 TBHP 125 TBHP 250 TBHP 500 TBHP 1000 Table 10:2: The vehicle and positive controls assessed to determine the optimum concentrations of hydrogen peroxide and TBHP to be used as positive controls.

The protocol detailed in Section 3.8.3 with modifications detailed in Section 10.3.1 and 10.3.2 was then carried out. Experiments were carried out in triplicate on separate days. The results shown in Figure 10:4 present the ratio of the geometric mean of the fluorescence of non-debris events of the positive control exposure to the vehicle control exposed cells.

The results show that cells exposed to 500 µM of TBHP and 500 µM of hydrogen peroxide showed the greatest increase in 8-OG binding compared to the untreated control cells. 500 µM was chosen as the optimal positive control concentration for both TBHP and hydrogen peroxide and this concentration was used in further assessments.

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10.3.4 Optimisation of compound exposure time To ensure that the 4 hour compound exposure used in DCFH-DA assessment and used in the positive control titrations was a suitable exposure time, cells were exposed to concentrations of 500 µM of TBHP and hydrogen peroxide as well as vehicle controls. Time points of 1, 2, 3, 4, 5, 6, 7 and 8 hours were carried out, with biological replicate studies conducted. The results shown in Figure 10:5 show that exposure times of 3, 4, 5 and 6 hours showed a clear increase in 8-OG antibody binding compared to the vehicle control but that those exposed for 1, 2, 7 and 8 hours did not. While the difference observed in the samples exposed for 4 hours was not as great as that observed following 6 hours, it was decided that a 4 hour exposure allowed better comparison with the DCFH-DA assay results and was chosen as the exposure time for further 8-OG antibody binding assessment.

10.3.5 Optimising the cell density for use with the 8-OG antibody. A titration of cell density against antibody concentration was conducted in order to achieve the greatest separation in fluorescence between positive and vehicle control samples. 5 × 106 cells in 5 ml of RPMI 1640 media were exposed to 500 µM of hydrogen peroxide for 4 hours. After 4 hours, the cells were centrifuged at 300 × g for 5 minutes, the supernatant was then discarded. The cells were re-suspended in 1 ml of RPMI. 400 µl were added to one tube, 300 µl to a second tube and 200 µl to a third tube leaving 100 µl in the original tube. This left the tubes with 2, 1.5, 1 and 0.5 × 106 cells respectively less any dead cells. The proportion of dead to viable cells would be equal across the 4 tubes. As shown in Figure 10:6, the samples containing 0.5 × 106 cells produced the greatest difference in fluorescence between the 2 conditions however the results were more variable between the replicate experiments than samples containing 1 × 106 cells. In light of these results, the number of cells to be used in further experiments was chosen to be 1 × 106 cells, leaving the protocol unchanged.

10.4 Assessment of 8-OG antibody binding in cells exposed to polyphenolic antioxidants

Twenty compounds were tested using the 8-OG antibody binding assay protocol detailed in Section 3.8.3 with the modifications detailed in Section 10.3. The twenty tested compounds are all detailed in Section 4 and included positive controls, hydrogen peroxide and TBHP. All twelve PPAs were tested to investigate that oxidative DNA damage might play in the positive results produced in the GADD45a-GFP assay by PPAs (see Section 5). CCCP was tested to assess if mitochondrial membrane disruption contributes to oxidative DNA damage. TBQ was tested to assess if, as a metabolite of the PPA tert-Butylhydroquinone (TBHQ) it contributed to oxidative DNA damage. MPA, BHT and NPAs, ascorbic acid and ethoxyquin were also assessed.

The results presented in Figure 10:7 show that 2 of the 12 PPAs (quercetin and resorcinol) increased the binding of the FITC conjugated 8-OG antibody. 2 of the 12 PPAs (EGCG and gamma-resorcylic acid) showed no significant change in 8-OG antibody binding and the remaining 8 of the 12 PPAs led to a reduction in 8- OG antibody binding. Both positive controls consistently increased 8-OG antibody binding. TBHQ metabolite, TBQ led to a decrease in 8-OG antibody binding. MPA, BHT, and NPA, ethoxyquin, both led to a reduction in 8-OG binding. Ascorbic acid produced no significant change in 8-OG binding.

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Figure 10:1 The addition of serum to PBS and the addition of ice cold ethanol to cells on a vortex reduced the number of “debris” events Panel A shows a scatter plot of FSC-H against SSC-H of cells that were fixed and permeabilised without the addition of extra steps to reduce cell shear stress (10.3.1). The events collected were of variable morphology indicating a large amount of debris generated by cells shearing. 66% of events were detected within regions R1 and R3 defined as “Debris”. Panel B shows the same plot following the addition of extra steps to reduce cell shear stress (10.3.1). A far more defined population is observed in green, 5% of events were detected in the debris regions.

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Figure 10:2 Histograms displaying the difference in antibody fluorescence between vehicle and positive control samples before the addition of an antibody “stop” step Panel A shows a histogram of the fluorescence of the FITC conjugated antibody in the vehicle control treated sample. A median value of 518 was calculated for the Panel B shows a histogram of the fluorescence within a sample treated with 100 µM TBHP. A median value of 495 was calculated for the sample. Histograms displaying the difference in antibody fluorescence of vehicle and positive control samples after the addition of an antibody “stop” step are displayed in Figure 10:3.

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Figure 10:3 Histograms displaying the difference in antibody fluorescence between vehicle and positive control samples after the addition of an antibody “stop” step Panel A shows a histogram of the fluorescence of the FITC conjugated antibody in the vehicle control treated sample. A median value of 19.6 was calculated for the sample. Panel B shows a histogram of the fluorescence within a sample treated with 100 µM TBHP. A median value of 33.4 was calculated for the sample. Histograms displaying the difference in antibody fluorescence after vehicle and positive control samples before the addition of an antibody “stop” step are displayed in Figure 10:2.

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Figure 10:4 Results from experiments to determine the optimal concentration of positive pro-oxidant controls to be used for FITC-conjugated 8-OG-specific antibody binding assay. Presented above are the fluorescence levels of cells exposed to various concentrations of TBHP, hydrogen peroxide and a vehicle control. Data are presented for cells treated with the FITC- conjugated 8-OG-specific antibody and antibody free controls. Fluorescence of antibody-free control cells presented in grey are relative to the fluorescence of pro-oxidant-untreated antibody-free control cells. Fluorescence of antibody treated test cells presented in green are relative to the fluorescence of pro-oxidant-untreated antibody-treated control cells. Raw values of the pro-oxidant-untreated cells are shown above their respective bars. Error bars represent the standard deviation among results of 3 independent experiments carried out on different days using freshly-prepared reagents. A dose-dependent increase in fluorescence is seen for antibody-treated cells exposed to both pro-oxidant controls. Cells treated with 1000 µM concentration of both pro-oxidant controls showed the greatest increase in fluorescence in antibody-treated cells but also greater variability in levels of fluorescence in both antibody-

193 treated and untreated cells. Cells treated with 500 µM concentration of both pro-oxidant controls showed a reproducible increase in fluorescence within antibody-treated cells.

Figure 10:5 A scatter chart showing the fluorescence of cells treated with hydrogen peroxide for between 1 and 8 hours relative to vehicle control cells. Error bars represent the standard deviation among results of 3 independent experiments carried out on different days using freshly-prepared reagents. An increasing trend in fluorescence relative to vehicle controls is observed with increasing exposure time until 6 hours. Cells exposed for 7 or 8 hours showed lower fluorescence relative to vehicle controls than cells exposed for between 3 and 6 hours. Cells exposed for 5 hours and longer show a greater level of variability fluorescence relative to vehicle controls between replicate experiments than cells exposed for a shorter period of time.

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Figure 10:6 Results from experiments to determine the optimal number of cells to use in samples to assess FITC-conjugated 8-OG-specific antibody binding. Presented above are the levels of raw fluorescence of antibody-treated cells measured by flow cytometry. Samples of 0.5, 1, 1.5 and 2.5 × 106 cells were treated with FITC-conjugated 8- OG-specific antibody. These samples included both cells treated with 500 µM hydrogen peroxide shown as dark orange bars and vehicle control-treated cells shown as light orange bars. Error bars represent the standard deviation among results of 3 independent experiments carried out on different days using freshly-prepared reagents. Above the bars are shown the relative increase in fluorescence of pro-oxidant control treated cells over the fluorescence of vehicle-control treated cells. Results show decreasing fold-increase in fluorescence as cell number in the sample increase. Samples containing 0.5 × 106 cells show the greatest increase (2.21 ×) in fluorescence in pro-oxidant-treated cells relative to vehicle-control cells. Samples containing 1 × 106 cells show a visible increase (2.01 ×) in fluorescence in pro-oxidant-treated cells relative to vehicle-control cells and shows less deviation in result between replicate experiments.

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Figure 10:7: A chart showing the relative increase in FITC-conjugated 8-OG antibody binding in cells exposed to test compounds for 4 hours. The Y axis shows the geometric mean of the fluorescence of non-debris events detected through the FL-1 (530 nm bandpass filtered) channel of a FACS Calibur flow cytometer relative to the fluorescence of the vehicle control. Results displayed are the mean of three biological replicate experiments and error bars represent the standard deviation within those results. Orange asterisks represent the p-value of the results calculated using a 2 tailed homoscedastic Student’s T-test. *P-value of less than 0.05 indicating a significant increase in fluorescence relative to the vehicle control, **p-value of less than 0.01 indicating a significant increase in fluorescence relative to the vehicle control, °p-value of less than 0.05 indicating a significant decrease in fluorescence relative to the vehicle control, °°p-value of less than 0.01 indicating a significant decrease in fluorescence relative to the vehicle control.

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10.5 Discussion

This chapter describes experiments to determine whether the observed increase in intracellular ROS (Section 8.4) by PPAs leads to a significant increase in oxidised DNA. The oxidised base, 8-OG, has been shown to be a valuable indicator of oxidative DNA damage (Section 2.4.4). The use of antibodies specific to 8-OG allows a relatively simple method to quantify the residue and has been shown effective in the detection 8-OG in various cell types, including lymphoblastoid cells (Gruhne et al. 2009; Futaki et al. 2002). It was shown here that the 8-OG antibody assay was able to detect a very significant increase in 8-OG in cells treated with the ROS-generator, positive controls, TBHP and hydrogen peroxide. A smaller but still significant increase in 8-OG was observed in cells treated with the direct-acting oxidant, potassium bromate. This difference is to be expected given the direct mechanism through which potassium bromate is able to oxidise DNA (Ballmaier & Epe 2006).

The results produced by the PPAs tested in the assay differ greatly from the results of the DCFH-DA assay indicating that although many PPAs appear to lead to an increase in ROS generation in cells, only two compounds caused an increase in 8-OG antibody binding. Of all compounds tested except the positive controls, only quercetin produced a positive result in the DCFH-DA assay and led to an increase in 8-OG binding. 8 of the twelve PPAs assessed as well as the MPA, BHT, and the NPA, ethoxyquin all reduced the level of 8-OG antibody binding significantly below that observed in the vehicle control. Two testable hypotheses arise from these findings: The level of ROS generated by PPAs may be sufficient to activate the cellular response to oxidative stress through p53 and Nrf2. Potentially, this could lead to an upregulation in GADD45a through a forkhead transcription factor-mediated stress-response (Furukawa-Hibi et al. 2002; Tran et al. 2002), leading to increased levels of DNA repair. A baseline level of 8-OG exists within the genome of a cell at any one time (Ravanat et al. 1998). An increased level of BER and NER caused by upregulating GADD45a could potentially lead to a reduction in 8-OG levels below that of vehicle control cells. Alternatively it could be hypothesised that ROS are being generated by many of the test compounds as a result of the compounds depolarising the mitochondrial membrane. Cells with depolarised mitochondria may undergo cell-death before the ROS are able to oxidise nuclear DNA. This would also explain CCCP not causing an increase in levels of 8-OG despite producing positive results in the DCFH-DA assay.

Other studies show similar results although no studies sought to assess such a broad set of PPAs. EGCG has been previously shown to effect no change upon the level of 8-OG in HCT116 cells (Thakur et al. 2010). Similarly to the result observed in this study, resveratrol was seen to reduce oxidised DNA damage in MCF- 10A cells. Unlike in this study however the cells had been pretreated with a polycyclic aromatic hydrocarbon, increasing the level of oxidised DNA above the baseline levels (Futaki et al. 2002). A similar result was seen in hydrogen peroxide pre-treated IMR-90 cells treated with propyl gallate (Chen et al. 2007). Using a comet assay to detect oxidised DNA, quercetin and EGCG were seen to reduce oxidised DNA in cells pretreated with n 3-morpholinosydnonimine (Johnson & Loo 2000). Only one result however was found showing PPAs reducing levels of oxidised DNA in cells not pretreated with a DNA-oxidising chemical. In the study, EGCG, which did not reduce the level of oxidised DNA in the results presented in this chapter, was seen to reduce levels of oxidised DNA (Johnson & Loo 2000). Positive controls TBHP and hydrogen

197 peroxide were previously shown to increase the level of 8-OG using the OxyDNA kit (Lee et al. 2008; Chen et al. 2007) but no study has previously tested cells treated with potassium bromate with the OxyDNA kit.

Time and monetary restraints made it unfeasible to carry out this experiment using dose ranges of the test compounds. This prevented the finding of whether the observed results are dose dependent. It is plausible that if tested at different doses, certain chemicals might produce different results to those recorded in this study. Some chemicals may be seen to increase levels of oxidised DNA at some doses and reduce them at other through a dose-dependent autoxidation reaction (Chvátalová et al. 2008).

10.6 Summary

 Steps were taken to optimise the 8-OG antibody binding assay for use with TK6 cells o Extra steps were added to the 8-OG antibody binding protocol to reduce cell shear stress and residual antibody levels o Both hydrogen peroxide and tert-butyl hydroperoxide were chosen to be used as positive controls . 500 µM was chosen to be the optimal concentration for both compounds o Experiments were carried out to optimise the compound exposure time, a 4 hour compound exposure was found to provide the clearest results o Experiments were carried out to optimise the number of cells to treat, samples where 1 × 106 cells were treated were found to provide the clearest results  17 chemicals including 2 positive controls, 12 PPAs and 3 other compounds were assessed using the 8-OG antibody binding assay o Compounds were assessed at the dose responsible for the greatest increase in DCFH-DA fluorescence without a significant decrease in cell viability as presented in Chapter 8.4 o Of the 12 assessed PPAs, 2 caused a significant increase in antibody binding, 2 caused no change and 8 caused a significant decrease in antibody binding indicating a decrease in oxidised DNA within the cells treated with 8 of the PPAs o Both of the oxidant chemicals with a radical-mediated pathway caused a highly significant increase in antibody binding, potassium bromate, a direct-acting oxidant caused a significant but smaller increase in antibody binding

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11 Conclusion and future perspective 11.1 Conclusions

The investigations presented in this thesis were conceived to gauge the propensity of phenolic antioxidants, specifically those with a polyphenolic structure, to generate positive results for genotoxicity within an in vitro mammalian assay. The results presented within Section 5, revealed that the majority of a diverse range of PPAs produced positive results in the GADD45a-GFP assay. This indicates that the induction of the DNA damage response within mammalian cells is not limited to a small number of PPAs but to a broad spectrum of compounds with polyphenolic structure and antioxidant character. The observed induction of the DNA damage response, measured as the induction of GADD45a was plausibly caused by the same, oxidative mechanism given the compounds shared characteristics and so an investigation of the mechanism involved was carried out. These positive results shouldn’t be seen as an indictment against the GADD45a- GFP as the results shown in Sections 4.2-4.4, each of the mammalian in vitro genotoxicity assay produces positive results for compounds that are widely recognised as safe for human consumption with no link to carcinogenicity. These results should however be taken into account when assessing any polyphenolic antioxidant in the GADD45a-GFP assay as the result may not necessarily be physiologically relevant. With studies such as the GADD45a-GFP that are often used as an early-stage compound screen, any potential source of plausibly misleading positive results must be further investigated.

The potential of certain phenolic antioxidants to produce hydrogen peroxide within cell culture medium has previously been observed (Section 2.5). It has also been reported that cells in culture undergo increased oxidative stress as a result of the presence of oxygen at a far higher partial pressure than would be observed in vivo (Turrens et al. 1982; Yusa et al. 1984; Tsukamoto et al. 2012).

PPAs have been seen to generate ROS via autoxidative mechanisms and it was considered plausible that an increased oxygen concentration during testing could cause this to happen more readily (Williams & Jeffrey 2000; Valko et al. 2006; Chvátalová et al. 2008). These observations suggest that the positive results for genotoxicity generated by PPAs were a consequence of an increased generation of ROS within cells.

To investigate this possibility experiments were designed to investigate whether GADD45a-GFP assessment in the presence of lower oxygen tension would lead to a reduction in the number of positive results generated for PPAs (Section 7). The assessment of genotoxicity in a reduced oxygen atmosphere (5% and 1%) did not produce qualitatively different results for any of the compounds tested. Although the doses at which some compounds were found to be genotoxic were twofold higher in the presence of a lower oxygen tension, these increases were not specific to PPAs. It was also observed that cells cultured in the presence of a lower oxygen tension were not able to reach a cell density as high as those cultured in the presence of an unmodulated oxygen tension. This indicates that in the presence of a lower oxygen tension, oxygen becomes a growth limiting factor. Therefore, the reduction of the level of oxygen present during the assessment from 20% to 5% was sufficient to remove the surplus of oxygen available to the cells.

The observations described suggest that this excess of oxygen in culture did not contribute to a higher level of GADD45a induction for any of the tested compounds. Although the level of oxygen present during GADD45a-GFP assessment did not appear to affect the result for the PPAs, it was still plausible that the

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PPAs could be damaging DNA by inducing oxidative stress within treated cells. To investigate this possibility experiments were carried out to investigate whether PPAs were able to lead to an increase in intracellular ROS.

In light of the results presented in Section 8.4, it was evident from the assessment of PPAs using the DCFH- DA assay that the majority causing an increase in intracellular ROS. This provides compelling support for the hypothesis that PPAs generate positive results for genotoxicity within the GADD45a-GFP assay due through an oxidative stress mechanism. It appeared, however, that PI was not a sensitive enough measure of cytotoxicity within the timeframe of the DCFH-DA assay (Section 8.5).

The experiments presented in Section 9 were designed to evaluate the use of JC-1 as an indicator of early commitment to cell-death. It was shown that a majority of PPAs were able to cause mitochondrial membrane depolarisation within the timeframe of the DCFH-DA assay, suggesting that cells exposed to the compounds were committing to a cell-death pathway. This suggests that an alternative explanation is need to explain the results from the DCFH-DA assessment. If the mitochondria of the cells had been compromised, then any observed increase in ROS could be the result of the contents of the electron transport chain entering into the cytosol (Turrens 2003). ROS generated in this manner would be contained solely within cells committed to a cell-death endpoint that would likely have degraded within the timeframe of the GADD45a-GFP assay leading to a cytotoxic, non-genotoxic result. This could mean that cells which produce a significant increase in DCF fluorescence following a four hour compound exposure, may be likely to degrade within the forty-eight hour exposure of the GADD45a-GFP assay and hence not be responsible for the observed GADD45a induction. Four of the tested PPAs did however produce a significant increase in DCF-fluorescence at doses that did not cause mitochondrial depolarisation. This indicates that at least some PPAs are able to increase the level of intracellular ROS without depolarising the mitochondrial membrane potential. To better understand whether the observed increase in ROS within cells treated with PPAs within the DCFH-DA assay was responsible for the induction of GADD45a within the GADD45a-GFP assay, it was necessary to measure the extent to which ROS generation translated into an increase in oxidised DNA bases.

Experiments to detect the generation of the oxidised DNA base, 8-OG were carried out using a FITC conjugated antibody specific to 8-OG. The results of this investigation (Section 10) indicate that while two PPAs (Quercetin and Resorcinol) were capable of increasing the level of 8-OG within cells, ten were not. Furthermore, of this ten, eight PPAs led to a significant decrease in 8-OG. These results initially seem at odds with the increase in ROS observed using the DCFH-DA assay. It is possible that the antibody assay may not be sufficiently sensitive to increasing levels of 8-OG and that another measure of 8-OG (e.g. gas chromatography/mass spectrometry) might produce more accurate results (Collins et al. 1997). Another possibility is that the increase in ROS within cells was sufficient to lead to an upregulation of Nrf2, in turn leading to a GADD45a response with minimal or no DNA damage (Section 2.4.3). In the eight samples that showed a decrease in 8-OG antibody binding, it is plausible that an induction of GADD45a may have led to oxidised DNA bases being repaired. This would lead to a lower number of oxidised bases compared to vehicle control-treated samples. These results, while far greater in scope, match results previously

200 described in the literature (Johnson & Loo 2000; Futaki et al. 2002; Chen et al. 2007; Lee et al. 2008; Thakur et al. 2010).

11.2 Summary of conclusions

It can be concluded from the research presented in this thesis that the majority of PPAs are capable of inducing GADD45a. This indicates that there is DNA or chromosome damage within treated cells. Furthermore, it can be concluded that the induction of GADD45a is not an artefact of a surplus of oxygen available to cells; that a majority of PPAs are able to increase the generation of ROS within cells and that the majority of PPAs do not lead to an increase in oxidised DNA bases.

11.3 Future perspective

Considering the results of this thesis with regards to safety assessment of xenobiotic chemicals, the following steps could be taken to highlight a plausibly misleading genotoxicity result for a polyphenol. Compounds that have a polyphenolic structure are likely to generate positive results in the GADD45a-GFP assay (11/12) and other mammalian in vitro genotoxicity assessment (7 of 8) (Section 4 & Section 5). Most (6 of 7) polyphenols that had previously been assessed in rodent carcinogenicity studies had produced negative results (Section 4). The difference in results between in vitro and in vivo studies is likely due to the more complex antioxidant response in vivo, the higher level of endogenous antioxidants and the pro- oxidant nature of some cell-culture media (Halliwell 2003). The difference in the concentration of oxygen present in tissue and in cultured cells does not appear to contribute to the conflicting results (Section 6). Most (7 of 9) polyphenols that had previously been assessed in bacterial mutagenicity studies had produced negative results (Section 4). This difference in results is likely due to the different response pathways to oxidative stress within bacterial cells (Lushchak 2011). Polyphenols that produce positive results for genotoxicity in mammalian cells but not in bacterial cells could be considered for further investigation. Most (10 of 12) polyphenols increased levels of intracellular ROS (Section 7). This presents a possible approach to the discrimination between polyphenols producing plausibly misleading and concerning positive results.

Therefore it would be wise to further investigate any compounds with a polyphenolic structure, with negative results for Ames bacterial mutagenicity, but positive results in one or more mammalian in vitro genotoxicity assay. Such follow up might include an assessment of the level of ROS generated by the compounds within treated mammalian cells. This could be carried out using the DCFH-DA assay. If the compound were to cause an increase in intracellular ROS, the compound may be genotoxic to mammalian cells in culture by means of a physiologically irrelevant mode of action. This could warrant the compound’s assessment in in vivo rodent studies.

Further work needs to be taken across the field of genotoxicity to investigate root causes of false and plausible misleading positive results to ensure that the results generated become more trustworthy, streamlining drug and chemical development and preventing potentially important discoveries from being shelved due to insufficiently specific genotoxicity assessment. Steps such as the movement from CA to MNT assessment and standardisation of cell types help greatly in this regard (Kirkland et al. 2007; Fowler et al. 2012). By embracing new technologies such as 3D cell culture and in silico tools, further steps may be taken

201 to provide more accurate and informative results in the future. For these advancements to have a great effect, regulatory bodies will have to be willing to move away from older, more established assays as better alternatives are developed.

11.4 Future Work

Clearly, the results of this investigation indicate that a wide range of PPAs are capable of producing positive results for genotoxicity in the GADD45a-GFP assay and other in vitro mammalian genotoxicity assays. It has also been shown that the compounds are capable of producing positive results in the GADD45a-GFP assay without a surplus of oxygen available to cells. It is also evident that ROS are generated within cells treated with these PPAs. Surprisingly however, it has also been shown, using the 8-OG antibody binding assay, that for many PPAs, this increase in ROS generation does not translate into an increase in oxidative DNA damage. Further work is needed to investigate the reasons underlying the difference between these observations.

If the reason for the disparity in results is due to the DCFH-DA assay not being sufficiently specific to the generation of intracellular ROS, an alternative measure could provide a clearer understanding of which specific ROS are being generated (e.g. DPBF [superoxide & singlet oxygen], 2-(2-Pyridil)-benzothiazoline

[superoxide], amplex red [H2O2]). This may provide a clearer indication of their source as the ROS. Superoxide has been shown to be capable of activating mitochondrial uncoupling proteins (Echtay et al. 2002) whereas the ROS linked with autoxidation of organic compounds are peroxyl radicals (Halliwell 2008; Long et al. 2010). It has however been shown that EGCG is capable of producing superoxide (Sang et al. 2005). By carrying out live cell imaging, a clearer understanding of where and when ROS are being generated could be developed. This could provide insight into whether ROS are causing the depolarisation of mitochondria or whether mitochondrial depolarisation is causing the release of ROS. Furthermore, live cell imaging could offer an indication of the degree to which cell-death caused by PPAs may interfere with the detection of ROS.

Using antibodies to detect an increase in DNA-oxidation may lead to non-specific binding, so it might be better to find another method of detecting 8-OG, or another oxidised residue. Levels of various oxidised DNA in lymphoblastoid cells have been measured by gas chromatography/mass spectrometry (Seager et al. 2012; Jaruga & Dizdaroglu 1996). Measuring 8-OG by this method has been shown to produce higher values than other methods, though this may be due to the methodological introduction of artefacts (Collins et al. 1997).

The potential of the PPAs tested here to induce apoptosis, using flow cytometric quantification of annexin V binding and caspase 3 & 7 activation, could also be assessed. This would provide further understanding of whether apoptosis pathways contribute to the strong JC-1 response to PPAs, whether apoptosis is the result of ROS-mediated mitochondrial membrane depolarisation or whether it plays no role.

To investigate the hypothesis that PPAs activate oxidative stress pathways leading to DNA damage repair, despite little or no oxidative DNA damage, several steps could be taken. Quantifying the upregulation of Nrf2 in response to PPAs could indicate the level to which cells are responding to oxidative stress. It has already been shown in the Nrf2-luc reporter assay and the ToxTracker™ assay (Hendriks et al. 2012;

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Westerink et al. 2010) that two PPAs, TBHQ and propyl gallate are able to cause an Nrf2 response and a genotoxic response. Cytogenetic evaluation of cells using the chromosomal aberrations assay or micronucleus test could indicate whether chromosomal damage is occurring in cells treated with PPAs, and MLA/HPRT/Pig-A could be used to quantify the potential for PPAs to cause mutation in mammalian cells. Although these assessments have been carried out to some degree already, they have produced mixed results, in a variety of cell types and varying protocols. By carrying out MNT in p53 competent, TK6 cells, it may be possible to identify whether PPAs are as potent genotoxic agents as the GADD45a-GFP assay suggests.

With regards to the results of the GADD45a-GFP assessment in the presence of low oxygen, it would be of interest to further investigate the reasons underlying the reduction of TK6 cell viability and cell-division rate in the presence of low levels of oxygen. Assessing the level of oxygen dissolved in the media could be carried out to discern how it differs in the assay between different conditions. Hypoxia-inducible factor (HIF) is able to provide an indication of cells’ response to hypoxia (Semenza et al. 1991; Zhu et al. 2005). It would be informative to evaluate the HIF response to lower levels of oxygen. While it would be expected that HIF would be upregulated in cells exposed to low (~1%) levels of oxygen; if HIF were upregulated in TK6 cells exposed to 5% oxygen, it may indicate that TK6 cells have become adapted to 20% oxygen. If this were the case, it might raise concerns regarding the use of cell lines that are adapted to 20% oxygen in the in vitro assessment of genotoxicity.

Continued work must be carried out on plausibly misleading positive genotoxicity results generated by PPAs and other compounds to provide the most accurate indication of the safety of xenobiotics. By doing so, fewer potentially valuable drugs will be needlessly discarded early in their development. Furthermore, by refining in vitro genotoxicity assessment to provide a more accurate prediction of in vivo genotoxicity and carcinogenicity assessment will help reduce reliance upon animal testing.

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13 Appendix 13.1 Derek Nexus™ databases

In order to calculate the predictivity of the alerts presented in Section 6, Derek Nexus™ compares the results of all compounds with the same alerting structure within existing genotoxicity and carcinogenicity databases to provide a figure of predictivity. Below are detailed the databases searched by the software. In using the predictivity figures produced Derek Nexus™ reminds users to take note that:

 Predictions in Derek associated with a reasoning level of equivocal or above have been considered positive;  The classification of compounds from the CPDB data set as active or inactive is based upon the "ActivityOutcome_CPDBAS_SingleCellCall" field as defined within DSSTox;  The classification of compounds from the ToxRefDB data set as active or inactive is based upon an overall call assigned from the tumorigenicity summary information. Any active in a single species leads to an overall active call;  The classification of compounds from the ISSCAN data set as active or inactive is based upon the "Canc" field in the data set, where 3 is classified as active and 1 is classified as inactive;  The classification of compounds from the Snyder data set as active or inactive is based upon carcinogenicity studies in rodents. Consistent reports of activity in a single species leads to an overall active call;  The classification of compounds from the CRD-AGES pesticide data set as active or inactive is based upon the “exp” field in the data set, where 1 is classified as active and 0 is classified as inactive;  The classification of compounds from the Brambilla data set (Brambilla et al. 2012) as active or inactive is based upon carcinogenicity studies in mammals. Consistent reports of activity in a single species leads to an overall active call;  No account has been taken of other alerts which may also be present in some compounds;  For in vivo carcinogenicity datasets, no comparison has been made between the species, strain, gender or route of administration dependency of positive experimental results and the expected profile which may be included in the comments for an alert;  For in vitro chromosomal damage datasets no comparison has been made between the protocol used to obtain positive experimental results, including exposure time and metabolic activation, and the expected profile which may be included in the comments for an alert;  For in vivo chromosomal damage datasets no comparison has been made between the protocol used to obtain positive experimental results, including exposure time, tissue examined or route of administration, and the expected profile which may be included in the comments for an alert;  For in vitro mutagenicity datasets no comparison has been made between the strain and S9 dependency of positive experimental results and the expected profile which may be included in the comments for an alert;  The classification of compounds from the Sofuni data set (Sofuni 1998) as positive or negative is based upon an overall result which includes both polyploidy and structural chromosome aberration results;

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 The classification of compounds from the FDA CFSAN data set as positive or negative is based upon a composite activity score;  The classification of compounds from the Snyder data set as active or inactive is based upon an overall in vitro cytogenetics result. Consistent reports of activity leads to an overall active call;  Predictions do not take into account (i) the tautomeric forms of compounds or (ii) the individual components of mixtures;  Compounds in the data sets assigned responses other than positive or negative have been excluded from the analysis;  Information from the data sets may have been used previously as supporting evidence for the derivation of some alerts;  Some compounds may be present in more than one of the data sets analysed.

13.1.1 in vitro chromosomal damage databases Sofuni data set A collection of in vitro chromosome aberration test data for 712 compounds (Sofuni 1998)

FDA CFSAN data set A collection of in vitro chromosome aberration test data for 2172 compounds derived from the FDA/CFSAN/OFAS knowledge base.

Mohr data set A collection of in vitro chromosome aberration test data for 940 compounds (Mohr et al. 2010)

Snyder data set A collection of in vitro chromosome aberration test data for 427 compounds (Snyder & Green 2001; Snyder et al. 2004; Snyder 2009)

CGX data set A collection of in vitro chromosome aberration test data for 488 compounds (Kirkland et al. 2005)

Vitic database A collection of National Toxicology Program in vitro chromosome aberration test data for 461 compounds extracted from Vitic Nexus (Lhasa Ltd, Leeds, UK) (18 September 2012).

13.1.2 in vivo chromosomal damage databases MMS data set A collection of in vivo micronucleus test data for 256 compounds from the Kirkland (CGX) data set, collated by the Japanese Mammalian Mutagenesis Study Group (MMS) (Suzuki et al. 2005).

FDA CFSAN (1) A collection of in vivo chromosome aberration test data for 449 compounds derived from the FDA/CFSAN/OFAS knowledge base.

FDS CFSAN (2) A collection of in vivo micronucleus test data for 1397 compounds derived from the FDA/CFSAN/OFAS knowledge base. 226

13.1.3 in vivo carcinogenicity databases CPDB data set A collection of carcinogenicity data for 1547 compounds from the following source: Carcinogenic Potency Database (CPDB) (EPA 2008).

ToxRefDB data set A collection of carcinogenicity data for 337 compounds from the following source: Toxicity Reference Database (ToxRefDB) Chronic & Cancer Endpoints data (EPA 2012).

ISSCAN data set A collection of carcinogenicity data for 1150 compounds from the following source: Instituto Superiore di Sanita, Chemical Carcinogens: Structures and Experimental Data (ISSCAN) (ISS 2011).

Snyder data set A collection of carcinogenicity data for 420 compounds (Snyder & Green 2001; Snyder et al. 2004; Snyder 2009)

CRD-AGES pesticide data set A collection of carcinogenicity data for 108 compounds (Worth et al. 2010)

Brambilla data set A collection of carcinogenicity data for 537 compounds (Brambilla & Martelli 2009; Brambilla et al. 2012).

13.1.4 Ames bacterial mutagenicity databases CGX data set A collection of Ames test data for 718 compounds (Kirkland et al. 2005).

Vitic database A collection of National Toxicology Program Ames test data for 2001 compounds extracted from Vitic Nexus (13 September 2012).

Snyder data set A collection of Ames test data for 554 compounds (Snyder & Green 2001; Snyder et al. 2004; Snyder 2009).

Proprietary data set 1 A proprietary collection of Ames test data for 841 chemicals.

Proprietary data set 2 A proprietary collection of Ames test data for 475 chemicals contributed by Bayer Schering Pharma AG.

FDA CFSAN data set A collection of Ames test data for 8421 compounds derived from the FDA/CFSAN/OFAS knowledge base.

Benchmark data set A collection of Ames test data for 6512 compounds from the following reference: (Hansen et al. 2009).

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