Optimisation and Use of Cell-Based Assays and in Vivo Assay for Screening Drugs for Alzheimer’S Disease

Optimisation and use of cell-based assays and in vivo assay for screening drugs for Alzheimer’s Disease

A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Science & Engineering.

2017

Maria Priscila del Castillo Frias

School of Chemistry

List of Contents

Chapter 1. Introduction ...... 21

1.1. Alzheimer’s Disease (AD) ...... 21 1.1.1 AD as a world health issue ...... 21 1.1.2 AD Overview and pathological hallmarks ...... 22 1.1.3 AD Genetics ...... 24 1.1.4 AD Enviromental factors ...... 25 1.1.5 APP metabolism ...... 25 1.1.6 Aβ Clearance ...... 27 1.1.7 Tau protein ...... 28 1.1.8 Amyloid cascade hypothesis ...... 28 1.1.9 Oligomers: refined cascade hypothesis ...... 30 1.1.10 Role of oxidative stress in AD...... 31

1.2. Approved drugs to treat AD ...... 32

1.3. Therapeutic strategies to treat AD...... 33 1.3.1 Modifications in APP metabolism ...... 33 1.3.2 Immunotherapy ...... 36 1.3.3 Drugs targeting Aβ aggregation ...... 37 1.3.4 Reducing Aβ toxicity (oxidative stress, Calcium) ...... 38 1.3.5 Drugs targeting Tau...... 39

1.4. Drug repositioning to find drugs for AD...... 40 1.4.1 Current candidates for drug repositioning based on rationale approaches 42 1.4.2 Drug repositioning with no therapeutic rationale. Drug library screening. 49

1.5. Cellular models for AD ...... 50

1.6. Animal Models for AD ...... 52 1.6.1 Natural/ Spontaneous models ...... 52 1.6.2 Transgenic models ...... 53

1.7. Project Aims...... 56

Chapter 2. Optimisation of in vitro assays ...... 57

2.1. Methods...... 58 2.1.1 SH-SY5Y cell culturing and maintenance...... 58 2.1.2 Aβ42 preparation using HFIP and DMSO ...... 58 2.1.3 Aβ42 oligomers preparation using NaOH ...... 59 2.1.4 MTT (Optimised method) ...... 59 2.1.5 Evaluation of Aβ42 oligomers toxicity in SH-SY5Y by MTT .. 60 2.1.6 LDH (Cyto Tox-OneTM assay Promega)...... 60 2.1.7 CytoTox-GloTM ...... 61 2.1.8 Meso scale Discovery system ...... 61 2.1.9 DCFH ...... 63 2.1.10 ROS-GloTM ...... 64 2.1.11 GSH/GSSG- GloTM assay...... 64

2.2. MTT ...... 65 2.2.1 MTT principle ...... 65 2.2.2 Optimisation of MTT assay ...... 66 2.2.3 Optimal Cell Density for MTT assay...... 68 2.2.4 Evaluation of Aβ42 toxicity using MTT assay ...... 70

2.3. LDH principle ...... 71 2.3.1 LDH optimisation ...... 72

2.4. CyToTox-GloTM ...... 73 2.4.1 Optimal cell density ...... 74 2.4.2 Experimental sensitivity...... 75 2.4.3 Evaluation of the toxicity of Aβ42 using CytoTox-GloTM ...... 76

2.5. MSD immunoassay principle ...... 77 2.5.1 MSD optimisation ...... 78

2.6. Oxidative Stress assays ...... 80 2.6.1 DCFH principle ...... 80 TM 2.6.2 ROS-Glo H2O2 principle ...... 83 2.6.3 GSH/GSSG principle ...... 86

2.7. Discussion ...... 88

Chapter 3. Primary Screening...... 95

3.1. Introduction ...... 95

3.2. Methods ...... 95 3.2.1 Selection of drugs for primary screening ...... 95 3.2.2 Preparation of Aβ42 and LOPAC compounds for MTT screening. 97 3.2.3 Drug setup for MTT screening ...... 98 3.2.4 MTT drug screening ...... 99 3.2.5 MSD drug preparation and setup ...... 99 3.2.6 MSD immunoassay drug screening ...... 100

3.3. Results: MTT LOPAC sub-library screening ...... 100 3.3.1 MTT first round ...... 101 3.3.2 MTT LOPAC second round ...... 104 3.3.3 MTT LOPAC third round ...... 107 3.3.4 Aβ42 Variation from batch to batch ...... 110 3.3.5 Hits confirmation ...... 111 3.3.6 Non-LOPAC compounds screening ...... 115

3.4. MSD immunoassays ...... 117 3.4.1 MSD immunoassays primary screening LOPAC ...... 117 3.4.2 MSD immunoassays hits confirmation ...... 127

3.5. Discussion ...... 131

Chapter 4. Secondary screening ...... 137

4.1. Introduction ...... 137

4.2. Methods ...... 138 4.2.1 Dose-response curve drug preparation ...... 138 4.2.2 Preparation of drug combinations ...... 138 4.2.3 Preparation of drugs for oxidative stress assays panel ...... 139 4.2.4 MTT and DCFH assay ...... 139 4.2.5 GSH/GSSG- GloTM assay...... 140

4.3. MTT dose-response curves ...... 140

4.4. MTT Drug combination screening...... 143

4.5. Oxidative stress assay panel...... 144 4.5.1 DCFH ...... 144 4.5.2 GSH/GSSG ratio ...... 147

4.6. Discussion ...... 149

Chapter 5. Drosophila melanogaster assays ...... 151

5.1. Introduction ...... 151

5.2. Methods...... 152 5.2.1 Drosophila melanogaster stocks ...... 152 5.2.2 Fly husbandry...... 153 5.2.3 Male/Female sorting ...... 154 5.2.4 Virgin collection ...... 154 5.2.5 Quantification of soluble and insoluble Aβ42 ...... 155 5.2.6 Lifespan assays ...... 155 5.2.7 Survival curves, statistical analysis ...... 155

5.3. Results ...... 156 5.3.1 Quantification of Aβ42 ...... 156 5.3.2 Longevity comparison of Aβ42 and GFP Drosophila lines ..... 157 5.3.3 Effect of EGCG and SEN304 on the lifespan of Aβ42 and GPF lines 158

5.4. Discussion ...... 159

Chapter 6. General discussion...... 163

6.1. Conclusion ...... 168

Appendix A. Tailored LOPAC library ...... 171

References ...... 199

Word count: 59900

List of Figures

Figure 1.1 AD neuropathological hallmark lesions...... 23 Figure 1.2 Amyloidogenic and non-amyloidogenic APP processing. 26 Figure 1.3 The amyloid cascade hypothesis...... 29 Figure 2.1 Summary of CytoTox-One protocol ...... 61 Figure 2.2 Summary of the protocol for CytoTox-GloTM ...... 61 Figure 2.3 Summary of the protocol to perform DCFH assay...... 63 Figure 2.4 MTT principle ...... 65 Figure 2.5 MTT protocol...... 66 Figure 2.6 A) Effect of changing medium before the addition of MTT.. B) Effect of replacing medium with isopropanol after incubation of SH-SY5Y cells with MTT...... 68 Figure 2.7 Cell density curve...... 69 Figure 2.8 SH-SY5Y cells at different densities incubated for 40 hours...... 70 Figure 2.9 Evaluation of Aβ42 oligomers toxicity in SHSY5Y cells using MTT .... 71 Figure 2.10 CytoTox-ONE (Promega) principle...... 72 Figure 2.11 LDH Optimisation...... 73 Figure 2.12 CytoTox-Glo™ assay principle ...... 74 Figure 2.13 Cell density curve for CyTox-GloTM...... 75 Figure 2.14 Experimental sensitivity of CytoTox-GloTM. 3...... 76 Figure 2.15 Aβ42 toxicity measured by CytoTox-GloTM...... 77 Figure 2.16 MSD immunoassay principle...... 78 Figure 2.17 Evaluation of drugs affecting APP processing in MSD immunoassays...... 79 Figure 2.18 DCFH principle DCFHDA ...... 81 Figure 2.19 Use of Opti-MEM vs PBS to dissolve DCFH-DA...... 81 Figure 2.20 Aβ toxicity in DCFH assays...... 83 TM Figure 2.21 ROS-Glo H2O2 assay principle...... 84 TM Figure 2.22 ROS-Glo H2O2 Optimisation...... 85 Figure 2.23 A) Glutathione peroxidise dependant system to neutralize ROS. B) GSSG/GSSHTM-Glo Assay principle ...... 87

Figure 2.24 GSH/GSS of SH-SY5Y cells treated with 0.5% DMSO, 1, 2.5 and 5µM...... 88 Figure 3.1 Features of sub-library of LOPAC...... 97 Figure 3.2 First round of individual LOPAC sub-library compounds to identify hits (compounds) that attenuate toxicity (reduction in cell metabolism) caused by Aβ42...... 103 Figure 3.3 Second round of individual LOPAC sub-library compounds to identify hits (compounds) that attenuate toxicity (reduction in cell metabolism) caused by Aβ42...... 106 Figure 3.4 Third round of individual LOPAC sub-library compounds to identify hits (compounds) that attenuate toxicity (reduction in cell metabolism) caused by Aβ42...... 109 Figure 3.5 Variability of the MTT reduction for the three different batches of Aβ42 used in the three independent rounds of LOPAC screening...... 110 Figure 3.6 Confirmatory screening...... 114 Figure 3.7 MTT screening of non-LOPAC drugs...... 116 Figure 3.8 Single-well MSD screening of sub-LOPAC library...... 126

Figure 3.9 SH-SY5Y APP695 cells were incubated with 0.5% 10µM DAPT, 5µM βIV, 10 µM carbachol as controls or 5µM of hits from LOPAC and the non-LOPAC compounds for 24h...... 130 Figure 4.1 MTT dose-response curves of SEN304, EGCG, vescalagin and castalagin...... 142 Figure 4.2 Two-drug combination MTT assay...... 143 Figure 4.3 ROS levels measured using DCFH-DA...... 144 Figure 4.4 ROS levels dose-response curve, EGCG...... 146 Figure 4.5 GSH/GSSG ratio was measured using GSH/GSSG- GloTM...... 147 Figure 4.6 GSH/GSSG ratio dose-response curve, EGCG...... 148 Figure 5.1 Control GFP line was generated by crossing female virgin ElavGal4 with UAS-GFP males...... 153 Figure 5.2 UAS-Aβ42 line...... 153 Figure 5.3 Male/Female morphological differences ...... 154 Figure 5.4 Quantification of Aβ42 in the soluble and non-soluble fraction of Drosophila melanogaster overexpressing the Aβ42 peptide...... 157

Figure 5.5 Comparison of the longevity of the control line elav-GAL4; UAS-GFP; + (GFP) vs. the elav-GAL4; UAS-Aβ42 line (Aβ42)...... 158 Figure 5.6 Lifespan comparison of GFP and Aβ42 lines treated with EGCG and SEN304...... 159 Figure 6.1 Summary of drug screening assay...... 169

List of Tables

Table 3.1 Summary of hits found in three rounds of MTT primary screening ...... 111

Table 4.1 EC50 of the MTT “hit compounds” ...... 141

Abbreviations

AD Alzheimer’s Disease

ADAM a disintegrin and metalloprotease domain

ADLs diffusible ligands ADDLs

AICD an intracellular signalling domain

AICD intracellular signalling domain

APH-1 anterior pharynx-defective

ApoE apolipoprotein E

APP Amyloid Precursor Protein

Aβ β-amyloid peptide

BACE1 a type I integral membrane protein

BBB Blood Brain Barrier

ChE Acetylcholinesterase

ChEI Acetylcholinesterase inhibitors

DCFH-DA Dichloro-dihydro-fluorescein diacetate

DMSO Dimethyl sulfoxide

EGCG Epigallocatechin gallate

FAD familial forms of Alzheimer’s

GABA glutamate  aminobutyric acid

GSH Glutathione

GSSG Glutathione disulphide

HFIP 1,1,1,3,3,3-hexafluoroisopropanol

IDE insulin degrading enzyme

LDH Lactate dehydrogenase

LOAD Late Onset Alzheimer’s Disease

LOPAC The Library of Pharmacologically Active Compounds

LRP low-density lipoprotein receptor- related protein

MAPK Microtubule Associated Protein Kinase

MAPs microtubule-associated proteins

MSD Mesoscale discovery

MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide)

NEP neprilysisn

NFT Neurofibrillary Tangle

NMDA N-Methyl-D-aspartatic acid

NO Nitric acid

PHFs paired helical filaments

PSEN1 and PSEN2 presenillin 1 or 2

RAGE receptor for advanced glycation endoproducts

ROS Reactive Oxidative Stress

Abstract

Alzheimer’s Disease (AD) is the most prevalent form of dementia among elderly people. This disease is a major world health issue as the numbers of patients with this type of dementia will significantly increase in the next few decades. The aetiology of AD is not completely fully understood. The amyloid cascade hypothesis that identifies β-amyloid peptide (Aβ) as the initiator of the AD pathology leads the drug development efforts for AD. Currently only five drugs are approved by the FDA for AD but these drugs just ameliorate the symptoms of the disease for a limited time and do not target the underlaying pathology. Unfortunately, none of the drugs targeting the underlying disease has fully succeeded in clinical trials.

In this project, we screened a drug library of 174 compounds that have been previously approved and/or undergone clinical trials and five drugs that have been previously identified to have a potential effect as AD therapies. We used SH-SY5Y cells acutely treated with Aβ42, and SH-SY5Y695 cells, as models to mimic the neurotoxic effects of Aβ in AD. First, we evaluated the feasibility and optimised different in vitro assays to be used as primary and secondary screening assays. MTT was selected as one of the primary screening methods as cells treated with Aβ42 decreased MTT reduction compared to non-treated cells. Mesoscale Discovery (MSD) immunoassays were also selected as another primary screening assay using SH-

SY5Y695 cells. This immunoassay allowed us to simultaneously measure the concentration of the following proteins involved in APP processing: sAPPα, sAPPβ, Aβ38, Aβ40 and Aβ42. The primary screening using MTT identified SEN304, EGCG and castalagin as hits. MSD immunoassays identified compounds 4 (Acetyl-beta- methylcholine), 8 (arecoline), 133 (Tranylprimine), 169 (tropisetron), castalagin and SEN304 as possible hits.

The first part of secondary screening focused on assessing the potency of the hits identified from the MTT primary screening and evaluating the possible advantage of a two-drug combination approach. EGCG was the most potent compound at inhibiting Aβ toxicity measured by MTT, followed by SEN304, castalagin and vescalagin. The two-drug combination approach did not show any synergetic effect. The second part of the secondary screening focused in assessing whether the hits from

both MT and MSD immunoassays could hinder the increase of ROS generated in SH- SY5Y cells treated with Aβ42. EGCG was the only drug that decreased the ROS caused by Aβ42.

A Drosophila melanogaster model overexpressing Aβ42 decreased the longevity of fruit flies compared to a control fly model not expressing Aβ. We performed survival assays to evaluate whether EGCG or SEN304 increased the lifespan of the Aβ42 flies. None of the drugs evaluated increased the life span of flies overexpressing Aβ42.

Overall the assays presented in this thesis allowed us to identify possible hits for the treatment of AD. The results suggest that SEN304, EGCG, vescalagin an castalagin have multiple mechanism of action to inhibit Aβ toxicity. Mesoscale Discovery immunoassays are a promising drug screening platform as it is possible to evaluate various targets simultaneously.

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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Acknowledgements

I would like to thank Prof. Andrew Doig for giving me the opportunity to undertake this PhD adventure under his supervision and for helping me and motivate me every time I needed it. Thank you to Prof. Andreas Prokop for providing me with the necessary fly stocks and for his help designing the experiments for the “fly chapter”. I would also like to thank to Sanjai Patel for his hard work performing the fly crosses and for helping me to learn to work with fly fruits. I would like to thank to Prof. Nigel Hooper for provide us with SH-SY5YAPP695 cell line and for allowing me to use his lab facilities to perform the MSD immunoassays. A big thank you goes to Ph.D. Kate Kellet for being so helpful when performing the MSD immunoassays. A very special mention and all my gratitude to Ph.D.-to-be Nishta Chandra for her constant support, particularly during this writing period.

Thank you to Peter Feller, Olivia Berthoumieu and Philippe Derremaux for providing me with the drugs from natural sources using in this project

I would also like to thank my parents for all their love and unconditional support. Thank you, Liliana for always being there for me. To my husband Benjamin, thank you for all his support and for helping to always look on the bright side of life.

Finally, I would like to thank CONACyT for funding this PhD.

Chapter 1. Introduction

1.1. Alzheimer’s Disease (AD) 1.1.1 AD as a world health issue Alzheimer’s disease (AD) is the most common form of dementia among elderly people. AD is a progressive neurodegenerative disorder that damages neurons causing cognitive decline, memory deterioration, personality changes and language impairment, and gradually prevents patients from performing their daily activities independently [1, 2].

The increasing prevalence of AD, high treatment costs and the lack of disease- modifying therapies (preventing onset, slowing progression or curing AD) make AD a major global health concern. It has been estimated that 35.6 million people lived with dementia worldwide in 2010, with numbers expected to almost double every 20 years, to 65.7 million in 2030 and 115.4 million in 2050 [3]. The global costs associated with dementia in 2015 were approximately $600 billion, which is equivalent to 1% of the entire world’s GDP. The rising costs of AD are leading to a huge burden that national health institutions, insurance companies and patients are struggling to afford.

Five drugs have been FDA approved for the treatment of AD but they only ameliorate the symptoms of the disease for 6 to 12 months and do not target the underlying pathology. Research groups and pharmaceutical companies are keen to develop disease-modifying drugs that change the progression/outcome of AD. Nevertheless, most novel drugs have failed to demonstrate efficacy or suitable safety profiles in clinical trials, possibly because the aetiology of AD has not been fully elucidated, drugs are administered too late and assignment of patients into AD or non- AD groups may be in error. The last approved AD drug was memantine in 2004. From 2002 to 2012, 413 AD drug trials were performed: including 83 at Phase 3, with a 99.6% failure rate [4]. New strategies are therefore needed to generate efficient, safe and economic drugs to treat AD, notably drug repositioning and drug screening of natural products are attractive strategies.

1.1.2 AD Overview and pathological hallmarks

th In the early 18 century mental disorders were not considered as diseases, and individuals suffering from these pathologies were treated as criminals and condemned to isolation. In 1838, senile dementia was characterised for the first time by Esquirol [5]. He defined senile dementia as the progressive cognitive deterioration in which memory and the ability to learn and retain new information is lost –which is very similar to the current definition. This promoted differential diagnosis, hence, around 1850, senile dementias were classified as a different form of other known dementias as these were associated with abnormal cognitive deterioration in elderly people [6] . In the last decade of the 19th century, Philipe Pinel convinced the medical academia that it was appropriate to implement medical care for mental diseases [7]. The new interest of the medical community in mental diseases was paramount in their identification, classification and aetiology research.

In 1906, Alois Alzheimer described what he considered to be a new kind of mental disease after examining the brain of a 51-year-old female patient called Auguste D. She presented clinical symptoms similar to those already described as senile dementia; however, they were more severe and she died rapidly just after four and half years of the onset of the symptoms [8] After her death, Dr. Alzheimer performed histological studies, using the Biechowksky silver staining method. These studies showed that one quarter to one third of cerebral cortical neurons had focal lesions (amyloid plaques) and the remainder contained thick, coiled masses of fibres within the cytoplasm (neurofibrillary tangles (NFTs)). Alzheimer attributed these findings to chemical changes occurring in neurofibrils. As tangles were free in the cytoplasm, he suggested that they caused the death of the cells and remained as marks of cell death [9].

After the August D. case, another 12 cases exhibiting the same clinical and pathological features were reported. In 1910, Dr. Emil Kraeplin named Alzheimer’s disease as a new and unique type of senile dementia (presenile dementia) - as it is a more severe form of dementia and the onset appeared in people younger than 60 years old. However, doubts were presented when cases in persons over 60 appeared. For decades, there was an intense debate to define symptoms and pathological features of AD.

In 1927, Divry confirmed that the senile plaques previously described by Alzheimer behaved like amyloid by using Congo red staining [10]. However, it took several decades to characterise the main components of the two hallmark features of AD: amyloid-plaques and Neurofibrillary tangles (NFTs) (Figure 1.1).

Figure 1.1 AD neuropathological hallmark lesions. Amyloid plaques are mainly extracellular lesions primarily formed by Aβ42. On the other hand, neurofibrillary tangles are mainly composed of hyperphosphorylated Tau.

During the 1960’s, electron microscopy studies and X-ray revealed that the amyloid-like plaques contained a high proportion of β-sheet protein fibrils [11, 12]. But it was during the 80’s when the main components of AD hallmarks were unveiled. Aβ was identified and characterised as the main component of the cerebral vascular amyloid plaques from brains of a patient with AD and an adult with Down’s syndrome [13, 14]. Just one year after this discovery , it was confirmed that Aβ was the main component of the neuritic amyloid plaques in AD [15]. Almost in parallel, Tau protein was identified as the primary component of NFTs [16]. Thanks to the partial sequence of the amyloid protein found by Glenner and Wong, the amyloid precursor protein (APP) gene was located in chromosome 21 [17].

The above discoveries allowed us a new understanding of AD and were seminal for the amyloid cascade hypothesis, which is one of the leading explanations for AD pathology.

1.1.3 AD Genetics Alzheimer’s disease can be classified as early-onset AD (EOAD) and Late- onset Alzheimer’s (LOAD). EOAD is the less common form of AD, accounting for less than 5% of the cases. In this type of AD, symptoms appear before the age of 65. EOAD shows a dominant pattern of inheritance. Three genes are related to this form of AD: (APP) and two presenilin genes (PSEN1 and PSEN2) [18].

APP mutations are responsible for around 14% of dominant EOAD cases and to date more than 50 mutations have been identified [18]. Most of the known mutations are found within the Aβ gene or its adjacent regions. The region where the APP mutation is located impacts APP function [19]. For instance, the Swedish mutation (KM670/671NL) [20],which is located near the N-terminal site, affects β- secretase efficiency and increases by two to three fold the Aβ42 concentrations in plasma [21]. Mutations located near the C-terminus, such as the London mutation (V1717l) [22], alter -secretase function by increasing Aβ42 over Aβ40 formation, thereby increasing the Aβ42/Aβ40 ratio [23]. Moreover, mutations within the Aβ region like the Arctic mutation (E693G) [24] and Dutch mutation (E693Q) [25], promote Aβ aggregation [26]. Additionally, APP dosage has a similar effect as N- terminal site mutations; patients with extra copies of APP have significantly higher levels of Aβ42 [27], such as in Down’s Syndrome.

PSEN1 and PSEN2 are part of the -secretase complex. There are around 185 mutations of PSEN1 that account for around 80% of EOAD. In contrast, 13 dominant mutations of PSEN2 have been identified and account for 5% of FAD cases [27]. Mutations of both PSEN1 and PSEN2 are distributed across the whole protein and their effect on APP processing is to alter Aβ40/Aβ42 ratios [28].

In contrast, LOAD is the most common form of AD accounting for ~95% of cases. The onset of symptoms appears after 65. Genetically, the risk of developing late-onset AD is associated with two genes, apolipoprotein E (APOE) — specifically with APOE ε4 isoform — and trigger Receptor Expressed On Myeloid Cells 2(TREM2); TREM2 R47H mutant.

APOE has three main isoforms: APOE ε2, APOE ε3 and APOE ε4 where APOE ε3 is the most commonly occurring isoform within the population. APOE ε4

increases the risk of LOAD having a dose-dependent effect on the age of onset [29]. Finally, APOE ε2 is protective against the onset of AD [30]. The way APOE influences AD aetiology is complex and remains not fully understood, but studies have shown that it binds to Aβ and could affect its clearance or aggregation mechanisms [31, 32].

TREM2 gene is located in chromosome 6.p21.1 and the TREM2 protein is an immune receptor, from the Ig superfamily. TREM2 protein is expressed on the cell surface of microglia, macrophages, osteoclasts ad immature dendritic cells [33]. TREM2 promotes phagocytosis, possess anti-inflammatory properties and triggers immune response by binding polyanionic ligands [34]. Additionally, amyloid places promote TREM2 expression correlating with amyloid phagocytosis in outer microglia[35]. In contrast, mutated TREM2 R47H reduces phagocytosis and increases the release of pro-inflammatory cytokines [36] .

1.1.4 AD Enviromental factors There are environmental factors that may increase the risk of developing AD such as smoking, obesity, diabetes, high cholesterol, hypertension, low level of education and lack of social or cognitive activities. However, The real contribution of the above factors to increase the risks of Alzheimer’s disease is not conclusive [37].

Studies have demonstrated that moderate to severe brain injury increases the probability of developing dementia –including AD – two to five times in comparison with healthy individuals [38]. Also, people constantly exposed to head injuries, such as American football players or boxers, have an increased risk to develop different types of dementia [39]. Unlike the genetic risks factors, many environmental factors can be modified thus decreasing the likelihood of developing AD.

1.1.5 APP metabolism The Aβ peptide is 38-43 amino acids long. It is a proteolytic product of a larger transmembrane protein known as APP coded by single multiexonic gene located on chromosome 21. As previously mentioned, mutations and multiple copies of APP are known to increase Aβ formation causing the familial form of AD [40]. For instance, the high occurrence of AD in individuals with Down’s syndrome (trisomy 21), where

APP gene is triplicate, has been seminal evidence of the role of APP and Aβ in AD pathogenesis.

The APP encodes an integral membrane type I protein consisting of a large N- terminal extracellular domain and a short C-terminal cytoplasmic domain [41]. APP can be spliced into three different isoforms: APP751, APP770 and APP695. The APP695 isoform is the one majorly expressed in neurons while APP751 is expressed in astrocytes [42].

APP processing involves two sequential proteolytic cleavages: first within the extracellular domain by either α- or β-secretase, and then in the transmembrane region by -secretase (Figure 1.2). Three enzymes from the ADAM family (a disintegrin and metalloprotease domain) including ADAM9, ADAM10 and ADAM17 have shown α- secretase activity [43]. The β-site APP-cleaving enzyme BACE1 (a type I integral membrane protein) has been identified as the β-secretase [44]. -Secretase is a complex of the enzymes presenilin 1 or 2 (PSEN1 or PSEN2), nicastrin, the anterior pharynx-defective (APH-1) and the presenilin enhancer 2 (PEN-2) [45, 46].

Figure 1.2 Amyloidogenic and non-amyloidogenic APP processing. (Modified from [47]).

APP can be processed via the non-amyloidogenic or amyloidogenic pathways. In the non-amyloidogenic pathway, α-secretase (commonly ADAM10) cleaves the APP within the Aβ domain, producing a soluble s-APPα and a C terminal fragment of 83 amino acid residues (C83). -Secretase then cleaves the C83 fragment resulting in a N-terminally truncated Aβ (P3) which is non-neurotoxic, and an intracellular

signalling domain (AICD) (Figure 1.2). In the amyloidogenic pathway, the β-secretase cleaves APP to a soluble APPβ (s-APPβ) leaving intact the Aβ domain in the C- terminal fragment of 99 amino acid residues (C99). Then -secretase cleaves C99 between the 38-43 amino acids forming Aβ peptides and an intracellular AICD [48].

This cleavage forms two main Aβ species of 40 and 42 amino acids in length (Aβ40, Aβ42) in a ratio 10:1. Aβ42 is more neurotoxic than Aβ40 and is the major component of the amyloid plaques, a characteristic hallmark of AD [49]. N-terminally truncated forms of Aβ with pyroGlu groups at position 3 are found in AD brains and are more toxic and aggregation prone than Aβ42 [50]. N-terminally extended forms of Aβ also appear to be important in cell culture models of AD, though their relevance in vivo is unclear [51].

APP is synthesised in the rough endoplasmic reticulum (ER) and following maturation in the Golgi apparatus, Aβ is secreted to the plasma membrane by secretory vesicles [52]. In fact, Aβ that is secreted extracellularly comes from the above mentioned intracellular locations and not from the plasma membrane [53].

1.1.6 Aβ Clearance In addition to overproduction, accumulation of Aβ in the brain could result from the imbalance between its production and clearance to the bloodstream. Aβ clearance can be performed via: mediated transport of Aβ, proteolytic degradation, and from efflux from interstitial fluid to bloodstream [54]. The transport of Aβ is mediated by multi-ligand cell surface receptors: LRP and RAGE. LRP is a low-density lipoprotein receptor related protein which mediates the flux of Aβ from the brain to the periphery. Mice injected with an LRP antagonist and radio-labelled Aβ40 showed a reduction up to 90% in the efflux of Aβ from brain to plasma thus demonstrating the role of LRP in Aβ clearance [55]. RAGE is a receptor for advanced glycation products that mediates the influx of Aβ from plasma to brain. In animal models with down- regulated RAGE, the efflux of Aβ to the periphery is inhibited [56].

Proteolytic removal of Aβ is mediated by zinc metalloendopeptidases: insulin degrading enzyme (IDE) and neprilysisn (NEP). IDE is located in the cytosol and hydrolases regulatory peptides including insulin, glucagon, Aβ and AICD [57]. The role of IDE in Aβ degradation was demonstrated in mice where the IDE gene was knocked out resulting in increased levels of Aβ and AICD [58]. Neprilysin, a type II

membrane protein, has been identified as the rate-limiting peptidase which cleaves Aβ. In vivo studies have demonstrated that the absence of neprilysin increases Aβ accumulation resulting in amyloid deposition [59].

1.1.7 Tau protein Microtubule associated protein Tau (MAPT) is one of the microtubule- associated proteins (MAPs) mainly found in axons. In normal conditions, tau is hydrophilic, highly soluble and natively unfolded. Tau plays a key role interacting with tubulin and promoting the assembly of microtubules, stabilising its structure. Tau also helps with intracellular transport of organelles and biomolecules, and prevents apoptosis by stabilising β-catenin. Tau is subject to different post-translational modifications including glycosylation, ubiquitination and phosphorylation. Phosphorylation is most widely studied because of its relevance to AD. In healthy neurons, two to three residues of tau can be phosphorylated, whereas in patients with AD, phosphorylated sites account to approximately 9 per molecule.

Hyper-phosphorylation occurs due to an alteration in the balance of tau kinase and tau phosphatase activity (mainly protein phosphatase 2), causing phosphorylation of different serine, threonine and tyrosine residues. As a result, loss of microtubule binding may contribute to a breakdown of intracellular traffic and consequently neuronal death. Likewise, there is a redistribution of tau from axonal to somatodentritic compartments. Tau then starts aggregating to paired helical filaments (PHFs) which turn in bundle to form neurofibrillary tangles [60, 61]. Studies made in animal models suggest that the hyper-phosphorylation of Tau is caused by multiple processes, including accumulation of Aβ plaques, disruption in glucose metabolism and inflammation [62].

1.1.8 Amyloid cascade hypothesis The amyloid cascade hypothesis points to deposition of Aβ as the initiating step leading to AD pathogenesis [63]. The deposition of Aβ can occur due to: increased processing of APP through the β- and then -secretase pathway, imbalance between the production and clearance of Aβ, or increased ratio of Aβ42/Aβ40. According to this hypothesis, the increased levels of Aβ, especially Aβ42, are toxic to neurons [64] by disrupting synapses, activating inflammatory responses, increasing oxidative

injury, and altering kinase/phosphatase activity leading to tau hyper-phosphorylation and tangle formation (Error! Reference source not found.). These alterations result i n neuronal dysfunction and ultimately neuronal death [65].

Figure 1.3 The amyloid cascade hypothesis points to Aβ42 aggregation as the initial event of the AD pathogenesis. Aggregation could be due to overproduction of Aβ42 or failure in clearance mechanisms. (modified from [41]).

Strong evidence supporting the amyloid cascade hypothesis relies on the genetic information obtained from familial forms of AD (FAD). As previously mentioned in section 1.1.3, the discovery of APP autosomal dominant mutations linked to FAD in the APP, PSEN1 and PS2 genes increase the processing of APP through the amyloidogenic pathway thus elevating the normal production of Aβ [40, 66, 67]. Both in vivo and in vitro models expressing FAD-linked mutations in APP, PSEN1 and PSEN2 genes, showed elevated levels of Aβ42 [67] [68] [69].

1.1.9 Oligomers: refined cascade hypothesis The amyloid cascade hypothesis was challenged by a lack of correlation between the amount of amyloid plaques and the degree of dementia in AD patients or animal models [70]. For instance, amyloid plaques were found in healthy individuals [71] and a low correlation between the amyloid plaque and detriment of cognition was found in patients diagnosed with AD [72].

Further research found that soluble Aβ species (those that do not pellet after high speed centrifugation) may be a more toxic form [73], such as dimers, trimers, Aβ derived diffusible ligands (ADDLs) and protofibrils [74]. They have shown synaptotoxic effects in cell cultures and animal models: blocking long term- potentiation (LTP) and causing synaptic loss by decreasing the activity of NMDA receptors and calcineurin [75] [76]. The blockage of LTP was confirmed in vivo after adding dimers extracted from brain tissue of patients with AD to hippocampal slices [75]. In addition, dimers trigger hyper-phosphorylation of tau resulting in microtubule abnormalities and paired helical filament formation [76]. Some of these “dimers” may be misidentified as extended forms of A such -40 to +40 [51]. Indeed, a whole “peptide soup” of N-terminally extended forms of A has been proposed to be of importance in AD [77].

Even though the actual role of Aβ in AD pathology has not been fully elucidated, the evidence overwhelmingly suggests that Aβ does have a significant role in AD pathology. Hence, it is not surprising that all potential AD therapies are developed to tackle different parts of the amyloid cascade from reducing/altering Aβ generation to reducing Aβ toxicity (oxidative stress/ inflammation).

1.1.10 Role of oxidative stress in AD Oxidative stress can be defined as the imbalance between the production of reactive oxidative species (ROS), such as free radicals and the ability of the cells to detoxify or repair the damage caused by oxidative stress. One of the major risks of AD is aging and it has been found that an increase of oxidative stress is involved in AD pathology, especially at early stages [78]. For instance, studies suggest that oxidative stress could activate many cell signalling pathways initiating neurodegenerative processes [79, 80]. Additionally, the concentrations of antioxidant enzymes seem depleted in the brains of patients with AD [81].

Neurons can be very sensitive to free radicals as they generate energy through mitochondrial oxidative phosphorylation [82]. Additionally, neuronal membranes are constituted by polyunsaturated fatty acids that are common substrates of lipid peroxidation reactions [83]. And finally, compared to other tissues, neurons possess lower levels of glutathione which is a natural free radical scavenger involved in the metabolism of xenobiotics [84].

.- Research has identified superoxide (O2 ), and nitric oxide (NO) as the main .- ROS involved in AD. The nitric oxide reacts with O2 and generates peroxynitrtite radical (ONOO.-) that reacts with lipids, proteins and DNA, activating mitochondrial enzymes that ultimately increase the generation of calcium causing apoptosis [85].

Neurons have mechanisms, composed of enzymatic and non-enzymatic antioxidants, which balance and neutralise the concentration of ROS. The enzymatic antioxidant system includes three enzymes. a) Superoxide dismutase (SOD) .- transforms (O2 ) into H2O2. It is located in the mitochondria, the cytosol and outside the cell. b) Selenium-dependent glutathione peroxidase (GSH-Px) and c) catalase enzymes that convert H2O2 into H2O [84].

Non-enzymatic antioxidants also play a role in balancing and neutralising ROS concentration. Small molecules such as Vitamin E react with free radicals and neutralise their activity. Additionally, chelating proteins (metallothioneins) of low molecular weight with thiol groups such as glutathione possess strong redox potential. Glutathione neutralises oxidative species via glutamate peroxidases, forming glutathione disulphide (GSSG), also known as oxidised glutathione [84].

There are several studies that support the role of oxidative stress in AD pathology. For instance, Aβ oligomers and fibril are metalloproteins that reduce metal ions (Fe or Cu), trap O2 and generate H2O2 [86]. Aβ fibrils are also able to further degraded H2O2 to OH and increased carbonyl groups (that are an indicator protein damaged caused by ROS) [87]. Toxicity caused by the addition of Aβ on PC12 cells was ameliorated by Vitamin E [88]. Another study showed Aβ toxicity was reduced when treated with catalases (enzymes which transform H2O2 into H2O) [78], hence suggesting that H2O2 could be somehow mediating Aβ toxicity. Furthermore, levels of catalase and glutathione peroxidase were considerably higher in PC12 Aβ toxicity resistant cells [89]. In transgenic models with APP and PSEN1 mutations, there is an increase in H2O2 and NO production as well as lipid and protein peroxidation, thus suggesting again that Aβ promotes oxidative stress [90, 91]. Additionally, transgenic mice with a defective antioxidant defence system increases the ROS and promotes Aβ production, thereby suggesting that ROS also promote Aβ production [92, 93]. In summary, ROS promotes the synthesis of Aβ which binds to ions and generates more ROS. This causes a toxic loop that could play a key role in AD pathogenesis.

1.2. Approved drugs to treat AD Five drugs have been approved by the U.S Food and Drug Administration (FDA) for the treatment of AD: cholinesterase inhibitors (ChEI), for mild to moderate AD, and an N- methyl-D-aspartate (NMDA) receptor antagonist for moderate to severe AD.

The four ChEIs approved are: donepezil, galantamine, rivastigmine and tacrine. The use of ChEIs for the treatment of AD is supported by the cholinergic hypothesis of AD. According this hypothesis, the basal forebrain of individuals with AD shows a deficit in choline acetyl-transferase resulting in a reduction in the production of acetylcholine and cholinergic dysfunction. This cholinergic dysfunction prompts progressive deterioration in cognitive function. The ChEIs inhibit the acetyl and butyryl-cholinesterases responsible for the degradation of the acetylcholine. Hence, increasing the amount of this neurotransmitter at the synaptic cleft improves the cholinergic transmission and reduces the cholinergic deficit [94]. Patients treated with ChEIs improve cognitive function for up to 12 months reporting mild adverse events including nausea, vomiting and fatigue [95].

Glutamate is the main excitatory neurotransmitter in the central nervous system and is regulated by glutamate receptors such as NMDA. Studies suggest that the excessive activation of NMDA receptors increases the accumulation of calcium in the cholinergic cells accelerating the neurodegeneration process [96]. Memantine, a NMDA non-competitive glutamate receptor antagonist, blocks the NMDA receptor regulating glutamate concentration thus preventing neuronal damage [97]. Patients with moderate AD treated with memantine showed mild to moderate improvement in cognitive function for a limited period of time (about 12 months) [98].

Available drugs to treat AD are just a temporary solution, because they only ameliorate the symptoms of AD, but do not target the underlying pathology of the disease [94]. Now, research is focusing in developing disease-modifying drugs to prevent the onset, slow the progression or modify the course of the disease.

1.3. Therapeutic strategies to treat AD Since current treatments are merely symptomatic, new strategies to generate drugs for the treatment of AD are focused on disease-modification. The aim of such drugs is to slow the progression of the pathology in the disease; the agents under development target mainly the amyloid cascade and Tau hyper-phosphorylation. Some drugs can be classified according their targets: to decrease the production of Aβ, to decrease Aβ aggregation, and to promote Aβ clearance [97]. In addition, some research groups have focused their research on the inhibition of tau phosphorylation.

1.3.1 Modifications in APP metabolism Drugs aiming to reduce the production of Aβ most often focus on α-, β-, or - secretase because they process APP.

One of the possible targets is α-secretase, as up-regulation of α-secretase promotes APP processing through the non-amyloidogenic pathway, thereby, reducing the formation of Aβ. Steroid hormones, protein kinase inhibitors, activators of muscarinic or glutamate  aminobutyric acid (GABA) receptor and statins have been demonstrated to activate α-secretase [99, 100]. For instance, in Phase II clinical trials, patients taking etazolate (a GABA receptor modulator) showed cognitive improvement in some subjects and a good safety profile [101]. Also, carbachol (a

cholinergic agonist) increased sAPPα in cells overexpressing M1 and M3 muscarinic receptors [102].

Another strategy consists of targeting drugs that target β-secretase. In late 90’s, the β-secretase enzyme was identified and characterised as a type 1 transmembrane aspartatic protease and it was named as BACE [44]. This enzyme is mainly localised in acidic intracellular compartments including the trans-Golgi network (TNG) [103], plasma membrane and endosomes [104], and it is highly expressed in neurons. Two BACE homologs have been identified: BACE1 and BACE2, however only BACE1 has been identified as a β-secretase [105]. To study effects of targeting BACE1, transgenic APP mice with BACE1 knock out have been developed. The studies of these models showed a complete absence of Aβ production validating the role of BACE1 as β-secretase [106]. Moreover, knockout BACE1 mice did not show memory problems [107] but alter their performance in cognition and emotions tests [108] . BACE1 has been implicated in the metabolism of other important proteins including neuregulin 1 (NRG1) [109], which is involved in the myelination of neurons and L1 which is a transmembrane protein involved in cell adhesion [110]. The alternate functions of BACE1 besides acting as a β-secretase have raised concerns over its use as an AD therapeutic target. Recently, it has been proposed that targeting endosomal BACE1 only could reduce possible undesirable adverse effects, as it has been shown that cleavage of other substrates mainly happens in non-endosomal compartments [111]. The design of BACE1 inhibitors is challenging and has been mainly driven by peptide design making the possible candidates too large and with low BBB permeability [112]. In 2004, a potent non- peptidic BACE-1 inhibitor (BACEIV) was designed. This compound is cell permeable, very potent and selective to BACE1. This inhibitor is widely used in vitro or in vivo models of AD.

AZD3293 is a BACE1 inhibitor currently in Phase 3 clinical trials. Preliminary results from phase I trials suggest that the drug is safe at the doses administered and reduces Aβ concentration in plasma and CSF [113].

One of the most promising β-secretase inhibitors is Verubecestat also known as MK-8931. Patients enrolled in the phase I clinical trials were subjected to three different doses of Verubecestast ranging from 12 to 60mg for a week. Compared to

previous trials with β-secretase inhibitors, the drug showed a good safety profile as none of the subjects were withdrawn due to adverse events. Moreover, the levels of Aβ40, Aβ42 and sAPPβ were reduced in both healthy patients and the patients with AD [114]. This encouraging result provided enough evidence to perform further clinical trials. Clinical EPOCH was a Phase 2/3 randomized clinical trial with the objective of evaluating both efficacy and safety of two different doses of verubecetstat (12 or 40mg). The study enrolled patients with mild to moderate AD [115]. Unfortunately, the study was suspended early this year after an interim analysis showed that the drug would not demonstrate any efficacy if the study was to be continued. One of the possible reasons of failure is the that the drug was administered in phases where the damage already produced by Aβ is irreversible [116]. Still, there is another ongoing trial named APECS that also evaluates efficacy and safety of the drug in subjects with prodromal AD [117]. It is expected that the results of this trial will shed light whether one of the reasons of failure in AD drug development is due to late intervention.

-secretase inhibitors prevent the generation of Aβ in the last step of APP processing. However, -secretase also cleaves other proteins, including Notch. When Notch is cleaved, an intracellular domain is released to the nucleus activating transcription factors which control important functions such as cell differentiation. When the -secretase inhibitor Semagacestat went into clinical trials, it was demonstrated to be effective in reducing Aβ concentrations in plasma and CNS, but worsened cognition in patients and caused severe adverse effects, including abnormal bleeding, gastrointestinal and skin toxicity [46]. These adverse events were attributed to the blockage of Notch processing and the trial was halted. After these failures, drug developers attempted to develop Notch sparing -secretase inhibitors which do not interfere with Notch cleavage [118]. Tarenflurbil was tested, but in Phase III clinical trials failed to demonstrate efficacy, perhaps because of low penetration through the blood-brain barrier, and adverse events including anaemia and dizziness [119].

Avegacestat, was a promising -secretase inhibitor that avoided effects on Notch processing. However, Phase II clinical trials of this drug were halted due to a high drop out of patients that presented side effects including diarrhoea, nausea and dermatological problems. The trial showed a reduction in Aβ only in some patients

that were administered with the higher doses and also showed deterioration in cognition compared to placebo [120].

1.3.2 Immunotherapy Immunisation started to be suggested as a possible therapeutic intervention considering that antibodies against Aβ could help to reduce its concentration in the brain. Both active (Aβ antigens) and passive (Aβ antibodies) immunisation have been used as strategies to clear Aβ. In late 90’s Schenk et al. showed that active immunization of Aβ42 with an immune Freund’s adjuvant in PDAPP mouse prevented amyloid plaque formation in young mice and reduced AD progression in older mice [121]. These findings encouraged further development of active and passive immunotherapies for AD.

The first immunotherapy in clinical trials was based on active immunisation. AN1792 consisted of an Aβ42 peptide with a QS21 adjuvant that promoted T-cell mediated immune responses. Phase I studies showed that the drug was well tolerated and Phase II trials were launched. Unfortunately, this study was terminated as around 6% of the subjects presented meningoencephalitis as an adverse effect [122]. Further research suggests that this could be due to an increased Th-1 mediated response [123]. Despite patients treated with the vaccine showing a reduction in Aβ accumulation, it was not correlated to a survival increased or cognition improvement [124].

Solanezumab is a humanised monoclonal IgG1 antibody that binds to the mid- domain of monomeric Aβ, known to be the site for Aβ oligomerisation [125, 126]. Preclinical studies in APP-transgenic mice treated with solanezumab showed that a single shot of the drug was able to reverse memory deficits, but had no effect on the amyloid plaques, suggesting that the drug was targeting soluble Aβ species [127]. Initial Phase I and Phase II trials showed that administration of solanezumab was safe. Additionally, the study showed an increment of Aβ species in plasma but no significant effects improving cognition. Both Phase III clinical trials performed in patients with mild to moderate AD confirmed that the drug was safe but failed to demonstrate a significant cognitive improvement [128]. Still, in 2016 a new Phase III study was initiated in patients with prodromal AD, however it was terminated in

January 2017 as it failed to show significant cognitive improvement and a change in the CSF biomarkers of AD [128].

Bapineuzumab, a monoclonal antibody that binds to the N-terminus of Aβ, facilitates the clearance of Aβ by crossing the blood-brain barrier. Bapineuzumab made it to clinical trials, but was discontinued in Phase III due to lack of improvement in cognition in patients with mild and moderate AD [129].

Based on the evidence, passive immunisation seems to be better tolerated than active immunisation. Still, passive immunization has not shown the significant therapeutic effect expected from this therapy. Researchers hypothesised that this failure could be due to a reduced BBB permeability of the antibodies and that the vaccine has been applied in later stages of AD where the damage is irreversible. Current efforts have targeted both problems. For instance, clinical trials are currently designed to include patients in early stages of AD or subjects that possess a genetic risk towards AD. On the other hand, groups are working in developing better delivery systems that increase antibody penetration trough the BBB [130].

1.3.3 Drugs targeting Aβ aggregation Aβ aggregates to different forms, however, not all the aggregated forms seem to be toxic. Considering this, altering Aβ aggregation is an attractive strategy. Anti- aggregation drugs can tackle aggregation in three different ways: a) bind to monomers, and prevent oligomerisation but not fibrilisation, b) bind to monomers and completely inhibit aggregation or, c) prevent fibrilisation but not oligomerisation [131] . These possible mechanisms of actions suggest that aggregation of Aβ is not linear and that molecules that take Aβ “off pathway” could avoid the toxicity of Aβ.

Scyllo-inositol is a drug that prevents Aβ aggregation into toxic oligomer forms. Studies in mouse showed that this drug reduced plaque accumulation and concentration of soluble Aβ [132]. It also showed restoration of learning deficits and improved synaptic transmission [133, 134]. Scyllo-inositol was well tolerated in Phase I clinical trials. However, in Phase II clinical trials it showed possible adverse events at doses higher than 500 mg/day and no significant cognitive improvement and thus was terminated [135].

SEN304 is a short sequence peptide based anti-aggregator. It was designed based on the self-recognition sequence (SRS) KLVFF from Aβ [136]. This sequence is considered a binding site for Aβ-Aβ peptide thereby the blockage of this sequence would prevent Aβ aggregation. SEN304 has proven to rescue SH-SY5Y cells from Aβ42 toxicity and biophysical assays corroborate that its mechanism of action consists in triggering “off pathway” Aβ aggregation [137, 138].

Curcumin is a natural polyphenol found in turmeric. In vitro studies showed that curcumin inhibited the formation of Aβ-fibrils and also oligomer formation in a dose-dependent manner [139-141]. It has been shown that curcumin is an antioxidant and prevents Aβ-Induced inflammation [142]. In Tg257 APP695 mice, a low dose of curcumin (160ppm) reduces ROS, inflammation, and decreases the level of soluble and insoluble Aβ [143]. In Sprague-Dawley rats injected with Aβ, those treated with curcumin performed better in spatial-memory tests compare to control group[144],

Five clinical trials using curcumin have been performed: four of these have been completed and one is ongoing. Unfortunately, only one of the completed trials has published its results. This was a phase II, double-blinded, randomized study performed in 34 patients with AD. This study showed that curcumin treatment does not produce changes in the Aβ levels in serum. It also showed that the treatment does not improve the mini-mental state examination (MMSE) score [145]. The ongoing clinical trial is a phase II study to test the clinical benefits of curcumin combined with exercise (Yoga) in patients with mild cognitive impairment [146].

1.3.4 Reducing Aβ toxicity (oxidative stress, Calcium) There is a considerable evidence that oxidative stress is important for the initiation and commencement of AD, causing mitochondrial dysfunction [147]. Anti- oxidants and metal chelators may therefore be beneficial in AD.

Natural compounds including EGCG have been widely studied as a possible AD therapy. Indeed, EGCG in vitro studies show that it decreases ROS [148] but several studies have also shown that it could activate sAPPα [149], or act as an Aβ anti-aggregator [150].

After it was found that copper and zinc were elevated in the neocortex of AD patients, [151], it was suggested that clioquinol, that has a chelator activity, could be

a drug for Alzheimer’s disease. In vivo studies in transgenic mice decreased Aβ deposition [152]. Later, this drug was tested in Phase 2 clinical trials and showed an Aβ biomarker reduction and had a positive effect in cognition [153] . Before the next trial was about to start, it was announced that clioquinol contained toxic contaminants and that a second generation of the drug hydroxyquinoline would be used. When Phase 2 trials were performed, the drug did not show any benefit in patients with AD [154].

AD is associated with an inflammatory response, as shown by an increased presence of activated microglia and astrocytes, activated complement proteins, cytokines, and reactive oxygen, nitrogen, and carbonyl species. While inflammation may be beneficial in the short term, prolonged chronic inflammation may be highly damaging. Triggers such as A may cause microglial activation. These release neurotoxic factors such as cytotoxic cytokines and reactive oxygen/nitrogen species damaging neighbouring neurons. Damaged or dying neurons release additional microglial activators, resulting in a vicious cycle of neurotoxicity. Anti- inflammatories, such as curcumin, apigenin and tenilsetam, may therefore be beneficial for AD [155].

1.3.5 Drugs targeting Tau The amyloid cascade hypothesis proposes that Aβ leads to tau phosphorylation, making tau a possible target to treat AD. The main strategies targeting tau include inhibition of tau phosphorylation and aggregation, microtubule stabilization and tau immunotherapy [157].

Methylene blue prevented cognition deterioration in mice expressing human tau [158]. These results suggest the use of this drug as a possible treatment despite its low tolerability and bioavailability. LMTX, a derivative of methylene drug, acts as inhibitor of tau phosphorylation, and has a good safety profile. In a phase II study, LMTX significantly mitigated the cognitive decline of patients treated with the drug [159]. Two phase III clinical trials tested LMTX as add-on treatment with donepezil and rivastigmine, where it showed no benefit. However, LMTX administered by itself did demonstrate clinical benefits [160]. Currently, two more phase III clinical trials are being designed to test the efficacy of LMTX as monotherapy for AD. Studies in animals showed that valproate inhibits glycogen synthase kinase 3-β (GSK3β)

involved in Tau phosphorylation. In clinical trials, adverse effects and lack of efficacy discouraged further evaluation of the compound [156].

The microtubule stabilizers epothilone D (BMS-241027) reached phase I clinical trials but were discontinued [161]. Another microtubule stabilizer, TPI-287, a derivative of taxol is undergoing Phase I clinical trials [162].

Immunisation has been used as another strategy against tau. AADvac is a synthetic peptide derived from the tau protein that targets shortened, non-native tau. A phase I clinical trial showed that the vaccine has favourable safety profile and 97% of the patients developed immune response. More clinical trials using this vaccine are expected given the success of this study [163].

Despite the efforts to develop a disease-modifying therapy for AD, current strategies based on targeting steps in the -amyloid hypothesis have not yielded any marketable drug. Common reasons for failures are that drugs fail to show a suitable safety profile and do not demonstrate significant benefits improving or slowing progression of AD, in some cases because they have low or no permeability through the blood brain barrier. It is plausible that drugs have been tested in patients with irreversible advanced AD, where they might have shown benefit at an earlier stage. A good alternative to take advantage of the available models and find successful drug modifying drugs for AD could be the use of alternative strategies to screen drugs, such as drug repositioning.

1.4. Drug repositioning to find drugs for AD Drug repositioning can be defined as the search for new therapeutic uses for already existing drugs. It is based on two principles: the “promiscuous” nature of drugs- a drug may interact with more than one target relevant to a particular disease [164, 165] and that the target of a drug could be relevant to different diseases [165, 166].

There are two main approaches to pursue drug repositioning. The knowledge based approach consists in finding links between drugs and possible new targets using knowledge generated in clinical trials, “omics”, clinicians’ experience or by using bioinformatics software [167, 168]. The second is the non-hypothesis driven approach which promotes the phenotypic screening of drugs in not previously linked targets.

Many authors agree that the second could be the best approach to address complex diseases such as AD [169, 170]. The rationale is that it is not necessary to know in depth the mechanism of action of a drug to find unknown target-drug relationships that may result in successful repositioned drugs [171].

The development of drugs by drug repositioning could be cheaper and shorter in comparison with de novo drugs [165, 172]. Repurposed drug candidates have already being tested in humans, hence, information regarding their safety profile, pharmacokinetics, pharmacodynamics and dosing is already available [166]. Assessing drugs for AD that are already considered safe in humans would reduce the failure rate of promising drugs in late stages of drug development due to severe adverse events. Besides, the drug development process would be faster and drugs showing efficacy in pre-clinical trials could go directly in to Phase II clinical trials. In fact, this approach has already yielded a promising candidate for AD, liraglutide. This drug -commonly used to treat Diabetes Mellitus II – was tested in AD mice reducing oligomer concentration and improving memory skills [173]. Because this drug is well characterized, in terms of safety, pharmacokinetics and pharmacodynamics, it is now being tested in humans in a Phase II clinical trial [174].

Probably the best example of drug repositioning for AD is Galantamine which was discovered in the 1950s in the bulbs and flowers of wild Caucasian snow drops, Galanthus woronowii [175]. It was first used to reverse the effect of the alkaloid poison curare, which functions by competitively inhibiting the nicotinic acetylcholine receptor. Galantamine inhibits acetylcholinesterase, raising levels of acetylcholine to compensate for its receptor being blocked [176]. Galantamine was later used for various other diseases of the peripheral and central nervous systems. Since Galantamine has the same mode-of-action as the first Alzheimer’s drugs, it was successfully repositioned as an AD drug, and approved by the FDA in 2001.

1.4.1 Current candidates for drug repositioning based on rationale approaches

1.4.1.1 Bioinformatics to find new drug candidates for AD. The initial screening to identify new hits could be through computational and target based or phenotypic assays. Bansode et al. used virtual screens against acetylcholinesterase, BACE1, and Aβ aggregation to search for new AD drugs [177]. Virtual docking was tested with 140 FDA-compounds against crystal structures of all three targets. Remarkably, five antidepressant drugs (protriptyline, amytriptyline, maprotiline, doxepin and nortriptyline) with related structures, showed strong predicted binding affinity to all three targets, supported by experiment. All five had submillimolar binding against acetylcholinesterase. Protriptyline inhibited aggregation of A13-22, had an IC50 of ~0.025 mM for BACE1 inhibition and was not a general protease inhibitor. Affecting multiple targets simultaneously is a promising strategy for any disease. Protriptyline is a FDA approved drug for the treatment of depression and narcolepsy, Attention Deficit Hyperactivity Disorder (ADHD) and headaches, and is already known to cross the BBB.

Levetiracetam is an orally available anti-epileptic drug that modulates the synaptic vesicle protein modulator SV2A. A human transcriptome study on how the ApoE4 risk allele affects APP processing implicated SV2A. Even though this work has been partly retracted, levetiracetam was effective in reducing Aβ generation in cells cultured from ApoE4 carriers. Levetiracetam reduced epileptiform activity and reversed cognitive deficits in human APP transgenic mice [178, 179]. A small trial in 17 people with mild cognitive impairment (MCI) showed that levetiracetam reduced hippocampal activity and improved performance on a hippocampal memory task [180]. A one-year study of Levetiracetam in AD patients who had seizures reported improved attention, verbal fluency, and controlled seizures [181]. Additional clinical studies of Levetiracetam are going ahead, following these encouraging data.

1.4.1.2 Angiotensin Converting Enzyme Inhibitors Inhibition of the renin–angiotensin system, which regulates blood pressure and fluid balance, has been suggested as a potential therapeutic strategy for AD and other neurodegenerative disorders[182] [183] [184] [185] [186]. Angiotensin converting

enzyme (ACE) produces the AT-II peptide, where elevated levels of AT-II increases Aβ induced neurotoxicity[187]. In particular, the elevation of ACE activity has been reported in the brains of AD patients[188], suggesting that brain-penetrating ACE inhibitors may be beneficial in preventing AD. Several ACE inhibitors have been reported to show promise in AD models:

Dong et al. compared the brain-penetrating ACE inhibitor perindopril with the non-penetrant ACE inhibitors imidapril and enalapril, using mice that underwent intracerebroventricular injection of Aβ1–40 on PS2APP double-transgenic mice that overexpress Aβ in brain tissue. Perindopril lowered hippocampal ACE activity and prevented cognitive impairment, brain inflammation and oxidative stress, compared to the non-brain-penetrating ACE inhibitors [189]. Perindopril was also effective in rodent model for vascular dementia [190].

Intracerebroventricular injection of streptozotocin (STZ) can induce AD-like dementia in mice, producing impaired learning and memory. Administration of ilinisopril, an ACE inhibitor, significantly reduced STZ induced behavioural and biochemical changes [191]. The ACE inhibitor captopril was tested on the widely used mouse AD model Tg2576. Captopril decreased the excessive hippocampal ACE activity of AD mice, reducing neurodegeneration, by decreasing amyloidogenic processing of APP and hippocampal ROS [192].

Nilvadipine is an L-type calcium channel antagonist used for the treatment of hypertension and chronic major cerebral artery occlusion. It was found to be effective on Aβ induced vasoconstriction in isolated arteries in Tg2576 transgenic mice [193]. Nilvadipine is an L-type calcium channel (LCC) antagonist with (+)-nilvadipine the active enantiomer. Both enantiomers inhibited Aβ production and increased its clearance across the BBB, revealing that this effect was not LCC related. Further studies of nilvadipine in PS013 mutant human tau transgenic mice revealed that (-)- nilvadipine reduced tau phosphorylation at various AD pertinent epitopes. Elucidation of (-)-nilvadipine’s mechanism of action showed that it attained its effects by inhibiting Syk, resulting in the activation of Protein Kinase A (PKA), which phosphorylates the inhibitory residue Ser9 in GSK3β, resulting in a decrease in tau hyperphosphorylation. Similarly, PKA also phosphorylates CREB, which is essential in neuroprotection and cognition. Furthermore, inhibition of Syk impedes the

stimulation of the NFκ B pathway resulting in a decrease in neuroinflammation, BACE1 expression and subsequent reduction in the accumulation of Aβ [194]. These multiple effects mean that the inhibition of Syk by nilvadipine or derivatives represents a very attractive therapeutic target for the treatment of AD. A pilot study of nilvadipine in 55 patients with AD found stabilisation of cognition and improvement in executive function in treated individuals [195]. A clinical trial on 500 people with mild to moderate Alzheimer's is currently underway [196].

1.4.1.3 Drugs targeting Tumour necrosis factor alpha (TNFα) Tumour necrosis factor alpha (TNFα) is a cytokine produced by macrophages. It was discovered and named after its ability to kill cancerous cells in mice. Later it was found that TNFα plays a key role mediating inflammatory and immunity processes. In the brain, this cytokine is expressed by neuronal cells and glial cells in response to brain injury, viral infection or degenerative disorders [197], such as AD. The amyloid cascade hypothesis suggests that inflammation might be activated downstream of A overproduction and deposition. Based on this possible relationship, investigators studied in depth the possible role of inflammation in AD. TNFα is found to be upregulated in patients with neurodegeneration [198]. In addition, there is evidence that cytokines, including TNFα, upregulate BACE1 expression, thus increasing Aβ load [199, 200].

As TNF-α seems to affect APP processing and Aβ plaque generation, it has been suggested as a drug target for AD. The inhibition of TNF-α could improve or slow down the cognitive decline in patients with AD. TNF-α inhibitors available in the market are monoclonal antibodies such as infliximab; fusion proteins such as etanercept; or small molecules like thalidomide. The use of these inhibitors includes rheumatoid arthritis, leprosy, Crohn’s disease, and multiple myeloma. Despite being successful treatments, TNF-α inhibitors adverse effects are considerable. In this section, I will describe the current efforts to use these drugs to treat AD.

Infliximab is a chimeric (mouse-human) immunoglobin type 1 (IgG1) monoclonal antibody which is not able to cross the BBB. It specifically binds to both monomers and trimers and membrane-bound and soluble TNF-α [201, 202]. Infliximab neutralises the biological activity of TNF-α by blocking its binding to natural receptors. This drug is approved by the FDA for the treatment of autoimmune

diseases, such as rheumatoid arthritis, psoriasis and Crohn’s disease. It has been further investigated for the treatment of other diseases, including inflammatory bowel disease and AD.

In preclinical trials, Infliximab reduced the number of amyloid plaques, the levels of TNF-α and Tau phosphorylation in APP/PSEN1 double transgenic mice [203]. In addition, it improved memory and cognition in rats with induced dementia [204].

Etanercept (Enbrel) is a soluble dimeric fusion protein receptor for TNF-α. It is a fusion between Human TNFR2-α receptor and Human IgG1 Fc domain. This artificial receptor has greater affinity for TNF-α than its natural receptors thereby decreasing the inflammatory response triggered by TNF-α [205]. Similar to Infliximab, Etanercept is approved for rheumatoid arthritis, psoriasis and ankylosing spondylitis.

Given the evidence of the possible role of TNF-α in AD, investigators were keen to use etanercept to treat AD, even though etanercept poorly penetrates the BBB. An alternative route of administration, consisting of injection into the tissues close to the spinal column (perispinally), was proposed to facilitate etanercept delivery to the brain through the cerebrospinal venous system [206]. In a six month, open-labelled study, etanercept was administered weekly by perispinal administration in 15 patients with mild to severe AD. Patients showed significant cognitive improvement thus providing sufficient clinical evidence to perform further trials [206, 207]. One patient with late-onset AD reported a rapid cognitive improvement (just 2 hours) after the perispinal administration of etanercept. The authors’ hypothesis is that this rapid improvement could be related to the effect of the drug inhibiting TNF-α [207].

These studies have prompted the further study of etanercept as an AD drug. An ongoing phase I clinical trial is assessing the safety and efficacy of the administration of perispinal etanercept plus dietary supplements (resveratrol, curcumin, Omega-3 and quercetin) versus the administration of dietary supplements only. The University of Southampton recently finished a randomised, double-blind, placebo-controlled phase II clinical trial to evaluate the safety and tolerability of etanercept in patients with AD. As a secondary outcome, the study evaluated cognitive and behavioural functions, as well as systemic cytokine levels. In contrast to previous

studies, etanarcept was administrated subcutaneously instead of perispinally. Etanercept was well tolerated and no new adverse events were recorded. There was not a significant difference in cognition or behaviour. However, the study found that the cognition decline of patients treated with placebo was twice as bad than expected and that patients treated with etanercept did not worsen or improve compared to baseline. This study suggests that a larger clinical trial should be performed [208].

Thalidomide was initially used to alleviate nausea and morning sickness during pregnancy. However, around 100,000 newborns suffered congenital malformations which in many cases resulted in death. For this reason, it was withdrawn in many countries, including the USA and United Kingdom. Despite its serious adverse events, the drug was investigated in other countries and was found to be useful as a treatment for leprosy or multiple myeloma, and finally approved by the FDA in 1998.

Thalidomide, as an immunomodulatory agent, inhibits the tumour necrosis factor-alpha (TNF-α) cytokine, which plays a key role in producing an inflammatory response. When TNF-α is inhibited, the inflammatory response is reduced. Early preclinical studies in Alzheimer’s disease mouse models showed that thalidomide has neuroprotective effects and decreases microglial activation [209, 210]. It also reduced tau phosphorylation, APP and A plaque load [210]. In a later study in APP Swedish mutation transgenic APP23 mice, thalidomide decreased BACE1 activity [211]. The evidence gathered in these studies supported the use of this drug in clinical trials. A Phase II study to evaluate the effects of thalidomide in patients with mild to moderate AD is ongoing in the USA. The primary endpoint of this study is to evaluate if the administration of the drug improves cognition and shifts CSF and plasma biomarkers.

1.4.1.4 Diabetes and Alzheimer’s disease Type 2 diabetes mellitus (T2DM) and AD are common diseases in elderly people. This observation prompts the question of a possible relationship between these diseases. The Rotterdam study, which investigated the possible influence of T2DM on the risk of dementia and AD, found that T2DM almost doubled the risk of dementia [212], calling for more epidemiological studies of this relationship. Recently, a meta- analysis of these epidemiological studies confirmed that there is a correlation between T2DM and the risk of developing AD [213]. The possible biochemical basis of the relationship between T2DM and AD has been widely studied, in in vitro models, in

vivo models, and humans. Insulin is well known for decreasing glucose levels in blood. However, it has been discovered that it has other important functions, in the brain including neuroprotective functions and regulation of Aβ levels and phosphorylated tau [214]. It has also been demonstrated that low levels of insulin cause cognitive impairment and reduce long term potentiation in the hippocampus [215].

In both patients with AD and T2DM, insulin receptors in the brain seem desensitised, impairing insulin signalling [214, 216]. Based on this premise, restoring the insulin receptors sensitivity could be a good alternative target for AD. Glucagon- like-peptide-1 (GLP-1) is an incretin hormone that regulates insulin secretion and levels of glucose in blood. As GLP-1 agonists, such as liraglutide, exenatide and lixisenatide, are the standard treatments for T2DM, they might also be an interesting option to treat AD. In addition, other treatments used to treat diabetes like insulin or metformin have been considered and tested in patients with AD.

In preclinical trials, rats administered with insulin enhanced memory in a passive-avoidance task [217]. In humans, intranasal administration of insulin was proposed as an alternative route as it could improve the delivery of insulin to the brain. Phase I clinical trials in healthy subjects demonstrated that this route of administration was safe. In this trial, insulin also improved cognition and memory in the treated subjects [218]. After these encouraging results, several Phase II and III trials have been carried out.

In a pilot study, insulin or placebo was randomly administered to a group of 40 patients diagnosed with AD and 64 with MCI for a four-month period. This study measured the patients’ cognition at different time points, collected CSF samples, and performed positron emission tomography with fludeoxyglucose. The results showed that cognition and memory were improved in patients treated with insulin. Though there was not a significant difference in the biomarker levels between groups, improvements in memory and cognition were attributed to changes in Aβ42 levels [219]. The AD cooperative study had a series of clinical trials named Study of Nasal Insulin to Fight Forgetfulness whose purpose is to assess the effects of insulin in AD patients. In the SNIFF-LONG 21 trial, none of the groups met the expected primary outcome measure, as they did not improve on the verbal composition test, though participants in a higher dose group did improve in visual retention and working

memory. APOE4 carriers assigned to the higher dose group performed better in verbal memory while non-carriers declined.

Metformin is a biguanidine antihyperglycemic drug, used as a first line of treatment for T2DM. Its mechanism of action consists in decreasing hepatic glucose production and intestinal absorption of glucose. It is believed that these effects are because metformin activates AMP-activated protein kinase (AMPK) which regulates lipid and glucose metabolism and increase insulin sensitivity [220].

An in vitro study in N2a695 cell line showed that metformin upregulates BACE1 thus increasing the levels of Aβ. Authors hypothesized that this effect might be mediated by AMPK. They also found that when cells were treated with a combination of metformin and insulin, Aβ decreased, suggesting that metformin alone might worsen effects of AD [221]. In a phase II study, 80 overweight subjects aged 55 to 90 diagnosed with Amnesic MCI were treated with metformin and placebo. The purpose of this study was to evaluate if the administration of metformin improves memory and cognition. Though the trial is terminated, there are no published results yet. An ongoing Phase II pilot study carried on by the University of Pennsylvania is assessing the effect of different doses of metformin versus placebo for four weeks in patients with MCI and AD. This study will evaluate cognition and CSF biochemical biomarkers of AD.

1.4.1.5 Other approaches Patients with cancer are less likely to have AD [222], suggesting that anti- cancer drugs may be beneficial for AD. Hayes et al. therefore screened FDA-approved oncology drugs using CHO cells stably expressing APP751wt to measure changes in the secretion of Aβ [223]. Carmustine decreased secreted Aβ levels and increased sAPPα, without inhibiting -β- and γ-secretases, suggesting that the activity of the drug may arise from altered trafficking and processing of APP. Carmustine, decreased plaque burden in mice, suppressed microglial activation and decreased levels of Aβ40, and increased sAPPα in mouse brains [223, 224].

Minocycline is a lipid-soluble tetracycline-class antibiotic, often used to treat acne and numerous other bacterial conditions. Tetracylin antibiotics were first shown to inhibit A aggregation [225]. Minocycline has anti-inflammatory properties so was investigated in microglia. It inhibited neuronal death and glial activation induced by 48

hippocampal injection of A in rat hippocampus [226]. In a transgenic mouse, it reduced A accumulation, neuroinflammatory markers, and behavioural deficits [227, 228].

Acitretin is a vitamin A retinoid analogue. It is orally delivered to treat the skin disease psoriasis. It promotes activity of retinoic acid receptors which are known to be impaired in AD, possibly causing deposition of A [229, 230]. ADAM10 activity is regulated by retinoic acid. A test of synthetic retinoic acid derivatives found strong enhancement of non-amyloidogenic processing of APP by the vitamin A analog acitretin [231]. Acitretin was tested for activation of α-secretase disintegrin and ADAM10 in patients with mild to moderate AD. Measurement of CSF levels of sAPP, the product of α-secretase on APP, showed a significant increase in CSF sAPPα levels [232].

1.4.2 Drug repositioning with no therapeutic rationale. Drug library screening. In addition to rationale approach of drug repositioning- which means choosing drugs based on their known functions, that have an effect on AD pathology - we used a non-rationale approach. The non-rationale approach involves screening drugs from a wide variety of classes without considering their already known mechanisms of action.

The drug non-rationale approach could be advantageous; it is possible that the drugs have some unknown function and that might have an effect on the particular disease we are looking at, or their known target is unexpectedly involved in AD pathology.

We can narrow down our search by applying simple criteria. For instance, in AD it is probably necessary that a drug should be able to cross the BBB. So, we can narrow down a non-rationale search to those drugs which can cross the BBB.

There are available drug libraries consisting of drugs that have been previously tested in humans and/or are approved for other diseases, such as The Library of Pharmacologically Active Compounds (LOPAC) and the National Center for Advancing Translational Sciences (NCATS) Pharmaceutical collection. The existence of such libraries allowed us to screen a wide number of compounds simultaneously in

one assay in a primary screening, obtaining possible hits that can be later further characterised using other assays.

1.5. Cellular models for AD Cellular models are commonly the initial starting point for studying a disease as they are cost-effective, can be grown and expanded relatively easily, and allow us to elucidate the underlying pathology of the disease by modifying specific proteins or pathways at a time. This can be achieved by techniques such as knocking out specific genes or overexpressing specific genes. They also allow us to screen compounds as possible therapeutic agents and give us insights about their possible mechanisms of action. In the following sections, we will discuss some of the common cell lines used in AD.

1.5.1.1 PC12 PC12 cells were developed in the mid 70’s and are derived from the rat adrenal pheochromocytoma with an embryonic origin. PC12 are attractive model for neurodegenerative diseases as they can be differentiated into neuron-likes cells when treated with nerve grow factor or dexamethasone. These cells produce neurotransmitters including norepinephrine and dopamine [233]. PC12 cells have been widely used as a model for AD. As Aβ42 is not expressed in this line neurotoxicity of the peptide has been studying by adding it to the culture [234].

1.5.1.2 Stem Cells In 2006, Yamanaka’s lab was able to re-program adult cells to a stem cell-like state. The protocol used showed that fibroblast cells were re-programmed to induce pluripotent stem cells (iPSCs) by introducing four pivotal genes that encode transcription factors Oct4, Sox2, Klf4 and cMyc [235, 236] . This discovery allowed the generation of iPSCs cells from patients with a known disease phenotype and/or genotype. Since then, several iPSCs lines for AD have been generated from patients carrying different mutations in genes related with AD such as PSEN1 or APOE4ref.

Although the use of iPSCs sounds promising to further study AD, it is still challenging. For instance, in some cases the iPSCs show a weaker phenotype than expected. Compared to other human cell lines, they are more difficult to culture and

they considerably more expensive. Also, they can be expanded only a few times before starting losing its features thus limiting their use for drug screening.

1.5.1.3 SH-SY5Y cell line The SH-SY5Y semi-adherent cell line is one of the three sublines of the SK- N-SH cell line. SK-N-SH was originated from the bone marrow biopsy of a four-year- old female with neuroblastoma. SK-N-SH has three subclones with different phenotypes: neuronal (N type), Schawannian (S type) and intermediary (I type) [237]. SH-SY5Y belongs to N type and expresses neuroblastic characteristics. This cell line shares some biochemical and physiological similarities with human neurons, specifically it shows a dopaminergic phenotype [238]. They express tyrosine and dopamine- β-hydroxylases, synthesise dopamine and its transporter [239]. Also, they express neurofilament proteins, opioid, muscarinic receptors, and nerve growth factors receptors [239].

Because they have similarities with human neurons, SH-SY5Y cells have been widely used in neuronal functional studies, such as neurodegeneration, and as in vitro models for Parkinson’s disease and AD.

SH-SY5Y can be differentiated by the addition of retinoic acid (RA) [240]. RA modifies SH-SY5Y cells biochemically and structurally making them more similar to human neurons compared with undifferentiated cells [240]. Moreover, differentiated cells show a close cholinergic neuronal phenotype [240]. A study comparing the susceptibility of differentiated and undifferentiated SH-SY5Y cells to

Aβ42 showed that differentiated cells are more resistant to Aβ42 in comparison with undifferentiated cells [241]. Because cells differentiated with RA develop tolerance to the neurotoxic effects of Aβ42 they are not suitable for neurotoxicity studies [242]. We decided to use non-differentiated cells as our in vitro model as they will provide a more reliable, faster and cost-effective model to assess the effect of drugs against Aβ42 toxicity than differentiated cells.

We also used a transgenic SH-SY5Y line transfected with the Swedish mutant of APP695. This model showed an increased expression of Aβ compared to the APP695 wild type [243].

1.6. Animal Models for AD Animal models have been used to characterise the pathology of many diseases by identifying disease pathways. They have also been used to identify compounds that could be used as possible treatments in humans and study their pharmacokinetics and pharmacodynamics. A good animal model is where the phenotypic and genotypic features in humans are observed. Animal models can be classified in three categories: natural/ spontaneous models, transgenic models and interventional models. Natural models refer to human diseases that are analogous in the animal model chosen to study the disease [244]. Transgenic models are those that are genetically modified to express certain genotype that mimics the interested human pathology. Interventional models are those where an external substance related to the disease is administered to the animal model to prompt the desire genotype. In the following section, we will describe the natural/ spontaneous and transgenic models.

AD is very complex and the full underlying pathology has not been completely elucidated thus making the use and development of AD animal models quite challenging.

1.6.1 Natural/ Spontaneous models There are a few natural models that have been identified for AD that show some of the pathological features observed in AD. For instance, dogs, cats, goats and primates like the Rhesus can develop plaques and tauopathies and in some cases, show cognitive deficits [245]. From these animals, the dog has been pointed to as one of the most suitable models to study AD as its pathological features are very similar to the ones shown in AD. For instance, the canine Aβ42 amino acid sequence is identical to the human one. Additionally, cognitive deterioration can be related to that one shown in early AD in humans [246]. Despite these similarities to AD, the used of dogs is widely limited as their study is very expensive and there are many ethical concerns that hamper the use of dogs for AD. The research in dogs is mainly reserved to pre-clinical trials to evaluate if the safety and efficacy of the compound seem reliable enough to proceed to human trials.

1.6.2 Transgenic models Molecular advances have helped to identify genes implicated in the processing of APP and genes related to FAD and transgenic models have been developed based on these discoveries.

1.6.2.1 Mouse models Mouse models are the most widely animal model for Alzheimer’s disease. The first animal model to be developed was the PDAPP which expresses human APP carrying the Indiana familial AD mutation (V717F). This model supports the amyloid cascade hypothesis as it shows similar pathogenesis to AD including: Aβ deposition and hippocampal atrophy, and cognitive deficits [247]. There are several mouse models but most of them only exhibit Aβ related abnormalities without tau hyper- phosphorylation or NFTs [248, 249]. The most complete mouse model is the triple transgenic model (3xTg-AD) which expresses amyloid plaques and NFT’s in the same regions as human brains with AD. This model was developed by harbouring three mutant genes: βAPP, PSEN1 and tauP301L [250].

1.6.2.2 Rat models Mice are the most popular rodent model for AD. This is because the complexities involved in generating transgenic rat models compared to mice. For instance, the survival of rats after transgene injections is lower than that of mice [251]. On the other hand, rats perform better in cognition tasks and they display a more complex social behaviour than mice [252]. This makes rats a more reliable model to study the deterioration of cognition that is characteristic in AD.

The first transgenic rat model for AD was developed in 2004. This model carried APP6swe mutation, but the rats did not display amyloid plaques and better performance in cognitive tasks compared to controls [253]. Most of the transgenic rat models carried one or more APP mutation in Wistar or Sprague Dawley rats. These models include McdGill-R-Thy1-APP, Tg6590, APP21 and Tg11587. However, these models do not consistently display the expected AD hallmarks compared to equivalent mice models [254]. The TgF344-AD model which express APPsw and PS1 does show age-dependent amyloidosis that precedes tauopathy in agreement with the amyloid cascade. Additionally, rats show loss of cognition and neuronal death in the cerebral

cortex and hippocampus[255] . The completeness of this model makes it a robust alternative to study AD.

1.6.2.3 Drosophila melanogaster Modelling AD has been challenging as many of the genes involved in the generation of Aβ are not conserved. Research has confirmed that flies possess APPL which is a fly homolog of the human APP and it is also located in neuronal tissue [256, 257]. Additionally, D. melanogaster has a -secretase that cleaves human APP. Because of differences between the human APP and the lack of an enzyme that acts as a β-secretase [245] Aβ is not naturally produced in fruit flies.

The use of fruit flies to model AD is appealing compared to other animal models such as mouse for many reasons. Similarly to humans, the fruit fly brain is organized in sections with well-defined cognitive and olfactory areas [258]. It is also possible to evaluate motor function by conducting climbing assays and Pavlovian olfactory conditioning assays as a way to measure neurodegeneration [259]. Compared to mouse models they cheaper to maintain, have a shorter life span which speeds-up the generation of results and there are not many ethical concerns around the number of flies used per experiment [260, 261]. Several groups have taken advantage of these features and have chosen fruit flies to perform drug screening. For instance, a fly fruit model overexpressing Aβ42 identified that curcumin reduces Aβ toxicity by reducing Aβ oligomers formation [262].

Some transgenic models focus in creating a complete APP- processing model by using flies encoding human APP and BACE1. These models allow the processing and expression of Aβ. In these flies Aβ aggregation is observed. Additionally, the mutations shortened the life-span of the flies [263].

Another approach is to directly modify the fly to express Aβ42. This is achieved by fusing the Aβ sequence with a secretion protein. These flies showed an age-related Aβ accumulation, lifespan reduction in a dose-dependent manner and defective locomotor behaviour [264]. These models are mainly used to explore the toxic effects of Aβ. Additionally, they have been successfully used as a platform for drug screening.

In this thesis, we used the Aβ model developed by Crowther et al. To develop this model, they fused Aβ40, Aβ42 or the Arctic form of Aβ (Glu22Gly) with a necrotic secretion signal peptide. They used the pan-neuronal driver elav-GAL4c155 to generate the expression of the Aβ40, Aβ42 or Arctic Aβ42. They found that the expression of Aβ42 or Arctic Aβ42 decreased life-span in a gene dose dependent fashion, showing age-dependent neurodegeneration. Additionally, they found that Aβ accumulates over age [264]. We considered this was a suitable in vivo model as the in vitro assays conducted in this thesis were focused on the toxicity of Aβ42. The full pathology of AD has never been observed in species that are suitable for laboratory experiments.

1.7. Project Aims The aim of this project was to screen a tailored drug library to assess if any of the compounds could be a candidate for treating AD. The tailored library was composed of drugs that have gone through clinical trials and are approved for different therapeutic indications, and 5 more drugs that have previously identified possible treatments for AD. To achieve this aim, we first screened the drug library in SH-SY5Y cells treated with Aβ42 to mimic the neurotoxic effect of this peptide in AD. We used different viability assays, immunoassays (in SH-SY5Y APP695) and oxidative stress assays to evaluate whether any of the compounds from the drug library reverse the cytotoxicity caused by Aβ42. We also used a Drosophila melanogaster model expressing Aβ42 to evaluate if any of the hits identified from the in vitro assays could increase the longevity in this model.

The specific aims of this project were:

1) To evaluate and optimise the suitability of different cell viability assays, immunoassays and oxidative stress assays to screen the drug library. (Chapter 2) 2) To perform a primary screening of the drug library using MTT in SH- SY5Y cells treated with Aβ42. To screen the drug library in SH-

SY5YAPP695 using immunoassays to measure the concentration of secreted Aβ species, sAPPα and sAPPβ. (Chapter 3) 3) To assay the hits found in the primary screening at a range of concentrations using the MTT assay. To evaluate whether a combination of drugs has a synergetic effect. To evaluate if the hits identified in the primary screening reduce oxidative stress in SH-SY5Y cells treated with Aβ. (Chapter 4) 4) To evaluate if the hits from the in vitro assay increase the longevity of Drosophila melanogaster expressing Aβ42 (Chapter 5)

Chapter 2. Optimisation of in vitro assays

Cell-based assays became a popular tool in early stages of drug development. This is because, compared to molecular assays, they offer a more faithful representation of diseases, and a better predictability of drug performance in further stages. In addition, they are amenable for high-throughput screening of drugs in a cost- effective fashion.

As we described in Chapter 1, the toxicity of Aβ aggregates plays a key role in AD pathology. Many cellular-based assays have been developed around this hypothesis to find possible candidates to treat AD. We used either SH-SY5Y cell line treated with a low concentration Aβ42 or SH-SY5Y expressing the Swedish mutant of APP695, as an in vitro model for AD to mimic Aβ accumulation and neurotoxicity in the human brain.

This chapter describes the development and optimisation of the different primary and secondary assays that we used to screen drugs as potential AD therapies. Normally, primary screenings measure cell growth or cell toxicity after adding a foreign stimulus. Secondary screenings further characterise the mechanism of actions of the drugs selected during primary screenings. For our primary screening assays, we measured cell viability (MTT) and cell toxicity (LDH) of SH-SY5Y cells treated with Aβ42. We also used a sandwich immunoassays coupled with electrochemiluminescence from Meso scale Discovery (MSD). These kits allow us to measure Aβ38, Aβ40 and Aβ40 and sAPPα and sAPPβ simultaneously using SH-

SY5Y APP695 cells. Our secondary assays focused on measuring the shifts of the oxidative stress activity in SH-SY5Y cells treated with Aβ42.

2.1. Methods 2.1.1 SH-SY5Y cell culturing and maintenance. SH-SY5Y human neuroblastoma cells, from European Collection of Cell Cultures (ECACC), were maintained in MEM Earle’s medium/ Ham’s F12 (1:1) supplemented with 10% foetal bovine serum (FBS), L-Glutamine (L-Q), 1% penicillin-streptomycin (P/S), and 1% non-essential amino acids (n-aa). SH-SY5Y cells were cultured in tissue flasks and incubated at 37 °C, 5% CO2 atmosphere. When cells reached ~80% confluency, they were either harvested for cell viability assays or passaged in to new flasks. Cells were used up to passage 20 because after that passage number they start losing their neuron-like characteristics. Cells were harvested by tryptinisation when they reached 80% confluence. Then cells were dyed with trypan blue and counted using a haemocytometer to determine its concentration in the flask and then further diluted at the necessary concentrations to be seeded.

2.1.2 Aβ42 preparation using HFIP and DMSO

The β-amyloid peptide is a recombinant Aβ42 peptide from a DNA sequence encoding the human Aβ42 peptide expressed in E. coli. The Aβ42 peptide was purchased from rPeptide.

Aβ42 lyophilised powder was dissolved in 1,1,1,3,3,3-hexafluoroisopropanol (HFIP) at a concentration of ~1mg/ml and vortexed in three cycles of 30s to mix. HFIP was used as it is a well characterised hydrogen bond-forming solvent commonly used for disaggregating peptides. After adding HFIP, the peptide was incubated at room temperature for 1h to dissolve it completely. Then it was aliquoted into 20 Eppendorf tubes of 50µl (50µg) each. Aliquots were lyophilized by streaming gaseous N2 transforming HFIP into its gas state, leaving the peptide coated onto the wall of the tube. The resulting lyophilized peptide aliquots were stored at -20°C until required.

Anhydride DMSO was added to the lyophilised aliquots of Aβ42 to obtain a concentration of 1M. As DMSO is toxic for SH-SY5Y cells when it is present in concentrations above 1%, this stock was diluted in non-supplemented Opti-MEM without phenol medium to obtain a final concentration of 1μM Aβ42 and 0.1%

DMSO when added to the cells [265]. Opti-MEM was used instead of MEM Earle’s medium/ Ham’s F12 (1:1) as it does not contain phenol which might interfere with colorimetric assays.

2.1.3 Aβ42 oligomers preparation using NaOH 1mg of the Aβ42 peptide purchased from rPeptide was dissolved using 2mM NaOH (pH 10.5) to obtain a concentration of ~1mg/ml. The solution was left at room temperature for ~3min to allow complete solvation and homogenous appearance. Then it was sonicated in a bath-type sonicator for 1 min. The clear solution was then aliquoted out into Eppendorf’s tubes in 20 lots of 50µl (50µg) each, and lyophilized by freeze drying. Aliquots where frozen at -20°C until required.

Aliquots were dissolved in a 1:1 mixture of highly purified water and 20mM phosphate buffer to obtain a concentration of 2mg/ml and sonicated for 1min. The Aβ42 solution was transferred to a Microcon YM-10 filter unit which was previously washed twice with 200μl of 10mM phosphate buffer, pH 7.4, and centrifuged at 16,000g for 20 min. The transferred solution added to the Microcon YM-10 filter was centrifuged at 16,000g for 30min. The filtrate containing Aβ42 oligomers was dissolved in non-supplemented Opti-MEM without phenol medium to obtain a final concentration of 1μM Aβ42 when added to the cells [266].

The presence of oligomers following the above protocols was confirmed by Olivia Berthoumieu using Thioflavin T (ThT) and Atomic Force Microscopy (AFM).

2.1.4 MTT (Optimised method) 100μl of SH-SY5Y cells were seeded in 96-well plates at different concentrations ranging from 105 to 106 cells/ml in MEM Earle’s medium/ Ham’s F12 (1:1) supplemented with 10% FBS, 1%L-Q, 1% n-aa, and 1% P/S

The cells were incubated for ~40h in a humidified incubator at 37°C with 5%

CO2. For the positive controls, media was replaced with 50 μl of 0.1% Triton X to kill the cells. For the rest of the wells, 50μl of media was removed and 10μl of sterile MTT (2.5mg/ml) was added to each well. The cells were incubated for 3hr at 37°C with 5%

CO2. Then, 100μl of acid-isopropanol (stock solution 100ml of isopropanol and 398μl of HCl 37%) was added. To allow solubilisation of the formazan crystals, the bottom of the wells was scraped with the micropipette tip and mixed thoroughly. The plates

were covered with foil and placed in a plate-shaker for 15 min. The absorbance of the plates was measured using a Tecan Infinite M200 Pro microplate reader at 570nm.

2.1.5 Evaluation of Aβ42 oligomers toxicity in SH-SY5Y by MTT 100μL of SH-SY5Y cells were seeded in 96-well plates at a concentration of 3x105 cells/ml using MEM Earle’s medium/ Ham’s F12 (1:1) supplemented with 10% FBS, 1%L-Q, 1% P/S, and 1% n-aa The cells were incubated overnight in a humidified incubator at 37°C with 5% CO2 to allow the cells to attach to the well. After incubation, the media in each well was replaced with 100μL of medium containing different concentrations of Aβ42 oligomers prepared using either NaOH or DMSO. The cells were incubated for 24hr. Then the MTT assay was performed as indicated in Section 2.4. Percentage of MTT reduction (cell viability) was calculated as:

X-A % MTT reduction= x 100% B-A

Where X is the absorbance value of each well, A is the mean absorbance of the blank, and B is the mean absorbance of the non-treated cells.

2.1.6 LDH (Cyto Tox-OneTM assay Promega) After the treated cells, were incubated overnight without treatment and then treated for 24h, 2µl of 9% w/v Triton-X solution was added to the positive control wells (maximum LDH release). Then the plate was equilibrated to room temperature for 30 min. 100µl of CyTox-ONETM solution was added to all the wells and incubated at room temperature for 10 min. Then 50µl of stop solution was added to all wells, and read at an excitation 560mn and 590 emission wavelengths using a Tecan Infinite M200 Pro microplate reader. The % of LDH released was calculated as follows:

X-B % of LDH= x 100% M-B

Where X is the fluorescence value in each individual well, B is the mean fluorescence of the culture media background (wells without cells) and M is the mean fluorescence of the positive control 100% LDH release.

Figure 2.1 Summary of CytoTox-One protocol

2.1.7 CytoTox-GloTM After incubating the SH-SY5Y cells in 96-well plates for the desired time and for the desired treatment the CytoTox-GloTM assay was performed as per manufacturer instructions. For measuring dead cells, 50µl of assay reagent were added to all wells incubated at room for 15 min. The luminescence of the plate was read in a Promega Glo-Max-Multi Detection system for measuring the total number of cells in each well plate, 50µl of lysis buffer were added, then the plate was incubated for another 15 min and the luminescence was read in the luminometer again. Figure 2.2 summarises Cyto Tox-GloTM protocol

To calculate the percentage of cell toxicity, the following formula was used:

푇 − 퐼 % of cell toxicity= x 100 B - I

where T is the luminescence signal of the total number of cells I is the experimental signal of dead cells and B is the average signal for 100% viable/control cells.

Figure 2.2 Summary of the protocol for CytoTox-GloTM

2.1.8 Meso scale Discovery system

SH-SY5Y APP695 cells which overexpress Aβ, were kindly provided by the Hooper laboratory. These cells were cultured and maintained the same way as un- transfected SH-SY5Y cells as described in Section 2.1.1.

When SH-SY5Y APP695 cells reached 80% confluence they were harvested by tryptinisation and re-suspended in Opti-MEM medium supplemented with 1% L-

Q, 1% and 1% n-aa. SH-SY5Y APP695 cells were dyed with trypan blue and counted 4 with a haemocytometer. SH-SY5Y APP695 were seeded at a 5x10 cells/well in a 96- well plate. Cells were incubated overnight at 37 °C, 5% CO2, to allow cell adherence. Then 150µl of 10µM DAPT (ϒ-secretase inhibitor), 5µM β-secretase inhibitor IV (βIV) (BACE1 inhibitor), or 10µM Carbachol (α-secretase activator) dissolved in non- supplemented Opti-MEM were added to the assigned wells. All drugs were dissolved in DMSO at the concentrations indicated above and had a final concentration of 0.5% DMSO. Cells were returned to the incubator for another 24 h. 120µl media sample was removed for sAPPα/β and Aβ analysis. 12µl of HEPES MSD buffer (500mM HEPES, pH 7.3) was added to each well before being run on sAPPα/β and Aβ peptide panel MSD plates.

For measuring Aβ38, Aβ40 and Aβ42 a MSD Aβ peptide panel was used. First, the plate was blocked by adding 300µl of diluent 35 to each well, the plate was sealed and incubated for 1h at room temperature in a plate shaker. Diluent 35 reduces matrix interferences, and help buffer any pH changes that have occurred when culturing or collecting media samples. The plate was washed 3 times with wash buffer. Then 25µl of detection antibody (6E10) were added to each well. 25µl of the sample was added to each well, the plate was sealed and incubated for 2h at room temperature. The plate was washed 3 times with wash buffer. 150µl of 2X read buffer was added to each well. The plate was read on a MSD reader. The read buffer provides the adequate environment for electroluminescence.

For measuring sAPPα/sAPPβ a MSD 96-well MULTI-SPOT sAPPα/sAPPβ assay was used. The plate was blocked by adding 150µl of 3% Blocker A solution and incubated for 1h at room temperature using a plate shaker. The plate was washed 3 times with 300µl/well of 1X Tris Wash Buffer. 25µL of sample were added to each well. The plate was incubated at room temperature in a plate shaker for 1 hour. The plate was washed three times with 300µl/well of 1X Tris Wash Buffer. 25µl of detection antibody (Anti-APP) was added per well and incubated at room temperature for 1 hour in plate shaker. The plate was washed three times with 1X Tris Wash Buffer. 150µL of 1X Read buffer was added to each well. The plate was incubated at room temperature for 10 mins and the plate was read on QuickPlex SQ 120.

Data were normalised by the MSD Work bench analysis, which uses a 4- parameter logistic curve fitting model.

푡표푝 − 푏표푡푡표푚 푦 = 푏표푡푡표푚 + 푋 ℎ𝑖푙푙 푠푙표푝푒 1 + ( ) 퐸퐶50

2.1.9 DCFH 100µl of SH-SY5Y cells at 3x105 cells/ml per well were seeded in MEM Earle’s medium/ Ham’s F12 (1:1) supplemented with 10% FBS, 1%L-Q, and 1% n- aa penicillin-streptomycin in a 96-black plate. The cells were incubated overnight at

37°C with 5% CO2 to let the cells attach to the bottom of the black 96-well plate. The cells were incubated for another 24h or treated with Aβ42 at different concentrations. A mother stock of DCFH at 100M in DMSO was dissolved in PBS to achieve a concentration of 100µM DCFH and 0.1% DMSO. The media was replaced from all wells with the diluted DCFH solution and the plate was returned to the incubator for 30min. Afterwards, each well was washed with 200µl of PBS to eliminate fluorescence coming from the media and ensure the measured fluorescence was coming from the cells only. The fluorescence was read using a Tecan Infinite M200 at an excitation of 480nm and 530nm emission. Figure 2.3 summarises DCFH protocol. Data was normalised using the following formula:

X-A % DCF fluorescence = x 100% B-A

Where X is the fluorescence value of each well, A is the mean fluorescence of the blank, and B is the mean fluorescence of the non-treated cells

Figure 2.3 Summary of the protocol to perform DCFH assay. After the 24h incubation of the cells with drugs and/or Aβ42 cells treated with 100µM DCFH-DA for 30 mins. After adding DCFH wells will show a greenish colour depending on the number of damaged cells. Wells are a washed with PBS twice to remove possible DCFH auto-florescence from the media. Then the plate was read at an excitation wavelength of 480nm and emission of 530nm.

2.1.10 ROS-GloTM 100µl of SH-SY5Y cells at 1x105 cells/ml were seeded per well in MEM Earle’s medium/ Ham’s F12 (1:1) supplemented with 10% FBS, 1%L-Q, and 1% n- aa penicillin-streptomycin. The cells were incubated overnight at 37°C with 5% CO2 to let the cells attach to the bottom of the white 96-well plate. Then the media from the wells was replaced with 80µl of non-supplemented media of either 1, 2.5 or 5µM of Aβ42, 0.5% DMSO or 25µM menadione, and the cells incubated for 3h or 19h depending of the selected endpoint. Menadione, has previously showed to increase ROS in a dose-dependent manner in mammalian cells so we selected it as control [267,

268]. After the desired incubation, 20µl of H2O2 substrate solution was added to each well including blanks. Then the plates were returned to the incubator for another 5 hours. 100µl of detection solution were added to each well and the plates were incubated at room temperature for 20 min before reading the luminescence using Promega Glo-Max-Multi Detection system.

2.1.11 GSH/GSSG- GloTM assay 100µl of SH-SY5Y cells at 1x105 cells/ml were seeded in MEM Earle’s medium/ Ham’s F12 (1:1) supplemented with 10% FBS, 1%L-Q, and 1% n-aa penicillin-streptomycin, and 1% L-Glutamine and incubated overnight at 37°C with

5% CO2 to let the cells attach to the bottom of the 96-white well plate. The cells were incubated with 0.5% DMSO or with Aβ42 at 1, 2.5 or 5µM for 24h. For this assay, there were two sets per triplicate for each the treatment — one set was used to measure total glutathione and one set to measure oxidized glutathione. After the incubation period, the media containing the treatments was replaced with 50µl of Total Glutathione Lysis Reagent or Oxidized Glutathione Lysis Reagent, as appropriate for each set. The plate was agitated in a plate shaker for 5 min at room temperature. 50µl of Luciferin Generation Reagent was added to all wells and the plate was incubated for 30 mins at room temperature. 100µl of Luciferin Detection Reagent was added to each well. The plate was incubated for 15 mins before the luminescence was read in a Promega Glo-Max-Multi Detection system. The data was normalised to GSH/GSSG ratio using the following formula

T-O GSH/GSSG= O/2

Where T is total glutathione RLU, O is oxidized glutathione. The oxidized concentration of glutathione was divided by two because when a mole of GSSG is reduced it produces two moles of GSH.

2.2. MTT 2.2.1 MTT principle MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide) assay is a robust and economic method to measure cell metabolism in a high throughput fashion. The assay principle is based on measuring the cell capacity to reduce MTT into formazan. MTT is a yellow positively charge tetrazolium salt that is able to penetrate viable eukaryotic cells [269]. The exact mechanism by which cells are able to reduce MTT is not completely elucidated, but evidence points to dehydrogenase enzymes in the mitochondria as the main responsible agent of this reduction [270] (Figure 2.4.A). Because mitochondrial activity depends on cell metabolism, the reduction of MTT (yellow) into formazan (purple) is a straightforward colorimetric method to measure cell metabolism. There is a direct correlation between the metabolic activity and the concentration of formazan[269] (Figure 2.4.B). Recently, it has been demonstrated that MTT is reduced in other parts of the cell besides the mitochondria, including the cytoplasm, endosomes and lysosomes[271, 272].

Figure 2.4 MTT principle A) Yellow MTT is reduced into purple formazan by reductase enzymes. B) The reduction of MTT into formazan is directly proportional to the number of viable cells in the culture and can be measured by the intensity of light at 570 nm wavelength. MTT is routinely used to study the in vitro toxicity of different Aβ species (oligomer and/or fibrils). Most studies agree that treating SH-SY5Y with Aβ decreases MTT reduction into formazan and that Aβ toxicity is concentration dependent.

Additionally, studies show that at concentrations below ~20µM, Aβ decreases MTT reduction without necessarily causing cell death [138], [273].

Reducing compounds, such as ascorbic acid, can reduce MTT to formazan, increasing formazan concentration, thus causing interference with the actual viability readings [274, 275].

2.2.2 Optimisation of MTT assay We optimised the MTT protocol as the one we were following [265] gave us low Optical density (O.D) readings and high variations between samples with the same treatment. We evaluated different parameters, such as: replacement of fresh medium before incubation with MTT and replacement of solubilisation solution after cells incubation with MTT, which according the literature [276, 277], are critical to optimise an MTT assay. Figure 2.5 summarises the parameters assessed.

Figure 2.5 A) (black arrows) Initial MTT protocol. B) (red arrows) After initial incubation, old medium was replaced with new medium before adding MTT (yellow colour). C) (purple arrows) After 3h incubation with MTT, medium with MTT was replaced with 160µl of isopropanol. D) (green arrows) After adding 100µl isopropanol we scraped the bottom of the well with the pipette tip and the we mix thoroughly to allow the purple colour from formazan to dissolve in isopropanol.

According to Sylvester [72], the addition of fresh medium before adding MTT to cells (Figure 2.5.B) is very important to ensure that cells will have enough nutrients to perform their metabolic functions, particularly to convert MTT into formazan 5 crystals. Based on this premise, 100μl of SH-SY5Y cells at a concentration of 4x10 cells/ml were seeded in a 96-well plate. Cells were incubated for 24hr in a humidified incubator at 37°C with 5% CO2. After incubation, cells were divided in two different groups. In the first group, all the medium was removed from the wells and replaced with 50μl of fresh Opti-MEM without phenol red supplemented medium. In the second group, just 50μl of the medium was removed from the wells. Afterwards, 10μl of MTT

(2.5mg/ml) was added to the wells and the assay continued as indicated in Figure 2.5.B. As shown in Figure 2.6.A, similar OD measurements were obtained for both groups. The addition of fresh medium before adding MTT assay does not increase OD readings compared with leaving old medium (p >0.05). This result suggests that in SH-SY5Y cells at a concentration of 4x105 cells/ml, nutrients are not depleted after 40 hours. Based on these results we decided that in further MTT assays the medium will not be replaced with fresh medium before adding MTT.

Other authors suggest that after the incubation of the cells with MTT it is necessary to completely replace the medium with the solubilisation solution [269, 278]. The rationale is that the medium may interfere with the OD measurements of the samples and that the removal is needed to fully solubilise the formazan crystals (Figure 2.5.C). In contrast, some studies indicate that the solubilisation solution must be added to the sample without the removal of the medium, and that thorough agitation is indispensable to dissolve formazan crystals and obtain reliable and homogenous OD measurements (Figure 2.5.D) [277]. We decided to test if there was a difference between these procedures. For these purposes, 100μl of SH-SY5Y cells at a concentration of 4x105 cells/ml was seeded in a 96-well plate. Cells were incubated for 40hr in a humidified incubator at 37°C with 5% CO2. Then MTT was added to the wells and the plates incubated for 3 hrs. After the incubation with MTT, cells were divided in two different groups. In the first group, the medium with MTT was removed and replaced with 160μl of solubilisation solution (isopropanol) (Figure 2.5.C). In the second group, 100μl of isopropanol were added to the medium with MTT and mixed thoroughly (Figure 2.5.D). After this step, the wells showed a purple coloration, but the coloration was more intense when isopropanol was added to the media compared with isopropanol only. The plate was covered in foil and placed in a shaker. After 15 min, we checked the plates visually and in both cases the purple coloration was more intense than just after the addition of isopropanol so we proceeded to read the plates. As Figure 2.6.B shows, we obtained a much better O.D measurement when the isopropanol was added to the media, the wells were mixed thoroughly and the bottom part of the well was scraped with the micropipette tip. After reading the plate at 570nm, we cover it with foil again and placed in a shaker for 6 hours as our initial MTT protocol indicated. When the foil was removed, we observed that in both cases the colour has faded considerably and we did not read the plate again. These results

showed that mixing thoroughly and scraping the bottom of the well were key steps to optimise the MTT assay. This is because after incubating SH-SY5Y semi adherent cells with MTT, most of the formazan crystals remain inside the cells at the bottom of the well. After adding isopropanol and scraping the bottom of the wells the cells are detached allowing the formazan crystals to solubilise. After assessing all these parameters, we optimised the MTT protocol as indicated in Section 2.1.4. The optimised protocol reduced the total time of the MTT assay by ~6 hours.

Figure 2.6 A) Effect of changing medium before the addition of MTT. There is no significant difference between adding fresh medium or using old medium before the addition of MTT, p >0.05, n=6, T-test (two sided-way); bars represent means of the groups, error bars represented (SEM). B) Effect of replacing medium with isopropanol after incubation of SH-SY5Y cells with MTT. The O.D measurements are higher when isopropanol is added to medium, compared to replace medium with isopropanol. SEM is also smaller, p<0.001, n=6, T-test(two-sided) bars represent the means of the groups, error bars represent SEM. 2.2.3 Optimal Cell Density for MTT assay After identifying the critical parameters to perform the MTT assay we constructed a density curve to determine the best cell density to perform the primary screening of drugs. We seeded SH-SY5Y cells in a 96-well plate at ten different cell 5 6 densities from 1x10 to 1x10 cells/ml. The cells were incubated for 40hr in a humidified incubator at 37°C with 5% CO2. Then we performed the optimised MTT assay as described in Section 2.1.4, namely after adding MTT we added isopropanol and read the plates at 2, 3 and 4h at 570 nm. The O.D increased between 2 and 3h of incubation after adding MTT (Figure 2.7). At 4h incubation O.D remains similar to 3h reaching a plateau, meaning that cells are not able to further reduce MTT into formazan. Based on this graph we decided that the optimal incubation with MTT was 5 5 3h. Figure 2.7 also shows that O.D increases with seeded density from 1x10 to 4x10 5 5 cells/ml. Between 5x10 and 6x10 cells/ml the O.D increases slowly then it reaches a

5 5 5 plateau from 7x10 to 8x10 cells/ml and finally starts decreasing slowly from 9x10 6 to 1x10 cells/ml.

Figure 2.7 Cell density curve. SHSY5Y cells were incubated for 40h at different cell densities. Isopropanol was added 2, 3 or 4h after adding MTT. Data are represented as the means of the groups; error bars represent SEM, n=6.

5 5 The increase of O.D between 1x10 to 4x10 cells/ml shows that the cells have 5 5 enough nutrients and surface to grow. Between 5x10 and 8x10 cells/ml the cells are depleting the nutrients and reaching 90-100% confluence. The decrease of O.D from 5 6 9x10 to 1x10 cells/ml is because the cells have depleted the medium and started dying; another reason could be that they reached 100% confluence and they do not have space to keep growing. Based on the results of the curve we decided that the optimal cell density to perform our primary screening of drugs would be either 3X105

5 or 4x10 cells/ml. In addition, we took pictures of the well to check the confluence of the cells at different cell densities. Figure 2.8A shows that at cell density of 1x105 cells/ml are around 40-50% confluent. Cells at 3x105 cells/ml (Figure 2.8.B) are 80- 90% confluent, cells at 5x105 cells/ml (Figure 2.8.C) are 100% confluent, in addition we start seeing black dots in the photographs which are dead cells. Cells 9x105 cells/ml (Figure 2.8.D) are fully confluent and we can see more black dots meaning there are even more cells dying. Considering the cell density curve and the photographs at different cell densities, we choose 3x105 cells/ml as the optimal cell density to perform the MTT assay. At this cell density, the O.D and standard error are small. In addition, cells are 80-90% confluent meaning there is no nutrient depletion and there is still surface for the cells to grow

A) B)

C) D)

Figure 2.8 SH-SY5Y cells at different densities incubated for 40 hours A) SH-SY5Y cells at a density of 1x105cells/ml (10% of the surface of the flask is covered by cell monolayer) B) SH-SY5Y cells at a density of 3x105cells/ml (80-85% of the surface of the flask is covered by cell monolayer C) SH-SY5Y cells at a density of 5x105cells/ml 100% of the surface of the flask is covered by cell monolayer D) SH- SY5Y cells at a density of 9x106cells/ml 100% of the surface of the flask is covered by more than one layer. Scale bar=100μm. 2.2.4 Evaluation of Aβ42 toxicity using MTT assay

The final step to optimise MTT assay was to test the toxic effects of Aβ42 oligomers in SH-SY5Y cells. We tested two different protocols to prepare the oligomers, with NaOH [266] or with HFIP. We prepared the oligomers as indicated in Sections 2.1.2 and, 2.1.3 and we performed the MTT assay as indicated in Section 2.1.5.

We evaluated the toxicity of four different concentrations of Aβ42: 5µM, 2.5µM, 1µM and 0.5µM. We achieved similar effects with both protocols without significant differences between them at the tested concentrations. For both oligomer preparations methods (NaOH, HFIP), the toxicity was concentration dependent. Aβ42 preparations at 5 µM were the more toxic ones reducing cell viability to ~40%, followed by 2.5 µM ~50% MTT reduction, 1µM ~60% MTT reduction and 0.5µM ~80% (Figure 2.9). We decide to continue preparing Aβ42 oligomers with HFIP, as

results showed that both protocols caused similar inhibition of MTT reduction. This would also make it easier to relate our findings to previous works that followed the HFIP protocol.

One of the limitations of cellular-based and other in vitro assays, is that the concentration of Aβ in vitro needed to show an effect is considerably higher than the actual concentration in patients with AD. For this reason, we chose 1µM as the concentration for further assays, as it was the lowest concentration that caused inhibition of MTT reduction. This choice would increase the predictability of efficacy of the screened drugs in further stages of drug development.

Figure 2.9 Evaluation of Aβ42 oligomers toxicity in SHSY5Y cells using MTT. SH-SY5Y cells were treated with Aβ42 for 24hr. The Aβ42 was prepared either with NaOH (blue) or HFIP (red). There was no significant difference between groups at any of the tested concentration. p > 0.05, n=3, T-test (two- sided), bars represent means of the groups, error bars represent SEM. 2.3. LDH principle When the cell membrane is damaged, there is a leak of enzymes from the cytoplasm to the culture medium. LDH is one of these enzymes and its leakage is used as an indicator of non-viable cells in the CytoTox-ONE (Promega assay). This assay uses coupled enzymatic reactions to measure the release of LDH. First, an excess of lactate and NAD+ is added to the media resulting in the conversion of lactate to pyruvic acid as it converts NAD+ to NADH. Then diaphorase and resazurin are also added causing the NADH to convert resazurin to the fluorescent resorufin by the

catalytic action of diaphorase (Figure 2.10). The concentration of resorufin is proportional to the concentration of LDH released.

Figure 2.10 CytoTox-ONE (Promega) principle. Released LDH is measured by coupled enzymatic reactions using lactate, NAD+, resazurin and diaphorase to produce fluorescent resorufin which is proportional to the concentration of LDH (Modified from [279]). 2.3.1 LDH optimisation First, we decided to find the most appropriate cell density to perform this assay by creating a cell density curve. We seeded SH-SY5Y cells in 96-well plates at different densities from 1 to 5x105cells/ml, with six replicates for each cell density and incubated for 4h. Then we performed the LDH assay as indicated in Section 2.1.6. We added lysis buffer to 3 wells for each density to be referred as a positive control (maximum LDH release).

Figure 2.11.A shows that there is a linear relationship between the cell number and fluorescence when cells are treated for LDH maximum release (positive control). The fluorescence for the non-treated cells remains almost the same regardless of cell density. This linearity indicates the sensitivity and reliability of the assay at different cell densities. We decided to use 3x105cells/ml as it is the same cell density we are using for MTT assays.

After choosing a cell density, we tested the toxicity of Aβ42 at the following concentrations: 50, 25, 10, 5, 2.5, 1 and 0.5 µM. To perform this assay, we kept the concentration of DMSO constant at 0.5% which is not toxic to cells. The toxicity of Aβ42 remains low (~5%) when cells are treated with 0.5,1 or 2.5µM Aβ42, but the toxicity increases to ~15% when cells are treated with 10µM Aβ42 and then increased linearly to 45% when cells are treated with 50µM Aβ42 (Figure 2.11.B). These results

suggest that LDH is not as sensitive as MTT to detect Aβ42, because MTT measures cell viability through damage in the mitochondria and LDH measures cells toxicity due to the disruption of cell membrane, as an indicator of cell death.

We decided that the LDH was not adequate as an assay for testing the drugs as it is necessary to use high concentrations of Aβ42 to achieve toxicity, thus reducing the predictability of efficacy of drug hits in further assays.

Figure 2.11 LDH Optimisation. A) We seeded SH-SY5Y cells at different cell concentrations. After 40 h of incubation we perform the LDH assay. Cells treated for maximum LDH release with triton-X (blue) showed a linear relationship between cell density and fluorescence intensity (r2>0.99). Cells not treated barely increased fluorescence when cell density was increased. Error bars represent SEM, n=3.B) 3x105 SH-SY5Y cells were treated with different concentrations of Aβ42 from 0.5 to 50µM. Aβ42 toxicity gradually increased with the concentration. 2.4. CyToTox-GloTM The CytoTox- GloTM assay from Promega allow us to measure the number of dead cells in the cell population. This assay measures “dead-cell proteases” that release into the media when the cells are damaged. In contrast with the CytoTox-One™ assay which only measures LDH, CyTox-Glo™ measures the concentration of other proteolytic enzymes from the cytoplasm, also released when cell damage occurs. Based on inhibition studies tripeptidyl peptidase seems to be a dead-cell protease.

The CytoTox-Glo™ assay uses alanyl-alanylphenylalanyl-aminoluciferin (AAF-Glo™) as a substrate to measure “dead-cell protease”. As this substrate cannot cross the cell membrane the signal obtained from this assay only corresponds to dead cells. The AAF-Glo™ is cleaved by the “dead cell protease” into aminoluciferin, which is a substrate for luciferase thus generating a luminescent signal proportional to the amount of “cell-dead proteases” (Figure 2.12).

Figure 2.12 CytoTox-Glo™ assay principle AAF-Glo™ is cleaved by the dead-cell protease. Then the aminoluciferin works a substrate for Luciferase generating a luminescent signal We decided to try CytoTox-Glo™ for two reasons: First, this assay is so sensitive that it can detect as little as 200 dead cells regardless of the cell density. In addition, we can estimate the signal of the total cells in each well. After measuring the activity of the “cell-dead protease” in the assay, we can add a lysis buffer and obtain the total signal of the cells in each well. With this method, we can estimate the viability of each well by subtracting the luminescent signal of the treated cell death from the signal of all the cells in the well.

2.4.1 Optimal cell density We seeded 500, 1000, 10 000, 30 000 and 50 000 SH-SY5Y cells in triplicate in a 96-well plate. We incubated them for 40h. After incubation, we added 50µl of lysis buffer to all wells and mixed in a plate shaker for 1 min. Then we added 50µl of assay reagent to all well plates, incubated for 15 min at room temperature and proceeded to read the results in a plate reader.

The objective of this assay was to determine the linear range for SH-SY5Y cells in the CytoTox- GloTM assay. We found a linear relationship up to cells seeded at 10,000 per well. After this cell density, the linearity is lost and the signal reaches a plateau at 30,000 and 50,000 cells per well (Figure 2.13). Based on this data we decided to use 10,000 cells per well as the optimal cell density for this assay.

Figure 2.13 Cell density curve for CyTox-GloTM. We seeded SH-SY5Y cells at different cell densities from 500 to 50,000 cells/well. There is a linear correlation for cells from 500 to 10000 per plate (r2>0.99). The curve reaches a plateau at 30,000 cells per well plate. Error bars represent SEM, n=3.

2.4.2 Experimental sensitivity The objective of this experiment was to determine the sensitivity of this assay. We harvested the SH-SY5Y cells and divided them into pools with a density of 100,000 cells/ml- one for viable cells (vial A) and the other for the dead cells (vial B). We sonicated vial B at an amplitude of 25%, for a 10 second pulse, 10 sec pause for 1 min to disrupt cell membranes. After sonication, we took a small aliquot of solution B and mixed in trypan blue to confirm cell disruption. For 100% viability, we took 1 ml cells from vial A. For 50% viability, we added 0.5 ml from vial A and 0.5 ml from vial B, and for 0% viability, we took 1ml of the cells from vial B. We added 100µl of each experimental group in a 96-well plate in triplicate. The number of live cells for each group were as follow 100%= ~10,000 cells/well, 50%= ~5,000 and 0%= ~0%. Immediately after seeding the cells we added the 50µl of assay reagent to all well plates, incubated for 15 min at room temperature and proceeded to read the results in a plate reader.

After normalising the data, we found that the mean cell viability values corresponded well to the expected values (Figure 2.14). These results proved the sensitivity of the CytoTox-GloTM as a cell toxicity assay.

Figure 2.14 Experimental sensitivity of CytoTox-GloTM. SH-SY5Y cells were mixed in different proportions with dead cells to test the sensitivity of the assay. The values of the experimental percentages correspond to the expected percentage value. Bars represent means of the groups, error bars represent SEM,n=3.

2.4.3 Evaluation of the toxicity of Aβ42 using CytoTox- GloTM SH--SY5Y cells were seeded at a cell density of 10,000 cells/well and incubated overnight. Then, given the sensitivity showed by the assay, cells were treated with either 1 or 5µM of Aβ42 and incubated for 24h. Then we performed the CytoTox-GloTM as indicated in Section 2.1.7.

The results showed that Aβ42 was slightly toxic at both 1 or 5µM, but not enough to be significantly different from non-treated cells (Figure 2.15). These results agree with what we previously found with LDH assay, suggesting that to disrupt the cell membrane integrity a higher concentration of Aβ42 than tested here is necessary.

Figure 2.15 Aβ42 toxicity measured by CytoTox-GloTM. SH-SY5Y cells were seeded at 10,000 cells per well. Then they were treated with either 1 or 5 µM Aβ42. There was no significant difference between groups at any of the tested concentration. p > 0.05, n=3, ANOVA(one-sided) Dunnett’s post hoc test, bars represent means of the groups, error bars represent SEM. 2.5. MSD immunoassay principle The MSD MULTI-SPOT plate is a sandwich immunoassay coupled with electrochemiluminescence. The plates are pre-coated with different capture antibodies in independent spots inside the same well. First, the analytes are captured by their respective capture antibodies, and then the analyte binds to the detection antibody conjugated with electrochemiluminescent labels (SULFO-TAG). After the addition of read buffer, a patented buffer that provides the adequate environment for electrochemiluminescence, the plate is read in a QuickPlex SQ 120 that applies voltage to the plates electrodes so they emit light. The intensity of emitted light is measured by the plate reader and processed. The amount of light is proportional to the concentration of analytes. Figure 2.16 shows the setup of the MSD plates used in this project.

Figure 2.16 MSD immunoassay principle. MSD is an immunoassay coupled with electrochemiluminescence. The amplification of well 1A shows the setup of the Aβ peptide panel assay. The amplification of well 1B show the setup of the MULTI-SPOT sAPPα/sAPPβ Assay. The dots not used are blocked with bovine serum albumin (BSA). (Modified from [280]). 2.5.1 MSD optimisation We tested MSD plates as a possible platform for screening drugs for AD. For this purpose, we used the Aβ peptide panel assay which measures the amount of three different Aβ species: Aβ38, Aβ40 and Aβ42. We also selected the sAPPα/sAPPβ assay, which measures the amount of APP cleaved by -secretase into sAPPα or cleaved by β-secretase forming sAPPβ. Then, sAPPα and sAPPβ are further cleaved by -secretase into P3 peptides and Aβ species respectively.

To test the sensitivity of the assay, we screened drugs with well-known mechanisms of action in the APP processing pathways. SH-SY5Y APP695 cells were treated with 10µM DAPT (-secretase inhibitor), 5µM βIV (BACE1 inhibitor), or 10µM Carbachol (α-secretase activator). None of the drugs affected the production of Aβ38 (Figure 2.17A). The production of Aβ40 and Aβ42 was significantly reduced by the three drugs tested (Figure 2.17.B and C). The secretion of sAPPα was significantly increased by carbachol around 20x (Figure 2.17.D). The secretion of sAPPβ was significantly reduced by βIV and carbachol. (Figure 2.17.E). The results agree with the mechanism of action of DAPT because the inhibition of -secretase explains the reduction of Aβ species and the lack of any effect in the sAPP secretion. βIV also behaved as expected, being a BACE1 (β-secretase) inhibitor it reduced the secretion of sAPPβ. The latter is an Aβ precursor which explains the observed reduction in Aβ species. Carbachol, an α-secretase activator greatly increased the

Figure 2.17 Evaluation of drugs affecting APP processing in MSD immunoassays. SH-SY5Y APP695 cells were treated with 5µM βIV, 10µM DAPT or 10µM Carbachol. After 24h incubation we measured the amount of Aβ38, Aβ40, Aβ42, sAPPα or sAPPβ. A) Aβ38 signal in SH-SY5Y APP695 cells. There was no significant difference between treatments and controls. B) Compared to the control cells (0.5% DMSO) the signal of Aβ40 decreased significantly in SH-SY5Y APP695 cells treated with 5µM βIV, 10µM DAPT or 10µM Carbachol. C) Compared to control cells (DMSO 0.5%), the signal of Aβ42 decreased significantly in SH-SY5Y APP695 cells treated with 5µM βIV, 10µM DAPT or 10µM Carbachol p>0.001 D). Compared to the control cells the sAPPα signal, significantly increased in cells treated with 10µM Carbachol. p>0.001. E) Compared to the control cells sAPPβ signal, significantly decreased in cells treated with 5µM βIV, and it significantly increased in cells treated with 10µM Carbachol. p>0.001. We performed ANOVA(one-sided) Dunnett’s post hoc test, n=2 bars represent means of the groups, error bars represent SEM.

secretion of sAPPα. It also reduced the concentration of Aβ species which is expected considering sAPPα is part of the non-amyloidogenic pathway of APP. Inexplicably; it also increased the amount of sAPPβ.

In general, the drugs had the expected effects in this assay. The above results showed that MSD is a sensitive assay for the screening of drugs for AD.

2.6. Oxidative Stress assays The Aβ-induced oxidative stress hypothesis suggests that Aβ oligomers disturb cell metabolism around membranes causing the oxidative stress observed in the brain of AD patients. The presence of hemeoxygenase, and other proteins induced by oxidative stress, around plaques of Aβ support the hypothesis that oxidative stress is part of the AD pathology [281]. Based on this theory we chose to study oxidative stress as a secondary assay to screen drugs to treat AD.

There are different markers to measure oxidative stress including lipid peroxidation, protein oxidation, DNA/RNA damage, reactive oxidative species and antioxidant. We decided to measure reactive oxygen species (ROS) such as superoxide and hydrogen peroxide which are produced in excess when there is oxidative stress, TM by using DCFH and ROS-Glo H2O2. One of the mechanisms by which cells neutralise ROS is through antioxidant enzymes, such as glutathione (GSH). We measured glutathione using the GSH/GSSG-GloTM assay. In the next sections, we will describe the optimisation of these techniques.

2.6.1 DCFH principle The acetate ester 2,7-dichlorodihydrofluorescein diacetate (DCFH-DA) crosses the cell membrane by diffusion (Figure 2.18). Once inside the cell, the cellular esterases deacetylate DCFH-DA into DCFH, which remains trapped in the cell due to its ionic nature. Then DCFH is oxidised by ROS, such as HO. and ROO. , into the fluorescent DCF. The fluorescence intensity is proportional to the concentration of

ROS in the cell.

Figure 2.18 DCFH principle DCFHDA crosses the cell membrane and it is deacetylated to DCFH by cellular esterase. DCFH is transformed by different ROS into the fluorescence DCF.

2.6.1.1 DCFH optimisation One of the limitations of this assay is the oxidation of DCFH to DCF by the serum present in the cell media, which hinders the reading of ROS species in the cells [282]. To minimize the artefacts caused by serum we evaluated the use of Opti-MEM reduced in BS (~1%) or PBS to dissolve DCFH-DA. We used 100µl of 3x105 SH- SY5Y cells/ml per well as literature suggest this to be an optimal cell density [283,

284]. We incubated the cells for 40 hours at 37°C with 5% CO2. We replaced media with 100µl of media containing either 25µM of menadione (DMSO concentration 0.1%) or 0.1% DMSO, and incubated for one hour. We diluted the DCFH 100mM stock with either PBS or Opti-MEM to make concentrations ranging from 150 to 1.5µM. DMSO was maintained at 0.15% for all the DCFH concentrations. We replaced the media of all the wells with 100µl of the corresponding DCFH concentration; each concentration was evaluated in triplicate. We returned the plate in the incubator for 30 mins. Then we washed each well with 200µl of either Opti-MEM or PBS. Finally, we read the fluorescence in a plate reader at an excitation wavelength of 480nm and emission wavelength of 530nm.

Figure 2.19 Use of Opti-MEM vs PBS to dissolve DCFH-DA. SH-SY5Y cells were either treated with 25µM of menadione or 0.1% DMSO. Then DCFH assay was performed using Opti-MEM or PBS to dissolve DCFH-DA. Error bars represent SEM, n=3.

DCFH dissolved in Opti-MEM showed a RFU reading increment with the concentration of DCFH-DA in both cells treated with 25µM of menadione and 0.1% DMSO (Figure 2.19). The increment of signal in cells treated with 0.1% DMSO could have been caused by the serum present in the media. This unwanted interference leads to wrong estimates of DCFH oxidation. There is little difference between the signal of cells treated with Opti-MEM and 25µM menadione (blue line in Figure 2.9) and those treated with Opti-MEM and 0.1%DMSO (red line in Figure 2.19). This small difference produces a reduced sensitivity of the assay.

DCFH dissolved in PBS showed an increase of RFU signal in cells treated with 25µM of menadione up to a DCFH concentration of 100µM, then the signal decreased at 150µM. In SHSY-5Y cells treated with 0.1% DMSO, RFU signal remained constant for all the concentrations of DCFH dissolved in PBS.

These results suggest that even small concentrations of serum interfere with the readings of DCFH thus reducing the reliability of the assay. PBS is a good alternative for dissolving DCFH without introducing significant artifacts in the assay. Based on these results we decided to use PBS to dissolve DCFH. We selected 100µM as the optimal concentration of DCFH because it showed the highest signal and higher sensitivity compared to other concentrations.

2.6.1.2 Evaluation of Aβ42 toxicity using DCFH assay We evaluated the toxicity of Aβ42 at three different concentrations (5, 2.5 and 1µM) at a constant concentration of 0.5% DMSO. We incubated the cells with each concentration of Aβ42 for 3, 6, 8 or 24 hours. The obtained values were normalised, considering the fluorescence of cells treated with 0.5% DMSO as 100%.

Figure 2.20 Aβ toxicity in DCFH assays. SHSY5Y cells were treated with 1, 2.5 or 5µM of Aβ. Cells were incubated for either 3, 6, 8 or 24 hours. Error bars represent SEM, n=3. Cells treated with 1µM Aβ42 did not show a fluorescence intensity increase maintaining a signal intensity of 100 to 107% at all the time points. Cells treated with 2.5µM Aβ42 modestly increased the fluorescence intensity at 3 (106%), 6 (107%) and 8 hours (112%); at 24 hours, the fluorescence intensity reached 126%. The fluorescence intensity of cells treated with 5µM Aβ42 increased gradually over the time and reached a maximum signal of 145% at 24 hours (Figure 2.20).

Cells treated with 1µM Aβ42 presumably neutralise the ROS generated by the addition of Aβ42. As we increase the concentration of Aβ42 the cells seem to struggle to counter the ROS thus increasing the fluorescence intensity. Time seems another important factor for measuring ROS, as fluorescence intensity increases over time. This could indicate that with time Aβ42 aggregates into more toxic forms thus reducing the ability of cells to neutralise ROS.

We selected 5µM Aβ42 as the concentration to be used in further assays because it showed the highest fluorescence intensity at all time points. Additionally, we decided to incubate it for 24h as the generation of ROS is the highest at this time.

TM 2.6.2 ROS-Glo H2O2 principle

TM We chose ROS-Glo H2O2 from Promega as a complementary assay to measure ROS. This is because, as we mentioned previously, the DCFH-DA assay measures different oxidative species without being able distinguish them. ROS-GloTM

H2O2 selectively measures H2O2, which is known for being one of the most toxic ROS.

In addition, H2O2 has the longest half-life among the ROS and it is well documented that many ROS are ultimately transformed into H2O2 [285].

The H2O2 substrate reacts with H2O2 to produce a luciferin precursor. Then after adding ROS-GloTM detection reagent, containing a recombinant luciferase and D-Cysteine, the precursor is converted to luciferin by D-cysteine. The produced luciferin reacts with the recombinant luciferase generating light that is proportional to the amount of H2O2 (Figure 2.21).

TM Figure 2.21 ROS-Glo H2O2 assay principle. Modified from [286].

TM 2.6.2.1 ROS-Glo H2O2 Optimisation SHSY5Y cells were treated with 1, 2.5 or 5µM of Aβ42, and the ROS-GloTM

H2O2 assay was performed indicated in Section 2.1.10. We chose 8 and 24h as the incubation times because when measuring ROS with DCFH these were the endpoints that showed a higher signal for ROS (Figure 2.20).

TM Figure 2.22 ROS-Glo H2O2 Optimisation. A) SH-SY5Y cells were treated with 5, 2.5 or 1µM of Aβ42 or 0.5% DMSO. Cells were incubated for either 8 or 24 h. There was no significant difference when comparing cells treated with 0.5% DMSO with any of the concentrations of Aβ42, p > 0.05, n=3, ANOVA(one-sided), bars represent means of the groups, error bars represent SEM. B) SH-SY5Y cells were treated with 0.5% DMSO or 25µM menadione for 8 or 24 hours. There was a significant increase in the RLU when comparing cells treated with 0.5% DMSO or 25µM menadione at both time points (8 or 24h). n=3 T-test(two-sided) bars represent the means of the groups, error bars represent SEM.

After 8 h of incubation, the signals of SH-SY5Y cells treated with 0.5% DMSO or Aβ42 (regardless of the concentration) were all around 360,000 RLU. This means that after acutely treating cells with Aβ42 for 8 hours, the amount of H2O2 did not increase in cells compared to cell treated with 0.5% DMSO. The same trend is observed when cells are treated with Aβ42 for 24h (Figure 2.22.A). We performed a

TM MTT assay in parallel using the same batches of Aβ42 used for the ROS-Glo H2O2 assay to confirm the cell toxicity of these batches. The Aβ42 batches showed a concentration dependant cell toxicity that agrees with the results obtained during the MTT optimisation. Furthermore, in each assay we used menadione as positive control. Studies have found that menadione causes cell death by multiple redundant pathways through the generation of oxidative stress in multiple compartments (e.g. mitochondria and cytosol) [267]. The RLU signal of cells treated with 25µM of menadione is 100x larger than cells treated with 0.5% DMSO (Figure 2.22). This trend is observed when SH-SHSY5Y cells are incubated at 8 or 24h. This means that menadione does increase the amount of ROS, in this case H2O2, dramatically. In summary, there was no increase in H2O2 concentration in cells treated with Aβ42 at different concentrations.

2.6.3 GSH/GSSG principle Glutathione is a peptide formed by three amino acids, L-glutamate, L-cysteine and glycine (Figure 2.23). The sulfhydryl (-SH) group in the cysteine portion makes glutathione the smallest and more abundant intracellular non-protein thiol, and confers its redox potential. Glutathione neutralises oxidative species via glutamate peroxidases, forming glutathione disulphide (GSSG), also known as oxidised glutathione (Figure 2.23.A). GSSG can be reduced back to GSH by glutathione reductase and this reaction depends on NADPH [287, 288]. Glutathione is mostly found in its reduced form GSH, but when cells are exposed to oxidative stress, GSH is oxidized to GSSG to tackle further damage. Thus, the ratio GSH/GSSG is a good measure of oxidative stress. For instance, in healthy cells GSH/GSSG is 100 but with oxidative stress can decreases to 30 in the presence of ROS [289].

The GSH/ GSSG-GloTM Assay couples with luciferase to measure the amount of GSH and GSSG. To measure the amount of total glutathione (GSH+GSSG) the cell is lysed and the GSSG present in the cell is reduced to GSH by GSH reductase. To measure GSSG only, first it is necessary to block GSH using a mix containing N- ethylmaleimide (NEM), so it cannot contribute to the luminescent signal. Luciferin- NT is transform into Luciferin in a redox reaction catalysed by glutathione S transferase (GST). Simultaneously, GSH is converted to GSH-NT. The Luciferin is transformed in to luciferase generating light. The amount of light is proportional to the amount of GSH in the cell (Figure 2.23B).

Figure 2.23 A) Glutathione peroxidise dependant system to neutralize ROS. B) GSSG/GSSHTM-Glo Assay principle [290]

2.6.3.1 GSH/GSSG Optimisation SH-SY5Y cells were treated for 24h with 0.5% DMSO as a control, 1, 2.5 or 5µM of Aβ42. After 24h incubation, both total and oxidised glutathione were measured and the ratio was used to determine the amount of damage caused by ROS. The ratio of GSH to GSSG decreased proportionally to the concentration of Aβ42. However, only the results for 2.5 and 5 µM are statistically significant (Figure 2.24). Furthermore, these results agree with the DCFH assay where the amount of ROS increased proportionally to the Aβ42 concentration. We selected 5 µM Aβ42 as the concentration to be used in further assays which is the same we used for DCFH assays and make the results comparable.

Figure 2.24 GSH/GSS of SH-SY5Y cells treated with 0.5% DMSO, 1, 2.5 and 5µM. There was a significant difference between cells treated with 2.5 or 5µM, p<0.01. n=3, ANOVA(one-sided) Dunnett’s post hoc test, bars represent means of the groups, error bars represent SEM. 2.7. Discussion The MTT assay has proved to be useful to evaluate the cell metabolism in both eukaryotic and prokaryotic cells. Since its discovery, it has been widely used as an in vitro tool to measure the cytotoxicity of drugs or cytotoxic agents, such as Aβ42 [291]. However, the adjustment of critical parameters to increase assay sensitivity, reliability and reproducibility is essential. We optimised our MTT assay in SH-SY5Y cell line successfully. The most significant improvement was that the total time of the assay was reduced by 6h.

In general, MTT reduction capacity is interpreted as a direct measurement of cell viability. This is because it was hypothesised that MTT was reduced exclusively by mitochondrial enzymes. Further studies showed that MTT is also reduced by enzymes found in the endosomes, lysosomes and cytoplasm [271, 272]. Many groups still interpret the inhibition of MTT reduction caused by Aβ species as cell toxicity [292, 293]. In contrast, we prefer to interpret that the reduction on the formazan signal is caused by an inhibition in cell metabolism (especially in the mitochondria), and that this is an early indication of impairment in cellular function. Thus, the results presented in Section 2.2.4 show that the inhibition of cell metabolism increases with the concentration of Aβ42 without necessarily causing cell death at the concentrations tested. This is considering that when we tested cell toxicity using LDH, which

measures cell toxicity based on cell disruption, we did not find a significant increase of LDH release at concentrations below 25µM( Figure 2.11).

Initially, the amyloid cascade hypothesis proposed Aβ deposition in brain as the culprit behind AD pathology. The hypothesis was challenged later when it was found that in some patients there was no correlation between Aβ deposition and neuronal loss and cognitive decline [72, 294]. Further research found that soluble Aβ oligomers are toxic and correlate better with AD pathology [41, 73]. Several studies focus on elucidating the mechanism of Aβ aggregation and depicting its toxic forms. So far, many toxic soluble forms have been identified ranging from dimers, trimers, hexamers and protofibrils [295]. The study of soluble forms of Aβ is controversial as there is no consensus on the toxic Aβ forms that contribute to AD pathology and whether the conformational forms found in vitro accurately represent Aβ assemblies in patients with AD. When developing an in vitro model for AD it is important to consider its limitation and choose a protocol to prepare Aβ oligomers that are toxic to cells at the lowest possible concentration to better mimic AD pathology.

Considering the above-mentioned points, we compared the cellular toxicity of the method used by our group (Section 2.1.2) with the one used by Teplow’s group (Section 2.1.3) using MTT. The method preferred by our group involves the dissolution of the peptide in HFIP, then lyophilisation and dissolution in DMSO. Most of the available protocols for biological or functional studies with Aβ involve the use of HFIP [296, 297]. HFIP is widely used to maintain Aβ in its monomeric form as it disrupts hydrophobic interactions in the aggregated peptide and promotes its α-helical conformation [298, 299] . Further dilution of the lyophilised peptide in DMSO also ensures Aβ monomeric structure. However, it has been reported that Aβ pre-treated with HFIP increases the rate of non-toxic fibrillogenic forms, even at low concentrations in aqueous media [300]. The protocol developed by Teplow’s group uses basic conditions (NaOH) to solubilise Aβ. The rationale is that when lyophilised Aβ previously treated with HFIP is dissolved in an aqueous solution, such as Opti- MEM, the Aβ with an acidic pH is neutralised by the media, passing through the isoelectric point of Aβ (5.5). At the isoelectric point, Aβ would prompt to aggregation thus reducing soluble species [301]. This method uses NaOH to dissolve the peptide, so after lyophilisation when the peptide is reconstituted in an aqueous solution it

avoids the isoelectric point of Aβ. Additionally, the filtration of Aβ guarantees the formation of soluble forms. A report indicates that using alkaline solvents produces higher proportions of oligomeric forms compared to aggregated species [302].

We were expecting that Aβ42 in NaOH to be more toxic than Aβ dissolved in HFIP and that we could use nM concentrations of the peptide to better mimic AD pathology. However, both methods showed similar cytotoxicity at all the tested concentrations (Figure 2.9). This could be explained because both preparations of Aβ were subject to the same conditions after being adding to the SH-SY5Y cells. During the 24h during which they were incubated with the cells they could have been exposed to a slow, but continuing decrease of pH due to nutrient depletion in the media and enzymatic products formed by the cells. Although, we do not use media supplemented with the pH indicator phenol red in MTT assays. We have observed that SH-SY5Y incubated in media supplemented with phenol red at the same density for 48 hours turn into pale orange as a result of pH changes in the media. These results suggest that when measuring cytotoxicity of Aβ in vitro the incubation conditions and not the initial method of Aβ solubilisation are largely responsible of the Aβ cytotoxicity. This finding highlights the difficulties of creating a simple but reliable AD model. Still, the results show that with both protocols (NaOH, HFIP) the toxicity of Aβ is concentration dependent. We decided to keep using HFIP to solubilise Aβ as it is widely used, so it would be easier to relate our findings with previous work. It is also relatively less laborious than preparing oligomers with NaOH and the toxicity is very similar. Additionally, we chose 1µM as the concentration to be used in the primary screening of drugs, because it provided us with a suitable window to evaluate Aβ42 toxicity at low concentration.

To evaluate the cell toxicity of Aβ42 in SH-SY5Y cells we measured LDH and “dead-cell protease” leakage. We found that higher concentrations of Aβ42 (at least 25µM) are necessary to produce cell death compared to the concentrations needed to inhibit cell metabolism (Figure 2.11.B). Our results agreed with Zou et al. who found that the percentage of maximum LDH release was 25% when SH-SY5Y were treated with 20 µM of Aβ42 [303]. Like LDH, CytoTox-Glo measures cell toxicity by assessing the cell integrity. However, CytoTox-Glo measures the leakage of different proteases which makes it more sensitive than LDH. We performed this assay expecting

that the sensitivity of the assay allowed us to have a suitable window to assess the toxicity of Aβ42 at lower concentrations. Still, when cells were treated with low concentrations of Aβ42(1 and 5µM) this assay did not show a significant increase the leakage of “dead cell protease” (Figure 2.15) thus confirming that at these concentrations Aβ does not cause cell death. These results support the conclusion that the inhibition of MTT reduction does not correlate with cell viability. We discarded LDH assay as possible platforms for drug screening because the concentration of Aβ42 that is necessary to cause significant cell death is too high and not appropriate for large drug screening.

Using the MSD immunoassay sounded promising as this system allows us to evaluate simultaneously different proteins involved in the processing of APP as targets for drugs to treat Alzheimer’s. In this project, we measured the concentration of Aβ38, Aβ40 and Aβ42, as well as the activity of sAPPα and sAPPβ. We were looking for drugs that reduced the concentration of Aβ42 or increased the relative concentrations of Aβ40 or Aβ38, or drugs that showed an increase in sAPPα or a reduced of sAPPβ.

To evaluate the sensitivity of the kits we selected DAPT and βIV as control drugs for measuring Aβ concentration. We selected carbachol as the control drug to measure sAPPα and again βIV to measure sAPPβ.

DAPT is a -secretase inhibitor developed by Elly Lilly in early 2000s. This compound reduced the levels of Aβ both in vivo and in vitro in a concentration- dependent fashion. In the same study showed no alteration in the levels of sAPPα or sAPPβ, consistent with the reduction of Aβ being due to inhibition of -secretase [304]. DAPT showed a decrease in Aβ40 and Aβ42 in the CSF of young mice [305]. Our results agree with the mechanism of action reported in the literature. DAPT decreased the concentration of Aβ40 and Aβ42 and it does not affect the concentrations of sAPPα or sAPPβ (Section 2.5. ).

βIV is a low molecular weight, non-peptidic and cell permeable β-secretase inhibitor which inhibits BACE1 and BACE2 activity [306]. Since its discovery, βIV has been a gold standard for measuring BACE inhibition. In this assay, we assessed β-secretase inhibition by βIV in two fashions by measuring the concentration of sAPPβ and Aβ species. The results confirmed the inhibition of BACE1 as the

concentrations of Aβ40, Aβ42 and sAPPβ were significantly reduced. Curiously, the inhibitory effect was greater for Aβ40 than for Aβ42.

We used carbachol, a non-specific muscarinic agonist primarily used to treat glaucoma, as an α-secretase activator. In the early 1990’s, it was discovered that stimulation of cells overexpressing M1 and M3 receptors with carbachol increases the secretion of sAPPα in a protein kinase dependent way[102]. The increase of sAPPα reduces APP processing by β-secretase, thus resulting in a decrease of Aβ production[307]. In general terms, carbachol behaved as expected in the immunoassay. Carbachol significantly increased the amount of sAPPα which can be interpreted as carbachol strongly activating α-secretase and favouring APP processing via the non-amyloidogenic pathway. We also see a reduction of Aβ40 and Aβ42. Carbachol decreased Aβ42 considerably more than DAPT and βIV. However, carbachol also shows an increase of sAPPβ which suggests a stimulation of β- secretase. These observations will be further addressed in the discussion of the next chapter.

Overall, our results show that MSD immunoassays are a promising platform for screening of drugs for AD.

Many studies have shown a relationship between oxidative stress and AD. Evidence suggests that Aβ triggers oxidative stress at early stages of AD [78, 308]. However, some studies also suggest that the excess of oxidative stress prompts APP processing through the amyloidogenic pathway increasing Aβ production [309, 310] . This means that oxidative stress could be both preceding AD pathology, as well as promoting Aβ production. Nevertheless, these facts suggest that the reduction of ROS species could be an interesting target to delay the onset or progression of AD.

Under normal conditions, ROS acts a signalling molecule regulating physiological functions in the cells, such as protein expression, immunity and metabolic adaptation [311]. The mitochondria regulate ROS concentrations with molecules such as antioxidants and electron carriers. However, some insults, such as Aβ, could cause an imbalance in the production, utilisation and neutralisation of ROS, thus reducing mitochondrial activity and causing cell damage [312]. Our experiments measuring mitochondrial activity with MTT showed that Aβ42 decreases cell metabolism in a concentration-dependent fashion. Considering the evidence that

shows a possible relationship between oxidative stress and Aβ, we decided to further investigate oxidative stress as a secondary assay for our drug screening platform.

DCFH is widely used to measure oxidative stress in vitro, but it has some limitations and caveats. The assay relies of the oxidation of DCFH-DA to fluorescent DCFH by ROS so the resulting fluorescence would reflect any shift in ROS. However, DCFH-DA can be autoxidised causing artifacts hindering the measurement of actual ROS [313]. It has been shown that the presence of bovine serum at concentrations above 5% can promote DCFH-DA autoxidation [314]. Considering this we prepared DCFH-DA in Opti-MEM at 1% serum and in PBS. Still, our results showed that even at very low concentrations bovine serum promotes autoxidation of DCFH. Using PBS to dissolve DCFH-DA proved to be a good alternative preventing its autoxidation (Section 2.6.1.1).

Once we prevented possible artifacts, we used DCFH to assess whether Aβ42 shifted ROS production in SH-SY5Y cells. The data suggests that the concentration of ROS remains unchanged when cells are treated with 1µM of Aβ42. However, when cells are treated at 2.5 or 5µM Aβ42 the ROS increases with the concentration. We can also observe that in both cases ROS gradually accumulates over time up to 24h (Section 2.6.1.2). The data indicates that 1µM Aβ42 causes impairment in the metabolism of the cells (MTT assay), but not a disturbance in the balance of ROS production. It could also indicate that cells could mitigate the imbalance of ROS caused by Aβ up to a certain threshold, thus when Aβ accumulates beyond this threshold it could trigger ROS overproduction. We decided to use 5µM as it was the concentration that showed the greatest increase in ROS production.

When the DCFH assay was developed the oxidation of DCFH-DA was mainly attributed to hydrogen peroxide[315]. However, further experiments demonstrated that DCFH-DA is oxidised by many types of ROS species besides H2O2 including . . hydroxyl radicals ( OH), and nitrile radicals ( NO2) and that it could be even possible that DCFH-DA do not directly react with H2O2 [313]. Still, many groups interpret fluorescence of DCFH as a direct assay to measure oxidative stress caused by H2O2

[316]. To know the real contribution of H2O2 to oxidative stress caused by Aβ42, we ran a ROS-Glo H2O2 assay which only measures the concentration of H2O2. We found that none of the concentrations of Aβ showed a shift in H2O2 signal. We used

menadione as a control to test the functionality of the assay and it increased the signal for H2O2. Because most of the literature point H2O2 as the major ROS component we wondered if the results previously observed for DCFH-DA could be due an artefact caused by Aβ42. We incubated the same concentrations of Aβ42 previously tested with Opti-MEM only for 3, 6, 8 and 24h and carried out DCFH-DA. The Aβ42 did not shift the fluorescence at any endpoint at any of the Aβ42 concentrations. These results could be interpreted as: (i) At the concentrations tested Aβ42 causes an increase of ROS excluding H2O2. (ii) The sensitivity of the assay is not enough to measure the concentration of H2O2 generated from the Aβ42 concentrations at which the cells were subject. (iii) It could be possible that in some of the studies that identified H2O2 the main ROS generated from Aβ42 treatment in vitro could be erroneous interpretations of DCFH-DA assay [78].

We also used the GSH/GSSG ratio as another assay to monitor oxidative stress. Glutathione is mainly found in cells as GSH, but is oxidised to GSSG to neutralize ROS. Our results showed that Aβ42 decreases the GSH/GSSG ratio in SH-SYS5Y cells in a concentration-dependent manner. This data confirmed that Aβ42 does increases oxidative stress in SH-SY5Y cells.

In this chapter, we explored the feasibility of different assays to screen drugs for AD. Despite all assays show certain limitations, we found a set of relative simple and scalable assays suitable for drug screening. The assays tested also agree with previous findings that support the amyloid cascade hypothesis. For instance, we found that an early event caused by Aβ is impairment in cell metabolism (mainly in the mitochondria) and increment in oxidative stress.

Chapter 3. Primary Screening

3.1. Introduction Primary screening is one of the first steps in the long path of drug development. It can be described as an initial assay(s) that allow us to identify promising drugs for treating diseases. Normally, primary screening platforms are amenable for high throughput screening (HTS) to evaluate a large number of drugs in a short time. These platforms are also relatively simple assays that allow us to evaluate specific target/targets. Also, it is desirable that assays are relatively cheap and accurate enough to reduce false negative/positives.

Based on the results of the previous chapter, we used two assays as our primary screening methods: MTT and MSD immunoassays. The first assay, MTT, evaluated the capacity of the tested drugs to inhibit cell metabolism damage caused by the addition of Aβ42. On the other hand, MSD immunoassays, allowed us to simultaneously evaluate if the tested drugs affect different targets involved in Aβ production.

In the next sections of this chapter, we will describe the features of the drug library used, the experimental approach to screen these drugs with our primary screening assays, and the results from this primary screening.

3.2. Methods

3.2.1 Selection of drugs for primary screening The Library of Pharmacologically Active Compounds (LOPAC) was used as the source for our customised drug library. LOPAC is collection of 1280 pharmacologically-active compounds with a wide variety of pharmacological classes. It encompasses drugs with 17 different targets including lipids, cell signalling, G proteins, and neurotransmitters. The latter accounts for more than half of the targets.

Compounds in LOPAC are highly pure and pre-solubilised in DMSO to facilitate their use in drug screening. An attractive feature of this library is that it contains several drugs that have been tested in clinical trials (mostly discontinued for lack of efficacy but not for safety issues) and approved by regulatory entities such as FDA. This means that some of the drugs contained in this library are commercially available treatments for different diseases.

LOPAC was used to create a sub-library suitable for drug repositioning for AD. PubChem [317] and DrugBank [318] were the main sources of information to classify the LOPAC drugs. First, drugs that were tested in humans and/or approved by a regulatory entity were selected. From these, drugs that cross the blood brain barrier (BBB) were selected. This was done to eliminate drugs that perform well in vitro but fail in further stages because they lack BBB permeability. For some drugs, there was no information regarding their BBB permeability, still it was decided to include them as part of our sub-library in case further information could confirm their BBB permeability. After the drug screening, a new literature search was conducted and we found that three drugs that were considered for screening do not permeate the blood brain barrier. The sub-library contains 175 drugs. Among these 147 (85%) are approved by FDA and/or another regulatory entity and 167 (95%) cross the blood brain barrier. Also, 90 of these drugs (52%) target the Central Nervous System (CNS). Interestingly, the pharmacological indications of 31 of these are anxiety and/or depression. Figure 3.1summarises the features of the library. A detailed list of drugs of our sub-library is available in the Appendix A. The LOPAC library was generously provided by Protein Technologies Ltd.

Figure 3.1 Features of sub-library of LOPAC. A) Classification according to the regulatory status B) Classification according to BBB permeability C) Classification according to pharmacological activity D) Sub-classification of drugs targeting Central Nervous System (CNS)

Also, five drugs that are not in the LOPAC were screened. These are vescalagin, castalagin, epigallocatechin gallate (EGCG), resveratrol, and SEN304. The first four are derived from natural sources and the latter is a designer peptide that targets Aβ aggregation. These drugs were screened because their study was part of collaboration with Professors Phillipe Derreumaux and Peter Faller. They performed biophysical assays on these drugs (Atomic force microscopy (AFM), Thioflavin fluorescence (ThT-fluorescence, and NMR) and we performed cell-based assays.

3.2.2 Preparation of Aβ42 and LOPAC compounds for MTT screening. The LOPAC library is arranged in 16 plates of 96 wells each; it contains 80 drugs per plate at 10mM concentration. The whole library was diluted from 10mM to 1mM in DMSO. Then, aliquots of 50µl of the diluted drugs were transferred into 16 Corning 96 v-bottom sterile plates in the same format as the original library. These plates were named as “mother stock” library and were store at -80°C until required.

For the non-LOPAC drugs, “mother stocks” of 1mM in DMSO were prepared in 1.5 mL Eppendorf tubes, except for EGCG that was dissolved in water. The drugs were also stored at -80°C until required.

For the primary screening of the sub-LOPAC library, an Aβ42 batch is defined to contain 2mg of Aβ42. Each batch was further dissolved in HFIP and a total of 40 aliquots of 50µl each were made per batch. The detailed procedure of Aβ42 preparation is described in Section 2.1.2. Three different batches of Aβ42 were used for MTT LOPAC screening; batches LR1, LR2 and LR3 were used for round 1, round 2 and round 3, respectively.

From an aliquot of 50µg of lyophilised Aβ42, a 1mM Aβ stock was prepared by adding 10µl of DMSO into the Eppendorf’s tube. In the same tube, 990 µl non- supplemented Opti-MEM was added to prepare Aβ42 at 10µM containing 1% DMSO.

An aliquot of the drugs was prepared by dissolving 1µl of the 1mM drug in 99µl non-supplemented Opti-MEM to achieve a concentration of 10µM containing 1% DMSO.

An Aβ42-drug mixture of 400µl was prepared by adding 40µl of Aβ42 10µM, 40µl of drug 10µM and 320µl of non-supplemented Opti-MEM. The final concentration of this solution was 1µM Aβ42, 1µM drug and 0.2% DMSO. The above- mentioned solutions were prepared just before performing the drug primary screening.

A preparation of Aβ42 1µM to be used as a positive control was prepared by adding 40µl of 10µM Aβ42 and 360µL of non-supplemented Opti-MEM. 0.2% DMSO solution in non-supplemented Opti-MEM was also prepared for the negative control wells by mixing 2µl of DMSO and 398µl of non-supplemented Opti-MEM.

3.2.3 Drug setup for MTT screening To screen the 175 drugs of the LOPAC sub-library, they were separated in 11 plates. Plate 1 contained 14 drugs, plates 2 through 10 contained 17 drugs each, and plate 11 contained the remaining 8 drugs. The setup is shown in Figures 3.2, 3.3 and 3.4. Each drug was screened in its allocated plate in triplicate and each plate contained a positive control with 1µM Aβ42 and a negative control 0.2% DMSO also in triplicate. Three full independent rounds of the above-described screening were performed, where in each round every drug was screened in triplicate in its plate (triplicate in each plate) x (three rounds). Within each round, each plate was analysed independently using an ANOVA (one-sided) Dunnett’s post hoc test. The drugs considered “hits” were re-screened to confirm their activity.

Similarly, the non-LOPAC drugs were later screened also in three different rounds, in triplicate per round.

3.2.4 MTT drug screening SH-SY5Y cells were harvested when they reached ~80% confluence and re- suspended in MEM Earle’s medium/Ham’s F12 (1:1) supplemented with 10% FBS, 1%L-Q, 1% P/S, and 1% n-aa. Then the cells were seeded at a density of 3x104 cells/well in a 96-well plate. The plate was incubated overnight at 37°C with 5% CO2 to allow cell adherence.

After the incubation time, media was carefully removed from each well and 100µl of the correspondent 1:1 µM Aβ42-drug mixture (described in section 3.2.2) was added to wells in triplicate using reverse pipetting. The media from the control wells was also removed and replaced with the corresponding control solution. The plate was returned for incubation for 24h at 37°C with 5% CO2. After incubation, MTT assay was performed as described in Section 2.1.4 and the data was normalised as indicated in Section 2.1.5.

3.2.5 MSD drug preparation and setup To screen the LOPAC sub-library using MSD immunoassays, the drugs were divided into three plates. The first the plate contained 81 drugs (1-81), the second one, 68 drugs (82-149), and the third, the remaining 26 (150-175). Due to limited resources, the drugs were screened once per plate. Each plate was incubated with 5 wells, each of them containing the respective positive controls: DAPT, βIV, and carbachol and the negative control with 0.5% DMSO Only. The non-LOPAC drugs were only screened during the confirmatory round for LOPAC as they were included in a later time after the primary screening of LOPAC drugs.

Stock aliquots of 5µM were prepared in a 1.0 ml Eppendorf tube by adding 2.5 µl of each drug (LOPAC and non-LOPAC) at 1mM and 497.5µl of non-supplemented Opti-MEM. After preparing the drugs, 200µl of each drug was transferred to an empty 96-well plate identified as “stock plate”. There, the drugs were sorted such that they could be loaded into the assay plates containing cells with a multi-channel pipette, and

where their final order corresponded to the assay plan summarised in the above paragraph.

3.2.6 MSD immunoassay drug screening

SH-SY5Y APP695 cells were harvested after they reached ~80% confluence. The cells were re-suspended in MEM Earle’s medium/ Ham’s F12 (1:1) supplemented with 10% FBS, 1%L-Q, 1% P/S, and 1% n-aa and plated in a 96-well plate at a cell density of 5x104cells/well. Then, the cells were incubated overnight at 37 °C, 5%

CO2, to allow cell adherence. After this incubation, media from all wells was carefully removed and replaced, using a multichannel pipette, with 150µl per well of the “stock plate” that contained the drugs sorted for the drug screening. Then the plates were returned to the incubator for another 24h. After incubation, 25µl of media from the cells was placed in the MSD V-Plex Aβ peptide panel for measuring Aβ38, Aβ40 and Aβ42 concentration. Another 25µl was added to the MSD sAPPα/ sAPPβ kit to measure sAPPα and sAPPβ concentrations. MSD immunoassay was then performed as indicated in Section 2.1.8.

3.3. Results: MTT LOPAC sub-library screening

We performed the MTT assay in three independent rounds for each of the 175 compounds of the LOPAC sub-library. The aim of this screening was to identify compounds that could decrease the cell metabolism damage caused to SH-SY5Y cells incubated with Aβ42.

After running the three independent rounds of LOPAC sub-library screening, we decided that the best way to analyse the data was in an independent fashion per plate and per round by performing an ANOVA (one-way) with Dunnett’s post hoc test. In the following sections, we will report and describe the main findings of each round and the rationale for the statistics test.

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3.3.1 MTT first round An aliquot of 50µl Aβ42 from batch LR1 was used for each plate of the round to prepare the positive controls of 1µM Aβ42. Each drug of the LOPAC sub-library was screened in triplicate in the assigned plate.

In this round, most of the compounds did not decrease the inhibitory effect of 1µM Aβ42 on the cell metabolism of SH-SY5Y cell line (Figure 3.2). Only two out of the 175 screened compounds could partially decrease the inhibitory effect of 1µM Aβ42 on cell metabolism (Figure 3.2.I). These compounds were number 136 (prochlorperazine dimaleate) and 140 (spiperone hydrochloride) and showed to significantly increase the MTT reduction to 86% and 90% respectively compared to the 76% showed by 1µM Aβ42. Prochlorperazine is a dopamine (D2) receptor agonist indicated for nausea in oncology patients [319] and vertigo [320]. Although rarely prescribed, it can be used as antipsychotic to treat schizophrenia [321]. Spiperone hydrochloride is a dopaminergic antagonist identified as the first-generation of antipsychotics [322].

None of the screened compounds were more toxic than Aβ42 1µM alone. On average, the percentage toxicity of 1µM Aβ42 was similar within all plates (around 64–76%), except for plate 10 where 1µM Aβ42 MTT reduction was 88% (Figure 3.2.J).

We did not consider the data from this plate as the unexpected low toxicity of 1µM Aβ42 made it very difficult to ascertain whether the drugs have any effect on the cell metabolism.

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Figure 3.2 First round of individual LOPAC sub-library compounds to identify hits (compounds) that attenuate toxicity (reduction in cell metabolism) caused by Aβ42. (A-K) SH-SY5Y were treated with each of the 175 compounds in the LOPAC library and 1µM Aβ42 (batch LR1). After 24h incubation at 37°C, an MTT assay was performed to measure the cell metabolism (% of MTT reduction) and each compound’s ability to attenuate toxicity caused by Aβ42 was evaluated. Data are represented as mean percentage of % of MTT reduction, with 0.2% DMSO controls set as 100%, n=3 and the error bars represent standard error of the mean (SEM). Significance symbol meaning * for p < 0.05, ** p < 0.01, *** p < 0.001 and **** for p < 0.0001.

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3.3.2 MTT LOPAC second round The compounds were screened in the same fashion as the first round with the only difference that the Aβ42 used as control for this round was from the batch LR2. In the plate 1, compounds 4 and 9 significantly increased cell metabolism by showing 106% and 96% of MTT reduction respectively when compared to 77% showed by cells treated with 1µM Aβ42 only (Figure 3.3.A). From all the compounds that showed a positive significant effect in attenuating cell metabolism effects of Aβ, compounds 4 and 9 were the only ones that recovered cell metabolism to ~100%. In the plate 2, Compounds 15, 18, 20, 22, 25 and 27 significantly increased cell metabolism by showing 89%, 95%, 84%, 89%, and 84% of MTT reduction respectively when compared to 72% of Aβ42 (Figure 3.3.B). In the plate 3, compounds 32 to 35, 38, 40 and 41, 43, 47 and 48, significantly increased cell metabolism by showing an MTT reduction of around ~80% for all the mentioned compounds compared to the 65% showed by the cells only treated with Aβ42 (Figure 3.3.C). In the Plate 5, compounds 68, 70, 71, 74 and 76 showed a significant increase of MTT reduction of 67%, 69%, 66%, 66% and 67% respectively compared to the 59% of MTT reduction showed by Aβ42 (Figure 3.3.E). In plate 7, compound 107 significantly increased MTT reduction at 64% compared to 57% showed by cells treated with Aβ42 alone (Figure 3.3.G). In plate 8, compounds 123 and 128 showed an MTT reduction of 74% and 75% compared to 65% of Aβ42 (Figure 3.3.H). In plate 10, compounds 157 and 159 significantly increased MTT reduction to ~82% compared to cells treated with Aβ only that showed an MTT reduction of 61% (Figure 3.3.J). Plates 4, 6, 7-9 and 11 did not show any compound that could ameliorate Aβ42 toxicity. The average of Aβ42 MTT reduction for round 2 was at 66%. Only compound 104 located in plate 7 was possibly be more toxic compared to Aβ42 alone (Figure 3.3.G). In total, we found 28 possible hits from the second round of the LOPAC sub-library.

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Figure 3.3 Second round of individual LOPAC sub-library compounds to identify hits (compounds) that attenuate toxicity (reduction in cell metabolism) caused by Aβ42 (Batch LR2). (A-K) SH-SY5Y were treated with each of the 175 compounds in the LOPAC library and 1µM Aβ42. After 24h incubation at 37°C a MTT assay was performed to measure the cell metabolism (% of MTT reduction) and each compound’s ability to attenuate toxicity caused by Aβ42 was evaluated. Data are represented as mean percentage of MTT reduction, with 0.2% DMSO controls set as 100%, n=3 and the error bars represent SEM. Significance symbol meaning * for p < 0.05, ** p < 0.01, *** p < 0.001 and **** for p < 0.0001.

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3.3.3 MTT LOPAC third round The third round of MTT LOPAC screening was performed using Aβ42 aliquots from the LR3 batch. In plate 5, compounds 67 and 76 significantly increased MTT reduction to 67% and 65%, respectively compared to the MTT reduction after Aβ42 addition alone of 53% (Figure 3.4.A). In plate 7, compounds 108 and 109 significantly increased MTT reduction to 57 and 52% compared to 44% MTT reduction of cells treated with Aβ42 alone (Figure 3.4.G). In plate 9, compounds 134, 139, 143, 144 and 147 showed a significant increase in MTT reduction of 76%, 73%, 72%, 76% and 73% respectively compared to the 64% MTT reduction showed by Aβ42 alone (Figure 3.4.I). In plate 10, compound 160 significantly increased MTT reduction to 86% compared to 65% of Aβ42 alone (Figure 3.4.J). In plates 1–4, 6, 8 and 11 none of the compounds showed a significant increase in MTT reduction.

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Figure 3.4 Third round of individual LOPAC sub-library compounds to identify hits (compounds) that attenuate toxicity (reduction in cell metabolism) caused by Aβ42. (A-K) SH-SY5Y were treated with each of the 175 compounds in the LOPAC library and 1µM Aβ42 (Batch LR3). After 24h incubation at 37°C, an MTT assay was performed to measure the cell metabolism (% of MTT reduction) and each compound’s ability to attenuate toxicity caused by Aβ42 was evaluated. Data are represented as mean percentage of % of MTT reduction, with 0.2% DMSO controls set as 100%, n=3 and the error bars represent standard error of the mean (SEM). Significance symbol meaning * for p < 0.05, ** p < 0.01, *** p < 0.001 and **** for p < 0.0001

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3.3.4 Aβ42 Variation from batch to batch One of the challenges of this screening was working with Aβ42 due to its experimental variability in toxicity. In the literature, is often discussed how the solubilisation and dilution method affects the conformational state of Aβ42[266, 323]. Some studies also included cell-based assays to assess the toxicity of these preparations [323, 324]. However, the experimental variability in toxicity between batches using a given solubilisation method is little discussed or highlighted in papers, although, this is a topic discussed informally among researchers [324] .

One of the reasons to analyse the data from the MTT LOPAC screening per plate per round was due to the variability in toxicity showed by Aβ preparations. Figure 3.5A shows the individual values of 1µM Aβ42 per batch. The mean of the three used batches is very similar at 68–70%, but the dispersion of the individual data values varies between batches. The data also indicates that the standard deviation (SD) of the LR3 batch is higher compared to the SD of LR1 and LR1. Overall, the data shows that although the toxicity within batches varies, the mean of the batches is very similar, thus highlighting that toxicity of Aβ42 1µM is experimentally reproducible.

We also elaborated a histogram using the individual concentrations of three rounds repeats of 1µM Aβ42 that showed a Gaussian distribution, thus corroborating that the use of ANOVA is appropriate for analysing the data (Figure 3.5B).

Figure 3.5 Variability of the MTT reduction for the three different batches of Aβ42 used in the three independent rounds of LOPAC screening. A) Scatter plots showing individual values of Aβ42 at 1µM concentration for MTT reduction per batch. B) Histogram of Aβ42 values of three batches used for LOPAC MTT drug screening shows that it fits a normal distribution

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3.3.5 Hits confirmation Our primary screening showed 38 possible hits in three rounds but just one drug was a repeated hit in two out of three rounds (Table 3.1). This was drug 76, Haloperidol, which is approved to treat schizophrenia[325] , psychosis[326] and Tourette’s syndrome[327].

We re-screened the 38 possible hits in three rounds, each round containing each compound in triplicate, see Figure 3.6. In the three confirmatory rounds, none of the compounds increased MTT reduction compared to Aβ42 alone (Figure 3.6).

When running primary screenings, random errors can cause false positives. Confirmatory screenings eliminate these false hits; the repeats significantly decrease the possibility of false hits due to random error [328].

Table 3.1 Summary of hits found in three rounds of MTT primary screening

" Hits Repeat 1" "Hits Repeat 2" "Hits Repeat 3" 136 Prochlorperazine dimaleate 4 Acetyl-beta-methylcholine chloride 67 Phenserine 140 Spiperone hydrochloride 9 Aminoguanidine hemisulfate 76 Haloperidol 15 Paroxetine hydrochloride hemihydrate 108 Nialamide 18 Amoxapine 109 Nortriptyline hydrochloride 20 L-Aspartic acid 134 Propantheline bromide 22 Bupropion hydrochloride 139 Spermidine trihydrochloride 25 Benztropine mesylate 143 Tetrabenazine 27 Buspirone hydrochloride 144 Ropinirole hydrochloride 32 Chlorzoxazone 160 L-Tryptophan 33 Citalopram hydrobromide 34 Clonidine hydrochloride 35 Clozapine 38 CNS-1102 Dextromethorphan hydrobromide 40 monohydrate 41 Doxepin hydrochloride 43 Cirazoline hydrochloride 47 N-Methyldopamine hydrochloride 48 Eletriptan hydrobromide 68 Felbamate 70 Gallamine triethiodide 71 Fluphenazine dihydrochloride 74 Paliperidone 76 Haloperidol 107 Methysergide maleate 123 (+)-Pilocarpine hydrochloride 128 Phenelzine sulfate 157 Trimipramine maleate 159 Granisetron hydrochloride

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Figure 3.6 Confirmatory screening. The 38 “possible hits” from the three rounds of screening were re- screened in triplicate in three independent rounds. SH-SY5Y cells were treated with each of the 38 hits from the LOPAC library and 1µM Aβ42. After 24h incubation at 37°C, an MTT assay was performed to measure the cell metabolism (% of MTT reduction) and each compound’s ability to attenuate toxicity caused by Aβ42 was checked. Data are represented as mean percentage of MTT reduction, with 0.2% DMSO controls set as 100%, n=3 and the error bars represent standard error (SEM). Significance symbol meaning * for p < 0.05, ** p < 0.01, *** p < 0.001 and **** for p < 0.0001.

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3.3.6 Non-LOPAC compounds screening As previously mentioned, five compounds that were not part of the LOPAC were also screened. These five compounds have been previously assessed as possible treatments for AD. These findings will be further examined in the discussion of this chapter. The compounds were screened in triplicate and in three independent rounds. Within the three rounds, cells treated with vescalagin, castalagin, EGCG and SEN304 showed to significantly increase MTT reduction compared to cells only treated with Aβ42. Vescalagin and castalagin showed a milder effect compared to EGCG and SEN304 (Figure 3.7).

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Figure 3.7 MTT screening of non-LOPAC drugs. SH-SY5Y were treated with each of the five non- LOPAC drugs and 1µM Aβ42 three times. After 24h incubation at 37°C and MTT assay was performed to measure the cell metabolism (% of MTT reduction) and each compound’s ability to attenuate toxicity caused by Aβ42 was checked. n=3 and the error bars represent standard error of mean (SEM). Significance symbol meaning * for p < 0.05, ** p < 0.01, *** p < 0.001and **** for p < 0.0001.

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3.4. MSD immunoassays 3.4.1 MSD immunoassays primary screening LOPAC Drugs from the LOPAC sub-library were screened as indicated in Sections 2.2.5 and 2.2.6. Briefly, drugs were screened once per plate at a concentration of 5µM each in SH-SY5Y695 cells. The drug 2 (amantadine) was not screened as we had limited quantities and we were not able to get a replacement for the LOPAC screening. Due to a typological error, drugs 8 to 11 were not screened in the first plate as initially planned, and were screened in the second plate instead. The final location of the drugs can be observed in Figure 3.8. Each of the three plates included 5µM βIV, 10µM DAPT and 10µM carbachol as a positive control and cells treated with 0.5% DMSO as a negative control. We did not consider the results of Aβ38 as its concentration was too low to be assessed.

For plate 1 and 3, most of the compounds increased secreted Aβ40 and Aβ42 levels compared to control cells (Figure 3.8A-F). Control drugs βIV and DAPT behaved as expected in the three plates: both decreased Aβ40, Aβ42 and sAPPβ concentration compared to the control cells. Carbachol dramatically increased the concentration of Aβ40, Aβ42, sAPPα and sAPPβ. In contrast, in the optimisation trial (Section 2.5.1), carbachol showed a closer behaviour to that previously reported in literature by decreasing Aβ40, Aβ42 and increasing sAPPα. However, it still increased the amount of sAPPβ.

For MSD immunoscreening, we selected the “hits” using a percentage cut-off of 20% above for sAPPα and 20% below for Aβ40, Aβ42 and sAPPβ. Based on the cut-off, we identified 11 drugs as hits. The compound 4 (Acetyl-beta-methylcholine chloride) showed a similar behaviour as carbachol by increasing sAPPα, sAPPβ, Aβ40 and Aβ42 (Figure 3.8.A, D, G, J). Acetyl-beta-methylcholine is used as a diagnostic drug for bronchial asthma [329].

The compound 8 (arecoline) increased sAPPα, Aβ40 and Aβ42 and did not show any effect on sAPPβ (Figure 3.8.B, E, H, K). Arecoline is a natural alkaloid extracted from the areca nut. It acts as a muscarinic and nicotinic acetylcholine receptors agonist [140] . Like caffeine and alcohol, arecoline is one of the most psychoactive drugs. It is mainly ingested by chewing areca nut, by Africa n and

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Asian inhabitants [330]. Due to its positive effects in the cholinergic system and given that one of the earliest symptoms in patients with AD is a decline in this system, arecoline has been tested as a potential treatment for AD. Despite the trials showing a modest improvement in verbal and spatial memory in patients with AD [331] further trials were halted as in vitro studies showed neuronal arecoline toxicity by increasing oxidative stress at concentrations above 50µM [332].

Compound 26 (biperiden) decreased sAPPβ to 87% and increased Aβ40 and Aβ42 (Figure 3.8.A, D, G, J). Biperiden is a muscarinic antagonist that is primarily used as symptomatic therapy of Parkinson’s disease and other movement disorders [333]. Cholinesterase inhibitors (ChEIs) in conjunction with antipsychotics are used to ameliorate behavioural and psychological symptoms of dementia (BPDS) [334]. However, antipsychotics can produce extrapyramidal side effects (EPS). It has been suggested that due to their mechanism of action, muscarinic antagonists such as biperiden can be used to prevent EPS caused by antipsychotics [335]. It is not advisable to prescribe such drugs in patients with AD, however, as muscarinic antagonists could ultimately impair cognitive function [336].

Compound 133 (Tranylcypromine) increased sAPPα and increased sAPPβ concentration by more than 20%. It also increased Aβ40 and Aβ42 (Figure 3.8.B, E, H, K). Tranylcypromine is used as an antidepressant and anxiolytic to treat major depression disorder and anxiety [337]. It works as a non-selective and irreversible inhibitor of monoamine oxidase (MAO) [338]. Studies show that MAO prompts the expression of β and  secretase thus increasing the production of Aβ [339], thereby suggesting that MAO inhibitors could be a good treatment for AD. In vitro studies in rat cortical neurons showed that above 10 µM, tranylcypromine inhibits Aβ toxicity. Moreover, biophysical assays using ThT to monitor Aβ fibrilization showed that tranylcypromine delays Aβ aggregation [340].

Compound 156 (tulobuterol) decreased the concentration of Aβ42 below 80% and increased Aβ40 (Figure 3.8.C and F). Tulobuterol is a β2-adrenergic receptor agonist that is used for asthma treatment as a transdermal patch [341]. Due to its pharmaceutical form, tulobuterol reaches the airways via the systemic circulation, thus allowing it to have a long-term effect compared to other asthma treatments [342]. It

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was first approved in Japan in 1998 and it is available in other countries such as Germany and Bangladesh.

Compound 161 (tetraethylammonium) increases sAPPα but also increases sAPPβ. It also increases Aβ40 but decreases Aβ42 below the 20% cut-off (Figure 3.8.C, F, I, L) Tetraethylammonium was initially investigated in clinical trials as a possible treatment for hypertension due to its inhibitory effect on the autonomic ganglia [343]. It was marketed for this purpose, but withdrawn due to its adverse events. Further research has found that this drug blocks voltage-dependant potassium channels in nerves [344]. It has also shown to be a competitive inhibitor of nicotinic acetylcholine receptors [345]. Additionally, Tetraethylammonium was shown to block calcium-activated potassium channels [346] .

Compound 162 (Theobromine) decreased the concentration of Aβ42, but increased Aβ40 and sAPPα and sAPPβ (Figure 3.8. C, F, I, L). Theobromine is a natural xanthine alkaloid that can be found in the cacao plant, and is thus present in chocolate. Although is not commercialised as a drug, it is known for having a bronchodilator and vasodilator and diuretic effect [347]. A study in a rat AD model showed that theobromine improved cognitive functions and decreased Aβ levels [348].

Compound 166 (trifluoperazine) increased sAPPα, Aβ40 and Aβ42 ( Figure 3.8.C, F, I, L). Trifluoperazine is a phenothiazine that is used as an antipsychotic and antiemetic, its mechanism of action is to block dopamine receptors thus reducing hallucinations caused by an excess of dopamine [349]. It also exhibits antiadrenergic effects [350]. Some studies have raised concern that the use of this compound to control behavioural symptoms in AD could contribute to neurofibrillary degeneration [351, 352].

Compound 168 (Thioridazine) increased sAPPα and decreased both sAPPβ and Aβ42 (Figure 3.8.C, F, I, L). Thioridazine is an antipsychotic phenothiazine used for treating schizophrenia [347]. It was developed and marketed by Novartis who withdrew the drug back in 2005 due to severe adverse events. Still, generic versions of the drug are still commercialised. Thioridazine was also used to treat anxiety in patients with dementia but with limited effects [353].

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Compound 169 (tropisetron) decreased the concentration of Aβ42, increased Aβ40 above 20% and slightly increased sAPPα to 13% above the control (Figure 3.8.C, F, I). Tropisetron is a serotonin receptor antagonist that blocks the action of serotonin in 5HT3 receptors [354]. It is mainly used as antiemetic in patients in chemotherapy. A study identified that toprisetron increased the levels of sAPPα in CHO-7W cells transfected with APP wt. In in vivo studies in J20 mice, it showed to increased sAPPα/Aβ ratio and improved spatial and working memory. This study also found that toprisetron binds to the APP ectodomain [355].

Compound 173 (Xylometazoline) decreased Aβ42 and had a modest increase in sAPPα (Figure 3.8, F, I). Xylometazoline is a drug marketed for the treatment of nasal congestion. Its mechanism of action is to bind to the same receptors as adrenaline [356].

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Figure 3.8 Single-well MSD screening of sub-LOPAC library. SH-SY5YAPP695 cells were incubated with 0.5% 10µM DAPT, 5µM βIV, 10 µM carbachol as controls or 5µM of the LOPAC sub-library for 24h. After incubation, 25µl of media from the cells was placed in MSD V-Plex Aβ peptide pane for measuring Aβ38, Aβ40 and Aβ42 concentration. Another 25µl were added to the sAPPα/ sAPPβ kit to measure sAPPα and sAPPβ concentrations. Hits were selected using a 20% cut-off. A-C) Aβ40 concentrations in cells treated with LOPAC sub-library. D-F) Aβ42 concentrations in cells treated with LOPAC sub-library G-I) sAPPα concentrations in cells treated with LOPAC sub-library J-L) sAPPβ concentrations in cells treated with LOPAC sub-library.

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3.4.2 MSD immunoassays hits confirmation To confirm the hits found in the single well screen in MSD immunoassay plates (V- Plex Aβ peptide panel and sAPPα/sAPPβ kit), we re-screened the hits and control drugs in duplicate. Additionally, we screened the 5 non-LOPAC drugs also in duplicate. Figure 3.9 shows the results from the confirmatory screening.

In this run, compound 4 (Acetyl-methylcholine chloride) increased sAPPα to 210%, sAPPβ to 136% and slightly increased Aβ40 and Aβ42 to 117%. This result agrees with what we found in the primary screening. Compound 8 (Arecoline) increased sAPPα to 134% and sAPPβ to 126%, Aβ40 and Aβ42 to ~120%. In this round arecoline also increased sAPPβ, but in the last/previous round it had no effect on sAPPβ. Compound 26 (biperiden) increased sAPPα to 131% and Aβ40 and Aβ42 to 110%. Compared to the previous round where compound 26 decreased sAPPβ, in this round, it showed no effect. Compound 133 (tranylcypromine) increased the amount of sAPPα to 134%, and slightly increased Aβ40 and Aβ42 to 117%. In the last round, tranylcypromine also increased sAPPβ, but in this round, it had no effect on it. Compound 156 (tulobuterol) slightly increased Aβ40, Aβ42 and sAPPα to 112% as it was below the 20% cut-off we did not consider as a hit. In the previous round this compound decreased Aβ42 below 80% and increased sAPPβ. Compound 161 (Tetraethylammonium) showed no significant effect on any of the targets tested. In the previous round, this compound decreased Aβ42 below 80%. Compound 162 (Theobromine) had no effect on Aβ40, Aβ42 and sAPPβ, and slightly increased sAPPα to 114%. In the previous round this compound decreased Aβ42 below the 20% cut-off and increased sAPPβ to 117%. Compound 166 (trifluoperazine) had no effect on any of the tested targets. In the previous/prior round, trifluoperazine increased sAPPα and Aβ40 and Aβ42. Compound 168 (thioridazine) had a very mild increase in sAPPα to 112% and a decrease in sAPPβ to 87%, but not enough to reach the 20% cut-off. In the previous round, this compound decreased Aβ42 and sAPPβ. Compound 169 (tropisetron) increased sAPPα to 124%. In the previous round, this compound decreased Aβ42 and slightly increased sAPPα to 113%. Compound 173 (Xylometazoline) did not show a difference in any of the parameters studied. In the previous round, it decreased Aβ42 and slightly increased sAPPα. SEN304 decreased produ ction of Aβ40 and Aβ42 below the 80%. It also increased sAPPα to 116% and decreased sAPPβ below 80%. Castalagin reduced Aβ40 to 88%, Aβ42 to 88%, sAPPα to 68% and sAPPβ to 73%. Vescalagin, increased Aβ42 but had no effect on the rest of the parameters evaluated. Resveratrol did not show any effect on the parameters measured. 127

In this confirmatory round only compounds 4, 8 and 133 behaved like in the primary screening round. The rest of the compounds showed no significant effect on the parameters evaluated. For instance, compound 161 and 162 that decreased Aβ42 by more than 50% in the primary screening round showed no effect on Aβ42 in the secondary round. Based on the obtained results, we decided to follow-up compounds, 4 (Acetyl-beta-ethylcholine), 8 (arecoline) and 133(tranylcypromine) as they showed consistent behaviour in both repeats. Still, we should consider that although the three compounds seem to activate sAPPα, compound 6 and 8, also increased sAPPβ, Aβ40 and Aβ42.

We also selected compound 169 (tropisetron) for a follow-up because it increased sAPPα in the confirmatory round. This is despite the drug not decreasing Aβ42 as it did in the initial round. Besides, the consulted literature supports that compound 169 has an interesting effect as an AD treatment [355]. In case of the non-LOPAC compounds, SEN304 and castalagin were considered hits as they increased sAPPα and decreased sAPPβ, Aβ40 and Aβ42. Table 3.2 summarises the effect of the hits from the Mesoscale screening

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Figure 3.9 SH-SY5Y APP695 cells were incubated with 0.5% 10µM DAPT, 5µM βIV, 10 µM carbachol as controls or 5µM of hits from LOPAC and the non-LOPAC compounds for 24h. After incubation, 25µl of media from the cells was placed in MSD immunoassay V-Plex Aβ peptide panel for measuring Aβ38, Aβ40 and Aβ42 concentrations. Another 25µl were added to the sAPPα/ sAPPβ kit to measure sAPPα and sAPPβ concentrations. Data are represented as the mean of each parameter evaluated, n=2 and the error bars represent standard deviation.

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Table 3.2 Summary of results from the Mesoscale screening

1st screening Confirmatory screening Drug Drug name Aβ40 Aβ42 sAPPα sAPPβ Aβ40 Aβ42 sAPPα sAPPβ Number 4 Acetyl-beta- + + + + + + + + methylcholine Chloride 8 Arecoline + + + = + + + + 26 Biperiden + + = - + + + = 133 Tranylcypromine + + + + + + + = 156 Tulobuterol + - = = + + + = 161 Tetraethylammonium + - + + = = + = 162 Theobromine + - + + = = + = 166 Trifluoperazine + + + = = = = = 168 Thioridzine = - + - = = + - 169 Tropisetron + - + = = = + = 173 Xylometazoline = - + = = = = = NA SEN304 - - + - NA Cantalanin NA - - - - NA Vescalagin = = = =

- decrease + increase = no change

3.5. Discussion Cell-based assays are often one of the earliest steps in drug discovery. Their objective is to help identify drugs that might be used as therapeutics. An ideal primary screening would be the one that allows us to accurately discern between compounds that have a true bioactive activity and those that are non-active for the intended target. Unfortunately, such screening is often still not achievable with current technology. This is mainly because cell-models only exhibit certain features of the disease, which make it difficult to predict whether drugs will behave the same when tested in other disease models or clinical trials. Still, cell-based assays are a widely used tool for drug screening as they allow a large number of drugs to be tested in a cost-effective fashion. In this chapter, we used two cell-based assays to select possible therapeutics for AD: MTT and MSD immunoassays.

Our customised library mainly included drugs that have been previously tested in humans, and/or approved by a regulatory entity. Drug repositioning is a forthcoming approach, as it could significantly decrease the costs and time of drug development. Besides selecting drugs that can be repositioned, we also added the ability of crossing the BBB as an additional criterion. This was considering that de-novo compounds that have demonstrated bioactive

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properties for AD in early drug development stages have been discarded or delayed in further stages because of their inability to cross the BBB [357]. Also, our tailored library could be used for drug screening for other CNS diseases such as Parkinson’s.

The primary screening using MTT assays showed 38 possible hits among the three independent rounds. However, each round in the primary screening showed different compounds as possible hits. This can be explained by considering within-plate variations. Within-plate variations only affect compounds within the same plate; cell seeding, cell synchronisation, effects of location of drugs in the plate, the solubility and stability of each drug, the batch of Aβ42 used, and pH changes are examples of within-plate variations [358]. Before seeding, cells are counted using a haemocytometer; however, given this counting is only approximate, it is impossible to seed the exact number of cells in each repeat and in each round. Also, it is possible that the cells are not synchronized thus being in different stages of the cell cycle. Drug solubility could be another source of variation—as we mentioned all drugs were dissolved in DMSO at 1mM and later dissolved to 1µM using Opti-MEM. It is possible that some of the drugs were not soluble in the cell media and had precipitated thereby losing any possible effect in the assay. In this case, the Aβ42 variability between plates was one of the main sources of variations between-plates. Considering these variations, we decided that the best way to analyse the results was to consider each plate individually. For instance, in the plate 5 of the third round (Figure 3.3E), Aβ42 MTT reduction was 53% and compound 76 that increased MTT reduction to 65% was identified. If we had not considered within-plate variations and use the overall average of Aβ toxicity in the round (68%) and a t-test as a statistical test, this possible hit could have been overlooked.

When performing drug screening, it is possible to make two types of errors: false positive or false negatives. A false positive is when an assay identifies as a possible hit, a compound that does not have a significant activity. On the other hand, a false negative is when the assay fails to identify an active compound. It is evident that the statistical method that we used to analyse MTT primary screening increased the probability of false positives, but it also decreased the probability of false negative errors due to low efficacy at the assessed concentration. However, false positives can be spotted by performing repetitions [328], as we did in the confirmatory round for the LOPAC sub-library.

The primary screening using MSD immunoassay plates was carried as single-well due to limited resources. When it comes to selection of hits, particularly for single-well screenings

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there is not a defined rule or technique to select hits. We also decided to increase the concentration at which the drugs were screened from 1µM to 5µM to enhance the sensitivity of the assay. Normally, a threshold is established as a go/no-go point. The threshold (cut-off) determines the rate of false positive or false negative hits. A low threshold decreases the probability of false negatives but increases the possibility of false positives [328]. The selection of the threshold purely depends on the type of assay and the resources of the laboratory that is performing it. For instance, for tough targets it is advisable to set low threshold to decrease false negatives, and false positives can be discarded in further stages [359] . In the case of the MSD immunoassay, we decided a threshold of 20% as compared to the non-treated cells. We considered this threshold so that it could allow us to detect the major number of hits, as false positives would be detected later.

We found 11 hits from the primary screening; these hits were re-screened in a confirmatory assay in two replicates. Only three compounds showed a similar behaviour to the primary screening round. For instance, in the primary round compounds 161 and 162 showed the highest decrease in concentration of Aβ42. However, in the confirmatory round, none of these compounds showed any effect on Aβ42. This is not uncommon and could be an artefact called regression toward the mean [328]. This artefact can happen when a compound shows a great effect in the first round and in a follow-up round its effect is closer to the group mean [360].

Overall the controls behaved as expected except for carbachol which simultaneously increased sAPPα, sAPPβ, Aβ40 and Aβ42. Such pattern was consistent in the three plates and both in the one-well primary screening and the confirmatory assay that was performed using duplicates. As mentioned in the discussion of the previous chapter, carbachol is a non-selective muscarinic agonist that increases sAPPα production [361] in a protein kinase dependent manner [102]. Also, it has been found that carbachol can upregulate the activity of α-secretase (ADAM17) [362]. Literature has also shown that it has no effect on BACE1 activity unless used with a M2 antagonist, in which case BACE1 is inhibited [363]. The results found in the MSD immunoassay do not agree with the consulted literature. However, in the consulted literature, none of the experimental designs evaluated the same parameters that we evaluated simultaneously. Both α- and β-secretase activity seem to be somehow regulated by muscarinic receptors. Considering that carbachol is a non-specific muscarinic agonist, the effect we are observing in our assay could be a hint that the regulation of α- and β-secretase is more complex than is currently known. 133

The screening of the sub-LOPAC library using MTT did not identify any hits. On the other hand, compounds 4, 8, 133 and 169 were selected as hits in the MSD immunoassay.

Similar to carbachol, compound 4 (Acetyl-beta-methylcholine), is a quaternary ammonium compound that also works as a muscarinic agonist. This could explain why it is similar to carbachol in the way that it increased all the parameters measured with the MSD immunoassay system. In the assay, it seems that the effect of acetyl-beta-methylcholine is milder compared to the activity of carbachol. However, we cannot have an accurate comparison on the potency of both drugs, as carbachol was tested at 10µM whereas acetyl-methylcholine was tested at 5µM. Still acetyl-beta-methylcholine’s behaviour supports that muscarinic receptors regulate both α and β-secretase activity.

Compound 8 (arecoline) is both a muscarinic and nicotinic receptor agonist [140]. In the MSD immunoassay, it showed a similar effect as methylcholine and carbachol. Arecoline seems to have a milder effect compared to methylcoline. The behaviour of arecoline can again be explained due to its activity as a non-selective muscarinic agonist. In fact, as we previously mentioned, it had already tested in humans and did show modest improvement in memory of patients with AD [331]. However, it could be possible that the limited activity of this compound could be due to its lack of specificity as it affects multiple targets in the APP processing pathway. Also, at high doses it has shown to increase oxidative stress [332].

Compound 133 (Tranylprimine) is a MAO inhibitor that reduces Aβ toxicity and decreases Aβ aggregation [340]. However, our findings do not align with the literature as tranylcypromine seems to increase sAPPα as well as Aβ40, Aβ42 and sAPPβ. Moreover, this drug also decreased Aβ42 toxicity in cortical neurons when treated with 10µM of tranylcypromine in a MTT assay. In the same assay cells treated with 1µM of tranylcypromine had no effect on Aβ42 toxicity. These results, agree with our findings in the MTT primary screening where tranylcypromine had no effect on Aβ42 toxicity.

Compound 169 (tropisetron) decreased Aβ42, and increased Aβ40 and sAPPα, with no effect on sAPPβ. These findings agree with a study that found that tropisetron increased the levels of sAPPα and Aβ40/Aβ42 ratio resulting in improvement in the memory of J20 mice [355].

Four out of the five compounds that were not part of the LOPAC library were hits for the MTT screening. All the compounds screened have previously been reported to have a

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potential activity as an AD therapeutic. Only castalagin and SEN304 were considered hits in the MSD immunoassay screening.

Interestingly, resveratrol—a polyphenol from the stilbene sub-family found in red wine, that has shown a neuroprotective action against different Aβ species [291, 364]—did not increase MTT reduction in our screening. A possible explanation could be that in similar assays in the literature both the concentrations of Aβ and resveratrol exceed 10µM [365].

Castalagin and vescalagin are enantiomers and polyphenols present in red wine. They are the main representatives of the hydrolysable tannins. Both compounds are found in oak and chestnut wood. Oak barrels are used to age red wine thus transferring these tannins into the wine [366]. A study using ThT and AFM imaging carried out by our collaborators identified that castalagin significantly decreased -amyloid aggregation [367] thus acting as an aggregation inhibitor. Both castalagin and vescalagin have also been highlighted as a possible treatment for AD given their antioxidant properties [368, 369]. Castalagin was also selected as a hit in the MSD immunoassay screening as it significantly decreased both sAPPα and sAPPβ.

EGCG is the main catechin (antioxidant flavonoid) found in green tea. Several in vitro and in vivo studies have pointed to EGCG as a potential treatment for AD. For instance, EGCG seems to inhibit Aβ toxicity in PC-12 and neuroblastoma mice cells when measured with MTT [370, 371]. It seems that one of the possible mechanism of actions for EGCG is as an aggregation inhibitor, by redirecting Aβ aggregation to off-pathway oligomers that are not as toxic as on-pathway oligomers [149] . In primary neurons from Swedish mutant APP mice, it was found that EGCG activates APPα processing [150]. Our data shows that EGCG increased MTT reduction in cells treated with Aβ42, but did not activate sAPP-α or decrease Aβ40 or Aβ42 in the MSD immunoassay.

SEN304 is an optimised peptide based on the site recognition sequence (SRS) KLVFF corresponding to residue 16-20 of Aβ [136] . This sequence was identified as the binding site involved for Aβ-Aβ interactions [372]. For this reason, this SRS was used as a template to design possible Aβ aggregation inhibitors, including SEN304. This peptide has shown both in biophysical and in vitro assays that it works by promoting a rapid aggregation of monomers in an alternative aggregation mode that produces larger but less toxic aggregates [137, 138]. Our results agreed with previous observation that SEN304 attenuates Aβ42 toxicity in SH-SY5Y cells assessed by MTT [138]. SEN304 also decreased Aβ40, Aβ42 and sAPPβ and increased

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sAPPα. In the consulted literature, we did not find any previous studies that investigated alternative mechanisms of actions for SEN304 that hypothesised about its possible effects in APP processing. However, 6E10 antibody, which is the capture antibody for the MSD Aβ42 panel was previously used by Amijee et al. as the monoclonal antibody for a single-site ELISA to assess SEN304 effect on Aβ oligomer formation [138]. They found that SEN304 did reduce Aβ signal in the ELISA assay and later confirmed that this was because SEN304 binds to Aβ monomers and oligomers. Considering this, and the fact that both SEN304 and 6E10 bind to the same Aβ region, it is possible that the decrease in Aβ40 and Aβ42 in the MSD assay is because SEN304 is already bound to the SRS KLVFF blocking the binding of 6E10. However, this does not explain why SEN304 decreases sAPPβ and increases sAPPα. Another possibility is that the MSD assay is suggesting that SEN304 has an additional mechanism of action besides the one it was initially designed for.

The screening of multiple drugs using the MTT assay proved to be challenging as the compounds were screened without any automatisation process thus increasing the probability of random errors. The variability of Aβ42 toxicity even within batches was another source of errors. Still, we managed to work around these drawbacks by using triplicates and by performing independent rounds to evaluate and confirm the activity of possible hits. In the case of the MSD immunoassay, we faced a limitation of resources that restricted our ability to further study the hits found during the performed studies. Nevertheless, one of the advantages of the MSD immunoassay platform is the ability to study different targets in the same pathway, in this case APP processing, simultaneously. The results from the MSD immunoassay showed the possibility that the study of targets in an isolated fashion could overlook possible multi- target effects. Overall, both MTT and MSD immunoassays helped us to identify compounds that could help as therapeutics in the treatment of AD.

Among the hits, EGCG and SEN304 produced the highest increase in MTT reduction. The MSD immunoassay SEN304 decreased Aβ and sAPPβ but increased sAPPα thereby suggesting that it could have an alternate mechanism of action

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Chapter 4. Secondary screening

4.1. Introduction In Chapter 2, we studied the toxicity of Aβ in SH-SY5Y cells as an AD model by using different cell-based assays. The primary screenings (MTT and MSD immunoassays) of compounds from LOPAC and other sources identified 8 possible drugs as hits. EGCG, SEN304, vescalagin and castalagin were identified as hits from the MTT assay and methylcholine (compound 6), arecoline (compound 8), tranylcypromine (compound 133) and tropisetron (compound 169) were identified as hits from the MSD immunoassay. Only SEN304 and castalagin were identified as possible hits in both assays.

In this chapter, we used the MTT assay to further investigate and compare the potency of the four hits that decreased Aβ toxicity in the primary screening by creating dose-response curves. Additionally, we used a two-combination compound approach to test whether any of the possible combinations could have a synergistic effect, thereby increasing the potency of the tested drugs. In this case, we did not use the LOPAC hits from MSD immunoassay as the primary screening using MTT did not highlight any drug as a potential hit decreasing Aβ toxicity in the MTT assay.

As previously mentioned in Chapter 2, several studies suggest that an increase of oxidative stress could be one of the factors preceding AD and that it could also promote Aβ production [78, 308]. Nevertheless, studies suggest that oxidative stress plays an important role in AD pathology, therefore, drugs that reduce ROS could be beneficial as a treatment for AD. Moreover, in some studies, EGCG, vescalagin and castalagin have been identified as antioxidants and as potential treatments for AD. Based on the important role of ROS in AD, we decided to focus our secondary assays in studying oxidative stress using two different assays: DCFH and GSH/GSSG-GloTM. DCFH assay allowed us to assess whether any of the hits from both primary assays decrease the excess of ROS in SH-SY5Y cells treated with Aβ42. Furthermore, we used GSH/GSSG-GloTM to evaluate if the tested compounds increased the GSH/GSSG ratio which is reduced in SH-SY5Y cells treated with Aβ42.

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4.2. Methods 4.2.1 Dose-response curve drug preparation Dose-response curves were prepared for each of the MTT hits: castalagin, vescalagin, SEN304 and EGCG. In total, 10 different concentrations of drugs ranging from 50 to 0.01µM were tested by incubating the cells with 1µM Aβ42 and the drug. The drugs in different concentrations without Aβ42 were used as control. All tested groups were incubated in triplicate. 400µl of each tested concentration was prepared containing 1µM Aβ42 and a constant concentration of 0.6% DMSO. Aβ42 was prepared as indicated in section 2.1.2 with the following variations. 10µl of Aβ42 at 1mM in DMSO was further diluted to 2µM in Opti- MEM by adding 4990µl of non-supplemented Opti-MEM with a final concentration of 0.2% DMSO. Drug stocks, 40x more concentrated than each required concentration, containing 20% DMSO were prepared. Then 10µL of the corresponding drug stock were mixed with 190µl of media and 200µl of 2µM Aβ42. All the drugs were dissolved in DMSO except for EGCG that was dissolved in water. In this case, drug concentrations were prepared as mentioned above with the only difference that the final concentration of DMSO was 0.1% for both EGCG and controls corresponding to EGCG.

For drugs controls without Aβ42, 10µl of the corresponding drug stocks 40x with 389.6µl of non-supplemented Opti-MEM and 0.4µl of DMSO was mixed.

Drug response curves were constructed using a 4PL model using the equation described in section 2.1.8.

4.2.2 Preparation of drug combinations A two-drug combination approach was undertaken to explore the possible synergistic activity between hits. The total 6 possible two-drug combinations of the 4 hits from the MTT primary screening were considered for this assay. Each two-drug combination was tested in triplicate at concentrations of 1.25µM for each drug. Likewise, single drugs were screened at 2.5µM to compare the activity of the drugs combined with the single drugs. Aβ42 at 1µM and DMSO 0.35% were used as controls.

Aliquots of castalagin, vescalagin, SEN304 and EGCG at 1mM in DMSO were prepared. For this assay EGCG was dissolved in DMSO instead of water as an adjustment to keep DMSO at a constant concentration in all the assays. Drugs at 1mM, were further diluted

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to 100µM using non-supplemented Opti-MEM. Aliquots of Aβ42 were prepared as indicated in section 4.2.1.

For screening at 1.25µM, each combination received 5µl of its respective two drugs at 100µM, 200µl of Aβ42 at 2µM and 190µl of non-supplemented Opti-MEM. For single drugs, 10µl of the respective drug was added to achieve a 2.5µM concentration; Aβ42 and non- supplemented Opti-MEM were added in the same amounts as described above for combination of drugs.

To prepare 1µM Aβ42 controls, 200µl of Aβ42 at 2µM were mixed with 198µl of non- supplemented Opti-MEM and 1µl of DMSO. To prepare DMSO at 0.35%, 1.5µl of DMSO was added to 498.5µl of non-supplemented Opti-MEM. The final concentration of DMSO was at 0.35% for both drugs and controls.

4.2.3 Preparation of drugs for oxidative stress assays panel Hits from both MTT and MSD screening were tested in the oxidative stress panel. For both DCFH and GSH/GSSG assays, drugs were tested at a concentration of 5µM with 5µM Aβ42 and 0.6% DMSO as controls in triplicates. Two aliquots of Aβ42 containing ~50µg of lyophilized Aβ42 were dissolved in 10µl of DMSO each to achieve a concentration of 1mM. Then the two aliquots were mixed to have 20µl of Aβ42 that were further diluted in 1980µl of non-supplemented Opti-MEM to achieve a concentration of 10µM Aβ42. A stock of 5mM was prepared for each of the drugs and then this was further diluted to 100µM in non-supplemented Opti-MEM. From this solution 20µl of the respective drug, 200µl of 10µM Aβ42 and 180µl of Opti-MEM were added to achieve a final concentration of 5µM drug, 5µM Aβ42 and 0.6% DMSO. 1µM Aβ42 and 6% DMSO controls were prepared as indicated in section 4.2.2.

4.2.4 MTT and DCFH assay For MTT and DCFH assays 100µl of SH-SY5Y cells was seeded at a concentration of 3x105 cells/ml in a 96-well plate in MEM Earle’s medium/ Ham’s F12 (1:1) supplemented with 10% FBS, 1%L-Q, and 1% n-aa, 1%P/S, and incubated overnight in a humidified incubator at

37°C with 5% CO2. Then media was replaced with 100µl of non-supplemented Opti-MEM containing its respective concentration of drug or control. After the drugs and controls were added to the well, the plates were returned to the incubator for another 24hrs. Then the MTT assay was performed as indicated in section 2.1.4 and DCFH assay was performed as described in section 2.1.9.

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4.2.5 GSH/GSSG- GloTM assay. 100µl of SH-SY5Y cells at 1x105 cells/ml were seeded in MEM Earle’s medium/ Ham’s F12 (1:1) supplemented with 10% FBS, 1%L-Q, 1% n-aa and 1%P/, and incubated overnight at 37°C with 5% CO2 to let the cells attach to the bottom of the white 96-well plate. Then the media was replaced with 100µl of non-supplemented Opti-MEM containing its respective concentration of drug or control and incubated for another 24h. For this assay, there were two sets in triplicate for each the treatment: one set was used to measure total glutathione and one set to measure oxidised glutathione. GSH/GSSG- GloTM assay was performed as indicated in section 2.1.11

4.3. MTT dose-response curves The four hits found in the MTT primary screening: SEN304, EGCG, castalagin and vescalagin were co-incubated for 24h at 10 different concentrations ranging from 50µM to 10nM with SH-SY5Y cells treated with 1µM Aβ42 using MTT assay. Additionally, we also incubated the cells with drugs alone. The MTT reduction of control cells treated with 1µM Aβ42 alone was on average ~64% for all the compounds. The data were normalised considering the cells treated with 0.6% DMSO only as 100% of MTT reduction.

The four compounds increased MTT reduction in a concentration-dependent manner. EGCG seems to be the most potent of the compounds evaluated as it restores MTT reduction to 100% in cells treated with only 2.5µM; EGCG seems to keep its potency up to the tested concentration of 50µM (red line Figure 4.1.A). SEN304 also restored MTT reduction to 100% when cells were treated with 10µM of this drug. Like EGCG, SEN304 maintained its potency up to the highest tested concentration (blue line Figure 4.1.A). Castalagin restored MTT reduction to 95% when cells were treated with 25 or 50µM of the drug (purple line Figure 4.1.A). Vescalagin, showed the lowest potency as it just restored MTT reduction to 93% when cells were treated with 25 or 50µM (green line Figure 4.1.A). SH-SY5Y cells only treated with drugs did not affect the MTT reduction of the cells (dotted lines Figure 4.1.A).

To further compare the activity and potency of the drugs, we constructed dose-response curves of all the drugs and fitted them using a 4PL dose response model in Graph Pad Prism software (Figure 4.1.B). With these curves, we calculated the half-maximal effective concentration (EC50) of all the four compounds (Table 4.1). Comparing the EC50 of the four

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screened compounds, EGCG seems to be most potent followed by SEN304, castalagin and vescalagin respectively.

Table 4.1 EC50 of the MTT “hit compounds”

Compound EC50 (µM) SEN304 ~2.5 EGCG ~0.8 Castalagin ~3.5 Vescalagin ~6.08

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Figure 4.1 MTT dose-response curves of SEN304, EGCG, vescalagin and castalagin. A) Compounds were incubated at different concentrations ranging from 50µM to 10nM along with 1µM Aβ42 (solid lines). Compounds were also incubated at the same concentrations without Aβ42 (dotted lines). Error bars represent SEM, n=3. B) Data from the drug response curves of all the compounds was fitted using a 4PL dose response model using the equation from section 2.1.8 and their EC50 was calculated.

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4.4. MTT Drug combination screening We used a two-drug combination approach to evaluate possible synergistic effects of the hits from MTT. In total, 6 possible combinations were evaluated. The cells treated with Aβ42 decreased MTT reduction to 67%. None of the two-drug combinations showed a possible synergistic effect inhibiting Aβ42 toxicity compared to drugs screened alone (Figure 4.2). Moreover, the effectiveness of the two-drug combinations seems to be driven by the most potent drug in the combination. For instance, all the drug combinations that included EGCG had similar effects to EGCG alone.

Figure 4.2 Two-drug combination MTT assay. A two-drug combination approach was used to evaluate possible synergistic effects. Each two-drug combination was tested at concentrations of 1.25µM for each drug (blue bars). Single drugs were screened at 2.5µM to compare the activity of the drugs combined with the single drugs (green bars). Error bars represent SEM. E = EGCG, S = SEN304, C = Castalagin, V= vescalagin.

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4.5. Oxidative stress assay panel 4.5.1 DCFH The 8 hits selected from both MTT and MSD primary assays were screened to evaluate their ability to hinder the increase of ROS in SH-SY5Y cells treated with 5µM Aβ42. The concentration of all drugs was also 5µM. The data were normalised considering healthy cells as 100%. Cells treated with 5µM Aβ42 increased ROS to ~160%. Among the screened drugs, only EGCG decreased the oxidative stress to ~100%. The literature suggests castalagin and vescalagin as possible treatments for AD due to their antioxidant properties [368, 369]. Despite this, when screened at 5µM, they did not show any potential activity in reducing the amount of ROS in cells treated with 5µM Aβ42. In vitro studies suggest that arecoline increases oxidative stress above 50µM [332]; according to our data, 5µM of arecoline does not seem to further increase ROS in SH-SY5Y cells treated with 5µM of Aβ42 (Figure 4.3A). In addition, cells incubated with 5µM of arecoline do not seem to have an effect in the ROS compared to control cells treated with 0.6% DMSO. None of the other drugs tested seem to have a significant effect in the ROS of SH-SY5Y cells when added at 5µM without Aβ42 (Figure 4.3.B).

Figure 4.3 ROS levels measured using DCFH-DA. A) SH-SH5Y cells were incubated with 5µM of each of the primary screening hits and 5µM of Aβ42 5µM. Controls were treated with 0.6% DMSO or 5µM Aβ42. After 24h incubation at 37°C, DCFH-DA assay was performed to measure ROS levels. B) SH-SY5Y cells were incubated with 5µM of each of the primary screening hits alone to test their possible effects on ROS. Data are represented as the mean % of relative ROS levels, with 0.6% DMSO controls set as 100%. n=3 and the error bas represent (SEM). Data was analysed using ANOVA (one-sided) Dunnett’s post hoc test. Significance symbol meaning **** for p < 0.0001.

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4.5.1.1 EGCG, ROS dose-response curve To further investigate the potency of EGCG to decrease ROS caused by 5µM Aβ42, we tested EGCG at 10 different concentrations from 50µM to 10 nM. EGCG seems to completely restore ROS level to 100% from 5 to 50µM. From 100nM to 5µM, EGCG seems to partially reduce ROS levels from cells treated with Aβ42 (solid line Figure 4.4. When SH- SY5Y cells were treated with drug only they did not have any effect on ROS levels (dotted line Figure 4.4.A)

We also made a dose-response curve and fitted it using a 4PL dose response model in

Graph Pad Prism software. We then calculated the half-maximal inhibitory concentration IC50 to be 0.5µM (Figure 4.4.B).

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Figure 4.4 ROS levels dose-response curve, EGCG. A) EGCG was incubated at different concentrations ranging from 50µM to 10nM and 5µM Aβ42 (solid line). EGCG was also incubated at the same concentrations without Aβ42 (dotted line). Error bars represent SEM, n=3. B) Data from the drug response curve was fitted using a 4PL dose response model using the equation from section 2.1.8 and their IC50 was calculated.

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4.5.2 GSH/GSSG ratio As we previously mentioned, in Section 2.6.3, glutathione is mainly found in cells in its reduced form (GSH) but when cells increase ROS it is oxidised to GSSG to neutralise ROS. Thus, the ratio of GSH/GSSG is reduced in cells that are exposed to ROS. We tested the 8 hits selected from the primary screening assay to evaluate whether they could restore the ratio of GSH/GSSG in cells treated with 5µM Aβ42. The concentration of the drugs was also 5µM. The GSH/GSSG ratio decreased from 24 to 12 when cells were treated with 5µM Aβ42. From all the drugs screened, only EGCG partially restored GSH/GSSG ratio when compared with cells treated with 0.6% DMSO (Figure 4.5.A). The compounds at 5µM were also tested without Aβ42. None of the compounds seemed to affect GSH/GSSG ratio significantly (Figure 4.5.B).

Figure 4.5 GSH/GSSG ratio was measured using GSH/GSSG- GloTM. A) SH-SH5Y cells were incubated with 5µM of each of the primary screening hits and 5µM of Aβ42 5µM. Controls were treated with 0.6% DMSO or Aβ42 5µM. After 24h incubation at 37°C GSH/GSSG- GloTM assay was performed to measure GSH/GSSG ratio. B) SH-SY5Y cells were incubated with 5µM of each of the primary screening hits alone to test its possible effects on GSH/GSSG ratio. Data are represented as the mean % of GSH/GSSG ratio, with 0.6% DMSO controls set as 100%. n=3 and the error bas represent (SEM). Data was analysed using ANOVA (one-side) Dunnett’s post hoc test. Significance symbol meaning **** for p < 0.0001. 4.5.2.1 EGCG, GSH/GSSG ratio dose-response curve As EGCG was the only drug that increased the GSH/GSSSG ratio, we made a dose- response curve by testing 8 different concentrations of EGCG ranging from 50µM to 10 nM in SH-SY5Y cells treated with 5µM Aβ42. Cells treated with 0.6% DMSO only showed a GSH/GSSG ratio of ~30 compared to cells treated with 5µM Aβ42 that decreased this ratio to ~15. EGCG increases the GSH/GSSG ratio of cells treated with Aβ42 in a concentration- dependent manner. Only cells treated with 50µM EGCG almost restore GSH/GSSG ratio to 147

similar ratio as control 0.6% DMSO (solid line Figure 4.6.A). Cells only treated with drugs do not decrease the ratio of GSH/GSSG compared to control cells (dotted line Figure 4.6.A).

We also constructed a drug-response curve and fitted it using a 4PL dose response model in Graph Pad Prism software. We then calculated the half-maximal effective concentration IC50 to be 1.8 µM (Figure 4.6.B).

Figure 4.6 GSH/GSSG ratio dose-response curve, EGCG. A) EGCG was incubated with 8 different concentrations ranging from 50µM to 10nM and 5µM Aβ42 (solid line). EGCG was also incubated at the same concentrations without Aβ42 (dotted line). Error bars represent SEM, n=3. B) Data from the drug-response curve was fitted using a 4PL dose-response model using the equation from section 2.1.8 and their EC50 was calculated.

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4.6. Discussion Once possible hits have been identified, one of the next steps in early drug development is to further characterise these compounds. One of these steps consists of performing potency assays, that can be defined as the ability of a drug to cause a desired effect at certain concentrations in a specific in vitro assay [373]. At this stage of drug discovery, potency assays are useful to compare and rate the efficacy of the different possible hits. To further characterise the potency of drugs found in the primary screening using MTT assay only, we created dose- response curves of these hits. MTT assays have been widely used to study the cytotoxicity of Aβ42 in SH-SY5Y cells [374, 375], and as platform to screen potential drugs for AD[137]. The average cytotoxicity in the MTT assays for the secondary screening (64%) is consistent with the MTT assays performed in the primary chapter (67%), thereby allowing the comparison of dose-response curves of the compounds tested. The primary indicator of potency assays is the EC50; this variable is defined as the concentration of a compound that induces a half- maximal response. The dose-response curves showed that EGCG was the most potent compound as it restored MTT reduction to 100% and had an EC50 of 0.8µM. SEN304 also restored MTT reduction to 100% and had an EC50 of 2.45µM. In a similar assay, SEN304 had a EC50 of 10µM, but the difference can be explained because in this assay SH-SY5Y cells were treated with 10µM of Aβ42 instead of 1µM [138]. Castalagin and vescalagin increased MTT reduction to 90% and had an EC50 of 3.4 and 6.0 µM respectively. The literature suggests that castalagin and vescalagin could be possible treatments for AD [366] but we are not aware of any in vitro assays further studying these compounds.

In this chapter, we also assessed whether a two-drug combination of the hits found for MTT primary screening could have a synergistic effect reducing Aβ42 toxicity. Drug combinations are an interesting strategy for AD as their possible synergistic effect could help to slow down or stop the progression of AD by affecting different targets that are relevant in AD pathology. For this purpose, we screened drugs at 2.5µM, we found that none of the drug- combinations improve the effectiveness of the drugs alone. According to the consulted literature, drug-combinations that exceed the effectiveness of single drugs are rare [376].

Considering the potential antioxidant activity of EGCG, vescalagin and castalagin, we performed two secondary assays to study their activity in reducing ROS causing by Aβ. We also performed these assays using all the hits found in the primary MTT round to evaluate whether their potential as AD drug was related to ROS regulation. After DCFH-DA permeates

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the cellular membrane, it is oxidised in the presence of ROS to its fluorescent form DCF; thereby the fluorescence intensity is proportional to the amount of ROS present in the cells. After performing DCFH assay, our data suggests that SH-SY5Y cells treated with 5µM Aβ42 increase the ROS activity to 160% compared to cells treated with 0.6% DMSO only. When assessing the potential activity of the hits from the primary screening to decrease the ROS activity caused by Aβ, we found that EGCG was the only compound that restored ROS activity to 100%. We then performed a dose-response curve using DCFH to evaluate EGCG potency to decrease ROS activity and found that the IC50 (0.5µM) concentration was very similar to the

EC50 (0.8µM) of EGCG dose-response curve for MTT reduction. Furthermore, we used GSH/GSSG ratio to evaluate if any of the hits increased the ratio of GSH/GSSG in SH-SY5Y cells treated with Aβ. Again, we found that only EGCG could significantly increase the ratio of GSH/GSSH and performed a dose-response curve using GSH/GSSG assay. In this assay,

EGCG showed to be less potent than in the previous dose-response curves showing an EC50 of 1.8µM.

EGCG has been previously studied as a possible treatment for AD. Our data agrees with previous studies that suggest that EGGC inhibits Aβ toxicity when measuring with MTT in a concentration dependent manner [377]. Moreover, other groups have studied the increase of ROS in cells treated with Aβ fragments and have also found that EGCG significantly decreased the ROS signal [148]. Our data also shows that EGCG increased the ratio of GSH/GSSG in cells treated with Aβ. The effect of EGCG decreasing oxidative stress could be due to its action as ROS scavenger but studies also suggest that it could be because it promotes the production of glutathione [378].

Although our data show that castalagin and vescalagin did not have an effect in decreasing ROS activity at the tested concentrations, they might have an alternative mechanism to alleviate Aβ toxicity. It is also possible that, as MTT drug responses suggest, these compounds are less potent than EGCG so it would be necessary to increase the concentration of the drugs to see any effect.

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Chapter 5. Drosophila melanogaster assays

5.1. Introduction Despite the absence of an ideal animal model for AD, transgenic animals modified to mimic the hallmark features of AD have been used to study this disease. As compared to other commonly used animal models (such as mice), Drosophila melanogaster is an attractive in vivo model as it is easier to maintain and handle. The generation time from egg to adult is ~10 days, and it has a short life span (50-90 days). One of the biggest challenges of finding drugs for AD is their penetration through the Blood Brain Barrier (BBB). Studies show that Drosophila BBB is very similar to the mammalian one, making Drosophila a good model for testing drugs for AD [379].

The available transgenic AD models in Drosophila melanogaster are focused on either Aβ or Tau production [259, 264]. Considering the scope of this thesis is on studying the effects of Aβ toxicity, we chose to use a Drosophila melanogaster model that over-expressed Aβ42. The model we chose was developed by Crowther’s lab and they fused Aβ42 sequence to a secretion signal peptide thereby allowing the secretion of Aβ42 in the nervous system of the fly [264]. The overexpression of human Aβ42 in Drosophila’s nervous system mimics the accumulation of Aβ42 in the extracellular neuron space found in the human brain. In addition, flies expressing human Aβ42 develop a similar AD pathology, including age–dependent memory defects, locomotor dysfunction, neurodegeneration and decreased lifespan[264, 380, 381]. Moreover, young flies from this AD model showed less Aβ42 compared to adult flies suggesting Aβ42 accumulates over time. Interestingly, the levels of Aβ42 were higher in the non-soluble fraction, suggesting the presence of aggregate forms of Aβ [262].

The generation of the AD model used here was based on the GAL4/UAS expression system developed in the early 90’s to allow the gene expression of inserted genes, including Drosophila [382]. The system involves the use of two different parent lines: one containing a yeast transcriptional activator GAL4, and the other containing the Upstream Activating Sequence (UAS) enhancer to which GAL4 selectively binds to activate transcription of a downstream gene. The parent line containing GAL4 is known as the driver and the UAS fused to the gene of interest is known as a responder. The system is activated when both parent lines

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containing driver and the responder are crossed, thereby the protein fused with UAS will be expressed in the offspring. In the model used by Crowther, Aβ42 is fused to UAS responder line.

In previous chapters, we screened the potential of different compounds from a tailored drug library to treat AD using different in vitro models. The assays described in these chapters highlighted two drugs as potential hits for AD- EGCG and SEN304. Considering that one of the features of Aβ42 expression in flies is a reduction in the lifespan due to Aβ42 accumulation and neurotoxicity, we performed survival assays to evaluate whether EGCG or SEN304 increased the lifespan of the Aβ42 flies.

5.2. Methods All Drosophila melanogaster stocks were kindly provided by the Dr. Andreas Prokop laboratory. The fly crosses, fly husbandry and culturing were performed by Sanjai Patel. The survival and MSD immunoassays were performed by us.

5.2.1 Drosophila melanogaster stocks 5.2.1.1 Elav-GAL The Elav-GALc155 that expresses Gal4 [64] in neurons was obtained from the Bloomington Drosophila Centre at the University of Indiana.

5.2.1.2 UAS-GFP The UAS-GFP line expressing GFP [383] was obtained from the Bloomington Drosophila Centre at the University of Indiana.

5.2.1.3 Elav-GALc155; UAS-GFP Female virgins of the line Elav-GalC155 (Elav-GAL4/Elav-GAL4;+/+) were crossed with virgin females of the UAS-GFP/UAS-GFP; + line. The male offspring from this cross — which contains the Elav-GAL4/+; +/UAS-GFP; +/+ genotype — was used as the control line for the experiments performed in this chapter. One copy of the GFP transgene was inserted in the 2nd chromosome (Figure 5.1).

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Figure 5.1 Control GFP line was generated by crossing female virgin ElavGal4 with UAS-GFP males. The males from this offspring expressing GFP were selected as the fly control lines 5.2.1.4 UAS-Aβ42 In this stock, the Aβ42 peptide was fused to a secretion signal peptide from the Drosophila necrotic gene (MASKVILLLLTVHLLAAQTFAQDAEFRHDSGYEVH

HQLVFFAEDVGSNKGAIIGLMVGGVVIA)[264]. The Aβ42 line has one copy of the transgene inserted on the 2nd chromosome. The expression of this peptide was driven by the Elav-GALc155 driver (Figure 5.2).

Figure 5.2 UAS-Aβ42 line. The driver parental line Elav-GAL4 was crossed with the UAS-Aβ42. The offspring containing the Gal4 protein binds to the UAS promoter and activates the transcription of the transgene fused to the promoter, in this case the Aβ42 peptide and is then expressed (modified from [19, 384]). 5.2.2 Fly husbandry The different stocks were maintained in 50 ml plastic vials containing standard media (cornmeal, glucose, yeast and agar) at 18°C or 25°C on a 12-hour dark/light cycle and at 65%

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humidity. As per facility good practice, two vials of each stock are kept at 18°C where one of the vials should be two weeks-old whereas the other should be a new one. This guarantees that when flies are transferred every two weeks one of the vials (being one-month old) contains new hatched flies that are transferred into a new vials, whilst the new vial (two-weeks old) will contain larvae [385] .

5.2.3 Male/Female sorting The sorting of male/females was performed under an optical microscope and flies were anaesthetised using a mild CO2 influx and a paint brush. There are different features that allow male/female sorting. Females are larger than males and have a stripped pattern at the posterior bottom of the abdomen. On the other hand,, males possess a rounded abdomen and the bottom part of it is completely dark (Figure 5.3.B). However, differentiation in recently enclosed flies can be difficult as the abdomen coloration of males could be pale. In these cases, males can be distinguished by sex combs on their forelegs (Figure 5.3.A). The sex combs are observable as a row of thick dark bristles [385].

Figure 5.3 Male/Female morphological differences A) Sex combs found in males only as a row of thick dark bristles B) Females are larger compared than males and have a striped abdomen at the bottom compared to the round and dark abdomen of male. Modified from [386, 387] 5.2.4 Virgin collection Collection of virgin females is essential to perform crosses. The selection of female virgins avoids random fertilisation thereby ensuring mating to obtain the desired genotypes. After eclosion, males incubated at 18°C will reach sexual maturity within 10-16 hours. To ensure female virgin collection, the following routine was performed. Vials with eclosing flies were kept overnight at 18°C and virgin flies were sorted early morning into new vials using very light CO2. Female virgins can be identified as they are very pale and possess a dark spot in the abdomen (meconium).

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5.2.5 Quantification of soluble and insoluble Aβ42 At day 4, five flies of the newly-eclosed UAS-Aβ42 strain were placed in 1.5ml Eppendorf tubes and decapitated by freezing the tubes in liquid nitrogen and subsequent vortexing. This procedure was also performed at days 24 and 50. Fly heads were separated using a paintbrush, placed in 1.5ml Eppendorf tubes and kept at -80°C till further use. When required, each batch of 5 heads was homogenized for 2 min and vortexed in 50µl of soluble extraction buffer (50Mm Hepes pH 7.3, 5mM EDTA, and complete EDTA-free protease inhibitor (Roche Diagnostics)). Then the homogenate was incubated at room temperature for 10 mins, sonicated for 4 min in water bath and centrifuged at 13,000 rpm for 5 mins to separate the soluble (supernatant) and insoluble (pellet) fraction. 20µl of the soluble fraction was 10x diluted in a dilution buffer (25Mm Hepes pH 7.3 and 1mM EDTA). This fraction was stored in ice till use. The insoluble fraction was dissolved in extraction buffer (5M Guanidine (Gn)HCl, 50Mm Hepes buffer pH 7.3 and 1mM EDTA) and homogenised for 1 min followed by vortexing. 20 µl of the insoluble fraction was also diluted 10X using the same dilution buffer [262, 388]. Soluble and insoluble fractions were assayed in duplicates using the MSD Aβ peptide panel as indicated in section 2.1.8. The total amount of protein was quantified using the Bradford Bio-Rad protein assay so the concentrations of Aβ42 were adjusted as a ratio of (ng Aβ42/ g of protein).

5.2.6 Lifespan assays Within 1-3 days after eclosion, male flies were transferred into separate vials containing 25 or 30 flies per vial. For each group treatment, we used a population of 90 or 100 flies depending on the assay. Each vial contained 3 ml of Nutri-Fly Instant media (from Genesee Scientific) mixed with 3ml of water with either 0.2% DMSO, 10µM EGCG, or 10µM SEN304. We used UAS-Aβ42 flies as our test subject and UAS-GFP as controls. Flies were transferred to new vials containing fresh food every 2-3 days. During every transfer dead flies or censored flies (i.e. escaped during transfer) were recorded/documented.

5.2.7 Survival curves, statistical analysis Survivorship was plotted using Kaplan-Meier curves. A Kaplan Meier curve is a non- parametric statistic tool that records the survivorship of a population considering censored data. Censored data is when we lose track of a subject before it reaches the endpoint of the study - in this particular case, when the flies escape during transfer or if they are accidently crashed.

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In the graph, censored data is represented by small vertical ticks. In Kaplan Meier curves, the survivorship probability is cumulative, which means that survivorship at a specific time point is calculated based on the previous time point survivorship.

Log-Rank is a non-parametric statistical test, used to compare two survival curves. Log-Rank is a Chi-square test that allows working with censored data and uses the Kaplan- Meier estimates. We used the Bonferroni correction to adjust critical p-values, as we were comparing multiple pairs of groups (e.g. control vs T1, control vs T2). Statistical analyses were performed using GraphPad Prism version 7.0.

5.3. Results

5.3.1 Quantification of Aβ42 To confirm the expression and aggregation of Aβ42 in the offspring of the Elav-GAL x UAS-Aβ42 cross, we used MSD immunoassays to measure the amount of Aβ42 produced by these flies at different days of age (4, 24 and 50). The amount of protein was quantified in both the soluble and insoluble fraction of the collected samples. Regardless of the age, we found that most Aβ42 was contained in the insoluble fraction of the protein extracts; this was consistent with previous findings using the same fly line [389]. The Aβ42 concentration increased over the age in both soluble and insoluble fractions suggesting Aβ42 accumulates as flies aged (Figure 5.4). At the measured timepoints, the soluble fractions accounted for ~10% of the total Aβ42. Overall, our results agree with previous studies: Aβ42 was mainly found in the insoluble protein fraction and it accumulates over time [19, 262].

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Figure 5.4 Quantification of Aβ42 in the soluble and non-soluble fraction of Drosophila melanogaster overexpressing the Aβ42 peptide. The heads of 5 flies at different days of age were homogenised and the protein content was separated into soluble and insoluble fraction. The concentration of Aβ42 was quantified using a MSD Aβ peptide panel. A) The concentration of Aβ42 in the insoluble fraction increased over the time and constitutes around 90% of the total Aβ42 B) The concentration of Aβ42 in the soluble fraction also increased over the time, however it only constituted around the 10% of the total protein. Bars represent the mean value of each sample, Error bars represent SEM, n=2. 5.3.2 Longevity comparison of Aβ42 and GFP Drosophila lines One of the characteristics of the Aβ42 Drosophila line developed by Crowther is that the expression of Aβ42 decreases the longevity of the flies. To evaluate the effect of Aβ42 over the flies’ lifespan, we compared the longevity of the control line elav-GAL4; UAS-GFP; versus the elav-GAL4; UAS-Aβ42 flies. 100 flies of each strain at a density of 25 flies per vial were fed with 3 ml of Nutri-Fly Instant media mixed with 3ml water or 0.2% DMSO. The flies were transferred to vials with fresh media every other day. During each transfer, the total number of living flies was recorded (including the censored ones).

When comparing the survivorship curves of the flies treated with water only, the median life of the Aβ42 line was shorter (44 days) compared to the median life of the GFP line (56 days)(Figure 5.5.A). The median life of the flies treated with 0.2% DMSO showed a similar trend where the median life of the Aβ42 line was also shorter (48 days) compared to the median life of the GFP line (54 days)

Figure 5.5.B). Hence, the data suggest that the secretion and accumulation of Aβ42 decreases the life span of Drosophila. When comparing the median life of the GFP line treated with water or 0.2% DMSO, the median life is very similar and the Log-Rank test does not indicate a significant difference between these survival curves. Similarly, there is no significant difference when comparing the survival curves of Aβ42 line treated with water or 0.2% DMSO.

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Therefore, these data suggest that 0.2% DMSO does not affect the survival median of the tested Drosophila models.

Figure 5.5 Comparison of the longevity of the control line elav-GAL4; UAS-GFP; + (GFP) vs. the elav-GAL4; UAS-Aβ42 line (Aβ42). A) Flies were fed with 3ml of Nutri-Fly Instant mixed with water. Kaplan Meier curves of the Aβ42 and GFP were plotted. Log rank test performed with Bonferroni transformation (p>0.0001) GFP median= 56 days, Aβ42 median= 44 days B) Flies were fed with 3ml of Nutri-Fly Instant mixed with 0.2% DMSO. Kaplan Meier curves of the Aβ42 and (GFP) were plotted. Log rank test performed with Bonferroni transformation (p>0.0003) GFP median= 54 days, Aβ42 median= 48 days. 5.3.3 Effect of EGCG and SEN304 on the lifespan of Aβ42 and GPF lines The previous experiment showed that the survival of Aβ42 line is reduced compared to the GFP line. In this experiment, we evaluated whether EGCG and SEN304 increased the lifespan of Aβ42 flies. After eclosion, both fly lines were fed with 3ml Nutri-Fly Instant media mixed with 3ml of 10µM of EGCG dissolved in water, 3ml of 10µM SEN304 with 0.2% DMSO, 3ml of water only, or 3ml of water at 0.2% DMSO. Flies treated with water were the control for flies treated with EGCG and water at 0.2% DMSO was the control for flies treated with SEN304.

The median survival lives of the flies from the GFP line treated with water and 0.2% DMSO were 64 and 66 days respectively. This is larger than the median survival life of Aβ42 line treated with water or 0.2% DMSO which is 56 days in both cases (Figure 5.6.A). None of treatments affected the median survival life of the GFP line flies (Figure 5.6.B). When comparing the lifespan of the Aβ42 flies treated with 10µM EGCG to the water controls, we found that EGCG does not increase the lifespan of Aβ42 flies (Figure 5.6.C). The median

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survival of flies treated with EGCG is 54 days compared to 56 days for flies treated with water. Likewise, when comparing the Aβ42 flies treated with 10µM SEN304 to the controls treated with water at 0.2% DMSO, the median survival life was 56 for both treatments (Figure 5.6.D). Overall, the collected data suggests that at 10µM neither EGCG or SEN304 have any effect on the lifespan of Aβ42 fly line.

Figure 5.6 Lifespan comparison of GFP and Aβ42 lines treated with EGCG and SEN304. A) Survival curves of Aβ42 line and GFP line treated with water or 0.2% DMSO. Aβ42 water median=56 days, GFP water median=64 days, Aβ42 02% DMSO = 56 days, GFP 0.2% DMSO = 56 days. Log rank test Aβ42 water vs GFP water performed with Bonferroni transformation (p > 0.0001). Log rank test Aβ42 water vs GFP 0.2% DMSO performed with Bonferroni transformation (p > 0.0001). B) Survival curves of GFP fly line treated with water, 10µM EGCG, 10µM SEN304, or 0.2% DMSO. Log rank test (p >0.001). GFP EGCG median= 62 days, GFP SEN304= 62 days C) Survival curves of Aβ42 fly line treated with water and 10µM EGCG. Log rank test (p >0.01) Aβ42 EGCG median = 54 days D) Survival curves of Aβ42 fly line treated with 0.2% DMSO and 10µM SEN304. Log rank test (p < 0.05) Aβ42 SEN304 =56 days. 5.4. Discussion We selected Drosophila melanogaster as an animal model because of its advantages in terms of handling, economics and life span compared to more complicated animal models such as mice. In the last decade, a few research groups have focused on developing a suitable transgene Drosophila model for AD [264, 380]. In this chapter, we used Crowther UAS-Aβ42

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because it agrees with the line of investigation of this thesis which is focused on using Aβ42 to mimic AD features. Additionally, this model has been used by other groups for similar purposes so we can compare our findings with the available literature [19, 262].

To assess the suitability of the UAS-Aβ42 model for the screening of drugs, first we measured the concentration of Aβ42 at different days of age in flies. Our data suggests that Aβ42 concentration increases over the age of flies. We interpret this increase (in concentration) over time as an accumulation of Aβ42 and not as an increase of Aβ42 secretion. This is due to the constant expression of Aβ42 over the different stages of development of the flies and Aβ42 proclivity to aggregate. Moreover, at the different evaluated endpoints, the concentration of Aβ42 in the soluble fraction accounted for around 10% of the total Aβ42. These findings are in alignment with Caesar et. al., where they found that Aβ42 also increases over time and that most of the Aβ42 is found in the insoluble fraction [262]. However, the concentrations of Aβ42 measured by these authors were considerably higher (~100 ng) than the ones measured by us (~250pg). The difference could be attributed to two factors: the sensitivity of the immunoassays used to measure Aβ42 and that they have two copies of the UAS-Aβ42. In the initial characterisation of the UAS-Aβ42 model, Crowther et. al. found that flies carrying two copies of the transgene increased the toxic effects of Aβ42.

One of the features of the Aβ42 model designed by Crowther is that Aβ42 reduces the lifespan of Drosophila. We used UAS-GFP to assess whether Aβ42 decreased the flies’ lifespan. UAS-GFP was selected as it also a fly model based on the GAL4/UAS expression system. Besides, this model has been previously used as a control for other Aβ42 transgene models [380]. We did find that the expression of Aβ42 decreased the lifespan of flies compared to flies expressing GFP. However, the median lifespan found in this assay was considerably longer to the ones found in previous assays. For instance, in our assay the median life of flies with one Aβ42 transgene was 44 days compared to 16 days in Crowther’s paper and 25 days in Caesar’s paper [262, 264]. We attribute this change in the median life to the difference in temperature at which the assays were performed. Crowther and Caesar used a temperature of 29°C and we performed the same assays at 25°C. They used such higher temperature to promote the expression of the transgene. We, however, chose to keep the temperature at 25°C as the expression of GFP transgene inserted in the control line would increase and might have had a negative effect on the lifespan. Even though the difference in lifespan between the

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controls and Aβ42 flies was not as big as expected, we consider it was good enough to evaluate the effect of in-vitro drug hits on the Aβ42 fly lifespan.

The administration of drugs in Drosophila is challenging; there are a few available drug-delivery routes. These include drug vaporisation and drug injection in the abdominal region[390]. The latter one is considered the most reliable route of administration, but is unpractical in long experiments such as survivorship. For this reason, the most common route of administration is the one used in this thesis which consists in mixing the drug with the fly food. Although, this is the most used technique due to its practicality, there are few considerations that need to be taken. For instance, after the drug is mixed in water where it is dissolved homogenously, it is mixed with the powder media where the distribution of the drug can be affected. Also, we should consider that the food intake varies among the flies so the drug dose ingested by each fly is different. Considering the above, is not surprising that the threshold at which a drug could be physiologically active varies from 0.01 to 100µM [391]. In this experiment, we were only able to test EGCG and SEN304 at 10µM as we had a limited stock. At the tested concentration, none of the drugs affected the median survival life of Aβ42 flies. However, this does not necessarily mean that the drugs lack any effect on this AD model. Further experiments using higher concentrations of drugs can clarify the issue.

Overall, the Aβ42 model designed by Crowther et. al. seems a robust in vivo model for the screening of drugs for AD and it would advisable to further optimise it. For instance, instead of using UAS-GFP as a control line we could use an isogenic wild-type fly such as w118. In this way, we could perform the lifespan assays at 29°C to promote the expression of the Aβ42 transgene and this might result in a more significant lifespan decrease. In addition, we could also increase the number of copies of the Aβ42 gene. Such changes would allow us to increase the drugs’ doses as the reported median life of flies with the above-mentioned genotype is considerably less than the one showed in this chapter

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Chapter 6. General discussion

The major risk factor associated with Alzheimer’s disease is aging. Given the demographic changes, in the next few decades the elderly population is expected to dramatically increase: the number of cases of AD is expected to double every 20 years worldwide [3]. While AD pathology has not been completely elucidated, significant progress has been made in the last thirty years. The Alzheimer’s amyloid cascade hypothesis, that hypothesized Aβ as the initiator of AD pathology, has driven most of the research around the disease. The development of drugs has also been heavily focused on targets to alter Aβ levels or aggregation patterns as way to address AD pathology. Still, none of the drugs have demonstrated enough effectiveness to prevent or stop the symptoms associated with AD. These constant failures have questioned whether the amyloid hypothesis is correct or if it is a matter related to the design of clinical trials that suggests patients should be enrolled at earlier stages of the disease. This panorama highlights that AD pathology is very complex and that there are many of missing pieces to fully understand this disease. Nevertheless, the evidence that Aβ plays a central role in AD pathology is overwhelming. For this reason, we designed the cellular and Drosophila assays around Aβ neurotoxicity.

Cell-based assays are one of the first steps for drug screening. Among the available cell models iPSCs are the most representative models for AD as they are directly derived from patients with sporadic or FAD. However, iPSCs use is still limited as they are very expensive compared to other existing cell models, and have some technical hurdles. For instance, they are fragile which makes them difficult grow and expand, and their differentiation processes could take months. Moreover, in some cases the phenotype expression results are weaker than expected. Undoubtedly, iPSCs are a promising model for AD, but at the moment due to their technical difficulties their applications for drug screening are limited.

In this project, we used SH-SY5Y cells as our cellular model. These are neuro-like cells that have been used widely for studying neurodegenerative diseases, including AD. They are easy to grow and expand making them a reliable, fast and cost-effective model. Differentiation of SH-SY5Y5Y cell lines with retinoic acid make the cells even more similar to human neurons. However, it also makes the cell more resistant to Aβ toxicity compared to undifferentiated cells [242]. We decided to use non-differentiated SH-SY5Y as our cell model as they have been previously used for drug screening for AD by evaluating the neurotoxic

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effects of Aβ [138, 265]. This allowed us to compare our findings with previous studies more easily.

Considering that Aβ consistently shows toxicity in SH-SY5Y cells we thought that cell viability assays would be a good primary screening assay. We evaluated MTT, LDH assay and CytoTox-Glo as possible assays. One of the critical parameters in the evaluation of these assays was to find the lowest concentration of Aβ42 that demonstrates significant cell toxicity. This is because the concentration of Aβ in patients with AD is in the pM range [392]. Our results found that for MTT, 1µM is the lowest concentration that showed inhibition of Aβ toxicity. The LDH assay showed that at least 25µM Aβ is necessary to cause cell death. We also used CytoTox-Glo which measures cell death by the release of “dead-cell proteases” and is considered more sensitive than LDH. Our data showed that treating cells with 1 or 5µM Aβ42 did not released a significant amount of dead-cell proteases. These results agreed with previous findings which suggest that at low concentrations of Aβ42 the decrease of MTT reduction does not necessarily produce cell death [138],[273]. Based on these results we selected MTT as our primary screening platform as it showed cell toxicity at significantly lower concentrations than LDH or CytoTox-Glo. There is a massive difference in the concentrations of Aβ found in patients with AD (pM) and the concentrations needed to show neurotoxicity in cellular models (µM). This difference can be explained by the fact that in patients with AD the accumulation of Aβ is gradual over many years, in contrast to a cellular model where Aβ is acutely applied to evaluate neurotoxic effects within a very short period of time.

In addition to the cell viability assays, we explored the use of the MSD immunoassays as a platform for drug screening. In this project, we used the V-Plex Aβ peptide panel assay which measures the amount of three different Aβ species: Aβ38, Aβ40 and Aβ42. We also measured sAPPα and sAPPβ

The MSD immunoassay allowed us to simultaneously measure different proteins involved in the processing of APP as targets for drugs to treat Alzheimer’s. Another advantage of the MSD immunoassay is that it does not use all of the media where the cells grow. This is very attractive, because the remaining media and cells can be used for other assays. To assess the suitability of this assay as a drug testing platform we used three drugs with well defined mechanism of action: DAPT (-secretase inhibitor) [304], βIV (BACE1 inhibitor) [306], and Carbachol (α-secretase activator) [102]. The assay showed the expected results for DAPT (decrease in Aβ40, Aβ42) and βIV (decrease in Aβ40, Aβ42 and sAPPβ). However, carbachol

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increased sAPPα, decreased Aβ40 and Aβ42, but increased sAPPβ as well. In the primary screening carbachol consistently increased sAPPα, sAPPβ, Aβ40 and Aβ42 which is quite puzzling. Our results do not agree with the consulted literature that suggests that the nonselective muscarinic agonist primarily increases sAPPα production [361] and that BACE1 activity could be inhibited when carbachol is co-administered with a M2 antagonist [363]. We hypothesised that the upregulation of all the assessed parameters could be a clue that both α- and β-secretase activity regulation by muscarinic receptors is more complex than previously thought. In general, the MSD immunoassay seems to be an appealing and innovative platform for drug screening. There are no previous studies using this MSD immunoassays as a platform to screen a large number of drugs.

For the secondary assays, we focused on studying oxidative stress caused by Aβ42 for two reasons: evidence suggests that ROS are involved in AD pathology and because castalagin, vescalagin and EGCG have been suggested them as possible antioxidants. First, we used DCFH-DA, which is widely used to measure ROS shifts. Our data showed that SH-SY5Y cells treated with 5µM Aβ increased the concentration of ROS the most so these conditions were selected for primary screening. We then tried to optimise the ROS-Glo-H2O2 assay to measure the H2O2 contribution to the ROS signal. We were not able to detect shifts in H2O2 with this assay. Finally, we used GSH/GSSG ratio as another approach to measure oxidative stress. Glutathione is mainly found in its reduced form but it is oxidised to neutralize ROS, thereby a decrease of GSH/GSSG is indicative of a shift in oxidative stress. We found that cells treated with either 2.5 or 5 µM of Aβ42 decreased the GSH/GSSG ratio significantly. We select 5µM to use a consistent concentration in both oxidative stress assays.

We screened a tailored version of the LOPAC drug library including only drugs that had been previously tested in humans and/or approved by a regulatory entity, and that were able to cross the BBB. We also screened vescalagin, castalagin, EGCG, resveratrol, and SEN304 as part of collaboration project with Professors Phillipe Derreumaux and Peter Faller. The primary screening of LOPAC using MTT assays did not identify any compound as hits. We ran three independent rounds with triplicates of each drug and performed three confirmatory rounds of the possible hits, which ensured that none of the LOPAC drugs screened could be a possible treatment to AD. The discussion of Chapter 4 (Primary screening) carefully discusses and justifies the experimental approach. The screening of the non-LOPAC drugs identified vescalagin, castalagin and SEN304 as hits. EGCG and SEN304 showed the strongest neuroprotection in the MTT assay.

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The MSD immunoscreening of LOPAC was done in a single well fashion due to limited resources and identified 11 compounds as possible hits. The confirmatory round was done in duplicate and included the 5 non-LOPAC drugs. The confirmatory screening identified: compound 4 (Acetyl-beta-methylcholine), 8 (arecoline), compound 133 (Tranylprimine), compound 169 (tropisetron), castalagin and SEN304 as possible hits.

The first part of the secondary screening was focused evaluating the potency of the hits identified from the MTT primary screening and explored if a two-drug combination of the hits could show a synergetic effect. The most potent compound was EGCG that showed an EC50 of

0.8µM followed by SEN304 with an EC50 of 2.5µM. The two-drug combination assay did not show any synergetic effect and the results seem to be driven by the most potent drug in the combination.

The next part of the secondary screening assessed if any of the hits from both LOPAC and MSD immunoscreening decreased the amount of ROS generated by the treatment of SH- SY5Y with Aβ42. The first assay used was DCFH-DA that allowed us to measure changes in ROS. From the screened hits only EGGC restore ROS levels to 100%. EGCG was also the only drug that partially restored the GSH/GSSG to similar levels compared to non-treated cells. We also evaluated the potency of EGCG as an antioxidant. For the DCFH-DA assay it showed a similar EC50 compared to the MTT assay suggesting that it has a similar potency inhibiting Aβ toxicity in both assays. For the GSH/GSH assay it showed a lower potency compared to MTT and DCFH-DA.

EGCG and SEN304 were further tested using a Drosophila model that overexpresses Aβ42 thereby reducing the longevity of the flies. None of the drugs increased the lifespan of Aβ42 flies compared to controls.

The Aβ42 model designed by Crowther et. al. is a very flexible model that has been already used by other groups as a platform for AD drug screening [262]. This model could be further manipulated and used for the screening of drugs. One of the changes that could increase the toxic effect of Aβ42 would be to implement fly crosses that give a duplicate copy of the Aβ42 gene or to use the fly model also designed by Crowther that has an Arctic mutation of Aβ42. Complementary to this it would be advisable to perform further experiments assays at 29°C to promote the expression of the Aβ42 transgene. This approach could reduce the lifespan of the flies significantly, thereby reducing the total amount of drugs needed per assay. Another

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change that could be implemented is the use of isogenic wild type flies instead of the UAS- GFP model. Additionally, climbing assays could be used to measure of neuronal dysfunction.

Figure 6.1 shows a summary of the drug screening results Castalagin and vescalagin were previously suggested as possible treatments for AD, but we did not find previous in vitro assays from other groups further studying this potential [366]. Our results suggest that castalagin and vescalagin partially restore cell metabolism (MTT assay) in cells treated with Aβ and a biophysical assay performed by our colleagues showed that this drug could also wok as an Aβ anti-aggregator [367] . MSD immunoassays suggest that only castalagin could be an sAPPα activator and reduced Aβ concentrations. Curiously, the literature suggests that its possible mechanism of action could be as an antioxidant, but in the assays, that assessed oxidative stress there was no significant activity. This does not necessarily mean that castalagin and vescalagin do not have an effect against oxidative stress. MTT assays showed that these drugs are less potent than EGCG so it is possible that larger concentrations of castalagin and vescalagin are necessary to observe an antioxidant effect.

EGCG has been previously study by other groups as a possible treatment for AD. Previous finding suggest that EGCG decreases Aβ toxicity when measured with MTT [370, 371], that it could work as an Aβ anti-aggregator [149], sAPPα activator [150] and that it is able to hinder the ROS increase in cells treated with Aβ [148]. Our results, partially agree with previous findings. We found that from the hits EGCG was the most potent at inhibiting Aβ toxicity in MTT assays and that it decreases the ROS probably working as a ROS scavenger or by promoting the production of glutathione [378]. However, the MSD immunoassays do not suggest that it could have an activity as sAPPα activator

SEN304 has been characterised by previous colleagues in our group as an Aβ anti- aggregator [137, 138, 367]. Additionally, previous assays also show that SEN304 decreases Aβ toxicity when measured with MTT. Our results from the MSD immunoassay, suggest that besides its already known mechanism of actions SEN304 could somehow affect the APP processing by decreasing sAPPβ and increasing sAPPα.

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6.1. Conclusion The development of treatments for AD is challenging as the pathology of the disease is still not well understood. Still the available knowledge has been used to attempt to develop new therapies for the disease and has shown new hints towards the full elucidation of AD pathology.

In this project, we evaluated the suitability of different cell-based assays to create a drug screening platform for AD drug screening using SH-SY5Y cells treated with Aβ42. From the evaluated assays, we selected MTT and MSD mesoscale as the more suitable primary screening assays. During the optimisation phase only the MTT assay was sensitive enough to measure cell-toxicity at low Aβ concentrations (1µM). Mesoscale assay was selected as a primary screening as it represented a good screening strategy, as it allowed us to measure different targets related to APP processing simultaneously. For the secondary screening assays, we focused on oxidative stress as the role of ROS is well documented in AD pathology. Compared to MTT assay it was necessary to use higher concentration of Aβ42 (5µM) to measure ROS. DCFH and GSH/GSSG were successfully optimised to measure the shift of different ROS caused by Aβ42.

The primary screening of drugs using MTT and mesoscale identified 8 possible hits: EGCG, SEN304, vescalagin, castalagin, methylcholine, arecoline, tranylcypromine and tropisetron. These hits were further examined to test its potency and ability to decrease ROS in cells treated with Aβ42. Based on the results from the primary and secondary screening we selected EGCG and SEN304 to be tested in our in vivo model. Our primary screening includes vescalagin and castalagin among the tested drugs. Some authors suggest these drugs as possible AD treatments and this is the first documented experimental test of these drugs. These drugs did not yield a hit in our assays.

The in vivo assay using Aβ42 transfected Drosophila did not highlight any of the two screened drugs as a possible treatment for AD. However, this could be because this assay only measured survivorship which is not very robust. It would be necessary to perform other assays to measure neuronal dysfunction and concentration of Aβ42.

We evaluated the use of different in vitro assays and an animal model as a possible platform for AD drug screening. The assays presented in this project could be adapted to be used in other cellular models or could be used to screen a different drug library. Likewise, the

168

drug library used in this assay could be screened for other diseases where the penetration of the BBB is paramount.

Figure 6.1 Summary of drug screening assay.

169

Appendix A. Tailored LOPAC library

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

2 Amantadine Dopamine Releaser Dopamine releaser used in the treatment of Yes Yes Yes hydrochlorid Parkinsonism and drug- e related extrapyramidal reactions; antiviral (Influenza A)

3 Aminophylli Adenosine Antagonist A1/A2 A1/A2 adenosine receptor antagonist; diuretic; Yes Yes No ne cardiac stimulant; smooth muscle relaxant ethylenedia mine

4 Acetyl-beta- Cholinergic Agonist M1 M1 muscarinic acetylcholine receptor agonist Yes Yes ? methylcholin e chloride

5 Atropine Cholinergic Antagonist Muscarin Competitive muscarinic acetylcholine receptor Yes Yes ? methyl ic antagonist bromide

171

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

6 Amperozide Serotonin Ligand Atypical antipsychotic drug with high affinity for Yes Yes, Veterinary ? hydrochlorid serotonin receptors and low affinity for D2 use e dopamine receptors

7 Varenicline cholinergic Agonist a4ß2 Varenicline tartrate is a partial a4ß2 nicotinic Yes Yes ? tartrate receptors receptor agonist and a7 full agonist. Varenicline competitively binds to a4ß2 receptors and partially stimulates without creating a full nicotine effect

8 Arecoline Cholinergic Agonist Acetylcholine receptor agonist Yes No ? hydrobromid e

9 Aminoguani Nitric Oxide Inhibitor NOS Selective nitric oxide synthase inhibitor Yes No ? dine hemisulfate

10 Reserpine Serotonin Inhibitor Uptake Inhibits vesicular catecholine and serotonin uptake. Yes Yes ?

11 (+)- Dopamine Antagonist Dopamine receptor antagonist Yes NA ? Butaclamol hydrochlorid e

12 Apomorphin Dopamine Agonist Non-selective dopamine receptor agonist Yes NA ? e

172

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

hydrochlorid e hemihydrate

13 L-Arginine Nitric Oxide Precursor Nitric oxide precursor Yes Yes ?

14 N-Acetyl-L- Glutamate Antagonist Amino acid analog that partially improves neuronal Yes yes Yes Cysteine survival following transient forebrain ischemia

15 Paroxetine Serotonin Inhibitor Reuptake Selective serotonin reuptake inhibitor; Yes Yes Yes hydrochlorid antidepressant e hemihydrate (MW = 374.83)

16 Amitriptylin Adrenocepto Inhibitor Uptake Tricyclic antidepressant Yes Yes Yes e r hydrochlorid e

17 Aniracetam Glutamate Agonist AMPA Increases ion conductance through AMPA Yes Aproved by Yes glutamate receptors; reported to display EMEA only

anti-amnesic activity

173

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

18 Amoxapine Adrenocepto Inhibitor Uptake Tricyclic antidepressant; inhibits neuronal uptake of Yes yes Yes r norepinephrine

19 Adenosine Adenosine Agonist Endogenous neurotransmitter and neuromodulator Yes Yes No, but increases BBB permeabilit y

20 L-Aspartic Glutamate Agonist Endogenous excitatory amino acid neurotransmitter Yes Yes Yes acid

21 S(-)- Adrenocepto Antagonist beta1 beta1 adrenoceptor antagonist Yes Yes ? Atenolol r

22 Bupropion Dopamine Blocker Reuptake Selective dopamine reuptake inhibitor; Yes Yes Yes hydrochlorid antidepressant e

23 Chlorprothix Dopamine Antagonist D2 D2 dopamine receptor antagonist; blocks a subset Yes Yes, UK, Yes ene of GABA-A receptors in rat cortex that is also Australia hydrochlorid blocked by clozapine e

174

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

24 Betaxolol Adrenocepto Antagonist beta1 Selective beta1 adrenoceptor antagonist Yes Yes Yes hydrochlorid r e

25 Benztropine Cholinergic Antagonist Muscarin Muscarinic acetylcholine receptor antagonist Yes Yes Yes mesylate ic

26 Biperiden Cholinergic Antagonist muscarin Non-selective muscarinic acetylcholine receptor Yes Yes ? hydrochlorid ic antagonist; antiparkinsonian e

27 Buspirone Serotonin Agonist 5-HT1A 5-HT1A Serotonin receptor agonist; non- Yes Yes ? hydrochlorid benzodiazepine anxiolytic e

28 Alfuzosin Adrenocepto Blocker alpha-adrenergic blocker used to treat benign Yes Yes Yes hydrochlorid r prostatic hyperplasia (BPH) e

29 Caffeine Adenosine Inhibitor Phospho Phosphodiesterase inhibitor; central stimulant Yes Yes Yes diesteras e

30 Chlorpromaz Dopamine Antagonist Dopamine receptor antagonist; anti-emetic; Yes Yes Yes ine antipsychotic

175

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

hydrochlorid e

31 D- Glutamate Agonist NMDA- Excitatory amino acid; partial agonist at the glycine Yes Yes Yes Cycloserine Glycine modulatory site of the NMDA glutamate receptor

32 Chlorzoxazo Nitric Oxide Inhibitor iNOS Inducible nitric oxide synthetase inhibitor; skeletal Yes Yes Yes ne muscle relaxant

33 Citalopram Serotonin Inhibitor Reuptake Selective serotonin reuptake inhibitor YES Yes Yes hydrobromid e

34 Clonidine Adrenocepto Agonist alpha2 alpha2 Adrenoceptor agonist; antihypertensive Yes Yes Yes hydrochlorid r e

35 Clozapine Dopamine Antagonist D4 > Atypical neuroleptic agent which display greater Yes Yes Yes D2,D3 affinity for the D4 dopamine receptors over D2 or D3

36 Cyclobenzap Serotonin Antagonist 5-HT2 5-HT2 serotonin receptor antagonist Yes Yes Yes rine hydrochlorid e

176

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

37 Clomiprami Serotonin Inhibitor Reuptake Serotonin reuptake inhibitor; antidepressant Yes Yes Yes ne hydrochlorid e

38 CNS-1102 Glutamate Antagonist NMDA Noncompetitive NMDA glutamate receptor Yes No ? antagonist.

39 Cyclothiazid Glutamate Agonist AMPA Blocks the rapid desensitization of AMPA Yes Yes Yes e glutamate receptors

40 Dextrometho Glutamate Antagonist NMDA Allosteric antagonist at NMDA-controlled ion Yes Yes Yes rphan channels hydrobromid e monohydrate

41 Doxepin Adrenocepto Inhibitor Uptake Antidepressant; antipruritic Yes Yes Yes hydrochlorid r e

42 Droperidol Dopamine Antagonist D1/D2 D1, D2 dopamine receptor antagonist Yes Yes Yes

43 Cirazoline Adrenocepto Agonist alpha1A Selective alpha1 adrenoceptor agonist; non- Yes No ? hydrochlorid r selective imidazoline binding site ligand e

177

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

44 Desipramine Adrenocepto Inhibitor Uptake Antidepressant Yes Yes Yes hydrochlorid r e

45 Chlormezan Neurotransm Modulator Muscle Anxiolytic; muscle relaxant Yes Yes Yes one ission relaxant

46 Decamethon Cholinergic Agonist Nicotinic Nicotinic acetylcholine receptor partial agonist; Yes Yes Yes ium depolarizing neuromuscular blocker dibromide

47 N- Dopamine Agonist Dopamine receptor agonist Yes No ? Methyldopa mine hydrochlorid e

48 Eletriptan Serotonin Agonist 5- Eletriptan hydrobromide is a serotonin 5-HT1B/1D Yes Yes Yes hydrobromid HT1B/1 receptor agonist; second generation anti-migraine e D drug.

49 Domperidon Dopamine Antagonist D2 Peripheral dopamine receptor antagonist that does Yes Yes Yes e not cross the blood-brain barrier; anti-emetic

178

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

50 Dicyclomine Cholinergic Antagonist Muscarin Competitive muscarinic acetylcholine receptor Yes Yes Yes hydrochlorid ic antagonist e

51 Propofol Cholinergic Inhibitor Muscarin Short acting general anaesthetic Yes No Yes ic

52 Etazolate Adenosine Inhibitor Phospho Phosphodiesterase inhibitor Yes No Yes hydrochlorid diesteras e e

53 Nefiracetam Neurotransm Nootropic Pyrrolidonetype nootropic agent with various Yes No Yes ission pharmacologic as well as cognition-enhancing effects.

54 N- Cholinergic Antagonist 5-HT2 Major metabolite of clozapine; potent 5-HT2 Yes No Yes Desmethylcl Serotoni serotonin receptor antagonist and a ligand for the ozapine n/M1 cloned 5-HT6 and 5-HT7 serotonin receptors. Also, Muscarin an allosteric potentiator of M1 muscarinic receptors ic

55 Venlafaxine Serotonin Reuptake 5-HT and Dual serotonin and norepinephrine reuptake Yes Yes Yes hydrochlorid NE inhibitor. Antidepressant. e

179

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

56 L-3,4- Dopamine Precursor Precursor to dopamine; antiparkinsonian agent Yes Yes Yes Dihydroxyp henylalanine

57 Naratriptan Serotonin Agonist 5- Naratriptan hydrochloride is a Serotonin 5- Yes Yes Yes hydrochlorid HT1B/1 HT1B/1D receptor agonist e D

58 Eliprodil Glutamate Antagonist NMDA- NR2B selective NMDA receptor antagonist Yes No Yes polyamin (polyamine site); potential clinical use to treat both e acute and chronic pain.

59 Doxazosin Adrenocepto Blocker alpha1 alpha1 adrenoceptor blocker Yes Yes Yes mesylate r

60 (-)- Cholinergic Inhibitor Cholinest Cholinesterase inhibitor Yes Yes Yes Physostigmi erase ne

61 Fluvoxamine Serotonin Inhibitor Reuptake Selective serotonin reuptake inhibitor Yes Yes Yes maleate

62 Fenspiride Adrenocepto Antagonist alpha alpha-Adrenoceptor antagonist; NSAID Yes Yes, Approved ? hydrochlorid r in Russia e

180

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

63 Flumazenil Benzodiazep Antagonist Benzodiazepine receptor antagonist Yes Yes Yes ine

64 Edrophoniu Cholinergic Inhibitor Acetylch Acetylcholinesterase inhibitor Yes Yes Yes m chloride olinester ase

65 Fluspirilene Dopamine Antagonist D2/D1 Dopamine receptor antagonist; antipsychotic Yes Yes Yes

66 Guanfacine Adrenocepto Agonist alpha2 alpha2-Adrenoceptor agonist Yes Yes Yes hydrochlorid r e

67 Phenserine Neurotransm Inhibitor Acetylch Selective, non-competitive acetylcholinesterase Yes Yes Yes ission olinester (AChE) inhibitor. ase

68 Felbamate Glutamate Antagonist Anticonvulsant; glutamate receptor antagonist Yes Yes Yes

69 Flupirtine Glutamate Antagonist NMDA NMDA glutamate receptor antagonist; centrally Yes Yes Yes maleate acting analgesic

70 Gallamine Cholinergic Antagonist M2 M2 muscarinic acetylcholine receptor antagonist; Yes Yes Yes triethiodide muscle relaxant

71 Fluphenazin Dopamine Antagonist D1/D2 Dopamine receptor antagonist; antipsychotic Yes Yes Yes e

181

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

dihydrochlor ide

72 Fenoldopam Dopamine Agonist D1 Peripheral D1 dopamine receptor agonist Yes Yes Yes bromide

73 Fluoxetine Serotonin Inhibitor Reuptake Selective serotonin reuptake inhibitor Yes Yes Yes hydrochlorid e

74 Paliperidone Neurotransm Antipsychotic Atypical antipsychotic; active metabolite of Yes Yes Yes ission risperidone.

75 L- Cholinergic Antagonist Competitive antagonist at post-ganglionic synapses Yes Yes Yes Hyoscyamin and on smooth muscle e

76 Haloperidol Dopamine Antagonist D2/D1 Dopamine receptor antagonist; antipsychotic Yes Yes Yes

77 Dopamine Dopamine Agonist Endogenous neurotransmitter Yes Yes Yes hydrochlorid e

78 Ipratropium Cholinergic Antagonist Muscarin Muscarinic acetylcholine receptor antagonist; Yes Yes Yes bromide ic bronchodilator

182

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

79 Hydralazine Neurotransm Inhibitor MAO- Non-selective MAO-A/B inhibitor; Yes Yes Yes hydrochlorid ission A/B antihypertensive e

80 Guanabenz Adrenocepto Agonist alpha2 Centrally acting alpha2 adrenoceptor agonist; Yes Yes Yes acetate r antihypertensive

81 Lithium Neurotransm Inhibitor Inositol Anti-manic drug used in the treatment of bipolar Yes Yes Yes Chloride ission monopho depression; inhibitor of inositol monophosphatase sphates’

82 Iproniazid Neurotransm Inhibitor MAO Monoamine oxidase inhibitor Yes No Yes phosphate ission

83 (±)-Ibotenic Glutamate Agonist NMDA Potent NMDA/metabotropic glutamate receptor Yes No Yes acid agonist; excitotoxin; originally isolated from Amanita pantherina and Amanita muscaria

84 Molindone Dopamine Antagonist D2 D2 dopamine receptor antagonist; monoamine Yes Yes Yes hydrochlorid oxidase inhibitor e

85 Imipramine Serotonin Blocker Reuptake Tricyclic antidepressant; blocks reuptake of Yes Yes Yes hydrochlorid serotonin and norepinephrine e

183

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

86 Imiloxan Adrenocepto Antagonist alpha2B Selective alpha2B-adrenoceptor antagonist Yes Yes ? hydrochlorid r e

87 Loxapine Dopamine Antagonist Dibenzoxazepine antipsychotic agent Yes Yes Yes succinate

88 TMPH Cholinergic Inhibitor Nicotinic Potent inhibitor of neuronal nicotinic receptors. Yes Yes ? hydrochlorid Subtype specific (alpha3/4 and beta 2/4 e combinations).

89 Molsidomin Nitric Oxide Donor Vasodilator; converted by the liver to the active Yes Yes No, helps to e metabolite, SIN-1 permeate barrier

90 Mianserin Serotonin Antagonist Serotonin receptor antagonist Yes Yes Yes hydrochlorid e

91 NG- Nitric Oxide Inhibitor NOS Nitric oxide synthase inhibitor; blocks formation of Yes Yes ? Monomethyl endothelium-derived relaxing factor (EDRF) -L-arginine acetate

184

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

93 MG 624 Cholinergic Antagonist Nicotinic Nicotinic acetylcholine receptor antagonist; Yes Yes ? selectively inhibits alpha-bungarotoxin sensitive receptors that contain the alpha7 subunit

94 N-Methyl- Glutamate Agonist NMDA NMDA glutamate receptor agonist Yes Yes Yes D-aspartic acid

95 S-Methyl-L- Nitric Oxide Inhibitor NOS Potent inhibitor of NOS; more potent than L- Yes Yes ? thiocitrulline thiocitrulline acetate

96 Methylergon Dopamine Antagonist Dopamine antagonist; ergot alkaloid which Yes Yes Yes ovine interacts with serotonergic, dopaminergic and maleate alpha-adrenergic systems

97 ML 10302 Serotonin Agonist 5-HT4 Potent, selective 5-HT4 serotonin receptor agonist. Yes Yes ?

98 Rufinamide Neurotransm Anticonvulsant Broad-spectrum anticonvulsant. Yes Yes Yes ission

99 Maprotiline Adrenocepto Inhibitor Reuptake Selective norepinephrine reuptake inhibitor Yes Yes Yes hydrochlorid r e

185

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

100 Neostigmine Cholinergic Inhibitor Acetylch Reversible acetylcholinesterase inhibitor. Yes Yes ? bromide olinester ase

101 Moclobemid Neurotransm Inhibitor MAO Reversible monoamine oxidase A inhibitor Yes Yes Yes e ission (MAOI); antidepressant.

102 Naphazoline Adrenocepto Agonist alpha alpha-Adrenoceptor agonist; imidazoline receptor Yes Yes Yes hydrochlorid r agonist; vasoconstrictor e

103 Levetiraceta Neurotransm Anticonvulsant Anticonvulsant; antiepileptic; exact mechanism of Yes Yes Yes m ission action is unclear but may be related to a synaptic vesicle protein.

104 7- Nitric Oxide Inhibitor nNOS Selective inhibitor of brain nitric oxide synthase Yes Yes Yes Nitroindazol e

105 Mecamylami Cholinergic Antagonist Nicotinic Nicotinic acetylcholine receptor antagonist Yes Yes Yes ne hydrochlorid e

186

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

106 Memantine Glutamate Antagonist NMDA Antagonist of NMDA glutamate receptors; Yes Yes Yes hydrochlorid stimulates dopamine release e

107 Methysergid Serotonin Antagonist Serotonin receptor antagonist; antimigraine Yes Yes Yes e maleate

108 Nialamide Neurotransm Inhibitor MAO Monoamine oxidase inhibitor Yes Yes Yes ission

109 Nortriptyline Adrenocepto Inhibitor Uptake Tricyclic antidepressant Yes Yes Yes hydrochlorid r e

110 Sertraline Serotonin Inhibitor Reuptake Selective serotonin reuptake inhibitor; Yes Yes Yes hydrochlorid antidepressant e

111 Ethopropazi Neurotransm Inhibitor Butyrylc Butyrylcholinesterase inhibitor; antiparkinsonian Yes Yes Yes ne ission holineste hydrochlorid rase e

112 Nomifensine Dopamine Inhibitor Reuptake Dopamine reuptake inhibitor; antidepressant Yes Discontinued Yes maleate

187

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

113 Orphenadrin Cholinergic Antagonist Muscarin Muscarinic acetylcholine receptor antagonist; H1 Yes Yes Yes e ic histamine receptor antagonist hydrochlorid e

114 Pancuroniu Cholinergic Antagonist Aminosteroidal neuromuscular blocking agent; Yes Yes Yes m bromide skeletal muscle relaxant

115 Pentolinium Cholinergic Antagonist Nicotinic Peripheral ganglionic nicotinic acetylcholine Yes Yes Yes di[L(+)- receptor antagonist tartrate]

116 3-n- Adenosine Antagonist A1 > A2 Weak competitive antagonist at both A1 and A2 Yes Yes Yes Propylxanthi adenosine receptors ne

117 Oxybutynin Cholinergic Antagonist Muscarin Muscarinic acetylcholine receptor antagonist Yes Yes Yes Chloride ic

118 Pentamidine Glutamate Antagonist NMDA NMDA glutamate receptor antagonist; Yes Yes Yes isethionate neuroprotective agent; antimicrobial agent prescribed for the treatment of AIDS-associated Pneumocystis carinii pneumonia

119 Perphenazin Dopamine Antagonist D2 D2 dopamine receptor antagonist; sigma receptor Yes Yes Yes e agonist; phenothiazine antipsychotic

188

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

120 Parthenolide Serotonin Inhibitor Inhibits serotonin release from platelets; inhibits Yes No ? production of leukotriene B4 and thromboxane B2

121 Pimozide Dopamine Antagonist D2 Ca2+ channel antagonist; antipsychotic; D2 Yes Yes Yes dopamine receptor antagonist

122 Piracetam Glutamate Modulator AMPA Prototypical nootropic; modulates Na+-flux at Yes No Yes AMPA glutamate receptors

123 (+)- Cholinergic Agonist Muscarin Cholinergic receptor agonist Yes Yes Yes Pilocarpine ic hydrochlorid e

124 Pindolol Adrenocepto Antagonist beta Nonselective beta adrenoceptor antagonist; Yes Yes Yes r vasodilator

125 Pilocarpine Cholinergic Agonist Muscarin Nonselective muscarinic acetylcholine receptor Yes Yes Yes nitrate ic agonist

126 Promazine Dopamine Antagonist D2 D2 dopamine receptor antagonist; phenothiazine Yes Yes Yes hydrochlorid antipsychotic e

189

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

127 Pirenzepine Cholinergic Antagonist M1 Selective M1 muscarinic acetylcholine receptor Yes Yes Yes dihydrochlor antagonist ide

128 Phenelzine Neurotransm Inhibitor MAO- Non-selective MAO-A/B inhibitor Yes YES Yes sulfate ission A/B

129 Protriptyline Adrenocepto Blocker Reuptake Norepinephrine reuptake blocker Yes Yes Yes hydrochlorid r e

130 Pergolide Dopamine Agonist D2/D1 Dopamine receptor agonist; antiparkinsonian Yes Yes Yes methanesulf onate

131 Quinolinic Glutamate Antagonist NMDA Neurotoxin which has neuroexcitatory activity; Yes NO Yes acid metabolite of trytophan

132 Prazosin Adrenocepto Antagonist alpha1 Peripheral alpha1 adrenoceptor antagonist Yes Yes Yes hydrochlorid r e

133 Tranylcypro Neurotransm Inhibitor MAO MAO inhibitor; antidepressant Yes YES Yes mine ission hydrochlorid e

190

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

134 Propanthelin Cholinergic Antagonist Muscarin Muscarinic acetylcholine receptor antagonist; Yes YES Yes e bromide ic antispasmodic

135 Ziprasidone Neurotransm Antipsychotic Atypical antipsychotic; FDA approved for the Yes Yes Yes hydrochlorid ission treatment of schizophrenia. e monohydrate

136 Prochlorpera Dopamine Antagonist Antipsychotic agent; used in the treatment of Yes Yes Yes zine spastic gastrointestinal disorders dimaleate

137 Pyridostigmi Cholinergic Inhibitor Cholinest Cholinesterase inhibitor Yes Yes Yes ne bromide erase

138 Enalaprilat Neurotransm Inhibitor Enalaprilat is an inhibitor of angiotensin converting Yes Yes Yes dihydrate itters enzyme (ACE), antihypertensive, and a Bradykinin B1 receptor activator. Enalaprilat has nM potency versus ACE and also activates B1 receptors to release NO.

139 Spermidine Glutamate Ligand NMDA- Binds to the polyamine modulatory site of the Yes No yes trihydrochlor Polyamin NMDA glutamate receptor ide e

191

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

140 Spiperone Dopamine Antagonist D2 Selective D2 dopamine receptor antagonist. Yes Yes Approved Yes hydrochlorid in japam e

141 (-)- Cholinergic Antagonist Muscarin Cholinergic receptor antagonist; isolated from Yes Yes Yes Scopolamine ic members of the Solanaceae family hydrobromid e

142 Spermine Glutamate Antagonist NMDA- Binds to the polyamine modulatory site of the Yes Yes Yes tetrahydroch Polyamin NMDA glutamate receptor, attenuating both loride e NMDA and quisqualate mediated responses in vivo.

143 Tetrabenazin Neurotransm Inhibitor VMAT Reversible type 2 vesicular monoamine transporter Yes Yes Yes e ission (VMAT) inhibitor. It depletes dopamine stores.

144 Ropinirole Dopamine Agonist D3 Agonist at the D2 and D3 dopamine receptor Yes Yes Yes hydrochlorid subtypes, binding with higher affinity to D3 than to e D2 or D4

145 (-)- Cholinergic Antagonist Muscarin Competitive muscarinic acetylcholine receptor Yes Yes Yes Scopolamine ic antagonist methyl nitrate

192

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

146 (-)- Cholinergic Antagonist Muscarin Cholinergic receptor antagonist Yes Yes Yes Scopolamine ic ,n-Butyl-, bromide

147 Tizanidine Adrenocepto Agonist alpha2 alpha2-adrenoceptor agonist. Yes Yes Yes hydrochlorid r e

148 1-(2- Serotonin Agonist 5-HT1 > Selective 5-HT1 serotonin receptor agonist Yes Yes ? Methoxyphe 5-HT2 nyl)piperazi ne hydrochlorid e

149 Tiapride Dopamine Antagonist D2/D3 D2 and D3 dopamine receptor antagonist; Yes Yes ? hydrochlorid antipsychotic e

150 Trihexyphen Cholinergic Antagonist Muscarin Muscarinic acetylcholine receptor antagonist; Yes Yes Yes idyl ic centrally acting anticholinergic hydrochlorid e

193

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

151 Amisulpride Dopamine Antagonist D2/D3 selective D2/D3 dopamine receptor antagonist and Yes Yes Yes atypical antipsychotic

152 Taurine Glycine Agonist Non-selective endogenous agonist at glycine Yes No Yes receptors

153 Theophyllin Adenosine Antagonist A1 > A2 Selective A1 adenosine receptor antagonist. Yes Yes Yes e

154 Tetrahydroz Adrenocepto Agonist alpha alpha-Adrenoceptor agonist; imidazoline binding Yes Yes ? oline r site ligand; vasoconstrictor hydrochlorid e

155 Trifluproma Dopamine Antagonist D2 D2 dopamine receptor antagonist; phenothiazine Yes Yes Yes zine antipsychotic hydrochlorid e

156 Tulobuterol Adrenocepto Agonist beta beta-Adrenoceptor agonist related to structurally to Yes Yes Approved ? hydrochlorid r terbutaline; bronchodilator in Japan e

157 Trimipramin Serotonin Inhibitor Reuptake Serotonin reuptake inhibitor that also blocks Yes Yes Yes e maleate norepinephrine reuptake; antidepressant

194

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

158 Trazodone Serotonin Inhibitor Reuptake Atypical antidepressant Yes Yes ? hydrochlorid e

159 Granisetron Serotonin Antagonist 5-HT3 serotonin 5-HT3 receptor antagonist and antiemetic Yes Yes Yes hydrochlorid e

160 L- Serotonin Precursor Antidepressant; precursor of serotonin Yes Yes Yes Tryptophan

161 Tetraethyla Cholinergic Antagonist Nicotinic Nicotinic acetylcholine receptor antagonist; K+ Yes Discontinueed Yes mmonium channel blocker chloride

162 Theobromin Adenosine Antagonist A1 > A2 Weak adenosine receptor antagonist; weak Yes Yes Yes e phosphodiesterase

inhibitor; diuretic; smooth muscle relaxant

163 Tomoxetine Adrenocepto Inhibitor Reuptake Norepinephrine reuptake blocker Yes Yes Yes r

164 UK 14,304 Adrenocepto Agonist alpha2 alpha2 Adrenoceptor agonist Yes Yes Yes r

165 Tropicamide Cholinergic Antagonist M4 M4 muscarinic acetylcholine receptor antagonist Yes Yes Yes

195

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

166 Trifluoperazi Dopamine Antagonist D1/D2 Calmodulin antagonist; dopamine receptor Yes Yes Yes ne antagonist; antipsychotic; sedative dihydrochlor ide

167 Xylazine Adrenocepto Agonist alpha2 alpha2 Adrenoceptor agonist; anesthetic Yes Yes Yes hydrochlorid r e

168 Thioridazine Dopamine Antagonist D1/D2 Dopamine receptor antagonist; Ca2+ channel Yes Yes Yes hydrochlorid antagonist; antipsychotic e

169 Tropisetron Serotonin Antagonist 5-HT3 Selective 5-HT3 serotonin receptor antagonist Yes Yes, Mexico ?

170 Caroverine Glutamatergi Antagonist NMDA/ Nonselective NMDA and AMPA glutamate Yes Yes ? hydrochlorid cs AMPA receptor antagonist. e

171 Urapidil Adrenocepto Antagonist alpha1 alpha1 Adrenoceptor antagonist and 5-HT1A Yes Yes ? hydrochlorid r serotonin receptor partial agonist; antihypertensive e

174 Urapidil, 5- Adrenocepto Antagonist alpha1A Selective alpha1A adrenoceptor antagonist; Yes Yes ? Methyl- r antihypertensive

196

Drug Name Class Action Selectivi Description Tested in Approved by Cross BBB # ty Humans Regulatory agency

1 DL-alpha- Neurotransm Inhibitor Tyrosine Inhibitor of catecholamine synthesis and tyrosine Yes No Yes Methyl-p- ission hydroxyl hydroxylase tyrosine ase

175 Zimelidine Serotonin Inhibitor Reuptake Serotonin reuptake inhibitor; antidepressant Yes Yes Yes dihydrochlor ide

197

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