9-aminoacridine derivatives as

9-AMINOACRIDINE DERIVATIVES AS

POTENTIAL ANTIALZHEIMER’S AGENTS:

INSILICO ANALYSIS, SYNTHESIS AND

BIOLOGICAL EVALUATION

A Thesis submitted in partial fulfillment for the award of

Doctor of Philosophy (Ph.D.)

RABYA MUNAWAR (B.Pharm., M.Phil.)

Department of Pharmaceutical Chemistry

Faculty of Pharmacy and Pharmaceutical Sciences

University of Karachi

Karachi-75270,

Pakistan

2019

AUTHOR’S DECLARATION

I Rabya Munawar here by state that my Ph.D. thesis titled “9-aminoacridine

derivatives as potential antialzheimer’s agents: insilico analysis, synthesis and

biological evaluation” is my own work and has not been submitted previously by me

for taking any degree from this University of Karachi.

At any time if the statement is found to be incorrect even after, the University has

right to withdraw my Ph.D. Degree.

___________________ Rabya Munawar

March, 2019

CERTIFICATE

This is to certify that Ms. Rabya Munawar has completed her Ph.D. thesis entitled

“9-aminoacridine derivatives as potential antialzheimer’s agents: insilico

analysis, synthesis and biological evaluation” under my supervision in the

Department of Pharmaceutical Chemistry, Faculty of Pharmacy and Pharmaceutical

Sciences, University of Karachi. Her research work is original and the dissertation is

worthy of presentation to the Advanced Studies and Research Board (ASRB) ,

University of Karachi for the award of degree of Doctor of Philosophy (Ph.D.) in

Pharmaceutical Chemistry.

_________________________

Prof. Dr. Nousheen Mushtaq Research Supervisor Chairperson Department of Pharmaceutical Chemistry Faculty of Pharmacy and Pharmaceutical sciences University of Karachi

To,

my parents

Mr. Munawar Pasha, Mrs. Rukhsana Begum (Late),

my respected teachers,

my husband

Mr. Muhammad Umar Sahool Usmani,

and

my family

9-Aminoacridine derivatives as potential Antialzheimer’s agents: Insilico analysis, Synthesis and Biological evaluation

CONTENTS

i. Summary.……………………………………………………………………….i-iii

ii. Summary in Urdu (Khulasa).……………………………………………………..iv

iii. Acknowledgments.………………………………………………………..……v-vi

iv. Aims and objectives.…………………………………………………….……….vii

v. Abbreviations and symbols.……………………………………..………..…viii-xii

vi. List of instruments.……………………………………………………………...xiii

vii. List of synthesized derivatives.………………………………………………….xiv

viii. List of tables.………………………………………………………………….….xv

ix. List of graphs.…………………………………………………………………... xvi

Chapter 1: INTRODUCTION AND LITERATURE SURVEY

1.1 Alzheimer’s Disease.…………………………………………………………….2

1.1.1 Types of Alzheimer’s disease.………………………………………......3-4

1.1.2 Phases and stages of Alzheimer’s disease.…………..……………………5

1.1.3 Sign and symptoms of Alzheimer’s disease.………………………..……..5

1.1.4 Diagnosis of Alzheimer’s disease.………………………………………5-6

1.1.5 Reported causes of Alzheimer’s disease.………………………………..6-7

1.1.5.1 Amyloid aggregation.…………………………………………7-10

1.1.5.2 Tau protein.………………………………………………...........10

1.1.5.3 Free radicals and oxidative stress.…………………………...10-11

1.1.5.4 Neurotransmitter and enzyme.……………………………….11-12

1.1.5.4.1 Acetylcholine.…………………………………….12-13

1.1.5.4.2 Acetylcholinesterase enzyme.…………………….13-16

1.1.5.4.3 Binding of acetylcholine to acetylcholinesterase

enzyme……….…………………………………...17-19

1.1.6 Management and treatment of Alzheimer’s disease.…………………19-21

1.1.7 Acetylcholinesterase inhibitors….……………………………………21-23

1.2 Acridine.....………………………………………………………………….24-25

1.2.1 9-aminoacridine.……………………………………………………...25-26

1.2.2 9-aminoacridine derivative.…………………………………………..26-31

1.3 Computer Aided Drug Design (CADD).…………......................................31-32


1.3.1 Types of computer aided drug design.……………………………..…32-33

1.3.1.1 Structure based drug design.……………………………….…….34

1.3.1.2 Ligand based drug design.…..………………………..............34-35

1.3.2 Drug design and molecular modeling.………………………….………..35

1.3.2.1 Molecular docking.…………………………………………...35-36

1.3.2.1.1 Protein ligand docking...………...…………………36-37

1.3.2.1.2 Molecular docking and drug likeness ……………37-38

PLAN OF WORK.…………………………………...……………………………..39

Chapter 2: INSILICO STUDIES

2.1 Molecular Docking

2.1.1 Methodology

2.1.1.1 MOE

2.1.1.1.1 Docking protocol.……………………………….….....41

2.1.1.1.2 Target protein.…………………………………….…..41

2.1.1.1.3 Validation of protocol by screening binding database

against target protein.…………………………...…41-42

2.1.1.1.4 Molecular docking of 4EY7.…………………..….…..42

2.1.1.2 Autodock Vina (PyRx)

2.1.1.2.1 Preparation of molecules library.……………….……..42

2.1.1.2.2 Protein selection.………………………………………42

2.1.1.2.3 Preparation of protein.……………………………...…43

2.1.1.2.4 Molecular docking method..………………….…....43-44

2.1.2 Results

2.1.2.1 Proposed library 9-aminoacridine derivatives for docking with

4EY7.........................................................................................45-48

2.1.2.2 Standards for docking with 4EY7.………………………………48

2.1.2.3 Docking sores of standards, parent and selected structures (ligands)

for synthesis.………………………………………………….49-54

2.1.2.4 3D interactions of standards, parent and top ranked ligands with

4EY7 by MOE.…………………………………………...…..55-56

2.1.2.5 3D interactions of standards, parent and top ranked ligands with

4EY7 by Autodock Vina (PyRx).……………………….……57-58


2.1.2.6 Common aminoacid residues involved in interaction of ligands

with 4EY7 in MOE and Autodock Vina (PyRx).……….……59-60

2.1.2.7 Ligand interacting with PAS and CAS residues of protein......61-62

2.1.2.8 3D pictures of acetylcholinesterase interacting with standards,

parent and ligands after docking by MOE..…..………………63-71

2.1.2.9 3D pictures of acetylcholinesterase interacting with standards,

parent and ligands after docking by Auodock Vina

(PyRx)………………………………………...………………72-80

2.1.3 Discussion.……………………………………………………………..81-89

2.2 Drug Likeness

2.2.1 Methodology.……………………………………………………………...90

2.2.2 Results.…………………………………………………………………….90

2.2.3 Discussion.………………………………………………….......................91

Chapter 3: SYNTHESIS OF DERIVATIVES

3.1 Chemicals and reagents.……………………………….…………….................93

3.2 Instruments.…………………………………….…………………………..…...93

3.3 Parent and reactants for synthesis.………………………….……………..94-95

3.4 Procedure of synthesis

3.4.1 General procedure for synthesis of 9-aminoacridine derivatives…………96

3.4.2 Confirmation of synthesized compounds

3.4.2.1 Chromatography.……………………………………………...….96

3.4.2.2 Melting point.……………………………………………..……...96

3.4.2.3 Spectroscopy.………………………………..…………………...96

3.4.3 Reaction scheme with list of product.…………………………………..97-98

3.5 Physical and spectral data of synthesized compounds

3.5.1 PS12.……………………………………………………......................99-100

3.5.2 PS13.………………………………………………………………..……..101

3.5.3 PS23.………………………………………………………………………102

3.5.4 PS24.………………………………………………………………………103

3.5.5 PS25.………………………………………………………………………104

3.5.6 PS26.………………………………………………………………………105

3.5.7 PS27.………………………………………………………………………106


3.5.8 PS28.………………………………………………………………………107

3.5.9 PS32.………………………………………………………………………108

3.5.10 PS33.…………………………………………………………………….109

3.6 Discussion.…………………………………………………………………110-111

Chapter 4: BIOLOGICAL EVALUATION

4.1 Acetylcholinesterase Inhibiting Activity

4.1.1 Methodology.…………………………………………………………….113

4.1.2 Results.……………………………………………………………...114-117

4.1.3 Discussion.…………………………………………………..............118-121

4.2 Antioxidant Activity (DPPH Scavenging Activity)

4.2.1 Methodology.………………………………………………….................122

4.2.2 Results.……………………………………………………………...123-126

4.2.3 Discussion.…………………………………………………………..127-130

4.3 Amyloid Disaggregation Activity

4.3.1 Methodology.……………………………………………..................131-132

4.3.2 Results.………………………………………………………………...…132

4.3.3 Discussion.…………………………………………………………..133-134

4.4 3T3 Cell Line Toxicity

4.4.1 Methodology.………………………………………………………..…...135

4.4.2 Results.………………………………………………………….…..136-139

4.4.3 Discussion.……………………………………………………..……140-142

CONCLUSION ……………………………………………………………….143-144

REFERENCES.……………………………………………………………….145-173

PUBLICATIONS.………………………………………………………………….174

TURNITIN REPORT.……………………………………………………………..175


i

SUMMARY

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder mainly

characterized by progressive deterioration of memory and impaired cognitive

function. It is the leading cause of dementia, responsible for about half of all cases

worldwide. Cholinergic enzyme deficiency, oxidative stress, formation of amyloid

beta (Aβ) plaques and neurofibrillary tangles are known main factors involved in the

pathogenesis of AD.

The most promising approach for symptomatic relief of AD is to inhibit

acetylcholinesterase (AChE), which primarily catalyzes the hydrolysis of

acetylcholine (ACh), thereby increasing synaptic levels of ACh in the brain. Crystal

structures revealed that it has a peripheral anionic site (PAS) located at the mouth of

the narrow gorge entry lined with multiple conserved amino acid residues and

catalytic active site (CAS) having choline binding site, an acyl pocket, oxyanion hole

and esteratic subunit (catalytic triad). It is also found that AChE present in the

cholinergic terminals accelerates Aβ plaque aggregation.

Tacrine (1,2,3,4-tetrahydro-9-aminoacridine) the first approved drug as an AChE

inhibitor for the treatment of AD is a derivative of 9-aminacridine (9AA). In the

present research work a comparative molecular docking approach using MOE and

Autodock was taken to identify the potential 9AA analogues as AChE inhibitors.

Moreover to test these molecules for having ability to reduce the oxidative stress as

well as inhibition of fibril aggregation.

In-house library containing forty six proposed 9AA derivatives was docked against

human acetylcholinesterase (hAChE) (PDB ID: 4EY7), retrieved from virtual protein

databank (PDB). The docking protocol as validated by reproduction of binding pose

of the co-crystallized ligand donepezil in the enzyme active site. To further

substantiate the protocol, some reported AChE inhibitors like tacrine, physostigmine,

rivastigmine and galantamine were also docked within the active site. In addition,

drug-likeness score responsible for a good pharmacokinetic property was also


ii

calculated. All the compounds followed Lipinski’s rule of five, making them

potentially promising drug candidates for the treatment of Alzheimer’s disease.

Top Ten molecules were selected for synthesis and biological investigation based on

best docking energy and conformations in which compounds were bound to PAS and

CAS regions of AChE through hydrogen bonding, π-π, π-CH and hydrophobic

interactions. All compounds were accommodated in the active site by blocking the

entrance of gorge area (PAS) and extending to CAS region mostly touching choline

and acyl binding regions of AChE. Most common active site residues displayed by

both soft wares were Asp74, Trp86, Tyr124, Trp286, Phe295, Phe297, Tyr133,

Tyr337, Phe338 and Tyr341.

Molecules were synthesized by targeting the 9-amino group of aminoacridine with

substituted and unsubstituted benzoyl, phenacyl, sulphonyl and naphthoyl halides.

Physical, chromatographic and spectroscopic techniques were used to confirm the

synthesis and structure elucidation of molecules. Designed molecules comprised three

main structural features first acridine ring with primary amine, second central

sulphonyl, acyl and carbonyl moieties linking acridine amine and aromatic ring

system and third, terminal substituted/unsubstituted single or fused aromatic ring

system. These features makes the molecules somewhat similar to endogenous

substrate ACh and enhancing affinity and binding with target active site.

Invitro AChE inhibition was investigated by Ellman’s method. All derivatives

effectively inhibited AChE with potencies in the micromolar ranges (IC50 0.261-

26.183µM). Outcomes of the enzyme inhibition study justified the molecular docking

results. Promising enzyme blocking potential of all compounds specially PS23, PS25

and PS28 signified the importance of the connecting moiety and substitution on

phenyl ring and suggesting their incorporation in the therapeutic activity. Sulphonyl

and carbonyl oxygen presenting opportunity for hydrogen bonding along with

acridine amines while aromatic ring substituted with lipophilic group (para position)

along with the acridine ring system helping the molecules to fit in the active area with

the help of π-π and hydrophobic interactions. These features providing not only the

best affinity for target enzyme but also stabilized the complex more efficiently.


iii

Antioxidant activity through DPPH scavenging ability showed pronounced results

with IC50 values ranging from 0.0294 to 0.811µM. Although all ligands demonstrated

better results than parent and standard but PS25 and PS28 are supposed to be best

candidates because of their optimal antioxidant property. Potential of the molecules to

inhibit the fibril aggregation was also investigated and all compounds were unable to

stop the fibril formation process at tested doses.

Cytotoxicity screening of all derivatives were performed by using 3T3 cell line. All

compounds showed better safety profile as compared to reference cytotoxic drug in

terms of higher IC50 values. PS24, PS32 and PS33 displayed best results among all

derivatives, PS25 and PS28 also exhibited good results.

Amongst all synthesized tested ligands PS23, PS25 and PS28 appeared as most

promising multitargeted candidates. The molecular modeling studies indicated that

our synthetic derivatives have significant binding affinity with both CAS and PAS of

the AChE. They exhibited profound AChE inhibition as main therapeutic target and

endowed with advantageous antioxidant power as additional supportive therapy which

can potentially increase memory, decrease free radical levels and protect neurons

against cognitive deficit. Over all this study suggest that compounds PS23, PS25 and

PS28 offer an attractive starting point for further lead optimization in the drug

discovery process against AD.


iv


v

ACKNOWLEDGMENTS

I am very thankful to Almighty ALLAH for His uncountable blessings bestowed on

me and giving me strength and capability to conduct my research studies and

complete it successfully.

I am deeply indebted to my Supervisor, Prof. Dr. Nousheen Mushtaq, Chairperson

Department of Pharmaceutical Chemistry, for her tireless efforts, exceptional

contributions and valuable guidance. Her advices played essential role in the success

of my research studies.

I am thankful to Dr. Ahsaan Ahmad, In charge of Insilico Research Facility (ISRF) of

our research lab in the Research institute of Pharmaceutical Sciences (RIPS) for

supervision and help to conduct the Insilico research studies. I also wish to thank Dr.

Saman Usmani for her support and guidance during computational studies.

I am highly obliged and thankful to Prof. Raheela Ikram (present Dean) and Prof. Dr.

Iqbal Azhar (former Dean), Faculty of Pharmacy and Pharmaceutical Sciences, for

their cooperation and encouragement.

Very special thanks to Prof. Dr. Zafar Saeed Saify, Prof. Dr. Shamim Akhtar, Prof.

Dr. Muhammad Arif and Prof. Dr. Faiyaz H.M. Vaid for their wealth of creative

ideas, sincere advices and their precious time throughout my research studies. I would

like to extend my thanks to all teachers of Faculty of Pharmacy especially teachers of

Department of Pharmaceutical Chemistry for their support and best wishes.

I am thankful to all non-teaching staff, Department of Pharmaceutical Chemistry for

their support and help during my research studies.

This research study is funded by the Higher Education Commission (HEC) of

Pakistan through National Research Program for Universities (NRPU) project. I am

also thankful of HEC for funding the spectral studies of synthesized compounds from

International Center of Chemical and Biological Sciences (ICCBS), University of

Karachi.


vi

I extend my gratitude to Dr. Ghuffran Saeed (Assistant Professor), Faculty of Food,

Science and Technology for his kind support to provide the facility for enzyme

inhibition activity.

I pay thanks to Late Prof. Dr. Ali Akber Sial (Former Dean), Prof. Dr. Anwer Ejaz

Baig, all teachers and non-teaching staff of Faculty of Pharmacy, Ziauddin University.

I also pay thanks to Prof. Dr. Sumbul Shamim (Dean), all teachers and non-teaching

staff of Dow College of Pharmacy, Dow University of Health Sciences.

I wish to express my sincere thanks to my research fellows, colleagues and friends for

valuable discussions and advices, continuous moral and physical support during my

research work.

I am highly obliged and thankful to my parents Mr. Munawar Pasha and late Mrs.

Rukhsana Begum (passed away five years ago) for their kind and continuous moral

and practical support, care and trust at every step of my life.

Finally, a special note of thanks to my siblings Um-e-Salma, Um-e-Hanee, Bushra

Munawar, Momal Munawar and Muhammad Ahsan for their understanding, support,

encouragement, forbearance throughout the process required to complete this research

work. I also thank to my brothers in-law. I thank to my husband Mr. Muhammad

Umar Sahool Usmani and my in-laws (Usmani family) for their understanding, kind

support and encouragement. In the last I want to express my love to my niece and

nephews Syeda Ayesha Minhas, Abdur Rehman, Syed Umar Minhas and Syed

Hamza Jawwad who helped me to release all my stress and putting lots of energy in

me to work with fresh mind.

Rabya Munawar


vii

AIMS AND OBJECTIVES

The main aim and objectives of the present study are

Targeted synthesis of therapeutically active molecules for Alzheimer’s

disease

To determine the possible binding mode/interaction pattern of molecules at

active target sites.

Exploring the structural features of novel compounds that are possibly

involved in drug receptor interaction.

Explore the therapeutic potential by direct and supportive invitro experiments

of targeted synthesized molecules.


viii

ABBREVIATIONS AND SYMBOLS

AD Alzheimer’s disease

MCI Mild cognitive impairment

ACh Acetylcholine

Aβ Amyloid-beta

NFTs Neurofibrillary tangles

NMDA or R N-methyl-D-aspartate or receptor

GSK3β Glycogen synthase kinase 3β

CDK5 Cyclin-dependent kinase 5

MRI Magnetic resonance imaging

BuChE Butyrylcholinesterase

AChE Acetylcholinesterase

AChEIs Acetylcholinesterase inhibitors

CTF C-terminal fragment

sAPPβ Amyloid precursor protein β

PSEN Presenilin

APH1 Anterior pharynx defective-1

AAO Age at onset

NEP Neprilysin

IDE Insulin degrading enzyme

FAD Familial Alzheimer’s disease

SAD Sporadic Alzheimer’s disease

LOAD Late onset Alzheimer’s disease

fLOAD Familial late onset Alzheimer’s disease

τ Tau

BACE1 Beta-site amyloid precursor protein cleaving enzyme 1

PHFs Paired helical filaments

ROS Reactive oxygen species

TBI Traumatic brain injury


ix

CNS Central nervous system

PDH Pyruvate dehydrogenase

NAA N-acetylaspartate

RNS Reactive nitrogen species

O-2 Superoxide

OH· Hydroxyl radical 1O2 Singlet oxygen

H2O2 Hydrogen peroxide

ONO2- Peroxynitrate

NO Nitric oxide

DNA Deoxyribonucleic acid

ChE Cholinesterase

MTT 3-[4, 5-dimethylthiazole-2-yl]-2, 5-diphenyl-tetrazolium

bromide

HLA Human leukocyte antigen

PNS Parasympathetic nervous system

Ch Choline

APOE4 Apolipoprotein E

PAS Peripheral anionic site

CAS Catalytic active site

APP Amyloid precursor protein

ChEIs Cholinesterase inhibitors

9AA 9-Aminoacridine

Gly Glycine

Ala Alanine

Phe Phenylalanine

Ser Serine

Asp Aspartic acid

Glu Glutamic acid

Tyr Tyrosine

Trp Tryptophan

His Histidine


x

Leu Leucine

AA Amino acid

PKC Protein kinase C

MAP Mitogen activated protein

FDA Food and drug administration

CYP Cytochrome

7-MEOTA 7-methoxytacrine

2tBuTHA 2-tertiary-butyl-9-amino-1,2,3,4-tetrahydroacridine

MTDLs Multitarget directed ligands

CADD Computer aided drug designing

QSAR Quantitative structural activity relationship

SBDD Structure based drug design

LBDD Ligand based drug design

3D Three dimensional

LBVS Ligand based virtual screening

MM Molecular modeling

ADMET Absorption, distribution, metabolism, excretion and

toxicity

logP Partition coefficient

BBB Blood brain barrier

PPB Plasma protein binding

TcAChE Torpedo Californica acetylcholinesterase

hrAChE Human recombinant acetylcholinesterase

EeAChE Electric eel acetylcholinesterase

THF Tetrahydrofuran

TEA Triethylamine

rt Room temperature

NaOH Sodium hydroxide

PDB Protein data bank

DPPH Diphenylpicrylhydrazine

SAR Structure activity relationship 1HNMR Proton Nuclear magnetic resonance


xi

UV Ultraviolet spectroscopy

IR Infrared spectroscopy

MS Mass spectra

ppm Parts per million

d6-DMSO Deutrated-dimethyl sulfoxide

s Singlet

d Doublet

t Triplet

m Multiplet

J Coupling constant

H Proton

m/z Mass to charge ratio

MeOD Deutrated methanol

υmax Frequency maximum

M+ Molecular ion

µM Micromolar

µL Microliter

ml Mililiter

nM Nanomolar

mm Millimeter

mM Millimolar

M Molar

min Minute

IC50 Inhibitory concentration 50 percent

SD Standard deviation

SEM Standard error mean

THA 1,2,3,4-tetrahydro-9-aminoacridine (tacrine)

b.p Boiling point

m.p Melting point

δ Chemical shift

ε Epsilon

ATCI Acetylthiocholine iodide


xii

DTNB 5,5-dithiobis-(2-nitrobenzoic acid)

KH2PO4 Potassium dihydrogen phosphate

K2HPO4 Dipotassium hydrogen phosphate

NaCl Sodium chloride

CR Congo red

CO2 Carbondioxide

°C Degree centigrade

3T3 Mouse fibroblast

cm2 Centimeter square

FBS Fetal bovine serum

DPPH 2,2'-diphenyl-1-picrylhydrazyl

DPPH-H 2,2'-diphenyl-1-picrylhydrazine

MTT 3-[4, 5-dimethylthiazole-2-yl]-2, 5-diphenyl-tetrazolium

bromide

CWE Chicken egg white

CSF Cerebral spinal fluid

TLC Thin layer chromatography

Fig Figure

MHz Mega hertz

GF Gypsum and florescent agent

mol Mole

g Gram

LOs Lipoxygenase

GAPDH Glyceraldehyde-3-phosphate dehydrogenase

NOS Nitric oxide synthase

RNAi Ribonuclic acid interference


xiii

LIST OF INSTRUMENTS

i. HP-UVIS Desaga (Heidelberg, Germany)

ii. Memmert Hot Air Oven (Germany)

iii. Analytical balance (PA214, OHAUS Corporation, U.S.A)

iv. Hot plate-Stirrer (Bibby Sterilin Ltd, UK)

v. STUART Melting point apparatus (U.S.A)

vi. Shimadzu UV-visible (UV-1601) spectrophotometer (Japan)

vii. ALPHA II FTIR (Bruker, Germany)

viii. FAB JEOL 600H-2 (U.S.A.)

ix. Bruker Advance AV-400 and AV-500 MHz (France)

x. UV-1800 (Shimadzu, Japan)

xi. Shaking water bath SHZ-82 (China)

xii. Micro plate reader (Spectra Max plus, Molecular Devices, CA, USA)


xiv

LIST OF SYNTHESIZED DERIVATIVES

i. PS12 : N(4'-phenylphenacyl)-9-aminoacridine 9-100

ii. PS13: N(2',4'-dimethoxyphenacyl)-9-aminoacridine 101

iii. PS23: N-(acridin-9-yl)-4'-methylbenzene sulfonamide 102

iv. PS24: N-(acridin-9-yl)-4'-nitrobenzene sulfonamide 103

v. PS25: N-(acridin-9-yl)-4'-bromobenzene sulfonamide 104

vi. PS26: N-(acridin-9-yl)-2',4',6'-trimethylbenzene sulfonamide 105

vii. PS27: N-(9-acridinyl) benzamide 106

viii. PS28: N-(acridin-9-yl)-4-methylbenzamide 107

ix. PS32: N-(acridin-9-yl)-3-bromobenzamide 108

x. PS33: N-(acridin-9-yl)-2-naphthamide 109


xv

LIST OF TABLES

Table-1: List of FDA approved acetylcholinesterase inhibitors 23

Table-2: Proposed library of 9AA derivatives for docking with 4EY7 45-48

Table-3: Standard drugs for docking with 4EY7 48

Table-4: Docking sores of standards, parent and top ranked ligands for synthesis

49-52

Table-5: 3D Interactions of standards, parent and top ranked ligands with 4EY7 by

MOE 55-56

Table-6: 3D Interactions of standards, parent and top ranked ligands with 4EY7 by

Autodock Vina (PyRx) 57-58

Table-7: Common amino acid residues involved in interactions of ligands with 4EY7

in MOE and Autodock Vina (PyRx) 59-60

Table-8: Ligands interacting with PAS and CAS residues of protein 61-62

Table-9: Parent and reactants for synthesis 94-95

Table-10: List of products with substitutions at different sites of structure 97-98

Table-11: Acetylcholinesterase inhibiting activity of 9AA derivatives 114-116

Table-12: Antioxidant activity (DPPH scavenging activity) of 9AA derivatives

123-125

Table-13: Disaggregation of fibrils by 9AA derivatives 132

Table-14: 3T3 cell line toxicity of 9AA derivatives 136-138


xvi

LIST OF GRAPHS

Graph-1: Comparison of docking scores of standards between MOE and Autodock Vina (PyRx) 53

Graph-2: Comparison of docking scores of 9AA and top ranked ligands between MOE and Autodock Vina (PyRx) 54

Graph-3: Acetylcholinesterase inhibitory activity of 9AA derivatives 117

Graph-4: Antioxidant activity (DPPH scavenging activity) of 9AA derivatives 126

Graph-5: 3T3 cell line toxicity of 9AA derivatives 139


1

Chapter 1

INTRODUCTION

AND

LITERATURE SURVEY


2

1.1 Alzheimer’s disease

Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disorder

that has emerged as the most prevalent form of late-life mental failure in humans.

(Inestrosa et al. 2008; Cavdar et al. 2019; Yang et al. 2019).

Most common form of dementia is Alzheimer’s disease. The first signals of AD are

memory failure and cognitive impairment characterized by a slow and silent damage

of the human brain (Youn et al. 2014; Chierrito et al. 2017; Chen et al. 2019). Alois

Alzheimer (German psychiatrist and neuropathologist) was first described this

“unusual disease of the cerebral cortex” in 1906 (Spilovska et al. 2013; Chierrito et al.

2017; Madaiah et al. 2017; Pascoini et al. 2018). Identification of Alzheimer’s disease

was made hundred years ago but its symptoms, causes, risk factors and treatment has

explored only the past 30 years (Association 2013).

Age is a major risk factor in making AD a serious public health problem being

expected to lead to epidemic levels (Chierrito et al. 2017). AD period from diagnosis

to death is 3–20 years. It is the fifth-leading cause of death among people of 65 years

or above. One in nine of 65 years older and one-third people of age above 85 years are

affected by this disorder (Minati et al. 2009; Spilovska et al. 2013; Ambure et al.

2014; Brogi et al. 2014; Association 2017; Basiri et al. 2017; Peauger et al. 2017;

Chen et al. 2019).

46.8 million persons already have AD worldwide and every year approximately 7.7

million new cases being identified. The number of people with dementia will increase

and become double up to 65.7 million by year 2030 and this number will reach around

135.5 million in 2050 and 60%-70% of these cases have been assigned to Alzheimer’s

disease. It was predicted that 1 in 85 people affected by Alzheimer’s disease till 2050

globally (Amat-ur-Rasool and Ahmed 2015; Adav and Sze 2016; Bacalhau et al.

2016; Chierrito et al. 2017; Lan et al. 2017; Madaiah et al. 2017; Tung et al. 2017;

Zhou et al. 2017; Alam et al. 2018; Jannat et al. 2019; Parsons and Gamble 2019).

Alzheimer’s disease is a very high paid condition and economic burden for society.


3

It was estimated that annual cost of $818 billion in 2015 and $1 trillion in 2018 and

will be $2 trillion in 2030 on AD. It indicates that AD is costly and required lifelong

care of individuals (Association 2017; Jannat et al. 2019).

AD is a chronic disease of brain with progressive cognitive and motor impairment,

characterized by defects in cognitive abilities such as problems with language,

memory, difficulty in decision making and others that affect a person’s daily life.

Neurons of the brain are injured or destroyed in Alzheimer’s disease especially

deterioration of basal forebrain cholinergic neurons responsible for learning, memory

and daily physical functions. In advanced AD, the brains of patient show

inflammation, shrinkage due to cell loss and extensive debris from dead neurons. In

the last stage, AD patient are on bed and require 24hours care. Enhancement of

cholinergic neurotransmitter system strategies were designed to combat with the

cognitive impairment present in AD (Acosta et al. 2017; Aouani et al. 2017;

Association 2017; Fernández et al. 2017; Kristofikova et al. 2017; Peauger et al. 2017;

Yang et al. 2019).

1.1.1 Types of Alzheimer’s disease

Genetic and non-genetic causes making it multifactorial illness. Strong genetic

component and number of genes have been involved in its pathogenesis. Alzheimer’s

disease which involved known genes mutation is referred as Familial Alzheimer’s

disease. Some families carry known pathogenic mutations. Familial form of AD

presents a late onset at the age of 65–70 years with complex genetic architecture

(familial late-onset AD, fLOAD) (Cifuentes and Murillo-Rojas 2014; Marr and Hafez

2014; Youn et al. 2014; Chan et al. 2016; Cruchaga et al. 2018; Pascoini et al. 2018;

Zoltowska and Berezovska 2018; Mitra et al. 2019).

In 1993, after discovery of ε4 allele of the apolipoprotein E (apoE4) gene encoding

was considered strongest genetic risk factor for AD. Increase risk of developing AD

by 3.7-12 folds in individuals that carry one or two copies of the ε4 allele. Mutation in

the genes of amyloid precursor protein (APP, 36 mutations) and presenilins (180

mutations in PSEN1, 20 mutations in PSEN2) leads to amyloid-β (Aβ) protein

elevation and initiates a cascade of pathophysiological changes in AD (Marr and

Hafez 2014; Vassar 2017; Mitra et al. 2019).


4

The majority cases of Alzheimer’s disease occur with unknown etiology called

sporadic AD. In sporadic type, it is hypothesized that amyloid β has critical part in the

pathogenesis of Alzheimer and age-related decrease in extracellular Aβ catabolism

causes Aβ accumulation. Neprilysin (NEP) and insulin degrading enzyme (IDE) are

two major candidates considered to degrade extracellular Aβ in brain. NEP deficiency

significantly increased pathological Aβ deposition whereas IDE deficiency decreased

Aβ deposition. These observations establish NEP as the major Aβ-degrading enzyme

invivo. NEP can become an ideal therapeutic target for reducing Aβ burdens in the

preclinical stage of AD patients (Inestrosa et al. 2008; Marr and Hafez 2014; Sasaguri

et al. 2017; Jannat et al. 2019; Mitra et al. 2019).

Up till now very limited knowledge about the physiology of aging that take part in

AD process. Regardless of the difference in age at onset (AAO), both AD forms have

common neuropathological features including Aβ deposition and senile (neuritic)

plaques (Inestrosa et al. 2008; Vassar 2017).

Fig-1 Phases and Stages of AD

1. Preclinical AD

Stage I Neurofibrillary tangles

and amyloid protein deposits in the

transentorhinal cortex

Stage II Degeneration of the

entorhinal region increasing pathological

conditions in the transenorhinal cortex

2. Mild Cognitive

Impairment (MCI) due to

AD

Stage III Severe degeneration of the transentorhinal and entorhinal regions with slight modification of

the hippocampal formation

Stage IV The progession of

disease to the neocortical regions

3. Dementia due to AD

Stage V Damage of neocortex

Stage VI The process extends into

the motor and sensory fields


5

1.1.2 Phases and Stages of Alzheimer’s disease

The three phases of AD [Fig-1] has been proposed and these phases further classified

in six different stages through autopsy of damaged neurons and severity of the

pathology.

It is unfortunate that initial diagnosis of AD is made when major pathological changes

ensued in the brain (Samuel et al. 1996; Nagy et al. 1997; Petersen et al. 1999;

Hänninen et al. 2002; Shoghi-Jadid et al. 2002; Lopez et al. 2003; Roberts et al. 2008;

Ganguli et al. 2011; Association 2013; Adav and Sze 2016).

1.1.3 Signs and Symptoms of Alzheimer’s disease

Dementia and Alzheimer’s disease are not same. All dementia does not have

Alzheimer’s disease but all AD have dementia and just one type of dementia

(Nogrady and Weaver 2005). AD is associated with number of features comprises of

cholinergic neurons damage that slowly destroy thinking ability, memory and daily

life activity (Birks 2006; Ambure et al. 2014; Ado et al. 2015; Bacalhau et al. 2016;

Yang et al. 2019). The cognitive damage is appeared after long preclinical phase (15

to 20 years) and only 1% of cases present clinical symptoms before 65 years of age

(early AAO) (Cruchaga et al. 2018).

Initial symptoms include trouble in remembering names or events or recent

conversations, apathy and depression. Advanced symptoms include behavior changes,

confusion, weakened communication, poor decision and difficulty in swallowing,

speaking and walking. The extensive synaptic differences in the cerebral cortex;

hippocampus or other brain areas are main histopathological signs of the disease

which are essential for cognitive functions (Schelterns and Feldman 2003; Castro and

Martinez 2006; Association 2013; Association 2017).

1.1.4 Diagnosis of Alzheimer’s disease

Earlier it was supposed that Ischemic cerebral vascular disease is the only reason of

AD (de la Torre 2012; Kalaria and Ihara 2013; Adav and Sze 2016). Up till now, AD

is not diagnosed by direct single test. A team of geriatricians, neurologist and


6

physicians work in collaboration to make a diagnosis by using a variety of approaches

and tools. The diagnosis of AD includes the following steps:

• Collection of family and medical history of AD patient including history of

psychiatric, cognitive and behavioral changes

• Inquiring the family members to collect the information about changes in thinking

skills and ability

• Conducting cognitive tests with physical and neurologic examinations

• Detection of tumor via brain imaging or certain vitamin deficiencies by blood tests

to find out the possible reasons of AD

For proper diagnosis first categorize the stage of a disease and secondly identification

of biomarkers such as imaging of brain for amyloid plaques, determination of brain

volume changes and tau and/or amyloid measurement in spinal fluid (Association

2013; Babitha et al. 2015; Association 2017; Karlawish et al. 2017).

1.1.5 Reported Causes of Alzheimer’s disease

Numbers of biological targets and multiple cellular changes played significant role in

the pathophysiology of the AD like gathering of abnormal amyloid beta (Aβ) and Tau

(τ) proteins, dyshomeostasis of biometals, oxidative stress, synaptic loss,

mitochondrial abnormalities, inflammatory responses, acetylcholine (ACh) low levels

and N-methyl-D-aspartate (NMDA) receptor, β secretase, glycogen synthase kinase

3β (GSK3β), cyclin-dependent kinase 5 (CDK5), etc. (Spilovska et al. 2013; Ambure

et al. 2014; Brogi et al. 2014; Bacalhau et al. 2016; Chen et al. 2016; Chen et al. 2019;

Jannat et al. 2019).

Fibrin, Fibrinogen and coagulation factor XII involved in neuroinflammation of AD

brain (Ahn et al. 2010; Davalos and Akassoglou 2012; Noguchi et al. 2014;

Zamolodchikov et al. 2016; Ahn et al. 2017).


7

Elevated concentrations of transition metals like iron, copper and zinc plays vital role

in Aβ aggregates deposition, neurotoxicity and induce formation of ROS (Liu et al.

2014b; Gurjar et al. 2018; Janaszewska et al. 2018).

Traumatic brain injury (TBI) is related with neuroinflammation, white matter

deterioration, cortical atrophy and often parade deposition of amyloid proteins due to

TBI are the main reasons of AD (Johnson et al. 2010; Johnson et al. 2013; Abu

Hamdeh et al. 2017).

Astrocytes (space-filling support cells) plays critical role in a central nervous system

(CNS) normal physiology, neurodegenerative disease like AD or amyloid β protein

(neurotoxin) produce changes in morphology and functions of astrocytes (Jalbert et al.

2008; Acosta et al. 2017).

In normal brain N-acetylaspartate (NAA) is found in relatively high concentrations,

deficiency of acetyl-CoA leads to the fall in oxidative phosphorylation and a decrease

in brain NAA, used as a sign of neuronal dysfunction in AD (Chen et al. 2016).

N-methyl-D-aspartic acid receptors (NMDAR) is a type of ionotropic glutamate

receptors, they can mediate calcium influx to trigger various intracellular processes

and blockers of NMDAR channel are promising neuroprotective agents (Paoletti and

Neyton 2007; Barygin et al. 2009; Gurjar et al. 2014).

Human leukocyte antigen (HLA) is one of the determinant of AD because immune

inflammatory responses regulated by genes from HLA. Two genes i.e. HLA-A2 and

HLA-B7 have been reported in excess in AD patients brain and also involved in

reducing the AAO of AD occur (Harris et al. 2000; Candore et al. 2004; Cifuentes and

Murillo-Rojas 2014).

Among all of them some targets are directly involve in development of disease and

supposed to be the most important reason of AD.

1.1.5.1 Amyloid aggregation

The post mortem histopathology of Alzheimer’s patient brain exposes “plaques and

tangles”. These are of two types, neuritic plaques containing extracellular deposits of


8

amyloid β fibrils and neurofibrillary tangles (NFTs, hyperphosphorylated,

microtubule-associated tau protein) (Hensley et al. 1994; Thomsan Nogrady 2005;

Gurjar et al. 2014; Saturnino et al. 2014; Acosta et al. 2017). The Aβ have normal

physiological functions because it is a naturally occurring endogenous peptide like in

picomolar concentrations increased long-term potentiation resulting in memory

improvement (Inestrosa et al. 2008; Marr and Hafez 2014).

According to the amyloid hypothesis, aberrant deposit of the protein fragment called

amyloid beta (Aβ) plaques or aggregates outside the neurons and these neurotoxins

interfering neuron-to-neuron communication at synapses, concurrently connected with

degeneration in the CNS cholinergic pathways and responsible for deterioration of

memory, learning functions and other symptoms of AD and become a reason of cell

death. According to recognized amyloid theory “pathogenic cascade like

inflammation, neuronal dysfunctions, imbalance of kinase and phosphatase activities

and formation of neurofibrillary tangles are result of accumulation of β-amyloid

peptides” (Thomsan Nogrady 2005; Association 2017; Youssef et al. 2018). Upset of

amyloid precursor protein processing mainly due to mutations in PSEN-1, PSEN-2,

APP and Apo-E4 genes (St George-Hyslop and Petit 2005; Cifuentes and Murillo-

Rojas 2014; Youn et al. 2014; Chan et al. 2016; Vassar 2017; Pascoini et al. 2018;

Zoltowska and Berezovska 2018). APP is a primary source for the production of

amyloid β protein. APP is cleaved by three unique aspartic proteases in sequence

called α-, β-, and γ-secretases and each hews at a distinctive site (Thomsan Nogrady

2005; Acharya et al. 2011; Gurjar et al. 2014; Youn et al. 2014; Ambure et al. 2018).

APP can be processed by non-amyloidogenic and amyloidogenic pathways. In the

amyloidogenic pathway, N-terminal ectodomain was shedding or β-cleavage of APP

by β-secretase (BACE-1) liberating a big soluble extracellular fragment (sAPPβ) and

membrane-associated beta carboxy-terminal fragment (βCTF/C99). PSEN1/γ-

secretase cleaves βCTF of APP at several positions and construction of the pathogenic

species, amyloid β protein (Aβ protein) [Fig-2]. β-secretase constantly hews APP at

single residue aspartate while no specific cleavage by γ-secretase, resulting into

generation of variable length peptides in humans composed of 39 to 43 amino-acid

(Aβ38 to Aβ43) (Marr and Hafez 2014; Youn et al. 2014; Acharya et al. 2016;

Awasthi et al. 2018a; Hojati et al. 2018; Passeri et al. 2018; Zoltowska and


9

Berezovska 2018). Aβ40 and Aβ42 are most abundant among them with a ratio of 1:9

in the brain. Fibril formation of Aβ40 takes several hours whereas Aβ42 fibril forms

within minutes. Aβ42 has two amino acids more than Aβ40 and the hydrophobicity

can greatly intensify by these two additional amino acids and turn it into much more

neurotoxic, causes more oxidative damage in brain than Aβ40 (Hensley et al. 1994;

Youn et al. 2014; Acharya et al. 2016; Hojati et al. 2018).

Amyloid plaques deposition is an crucial measure of neurodegeneration in AD (Dutta

and Mattaparthi 2018; Hojati et al. 2018; Padmadas et al. 2018).

BACE-1(β-secretase) responsible for formation of amyloid protein and its inhibitors

can be mostly categorized into two classes: peptidomimetic and non-peptide inhibitors

(Huang et al. 2009; Gurjar et al. 2014; Jannat et al. 2019).

Fig-2 Formation of Amyloid β (Aβ) proteins

The Aβ clearance is also significant in development of AD. Normally accumulation of

Aβ is limited due to greater clearance than production. In humans, Aβ production rate

is 7.6% per hour but cleared with a rate of 8.3% per hour. According to available

β-secretase γ-secretase

APP

β-Secretase Inhibitors

γ-Secretase Inhibitors

APP Production Inhibitors

sAPPβ

Aβ40 Aβ4

0

CTFβ CTFγ

Aβ Production Modulators

α-Secretase Activators


10

human data, accumulation also results from reduced clearance instead of increased

production of Aβ. Aβ degrading enzyme, Neprilysin (NEP) is one of the enzymes,

controls the Aβ levels in brain and NEP inhibitors elevates Aβ deposition in AD

brain. Although Aβ is strongly genetically associated with AD, its relation to NFT is

extensive (Marr and Hafez 2014; Nilsson et al. 2015; Mitra et al. 2019).

1.1.5.2 Tau protein

In brain, abnormal protein accumulation due to disintegration of microtubules which

blocks the nutrients and other essential contents transportation inside the neurons

called tau (τ) tangles or tau proteins. Deregulation of different protein kinases

including cyclin-dependant kinase 5 (CDK5), glycogen synthase kinase 3β (GSK-3β)

and mitogen-activated protein kinases etc. are due to the hyperphosphorylation of the

tau proteins. Neurofibrillary tangles (NFTs) are form by gathering of paired helical

filaments (PHFs) of Tau proteins and become more toxic, mediate dementia,

neurodegeneration and worsen the dementia pathology in AD. Microtubules forms

cytoskeleton and Tau protein stabilizes the microtubules under normal conditions.

Toxic concentrations of Aβ also produced changes and activate tau and neurofibrillary

tangle formation (García et al. 2011; Cárdenas-Aguayo et al. 2014; Adav and Sze

2016; Association 2017; Chierrito et al. 2017; Ambure et al. 2018; Azam et al. 2018;

Mitra et al. 2019).

1.1.5.3 Free radicals and oxidative stress

The human body is continuously faced many oxidative and electrophilic chemicals.

This exposure starts different redox reactions which are vital for many natural

physiological processes. The imbalance in these biochemical processes leading to the

production of oxidative and electrophilic species. Excess reactive oxygen species

(ROS) and reactive nitrogen species (RNS) are the main cause of oxidative stress

generated by both exogenous and endogenous sources. ROS include superoxide (O2-),

hydroxyl radical (OH·), singlet oxygen (1O2) and hydrogen peroxide (H2O2) and cause

DNA damage by oxidation. Other DNA oxidants are RNS include peroxynitrate

(ONO2-) and nitric oxide (NO) (Bharathi et al. 2014; Abed et al. 2015; Ado et al.

2015; Ambure et al. 2018). Essential biomolecules damage such as nucleic acids,

proteins, polyunsaturated fatty acids, lipids, carbohydrates as well as DNA mutation


11

by these leads to inflammation, aging and AD (Bharathi et al. 2014; Abed et al. 2015;

Aksu et al. 2016). Oxidative stress plays a fundamental role in AD pathogenesis by

increased ROS indices in the regions of the brain affected by neurodegeneration

(Youssef et al. 2018; Goschorska et al. 2019) and starts early aggregation of Aβ and

tau protein (Ademosun et al. 2016; Shaik et al. 2016). Direct reduction of the ROS

and RNS in antioxidant defense system by low molecular mass compounds from

endogenous sources or our diet [Fig-3] (Abed et al. 2015). Additionally, a significant

depletion in the levels of antioxidants has been found to be an inevitable factor that

exaggerates the disease risk (Bhatt et al. 2018).

Fig-3 Antioxidant Defense System

Monoamine oxidase (MAO-A and MAO-B) catalyzes the oxidative deamination of

biogenic and xenobiotic amines with the simultaneous hydrogen peroxide production.

ROS is directly damage neuronal cells also formed by MAO-B. Inhibitors of MAO

has been reported for the possible treatment of AD (Wang et al. 2014; Ambure et al.

2018; Yang et al. 2019).

1.1.5.4 Neurotransmitter and enzyme

Chemicals that cause stimulation of the parasympathetic nervous system are called

cholinomimetic or more specifically for sympathomemetic agents. Cholinomemetic


12

agents might be agonists that act directly on cholinergic receptors or function as

acetylcholinesterase inhibitors (AChEIs) (Fifer 2008).

1.1.5.4.1 Acetylcholine

Acetylcholine (ACh) was first confirmed by Loewi in 1921 in the heart of frog by

vagus nerve stimulation (Aksu et al. 2016; Gocer et al. 2016). Endogenous

neurotransmitter ACh present in brain and body secreted by nerve cells as organic

chemical working as a communicator among brain cell (Tiwari et al. 2013). ACh

functions in both CNS and PNS (Kellogg Jr et al. 2005) and act as a neurotransmitter

for the sympathetic and parasympathetic nervous system (Tiwari et al. 2013). In the

area of nucleus basalis of Meynert in the basal forebrain, cholinergic neurons are

capable of producing ACh (Smythies 2009; Smythies and de Lantremange 2016;

Mitra et al. 2019). ACh is generated by the action of enzyme choline acetyl-

transferase to form ester linkage between acetyl coenzyme A and choline [Fig-4 and

5] in the presynaptic cholinergic neurons. An energy-dependent pump is responsible

for the uptake of major portion of the ACh into 100nm storage vesicle in nerve

endings and small portion is free in the cytosol. ACh release from these vesicles into

the synaptic cleft and binds to ACh (nicotinic and muscarinic) receptors on the

postsynaptic membrane (Kostenis et al. 1998; Hossain et al. 2018). Cholinergic

system damage decreases level of ACh caused impairment of the cholinergic

neurotransmission in brain showing cognitive decline and memory deficits. Reduction

in the ACh metabolism is a competent methodology to improve the cholinergic

neurotransmission in AD brain (Francis et al. 1999; Li et al. 2017).

In the synaptic space acetylcholine is degraded after its release into acetic acid and

choline by the action of acetylcholinesterase [Fig 4 and 6] (Kostenis et al. 1998; Aksu

et al. 2016).

Acetylcholinesterase (AChE) is a key enzyme for terminating neurotransmission by

acetylcholine hydrolysis and reduce agglomeration of this active neurotransmitter in

the synapse (Aouani et al. 2017; Dastan et al. 2017; Parsons and Gamble 2019).


13

Acetyl Co A

Choline

ACh

ACh

ChAT

BuChE

BuChE

AChE

Acetate

Choline

Choline

Presynaptic Neuron

Cholinergic receptor: N type

Synaptic Cleft

Postsynapticneuron

Cholinergic receptor:M or N type

AChE

Fig-4 Mechanism of Acetylcholine in neuron

HO N+H3C

CH3

CH3H3C SCoA

O

O N+

CH3

H3CCH3

O

CholineAcetyltransferase

Acetyl CoA Choline Acetylcholine

H3CSHCoA

Fig-5 Synthesis of Acetylcholine

H3C O

N(CH3)3

O

Cl

H2O, AChEH3C OH

O

HO

N(CH3)3

Cl

Fig-6 Hydrolysis of Acetylcholine

1.1.5.4.2 Acetylcholinesterase enzyme

Acetylcholinesterase (AChE) has been involved in both CNS and PNS processes.

AChE (EC 3.1.1.7), belongs to the α/β hydrolase protein superfamily distributed at

cholinergic brain synapses, neuromuscular junctions and muscles. AChE itself is

found in all vertebrates and invertebrate groups and neurons degenerate, its level

decline. Rapid hydrolysis of ACh into acetate and choline is done by AChE [Fig-6]

(Johnson and Moore 2006; Inestrosa et al. 2008; Weinstock and Groner 2008; Aksu et

al. 2016; Gocer et al. 2016; Cheng et al. 2017; Cavdar et al. 2019). The AChE enzyme

has a fascinating tree-like structure. The trunk of the tree is a collagen molecule which


14

is anchored to the cell membrane. There are three branches (disulphide bridges)

leading from the trunk, each of which holds the acetylcholinesterase enzyme above

the surface of the membrane. The enzyme itself is made up of four protein subunits

with an active site. Therefore, each enzyme tree has twelve active sites. The trees are

rooted immediately next to the acetylcholine receptors so that they will efficiently

capture acetylcholine molecules as they depart the receptors. The acetylcholinesterase

has turnover of 104 per second (Patrick 2001; Johnson and Moore 2006; Inestrosa et

al. 2008).

Organization of the active site of AChE and its catalytic mechanism are

characteristics features (Talesa 2001). AChE catalytic rate is very fast (~109 M−1s−1)

on ACh. However, crystal structures revealed that it has two sites: a peripheral

anionic site (PAS) located at the mouth of the narrow (~5 °A) gorge lined with

multiple conserved aromatic amino acid (AA) residues and catalytic active site (CAS)

buried at the bottom, deep in the gorge (~20 °A, not easy to access). CAS is also

called the catalytic triad or anionic subsite of the active site or esteratic site with other

subsites in gorge. The PAS comprises of 5 residues crowded around the ingress to the

active site gorge including Tyr72, Asp74, Tyr124, Trp286 and Tyr341. The large

omega loop Cys69–Cys96 is associated with the PAS and incorporates Tyr72 and

Asp74 and outer wall of the gorge forms by latter section of this loop includes the

principal component of the CAS i.e. Trp86. The surface loop 275-305 includes

Trp286, present in the gorge opposite site. In first step, substrate attracted by the PAS

and prevents its further movement. The changes has been induced at PAS by binding

of ligands (accelerate carbamoylation at the active site). Recent evidence has shown

the conformational fluctuations in the gorge induced by PAS and CAS inhibitors

because the loop is highly flexible and allowing transient opening and closing to alter

substrate accessibility. Acetylcholine first binds in the active site, serine, histadine and

glutamic acid residue (esteratic site) which causes hydrolysis of the ester portion of

ACh [Fig-7a & b]. Quaternary ammonium group of ACh binds (choline binding site)

to aromatic moieties of tryptophan, tyrosine and phenylalanine residues (Trp86,

Tyr133, Tyr337 and Phe338) [Fig-7b] in the CAS through cation-π interactions. The

acyl pocket is composed of Phe295 and Phe297, responsible for substrate selectivity.

The oxyanion hole (Gly121, Gly122 and Ala204) links through hydrogen bonds.

Hydrophobic sites of AChE binds alkyl portion of ACh [Fig-7a]. There are 14


15

aromatic residues (Tyr72, Trp86, Phe123, Tyr124, Tyr133, Trp236, Trp286, Phe295,

Phe297, Tyr 341, Tyr337, Phe 338 Tyr449 and Trp439) in AChE (Barak et al. 2002;

Castro and Martinez 2006; Johnson and Kotermanski 2006; Berg et al. 2011; Wilson

et al. 2011; Cheung et al. 2013; Nachon et al. 2013; Ambure et al. 2014; Amat-ur-

Rasool and Ahmed 2015; Dighe et al. 2016; Cheng et al. 2017; Rosenberry et al.

2017; Sukumaran et al. 2018).

PAS of AChE also associated with formation of β-amyloid (Talesa 2001; Wilson et

al. 2011; González-Naranjo et al. 2014; Liu et al. 2014b). At physiological pH, most

of the AChE inhibitors mimic ACh by become positively charged species and by

keeping a quaternary amine or basic nitrogen. The traid of these AA residues

contribute to the high catalytic efficiency of AChE. Acetyl, carbamyl, and phosphoryl

are three different chemical groups may react with the AChE with similar chemical

reaction but kinetic constraints for every moiety vary and consequence among toxicity

and effectiveness is also different (Berg et al. 2011; Wilson et al. 2011).

Different recognized sites of AChE have been reported for pharmacological

interaction of AChE inhibitors especially interfere with the Aβ metabolism. In non-

amyloidogenic pathway: levels of acetylcholine can be increase by AChE inhibitors

and stimulate cholinergic pathway processes. Protein kinase C (PKC) and mitogen-

activated protein (MAP) kinase regulating the synthesis or turnover of Aβ and

activation of either or both would increase sAPP levels and reduce Aβ. AChE

inhibitors could potentially target APP processing enzymes such as β-secretase

(BACE-1) and γ-secretase and glycosylation, phosphorylation and trafficking of

secretory proteins after APP synthesis.


16

O N+

O

Peripheral Anionic Site

Catalytic Anionic Site

Choline Binding Pocket

Oxyanion Hole

Catalytic Site

AcylBinding Site

ACh

Hydrophobic region

Hydrophobic region

Hydrophobic region

Fig-7(a) Different binding regions of AChE responsible for hydrolysis of ACh

Fig-7(b) Binding of acetylcholine with different amino acids of acetylcholine during

hydrolysis

Also hydrophobic linning near the PAS encourages the Aβ fibrils formation.

Simultaneously interaction of AChE inhibitors with both the PAS and CAS appear to

be a very good strategy to delay the amyloid plaque formation and elevate the

acetylcholine level (Castro and Martinez 2006; Inestrosa et al. 2008; Valasani et al.

2013; Cheng et al. 2017; Chierrito et al. 2017).


17

1.1.5.4.3 Binding of Acetylcholine to the Acetylcholinesterase enzyme

Acetylcholinesterase X-ray crystal structure revealed important binding regions in the

enzyme responsible for hydrolysis of acetylcholine based on several stages (Patrick

2001; Silverman 2004a).

Stage 1: Acetylcholine approaches and binds to AChE enzyme. Serine acts as a

nucleophile and to form a bond to the ester of acetylcholine by using a lone pair of

electrons. Nucleophilic addition to the ester takes place and opens up the carbonyl

groups (R=CH2CH2NMe3)

CH3C

O

O CH2CH2NMe3

N

NH

O HN

NH

O

C OH3C

OR

HStage 1

Serine(Nucleophile)

Histidine(Base)

Histidine (Base catalyst)

Stage 2: The histidine residue catalyzes this reaction by acting as a base and removing

a proton, thus making serine more nucleophilic

NNH

O

C OH3C

OR

HN

NHO

C OH3C

OR

NNH

O

CH3C

O

Histidine (Base catalyst)

H ORH

Stage 2

Histidine (Acid catalyst)

Histidine

Stage 3: The histidine now acts as an acid catalyst and protonates the OR

(R=CH2CH2NMe3) portion of the intermediate, turning it into a much better leaving

group

NNH

O

CH3C

O

ORH

Histidine (Acid catalyst)

NNH

O

CH3C

O

OR

Histidine

Stage 3H


18

Stage 4: The carbonyl group reforms and expels the alcohol portion of the ester (i.e.

choline)

NNH

O

CH3C

O

OR

Histidine

Stage 4H

NNH

O

CH3C

OROH

H2O

Stage 5: The acyl portion of acetylcholine is now covalently bound. Choline detaches

and is replaced by water at active site

Stage 5 NNH

O

CH3C

O

NNH

O

CH3C

OROH

H2O

O

H

H

Histidine

Stage 6: Water now acts as a nucleophile and uses a lone pair of electrons on oxygen

to attack the acyl group.

Stage 6 NNH

O

CH3C

NNH

O

CH3C

O

O

H

H

O

OH

H

Histidine

Stage 7: Water is normally a poor nucleophile, but once again histidine aids the

process by acting as a basic catalyst and removing a proton

Stage 7 NNH

O

CH3C

O

OH

NNH

O

CH3C

O

OH

H H


19

Sage 8: Histidine now acts as an acid catalyst by protonating the intermediate

Stage 8 NNH

O

CH3C

O

OH

NNH

O

CH3C

O

OH

H

H

Histidine

Stage 9: The carbonyl group is reformed and the serine residue is released. Since it is

protonated, it is a much better leaving group

Stage 9NNH

O

CH3C

O

OH

H

Histidine

NNH

OH

CH3C

O

OH

Histidine

Stage 10: Ethanoic acid leaves the active site and the cycle can be repeated

1.1.6 Management and Treatment of Alzheimer’s disease

Disease modifying therapy is important for this devastating neurodegenerative

disorder and if one is not developed then an epidemic of AD will ensue in the coming

decades in aged population (Vassar 2017).

There are several therapeutically important targets for the management and treatment

of AD [Fig-8] (ul Islam and Tabrez 2017).

The relationship between several targets [Fig-8] may demand multi-targeting drug

development approaches (Chierrito et al. 2017).


20

AD Targets

Fig-8 Treatment Approaches or Targets for Alzheimer's disease (ul Islam and

Tabrez 2017)

In the decade of 2002-2012, clinicaltrials.gov, a National Institutes of Health registry

of publicly was registered and tested 244 drugs in clinical trials for AD. Among 244

drugs, only one successfully completed clinical trials and get FDA approval.

Development of effective treatment of AD is difficult to make due to these factors

such as high drug development cost, long duration of use and the structure of the brain

through which only very specialized small-molecule drugs can cross. None of the

medications available today to slows or stops the damage and destruction of neurons

that originate AD symptoms and make it lethal (Association 2017).

1. Proein Deposition • Aβ Amyloid • Tau protein

2. Enzymes • Acetylcholinesterase • Lipoxygenase (LOs) • Secretases a) β

secretase (BACE-1) b) γ secretase

• Caspases • Sirtirins • Glycogen synthase

kinase (GSK-3) • Glyceraldehyde-3-

phosphate dehydrogenase (GAPDH)

• Nitric Oxide synthase (NOS)

3. Misellaneous • Oxidative imbalance • Increasing

Autophagocytosis • Nucleicacid-based

medicines • Increasing

synaptogenesis • RNA Interference

(RNAi)


21

Complexity or multiple targets of AD make one molecule-one target solution

ineffective. Multitargeted strategy generates a solo analogue to interact with

numerous targets simultaneously in the complex neuronal cascades, like BACE-1, Aβ

aggregation inhibition with antioxidant, antiacetylcholinesterase, MAO inhibition and

many other (Cavalli et al. 2008; Kozurkova et al. 2011; Di Santo et al. 2012;

Hamulakova et al. 2012; Spilovska et al. 2013; Yang et al. 2019). Initial target is

cholinergic system but drug treatment with cholinergic agonists or choline

replacement was of negligible value (Thomsan Nogrady 2005). Acetylcholine

signaling is terminated in synaptic cleft by its cleavage through acetylcholinesterase.

AChE inhibitors are giving a therapeutic tactic to boost cholinergic signaling in AD

patients by limiting acetylcholine breakdown and able of varying the growth of AD

(Nwidu et al. 2017; Yan et al. 2017; Pascoini et al. 2018).

1.1.7 Acetylcholinesterase Inhibitors (AChEIs)

Up till now no cure has been established with the current anti-Alzheimer drugs.

Available drugs only for symptomatic treatment by increasing the quantity of

neurotransmitter in the brain but none of these stop the disease itself. They only slow

down the progression but when a patient discontinues the drugs, the deterioration

continues. Patient to patient the effectiveness of marketed drugs varies and also its

duration of action is limited (Ambure et al. 2014; Valasani et al. 2014; Association

2017).

Reversible AChEIs are those compounds that are substrate for and react with AChE to

form an acylated enzyme, which is more stable than the acetylated (acetylcholine with

AChE) form but still capable of undergoing hydrolytic regeneration. These reversible

AChEIs binds with greater affinity than acetylcholine to AChE but do not act as a

substrate. Those that acylate AChE include the acyl carbamates e.g. physostigmine

[Fig-9]. Alkyl carbamates such as carbachol [Fig-10] and bethanechol [Fig-11], are

structurally related to acetylcholine, substrates for and competitively inhibit AChE,

because they are hydrolyzed very slowly by AChE (Fifer 2008).


22

O

NH

ON

N

Fig-9 Physostigmine

Fig-10 Carbachol Fig- 11 Bethanechol

Different drugs were approved by FDA for the treatment of AD and most of them are

inhibitor of acetylcholinesterase [Table-1] namely Tacrine [Cognex], Donepezil

[Aricept], Galantamine [Reminyl] and Rivastigmine [Exelon]. These approved drugs

are useful only for symptomatic relief in mild to moderate disease state but unable to

stop the neuron damage (Anand and Singh 2013; Ambure et al. 2014; Bacalhau et al.

2016; Nwidu et al. 2017; Chen et al. 2019; Yang et al. 2019). Current clinical

inhibitors of AChE (to increase the acetylcholine levels) are most valuable approach

and major consideration for AD treatment but parade numerous side effects such as

gastrointestinal troubles, insomnia, fatigue, depression and liver toxicity (Senol et al.

2011; Ado et al. 2015).

H2N O

N

O CH3

CH3

CH3

ClH2N O

N

O CH3

CH3

CH3

Cl


23

List of FDA Approved Acetylcholinesterase Inhibitors (Table-1)

FDA

Approved

drugs

Structures Approval

Year

Mechanism of

Action References

Tacrine

N

NH2

1993

reversible

inhibitor of

AChE and

BuChE

(Petersen et

al. 2005;

Bacalhau et

al. 2016)

Donepezil

O

N O

O

1996

reversible and

selective AChE

inhibitor

(Colombres et

al. 2004;

Bacalhau et

al. 2016;

Goschorska et

al. 2019)

Galantamine

NO

O

H

HO

2000

reversible and

selective AChE

inhibitor

(Ballard CG

2003; Birks

2006; Anand

and Singh

2013;

Bacalhau et

al. 2016)

Rivastigmine

ONH3C

NCH3

CH3O

CH3CH3

H

2000

pseudo-

irreversible,

dual inhibitor

of

cholinesterases

(Ballard CG

2003;

Colombres et

al. 2004;

Birks 2006;

Bacalhau et

al. 2016;

Goschorska et

al. 2019)


24

1.2 Acridine

The organic dyes have been used since early 20th century as a medicinal agent.

Acridine [Fig-12] is one of the important molecule among these organic dyes (Fifer

2008). Heterocyclic compounds containing nitrogen form a new class of acridine

derivatives with important pharmaceutical properties. Acridine derivatives are

characterized by distinctive physical and chemical properties, biological activities and

industrial applications. In nineteenth century acridine derivatives were used as

pigments and dyes in different industries (Gensicka-Kowalewska et al. 2017).

Fig-12 Acridine

Acridine is a constituent of coal tar (Glenn L. Jenkins 1957), having a structural

feature of a quinoline ring or quinoline with additional benzene added (Fullerton

1998). It is a weak base with chemical properties similar to quinoline and medicinally

important chiefly for its derivative (Glenn L. Jenkins 1957). Acridine is known to be

biologically versatile compound possess several biological activities (Chen et al.

2002; Charmantray et al. 2003). Acridine based compounds, which were identified as

by-product of aniline dye manufacturing, first time used in clinical medicine in the

late 19th century against malaria. In First World War acridine derivatives such as

proflavin [Fig-13] used extensively as a local antibacterial agent (Silverman 2004b).

NH2N NH2

Fig-13 Proflavin

Acridine based pharmacophores bearing a heterocyclic/aromatic ring system

displayed wide bioactivities such as antimicrobial, anticancer, antiparasitic, antiviral,

antitubercular, anticonvulsant, antihypertensive, anti-inflammatory, antimalarial,

analgesic, DNA intercalating and fungicidal agent. Acridine derivatives are effective

N


25

acetylcholinesterase inhibitors and also used as dyes, fluorescent materials for

biomolecules visualization and in laser technologies (Baguley et al. 1995; Petrikaitė et

al. 2007; Patel et al. 2010; Gensicka-Kowalewska et al. 2017).

Acridine derivatives are also found in nature like plants and various marine

organisms. Cystodytin A (isolated from various marine organisms) and acronycine

(isolated from bark of Australian scrub ash tree) are two best examples. For diagnosis

of neurodegenerative disorders such as AD, acridines can also be used. 125I-labeled

acridines holding high affinities for Aβ aggregates and 6-iodo-2-methoxy-9-

methylaminoacridine and 2,9-dimethoxy-6-iodoacridine as potential imaging agents

for amyloid in living brain particularly for AD (Gensicka-Kowalewska et al. 2017).

1.2.1 9-Aminoacridine

9-aminoacridine (9AA) [Fig-14] is a highly fluorescent, dibasic crystalline yellow dye

derived from acridine having a molecular formula C13H10N2 and formula weight of

194.69g with melting point of 240°C (464.9°F) (Manzel et al. 1999). 9AA is a

moderately strong base (pKa=9.99) (Vaidyanathan and Goodacre 2007).

Fig-14 9-Aminoacridine

Aminoacridines get more importance among acridine based chromophores which

were synthesized and tested (Patel et al. 2010). Aminoacridine or 9-aminoacridine

primarily reported as antibacterial agent (Kastrup 1996). Later Elberfeldin Germany

brought about the introduction of Quinacrine [Fig-15] in the dye industry during

chemotherapeutic studies of antimalarial synthetic products in 1930. Quinacrine

[Mepacrine, 6-chloro-9-(4-dimethylamine-1-methylbutyl amino)-2-methoxyacridine,

Fig-15] is effective in the treatment of malaria (Glenn L. Jenkins 1957). In 1960s and

early 1970s various aniline-substituted analogues of 9-anilinoacridine were prepared

N

NH2


26

and test for antitumor activity and found that the most potent analogues had electrons

donating substituent in addition to the sulfonamide group. Amsacrine [Fig-16]

(R=OMe, R'=NHSO2Me; Amsidyl) was most potent among these tested compounds

and now use in the treatment of leukemia (Silverman 2004b).

Fig-15 Quinacrine Fig-16 Amsacrine

1.2.2 9-Aminoacridine derivatives

1,2,3,4-tetrahydro-9-aminoacridine (Tacrine, THA, Table-1), is a member of 9-

aminoacridine class and reported as a reversible inhibitors of AChE (Walker et al.

1995; Chierrito et al. 2017).

Tacrine is a first FDA approved drug clinically use as AChEI in 1993 for AD

management and treatment (Korabecny et al. 2010). Tacrine was synthesized in

1930s as nonclassical cholinesterase inhibitor that binds to both acetylcholinesterase

(AChE) and butyrylcholinesterase (BuChE) (Shutske et al. 1989). Approximately

20% among tacrine-treated patients show improvement but use become limited due to

its hepatotoxicity. Tacrine produces three metabolites due to extensive metabolism by

CYP450. Toxic and active one is 1-hydroxy-tacrine metabolite, excreted via urine

with elimination half-life 1.5-4hrs and responsible for the hepatotoxicity (Marx 1987;

Ames et al. 1988; Fifer 2008; Patocka et al. 2008).

Shaw and Bentley (1949) was first described THA pharmacologically in Australia as

an analeptic agent, use to morphinized dogs and cats. Early THA was used for the

treatment of anesthetic induced delirium and initiation of the muscle relaxing effect of

N

H3CO

Cl

NHCH(CH3)CH2CH2CH2N(C2H5)2

N

NH

R R'


27

succinylcholine. Later Heilbronn (1961) was a first one who described that Tacrine is

a reversible inhibitor of AChE and BuChE (Giacobini 1998).

Since 1986 a group of scientist reported a series of THA derivatives. The reversible

competitive inhibitor, octyl-THA [Fig-17] is a THA derivative, found less toxic than

THA (BRUFANI et al. 1986; Marta and Pomponi 1988; Hunter et al. 1989; Harel et

al. 1993; Pomponi et al. 1997) .

Fig-17 Octyl-THA Fig-18 2tBuTHA

A novel aminoacridine derivative was synthesized, 2-tertiary-butyl-9-amino-1,2,3,4-

tetrahydroacridine (2tBuTHA) [Fig-18] and reported as cytotoxic to the neuronal cell

and least potent AChE inhibitor as compared to tacrine (Walker et al. 1995). Scientist

investigated the comparative effect of tacrine with 7-methoxytacrine (7-MEOTA)

[Fig-19]. 7-methoxytacrine 4-5 times more stronger than tacrine to interfere with

functioning of muscarinic receptors (Musilkova and Tuček 1991). 7-MEOTA based

fourteen new analogues (N-alkyl-7-methoxytacrine) were synthesized and reported

with better AChE inhibition (Korabecny et al. 2010).

Fig-19 7-Methoxytacrine Fig-20 2-aminopyridine-3,5-dicarbonitrile

Fig-21 2-chloropyridine3,5 dicarbonitrile Fig-22 Tacrine analogue

N

NH2

N

NH2 CH3

CH3

N

NH2

H3CO

N

R

NC

X

CN

NH2

N

R

NC

X

CN

Cl

Y

NN

NH2

CN

X

R


28

2-aminopyridine-3, 5-dicarbonitriles [Fig-20] and 2-chloropyridine-3,5-dicarbonitriles

[Fig-21] were fused with tacrine to form a new tacrine analogue [Fig-22]. By

substituting different groups at R, X and Y position generated twenty two different

new derivatives [some examples are given below in Fig-23 a-d] as inhibitors of AChE

as well as amyloid deposition (Samadi et al. 2011).

Fig-23 a b c d

Potential multifunctional drugs are not only inhibiting AChE but also inhibits AChE-

induced β-amyloid deposits and BACE-1 activity. Novel tacrine-8-hydroxyquinoline

hybrids [Fig-24] and 6-chlorotacrine bearing hybrids [Fig-25] are come under this

class and more potent than tacrine in nanomolar concentration (Hardy and Selkoe

2002; Skovronsky et al. 2006; Camps et al. 2009; Camps et al. 2010; Fernández-

Bachiller et al. 2010; Tumiatti et al. 2010).

Fig-24 Where: R1, R2=H, Z= (CH2)7-10 Fig-25 6-chlorotacrine

To improve aqueous solubility, novel series of AChEIs have been synthesized bearing

tacrine pharmacophore containing pyrazoline rings [Fig-26 (n=4 & 7)] while other

containing imidazole rings [Fig-27 (n=4 & 7)] (Lange et al. 2010).

NN

NH2

CN

Me2N NN

NH2

CN

MeO NN

NH2

CN

Me2N NN

NH2

CN

Cl

N

HN zHN

N

OH

R1

R2 N

NH2

Cl


29

Substitution in the THA at positions 6 and 7 [Fig-1] built analogues [Fig-28 a-c]

having inhibitory potency in the micro molar concentration to AChE (Recanatini et al.

2000).

Fig-26 (n= 4 & 7) Fig-27 (n=4 & 7)

Fig-28 a b c

Bis-tacrine derivatives [Fig-29] and planar acridines, spiroacridines,

tetrahydroacridines [Fig-30, 31 and 32] have negligible cytotoxicity work as

multifunctional compounds concurrently inhibit AChE as well as Aβ aggregation

induced by AChE and more potent than tacrine (Bolognesi et al. 2007; Antosova et

al. 2011).

NNH(CH2)nHNN

N

N

Cl

SOO

Cl

NHN(CH2)nHN

O

N

N

Cl

Cl

N

NH2

R1

R2 N

NH

R1

R2N

NH

R1

R2

C7H15

NN NH HNY

O

NH

HN

O

OO

O


30

Fig-29 Y= a b c

N

NH

NH

OHS

N

NH

NH

HS

N

NH

HS

HN

Fig-30 Fig-31 Fig-32

Synthesis of new heterodimers of 7-MEOTA were done and these possesses human

AChE and BChE inhibitory activity especially compound with six methylene [Fig-

33] and compound with a five methylene linker [Fig-34] (Spilovska et al. 2013).

N

OH3C

HNNH2

N

OH3C

HN NH

NH

S

Fig-33 Fig-34

The tacrine carbazole hybrids were composed by tacrine and 7-methoxyheptaphylline

with 5-methylene linkage between them [Fig-35] showed AChE inhibitory and

antioxidant action with great strength (Thiratmatrakul et al. 2014).

N NH N

H

NHHO

OCH3

Fig-35


31

Methoxy substituted coumarin–tacrine hybrids, p-cholorophenyl substituted

rivastigmine-tacrine and fluoxetine-tacrine hybrid compounds appeared as a potential

candidate for AChE inhibition (Babitha et al. 2015). Bis-tacrine compounds [Fig-36]

are multitarget directed ligands (MTDLs) inhibit Aβ aggregation with enzyme

inhibition potency and less toxic for human hepatocytes. On the basis of given

properties these compounds are supposed to be a new pharmacological tool for

multifactorial nature of AD (Brogi et al. 2014).

N

HN

NH

N

HN

NH

HN

NH

O

O

O

O

NH

O

O

O

O

Fig-36

1.3 Computer Aided Drug Designing (CADD)

Developing a new drug is a complex process from novel plan to the inauguration of a

final product. Selection of modulator is based on screening of huge libraries of small

molecules or peptides against the selected target to screen out potential candidate.

This potential candidate will then be screened under medicinal parameters for drug

candidate such as potency is determined and converted into potential lead. The whole

process of analyzing medicinal aspect of dug candidate is obviously very lengthy,

monotonous and time taking. Period of 12–15 years and cost of $1 billion or more

required to develop a single pharmaceutical product (Hughes et al. 2011). Insilico

based approaches are now being developed to decrease the cost and time for

discovering a new drug molecule more efficiently. Computational studies overcome

this problem which includes computer aided drug designing (CADD) in order to

produce number of derivatives with improved absorption, bioavailability, metabolism,

potency and safety profile (Aboul-ela and Varani 1995; Drews and Ryser 1997;

Hughes et al. 2011).


32

The use of silicon in semiconductor computer chips for performance of experiments

generated a term insilico. This new area introduced in the mid-1980s and now

developed and widely used in industry, academia, especially in drug design field for

treating various diseases by exploring new biologically active compounds. Before

synthesis of any molecule, chemical structure inspection, recognizing features for

definite biological activity, thus allowing estimation and validation of large set of

compounds theoretically and by statistical data (Scotti et al. 2018).

However, insilico study plays an important role in cost effective MTDLs investigation

in less time. CADD also involves various techniques like visualization, energy

minimization, homology modeling, molecular docking, molecular dynamic and

QSAR (Taft and Da Silva 2008; Rahman et al. 2012; Ambure et al. 2018).

In biomedical field, insilico methods have been used to accelerate and expedite hit

identification, hit-to-lead selection, improving the pharmacokinetic parameters and

reducing toxicity data of drugs. The use of new drug designing technology in research

& development would reduce the cost up to 50% (Geldenhuys et al. 2006). Currently,

research oriented pharmaceutical industries employing latest computational chemistry

tools to develop structure-activity relationships, pharmacodynamics profile (potency,

affinity, efficacy and selectivity) and pharmacokinetic properties (Lipinski et al. 2001;

Hughes et al. 2011). CADD had extensively provided its variety of extended

applications in the post genomic era, bridging nearly all steps of the drug discovery

i.e. starting from identification of target to lead discovery and finally to clinical trials

[Fig-37] (Rücker et al. 2004).

1.3.1 Types of Computer Aided Drug Design

CADD can be categorized mainly into two types such as ligand based drug design

(LBDD) and Structure based drug design (SBDD) (Ambure et al. 2018). These drug

discovery methodologies have been used in academics as well as industry for studying

structural, chemical and biological data [Fig-38] (Drwal and Griffith 2013; Valasani

et al. 2014).


33

Fig-37 Application of CADD in Drug Development

Fig-38 Types of CADD

CADD

Disease-related

Genomics Target

Identification

Bioinformatics

Reverse

Docking Protein

Structure Prediction

Target Validation

Target Druggability

Tool Compound

Design

Lead discovery

Library Design

Docking Score

De nono Design

Pharma-cophore

Target Flexibility

Lead Optimizatio

n QSAR 3D-QSAR

Structure Based

Optimization

Preclinical Test

Insilico ADMET Prediction

Physiological Based Pharmacokinetics PBPK

Simulation

Clinical Trials

CADD

SBDD De novo Design

Virtual Screening

LBDD

QSAR 2D

3D

Comparative Molecular Field

Analysis CoMFA

Comparative Molecular Similaraty

Indices Analysis CoMSIA

Scaffold Hopping

Pharmacophore Qualitative Quantitative

Modeling

Pseudo Receptors


34

1.3.1.1 Structure Based Drug Design (SBDD)

In SBDD [Fig-39] three dimensional (3D) information of protein structure of

biological targets used as important factor in modern medicinal chemistry (Salum et

al. 2008). Different strategies used in SBDD such as virtual screening, molecular

dynamics and molecular docking involved in the key ligand-protein interactions,

conformational changes and binding energies investigation (Kalyaanamoorthy and

Chen 2011; Manoharan and Ghoshal 2018). With the help of 3D model of ligand

receptor complex, several intermolecular features like interacting residues, unknown

binding sites and ligand induced conformational changes can be determined [Fig-39]

(Kahsai et al. 2011; Shoichet and Kobilka 2012).

Fig-39 Outline of Structure Based Drug Designing (SBDD).

1.3.1.2 Ligand Based Drug Design (LBDD)

LBDD is based on the use of data-banks or libraries of small-molecule (ligand) that

are biologically active. There is a unique chemical range available to make

interactions with a specific macromolecule. It is also used as ligand based virtual

screening (LBVS), QSAR modeling, similarity searching and pharmacophore

modeling which could provide predictive models for lead identification and

optimization (Bacilieri and Moro 2006; Leach and Gillet 2007; Acharya et al. 2016;

Ambure et al. 2018).


35

SBDD and LBDD together becomes a powerful tool to investigate multi-targeted

ligands for different biological targets (Manoharan and Ghoshal 2018; Passeri et al.

2018).

1.3.2 Drug Design and Molecular Modeling

Molecular modeling (MM) gives new options for discovering, identifying,

determining and understanding of a lead molecule in drug design. MM had been used

in different science related fields like computational chemistry, biology and other

material sciences for learning from small molecules to large complex systems

(Nadendla 2004).

It includes all modeling procedures and theoretical methods used to identify the

behavior of molecules. Mostly MM involves three stages:

a. selection of a model to describe the intramolecular and intermolecular interactions.

Quantum and molecular mechanics are two most common models which calculate the

energy of arrangement and interactions of different atoms and molecules of the

system.

b. energy minimization, molecular dynamics or conformational search.

c. analysis of calculations and to pattern that it has been performed accurately.

MM also allowed researchers to produce and display molecular data such as energies

[heat of formation, activation energy, etc.], geometries [bond angles, bond lengths and

torsion angles], bulk properties [volumes, viscosity, surface areas, diffusion, etc.],

spectroscopic properties [chemical shifts and vibrational modes] and electronic

properties [charges, electron affinity and ionization potential] (Redhu and Jindal

2013).

1.3.2.1 Molecular Docking

Molecular docking is use to determine ligands and protein interaction at atomic level

and different poses of ligand ranked by a scoring function. These ligand–receptor

interactions govern by intermolecular forces including van der Waals force, hydrogen


36

bonding, hydrophobicity and electrostatic interactions. A major advantage of

molecular docking is to perform virtual screening with a big number of binders

forming complex with receptor and selected on the basis of binding energy or scores

(López-Vallejo et al. 2011; Meng et al. 2011; Begum et al. 2018; Scotti et al. 2018).

Three dimensional (3D) structures of the known proteins are found from different

literatures and protein data banks. Now, numbers of computer aided models have been

developed and used to screen several thousand molecules for different biological

activities. For this purpose, the methods of choice are computational programs,

docking of ligands to 3D structure of proteins or construction of new ligands within a

known binding site (Kubinyi 1998; Taft and Da Silva 2008).

This method is based on two models, first the “key-lock” model proposed by Fisher in

1894 stated that a compound would bind specifically to the rigid active site of a

macromolecule and second “induced fit” by Koshland in 1958 stated that the

macromolecule was not a rigid structure and can be presented in different

conformations induced by binder.

Three dimensional structure of the ligand and the macromolecule must be known for

succeed molecular docking. The software created different types of conformations of

the ligand ranked according to a score function (binding energies), separating the

docked compounds according to their binding score and allows the comparison

between residues of the interacting macromolecule with ligand and the reference

ligand (drug candidates). Another possibility is called blind docking in which all the

macromolecules considered as the interacting area (Huang and Zou 2010; Scotti et al.

2018).

1.3.2.1.1 Protein-Ligand Docking

Protein-ligand docking signifies a important, well-established methodology of the

current drug discovery process in the field of molecular docking (Kitchen et al. 2004;

Sousa et al. 2006; Grosdidier and Fernández-Recio 2009). The most widely used

computational tool is protein-ligand docking that helps to provide favorable complex

between a protein and a small molecule (ligand). It can be regarded as part of the

more general field of molecular docking, which aims to give most promising


37

intermolecular complex formed between two or more generic constituents (Sousa et

al. 2006; Grosdidier and Fernández-Recio 2009). These progressions depend on a

variety of atomic-level scale such as ligand-protein, enzyme-substrate, ligand-nucleic

acid and protein-protein recognition (Huang and Zou 2010). Protein-ligand docking

involves the exploration of different ligand conformations and orientations (the pose)

within a target protein and the measure of the binding affinity of the different

alternatives (the scoring) [Fig-40].

Fig-40 Drug-receptor complex interactions showing binding energy (Jacob et al.

2012)

Conformational sampling methods use as docking algorithms for performing virtual

screening in which active site of the target macromolecule is occupied by ligand.

Conformational sampling methods are numerous types like Genetic Algorithms,

Monte Carlo simulation and Simulated Annealing used in calculation of docking

results. All conformational sampling methods are directed by a function that estimates

interactions between the protein and ligand is evaluated by scoring function.

knowledge, empirical and force-field based three methods for score calculation.

Combining of different scoring functions produced consensus scoring and its high

value directs better attachments of ligand with target protein (Majeux et al. 1999; Zou

et al. 1999; Halim et al. 2010; Begum et al. 2018).

1.3.2.1.2 Molecular Docking and Drug Likeness

Drug-like properties or oral bioavailability analyzed by Lipinski’s rule of five.

According to this rule the potential therapeutic compounds must possess molecular

weight <500 daltons, partition coefficient (logP) less than 5, hydrogen bond acceptors


38

≤ 10, hydrogen bond donors ≤ 5 and molecular reactivity between 40 and 130. Blood

brain barrier is an important descriptor of absorption, distribution, metabolism,

elimination and toxicity (ADMET) which determines the ability of an unbound drug

to penetrate the selectively permeable layer of CNS cells and its capability to

reallocate itself from blood plasma to the lipid of the plasma membranes. Its relative

affinity for lipid and water is determined by its lipid/water partition coefficient (logP).

Most CNS active drugs with logP value of <5 have been reported to easily penetrate

the BBB while those with logP value of <0 are unable to penetrate across BBB. It is

well known that poor drug solubility is detrimental for its good and complete oral

absorption suggesting that evaluation of this property in early stages of drug discovery

is of great importance. In addition to these parameters, the metabolism of the drug

within the body is also an important step. By ADMET analysis, avoid long-term

toxicity and side effects. The main enzymes involved in metabolism of the drug, such

as CYP1A2, CYP2A6, CYP2C9, CYP2C19, CYP2D6, CYP2E1 and CYP3A49,

belong to the cytochrome P450 (CYP) family among which CYP2D6 accounts for the

majority of xenobiotic metabolism and almost 50% of the known pharmaceuticals. It

is noteworthy that the quantity of the drug in general blood circulation reduces after

binding to plasma proteins while an unbound drug can traverse more efficiently across

cell membranes. Thus, plasma protein binding (PPB) is another important

pharmacokinetic descriptor for ADMET analysis and is predicted on the basis of

binding of the drug to human serum albumin. Since human toxicity can be influenced

by drug metabolism therefore, hepatotoxicity can be considered as one of the major

manifestations of drug toxicity. Thus, many drug research and development

laboratories have adopted intact hepatocytes as an approach for toxicity screening

(Amat-ur-Rasool and Ahmed 2015; Awasthi et al. 2018b).


39

PLAN OF WORK

Protein Structure from PDB

Protein Preparation using chimera

Ligands sketch using chemdraw

& Standards retrieved from

Pubchem database

Ligands preparation

by MOE

Protein selection

Protein Structure from PDB

Protein Preparation by Quickprep MOE

Ligands sketch using chemdraw &

Standards retrieved from Pubchem

database

Ligands preparation using Open Bable

Receptor Grid Generation

Receptor Grid Generation Docking using Autodock Vina

(PyRx)

Docking using MOE

DOCK

Docking Scores

Analysis of docked complex

Filter 10 compounds for synthesis

A Selection of reagents and solvents

Synthesis

Separation & confirmation of synthesized compound

Acetylcholinesterase inhibition

Antioxidant activity

Biological activity Significant activity

Amyloid disaggregation activity

Potent molecules for Alzheimer’s

disease

Protein selection

Cytotoxic activity


40

Chapter 2

INSILICO STUDIES


41

2.1 Molecular Docking

2.1.1 Methodology

2.1.1.1 MOE

2.1.1.1.1 Docking protocol

Molecular docking is a dynamic technique for discovering the interactions between

the target protein and a small molecule. In order to find out the structure and activity

relationship of the newly synthesized AChE inhibitors, molecular docking was

performed by using windows based Molecular Operating Environment software

package MOE 2018.01 (Chemical Computing Group. Inc., Canada).

2.1.1.1.2 Target protein

X-ray structure of acetylcholinesterase were downloaded from Protein Data Bank

(PDB) (www.rcsb.org), using PDB: 4EY7 (Cheung et al. 2012). Dimeric chain was

refined and used only monomer. Removed all water molecules, heteroatoms (except

donepezil) and add hydrogen atom/s, missing residues and partial charges by using

Quickprep of MOE. Moreover, energy minimization of target protein was carried out

under MMFF94x force field (Halgren 1996; Halgren 1999). The active sites were also

finding with the help of MOE alpha site finder.

2.1.1.1.3 Validation of protocol by screening binding database against target protein

Before starting the docking, validation of molecular docking was performed to check

the accuracy of the software. For that, online available binding database were used by

making different data sets after screening against AChE target. MOE DOCK module

was used under a defined force field and rescoring.

Two-steps calculation, in first step, without energy minimization docking was carried

out to find whether in-house library will bind to an active pocket and the placement

algorithm was set to Proxy Triangle and the scoring function (Aplha HB). Docking

step with induced fit and GBVI/WSA dG approach with energy minimization


42

calculation (MMFF94x) select best poses of every ligand of different data set. The

same score function and other parameters were used as in the first step.

The ligand molecule (E20) using the alpha site finder module and to confirm the

parameters, re-docked on 4ey7.pdb showed a root mean square deviation (RMSD) <1 °A, signifying appropriate repeatability of the method.

2.1.1.1.4 Molecular docking of 4EY7

Validated docking protocol used was based on docking placement methods (i)

Induced fit method and (ii) Proxy Triangle + alpha HB + GBVI/WSA dG approach.

2.1.1.2 Autodock Vina (PyRx)

2.1.1.2.1 Preparation of Molecules Library

A virtual library of proposed 9-aminacridine compounds (Table-2) with structural

diversity has been sketched using ChemDraw Ultra 8.0, followed by their

compatibility to molecular environment using OpenBable. Biologically known

compounds (Table-3) were retrieved from pubchem database

(http://www.ncbi.nlm.nih.gov/pccompound/) to be included in dataset for comparison.

The library was then subjected to protonation and energy minimization for their

geometry optimization using MMFF94 with optimization algorithm conjugate

gradients and 1000 number of steps for attaining least energy conformations and

stored as pdb files.

2.1.1.2.2 Protein Selection

Before starting docking, the sequence similarity between target proteins of different

organism i.e. electric eel and human acetylcholinesterase was calculated using an

online server http://www.ebi.ac.ukools/psa/emboss_needle/ by aligning the amino

acid sequence of two proteins and value is 74.7%. The purpose of this calculation is to

intimate the electric eel based testing to the human application based therapeutics,

initially.


43

2.1.1.2.3 Preparation of Protein

X- ray crystallographic structure of human acetylcholinesterase (hAChE) (PDB ID:

4EY7, resolution: 2.35 Å) [Fig-41] was retrieved from virtual protein databank (PDB)

(www.rcsb.org) for pursuing computational studies. 4EY7 as a complex intact

structure contain chain A and B with 524 aminoacids (Sukumaran et al. 2018). Chain

B of 4EY7 was selected for insilico studies because it is bound with donepezil as its

cognate ligand. In preparation of protein, the removal of surplus charges, non-bonded

inhibitors and non-essential water molecules; however, polar hydrogen addition and

non-polar merging ones by Chimera 1.10.2, UCSF. Gasteiger charges were assigned

to all atoms and protein is allowed to relax fully by selecting 1000 descent steps and

saved in pdb.

2.1.1.2.4 Molecular Docking Method

All molecular docking studies were performed on Autodock Vina PyRx0.9.2 software

package (Jacob et al. 2012; Muhammad and Fatima 2015) using WINDOWS 8.1

work station running under Microsoft. The prepared target was uploaded in software

folder as macromolecule and prepared library of compounds [Table-2] was allowed to

dock against it. For the docking, both macromolecule and ligands were converted into

pdbqt as software compatible format. The active site was specified by generating a

centroid grid box; following xyz coordinates were applied against bound ligand or

active site residues (10.698, -58.115, -23.192). Molecular docking was carried out in

Vina Wizard (PyRx-Virtual Screening Tool-Version 0.9.2) program utilizing

computer resources. Results appeared in eight different conformers with different

binding energies or affinities and minimum predicted Gibbs binding energy were

selected as the top-scoring modes. Rendering the graphic and visualization

representations of docked 3D poses, Chimera 1.10.2 was utilized.


44

Chain A Chain B

Fig-41 Structure of recombinant human acetylcholinesterase (PDB:4EY7) with

donepezil complex and represent in a ribbon diagram. The crystal structure has two

independent molecules A and B in green and red colour, respectively. Donepezil is in

stick model and coloured cyan.


45

2.1.2 RESULTS

2.1.2.1 Proposed Library of 9AA Derivatives for Docking with 4EY7

(Table-2)

Code

Proposed Derivatives Code Proposed Derivatives

PS1

N

NH2

O

Cl

PS9

N

NH2

O

OCH3

PS12

N

NH2

O

PS13

N

NH2

O

OCH3

H3CO

PS23

N

NH2

O2S

CH3

PS24

N

NH2

O2S

NO2

PS25

N

NH2

O2S

Br

PS26

N

NH2

O2S

CH3H3C

CH3

PS27

N

NHO

PS28

N

NHO

CH3


46

PS29

OCH3

N

NHO

H3COOCH3

PS32

N

NHO

Br

PS33

N

NHO

PS36

N

NH2+

Br

PS37 N NH2

+

PS38 N NH2

+

PS39 N NH2

+

PS40

N

NH2

PS41

N

NH2

PS42

N

+H2N

PS 43

N

H2N

PS 44

N

H2N

PS 45

N

H2N

PS 46

N

NH2

PS 47

N

NH2

PS 48

N

H2N

PS 49

N

H2N


47

PS 50 N

H2N

PS 51

N

H2N

PS 52

N

H2N

PS 53

N

H2N

PS 54

N

H2N

PS 55

N

H2N

PS 56

N

H2N

PS 57

N

H2N

PS 58

N

H2N

PS 59

N

H2N

PS 60

N

NH2

PS 61

N

H2N

PS 62

N

H2N

PS 63

N

H2N

PS 64

N

H2N

PS65

N

NH2

PS 66

N

H2N


48

PS67

N

H2N

PS 68

N

H2N

2.1.2.2 Standards for docking with 4EY7 (Table-3)

S.No. Standard Drugs

Structures

1. Tacrine

N

NH2

2. Donepezil

O

N O

O

3. Galantamine NO

O

H

HO

4. Physostigmine

O

NH

ON

N

5. Rivastigmine ONH3C

NCH3

CH3O

CH3CH3

H

2.1.2.3 Docking Sores of Standards, Parent and Top Ranked Ligands

for synthesis (Table-4)

S.No. Standards Structures Docking Docking


49

Sores

with

4EY7 By

MOE

Sores with

4EY7 By

Autodock

Vina

1. Tacrine

N

NH2

-5.60 -8.5

2. Donepezil

O

N O

O

-8.86 -11.3

3. Galantamine NO

O

H

HO

-6.92 -8.9

4. Physostigmine

O

NH

ON

N

-6.95 -8.5

5. Rivastigmine ONH3C

NCH3

CH3O

CH3CH3

H

-6.65 -7.6

Lead Structure

Docking

Sores

with

4EY7 By

Docking

Sores with

4EY7 By

Autodock


50

MOE Vina

1. 9AA

N

NH2

-5.46 -8.6

Top Ranked

ligands Structures

Docking

Sores

with

4EY7 By

MOE

Docking

Sores with

4EY7 By

Autodock

Vina

1. PS12

N

HN

O

-8.65 -12.7

2. PS13

N

HN

O

O CH3

O

H3C

-6.90 -10.1

3. PS23

N

HN

S OO

CH3

-6.55 -10.2


51

4. PS24

N

HN

S OO

NO2

-8.02 -10.4

5. PS25

N

HN

Br

S OO

-6.93 -9.3

6. PS26

N

NH

SO O

CH3H3C

CH3

-7.86 -10.6

7. PS27

N

NH

O

-6.85 -11.1

8. PS28

N

NH

O

CH3

-6.43 -10.2


52

9. PS32

N

HN

C

Br

O

-6.66 -10.9

10. PS33

N

HN

C O

-6.78 -12.5


53

Graph-1: Comparison of docking scores of standards between MOE and

Autodock Vina (PyRx)

-12 -10 -8 -6 -4 -2 0

Tacrine

Donepezil

Galantamine

Physostigmine

RivastigmineMOEAutodock Vina

S

tand

ard

Dru

gs

Binding Affinity (kcal/mol)


54

Graph-2: Comparison of docking scores of 9AA and selected derivatives between

MOE and Auodock Vina (PyRx)

-14 -12 -10 -8 -6 -4 -2 0

9AA

PS12

PS13

PS23

PS24

PS25

PS26

PS27

PS28

PS32

PS33

MOE

Autodock Vina

Binding Affinity (kcal/mol) 9A

A a

nd S

elec

ted

Der

ivat

ives

Binding Affinity (kcal/mol)

9AA

and

Sel

ecte

d D

eriv

ativ

es


55

2.1.2.4 3D Interactions of Standards, Parent and Top Ranked

Ligands with 4EY7 by MOE (Table-5)

S.No. Code Hydrogen Bond/s π-π and π-CH

Stacking

Hydrophobic

Interaction/s

1. Tacrine NH----O Tyr124 - Trp86, Tyr337,

Tyr341

2. Donepezil O----HN Phe295

NH----O Tyr337 Trp86,Tyr337

Trp86, Phe297,

Tyr337, Phe338,

Tyr341, His447

3. Galantamine

O----HN Gly121

O----HN Gly122

NH----O Tyr124

OH----O Glu202

O----HO Ser203

- Trp86, Phe297,

Phe338, His447

4. Physostigmine NH----O Tyr124

NH----O Tyr133 Tyr341

Trp86, Tyr337,

Phe338, His447

5. Rivastigmine O----HN Phe295 Tyr337, Phe338 Trp86, Tyr341

6. 9AA HN----HO Tyr124

NH----O Ser125 -

Trp86, Tyr337,

Tyr341

PHENACYL DERIVATIVES

7. PS12 NH----O Gly120 Trp86,Tyr341

Asp74, Trp286,

Tyr337, Phe338,

Tyr341

8. PS13 O----HN Phe295

O----HN Arg296 Tyr341

Leu76, Trp286,

Phe338, Tyr341,

Tyr337

SULPHONYL DERIVATIVES

9. PS23 O----HN Phe295 Tyr341

Tyr72, Trp286,

Leu289, Tyr337,

Phe338, Tyr341,

Leu76

10. PS24 O----HN Phe295 Trp86 Asp74, Trp86,

Phe297, Phe338,


56

Tyr341

11. PS25 O----HN Phe295

O----HN Arg296 -

Tyr72, Trp286,

Leu289, Phe297,

Phe338, Tyr341

12. PS26 NH----O Asp74

O----HN Phe295

Trp286, Tyr337,

Phe338

Tyr72, Asp74,

Trp86, Trp286,

Tyr341, Tyr337

BENZOYL DERIVATIVES

13. PS27 O----HO Tyr124 Trp286, Tyr337,

Phe338

Tyr72, Trp286,

Leu289, Tyr341

14. PS28 O----HO Tyr124 Trp286, Tyr341

Tyr72, Trp286,

Leu289, Tyr337,

Phe338, Tyr341

15. PS32 NH----O Tyr124

O----HN Phe295 -

Tyr72, Trp286,

Phe297, Tyr337,

Phe338, Tyr341

NAPHTHOYL DERIVATIVE

16. PS33 O----HO Tyr124

NH----O Tyr341

Trp86, Tyr337,

Tyr341

Tyr72, Trp286,

Phe297, Tyr337,

Phe338, Tyr341


57

2.1.2.5 3D Interactions of Standards, Parent and Top Ranked Ligands

with 4EY7 by Autodock Vina (PyRx) (Table-6)

S.No. Code Hydrogen Bond π-π and π-CH

Stacking

Hydrophobic

Interaction/s

1. Tacrine NH----O Tyr124 Tyr337 Trp86, Phe338,

Tyr341

2. Donepezil O----HN Phe295

N----HO Tyr124 Trp86, Trp286

Tyr337, Phe338,

Tyr341,

3. Galantamine

O----HO Ser125

OH----O Ser125

OH----O Glu202

O----HO Ser203

OH----O Ser203

O----HN His447

OH----N His447

Tyr337 Asp74, Trp86,

Tyr337

4. Physostigmine N----OH Tyr124

O----HO Tyr341 Glu202 Trp86

5. Rivastigmine O----HO Tyr124

O----HO Ser125 Tyr337, Trp86

Tyr337, Phe338,

Tyr341

6. 9AA NH----OH Tyr124 Tyr337 Trp86, Phe338,

Tyr341


7. PS12 N----OH Ser125

Tyr341

Asp74, Trp86,

Trp286, Phe338,

Tyr341

8. PS13

NH----N Trp86

NH----O Ser125

N----HO Tyr337

NH----O Tyr337

Tyr341

Asp74, Trp86,

Phe297, Tyr124


9. PS23

NH----O Trp86

NH----O Tyr124

N----HO Tyr124

Tyr124, Tyr341

Asp74, Trp86,

Phe338, Tyr341


58

10. PS24

NH----O Trp86

NH----O Tyr124

N----OH Tyr124

O----HN Phe295

Tyr341

Asp74, Trp86,

Tyr124, Phe297,

Phe338

11. PS25 O----HN Phe295

NH----O Tyr341

Trp286, Tyr341

Trp286, Leu289,

Phe338, Tyr341

12. PS26 N----HO Ser125

NH----O Ser125 Trp86, Tyr341

Asp74, Tyr337,

Phe338, Tyr341

BENZOYL DERIVATIVES

13. PS27

NH----O Trp86

NH----OH Tyr124

N----HO Tyr124

Tyr124

Asp74, Trp86,

Tyr337, Phe338,

Tyr341

14. PS28 N----O Tyr124

Trp86, Trp286,

Phe297, Phe338,

Tyr341

15. PS32

NH----O Trp86

NH----OH Tyr124

N----HO Tyr124

Tyr124

Asp74, Trp86,

Tyr337, Phe338,

Tyr341


16. PS33

NH----O Trp86

N----HO Tyr124

O----OH Tyr341

Tyr341

Asp74, Trp86,

Trp286, Phe338,

Phe297


59

2.1.2.6 Common Amino Acid Residues Involved In Interactions of

Ligands with 4EY7 in MOE and Autodock Vina (PyRx)

(Table-7)

S.No. Code Interacting amino acids

MOE Autodock Vina (PyRx)

1. Tacrine Trp86, Tyr124, Tyr337,

Tyr341

Trp86, Tyr124, Tyr337,

Phe338, Tyr341

2. Donepezil

Trp86, Phe295, Phe297,

Tyr337, Phe338, Tyr341,

His447

Trp86, Tyr124, Trp286,

Phe295, Tyr337, Phe338,

Tyr341

3. Galantamine

Trp86, Gly121, Tyr124,

Glu202, Ser203, Phe297,

Phe338, His447

Asp74, Trp86, Ser125,

Glu202, Ser203, Tyr337,

His447

4. Physostigmine


Tyr337, Phe338, Tyr341,

His447

Trp86, Tyr124, Glu202,

Tyr341

5. Rivastigmine Trp86, Phe295, Tyr337,

Phe338, Tyr341

Trp86, Tyr124, Ser125,

Tyr337, Phe338, Tyr341

6. 9AA Trp86, Tyr124, Ser125,

Tyr337, Tyr341


Phe338, Tyr341


7. PS12 Asp74, Trp86, Trp286,



Trp286, Phe338, Tyr341

8. PS13

Leu76, Trp286, Phe295,

Arg296, Phe338, Tyr341,

Tyr337

Asp74, Trp86, Tyr124,

Ser125, Phe297, Tyr337,

Tyr341


9. PS23

Tyr72, Leu76, Trp286,

Leu289, Phe295, Tyr337,

Phe338, Tyr341


Phe338, Tyr341

10. PS24 Asp74, Trp86, Phe295,

Phe297, Phe338, Tyr341


Phe295, Phe297, Phe338,


60

Tyr341

11. PS25

Tyr72, Trp286, Leu289,

Phe295, Arg296, Phe297,

Phe338, Tyr341

Trp286, Leu289, Phe295,

Phe338, Tyr341

12. PS26

Tyr72, Asp74, Trp86,

Trp286, Phe295, Tyr337,

Phe338, Tyr341



BENZOYL DERIVATIVES

13. PS27

Tyr72, Tyr124, Trp286,

Leu289, Tyr337, Phe338,

Tyr341

Asp74, Trp86, Tyr 124,


14. PS28 Tyr72, Trp286, Leu289,


Trp86, Tyr124, Trp286,

Phe297, Phe338, Tyr341

15. PS32

Tyr72, Tyr124, Trp286,

Phe295, Phe297, Phe338,

Tyr341




16. PS33

Tyr72, Trp86, Tyr124,

Trp286, Phe297, Tyr337,

Phe338, Tyr341


Trp286, Phe297, Phe338,

Tyr341


61

2.1.2.7 Ligand Interacting with PAS and CAS Residues of Protein

(Table-8)

Residues (MOE) Residues (Autodock Vina)

Gln71 Gln71

Tyr72a Tyr72a

Val73 Val73

Asp74a Asp74a

Thr75 Thr75

Leu76 Leu76

Trp80 -

Gly82 Gly82

Thr83 Thr83

Trp86b Trp86b

Asn87 Asn87

Pro88 Pro88

Trp117 -

Tyr119 -

Gly120 Gly120

Gly121b Gly121b

Gly122b Gly122b

Tyr124a Tyr124a

Ser125 Ser125

Gly126 Gly126

Ala127 Ala127

Leu130 Leu130

Tyr133b Tyr133b

Val139 -

Glu202b Glu202b

Ser203b Ser203b

Ala204b Ala204b

Trp236 -


62

Trp286a Trp286a

His287 His287

Leu289 Leu289

Gln291 Gln291

Glu292 Glu292

Ser293 Ser293

Val294 Val294

Phe295b Phe295b

Arg296 Arg296

Phe297b Phe297b

Tyr337b Tyr337b

Phe338b Phe338b

Val340 -

Tyr341a Tyr341a

Gly342 Gly342

Trp439 Trp439

His447b His447b

Gly448 Gly448

Tyr449 -

Ile451 Ile451

Residues within 3°A of protein–ligand interactions are highlighted. aPeripheral anionic site (PAS)

residues and bCatalytic active site (CAS) residues.


63

2.1.2.8 3D pictures of Acetylcholinesterase interacting with standards

and ligands after docking by MOE

In general, ligand in ball and stick while protein atoms in stick form. Residue refers to

human acetylcholinesterase are numbered and shown in purple. Oxygen and nitrogen

atom are colored red and blue, respectively. Hydrogen bond representations are

colored yellow solid line. Hydrophobic interactions and π-π, π-CH stacking are

depicted by green solid line. For the better clarity, only active pocket residues were

displayed. The figure was created with MOE.

Fig-42 Active site of the AChE:Tacrine (blue) complex.


64

Fig-43 Active site of the AChE:donepezil (blue) complex.

Fig-44 Active site of the AChE:Galantamine (blue) complex.


65

Fig-45 Active site of the AChE:Physostigmine (light blue) complex.

Fig-46 Active site of the AChE:Rivastigmine (yellow) complex.


66

Fig-47 AChE active site bound with 9AA (red).

Fig-48 AChE active site bound with PS12 (brown)


67

Fig-49 AChE active site bound with PS13 (red).

Fig-50 AChE active site bound with PS23 (blue).


68

Fig-51 AChE active site bound with PS24 (yellow).

Fig-52 AChE active site bound with PS25 (brown).


69

Fig-53 AChE active site bound with PS26 (brown).

Fig-54 AChE active site bound with PS27 (shocking pink).


70

Fig-55 AChE active site bound with PS28 (white).

Fig-56 AChE active site bound with PS32 (dark grey).


71

Fig57 AChE active site bound with PS33 (light blue).


72

2.1.2.9 3D pictures of Acetylcholinesterase interacting with standards

and ligands after docking by Autodock Vina (PyRx)

In general, ligand in ball and stick while protein atoms are shown as sticks. Human

acetylcholinesterase residues are numbered shown in light grey, 9AA in hot pink,

standards in sandy brown and selected derivatives in cyan colour. Oxygen are red and

nitrogen atoms are blue. Hydrogen bond representations are coloured yellow dotted

line. π-π and π-CH stacking are depicted by orange in colour. Hydrophobic

interactions indicated by green colour. For the better clarity, only active pocket

residues are displayed. The figure was created with Chimera 1.10.1.

Fig-58 AChE active site bound with Tacrine


73

Fig-59 AChE active site bound with Donepezil

Fig-60 AChE active site bound with Galantamine


74

Fig-61 AChE active site bound with Physostigmine

Fig-62 AChE active site bound with Rivastigmine


75

Fig-63 AChE active site bound with 9AA

Fig-64 AChE active site bound with PS12


76




77




78




79




80



81

2.1.2 DISCUSSION Acetylcholinesterase (AChE) present in cholinergic brain synapses involved in rapid

hydrolysis of acetylcholine (ACh) into acetate and choline. Decreased level of ACh

causes impairment of the cholinergic neurotransmission in brain showing cognitive

decline and memory deficits. Reduction in the ACh metabolism is a competent

methodology to improve the cholinergic neurotransmission in Alzheimer disease

(AD) (Francis et al. 1999; Li et al. 2017) .

ACh is an endogenous neurochemical having small structure with three main parts i.e.

choline moiety (tertiary nitrogen), ethylene chain and acetate group. From

pharmacophoric point of view, all three components are essential for binding with

acetylcholinesterase enzyme. Tertiary nitrogen of structure is going to bind with

choline binding site, however, ethylene bridge bind to hydrophobic region and acetate

moiety to the esteratic site of pocket where the main hydrolysis occurred for the

release of degraded choline (Dvir et al. 2010). The three sites and their corresponding

binding regions within pocket sites are presented in figure-74.

Moreover, our selected synthesized compounds also comprised of three structural

features similar to ACh i.e acridine ring along with 9-amino group imitating the role

of choline amine, connecting moieties (Y), replacing the ethylene bridge and

substituted phenyl and naphthalene ring as an alternate of acetate group. Figure-75

presents all possibly alternative features that chemically correlate our synthetic

compounds with endogenous substrate (ACh) predicting their affinity with the target

enzyme.

Fig-74 Structure of acetylcholine highlighted different regions responsible to

interacting with AChE


82

Where Y= C

O

S

O O

C

O

CH2

Fig-75 General structure of 9AA derivatives showing resemblances with the ACh

AChE has two sites: a peripheral anionic site (PAS) located at the mouth of the gorge

and catalytic active site (CAS) present deep in the gorge. The PAS consists of 5

residues (Tyr72, Asp74, Tyr124, Trp286 and Tyr341) clustered at the entrance of the

gorge and trap substrate on its way to the active site (CAS). ACh binds with serine,

histadine and glutamic acid residue (esteratic site) which causes hydrolysis of the

ester portion of ACh. Quaternary ammonium group of ACh binds to aromatic

moieties of tryptophan, tyrosine and phenylalanine residues (Trp86, Tyr133, Tyr337

and Phe338) through cation-π interactions. The acyl pocket, responsible for substrate

selectivity, composed of Phe295 and Phe297. The oxyanion hole with Gly121,

Gly122 and Ala204, provides hydrogen bond donors. Hydrophobic site of AChE

binds alkyl portion of ACh. The ligands (inhibitors) at PAS and CAS induce extensive

conformational changes to alter substrate accessibility (Gocer et al. 2016; Cheng et al.

2017; Rosenberry et al. 2017; Sukumaran et al. 2018)

Insilico based approaches are now being developed to decrease the cost and time for

efficiently discovering a new drug molecule. Computer aided drug designing (CADD)

in drug discovery or design process helps in synthesis of molecules, with defined

chemical structure, recognizing features for certain biological activity, improved


83

determination of absorption, bioavailability, metabolism, potency and safety profile

thus allowing estimation and validation of large set of compounds theoretically and by

statistical data (Scotti et al. 2018).

MOE and PyRx are Virtual Screening soft wares for CADD that can be used to screen

libraries of compounds against potential drug targets. These soft wares used to

determine the interactions of 9-aminoacridine (9AA) and its different analogues

against acetylcholinesterase (AChE) enzyme as inhibitors.

FDA approved drugs are useful only for symptomatic treatment in mild to moderate

state of AD and most of them are inhibitors of AChE including tacrine, donepezil,

galantamine and rivastigmine. Current clinical inhibitors of AChE (to increase the

acetylcholine levels) are most valuable and major consideration for AD treatment

(Ado et al. 2015; Bacalhau et al. 2016; Nwidu et al. 2017).

Tacrine (1,2,3,4-tetrahydro-9-aminoacridine) is a FDA approved reversible AChE

inhibitor belongs to 9-aminoacridine class used for AD management and treatment

but withdrawn from the market because of its hepatotoxicity. 9-aminoacridine has

great importance among acridine based chromophores because of its number of

bioactivities, especially as antialzheimer’s agent by acting as effective

acetylcholinesterase inhibitor (Korabecny et al. 2010; Patel et al. 2010; Gensicka-

Kowalewska et al. 2017).

In the present study docking affinity in term of minimum energy indicated the good fit

of ligand to human acetylcholinesterase (hAChE) crystallographic structure (PDB

ID:4EY7, resolution: 2.35 Å). The active site with hotspot residues was calculated by

the cognate ligand of protein retrieved from protein databank (PDB) with code 4EY7.

Tyr124, Phe295 Trp86, Tyr341, Trp286, Tyr337 and Phe338 are among the important

amino acid residues involved in fixing the ligand into the active pocket site of target

ensuring their ability to have role in physiological regulation.

Molecular Docking by MOE:

All compounds showing better energy score and binding interactions than all

standards except donepezil. They interacted with active target site via hydrogen


84

bonding, π-π stacking, π-CH stacking and hydrophobic interactions [Table-5, Fig-42-

57]. Amino acid residues which are part of CAS and PAS regions are given in Table-

8.

Standards

Tacrine is a FDA approved derivative of 9AA presented the only hydrogen bonding

between its amine and oxygen of Tyr124 while surrounded by Trp86, Tyr337 and

Tyr341 producing hydrophobic surface. Donepezil with the best docking score

involved with amine of Phe295 and oxygen of Tyr337 to form hydrogen bonds while

adjusted in the active pocket with the help of amino acid residues (Trp86, Tyr337,

Phe338, Tyr341, Phe297 and His447) creating hydrophobic lining. Trp86 and Tyr337

interacted with donepezil through π-π stacking. Galantamine fit into the target site

with maximum number of hydrogen bonds (Gly121, Gly122, Tyr124, Glu202, and

Ser203). Whereas Trp86, Phe297, Phe338 and His447 aligned with the molecule

creating hydrophobic region. Amine of Physostigmine connected with Tyr124 and

Tyr133 through hydrogen bonds. Tyr341 was a part of π-π stacking and hydrophobic

surface presented interactions with Trp86, Tyr337, Phe338 and His447. Rivastigmine

showing very few amino acids involved in hydrogen, π-π and hydrophobic

interactions.

9AA and its Derivatives

9AA involved in hydrogen bonding with hydroxyl of Tyr124 and Ser125 and few

amino acid residues (Trp86, Tyr337 and Tyr341) formed hydrophobic lining.

PS12 is phenacyl derivative having para phenyl group. In this molecule amine of

acridine creating hydrogen bond with Gly120. π-π stacking presented with Trp86 and

Tyr341.Amino acid residues Asp74, Trp286, Tyr337, Phe338 and Tyr341 were part

of hydrophobic interactions. In PS13 presence of ortho, para methoxy groups

increased the numbers of hydrogen bonding i.e. one with Phe295 and two with

Arg296 while π-π stacking and hydrophobic interactions were almost similar to PS12.

PS23, PS24, PS25 and PS26 are substituted sulphonyl derivatives of 9AA. PS23 has

para methyl substitution generated hydrogen bond between its sulphonyl oxygen and


85

amine of Phe295.whereas Tyr341 involved in π-CH stacking. Seven amino acid

residues (Tyr72, Trpp286, Leu289, Tyr337, Phe338, Tyr341, and Leu76) stabilized

the molecule in active pocket via hydrophobic interactions.

PS24 having nitro group in place of methyl group. This change in functional group

change the conformation of phenyl ring in a way that adjust it on hydrophobic surface

with different amino acid residues (Asp74, Trp86, Phe297) except Phe338 and

Tyr341. Hydrogen bond generated by this molecule is same as in PS23 while π-π

stacking involved Trp86.

PS25 is para bromo sulphonyl analogue. This molecule displayed additional

hydrogen bonding with Arg296 along with Phe295. Ring conformation is allowing the

molecule to orient in almost same fashion as PS23 to fix in hydrophobic region

(Tyr72, Trp286, Leu289, Phe297, Phe338 and Tyr341).

In PS26 three methyl groups are attached on 2, 4 and 6 positions of phenyl ring which

stabilized the ligand target complex with increased number of amino acids (Trp286,

Tyr337 and Phe338) for π-π stacking as compared to other derivatives. Hydrogen

bonds produced with Asp74 and Phe295 whereas hydrophobic area occupied by the

ligand with amino acids including Tyr72, Asp74, Trp86, Trp286, Tyr341 and Tyr337.

Among benzoyl derivative PS27 having unsubstituted phenyl ring. Carbonyl oxygen

of PS27 formed one hydrogen bond with hydroxyl of Tyr124 while Trp286 and

Phe338 and Tyr337 involved in π-π and π-CH stacking. Hydrophobic interactions

generated by Tyr72, Trp286, Leu289 and Tyr341.

PS28 differs from PS27 by attachment of methyl group at para position. This change

in substitution influenced on the attachment of molecule mostly via hydrophobic

forces with increased numbers of amino acids (Tyr72, Trp286, Leu289, Tyr337,

Phe338 and Tyr341). Face to face interaction (π-π stacking) formed with Trp286.and

Tyr341, while engaged with same type of hydrogen bonding as shown in PS27.

PS32 produced complex with target active site via two hydrogen bonds with Tyr124

and Phe295. Molecule is surrounded by hydrophobic interactions via Tyr72, Trp286,

Phe297, Phe338 and Tyr341. Bromo group at ortho position in PS32 may be


86

responsible for the absence of the face to face interaction (π-π stacking) and π-CH

stacking.

PS33 is a naphthoyl derivative connected with the active site with two hydrogen

bonds, one with hydroxyl of Tyr124 and second with oxygen of Tyr341. π-CH and π-

π stacking formed with Trp86, Tyr341 and Tyr337. Hydrophobic area is generated

with Tyr72, Trpp286, Phe297, Tyr337, Phe338 and Tyr341.

In all derivatives changes in the terminal aromatic ring or substitution of fused ring

system mostly effected on π-CH, π-π stacking and hydrophobic interactions but

conformations of all the molecules favour the formation of same king of hydrogen

bonding specially involving the sulphonyl and carbonyl oxygen of linking chain

except PS13 where dimethoxy substitution on ring also taking part in hydrogen bond

generation.

Autodock Vina:

Table-4 & 6 presented interaction affinities of selected compounds shortlisted through

their binding capability with pocket side residues and fitting energy scores by

Autodock Vina (PyRx). Newly designed analogues docked better than all standards

with higher docking scores except donepezil. Donepezil is one of the most frequently

used drugs, presented following interactions with target protein: Carbonyl of ligand

established hydrogen bonding with Phe295. Nitrogen of piperidine ring formed

hydrogen bond with Tyr124. Face to face (π-π) interaction of Trp86 and Trp286 with

heterocyclic ring. Hydrophobic interactions with Tyr337, Phe338 and Tyr341 also

support the ligand accommodation within pocket site. Moreover, interactions made by

hotspot residues to the galantamine, physostigmine, rivastigmine and tacrine showed

their capability to block active pocket sites. Conclusively, Trp86, Ser125, Tyr124,

Tyr337 and Tyr341 are essential residues observed in binding of all standard

molecules [Fig-58-61].

Comparatively, 9-aminoacridine (9AA) binds with enzyme by its primary amine

which mediates a hydrogen bond with hydroxyl of Tyr124. π-π interaction with

Tyr337. Hydrophobic interactions (Trp86, Phe338 and Tyr341) further facilitated the


87

capturing capabilities of 9AA. As compared to 9AA it derivatives produced better and

enhanced interacting potential with the target protein [Fig-62].

Synthesized compounds exhibited better binding potential than standards except

donepezil. Chemical changes in the structure allowed the molecules to take unique

conformations within active site and showed varied binding behaviors [Fig-63-73].

In PS12, hydroxyl of Ser125 is interacting with nitrogen of acridine ring via hydrogen

bond. Tyr341 is involved in π-π and π-CH interaction. Asp74, Trp86, Trp286, Phe338

and Tyr341 contributed to make hydrophobic region for ligand.

In case of PS13, four hydrogen bonds were observed involving central ring nitrogen

and 9-amino of ligand with hydroxyl of Tyr337, carbonyl of Ser125 and amine of

Trp86. Face to edge (π-CH) interaction occurred with Tyr341. Hydrophobic area is

created by Asp74, Trp86, Phe297 and Tyr124.

In PS23, two hydrogen bonds formed by amines of 9AA with Tyr124 and central ring

nitrogen formed another hydrogen bond with Tyr86 while Tyr341 and Tyr124

communicated through π-π and π-CH interaction. Hydrophobic line comprised by

Asp74, Trp86, Phe338 and Tyr341.

PS24 showed affinity towards varied pocket regions with different interaction

capacities. Nitro of PS24 interacted with amine of Phe295; 9-amino group produced

bidentate hydrogen bonds with carbonyl of Trp86 and hydroxyl of Tyr124. π-π

interaction observed with Tyr341 while Asp74, Trp86, Tyr124, Phe297 and Phe338

were part of hydrophobic environment surrounding the ligand.

In PS25 combinations of bromine substitution in the ring with sulphonyl linker

reduced the ligand affinity with reference to hydrogen bonding comparing to PS24.

Sulphonyl oxygen of PS25 generated hydrogen bond with Phe295. Nitrogen of central

ring made hydrogen bond with carbonyl of Trp86. Two π-π interactions were seen

with Trp286 and Tyr341. Hydrophobic region included Trp286, Leu289, Phe338 and

Tyr341 which are somewhat different from commonly observed residues in the same

series.


88

Ser125 shared its hydroxyl for two hydrogen bondings with ligand amine of PS26.

Additionally π-π stacking with Trp86 was observed. π-CH (T-shape) contact with

Tyr341 also supported the binding. However, Asp74, Tyr337, Phe338 and Tyr341

covered hydrophobic portion. Thus, addition of hydrophobic groups on phenyl ring

showed positive effect on whole structural conformation and interactive position.

PS27 amine connected through hydrogen bond with hydroxyl of Tyr124. Another

hydrogen bond was observed between carbonyl of Trp86 with amine of acridine ring.

Face to face (π-π) interaction displayed with Tyr124. Hydrophobic interaction

involved Asp74, Trp86, Tyr337, Phe338 and Tyr341.

PS28 amine mediated a hydrogen bond towards hydroxyl of Tyr124. Hydrophobic

interaction mainly based on following amino acids residues Trp86, Trp286, Phe297,

Phe338 and Tyr341. Hydrophobic region produced by this molecule presenting

comparable area as presented by PS25.

Carbonyl of Trp86 and hydroxyl of Tyr124 formed hydrogen bond with amines of

PS33. Tyr341 also formed hydrogen bond by its hydroxyl with carbonyl of ligand.

Tyr341 made π-π interactions while Asp74, Trp86, Trp286, Phe338 and Phe297 were

part of hydrophobic surface.

Molecular docking study performed by MOE and Autodock Vina showing

comparable results. More stabilized complex is shown by Autodock in terms of

binding energy and amino acids involved in hydrogen bonding. Both soft wares

predicted same compounds as top most effective binders of the target enzyme. The

common amino acids by both soft wares cover PAS and CAS area of the protein.

Additional amino acids (binding regions) showed adaptability of the compounds

conformations to bind with target enzyme [Table-7].

Conclusively, all derivatives showed hydrogen bonds with the hotspot residues

similar to standards with some additional interactions in both PAS and CAS region.

Tyr124 and Phe295 were the prominent common amino acids engaging the linking

chain and acridine amines in hydrogen bonding. MOE projected mostly the

involvement of sulphonyl and carbonyl oxygen while Autodock presented greater

number of hydrogen bonds with the additional participation of acridine amines.


89

Standards and ligands interacted with PAS and CAS by π-π and π-CH interactions

with the contribution of Trp86, Tyr341, Trp286 and Tyr337. These are the reported

amino acids in the active pocket of the enzyme stabilizing the endogenous substrate

with same interactions.

Maximum number of hydrophobic interactions presented in ligand target complex

formation and played important role in capturing the acridine and terminal aromatic

ring to fix with Trp86, Tyr337, Phe338 and Tyr341 as mostly presented hot spot

residues.

Similar to the standards, ligands were also bind in the entrance of the receptor i.e.

PAS due to this they will capable to inhibit entrance of acetylcholine in

acetylcholinesterase and acetylcholine hydrolysis will inhibit. On the other hand those

derivatives which were bind to CAS similar to standards would able to inhibit binding

of acetylcholine at catalytic site of the receptor. PAS and CAS bonded ligands

considered as dual inhibitors. It is suggested that inhibition of acetylcholine

hydrolysis by blocking acetylcholinesterase, increases the level of neurotransmitter in

the brain and helps to improve cognitive function.

Literature revealed that the molecules bind to PAS can be used as dual inhibitor of

AChE and Aβ fibrillations, two pathological targets of Alzheimer’s disease (Cheng et

al. 2012; Szymański et al. 2013; Liu et al. 2014a; Basiri et al. 2017). It means when

the drug bind to that region blocks the entry of acetylcholine to active gorge for

hydrolysis as well as has capability to disintegrate β amyloid aggregates. So, all

selected ligands could be the successful candidates for the treatment of Alzheimer’s

diseases by targeting AChE and Aβ fibrils.


90

2.2 Drug likeness

2.2.1 Methodology

The “PreADMET 2.0” (Republic of South Korea) is computer-based program for

rapid prediction of ADME properties, physicochemical, drug absorption and drug-like

properties. The prediction of these properties including drug likeness checked through

this online software. Before determining these properties, structures (parent and

selected ligands) were drawn directly on software and submit for prediction. After

few minutes results had displayed on screen and save in excel sheet then convert into

word document.

2.2.2 Results

S.No. Drug Codes Drug likeliness

Rule of Five

1. 9AA Suitable

2. PS12 Suitable

3. PS13 Suitable

4. PS23 Suitable

5. PS24 Suitable

6. PS25 Suitable

7. PS26 Suitable

8. PS27 Suitable

9. PS28 Suitable

10. PS32 Suitable

11. PS33 Suitable


91

2.2.3 Discussion

In the pharmaceutical ground, preferred delivery route is oral. The major challenge for

medicinal chemist is to discover molecules that not only bind to the specific receptor

but also possesses particular physicochemical properties to reach the target site. Role

of five (RO5) (also known as ‘Lipinski’s rule’) is used to determine drug-like

property including physicochemical features and structural features like a drug. The

original RO5 defines four simple physicochemical parameters includes molecular

weight <500 daltons, log P value <5, hydrogen bond donors <5, hydrogen bond

acceptors <10 when there are >5 hydrogen-bond donors, the molecular mass is >500

daltons, calculated log P is >5, and the sum of nitrogen and oxygen atoms (hydrogen

bond acceptor) in a molecule is greater than 10 results in poor absorption or decreased

permeability of a compound. From last 10 years, RO5 is a routine procedure in drug

discovery associated with ‘drug-likeness’. Computer algorithms (combinatorial

chemistry) can freely use to calculate different parameters of RO5 by screening large

numbers of compounds libraries. It is very difficult to predict drug absorption,

distribution, metabolism, excretion and toxicity (ADMET) by invivo testing because

invivo studies are slow and expensive. Virtual RO5 determination is helpful to reduce

the cost, duration and complexity of drug development. However, only half of all

FDA approved small-molecule drugs used orally, compliant with the ‘rule-of-five’

(Tice 2001; Lipinski 2004; Zhang and Wilkinson 2007; Choy and Prausnitz 2011).

Lipinski’s Rule of Five has provided a simple method to identify suitable

physicochemical properties as well as ADMET. In this study, synthesized compounds

were run for drug likeness property on PreADMET 2.0 software. All compounds

having drug like property by obeys Lipinski’s rule of five parameters. More

specifically concluded that drug candidates follow the Rule of Five by covering all

parameters and good for oral route.


92

Chapter 3

SYNTHESIS OF

DERIVATIVES

3.1 Chemicals and Reagents

3.2 Instruments

3.3 Parent and Reactants for Synthesis

3.4 General Procedure of Synthesis

3.5 Physical and Spectral data of Synthesized Compounds


93

3.1 Chemicals and Reagents

All reagents and chemicals purchased from Merck and Sigma-Aldrich Company

(Germany). All solvents were reagent grade and distilled twice before used in

experiment. Precoated 0.25mm thick TLC plates with silica gel 60 GF254 were used to

monitor the reactions and check the purity of the chemicals. Solvent system used for

TLC including ethanol, chloroform and ethyl acetate. Recrystallization was done by

using tetrahydrofuran (THF) and alcohol. All the derivatives were dried over silica

beads of E.Merck as adsorbent in vacuum desiccators.

3.2 Instruments

In TLC, spots were visualized using UV light at 254 and 365nm on HP-UVIS Desaga

(Heidelberg, Germany). All glass wares were dried in Memmert Hot Air Oven

(Germany). Analytical balance (PA214, OHAUS Corporation, U.S.A) was used for

weighing of chemicals. The stirring and heating of the reaction mixtures has been

done on Hot plate-Stirrer (Bibby Sterilin Ltd, UK). Melting points determined on

STUART Melting point apparatus (U.S.A). The confirmation of final products was

done by spectral techniques. Shimadzu UV-visible (UV-1601), Japan

spectrophotometer was used for UV spectra. Infrared (IR) spectra were taken on

ALPHA II FTIR, Bruker, Germany. Fast Atomic Bombardment (FAB) technique was

used for mass determination of products on JEOL 600H-2, U.S.A. In d6-DMSO and

deutrated methanol (MeOD), Nuclear magnetic resonance (1HNMR) spectra were

recorded on Bruker Advance AV-400 and AV-500 MHz, France.


94

3.3 Parent and Reactants for Synthesis (Table-9)

Name of Chemicals Structures Molecular

Formula

Physical

State

Molecular

Weight

(a.m.u)

m.p/b.p

(°C)

Pare

nt

9-aminoacridine

(9AA)

C13H10N2 Solid 194.2 241-242

Rea

ctan

ts

2-bromo-4'-

phenylacetophenone O

Br

C14H11OBr Solid 196.04 47-49

2-bromo-2',4'-

dimethoxyacetophenone O

O

O

Br

C10H11O3Br Solid 259.10 102-104

4-methyl benzene

sulphonyl chloride S

O

O

Cl

C7H7SO2Cl Solid 190.65 65-69

4-nitro benzene


O

O

ClN+

O

-O

C6H4NO2SO2Cl Solid 221.5 75

4-bromo benzene


O

O

ClBr

C6H4BrSO2Cl Solid 255.52 73-75

2,4,6-trimethyl benzene


O

O

Cl

C9H11SO2Cl Solid 218.7 55-57

Benzoyl bromide O

Br C7H5OBr Liquid 185.02

-24/218-

219


95

4-methyl benzoyl

chloride O

Cl

C8H7OCl Liquid 154.59

-4 to -

2/225 to

227

3-bromo benzoyl

chloride

Cl

O

Br C7H4BrOCl Liquid 219.46 74-75

2-naphthoyl chloride O

Cl C11H7OCl Solid 190.63 50-52


96

3.4 Procedure for Synthesis

3.4.1 General Procedure for synthesis of 9-Aminoacridine

Derivatives

A mixture of tetrahydrofuran (THF) based solution of 9-aminoacridine (0.0025M) and

phenacyl, sulphonyl, benzoyl and naphthoyl halides (0.0025M) were stirred at room

temperature (rt) for 5-25hours at alkaline pH and refluxed at 50°C for 15-20 hours.

Reactions were monitored and confirmed by TLC using solvent system of ethanol and

chloroform with few drops of ethyl acetate. After cooling, the resulting product

[Table-10] precipitates were collected by filtration under reduced pressure, washed

with THF, recrystallized by THF and alcohol and dried under vacuum over silica.

Melting points were recorded and uncorrected.

3.4.2 Confirmation of Synthesized Compounds

3.4.2.1 Chromatography

Thin layer Chromatography (TLC) was used to confirm the synthesized derivatives

and to find out their purity using precoated 0.25mm TLC plate with silica gel 60

GF254 (Merck, Germany), using ethanol:chloroform with few drops of ethyl acetate as

eluent. TLC spots were visualized under ultraviolet light at 254 and 365nm on

HPUVIS Desaga (Heidelberg).

3.4.2.2 Melting point

Synthesis of analogues were also confirmed with melting point by using STUART

melting point apparatus.

3.4.2.3 Spectroscopy

Different spectroscopic techniques were used for the structure elucidation; detail is

given with the individual compounds.


97

3.4.3 Reaction Scheme

N

NH2

R

Y

XN

NH

Y

R

Stirring 5-25hrs (rt)

Reflux 15-20hrs (50°C)HX

List of products with substitutions at different sites on structure

(Table-10)

S.No. Product

Codes Y R X

1. PS12 C

O

CH2

Br

2. PS13 C

O

CH2 H3CO

OCH3

Br

3. PS23 S

O O

CH3

Cl

4. PS24 S

O O

NO2

Cl

5. PS25 S

O O

Br

Cl


98

6. PS26 S

O O

H3C

CH3

H3C

Cl

7. PS27 C

O

Br

8. PS28 C

O

CH3

Cl

9. PS32 C

O

Br

Cl

10. PS33 C

O

Cl


99

3.5 Physical and Spectral Data of Syntheszied

Compounds 3.5.1 N(4'-phenylphenacyl)-9-aminoacridine (PS12)

N

HN

O

1

2

3

4 5

6

7

89

1'

6'

3'4'

1''2''

7'

8'

5'

9'11'

12'

10'

2'

State and colour: dull yellow powder

Yield: 74.725%

Melting Point: 253°C (decomposed)

Molecular formula: C27H20N2O

Formula Weight: 309.24 a.m.u

Solubility: methanol, ethanol, DMSO

UV (MeOH) ε: 8095.9032 mol-1cm-1

IR (νmax) cm-1: 750 (aromatic C-H bending), 1450 (aliphatic CH2 bending), 1580

(aromatic C=C stretching), 1780 (C=O stretching), 3030 (aromatic C-H stretching).

3100 (N-H stretching)

FAB positive (m/z) M+1: 310


100

1H-NMR (MeOD, 500 MHz), δ(ppm): 8.642-8.663 (d, 4H, H-4, 5, 2', 6' J=8.4Hz),

7.950-7.990 (t, 5H, H-2, 7, 9', 10', 11' J=16Hz), 7.531-7.568 (t, 4H, H-3, 6, 3', 5'

J=14.8Hz), 7.877-7.898 (d, 4H, H-1, 8, 8', 12' J=8.4Hz), 2.486 (s, 2H, H-2'')


101

3.5.2 N(2',4'-dimethoxyphenacyl)-9-aminoacridine (PS13)

N

HN

O

O CH3

O

H3C

1

2

3

4 5

6

7

89

1' 6'

2'

3' 4'

5'

1''2''

7'

8'

State and colour: Bright yellow powder

Yield: 66.56%

Melting Point: 170°C

Molecular formula: C23H20N2O3



UV (MeOH) ε: 4031.77 mol-1cm-1

IR (νmax) cm-1: 760 (aromatic C-H bending), 1020 (C-O stretching), 1250 (C-O

stretching), 1410 (CH3 bending), 1480 (CH2 bending), 1600 (aromatic C=C

stretching), 1660 (C=O stretching), 3000 (aromatic C-H stretching), 3080 (N-H

stretching)

FAB positive (m/z) M+1: 418 1H-NMR (d6-DMSO, 400 MHz) δ (ppm): 3.872 (s, 3H, H-7'), 3.957 (s, 3H, H-8'),

4.717 (s, 2H, H-2''), 8.638-8.659 (d, 2H, H-4, 5 J=8.4Hz), 8.008-8.046 (t, 2H, H-3, 6

J=15.2Hz), 7.862-7.883 (d, 1H, H-1, 8 J=8.4Hz), 6.685-6.691 (d, 1H, H-5' J=14Hz),

6.715 (s, 1H, H-3'), 7.780-7.802 (d, 1H, H-6' J=8.8Hz)


102

3.5.3 N-(acridin-9-yl)-4'-methylbenzene sulfonamide (PS23)

N

HN

S

1

2

3

4 5

6

7

89

1'

6'

3'4'

7'

5'

2'

OO

CH3

State and colour: sharp yellow powder

Yield: 53.158%


Molecular formula: C20H17N2SO2



UV (MeOH) ε: 8234.994 mol-1cm-1

IR (νmax) cm-1: 750 (aromatic C-H bending), 1470 (CH3 bending), 1590 (aromatic

C=C stretching), 3020 aromatic C-H stretching), 3120 (N-H stretching),

FAB positive (m/z) M+1: 349 1H-NMR (d6-DMSO, 400 MHz), δ (ppm): 2.267 (s, 3H, H-7'), 8.725-8.747 (d, 2H,

H-4, 5 J=8.8Hz), 7.094-7.114 (d, 2H, H-2', 6' J=8Hz), 7.488-7.508 (d, 2H, H-1, 8

J=8Hz), 7.536-7.57 (t, 2H, H-2, 7 J=14Hz), 7.953-8.007 (q, 4H, H-3, 6, 3', 5')


103

3.5.4 N-(acridin-9-yl)-4'-nitrobenzene sulfonamide (PS24)

N

HN

S

1

2

3

4 5

6

7

89

1'

6'

3'4'

5'

2'

OO

NO2

State and colour: yellow powder

Yield: 77.5%





UV (MeOH) ε: 9506.544 mol-1cm-1

IR (νmax) cm-1: 650 (aromatic C-H bending), 1580 (aromatic C=C stretching), 3090

(aromatic C-H stretching), 3200 (N-H stretching)

FAB positive (m/z) M+1: 380 1H-NMR (d6-DMSO, 400 MHz), δ (ppm): 7.573-7.611 (t, 2H, H-2, 7 J=15.2Hz),

7.814-7.836 (d, 2H, H-2', 6' J=8.8Hz), 7.897-7.918 (d, 2H, H-1, 8 J=8.4Hz), 7.997-

8.035 (t, 2H, H-3, 6 J=15.2Hz), 8.171-8.193 (d, 2H, H-3', 5' J=8.8Hz), 8.669-8.691

(d, 2H, H-4, 5 J=8.8Hz)


104

3.5.5 N-(acridin-9-yl)-4'-bromobenzene sulfonamide (PS25)

N

HN

Br

S OO

1

2

3

54

6

7

8

1'

2'

3'4'

5'

6'

9


Yield: 72.729%


Molecular formula: C19H13N2SO2Br



UV (MeOH) ε: 10334.63 mol-1cm-1


(aromatic C-H stretching), 3100 (N-H stretching)

FAB positive (m/z) M+1: 414 1H-NMR (d6-DMSO, 400 MHz), δ (ppm): 7.567-7.606 (t, 2H, H-2, 7 J=15.6Hz),

7.903-7.925 (d, 2H, H-1, 8 J=8.8Hz), 7.994-8.033 (t, 2H, H-3, 6 J=15.6Hz), 8.675-

8.697 (d, 2H, H-4, 5 J=8.8Hz), 7.488-7.541 (m, 2H, H-2', 6'), 8.501-8.521 (d, 2H, H-

3', 5' J=8.0Hz)


105

3.5.6 N-(acridin-9-yl)-2',4',6'-trimethylbenzene sulfonamide

(PS26)

N

NH1

2

3

46

7

89

5

1'

2'

3 '

4'

5'

6'

SO O

CH3H3C

8 '

9 '7 '

CH3


Yield: 53.815%





UV (MeOH) ε: 9628.312mol-1cm-1

IR (νmax) cm-1: 760 (aromatic C-H bending), 1450 (aliphatic CH3 bending), 1580

(aromatic C=C stretching), 2900 (aromatic C-H stretching), 3100 (N-H stretching)

FAB positive (m/z) M+1: 377 1H-NMR (d6-DMSO, 400 MHz), δ (ppm): 6.729 (s, 2H, H-3', 5'), 2.146 (s, 9H, H-7',

8', 9'), 7.526-7.564 (t, 2H, H-2, 7 J=15.2Hz), 7.839-7.914 (d, 2H, H-1, 8 J=8.4Hz),

7.956-7.994 (t, 2H, H-3, 6 J=15.2Hz), 8.655-8.676 (d, 2H, H-4, 5 J=8.4Hz)


106

3.5.7 N-(9-acridinyl) benzamide (PS27)

N

NH

1

2

3

46

7

89

5

1'

2'

3'

4'

5'

6'

O


Yield: 87.458%





UV (MeOH) ε: 7243.7638 mol-1cm-1


(C=O stretching), 3010 (aromatic C-H stretching), 3130 (N-H stretching)

FAB positive (m/z) M+1: 299 1H-NMR (d6-DMSO, 400 MHz), δ (ppm): 8.748-8.769 (d, 2H, H-4, 5 J=8.4Hz),

7.975-8.028 (q, 4H, H-2, 7, 3', 5'), 7.617-7.654 (t, 1H, H-4' J=14.8Hz), 8.226-8.245

(d, 2H, H-2', 6' J=7.6Hz), 7.566-7.593 (t, 2H, H-3, 6 J=10.8Hz), 8.307-8.328 (d, 2H,

H-1, 8 J=8.4Hz)


107

3.5.8 N-(acridin-9-yl)-4-methylbenzamide (PS28)

N

NH

1

2

3

4

6

7

89

5

1'

2'

3'

4'

5'

6'

O

CH37'


Yield: 91.563%





UV (MeOH) ε: 5140.2934 mol-1cm-1

IR (νmax) cm-1: 750 (aromatic C-H bending), 1450 (CH3 bending), 1580 (aromatic

C=C stretching), 1660 (C=O stretching), 2980 (aromatic C-H stretching), 3110 (N-H

stretching)

FAB positive (m/z) M+1: 313 1H-NMR (d6-DMSO, 400 MHz), δ (ppm): 1.165-1.202 (t, 3H, H-7' J=14.8Hz),

8.724-8.745 (d, 2H, H-4, 5 J=8.4Hz), 8.109-8.28 (d, 2H, H-3', 5' J=7.6Hz), 7.994-

8.033 (t, 2H, H-3, 6 J=15.2Hz), 7.953-7.973 (d, 2H, H-1, 8 J=8Hz), 7.562-7.600 (t,

2H, H-2,7 J=15.2Hz), 7.426-7.445 (d, 2H, H-2', 6' J=7.6Hz)


108

3.5.9 N-(acridin-9-yl)-3-bromobenzamide (PS32)

N

HN

C

1

2

3

4 5

6

7

89

O

1'

2'

3'

4'

5'

6'

Br

State and colour: dark yellow powder

Yield: 93.70%

Melting Point: 222-2227°C

Molecular formula: C20H14N2COBr


Solubility: methanol, ethanol, DMSO, acetone, chloroform

UV (MeOH) ε: 9017.8956 mol-1cm-1


(C=O stretching), 3000 (aromatic C-H stretching), 3080 (N-H stretching)

FAB positive (m/z) M+1: 378 1H-NMR (d6-DMSO, 500 MHz), δ (ppm): 8.345-8.362 (d, 2H, H-1, 8 J=8.5Hz),

7.582-7.625 (m, 2H, H-3, 6), 7.841-7.858 (d, 1H, H-6' J=8.5Hz), 8.520-8.539 (d, 1H,

H-2' J=9.5), 8.492-8.509 (d, 2H, H-4, 5 J=8.5), 8.410-8.417 (t, 1H, H-5' J=17Hz),

8.292-8.325 (t, 2H, H-2, 7 J=16.5Hz), 8.205-8.226 (d, 1H, H-4' J=10.5)


109

3.5.10 N-(acridin-9-yl)-2-naphthamide (PS33)

N

HN

C

1

2

3

4 5

6

7

89

O

1'

2'

3'

4'

5'

6'

7'

8'

State and colour: Bright yellow powder

Yield: 91.812%


Molecular formula: C24H16N2CO



UV (MeOH) ε: 9244.6782 mol-1cm-1


(C=O stretching), 3010 aromatic C-H stretching), 3100 (N-H stretching)

FAB positive (m/z) M+1: 349 1H-NMR (d6-DMSO, 400 MHz), δ (ppm): 7.331-7.369 (t, 2H, H-5', 6' J=15.2Hz),

7.553-7.634 (m, 2H, H-3, 6), 7.675-7.713 (t, 2H, H-2, 7 J=15.2Hz), 7.835-7.857 (d,

2H, H-1, 8 J=8.8), 7.947-8.004 (m, 3H, H-7', 4', 3'), 8.066-8.086 (d, 1H, H-2' J=8Hz),

8.409-8.431(d, 2H, H-4, 5 J=8.8Hz), 8.566 (s, 1H, H-8')


110

3.6 Discussion Ten 9AA derivatives were successfully synthesized by targeting amino group of

acridine ring present at 9th position. All the reactants attached to the amino group

forming alkylamine, sulphonamide and amide bonds. Aromatic ring in all the

compounds has substitution except PS27, while naphthoyl derivative is different from

all other molecules where two rings are fused together.

Structures of all the selected molecules from the in-house library can be divided into

three main segments.

1. Acridine ring with primary amine attached at 9th position of ring

2. Acyl, carbonyl and sulphonyl moiety in the center as bridge to connect acridine

ring via amino group to the terminal aromatic ring

3. Aromatic ring single or fused, with or without substitution. Methyl, nitro, bromo

and methoxy groups are present on different positions of ring.

All three regions in the chemical structures of selected ligands showed involvement

in the formation of required conformation and best interactions to interact with the

target binding sites.

All products were pale/light yellow to bright/dark yellow in colour with yields in the

range of 53%-93% and showed solubility in ethanol, methanol and DMSO. The

completion of reaction is confirmed by TLC and melting point. Single circular spot

of product appeared on TLC at the different position from 9AA and other primary

reactants. Melting points of all the products were examined after separation. Some

compounds showed sharp and some decomposed at higher temperatures. Structure

elucidations done by four different spectroscopic techniques. In UV/visible

spectrophotometer, epsilon (ε) values calculated in the range of 4031.77-10334.63

mol-1cm-1. Infrared spectrophotometry was used to determine the major functional

groups which are the part of structure in the form of prominent peak. Molecular mass

of all compounds appeared in the form of M+1 peak by FAB positive technique.

Proton nuclear magnetic resonance (1HNMR) used to confirm the number of protons

present in a structure. 1HNMR spectra generated on Bruker Advance AV-400 and

AV-500 MHz (France) in d6-DMSO and deutrated methanol and chemical shifts were

reported in ppm. Selected data were reported as follows: chemical shift (δ),


111

multiplicity (s=singlet, d=doublet, t=triplet, m=multiplet), coupling constant J (Hz),

number of protons (1H=one proton, 2H= two protons………….nH=n protons)


112

Chapter 4

BIOLOGICAL ACTIVITIES


4.2 Antioxidant Activity (DPPH Scavenging Activity)




113


4.1.1 Methodology

All reactions were performed on UV-1800 spectrophotometer (SHIMADZU, Japan).

Acetylcholinesterase (AChE, E.C. 3.1.1.7, from electric eel in lyophilized powder,

≥1000 unit), acetylthiocholine iodide (ATCI) and 5,5-dithiobis-(2-nitrobenzoic acid)

(DTNB) and other chemicals purchased from Sigma-Aldrich (St. Louis. Mo, USA).

Modified Ellman’s method used for acetylcholinesterase inhibiting activity. Mixture

of DMSO and methanol (1:1) were used to prepared sample solution of test

compounds and obtain final assay concentrations with 0.1M KH2PO4/K2HPO4 buffer

(pH 8). For IC50 values, six different concentrations of each compound in triplicate

were tested. All experiments were done at 25°C. The enzyme was prepared in pH 8

buffer in final concentration of 0.22units/ml. 3mM DTNB was prepared in buffer of

pH 8 and 3mM ATCI in water, used as substrate of the reaction. Each sample mixture

contained 50µl potassium phosphate buffer, 25µl sample and 25µl enzyme. They

incubated for 15min at room temperature then 25µl ATCI and 125µl DTNB were

added. After 15 min, hydrolysis of ATCI by AChE was checked at 412nm by

spetrophotometer. Graph between inhibitory concentration and percent of inhibition

used to determine the IC50 values (inhibition curves). A control and the blank

experiments were performed. Buffer, water, DTNB and substrate were part of blank

and control ran under the same conditions of sample without inhibitor (Ellman et al.

1961; Mohammadi-Khanaposhtani et al. 2015).


114

4.1.2 Results

Acetylcholinesterase Inhibiting Activity of 9AA Derivatives (Table-11)

S.No. Compounds Code Structures IC50 ± SD

Micromolar (µM)

1. PS12

N

HN

O

1

2

3

4 5

6

7

89

1'

6'

3'4'

1'' 2''

7'

8'

5'

9'11'

12'

10'

2'

2.400 ± 0.0482

2. PS13

N

HN

O

O CH3

O

H3C

1

2

3

4 5

6

7

89

1' 6'

2'

3'4'

5'

1''2''

7'

8'

26.138 ± 1.0327

3. PS23

N

HN

S

1

2

3

4 5

6

7

89

1'

6'

3'4'

7'

5'

2'

OO

CH3

0.906 ± 0.0706

4. PS24

6.369 ± 0.1916

N

HN

S

1

2

3

4 5

6

7

89

1'

6'

3'4'

5'

2'

OO

NO2


115

5. PS25

N

HN

Br

S OO

1

2

3

54

6

7

8

1'

2'

3'4'

5'

6'

9

0.711 ± 0.0038

6. PS26

N

NH1

2

3

46

7

89

5

1'

2'

3 '

4'

5'

6'

SO O

CH3H3C

8 '

9 '7 '

CH3

6.329 ± 0.0992

7. PS27

N

NH1

2

3

4

6

7

89

5

1'

2'

3'

4'

5'

6'

O

5.062 ± 0.2048

8. PS28

N

NH1

2

3

46

7

89

5

1'

2'

3'

4'

5'

6'

O

CH37'

0.261 ± 0.0118

9. PS32

N

HN

C

1

2

3

4 5

6

7

89

Br

O

1'2'

3'4'

5'

6'

9.316 ± 0.2051


116

10. PS33

N

HN

C

1

2

3

4 5

6

7

89

O

1'

2'

3'

4'

5'

6'

7'

8'

7.683 ± 0.0276

11. 9AA N

NH2

152.54 ± 0.4342

12. Galantamine NO

HO

O

H

60.800 ± 0.4910


117

Graph-3: Acetylcholinesterase Inhibiting Activity of 9AA Derivatives

0

20

40

60

80

100

120

140

160

2.4

26.138

0.9069 6.369 0.7119 6.329 5.062

0.261 9.316

7.683

152.54

60.8

Compounds

IC50

(µM

)


118

4.1.3 Discussion

Acetylcholine (ACh) is an important neurotransmitter in the brain as they release into

the synaptic cleft and binds to ACh receptors (nicotinic and muscarinic). Decreased

levels of ACh cause impairment of the cholinergic neurotransmission in brain

showing cognitive decline and memory deficits which is considered to be a critical

determinant of Alzheimer’s pathogenesis and progression. Reduced level of ACh can

be overcome by inhibiting its hydrolytic enzyme, acetylcholinesterase (AChE). Level

of ACh in the brain increased for longer period by inhibition of AChE. In particular, it

has been the prime target for the development of first generation anti-Alzheimer’s

drugs (Cheng et al. 2017).

The selected synthesized compounds from the inhouse library were investigated for

their AChE inhibitory potential in invitro experimental model. Results mentioned in

Table-11.

9-Aminoacridine (9AA):

9AA when tested for its AChE inhibiting ability showed IC50 value 152.54µM, while

the standard galantamine displayed IC50 60µM. All synthesized derivatives exhibited

promising results as compared to 9AA and galantamine.

Phenacyl Derivatives:

PS12 showed IC50 value 2.4µM while PS13 exhibited IC50 value 26.138µM. This

difference in IC50 values can be correlated with the type and position of substitution at

phenyl ring. Presence of methoxy group at ortho and para position increased the

potential of PS13 more than ten times as compared to PS12 where phenyl ring is

present at para position. In insilico study PS12 presented better binding energy as

compared to PS13 but in terms of chemical interaction, PS13 was superior and

produced more hydrogen bonds as compared to PS12.


119

N

HN

O

1

2

3

4 5

6

7

89

1' 6'

2'

3'4'

5'

1''2''

R2

R1

R1=phenyl ring, R2=H (PS12) R1, R2=OCH3 (PS13)

Sulphonyl Derivatives:

Sulphonyl derivatives also acting as good AChE inhibitors with IC50 values ranging

between 0.711-6.369µM. When comparing all derivatives within the class, PS25 with

bromo group at para position exhibited greater inhibitory activity (0.7119µM) against

the AChE. Replacing bromo with methyl group at the same position (PS23), activity

slightly reduced with IC50 value of 0.906µM. In PS24 presence of nitro group at para

position attenuated the inhibitory effect more than six folds (IC50 6.369µM).

Interestingly in PS26 presence of three methyl groups one at para and two at ortho

positions again reduced the inhibitory power of PS26 as compared to PS23where only

para methyl group is present. This finding suggested that number of lipophilic groups

as well as their position is important for good inhibitory potential.

N

HN

S

1

2

3

4 5

6

7

89

1'

6'

3'4'

7'

5'

2'

OO

R2

R3 R1

R1=H, R2=CH3, R3=H (PS23) R1=H, R2=NO2, R3=H (PS24) R1=H, R2=Br, R3=H (PS25)

R1, R2, R3=CH3 (PS26)


120

Benzoyl and Naphthoyl Derivative:

Benzoyl derivatives including PS27, PS28 and PS32 showed AChE inhibiting activity

in the range of 0.269-9.316μM. PS27 is unsubstituted, PS28 with methyl at para

position and PS32 have bromo group at meta position. Among all these PS28

produced greater activity (0.261µM) as compared to PS27 and PS32 due to its para

methyl substituion. PS33 contains naphthoyl ring and showed activity with IC50

7.683µM.

N

NH1

2

3

4

6

7

89

5

1'

2'

3'

4'

5'

6'

O

R1

R2

N

HN

C

1

2

3

4 5

6

7

89

O

1'

2'

3'

4'

5'

6'

7'

8'

R1=H, R2=H (PS27) PS33 R1=CH3, R2=H (PS28) R1=H, R2=Br (PS32)

All derivatives showed good enzyme inhibiting potential when compared with 9AA

as well as reference standard galantamine. Overall results showed that in the given

series of compounds lipophilic group at para position is important for good inhibition

of AChE. At this position bromo and methyl substitution was showing significant

activity and between these, compound having methyl substitution particularly in

benzoyl derivative showed best activity with excellent IC50 value.

Outcomes of the Enzyme inhibition study justified the molecular docking results.

Promising enzyme blocking potential of PS23, PS25 and PS28 signified the

importance of the connecting moiety and substitution on phenyl ring and suggesting

their incorporation in the therapeutic activity Sulphonyl and carbonyl oxygens

presenting opportunity for hydrogen bonding along with acridine amines while

aromatic ring substituted with lipophilic group (para position) along with the acridine

ring system helping the molecules to fit in the active area with the help of π-π and

hydrophobic interactions. PS23 and PS28 clearly indicating the role of carbonyl


121

functionality along with para substituted methyl group for enhanced enzyme blocking

response. These features providing not only the best affinity for target enzyme but

also stabilized the complex more efficiently.


122

4.2 Antioxidant Activity (DPPH Scavenging Test)

4.2.1 Methodology

2,2'-diphenyl-1-picrylhydrazyl (DPPH) 100μM were prepared in methanol. Test

compounds were prepared in different concentrations (0–200μM) in methanol than

mixed with DPPH in equal volumes, mixed well and kept in dark for 20min a room

temperature. By using the spectrophotometer UV-1601, Shimadzu (Japan), the

absorbance at 517nm was measured. The percentage scavenging was calculated from

the following equation

% scavenging = Absorbance of blank - Absorbance of test X 100 Absorbance of blank

The plot between concentration of test compounds and percent (%) scavenging were

used for obtaining IC50 value. For comparison, Ascorbic acid was used as standard.

Experiments were performed in triplicate (NARLA and Rao 1995; Venkatachalam et

al. 2012).


123

4.2.2 Results

Antioxidant Activity of 9AA Derivatives (Table-12)

S.No. Compounds Code Structures IC50 ± SD

Micromolar (µM)

1. PS12

N

HN

O

1

2

3

4 5

6

7

89

1'

6'

3'4'

1'' 2''

7'

8'

5'

9'11'

12'

10'

2'

0.235 ± 0.0036

2. PS13

N

HN

O

O CH3

O

H3C

1

2

3

4 5

6

7

89

1' 6'

2'

3'4'

5'

1''2''

7'

8'

0.583 ± 0.0238

3. PS23

N

HN

S

1

2

3

4 5

6

7

89

1'

6'

3'4'

7'

5'

2'

OO

CH3

0.650 ± 0.0349

4. PS24

0.068 ± 0.0041

N

HN

S

1

2

3

4 5

6

7

89

1'

6'

3'4'

5'

2'

OO

NO2


124

5. PS25

N

HN

Br

S OO

1

2

3

54

6

7

8

1'

2'

3'4'

5'

6'

9

0.0294 ± 0.0013

6. PS26

N

NH1

2

3

46

7

89

5

1'

2'

3 '

4'

5'

6'

SO O

CH3H3C

8 '

9 '7 '

CH3

0.0779 ± 0.0011

7. PS27

N

NH1

2

3

46

7

89

5

1'

2'

3'

4'

5'

6'

O

0.3944 ± 0.0214

8. PS28

N

NH1

2

3

46

7

89

5

1'

2'

3'

4'

5'

6'

O

CH37'

0.035 ± 0.0006

9. PS32

N

HN

C

1

2

3

4 5

6

7

89

Br

O

1'2'

3'4'

5'

6'

0.702 ± 0.0035


125

10. PS33

N

HN

C

1

2

3

4 5

6

7

89

O

1'

2'

3'

4'

5'

6'

7'

8'

0.811 ± 0.0131

11. 9AA N

NH2

121.57 ± 0.3637

12. Ascorbic acid

OH

OH

HO

OO

HO

3.05 ± 0.0605


126

Graph-4: Antioxidant Activity of 9AA Derivatives

0

50

100

150

PS12

PS13

PS23

PS24

PS25

PS26

PS27

PS28

PS32

PS33

9AA

Asc

orbi

c…

0.235 0.583 0.65 0.068 0.0294 0.0779 0.3944 0.035 0.702 0.811

121.57

3.05

Compounds

IC50

(μM

)


127

4.2.3 Discussion

Oxidative stress refers to the excessive oxidation of biomolecules leading to cellular

damage and it is carried out by reactive oxygen species (ROS). The ratio between

generation and detoxification of ROS can be balance by several defense systems. The

overproduction of ROS or the impairment of antioxidant defense systems trigger by

endogenous and exogenous species, therefore leading to oxidative stress. Neuronal

systems seem to be sensitive to oxidations and a large number of neurodegenerative

disorders occurred due to oxidative damage of nerve cells. The histopathological and

the experimental evidence support the impact of oxidation on the pathogenesis of

Alzheimer’s disease. Elevated levels of different oxidized formations found in brain

as well as in cerebrospinal fluid (CSF), urine and blood of AD patients. Antioxidants

defense mechanism in brain and plasma also weakened with increase in age and

become a reason of age-related memory impairments (Marcus et al. 1998;

ALZHEIMER’S 2002; Montine et al. 2002; Feng and Wang 2012; Ado et al. 2015).

Antioxidant term mainly related to the determination of drug capability to save living

system from free radicals. These free radicals are the main and basic reason of

generation of number of diseases. 2,2-Diphenl-1-picrylhydrazyl (DPPH) scavenging

experiment is used to evaluate the antioxidant activity of different natural and

synthetic compounds.

DPPH is a stable free radical. It converted to stable diamagnetic molecule DPPH-H by

accepting an electron or hydrogen radical. DPPH radical reduction ability is

determined by the fall in its absorbance persuaded by antioxidants as compared to

standard or control at 517nm (Aazza et al. 2011). DPPH solution (purplish blue

colour) will turns yellow due to the formation of diphenylpicrylhydrazine (DPPH-H)

(Ado et al. 2015). Table-12 depicts the DPPH scavenging activity of the synthesized

derivatives with parent 9-aminoacridine (9AA) and well known reference standard

ascorbic acid.

9AA:

9AA presented minimum antioxidant activity with IC50 121.57µM while ascorbic

acid used as reference standard with IC50 value of 3.05µM.


128


PS12 having phenyl ring at 4' position showed significant IC50 value 0.235µM. PS13

having methoxy groups at 2' and 4' position exhibited IC50 0.583µM. This difference

in the antioxidant potential may be correlated with the substitution at the terminal

aromatic ring and signifying the presence of para phenyl in PS12 for imparting better

activity than PS13.

N

HN

O

1

2

3

4 5

6

7

89

1' 6'

2'

3'4'

5'

1''2''

R2

R1



Excellent antioxidant effect is presented by PS25 with the lowest IC50 0.0294µM.

PS24 also exhibited significant result with good DPPH scavenging activity giving

IC50 0.068µM. PS26 depicted almost similar result as shown by PS24 with the value

of 0.0779µM. PS23 is the only compound in its series which showed lowest potential

as antioxidant molecule with IC50 value of 0.68 µM but again its activity is more than

three folds higher than standard. When taking a review of the activity and its

relationship with the structures it is evident that presence of bromo group (PS25) at

para position producing best results as compared to other substitutions in the phenyl

ring. Similarly presence of nitro group (PS24) at para position also exhibited

significant result but the antioxidant potential reduced to half as compared to PS25.

Presence of three methyl groups and their positions in PS26 attributed in the good

antioxidant activity because removal of two methyl groups (PS23) reduced the

activity more than eight times.


129

N

HN

S

1

2

3

4 5

6

7

89

1'

6'

3'4'

7'

5'

2'

OO

R2

R3 R1


R1, R2, R3=CH3 (PS26)

Benzoyl and Naphthol Derivatives:

Benzoyl derivatives, PS27, PS28 and PS32 were tested for antioxidant activity.

Among these four molecules, PS27 is without substitution on its phenyl ring while

PS28 and PS32 are with methyl and bromo group respectively. PS28 showed

maximum potential as antioxidant in terms of lowest IC50 value (0.035µM) due to

methyl group at para position in structure. PS27 demonstrated lesser degree of

antioxidant power with IC50 of 0.394µM and this decline in activity could be

connected to the unsubstituted ring. Presence of meta bromo group (PS32) further

reduced its antioxidant activity many times i.e. 0.702 µM. These results outcome

signifying that presence or absence moreover position and type of substitution

involved in making the molecule more or less active. Methyl group at fourth position

drastically enhanced the effect. Naphthoyl analogue PS33 (0.811µM) produced the

comparable result with PS23 (0.650µM) and PS32 (0.702µM).

N

NH1

2

3

46

7

89

5

1'

2'

3'

4'

5'

6'

O

R1

R2

N

HN

C

1

2

3

4 5

6

7

89

O

1'

2'

3'

4'

5'

6'

7'

8'



130

PS23 and PS28 given interesting comparison related to structure activity relationship;

both are similar with the only difference in the connecting chain. Sulphonyl

derivatives displayed better results among all, highest activity shown by PS25 which

was comparable to benzoyl derivative PS28. Presence of carbonyl moiety in place of

sulphonyl enhanced antioxidant power of PS28 more than twenty times. This result

indicated that possible role of connecting chain along with the different substitution at

aromatic ring of molecules. All the ligands produced hundred to thousand times better

results than standard ascorbic acid and parent 9AA.

PS25 and PS28 are supposed to be good therapeutic candidates because their

significant antioxidant property and promising acetylcholinesterase inhibition is an

excellent combination to protect the brain from neurodegenerative damage and can

play pivotal role as for the treatment of for Alzheimer’s disease.


131


4.3.1 Methodology

Amyloid Fibrils/Aggregates preparation

Lyophilized powder of lysozyme from chicken egg white (CEW lysozyme,

≥40,000units/mg protein) purchased from Sigma-Aldrich. Protein concentration was

determined spectrophotometrically at 280nm, using an extinction coefficient (ε280) of

2.65Lg-1cm-1. Glycine buffer 100mM of pH 2 was prepared containing 100mM NaCl

(50ml). 70µM protein solution prepared in glycine buffer 10ml (final volume). Then

protein solution was kept at 75°C for 48hours with shaking.

Confirmation of prepared Fibrils/aggregates

The lysozyme amyloid aggregates were examined by specific binding with Congo red

(CR) ensued in the maximum absorbance in red shift of CR with lysozyme sample

solutions and the free dye controls, specifically peak should appear around 540nm.

The spectrum was recorded from 400 to 700nm by Shimadzu UV-visible (UV-1601),

Japan spectrophotometer. 20µM CR was freshly prepared in 100mM phosphate buffer

of pH 7.4 then mix 0.5ml prepared fibrils containing protein solution and 3ml Congo

red solution, incubated at room temperature for at least 30min along with CR alone as

control before recording the absorption spectrum. Presence of fibrils will be

characterized by peak shift from 492-512nm.

Disaggregation of Fibrils

Prepare drug sample solutions of different concentration in DMSO and 100mM

glycine buffer. The stock solutions of drugs were freshly prepared in DMSO with

volume of lower than 2% then final volume makeup with 100mM glycine buffer.

Take 0.5ml protein aggregates solution and add 0.5ml drug sample solution and

incubated 24 hours at room temperature to check the disaggregation potential of

ligands. After that add 3ml 20µM CR solution in each drug and protein mixture as

well as protein alone and leave for at least 30min. Now take UV absorption scan from


132

400-700nm. For disaggregation peak shift between 500 to 510nm (Gazova et al. 2008;

Ramshini et al. 2015).

4.3.2 Results

Disaggregation of Fibrils by 9AA Derivatives (Table-13)

S.No. Samples Wavelength Disaggrgation

activity

1. Congo red 497.5

2. Fibrils 279

3. Fibrils-CR complex 513

4. PS12 515 -

5. PS13 515 -

6. PS23 514.5 -

7. PS24 514.5 -

8. PS25 515 -

9. PS26 515 -

10. PS27 515 -

11. PS28 513.5 -

12. PS32 513.5 -

13. PS33 514 -

- means no diaggregation activity


133

4.3.3 Discussion

Protein amyloid aggregation is a reason of Alzheimer’s disease and other 20 or more

human diseases. In the developed world, the amyloid diseases are one of the most

important in brain pathologies. Protein deposits (amyloid fibrils) actually the

conversion of a soluble natural protein into insoluble form with primary and tertiary

structures of different size deposits in organs and tissues. Single leading protein

component become a characteristic of each disease. Cell impairment and death also

due to protein aggregated in different cell types. This concept becomes a reason to

investigate Aβ oligomers neurotoxicity in Alzheimer’s disease. By amyloid toxicity

membrane permeability increases with disruption integrity, formation of ion channels,

oxidative stress and deregulation of cell homeostasis by its intracellular accumulation.

By the single point mutants, wild-type human lysozymes and hen egg white lysozyme

having ability to form amyloid aggregates invitro. cross-β structural motif of amyloid

fibrils, selectively binds with the aromatic dyes including Congo red and Thioflavin T

(Gazova et al. 2008).

According to amyloid hypothesis, AD is caused by an imbalance between Aβ

production due to alteration of beta and gamma secretase activity and clearance of Aβ

by its decreased catabolism and increased amounts of Aβ by other mechanisms in

numerous forms such as monomers, oligomers, insoluble fibrils and plaques in the

CNS (Mawuenyega et al. 2010).

It was reported that those molecules binds to peripheral anionic site (PAS) of receptor

will be good inhibitor of acetylcholinesterase as well as inhibit Aβ fibrils. To

improves cognitive decline, fibril formation inhibition or disaggregation supposed to

be one of the target therapy (Liu et al. 2014a; Basiri et al. 2017).

To prevent the formation of amyloid fibrils or deposits or to break them down once

formed would be the ultimate goal. Synthesized derivatives were tested for

disaggregation of fibrils at concentrations which showed fifty percent inhibition of

acetylcholinesterase.

Congo red is used as a red dye to form complex with fibrils and indicates its presence.

The maximum absorbance (λmax) of Congo red was recorded at 497.5nm. Fibrils


134

synthesis was confirmed with the help of CR and showed λmax at 513nm without CR

appeared at 273.5nm. The quantity of fibrils reduces when disaggregation happens

and fewer amounts of fibrils are available to bind with CR. This disaggregation is

confirmed by peak shift.

All compounds showed absorbance peak between 513-515nm, no significant peak

shift from fibrils indicated no disaggregation at these concentrations. All derivatives

did not show disaggregation ability at the tested doses.


135


4.4.1 Methodology

Colorimetric assay was used for cytotoxic activity of compounds by using 96-well

flat-bottomed micro plates with standard 3-[4, 5-dimethylthiazole-2-yl]-2, 5-diphenyl-

tetrazolium bromide (MTT). 3T3 (mouse fibroblast) cells were cultured in Dulbecco’s

Modified Eagle Medium, added with 5% of fetal bovine serum (FBS), 100IU/ml of

penicillin and 100µg/ml of streptomycin in 75cm2 flasks and kept in 5% CO2

incubator at 37°C. Growing cells were harvested, haemocytometer used for counting

cells and diluted with a particular medium. Cell culture with the concentration of

5x104cells/ml was prepared and introduced (100µL/well) into 96-well plates leave for

incubation overnight. After incubation, 200µL of fresh medium was added with

different concentrations of compounds (1-30µM) after removal of older medium.

After 48hrs, 200µL MTT (0.5mg/ml) was added to each well and incubated further

for 4hrs. Subsequently, 100µL of DMSO was added to each well. Micro plate reader

(Spectra Max plus, Molecular Devices, CA, USA) was used to detect MTT reduction

within cells to formazan and measuring the absorbance at 540nm. The cytotoxicity

was recorded as IC50 for 3T3 cells and percent inhibition was calculated by using the

following formula:

% inhibition = 100-((mean of absorbance of test compound – mean of absorbance of

negative control)/ (mean of absorbance of positive control – mean of absorbance of

negative control)*100).

The results (% inhibition) were processed by using Soft-Max Pro software (Molecular

Device, USA) (Mosmann 1983).


136

4.4.2 Results 3T3 cell line Toxicity of 9AA Derivatives (Table-14)

S.No. Compounds

Codes Structures

IC50 ± SD

Micromolar (µM)

1. PS12

N

HN

O

1

2

3

4 5

6

7

89

1'

6'

3'4'

1'' 2''

7'

8'

5'

9'11'

12'

10'

2'

3.5 ± 2.0

2. PS13

N

HN

O

O CH3

O

H3C

1

2

3

4 5

6

7

89

1' 6'

2'

3'4'

5'

1''2''

7'

8'

4.7 ± 0.8

3. PS23

N

HN

S

1

2

3

4 5

6

7

89

1'

6'

3'4'

7'

5'

2'

OO

CH3

3.5 ± 1.9

4. PS24

6.5 ± 1.0

N

HN

S

1

2

3

4 5

6

7

89

1'

6'

3'4'

5'

2'

OO

NO2


137

5. PS25

N

HN

Br

S OO

1

2

3

54

6

7

8

1'

2'

3'4'

5'

6'

9

4.7 ± 1.0

6. PS26

N

NH1

2

3

4

6

7

89

5

1'

2'

3 '

4'

5'

6'

SO O

CH3H3C

8 '

9 '7 '

CH3

3.1 ± 1.1

7. PS27

N

NH

1

2

3

4

6

7

89

5

1'

2'

3'

4'

5'

6'

O

3.3 ± 0.7

8. PS28

N

NH1

2

3

46

7

89

5

1'

2'

3'

4'

5'

6'

O

CH37'

3.5 ± 2.3

9. PS32

N

HN

C

1

2

3

4 5

6

7

89

Br

O

1'2'

3'4'

5'

6'

7.5 ± 1.0


138

10. PS33

N

HN

C

1

2

3

4 5

6

7

89

O

1'

2'

3'

4'

5'

6'

7'

8'

6.3 ± 1.7

11. 9AA

N

NH2

4.0 ± 1.8

12. Cyclohexamide NH

O

O

O OH

H

0.8 ± 0.2


139

Graph-5: 3T3 Cell Line Toxicity of 9AA Derivatives

012345678

3.5

4.7

3.5

6.5

4.5

3.1 3.3 3.2

7.5

6.3

4

0.8

IC50

(µM

)

Compounds


140

4.4.3 Discussion

Early determination of toxicity profile of drug candidates is crucial in the drug

discovery process. Cell-based assays have been used as suitable substitute methods

for animal experiments in development of drugs and toxicological studies. Invitro

cytotoxicity tests could be used as aides to the alternative animal tests to improve dose

level selection in invivo studies by calculating IC50 values. BALB/3T3 and 3T3-L1

cell lines were recommended that may have a useful role to play in the prediction of

acute systemic toxicity as a replacement for animal studies. Routine toxicological

laboratories need reliable cell-based assays with automatic and easy operating features

for cytotoxicity assessment to accurately predict acute toxicity under dynamic

conditions (Harada et al. 1992; Viravaidya and Shuler 2004; Xing et al. 2006)

3T3, a normal cell line was selected for cytotoxicity testing because drugs with

therapeutic efficacy should not damage normal cell cycle and not become the reason

of cell death. For this reason the synthesized derivatives having therapeutic potential

are tested for their toxicity level on 3T3 cell line and their IC50 values were

determined with parent molecule 9–aminoacridine (9AA) and cyclohexamide

(reference drug). Low IC50 value indicates that drug produce its toxic effect on normal

cell in lower dose while higher IC50 value means that molecule has good safety

margin. Results displayed in Table-14.

9AA:

9AA tested for cytotoxicity on 3T3 cells showed IC50 value 4.0µM while the IC50

value of cyclohexamide was 0.8µM. The significant difference between IC50 values of

9AA and reference compound was showing the better safety level of 9AA than

reference.


PS12 contains phenyl ring at para position produced IC50 value 3.5µM while PS13

having ortho, para dimethoxy groups showing IC50 4.7µM. According to values,

substitution at different positions with different groups changed the cytotoxicity level


141

of drugs. Though phenacyl analogues presented no marked difference in the IC50

values but PS13 showed comparatively better IC50 value than PS12.

N

HN

O

1

2

3

4 5

6

7

89

1' 6'

2'

3'4'

5'

1''2''

R2

R1


This change in cytotoxicity level may be correlated with the structure. Replacement

of phenyl with methoxy group increases safety of drug in terms of increased IC50

value i.e. dimethoxy derivative is less cytotoxic than the compound with phenyl

moiety.


Four sulphonyl derivatives of 9AA showing different IC50 values as they have

different substitution at different positions on phenyl ring. PS23 and PS26 are methyl

substituted but difference is in number of methyl moieties i.e. PS23 has one methyl

while PS26 has three methyl groups. Both the analogues showed almost same level of

safety with IC50 values 3.5µM (PS23) and 3.1µM (PS26).

N

HN

S

1

2

3

4 5

6

7

89

1'

6'

3'4'

7'

5'

2'

OO

R2

R3 R1


R1, R2, R3=CH3 (PS26)


142

This finding clearly indicated that number and position of methyl groups is not

imparting any influence. PS24 (6.5µM) and PS25 (4.7µM) can be compared because

of different substitution a para position. Investigation revealed that presence of nitro

group (PS24) improving safety level (IC50 6.5µM) as compared to bromo group

(PS25, IC50 4.7µM) at the same position. Among all derivatives, safest value showed

by PS24.

Benzoyl Derivatives:

Among benzoyl derivatives, PS27 and PS28 showing almost same safety level with

IC50 value of 3.3µM and 3.2µM respectively. PS32 demonstrated best result with

highest IC50 value 7.5µM depicting the significance of bromo substitution at specific

position which generated the safety level more than two times as compared to the

PS27 and PS28. These values showed that presence of methyl group in the ring is not

making any difference in increasing and decreasing safety level of compounds.

N

NH1

2

3

4

6

7

89

5

1'

2'

3'

4'

5'

6'

O

R1

R2

N

HN

C

1

2

3

4 5

6

7

89

O

1'

2'

3'

4'

5'

6'

7'

8'


Naphthoyl Derivative:

PS33 produced good safety level (IC50 6.3µM) which is comparable to PS24. Over all

PS24, PS32 and PS33 displayed best results among all derivatives.

It is noteworthy to mention that the IC50 values of all the compounds against 3T3cells

were very high in comparison to IC50 value of reference drug (cyclohexamide) which

indicated that all synthesized derivatives are better in terms of safety than standard.


143

CONCLUSION

An increase prevalence and severity of neurodegenerative pathology and lack of an

effective treatment for Alzheimer’ disease (AD) in market, boost medicinal chemists

to look for new drugs. Currently, only acetylcholinesterase (AChE) inhibitors and

NMDA-receptor antagonist have been approved palliative treatment of AD. AChE

has been the prime target making first generation antialzheimer’s drug class and there

is continued interest in discovering new and specific AChE inhibitors.

A comparative molecular docking approach using MOE and Autodock Vina was

taken to identify the potential acridine analogues as AChE blockers. It is evident from

the results that this insilico assistance in search of effective AChE inhibitors leads to

the targeted active molecules excluding the inactive structures from the huge library

which is time and cost effective.

Docking study revealed the binding pattern of molecules with the enzyme active site.

All molecules are docked within the active pocket. Acridine flat ring enclosed the

peripheral anionic site (PAS) while 9-amino group and extending part slide in the

gorge reached to catalytic active site (CAS) and made connections with acyl and

choline binding regions involving all important amino acid residues. All features of

the structures are important and communicating with target through hydrogen bonds

(acridine amine and oxygen of connecting chain), π-π, π-CH and hydrophobic

interactions (acridine ring and terminal aromatic ring). Substitutions on ring and

presence of single and fused rings with lipophilic substitution are responsible to create

active stabilized conformations.

All compounds exhibited profound results in AChE inhibitory activity justifying the

insilico results. Moreover, these derivatives also exhibited potent radical scavenging

activity except fibril antiaggregation. They also exhibited better toxicity profile as

compared to standard used in assy.

In this study we focused on the development of new AChE inhibitors, also acting as

antioxidant. Though directly they did not show amyloid antiaggregation but as it is

found that AChE present in the cholinergic terminals accelerates Aβ plaque


144

aggregation, indirectly these compounds can also help to slow down the process of

fibril formation. Present research investigations suggest that among all compound

PS23, PS25 and PS28 can be considered very promising lead compounds offered an

attractive starting point for further lead optimization in the drug-discovery process

against AD.


145

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174

PUBLICATIONS

1. Synthesis of 9-Aminoacridine Derivatives as Anti-Alzheimer Agents

Rabya Munawar, Nousheen Mushtaq, Sadia Arif, Ahsaan Ahmed, Shamim Akhtar,

Sumaira Ansari, Sadia Meer, Zafar S. Saify and Muhammad Arif

American Journal of Alzheimer’s Disease & Other Dementias®

May 2016, 31(3): 263-269

2. Synthesis, Pharmacological Evaluation and In-Silico Studies of Some

Piperidine Derivatives as Potent Analgesic Agents

Sumaira Ansari, Sadia Arif, Nousheen Mushtaq, Ahsaan Ahmed, Shamim Akhtar,

Rabya Munawar, Huma Naseem, Sadia Meer, Zafar S. Saify, Muhammad Arif

and Qurratul-ain Leghari

Journal of Developing Drugs

2017, 6(1): DOI: 10.4172/2329-6631.1000170

3. Major Risk Factors Responsible for Osteoporosis and Osteoarthritis in

General Population of Karachi, Pakistan

Rabya Munawar, Qurratul-ain Leghari, Saira Shahnaz, Hammad Ahmed International Journal of Biomedical and Advance Research 2018, 9(11): 362-366


175

TURNITIN REPORT

9-aminoacridine derivatives as

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