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Computer-guided Design and Synthesis of IL-2 Inhibitors asImmunomodulating Agents

Islamabad

A dissertation submitted to the Department of Chemistry,Quaid-i-Azam University, Islamabad, in partial fulfillment

of the requirements for the degree of

Doctor of Philosophy

In

Organic Chemistry

by

Saima Kalsoom

Department of ChemistryQuaid-i-Azam University

Islamabad, Pakistan2016

Dedicated

to

My Parents

and

My Sons

(Muhammad Jibran & Muhammad Ayan)

I

DEDICATED

TO

My loving Parents & My Sons

(Muhammad Jibran & Muhammad Ayan)

(In the name of Allah, the Beneficent, the most Merciful)

CONTENTS

Acknowledgments……………………………………………………………………………………………………………………..i

List of Abbreviations and Symbols…………………………………………………………………………………………….iii

Abstract……………………………………………………………………………………………………………………………………..vi

List of Schemes………………………………………………………………………………………………………………………….vii

List of Figures…………………………………………………………………………………………………………………………….viii

List of Tables……………………………………………………………………………………………………………………………….x

1. INTRODUCTION………………………............................................................................................1-44

1.1.Disorders of human immunity…………………………………………………………………………………………….1

1.1.1. Immunodeficiencies………………………………………………………………………………………………….2

1.1.2. Autoimmunity……………………………………………………………………………………………………………2

1.1.3. Hypersensitivity…………………………………………………………………………………………………………3

1.2. Interleukin-2……………………………………………………………………………………………………………………….4

1.2.1. Cellular sources and production of IL-2…………………………………………………………………4

1.3. Interleukin-2 receptor………………………………………………………………………………………………………..5

1.4. IL-2/IL-2 Receptor binding………………………………………………………………………………………………….6

1.5. IL-2 inhibitors………………………………………………………………………………………………………………………7

1.6.Computer-aided drug design………………………………………………………………………………………………9

1.7.Strategies of CADD……………………………………………………………………………………………………………..10

1.7.1. Ligand based drug designing…………………………………………………………………………………….10

1.7.1.1. 2D & 3D similarity search…………………………………………………………………………….10

1.7.1.2. Pharmacophore modeling…………………………………………………………………………..11

1.7.1.3. Quantitative structure activity relationship…………………………………………………12

1.7.2. Structure based drug designing……………………………………………………………………………….13

1.7.2.1. Homology Modeling……………………………………………………………………………………….14

1.7.2.2. Molecular docking ………………………………………………………………………………………….15

1.7.2.2.1. Types of molecular docking…………………………………………………………….16

1.7.2.2.2. Docking Algorithm………………………………………………………………………….17

1.7.2.3. Molecular dynamic simulations……………………………………………………………………..19

1.8. Virtual screening……………………………………………………………………………………………………………….19

CONTENTS

1.9. Wet Lab…………………………………………………………………………………………………………………………….20

1.9.1. Chalcones………………………………………………………………………………………………………….21

1.9.1.1. Synthesis of chalcones………………………………………………………………………22

1.9.2. Dihydropyrimidines…………………………………………………………………………………………..23

1.9.2.1. Synthesis of dihydropyrimidines……………………………………………………….24

1.9.3. Heteroazepines…………………………………………………………………………………………………27

1.9.3.1. Synthesis of heteroazepines…………………………………………………………….28

1.9.4. Bezimidazoles…………………………………………………………………………………………………….30

1.9.4.1. Synthesis of bezimidazoles………………………………………………………………..31

1.9.5. Anthraquinone sulfonamide derivatives ………………………………………………………….32

1.9.5.1. Synthesis of anthraquinone sulfonamide derivatives……………………….33

1.9.6. Schif bases………………………………………………………………………………………………………….35

1.9.6.1. Synthesisof Schif base derivatives…………………………………………………….36

1.9.7. Pyrazoles……………………………………………………………………………………………………………37

1.9.7.1. Synthesis of pyrazoles……………………………………………………………………….38

1.9.8. Benzamides……………………………………………………………………………………………………….41

1.9.8.1. Synthesis of benzamides……………………………………………………………………41

1.10. Aim of the study………………………………………………………………………………………………………………43

2. RESULTS AND DISCUSSION………………………………………………………………………………………………45-90

2.1. In silico identification of IL-2 inhibitors (Dry lab)……………………………………………………………….45

2.1.1. Target identification………………………………………………………………………………………….45

2.1.2. Determination of active site……………………………………………………………………………..46

2.1.3. Designing a training set……………………………………………………………………………………..46

2.1.4. Generation of structure based pharmacophore model……………………………………50

2.1.5. Validation of pharmacophore model………………………………………………………………..52

2.1.6. Pharmacophore based virtual screening…………………………………………………………..53

2.1.7. Highthroughput docking……………………………………………………………………………………54

2.1.8. Binding mode analysis of the Novel IL-2 Inhibitors…………………………………………….55

2.2. Synthesis of analogs of different heterocycles from PBVS ……………………………………………..69

CONTENTS

2.2.1. Synthesis of chalcones (1-7)………………………………………………………………………………69

2.2.1.1. Characterization of chalcones derivatives………………………………………….70

2.2.2. Synthesis of dihydropyrimidines (8-13)……………………………….…………………………….70

2.2.2.1. Characterization of dihydropyrimidines……………………………………………..71

2.2.3. Synthesis of heteroazepines (14-19)………………………………………………………………….72

2.2.3.1. Characterization of heteroazepines……………………………………………………73

2.2.4. Synthesis of pyrazole carbaldehydes (20-25)…………………………………………………….74

2.2.4.1. Characterization of pyrazole carbaldehydes………………………………………74

2.2.5. Synthesis of pyrazole carbothioamides (26-28)………………………………………………….76

2.2.5.1. Characterization of pyrazole carbothioamides……………………………………76

2.2.6. Synthesis of benzimidazoles (29-32)…………………………………………………………………..77

2.2.6.1. Characterization of benzimidazoles…………………………………………………….78

2.2.7. Synthesis of anthraquinone sulfonamide derivatives (33-36)…………………..........78

2.2.7.1. Characterization of anthraquinone sulfonamide derivatives …………….79

2.2.8. Synthesis of Schiff base derivatives (37-41)……………………………………………………….80

2.2.8.1. Characterization of Schiff base derivatives…………………………………………80

2.2.9. Synthesis of pyrazoles (42-43)…………………………………………………………………………….81

2.2.9.1. Characterization of pyrazoles……………………………………………………………..82

2.2.10. Synthesis of benzamides (44-46)………………………………………………………………………83

2.2.10.1. Characterization of benzamides……………………………………………………….84

2.3. IL-2 Inhibition assay……………………………………………………………………………………………………………84

2.3.1. Compounds (Chalcones) 1-7 ………………………………………………………………………………84

2.3.2. Compounds (Heterocycles derived from chalcones) 8-28…………………………………85

2.3.3. Compounds (Benzimidazoles and anthraquinone sulfonamide derivatives)

29-36…………………………………………………………………………………………………………………87

2.3.4. Compounds (Schiff base derivatives) 37-41 ……………………………………………………..87

2.3.5. Compounds (Pyrazole and benzamide derivatives) 42-46 ……………………………….88

3. EXPERIMENTAL………………...........................................................................................91-120

3.1. In silico guided identification of IL-2 inhibitors (Dry lab)…………………………………………………….91

3.1.1. Hardware specifications……………………………………………………………………………………..91

CONTENTS

3.1.2. Selection and preparation of protein protein complex…………………………………….91

3.1.3. Determination of active site of protein…………………………………………………………….91

3.1.4. Pharmacophore generation………………………………………………………………………………91

3.1.5. Excluded volumes……………………………………………………………………………………………..92

3.1.6. Generation of training set (S-1 to S-30)…………………………………………………………….93

3.1.7. Building of compounds……………………………………………………………………………………..93

3.1.8. Validation of pharmacophore model………………………………………………………………..97

3.1.9. Virtual screening……………………………………………………………………………………………….98

3.1.10. Docking accuracy…………………………………………………………………………………………….98

3.1.11. Descriptor based filtering of the database……………………………………………………..99

3.1.12. 3D pharmacophore based filtering of database…………………………………………….100

3.1.13. Receptor based virtual screening of hits………………………………………………………100

3.2. Hits optimization……………………………………………………………………………………………………………..101

3.2.1. Pharmacophore mapping………………………………………………………………………………..101

3.2.2. Molecular docking……………………………………………………………………………………………101

3.3. Synthesis of hit compounds (Wet lab)…………………………………………………………………………….101

3.3.1. Chemicals used.……………………………………………………………………………………………….101

3.4. Purification of solvents……………………………………………………………………………………………………102

3.4.1. Acetone……………………………………………………………………………………………………………102

3.4.2. Absolute ethanol and methanol………………………………………………………………………102

3.4.3. Ethyl acetate…………………………………………………………………………………………………….102

3.4.4. Chloroform and dichloromethane……………………………………………………………………102

3.4.5. Absolute methanol…………………………………………………………………………………………..102

3.4.6. Dimethyl formamide (DMF)……………………………………………………………………………..103

3.4.7. Dimethyl sulfoxide (DMSO)……………………………………………………………………………..103

3.5. Instruments used…………………………………………………………………………………………………………….103

3.5.1. Melting point ……………………………………………………………………………………………………103

3.5.2. NMR-Spectrometer………………………………………………………………………………………….103

3.5.3. Mass Spectrometer………………………………………………………………………………………….103

CONTENTS

3.6. Chromatographic Techniques…………………………………………………………………………………………103

3.6.1. Thin layer chromatography (TLC)…………………………………………………………………..103

3.7. Synthesis of chalcones (1-7)……………………………………………………………………………………………104

3.7.1. 3-Phenyl-1-phenylprop-2-en-1-one (1)………………………………………………………….104

3.7.2. 3-(4-Fluorophenyl)-1-phenylprop-2-en-1-one (2)………………………………………….104

3.7.3. 3-(4-Chlorophenyl)-1-phenylprop-2-en-1-one (3)………………………………………….104

3.7.4. 3-(4-Bromophenyl)-1-phenylprop-2-en-1-one (4)………………………………………….105

3.7.5. 1-(3-Hydroxyphenyl)-3-(3-nitrophenyl)prop-2-en-1-one (5)…………………………105

3.7.6. 3-(2-Hydroxyphenyl)-1-(3-hydroxyphenyl)prop-2-en-1-one (6)……………………105

3.7.7. 1-(4-Aminophenyl)-3-(3-chlorophenyl)prop-2-en-1-one (7)………………………….105

3.8. Synthesis of dihydropyrimidines (8-13)…………………………………………………………………………..105

3.8.1. 4-(3-Hydroxyphenyl)-6-(3-nitrophenyl)-5,6-dihydropyrimidine-2(1H)-thione

(8)………………………………………………………………………………………………………………………105

3.8.2. 6-(2-Hydroxyphenyl)-4-(3-hydroxyphenyl)-5,6-dihydropyrimidine-2(1H)-thione

(9)……………………………………………………………………………………………………………………106

3.8.3. 4-(4-Aminophenyl)-6-(3-chlorophenyl)-5,6-dihydropyrimidine-2(1H)-thione

(10)………………………………………………………………………………………………………………….106

3.8.4. 4-(3-Hydroxyphenyl)-6-(3-nitrophenyl)-5,6-dihydropyrimidin-2(1H)- one

(11)………………………………………………………………………………………………………………….106

3.8.5. 6-(2-Hydroxyphenyl)-4-(3-hydroxyphenyl)-5,6-dihydropyrimidin-2(1H)-one

(12)…………………………………………………………………………………………………………………..107

3.8.6. 4-(4-Aminophenyl)-6-(3-chlorophenyl)-5,6-dihydropyrimidin-2(1H)- one

(13)…………………………………………………………………………………………………………………..107

3.9. Synthesis of heteroazepines (14-19)……………………………………………………………………………….107

3.9.1. 4-(4-(3-Chlorophenyl)-4,5-dihydro-3H-benzo[b][1,4]diazepin-2-yl)-benzenamine

(14)…………………………………………………………………………………………………………………..108

3.9.2. 3-(4-(3-Nitrophenyl)-4,5-dihydro-3H-benzo[b][1,4]diazepin-2-yl)-phenol (15)..108

3.9.3. 3-(4-(2-hydroxyphenyl)-4,5-dihydro-3H-benzo[b][1,4]diazepin-2-yl)-Phenol (16)…..

…………………………………………………………………………………………………………………………108

CONTENTS

3.9.4. 4-(2-(3-Chlorophenyl)-2,3-dihydrobenzo[1,4]thiazepin-4-yl)benzenamine

(17)…………………………………………………………………………………………………………………..108

3.9.5. 3-(2-(3-Nitrophenyl)-2,3-dihydrobenzo[b][1,4]thiazepin-4-yl)phenol (18)…….109

3.9.6. 3-(2-(2-Hydroxyphenyl)-2,3-dihydrobenzo[b][1,4]thiazepin-4-yl)- phenol (19)..109

3.10. Synthesis of pyrazole carbaldehydes (20-25) and pyrazole carbothioamides (26-28)……109

3.10.1. 3-(3-Hydroxyphenyl)-5-(3-nitrophenyl)-4,5-dihydropyrazole-1-carbaldehyde

(20)…………………………………………………………………………………………………………………109

3.10.2. 5-(2-Hydroxyphenyl)-3-(3-hydroxyphenyl)-4,5-dihydropyrazole-1-carbaldehyde

(21)…………………………………………………………………………………………………………………110

3.10.3. 3-(4-Aminophenyl)-5-(3-chlorophenyl)-4,5-dihydropyrazole-1-carbaldehyde

(22)…………………………………………………………………………………………………………………110

3.10.4. 5-(4-Bromophenyl)-3-phenyl-4,5-dihydro-1H-pyrazole (23)…………………………110

3.10.5. 1-(3,5-Diphenyl-4,5-dihydropyrazol-1-yl)ethanone (24)…………………..…………..111

3.10.6. 1-(5-(4-Bromophenyl)-3-phenyl-4,5-dihydropyrazol-1-yl)ethanone(25)………111

3.10.7. 3,5-Diphenyl-4,5-dihydropyrazole-1-carbothioamide (26)…………………………….111

3.10.8. 5-(4-Fluorophenyl)-3-phenyl-4,5-dihydropyrazole-1-carbothioamide(27)……112

3.10.9. 5-(4-Chlorophenyl)-3-phenyl-4,5-dihydropyrazole-1-carbothioamide(28)……112

3.11. Synthesis of benzimidazoles (29-32)………………………………………………………………………………112

3.11.1. 2-(1-(4-Isobutylphenyl)ethyl)-1H-benzo[d]imidazole (29)…………………………….112

3.11.2. 2-(2-(4-(4-Chlorophenyl)(phenyl)methyl)piperazin-1-yl)-ethoxy)methyl)-1H-

benzo[d]imidazole (30)…………………………………………………………………………………..113

3.11.3. N,N-dimethyl-4-(6-nitro-1H-benzo[d]imidazol-2-yl)benzenamine(31)………...113

3.11.4. 4-(1H-benzo[d]imidazol-2-yl)-N,N-dimethylbenzenamine (32)…………………….113

3.12. Synthesis of anthraquinone sulfonamide derivatives (33-36)……………………………………….114

3.12.1. 2-(2-(4-(Dimethylamino)phenyl)-1H-benzo[d]imidazol-1-ylsulfonyl)- anthracene-

9,10-dione (33)……………………………………………………………………………………………….114

3.12.2. 2-(2-(4-(Dimethylamino)phenyl)-6-nitro-1H-benzo[d]imidazol-1- ylsulfonyl)-

anthracene-9,10-dione (34)…………………………………………………………………………..115

3.12.3. 2-(2-(1-(4-Isobutylphenyl)ethyl)-1H-benzo[d]imidazol-1-ylsulfonyl)-anthracene-

9,10-dione (35)……………………………………………………………………………………………….115

CONTENTS

3.12.4. 2-(2-(2-(4-(4-Chlorophenyl)(phenyl)methyl)piperazin-1-yl)ethoxy)-methyl)-1H-

benzoimidazol-1-ylsulfonyl)anthracene-9,10-dione(36)……………………………….115

3.13. Synthesis of Schiff bases (37-41)……………………………………………………………………………………116

3.13.1. 1,5-bis-(3-Hydroxybenzylideneamino)anthracene-9,10-dione (37)………………116

3.13.2. 1,5-bis-(4-Fluorobenzylideneamino)anthracene-9,10-dione (38)…………………116

3.13.3. 3-(3-(Naphthalen-1-ylimino)prop-1-enyl)-5-nitrophenol (39)………………………117

3.13.4. bis-N-3-phenylallylidene)biphenylamine (40)……………………………………………….117

3.13.5. bis-N-(2,4-dichlorobenzylidene)biphenylamine (41)……………………………………..117

3.14. Synthesis of pyrazole derivatives (42-43)……………………………………………………………………….117

3.14.1. 1-(4-((3-Nitrophenyl)diazenyl)-3,5-dimethyl-1H-pyrazol-1-yl-)-2- (quinolin-8-

yloxy)ethanone (42)……………………………………………………………………………………….118

3.14.2. 1-(4-((4-Hydroxyphenyl)diazenyl)-3,5-dimethyl-1H-pyrazol-1-yl-)2-(quinolin-8-

yloxy)ethanone (43)……………………………………………………………………………………….118

3.15. Synthesis of benzamides (44-46)……………………………………………………………………………………118

3.15.1. 2-(4-Isobutylphenyl)-N-(naphthalen-2-yl)propanamide (44)…………………………119

3.15.2. 2-(2-(4-(4-Chlorophenyl)(phenyl)methyl)piperazin-1-yl)ethoxy)-N-(naphthalen-

2-yl)acetamide (45)………………………………………………………………………………………..119

3.15.3. bis-N-biphenylacetamide (46)……………………………………………………………………….119

3.16. Biological assay………………………………………………………………………………………………………………120

3.16.1. IL-2 production and quantification…………………………………………………………………120

4. CONCLUSION AND FUTURE PLAN………………………………………………………………………………..121-122

4.1. Conclusion……………………………………………………………………………………………………………………….121

4.2. Future Recomendation…………………………………………………………………………………………………..122

REFERENCES…………………………………………………………………………………………………………………….123-140

ANNEXURE……………………………………………………………………………………………………………………...141-152

LIST OF INTERNATIONAL PUBLICATIONS…………………………………………………………………..……153-155

ACKNOWLEDGEMENTS

i

All praises to ALMIGHTY ALLAH, who showered upon me all His blessings throughout my life

especially for giving me the strength for the completion of this research work. Many thanks to

Him as He blessed us the Holy Prophet, Hazrat Muhammad (PBUH) for whom the whole universe

is created and who enabled us to worship only ALLAH. He (PBUH) brought us out of darkness and

enlightened the way of Heaven.

I feel great pleasure in expressing my ineffable thanks to my encouraging and motivating Co-

Supervisor, Prof. Dr. Farzana Latif Ansari (TI) for her excellent supervision, and guidance. Her vision,

patience and motivation at every step of this study made it possible for me to work in this exciting

and interesting field of research.

My research for this dissertation was made more extensive and proficient by the use of resources

at Department of Pharmacy, University of Padova, Italy and I wish to express my gratitude to Prof. Dr.

Stefano Moro and his research group, with whom I completed a part of my research work. In

continuation, my heartfelt gratitude goes to Higher Education Commission (HEC) Pakistan for

providing me fellowship under International Research Support Initiative Program (IRISP) during my six

months stay at Italy.

I am deeply indebted to my Supervisor, Prof. Dr. Humaira Siddiqi, for her excellent administrative

guidance and opportunity to undertake research within her group. Without her cooperation, it would

have been impossible to complete this research

I owe my sincere gratitude to Dr. Zaheer-ul-Haq Qasmi and Dr. Ahmed Mesaik and Dr. Almas Jabeen,

PCMD, Karachi, for doing in vitro IL-2 inhibition activity.

I am thankful to Prof. Dr. Muhammad Siddiq, the Chairman, Department of Chemistry, Quaid-i-Azam

University, Islamabad and Prof. Dr. Shahid Hameed, Head of Organic Section, for providing necessary

research facilities. Many thanks are due to all the faculty members of Chemistry Department, especially

to all teachers of Organic Section for being a source of inspiration and enlightenment.

I also owe my recognition to all my lab fellows/friends, especially Dr. Awais Shaukat and Dr. Iram

Batool for their help at crucial times of my research work. I am also very thankful to my friend Uzma

Bilal for her well wishes during my thesis write-up.

I am very obliged to the supporting staff of the department for their all-time help.

ACKNOWLEDGEMENTS

ii

I have no words to acknowledge the sacrifice, efforts, prayers, guidance, support, encouragement and

firm dedication of my family members. Their endless prayers contributed to the successful completion

of this thesis. Words become meaningless when I look at them as icons of strength for being what I am

today. I pay especial gratitude to my Mother, elder brother Tariq Mehmood who not only supported

me but also boosted my morale during the toughest time of my life. My apologies to my husband

Zulfqar Ali Fraz and my kids Jibran and Ayan for their sufferings when I had to but ignore them.

Finally I would like to thank everybody who directly or indirectly supported me in the completion of my

thesis. I extend my apology to those whom I could not mention individually by name in this

acknowledgement.

Saima Kalsoom,

Chak No, 90WB, Tehsil : Melsi,

District: Vehari,

LIST OF ABBREVIATIONS AND SYMBOLS

iii

Abbrev. Symbols Name

Ɵ AngleA Hydrogen bond acceptor interaction site

Å Angstrom (10-10m)Acc Hydrogen bond acceptors feature

Acc&ML Hydrogen bond acceptor and metal ligation feature

ADME Absorption, distribution, metabolism and excretion

ADMET Absorption, distribution, metabolism, excretion & toxicityamu Atomic mass unit

Aro Aromatic centerAro|Hyd Aromatic and hydrophobic feature

AutoDOCK Software

bs Broad singletCADD Computer-aided drug designing

CART Classification and regression trees°C Celsius

13C NMR Carbon Nuclear Magnetic Resonance SpectroscopyCATALYST Software

2D Two dimensional3D Three dimensional

∆ TriangleDCM Dichloromethane

DDD Drug Discovery and Development

DHFR Dihydrofolate reductaseDMF Dimethylformamide

DMSO Dimethylsulfoxide

DNA Deoxyribonucleic acid

DISCO Distance comparison

DOCK Software

Don Hydrogen bond donor feature

D Hydrogen bond donor interaction siteEt3N TriethylamineELISA Enzyme linked immunosorbent assayF Pharmacophoric featureFDA U.S. Food and Drug AdministrationFlexX SoftwareFT-IR Fourier transform infrared spectroscopyGOLD SoftwareEt3N Triethylamine1H NMR Proton Nuclear Magnetic Resonance Spectroscopy


iv

H Hydrophobic interaction site

HBA Number of hydrogen bond acceptors

HBD Number of hydrogen bond donors

Hyd Hydrophobic feature

IC50 50% inhibitory concentrationsIL-2 Interlukin 2

J Coupling constant

JAK/STAT Janus kinase/Signal Transducer and Activator of Transcription

kcal Kilo Calories

LBDD Ligand-based drug design

LC-MS Liquid chromatography-mass spectrometry

LIGANDSCOUT Software

LogP Lipophilicity

m Multiplet

m.p. Melting point

MC Monte Carlo

MD Molecular dynamics

ML Metal ligation feature

MMFF94x Merck Molecular Force Field 94x

MOE Molecular Operating Environment Software

MW Microwave

M.wt Molecular weight

nm Nanometer

NMR Nuclear Magnetic Resonance Spectroscopy

PCl5 Phosphorus pentachloride

PDB Protein Data Bank

PHA Phytoheamagglutinin

PMA Phorbol myristate acetate

ppm Parts per million

q2 Correlation coefficient between predicted and

observedQSAR Quantitative Structure Activity Relationship

MD Molecular dynamics

ML Metal ligation feature

R2 Correlation coefficient

Rf Retardation factor


v

RMS Root mean squareRMSD Root mean square deviationRo5 Lipinski’s rule of five

s Singlet

SBDD Structure-based drug design

TLC Thin layer chromatography

TMS Tetramethyl silane

TPSA Total polar surface area

V Excluded volumes

δ Chemical shift

μ 10-6 (one millionth)

υ Frequency

ABSTRACT

vi

ABSTRACT

Cytokine Interleukin-2 (IL-2) has a prevalent role in the growth, activation, and differentiation

of T-cells. To suppress immune responses associated with organ transplant rejection and

other autoimmune diseases, it is important to disrupt the interaction of IL-2 with its receptor

system. IL-2 is now emerging as a target in the discovery of novel therapeutics for addressing

the problems related to immune system. The main goal of this study was to establish an

effective in silico protocol for identification of IL-2 inhibitors. It describes a pharmacophore

based virtual screening combined with docking study as a rational strategy for identification of

novel IL-2 inhibitors. Structure based pharmacophore model was developed using the crystal

structure of IL-2/IL-2Rα (PDB ID: 1Z92) complex.

The predictive pharmacophore model consisted of three features, two hydrophobic and one

cationic feature with three excluded volumes. The pharmacophore was validated using a

training set of thirty known IL-2 inhibitors. Pharmacophore model as a 3D search query was

searched against ZINC and MOE database, in order to retrieve new chemical scaffolds that

may be potent IL-2 inhibitors. The hits retrieved from this search were filtered based on their

RMSD values and pharmacophoric features. Hits that were retained were used in a molecular

docking study to find the binding mode and molecular interactions with crucial residues at the

active site of the target. Pharmacophore based molecular docking was carried out on virtually

screened compounds using 1Z92 as target by MOE software that led to the identification of 15

hits belonging to diverse classes of heterocycles. These hits were further optimized and a

library of forty six compounds including 5-6 membered azaheterocycles namely

dihydropyrimidines, heteroazepines, pyrazoles and benzimidazoles besides some compounds

such as chalcones and Schiff bases, was designed and synthesized.

All newly synthesized compounds were characterized by their MS and NMR spectral analysis.

IL-2 inhibition studies on the members of the synthesized library led to the identification of

novel IL-2 inhibitors with IC50 values ranging from <2- >50μg/ml using cyclosporine as a

standard drug. This entire set of experiments in both dry and wet labs led to a successful

designing and synthesis of a variety of compounds as novel scaffolds that may be developed

into interesting immunomodulators.

LIST OF SCHEMES

vii

LIST OF SCHEMES PAGE

Scheme 2.1: Synthesis of chalcones (1-7)........................................................................................................69

Scheme 2.2: Synthesis of dihydropyrimidines (8-13).......................................................................................71

Scheme 2.3: Synthesisof heteroazepines (14-19)............................................................................................72

Scheme 2.4: Synthesis of pyrazole carbaldehydes (20-25)...........................................................…………………74

Scheme 2.5: Synthesis of pyrazole carbothioamides (26-28)..........................................................................76

Scheme 2.6: Synthesis of benzimidazoles (29-32)…………………………………………………………………………………………77

Scheme 2.7: Synthesis of (benzimidazolylsulfonyl)anthracene-dione derivatives (33-36)……………………………79

Scheme 2.8: Synthesis of Schiff-base derivatives (37-41)……………………………………………………………………………..80

Scheme 2.9: Synthesis of pyrazole derivatives (42-43)………………………………………………………………………………….82

Scheme 2.10: Synthesis of benzamides (44-46)…………………………………………………………………………………………….83

LIST OF FIGURES

viii

LIST OF FIGURES PAGE

Figure 1.1: immune system fight against different pathogens………………………………………………………………..1

Figure 1.2: Bone marrow with weak immune system resulting different diseases………………………………….2

Figure 1.3: Types of Hypersensitivity disorders……………………………………………………………………………………..3

Figure 1.4: Top view of interlukin-2………………………………………………………………………………………………………..5

Figure 1.5: 3D structure of Human IL-2/IL-2R receptor (α, β and ϒ)………………………………………………………..7

Figure 1.6: Drug discovery and development pipeline…………………………………………………………………………………………10

Figure1.7: Ligand based drug designing………………………………………………………………………………………………..11

Figure 1.8: Pharmacophore of morphine a) Pharmacophoric features b) Distances between

pharmacophoric features……………………………………………………………………………………………………12

Figure1.9: Structure based drug designing…………………………………………………………………………………………….13

Figure 1.10: Algorithm used for Molecular docking……………………………………………………………………………….18

Figure 1.11: Designing and synthesis of IL-2 inhibitors by CADD…………………………………………………………..42

Figure 2.1: Different steps used in dry & wet labs for identification of IL-2 inhibitors…………………………..44

Figure 2.2: 3D Structure of protein-protein complex, IL-2 (red), IL-2Rα (green ribbon)………………………….45

Figure 2.3: A close-up view of the binding pocket showing hydrophobic (green) and hydrophilic surface

(magenta)…………………………………………………………………………………………………………………………….46

Figure 2.4: 3D view of structure based pharmacophore in the active site of 1Z92. Blue sphere indicates

cationic feature and yellow spheres indicate hydrophobic feature. Excluded volumes shown

as white meshed spheres……………………………………………………………………………………………………51

Figure 2.5: 3D view of structure based pharmacophore model for 1Z92, showing a) distances b) angles in

the active……………………………………………………………………………………………………………………………..52

Figure 2.6: The superimposition of standards docked compounds in binding site of 1Z92…………………….53

Figure 2.7: Chemical structures of top 15 compounds retrieved from PBVS…………………………………………..56

Figure 2.8: Superimposed view of pharmacophore based docked synthesized compounds (9, 22 & 45) in

active site of 1Z92. All interacting amino acids are in red color…………………………………………….60

Figure 2.9: The binding mode analysis of compounds 5, Hydrogen bond is displayed in blue dotted line

and π-π interactions are in green color. A) 3D and B) 2D docked view…………………………………61

Figure 2.10: The binding mode analysis of compound 22, Hydrogen bonds and π-π interaction is

displayed in green dotted lines. A) 3D and B) 2D docked view……………………………………………63

Figure 2.11: The binding mode analysis of compounds 35, Hydrogen bonds and distances are displayed

in blue dashed line and π-π interactions are in green color. A)3D & B)2D docked view……….65

LIST OF FIGURES

ix

Figure 2.12: The binding mode analysis of compounds 37, A) 3D B) 2D…………………………………………………66

Figure 2.13: Bar graphs for in vitro IL-2 inhibition assay of chalcones (1-7)……………………………………………85

Figure 2.14: Bar graphs for in vitro IL-2 inhibition assay of compounds (8-28)……………………………………….86

Figure 2.15: Bar graphs for in vitro IL-2 inhibition assay of compounds (29-36)……………………………………..87



Figure 2.18: Active compounds identified for IL-2 inhibition………………………………………………………………….90

Figure 3.1: Schematic presentation of virtual screening protocol………………………………………………………….99

Figure 3.2: Receptor based virtual screening protocol…………………………………………………………………………100

LIST OF TABLES

x

LIST OF TABLES PAGE

Table 2.1: The structures and inhibitory activities of IL-2 inhibitors used in training set……………………..47

Table 2.2: Distances of pharmacophoric features………………………………………………………………………………..51

Table 2.3: Structure of 46 novel and potent IL-2 inhibitors………………………………………………………………….56

Table 2.4: Binding interactions observed in docked compounds (1-7) with 1Z92………………………………..61

Table 2.5: Binding interactions observed in hits optimized from PBVS (8-28) with 1Z92……………………..62

Table 2.6: Binding interactions observed in docked compounds (29-36) with 1Z92…………………………….64



Table 2.9: Drug likeness of the synthesized compounds (Lipinski’s RO5)…………………………………………….68

Table 2.10: Physical data of synthesized chalcones (1-7)……………………………………………………………………..70

Table 2.11: Physiochemical & Spectral data of synthesized dihydropyrimidine derivatives (8-13)………72

Table 2.12: Physiochemical & Spectral data of synthesized heteroazepines (14-19)……………………………73

Table 2.13: Physiochemical & Spectral data of synthesized pyrazole derivatives (20-25)…………………….75

Table 2.14: Physiochemical & Spectral data of synthesized pyrazole carbothioamides (26-28)……………77

Table 2.15: Physiochemical & Spectral data of synthesized benzimidazoles (29-32)…………………….………78

Table 2.16: Physiochemical & Spectral data of synthesized sulfonamides (33-36)……………………………….80

Table 2.17: Physiochemical & Spectral data of synthesized Schiff base derivatives (37-41)…………………81

Table 2.18: Physiochemical & Spectral data of synthesized pyrazole derivatives (42-43)…………………….83

Table 2.19: Physiochemical & Spectral data of synthesized benzamides (44-46)………………………………….84

Table 2.20: The IL-2 inhibitory activities of hits (1-7)……………………………………………………………………………..85

Table 2.21: The IL-2 inhibitory activities of optimized hits (8-28)…………………………………………………………..86

Table 2.22: The IL-2 inhibitory activities of optimized hits (29-36)………………………………………………………...87

Table 2.23: The IL-2 inhibitory activities of optimized hits (37-41)…………………………………………………………88

Table 2.24: The IL-2 inhibitory activities of optimized hits (42-46)…………………………………………………………88

Chapter 1

Introduction

CHAPTER 1: INTRODUCTION

1

Human health is directly influenced by immune system and its performance which are

fundamentally designed for the protection against the attack of foreign invaders. However the

beginning of almost all infectious and degenerative diseases is largely due to inadequate or

hyperactive immune system1. The immune system is the body's defense against infectious

organisms and other invaders. Through a series of steps called the immune response, the immune

system attacks organisms and substances that invade body systems and cause disease. In order to

function properly, an immune system must detect a wide variety of agents, known as pathogens,

viruses, bacteria and parasitic worms and distinguish them from the organism's own healthy

tissue2 as shown in Figure 1.13.The immune system is made up of a network of cells, tissues and

organs that work together to protect the body. The cells involved are white blood cells or

leukocytes, which come in two basic types that combine to seek out and destroy disease causing

organisms or substances4.

Figure 1.1: Immune system fight against different pathogens3

1.1 Disorders of human immunity

Disorders of the immune system can result in immune deficiency, hypersensivity5 autoimmune

diseases that occurs when the immune system is resulting in recurring and life-threatening

infections.


2

1.1.1 Immunodeficiencies

Immunodeficiencies occur when one or more of the components of the immune system are

inactive. The ability of the immune system to respond to pathogens is diminished in both the

young and the elderly, with immune responses beginning to decline at around 50 years of age due

to immunosenescence6. In developed countries, obesity, alcoholism, and drug use are common

causes of poor immune function. However, malnutrition is the most common cause of immune

deficiency in developing countries7. Additionally, the loss of the thymus at an early age through

genetic mutation or surgical removal results in severe immune deficiency and a high susceptibility

to infection8. Immunodeficiencies can also be inherited or acquired9. Chronic granulomatous

disease, where phagocytes have a reduced ability to destroy pathogens, is an example of an

inherited or congenital immune deficiency as shown in Figure 1.210. AIDS and some type of

cancer [particularly bone marrow and blood cells] cause acquired immunodeficiency11-12.

Figure 1.2: Bone marrow with weak immune system resulting different diseases10

1.1.2 Autoimmunity

Over active immune responses include the other end of immune dysfunction, particularly the auto

immune disorders. Here, the immune system fails to properly distinguish between self and non-

self and starts attacking part of the body. Under normal circumstances, many T-cells and

antibodies react with self-peptides13. One of the functions of specialized cells is to present young

lymphocytes with self-antigens produced throughout the body and to eliminate those cells that


3

recognize self-antigens, preventing autoimmunity14. Ankylosing spondylitis, rheumatoid arthritis,

type 1 diabetes mellitus, adrenal insufficiency and multiple sclerosis are some common

autoimmune diseases15.

1.1.3 Hypersensitivity

Hypersensitivity is an immune response that damages the body's own tissues. They are divided in

to four classes [Type I–IV] as shown in Figure 1.316 based on the mechanisms involved and the

time course of the hypersensitive reaction. Type-I hypersensitivity is an immediate or anaphylactic

reaction, often associated with allergy. Symptoms can range from mild discomfort to death. Type-

I hypersensitivity is mediated by Ig E, which triggers degranulation of mast cells and basophiles

when cross-linked by antigen. Type-II hypersensitivity occurs when antibodies bind to antigens on

a patient's own cells, marking them for destruction. This is also called antibody-dependent

hypersensitivity17. Immune c o m p l e x e s deposited in various tissues trigger Type-III

hypersensitivity reactions. Type-IV hypersensitivity usually takes between two and three days to

develop. Type-IV reactions are involved in many autoimmune and infectious diseases. These

reactions are mediated by T-cells, monocytes and macrophages18.

Figure 1.3: Types of hypersensitivity disorders16


4

1.2 Interleukin-2

Interleukin 2 [IL-2] is an interleukin, a type of cytokine signaling molecule in the immune system.

It is a protein that regulates the activities of white blood cells (leukocytes) that are responsible for

immunity. IL-2 is part o f the body's natural response to microbial infection, toxin and

pollutants19. IL-2 is a15.5kD a glycoprotein cytokine secreted by activated T-cells as a growth

factor for T lymphocytes, natural killer cells, and lymphokine-activated killer cells20. The gene for

IL-2 was cloned in 1983 by Degrave et al.21,22 and its crystal structure was determined in1987 by

Brand huber et al.23.X-ray crystallographic studies revealed that human IL-2 molecule contains

four α helices A,B,C and D, which are connected by a long downward A-B loop that also contains

a short helical segment, a B-C loop and a long C-D cross over loop (Figure 1.4)24. IL-2 plays an

important role in regulating lymphocytes that has led to exciting new directions for use in cancer

immunotherapy. IL-2 is a key mediator of the T-helper 1 [Th1] immune response25-26. Irregular

Th1 immune responses play a central role in the development of autoimmune disorders such as

rheumatoid arthritis, multiple sclerosis, psoriasis and graft rejection27. It prevents

autoimmune diseases by promoting the differentiation of certain immature T-cells into regulatory

T-cells, which kill off other T-cells that are primed to attack normal healthy cells in the body. IL-

2 also promotes the differentiation of T cells into effectors T-cells and into memory T-cells when

the initial T-cells are also stimulated by an antigen, thus helping the body for fight against

infections. Its expression and secretion is tightly regulated and functions as part of both transient

positive and negative feedback loops in rising immune responses and forcing them down. Through

its role in the development of T-cell immunologic memory, which depends upon the expansion of

the number and function of antigen-selected T-cell clones, it also plays a key role in enduring cell-

mediated immunity22.

1.2.1 Cellular sources and production of IL-2

IL-2 is exclusively produced by T lymphocytes, CD4+ and CD8+ T-cells. THI and TH2 can

produce IL-2, although THl cells may produce higher levels than TH2 cells. T-cells require

stimulus with antigen or mutagenic lectins to secrete IL-2. Activation of mature, resting T-cells

initiates a complex cascade of signaling pathways that leads to cellular responses including

induction of genes essential for T-cell activation and induction of genes that encode IL-2,


5

IL-2Rα and IL-2Rβ. Rapid induction of IL-2 gene expression activates a number of signaling

pathways including protein Kinase C [PKC] and calcium pathways which are required for

regulation of IL-2 gene expression24-25.

Figure 1.4: Top view of IL-224

1.3 IL-2 receptor

The receptor for IL-2 was initially characterized as a single 55-kDa chain. Subsequent studies

demonstrated the existence of a second 70-75-kDa IL-2R subunit, termed the β chain, and it was

established that IL-2Rs existed in Iow, intermediate, and high-affinity forms consisting of α,β and

αβ subunits, respectively28-29. However, whence DNAs for the a and β subunits were cloned and

stated in both lymphoid and non-lymphoid cells, it became evident that some additional,

lymphoid cell-specific component was required to completely reconstitute the intermediate and

high-affinity forms of IL-2R30-32.Takeshita et al33 first observed a 64-kD a molecule in high-

affinity IL-2R complexes that was only detectable in the presence of IL-2.This molecule was

termed as IL-2Rγ chain. The co-structure of IL-2, IL-2Rα was determined in 200534 and

confirmed the IL-2 hotspot. IL-2Rα is an elbow-shaped protein consisting of two β-sheet sushi.

The IL-2/IL-2Rα interactions are defined by an ear parallel packing of IL-2 and IL-2Rα

secondary structures, with twenty IL-2 ligand side chains and twenty one IL-2Rα receptor

residues burying an areaof~1,900A°35.


6

The IL-2/IL2Rα hotpot is composed of hydrophobic patches, including IL-2 side chains Phe42 and

Leu72, projecting into a complementary cavity formed by Leu42, Tyr43, and Met25 on the surface

of IL-2Rα and a buried salt-bridge between Glu62 [IL-2] and Arg36 [IL-2Rα]. Numerous polar and

salt-bridge interactions surround the hotspot36. Site-specific mutagenes are identified a set of

surface residues [Lys35, Arg38, Phe42, and Lys 43] critical for receptor binding these residues lie

on a concave face of IL-237. The structure of the quaternary high-affinity and biologically active

complex [IL-2/IL-2Rα/IL-2Rβ/IL-2γ] was reported five months after the IL-2/IL-2Rα structure

(Fig. 1.5)38. Structural studies of IL-2 have shown that the binding interface for the α-chain of IL-

2R contains both rigid and flexible portions of protein structure39.

Figure 1.5: 3D structure of human IL-2/IL-2R receptor (α, β and ϒ)38

1.4 IL-2/IL-2 receptor binding

To modulate immune responses, IL-2 needs to interact with its cognate receptor on the effector

cells. The IL-2Rα binds IL-2 with low affinity [kd10-8M], while the IL-2Rβ shows even lower

affinity for IL-2 (kd10-7M). The combination of all three chains results in increased affinity for IL-

2.Co-expression of hetero-dimmer of βγ or αγ chains shows intermediate affinity for IL-2.

However the affinity of hetero-trimmers of αβγ chains for IL-2 is dramatically increased and the

expression of receptors constitute high affinity binding. The high affinity receptors are only


7

detected on activated T-cells. The low and intermediate affinity receptors are expressed on T-cells

and resting lymphocytes, respectively40.

Small, organic molecules that bind to IL-2 at the IL-2Rα site take advantage of this flexibility.

Present work focuses on studies of IL-2/IL-2Rα, which is considered to be an important therapeutic

target for immune disease and training ground for developing approaches toward small-molecule

drug discovery at protein-protein interfaces.

1.5 IL-2 Inhibitors

IL-2 inhibitors are drugs that inhibit the action of IL-2. The first small molecule shown to inhibit

the IL-2/IL-2Rα interaction was reported by Roche39. Compound a, was an enantiomer-specific,

competitive inhibitor of IL-2Rα with an IC50 =6 µM.

(a)

NHN

H2N

O

HNO

O CH3

C C

A fragment-minded approach was used to evolve compound a in to a more potent and drug-like

inhibitor. Braisted et al reported a compound (b, IC50 = 6 µM) that binds to IL-2, preventing its

association with IL-2Rα41.

NHN

N

ONH

O HN NH2

NH

Cl Cl

(b)

Optimization of compound b included the introduction of a furanoic acid fragment onto the

dichlorophenyl ring (c, IC50 = 0.060 µM) which offered a 30-fold increase in activity.


8

NN

N

ONH

OHN NH2

NH

Cl Cl

(c)

MeO

OHOOC

Some natural products were isolated from lindolefiastylosa which showed the ability to modulate

the immune response42.

For the development of new immunomodulators, current therapies target IL-2 production or the

IL-2 signaling pathway43. For instance, several IL-2 drugs such as corticosteroids, cyclosporine

and tacrolimus inhibit IL-2 production by antigen-activated T-cells and are used in the treatment

of autoimmune diseases and the suppression of graft rejection. Similarly, sirolimus blocks IL-2R

signaling, thereby preventing the clonal expansion and function of antigen- selected T-cells.

Clinical data with antibodies directed against IL-2Rα suggest that specific inhibition of the IL-

2/IL-2Rα interaction targets the Th1 response without causing toxicity which has been observed

with general immunomodulators44. Therapeutic antibodies have several drawbacks, including high

cost and lack of oral bioavailability. A small molecule inhibitor of IL-2/IL-2Rα interaction could

offer a significant improvement in immunomodulating therapy. Thus far, small molecule inhibitors

of IL-2/IL-2R interaction have been difficult to identify45. In contrast, computational drug design

approaches can be effectively followed resulting in lower cost and less screening time at onset46.

In silico protocol was also used for identification of IL-2 inhibitors. VS of a database of 6,000

compounds resulted in the identification of three novel and moderately active hits. Furthermore,

the effect of these three compounds on the expression of IL-2Rα was measured. The three active

hits showed dose-dependent inhibitory effects on the expression of IL-2Rα with an IC50 ranging

from 5.8-140μM43. The structures of these compounds (d-f) are shown below.

O

N N(d)

NN

NNO

O

OO

(e)


9

NH

N O

(f)

HN

OHN

NO

O

1.6 Computer-aided drug design (CADD)

For the success of a drug research, the choice of a right bioassay is essential. The chosen test

should be simple, quick and relevant. Human testing is not possible at such an early stage and so

the test has to be done in vitro or in vivo. In general in vitro tests are preferred over in vivo tests. In

vivo tests on animals often involve a clinical condition in an animal to produce observable

symptoms. The animal is then treated to see whether the drug alleviates the problem by

eliminating the observable symptoms. There are several problems associated with in vivo testing.

Such testing is slow and also causes animal suffering. In vitro tests do not involve live animals,

instead, specific tissues, cells or enzymes are used. Besides in vitro and in vivo testing of

pharmacologically active compounds, in silico studies, are also carried out on interesting

compounds in search of potential drug candidates. In silico studies, also described as

computational studies, have played a very significant role in drug discovery and development.

The drug discovery process is both time-consuming and expensive47 yet new drugs are required to

satisfy the numerous unmet clinical needs in many disease indications. A discovery of safe and

effective drug molecule and bringing it to market takes around12-15 years. A vast range of

technologies and specialties are involved to expedite the drug process which cost from 800 million

to more than 1 billion48. Computational and molecular modeling tools have become a close

counterpart to experiment in the understanding of molecular aspects of biological systems49-51.

The computational approaches like homology modeling, molecular docking, and quantitative

structure activity relationships (QSAR) and molecular dynamics [MD] are widely employed to

discover the novel hits for various therapeutic targets52. Generally the following eight steps are

undertaken for the identification of drugs for any disease as shown in Figure 1.653. Among the

eight steps, mentioned in Figure1.6. The lead characterization is the most crucial step in the

process. A lead is obtained by the screening of thousands of chemicals in a chemical library.


10

Figure 1.6: Drug discovery and development pipeline53

1.7 Strategies of CADD

Computational screening has become a popular tool in the search for the new drug leads which

have potential to amplify other drug like properties. There are two approaches towards CADD.

1.7.1 Ligand based drug designing (LBDD)

LBDD, also called indirect drug design, relies on the knowledge of other molecules that bind to

the biological target of interest. Ligand based drug design efforts are based on the analysis of the

biological and chemical properties of a set of ligands and are often used when little or no

information about the structure of the target protein is available as shown in Figure 1.754.

2D & 3D similarity searching, pharmacophore modeling and quantitative structure

activity relationship (QSAR) are most popular techniques used in LBDD52.

1.7.1.1 2D & 3D similarity searches

2D chemical similarity analysis methods are used to scan a database of molecules again stone or

more active ligand structure in LBDD. It is based on searching molecules with shape similar to

that of known actives, as such molecules will fit the target's binding site and hence will be likely

to bind to the target. There are a number of prospective applications of this class of techniques in

CLINICAL TRIALSTARGET IDENTIFICATION

Protein IdentificationGenome Analysis

PRE-CLINICAL TRIALSPharmacokinetics

Toxicology

LEAD OPTIMIZATIONDrug Leads

Rational Drug DesignMARKETING OF

DRUGSTARGET VALIDATIONBiological Validation

LEAD DISCOVERYNatural Ligands

Virtual Screening

INTERFACECHARACTERIZATION

NMRX-Ray

Crystallography


11

the literature55-56. Various successful examples of 2D similarity searches are available.

Topological indices, 2D fingerprints and maximum common sub graph similarity methods are

used 2D similarity method57.

Figure 1.7: Ligand based drug designing54

1.7.1.2 Pharmacophore modeling

One of the most enduring concepts of CADD is that of a ‘pharmacophore’. A pharmacophore is

the spatial arrangement of functional groups essential for biological activity; it is a three

dimensional pattern that emerges from a set of biologically active molecules. The concept of a

pharmacophore was introduced by Monty Kier in a series of papers which were published1967-

197158.

According to IUPAC, "a pharmacophore is the ensemble of steric and electronic features that is

necessary to ensure the optimal supramolecular interactions with a specific biological target

structure and to trigger its biological response"59. The pharmacophore of morphine is given in

Figure 1.8 to illustrate concept of pharmacophore.


12

Figure 1.8: Pharmacophore of morphine a) Pharmacophoric features b) Distances between

pharmacophoric features60

Typical pharmacophore features include hydrophobic centroids, aromatic rings, hydrogen

acceptors or donor, cations and anions. These pharmacophoric points may be located on the

ligand itself or may be the projected points presumed to be located in the receptor. The features

need to match different chemical groups with similar properties, in order to identify novel ligands.

Ligand-receptor interactions are typically “polar positive”, “polar negative” or “hydrophobic”. A

well-defined pharmacophore model includes both hydrophobic volumes and hydrogen bond

vectors. In 2D search usually similar compounds are searched while pharmacophore searching is

conducted with a goal to discover new chemical series structurally different from known chemical

series. Ligand Scout, Catalyst, PHASE, MOE, GASP and GALAHAD softwares are mainly used

for pharmacophore modeling61.

1.7.1.3 Quantitative structure activity relationship (QSAR)

QSAR is a descriptor based technique used to explain the biological activities of structurally

similar compounds to their physiochemical properties. The nature of the molecular properties used

and the extent to which they describe the structural features of molecules can be related to its

biological activity, which is an important part of QSAR study. QSAR studies use chemometric

methods to describe how a given biological activity or a physicochemical property varies as a

function of the molecular descriptors. Thus, it is possible to replace costly biological tests or

experiments using a given physicochemical property with calculated descriptors that can, in turn,

be used to predict the responses of interest for new compounds62. Two main steps are involved in

QSAR63.


13

1) A wide variety of molecular descriptors (hydrophobic, steric and electronic) is computed

for each drug like candidate in the data set.

2) A quantitative correlation between the activity and molecular descriptors is derived64.

QSAR may be 2D or 3D QSAR. 2D-QSAR methods were devised in the early days of drug

designing b y Free, Wilson, Hansch, and Fujitain 1964. In 2D-QSAR methods, 2D molecular

substituents and their physicochemical properties are used to perform quantitative predictions.

They do not require any substituent parameters or descriptors to be defined; only activity is

needed. In 3D-QSAR methods, macroscopic properties of target are correlated with atom-based

descriptors derived from the spatial 3D arrangement of the molecular structure65.

1.7.2 Structure based drug designing (SBDD)

Structure based drug design (SBDD), also known as receptor based drug design or direct drug

design, applies when a protein with its 3D structure is known and the structure of the proposed

ligand is under investigation as shown in Figure 1.954.

Figure 1.9: Structure based drug designing54

If an experimental structure of a target is not available, it may be possible to create a homology

model of the target based on the experimental structure of are lated protein66. The first

computational structure-based drug design methods came into existence in the early 1980s.

There have been a few successes to date. Dihydrofolate reductase (DHFR) was the first target


14

protein solved in complex with a drug methotrexate (g), bound to a bacterial enzyme; it was an

important first step67. Three-dimensional structures of dihydrofolate reductase have been the

basis for the design of several improved inhibitors68. Another example of the role of a defined

molecular target coupled with structure-based drug design was the discovery of imatinib

mesylate (h), as elective tyrosine kinase inhibitor approved for the treatment of chronic

myelogenous leukemia69

N

NN

N

H2N

NH2

NHN

OH

OHO O

O

(g)

NN

NHN

NH

N

N

O

(h)

The ability to work at higher solution with both proteins and drug compounds makes SBDD one

of the most powerful methods in drug design. Chemists may be guided to subsets of compounds

with desired features to complement 3D shape of the site. From the geometry and functional

features of the binding site, complementary structures of a drug are so designed as to have high

binding affinity with the target molecule. It is a powerful technique to design a corresponding

drug specifically interacting with the target, particularly for the development of a novel

therapeutic through stimulation or inhibition of the receptor protein70. Homology modeling,

molecular docking, structure based pharmacophore docking, MD simulations are most popular

techniques used in SBDD.

1.7.2.1 Homology Modeling

Homology modeling is the process by which one or more template proteins with

knownstructures,withsequencessimilartoaproteinofinterestthatlacksaknownstructure, is used to

model the unknown structure introduced in 1970s71. Molecular modeling has become an

essential tool in several fields of science, including chemistry, physics, drug discovery, and

biochemistry. If the 3Dstructureofaproteinisresolvedandthe sequence of a related protein of

interest is known, the approach of homology modeling becomes applicable. In particular, the

structural information of the template protein can then be used as a scaffold for the generation

of a model of the protein of interest (target protein).Thus knowledge-based approaches were

developed to predict the 3D structure of proteins based on experimental data of the 3D structure


15

of homologous reference proteins72. The accuracy of the model is justified by the sequence

identity between the model a n d template structure. A sequence identity of <30% suggests that

the model have serious errors, while>50%identity suggests that the model is reliable71.

1.7.2.2 Molecular docking

Molecular docking is a computational procedure performed on structure-based rational drug

design to identify correct conformations of small molecule ligands and also to estimate the

strength of the protein-ligand interactions, usually one receptor and one ligand73-75. The most

common docking programs and software include Autodock76, Autodock Vina77 ,GOLD78, FlexX79

and MOE80. Docking calculations allow the prediction of the structures of all the complexes

between the enzymes and ligands, thereby, suggesting different kinds of interactions81. Two

approaches are particularly popular within the molecular docking community. One approach is

shape complementarity t h a t uses a matching technique which describes the protein and the

ligand as complementary surfaces82-84. The second approach is simulation which is the actual

docking process in which the ligand-protein pairwise interaction energies are calculated85. Both

approaches have significant advantages as well as some limitations. These are outlined below.

Shape complementarity methods describe the protein and ligand as a set of features that

make them dockable86. These features may include molecular surface/complementary

surface descriptors. In this case, the receptor’s molecular surface is described in terms of its

solvent-accessible surface area and the ligand’s molecular surface is described in terms of

its matching surface description. The complementarity between the two surfaces amounts

to the shape matching description that may help finding the complementary pose of

docking the target and the ligand molecules. Another approach is to describe the

hydrophobic features of the protein using turns in the main-chain atoms. Yet another

approach is to use a Fourier shape descriptor technique87-88. Shape complementarity

methods can quickly scan through several thousand ligands in a matter of second sand

actually figure out whether they can bind at the protein’s active site, and are usually

scalable to even protein-protein interactions. They are also much more cooperative to

pharmacophore based approaches, since they use geometric descriptions of the ligands to

find optimal binding89.


16

Simulating the docking process as such is much more complicated. In this approach, the

protein and the ligand are separated by some physical distance, and the ligand finds its

position into the protein’s active site after a certain number of moves in its

conformationalspace.SimulationappliesthesimplificationofNewton's equation of motion.

There are two simulation methods available: molecular dynamics (MD) and pure energy

minimization methods. MD has wide range of applications. MD methods are gaining

popularity in docking because they consider flexibility of protein during docking but it

takes lengthy timeframe for sampling the conformational space90. The obvious advantage

of docking simulation is that ligand flexibility is easily incorporated, whereas shape

complementarity techniques must use in genius methods to incorporate flexibility in

ligands. Also, it more accurately models reality, whereas shape complimentary techniques

are more of an abstraction. Clearly, simulation is computationally expensive, having to

explore a large energy landscape. Grid-based techniques, optimization methods and

increased computer speed have made docking simulation more realistic91.

1.7.2.2.1 Types of molecular docking

Protein-ligand docking

The goal of protein–ligand docking is to predict the position and orientation of a ligand when it is

bound to a protein receptor or enzyme92. The protein- ligand docking procedure can be typically

divided into two parts: rigid body docking and flexible docking. Rigid docking treats both the

ligand and the receptor as rigid and explores only six degrees of translational and rotational

freedom, hence excluding any kind of flexibility. Most of the docking suites employ rigid body

docking procedure as a first step93.

Flexible docking is a more common approach to model the ligand flexibility while assuming

having a rigid protein receptor, considering thereby only the conformational space of the ligand.

Ideally, however, protein flexibility should also be taken into account and some approaches in

this regard have been developed. There are three general categories of algorithms to treat ligand

flexibility: systematic methods, random or stochastic methods and simulation methods94.

Protein-protein docking


17

Macromolecular docking is the computational modelling of the quaternary structure of

complexes formed by two or more interacting biological macromolecules.Protein–

proteincomplexesarethemostcommonlyattemptedtargetsof such modelling. In 1996 the results of

the first blind protein-protein docking were published95 in which six research groups attempted

to predict the complex structure of TEM-1 beta-lactamase with beta-lactamase inhibitor protein

(BLIP). The goal of protein–protein docking is to determine the structure of a complex

from the separately determined coordinates of its component proteins. Since the

molecule unbound structures generally differ from bound structures in the complex,

docking is not simply the rigid-body geometric problem of fitting together two

complementary shapes96. The quality of the predicted complex in test problems is

usually assessed in terms of the following measures:

1) Fraction of native contacts: defined as the number of correct residue-residue

contacts in the predicted complex divided by the number of contacts in the

target complex. A pair of residues on different sides of the interface is

considered to be in contact if any of their atoms are within10A.

2) Ligand root-mean-square deviation (RMSD): The RMSD of the backbone

atoms of the ligand in the predicted target complex versus target complex

after super imposition of the receptor.

3) Binding site RMSD: Ligand RMSD is calculated only for those ligand residues

in contact with the receptor.

The goal of rigid-body search is to generate as many hits as possible, whereas the re-

scoring and ranking step differentiates true hits among the large number of structures

obtained in the first step. The discrimination might require flexible refinement of

selected structures97.

1.7.2.2.2 Docking algorithm

The strength of a successful docking program depends on two components, i.e. A ) Search

algorithm, B) Scoring function, explained in Figure.1.1098.


18

Figure 1.10: Algorithm used for molecular docking98

A. Search algorithm

It consists of all possible orientations and conformations of the protein paired with the ligand. The

search algorithm should create an optimum number of configurations that include the

experimentally determined binding modes. Although a rigorous searching algorithm would go

through all possible binding modes between the two molecules, this search would be impractical

due to the size of the search space and amount of time it might take to complete. As a

consequence, only a small amount of the total conformational space can be sampled, so a balance

must be reached between the computational expense and the amount of the search space examined.

Some common searching algorithms include molecular dynamics, Monte Carlo methods, and

genetic algorithms, fragment-based, point complementary and distance geometry methods, Tabu

and systematic searches99.

B. Scoring function

The scoring function takes a pose as input and returns a number indicating the likelihood that the

pose represents a favorable binding interaction. Scoring function consists of a number of

mathematical methods used to predict the strength of the non-covalent interaction called the

binding affinity. In molecular docking scoring functions are classified into empirical, force field

and knowledge based scoring functions. In all computational methodologies, one important

problem is the development of an energy scoring function that can rapidly and accurately describe


19

the interaction between the protein and ligand. Several reviews on scoring are available in the

literature100-101.

1.7.2.3 Molecular dynamics simulations (MDS)

MD is a versatile and powerful computational technique used to simulate the dynamics of

complex biochemical, chemical and physical systems. The availability of powerful computers

and algorithms contributes in the success of MD. The thermodynamic properties, dynamic

aspects and kinetics of organic and bio-molecules, molecular recognition process and

macromolecular stability can be explained by MD simulation. Statistical mechanics gives

strength to MD simulations which connect microscopic simulation with macroscopic

observables102(97).The distribution and motion of atoms are related with macroscopic

observables such as temperature; pressure, heat capacity and free energies by using

mathematical expressions provided by statistical mechanics. The binding free energy of a

protein ligand complex and the conformational changes of protein can be explored in this way.

We have already reported BChE inhibition activities of 2,3-dihydro-1,5-benzothiazepines

through MD simulation studies. It was reported that the availability of BChE to catalyze

various substrates depends on the difference between amino acid residues that line the

enzyme cleft at about 20Å depth. Such structural information allow the medicinal chemist to

explore the region specific for BChE so that a combination therapy can be employed103.

AMBER, CHARMM and GROMACS are widely used for MD simulation 104.

1.8 Virtual screening

It is commonly accepted that there are several steps in the drug discovery process; including

disease selection, target hypothesis, lead identification, lead optimization, pre-clinical trials,

clinical trials and pharmacological optimization. Traditionally, these steps are carried out

sequentially and if one of the steps is slow, the entire process is delayed. Because it is not possible

to speed-up clinical trials, it seems that the only way to accelerate the process of DD is to act on the

preclinical steps. Among the various techniques used to facilitate hit identification, high throughput

screening (HTS) represents probably the most investigated one. The perspective of screening

millions of compounds on a specific target can be powerful to identify hits105.

In virtual database screening, computational techniques are used to search databases of

compounds for small molecules predicted to bind to a drug target106. Such predictions are not


20

meant to replace experimental affinity determination, but virtual screening method scan

complement the experimental methods by producing an enriched subset of a large chemical

database; the enriched subset is one in which the proportion of compounds that actually bind to

the drug target of interest is increased, compared to the proportion within the whole database107.

Thus, compounds from the subset that pass the initial virtual screening are found to be

pharmaceutically interesting at a higher rate and at a lower cost. In principle, the methods used in

virtual screening may be applied to any number of conceivable compounds, but in practice one

usually focuses on curated libraries of purchasable or synthesizable compounds or close analogues

of such compounds. Some examples include Accelrys available Chemical Directory, e Molecules

Database and free ZINC database108.

There are two general types of virtual screening, ligand-based virtual screening (LBVS) and

structure-based virtual screening (SBVS). In ligand-based virtual screening, properties of a set of

ligands known to bind to the drug target of interest are used to build a model for common features

believed to be important for a ligand’s biological effect. This model can then be used to find new

ligands that share these common features109. In SBVS, the ligands are modeled as physical entities

and scoring functions are used to predict the affinity of the ligand for the binding site of interest110.

Structure-based virtual screening typically employs docking software that is designed to explore

the possible binding modes of a ligand within a binding site of interest92, 111-113.

1.9 Wet Lab

Having identified some hits as small molecules of simple and easy to synthesize scaffolds in wet

lab were selected. Some important scaffolds that possess diverse pharmaceutical applications have

been discussed in the following sections.

Their methods of synthesis are already reported in literature along with their biological potential.

Chalcones, for example is one of the most widely studied scaffold, synthesized by Claisen Schmidt

condensation reaction, while dihydropyrimidines and benzimidazoles can be synthesized by

Biginelli and Philip reaction respectively.

1.9.1 Chalcones

One of the most easily synthesized scaffolds for DD or structure optimization is chalcone.


21

Chalcones, also known as α,β-unsaturated ketones are not only important precursors for synthetic

manipulations but also form a major component of the natural products.

Chalcones as well as their synthetic analogues display enormous number of biological activities

such as IL-2 inhibitors i43, anticancer agents ii114, iii115, anti-malarial iv116, v117, antimicrobial vi118,

vii119, anti-inflammatory viii120, anti-protozoal ix121, antioxidant x122.

O

N Ni

O

OOCH3

HN

O

N

O

H3CO

H3COOCH3

O

OO

ii

O

O

NN

N

NO

O

iii

N

NH (CH2)6 NH

O

O

O

Cl ivNH

N

ONO2

v

vi

O

SN

COOHS

O

N

S O

NO2

NH vii


22

NN

O

N

N

Cl

O

OO

viii

O

O

O

NH

N

OH

ix

O

HO

OH

x

1.9.1.1 Synthesis of chalcones

Bandgaret al synthesized 3-(2,4-dimethoxy-phenyl)-1-phenyl-propenone by reacting substituted 1-

phenyl ethanone and 2,4-dimethoxy-benzaldehyde or 3,4,5 trimethoxybenzaldehyde using NaOH

as catalyst (Scheme 1.1)123.

H

O

R

CH3

O

R'

O

R R'NaOH/EtOH

rt

Scheme 1.1

Hans et al synthesized acetylenic chalcones from commercially available hydroxyl-acetophenone

or benzaldehyde and commercially available vanillin or acetovanillone by O-alkylation followed

by Claisen-Schmidt condensation as shown in Scheme 1.2124.

O

O R'

CH

R'

O

OHC

R'

O

HO

i ii

R' =H, CH3i = Propagyl bromide, K2CO3, DMFii = Metoxylated acetophenone, NaOH, MeOH, rt

Scheme 1.2

Kumar et al synthesized indolyl chalcones by reacting indol-3-carboxaldehyde and appropriate

acetophenone in the presence of piperidine under reflux (Scheme 1.3)125.


23

CH3

O

R' NR

CHO

NR

O

R'

R' = 4OH, 4F, 4OCH3 etc.Scheme 1.3

1.9.2 Dihydropyrimidines

Another important scaffold in CADD is a pyrimidine nucleus. Pyrimidines are the most important

six member heterocyclic compounds containing two nitrogen atoms at 1 and 3 positions, called 1,3-

diazines B. Other structurally isomeric diazines are pyridazineA (1,2-diazine) and pyrazine C (1,4-

diazine). The numbering of 3-isomers of diazines is shown below.

NN

N

N

N

N2

3

45

6

1

A B C

2

3

4

5

6

1

2

3

45

6

1

The chemistry of pyrimidines has become increasingly important as a result of recent developments

in medicinal chemistry. Pyrimidine derivatives possess a broader spectrum ofbiological activities

such as antiproliferativexi126, antibacterialxii127, anticancer xiii128, cytotoxic xiv129, antifolatesxv130,

inhibitor of dihydrofolate reductase (DHFR) of malarialplasmodia xvi131, and antifungal xvii132etc.

N

NH3C

F3CNH

O

N

N

N

NH

xi

O

N

NH

N

S

N

N

N

CNO

SCH3

NGNH2

xii


24

O

S

NHN

N

N

NH2

OCH3

S NH2O

Oxiii

NH

N

NHO NH2

H2N

xivBr

O O

N

N NH2

NH2

xv

CH3SO

O

NN

CH3

xvi

HNH3C

NO

OH

xvii

1.9.2.1 Synthesis of dihydropyrimidine derivatives

Mainly, pyrimidine synthesis can be classified according to the fundamental nature of fragments,

which combine together to form pyrimidine nucleus. Some of the synthetic routes to

dihydropyrimidinones are given below.

One of the prominent multicomponent reactions that produces an interesting class of nitrogen

heterocycles is the “Biginelli dihydropyrimidines synthesis.” 3,4-dihydropyrimidinones and 3,4-

dihyrpyrimidine-2-thiones have been synthesized by Biginelli condensation of differently

substituted aryl aldehydes with different 1,3 dicarbonyl compounds in the presence of urea or

thiourea and stannous chloride by conventional heating or at reflux temperature as shown in

Scheme 1.4133.

+

+CHO

H2N

NH2

X

O

O NH

NH

X

OSnCl2 . 2H2O

ACN, Reflux

X=O,S

Scheme 1.4


25

The reaction of substituted aromatic aldehydes, 4-chloro acetanilide ethanol, and potassium

hydroxide solution resulted in the formation of chalcone derivatives which was then treated with

guanidine nitrate in presence of ethanol and 40% sodium hydroxide solution to give pyrimidine

derivatives as shown in Scheme 1.5134.

Scheme 1.5

HN

Cl

CH3

O

RR1

R2

OHC Ethanolic KOHHN

ClO

RR1

R2

Ethanolic KOHGuinidine nitrate

HN

ClO

NN

NH2

RR1

R2

C6H6CHO

EtOH

NH

Cl

ONN

N

RR1

R2

The reaction of aromatic hydrazine and ethyl 3-oxobutanoate resulted in the formation of an

intermediate which was then reacted with substituted benzaldehyde to form another intermediate

which when treated with urea and KOH in DMF under microwave irradiations yielded pyrimidine

derivatives (Scheme 1.6).135

NO2

O2N

NH2-NH2

H3C OC2H5

O OCH3COOH

DMF

NO2

O2N

NN

O

KOH RC6H5CHO

NO2

O2N

NN

O

CH-RUrea

KOH

NO2

O2N

NN

NNH

O

R

Scheme 1.6


26

Pyrimidine derivatives have been synthesized by the reaction of 2-amino-3-carbonitrile,

triethylorthoformate and acetic anhydride in 1,4-dioxane followed by the reaction of an

intermediate formed with ammonia or primary amine in ethanol as described in Scheme 1.7136.

O

CN

NH2

CH(OEt)3/Ac2O

O

CN

N OC2H5

O N

N-R

NH

NH2-R

Scheme 1.7

The reaction of 6-methyl-5-(2-oxobutanoyl)-4-phenyl-3,4-dihydropyrimidine derivative (prepared

from the reaction of ethyl ester with thiosemicarbazide in acetone) with carbothioamideat reflux

temperature resulted an intermediate (Carbothioamide)-3,4-dihydro-6-methyl-4-phenylpyrimidin-

2(1H)-one. Then gradually adding the Iodine solution in KI and heating the reaction mixture

continuously for 5 hr gave desired pyrimidine derivatives as shown in Scheme 1.8.137

NH

NH

OC2H5

OX

RR1

NH2-NHCH3-CS-NH2

Reflux 10-12h

NH

NHNH

O

X

RR1

HNH2N

S

NaOH

KI

NH

NH

X

RR1

NN

OH2N

Scheme 1.8

Another reported method for the synthesis of dihydropyrimidine derivatives involves the

condensation of separately synthesized chalcone (3-(4-substitutedphenyl)-1-(pyridine-4-yl)prop-2-


27

en-1-one) with urea or guanidine hydrochloride in the presence of acetic acid as shown in Scheme

1.9.138

Scheme 1.9

NCH3

OROHC

C2H5OH

KOH

N

NN

NH2

R

NR

O

HClNH2CNHNH2 NH2CONH2

N

NHN

OH

R

1.9.3 Heteroazepines

Heteroazepines are important seven membered heterocyclic compounds used as tranquillizers, such

as Librium and Valium. 1,5-benzothiazepines have recently emerged as important target molecules

due to their pharmacological properties such as p53-MDM2 Inhibitors xviii, xix139, antimicrobial

xx140, antioxidant xxi141, acetylcholinesterase inhibitors xxii.142

NH

N

O

O

Cl

COOH

Cl

I

xviii

Cl

N

Cl

OHO

COOH

xix


28

NH

HN

CH3

N

xx

S

S

N

F3C xxi

NSX

C8H17

xxii

1.9.3.1 Synthesis of heteroazepine derivatives

Synthesis of a series of some new 1,5-benzodiazepines has been reported by the condensation of o-

phenylenediamine and various substituted chalcones in the presence of DMF as solvent (Scheme

1.10).143

Scheme 1.10

N Cl

OR

N

Cl

R1

N NH

NN Cl Cl

RR1

H2N NH2

PiperidineDMF

Reflux

Chalcone derivatives have also been prepared by the Claisen-Schmidt condensation reaction of 1-

(4-chlorophenyl)-1H-pyrazole- 4-carbaldehyde and variously substituted o-hydroxyacetophenones

in the basic medium followed by Michael addition of o-amino thiophenol to chalconesin acetic

acid/ethanol (Scheme 1.11).144


29

Scheme 1.11

N

Cl

N

HO

CH3

O

OHR1

R2

R3

O

OHR1

R2

R3

NN

Cl

OHR1

R2

R3N S

NN

Cl

10% KOH

EtOH

AcOH/EtOHHS

H2N

The reaction of gem-acylnitrostyrenes witho-aminothiophenol afforded gem-benzoylnitrostyrenes

which on heating under reflux in EtOH in the presence of methanolicHCl gave 2-aryl-3-nitro-4-

phenyl-2,5-dihydro-1,5-benzothiazepine derivatives (Scheme 1.12).145

Scheme 1.12

HS

H2N

NO2

O CH3OH

18-20 °C10-20 min

S

NH2

NO2

O N

SNO2

NH

SNO2

CH3OH

Heat, HCl

A mixture of 2-aminothiophenol or 2-aminophenol or o-phenylenediamine, 1,3-diketone and N,N-

dimethylformamide dimethyl acetal in distilled water was stirred well and subjected to microwave


30

irradiation at 130-140 °C that resulted in the formation of heteroazepines as illustrated in Scheme

1.13.146

Scheme 1.13

OTs

OO

X

NH2

X=O,S,NHMicriwave

N

X

O

1.9.4 Benzimidazoles

Benzimidazoles are regarded as a promising class of bioactive heterocyclic compounds, they are

remarkably effective compounds both with respect to their inhibitory activity and their favorable

selectivity ratio.147The most prominent benzimidazole in nature is N-ribosyl-dimethyl

benzimidazole, which serves as an axial ligand for cobalt in vitamin B12.148Benzimidazoles exhibit

a range of pharmaceutical activities like antiulcer xxiii, xxiv149 proton pump inhibitorxxv150,

antihelmenthic xxvi151, antifungal xxvii152, antimicrobial and antibacterial activity xxviii,

xxix153,154, antiviral xxx155, anthelmintic xxxi156, HIV inhibitors xxxii157, antihypertensive xxxiii158,

anti-inflammatory xxxiv159, analgesic xxxv160.

N

HN

XHN

NH O

O

NO2

xxiii

N

N

S OO

xxiv

N

HN

SO N

OF

FF

xxv

S

N

HN

NH

O

O

Oxxvi

N

NH

N

S

xxvii


31

N

HN

N

NNN

xxviii

N N

CH3

HN N

O

O

H3CCl

xxix

N

HN

CH3

H3C

H3C

xxx

N

HN

xxxi

S

NO

N

N

xxxii

S

FF

N

N

xxxiii

HOOC

N

HN

COOH

R

xxxiv

N

N

N

N

SRxxxv

1.9.4.1 Synthesis of benzimidazole derivatives

Pyridinyloxyphenyl benzimidazoles have been obtained by reacting chloropyridinyloxy

benzaldehyde (prepared by reacting 2,3,5,6-tetrachloropyridine with m-hydroxy benzaldehyde

using anhydrous K2CO3 in DMF medium) with o-phenylene diamine in presence of sulfonium

bromide catalyst (Scheme 1.14)161.


32

Scheme 1.14

N

ClCl

OCl

N

HN

Cl

N

Cl

Cl

Cl

Cl

OH

CHON

ClCl

OCl

CHOK2CO3, DMF

80 C, 2 hr

Me2SBr2,AcCN,Reflux, 4hr

NH2

NH2O2N

The o-Phenylenediamine was reacted with benzaldehyde followed by condensation with aryl

aldehydes afforded corresponding benzimidazoles in good yield (Scheme 1.15).162

Scheme 1.15

O

O

H

NH2

NH2

Glycerol, 90 °C N

HN

N

N

59%41%

1.9.5 Anthraquinone sulfonamide derivatives

Sulfonamides (sulfa drugs) were the first drugs largely employed and systematically used as

preventive and chemotherapeutic agents against various diseases163. These compounds are well

known for their use as cytotoxic against human cancer cells xxxvi164, antimicrobial xxxvii,

xxxviii165, and are known to be involvedin a number of biological reactions.

N3 S NHO

OOCH3

xxxvi


33

S NHO

OH2N

O

N

O

HN

xxxvii

SO

OH2N

xxxviii

NH

N

NHS

1.9.5.1 Synthesis of anthraquinone sulfonamide derivatives

Due to the broad applicability of sulfonamides, it is desirable to find general and effective methods

for their synthesis. Recently, a direct oxidative conversion of thiols into sulfonamides with H2O2-

SOCl2 was reported by Bahramiet al.166 leading to corresponding sulfonamides were obtained in

excellent yields in very short reaction time (Scheme 1.16).167

Scheme 1.16

RSH R S ClO

OR S NH

O

O

R1R1 NH2

Pyridine

aq.H2O2, SOCl2

MeCN

A method of formation of sulfonamides from thiols was reported by Wright et al (Scheme

1.17)168,requiring in situ synthesis of a sulfonyl chloride using sodium hypochlorite-mediated

oxidation of thiol.

Scheme 1.17

RSH R S ClO

O R S NHO

O

NOClH2N

Another innovative example for the synthesis of sulfonamides was illustrated by the synthesis of 2-

amino-9H-purin-6-sulfonamide (Scheme 1.18). Mild and selective oxidants were used by

Revankaret al.169. They reported the oxidation of 2-amino-9H-purin-6-sulfonamide using one

equivalent of m-chloro para-benzoic acid in 48% yield.

Scheme 1.18

N

N NH

N

H2N

SH2N

N

N NH

N

H2N

SH2N

OO

m-CPBA


34

Chavasiret al.170 reported the use of trichloroacetonitrile-triphenylphosphine complex

(Cl3CCN/PPh3) for sulfonamide construction (Scheme 1.19).

Scheme 1.19

R S OHO

O Cl3CCN, PPh3, DCM

RNH2, 4-PicolineR S NHR

O

O

An effective method for N-arylation on sulfonamide using copper (II) acetate in air and arylboronic

acid to get N-arylsulfonamide is given in Scheme 1.20.171

Scheme 1.20

SNH2

OO

CH3

BHO OH

SNH

OO

CH3

Cu(OAc)2, TEMPO

NEt3, DCM

The sulfonamide derivatives have also been synthesized by reacting substituted benzene

sulphonamide and naphthofuroic acid, phosphorus oxychloride at 40-45 °C (Scheme 1.21).172

Scheme 1.21

ORCOOH

S R1H2NO

O

POCl3/Heat, 10 min,40-45 °C

OR O

HN SO

OR1

The reaction of celecoxib (prepared according to the reported method173 and 2-chloropropanoyl

chloride, or chlorobutyryl chloride resulted in the formation of an intermediate which was then

subjected to condensation with 2-aminoanthraquinone to give alkanamide derivatives (Scheme

1.22).174


35

Scheme 1.22

O

O

HNCl

OXCl

O

O

NH2

O

XCl

O

O

HN

X NH

SO

ONN

CF3

H2NS

O

ONN

CF3

O

Ethanol, DMF,reflux, 4–5 h.

X=O, S

1.9.6 Schiff base derivatives

Schiff bases and their metal complexes have been extensively investigated due to their wide range

of applications including antifungal xxxix175, antitumor xl176, anti-inflammatory xli177, antiviral

xlii178, antioxidant xliii179, angiotension-II receptor antagonist xliv180, antibacterial xlv181,

antiglycation xlvi182, anticonvulsant xlvii183 and anti-tuberculosis xlviii184.

OSn

PhPhPh

NN

OSnPh

PhPh

xxxix

O (CH2)11 CH3

xl

NHOOC

N

N

N

CN

O

N

NO2

xli

N

N

ON

H OH

xlii

OO H

OHN(H3C)7

O

xliii


36

N

OO2NR

R1

xliv

O2S

N

NH2

xlv

NH

O

N NNO2

xlvi

N

N

OI

NNH

S

NH2

F

xlvii

N

OH

SN

N

HN

xlviii

1.9.6.1 Synthesis of Schiff base derivatives

The reaction of primary aromatic amines with aryl aldehydes is found to be catalyzed by lemon

juice as natural acid under solvent-free conditions to give the corresponding Schiff bases in good

yield (Scheme 1.23).185

Scheme 1.23

OHCR2

R1

NH2N

R1

R2

A mixture of 2-aminobenzothiazoles and different aromatic aldehydes were taken in mortar, added

to conc. H2SO4, water and stirred at room temperature for 30 minutes (Scheme 1.24).186

Scheme 1.24

S

NH2NH3CO

O

H S

NN

H3CO

A simple and efficient method has been developed for the synthesis of some novel Schiff bases by

the reaction of aromatic aldehydes with 2-aminobenzimidazole by using catalytic amount of

M(NO3)2 .xH2O in an organic solvent at room temperature (Scheme 1.25).187


37

Scheme 1.25

N

HN

NH2

CHO

N

HN

N

NO2

NO2

Ni(NO3)2

20-3- mint, r.t

1.9.7 Pyrazoles

Pyrazoles are an important class of heterocyclic compounds with two adjacent nitrogens in a five-

membered ring system. Among the two nitrogen atoms; one is basic and the other is neutral in

nature.

Pyrazoles are widely found as the core structure in a large variety of compounds that possess

important agrochemical and pharmaceutical activities such asantitumor xlix188, antitubercular l189,

anticonvulsant li190, lii191, antihepatotoxic liii192, anti-inflammatory liv193, antimicrobial lv194,

antibacterial lvi195, antioxidant lvii196 etc.

R1

N NNN

R3

R2

xlix

NN

SN

N

l

O

O

N NO

NH

li

N NO

H2N

R1 R2

lii

R

N NHO

O

liii

O NN N O

Rliv

NNR

NO2

lv

NHN

O

R

lvi

NN

O

NN

O

lvii


38

1.9.7.1 Synthesis of pyrazole derivatives

The synthetic strategy of pyrazole derivatives is illustrated in Scheme 1.26. The acetophenone and

substituted phenyl hydrazine were exposed to microwave irradiations at 200 W.197

O

CH3

R1

R2

NH

+ R1

R2

N-N

R1

R2

NN

OH

MW

MW

Scheme 1.26

H2N

A series of novel indeno-pyrazole derivatives were synthesized by the reaction of α- and β-

unsaturated ketones with phenyl hydrazine in polyethylene glycol-400 (PEG-400) and few drops of

acetic acid (Scheme 1.27).198

Scheme 1.27

OAr/Het

NH-NH2

N N

Ar/HetPEG-400/3-4hAcOH, 2-4 drops

O

H Het/Ar

OBleaching Earth

PEG -400/Heat

A convenient synthesis of 3-substituted pyrazole derivatives by a mixed anhydride method using

isobutylchloroformate and N-methylmorpholine at -20°C in THF (Scheme 1.28).199


39

Scheme 1.28

O CH3

CH3Cl

O O

O

Cl Cl

H2N-HNO CH3

CH3Cl

O N

O

Cl Cl

HN

Cl Cl

N N

Cl

CH3

COOH

Isobutylchloroformate

THF, NMM

Cl Cl

N N

Cl

CH3

O

R

R-NH2+

EtOH/AcOH

EtOHKOH

A series of novel N’-[(aryl)methylene]-5-substituted-1H-pyrazole-3-carbohydrazide derivatives

were synthesized by the reaction of substituted pyrazole carbohydrazide with functionalized

aromatic aldehydes (Scheme 1.29).200

Scheme 1.29

R

CH3O

Diethyloxalate

NaOEtR CO2Et

O ONH2-NH2.H2O

H2SO4N N

R OEt

O

N N

R NH-NH2

O

NH2-NH2Reflux

ArCHO/EtOH

RefluxN N

R N-N

O

Ar

A strategy based on the concept of acid-catalyzed dimerization was applied to the synthesis of

methylene bis-salicylaldehyde from readily available salicylaldehyde, which then underwent

reaction with substituted acetophenones, leading to the formation of methylene bis-chalcones.

Construction of the pyrazole ring was accomplished by the reaction of chalcones with hydrazine

hydrate under microwave irradiation with or without acetic acid as shown in Scheme 1.30.201


40

Scheme 1.30

OH

CHO

Trioxane

H2SO4

HO

OHC CHO

OH

KOH/EtOHO

HO

OH

O

O

R

NH2-NH2H2O

HO OH

N NH

R

R

R

N NH R

In another method3,5-bis-trifluoroacetophenone was treated with diethyl oxalate and sodium

hydride in toluene at rt. The intermediate ethyl ester was then reacted with 4-fluorophenyl

hydrazine hydrochloride in ethanol:acetic acid (2:1) that resulted in the formation of ethyl ester

which was added to the ethanolic NaOH solution and the mixture was stirred at rt for 4h to afford

pyrazole derivatives (Scheme1.31).202

NaH, toluene, rt HCl, EtOH, 1000oC

NaOH, ethanol, rt

CF3

F3CO

CF3

F3CO

OEtO

O

F3C

F3C

OEt

O

N N

F

F3C

F3C

OH

O

N N

F

diethyl oxalate4F-C6H4-NH-NH2

Scheme 1.31


41

1.9.8 Benzamides

Benzamides are an important class of organic compound which are derived from benzoic acid.

Benzamide and their heterocyclized products display diverse biological activities including enzyme

inhibitors against BchE,AchE and lipoxygenase enzymes lviii203, antibacterial lix204, cell cycle

inhibition in HEPG2 cells lx205, anti-inflammatory lxi, Synthesis, anticancer and antibacterial

activity of salinomycinn-benzyl amides206, angiotensin-i-converting enzyme inhibition, antioxidant

lxii207 and anticancer lxiii208.

HN

O

Olviii

NH

NH

O

NO2

NO2

lix

ONH

ON

F

F

lx

O

HN

O

NO

NN

S

lxi

O

NHOO

NHN

S

lxii

NH

O

O O

OOHO

OH

O

OHF

lxiii

1.9.8.1 Synthesis of benzamide derivatives

N-(3-hydroxyphenyl) benzamide derivatives were synthesized by reacting 3-hydroxyaniline with

benzoyl chloride in aqueous medium, which was then treated with different alkyl halides to

synthesis new 3-O-derivatives via O-alkylation (Scheme 1.32).203

Scheme 1.32

NH2

OH

Cl

O HN

OH

O C2H5ONa

RI

HN

OR

O

Aq. medium

stirr. 30 min.


42

The reaction of benzoyl chloride, ammonium hydroxide and water on stirring yielded the amide

derivatives (Scheme 1.33).204

Scheme 1.33

Cl

ONH3

30% NH4OH

NH2

O

HCHO, ethanol,reflux N

H

OCH2-R

Amide derivatives have been synthesized by a three-step reaction First, a Buchwald-Hartwig C-N

or C-O coupling was carried out by an ester hydrolysis reaction that lead to an intermediate. Next,

1-(3-dimethylaminopropyl)-3-ethylcarbodiimide (EDCI) and 1-hydroxybenzotriazole hydrate

(HOBT) were used to catalyze amide formation as shown in Scheme 1.34.205

Scheme 1.34

R'R'

FNH2

HO

OC2H5

OX

OH

OR'

X

NH

OR'

NH

+

HOBT EDCI

The reaction of methyl-2-aminobenzoxazole-5-carboxylate in DMF and NaH, substituted benzoyl

chloride afforded the desired amide in 60-70% yield (Scheme 1.35).206

Scheme 1.35

H3CO

O

O

NNH2

R

O

Cl H3CO

O

O

NNH

O

RNaH

DMF


43

1.10 Aim of the study

With view to address the challenges of immunomodulating issues as a scoring health hazard it was

planned to design some novel IL-2 inhibitors by making use of the most rapidly emerging

computational tools commonly referred to as computer-aided drug designing, the proposed strategy

would involve the initial study of a novel drug target i.e. IL-2-IL-2R, followed by detail study of its

binding site. This would follow the extraction of pharmacophoric features expected to be important

for IL-2 inhibitory using the structure based pharmacophore protocol. The analogs of the hits

identified in the 1st round of experiments in dry lab will be synthesized for their potential as IL-2

inhibitors. Molecular Operating System (MOE) software will be used for the entire set of study

(Figure 1.1).

Figure 1.11: Designing and synthesis of IL-2 inhibitors by CADD

The discovery of small-molecule inhibitors and the study of their interactions with IL-2/1L-2Rα

will help in elucidating the inhibitors' mechanisms of action by using following steps.


44

1) Establishing structure-based 3D-Pharmacophore models for IL-2 inhibitors.

2) Establishing an appropriate virtual screening protocol.

3) Filtration of IL-2 inhibitors by virtual screening.

4) Identification of hits.

5) Determination of binding modes of known natural and synthetic IL-2 inhibitors by

molecular docking method.

6) Synthesis and IL-2 inhibition studies on the analogs of hits structures identified in dry lab

and synthesized later in wet lab.

Chapter 2

Results and Discussion

CHAPTER 2: RESULTS & DISCUSSION

44

The function of the immune system depends largely on the action of interleukins which are a

group of cytokines that were first seen to be expressed by white blood cells. The majority of

interleukins are synthesized by helper CH4T lymphocytes, as well as through monocytes,

macrophages and endothelial cells. They stimulate the development and differentiation of T and

B lymphocytes and hematopoietic cells209. The rare deficiencies in the number of interleukins

cause autoimmune diseases or immune deficiency. Hence, IL-2 holds considerable promise as a

therapeutic target for the treatment of autoimmune disorders. For the development of new

immunomodulators, current therapies target IL-2 production or the IL-2 signaling pathway. A

small molecule inhibitor of the IL-2/IL-2Rα interaction could offer a significant improvement in

immunosuppressive therapy. The present study describes the usefulness of in silico tools in

identification of new IL-2 inhibitors. Such studies also help in the identification and improving

the physiochemical properties of the existing drugs. Today, in silico screening has also become a

popular tool in identifying novel drugs at a much faster rate. The identification of novel IL-2

inhibitors was carried out by the steps followed both in dry and wet labs in different rounds of

experiments.

Figure 2.1. Different steps used in dry & wet labs for identification of IL-2 inhibitors

Target Selection

Active sitedetermination

Data SetGeneration

PharmacophoreDesigning

PharmacophoreValidation

VirtualScreening

PharmacophoreBased Docoking

HitsIdentification

HitsOptimization

PharmacophoreBased Docoking

Synthesis ofoptimized Hits

IL-2 inhibitionassay


45

STEP 1 (Dry lab)

2.1. In silico identification of IL-2 inhibitors

In an attempt to identify novel IL-2 inhibitors, structure-based pharmacophore designing and

virtual screening was conducted. A diverse group of the compounds that passed the VS steps

was selected for optimization and retested by 3D pharmacophore mapping and molecular

docking. The optimized VS hits were subjected to synthesis. All synthesized hits were tested

for IL-2 inhibitory activity. MOE software has been used for carrying out all computational

studies.

2.1.1. Target identification

The first step of any drug designing project is the target identification. The target was selected

on the basis of the criteria:

1) The complex should be of human origin.

2) The target should have high quality of good resolution (<3.0° A).

3) The protein-protein or protein-ligands are interacted with each other through non-

covalent interactions and the experimental binding data should be available.

X-ray structure of the drug target i.e. IL-2/IL-2Rα complex (1Z92) was obtained from Protein

Data Bank. The structure of complex is given in Figure 2.2. It’s a dimer with two chains (A &

B) and has a resolution 2.8 °A. Chain A represents IL-2 (red) while chain B is IL-2Rα (green).

Figure 2.2. 3D Structure of protein-protein complex, 1L-2 (red), IL-2Rα (green ribbon)

IL-2

IL-2α


46

2.1.2. Determination of active site

The active site of 1Z92 was determined through ‘Site Finder’ tool of MOE. It created α-spheres

at the active site. The site having key contributing amino acids was selected as the active site

which was usually larger in size. The key contributing amino acid residues in the active site were

identified by literature33,206. These amino acids were Lys35, Arg38, Met39, Thr41, Phe42,

Phe44, Tyr45, Glu62, Lys65, Pro65, Glu68, Val69, Aln71, Leu72, Ala73, Thr111 and Thr113.

The molecular surface of the binding pocket was visualized by calculating molecular surface and

volume through the ‘surfaces and maps’ option present in MOE as shown in Figure 2.3. After

identification of the active site of 1Z92 the next step was the generation of a training set for

pharmacophore modeling.

Figure 2.3. A close-up view of the binding pocket showing

hydrophobic (green) hydrophilic (magenta)

2.1.3. Designing a training set

For pharmacophore generation a training set of the compounds was built, which contains the

standard drugs as well as drugs present in complex structure of IL-2 complexes. These

compounds were selected from literature41, 44, 210-212 and all were structurally diverse and were

known to strongly and selectively inhibit IL-2. All these compounds have IC50 values from 0.06-

80μg/ml. Firstly, all these structures were built using MOE Builder then hydrogen atoms were


47

added and energy minimized by using MOE. The minimized structures of all these compounds

were added to the training database. The chemical structures and experimental IC50 values of the

training set are given in Table 2.1.

Table 2.1: Structures and IC50 of IL-2 inhibitors used in training set

No. Code Structure IC50

S-1 CHEMBL104594

H2N

H2N NO

NH N

O ClCl

O OH

O

NN 0.2

S-2 CHEMBL107320N

NH

O N

O

NH2

NH2

NNClCl

2

S-3 CHEMBL108123

H2N

H2N NO

NH N

O ClCl

O OH

O

NN0.4

S-4 CHEMBL421412

H2N

H2N NO

NH N

O ClCl

O

NN 3

S-5 CHEBI:47417(SP-4206) H2N

H2N NO

NH N

O ClCl

O

NN

OO

OH

0.06

S-6 CHEMBL106951H2N

H2N NO

NH N

O ClCl

O

NN

O

OH

0.6

S-7 CHEMBL317898

C COO

NH

O HN

O

N NH2

NH29

Continued


48

S-9 CHEMBL181983 ClCl

NO

HN

ON

NH2N

NH2N N

20

S-10 CHEMBL104746 ClCl

N NN

O

NH NNH2

NH2O

20

S-11 CHEMBL104914H2N

NH2

NO

NH

N

OO

ClCl

O

O

NN40

S-12 CHEMBL182704

SN

O

Cl Cl

O OH

ONH

O

ONN

N

NHH2N

0.6

S-13 CHEMBL182098 NH2

NHN

SNO

OHNN NCl

Cl48

S-14 CHEMBL350354OHF

F

NHO

F

FF

N

OH

F

FF

6.5

S-15 CHEMBL164641HO

N

HO F

N

F

OH

ClCl0.8

S-16 CHEMBL166149

N

Cl

N

OHF

N

F

N

Cl

2

Continued


49

S-17 CHEMBL103894NH

ON

NN

OF

F

FF

0.26

S-18 CHEMBL100390NN

O

NH

OF

F

FF

F

0.37

S-19 CHEMBL101202

NN

F

FF

NH

OF

N0.30

S-20 CHEMBL167745HO

NN

OO

NN

ClCl0.9

S-21 CHEMBL350780F

OH

N N

ClClOH OH 0.6

S-22 CHEMBL164498O

F

N NN N

ClCl

FHO 0.8

S-23 CHEMBL419362C C

OO

NH

O N NH2

NH

3.0

S-24 CHEMBL106262N

OHN O

N

NH2H2N

NNCl40

Continued


50

S-25 CHEMBL106518 NO

HN

O

N

NH2H2N

NNCl

Cl

10

S-26 CHEMBL107371 CCO O

HN

ONH

O

NH2N

H2N

20

S-27 CHEMBL449094

NO

HN

ONH2N

H2N

N N ClCl

50

S-28 CHEMBL318396 CCO O

HN

ONH N

OH2N

NH2 80

S-29 CHEMBL107739

O

HN

HN

N

O

NH

O

N NH2

NH2Cl

30

S-30 CHEMBL107685

NH2N

H2N

NH

O

N

O

NHN

ClO

50

2.1.4. Generation of structure based pharmacophore model

The pharmacophore model was generated by the Pharmacophore Generating Program of

MOE. In MOE, additional features were developed using the MOE Pharmacophore Query

Editor. Several possible hypotheses were generated by considering key amino acids of IL-2Rα.

After testing 60-70 hypotheses, three amino acids were identified as pharmacophoric features

for IL-2 inhibition. One cationic center F2 (Cat.) and two hydrophobic groups F1 & F3 (Hyd)

were concluded as key features for inhibitors.


51

Besides, the program automatically generated three excluded volumes in the model. The

cationic feature (F2) was located on side chain amines of arginine (Arg36B). The hydrophobic

features F1 and F3 were located on imidazole group of His120B and the methyl group of

leucine (Leu2B) respectively. F1 and F3 were depicted by yellow spheres and F2 by blue

sphere in Figure 2.4.

Figure 2.4. 3D view of structure based pharmacophore in the active site of 1Z92.

Blue sphere indicates cationic feature, Yellow spheres indicate hydrophobic features.

Excluded volumes are shown as white meshed spheres.

Prior to screening, it was necessary to make a number of adjustments in term of radii and

distances. The radii of features were optimized for best hit search213. F1 & F3 had radii 2.2 °A

and 1.8°A respectively while cationic feature F3 had a radius of 2.1°A as shown in Figure 2.5.

All possible distances among these features are shown in Table 2.2. The pharmacophore model

for IL-2 inhibitors was of triangle shape, the following angles of three features were calculated

as depicted in Figure 2.5.Table 2.2: Distances of pharmacophoric features

No. Feature type Distances (A°) Feature type Angle

1 F1-F2 12.07 ∆F1.F2.F3 21.9°

2 F1-F3 5.96 ∆F2.F3.F1 43.7°

3 F2-F3 16.97 ∆F3.F1.F2 114.4°


52

Figure 2.5. 3D view of structure based pharmacophore model for 1Z92, showing

a) Distances b) Angles in the active.

2.1.5. Validation of pharmacophore model

For any conformation of compound to bind with the active site of target it should reasonably

match the pharmacophoric features of the active site. In addition the conformation of the

compound should never overlap with the excluded volume around the active site of target

protein. To find out such conformations, the training set was thoroughly searched by using

pharmacophore search through the in built option present in “Pharmacophore Query Editor”

module of MOE and for this purpose a validation database of 30 compounds was screened after

virtual screening. All these compounds correctly mapped by the modified pharmacophore model

as shown in Figure 2.6. The RMSD values of these hits were in the acceptable range of 0.05 to

2.73. The results verified the validity of modified pharmacophore model that can be used for the

screening of large databases.

The above validated pharmacophore was further used as a search query in virtual screening to

screen all those compounds which exhibited the same pharmacophoric features. In the current

study this virtual database was built by selecting compounds from ZINC & MOE databases.


53

Figure 2.6.The superimposition of 3 standards docked in binding site of 1Z92.

2.1.6. Pharmacophore based virtual screening

The modified validated pharmacophore model was then used as in silico filter to screen the

ZINC and MOE databases of commercially available compounds. ZINC database contains a total

of 1,594,931 compounds out of which 1.5 million compounds passed the filtration criteria of

Li p i n s k i ’ s R O 5 a n d 15,000 compounds were randomly selected f o r virtual screening i n

t h e n e x t s t e p . Enrichment studies were performed using 30 compounds including quite a

few standard drugs selected from literature with IC50 ranging from 0.06-80μg/ml against human

IL-2. These active compounds were seeded into 1 5 , 0 0 0 decoy molecules selected from

ZINC database. The resulting data set of 15,030 compounds was imported into MOE database

containing 10,000 compounds leading to a new set of database containing 25, 030 compounds

having a variety of chemical scaffolds. Geometry optimization was performed using protonate

3D command in MOE. The output files of compounds of both databases were loaded into MOE

environment in SDF format where the 3D structure of each compound was modeled using

MMFF94x force field. The Conformation Import methodology was applied to generate low-

energy conformations for each compound. All these compounds and their respective

conformations were saved in MOE database. The new and extended database was exhaustively

searched through the Search option present in the ‘Pharmacophore Query Editor’ of MOE and all

those conformations which matched the pharmacophoric features were selected as hits in the


54

output database. The output database was sorted on the basis of RMSD in ascending order and

only those conformations having RMSD up to 2.0 were retained and those having RMSD above

2.0 were removed from the database. The next step i.e. pharmacophore-based virtual screening,

reduced the number of compounds from 25, 030 to 500 compounds that mapped on the

developed pharmacophore model. The output database of the resulting 500 compounds was

sorted out on the basis of RMSD in ascending order. These initially identified hits were selected

for further evaluation using docking studies. To reduce the data of identified hits, they were

docked into the identified binding pocket of IL-2/IL-2Rα complex.

2.1.7. High throughput docking

To prioritize the compounds for synthesis and biological testing, they were subjected to high-

throughput docking using MOE. These initially identified 500 compounds from the output file

were selected for further evaluation using molecular docking. The top ranked docked poses

were re-scored and binding energies were calculated using LIGX option implemented in MOE

to prioritize these compounds. Analysis of the docking results, on the basis of the scoring

functions and visual inspection of docked poses of top 5% compounds led to identification of

15 hits (H1-H15) for optimization. The chemical structures of these 15 hits are shown in

Figure 2.7.

These hits included the compounds such as chalcone (H-11), dihydropyrimidine (H-5),

sulfonamide (H-13), Schiff base (H-14), benzamides (H-15). Moreover a variety of

azaheterocycles were also identified as hits for example benzimidazole (H-3), benzodiazepine

(H-8) and pyrazoles (H-10).

The next step was the synthesis of these hits in wet lab from readily available precursors by the

methods reported in literature. Different analogs (1-46) of these hits identified in dry lab were

synthesized in wet lab.


55

2.1.8. Binding mode analysis of novel IL-2 inhibitors

Using the previously presented VS strategy, forty six novel and potent IL-2 inhibitors were

identified (Table 2.3) to explore the binding behavior of compounds (1-46) with amino acid

residues of the binding pocket of 1Z92. All compounds of the library were successfully

docked in the active site of 1Z92 as shown in Figure 2.8.


56

SHN

N NHNNNN

O O

HN

NNH2

NHO

O

O

N SHN

NHNH2

NN

O

ON N

HN

H2N

O

ClCl

O

NN CH2N

SNHN

NO2

HO

NHNS

H2N

Cl

ON N

Cl

HN

ON N

NN O

O NHO

NHC

N CH

S N NO

O

O

O

NH

NN

O2N

OHO NO2

HNN N

N NHO

Me

Me

NH

N NO

OH2N

(H-1) (H-2) (H-3)

(H-4) (H-6)(H-5)

(H-7)(H-8)

(H-12)

(H-9)

(H-10)(H-11)

(H-13) (H-14) (H-15)

Figure 2.7. Chemical structures of top hits retrieved from PBVS.

Table 2.3: Structure of 46 novel and potent IL-2 inhibitors

No. Structure Code Structure1 2

3 4

5 6

Continued


57

7 8

NHN

S

HO NO2

9

NHN

S

HOOH

10

NHN

S

H2N

Cl

11 12

NHN

O

HOOH

13

NHN

O

H2N

Cl

14

NHN

H2NCl

15

NHN

NO2

HO

16

17 18

19 20

Continued


58

21

NNHO

HO

CH

O

22

NN CH

O

H2N

Cl23 NHN

Br

24

NN CH3C

O

25

NN CH3C

O

Br

26

NN CH2N

S

27 28

29

NH

N30

NH

N O NN

Cl

31

NH

NN

O2N

32

NH

NN

33 34

Continued


59

35

SN NO

O

O

O

36

37

O

O

NNHO

HO

38

O

O

N N FF

39

NO2N

HO 40

NHC

NCH

41

NN

CH

Cl

Cl

HC

Cl

Cl

42

NN

NN O

O NO2N

43 44

NH

O

45 46

HN NH

O

O


60

Some of the significant binding interactions such as hydrogen bonding, π-π interactions and

hydrophobic interactions between different ligand-receptor complexes were observed. Amino

acid residues Ile28, Leu36, Val69 and Phe42 were found having hydrophobic interactions.

Hydrogen bonding interactions were observed with amino acid residues Phe42, Glu62, Pro63,

Val69, Asn71 and Leu72. In addition to both these interactions strong π-π interactions were

also observed with Phe42 and Tyr43 in close vicinity of docked complexes as shown in Table

2.4.

Figure 2.8. Superimposed view of pharmacophore based docked view of some compounds 9

(dihydropyrimidine), 22 (pyrazole) & 45 (amide) in the active site of 1Z92.

All interacting amino acids are shown in red color.

The binding mode of chalcone is shown in Figure 2.9. The ligand-receptor complex was

stabilized by hydrogen bonding and π-π interactions. A hydrogen bond interaction was observed

between the hydroxyl hydrogen of chalcone and amino acid residue Leu72 at a distance of 2.94

Å. The complex was further stabilized by the π-π interaction between phenyl rings of compound

with Phe42 as shown in Figure 2.10.

The details of the binding modes of chalcones (1-7) are shown in Table 2.4. The strongest

complex was formed between chalcone 7 and IL-2/IL-2R as evidence from its lowest dG value (-

10.30 kcal/mol).


61

Table 2.4: Binding interactions observed in chalcones (1-7) with 1Z92.

No.H-bonding interaction π-π interaction Hydrophobic

interactionLondon dGkcal/mol

Distance(Å) Amino acid Amino acid Amino acid1 - - Tyr43 Leu72 -7.6132 2.92 Phe42 Phe42 Phe42,Met39 -8.7663 3.08 Leu72 Phe42 Leu72 -8.9364 2.71 Tyr43 Phe42 Ile28 -7.4365 2.94 Leu72 Phe42 Val69 -6.6626 1.83 Pro63 Phe42 Leu72,Val69 -9.4867 2.81 Tyr43 Phe42 Leu72,Val69 -10.308

Figure 2.9. The binding mode analysis of chalcone 5

Hydrogen bonding (blue) π-π interactions (green). A) 3D and B) 2D docked view

A)

B)


62

Different azaheterocycles (8-28) (dihydropyrimidines, benzothiazepines and pyrazoles) derived

from synthesized 3 different chalcones were also docked in the active site of 1Z92. Figure 2.10

depicts the preferred docked orientation of compound 22 (pyrazole) in the binding cavity of

receptor. The key residues have been highlighted in Table 2.5. The carbonyl group of pyrazole is

involved in H-bonding with Arg38 at distance of 2.7Å. Phenyl ring was involved in π-π

interactions.

Table 2.5: Binding interactions observed in heterocycles derived from chalcones (8-28) with 1Z92.

No. H-bonding interaction π-π interaction Hydrophobicinteraction

London dGkcal/mol

Distance Å Amino acid Amino acid Amino acidDihydropyrimidines

8 2.98 Phe42 Tyr43 Leu36 -6.5739 3.07 Glu62 Phe42 Ile28 -6.654

10 3.12 Val69 Phe42 Phe42, Val69 -9.51011 2.00 Asn71 His120 Leu36 -8.86812 2.21 Phe42 Tyr43 Leu36 -8.07313 3.71 Val69 Phe42 Leu72,Val69 -6.936

Heteroazepines14 1.92 Val69 Phe42 Ile28 -8.41015 1.89 Pro63 His120 Val69 -9.70116 2.11 Glu62 Tyr43 Leu72 -6.35817 2.14 Glu62 Tyr43 Leu72, Leu36 -9.16718 3.72 Asn71 - Phe42 -7.83219 2.01 Pro63 Phe42 Phe42 -8.949

Pyrazoles20 1.99 Phe42 Tyr43 Phe42 -6.53821 1.96 Asn71 Tyr43 Leu36 -6.08822 2.7 Arg38 His120 Leu36 -9.07123 2.15 Pro63 Tyr43 Val69 -9.54724 2.36 Phe42 Phe42 Val69 -7.89925 2.00 Phe42 - Val69 -8.79926 3.05 Phe42 Phe42 Val69 -8.37027 2.61, 1.95 Tyr43,Val69 - Val69, Phe42 -8.34128 3.28 Glu62 Tyr43 Ile28 -8.711

As a generalization, chalcones show stronger binding with 1Z92 compared to the derived

dihydropyrimidines as reflected from their lower dG values (Table 2.5). Different classes of

heterocycles (dihydropyrimidines, benzimidazoles, pyrazoles) were better in interacting with

residues of the active site of 1Z92.


63

Figure 2.10. The binding mode analysis of pyrazole 22, Hydrogen bonds and π-π interactions are

displayed in green dotted lines. A) 3D and B) 2D docked view

Generally speaking, the residues around the docked compounds anthraquinone sulfonamides (29-

36) remain almost the same in all compounds studied and the residues Phe42, Glu62, Pro63,

Glu67, Val69, Asn71 and Leu72 located in the binding pocket may have catalytic role in

inhibitory mechanism (Table 2.6). It may be seen the docking pattern of compound 35

(benzimidazole) in Figure 2.11. Its ability to interact with the binding pocket through H-bonding

A)

B)


64

interactions (Val69, Glu67) and π-π Interactions (Phe42) with several residues including catalytic

triad may account for the greater stability of the ligand-receptor complex.

Table 2.6: Binding interactions observed in benzimidazoles and anthraquinone sulfonamides(29-36) with 1Z92.

No. H-bonding interaction π-π interaction Hydrophobicinteraction

London dGkcal/mol

Distance Å Amino acid Amino acid Amino acidBenzimidazoles

29 2.86 Pro63 Tyr43 Phe42 -6.25530 2.93 Glu62 Phe42 Phe42 -7.78431 1.99 Glu62 Phe42 Phe42 -9.32332 1.05 Glu67 Phe42 Ile28 -7.315

Anthraquinone sulfonamides33 2.20 Pro63 - Leu36 -9.28934 2.16 Pro63 Tyr43 Phe42 -7.32135 2.29,2.94 Val69,Tyr43 Phe42 Phe42 -8.30036 2.19 Pro63 - Leu72 -8.287

Table 2.7 shows the interactions between Schiff bases (37-41) and receptor complex which is

stabilized by strong hydrogen bonding and hydrophobic interactions. The observed binding

mode of Schiff base37reveals H-bonding interactions between the carbonyl oxygen and hydroxyl

group of Schiff bases with the hydroxyl group of Tyr43 at 2.61 Å and with their backbone

carbonyl oxygen of Val69 at 1.95 Å, respectively as shown in Figure 2.12.


65

Figure 2.11. The binding mode analysis of anthraquinone sulfonamide 35, Hydrogen bonds and

distances are displayed in blue dashed line and π-π interactions are in green color.

A) 3D and B) 2D docked view

A)

B)


66

Table 2.7: Binding interactions observed in Schiff bases (37-41) with 1Z92.

No. H-bonding interactions π-π interactions Hydrophobicinteractions

London dGkcal/mol

Distance Å Amino acid Amino acid Amino acid37 1.95, 2.61 Tyr43

Val69- Val69 -8.271

38 1.93 Phe42 Tyr43 Val69 -9.32839 2.16 Glu62 - Val69 -7.29940 2.05 Glu62 Phe42 Leu72 -7.33641 2.68 Glu62 Tyr43 Leu36 -6.321

Figure 2.12. Binding mode analysis of Schiff base 37 A) 3D B) 2D

A)

B)


67

Table 2.8 shows the binding pattern of screened amides (42-46) as observed in almost all

pharmacophore based virtually. In summary, the binding modes of these compounds reveal that

each one is forming at least one interaction with amino acid residues Phe42, Tyr43, Pro63,

Leu72, thereby stabilizing the ligand-target complexes.

Table 2.8: Binding interactions observed in pyrazoles and benzamides (42-46) with 1Z92.

No.H-bonding interaction π-π interaction Hydrophobic

interactionLondon dGkcal/mol

Distance Å Amino acid Amino acid Amino acidPyrazoles

42 1.95 Glu62 Tyr43 Leu72,Val69 -7.29443 2.19 Phe42 Tyr43 Ile28 -8.289

Amides44 3.02 Leu72 Tyr43 Val69 -8.31445 2.24 Leu72 Tyr43 Leu36 -9.29346 2.63, Pro63 Phe42 Ile28 -4..523

The drug-likeness of all the analogs of identified hits was evaluated by calculating their

properties using MOE suit, these properties include molecular weight, hydrophobicity (logP),

Hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), and number of rotatable bond

(NOR) complete with full and short form and are embedded in Lipinski's RO5 (Table 2.9)

indicates that all these compounds follow RO5 and may serve as potential drug candidates.

All these compounds comply cut off limits of 500, for mol.wt less than 500 except compound

36while logP less than 5 with few exceptions. As far as hydrogen bond donating and accepting

capability, all compounds follow the cut off limits of 5 and 10 respectively. The same is true

for NOR (less than 10) without any exception. These results are evidence that these analogs of

hits to be synthesized may serve as potential drug candidates.


68

Table 2.9: Lipinski’s RO5 compliance of the synthesized compounds (1-46)

No. M.wt logP HBA HBD NOR

Chalcones

1 208.26 3.58 1 0 3

2 226.25 3.72 1 0 3

3 242.70 4.23 1 0 3

4 287.15 4.34 1 0 3

5 269.25 3.19 2 1 4

6 240.25 2.99 3 2 3

7 257.27 3.81 1 1 3

Dihydropyrimidines

8 327.36 3.20 3 2 3

9 298.36 3.01 4 3 2

10 315.82 3.82 2 2 2

11 311.29 3.03 3 2 3

12 282.29 2.83 4 3 2

13 299.76 3.66 2 2 2

Heteroazepines

14 347.12 5.16 1 2 2

15 359.13 5.07 2 3 3

16 330.14 4.87 3 3 2

17 364.08 6.37 2 1 1

18 376.09 5.75 3 2 1

19 347.10 5.55 2 3 2

Pyrazoles

20 311.09 2.70 3 1 4

21 282.10 2.50 4 2 3

22 299.08 3.52 2 1 3

23 300.03 3.98 1 1 2

24 264.13 3.47 2 0 3

25 342.04 4.27 2 0 3

26 281.10 3.17 2 1 3

27 299.09 3.31 2 1 3

28 315.06 3.83 2 1 3

Benzimidazoles

29 278.18 4.91 2 2 4

30 460.20 5.50 5 2 8

31 282.11 3.20 2 2 3

32 237.13 3.29 2 2 2

Sulfonamides

33 507.13 4.83 5 1 4

34 552.11 4.74 5 1 5

35 548.18 6.39 5 0 6

36 730.20 6.98 8 0 10

Schiff bases

37 446.45 4.37 6 2 4

38 450.12 6.24 4 0 4

39 318.10 4.86 2 1 4

40 412.19 8.16 2 0 7

41 496.01 9.05 2 0 4

42 430.14 5.01 5 0 5

43 401.15 4.90 6 1 4

Bnzamides

44 331.19 5.78 1 1 5

45 513.22 5.95 4 1 9

46 268.12 3.27 2 2 3


69

STEP 2 (Wet lab)

2.2. Synthesis of analogs of different heterocylces identified from PBVS

Chalcone derivatives, natural or synthetic, exhibit a variety of biological activities and therefore

have been used as precursors for pharmacological activities such as anticancer agents114,115, anti-

malarial116,117, antimicrobial118,119, anti-inflammatory120, anti-protozoal121, antioxidant122. In

addition, dihydropyrimidines and its derivatives are powerful antibacterial127 and anticancer128

agents. Similarly benzimidazoles also possess several bioactivies such as antiviral155,

anthelmintic156, HIV inhibitors157, antihypertensive158, anti-inflammatory159, analgesic160. Some

pyrazole derivatives have been shown to possess antitumor 188, antitubercular189,

anticonvulsant190,191, antihepatotoxic192, anti-inflammatory193, antimicrobial194, antibacterial195,

antioxidant196 etc. In continuation of ongoing chemistry research and in silico studies on these

classes of compounds, some optimized hits were synthesized for IL-2 inhibition studies.

2.2.1. Synthesis of chalcones (1-7)

One of the important hits identified in dry lab is an interesting scaffold i.e chalcone (H-11)

which, alongwith 6 analogs was synthesized.

OHO NO2

H-11

The synthesis of chalcones (1-7) was carried out by simply adding an ice cold solution of 4 M

NaOH in water into the mixture of benzaldehyde and acetophenone214. Then the mixture was

stirred at room temperature and the crude product was then purified and recrystallized with

ethanol (Scheme 2.1). The physical data of all synthesized chalcone derivatives (1-7) is given in

Table 2.9.

CH3

O

H

OO

NaOH+R1 R2

R2

(1-7)

R1

No. R1 R2 No. R1 R2

1 H H 5 3-OH 3-NO2

2 H 4-F 6 3-OH 2-OH3 H 4-Cl 7 4-NH2 3-Cl4 H 4-Br


70

Scheme 2.1: Synthesis of chalcones (1-7)

2.2.1.1. Characterization

FTIR spectral data of the chalcones (1-7) exhibited the characteristic C=O absorption at 1714-

1695 cm-1. All of the synthesized compounds were characterized by 1H NMR, 13C NMR, and

they gave satisfactory analytical and spectroscopic data. Physical data of chalcones is given in

Table 2.10.

Table 2.10: Physical data of synthesized chalcones (1-7)

No. m.p(°C) Yield

(%)

Rf* No. m.p(°C) Yield(%)

Rf*

1 71215 71 0.60 5 7564 75 0.65

2 76215 76 0.71 6 6164 61 0.68

3 69215 69 0.62 7 64214 64 0.59

4 73214 73 0.53

*Solvent system. Ethyl acetate: Pet ether (1:4)

1H NMR spectra showed the characteristic signal appearing in the range of δ 7.41-7.96 ppm

assigned as doublet for CH protons with coupling constant of J=7.0 Hz. In case of compounds 5

and 6, a characteristic singlet for one proton of OH appeared at δ 9.12 ppm and 9.23 ppm

respectively, while in case of compound 7, a singlet for two protons at δ 4.82 ppm has been

assigned to NH2. In 13C NMR spectra, a signal for carbonyl carbon appeared in the range of δ

172.40-188.30 ppm and a signal for CH carbon was observed ranging from δ 135.20-147.07 ppm

in different chalcones.

2.2.2. Synthesis of dihydropyrimidines (8-13)

Dihydropyrimidines (H-10) was also found to be important hit in dry lab and some of its

derivatives were synthesized in wet lab.

NHN

S

H2N

Cl

H-10

The synthesis of dihydropyrimidine derivatives (8-13) was achieved by reacting the synthesized

chalcones (5-7) with urea or thiourea in the presence of NaOH and ethanol (Biginelli


71

reaction)216. The reaction was monitored by TLC and product was purified and recrystallized

with ethanol (Scheme 2.2).

NH2

XH2N

X=S,O

NaOH, EtOH

NHN

X

X=S (8-10)

R1 R2

O

R2

(5-7)

R1

X=O (11-13)

No. R1 R2 X No. R1 R2 X

8 3-OH 3-NO2 S 11 3-OH 3-NO2 O

9 3-OH 2-OH S 12 3-OH 2-OH O

10 4-NH2 3-Cl S 13 4-NH2 3-Cl O

Scheme 2.2: Synthesis of dihydropyrimidines (8-13)


All the synthesized dihydropyrimidine derivatives (8-13) were obtained in good yield in the

range of 56-79 % and they were characterized by their physical constants and spectroscopic data.

Melting points of all compounds were recorded and found in the range of 148-161 °C.1H NMR (DMSO-d6) showed characteristic broad signal for one proton of NH as singlet ranging

from δ 8.42-10.85 ppm and another signal m appearing at δ 1.21-2.82 ppm was assigned to CH2

group. The dd for one proton of CH group appeared in the range of δ 3.71-3.96 ppm. In13C

NMR, the most deshielded carbon appeared at δ 184.94-185.83 ppm, which was assigned to

carbon of C=S group and the deshielded carbon of C=O was observed at δ 166.98-168.48 ppm

(Annexure 1 for 1H and 13CNMR spectra of compound 10). GCMS spectra of

dihydropyrimidines (8-13) were recorded. Molecular ion peak was observed in all the

compounds, but this peak did not correspond to the base peak. In compounds containing a Cl or

Br atom, in addition to M+• ion peak, M+•+2 peak was observed due to isotopic natural abundance

of 37Cl and 81Br (Annexure 2 for GCMS of compound 12). Physiochemical and spectral data of

all synthesized dihydropyrimidine derivatives (8-13) is given in Table 2.11.


72

Table 2.11: Yield, m.p and spectral data of synthesized dihydropyrimidine derivatives (8-13)

No. Yield(%)

m.p(°C)

1H NMR 13C NMR m/z(M+.)

δ(ppm)

Integration Multiplicity J(Hz)

Assignment CH CH2 C=N C=S

8 61 157-8 1.91 2H m - -CH2 54.5 45.3 165.2 185.4 327- - 3.96 1H dd 11.9,

5.2-CH - - - - -

8.42 1H s - -NH - - - - -9.22 1H s - -OH - - - - -

9 66 150-1217 - - - - - - - - - 29910 69 163-5 2.21 2H m - -CH2 41.5 40.8 166.4 315

3.77 1H dd 12.3,5.4

-CH - - - - -

5.53 2H s - -NH2 - - - - -9.18 1H s - -NH - - - - -

11 76 148-50217 - - - - - - - - - 31112 74 136-8217 - - - - - - - - - 28213 67 150-3217 - - - - - - - - - 299

2.2.3. Synthesis of benzoheteroazepines (14-19)

During pharmacophore identification of hits, some 7-member benzoazepine (H-8) was also

identified and its derivatives benzodiazepines (14-16) and benzothiazepines (17-19) were

synthesized by reacting the chalcones (1-7) with o-phenylenediamine and 2-aminobenzene-thiol

respectively in THF81.

NHN

NO2

HO

H-8

The resulted filtrate was concentrated under reduced pressure the crude product was

recrystallized with MeOH leading to azaheteroazepine derivatives (Scheme 2.3).

N X

R1

NH2

X

MeOH/H

X=NH2 (14-16) Benzodiazepines

O

R2

(5-7)

R1R2

X=SH (17-19) Benzothiazepines


73

No. R1 R2 X No. R1 R2 X14 4-NH2 3-Cl NH 17 4-NH2 3-Cl S15 3-OH 3-NO2 NH 18 3-OH 3-NO2 S16 3-OH 2-OH NH 19 3-OH 2-OH S

Scheme 2.3: Synthesis of heteroazepines (14-19)


Characterization of synthesized heteroazepines (14-19) was carried out by FTIR spectra with

appearance of C=N stretching band at 1538 cm-1 and disappearance of carbonyl absorption

bands. The characteristic strong absorption of the NH group appeared at 3412-3428 cm-1.In 1H

NMR spectra, a singlet peak at δ8.18-8.92 ppm for NH proton in case of compounds (14-16). A

dd for CH proton appeared at δ 3.44-3.88 ppm and the aromatic protons appeared in the range of

δ 6.46-8.48 ppm. In 13C NMR the characteristic peak for CH and CH2 carbon appeared at δ 40.9-

62.6ppm and imino carbon (C=N) in range of δ 168.70-187.77 ppm. LC-MS analysis of

heteroazepines (14-19) was performed. All the synthesized compounds (14-19) were

characterized by molecular ion peak (M-H)- (Annexure 3 for LCMS of compound 14 and

Annexure 4 for 1H and 13CNMR spectra of compound 15).. The physiochemical data of all

synthesized 2,3-dihydrobenzoazepines (14-19) is given in Table 2.12.

Table 2.12: Yield, m.p and spectral data of synthesized heteroazepines (14-19)

No. Yield(%)

m.p(°C) 1H NMR 13C NMR m/z(M-H)-

δ(ppm)


Assignment CH CH2 C=N

14 59 97-98 2.49 2H m - -CH2 62.6 40.9 168.6 3463.88 1H dd 11.2,

5.4-CH

5.49 2H s - -NH2

9.80 1H s - -NH15 83 89-92 3.18 2H m - -CH2 59.8 42.0 187.0 358

3.85 1H dd 12.0,6.0

-CH

8.18 1H s - -NH9.47 1H s - -OH

16 76 98-100216 - - - - - - - - 33017 79 84-86 2.92 2H m - -CH2 58.7 40.7 169.1 329

3.44 1H t 7.2 -CH5.45 2H s - -NH2

18 75 96-98 3.40 2H m - -CH2 58.8 41.7 169.0 3633.86 1H t 8.6 -CH9.70 1H s - -OH

19 68 99-101216 - - - - - - - - 346


74

2.2.4. Synthesis of pyrazole carbaldehydes (20-25)

Pyrazole carbaldehyde (H-7) was one of the hit identified in dry lab. Some of its derivatives

were synthesized by reacting the corresponding chalcones (5-7) with formohydrazide while

compound (23) was synthesized by using chalcone (4) and hydrazine hydrate218.

NN CH2N

S

H-7

The pyrazole ethanone derivatives (24-25) were synthesized by reacting the chalcones (1) and

(4) with acetohydrazide at reflux temperature in ethanol (Scheme 2.4).

R3COOH

NH2-NH2

R3=H (20-22)R3CHO R1

NN

R2

R3

O

R3=CH3 (24-25)

NH2-NH2 (23)R1

NHN

R2

R1

NN

R2

R3

O

O

R2

NH2-NH2

R1

No. R1 R2 R3 No. R1 R2 R3

20 3-OH 3-NO2 H 23 H 4-Br -21 3-OH 2-OH H 24 H H CH3

22 4-NH2 3-Cl H 25 H 4-Br CH3

Scheme 2.4: Synthesis of pyrazole derivatives (20-25)


All the synthesized pyrazole derivatives (20-25) were characterized by their 1H NMR and 13C

NMR data. The percentage yield of these compounds was generally good ranging from 59-72 %.

The 1H NMR spectra of compounds (20-25) displayed the characteristic signal for CH proton as

dd appearing at δ 3.88-5.54 ppm (J=12.9 and 3.3 ppm) and in case of compounds 24 and 25, a


75

singlet for three protons of CH3 group appeared at δ2.31 and 2.30 ppm respectively. In 13C NMR

a characteristic peak for carbonyl carbon appeared in range of δ 165.72-167.87 ppm and a peak

for imine carbon (C=N) at δ 150.98-154.14 ppm besides the signals for aromatic protons

(Annexure 5 for 1H and 13CNMR spectra of compound 25). In the GCMS and LCMS spectra of

synthesized compounds (20-25), the molecular ion peak was observed in almost all cases

although its intensity was low. The loss of aldehydic and ketonic fragments was a common

observation. M+2 was observed in all chloro- and bromo-substituted compounds as a result of

natural isotopic abundance (Annexure 6 for LCMS of compound 25). The physiochemical and

spectral data of all synthesized pyrazole derivatives (20-25) is given in Table 2.13.

Table 2.13: Yield, m.p and spectral data of synthesized pyrazole derivatives (20-25)

No. Yield(%)

m.p(°C)


δ(ppm)


Assignment CH CH2 C=N C=O

20 66 112-4 1.95 2H m - -CH2 62.6 42.7 155.3 163.2 312- 3.92 1H dd 12.9,

5.7-CH

5.94 1H s - OH8.08 1H s - -CHO

21 65 106-8 2.14 2H m -CH24.07 1H dd 12.3,

6.3-CH 41.1 28.2 150.3 205.3 283

4.86 2H S - -2OH8.93 1H s - -CHO

22 69 109-11 2.01 2H m - -CH2 62.7 42.6 155.3 176.5 3003.88 1H dd 11.4,

5.1-CH

5.49 2H s - -NH28.95 1H s - -CHO

23 72 98-100 3.16 IH dd 18.0,3.3

H-3a 63.3 42.8 176.5 - 302

3.92 IH dd 18.0,11.4

H-3b

5.94 IH dd 3.3,11.4

H-2

8.05 IH S - -NH24 70 102-3218 - - - - - - - - -25 69 106-8 2.30 - s - CH3 42.3 59.3 154.6 167.9 342

3.17 - dd 18.3,4.8

H-3a

3.87 - dd 18.0,12.0

H-3b

5.54 - dd 11.7,4.5

H-2

8.95 1H s - -CHO


76

2.2.5. Synthesis of pyrazole carbothioamides (26-28)

Pyrazole carbothioamides (26-28) were synthesized by reacting the corresponding chalcones

with thiosemicarbazide and refluxing the mixture in ethanol218 as shown in Scheme 2.5. The

product was then purified and recrystallized.

NN

S

NH2

NaOH, EtOH

NH2

SHN

H2N

(26-28)R2R1

O

R2

(1-3)

R1

No. R1 R2

26 H H

27 H 4-F

28 H 4-Cl

Scheme 2.5: Synthesis of pyrazole carbothioamides (26-28)


The synthesized pyrazole carbothioamides (26-28) were obtained in good yield ranging from 68-

72 %. 1H NMR spectra of compounds (26-28) showed the characteristic peak for CH proton as

dd appeared at δ 5.93 ppm. In13C NMR a characteristic peak for thionyl carbon appeared in

range of δ 176.58-178.18 ppm and a peak for imine carbon (C=N) at δ 154.02-155.85 ppm

(Annexure 7 for 1H and 13CNMR spectra of compound 28). LC-MS analysis of synthesized

compounds (26-28) was also performed. For all compounds [M+H]+ peak was observed. The

physiochemical and spectral data of all synthesized pyrazole carbothioamides (26-28) is given in

Table 2.14.


77

Table 2.14: Yield, m.p and spectral data of synthesized pyrazolecarbothioamides (26-28)

No. Yield(%)

m.p(°C)

1H NMR 13C NMR m/z(M+H)+

δ(ppm)


Assignment CH CH2 C=N C=S

26 71 183-5219 - - - - - - - - - 28127 68 261-5219 - - - - - - - - - 29928 70 183-9 3.18 1H dd 18.3,

3.6H-3a 62.7 42.6 155.3 176.5 315

3.91 1H dd 18.3,11.7

H-3b

5.93 1H dd 3.6,11.7

H-2

8.10 2H s - -NH2


2.2.6. Synthesis of benzimidazoles (29-32)

The benzimidazole (H-3) was one of the important hits found in dry lab.

NH

NN

O2N

H-3

Some benzimidazole derivatives (29-32) were synthesized according to literature procedure219 by

reacting an aqueous solution of sodium metabisulfite, different substituted benzoic acid or

aldehyde at 0-4 °C. In the next step o-phenylenediamine was added. The mixture was heated at

110 °C in DMF resulted in the formation of benzimidazoles Scheme 2.6).

NH2

NH2R2

O

HNH

NR2

R1

DMFNa2S2O5R2

O

HOor

(29-32)

R1

No. R1 R2 No. R1 R2

29 H 4-N,N-dimethylphenyl- 31 HO

OH

30 3-NO2 4-N,N-dimethylphenyl- 32 H

O

OHO

NN

Ph

Cl

Scheme 2.6: Synthesis of benzimidazoles (29-32)


78

The ability of carboxylic group in two commercially available drugs namely ibuprofen and

cetirizine were also exploited for their conversion to their corresponding amides 31 and 32.

Ibuprofen is a NSAID while cetirizine is anti-allergic drug. These drugs were used with an

expected new indication as immunomodulating activity.


In spectral analysis of compounds (29-32), 1H NMR showed characteristic broad signal for one

proton of NH as singlet at δ 11.09-11.36 ppm. 13C NMR spectral data showed the characteristic

peak of carbon (N-C-N) at δ 146.38-154.34 ppm. The physical data of all synthesized

benzimidazole (29-32) is given in Table 2.15. Please see the Annexure 8 for GCMS of

compound 31.

Table 2.15: Yield, m.p and spectral data of anthraquinone sulfonamide derivatives (29-32)

No. Yield(%)

m.p(°C)


δ(ppm)



29 70 331-3 1.28 6H d 11.7 2CH3 39.1 40.8 150.0 180.9 2781.57 3H d 8.7 -CH3

2.18 1H m - -CH3.88 1H q 8.1 -CH

30 62 183-4 2.07 4H m - -C2H4 - - 144.3 181.5 4602.33 4H m - C2H4

2.56 2H m - -CH2

4.01 2H s - -OCH2

31 69 109-11 2.67 6H m - 2CH3 20.0 - 152.3 180.9 282

7.0-8 14H m - Ar-H

32 68 278-80222 - - - - - - - - - 238

2.2.7. Synthesis of anthraquinone sulfonamide derivatives (33-36)

One important hit found in dry lab was (H-13) and its derivatives were synthesized by reacting

benzimidazoles (29-32) with freshly prepared anthraquinonesulfonyl chloride yielded their

corresponding anthraquinone sulfonamide derivatives (33-36) as shown in Scheme 2.7220.

SN NO

O

O

O

H-13


79

O

O

SCl

O

O

O

O

SOH

O

O

NH

NR2

SN N

R2

O

O

O

O

CH2Cl2HSO3Cl

(29-32)(33-36)

R1

R1

No. R1 R2 No. R1 R2

33 H 4-N,N-dimethylphenyl- 35 H O

OH

34 3-NO2 4-N,N-dimethylphenyl- 36 H

O

OHO

NN

Ph

Cl

Scheme 2.7: Synthesis of anthraquinone sulfonamide derivatives (33-36)


The anthraquinone sulfonamide derivatives (33-36) were synthesized in good yield in the range

of 60-78% and they were characterized by physical constants and spectral data. Melting points of

all these compounds were recorded and found in the range of 251-297°C. 1H and 13CNMR

spectra of synthesized compounds (33-36) were recorded and the data is given in Table 2.16

(Annexure 9 for 1H and 13CNMR spectra of compound 36). LCMS analysis of anthraquinone

sulfonamide derivatives (33-36) was performed. All the synthesized compounds (33-36) were

characterized by molecular ion peak (M+H)+ while base peaks were same for all compounds.

The physiochemical and spectral data of all synthesized anthraquinone sulfonamides (33-36) is

given in Table 2.16.


80

Table 2.16: Yield, m.p and spectral data of anthraquinone sulfonamide derivatives (33-36)

No. Yield(%)

m.p(°C)


δ(ppm)



33 76 281-2224 - - - - - - - - - 50834 78 297-8 2.67 6H m - 2CH3 20.0 22.2 152.3 180.9 553

7.0-8 14H m - Ar-H35 74 289-91 1.28 6H d 11.7 2CH3 39.1 40.8 150.0 180.9 549

1.57 3H d 8.7 -CH3

2.18 1H m - -CH3.88 1H q 8.1 -CH

36 64 273-5 2.07 4H m - -C2H4 - - 144.3 181.5 7312.33 4H m - C2H4

2.56 2H m - -CH2

4.01 2H s - -OCH2

5.25 1H s - -CH

2.2.8. Synthesis of Schiff base derivatives (37-41)

Schiff base (H-9) was found one of the important hits and its some derivatives were synthesized

in wet lab.

NHC

NCH

H-9

The reaction of aromatic amines with substituted benzaldehydes in the presence of acetic acid

and ethanol afforded Schiff base derivatives221 (37-41) as shown in Scheme 2.8.

O

O

N

N

Acetic acid

Ethanol

NH2H2N

or

NN

R

R

O

O

NH2

NH2

or

CHOR

R

R

NH2N R

(37-38)

(39)

(40-41)


81

No. R No. R37 3-OH-C6H4 40 3-Phenylacryl38 4-F-C6H4 41 2,4-diClC6H3

39 3-OH, 5-NO2-C6H3

Scheme 2.8: Synthesis of Schiff base derivatives (37-41)

2.2.8.1. Characterization1H NMR showed the singlet for CH proton that appeared in the range of δ 8.26-8.36 ppm. All

aryl protons appeared in the range of δ 6.34-7.81 ppm. In case of compounds 37 and 39, the

signal for one proton of OH appeared at δ 9.40 and 9.02 pm respectively. In 13C NMR the signal

for carbonyl carbon was observed at δ 175.12-178.52 ppm and a signal for imines carbon (C=N)

was observed at δ 162.89-167.12 ppm. LC-MS analysis of synthesized compounds (37-41) was

also performed. For all compounds [M+H]+ peak was observed which was also the base peak,

data is presented in Table 2.17. LCMS of synthesized compound 39 is presented in Annexure

10. The physiochemical and spectral data of all synthesized Schiff base derivatives (37-41) is

given in Table 2.17.

Table 2.17: Yield, physiochemical and spectral data of synthesized Schiff base derivatives (37-41)

No. Yield(%)

m.p (°C) 1H NMR 13C NMR m/z(M+H)+

δ(ppm)


Assignment CH CH C=N

37 58 200-1226 - - - - - - - - 447

38 67 214-6226 - - - - - - - - 451

39 57 183-4 3.89 1H d 12.8 -CH 119.9 138.2 163.8 3184.73 1H m - -CH6.12 1H d 12.8 -NCH

40 61 203-5 5.73 1H t 11.7 -CH 113.2 119.0 163.7 4135.90 1H d 12.6 -CH

41 67 204-5226 - - - - - - - - 499

2.2.9. Synthesis of pyrazole derivatives (42-43)

Pyrazole (H-10) was also an important hit found in dry lab and its derivatives were synthesized

in wet lab.

H-10

NN

NN O

O NHO


82

The reaction of different amines with acetylacetone in the presence of sodium nitrite and acid

yielded their corresponding intermediates, which were then reacted with separately synthesized

8-quinolinoxyacetic acid hydrazidein presence of acetic acid to afford pyrazole derivatives222

(42-43) as given in Scheme 2.9.

R

NH2 O O

ONaNO2, CH3COONa

NN

NN

O

O N

(42-43)

NHN

COCH3

COOC2H5

R

R

H2NHN

O

O N

(A)

No. R42 3-NO2

43 4-OHScheme 2.9: Synthesis of pyrazole derivatives (42-43)


FTIR spectral analysis of pyrazoles (42-43) showed the peaks for aliphatic sp3 hybridized

carbons that appeared in the range of 2951-2969 cm-1 and 2839-2859 cm-1. 1H NMR showed the

two singlets for two CH3 groups appeared at δ 1.85-1.90 ppm and δ 2.14-2.20 ppm. All aromatic

protons appeared in the range of δ 7.42-7.97 ppm. In 13C NMR the signals for two CH3 carbons

were observed at δ 19.49-19.56 ppm and δ 21.31-21.47 ppm and a signal for carbonyl carbon

(C=O) was observed at δ 168.26-168.75 ppm (Annexure 11 for 1H and 13CNMR spectra of

compound 43). LCMS spectra were recorded for the synthesized compounds (42-43) and

characterized by molecular ion peak [M+H]+, data is given in Table 2.18. The physiochemical

data of all synthesized pyrazole derivatives (42-43) is given in Table 2.18.


83

Table 2.18: Yield, m.p and spectral data of synthesized pyrazole derivatives (42-43)

No. Yield(%)

m.p(°C)


δ(ppm)



42 78 199-200 2.51 6H s - 2CH3 19.4 70.9 150.4 168.2 4313.36 2H s - -CH2

43 77 195-6 1.85 3H s - -CH3 18.2 70.4 158.2 168.1 4023.35 3H s - -CH3 - - - - -4.04 2H s - -CH2 - - - - -9.02 1H s - -OH - - - - -


2.2.10. Synthesis of benzamides (44-46)

One of the important hit found in dry lab was (H-15) benzamide.

HNO

N NN NH

H-15

The synthesis of amide derivatives (44-46) was carried out by reacting naphthyl amine or

benzidine with carboxylic acids or a carbonyl group containing drugs ibuprofen and citerazine by

using 5-methoxy-2-iodophenylboronic acid (MIBA) as catalyst under mild conditions at ambient

temperature in the presence of molecular sieves223 (Scheme 2.10).

HN RNH2

+

O

R

NH2H2N

orNHHN

RO

OR

(46)

MIBA, rt(44-45)

No. R No. R

44O

OH46

H3C OH

O

45

ON

N

Ph

Cl

OHO

Scheme 2.10: Synthesis of benzamides (44-46)


84

A possible catalytic cycle is based on the supposed formation of an acylborate intermediate.

MIBA is used to promote direct amidations of carboxylic acids and amines in catalytic amounts

and it avoids the requirement of pre-activation of the carboxylic acid or use of coupling reagents.

The final products were recrystallized from ethanol.


FTIR spectra of benzamides (44-46) showed the appearance of absorption band at 3256 and 3139

cm-1 for free and associated NH group along with the peak at 1697 cm-1 for carbonyl carbon. 1H

NMR indicated the NH as broad singlet at 8.84-9.31 ppm, while aryl protons appeared in range

of 6.82-7.94 ppm and alkyl protons observed in range of 1.20-4.69 ppm. In 13C NMR signal for

carbonyl carbon appeared at 169.26 ppm. Aromatic carbons appeared at 113.64-151.38 ppm

while alkyl carbons appeared in range of 77.42-61.52 ppm (Annexure 12 for 1H and 13CNMR

spectra of compound 45). The physiochemical and spectral data of all synthesized amides (44-

46) is given in Table 2.19.

Table 2.19: Yield, m.p and spectral data of synthesized benzamides (44-46)

No. Yield(%)

m.p(°C)


δ(ppm)


Assignment CH CH2 O-CH2 C=O

44 81 197-8229 - - - - - - - - - 33245 79 129-30 2.07 4H m - -NC2H4 77.4 59.2 61.5 186.2 515

2.34 4H m - -NC2H42.56 2H t 9.9 -CH24.06 2H s - -OCH24.54 2H t 13.8 -CH25.12 1H s - -CH8.94 1H s - -NH

46 78 316-8230 - - - - - - - - - 269

2.3. (STEP 3) IL-2 Inhibition assay

All the hits identified in dry lab were successfully synthesized in wet lab and they were

characterized with different spectroscopic techniques. The members of the synthesized library

(1-46) were subjected to in vitro IL-2 inhibitory activity following the protocol of Manger B. et

al.224 IC50 values were calculated in μg/ml and data is reported in Table 2.9 as mean +- SD. All

the compounds showed promising IL-2 inhibition activity. Positive and negative control means

cultures were performed in parallel in the presence of cyclosporin A (+CsA) or absence of (-

CsA) using a concentration of 1mg/ml. Cyclosporin has IC50 value of 0.06 µg/mL.


85

2.3.1. Chalcones (1-7)

All compounds from this group were found potently inhibiting IL-2 production. Their IC50 values

ranges from <2 to 5.30 µg/mL (Table 2.20), except for compounds 3 and 6, which showed low

and moderate level of inhibition. Noteworthy, compound 1 was found to possess the most potent

inhibitory activity of this group with IC50<2 µg/mL.

Table 2.20: IL-2 inhibitory activities of chalcones (1-7)

Figure 2.13: Bar graphs for in vitro IL-2 inhibition assay of chalcones (1-7)

2.3.2. Compounds (Heterocyclic compounds derived from chalcones) 8-28

Among all tested compounds, of this group were found to have strong inhibition on IL-2

production (Table 2.21). Their IC50 values range from <2 to 8.9 µg/mL. Compound 11 with an

IC50 value of <2 was found to be the most potent inhibitor in this group. Compounds 14, 19,

No. IL-2 InhibitionIC50(µg/mL)

Activity

1 <2 high

2 3.31 ± 0.05 high

3 >50 low

4 5.30 ± 0.14 high

5 5.06 ± 0.5 high

6 25.05 ± 2.6 moderate

7 2.26 ± 0.01 high


86

23 and 27 showed very weak to low level of inhibition their IC50 ranges between 41.7 to >50

µg/mL. All other compounds from this group were found to possess moderate inhibition on IL-

2 production (IC50 ranges from 10.7 to 32.4 µg/mL).

Table 2.21: IL-2 inhibitory activities of azaheterocycles (8-28)

Figure 2.14: Bar graphs for in vitro IL-2 inhibition assay of dihydropyrimidines,

benzimidazoles, pyrazoles (8-28)

No. IC50(µg/mL) Activity No. IC50(µg/mL) Activity8 24.7 ± 0.8 moderate 19 >50 low9 14.79 ± 0.4 moderate 20 10.78 ± 4.2 moderate

10 19.8 ± 0.8 moderate 21 41.75 ± 1.06 moderate11 <2 high 22 3.12 ± 0.02 high12 8.95 ± 1.2 high 23 >50 low13 14.95 ± 1.63 moderate 24 20.92 ± 4.08 moderate14 >50 low 25 5.83 ± 0.1 high15 11.51 ± 2.4 moderate 26 8.36 ± 0.5 high16 5.35 ± 0.21 high 27 >50 low17 4.30 ± 0.02 high 28 32.4 ± 10.1 moderate18 5.08 ± 0.3 high


87

2.3.3. Compounds (Benzimidazoles and anthraquinone sulfonamide derivatives) 29-36

Among all tested compounds of this group were found to exert potent inhibitory activity on IL-2

and their IC50 was recorded as 6.1 and 8.9 µg/mL respectively (Table 2.22). Moderate level of

inhibition was associated with compound 32 and 36 (IC50 11.5 and 15.4 µg/mL) and the

remaining compounds of this group showed very weak inhibitory effect (IC50 >50 µg/mL).

Table 2.22: IL-2 inhibitory activities of benzimidazoles and anthraquinone sulfonamidederivatives (29-36)

Figure 2.15. Bar graphs for in vitro IL-2 inhibition assay of benzimidazoles

and anthraquinone sulfonamide derivatives (29-36)

2.3.4. Compounds (Schiff base derivatives) 37-41

In this group compound 37 and 39 (IC50 7.5 and 3.6 µg/mL) showed potent inhibitory effect on

IL-2. Remaining compounds showed very weak level of inhibition (IC50 >50 µg/mL).

No. IC50

(µg/mL)Activity No. IC50

(µg/mL)Activity

29 >50 low 33 >50 low

30 >50 low 34 >50 low

31 6.13 ± 0.11 high 35 8.98 ± 0.39 High

32 11.55 ± 1.20 moderate 36 15.45 ± 0.92 moderate


88

Table 2.23: IL-2 inhibitory activities of Schiff bases (37-41)

Figure 2.16. Bar graphs for in vitro IL-2 inhibition assay of Schiff base derivatives (37-41)

2.3.5. Compounds (Pyrazole and benzamide derivatives) 42-46

In this group pyrazoles 42 and 43, (IC50 ranges from <2 to 3.65 µg/mL) showed very strong

inhibitory effect on IL-2 (Table 2.24). Compound 44 showed moderate level of inhibition (IC50

24.7 µg/mL). Remaining compounds showed very weak inhibitory effect on IL-2 cytokine (IC50

>50 µg/mL).

Table 2.24: IL-2 inhibitory activities of optimized hitspyrazoles and benzamides (42-46)

No. IC50(µg/mL) Activity No. IC50(µg/mL) Activity

37 7.58 ± 0.86 high 40 >50 low

38 >50 low 41 >50 low

39 3.63 ± 0.3 high

No. IC50(µg/mL) Activity No. IC50(µg/mL) Activity

42 3.65 ± 0.01 high 45 >50 low

43 <2 high 46 >50 low

44 24.7 ± 0.14 moderate


89

Figure 2.17. Bar graphs for in vitro IL-2 inhibition assay of pyrazoles and benzamides (42-46)

Current study of the computer aided identification of IL-2 inhibitors involves working in a

number of rounds of experiments both in dry lab and wet lab as discussed in the following

sections.

In vitro studies showed that all synthesized compounds showed IL-2 inhibitory potential to a

larger or ion expected to be good. The IL-2 inhibition activity of all these forty six compounds

was already predicted by using 3D pharmacophore mapping and molecular docking studies.

Hence all in silico studies carried out for these compounds showed that they exhibit good

binding energies and all necessary chemical features required for the binding in the active site. It

was observed that that all the active compounds were deeply embedded in the active site of IL-

2/IL-2Rα complex and all these compounds were stabilized by multiple hydrophobic and

hydrophilic interactions.

The most promising candidates for IL-2 inhibition identified through this rational designing are

described in Figure. 2.18.


90

O

H2N

Cl

7, * IC50 = 2.26

NHN

S

HOOH

NH

NN

O2NS

N NO

O

O

O

NO2N

HO

NH

O

NN O

O NNN

HO 43, * IC50 = <2

35, *IC50 = 8.98

39, *IC50 = 3.63

44, * IC50 = 24.7

9, *IC50 = 19.8

31, *IC50 = 6.13

Figure 2.18. Designed and synthesized potential candidates for developing intoimmunomodulators.

Apart from this the calculated molecular descriptors indicated that these compounds also possess

drug like properties hence these compounds may act as good inhibitors of IL-2. An inspection of

data given in Table 2.9 reveals that all the compounds obey RO5 and their molecular descriptors

lie in the range acceptable for drug-like properties. This study represents a successful

identification of new IL-2 inhibitors following a rational drug design approach. The compounds

synthesized in wet lab (1-46) were the synthetic analogs of hits (H1-H15) identified in dry lab.

All the synthesized analogs belonging to different classes of heterocycles showed varied

potential against IL-2 as drug target and can be considered as potential candidates for developing

into novel immunomodulating agents.

Experimental

Chapter 3

CHAPTER 3: EXPERIMENTAL

91

3.1. In silico guided identification of IL-2 inhibitors (Dry lab)

3.1.1. Hardware specifications

All the computational studies were performed using Molecular Operating Environment (MOE)80

version 2012.10, Chemical Computing Group. The program operated under ‘Windows Server

2003 R2’ operating system installed on an IBM System x3400 with 2048 Ram and four Intel (R)

Xeon (TM) CPU 3.00 GHz processors.

3.1.2. Selection and preparation of protein protein complex

Special care was taken to select protein-protein complexes. To consider the flexibility of

protein, human IL-2 protein-protein structures were downloaded from Protein Data Bank (PDB

entry code 1Z9234 for pharmacophore designing. The target was selected on the basis of criteria

below:

1) The complex should be of human origin.

2) The target should have high quality resolution (<3.0° A).

3) The protein-protein or protein-ligands interact with one another through non-

covalent interactions and the experimental binding data should be available.

Initially the cofactors, non-interacting water molecules and counter-ions were removed from

receptor-protein coordinate file. Hydrogen atoms were added to the receptor atoms by MOE.

The energy of the retrieved protein molecule was minimized using the default parameters of

MOE energy minimization algorithm (gradient: 0.05, Force Field: MMFF94X)80. The co-

crystallized protein IL-2 was extracted from its corresponding receptor and saved as the

reference structure which was used for RMSD calculation.

3.1.3. Determination of active site of protein

The IL-2Rα was taken for further computational studies. For structure based pharmacophore

modeling active site determination is a very crucial step and this was accomplished through Site

Finder tool of MOE. It created alpha spheres at the active site. The cavity having key

contributing amino acids is taken as the active site and is generally large in size.

3.1.4. Pharmacophore generation

When the 3D structure (X-ray crystal structure) of the molecular target is available, structure-

based pharmacophore model generation can be performed. Pharmacophore model was built by


92

using pharmacophore Query Editor of MOE. The pharmacophore scheme of Polarity-Charge-

Hydrophobicity (PCH) was applied throughout the study. Critical amino acids present in the

active site of IL-2Rα involved in binding interactions with IL-2 protein were a sufficient input to

generate the structure-based pharmacophore.

Qualitative 60-70 hypotheses were generated based on the key contributing amino acids on IL-2

binding surface. Multiple chemical features were detected and mapped. Alternative HBA and/or

HBD, Hyd/Aro, Cat/Anionic sites are considered simultaneously on the receptor surface within

the limits of geometric constraints. Structure based annotations were generated through

Pharmacophore Query panel of MOE and pharmacophoric features were generated over the key

amino acids. The features projecting into the binding pocket were selected while those that

projected out of the pocket were deleted. The generated pharmacophore was complimentary to

the receptor active site. Excluded volume spheres were also added to the structure-based model

in order to characterize the inaccessible areas for any potential ligand.

Pharmacophore model included three features which were two hydrophobic (Hyd) and one

cationic (Cat) while the tolerance radius of each feature was adjusted to achieve a balance

between sensitivity and specificity. The distances and angles among the pharmacophoric features

were calculated through the ‘Measure’ distances and angles panel of MOE.

3.1.5. Excluded volumes

To confine the search of compound conformations within a range, the concept of excluded

volume was introduced. The excluded volume is defined as an area in a protein that cannot be

occupied by drug225. The shape of the binding pocket was approximated by a set of excluded

volume spheres added as spatial restraints to the proteins nearby the binding pocket. Specifically

the generation of excluded volume was based on those amino acids which could not involve in

drug binding and were rendered as excluded volume spheres. Moreover an excluded volume was

added on the basis of all the atoms of the residues lining the binding pocket thus accounting its

shape and size. The generation of excluded volumes helped in preventing the identification of

leads that would fit the pharmacophore elements well but that would not overlap with the

receptor atoms. So a three point pharmacophore model was built which including three excluded

volumes.


93

3.1.6. Generation of training set (S-1 to S-30)

For the validation of the developed pharmacophore, a training set of thirty compounds of

different classes of heterocycles were selected. The selected compounds were mostly amides and

ketones along with standard drugs azathioprine, 6-mercaptopurine, tacrolimus, cyclosporine A

etc. Selected compounds were reported in literature41, 44, 210-212 as IL-2 inhibitors and had good

IC50 values ranging from 0.06-80.0µM (Table 2.1).

3.1.7. Building of compounds

Structures of all training set compounds were built using the Builder Interface of MOE program.

All the structures were then energy minimized using Force field MMFF94x and these energy

minimized structures were then saved in the mdb file format.

Table 2.1: The structure and IC50 values of IL-2 inhibitors from literature

Entry Code Structure IC50

S-1 CHEMBL104594 H2N

H2N NO

NH N

O ClCl

O OH

O

NN0.2

S-2 CHEMBL107320

NNH

O N

O

NH2

NH2

NNClCl

2


H2N NO

NH N

O ClCl

O OH

O

NN0.4


H2N NO

NH N

O ClCl

O

NN3

Continued


94

S-5 CHEBI:47417(SP-4206) H2N

H2N NO

NH N

O ClCl

O

NN

OO

OH

0.06


H2N NO

NH N

O ClCl

O

NN

O

OH

0.6

S-7 CHEMBL317898 C C

OO

NH

O HN

O

N NH2

NH29

S-8 CHEMBL181983

ClCl

NO

HN

ON

NH2N

NH2N N

20

S-10 CHEMBL104746

ClCl

N NN

O

NH NNH2

NH2O

20


NH2

NO

NH

N

OO

ClCl

O

O

NN40

S-12 CHEMBL182704

H2N

NHN

S N

OCl Cl

OOH

O

HN

OO

NN

0.6

Continued


95

S-13 CHEMBL182098 NH2

NHN

SNO

Cl

Cl

OHNN N

48

S-14 CHEMBL350354 F

F

F

HO NOH

F

FN

OH

FF

F6.5

S-15 CHEMBL164641

Cl

HO N

HOF

N

F

OH Cl0.8

S-16 CHEMBL166149 N

Cl

N

OHF

N

F

N

Cl

2

S-17 CHEMBL103894 NH

ON

F

NN

O

F

FF

0.26

S-18 CHEMBL100390 N

N

O

F

FF

NH

O

F

F

0.37

S-19 CHEMBL101202 N

N

F

FF

NH

OF

N0.30

Continued


96

S-20 CHEMBL167745

N

Cl

N

O HO

NO NCl

0.9

S-21 CHEMBL350780

HO

N

N

OH

Cl

HOF

Cl0.6

S-22 CHEMBL164498

Cl

N

N

OF

F

NNCl

HO

0.8

S-23 CHEMBL419362

C COO

NH

O N NH2

NH3.0

S-24 CHEMBL106262

Cl

N

N

NO

HN O

N

NH2H2N

40

S-25 CHEMBL106518 Cl

HN

N NO

NHO

N

NH2

H2NCl

10

Continued


97

S-26 CHEMBL107371

CCO O

HN

ONH

O

NH2N

H2N

20

S-27 CHEMBL449094

Cl

Cl

NNN

O

HN

ONH2N

H2N

50

S-28 CHEMBL318396

CCO O

HN

ONH N

OH2N

NH2 80

S-29 CHEMBL107739

O

HN

HN

N

ONH

O

N NH2

NH2

Cl

30

S-30 CHEMBL107685

NH2N

NH2

NH

ON

O

NHN

OCl

50

3.1.8. Validation of pharmacophore model

The quality of the pharmacophore model was tested using a literature active training set of thirty

compounds from diverse chemical scaffolds and with different activities. This pharmacophore

model was used to virtually filter the fitting hits from the training set in order to evaluate the

model. The training set of molecules was screened against the pharmacophore model using the

‘Pharmacophore search’ function of MOE.

All settings were kept as default. If a conformation of any compound can properly bind with the

binding site of the protein, the pharmacophore in this conformation should reasonably match the

pharmacophoric feature of the active site. In addition, the conformation of the compound should


98

never overlap with the excluded volume around the binding site. To find out such conformations,

the pharmacophore search was conducted exhaustively on the training set and the procedure was

performed on all conformations in the database. The conformations that appropriately match all

the pharmacophoric features of the binding site and free from steric clashes with the excluded

volumes were stored in output database.

3.1.9. Virtual screening

Pharmacophore-based virtual screening can be efficiently used to find novel potential leads for

further development from a virtual database. After 3D pharmacophore model generation and

validation, the pharmacophore model was used as a 3D search query for retrieving potent

molecules from the ZINC and MOE databases. Compounds were downloaded from ZINC

database and filtered according to Lipinski rule of five226 by FILTER program227-228. ZINC

database contains a total of 1,594,931 compounds out of which 1.5 million compounds passed

the filtration criteria of Li p i n s k i ’ s R O 5 a n d 15,000 compounds were randomly selected

f o r virtual screening in the next step. Enrichment studies were performed using 30

compounds including quite a few standard drugs selected from literature with IC50 0.06-

80μg/ml against human IL-2. These active compounds were seeded into 1 5 , 0 0 0 decoy

molecules selected from ZINC database. The resulting data set of 15,030 compounds was

imported into MOE database containing 10,000 compounds leading to a new set of database

containing 25, 030 compounds having a variety of chemical scaffolds. The next step i.e.

pharmacophore-based virtual screening, reduced the number of compounds from 25, 030 to

500 compounds that mapped on the developed pharmacophore model. The output database of

the resulting 500 compounds was sorted out on the basis of RMSD in ascending order. These

initially identified 500 compounds from the output file were selected for further evaluation

using molecular docking. The top ranked docked poses were re-scored and binding energies

were calculated using LIGX option implemented in MOE to prioritize these compounds. Fig

3.1 summarizes the protocol used for virtual screening in this study.

3.1.10. Docking accuracy

The predictive ability of docking was assessed by RMSD of the top ranked solution. The

following criteria were used.

1) Good: A top ranked docked solution with RMSD~2.0 A.


99

2) Fair: A top ranked docked solution with RMSD>2.0 and ~3.0A.

3) In accurate/wrong solution: top ranked docked solution with RMSD>3.0.

Figure 3.1: Schematic presentation of virtual screening protocol

3.1.11. Descriptor based filtering of the database

First filter applied was ‘Lipinski’s rule of five’ RO5 for all compounds of MOE database with the

‘Calculate descriptors’ function of MOE. RO5 states that an orally active drug could not violate

the following criteria226.

Not more than 5 hydrogen bond donors (nitrogen or oxygen atoms with one or

more hydrogen atoms)

Not more than 10 hydrogen bond acceptors (nitrogen or oxygen atoms)

A molecular mass less than 500 Daltons

An octanol-water partition coefficient log P not greater than 5

Screening of whole database was carried out on the basis of this rule. The number of hydrogen

bond donors, hydrogen bond acceptors, molecular weight and log P was calculated for each

compound of the database. The compounds which did not comply with Ro5 cut off limits were

eliminated.


100

3.1.12. 3D pharmacophore based filtering of database

A structure based 3D pharmacophore was used as search queries on the compounds, that cover

all pharmacophoric features were retained. Among these hits only those compounds were

selected which had RMSD up to 3.0. This filter further shortlisted the MOE database and

reduced the number of compounds to be chosen for the next step of molecular docking 229.

3.1.13. Receptor based virtual screening of hits

The 3D in-house virtual collection was generated using the MOE. All compounds which passed

through above mentioned filters were subjected further to molecular docking studies230. The

binding mode of all retrieved compounds in 3D structure of IL-2Rα/IL-2 was evaluated. The

binding energy for each docked pose was calculated and the docked mdb files were rearranged

on the basis of the binding energy in descending order and all compounds having high binding

energies were selected. The top rank docked poses were rescored. Following docking and

rescoring, the top 5% of the best-scored compounds were retrieved and their binding modes were

visually analyzed using MOE lig plot. The selection of the hits compounds was based on

ranking and molecular interactions. The receptor based virtual screening protocol is presented

in Fig 3.2.

Figure 3.2: Receptor based virtual screening protocol


101

After identification of hits (Figure 2.6) in dry lab the next step was the optimization and

synthesis of these hits in wet lab.

3.2. Hits optimization

In the next step, an in house library of 46 compounds was designed by using identified hits.

Pharmacophore mapping and molecular docking studies were done in order to know their

pharmacophoric behavior, binding mode and binding energies.

3.2.1. Pharmacophore mapping

The generated 3D pharmacophore was mapped on these optimized hits and RMSD values of all

the compounds were calculated which were in the range of 0.42 to 2.51.

3.2.2. Molecular docking

All these optimized hits were docked into the active site of 1Z92 and binding energy for each

docked conformation was calculated. To obtain the structural insight into the inhibitory

mechanism of IL-2 inhibitors, their binding mode in the active site was investigated. The binding

interactions were determined by docking of the inhibitors into the active site of the target and

their binding energies were also calculated. In order to identify the type of interactions which

were involved in binding the inhibitors in the active site of complex. Ligand-protein interaction

diagrams of each docked conformation were generated which showed the key amino acids

involved in binding.

3.3. Synthesis of hits identified (Wet lab)

3.3.1. Chemicals used

Acetophenone acetone, acetonitrile, stannous chloride, anhydrous calcium chloride, ethyl acetate,

sodium acetate, sodium hydroxide, anhydrous potassium carbonate, calcium oxide, ethanol, ethyl

acetate, dichloromethane, methanol, chloroform, dimethyl formamide (DMF), dimethyl

sulfoxide (DMSO), calcium hydride were purchased from E. Merck. Benzaldehyde, 4-

flourobenzaldehyde, 4-chlorobenzaldehyde, 4-bromobenz-aldehyde, 3-hydroxyacetophenone, 3-

nitrobenzaldehyde, 2-hydroxybenz-aldehyde, 4-aminoacetophenone, 3-chlorobenzaldehyde, 4-

N,N-dimethylbenzaldehyde, 3-hydroxy-benzaldehyde, 4-flourobenzaldehyde, 3-(3-hydroxy-5-

nitrophenyl)acryl aldehyde, 2,4-dichlorobenzaldehyde, acetaldehyde, cinnamaldehyde, 3-


102

nitroaniline, 4-hydroxyaniline, phenylenediamine, 3-nitrophenylenediamine, biphenylamine, 1-

napthylamine, 2,6-diamino anthraquinone, 8-quinolinoxyacetic acid hydrazide, hydrazine

hydrate, formohydrazide, acetohydrazide, thiosemicarbazide, 2-aminobenzenethiol, urea,

thiourea were purchased from BDH. Ibuprofen and cetirizine were purchased from Sigma

Aldrich.

3.4. Purification of solvents

All the solvents were used after necessary purification and drying according to standard

procedures. The dried solvents were stored over molecular sieves (4°A). A brief account of the

purification procedure follows.

3.4.1. Acetone

Anhydrous CaCl2 (200 g) was introduced into round bottom flask containing one litre of acetone

it was left for 4~5 hours. Pure acetone was distilled at 56 °C231.

3.4.2. Absolute ethanol

Commercial ethanol was distilled first and then the distilled ethanol was refluxed over calcium

oxide and distilled again. The 99 % ethanol obtained was refluxed after adding magnesium

turnings and iodine. When the color of iodine disappeared, it was distilled again at 77-78 °C231.

3.4.3. Ethyl acetate

Ethyl acetate was purified by shaking it with 5 % sodium carbonate, then with saturated brine

solution. It was then dried by stirring it with calcium hydride and distilled231.

3.4.4. Chloroform and dichloromethane

Chloroform and dichloromethane were dried over anhydrous calcium chloride for 3 hours and

then distilled at 39-41 °C and 64-65 °C respectively231.

3.4.5. Absolute methanol

Procedure for absolute methanol is the same as above for absolute ethanol which was distilled at

64-65 °C.


103

3.4.6. Dimethyl formamide (DMF)

Dimethyl formamide was purified and dried by keeping it over calcium hydride and then distilled

over sodium. Fraction boiling at 95 °C was collected231.

3.4.7. Dimethyl sulfoxide (DMSO)

Calcium hydride (200 g) was added to dimethyl sulfoxide (1000 mL) and allowed to stand

overnight. The solvent was filtered and fractionally distilled over calcium hydride231.

3.5. Instruments used

3.5.1. Melting Point

Melting points were determined in open capillaries using Gallenkampmelting point (MP-D).

3.5.2. NMR-spectrometer1H and 13C-NMR spectra were recorded on a Bruker spectrometer at 300 and 75 MHz

respectively in DMSO-d6 solution. Solvent was used as internal reference. Chemical shifts are

given in δ scale (ppm). Abbreviations s, d, t, q, m were used for singlet, doublet, triplet, quartet,

multiplet respectively. Coupling constants are presented in Hz.

3.5.3. Mass spectrometer

Mass spectra were recorded on Agilent technologies 6890N Gas chromatography (GC) and an

inert mass selective detector 5973 GC-mass spectrometer.

3.6. Chromatographic techniques

3.6.1. Thin layer chromatography (TLC)

The progress of all the reactions was monitored by TLC on 2.0 x 5.0 cm aluminum sheets

precoated silica gel 60F254 with a layer thickness of 0.25 mm (Merck).

The chromatograms were visualized under UV light (254-366 nm) or iodine vapors. They were

developed by using different solvent systems. Following solvent systems were used for the

development of chromatogram:

A Petroleum ether: Ethyl acetate (3:2)

B Petroleum ether: Ethyl acetate (7:3)

C Chloroform: Methanol (5:1)


104

3.7. Synthesis of chalcones (1-7)

General procedure214

Chalcones were synthesized to use as precursors for 2,3-dihydro-1,5-benzothiazepines. All

chalcones were synthesized by literature procedure. An Erlenmeyer flask fitted with mechanical

stir bar was loaded with solution of acetophenone (5 mmol) in rectified spirit (15 mL) the flask

was cooled in ice bath and solution of benzaldehyde (5 mmol) in rectified spirit (15 mL) was

added on stirring. To this mixture was added an ice cold solution of 4 M NaOH in water. The

mixture was stirred vigorously at room temperature and monitored through TLC. The mixture

was kept at 0-3 oC overnight, neutralized with ice cold aqueous HCl. The precipitates were

filtered and washed with distilled water thoroughly; the product obtained was pure in many

cases. In some cases the crude product was recrystallized using hot ethanol. Since chalcones

were already synthesized and documented by our group therefore characterization was not

reproduced here. Chalcones (1-7) were characterized through their physical constants, their

melting points were found in agreement with literature.

3.7.1: 3-Phenyl-1-phenylprop-2-en-1-one (1)

Compound (1) was synthesized by general procedure using

acetophenone and benzaldehyde. Rf = 0.60.Yield: 71 %. m.p. 86-88oC215.

3.7.2: 3-(4-Fluorophenyl)-1-phenylprop-2-en-1-one (2)


acetophenone and 4-flourobenzaldehyde. Rf = 0.71.Yield: 76 %. m.p.

84-86 oC215.

3.7.3: 3-(4-Chlorophenyl)-1-phenylprop-2-en-1-one (3)


acetophenone and 4-chlorobenzaldehyde. Rf = 0.62.Yield: 69 %.

m.p. 112-114 oC215.

O

Cl

O

F

O


105

3.7.4: 3-(4-Bromophenyl)-1-phenylprop-2-en-1-one (4)


acetophenone and 4-bromobenzaldehyde. Rf = 0.53.Yield: 73 %.

m.p. 113-115 oC214.

3.7.5: 1-(3-Hydroxyphenyl)-3-(3-nitrophenyl)prop-2-en-1-one (5)

Compound (5) was synthesized by general procedure using 3-

hydroxy acetophenone and 3-nitrobenzaldehyde. Rf= 0.65.

Yield: 75 %. m.p. 140-142 oC64

3.7.6: 3-(2-Hydroxyphenyl)-1-(3-hydroxyphenyl)prop-2-en-1-one (6)


hydroxyacetophenone and 2-hydroxybenzaldehyde. Rf =

0.68.Yield: 61 %. 148-149 oC64.

3.7.7: 1-(4-Aminophenyl)-3-(3-chlorophenyl)prop-2-en-1-one (7)


aminoacetophenone and 3-chlorobenzaldehyde. Rf = 0.59.Yield:

64 %. m.p. 83-85 oC214

3.8. Synthesis of dihydropyrimidines (8-13)


A mixture of chalcone (0.848 g, 0.002 mol), urea or thiourea (0.22 g, 0.002 mol) and 0.2 g

NaOH in 25 mL of 80% dilute ethanol was refluxed for 7.5 h, then concentrated and cooled, the

precipitates were filtered off and recrystallized from ethanol.

3.8.1: 4-(3-Hydroxyphenyl)-6-(3-nitrophenyl)-5,6-dihydropyrimidine-2(1H)-thione (8)


corresponding chalcone (5) and thiourea. Rf = 0.52. Yield:

61 %. m.p. 157-158 oC.1H NMR (300 MHz, DMSO-d6): δNHN

S

HO NO2

O

H2N

Cl

OHO

OH

OHO NO2

O

Br


106

1.91(m, 2H, CH2), 3.96 (dd, J=11.9 Hz, J=5.2 Hz, 1H, CH), 7.22-7.35 (Ar-H), 8.42 (s, 1H,

NH),9.22 (s, 1H, OH). 13C NMR (75 MHz, DMSO-d6): δ 45.32 (CH), 54.50 (CH2), 106.29-

135.99 (aryl C), 150.33 (C-NO2), 162.23 (C-OH), 165.26 (C=N), 185.43 (C=S). LCMS (m/z):

328 (M+H)+.

3.8.2: 6-(2-Hydroxyphenyl)-4-(3-hydroxyphenyl)-5,6-dihydropyrimidine-2(1H)thione (9)215


corresponding chalcone (6) and thiourea. Rf = 0.61. Yield: 66 %.

m.p. 150-151 oC.1H NMR (300 MHz, DMSO-d6): δ 2.21 (m, 2H,

CH2), 3.85 (dd, J=12.9 Hz, J=6.9 Hz, 1H, CH), 6.76-7.33 (Ar-H),

9.22 (s, 1H, NH).9.96 (s, 2H, OH), 13C NMR (75 MHz, DMSO-

d6): δ 39.05 (CH2), 48.90 (CH), 120.03-140.11 (aryl C), 158.89 (C-OH), 164.89 (C=N), 185.83

(C=S). LCMS (m/z): 299 (M+H)+.

3.8.3: 4-(4-Aminophenyl)-6-(3-chlorophenyl)-5,6-dihydropyrimidine-2(1H)-thione (10)


corresponding chalcone (7) and thiourea. Rf = 0.55. Yield: 69

%. m.p. 163-165 oC.1H NMR (300 MHz, CDCl3): δ 2.21 (m

2H, CH2), 3.77 (dd, J=12.3 Hz, J=5.4 Hz, 1H, CH), 5.52 (s,

2H, NH2), 7.82-8.27 (Ar-H), 9.19 (s, 1H, NH).13C NMR (75

MHz, CDCl3): δ 40.94 (CH2), 41.51 (CH), 109.11-137.96 (aryl C), 158.69 (C-Cl), 159.34 (C-

NH2), 166.46 (C=N), 189.74 (C=S). LCMS (m/z): 316 (M+H)+.

3.8.4: 4-(3-Hydroxyphenyl)-6-(3-nitrophenyl)-5,6-dihydropyrimidin-2(1H)-one (11)215


corresponding chalcone (5) and urea. Rf 0.67 (C). Yield: 76 %.

m.p. 148-150 °C. 1H NMR (300 MHz, DMSO-d6): δ 2.60 (m,

2H, CH2), 3.71 (dd, J=12.2, Hz, J=7.4 Hz, 1H, CH), 6.32-7.55

(Ar-H), 9.32 (s, 1H, OH), 10.45 (s, 1H, NH).13C NMR (75 MHz, DMSO-d6): δ 41.75 (CH2),

50.16 (CH), 108.93-139.56 (aryl C), 149.61 (C-NO2), 157.35 (C-OH), 164.37 (C=N), 165.94

(C=O). LCMS (m/z): 312 (M+H)+.

NHN

S

H2N

Cl

NHN

S

HOOH

NHN

O

HO NO2


107

3.8.5: 6-(2-Hydroxyphenyl)-4-(3-hydroxyphenyl)-5,6-dihydropyrimidin-2(1H)-one (12)215


corresponding chalcone (6) and urea. Rf 0.58 (C). Yield: 74 %. m.p.

136-138 °C. 1H NMR (300 MHz, DMSO-d6): δ 2.82 (m, 2H, CH2),

3.78 (dd, J=13.1 Hz, J=6.2 Hz, 1H, CH), 6.74-7.48 (Ar-H), 9.30 (s,

1H, OH), 10.61 (s, 1H, NH).13C NMR (75 MHz, DMSO-d6): δ 41.49 (CH2), 50.55 (CH), 112.73-

139.44 (aryl C), 158.75 (C-OH), 164.85 (C=N), 166.44 (C=O). GCMS (m/z, %): 282 (M+., 14),

265(M+.-17, 13), 249 (M+.-34, 100).

3.8.6: 4-(4-Aminophenyl)-6-(3-chlorophenyl)-5,6-dihydropyrimidin-2(1H)-one (13)215


corresponding chalcone (7) and urea. Rf 0.64 (C). Yield: 67 %.

m.p. 150-153 °C. 1H NMR (300 MHz, DMSO-d6): δ 2.79 (m

2H, CH2), 3.83 (dd, J=12.3 Hz, J=7.3 Hz, 1H, CH), 4.80 (s, IH,

NH2), 6.45-7.50 (Ar-H), 9.64 (s, 1H, OH), 10.78 (s, 1H,

NH).13C NMR (75 MHz, DMSO-d6): δ 41.57 (CH2), 51.34 (CH), 115.20-138.88 (aryl C), 143.75

(C-Cl), 153.22 (C-NH2), 164.48 (C=N), 165.29 (C=O). LCMS (m/z): 300 (M+H)+.

3.9. Synthesis of 2,3-dihydro-1,5-heteroazepines (14-19)

General procedure81

A solution of chalcone (4 mmol) in dry THF (10 mL) was taken in 100 mL Erlenmeyer flask.

Silica (8 g) was added and stirred for 5 minutes. Solvent was removed under reduced pressure,

1.2 equivalent of o-phenylenediamine was added to this mixture. The flask was fitted with a

three way stopcock, evacuated and stirred for 4 hours at 85 oC under nitrogen atmosphere. After

completion of reaction, the mixture was stirred with ethyl acetate (15 mL) and filtered through

cotton plug. The filtrate was concentrated under reduced pressure the crude product was

recrystallized with MeOH to afford crystalline solid. Silica was recycled by sequential washing,

stirring ethyl acetate (30 mL) and subsequent filtration and dried in oven for 2 hours.

NHN

O

H2N

Cl

NHN

O

HOOH


108

3.9.1: 4-(4-(3-Chlorophenyl)-4,5-dihydro-3H-benzo(b)(1,4)diazepin-2-yl)-benzenamine (14)


corresponding chalcone (7) and o-phenylenediamine. Rf =

0.59. Yield: 59 %. m.p. 97-98 oC.1H NMR (300 MHz,

DMSO-d6): δ 2.49 (m, 2H, CH2), 3.86 (dd, J2,3a=11.2 Hz,

J2,3b=5.4 Hz, 1H, CH), 5.49 (s, 2H, NH2), 7.21-7.35 (Ar-H ),9.80 (s, 1H, NH). 13C NMR (75

MHz, DMSO-d6): δ 40.92 (CH2), 62.67 (CH), 120.12-139.72 (aryl C), 150.02 (C-Cl), 158.27 (C-

NH2), 168.67 (C=N). LCMS: 346.43 (M-H)-.

3.9.2: 3-(4-(3-Nitrophenyl)-4,5-dihydro-3H-benzo(b)(1,4)diazepin-2-yl)phenol (15)


corresponding chalcone (5) and o-phenylenediamine. Rf =

0.63. Yield: 83 %. m.p. 89-92 oC.1H NMR (300 MHz, DMSO-

d6): δ 3.18 (m, 2H, CH2), 3.97 (dd, J2,3a=12 Hz, J2,3b =6 Hz,

1H, CH), 7.17-7.80 (Ar-H), 8.81 (s, 1H, NH). 9.47 (s, 1H,

OH).13C NMR (75 MHz, DMSO-d6): δ 42.60 (CH2), 59.89 (CH), 125.89-131.55 (aryl C), 154.61

(C-NO2), 167.86 (C-OH). LCMS (m/z):358 (M-H)-.

3.9.3: 3-(4-(2-hydroxyphenyl)-4,5-dihydro-3H-benzo(b)(1,4)diazepin-2-yl)phenol (16)216


corresponding chalcone (6) and o-phenylenediamine. Rf = 0.66.

Yield: 76 %. m.p. 98-100 oC.1H NMR (300 MHz, DMSO-d6): δ2.78 (m,2H, CH2), 3.20 (dd,J2,3a=12.3 Hz, J2,3b =6.2 Hz, 1H, CH),

6.53-7.26 (Ar-H), 8.10 (s, 1H, NH), 8.30 (s, 2H, OH). 13C NMR (75

MHz, DMSO-d6): δ 38.90 (CH2), 118.01-152.10 (aryl C), 158.33 (C-OH), 161.80 (C=N).LCMS

(m/z, %): 329(M-H)-.

3.9.4: 4-(2-(3-Chlorophenyl)-2,3-dihydrobenzo(1,4)thiazepin-4-yl)benzenamine (17)


Yield: 79 %. m.p. 84-86 oC.1H NMR (300 MHz, DMSO): δ 2.86

corresponding chalcone (7) and 2-aminobenzenethiol. Rf = 0.52. N


109

(m, 2H, CH2), 3.38 (t, J=7.2 Hz, 1H, CH), 5.45 (s, 2H, NH2), 6.96-8.20 (m, 12H, Ar-H). 13C

NMR (75 MHz, DMSO): δ 37.0(CH2), 58.0(CH), 114.1-158.1(Ar-Cs),169.1(C-NH2). LCMS

(m/z): 363(M-H)-.

3.9.5: 3-(2-(3-Nitrophenyl)-2,3-dihydrobenzo(b)(1,4)thiazepin-4-yl)phenol (18)


corresponding chalcone (5) and 2-aminobenzenethiol. Rf = 0.60.

Yield: 75 %. m.p. 81 oC.1H NMR (300 MHz, DMSO-d6): δ 3.40

(m, 2H, CH2), 3.40 (t, J=8.6 Hz, 1H, CH), 66.9-8.22 (m, 12H,

Ar-Hs), 9.70 (s, 1H, OH). 13C NMR (75 MHz, DMSO-d6): δ

58.7 (CH), 40.7 (CH2), 114.1-158.1 (Ar-Cs), 152.6 (C-NO2).

LCMS (m/z): 375(M-H)-.

3.9.6: 3-(2-(2-hydroxyphenyl)-2,3-dihydrobenzo(b)(1,4)thiazepin-4-yl)phenol (19) 216


corresponding chalcone (6) and 2-aminobenzenethiol. Rf= 0.58.

Yield: 68%. m.p. 99-101oC. 1H NMR (300 MHz, acetone-d6): δ 3.27 m, J=12.0 Hz, J=3.4 Hz, 2H, CH2), 3.83 (t, J=6.0 Hz, 1H, CH), 7.3-

8.20 (m, 12H, Ar-Hs), 8.81 (s, 2H, OH). 13C NMR (75 MHz,

acetone-d6): δ 29.5 (C-3), 48.0 (C-2), 115.7-159.8 (Ar-Cs), 168.0

(C-4). LCMS: 346.2(M-H)-

3.10. Synthesis of pyrazolecarbaldehydes (20-25) and pyrazolecarbothioamides (26-28)


A mixture of chalcone (0.84 g, 0.001 mol) and hydrazine hydrate (0.20 mL, 0.002 mol) was

heated under reflux for 10 h, in 25 mL absolute ethanol then cooled and the residual material was

filtered off and recrystallized from ethanol.

3.10.1: 3-(3-Hydroxyphenyl)-5-(3-nitrophenyl)-4,5-dihydropyrazole-1-carbaldehyde (20)


corresponding chalcone (5) and formohydrazide. Rf 0.43 (C).

Yield: 66 %. m.p. 112-114 °C. 1H NMR (300 MHz, DMSO-d6):3

N NC

H

OHO

NO22

pc

Cross-Out


110

δ 1.95 (dd, J=12.9 Hz, J=5.7 Hz, 2H, CH2), 3.93 (dd, J2,3a=12.9 Hz, J2,3b =5.7 Hz, 1H, CH), 5.94

(s, 1H, OH), 7.10-7.92 (Ar-H), 8.08 (CHO). 13C NMR (75 MHz, DMSO-d6): δ 42.75 (CH2),

62.87 (CH), 115.83-131.06 (aryl C), 131.31 (C-NO2), 139.63 (C=N), 155.39 (C-OH), 163.17

(C=O). LCMS: 312 (M+H)+.

3.10.2: 5-(2-Hydroxyphenyl)-3-(3-hydroxyphenyl)-4,5-dihydropyrazole-1-carbaldehyde (21)


corresponding chalcone (6) and formohydrazide. Rf 0.46 (C). Yield:

65 %. m.p. 106-108 °C. 1H NMR (300 MHz, DMSO-d6): δ 2.15 (dd,

J=12.3 Hz, J=6.3 Hz, 2H, CH2), 4.07 (dd, J2,3a =12.3 Hz, J2,3b =6.3 Hz, 1H, CH), 4.86 (s, 1H, OH), 5.07 (s, 1H, OH), 6.84-7.01 (Ar-H), 8.93

(CHO). 13C NMR (75 MHz, DMSO-d6): δ 28.20 (CH2), 41.13 (CH), 121.02-145.87 (aryl C),

151.39 (C=N), 205.11 (C-OH), 205.38 (C=O). LCMS: 283 (M+H)+.

3.10.3: 3-(4-Amiphenyl)-5-(3-chlorophenyl)-4,5-dihydropyrazole-1-carbaldehyde (22)


corresponding chalcone (7) and formohydrazide. Rf 0.44 (C).

Yield: 69 %. m.p. 109-111 °C. 1H NMR (300 MHz, DMSO- Cl d6): δ 2.01 (dd, J=11.4 Hz, J=5.1 Hz, 1H, CH2), 3.88 (dd,

J2,3a=11.4 Hz, J2,3b=5.1 Hz, 1H, CH), 5.49 (s, 2H, NH2), 7.21-7.68 (Ar-H), 8.95 (CHO). 13C NMR

(75 MHz, DMSO-d6): δ 40.95 (CH2), 60.98 (CH), 116.04-131.85 (aryl C), 132.16 (C-Cl), 160.95

(C=O). LCMS: 300 (M+H)+.

3.10.4: 5-(4-Bromophenyl)-3-phenyl-4,5-dihydro-1H-pyrazole (23)


corresponding chalcone (4) and hydrazine hydrate. Rf 0.48 (C).

Yield: 72 %. m.p. 98-100 °C. 1H NMR (300 MHz, DMSO-d6): δ

3.16 (dd, J3a,3b=18.0 Hz, J3a,2=3.3 Hz, 1H, H3a),3.92 (dd, J3b,3a=18.0

Hz, J3b,2= 11.4 Hz, 1H, H3b), 5.94 (dd, J2,3a=3.3 Hz, J2,3b=11.4 Hz, 1H, H2),8.05 (s, 1H, NH),

7.14-7.79 (Ar-H). 13C NMR (75 MHz, DMSO-d6): δ 42.86 (CH2), 63.30 (CH), 120.66-142.24

(aryl C), 155.38 (C-Br), 155.39 (C=N). LCMS: 302 (M+H)+.

N NHO

HO

CH

O

3 2

N NC

H

O

H2N

Cl

3 2

N NH

Br

3 2


111

3.10.5: 1-(3,5-Diphenyl-4,5-dihydropyrazol-1-yl)ethanone (24)


corresponding chalcone (1) and acetohydrazine. Rf 0.47 (C). Yield: 70 %. m.p. 102-103 °C. 1H NMR (300 MHz, DMSO-d6): δ 1.89 (s, 3H, CH3),

3.15 (dd, J3a,3b=18.0 Hz, J3a,2=4.5 Hz, 1H, H3a),3.85 (dd, J3a,3b =18 Hz,

J3b,2=12 Hz, 1H, H3b), 5.58 (dd, J2,3a=4.5 Hz, J2,3b=12.0 Hz, 1H, H2),

6.56-7.80 (Ar-H). 13C NMR (75 MHz, DMSO-d6): δ 22.19 (CH3), 52.60 (CH2), 59.89 (CH),

121.62-136.85 (aryl C), 154.61 (C=N), 176.86 (C=O). LCMS: 300 (M+H)+.

3.10.6: 1-(5-(4-Bromophenyl)-3-phenyl-4,5-dihydropyrazol-1-yl)ethanone (25)


corresponding chalcone (4) and acetohydrazide.Rf 0.39 (C). Yield:69 %. m.p. 106-108 °C. 1H NMR (300 MHz, DMSO-d6): δ 1.91 (s,

3H, CH3), 3.17 (dd, J3a,3b =18.3 Hz, J3a,2=4.8 Hz, 1H, H3a),3.87 (dd,

J3a,3b =18 Hz, J3b,2=12 Hz, 1H, H3b), 5.54 (dd, J2,3a=4.8 Hz, J2,3b=12

Hz, 1H, H2),6.86-8.05 (m, Ar-H). 13C NMR (75 MHz, DMSO-d6): δ 21.16 (CH3), 42.32 (CH2),

59.37 (CH), 120.52-131.06 (aryl C), 142.24 (C-Br), 154.67 (C=N), 167.94 (C=O). GCMS (m/z,

%): 344(M+.+2, 45), 342 (M+., 45), 300 (M+.-42, 100), 259 (M+.-93, 25).

3.10.7: 3,5-Diphenyl-4,5-dihydropyrazole-1-carbothioamide (26)


corresponding chalcone (1) and thiosemicarbazide. Rf = 0.40. Yield: 71

%. m.p. 105-107 °C. 1H NMR (300 MHz, DMSO-d6): δ 2.78 (dd, J3a,3b

=12.3 Hz, J3a,2=3.9 Hz, 1H, H3a), 3.20 (dd, J3a,3b =12.3 Hz, J3b,2=6.9 Hz,

1H, H3b), 4.63 (dd, J2,3a=3.9 Hz, J2,3b=6.9 Hz, 1H, H2),5.23 (s, 2H, NH2),

6.96-8.12 (Ar-H). 13C NMR (75 MHz, DMSO-d6): δ 40.38 (CH2), 54.09 (CH), 120.42-140.15

(aryl C), 154.02 (C=N), 176.58 (C=S). LCMS: 281 (M+H)+.

3

N NC

H3C

O2

N NC

H3C

O

Br

3 2

N NC

H2N

S

3 2


112

3.10.8: 5-(4-Fluorophenyl)-3-phenyl-4,5-dihydropyrazole-1-carbothioamide (27)


corresponding chalcone (2) and thiosemicarbazide. Rf 0.48 (C).

Yield: 68 %.m.p. 110-112 °C. 1H NMR (300 MHz, DMSO-d6): δ

3.34 (m, 2H, H3), 3.91 (dd, J2,3a=18.1 Hz, J2,3b=5.7 Hz, 1H, H2),5.94

(s, 2H, NH2), 7.05-8.33 (Ar-H). 13C NMR (75 MHz, DMSO-d6): δ

42.75 (CH2), 62.68 (CH), 115.42-138.19 (aryl C), 155.39 (C-F), 163.17 (C=N), 176.54 (C=S).

LCMS: 299 (M+H)+.

3.10.9: 5-(4-Chlorophenyl)-3-phenyl-4,5-dihydropyrazole-1-carbothioamide (28)

Compound (28) was prepared by general procedure using

corresponding chalcone (3) and thiosemicarbazide. Rf 0.47 (C).

Yield: 70 %. m.p. 116-118 °C. 1H NMR (300 MHz, DMSO-d6): δ

3.19 (dd, J3a,3b =18.5 Hz, J3a,2=3.6 Hz, 1H, H3a),3.91 (dd, J3a,3b =18.3

Hz, J3b,2=11.7 Hz, 1H, H3b), 4.63 (dd, J2,3a=3.6 Hz, J2,3b=11.7 Hz, 1H, H3),7.14 (s, 2H, NH2), 7.09-8.13 (Ar-H). 13C NMR (75 MHz, DMSO-d6): δ 42.63 (CH2), 62.77 (CH), 127.62-131.78

(aryl C), 142.46 (C-Cl), 155.38 (C=N), 176.53 (C=S). LCMS: 315 (M+H)+.

3.11. Synthesis of benzimidazoles (29-32)


All benzimidazole derivatives were synthesized according to following literature procedure. To

an aldehyde (15mmol/50 ml ethanol), an aqueous solution of sodium metabisulfite (1.6 g) was

added, then the reaction mixture was stirred vigorously maintaining the temperature at 0-4 °C

and the precipitate formed were filtered, dried. A mixture of adduct (2 mmol) and o-

phenylenediamine (2 mmol) was heated at 110 °C for 4 hours in DMF (5 ml), the reaction

mixture was cooled, poured into ice cold water and precipitates formed were filtered, washed and

dried.

3.11.1: 2-(1-(4-Isobutylphenyl)ethyl)-1H-benzo(d)imidazole (29)


ibuprofen and o-phenylenediamine. Rf = 0.48. Yield: 62 %. m.p.NH

N

3 2

N NC

H2N

S

Cl

23


113

183-184oC.1H NMR (300 MHz, DMSO-d6): δ 1.09 (d, J=9.7 Hz, 6H, (CH3)2), 1.73 (d, J=7.8 Hz,

3H, CH3), 2.10 (m, 1H, CHCH2), 2.83 (d, 2H, J=8.6 Hz, CHCH2), 4.42 (q, 1H, CHCH3), 7.02-

7.66 (Ar-H), 11.36 (s, 1H, NH). 13C NMR (75MHz, DMSO-d6): 19.21 (CH3), 23.21 (CH3)2),

29.12 (CHCH2), 40.34 (CHCH3), 48.16 (CHCH2), 112.24-137.27 (aryl C), 146.38 (N-C-N).

GCMS (m/z, %): 278 (M+., 44), 263 (M+.-15, 18), 235(M+.-43, 17), 221(M+.-57, 100).

3.11.2: 2-(2-(4-(4-Chlorophenyl)(phenyl)methyl)piperazin-1-yl)ethoxy)methyl)-1H-benzo-

(d)imidazole (30)

Compound (30) was synthesized by general

procedure using a commercial sample of cetirizine

and o-phenylenediamine. Rf = 0.50.Yield: 70 %.

m.p. 331-333 oC.1H NMR (300 MHz, acetone-d6):

δ 2.24 (t, J=12.8 Hz, 2H, CH2piperazine), 2.72 (t,

J=9.5 Hz, 2H, N-CH2), 3.67 (t, J=12.1 Hz, 2H, N-CH2CH2), 4.78 (s, 2H, O-CH2), 5.21 (N-CH),

6.98-7.81 (Ar-H), 11.24 (s, 1H, NH). 13C NMR (75 MHz, acetone-d6): δ 48.98 (CHN-

CH2piperazine), 52.76 (CH2piperazine), 55.09 (N-CH2), 64.87 (O-CH2), 69.05 (N-CH2CH2),

78.43 (N-CH), 118.65-140.54 (aryl C). LCMS: 460 (M+H)+.

3.11.3: N,N-dimethyl-4-(6-nitro-1H-benzo(d)imidazol-2-yl)benzenamine (31)


N,N-dimethylbenzaldehyde and 3-nitro-o-phenylenediamine. Rf

= 0.68. Yield: 74 %. m.p. 289-291 oC.1H NMR (300 MHz,

DMSO-d6): δ 2.86 (s, 6H, N(CH3)2), 6.87-8.20 (Ar-H), 11.09 (s, 1H, NH). 13C NMR (75 MHz,

DMSO-d6): δ 39.54 (N(CH3)2), 110.43-137.80 (aryl C), 145.12 (C-NO2), 150.35 (N-C), 154.06

(N-C-N). GCMS (m/z, %): 282(M+., 73), 267 (M+.-15, 31), 252 (M+.-30).

3.11.4: 4-(1H-benzo(d)imidazol-2-yl)-N,N-dimethylbenzenamine (32)

Compound (32) was synthesized by general procedure using 4-N,N-

dimethylbenzaldehyde and o-phenylenediamine. Rf = 0.63.Yield: 68

%. m.p. 278-280 oC.1H NMR (300 MHz, DMSO-d6): δ 2.79 (s, 6H,

N(CH3)2), 7.06-8.35 (Ar-H), 11.24 (s, 1H, NH). 13C NMR (75 MHz, DMSO-d6): δ 39.75

(N(CH3)2), 120.24-138.12 (aryl C), 151.22 (N-C), 154.34 (N-C-N). LCMS: 238 (M+H)+.

NH

NN

NH

NN

O2N

NH

N O NN

Cl


114

3.12. Synthesis of anthraquinone sulfonamide derivatives (33-36)


The anthraquinonesulfonyl chloride was synthesized according to literature procedure232. In a

500 mL, two neck round bottom flask equipped with are flux condenser under nitrogen

condition, mixed anthraquinone-2-sulfonic acid sodium salt (0.15 mol, 46.539 g) and PCl5 (0.15

mol, 31.236 g). The temperature of reaction mixture was maintained 175-180 °C. After four

hours, reaction mixture was cooled to room temperature and mixed with spatula and again

reaction temperature was raised to 175-180 °C. Following exercise of cooling to room

temperature and mixing after intervals of four hours was repeated for sixteen hours. After

completion of reaction, reaction mixture was cooled to room temperature and poured into 2 L

beaker containing 1Kg crushed ice. Resulting precipitates were filtered and solubilize in

chloroform to remove insoluble impurities by filtration. To remove water from chloroform

solution, anhydrous sodium sulfate was added and left overnight. Solution was then filtered and

concentrated on a rotary evaporator leading to formation of yellow crystals of anthraquinone-2-

slfonylchloride (m.p.196 °C).

Sulfonamides were prepared by the general procedure220. The benzimidazole (0.04 mol) was

condensed with anthraquinonesulfonyl chloride (0.03 mol) in 50 ml 4N hydrochloric acid. The

reaction mixture was stirred for about 4 h at 80 °C. The compound was precipitated by adding

ammonia filtered through suction and washed with cold water. Compound was recrystallized

from water and ethanol.

3.12.1: 2-(2-(4-(Dimethylamino)phenyl)-1H-benzo(d)imidazol-1-ylsulfonyl)-anthracene-9,10-

dione (33)


anthraquinone sulfonyl chloride and corresponding

benzimidazole (32). Rf = 0.55.Yield: 76 %.m.p. 281-282 oC.1H

NMR (300 MHz, DMSO-d6): δ 3.35 (s, 6H, N(CH3)2), 7.04-8.45

(Ar-H). 13C NMR (75 MHz, DMSO-d6): δ 40.09 (N(CH3)2),

110.67-139.70 (aryl C), 150.02 (N-C-N). 182.67 (C=O). LCMS:

508 (M+H)+.

SN N

N

O

O

O

O


115

3.12.2: 2-(2-(4-(Dimethylamino)phenyl)-6-nitro-1H-benzo(d)imidazol-1-ylsulfonyl)-

anthracene-9,10-dione (34)


anthraquinone sulfonyl chloride and corresponding

benzimidazole (31). Rf = 0.62. Yield: 78 %. m.p. 297-298 oC.1H

NMR (300 MHz, DMSO-d6): δ 2.67 (s, 6H, N(CH3)2), 6.99-7.97

(Ar-H). 13C NMR (75 MHz, DMSO-d6): δ 39.13 (N(CH3)2),

1116.38-132.42 (aryl C), 139.95 (C-NO2),150.09 (N-C-N),

180.95 (C=O). LCMS: 553 (M+H)+.

3.12.3: 2-(2-(1-(4-Isobutylphenyl)ethyl)-1H-benzo(d)imidazol-1-ylsulfonyl)-anthracene-

9,10-dione (35)

Compound (35) was synthesized by general procedure

using anthraquinone sulfonyl chloride and

corresponding benzimidazole (29). Rf= 0.66. Yield: 60

%. m.p. 251-252 oC. 1H NMR (300 MHz, DMSO-d6): δ

1.28 (d, J=11.7 Hz, 6H, (CH3)2), 1.57 (d, J=8.7 Hz, 3H,

CH3), 2.18 (m, 1H, CHCH2), 3.88 (q, J=8.1 Hz, 1H,

CHCH3), 7.22-7.68 (Ar-H). 13C NMR (75MHz, DMSO-d6): 39.13 (CH3), 39.14 (CH3)2), 40.80

(CHCH2), 40.95 (CHCH3), 116.04-132.16 (aryl C), 150.09 (N-C-N), 180.95 (C=O). LCMS: 549

(M+H)+.

3.12.4: 2-(2-(2-(4-(4-Chlorophenyl)(phenyl)methyl)piperazin-1-yl)ethoxy)methyl)-1H-

benzo-(d)imidazol-1-ylsulfonyl)anthracene-9,10-dione (36)


procedure using anthraquinonesulfonyl chloride

and corresponding benzimidazole (30). Rf =

0.63. Yield: 64 %. m.p. 273-275 oC. 1H NMR

(300 MHz, acetone): δ 2.07 (m, 4H, CH2

N

N O NN

Cl

SO OO

O

SN NO

O

O

O

SN N

N

O

O

O

O

O2N


116

piperazine), 2.33 (m, 4H, CH2 piperazine), 2.56 (m, 2H, CH2), 4.01 (s, 2H, O-CH2), 5.25 (s, 1H,

N-CH), 6.84-7.04 (Ar-H). 13C NMR (75 MHz, acetone-d6): δ 41.13 (CH2 piperazine), 41.19 (N-

CH2), 54.21 (O-CH2), 73.51 (N-CH), 121.02-138.30 (aryl C), 142.29 (C-Cl), 144.34 (C=N),

181.51 (C=O). LCMS: 732 (M+H)+.

3.13. Synthesis of Schiff base derivatives (37-41)


The Schiff base derivatives were synthesized by the following method. A solution of aromatic

amine (0.007 mol) was dissolved in absolute ethanol and then it was slowly added to a solution

of substituted benzaldehyde (0.014 mol) in ethanol. After stirring the reaction mixture for two

hours at 40-50˚C, then on cooling the precipitates formed were collected by filtration. The

product was washed several times with cold water and then recrystallized from ethanol.

3.13.1: 1,5-bis-(3-Hydroxybenzylideneamino)anthracene-9,10-dione (37)


procedure using 2,6-diamino anthraquinonone (7

mmol) and 3-hydroxybenzaldehyde (0.014 mol).

Rf = 0.52. Yield: 58 %.m.p. 200-201 oC.1H NMR

(300 MHz, DMSO-d6): δ 6.78-7.81 (Ar-H), 8.36

(s, 1H, CH), 9.40 (s, 1H, OH). 13C NMR (75 MHz, DMSO-d6): δ 112.6-145.70 (aryl C), 155.67

(C-OH), 163.4 (C=N), 175.12 (C=O). LCMS (m/z): 447 (M+H)+.

3.13.2: 1,5-bis-(4-Fluorobenzylideneamino)anthracene-9,10-dione (38)


procedure using 2,6-diamino anthraquinonoe (7

mmol) and 4-flourobenzaldehyde (0.014 mol). Rf

= 0.32. Yield: 67 %. m.p. 214-216 oC.1H NMR

(300 MHz, DMSO-d6): δ 6.34-7.60 (Ar-H), 8.30 (s, 1H, CH). 13C NMR (75 MHz, DMSO-d6): δ

112.6-145.70 (aryl C), 163.45 (C-F), 162.89 (C=N), 178.52 (C=O). LCMS: 451 (M+H)+.

O

O

N N FF

O

O

NN

OH

HO


117

3.13.3: 3-(3-(Naphthalen-1-ylimino)prop-1-enyl)-5-nitrophenol (39)


napthylamine (0.01 mol), and 3-(3-hydroxy-5-nitrophenyl)

acrylaldehyde (0.01 mol). Rf = 0.43. Yield: 57 %.m.p.183-

184oC. 1H NMR (300 MHz, DMSO-d6): δ 3.89 (d, J=12.8 Hz,

1H, CH), 4.73 (m, J=12.7 Hz, 1H, CH), 6.12 (d, J=12.8 Hz, 1H, N-CH), 7.16-7.81 (Ar-H). 9.02

(s, IH, OH), 13C NMR (75 MHz, DMSO-d6): δ 116.12 (NCH-CH), 102.75-130.20 (aryl C),

147.12 (C-NO2), 148.33 (C-N), 158.99 (C-OH), 166.70 (C=N). LCMS: 319 ((M+H)+.

3.13.4: bis-N-3-phenylallylidene)biphenylamine (40)

Compound (40) was synthesized by

general procedure using biphenylamine

(0.01 mol), and cinnamaldehyde (0.02

mol). Rf = 0.49. Yield: 61 %.m.p.70 oC.1H

NMR (300 MHz, CDCl3): δ 5.27 (d,

J=12.8 Hz, 1H, CH), 6.34 (t, J=12.8 Hz, 1H, CH), 7.10-7.79 (Ar-H). 13C NMR (75 MHz,

CDCl3): δ 112.29 (NCH-CH), 120.75-138.50 (aryl C), 147.91 (C-N), 167.12 (C=N). LCMS

(m/z): 413 ((M+H)+.

3.13.5: bis-N-(2,4-dichlorobenzylidene)biphenylamine (41)

Compound (41) was synthesized by

general procedure using biphenylamine

(0.01 mol), and 2,4-dichlorobenzaldehyde

(0.02 mol). Rf = 0.59. Yield: 67 %.m.p. 204-205 oC.1H NMR (300 MHz, DMSO-d6): δ 7.28-7.80

(Ar-H), 8.25 (s, 1H, CH). 13C NMR (75 MHz, DMSO-d6): δ 121.01-155.68 (aryl C), 169.85

(CH). LCMS (m/z): 499 (M+H)+.

3.14. Synthesis of pyrazole derivatives (42-43)


8-Quinolinoxyacetic acid hydrazide was prepared by the reaction of 0.001 molof 8-

hydroxyquinoline with ethyl chloro acetate (0.001 mol) in the presence of methanol and H2SO4.

The resulting intermediate was then treated with hydrazine hydrate at rt.

NN

Cl

Cl

ClCl

NHC

NCH

NO2N

HO


118

A mixtureof aniline (0.01 mol), conc HCl and water were stirred for 15 mints at 0°C. NaNO2was

added in reaction mixture maintaining the temperature at 0°C. The mixture of ethyl acetate (0.01

mol) and sodium acetate was then added in reaction mixture with stirring. The precipitates of

ethyl 3-oxo-2-(2-phenylhydrazono)butanoate(A) was collected. (Scheme 2.9)

8-Quinolinoxyacetic acid hydrazide (0.001 mol) was added to solution of ethyl 3-oxo-2-(2-

phenylhydrazono)butanoate (0.001 mol) in acetic acid and it was refluxed for 6-7 hours.

Precipitates were collected and recrystallized with ethanol.

3.14.1: 1-(4-((3-nitrophenyl)diazenyl)-3,5-dimethyl-1H-pyrazol-1-yl-)-2-(quinolin-8-

yloxy)ethanone (42)


procedure using 3-nitroaniline8-quinolinoxyacetic

acid hydrazide. Rf = 0.40. Yield: 78 %. m.p. 199-

200 oC.1H NMR (300 MHz, DMSO-d6): δ 3.36 (s,

6H, CH3),5.82 (s, 2H, O-CH2), 6.93-7.90(Ar-H). 13C NMR (75 MHz, DMSO-d6): δ 18.35 (CH3),

19.43 (CH3), 70.92 (OCH2), 113.17-146.93 (Ar-C), 168.26 (C-NO2). LCMS: 431 (M+H)+.

3.14.2: 1-(4-((4-hydroxyphenyl)diazenyl)-3,5-dimethyl-1H-pyrazol-1-yl-)2-(quinolin-

8-yloxy)ethanone (43)


procedure using 4-hydroxyaniline8-quinolinoxyacetic

acid hydrazide. Rf = 0.51. Yield: 77 %. m.p. 195-196oC.1H NMR (300 MHz, DMSO-d6): δ 1.85 (s, 3H,

CH3), 3.35 (s, 3H, CH3), 4.04 (O-CH2), 7.49‐7.85(Ar-H), 9.02 (s, IH, OH).13C NMR (75 MHz,

DMSO-d6): δ 19.56 (CH3), 21.47 (CH3), 70.42 (OCH2), 126.84-134.19 (aryl C), 158.86 (C-OH).

LCMS: 402(M+H)+.

3.15. Synthesis of benzamides (44-46)


Direct synthesis of amide derivatives was carried out by reacting the anilines with acids by using

5-methoxy-2-iodophenylboronic acid (MIBA) as catalyst under mild conditions at ambient

N N

NN

O

ONHO

NN

NN O

O NO2N


119

temperature in the presence of molecular sieves. A possible catalytic cycle was based on the

supposed formation of an acylborate intermediate. The product was recrystallized from ethanol.

3.15.1: 2-(4-Isobutylphenyl)-N-(naphthalen-2-yl)propanamide (44)

Compound (44) was synthesized by general procedure using a

commercial sample of ibuprofen and 1-naphthylamine. Rf = 0.58.

Yield: 81 %. m.p. 197-198 oC.1H NMR (300 MHz, DMSO-d6): δ

1.20 (d, J=9.7 Hz, 6H, (CH3)2), 1.67 (d, J=7.8 Hz, 3H, CH3), 2.34

(m, 1H, CHCH2), 2.72 (d, 2H, J=8.6 Hz, CHCH2), 4.05 (q, J=7.8

Hz, 1H, CHCH3), 6.82-7.46 (Ar-H), 11.15 (s, IH, NH). 13C NMR (75MHz, DMSO-d6): 18.78

(CH3), 23.86 (CH3)2), 30.27 (CHCH2), 41.45 (CHCH3), 47.55 (CHCH2), 104.64-140.38 (aryl C),

167.83 (C=O). LCMS: 332 ((M+H)+.

3.15.2: 2-(2-(4-(4-Chlorophenyl)(phenyl)methyl)piperazin-1-yl)ethoxy)-N-(naphthalen-2-

yl)acetamide (45)


a commercial sample of cetirizine and 1-naphthylamine. Rf

= 0.67. Yield: 79 %. m.p. 129-131oC.1H NMR (300 MHz,

acetone-d6): δ 2.07 (m, 4H, N-CH2CH2), 2.34 (m, 4H, N-

CH2CH2), 2.56 (t, J=13.8 Hz, 2H, N-CH2), 4.06 (s, 2H, O-

CH2), 5.12 (s, 1H, N-CH), 6.84-7.46 (Ar-H), 8.94 (s, 1H,

NH). 13C NMR (75 MHz, acetone-d6): δ 58.40 (CH2piperazine), 59.22 (N-CH2), 61.52 (O-CH2),

77.42 (N-CH), 113.17-135.42 (aryl C), 150.41 (C-Cl), 168.26 (C=O). LCMS (m/z): 515 (M+H)+.

3.15.3: bis-N-biphenylacetamide (46)


biphenylamine and acetaldehyde. Rf = 0.70. Yield: 78 %. m.p.

316-318oC.1H NMR (300 MHz, DMSO-d6): δ 2.51 (s, 3H,

CH3), 7.38-8.42 (m, Ar-H), 11.18 (s, 1H, NH). 13C NMR (75

MHz, DMSO-d6): δ 21.50 (CH3), 128.06-136.67 (aryl C), 169.67 (C=O). LCMS: 269 (M+.), 253

(M+H)+.

HN NH

O

O

ON N

Cl

HN

O

NH

O


120

3.16. IL-2 inhibitory Studies

3.16.1 IL-2 Production and quantification

Jurkat (human T lymphocyte leukemia) cells were kindly provided by Prof. Daniel Hoessli from

University of Geneva, Switzerland. The cells were maintained in RPMI-1640 (BioM

Laboratories, Chemical Division, Malaysia) supplemented with 5% FBS and 1% penicillin /

streptomycin (GIBCO New York U.S). Upon 70% confluency cells were plated in 96-well flat

bottom plates (Costar, NY, USA) at a concentration of 2×106 cells/mL. The cells were activated

by using 20 ng/mL of phorbolmyristate acetate (PMA) and 7.5 µg/mL of phytoheamagglutinin

(PHA) (SERVA, Heidelberg, Germany). Cells were then treated with three different

concentrations (2, 10, 50 μg/mL) of compounds and plate was incubated for 18 hours at 37 ºC in

5% CO2. Supernatants were collected and IL-2 quantification was performed using human IL-2

Kits Duo Set (R&D systems, Minneapolis, USA) followed manufacturer’s instructions224.

CHAPTER 4: CONCLUSIONS

121

4.1 CONCLUSION

Current study aims at establishing an effective CADD protocol for identification of IL-2

inhibitors as immunomodulating agents following structure based pharmacophore protocol using

MOE software. Pharmacophore based virtual screening and molecular docking studies were used

for hits identification. The binding mode analysis of these hits revealed that these compounds

were able to block binding site of IL-2/IL-2Rα. In the next step these hits were optimized and

forty six synthetic analogs of these hits were synthesized in wet lab using different experimental

procedures.

Synthesis of chalcones (1-7) was carried out by Claisen Schmidt condensation. These

synthesized chalcones were used as precursor for the synthesis of dihydropyrimidines (8-13),

benzodiazepines (14-16), benzothiazepines (17-19), pyrazole carbaldehydes (20-25) and

pyrazole carbothioamides (26-28). A series of benzimidazole derivatives (29-32) was

synthesized by reacting an aldehyde with sodium metabisulfite, followed by the reaction of

adduct with phenylene diamine. Anthraquinone derivatives of benzimidazoles (33-36) were

prepared by condensation reaction of benzimidazoles with anthraquinone sulfonyl chloride.

Schiff base derivatives (37-41) were synthesized by reacting aromatic amines with different

substituted benzaldehydes. Multistep synthesis of pyrazole derivatives (42-43) was carried out by

the reaction of anilines and ethylacetoacetate followed by the reaction of product with 8-

quinolinoxy acetic acid hydrazide in acetic acid solution. Benzamide derivatives (44-46) were

synthesized by reacting ibuprofen and cetirizine with aromatic amines. The newly synthesized

compounds were characterized by their IR, MS, 1H and 13C NMR spectral data.

All the synthesized compounds were subsequently assayed for their ability to inhibit IL-2

production and they were found to be potent inhibitors with IC50 values ranging from < 2 to >50

μg/ml. These compounds may function as a starting point for the discovery of promising

candidates as immunomodulatory agents. Moreover the synthesized derivatives of cetirizine (30,

36, 45) and ibuprofen (29, 35, 44) may serve as potential candidates for the development of

repurpose drugs.

Current study resulted in a successful designing and synthesis of new IL-2 inhibitors in different

rounds of dry and wet labs. Since all the compounds obeyed RO5 cut off limits, therefore, they

may prove to be safe for human consumptions after their ADMET prediction.

CHAPTER 4: CONCLUSIONS

122

4.2 FUTURE RECOMMENDATION

A variety of novel IL-2 inhibitors having different chemical scaffolds were designed through

pharmacophore based designing protocol in dry lab subsequently synthesized in wet lab. All the

compounds were found to inhibit IL-2 with moderate to high activity. To date no data is reported

on the exact mechanism of action of these compounds, which deserves further investigation.

Moreover, pharmacological, toxicological and clinical studies would be essentially required to

elucidate the exact mechanism of IL-2 inhibition and to establish their efficacy and safety for

their use as new immunomodulators.

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141

Annexure 11H NMR & 13C NMR Spectra of compound 10

CH2

CH

NH2

NH

Ar-C

CH2

CH

Ar-CC=S C-Cl

142

Annexure 2

GCMS of compound 12

143

Annexure 3

LCMS of compound 14

144


CH2

zCHOH

Ar-C

NH

Ar-C

CH2CH

Ar-C

C-OHC-NO2

145

Annexure 5

GCMS of compound 25

146


CH3

23z

CH

Ar-H

CH2a

CH2b

CH2

CHAr-C

C=OC=N

CH3

147


NH2

CH

Ar-H

CH2a

CH2b

CH2

CH

Ar-C

C=SC-Cl

148

Annexure 8

GCMS of compound 31

149


CH2-piperazine

NCH

Ar-H

CH2

OCH2

CH2-piperazine

-NCHAr-H

CH2OCH2

C=O

150

Annexure 10

LCMS of compound 39

151


CH3

3z

Ar-C

C=OCH2

C-OH

152


CH2-piperazineNCH

Ar-H CH2

OCH2

NH

CH2-piperazineNCH

Ar-H CH2

OCH2

C=O

LIST OF INTERNATIONAL PUBLICATIONS

153

1. Microwave assisted synthesis, antibacterial activity against Bordetella bronchiseptica of

a library of 3΄-hydroxyaryl and heteroaryl chalcones and molecular descriptors-based

SAR. Farzana Latif Ansari, Muhammad Baseer, Fatima Iftikhar, Saima Kalsoom, Ahsan

Ullah, Samina Nazir, Awais Shaukata, Ihsan-ul-Haq, Bushra Mirza. ARKIVOC, 2009,

10(10), 318-332.

2. In silico modeling of the specific inhibitory potential of thiophene-2,3-dihydro-1,5-

benzothiazepine against BchE in the formation of β-amyloid plaques associated with

Alzheimer’s disease, Zaheer Ul-Haq, Waqasuddin Khan, Saima Kalsoom, Farzana Latif

Ansari. Theoretical Biology and Medical Modelling, 2010, 7:22doi:10.1186/1742-4682-

7-22.

3. Identification of type-specific anticancer histone deacetylase inhibitors: road to success,

Nighat Noureen, Hamid Rashid, Saima Kalsoom. Cancer Chemotherapy and

Pharmacology, 2010, 66 (4), 625-633.

4. Ligand based pharmacophore modeling of anticancer histone deacetylase inhibitors,

Nighat Noureen, Saima Kalsoom, Hamid Rashid. African Journal of Biotechnology,

2010, 9 (25), 3923-3931.

5. Homology modeling of ǳ-aminobutyrateaminotransferase, a pyridoxal phosphate-

dependent enzyme of Homo sapiens: Molecular modeling approach to rational drug

design against epilepsy, Hina Naz Khan, Hamid Rashid, Saima Kalsoom. African Journal

of Biotechnology, 2011, 10(31), 5916-5926.

6. In silico pharmacophore model generation for the identification of novel

butyrylcholinesterase inhibitors against Alzheimer’s disease, Sumra Wajid Abbasi, Saima

Kalsoom, Naveeda Riaz. Medicinal Chemistry Research, 2011, 21(9), 2716-2722.

7. Structural modeling of natural citrus products as potential cross-strain inhibitors of

Dengue virus, Naveeda Riaz, Ayma Aftab, Fouzia Malik, Ahsan Sheikh, Waseem Akram

and Saima Kalsoom. African Journal of Biotechnology, 2011, 10 (34), 6633-6639.

8. Investigations of drug–DNA interactions using molecular docking, cyclic voltammetry

and UV–Vis spectroscopy, Fouzia Perveen, Rumana Qureshi , Farzana Latif Ansari,

Saima Kalsoom, Safeer Ahmed. Journal of Molecular Structure, 2011, 1004, 67–73.


154

10. Ligand based pharmacophore model development for the identification of novel

antiepileptic compound, Hina Naz Khan, Saima Kalsoom, Hamid Rashid. Epilepsy

Research, 2011, 98(1), 62-71.

11. Electrochemical, spectroscopic and molecular docking studies of Anticancer

Organogermalactones. Fouzia Perveen, Rumana Qureshi, Afzal Shah, Farzana Latif

Ansari, Saima Kalsoom, Sumera Mehboob. International Journal of Pharmaceutical,

2011, 1(1), 1-8.

12. Microwave assisted synthesis and bioevaluation of some semicarbazones. Laila Jafri,

Farzana Latif Ansari, Maryam Jamil, Saim Kalsoom, Sana Khurram, Bushra Mirza,

Chemical biology and drug designing, 2012, 79(6), 950-959.

13. An efficient anticancer histone deacetylase inhibitor and its analogues for human

HDAC8, Nighat Noureen, Hamid Rashid, Saima Kalsoom, Medicinal Chemistry

Research, 2012, 21(5), 568-577.

14. In silico studies on 2,3-dihydro-1,5-benzothiazepines as cholinesterase inhibitors.

Farzana Latif Ansari, Saima kalsoom, Zaheer-ul-Haq, Zahra Ali, Farukh Jabeen.

Medicinal Chemistry Research, 2012, 21, 2329-2339.

15. In silico Molecular Docking and Design of Anti-Hepatitis B Drugs, Mahira Arooj,

Madiha Sattar, Muniba Safdar, Saima Kalsoom, Shaheen Shahzad. IOSR Journal of

Pharmacy and Biological Sciences, 2012, 3(6), 41-45.

16. Identification of an antiepileptic lead compound with a more selective activity and its

analogues for GABA-AT using in silico approach, Hina Naz Khan, Saima Kalsoom,

Hamid Rashid. Medicinal Chemistry Research, 2013, 22(2), 879-889.

17. In silico ligand-based pharmacophore model generation for the identification of novel

Pneumocystis carinii DHFR inhibitors, Yusra Sajid Kiani, Saima Kalsoom, Naveeda

Riaz. Medicinal Chemistry Research, 2013, 22, 949-963.

18. Design and Molecular Docking of Antioxidant Lead compound and its Analogues

Acting as Human Tyrosine kinase inhibitors, Ammara Khalid, Saima Kalsoom,

Naveeda Riaz, IOSR Journal of Pharmacy and Biological Sciences, 2013, 5(4), 75-80.

19. In vitro and in silico exploration of IL-2 inhibition by small drug-like molecules, Saima

Kalsoom, Umer Rashid, Awais Shaukat, Omer Mohamed Abdalla, Khalida Hussain,


155

Waqasuddin Khan, Samina Nazir, Mohammad Ahmad Mesaik, Zaheer-ul-Haq and

Farzana Latif Ansari. Medicinal Chemistry Research, 2013, 22(12), 5739-5751.

20. In Silico Approach for Lead Identification and Optimization Of Antidiabetic

Compounds Shabana bibi, Saima Kulsoom, Hamid Rashid, IOSR Journal of Pharmacy

and Biological Sciences, 2013), 7 (3), 36-46.

21. Ligand based approach for pharmacophore generation for identification of Novel

compounds having antidiabetic activity, Shabana bibi, Saima kalsoom, Hamid Rashid.

International Journal of Pharmacy and Pharmaceutical Sciences, 2013, 5( 4), 303-

314,

22. Pharmacophore model generation for microtubule-stabilizing anti-mitotic agents

(MSAAs) against ovarian cancer, Asma Abro, Saima Kalsoom, Naveeda Riaz.

Medicinal Chemistry Research, 2013, 22(9), DOI:10.1007/s00044-012-0445-8.

23. Experimental and theoretical studies on DNA binding affinities Of benzylidene

acetophenone and its derivatives, Jaweria Ambreen, Fouzia Perveen, Rumana Qureshi,

Saima Kalsoom, Safeer Ahmed, Musfirah khaleeq, Farzana Latif Ansari, Asian

Academic Research Journal of Multidisciplinary, 2014, 1(19), 648-671.

24. Synthesis, characterization and biological evaluation of Ferrocene based

Poly(azomethene)esters, Asghari Gul, Sehrish Sarfraz, Zareen Akhter, Muhammad

Siddiq, Saima Kalsoom, Fouzia Perveen, Farzana Latif Ansari, Bushra Mirza. Journal

of Organometallic Chemistry, 2015, 779, 91-99.

25. Lead identification and optimization of plant insulin-based antidiabetes drugs through

molecular docking analysis, Shabana Bibi, Saima Kalsoom, Hamid Rashid and Katsumi

Sakata. International Journal of Pharmacy and Pharmaceutical Sciences, 2015, 7(3),

337-343.

26. Synthesis, molecular docking studies and anticonvulsant evaluation of novel bis-

phenylhydrazones against chemically-induced seizures in mice, Humaira Masood

Siddiqi, Aneela Tabasum, Saima Kalsoom, Farzana Latif Ansari, Muhammad Shoaib

Akhtar, Sumera Qasim and Mariam Khalid, Submitted in Medicinal Chemistry

Research.

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