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Al-Azhar University–Gaza
Deanship of Postgraduate Studies
Faculty of Pharmacy
Master of Pharmaceutical Sciences
Computational Approach Towards Exploring the
Polypharmacology of Urtica dioica and Salvia officinalis Focusing on
Antidiabetic, Antihyperlipidemic and Anticancer Activities
By
Asmaa Mahmoud Rabie Rabie
Supervisor
Prof. Dr. Ihab Almasri
Prof. Dr. of Medicinal Chemistry & Drug Discovery
A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of
Master in Pharmaceutical Sciences
January-2021
i
Declaration
I declare that the thesis titled ―Computational Approach Towards Exploring the
Polypharmacology of Urtica dioica and Salvia officinalis Focusing on
Antidiabetic, Antihyperlipidemic and Anticancer Activities‖ submitted for the
degree of master of pharmaceutical sciences, is the result of my own research work
and the work provided in this thesis, unless otherwise referenced, is my own work,
and has never been submitted elsewhere for any other degree qualifications neither
for any academic titles, nor for any other academic or publishing institutions.
I declare that I will be responsible for academic and legal terms if this work
proves the opposite.
Signature:
Asmaa M R Rabie
Date: 20/1/2021
ii
Dedication
To my great parents, who never stop giving of themselves in countless ways,
To my dearest husband, who leads me through the valley of darkness with light
of hope and support,
To my beloved kids: Moath, Haya, Hala and Mohammed whom I can't force
myself to stop loving,
To my brothers and sisters, the symbol of love, generous and giving,
To my friends who encourage and support me,
To all the people in my life who touch my heart,
Asmaa M R Rabie
iii
Acknowledgment
In the Name of Allah, the Most Merciful, the Most Compassionate all praise be to Allah, the
Lord of the worlds; and prayers and peace be upon Mohamed His servant and messenger.
First and foremost, all my prayers and my limitless thanks to Allah, the Ever-Magnificent; the
Ever-Thankful, for His help and bless. I am totally sure that this work would have never become
truth, without His guidance.
I would like to express my profound gratitude and deep regard to my supervisor, Prof.Dr. Ihab
Almasri for his improving suggestions, advices, guidance, patience and constant encouragement
throughout this thesis, so I ask Allah to reward him on my behalf.
I owe a deep debt of gratitude to our university, Al-Azhar University, Gaza, for giving us an
opportunity to complete this work. I extend my appreciation to Faculty of Pharmacy and to all
the academic staff in it.
Thanks to the OpenEye Scientific company for Omega 2.5.1.4, & OEDOCKING 3.2.0.2
Softwares donation to Al-Azhar University, which were used in the study.
I owe profound gratitude to my husband, Raed, whose constant encouragement, limitless giving,
patience and great sacrifice, helped me accomplish my degree. Without his support, this study
would not have been possible.
My sincere thanks to my colleagues, pharmacy staff members and partner in working field in
Ministry of Health, especially in Shohadaa Jabalia Martyrs Health center as well as in Hala El-
Shawa Health Center,
Finally, my appreciation goes to all my beloved friends for their encouragement and support
along the way of doing my thesis.
iv
Abstract
Natural plants have long been considered as the cornerstones of drug discovery and
development. They have a wide range of diversity of multidimensional chemical structures; in
the meantime, the utility of natural products as broad biological target modifiers has also gained
considerable attention. Therefore, the identification of the molecular targets of natural
compounds is a critical step in rational design of more selective, potent and safer drugs. In this
work, we explored the polypharmacology of S. Officinalis and U. dioica and their
phytochemicals focusing on anticancer, antidiabetic and antihyperlipidemic activities using a
ligand-based target fishing approach. The fishing protocol was started with the generation of a
chemogenomic database that links individual targets with specific target ligands or group of
drugs. Targets profile was then generated using ROCS software. The applied method was able to
retrieve known on-targets as we had found that, many of our natural constituents could bind to
ER, MAPK14, PIK3CG and PPAR-γ such as apigenin, luteolin, oleanolic acid and quercetin.
The applied method was also able to identify potential off-targets. The validity of these off-
targets as potential targets were evaluated by docking simulation according to our adopted
experimental procedures, which include preparation of proteins, preparation of ligands and then
docking using FRED software version 3.3.1.2 (FRED, 2015) within the OEDockind suite in the
presence of the explicit water molecules. The obtained results clarified that the different
phytochemicals of S. Officinalis and U. dioica were successfully docked within the active sites of
the off-targets with relatively good scores such as DAPK1, Tank2, CDK2, CK2 and FGFR1.
These off-targets were consistent with recently identified bioactivities of S. Officinalis and U.
dioica and their phytochemicals.
In our results, the concept of polypharmacology is obviously clarified as multiple
phytochemicals could affect the same target on the disease pathway producing synergistic effect
or an individual compound could affect multiple targets that involved in the same disease.
Finally, further in in-vitro and in-vivo studies on the phytochemicals of S. officinalis and U.
dioica against the fished off-targets will provide more understanding of their pharmacological
properties as well as could provide good leads for designing new more potent and safer
anticancer, dantidiabetic and hypolipidemic drugs.
Keywords: Natural plants; Salvia officinalis; Urtica dioica; Polypharmacology; Similarity
search; Target fishing; Docking.
v
Abstract in Arabic
الملخص باللغة العربية
اليياشررا الميسياةيرر تشره مرر ياسردى سررفيررا تتسر دويةرر يتطهةرىرا فررا اشتذرال اأ ساسري الركيررزا اأ ت الطبيعير شباترراال تعتبرريعد تحديرد ك،ياس اىتسام كبير لذل حيهة على نطاقدتقبلات كسالشباتات الطبيعي اأ عادو ؛ فا نفس الهقت، حظيت متعددوا
خطررها حاسررس فررا الترررسيي العكلانررا لسركبررات أشوررر انتكاةيرر يفعاليرر يأمان ررا فررا ىررذا شباتررات الطبيعيرر للالجزةئيرر السدررتقبلاتيالسرهادو الميسياةير الشباتير الخابر يسرا مر التركيرز ي الكررة السرمير ات الدياةير الستعرددوا لشبتترا قسشرا اشتذرال الترر ير ،العسا
عسرا الجديردا للسركبرات ليرات الآ التعررل علرى نرةكر فرر حرحسيات الردم اسرتخدامي نذط السزرادوا للدررنان يالدر ر على اأ سدرتقبلاتات كيسياةي جيشهمير ترر ا القاعدا يان إنذاء بريتهكهل ىذا ال دأ تسادوا على مركبات ذات فعالي معريف الطبيعي اعكررد ل ((ROCS اسررتخدام رنررام السدررتقبلاتمحررددوا أي مجسهعرر مرر اأدويةرر رري ترري رنذرراء ملرر تعرةرر سدررتقبلاتالفردويرر حير يجردنا أن الس هنرات الطبيعير السختلفر السعريفر للسركبرات الطبيعير ى تحديرد السدرتقبلاتالطرةك السطبكر قرادو ا علرشانت ,ER مورررا اأ جيشررري ، اللهتيرررهلي ، حسررر اأيليانهلرررك يالمهةرسرررتي قرررادو ا علرررى ات تبرررا سدرررتقبلات معريفررر مترررا يللشبتتررر
MAPK14, PIK3CG وPPAR-γ ) أدوت أيزا الى التعررل علرى مدرتقبلات لري تمر معريفر مر قبرا الطرةك السطبك ،جرراءات تجر تشرا السعتسردا الترا نرةكر محاشراا اس سراء حدر ركييي فعالي ىذه السدتقبلات كسدتقبلات محتسلر اسرتخدام ي تي ت
أن ةي أفرادوت الشترا ) 3 3 1 1( نبعر FRED رنام ساء استخدام السركبات ي تطبيق اسشات ي تتزس تحزير البريتي ,DAPK1مورا مهاق ات تبا م السدتقبلات الجديدا ساء دواخااسقادو ا على كانت كرة ي ال السرمي العديد م مركبات
Tank2, CDK2, CK2وFGFR1 متهافكر مر اأنذرط الحيهةر الترا تري تحديردىا مر خر ا ( كسرا أن ىرذه السدرتقبلات الجديردا ي الكرة يالسهادو الميسياةي الشباتي الخاب يسا السرمي لشبتتا
جليا، حي تحظشا عدا مركبات م نفرس الشبتر تر ر علرى أحرد السدرتقبلات تعددو التر يرات الدياةي البح يظير مبدأ نتاة فا على السرض أي أن مرك ياحد ي ر على عدا مدتقبلات للسرض مسا يعطا تر يرا مزاعفا للسركبات الطبيعي التا ت ر
علررى السركبرات الطبيعيرر للسراميرا ي الكرررة ي السدرربقبلات يفرا اأجدررام الحيهةر أخيررا، السزةررد مر الد اسررات العسلير السخبرةرر كسرا سريهفر مركبرات جيردا لتررسيي أدويةر ليرذه السركبرات، الدياةير الجديدا التا تي اشتذافيا سهل ي دو للسزةد م فيي الخهاص
ي ضد الدرنان يالد ر ي فر ححهم الدممانا ي فعالأشور أ
لة:الكلمات الدا
ساء، اسسدتقبلاتدياةي ، ح التذا و، بيد ال، الكرة ، تعددو التر يرات الالسرمي الطبيعي ، شباتاتال
vi
List of contents
Declaration .................................................................................................................................. i
Dedication .................................................................................................................................. ii
Acknowledgment....................................................................................................................... iii
Abstract ..................................................................................................................................... iv
Abstract in Arabic ...................................................................................................................... v
List of contents .......................................................................................................................... vi
List of Tables ............................................................................................................................. x
List of Figures .......................................................................................................................... xii
List of Abbreviations ............................................................................................................... xiv
Chapter One Introduction ........................................................................................................... 1
1.1 Background ..................................................................................................................... 1
1.2 Problem statement ........................................................................................................... 2
1.3 Aim ................................................................................................................................. 2
4.1 Objectives ....................................................................................................................... 3
1.5 Justification of the Study ................................................................................................. 3
Chapter Two Literature Review .................................................................................................. 4
2.1 Drug discovery and importance of medicinal plants ........................................................ 4
2.2 Sage ................................................................................................................................ 5
2.2.1 Ethnomedicinal uses of S. officinalis ........................................................................ 6
2.2.2 Phytochemical constituents of S. officinalis .............................................................. 6
2.2.3 Pharmacological activities of S. officinalis ................................................................ 8
2.2.3.1 Anticancer effects .............................................................................................. 8
2.2.2.2 Antidiabetic effects ............................................................................................ 9
2.2.3.3 Antihyperlipidemic effects................................................................................. 9
2.2.3.4 Other effects ...................................................................................................... 9
2.2.4 Clinical studies ......................................................................................................... 9
2.3 Stinging Nettle .............................................................................................................. 10
2.3.1 Ethnomedicinal uses of U. dioica ........................................................................... 11
2.3.2 Phytochemical constituents of U. dioica ................................................................. 11
vii
2.3.3 Pharmacological activities of U. dioica ................................................................... 14
2.3.3.1 Antidiabetic effects .......................................................................................... 14
2.3.3.2 Antihyperlipidemic effects .............................................................................. 15
2.3.3.3 Anticancer effects ............................................................................................ 15
2.3.3.4 Other effects .................................................................................................... 15
2.4 Cancer .......................................................................................................................... 16
2.4.1 Causes & risk factors of cancer............................................................................... 16
2.4.2 Oxidative stress and cancer..................................................................................... 17
2.4.3 Signs and symptoms ............................................................................................... 18
2.4.4 Cancer treatment .................................................................................................... 18
2.4.4.1 Cancer treatment by drugs ............................................................................... 18
2.4.4.2 Cancer treatment by natural plants ................................................................... 20
2.5 Diabetes mellitus (D.M) ................................................................................................ 22
2.5.1 Classification of diabetes mellitus .......................................................................... 22
2.5.2 Signs and symptoms ............................................................................................... 23
2.5.3 Complications of diabetes mellitus ......................................................................... 23
2.5.4 T2DM pathogenesis and major risk factors ............................................................. 23
2.5.5 Management of T2DM ........................................................................................... 25
2.5.5.4 Non pharmacological treatment ....................................................................... 25
2.5.5.2 Pharmacological treatment............................................................................... 25
2.5.5.3 Diabetes management by natural plants ........................................................... 26
2.6 Hyperlipidemia ............................................................................................................. 27
2.6.1 Classification of hyperlipidemia ............................................................................. 27
2.6.2 Causes and risk factors of hyperlipidemia ............................................................... 27
2.6.3 Complications of hyperlipidemia ............................................................................ 27
2.6.4 Pathophysiology of hyperlipidemia ........................................................................ 27
2.6.5 Management of hyperlipidemia .............................................................................. 28
2.6.5.1 Non pharmacological management .................................................................. 28
2.6.5.2 Pharmacological therapy.................................................................................. 28
2.6.5.3 Management of hyperlipidemia by natural plants ............................................ 29
viii
2.7 Computer aided drug design (CADD( ........................................................................... 30
2.7.1 CADD classification............................................................................................... 31
2.7.2 Computational target fishing .................................................................................. 32
2.7.2.1 Polypharmacology ........................................................................................... 33
2.7.2.2 Drug repurposing ............................................................................................. 34
2.7.2.3 Computational methods for target fishing ........................................................ 35
Chapter Three Methodology ..................................................................................................... 42
3.1 Computational Method .................................................................................................. 42
3.1.1 Building 3D-ligands database ................................................................................. 42
3.1.2 ROCS similarity search .......................................................................................... 43
3.1.3 Docking simulations ............................................................................................... 44
3.1.3.1 Preparation of proteins ..................................................................................... 44
3.1.3.2 Preparation of ligands ...................................................................................... 45
3.1.3.3 Docking ........................................................................................................... 46
Chapter Four Results and discussion ......................................................................................... 47
4.1 Background ................................................................................................................... 47
4.2 Polypharmacologyof S. officinalis ................................................................................. 48
4.2.1 Reported anticancer, antidiabetic and hypolipidemic targets of S. officinalis ........... 48
4.2.2 Targets identified using S. officinalis constituents as queries in RTFA .................... 50
4.2.2.1 Apigenin.......................................................................................................... 50
4.2.2.2 Carnosol .......................................................................................................... 56
4.2.2.3 Cirsimaritin ..................................................................................................... 60
4.2.2.4 Corosolic Acid ................................................................................................ 61
4.2.2.5 Ellagic acid ...................................................................................................... 63
4.2.2.6 Ferruginol ........................................................................................................ 67
4.2.2.7 Genkwanin ....................................................................................................... 70
4.2.2.8 Hispidulin ........................................................................................................ 72
4.2.2.9 Luteolin ........................................................................................................... 75
4.2.2.10 Oleanolic acid ................................................................................................. 77
4.2.2.11 Quercetin ......................................................................................................... 79
ix
4.2.2.12 Rutin .............................................................................................................. 82
4.2.2.13 Rosmarinic acid ............................................................................................... 83
4.2.2.14 Ursoli acid ...................................................................................................... 85
4.3 Polypharmacology of Urtica dioica ............................................................................... 86
4.3.1 Reported anticancer antidiabetic and hypolipidemic targets of U. dioica .............. 86
4.3.2 Targets identified using U.dioica constituents as queries in RTFA.......................... 88
4.3.2.1 Caffeoylmalic acid .......................................................................................... 88
4.3.2.2 Chlorogenic acid ............................................................................................. 89
4.3.2.3 Isolariciresinol ................................................................................................. 89
4.3.2.3 Neoolivil ......................................................................................................... 92
4.3.2.5 Secoisolariciresinol ......................................................................................... 95
Chapter Five Conclusion ..................................................................................................... 97
Chapter six Recommendations .............................................................................................. 98
References ............................................................................................................................... 99
x
List of Tables
Table 2.1: Ethnomedicinal uses of S. officinalis ......................................................................... 6
Table 2.2: Chemical structures of main polyphenols and flavonoides isolated from S. officinalis 7
Table 2.3: Chemical structures of main terpenoids isolated from S. officinalis ........................... 8
Table 2.4: Summary of clinical studies on S. officinalis ................................................................ 10
Table 2.5: Ethnomedicinal uses of Urtica dioica L ................................................................... 12
Table 2.6: Structures of chemical constituents of U. dioica ...................................................... 13
Table 2.7: The Anticancer effects of U. dioica ......................................................................... 15
Table 2.8: Various types of anticancer drugs and their examples .............................................. 19
Table 2.9: Anticancerous medicinal plants ............................................................................... 21
Table 2.10: Marketed therapeutic agents for T2DM and respective mechanisms. ..................... 25
Table 2.11: Medicinal plants used in the treatment of T2DM ................................................... 26
Table 2.12: Antihyperlipidemic drugs and their mechanisms of action ..................................... 29
Table 2.13: Plants with hypolipidemic activity ......................................................................... 30
Table 3.1: Proteins data obtained from Protein data bank. ........................................................ 44
Table 4.1: On-targets and off-targets of S. officinalis ............................................................... 49
Table 4.2: Pharmacological profiling for apigenin using ROCS ............................................... 51
Table 4.3: Pharmacological profiling for carnosol using ROCS ............................................... 57
Table 4.4: Pharmacological profiling for cirsimaritin using ROCS ........................................... 60
Table 4.5: Pharmacological profiling for corosolic acid using ROCS ....................................... 62
Table 4.6: Pharmacological profiling for ellagic Acid using ROCS .......................................... 63
Table 4.7: Pharmacological profiling for ferruginol using ROCS ............................................. 67
Table 4.8: Pharmacological profiling for genkwanin using Rocs……………………………….72
xi
Table 4.9: Pharmacological profiling for hispidulin using ROcs ............................................... 74
Table 4.10: Pharmacological profiling for luteolin using ROCS ............................................... 75
Table 4.11: Pharmacological profiling for oleanolic using ROCS ............................................ 77
Table 4.12: Pharmacological profiling for quercetin using ROCS ............................................ 79
Table 4.13: Pharmacological profiling for rutin using ROCS ................................................... 82
Table 4.14: Pharmacological profiling for rosmarinic acid using ROCS ................................... 83
Table 4.15: Pharmacological profiling for ursolic acid using ROCS ......................................... 85
Table 4.16: On-targets and off-targets of U. dioica .................................................................. 87
Table 4.17: Pharmacological profiling for caffeoylmalic acid using ROCS .............................. 88
Table 4.18: Pharmacological profiling for chlorogenic acid using ROCS ................................. 89
Table 4.19: Pharmacological profiling for isolariciresinol using ROCS .................................... 90
Table 4.20: Pharmacological profiling for neoolivil using ROcs .............................................. 94
Table 4.21: Pharmacological profiling for secoisolariciresinol using ROCS ............................. 96
xii
List of Figures
Figure 2.1: Arial parts of Salvia officinalis L. .......................................................................................... 5
Figure 2.2: Parts of U. dioica plant. (a) Whole plant; (b) Flower; (c) Trichomes; (d) Roots; (e) Leaf. .... 11
Figure 2.3: Multiple defects contribute to the development of glucose intolerance in T2DM. ................. 24
Figure 2.4: CADD in drug discovery/design pipeline. ........................................................................... 32
Figure 2.5: Comparison between docking and reverse docking .............................................................. 33
Figure 2.6: Molecular descriptors based on the ROCS color force field. ................................................ 38
Figure 2.7: Main applications of molecular docking in current drug discovery .......................... 40
Figure 3.1: Workflow of ROCS-based target fishing approach (RTFA) ................................................. 42
Figure 3.2: Simple run window display in vRocs user interface after generation of quercetin query. ...... 43
Figure 4.1: Mechanism of action for GIP, GLP-1 analogues and DPP4 inhibitors in controlling T2DM . 58
Figure 4.2: A : Detailed view of docked carnosol and the corresponding interacting amino acid within
the binding site of Tank2, B : Detailed view of co-crystallized structure (G9W, PDB
code: 5C5Q) and the corresponding interacting amino acid within the binding site of
Tank2. ............................................................................................................................. 59
Figure 4.3: Detailed view of docked cirsimaritin and the corresponding interacting amino acids within
the binding site of PPAR-γ ............................................................................................... 61
Figure 4.4: Mechanism of activation of PKB (AKT), S6K and SGK by PDK1.. .................................... 65
Figure 4.5: Insulin signaling.. ................................................................................................................ 66
Figure 4.6: The physiological signal pathways involving PTP1B. .......................................................... 68
Figure 4.7: Detailed views of docked feruginol and the corresponding interacting amino acids within
the binding site of PTP1B. ............................................................................................... 69
Figure 4.8: The overview of PI3K/AKT/mTOR signaling pathway.. ...................................................... 71
Figure 4.9: A: Detailed view of docked genkwanin and the corresponding interacting amino acid
within the binding site of PIM1, B: Co-crystallized structure (HUL, PDB code: 4XH6)
and the corresponding interacting amino acids within the binding site of PIM1, C:
Overlay view of docked genkwanin within the binding site of EGFR ............................... 73
xiii
Figure 4.10: Signaling through MAPK14 cascade and its role in the regulation of cellular functions.. .... 81
Figure 4.11: Epidermal growth factor receptor (EGFR) and its downstream signaling proteins. ............. 91
Figure 4.12: A; Detailed view of docked Isolariciresinol and the corresponding interacting amino acid
within the binding site of EGFR, B; Detailed view of co-crystallized structure (1C9 ,
PDB code: 4I23 ) and the corresponding interacting amino acid within the binding site
of EGFR, C; Overlay view of docked Isolariciresinol within the binding site of EGFR ..... 93
Figure 4.13: A: Detailed view of docked Neoolovil and the corresponding interacting amino acid
within the binding site of DPP-4, B: Surface view of docked Neoolovil and the
corresponding interacting amino acid within the binding site of DPP-4. ........................... 95
xiv
List of Abbreviations
71β-HSD
17 Beta Hydroxysteriod Dehydrogenase 1
ACAT Acyl Coenzyme-A-Cholesterol Acyl Transferase
ACL Adenosine-Triphosphate Citrate Lyase
ADME/T Absorption, Distribution, Metabolism, Excretion And Toxicity
AKT Protein Kinase B
Aº Angstrom
b GP Brain Glycogen Phosphorylase
Bak Bcl-2 Homologous Antagonist Killer
Bax Bcl-2-Associated Protein X
BCG Bacillus Calmette-Guerin
Bcl2 B-Cell Lymphoma 2
CA Caffeoylmalic Acid
CADD Computer Aided Drug Design
CDK2 Cycline Dependent Kinase 2
CGA Chlorogenic Acid
CK2 Casine Kinase 2
COVID-19 Coronavirus Disease of 2019
Cpd.ID Co-Crystallized ligand Identification
CS Chemical Similarity
CSNAP Chemical Similarity Network Analysis Pull-Down
D Dimension
DAPK1 Death Associated Protein Kinase1
DB Drug Bank
DM Diabetes Mellitus
DMPK Drug Metabolism and Pharmacokinetic
DNA Deoxyribonucleic Acid
DPP-4 Dipeptidyl Peptidase 4
DS Discovery Studio
EA Ellagic Acid
EBV Epstein-Barr Virus
EGFR Epidermal Growth Factor Receptor
EIND Emergency Investigational New Drug
ERK Extracellular Signal-Regulated Kinase
xv
ERα Estrogen Receptor alpha
ERβ Estrogen Receptor beta
FDA Food And Drug Administration
FGFR1 Fibroblast Growth Factor Receptor 1
FRED Fast Rigid Exhaustive Protein-Ligand Docking
GIP Glucose-Dependent Insulin-Tropic Polypeptide
GLO-1 Glyoxalase
GLP-1 Glucagon Like Peptide 1
GLUT-4 Glucose Transporter 4
GP Glycogen Phosphorylase
GPR G-Protein Coupled Receptor
HbA1c Glycated Haemoglobin
HER2 Human Epidermal Growth Factor Receptor 2
HLD High Density Lipoprotein
HMG-CoA Hydroxy-3-Methylglutaryl Coenzyme A
HPVs Human Papilloma Viruses
HSP90-α Heat Shock Protein 90 alpha
IARC International Agency for Research on Cancer
IC50 Half-Maximal Inhibitory Concentration
IDDM Insulin Dependent Diabetes Mellitus
IDF International Diabetes Federation
ITC Isothermal Titration Calorimetry
Ki Inhibition Constant
KSHV Kaposi‘s Sarcoma-Associated Herpes Virus
l GP Liver Glycogen Phosphorylase
LBDD Ligand Based Drug Design
LDL Low Density Lipoprotein
LHRH Luteinizing Hormone Releasing Hormone
LPL Lipoprotein Lipase
m GP Muscle Glycogen Phosphorylase
MAPK14 Mitogen Activated Protein Kinase 14
Mcl-1 Myeloid Leukemia Cell Differentiation Protein 1
MMp Metalloproteinase
MOH Ministry of Health
MTP Microsomal Triglyceride Transfer Protein
mTOR Mammalian Target of Rapamycin
xvi
NCI National Cancer Institution
NF-kB Nuclear Transcription Factor-Kappa B
NIDDM Non–Insulin Dependent Diabetes Mellitus
NMR Nuclear Magnetic Resonance
NPs Natural Products
OA Oleanolic Acid
PARP Poly Adenosine-Diphosphate (ADP)-Ribose Polymerase
PCSK9 Proprotein Convertase Subtilisin/ Kexin type 9
PDB Protein Data Bank
PDK1 Phosphoinositide Dependent Protein Kinase 1
Pgp P-Glycoprotein
PI3Kγ Phosphatidylinositol 3-Kinase gamma
PIK3CG Phosphatidylinositol-4,5-Bisphosphate3-Kinase Catalytic Subunit gamma
PIK3Cα Phosphatidylinositol-4,5-Bisphosphate3-Kinase Catalytic Subunit alpha
PIM1 Proviral Integration Site for Moloney Murine Leukaemia Virus 1
PIN1 Peptidyl-prolyl cis/trans Isomerase Never In Mitosis A (NIMA)-
Interacting 1
PIP3 Phosphatidylinositol (3,4,5)-Triphosphate
PKACα Protein Kinase A Catalytic Subunit alpha
PMoH Palestinian Ministry Of Health
PPAR-α Peroxisome Proliferator Activated Receptor alpha
PPAR-γ Peroxisome Proliferator Activated Receptor gamma
PTEN Phosphatase and Tensin Homolog
PTP1B Protein Tyrosine Phosphatase 1B
QSAR Quantitative Structure Activity Relationship
RA Rosmarinic Acid
RAF Rapidly Accelerated Fibrosarcoma Kinase
Ras Rat Sarcoma Gene
RCS Reactive Chloride Species
Ref References
RNS Reactive Nitrogen Species
ROCS Rapid Overlay of Chemical Structures Software
ROS Reactive Oxygen Species
RPTP-γ Receptor Type Protein Tyrosine Phosphatase gamma
RS Reactive Species
RSS Reactive Sulfur Species
RTFA ROCS Based Target Fishing Approach
xvii
RTK Receptor Tyrosine Kinase
S. Salvia
SBDD Structure Based Drug Design
SGLT2 Sodium Glucose Co-Transporter 2
SIRT-1 Silent Information Regulator Proteins-1
T1DM Type 1 Diabetes Mellitus
T2DM Type 2 Diabetes Mellitus
Tank2 Tankyrase 2
TG Triglycerides
U. Urtica
UA Ursolic Acid
VEGF Vascular Endothelial Growth Factor
VEGFR Vascular Endothelial Growth Factor Receptor
VLDL Very Low Density Lipoprotein
VS Virtual Screening
WHO
World Health Organisation
4
1 Chapter One
Introduction
1.1 Background
Natural products and traditional medicines are of great importance. They have been recognized
for many years as a source of therapeutic agents and have shown beneficial uses. Discovery of a
new drug usually faces challenges in the form of time and money investment. On the other hand,
traditional medicine is affordable, of low level of side effects, easily accessible, and culturally
familiar (Katiyar, et al., 2012).
The world is decorated with medicinal herbs, which is a rich wealth of endurance. Every plant is
identified by its own different therapeutic properties due to the bioactive molecule. Herbal
medicine is based on traditional medicines which have their own importance and basic
philosophy. So exploration of the chemical constituents of the plants and their pharmacological
screening may provide us the basis for developing a lead molecule through herbal drug discovery
(Kar, 2006). Thus, there is a need of investigating the various bioactive fractions as well as
evaluating of pharmacological activity of herbal drugs for achieving the dreams of herbal drug
discovery.
In our research we have chosen two of these herbs to investigate their role in treatment
of serious diseases such as cancer, diabetes and hyperlipidemia. The two herbs are
Salvia officinalis and Urtica dioica which are widely used as a medicine due to their
many pharmacological and clinical effects.
Salvia officinalis (S. officinalis) is a plant from the family of Labiatae/ Lamiaceae. A wide range
of phytochemicals are well identified in S. officinalis include alkaloids, flavonoids, glycosidic
derivatives, phenolic compounds, steroids, terpenes/ terpenoids and waxes. Studies have
revealed many pharmacological activities of Salvia officinalis include anticancer, hypoglycemic,
hypolipidemic, anti-inflammatory, antinociceptive, antioxidant, antimicrobial, antimutagenic,
and antidemential effects (Badiee, et al., 2012).
Urtica dioica (U. dioica) belongs to Urticaceae family, is a perennial herb commonly known as
‗stinging nettle‘. Phytochemical studies revealed the presence of many valuable chemical
compounds such as alkaloids, tannins, flavonoids, steroids, terpenes and polyphenols. Urtica
dioica has been reported to have various pharmacological activities like antibacterial,
2
antioxidant, analgesic, anti-inflammatory, antiviral, im munomodulatory, hepatoprotective, and
anticancer effects (Joshi, et al., 2014).
Identifying the complete target spectrum of bioactive compounds is not only a critical step in the
understanding of the mechanism of action, but also is of particular interest for rationally
designing more effective and less toxic drugs, predict the adverse effects of a compound, or drug
repurposing (Cereto-Massague, et al., 2015).
In our research, we will use in-silico target fishing approach in order to explore potential new
targets for Salvia officinalis and Urtica dioica natural compounds focusing on anticancer,
antidiabetic and antihyperlipidemic targets.
1.2 Problem statement
On the basis of the available literature evidence, S. officinalis shows many pharmacological
effects. The possible therapeutic applications for these effects need to be elucidated (Ghorbani &
Esmaeilizadeh, 2017). Also, further investigations are necessary to understand the exact
molecular mechanisms responsible for the effects of S. officinalis , especially anticancer,
antidiabetic and antihyperlipidemic activities.
Urtica dioica is used for the treatment of various diseases due to its remarkable power of
healing. This plant has got the place among the top ranked evidence based herbal medicines
(Sepide, 2016). In folk medicine U. dioica was used for the treatment of arthritis and it showed
the presence of antiasthmatic, antidandruff, astringent, depurative, diuretic, galactogogue,
haemostatic and hypoglycaemic activities in preclinical experiments. Exploration of the exact
mechanisms of actions of U. dioica and its phytoconstituents are required to support its
traditional uses (Mueen & Parasuraman, 2014), especially anticancer, antidiabetic and
antihyperlipidemic activities.
1.3 Aim
To explore the polypharmacology of Urtica dioica and Salvia officinalis focusing on anticancer,
antidiabetic and antihyperlipidemic targets.
2
1.4 Objectives
To identify new potential anticancer, antidiabetic and antihyperlipidemic targets for the
natural U. dioica components.
To identify new potential anticancer, antidiabetic and antihyperlipidemic targets for the
natural S officinalis components.
To emphasize the rule of the components of U. dioica and S. officinais as anticancer,
antidiabetic and antihyperlipidemic agents.
To open new space for further researches to evaluate the natural compounds of U. dioica
and S. officinalis as lead compounds for new anticancer, antidiabetic and antihyperlipidi-
mic targets.
1.5 Justification of the Study
Herbal medicines are very important to cure the various ailments of human. Demands of the
herbal medicines are increasing in both developed and under developed countries due to growing
recognition of natural plants being lesser of side effect, easily available in the surrounding
geographical area with low cost.
Traditional medicine using herbal drugs exists in every part of the world. U. dioica and S.
officinalis are widely distributed in Palestine and have the advantage of being available for
patients in the surrounding places with low cost and less side effects.
In addition, cancer, diabetes mellitus (DM) and hyperlipidemia are serious problems affecting
humankind, with significantly increasing number of cases and deaths. At the same time, the
prevailing treatments are not effective enough and cause adverse side effects, which justifies the
need to discover other effective and safe agents.
Moreover, the use of in-silico target fishing approach to explore the polypharmacology of
endogenous medicinal plants is the first time to be applied in Palestine. This encourage and
attract us to investigate these aspects to well distributed and known medicinal herbs in our
country, U. dioica and S. officinalis, aiming to identify new potential targets that could help in
designing new therapeutic agents for the treatment of serious diseases such as cancer, diabetes
mellitus and hyperlipidemia.
1
2 Chapter Two
Literature Review
2.1 Drug discovery and importance of medicinal plants
Drug discovery using natural products (NPs) is a challenging task for designing new leads. It
involves a wide range of scientific disciplines, including biology, chemistry and pharmacology.
Research in drug discovery needs to develop robust and viable lead molecules, which step
forward from a screening hit to a drug candidate through structural elucidation and
identification. The development of new technologies has revolutionized the screening of natural
products in discovering new drugs. Utilizing these technologies gives an opportunity to perform
research in screening new molecules using a software and database to establish natural products
as a major source for drug discovery (Koparde, et al., 2019).
Natural products are one of the main sources of drug discovery. According to the data from
Newman, most new FDA-approved drugs between 1981 and 2014 were derived from NP
structures (Newman & Cargg, 2016).
Natural constituents are widely distributed in various natural sources, including plants,
microorganisms and invertebrates. Plant-derived molecules continue to make up a large portion
of the pharmaceuticals in the clinic. The most famous example to date is probably the synthesis
of the anti-inflammatory agent acetylsalicylic acid (aspirin), derived from salicin and isolated
from the bark of the willow tree Salix alba L . Other examples are morphine, codeine, digitoxin,
quinine and the antitumor agents paclitaxel, vincristine and vinblastine, and a long list of other
drugs. In addition, the production of antibiotics by microorganisms was one of the biggest
breakthroughs in the history of drug discovery in the twentieth century (Ramos, et al., 2018).
To better understand the huge impact of NPs, for example, on cancer pharmaceuticals, it is worth
mentioning that out of 155 small molecules used as chemotherapeutics, 73 are directly NPs and
another 40 are derivatives or synthetic NP mimetics (Newman & Cragg, 2007). Furthermore,
current research trends in the field suggest an optimistic future for NPs in cancer prevention and
new therapeutics drug discovery.
Because of the complex chemistry generated by centuries of evolution of NPs, more success is
expected in drug discovery with NPs than with synthetic molecules. However, that complexity of
the natural molecules requires a coordinated effort from the interaction of multidisciplinary
5
research areas with new and more sophisticated analytical and technical expertise in order to
extract, isolate, identify and turn them into promising leads (Ramos, et al., 2018).
Since only a small amount of the world‘s biodiversity has been evaluated for potential biological
activity, many more useful natural lead compounds await discovery, the challenge being how to
access this natural chemical diversity (Ramos, et al., 2018).
2.2 Sage
Salvia officinalis L. is a plant from the mint family Lamiaceae, subfamily Nepetoideae, tribe
Mentheae, and genus Salvia. Salvia is the largest genus of the Lamiaceae family, containing
around 1000 species. It is known as garden sage or common sage as seen in figure 2.1. It is an
aromatic perennial woody sub-shrub native to the mediterranean area region and widely
distributed over the hillsides and shores of southern Europe. Today's, it has been naturalized
throughout the world. It is cultivated throughout Europe and the USA, including Spain, Italy,
Yugoslavia, Greece, Albania, Argentina, Germany, France, Malta, Turkey, England and Canada
(Sharma & Schaefer, 2019).
Figure 2.1: Arial parts of Salvia officinalis L. (Ghorbani & Esmaeilizadeh, 2017)
Sage is a plant of both tropics and temperate region, grown for food, home remedies, and
commercial pharmaceuticals. It is a multipurpose plant, on which several research studies were
6
reported supporting its traditional utilization, biological effects, and mode of action (Sharma &
Schaefer, 2019).
2.2.1 Ethnomedicinal uses of S. officinalis
Historically, sage is known as the ―Salvation Plant‖, originating from the old Latin word
―salvarem‖, which means save or cure. It has been used to reduce perspiration, as a gargle for
sore throat, to improve regularity of a menstrual cycle and to reduce hot flashes in menopause, to
fight gastroenteritis and other infections, to improve lipid status and liver function in general, to
improve appetite and digestion, and to improve mental capacity (Jakovljevicet, et al., 2019).
Traditional medicinal uses of S. officinalis documented by different communities and regions of
the world are presented in table 2.1 (Sharma & Schaefer, 2019).
Table 2.1: Ethnomedicinal uses of S. officinalis (Sharma & Schaefer, 2019)
Region Traditional use
Europe and the
Mediterranean region
Sage leaf tea : relieve stress, indigestion, bloating, heartburn, acidity, sore throat,
and sunburn
Leaf extract : treat excessive sweating, bronchitis and Alzheimer‘s disease
Jordan Leaf extract : used as antibacterial, anti-inflammatory and antiseptic
South east Asia and India Fresh leaves and decoction: antispasmodic, refreshing tea, hypotensive and to treat
respiratory disorders
Latin America Sage tea and essential oil : treat convulsion, nerve related disorders and high blood
pressure
Turkey, Serbia, and Iran
Flower, leaf and stem extracts : as antiseptic in wounds, pharyngitis, mouth ulcers
and consumed internally for dysmenorrhea.
Leaf tea is : stimulant, digestive, sedative and analgesic
Brazil
Hydroalcoholic tincture and leaf tea : to relieve stress, inflammation, prevent
excessive bleeding and as a mouth freshener. Commonly used to increase memory
power.
Greeks and Romans
leaves : as a spice and appetite stimulant for easy digestion of fatty foods
Leaf tea : for ulcers, sore throats, and laryngitis
Leaf decoction : enhance memory and brain power
Valencia region of Spain
leaf decoction : as an appetizer and hypotensive .
Hot tea : for detoxification, cold, and throat infections Dry leaves : as a spice and essential oil extraction
2.2.2 Phytochemical constituents of S. officinalis
The major phytochemicals in flowers, leaves, and stem of S. officinalis are well identified. A
wide range of constituents include alkaloids, carbohydrate, fatty acids, glycosidic derivatives
(e.g., cardiac glycosides, flavonoid glycosides, saponins), phenolic compounds (e.g., coumarins,
flavonoids, tannins), polyacetylenes, steroids, terpenes/terpenoids (e.g., monoterpenoids,
7
diterpenoids, triterpenoids, sesquiterpenoids) , and waxes are found in S. officinalis (Ghorbani &
Esmaeilizadeh, 2017).
Several polyphenolic compounds like chlorogenic acid, ellagic acid, epicatecin, epigallocatechin
gallate and rosmarinic acid, flavonoids like quercetin, and rutin as well as several volatile
components such as borneol, cineole, camphor, elemene, α-pinene, and thujone have been
identified in infusion prepared from S. officinalis (Ghorbani & Esmaeilizadeh, 2017).
The chemical structures of the main polyphenols and flavonoids as well as terpenoids isolated
from S. officinalis are shown in table 2.2 and table 2.3, respecively (Ghorbani & Esmaeilizadeh,
2017).
Table 2.2: Chemical structures of main polyphenols and flavonoides isolated from S. officinalis
O
OH
OOH
HO
O
OH
OOH
O
O
O
OH
OOH
O
Apigenin (C15H10O5) Circimaritin (C17H14O6) Genkwanin (C16H12O5)
O
OH
OOH
HO
OH
OHO
OH O
OH
OH
OH
Hispidulin (C16H12O6) Luteolin (C15H10O6) Quercetin (C15H10O7)
OHO
OH O
OH
OH
O
O
OO
OH
OH
OH
OH
OH
HO
HO O
O
OH
OHO
OH
OH
O
O
O
OH
OH
O
HO
HO
Rutin (C27H30O16) Rosmarinic acid (C18H16O8) Ellagic acid (C14H6O8)
The highest yield of total phenolics and flavonoids in sage extracts was obtained using 40%
aqueous ethanol solution which confirms that water ethanol solvents are probably the most
suitable for extraction of phenolic compounds and flavonoids from the sage due to the different
polarity of the bioactive constituents, and the acceptability of this solvent system for human
consumption (Osmic, et al., 2019).
The most common monoterpenes include: α- and β-thujone, 1, 8-cineole, and camphor. The most
common diterpenes include: carnosic acid, carnosol, rosmadial, and manool. Triterpenes include
O
OH
OOH
HO
O
8
oleanolic acid, corosolic acid and ursolic acid. In addition, sesquiterpenes as α-humulene and
viridiflorol are also present in sage extracts (Jakovljevic, et al., 2019).
Table 2.3: Chemical structures of main terpenoids isolated from S. officinalis
OH
H
O
α-Pinene (C10H16) Borneol (C10H18O) Camphor (C10H16O)
OH
H
O
O
HO
H
H
O
Carnosol (C20H26O4) Caryophyllene (C15H24)
Cineole (C10H18O)
COOHH
HO
H
H
HO
Corosolic acid (C30H48O4) Cymene (C10H14) Elemene (C15H24)
HO
H
Ferruginol (C20H30O) Limonene (C10H16) Myrcene (C10H16)
COOH
HO
H
H
H
O
H
HO
H
HOH
OH
Oleanolic acid (C30H48O3) Thujone (C10H16O) Ursolic acid (C30H48O3)
2.2.3 Pharmacological activities of S. officinalis
2.2.3.1 Anticancer effects
Extracts of S. officinalis showed proapoptotic and growth inhibitory effects on cell lines of breast
cancer, cervix adenocarcinoma, colorectal cancer, insulinoma, laryngeal carcinoma, lung
carcinoma, melanoma, and oral cavity squamous cell carcinoma. In addition to antiapoptotic
9
action, S..officinalis has antiproliferative, antimigratory and antiangiogenic effects (Kontogianni,
et al., 2013).
These effects may be related to the presence of several cytotoxic and anticancer compounds in S.
officinalis as ursolic and rosmarinic acids. Ursolic acid inhibits angiogenesis and invasion of
melanoma cells (Jedinak, et al., 2006). Rosmarinic acid inhibits the growth of various human
cancer cells including breast adenocarcinoma, colon, chronic myeloid leukemia, prostate,
hepatocellular and small cell lung carcinoma (Yesil-Celiktas, et al., 2010).
2.2.3.2 Antidiabetic effects
Recent pharmacological investigations demonstrated that different extracts of aerial parts of S.
officinalis are able to decrease blood glucose in normal and diabetic conditions. The mechanisms
suggested for hypoglycemic effect of S. officinalis include an inhibition of hepatocyte
gluconeogenesis and decrease of insulin resistance (Christensen, et al., 2010, Ghorbani &
Esmaeilizadeh, 2017).
2.2.3.3 Antihyperlipidemic effects
In clinical trials, extract of S. officinalis leaf could lower the blood levels of triglyceride, total
cholesterol, low density lipoproteins (LDL), very low density lipoproteins (VLDL) and two hour
postprandial glucose in patients with hyperlipidemia and diabetes. The beneficial properties of S.
officinalis tea consumption on serum lipid profile have been also reported on nondiabetic healthy
volunteers (Kianbakht & Dabaghian, 2013). This activity may be related to flavonoids present in
the plant such as rosmarinic acid and rutin (Govindaraj & Sorimuthu, 2015).
2.2.3.4 Other effects
Several studies have established that extracts of S. officinalis possess various pharmacological
effects, including antioxidant (Ghorbani & Esmaeilizadeh, 2017), anti-inflammatory and
antinociceptive properties (Azevedo, et al., 2013), antimicrobial effects (Badiee, et al., 2012) as
well as cognitive and memory-enhancing effects (Miroddi, et al., 2014).
2.2.4 Clinical studies
Clinical studies on pharmacological properties of S. officinalis are summarized in table 2.4
(Ghorbani & Esmaeilizadeh, 2017).
41
Table 2.4: Summary of clinical studies on S. officinalis (Ghorbani & Esmaeilizadeh, 2017).
Category Study design Subjects Dosage Effects
Effects on
memory and
cognitive
functions
Randomized
placebo controlled
trial
Patients with
Alzheimer's
Disease
60 drops/day of
alcoholic extract
For 16 weeks
Improvement of cognitive
Functions
Randomized
placebo controlled
trial
Healthy young
participants
300-600 mg
encapsulated
dried leaf
Improvement of mood and
cognitive functions after
single Dose
Randomized
placebo controlled trial
Healthy old
participants
165-132 mg of ethan-
olic extract was administrated 1,2,4
and 6 hours before
assessment
Improvement of memory and
Attention
Randomized
controlled trial
Healthy adults
participants
5 drops of essential
oil were placed into
the testing cubicle
Improvement of prospective
memory and cognitive
performance
Effects on
pain
Randomized
controlled trial
Patient with
pharyngitis
15% spray containing
140 ml of the plant
extract per dose
Reduction of the throat pain
Intensity
Effects on
glucose
and lipids
Randomized
placebo controlled
trial
Patients
diagnosed with
primary
hyperlipidemia
500 mg encapsulated
hydroalcoholic
extract every 8 h for
2 months
⇊ of the blood levels of total
cholesterol, triglyceride, LDL
and VLDL;
Increase of HDL Level
Randomized
placebo controlled
trial
Hyperlipidemic
type 2 diabetic
patients
150 mg sage extract
3 times a day for
3 months
⇊ of the blood levels of total
cholesterol, glucose, HbA1c,
triglyceride, and LDL; Increase of HDL level
Randomized
placebo controlled
trial
Type 2 diabetic
patients
300 mL of sage
tea twice daily
for 4 weeks
No effect on fasting glucose,
HbA1c, triglyceride, LDL and
HDL
A pilot study (non-randomized trial)
Healthy female
volunteers
300 mL of sage tea twice daily
for 4 weeks
⇊ of total cholesterol and LDL;
No effect on fasting glucose;
Increase of HDL level
HbA1c: Glycated haemoglobin, HDL: High density lipoprotein, LDL: Low density lipoprotein, VLDL: Very
low density lipoprotein, ⇊: Reduction
2.3 Stinging Nettle
Nettle, or stinging nettle, is a perennial plant that is widely distributed throughout the temperate
and tropical areas around the world. It grows from two to four meters high and produces pointed
leaves and white to yellowish flowers and it belongs to the family of Urticaceaea and to the
genus of Urtica as seen in figure 2.2 (Joshi, et al., 2014).
The genus name Urtica comes from the Latin verb urere, meaning ‗to burn,‘ because of these
stinging hairs with a tuft (a small cluster of elongated flexible outgrowths attached or close
together at the base and free at the opposite ends) of hair at the apex. Leaves and stems contain
abundant non-stinging hairs, with touch sensitive tips, needles that will inject chemicals
including serotonin, histamine,
44
acetylcholine, leukotrienes and possibly formic acid into the skin. The irritant compounds
provoke pain, wheals or a stinging sensation (Fu, et al., 2006).
The species name dioica means ‗two houses‘ because the plant usually contains either male or
female flowers. In the last few years, Urtica dioica L., has been accepted as a healing plant
because of its considerable effects on human health in many countries all over the world (Joshi,
et al., 2014).
Figure 2.2: Parts of U. dioica plant. (a) Whole plant; (b) Flower; (c) Trichomes; (d) Roots; (e) Leaf (Joshi, et
al., 2014).
2.3.1 Ethnomedicinal uses of U. dioica
U. dioica is widely used by the traditional medicinal practitioners for curing various diseases
such as nephritis, haematuria, jaundice, menorrhagia, arthritis and rheumatism. The plant also
has been used as food, fiber, paint, manure and cosmetics (Joshi, et al., 2014). The
ethnomedicinal uses of stinging nettle in various countries are shown in table 2.5.
2.3.2 Phytochemical constituents of U. dioica
The phytochemical composition investigation on U. dioica revealed that it contains different
compounds, incuding alkaloids, terpenoids, flavonoids, phenolic acids, sterols, fatty acids,
42
polysaccharides and lignans (Esposito, et al., 2019). The categorizations of these compounds,
their molecular formulas and chemical structures are listed in table 2.6 (Ibrahim, et al., 2018).
Table 2.5: Ethnomedicinal uses of Urtica dioica L (Joshi, et al., 2014).
Region Ethnomedicinal uses
Brazil
Asthma, bronchitis, cough, bleeding, diabetes, diarrhea, dysentery, fever, liver support, lung
problems, menstrual disorders, pneumonia, skin disorders, ulcers, urinary problems, and to
increase perspiration
Cuba Burns, flu, hemorrhoids, urinary insufficiency and to treat wounds
Germany Arthritis, inflammation, prostate diseases, rheumatism, urinary insufficiency and urinary tract
disorders
Greece Asthma, inflammation, laxative, pleurisy, spleen disorders and urinary insufficiency
India Eczema, nosebleeds, skin eruptions and uterine haemorrhages
America Allergies, arthritis, bleeding, hair loss, hypertension, inflammation, prostatitis, rhinitis, sinusitis,
urinary insufficiency and wounds
In particular, it was observed that all the parts (roots, stalk and leaves) of U. dioica are a rich
source of phenols and polyphenols and that their content is higher in wild plants than in
domesticated plants. The polyphenol profile seems to be strongly dependent on the parts of the
plant investigated, but also on the harvest site and season (Esposito, et al., 2019).
Root samples from mediterranean cultivar were reported to contain phenol compounds, such as
ferulic acid and polyphenols such as naringin, ellagic acid, myricetin and rutin. The roots also
contained lignans (secoisolariciresinol, 9,9΄-bisacetyl-neo-olivil and their glucosides), phytoste-
rols (e.g., β-sitosterol), coumarins (e.g., scopoletin), simple phenols (e.g., p-hydroxybenz-
aldehyde), triterpenoic acids (e.g., oleanolic acic and ursolic acid) and monoterpendiols (e.g,
carvacrol, carvone) (Gul, et al., 2012; Esposito, et al., 2019).
U. dioica leaves are also constituted by flavonoid glycosides, mainly rutinosyl flavonols.
Chlorogenic acid and caffeoyl malic acid represented approximately 76.5% of total phenolic
compounds, whereas rutin was the most abundant flavonol derivative. Isorhamnetin-3-O-
rutinoside was found, together with rutin, quercetin-3-O-glucoside and kaempferol-3-O-
glucoside in methanolic extracts of U. dioica leaves and stalks (Otles & Yalcin, 2012).
Among lipid secondary metabolites, carotenoids were detected in the leaves and their total
content was estimated equal to 29.6 mg/100 g dry weight (Esposito, et al., 2019).
42
The leaves are rich in vitamins B, C and K as will as minerals such as calcium, iron, magnesium,
phosphorus, potassium and sodium (Wetherilt, 1992; Gul., et al., 2012).
Table 2.6: Structures of chemical constituents of U. dioica
O
OH
HO
HO
O
OH
O
O
O
OHHO
HO
O O
O
HO OH
HO
HO
Neoolivil (C20H24O7) Neoolivil; 4-O-b-d-Glucopyranoside (C26H34O12)
O
HO
O
OH
OH
OH
HO
O
OH
O
OH
HO
Isolariciresinol (C20H24O6) Secoisolariciresinol (C20H26O6)
Lignanes
O
O HO
OHHO
O
OH
HO
HO
O
OH
OH
O
O
O
HO
HO
Chlorogenic acid (C16H18O9) Caffeoylmalic acid (C13H12O8)
H
H
O
HO
OH
OH
OH
O
H
H
HO
O
Caffeic acid (C9H8O4) Ferulic acid (C10H10O4)
Phenolic acids
OHO
OH O
OH
OH
OH
O
HO
HO
O OH
OH
OH
OH
Quercetin (C15H10O7) Myricetin (C15H10O8)
41
O
OO
HO
OH
OH
OHHO
HO
O
OH
OH
OHO
OH O
OH
OH
O
O
OO
OH
OH
OH
OH
OH
HO
Isoquercitrin (C21H20O12) Rutin (C27H30O16)
Flavonoides
OH OH HO
O
O O
9,10 Pinanediol (C10H18O2) Diocanol (C16H22O4)
COOH
HO
H
H
H
HO
H
HOH
OH
Oleanolic acid (C30H48O3) Ursolic acid (C30H48O3)
H
H
H
HO
β-Amyrin (C30H50O) Carvacrol (C10H14O)
Terpenoides
2.3.3 Pharmacological activities of U. dioica
2.3.3.1 Antidiabetic effects
The aqueous extract of U. dioica has shown a significant glucose lowering effect against alloxan
induced diabetes in rats (Bnouham, et al., 2003). The fructose induced insulin resistance in male
rats has been shown to decrease serum glucose level on administration of hydroalcoholic leaf
extract (Ahangarpour, et al., 2012). The cold methanolic extract of leaves has also shown
significant antihyperglycemic effect in alloxan induced diabetes (Al-Wasfi, et al.,2012).
OH
45
2.3.3.2 Antihyperlipidemic effects
The plant has very potent antihyperlipidemic activity as it lowers the levels of lipids and
lipoproteins in blood. The aqueous extract of the plant at a dose of 150 mg/kg given for 30 days
to rats fed on normal or high-fat diet, improved the blood lipid profile (Daher, et al., 2006). The
ethanolic extract of the plant at a dose of 100 and 300 mg/kg has shown significant reduction in
the level of total cholesterol and LDL level in hypercholesterolemic rats (Avci, et al., 2006).
2.3.3.3 Anticancer effects
Various studies have recently demonstrated the cytotoxic and anticancer properties of U. dioica,
in particular, against colon, gastric, lung, prostate and breast cancers. Table 2.7 summarizes the
main findings regarding the anticancer properties of U. dioica with the plant extracts and parts
used (specifying collection sites), the cancer type or animal models tested and the effects.
(Esposito, et al., 2019).
Table 2.7: The Anticancer effects of U. dioica (Esposito, et al., 2019)
Used parts (site) Extracts Cancer Type Effects
roots (Iran) Ethanolic extract Human colon and gastric cancer ↓Proliferation, ↑Apoptosis
Aerial parts (Iran) Dichloromethane
extract human colon cancer ↓Proliferation, ↑Apoptosis
Leaves (Italy) Methanolic extract human lung cancer ↓Proliferation , ↑Apoptosis
↑caspase3
leaves (Iran) Aqueous extract prostate cancer ↓Proliferation, ↑Apoptosis
Root (Germany) Methanolic extract Human prostate cancer ↓Proliferation
Leaves (Iran) Dichloromethane
extract human breast cancer
↓Proliferation, ↑Apoptosis ,
↑caspase 3 and 9
Leaves (Germany) Dichloromethane
extract Mouse model of breast cancer
↓Metastasis , ↑Apoptosis ,
↑caspase 3 and 9
leaves and stems
(Jordan) Ethanol extract Human breast cancer ↓Proliferation
2.3.3.4 Other effects
Several studies have established that extracts of U. dioica possess various pharmacological
effects including analgesic and anti-inflammatory (Hajhashemi & Klooshani, 2013), antioxidant
(Gulcin, et al., 2004), antimicrobial (Turker, et al., 2008), immunomodulatory (Akbay, et al.,
2003), diuretic (Dizaye, et al, 2013) antiviral (Balzarini, et al., 2005), hepatoprotective and
anthelmintic (Kataki, et al., 2012).
46
2.4 Cancer
Cancer is a group of diseases characterized by the uncontrolled growth and spread of abnormal
cells. If the spread is not controlled, it can result in death. Other terms used are malignant tumors
and neoplasms (Costa, 2020)
Cancer is a major public health problem and the second leading cause of death worldwide while
the long-term prognosis is still unfavorable. The global cancer burden is estimated to have risen
to 18.1 million new cases and 9.6 million deaths in 2018. 1 in 5 men and 1 in 6 women
worldwide develop cancer during their lifetime, and one in 8 men and one in 11 women die from
the disease (International Agency for Research on Cancer, IARC, 2018).
Lung, prostate, colorectal, stomach and liver cancer are the most common types of cancer in
men, while breast, colorectal, lung, cervix and thyroid cancer are the most common among
women. Cancers of the lung, female breast, and colorectal are the top three cancer types in terms
of incidence, and are ranked within the top five in terms of mortality (first, fifth, and second,
respectively). Together, these three cancer types are responsible for one third of the cancer
incidence and mortality burden worldwide (IARC, 2018).
In 2015, Palestinian Ministry of Health official statistics revealed that the rate of cancer patients
in Palestine reached 83.8 per one 100 thousand persons, 83.9 cases per 100 thousand persons in
Gaza and 83.8 cases per 100 thousand persons in the West Bank. 52.5% of the new cancer cases
are females and 47.4% are males. Breast cancer ranks first since it constitutes 17.8% of all
cancer cases. Breast cancer also came first for cancers that affect women in Palestine, which
reached 33.7% with a rate of 33.1 new cases per 100 thousand females in Palestine annually,
Colon Cancer comes second regarding cancer cases with the rate of 9.4%. Lung cancer comes
third with the rate of 8.7%, but it its ranked first for cancers that affect males (PMoH, 2016).
2.4.1 Causes & risk factors of cancer
It is impossible to know why some people get cancer unlike others who don't, but there are many
risk factors that increase the possibility of some people to develop cancer without others. The
origin and advancement of cancer depend on many factors inside the cell as well as factors
external to the cells. The inside cell factors include inherited genetics (as Tumor suppressor
genes, oncogenes and DNA repair genes), mutations, immune conditions, hormones and ageing
while smoking, alcohol use ,unhealthy diet ,physical inactivity, chemicals, radiations, bacterial
infections as Helicobacter pylori and viral Infections as Human Papilloma Viruses (HPVs),
47
Epstein-Barr virus (EBV) and Kaposi‘s sarcoma-associated herpes virus (KSHV) represent the
external factors (NCI, 2015b).
These entire elements act together to cause abnormal cell behavior and uncontrolled
proliferation. Cancer develops as a result of changes to genes that control cells functions
specially growth and division. These genetic changes (mutations) might be inherited or acquired
during one's lifetime as chemical and radiation exposure (NCI, 2015a). Some genes are
completely linked to develop cancer which fall into 3 groups:
Tumor suppressor genes: are defensive genes such as p53. Normally, these genes slow down
cell growth, repair damaged DNA and control cells death (apoptosis). If a tumor suppressor gene
is missed or damaged, cells divide in uncontrolled manner and may eventually form a tumor.
More than 50% of all cancers involve mutated p53 gene (Rivlin, et al., 2011; NCI, 2015b).
Oncogenes: genes promote normal cell to turn into a cancerous one. The most common
oncogenes are Ras (Rat sarcoma gene) family and HER2 (Human epidermal growth factor
receptor 2). Ras is responsible for translating proteins involved in cell signaling pathways, cell
division, and cell survival (NCI, 2015b).
DNA repair genes: genes responsible for correcting DNA damage, so default gene won‘t
correct the damage of DNA and may lead to develop cancer (NCI, 2015b).
2.4.2 Oxidative stress and cancer
Eukaryotic cells generate energy through aerobic respiration process and free radicals (reactive
species RS) are produced as a result. Free radicals are classified into four groups, reactive
oxygen species (ROS), reactive nitrogen species (RNS), reactive sulfur species (RSS) and
reactive chloride species (RCS). ROS is the most abundant product that includes superoxide
anion (O2-), hydrogen peroxide (H2O2), hydroxyl radical (OH
-) singlet oxygen (
1O2) and ozone
(O3) (Sosa et al., 2013). ROS can serve as anticancer (e.g. through promoting cell cycle stasis,
apoptosis, senescence, necrosis and inhibiting angiogenesis) or as procancer (through promoting
angiogenesis, proliferation, metastasis, invasiveness and suppressing apoptosis). Other types of
RS have the same two faces (Halliwell, 2007). What determines which face will show up is the
equilibrium status between ROS oxidants and ROS scavengers, low concentration of ROS is
beneficial, unlike harmful high concentration of ROS (Reuter, 2010). This ROS-high
concentration is called oxidative stress. Oxidative stress is very related to a wide variety of
human diseases, such as cardiovascular disease, neurodegenerative disease, inflammation,
48
allergies, diabetes, immune system dysfunctions, aging and wide variety of different cancers
(Sosa et al., 2013). Since high concentration of ROS induces damage to cell structures and
molecules including nucleic acids, proteins, lipids and membrane, from here cancer initiation and
development is linked to oxidative stress by inducing DNA mutations and DNA damage (Valko,
et al., 2006).
2.4.3 Signs and symptoms
Possible signs and symptoms include a lump, abnormal bleeding, unusual breast changes,
prolonged cough, breathlessness, unexplained weight loss, fatigue, fever, skin changes, pain, and
a change in bowel movements. While these symptoms may indicate cancer, they can also have
other causes (Tyagi, et al., 2017).
2.4.4 Cancer treatment
There are different conventional treatment modalities that are available to treat and manage
cancer. The selection of treatment and its progress depends on the type of cancer, its locality, and
stage of progression. Surgery, radiation-based surgical knives, chemotherapy, and radiotherapy
are some of the traditional and most widely used treatment methods. Some of the modern
modalities include hormone-based therapy, anti-angiogenic modalities, stem cell therapies,
targeted therapies and immunotherapy (Abbas & Rehman, 2016; Shumaila, et al., 2016).
2.4.4.1 Cancer treatment by drugs
2.4.4.1.1 Chemotherapy
Chemotherapy is considered the most effective and extensively used modality in the treatment of
most types of cancers. Out of chemotherapeutic drugs discovered, a total of 132 are FDA
approved. These drugs are designed to specifically target tumor cells and kill them by genotoxic
effect, i.e., the production of reactive oxygen species. However, chemotherapeutic drugs also
target normal cells, which could result in a variety of side effects depending on the dosage such
as hair loss, nausea, fatigue and vomiting. As a result of vigorous chemotherapy treatment,
patients become immunocompromised; this can result in complicated infections and
consequently death ( Rodgers, et al., 2012).
Different types of chemotherapy include alkylating agents, antimetabolites, anthracyclines,
mitotic inhibitors and others are mentioned in table 2.8.
49
Table 2.8: Various types of anticancer drugs and their examples
Types Drugs Ref.
Ch
em
oth
erap
y
Alkylating Agents
Nitrogen mustards as mechlorethamine and chlorambucil
Nitrosoureas as streptozocin and carmustine
Alkyl sulfonates as busulfan
Triazines as dacarbazine and temozolomide
Ethylenimines as altretamine
Abbas & Rehman, 2016
Antimetabolites
Pyrimidine analogue as azacitidine, capecitabine and 5-fluorouracil
Adenosine analogue as cladribine
Purine analogue as ludarabine, mercaptopurine and thioguanine
Folate analogue as mehotrexate, pemetrexed and raltitrexed
Abbas & Rehman, 2016
Anthracyclines
Daunorubicin, doxorubicin, epirubicin and idarubicin Abbas & Rehman, 2016
Mitotic inhibitors
Taxanes as paclitaxel (Taxol®) and docetaxel (Taxotere®)
Epothilones as ixabepilone (Ixempra®)
Vinca alkaloids as vinblastine, vincristine and vinorelbine
Abbas & Rehman, 2016
Topoisomerase Inhibitors as amsacrine and mitoxantrone Abbas & Rehman, 2016
Ho
rmo
na
l T
her
ap
y
Antiestrogens as tamoxifen and fulvestrant
Aromatase Inhibitors as anastrozole and letrozole
Antiandrogens as apalutamide and enzalutamide
Androgen Biosynthesis Inhibitors as abiraterone
Androgen as testosterone
Corticosteroids as dexamethasone and prednisone
Somatostatin Analogues as lanreotide
Luteinizing Hormone Releasing Hormone Agonists as buserelin
Luteinizing Hormone Releasing Hormone Antagonists as degarelix
Progestins as mroxyprogesterone and megestrol
Prolactin Lowering Agents as bromocriptine
BC Cancer Agency, 2019
Immuno-
theraputic
Agents
Cytokin as interleukin, interferon and peginterferon.
Vaccine Therapy as bacillus calmette-guerin (BCG)
Immunomodulatory drugs as lenalidomide and pomalidomide
Differentiating agents as acitretin, bexarotene and tretinoin
Ventola , et al., 2017
Targeted
Therapies
Selective kinase inhibitors
Imatinib mesylate, Gefitinib, Lapatinib, Sunitinib and Sorafenib
Monoclonal antibodies
Catumaxomab, Denosumab, Rituximab and Trastuzumab
Falzone, et al., 2018
21
2.4.4.1.2 Hormonal therapy
Rapid development in the field of molecular biology has led to the understanding of the role of
hormone in cell growth and the concept of autocrine and paracrine regulation of malignant cells.
Approximately 25% of malignant tumours in men and 40% in women have a hormonal basis.
Hormonal treatment can result in dramatic response without toxicity associated with cytotoxic
chemotherapy (EBCTC Group 2005). Examples of these drugs are given in table 2.8.
2.4.4.1.3 Immunotherapeutic agent
Cancer immunotherapy is the artificial stimulation of the immune system to treat cancer,
improving on the immune system's natural ability to fight the disease. Categories and examples
of these agents are shown in table 2.8 (Ventola, et al., 2017).
2.4.4.1.4 Targeted therapy
Targeted therapy for cancer treatment is based on tyrosine and serine/threonine protein kinase
inhibitors and monoclonal antibodies. Examples of protein kinase inhibitors include epidermal
growth factor receptor (EGFR) inhibitors, vascular endothelial growth factor receptor (VEGFR)
inhibitors, rapidly accelerated fibrosarcoma kinase (RAF) inhibitors and mammalian target of
rapamycin (mTOR) inhibitors. Monoclonal antibodies are directed toward extracellular growth
factors or extracellular receptor tyrosine kinase. Examples of these drugs are given in table 2.8
(Falzone, et al., 2018.)
2.4.4.2 Cancer treatment by natural plants
Medicinal plants are potent natural sources of drugs to treat different disease since ancient time.
The positive effect of plants in cancer treatment have been studied extensively and has shown
advanced results (Kooti, et al,. 2017). Many of anticancer lead bioactive molecules such as vinca
alkaloid, vinblastine, vincristine, camptothecin, and taxanes have been characterized from
different medicinal plants and are used as therapeutic agents worldwide (Ali, et al.,2016).
Nowadays, most research has been established to estimate the effect of medicinal plant against
cancer treatment. Numerous naturally occurring compounds from plants are being used as
anticancerous agents and are currently undergoing medical development (Malinowsky et al.,
2015). Many plants like Petasites japonicas )Malinowsky et al., 2015), Curcuma longa
(Hosseinimehr, 2014), Astragalus hamosus (Kondeva-Burdina, et al., 2014), Achillea wilhelmsii
24
(Asadi-Samani, et al., 2016), Ammi majus (Mohammed, et al., 2014), Olea europae L(Ghanbari,
et al., 2012),Thymus vulgaris L(Al-Menhali, et al., 2105), Trigonella foenum-graecum L
(Alsemari, et al., 2014) and many other plants used as an anticancerous agents with minimum
toxicity as given in table 2.9.
Table 2.9: Anticancerous medicinal plants
Botanical
name of plants
Family
Name
Active
components /
main parts used
Medicinal importance Ref.
Petasites
Japonicas Asteraceae Roots Anticancer Activity
Malinowsky et al.,
2015
Curcuma longa Zingiberaceae Curcumin
Activity against leukemia,
lymphoma, breast, uterus,
ovary, lung, melanoma,
colon and brain tumors
Hosseinimehr, 2014
Astragalus
Hamosus Fabaceae Aerial parts
Hepatoprotective
and antioxidant activity
Kondeva-Burdina, et
al., 2014
Silybum
marianum Asteraceae Whole plant
causes cell cycle arrest and
apoptosis
Shariatzadeh, et al.,
2014
Artemisia
vulgaris Compositae aqueous extract
Treat breast, prostate and
colon cancers. Nawab et al., 2011
Nigella sativa Ranunculaceae Seeds
Great medicinal value in
liver, colorectal, gastric and
breast cancers
Padhye et al., 2008,
Khalife, et al., 2016
Achillea
wilhelmsii Asteraceae Leaves
Cytotoxic effects on colon
cancer cells
Asadi-Samani, et al.,
2016
Allium sativum Amaryllidaceae
organosulfuric
compounds, allicin
and ajoene
reduce the risk of cancer in
breast, larynx, colon, skin,
womb, gullet, bladder, lung
and prostate cancers.
Thomson & Ali,
2003
Ammi majus Apiaceae Comorian
compounds Treat breast cancer
Mohammed, et al.,
2014
Olea europae L Oleaceae Oleuropein and
oleanolic acid
Activity against colon and
breast cancer
Ghanbari, et al.,
2012
Thymus
vulgaris Lamiaceae
Whole plant
extract
Activity against colorectal,
breast and prostate cancer
Al-Menhali, et al.,
2105
Trigonella
foenum-
graecum L
Fabaceae
Ginger, cadence,
zingerone, vanillin
and eugenol
Anticancer effects in brain
and breast cancers Alsemari, et al., 2014
In addition, Silybum marianum is used against liver cancer and shown tumorigenic effects in-
vitro and in-vivo conditions by suppressing oxidative stress and proliferation (Shariatzadeh, et
al., 2014). Nigella sativa has been shown several pharmacological activities, including
antioxidant, anti-inflammatory, chemotherapeutic and antitumor activities (Padhye, et al., 2008),
as well as hepatoprotective activity (Khalife, et al., 2016). Artemisia vulgaris aqueous extract has
anticancer activity against human breast carcinoma, human prostate cancer and colon cancer
(Nawab, et al., 2011). Various researches have shown that Allium sativum, organosulfuric
22
compounds and allicin reduce the risk of cancer in breast, larynx, colon, skin, womb, gullet,
bladder, prostate and lung (Thomson, et al., 2003).
2.5 Diabetes mellitus (D.M)
Diabetes is a serious, chronic disease that occurs either when the pancreas does not produce
enough insulin (a hormone that regulates blood glucose), or when the body cannot effectively
use the insulin it produces. Raised blood glucose, a common effect of uncontrolled diabetes by
over time, lead to serious damage to the heart, blood vessels, eyes, kidneys and nerves (WHO,
2017).
Diabetes mellitus is the seventh leading cause of death globally. In 2019, it is estimated that,
approximately (one in 11) 463 million adults (20-79 years) were living with diabetes; by 2045
this will rise to 700 million. About 79% of adults with diabetes were living in low- and middle-
income countries. One in 2 (232 million) people with diabetes were undiagnosed. More than 1.1
million children and adolescents are living with type 1 diabetes. Ten Percentage of global health
expenditure is spent on diabetes (International Diabetes Federation (IDF), 2019).
The Palestinian Ministry of Health (PMoH) ranked T2DM as the fourth leading cause of death
and represented 8.9% of all deaths in 2014 (PMoH, 2016).
The International Diabetes Federation (IDF) reported the prevalence in diabetic patients aged
20–79 years in Palestine to be 9.1% .The percentage of DM between both sexes is equal. The
percentage of patients with T1DM is 4.7% and 95.3% of T2DM (IDF, 2015).
2.5.1 Classification of diabetes mellitus
Type 1 diabetes mellitus (T1DM) is an autoimmune disease that results in β cell destruction. It
comprises about 5%–10% of total cases of diabetes. It is typically recognized in childhood or
adolescence. It is used to be known as juvenile-onset diabetes or insulin dependent diabetes
mellitus (IDDM). It is associated with the presence of islet cell antibodies, and patients require
lifelong insulin (Ashcroft & Rorsman, 2012).
T2DM is a non–insulin dependent (NIDDM). It is due to insulin resistance or reduced insulin
sensitivity, combined with relatively reduced insulin secretion which in some cases becomes
absolute. The body tries to overcome this resistance by secreting more and more insulin. At least
90% of patients with diabetes have T2DM (Mau, et al., 2019).
22
Gestational diabetes is a form of diabetes that start during the second half of pregnancy and
produced by the hormones of pregnancy or by a shortage of insulin. Women who have
gestational diabetes are more likely than other women to have large babies and to develop type II
diabetes later in life. However, such kind of diabetes resolves as the nannies delivers (Mau, et al.,
2019).
2.5.2 Signs and symptoms
The classic symptoms of untreated diabetes are polyuria (increased urination), polydipsia
(increased thirst), polyphagia (increased hunger) and unintended weight loss. Symptoms may
develop rapidly (weeks or months) in type 1 diabetes, while they usually develop much more
slowly and may be subtle or absent in type 2 diabetes. Other Signs and Symptoms include
blurred vision, headache, fatigue, irritability, slow healing of cuts and itchy skin (WHO, 2019).
2.5.3 Complications of diabetes mellitus
Raised blood glucose can result in multiple complications as diabetic retinopathy that may
develop into loss of vision, end stage renal disease, cardiovascular events and lower extremities
amputation (WHO, 2017).
2.5.4 T2DM pathogenesis and major risk factors
The pathogenesis of T2DM is sophisticated and its corresponding mechanisms remain
conflicting. T2DM was generally considered as chromosome polygene recessive inheritance,
following with abnormality of insulin secretion (Morris, et al., 2012). Molecularly, obvious
alterations occurred in insulin genes compared with normal patients, suggesting that changes of
insulin genes may be one of contributing factors in the pathogenesis of T2DM (Scott, et al.,
2012).
With regard to the environment, various kinds of bacteriostatic agents, preservatives, antibiotics,
chemicals abuses and even lifestyle along with irregular dietary habits are external factors which
are always neglected (He, et al., 2019).
In T2DM, insulin resistance contributes to increased glucose production in the liver and
decreased glucose uptake in muscle and adipose tissue. In addition, β-cell dysfunction results in
reduced insulin release, which is insufficient for maintaining normal glucose levels. Indeed,
21
multiple defects contribute to the development of glucose intolerance and hyperglycemia in
T2DM as shown in figure 2.3 (DeFronzo, et al., 2014).
Figure 2.3: Multiple defects contribute to the development of glucose intolerance in T2DM. HGP, Hepatic
glucose production (DeFronzo, et al., 2014).
Importantly, dysfunction of glucose metabolism, incretin secretion and insulin sensitivity
mentioned with some key enzymes such as α-glucosidase, α-amylase, dipeptidyl peptidase 4
(DPP-4), peroxisome proliferator-activated receptor-γ (PPAR-γ), protein tyrosine phosphatase1
B (PTP1B) and glucose transporter-4 (GLUT-4) quietly account for the pathogenesis of T2DM
(He, et al., 2019).
Pharmacologically, α-amylase hydrolyzes macromolecules like starch into some oligoglucans.
These substances are further degraded by α-glucosidase into absorbable glucose at the brush
border of small intestine and then permeate blood circulation. Elevated blood glucose increase
glucagon-like peptide-1 (GLP-1) production to enhance insulin release and protect pancreatic β-
cells. DPP-4 can shorten the half-life of GLP-1,which interrupts glucose metabolism (Liu, et al.,
2017).
25
Insulin secreted by pancreatic β-cells can modulate lipid metabolism and hepatic and muscle
glycogen synthesis, which require the regulation of GLUT-4, PTP1B and PPAR-γ pathways (He,
et al., 2019).
2.5.5 Management of T2DM
2.5.5.1 Non pharmacological treatment
Modification of lifestyle, including weight loss, increasing physical activity and adopting a
healthy diet, remains one of the first line strategies for the management of T2DM (Zheng, et al.,
2018).
2.5.5.2 Pharmacological treatment
Current remedies for T2DM mainly include chemical or biochemical agents such as biguanides,
sulfonylureas, α-glucosidase inhibitors, etc. All these first line clinical therapeutic drugs and
corresponding mechanisms of action are shown in table 2.10 (He, et al., 2019).
Table 2.10: Marketed therapeutic agents for T2DM and respective mechanisms (He, et al., 2019)
Categories Familiar drugs Mechanism Adverse Effects
2nd
Generation
Sulfonylureas
Glibenclamide Gliclazide,
Glimepiride and Glipizide
Stimulation of pancreatic
insulin secretion
Hypoglycemia risk, Weight
gain
α-Glucosidase
INHIBITORS
Acarbose, Miglitol
Vocarbose
Disturbance of glucose
digestion and absorption in
the intestinal system
Gastrointestinal symptoms
Biguanides Phenformin and
Metformin
Increases insulin sensitivity
and hepatic glucose
utilization
Gastrointestinal symptoms,
Lactic acidosis, contraindi-
cated in renal insufficiency
DPP-4 Inhibitors Linagliptin, Sitagliptin
Saxagliptin, Vildagliptin
And Alogliptin
Decrease glucagon and
blood glucose levels
through elongating the half-
life of GLP-1
Nasopharyngitis, Headache,
Nausea and Hypersensitivity
Thiazolidinediones Pioglitazone and
Rosiglitazone
Amelioration of insulin
action by stimulating of the
PPAR-γ
Hepatoxicity, Weight gain,
Risk for heart failure, Fluid
retention
GLP-1 Agonists Exenatide , Liraglutide
Exenatide, and Albiglutide
Promotion of insulin
signaling; inhibition of
elevated glucagon levels
Risk for thyroid cancer
SGLT-2 Inhibitors
Canagliflozin,
Dapagliflozin and
Empagliflozin
Decrease of glucose
secretion in the renal organ
Genital infections and
Weight loss
DPP-4: Dipeptidyl peptidase-4, GLP-1: Glucagon-like peptide-1, SGLT2: Sodium-glucose cotransporter-2
26
2.5.5.3 Diabetes management by natural plants
Fortunately, natural occurring antidiabetic drugs have never been obsoleted and still play a
principal role in the management of T2DM, many of which are known to be effective against
diabetes (Bailey & Day, 2004).
Academically, He et al., reviewed the hypoglycemic effects of many plants, their identified
compounds, crude extracts and their molecular targets for treating T2DM from 2011 to 2017
(He, et al., 2019). Some of these plants are listed in table 2.11.
Table 2.11: Medicinal plants used in the treatment of T2DM (He, et al., 2019)
Target Plant specie Familly Part used Active ingredient
α-
Glucosidase
and α-
Amylase
Allium sativum L Alliaceae Ground bulbs Kaempferol , Luteolin and Quercetin 4'-glucoside
Salvia offcinalis L Lamiacea Leaf Luteolin and Quercetin4'-glucoside
Momordica charantia Cucurbitacea Fruit Crude extract
Swertia mussotii Gentianacea Whole plants Xanthones
Zingiber mioga Gentianaceae Whole plants Ethyl alcohol and water extracts
DPP-4
Abelmoschus
esculentus Malvacea Root
Quercetin, Glucosides and
triterpenes
Apocynum venetum Apocynaceae Leaf Isoquercitrin
Origanum vulgare Lamiaceae Seed Naringenin hispidulin, Cirsimaritin
and Carnosol
Pericarpium Citri
Reticulatae Rutaceae Peels Naringin
Pilea microphylla Urticaceae Wholeplants Flavonoids
PPAR-γ
Ampelopsis
grossedentata Vitaceae
Stem
Leaf Ampelopsin
Citrus junos Rutaceae Peel Pulp Ethanol extract of the pulp
Glycyrrhiza inflata Leguminosae Root Licochalcone E
Morinda citrifolia Rubiaceae Root Ethanolic extract
Opuntia humifusa Cactaceae Stem Flavonoids
PTP1B
Annona squamosal Ranunculaceae Fruit Hexane extract
Dodonaea viscosa Sapindaceae Aerialparts Polyphenolic compounds
Persea americana Lauraceae Lea Aqueous extract
Ramalina Americana Ramalinaceae Bark Trivaric acid
Syzygium cumini Myrtaceae Seeds Vitalboside
GLUT-4
Catharanthus roseus Apocynaceae Leaf Ethanolic extrac
Centratherum
anthelminticum Asteraceae Seeds Methanolic extract
Psoralea corylifolia Fabaceae Seeds Bavachin
Rosmarinus offcinalis Lamiaceae Leaf Rosmarinic acid
Zingiber officinale Zingiberaceae Root Ethyl acetate extract
DPP-4: Dipeptidyl peptidase-4, GLUT4: Glucose transporter 4, PPAR-γ: Peroxisome proliferator activated receptor-γ, PTP1B: Protein tyrosine phosphatase 1B.
27
2.6 Hyperlipidemia
Hyperlipidemia is an increase in one or more of the plasma lipids, including triglycerides,
cholesterol, cholesterol esters and phospholipids and or plasma lipoproteins including very low
density lipoprotein and low-density lipoprotein as well as a reduce in high-density lipoprotein
levels (Shattat, 2014).
2.6.1 Classification of hyperlipidemia
Hyperlipidemia can be claasified into primary hyperlipidemia (also called familial) and
secondary Hyperlipidemia. Primary hyperlipidemia takes place as a result of genetic problems
i.e., mutation within receptor protein, which may be due to single (monogonic) gene defect or
multiple (polygenic) gene defect. Secondary hyperlipidemia arises as a result of other
underlining diseases like diabetes, myxoedema, nephritic syndrome, chronic alcoholism and with
use of drugs like corticosteroids, oral contraceptives and beta blockers (Pe, et al., 2015).
2.6.2 Causes and risk factors of hyperlipidemia
The causes of hyperlipidemia include genetic factors with risk factors like excessive alcohol
consumption, obesity, medications (hormones or steroids), diabetes, metabolic syndrome, long
term kidney disease, premature menopause, an underactive thyroid gland, or hypothyroidism,
pregnancy and sedentary lifestyle (Singh & Nain, 2018).
2.6.3 Complications of hyperlipidemia
Hyperlipidemia increases morbidity and mortality when combined with other prevalent diseases
such as diabetes mellitus, hypertension and cardiovascular diseases and it may leads to several
harmful diseases like atherosclerosis, cardiovascular diseases, high blood pressure and many
other severe problems which seriously affect the human body (Singh & Nain, 2018).
2.6.4 Pathophysiology of hyperlipidemia
The pathophysiology of primary hyperlipidemia involve the idiopathic hyperchylomicronemia in
which defect in lipid metabolism leads to hypertriglyceridemia and hyperchylomicronemia
caused by a defect in lipoprotein lipase (LPL) activity or the absence of the surface apoprotein
CII31 (Gotto & Moon, 2010; Amit, et al., 2011).
28
In secondary hyperlipidemia, the postprandial absorption of chylomicrons from the
gastrointestinal tract occurs 30- 60 min after ingestion of a meal containing fat that may increase
serum triglycerides for 3-10 hours. The diabetes mellitus patients have been noted to possess low
LPL activity which further caused high synthesis of very low density lipoprotein (VLDL)
cholesterol by the liver ultimately leading to hyperlipidemia. Moreover, hypothyroidism induced
low LPL activity and lipolytic activity has been noted to reduce hepatic degradation of
cholesterol to bile acids. Furthermore, hyperadrenocorticism increased the synthesis of VLDL by
the liver causing both hypercholesterolemia and hypertriglyceridemia (Pe, et al., 2015).
2.6.5 Management of Hyperlipidemia
2.6.5.1 Non pharmacological management
Stress reduction, dietary modification, reduce the risk factors of atherosclerosis like body weight,
consumption of alcohol, smoking and treatment of diseases like hypothyroidism, DM,
hypertension are also important while starting hypolipidemic drug therapy (Atlee, 2020).
The objectives of dietary therapy are to decrease the intake of total fat, saturated fatty and
cholesterol progressively and to achieve a desirable body weight. The dietary therapy includes
reduction of saturated fat intake to 7 percent of daily calories, reduction of total fat intake to 25
to 35 percent of daily calories, reduction of dietary cholesterol to less than 200 mg per day,
eating 20 to 30 g a day of soluble fiber, which is found in peas, beans, and certain fruits and as
well as increasing the intake of plant stanols or sterols, substances found in nuts, vegetable oils,
corn and rice, to 2 to 3 g daily. Other foods that can help control cholesterol include cold-water
fish, such as mackerel, sardines, and salmon. These fish contain omega-3 fatty acids that may
lower triglycerides. Soybeans found in tofu and soy nuts and many meat substitutes contain a
powerful antioxidant that can lower LDL (Arun, et al., 2013).
2.6.5.2 Pharmacological therapy
Generally, the drugs involves in the treatment of hyperlipidemia are classified into several
groups such as statins, resins, fibric acid derivatives, niacin and novel drugs as shown in table
2.12 (Arun, et al., 2013; Atlee, 2020)
29
Table 2.12: Antihyperlipidemic drugs and their mechanisms of action (Atlee, 2020)
Drug Mechanism 0f action Side effects Effects on lipid
HMG-CoA reductase
inhibitors (Statins):
Lovastatin(20-40mg/day)
Simvastatin(5-20mg/day)
Atorvastin(10-40mg/day)
Rosuvastin(5-20mg/day)
↓ cholesterol biosynthesis
by inhibiting HMG-CoA
Rise serum transaminase
and creatine levels,
Muscle Tenderness,
Myopathy(rare)
↓LDL,
↑HDL and
↓TG
Ezetimibe (5-10 mg/day) ↓ Cholesterol absorption Contraindicated in
pregnancy and children ↓LDL
Bile acid sequestrants (Resins)
Cholestyramine(4-16mg)
Colestipol (5-30mg)
↓ bile acid absorption and
↑hepatic conversion
of cholesterol to bile acids
Nupalatable,
Flatulence,
Interference with drugs
↓ LDL and
↑HDL
Fibric acid derivatives
Gemfibrozil(1200mg)
Benzafibrate (600mg)
Fenofibrate(200mg)
↓ PPAR-α, ↑ activity of
LPL, ↓ release of FA from
adipose tissue
Skin rashes, Eosinophillia
Impotency, contraindica-
ted in pregnancy.
↓LDL,
↑HDL and
↓TG
Tesaglitazar ↓ PPAR-α
↓ PPAR-γ
Gastrointestinal
symptoms,
Respiratory infections
↓LDL, ↑HDL
And ↓TG
Niacin (2-8 gms/day) ↓ lipolysis in adipocytes,
↓ FA synthesis in liver
↓ VLDL
Flushing,
Itching
↓LDL, ↓TG
And ↑HDL
Novel drugs
PCSK9 inhibitors
Alirocumab
Evolocumab
PCSK9 promote LDL
receptor destruction on liver cells, prevent blood
clearance of LDL
.
Chest pain,fluid retention,
Hepatic steatosis, itching and irritation
↓ LDL
MTP inhibitors
Lomitapide
↓ Assembly of
apolipoproteins,
triglycerides and
cholesterol in liver
Hepatic steatosis and
Increase in liver
transaminase level are the
serious side effects
↓ LDL and
↓ TG
Bempedoic Acid ↓ACL (links carbohydrate
metabolism to pathways
for synthesis of Fas and
cholesterol).
Muscle pain, diarrhea and
pain in extremities
↓ Cholesterol
↓ FA and ↓LDL
ACAT: Acyl coenzyme A cholesterol acyl transferase, ACL: adenosine triphosphate citrate lyase, FA: fatty acid,
HMG-CoA: Hydroxy-3-Methylglutaryl Coenzyme A, HLD; High density lipoprotein, LDL ;Low density
lipoprotein, LPL: lipoprotein lipase, MTP: Microsomal Triglyceride transfer Protein, PCSK9: Proprotein
Convertase subtilisin/ kexin type 9, TG: Triglycerides, VLDL: Very Low density lipoprotein.
2.6.5.3 Management of hyperlipidemia by natural plants
Over the past decade, natural plants have become a topic of global importance, making an impact
on the world health. Natural plants continue to play a central role in the healthcare system of
large proportions of the world‘s population. Currently used hypolipidemic drugs are associated
with so many adverse effects which are not seen with herbal preparations. Plant parts or plant
21
extract are sometimes even more potent than known hypolipidemic drugs. Some plants which
had hypolipidemic activity are presented in table 2.13 (Arun, et al., 2013).
Table 2.13: Plants with hypolipidemic activity
Plant Family Parts used Ref
Amaranthus spinosus Amaranthaceae Leaves Arun, et al., 2013.
Glycyrrhiza glabra Fabaceae Root Arun, et al., 2013.
Withania somnifera Solanaceae Root Arun, et al., 2013.
Chlorophytum
borivilianum Liliaceae Root Arun, et al., 2013.
Moringa oleifera Moringaceae Leaves ,root, seeds Arun, et al., 2013.
Hibiscus cannabinus Malvaceae Fresh leaves Arun, et al., 2013.
Randia dumetorum Rubiaceae Fruit Arun, et al., 2013.
Medicago sativa Fabaceae Seeds Bahmani, et al., 2015.
Trigonella foenum-
graecun Fabaceae Seeds Bahmani, et al., 2015.
Allium sativum L Amaryllidaceae Alliin Bahmani, et al., 2015.
Silybum marianum L Asteraceae Silymaryne Bahmani, et al., 2015.
2.7 Computer aided drug design(CADD(
Drug discovery is challenging, an exhaustive and time-consuming process involving numerous
stages like target identification, validation, lead optimization, preclinical trials, clinical trials and
finally post marketing vigilance for drug safety. For instance, the total average cost of
developing a new drug, as per an estimate, ranges from $2 billion to $3 billion and it takes at
least 13–15 years to bring a drug to the market—starting from initial discovery to the approval
stage. The application of computer aided drug designing (CADD) is an indispensable approach
for developing safe and effective drugs. It is extensively used to reduce cost, time and speed up
the early stage development of biologically new active molecules (Scannell, et al., 2012).
CADD based approaches including pharmacophore modeling, molecular docking, inverse
docking, chemical similarity (CS), quantitative structure activity relationship (QSAR), virtual
screening (VS) and molecular dynamics simulations have been quite productive in predicting the
therapeutic outcome of candidate drugs/compounds besides saving precious time. Computational
approaches have led to the discovery of many drugs that have passed preclinical and clinical
trials and become novel therapeutics in the treatment of a variety of diseases. In addition, CADD
plays an important role in predicting absorption, distribution, metabolism, excretion and toxicity
(ADME/T) of candidate drugs. Overall, CADD represents an effective and much-needed strategy
for designing therapeutically effective drugs to combat human diseases (Dar, et al., 2018).
24
In a drug discovery campaign, CADD is usually used for three major purposes; filter large
compound libraries into smaller sets of predicted active compounds that can be tested
experimentally, guide the optimization of lead compounds, whether to increase its affinity or
optimize drug metabolism and pharmacokinetic (DMPK) properties including absorption,
distribution, metabolism, excretion and the potential for toxicity (ADME/T) and design novel
compounds, either by "growing" starting molecules one functional group at a time or by piecing
together fragments into novel chemotypes. Figure 2.4 illustrates the position of CADD in drug
discovery pipeline (Sliwoski, et al., 2013).
2.7.1 CADD classification
CADD methods can be broadly classified into two groups, namely structure based (SB) and
ligand based (LB) drug discovery as shown in figure 2.4 The CADD method used depends on
the availability of target structure information (Leelananda & Lindert, 2016; Pares, et al., 2017).
In order to use SBDD tools, information about target structures needs to be known. Target
information is usually obtained experimentally by X-ray crystallography or NMR (nuclear
magnetic resonance). When neither is available, computational methods such as homology
modeling may be used to predict the three dimensional structures of targets. Knowing the
structure makes it possible to use structure-based tools such as virtual high throughput screening
and direct docking on targets and possible drug molecules (Leelananda & Lindert, 2016).
The central goal of SBDD is to design compounds that bind tightly to the target, e.g., with large
reduction in free energy, improved DMPK/ADME/T properties, and are target specific
(Jorgensen, 2010).
When the target structure is not experimentally determined or it is not possible to predict the
structure using computational methods, ligand-based approaches are often used as an alternative
methods. These methods, however, rely on the information about known active binders of the
target. Pharmacophore modeling, molecular similarity approaches and QSAR modeling are some
popular LBDD approaches (Leelananda & Lindert, 2016).
22
Figure 2.4: CADD in drug discovery/design pipeline (Leelananda & Lindert, 2016).
2.7.2 Computational target fishing
Computational or in-silico target fishing which is also known as reverse screening as seen in
figure 2.5 has emerged as an interdisciplinary field with tremendous potential to advance in-
silico drug design. Target fishing, or target identification, is an important start step in modern
drug development, which investigates the mechanism of action of bioactive small molecules by
identifying their interacting proteins. It can also be used to find potential off-targets of
therapeutic compounds for the study of their side effects. In addition, target fishing can be used
to detect drug polypharmacology (Achenbach, et al., 2011) and for drug repurposing, (Liu, et al.,
2013).
Computer Aided Drug Design (CADD)
Target Identification
Structure Based Drug
Design (SBDD):
Structure prediction
Docking
De novo ligand design
Ligand Based Drug
Design (LBDD):
QSAR
Pharmacophore modeling
Similarity search
In Vitro Verification
Drug Candidate
Hit
Lead generation
22
Figure 2.5: Comparison between docking and reverse docking (Zheng et al., 2013).
2.7.2.1 Polypharmacology
Polypharmacological phenomena includes single drug acting on multiple targets of a unique
disease pathway, or single drug acting on multiple targets pertaining to multiple disease
pathways. In addition, polypharmacology for complex diseases is likely to employ multiple
drugs acting on distinct targets, that are part of a networks regulating various physiological
responses (Reddy & Zhang, 2013). The polypharmacological approaches aim to discover the
unknown off-targets for the existing drugs (also known as drug repurposing) (Achenbach, et al.,
2011). The approach needs the systematic integration of the data derived from different
disciplines including computational modeling, synthetic chemistry, in vitro / in vivo pharmacolo-
gical testing and clinical studies (Reddy & Zhang, 2013).
Almasri, 2018, used ROCS based target fishing approach (RTFA) to approve the
polypharmacology of some natural products as resveratrol, curcumin and berberine, he explored
some targets that were reported previously and other targets which weren't reported. In his study
docking was adopted for the unreported targets and the binding energy was calculated, e.g.,
resveratrol, among other compounds involved in the study, is non-flavonoid polyphenol has
antioxidant, phytoestrogenic, anti-inflammatory, anticarcinogenic, antiaging, cadioprotective and
neuroprotective activities. The exact mechanisms of action of some of these potential biological
activities wern't fully clarified, at his work, he discovered the targets that clarify these activities
21
as estogen receptor α (ERα) for anti-inflammatory activity, silent information regulator proteins-
1 (SIRT-1) which responsible for antiobesity, antiaging and anticancer activities, protein kinases
as casein kinase 2 (CK2) and (PIM1) for anticancer activity, in addition, resveratrol was found to
act on tankyrase 2 as anticancer target. Moreover, resveratrol captured two targets of
neuroprotective properties that were transthyretin and β-secretase (Almasri, 2018).
2.7.2.2 Drug repurposing
Drug repurposing (also known as repositioning, reprofiling, or rediscovering) is defined as
developing new uses for a drug beyond its original use or initial approved indication (Ashburn &
Thor, 2004; Mangione, et al., 2019).
The premise is that since most approved compounds have known bioavailability and safety
profiles, proven formulation and manufacturing routes, and reasonably characterized
pharmacology, repositioned drugs can enter clinical phases more rapidly and at a lower cost than
novel compounds. It is therefore not surprising that in recent years, of the new drugs that reach
their first markets, repositioned drugs have taken up to a percentage of approximately 30%. For
instance, of the 113 new drugs approved or launched in 2017, only seven were first-in-class
agents (an approved and launched first drug with a novel mechanism of action) while 36 were
repositioned drugs (Papapetropoulos & Szabo, 2018).
Most of the successful cases of drug repurposing have been serendipitous discoveries rather than
systematic, hypothesis-driven outcomes. There are many stories of repurposing that have gone
on to be profitable: bupropion, originally used for depression, was repurposed for smoking
cessation; and thalidomide, used as a treatment for morning sickness, is now used for multiple
myeloma (Baker, et al., 2018).
In addition, scientists from the University of Dundee have shown that the antitubercular drug
delamanid has the potential to be repositioned as an oral drug for visceral leishmaniases, one of
the major diseases seen in developing countries (Patterson, 2016).
In addition, Kinnings, et al., 2009, performed extensive structure-based studies on nine different
Mycobacterium tuberculosis InhA structures (InhA: 2-trans-enoyl-acyl carrier protein reductase
which is a target of the antituberculosis isoniazid) to evaluate whether the entacapone and
tolcapone drugs, approved for the treatment of parkinson‘s disease, might be repurposed against
tuberculosis. Their results allowed the identification of entacapone as a promising lead
compound against resistant strains of Mycobacterium tuberculosis (Kinnings, et al., 2009).
25
On the same line, Dakshanamurthy, et al., 2012, performed extensive docking-based virtual
screenings on a subset of compounds taken from Protein Drug Bank (PDB) and FDA databases
against several X-ray crystal structures of human proteins reported in the PDB. According to the
reported results, the authors discovered that the antiparasitic drug mebendazole is also an anti-
angiogenic VEGFR2 inhibitor. Moreover, they also successfully discovered that the COX-2
inhibitor celecoxib and dimethyl celecoxib bind to Cadherin-11, which is a protein mediating
calcium-dependent cell-cell adhesion that plays a crucial role in rheumatoid arthritis
(Dakshanamurthy, et al., 2012).
2.7.2.3 Computational methods for target fishing
Various computational methods have been developed to predict the molecular targets of a
compound. These methods were initially classified into four groups: molecular similarity
searching, data mining/machine learning, panel docking, and the analysis of bioactivity spectra.
Also, other classes, such as protein-structure-based methods, have been proposed (Zheng, et al.,
2013; Cereto-Massagué, et al., 2015).
2.7.2.3.1 Molecular similarity methods
This section describes chemical similarity methods and shape based similarity methods. The
simplest methods for target prediction are based on chemical similarity and the use of current
knowledge about the bioactivity of millions of small molecules. These methods are based on the
‗‗chemical similarity principle,‘‘ which states that similar molecules are likely to have similar
properties. Thus, the targets of a molecule can be predicted by identifying proteins with known
ligands that are highly similar to the query molecule. The advantage of these methods is that they
only require the computation of the similarity between compounds. In the chemical similarity
method, a small molecule is represented as a chemical fingerprint. Fingerprints are a way of
encoding the structure of a molecule. The most common type of fingerprint is a series of binary
digits (bits) that represent the presence or absence of particular substructures in the molecule. To
compare the fingerprints of two molecules, the Tanimoto coefficient or any other similarity
criterion can be used. The more similar two compounds are, the closer the Tanimoto coefficient
will be to 1. (Cereto-Massagué, et al., 2015).
Tanimoto coefficient also known as the Jaccard coefficient, one of the most common approaches
for database searching due to its simplicity, fast speed as its calculation does not involve any
square root, making it faster in calculation, easy implementation and results in drug discovery
(Yu, et al., 2015). Tanimoto binary variable formula for binary data, as shown in the equation:
26
When the molecules A and B, a = bits set to 1 in A, b = bits set to 1 in B, c = number of 1 bits
common to both (Himmat, et al., 2016).
Molecular similarity can be classified into two Dimension (2D) chemical similarity and three
Dimension (3D) molecular similarity.
The 2D fingerprints have been widely used for similarity searching in target fishing due to quick
calculation so in some cases, methods that use 2D fingerprints outperform those methods that use
3D fingerprints in correct target prediction (Nettles, et al., 2006). The 3D chemical descriptors
can also be used for similarity searching in target fishing, although calculating them is
computationally more expensive. Because they contain more information, the predictions based
on 3D fingerprints would be expected to be better than those based on 2D fingerprints. The 3D
descriptors work better in cases of low structural similarity (Nettles, et al., 2006).
3D similarity searching requires the conformation properties and the specification of an entire
target structure (the whole structure) rather than partial structure. Thus, the 3D method-based
similarity measurement was introduced and gained more attention recently because of their
potential to overcome the key limitation of 1D and 2D methods (Nettles, et al., 2006).
The 3D structures usually have three different angles of view, which is x-, y-, and z- axis. With
the one extra angle of view in 3D structure, it will provide an important information needed and
better conformation for the similarity search process. Another characteristic of 3D structure is, it
is better in capturing all aspects of the molecular structure (the size and the shape of molecular
structure). In order to measure how similar is the chemical structure is, a similarity measure is
needed (Cereto-Massagué et al., 2015).
Shape-based similarity methods use 3D shape comparisons between molecules, usually
comparing the shape of the molecular volume, but other ―shapes‖ can be compared, like the
electrochemical surface. This can be done with software such as ROCS, Phase Shape,
ESHAPE3D, PARAFIT, ShaEP and USR as some examples (Cereto-Massagué, et al., 2015)
Popular 3D similarity comparison programs (Shape-it, Align-it, and ROCS). These programs
aligned molecules based on either molecular shape (Shape-it and ROCS) or pharmacophore
(Align-it) features using a Gaussian-like function. CSNAP (chemical similarity network analysis
pull-down) for drug target profiling using chemical similarity networks.
Tanimoto binary variable formula: SA,B = c \ a+b-c
27
A known limitation of chemical similarity approaches is that this approach often suffers from
false positive and false negative predictions, particularly when inactive and active compounds
display structural similarities. In many cases (where there is no known biologically active
conformation for the molecule), a single low-energy conformer is used, although it can be
biologically irrelevant. Another approach is to get the conformation of the molecules by aligning
them to a known bioactive conformation of a known ligand. In other cases, combining chemical
and shape similarity measures significantly increases the target prediction accuracy (Gfeller, et
al.,2014).
In addition, statistical analysis has been added to traditional chemical similarity scores in order to
assess the statistical significance of similarity. For example, fitting with extreme value
distributions, the similarity ensemble approach models the possibility of the occurrence of higher
scores when comparing two ligand sets. This method has been successfully used in drug
repurpose and side effect prediction (Wang, et al., 2013).
Rapid overlay of chemical structures software (ROCS)
ROCS is a standard tool for the calculation of 3D shape and chemical (‗‗color‘‘) similarity.It is a
powerful virtual screening tool which can rapidly identify potentially active compounds by
shape/chemical matching between the natural compounds and the chemogenomic database.
ROCS is competitive with, and often superior to, structure-based approaches in virtual screening,
both in terms of overall performance and consistency. Novel and interesting molecular scaffolds
have been identified using ROCS against targets often considered very difficult for
computational techniques to address. ROCS software is designed to perform large scale 3D
database searches by using a superposition method that finds the similar but non-intuitive
compounds that are so valuable in the drug discovery process (Hawkins et al., 2007; ROCS
3.2.1.4, 2015; Kearnes and Pande, 2016).
ROCS is a shape-based superposition method, for definition of shape. Two entities will have the
same shape if their volumes exactly correspond. The more the volumes differ, the more the
shapes will differ but a volume is any scalar field. The special case for the common
understanding of volume is a specific scalar field that has a value of one inside an object and
zero outside (ROCS 3.2.1.4, 2015).
28
ROCS uses only the heavy atoms of a ligand, hydrogens are ignored. Since shape and volume in
this context are so closely related, a volume overlap maximization procedure is an excellent
method for gaining insights into similar shapes (ROCS 3.2.1.4, 2015).
Although ROCS is primarily a shape-based method, user specified definitions of chemistry
alignment, known as ‗color‘ can be included into the superposition and similarity analysis
process which facilitates the identification of those compounds which are similar both in shape
and chemistry (Hawkins, et al., 2007).
ROCS can routinely perform global shape and color alignments at the rate of 600-800
conformers per second (Kearnes and Pande, 2016).
Molecules are traditionally viewed as a set of fused spheres. Molecules are aligned by a solid
body optimization process that maximizes the overlap volume between them as seen in figure
2.6. Volume overlap in this context is not the hard-sphere overlap volume, but rather a Gaussian-
based overlap parameterized to reproduce hard-sphere. (Kearnes and Pande, 2016).
Figure 2.6: Molecular descriptors based on the ROCS color force field. Color components represent each
colortype independently. Color atom overlaps describe similarity in terms of individual color atoms in the query
molecule (bottom left). Color component values are Tanimoto scores. Note that color atom overlap volumes are
used without normalization, and that negative values indicate favorable interactions (Kearnes and Pande, 2016).
Medium-sized database searching (Ten‘s of millions of conformers) becomes tractable but slow
at this rate of superposition. Distributed computing makes the entire process much more facile
29
for screening larger numbers of compounds and conformers. ROCS can automatically split up
similarity searches over entire networks of computers in an efficient and manner taking full
advantage of parallel virtual machines. The coupling of shape and chemistry screening with a
distributed architecture makes ROCS an incredibly powerful tool for searching large 3D
databases (ROCS 3.2.1.4, 2015).
ROCS includes a query visualizer and editor, vROCS, which provides a graphical interface for
creating queries and evaluating the performance of ROCS (Kearnes and Pande, 2016).
Items used in ROCS Report
Name: this is the name of the database molecule. If the database contains multi-conformer
molecules, the specific conformer index is appended to the molecule name with an underscore.
ShapeQuery: This is the name of the query molecule. If the query is a multi-conformer
molecule, then the specific conformer index is appended to the molecule name with an
underscore.
Rank: It is the numerical ranking in the hit list, based on the chosen score to sort by.
TanimotoCombo: To provide a score that includes both shape fit and color, the Shape Tanimoto
is added to the Color Tanimoto, resulting in the TanimotoCombo score. This has a value between
0 and 2 and is the score used for ranking the hit list.
Shape Tanimoto: This column gives the ShapeTanimoto, a value between 0 and 1 as calculated
by the Tanimoto equation.
Color Tanimoto: This column gives the ColorTanimoto, a value between 0 and 1 as calculated
by the Tanimoto equation (Kearnes and Pande, 2016).
2.7.2.3.2 Data mining and machine learning methods
In data-mining or machine learning methods, the properties of known active compounds against
a target are analyzed carefully and statistical models are generated, which after rigorous training
are employed to predict the probable targets that associate with the query compound. The main
limitation of this method is that every target may bind structurally diverse classes of compounds
and hence one model may not cover all the features, consequently affecting the performance in
target fishing (Wang, et al, 2013; Ganesan & Barakat, 2016).
11
2.7.2.3.3 Molecular docking methods
Other computational target fishing methods use the protein structure of the targets to predict
novel bioactivities. Pharmacophore searching, protein−ligand interaction fingerprints or protein
docking can be used. These methods are limited to targets with resolved structures. Molecular
docking methods for target fishing employs a ‗reverse‘ virtual screening approach, in which a
compound of interest is docked into a wide array of protein structures in public databases as seen
in figure 2.5, such as protein data bank (PDB), and the target in the best scoring complex is
predicted to be a probable partner of the query compound. Several online servers, such as
TarFisDock, INVDOCK and idTarget, have been developed for this purpose. However, the
accuracies of these docking-based methods are dependent on the efficacies of the scoring
functions employed and the availability of high-performance supercomputers (Ganesan &
Barakat, 2016).
In fact, although it was first developed to investigate molecular recognition between large and
small molecules, it is now also widely used to assist different tasks of drug discovery programs,
such as hit identification and optimization, drug repositioning, target identification, multi-target
ligand design, and repositioning as shown in Figure 2.7.
Figure 2.7: Main applications of molecular docking in current drug discovery (Pinzi, & Rastelli, 2019)
14
Moreover, docking allows understanding the relationships between different molecular targets
involved in a given disease, which is also of high relevance for polypharmacology and modern
drug discovery in general (Pinzi, & Rastelli, 2019).
7.1.7.2.4 Methods based on analysis of bioactivity spectra
Bioactivity spectra analyses methods work on the principle that compounds binding to same
target should display similar bioactivity spectra (i.e., the readouts from microarrays, cell lines
and in vitro screening). The bioactive spectra collected from different targets and assays are later
employed in the computational method to predict targets for the drugs. The important caveat of
this method is the need to perform expensive and time consuming experiments to collect
bioactivity spectra for different targets (Wang, et al., 2013; Kearnes and Pande, 2016).
3
4
5
6
7
8
9
12
10 Chapter Three
Methodology
3.1 Computational Method
Figure 3.1 shows the workflow of ROCS-based target fishing approach (RTFA) implanted in this
thesis. The work involves the following steps: the first was generation of active ligands database;
The second was generation of query compound; The third was similarity search and finally
molecular docking simulations.
Figure 10.1: Workflow of ROCS-based target fishing approach (RTFA)
3.1.1 Building 3D-ligands database
In order to cover the chemogenomic space, two databases were downloaded as SDF files: a) The
approved drug molecules (e-Drug3D) obtained from the Cheminformatic Tools and Databases
(contain 1822 molecular structures with a molecular weight ≤ 2000, last update: July 2016).
b) The co-crystallized ligands database obtained from Protein Data Bank (PDB) (www.rcsb.org)
including ligands that are used currently in PDB structures (contains 217000 entries). The two
databases were filtered using FILTER Software (Filter, 2013). In order to get rid of large
12
polypeptide drugs in the drug database, filtering was carried out using molecular weight as a
filter (180-800), for PDB database, the following parameters were adopted: allowed elements
(H, C, N, O, F, P, S, Cl, Br, I): number of heavy atoms (15-40); molecular weight (200-600);
number of ring system (0-5). After filtration, the drug database encompassed 1660 approved
drugs and the PDB had 6548 ligands. Subsequently, an ensemble of energetically accessible
conformers of the target representatives was generated using OMEGA software (OMEGA, 2013)
with default parameters.
3.1.2 ROCS similarity search
The ROCS run was carried out using vROCS which provides a single user interface from which
the user can build/edit ROCS queries, set up ROCS runs and visualize/analyze the results. There
are four primary tasks in vROCS available: a) Perform a simple ROCS run; b) Create a query
with a wizard; c) Create or edit a query manually; d) Perform a ROCS validation.
Queries from natural compounds were built in the vROCS query editor after exporting a line
notation describing the structure of chemical species (SMILES) and are automatically added to
this list for the current vROCS session. The query highlighted in blue is the selected (active)
query as shown in figure 3.2.
Figure 10.2: Simple run window display in vRocs user interface after generation of quercetin query.
11
A simple run was performed which aligns a database of precomputed molecular conformers
against natural queries. For each molecule in the chemogenomic database it overlays every
conformer based on molecular shape and chemical features. The conformers are scored based
upon the Gaussian overlap to the query and the best scoring conformer is reported. The adopted
score in our research was TanimotoCombo (shape + color). The molecules in the database are
finally ranked by the scores for their best aligned conformers. The higher TanimotoCombo score,
the better shape and chemical-feature match exists between molecules.
The ROCS run was carried out with the following parameters: Score to use for ranking the hit
list (rank by = TanimotoCombo); Maximum number of overlays returned for each comparison of
a database molecule with a query molecule; Keep a hit list with the highest score; Other
parameters as kept as default.
3.1.3 Docking simulations
3.1.3.1 Preparation of proteins
The 3D coordinates of target proteins had been downloaded from Protein Data Bank, (PDB;
https://www.rcsb.org) as seen in table 3.1, as (.pdb) files. Subsequently, hydrogen atoms were
added to proteins using Accelrys Discovery studio (DS) Visualizer.
Table 10.1: Proteins data obtained from Protein data bank.
Target PDB code Cpd.ID Origin References
CDK2 2DUV 371 Homo sapiens Lee, et al., 2007
CK2α 3AMY AGI Homo sapiens Kinoshita, et al.,
2013
DAPK1 5AV3 KMP Homo sapiens Yokoyama, et al.,
2015
DPP-4 3HAC 361 Homo sapiens Edmondson, et al.,
(2009).
PPAR-γ 3SZ1 LU2 Homo sapiens Puhl, et al., (2012).
PDK1 3NUN JMZ Homo sapiens Medina, et al., 2010
PIM1 4XH6 HUL Homo sapiens Chao, et al., 2015
GSK-3β 3Q3B 55E Homo sapiens Coffman, et al.,
(2011).
Tank 2 4HL5 15W Homo sapiens Narwal,et al., 2013
CDK2:Cycline dependent kinase2, CK2α: Casine kinase 2 alpha, Cpd.ID: Co-Crystallized ligand identification,
DAPK1: Death associated protein kinase1, DPP-4: Dipeptidyl peptidase, ERα: Estrogen receptor alpha, ERβ:
Esrtogen receptor beta, GSK-3β: Glycogen synthase kinase-3, PDB: Protein data bank, PDK1: Phosphoinositide-dependent kinase-1, PIM1: Proviral integration site for moloney murine leukaemia virus,
PPAR-γ: Peroxisome proliferator activated receptor gamma, PTP1B: Protein tyrosine phosphatase 1B, Tank-2:
Tankyrase 2
15
The target proteins binding sites were identified using pdb2receptor module within OEDocking
version 3.2.0.2 software suite (OEDOCKING, 2015). PDB2RECEPTOR is a utility program for
converting a protein-ligand complex into a receptor. It takes as an input the structure of the
protein-ligand complex and the name of a residue identifying a ligand bound to the active site.
The output receptor files were saved as OEBinary file (.oeb) files which are compatible with the
docking software.
3.1.3.2 Preparation of ligands
The chemical structure of natural products were sketched in Marvin Sketch (version 16.10.24)
and saved in molfile format. Afterwards, conformation space of compounds were explored by
generating energetically accessible conformers using OMEGA software (OMEGA 2.5.1.4, 2013)
and the generated conformers were saved in (.Sdf) format.
OMEGA is a conformation generator of molecules. OMEGA is composed of two main
components; model building and torsion driving. The software builds initial models of structures
by assembling fragment templates along sigma bonds. Once an initial model of a structure is
constructed, or given as input, OMEGA generates additional models by enumerating (a) ring
conformations, (b) invertible nitrogen atoms. Ring conformations are taken from the same
fragment library used to build an initial model, OMEGA detaches all exocyclic substituents from
a ring system, aligns and attaches them relative to the new ring conformation. OMEGA attempts
to generate every possible combination of ring conformations possible for a given structure. On
the other hand, nitrogens that have pyramidal geometry, no stereochemistry specified, no more
than one hydrogen, are three valent, and have no more than three ring-bonds are considered by
OMEGA to be invertible.
OMEGA begins the torsion search process by examining the molecular graph and determining
the bonds that may freely rotate. Exhaustive depth first torsion search is performed on each
fragment, and the resulting conformers are placed into a list sorted by energy. Entire structures
are assembled by combining the lowest energy set of fragments, and then the next lowest set,
until the search is terminated. The search will terminate when the limit on the total number of
conformers that may be generated is exceeded, the fragment list is exhausted, or the sum of the
fragment energies exceeds the energy window of the global minimum structure. The best
conformers identified in the torsion search are rank ordered by energy.
16
3.1.3.3 Docking
Natural compounds were docked into active site of target proteins using FRED software version
3.2.0.2 (FRED, 2015) within the OEDocking suite in the presence of the explicit water
molecules.
FRED is a fast rigid exhaustive protein-ligand docking program, which makes use of a pre-
generated multi-conformers database and a single receptor file as input and output molecules
most likely to bind to the receptor (FRED, 2015). The input files to this program is: receptor
saved as specialized OEBinary file (.oeb) files obtained from crystallographic structures of the
target proteins as shown in protein preparation section above; and one or more drug-like
molecules to be docked. The output is the docked pose of the molecules and information about
the dock score (Chemgauss4). The protein structures and ligands conformers were treated as
rigid units during docking process. The top scoring poses were optimized and assigned a final
score.
Here we could remember that the receptor is a specialized OEBinary file (i.e., .oeb or .oeb.gz
file) that contains the structure of a target protein, a negative image that describes the shape of
the active site and additional information about the location and characteristics of its binding
pocket, also the structure of a ligand bound to the active site and extra molecules that do not
affect either docking or scoring generally as water or other solvent molecules.
This docking engine takes a multiconformer database of one or more ligands, a target protein
receptor, that contains the structure of a target protein and additional information about the
location and characteristics of its binding pocket. The ligand conformers and protein structure
are treated as rigid during the docking process. FRED's docking strategy is to exhaustively score
all possible positions of each ligand in the active site (OEDOCKING, 2015). The exhaustive
search is based on rigid rotations and translations of each conformer. Therefore, it avoids
sampling issues associated with stochastic methods; semi random method.
17
11 Chapter Four
Results and Discussion
11.1 Background
Natural products contribute significantly to drug discovery research with a rich source of
compounds and provide inherently large-scale of structural diversity than synthetic compounds.
Modern drug discovery aims to identify hits that induce desired biological effects in cell culture
or animal models. Historically, nature has been an important source for such molecules,
however, the binding mechanisms of the identified molecules are often unknown, and
determining their underlying molecular targets has become an integral part of the drug discovery
process (Schenone, et al., 2013).
Current experimental target identification approaches can rarely achieve large-scale drug target
profiling (Verhelst & Bogyo, 2005). As a result, the development of in-silico drug target
profiling approaches that can effectively prioritize supposed on and off-targets for experimental
validation will be important for the success of current and future drug discovery programs.
In-silico target fishing methods can be classified as structure based or ligand based approaches
(Schenone, et al., 2013). Currently, ligand based approaches remain the standard for
computational target prediction, as this approach does not depend on the availability of protein
structures or prior experimental measurements. The rationale behind ligand based approaches is
the chemical similarity principle, which states that structurally similar compounds often share
similar bioactivities. To compare chemical similarity between compounds, each molecule is
encoded as a substructure fingerprint, and the degree of similarity is quantified by shared bits
using a Tanimoto index. To predict the drug targets for the query ligands, the compounds are
used to search the bioactivity databases, and putative drug targets are inferred from annotated
ligands in the database that share the highest chemical similarity to the query ligand (Lo, et al.,
2016).
Target fishing is considered as complementary process to experimental screening approaches as
it is not possible to test each compound against every possible target. This work is ligand-based
computational method which involve an efficient similarity measure and reliable scoring method
(Schenone, et al., 2013).
18
This study was designed to explore the polypharmacology of S. officinalis and U. dioica, well
known plants in our region, using ligand based target fishing approach and focusing on their
reported anticancer, antidiabetic and antihyperlipidemic bioactivities. Only thet op ranked targets
had been taken into consideration in the discussion below.
4.2 Polypharmacologyof S. officinalis
S. officinalis has been conventionally used for the treatment of various ailments since ancient
times at various parts of the world. Findings from in-vitro and several clinical studies supporting
the evidence of its medicinal uses such as cognitive, antioxidant, antimicrobial, anticancer,
antidementia, hypoglycemic, and hypolipidemic agents (Sharma & Schaefer, 2019).
The main phytochemical components of S. officinalis species are terpenoids and phenolic
components. The phenolic components can be divided into phenolic acids (caffeic, vanillic,
ferulic, and rosmarinic acids) and flavonoids (luteolin, apigenin, and quercetin) (Jakovljevic, et
al., 2019).
Terpenoids possess antitumor, anti-inflammatory, antibacterial, antiviral, antimalarial,
cardiovascular, antioxidant and hypoglycemic activities (Yang, et al., 2019). Flavonoids are
shown to have antioxidant activity, free radical scavenging capacity, coronary heart disease
prevention, hepatoprotective, anti-inflammatory, and anticancer activities, while some flavonoids
exhibit potential antiviral activities (Kumar & Pandey, 2013).
4.2.1 Reported anticancer, antidiabetic and hypolipidemic targets of S. officinalis
Several suggested mechanisms for S. officinalis anticancer effects were reported in the literature.
The suggested mechanisms for the anticancer role of dietary intake of S. officinalis include the
interaction with signaling pathways, such as estrogen receptor (ER)-mediated, Mitogen-
Activated Protein Kinase/extracellular signal-regulated kinase (MAPK/ERK), nuclear
transcription factor-kappa B (NF-kB), Vascular endothelial growth factor/ Vascular endothelial
growth factor receptor (VEGF/VEGFR), phosphatase and tensin homolog/ phosphatidylinositol
3-kinase /Protein Kinase B (PTEN/PI3K/Akt), p53, and mitochondria-dependent pathways
(Xavier, et al., 2009; Jian et al., 2016).
Recent pharmacological investigations demonstrated that different extracts of aerial parts of S.
officinalis are able to decrease blood glucose in normal and diabetic conditions. The mechanisms
suggested for hypoglycemic effect of S. officinalis include an inhibition of hepatocyte
gluconeogenesis and decrease of insulin resistance through stimulation of peroxisome
19
proliferator activated receptor-γ (PPAR-γ) (Christensen, et al., 2010; Ghorbani & Esmaeilizadeh,
2017).
The possible mechanism of hypolipidemic activity of S. officinalis may be due to free radical-
scavenging and antioxidant activities which might be attributed to the presence of flavonoids. In
addition, the decrease in the level of cholesterol may be due to the activity of Cholesterol 7-α
hydroxylase which works to lower blood cholesterol level by converting it to bile salts (Uthandi
& Karuppasamy, 2012; Abdulhussein, et al., 2019)
Using RTFA, we had discovered that many of S. officinalis natural constituents could bind to
ER, MAPK14, PIK3CG and PPAR-γ such as apigenin, luteolin, oleanolic acid and quercetin.
As shown in table 4.1, four on-targets and twelve off-targets related to anticancer activity of S.
officinalis, only one on-target and four off-targets related to antidiabetic activity of S. officinalis,
in addition to one off-target related to hypolipidemic activity of S. officinalis, were identified by
the adopted RTFA protocol.
Table 11.1: On-targets and off-targets of S. officinalis
Anticancer Targets Antidiabetic Targets Hypolipidemic Targets
On-Target Off-Targets On-Targets Off-Targets On-Targets Off-Targets
1 ERα CK2 PPAR-γ GP PTP1B
2 ERβ PIM1 DPP-4
3 MAPK14 DAPK1 PTP1B
4 PIK3CG PDK1 PDK1
5 Tank2
6 GLO-I
7 FGFR1
8 RPTP-γ
9 CDK2
10 17β-HSD1
11 HSP90-α
12 Mcl-1
17β-HSD: 17 Beta Hydroxysteriod dehydrogenase 1,CDK2: Cycline dependent kinase 2, CK2: Casine kinase 2,
DAPK1: Death associated protein kinase1, DPP-4: Dipeptidyl peptidase-4, ERα: Estrogen receptor alpha,
ERβ:Estrogen receptor beta, FGFR1: Fibroblast growth factor receptor 1, GLO-1: Glyoxalase 1, GP: Glycogen
phosphorylase, HSP90-α: Heat shock protein 90 alpha, MAPK14: Mitogen activated protein kinase 14, Mcl-1:
myloid cell leukemia 1, PDK1: phosphoinositide-dependent kinase1, PIK3CG: phosphatidylinositol-4,5-
bisphosphat 3-kinase catalytic subunit gamma, PIM1: proviral integration site for moloney murine leukaemia
virus, PPAR-γ: peroxisome proliferator activated receptor gamma, PTP1B: protein tyrosine phosphatase 1B,
RPTP-γ: Receptor type protein tyrosine phosphatase gamma, Tank2: Tankyrase2.
On-Targets: Known targets, reported in the literature, for the specified bioactivity of the query compound.
Off-Targets:Unknown targets, not reported in the literature, for the specified bioactivity of the query compound.
51
Analysis of the results that retrieved from RTFA highlights the importance of this approach in
clarifying the polypharmacology of S. officinalis especially the anticancer, antidiabetic and
hypolipidemic activities. RTFA clarify the ability of S. officinalis to bind with reported
anticancer targets (on-targets) as MAPK14, ERα, ERβ and PIK3CG as well as to bind with the
reported antidiabetic target such as PPAR-γ. Moreover, we had discovered many off-targets for
cancer, diabetes and hyperlipidemia that had been caught by S. officinalis constituents as CK2,
PIM1, PTP1B, PDK1…etc.
4.2.2 Targets identified using S. officinalis constituents as queries in RTFA
4.2.2.1 Apigenin
Apigenin, known chemically as 4′,5,7-trihydroxyflavone (as shown in table 2.2), belongs to the
flavone subclass and is abundant in a variety of vegetables, fruits and medicinal plants. It has
several reported biological activities, including antioxidant, anti-inflammatory, antitumor,
antidiabetic, neuroprotective (thus beneficial in amnesia and Alzheimer‘s disease) and
cardioprotective effects (Salehi et al., 2019).
Using RTFA and apigenin as query template, several targets related to cancer and diabetes were
identified, where all of them were on-targets : casein kinase II alpha (CK2α), proviral integration
site for moloney murine leukaemia virus 1 (PIM1), Death-associated protein kinase 1 (DAPK1),
Tankyrase 2 (Tank-2), mouse Glyoxalase-1 (GLo-1), Estrogen receptor α (ERα), Estrogen
receptor β (ERβ) and Glycogen phosphorylase (GP) as shown in table 4.2. This indicates the
biological importance of apigenin and the research interest in its multiple biological activities.
The first target, with the highest rank value was CK2α as shown in table 4.2. It is an ubiquitous,
pleiotropic, highly conserved and constitutively active serine/threonine kinase (Litchfield, 2003).
In many reports, the implication of CK2α in several pathologies is described, like neurodege-
nerative diseases (Parkinson‘s and Alzheimer‘s disease), inflammation, viral and parasite
infections as wellas cancer (Baier, et al., 2018).
CK2α enhances cancer phenotype by blocking apoptosis and simultaneously stimulating cell
growth. A number of cancers are associated with hyper activation and overexpression of CK2α
including breast, lung, prostate, colorectal, renal, and hematological malignancies (Ortega, et al.,
2014), thus CK2α could be considered as a promising target for cancer treatment.
54
CX-4945 from Cylene Pharmaceuticals, also known as Silmitasertib, is the first inhibitor of CK2
that has been qualified for human clinical trials (Siddiqui-Jain, et al., 2010). It has successfully
completed phase I, and is currently in phase II for cholangiocarcinoma treatment, which granted
it an Orphan Drug status for cholangiocarcinoma by Food and Drug Administration (FDA) in the
USA in January 2017 (Masłyk, et al., 2017). CX-4945 is an ATP- competitive inhibitor of
protein kinase CK2 and a highly selective, orally administered small molecule studied in
different types of human cancer research (Siddiqui-Jain, et al., 2010). Human clinical
characterization of CX-4945 as a single agent in solid tumors and multiple myeloma has shown
its promising pharmacokinetic, pharmacodynamics, and safety profiles (Padgett, et al., 2010). In
addition, CX-4945; when used in anticancer therapy, it may simultaneously prevent cancer-
associated candidiasis (Masłyk, et al., 2017 ).
In addition, In July 2020, Silmitasertib was granted Rare Pediatric Disease Designation in
medulloblastoma by the US FDA. Moreover, an Emergency Investigational New Drug (EIND)
was granted by the US FDA on August 27, 2020, for use of Silmitasertib in the treatment of
sever coronavirus disease of 2019 (COVID-19). Silmitasertib has great potential as a therapeutic
for COVID-19 and as it targets the host protein kinase CK2 pathway, virus mutations are
unlikely to affect either antiviral or anti-inflammatory efficacy of Silmitasertib (John, 2020).
Table 11.2: Pharmacological profiling for apigenin using ROCS
Target Disease Tanimoto coefficient Ref.
CK2 α a Cancer 2.000
Lolli, et al.,2012 & Liu,
et al., 2015
PIM1 a Cancer 1.860
Gadewal and Varma.
2012
DAPK1 a Cancer 1.824 Yokoyama et al.,2015
Tank2 a Cancer 1.686 Narwal et al., 2013
mGLO-I a Cancer 1.651 Zhang et al.,2016
ERα b Cancer 1.523 Maruthanila, et al., 2019
ERβ b
Cancer 1.436 Powers & Setzer. 2015
GP b T2DM 1.261 Paramaguru, et al., 2014
CK2α: Casein kinase two alpha, DAPK1: Death-associated protein kinase 1, ERα: Estrogen Receptor alpha, ERβ: Estrogen Receptor beta, GP: Glycogen Phospholylase, mGLo-1: mouse Glyoxalase, PIM1: Proviral
integration site for moloney murine leukaemia virus 1, Tank2: Tankyrase2.
a Has in-vitro and/or in-vivo evidence, b Has in-silico evidence.
Herein, the RTFA had identified CK2α as an anticancer target and this finding was supported by
Lolli et al., who assayed a panel of 16 flavonoids and related compounds for their ability to
52
inhibit CK2α. Among the tested compounds, apigenin had an inhibition activity with IC50 value
of 0.80 μM (Lolli, et al.,2012). In addition, we found that apigenin had a tanimoto coeff. of 2
which means apigenin itself was the ligand for CK2α.
The second on-target was PIM1 from the proto-oncogene family (This family includes three PIM
genes PIM1, PIM2 and PIM3), represents a novel class of constitutively active serine/threonine
kinases. PIM1 had been shown to play a pivotal role in cell survival, proliferation, and
differentiation (Wang, et al., 2001). In addition, PIM1 has been implicated in avoidance of
apoptosis (Rebello, et al., 2018) by interacting with other tumorigenic pathways, such as the
PI3K /AKT pathway (Warfel & Kraft, 2015). Moreover, PIM up-regulation can cause resistance
to conventional chemotherapy, radiotherapy and other therapeutics (Zemskova, et al., 2008). In
humans, the PIM1 oncogene is expressed in lymphoid and haematopoietic cells, (Kumar, et al.,
2005; Meeker, et al., 1990), prostate malignancies (Valdman, et al., 2004), squamous cell
carcinomas of the head and neck region (Jeon, et al., 2004), gastric carcinomas (Sepulveda, et
al., 2002) and colorectal carcinomas (Xu, et al., 2011). An investigation found that chromosomal
translocation of PIM1 leading to overexpression of PIM1 is involved in diffuse large cell
lymphoma, the most common form of non-Hodgkin's lymphoma (Akasaka, et al.,2000).Thus, the
strong connection between overexpression and mutation of PIM1 and cancer suggests that PIM1
kinase inhibition is a promising drug target in the treatment of cancer.
To date, most efforts to inhibit PIM in cancer treatment have focused on the use of ATP-
competitive drugs that target the kinase action of the protein, preventing it from phosphorylat ing
its downstream effectors (Wang & Sun, 2019). A number of small molecule PIM inhibitors have
been developed and some have progressed to clinical stages such as flavonoid inhibitor, SGI-
1776 (Yang, et al., 2013) and AZD1208 (Keeton, et al., 2012). They can be classified as the first
generation inhibitor (SGI-1776) and the next generation inhibitor (AZD1208) (Mondello, et al.,
2014).
Studies demonstrated that SGI-1776, the first generation imidazopyridazine-based Pim kinase
inhibitor to enter clinical study, exhibited potent antitumor activity in vivo as well as in vitro.
SGI-1776 also showed to sensitize drug resistant cells to cancer chemotherapy drugs by
inhibiting p-glycoprotein (Pgp)-mediated efflux and inducing apoptosis (Burger, et al., 2008).
SGI-1776 was evaluated in a Phase I clinical trials, but failed to progress, due to its
cardiotoxicity (Le, et al., 2015).
The second generation inhibitors were quickly followed, aiming to remove cardiotoxicity while
maintaining the PIM potency. Thus, SGI-9481 (also known as TP-3654) was developed which is
52
a pyrazolopyrimidine-based PIM inhibitor with improved potency and decreased cardiotoxicity
compared to thiazolidinedione-based inhibitor. Notably, this drug selectively targets PIM1, with
a Ki (inhibition constant) of 5 nm (Luszczak, et al., 2020).
PIM kinase inhibitors in the clinical studies such as AZD1208 (Thiazolidinedione-based),
CXR1002 and LGH447 have been shown to induce apoptosis affecting cell proliferation and
migration, high lighting the potential for targeting PIM as an anticancer therapy (Pierre, et al.,
2011; Keeton, et al., 2012; Yadav, et al., 2019).
Gadewal and Varma, investigate the binding of PIM1 and apigenin and found that apigenin
exhibited PIM1 kinase inhibitory activity with IC50 value of 0.94µM (Gadewal & Varma, 2012),
this support our RTFA findings which identified PIM1 as a potential anticancer target for
apigenin and the whole plant.
The third on-target was DAPK1. It is a 160-kDa cytoskeletal associated protein kinase that
consists of 1430 residues and belongs to the superfamily of calcium/calmodulin (Ca+2
/CaM)
regulated serine/threonine protein kinases (Shiloh, R., et al., 2014). DAPK1 was identified as a
mediator of γ-interferon-induced cell death and a tumor suppressor. It is also linked to activation
of autophagy (Zalckvar, et al., 2009). In addition, knockdown of DAPK1 expression induced
TRAIL-mediated apoptosis in human endometrial adenocarcinoma cells, suggesting that DAPK1
may be a candidate molecule for advanced endometrial adenocarcinomas (Bai, et al., 2010).
Moreover, DAPK1 is an upstream negative regulator of peptidylprolyl cis/trans isomerase, never
in mitosis A(NIMA)-interacting 1 (Pin1) that activates numerous oncogenes and suppresses
many tumor suppressors, suggesting that activating DAPK1 effectively inhibits multiple
oncogenic signaling pathways (Huang, et al., 2014). It has also been reported that DAPK1 might
be oncogenic on certain cellular condition such as p53 mutant cancers (Zhao, et al., 2015).
Therefore, more studies of DAPK1 regulatory mechanisms and specific DAPK1 activators are
needed.
Yokoyama, et al., investigated the binding affinities of 17 natural flavonoids to DAPK1 and
explained their different affinities by means of the crystallographic analysis of DAPK1 with
selected flavonoids. The binding affinity of apigenin to DAPK1 had IC50 of 31 ± 3.6 µM
(Yokoyama, et al., 2015) and this go with our results from RTFA which identified DAPK 1 as a
possible target that might contribute to the anticancer activity of our plant.
51
The fourth on-target of apigenin was Tank2. Tank2 is a multifunctional poly ADP(adenisine
diphosphate)-ribose polymerase (PARP) that regulates a variety of cellular processes, including
telomere maintenance (allows continued proliferation), oncogenic pathways, mitosis and DNA
repair and cell death. Therefore, Tank2 inhibitors may be effective targets for cancer treatment
(Haikarainen, et al., 2014).
Numerous studies have reported the importance and utility of tankyrase inhibitors as cancer
therapeutics (Quackenbush, et al., 2016). Consequently, a number of tankyrase inhibitors with
promising therapeutic effects have been developed, including XAV939 (Huang, et al., 2009;
Bao, et al., 2012; Busch, et al., 2013;), IWR-1, G007-LK (Lau, et al., 2013), JW55 (Waaler, et
al., 2012), AZ1366 (Quackenbush, et al., 2016), JW 74 (Tian, et al., 2013; Stratford, et al., 2014)
and NVP-TNKS656 (Arqués, et al., 2016; Wang et al, 2016).
Narwal, et al., had performed a systematic screening of Tank2 inhibitory activity using 500
natural and naturally derived flavonoids and found that apigenin is Tank2 inhibitor with IC50 of
2.9 μM (Narwal, et al., 2013).
The fifth on-target was Glyoxalase I (GLO-I). GLO-I is the first and rate-limiting zinc enzyme in
the mammalian glyoxalase system for catalyzing the conversion of toxic α-oxoaldehydes to
nontoxic α-hydroxacids (Sousa, et al., 2103). The glyoxalase pathway is an antioxidant defense
mechanism. GLO-I has been reported to be frequently overexpressed in various types of cancer
cells, and has been expected as an attractive target for the development of new anticancer drugs
(Thornalley & Rabbani, 2011).
A novel inhibitor of human GLO-I, named TLSC702, was discovered by in silico screening
method with IC50 value of 2.0 µM. TLSC702 inhibits the proliferation of human leukemia and
lung cancer cells and induces apoptosis in a dose-dependent manner in both cancer cells. Taken
together, TLSC702 could become a unique seed compound for the generation of novel
chemotherapeutic drugs targeting GLO-I dependent human tumors (Takasawa, et al., 2016).
Apigenin inhibits the activity of GLO-1 and had IC50 value of 70 µM (Zhang, et al., 2016).
The sixth on-target was ERα, which is a member of the nuclear receptor superfamily of
transcription factors (Jameera, et al., 2107). It plays a vital role in the delineation and
maintenance of neural, skeletal, cardiovascular, and reproductive tissues (Hsu, et al., 2017).
However, ERα is considered as an oncogene and is mainly responsible for the breast cancer
initiation and progression (Folkerd, et al., 2010). Currently, compounds which effectively
55
modify ERα transcriptional activity are found beneficial in the treatment of osteoporosis,
cardiovascular disease, and breast cancer (Jordan, 2007; Hua, et al., 2018).
Herein, the RTFA had presented ERα as anticancer target and this supported by Maruthanila, et
al. In their study, they had screened selected natural ligands and their binding features with ERα
and found that apigenin has a strong binding to ERα with Glide Scores of -8.92 Kcal mol-1
using
molecular docking simulations (Maruthanila et al., 2019).
The seventh on-target was ERβ, which is a members of the nuclear receptor superfamily of
transcription factors. Estrogen is a steroid hormone that has critical roles in reproductive
development, bone homeostasis, cardiovascular remodeling and brain functions. However,
estrogen also promotes mammary, ovarian and endometrial tumorigenesis. ERβ is considered as
a tumor suppressor (Hua, et al., 2018) and its activation is an important mechanism for cancer
prevention (Yang, et al., 2015).
Powers & Setzer (2015) studded the binding of apigenin and ERβ and found that the molecular
docking energy of apigenin with ERβ had a value of −97.3 KJ/mol.
These findings could increase confidence in our approach (RTFA) for exploring the mechanisms
of the anticancer activity of S. officinalis.
The last on target was glycogen phosphorylase (GP) as shown in table 4.2. GP is a large protein
composed of two subunits (α and β) and present in muscles (m GP), brain (b GP) and liver
(l GP). GP exists under different conformational states symbolizing its activation degree (α
subunit is the phosphorylated and active unit while β subunit is the dephosphorylated and
inactive unit) (Gaboriaud & Skaltsounis, 2013). GP catalyzes the breakdown of glycogen and
largely contributes to hepatic glucose production. This breakdown of glycogen is enhanced
during fasting and reduced by GP inhibition. Moreover, GP inhibition enhances glycogen build
up in skeletal muscle and enhances hepatic glucose uptake that contributes to glucose clearance
from blood. These effects together makes GP inhibition an attractive target to modulate glucose
levels in diabetes (Nagy, et al., 2013; Stravodimos, et al., 2018).
Numerous GP inhibitors have been published in the last two decades. Indeed, a novel GP
inhibitor, Ingliforib in clinical study was able to reduce glucagon-induced hyperglycemia (Agius,
2007). Also it markedly reduces myocardial ischemic injury in vitro and in vivo; this may
represent a viable approach for both achieving clinical cardio protection and treating diabetic
patients at increased risk of cardiovascular disease (Tracey, et al., 2004).
56
KB228 is another novel and potent GP inhibitor was tested in vitro and in vivo under
normoglycemic and diabetic conditions. It reduced serum glucose levels and increased hepatic
glycogen content under both normoglyce-mic and insulin resistant, hyperglycemic conditions
(Nagy, et al., 2013).
Moreover, several GP inhibitors were tested in clinical trials and passing phase I, confirming the
safety of these drugs. Namely, CP-316819 (Pfizer), AVE56588 (Sanofi-Aventis) and
GSK1362885 (GlaxoSmithKline) completed phase I, while CP-368296 (Ingliforib, Pfizer) and
PSN-357(Prosidion, now Astellas Pharma) advanced to phase II. However, unfortunately, the
clinical studies on GP inhibitors were halted after phase II, but the reasons were not
communicated (Nagy,et al., 2018).
Herein, GP was identified as a potential target and this finding was supported by molecular
docking simulations study carried out by Paramaguruet, et al., 2014. They had investigated the
potential interaction of apigenin with GP using Autodock 4.2. and they had reported that
apigenin could bind with GP making three hydrogen bond interactions with the active site
residues Glu88, His377 and Asn484. The estimated free energy of binding of apigenin was found
to be −8.08 Kcal mol−1
with estimated inhibition constant (Ki) of 1.2 µM (Paramaguruet, et al.,
2014). These findings could be responsible, even partially, for its beneficial antidiabetic effects.
4.2.2.2 Carnosol
Carnosol is an ortho-diphenolic diterpene (as seen in table 2.3), naturally occurring compound of
the labiate herbs rosemary and sage. It has multiple beneficial medicinal effects including anti-
oxidante, anti-inflammatory, antimicrobial, antidiabetic and anticancer in various disease
models. (Johnson, 2011; Farkhondeh, et al., 2016).
By the application of RTFA on carnosol as query template, four targets related to cancer, and
diabetes were identified, that were off-targets as shown in table 4.3.
The first potential off-target was the human fibroblast growth factor receptor 1 (FGFR1),which
is a member of the FGFRs family that consists of four members (FGFR 1- 4). FGFRs belong to
the family of the receptor tyrosine kinases (RTKs) and play fundamental roles in several basic
biological and physiological processes by participating in the regulation of cell proliferation,
migration, differentiation, survival, apoptosis, metabolism, and angiogenesis (Carter, et al., 2015;
Ornitz & Itoh, 2015). Over activation of FGFR1 signaling occurs in many types of cancer due to
gene amplification, mutations or translocations (Wang, et al., 2014). FGFR1 is expressed in a
57
wide variety of cell types and tissues, including many malignancies such as breast, lung,
mammary gland, pancreatic and prostate cancers as well as the advanced stage of head and neck
squamous cell carcinoma. Moreover, the inhibition of FGFR-1 resulted in significant growth
inhibition in pancreatic, breast, prostate, lung and several other cancer cell lines (Navid, et al.,
2011). On this basis, FGFR has been validated as an attractive target for targeted cancer therapy
(Hallinan, et al., 2016; Giuseppe, et al., 2019).
Table 11.3: Pharmacological profiling for carnosol using ROCS
Target Disease Tanimoto coefficient Docking score
FGFR1 Cancer 1.081 -9.337
Tank2 Cancer 1.079 -11.561
CK2 Cancer 1.061 -08.344
DPP-4 T2DM 1.022 -07.457
CK2: Casine kinase 2, DPP-4: Dipeptidyl peptidase-4, FGFR1: Fibroblast growth factor receptor1, Tank2:
Tankyrase2.
In recent years, several small molecule FGFR inhibitors have been reported, and some of them
are now in clinical trials. The early examples of FGFR inhibitors are predominantly multi-
targeting drugs, such as nintedanib, lenvatinib, dovitinib, and lucitanib. Currently, several FGFR-
selective inhibitors have progressed into clinical trials including JNJ-42756493, AZD4547 and
NVP-BGJ398 (Zhang, et al., 2016).
Erdafitinib (Balversa™, Janssen Pharmaceutical Companies) is a pan-fibroblast growth factor
receptor (FGFR) inhibitor that was recently approved in the USA for the treatment of locally
advanced or metastatic urothelial carcinoma. The drug is also being investigated as a treatment
for other cancers including cholangiocarcinoma, liver cancer, non-small cell lung cancer,
prostate cancer, lymphoma and oesophageal cancer (Markham, 2019).
Herein, RTFA has identified carnosol as potential inhibitor of FGFR1. For further invistigation,
docking simulations using molecular docking has revealed the ability of carnosol to bind to the
FGFR1 target with good docking score (-9.34).
The obtained results highlight the possibility of FGFR1 to be a potential target and be partially
responsible for the anticancer activity of our plant and add in-silico evidence for the reported
polypharmacology of natural products.
Furthermore, the obtained data suggested that carnosol and apigenin, two different natural
compounds from S. officinalis, could have activity against the same targets (Tank2 and CK2) and
58
this support the fact that the biological activity of plants (herein, anticancer activity) could be
resulted from synergistic action of its multiple constituents on the same target or on different
targets related to pathogenesis of the same diseases as shown here.
The fourth off-target was the dipeptidyl peptidase-4 (DPP-4) which is a member of the prolyl
oligopeptidase family of related proteins and is one of the newest targets for T2DM treatment
(Deacon, et al., 2019).
DPP‐4 inhibitors are effective antihyperglycaemic agents by virtue of their ability to
inhibit the breakdown of the active form of the incretin hormones glucagon‐like
peptide1 (GLP‐1) and glucose‐dependent insulinotropic peptide (GIP) where both
hormones have very short half-lives (approximately 2 min) (Yang, et al., 2014). This
results in increased plasma levels of the intact and, thus, biologically active form of
both incretin hormones, which results in an improvement of glycaemic control
primarily via augmentation of glucose‐stimulated insulin secretion and inhibition of
glucagon release by the beta and alpha cells of the pancreas, respectively as shown in
figure 4.1 (Deacon, et al., 2019).
Figure 11.1: Mechanism of action for GIP, GLP-1 analogues and DPP4 inhibitors in controlling T2DM
(Deacon, et al., 2019)
59
Several structurally diverse DPP‐4 inhibitors have become established therapeutically, the most
widespread of which are sitagliptin, vildagliptin, saxagliptin, linagliptin and alogliptin. Presently,
no less than eleven DPP‐4 inhibitors have been approved by regulatory authorities worldwide,
although some with more limited geographical availability (Deacon, et al., 2019).
The validity of the captured four proteins as potential targets for carnosol was evaluated by
molecular docking simulation according to our adopted experimental procedures and the
obtained results were shown in table 4.3. These findings could promote further studies on these
new off-targets which could give rise to the discovery of new anticancer and antidiabetic agents.
Among these off-targets, Tank2 had the highest docking score and this attract us to further
investigate its binding with carnisol at molecular level as seen in figure 4.2.
A B
Figure 11.2: A : Detailed view of docked carnosol and the corresponding interacting amino acid within the
binding site of Tank2, B : Detailed view of co-crystallized structure (G9W, PDB code: 5C5Q) and the
corresponding interacting amino acid within the binding site of Tank2 (Green lines refere to hydrogen bonds
and black lines refere to hydrophobic interactions)
As shown in figure 4.2, a strong network of hydrogen bonds between hydroxyl groups and keto
group of carnosol and key amino acids in the binding site of Tank2 including ILE1075,
GLY1032, and SER1068 similar to the co-crystallized structure. In addition to the aromatic rings
which occupy a lipophilic cavity composed of TYR1060, TRY1050 and TYR1071. These
interactions indicated a valid and substantial a strong binding with Tank2 and support our
postulation that carnosol could contribute to anticancer effect of S. officinalis and it is a good
candidate for further in-vitro / in-vivo experimental validation as Tank2 inhibitor.
61
4.2.2.3 Cirsimaritin
Cirsimaritin also known as 4',5-Dihydroxy-6,7-dimethoxyflavone (as shown in table 2.2),
belongs to the class of organic compounds known as 7-O-methylated flavonoids. It is an active
flavone associated with several potent pharmacological effects including antioxidant, antiinfl-
ammatory, antimicrobial, antidiabetic, anticancer, antagonistic properties, neurological effects,
cardiovascular, and hepatoprotective (Mahmood & Alkhathlan. 2019).
As we had applied our adopted approach on cirsimaritin as query template, several targets related
to cancer and diabetes were identified, where all of them were off-targets as shown in table 4.4.
Table 11.4: Pharmacological profiling for Cirsimaritin using ROCS
Target Disease Tanimoto Coefficient Docking Score
PIM1 Cancer 1.870 -11.538
CK2 α Cancer 1.760 -12.627
mGLO-1 Cancer 1.635 -02.491
PPAR-γ T2DM 1.624 -10.582
DAPK1 Cancer 1.623 -11.661
Tank2 Cancer 1.472 -13.191
ERα Cancer 1.366 -14.323
ERβ Cancer 1.365 -13.639
CK2α: Casein kinase two alpha, DAPK1: Death-associated protein kinase 1, ERα: Estrogen Receptor alpha,
ERβ: Estrogen Receptor beta, GP: Glycogen Phospholylase, mGLo-1: Glyoxalase 1, PPAR-γ: Peroxisome
proliferator activated receptor gamma, PIM1: Proviral integration site for moloney murine leukaemia virus 1,
Tank2: Tankyrase 2.
In-silico target fishing had led to identification of eight new off-targets for cirsimaritin: PIM1,
CK2α, GLO-1, PPAR-γ, DAPK1, Tank2, ERα and ERβ. All of these targets were discussed in
section 4.2.2.1.1 except PPAR-γ.
PPAR-γ is a member of the nuclear hormone receptor family of ligand-activated transcription
factor. It is highly presents in adipose tissues and plays a key role in regulating the insulin
sensitivity, adipocyte differentiation, inflammation and cell growth. PPAR-γ is generally
regarded as a molecular target for the thiazolidinedione class of antidiabetic drugs, as it plays a
key role in the generation and development of diabetes. Recent studies have shown that PPAR-γ
agonists, including rosiglitazone and pioglitazone, may be used as insulin sensitizers in target
tissues to lower glucose, as well as fatty acid levels in T2DM patients (Jian, et al., 2018).
64
The validity of these proteins as potential targets for cirsimaritin was evaluated by molecular
docking simulation according to our adopted experimental procedures and the obtained results
were shown in table 4.4.
Here we had chosen PPAR-γ to show its docking image with cirsimaritin at molecular level as
seen in figure 4.3, where a potential hydrogen bonding between cirsimaritin and key amino acids
at the binding site of PPAR-γ including ILE281, HIS266 and LYS265. In addition, the aromatic
rings occupy a lipophilic cavity composed of ILE341 and CYS285.
Figure 11.3: Detailed view of docked cirsimaritin and the corresponding interacting amino acids within the
binding site of PPAR-γ (Green lines refere to hydrogen bonds and black lines refere to hydrophobic
interactions).
4.2.2.4 Corosolic Acid
Corosolic acid is a pentacyclic triterpene, also known as 2 alpha-hydroxy ursolic acid or glucosol
(as seen in table 2.3), discovered in numerous medicinal herb (Baba, et al., 2018). It is reported
to exhibit antidiabetic, antihyperlipidemic, antioxidant, antiinflammatory, antiproliferative,
antifungal, antiviral, antineoplastic, osteoblastic and protein kinase C inhibition activity (Miura,
et al., 2012; Wang, et al., 2020).
To identify the possible anticancer, antidiabetic and hypolipidemic targets for corosolic acid,
RTFA had been used and several targets had been identified where all of them were off-targets
as shown in table 4.5.
62
The first off-target identified was the human receptor type protein tyrosin phosphatase gamma
(RPTP-γ). It is a member of the protein tyrosine phosphatase (PTP) family. PTPs are known to
be signaling molecules that regulate a variety of cellular processes including cell growth,
differentiation, mitotic cycle and oncogenic transformation.This PTP possesses an extracellular
region, a single trans-membrane region, and two intra-cytoplasmic catalytic domains, and thus
represents a receptor-type PTP. RPTP is located in a chromosomal region that is frequently
deleted in renal cell carcinoma and lung carcinoma, thus is thought to be a candidate tumor
suppressor gene (Xu & Fisher, 2012).
Table 11.5: Pharmacological profiling for corosolic acid using ROCS
Target Disease Tanimoto Coefficient Docking score
RPTP-γ Cancer 0.993 -7.695
Tank 2 Cancer 0.985 -8.949
CDK2 Cancer 0.957 -6.233
PPAR-γ T2DM 0.945 -5.460
ERα Cancer 0.938 -11.21
CDK2: Cyclin dependent, ERα : Estrogen receptor alpha , PPAR-γ: Peroxisome proliferator activated receptor
gamma, RPTP-γ :Receptor type protein tyrosine phosphatase gamma,Tank2: Tankyrase 2.
Tank2 and ERα were discussed before in section 4.2.2.1, while the fourth target, PPAR-γ, was
discussed in section 4.2.2.3.
The third target was the human cyclin dependent kinases 2 (CDK2) which is a member of CDKs
family. CDKs are serine/threonine protein kinases. This family has critical functions in cell cycle
regulation and controlling of transcriptional elongation. Moreover, dysregulated CDKs have
been linked to cancer initiation and progression. Pharmacological CDK inhibition has recently
emerged as a novel and promising approach in cancer therapy (García-Reyes, et al., 2018).
Two CDK inhibitors have been approved for marketing. Palbociclib developed by Pfizer is the
first CDK inhibitor approved to enter the market in February 2015 for the treatment of metastatic
breast cancer (Gupta, et al., (2016). Ribociclib developed by Novartis is the second CDK
inhibitor approved by FDA in 2017 for the treatment of advanced breast cancer in combination
with an aromatase inhibitor (Zhao,et al., 2019)
In-silico molecular docking simulations were conducted to validate the possibility of binding of
corosolic acid with its new off-targets. The obtained results clarified that the corosolic acid was
62
successfully docked within the active site of RPTP-γ, Tank2, CDK2, PPAR-γ and ERα with the
relatively good scores as shown in table 4.5.
4.2.2.5 Ellagic acid
Ellagic acid (EA), a fused four ring compound ( as shown in table 2.2), is a naturally occurring
tannic acid derivative that is widely found in fruits, vegetables and other foods. Many studies
have shown that EA possesses strong antioxidant, anti-inflammatory, antiproliferative effects,
apoptosis induction, metastasis inhibition and anticarcinogenic properties, suggesting the strong
anticancer activity of ellagic acid (Cozza, et al., 2006; Sepúlveda,et al., 2012).
To identify the possible anticancer, antidiabetic and hyperlipidemic targets for EA, RTFA had
been used. Numerous targets had been identified, some of them were on-targets as CK2, ERα
and CDK2, while others were off-targets as shown in table 4.6.
4.2.2.5.1 On-targets of Ellagic acid
The first on-target was CK2. EA inhibitory capacity for CK2 had been reported with IC50 value
of 0.04 µM (Alchab, et al., 2015), as it considered as a potent inhibitor of CK2.
Table 11.6: Pharmacological profiling for ellagic Acid using ROCS
Target Disease Tanimoto
Coefficient DockingScore Ref.
CK2 a Cancer 1.188 Alchab, et al.,
2015
mGLO1 Cancer 1.173 -02.079
Tank2 Cancer 1.172 -12.392
PIM1 Cancer 1.133 -11.940
PDK1 Cancer & T2DM 1.127 -12.660
17β-HSD1 Cancer 1.108 -14.429
ERα a Cancer 1.092 Pang, et al., 2018.
CDK2 b Cancer 1.069
Mohan, & Latha,
2018
17β-HSD1: 17 Beta Hydroxysteroiddehydrogenase type1, CK2: Casine kinase 2, CDK2: Cycline dependent
kinase2, ERα: Estrogen Receptor alpha, mGLO1: mouse Glyoxalase I, PDK1: Phosphoinositide-dependent
protein kinase-1, PIM1: Proviral integration site for moloney murine leukaemia virus 1, Tank2: Tankyrase 2.
a: Has in-vitro or in-vivo evidence, b: Has in-silico evidence.
The second on-target was ERα. It was reported that EA could directly bind to the active pocket
of ERα with high affinity due to its phenolic hydroxyl group and conjugated structure. Pang, et
61
al., had measured the binding affinity of ERα to ellagic acid by green Polar Screen ERα
Competitor Assay and found that; ellagic acid had a binding affinity with IC50 value of 62.61 ±
9.34 μM (Pang, et al., 2018).
The third on-target was CDK2. Mohan and Latha had used the SCHRODINGER software to
study the binding features between ellagic acid and CDK2 and had found that EA had high
desirable potential to bind with the active site of CDK2. When CDK2 was docked with EA, the
glide score value was -9.899 Kcl/mol which indicates good interaction with the target protein
(Mohan & Latha, 2018).
CK2 and ERα were discussed in section 4.2.2.1, while in section 4.2.2.4, CDK2 was discussed.
4.2.2.5.2 Off-targets of Ellagic acid
In order to validate the identified potential off-target, in-silico molecular docking simulations
were conducted to reveal the possibility of binding of EA with its new target. EA was
successfully docked within Tank2, PIM1, PDK1 and 17β-HSD1 with a relatively high good
score as shown in table 4.6. This finding could promote further studies on these new off-targets
which could give rise to the discovery of new anticancer/antidiabetic agents against these
important targets.
For mouse GLO1, Tank2 and PIM1, they were discussed in section 4.2.2.1. PDK1 kinase (3-
phosphoinositide-dependent protein kinase-1), is a protein of 556 amino acids belongs to the
family of AGC kinases (protein kinase A (PKA), protein kinase G (PKG), protein kinase C
(PKC)), Although PDK1 was discovered for its ability to phosphorylate protein kinase B (PKB),
also known as AKT, many other kinases are now known to be downstream of PDK1. For
example the AGC kinases serum glucocorticoid dependent kinase (SGK), p70 ribosomal protein
S6 kinases (S6K), p90 ribosomal protein S6 kinase (RSK) and protein kinase C (PKC) isoforms,
are known to be direct targets of PDK1 which phosphorylates specific serine/threonine residues
of their activation loop as seen in figure 4.4. These targets play crucial roles in regulating
physiological processes relevant to metabolism, growth, proliferation and survival For this
reason PDK1 has been named the ―master regulator‖ of AGC kinase signal transduction and
have an important role in the signalling pathways activated by several growth factors and
hormones including insulin signaling (Mora, et al., 2004).
65
Increased levels of PDK1 have been reported in 45% of patients with acute myeloid leukaemia,
and a role for PDK1 in breast and ovarian cancer progression has also been proposed. PDK1
inhibitors can be considered as an important therapeutic target in cancer treatment.
Figure 11.4: Mechanism of activation of PKB (AKT), S6K and SGK by PDK1. Growth factor or insulin
stimulation activates RTKs which further activates PI3K. Activated PI3K converts PIP2 to PIP3, which provides
docking sites for signaling proteins such as PDK1.Once activated, PDK1 phosphorylates many downstream
effectors, including isoforms of protein kinase B (PKB)/Akt, p70 ribosomal S6 kinase (S6K), serum- and
glucocorticoid-induced protein kinase (SGK) and protein kinase C (PKC), which play crucial roles in regulating
physiological processes relevant to metabolism, growth, proliferation and survival. PTEN functionally antagonizes
PI3K activity by dephosphorylating PIP3 (Raimondi & Falasca, 2011).
On the other hand, PDK1 directly phosphorylates AKT. The activated AKT phosphorylates
many downstream substrates in various signaling pathways, making it a key node in insulin
signaling (Petersen & Shulman, 2018). The activated insulin signaling decreases glucose
production, increases glycogen synthesis, and also increases glucose uptake into peripheral
tissues such as skeletal muscle and adipose tissue as shown in Figure 4.5. The dysfunction of
insulin signaling will cause insulin resistance, which is closely linked to many pathways
66
including lipid metabolism, energy expenditure, and inflammation ( Guo, et al., 2020). For this,
PDK1 could be considered as a potential target for insulin resistance and diabetes treatment.
Although extensive data showed that PDK1 plays an important role in cancer, few PDK1-
specific inhibitors have been developed so far. This is largely due to the promiscuity within the
AGC kinase family which makes the design of specific ATP active site-directed inhibitors
difficult (Raimondi & Falasca, 2011).
New specific and potent inhibitors such as GSK2334470 and a pyridinonyl-based compound
were recently characterized and show high selectivity and high potency in inhibiting PDK1.
Interestingly, GSK2334470 inhibits S6K and SGK more potently than Akt. GSK2334470
inhibits PDK1 with an IC50 of ~10 nM, but does not suppress the activity of 93 other protein
kinases. However in the future it will be important to investigate in-vivo the pharmacological
inhibition of PDK1 on tumour models, and determine the pharmacokinetic and metabolic
properties of specific PDK1 inhibitors identified for preclinical trials (Nagashima, et al., 2011).
Figure 11.5: Insulin signaling. Insulin binds and activates insulin receptor (INSR), causing phosphorylation of
insulin receptor substrate (IRS). Tyrosine phosphorylated IRS proteins recruit phosphatidylinositide-3 (PI3K),
which catalyzes the production of phosphatidylinositol-3, 4, 5-tris-phosphate (PIP3) from PIP2. PIP3 then activates
PDK1, which phosphorylates activating protein kinase B (AKT). These effector proteins mediate the effects of
insulin on glucose production, utilization, and uptake, as well as glycogen synthesis ( Guo, et al., 2020).
The last off-target for EA was 17β-HSD1, which is a protein composed of 328 amino acids with
a molecular mass of 34.95 kDa. It is primarily expressed in the placenta and ovary, but it is also
67
expressed at lower levels in breast epithelium. It is a steroid-converting enzyme that has long
been known to play critical roles in estradiol synthesis and more recently in dihydrotestosterone
inactivation, showing a dual function that promotes breast cancer cell proliferation (Hilborn, et
al., 2017). Recent studies show that 17β-HSD1 increases breast cancer cell migration and
stimulates breast cancer cell growth (Aka, et al., 2012). These findings revealed that, 17β-HSD1
inhibition could be considered as an attractive target of cancer treatment.
4.2.2.6 Ferruginol
Ferruginol is a natural phenol and a meroterpene (a chemical compound containing a terpenoid
substructure) (as seen in table 2.3). It displays relevant pharmacological properties, including
antimicrobial, cardioprotective, antioxidative, antiplasmodial, leishmanicidal, antiulcerogenic,
anti-inflammatory and anticancer (Areche, et al., 2008). It has been shown to inhibit the growth
of cancer cells such as prostate cancer and nonsmall lung cancer (Ho, et al., 2015; Luo,er al.,
2019).
Using RTFA and ferruginol as query template, we had discovered several targets that were
related to cancer, diabetes and hyperlipidemia where all of them were off-targets as in table 4.7.
Table 11.7: Pharmacological profiling for ferruginol using ROCS
Target Disease Tanimoto Coefficient Docking Score
HSP90-α Cancer 1.228 -10.621
Tank2 Cancer 1.200 -12.532
CDK2 Cancer 1.138 -8.616
CK2 Cancer 1.118 -9.127
PTP1B T2DM & Hyperlipidemia 1.084 -4.047
PIK3CG Cancer 1.081 -9.560
CDK2: Cycline dependent kinase 2, CK2: Casine kinase 2, HSP90-α: Heat shock protein 90 alpha,
PIK3CG:Phosphatidylinositol-4,5-bisphosphat 3-kinase Catalytic Subunit Gamma, PTP1B: Protein tyrosine
phosphatase1B, Tank2: Tankyrase2.
The first off-target for ferruginol was the human Hsp90 (heat shock protein 90), which is a
chaperone protein that assists other proteins to fold properly, stabilizes proteins against heat
stress, and aids in protein degradation. The mammalian HSP90 family of proteins is a cluster of
highly conserved molecules that are involved in myriad cellular processes. Their distribution in
various cellular compartments underlines their essential roles in cellular homeostasis. HSP90 and
its co-chaperones orchestrate crucial physiological processes such as cell survival, cell cycle
68
control, hormone signaling, and apoptosis. Conversely, HSP90, contribute to the development
and progress of serious pathologies, including cancer and neurodegenerative diseases. Therefore,
targeting HSP90 is an attractive strategy for the treatment of neoplasms and other diseases
(Hoter, et al., 2018)
The fifth off-target for ferruginol was PTP1B which is type 1 trans-membrane protein that
catalyze tyrosine phosphorylated proteins, and is widely expressed in different organs in the
human body and involved in different signal transduction pathways including insulin signaling
pathway. It was reported that the binding of α subunits of the insulin receptor lead to a series of
phosphorylation and dephosphorylation cascade reactions including MAPK and PI3K/Akt signal
pathways to regulate metabolism. During the combination of insulin and its receptor, PTP1B
could catalyze insulin receptor and insulin receptor substrates (IRS) de-phosphorylation, which
resulted in down-regulation of insulin signal transduction. Besides, PTP1B could
dephosphorylate activated JAK2 and STAT3, and prevented leptin signal transduction as seen in
Figure 4.6. High expression of PTP1B influenced the activity of PTKs, which resulted in insulin
failing to combine with insulin receptor, induced the IR and leptin resistance, and caused T2DM
and obesity (Zhang & Lee, 2003; Sun, 2016; Abdelsalam et al., 2019).
Figure 11.6: The physiological signal pathways involving PTP1B (IR: Insulin receptor, IRS:Insulin receptor
substrate, AKT: Serine/threonine-specific protein kinase, PTP1B: Protein tyrosine phosphatase 1B, JAK2: Janus
kinase 2, STAT3: Signal transducer and activator of transcription 3) (Sun, 2016).
69
Genetically modified mice that lack PTP1B protein expression show enhancing in insulin
signaling and glucose tolerance, also they are protected against weight gain and have
significantly lower triglyceride levels when placed on a high-fat diet. This is likely to be
associated with increased energy expenditure owing to enhanced leptin sensitivity (Zhang &
Lee,2003; Sun, 2016).
ISI-113175 (ISIS Pharmaceuticals), an antisense oligonucleotide PTP1B inhibitor (100–200 mg
injected weekly), has completed a phase 2 trial in patients with T2DM on stable, maximal doses
of sulfonyl urea. After 13 weeks, the 200 mg/week cohort reported a 25 mg/dL decrease in
average weekly fasting self-monitoring of blood glucose values (P = 0.026 vs. placebo) and a
significant 65% increase in adiponectin was noted with ISI-113175 (Wang, et al., 2012).
PTP1B is an attractive target for T2DM and hyperlipidemia treatment and this encourage us to
choose it for further investigation. Figure 4.7 shows the binding between feruginol and PTP1B at
molecular level with the presense of hydrogen bonding with key amino acids at the binding site
of PTP1B as ASP181. In addition to the hydrophobic interactions between aromatic rings
composed of TYR46 and PHE182.
Figure 11.7: Detailed views of docked ferruginol and the corresponding interacting amino acids within the
binding site of PTP1B (Green lines refere to hydrogen bonds and black lines refere to hydrophobic
interactions).
The last off-target was the human Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic
subunit gamma isoform, PIK3CG (also kown as PI3Kγ) which belonges to 1B PI3K
(Phosphatidylinositol 3-kinases) class. PIK3CG is altered in 2.42% of all cancers with lung
adenocarcinoma, colon adenocarcinoma, cutaneous melanoma, breast invasive ductal carcinoma,
71
and conventional glioblastoma multiforme having the greatest prevalence of alterations
(Suvarna, et al., 2017).
PI3K is a group of plasma membrane-associated lipid kinases that exhibits a crucial role in cell
cycle, programmed cell death, DNA repair, angiogenesis, autophagy, motility, and cellular
metabolism (Suvarna, et al., 2017). PI3K consists of three subunits: p85 regulatory subunit, p55
regulatory subunit and p110 catalytic subunit. According to their different structures and specific
substrates, PI3K is divided into 3 classes: classes I, II,and III . Class I PI3Ks comprised of class
IA and class IB PI3Ks. Class IA PI3K, a heterodimer of p58 regulatory subunit and p110
catalytic subunit, is the type most clearly implicated in human cancer. Class IA PI3K contains
p110α, p110β and p110δ catalytic subunits produced from different genes (PIK3CA, PIK3CB
and PIK3CD, respectively), while p110γ produced by PIK3CG represents the only catalytic
subunit in class IB PI3K (Liu, et al., 2009).
PI3Ks have a crucial role in cell cycle, programmed cell death, DNA repair, angiogenesis,
autophagy, motility, and cellular metabolism. It is a potential and druggable target for cancer
therapy. Literature suggests that PI3K signaling pathway is activated in almost 30–50% of
various human cancers. Multiple components of the PI3K signaling pathway are activated and
mutated in human cancers as shown in figure 4.8 (Fruman, et al., 2017; Wang, et al., 2019).
In the last five years, four of the PI3K inhibitors Idelalisib, Copanlisib, Duvelisib and Alpelisib
were approved by the FDA for the treatment of different types of cancer and several other PI3K
inhibitors are currently under clinical development (Bheemanaboina, et al., 2020).
In order to validate the identified potential off-target, in silico molecular docking simulations
were conducted to reveal the possibility of binding of ferruginol with its new targets. The
obtained results revealed that ferruginol was successfully docked within the active site of off-
targets: HSP 90α, Tank2, CDK2, CK2, PTP1B and PIK3CG with relatively good scores as seen
in table 4.7. These findings could promote further studies on these new off-targets which could
give rise to the discovery of new anticancer/antidiabetic agents.
7.7.7.1 Genkwanin
Genkwanin is a monomethoxyflavone that is apigenin in which the hydroxy group at
position 7 is methylated (as shown in table 2.2). Previous pharmacological studies have
found that genkwanin has a variety of pharmacological effects including antibacterial,
74
radical scavenging, chemopreventive and inhibiting 17α-Hydroxysteroidsteroid
dehydrogenase type 1 activities (Gao, et al., 2014).
Using RTFA protocol, we had discovered several targets that were related to cancer, diabetes
and hyperlipidemia and all of them were off-targets as shown in table 4.8.
Figure 11.8: The overview of PI3K/AKT/mTOR signaling pathway. In physiologic conditions, PI3K is
normally activated by a variety of extracellular stimuli, such as growth factors, cytokines, and hormones,through the
activation of RTK. PIKCG (PI3Kγ) can also be activated by GPCRs. Activated PI3K converts PIP2 to PIP3, which
provides docking sites for signaling proteins such as PDK1 and serine-threonine kinase AKT. Once activated, AKT
phosphorylates many downstream effectors to regulate cell processes such as protein synthesis, cell survival,
proliferation, and metabolism. PTEN functionally antagonizes PI3K activity by dephosphorylating PIP3.
PIP2: Phosphatidylinosito 4,5 phosphatase, PIP3: Phosphatidylinosito 3,4,5 phosphatase, RTK: Receptor tyrosine
kinase, GPCR: G-Protin coupled receptor (Yang, et al., 2019).
All of these off-targets were discussed in pervious sections where PIM1,GP, Tank2, GLO1 and
ERα were illustrated in section 4.2.2.1 while details about PPARγ and HSP90α were presented in
sections 4.2.2.3 and 4.2.2.6 respectively .
For validation of the identified potential off-target, in-silico molecular docking simulations were
used to uncover the possibility of binding of Genkwanin with its new targets. The obtained
72
results appeared that the captured off-targets of Genkwanin were successfully docked within the
active site of these proteins with the relatively good score as seen in table 4.8. These findings
could promote further studies on these new off-targets which could give rise to the discovery of
new anticancer/antidiabetic agents.
Table 11.8: Pharmacological profiling for Genkwanin using ROCS
Target Disease Tanimoto Coefficient Docking Score
PIM1 Cancer 1.760 -12.754
PPAR-γ T2DM 1.730 -10.489
GP T2DM 1.686 -11.585
Tank2 Cancer 1.576 -15.911
mGLO1 Cancer 1.556 -03.608
ERα Cancer 1.483 -13.907
HSP90-α Cancer 1.330 -12.431
ERα: Estrogen receptor alpha, GP: Glycogen phosphorylase, HSP90-α: Heat shock protein 90 alpha, mGLO1 :
mouse Glyoxalase 1, PIM1: Proviral integration site for moloney murine leukaemia virus 1, PPAR-γ :
Peroxisome proliferator activated receptor gamma, Tank2 : Tankyrase 2.
The docked Genkwanin at the binding site of PIM1 had been illustrated at figure 4.9. The high
similarity (tanimotto coefficient is relatively high) had been supported by the good fitting and
valid bonding between Genkwanin and its predicted target by docking simulations using FRED
software. As shown in the figure, the presence of hydroxyl groups, ketone and methoxy group
produce a potential network of hydrogen bonding with key amino acids at the binding site of
ASP186, SER46 and LYS67, similar to the co-crystallized structure. Moreover, several
hydrophobic interactions are seen between aromatic rings of genkwanin and LEU174 and
ILE185 amino acids.
7.7.7.4 Hispidulin
Hispidulin (4′, 5, 7-trihydroxy-6-methoxyflavone or 6-methoxyapigenin) is a flavones derivative
(as seen in table 2.2) found in several plants. Hispidulin, a bioactive flavone, has been reported
as an effective antioxidant, antifungal, anti-inflammatory and anticancer agent (Patel, et al.,
2016).
By the application of RTFA on Hispidulin as query template, several targets related to cancer
and diabetes were identified, some of them were on-targets such as PIM1 and GP while others
were off-targets as shown in table 4.9.
72
A
B
C
Figure 11.9: A: Detailed view of docked genkwanin and the corresponding interacting amino acid within the
binding site of PIM1, B: Co-crystallized structure (HUL, PDB code: 4XH6) and the corresponding
interacting amino acids within the binding site of PIM1, C: Overlay view of docked genkwanin within the
binding site of PIM1 (Green lines refere to hydrogen bonds and black lines refere to hydrophobic
interactions).
71
7.7.7.4.7 On-targets of Hispidulin
Here we have two on-targets for hispidulin PIM1 and GP and both of them were discussed
previously in section 4.2.2.1.
Crystallographic analysis of PIM1 bound to hispidulin by Chao, et al., 2015 reveals that
hispidulin exhibited PIM1 kinase inhibitory activity with IC50 value of 2.71 μM. According to
the crystal structure of PIM1 kinase in complex with hispidulin, the A ring of hispidulin binds
deep inside the ATP-binding pocket where the methoxy group makes a van der Waals contact
with the hydrophobic residue LEU120. The 7-hydroxyl group and the oxygen of the 6-methoxy
form hydrogen bonds with the highly conserved residues LYS67. Additionally, the oxygen of the
6-methoxy interacts indirectly with PHE187 of the backbone and GLU89 of the side chain
through a water molecule (Chao, et al., 2015).
Table 11.9: Pharmacological profiling for hispidulin using ROCS
Target Disease Tanimoto
Coefficient DockingScore Ref.
PIM1 ab
Cancer 2.000 Chao, et al., 2015
mGLOI Cancer 1.730 -2.491
GP b T2DM 1.673 Nath Choudary, 201 4
ERα Cancer 1.427 -14.045
ERβ Cancer 1.360 -13.858
HSP90-α Cancer 1.357 -12.567
ERα: Estrogen receptor alpha, ERβ : Estrogen receptor beta, GP: Glycogen phosphorylase , HSP90-α: Heat
shock protein, mGLO1: mouse Glyoxalase 1, PIM1: Proviral integration site for moloney murine leukaemia
virus 1.
a: Has in-vitro or in-vivo evidence,b: Has in-silico evidence.
The second on-target was GP. In-silico docking study of the hispidulin with GP revealed that
hispidulin had a docking score of -21.189 Kcal/mol. This result showed the high potentiality of
hispidulin to become potential antidiabetic drug ( Nath, Choudhury, 2014).
4.2.2.8.2 Off-targets of Hispidulin
The off-targets that identified by RTFA for hispidulin were CK2α, GLOI, ERα, ERβ and HSP90-
α. All of them discussed before in section 4.2.2.1 except the last target which was discussed in
section 4.2.2.6.
75
For validation of the identified potential off-target, molecular docking simulations were used to
uncover the possibility of binding of hispidulin with its new off-targets. The obtained results
releaved that Hispidulin was successfully docked within the active site of these proteins; CK2α,
GLOI, ERα, ERβ and HSP90-α with a relatively good score as seen in table 4.9. These findings
could promote further studies on these new off-targets which could give rise to the discovery of
new anticancer/antidiabetic agents.
4.2.2.9 Luteolin
Luteolin is a tetrahydroxyflavone in which the four hydroxy groups are located at positions 3', 4'
5 and 7 (as shown in table 2.2). It is thought to play an important role in the human body as an
antioxidant, a free radical scavenger, an inflammatory agent and an immune system modulator as
well as being active against several cancers (Chen, et al., 2015).
Using RTFA protocol, luteolin had several targets related to cancer and diabetes, all of them
were on targets: PPAR-γ, Tank2, CK2, PIM1, ERα and HSP90-α exept PDK1which was off-
target as seen in table 4.10.
Table 11.10: Pharmacological profiling for luteolin using ROCS
Target Disease Tanimoto
Coefficient Docking score Ref.
PPAR-γ ab
T2DM 2.000 Wang., et al., 2014
Tank2 b Cancer 1.826 Pai, et al., 2017-
CK2 a Cancer 1.826 Lolli, et al.,2012
PIM1a Cancer 1.712
Gadewal & Varma,
2012; Rathod H. 2016.
ERα ab
Cancer 1.429 Maruthanila, et al.,
2019.
HSP90-α b Cancer 1.357 Fu, et al., 2012.
PDK1 Cancer & T2DM 1.297 -12.693
CK2: Casine kinase 2, ERα: Estrogen receptor alpha, HSP90-α: Heat shock protein90 alpha, PDK1:
Phosphoinositide-dependent kinase-1, PIM1: Proviral integration site for moloney murine leukaemia virus 1,
PPAR-γ: Peroxisome proliferator activated receptor gamma, Tank 2: Tankyrase 2.
a: Has in-vitro or in-vivo evidence, b: Has in-silico evidence.
4.2.2.9.1 On-Targets of Luteolin
The first target was PPAR-γ which bind with luteolin with high tanimotto coefficient (2), this
means that luteolin itself was the ligand co-crystallized with PPAR-γ in the PDB. In literature,
Wang, et al., 2014, had documented that luteolin bound to the PPAR-γ with IC50 of 3.9 µM
76
forming hydrogen bonds with LYS265 and HIS266 and builds hydrophobic contacts with
various amino acids (Wang, et al., 2014).
The second on-target was Tank2. Luteolin was subjected to the molecular docking process to
identify its binding with Tank2 and the result was promising with a dock score of -11.472
Kcal/mole. The results showed that luteolin had the ability to form additional hydrogen bonding
interactions at the active site of Tank2. The incorporation of hydrogen bond donor or acceptor
groups might increase the binding affinity at the active site of Tank2 (Pai, et al., 2017).
The third target was CK2α. Lolli et al., assayed a panel of 16 flavonoids and related compounds
for their ability to inhibit CK2α. Among the tested compounds, luteolin had an inhibition activity
with IC50 value of 0.50 μM (Lolli, et al., 2012).
The fourth target was PIM1. Gadewal & Varma, investigate the binding of PIM1 and luteolin
and found that luteolin exhibited PIM1 kinase inhibitory activity with IC50 value of 1.6 µM
(Gadewal & Varma, 2012), this support our finding by the RTFA which identified PIM 1 as an
anticancer target.
The fifth target was ERα. In-silico molecular docking study revealed that luteolin showed a high
Glide Score of -8.87 Kcal mol-1
. In addition, Luteolin showed a significant IC50 value of 58.3 ±
4.4 µM against MCF-7 cell line (Maruthanila, et al., 2019).
The last target was HSP90-α. The molecular modeling analysis with CHARMm–Discovery
Studio 2.1 indicated that luteolin could bind to the ATP binding pocket of HSP90. The SPR
technology-based binding assay confirmed the association between luteolin and HSP90. ATP-
sepharose binding assay displayed that luteolin inhibited HSP90-ATP binding (Fu, et al., 2012).
4.2.2.9.2 Off-targets of Luteolin
PDK1 was identified by RTFA as potential off-target and was discussed in section 4.2.2.5.2. For
the validation of this target, docking simulations were conducted to reveal the possibility of
binding of Luteolin with PDK1. Luteolin was successfully docked within the active site of PDK1
with a relatively good score (-12.693). These findings could promote further studies on these
new off-targets which could give rise to the discovery of new anticancer agents.
77
4.2.2.10 Oleanolic acid
Oleanolic acid (OA) is an oleanane-type pentacyclic triterpenoid ( as seen in table 2.3) that exist
widely in food, medicinal herbs and other plants. OA exists in nature as a free acid or as an
aglycone of triterpenoids and it is often ubiquitously found with its isomer, ursolic acid. It has
potent pharmacological activities such as antioxidant, anticancer, anti-inflammatory,
antidiabetic, antimicrobial and hepatoprotective effect (Ayeleso, et al., 2017; Ziberna, et al.,
2017).
Using of RTFA protocol on OA as query template, several targets related to cancer and diabetes
were identified, some of them were on-targets as CDK2, PPAR-γ and ERα while others were
off-targets as shown in table 4.11.
4.2.2.10.1 On-Targets of Oleanolic acid
CDK2 was the first on-target. Kim, et al., 2018 evaluated OA role in arresting cell cycle in breast
cancer cells and found the IC50 values for OA induced cytotoxicity was of 132.29 μg/mL. They
found that the treatment of the breast cancer cells with 100 µg/mL OA decreased the production
of CDK2 by 12.02 fold, when compared with that reported for the control group (Kim, et al.,
2018).
Table 11.11: Pharmacological profiling for oleanolic using ROCS
Target Disease Tanimoto
Cofficient Docking score Ref.
RPTP-γ Cancer 1.050 -7.871
CDK2 a Cancer 1.011 Kim, et al., 2018
PPAR-γ b T2DM 0.995 Salazar, et al., 2020
ERα b Cancer 0.972 Xie, et al., 2019
17β-HSD1 Cancer 0.971 -12.661
Tank2 Cancer 0.965 -9.238
Mcl-1 Cancer 0.951 -7.384
17β-HSD1: 17 Beta Hydroxysteroid dehydrogenase 1, CDK2: Cycline dependent kinase 2, ERα: Estrogen
receptor alpha, Mcl-1: Myeloid cell leukemia 1, PPAR-γ: Peroxisome proliferatoe activated receptor gamma,
RPTP-γ: Receptor protein tyrosine phosphatase gamma, Tank2: Tankyrase 2.
a: Has in-vitro or in- vivo evidence, b: Has in- silico evidence.
78
The second target was PPAR-γ. A study by Salazar, et al., 2020 showed that OA exhibited a
good theoretical affinity against PPAR-α and PPAR-γ with Moldock score values of −118.9 and
−114.2 respectivelly (Salazar, et al., 2020).
Xie, et al., 2019 investigated the binding of OA acid with ERα, their study showed that OA
binds the ERα and up-regulates the expression of ERα on gene and protein levels. In addition,
they docked OA with ERα and found that OA and estradiol have the same interaction site with
ERα, forming intermolecular forces between 3-OH-group of OA and LEU387 of ERα (Xie, et
al., 2019).
4.2.2.10.2 Off-targets of Oleanolic acid
In order to validate the identified potential off-target, docking simulations were conducted to
reveal the possibility of binding of OA with its newly discovered targets; RPTP-γ, 17β-HSD1,
Tank2 and Mcl-1. OA was successfully docked within the active site of these proteins with the
relatively good score as shown in Table 4.11. These findings could promote further studies on
these new off-targets which could be basic nucleus for further optimization that allow the
discovery of new anticancer/ antidiabetic agents.
All of these off-targets of OA were discussed in sections 4.2.2.4, 4.2.2.5.2 and 4.2.2.1
respectively except Mcl-1, which will be discussed here.
Myeloid cell leukemia 1 (Mcl-1) is an anti-apoptotic member of the B-cell lymphoma 2 (Bcl-2)
family of proteins that regulates apoptosis. The Bcl-2 family represented a new class of
oncogenes that promoted oncogenesis, not through upregulation of proliferation, but by
maintaining viability through inhibition of apoptosis. As predicted, dysregulation of Bcl-2
protein family expression and function has since been implicated in virtually all malignancies,
and a number of other pathologies. Mcl-1 blocks the progression of apoptosis by binding and
sequestering the pro-apoptotic proteins Bcl-2 homologous antagonist killer (Bak) and Bcl-2-
associated protein X (Bax), which are capable of forming pores in the mitochondrial membrane,
allowing the release of cytochrome c into the cytoplasm. In the cytoplasm, cytochrome c induces
the activation of a family of cysteine proteases named caspases which are responsible for much
of the macromolecular degradation observed during apoptosis (Thomas, et al., 2010).
Elevated levels of Mcl-1 contribute to tumorigenesis and resistance, not only to conventional
chemotherapies but also to targeted therapies. Accordingly, researchers in both the
79
pharmaceutical industry and academia have been actively seeking Mcl-1 inhibitors in the quest
for new anticancer drugs (Fletcher, 2019).
4.2.2.11 Quercetin
Quercetin is a pentahydroxyflavone having the five hydroxy groups placed at the 3-, 3'-, 4'-, 5-
and 7-positions (as shown in table 2.2). It is one of the most abundant flavonoids in edible
vegetables, fruit and wine. It has numerous biological and pharmacological activates as antiviral,
antibacterial, antioxidant, a protein kinase inhibitor, a phytoestrogen, a radical scavenger, a
chelator, an aurora kinase inhibitor, anticancer and many more. In addition, it produces anti-
inflammatory and anti-allergy effects mediated through the inhibition of the lipoxygenase and
cyclooxygenase pathways, thereby preventing the production of pro-inflammatory mediators
(Batiha, et al., 2020).
By using RTFA, we had discovered many targets that were related to cancer and diabetes had
been caught by quercetin, all of them were on-targets as seen in table 4.12. This reflects the
importance of quercetin and the interest in searching its different biological activates.
Table 11.12: Pharmacological profiling for quercetin using ROCS
Target Disease Tanimoto
Coefficient Ref.
PPAR-γ b T2DM 1.845 Srinivasan, et al., 2018
CK2 a Cancer 1.692 Lolli, et al.,2012
MAPK14 b Cancer 1.650 Baby, et al., 2016
PIM1 a Cancer 1.597 Bullock, et al., 2005
CDK2 b Cancer 1.340 Baby, et al., 2016
ERα b Cancer 1.306 Maruthanila, et al., 2019
HSP90-α b Cancer 1.277 Kıyga, et al., 2020; Singh, et al., 2015
DAPK1 a Cancer 1.266 Yokoyama, et al., 2015
ERβ b Cancer 1.212 Powers & Setzer, (2015)
CDK 2: Cycline dependent kinase 2, CK2: Casine kinase 2, DAPK1: Death associated protein kinase 1, ERα:
Estrogen receptor alpha, ERβ: Estrogen receptor beta, HSP90-α: Heat shock protein90 alpha, MAPK14:
Mitogen activated protein kinase 14, PIM1: Proviral integration site for moloney murine leukaemia virus 1,
PPAR-γ: Peroxisome proliferator activated receptor gamma.
a Has in-vitro or in-vivo evidence, b Has in- silico evidence.
81
From theses targets, PPAR-γ was discussed in section 4.2.2.3, CDK2 was discussed in section
4.2.2.4, HSp90-α was discussed in section 4.2.2.6, the other targets discussed in section 4.2.2.1
and MAPK1 will be discussed here.
The interactions of PPAR-γ with quercetin were extensively discussed. Srinivasan, et al., 2018
had documented that quercetin was an inhibitor for PPAR-γ with docking score of -6.958
Kcal.mol-1
and had interaction with PPAR-γ amino acid residue TYR473 by forming hydrogen
bonding with the OH-group of quercetin (Srinivasan, et al., 2018).
The second target was CK2. Lolli, et al., evaluated the ability of quercetin to inhibit CK2 and
found that quercetin had a potent inhibition activity with IC50 value of 0.55 μM (Lolli, et al.,
2012).
The third target was MAPK14. The effect of quercetin on MAPK14 had been investigated by
Baby, et al., 2016, they had documented that quercetin had hydrophobic bonding with VAL30,
VAL38, ALA51, ILE84, LEU108, ALA111, LEU167, MET179 and TYR182 with a Glide Score
of −6.69 Kcal/mol and binding energy of 44.85 Kcal/mol (Baby et al., 2016).
Similarly, Baby, et al., had investigated the interactions between quercetin and CDK and found
that quercetin had hydrophobic bonding with THR14, LYS33, LEU83, GLN131, ASP145,
ILE10, VAL18, ALA31 and LEU134 with a Glide Score of −9.33 Kcal/mol and binding energy
of −64.14 Kcal/mol (Baby et al., 2016).
MAPK14 is a 41 kDa protein composed of 360 amino acids. MAPK14, also called p38-α,
belongs to the p38 MAPK family which is composed of four members (MAPK14/p38α,
MAPK11/p38β, MAPK12/p38γ and MAPK13/p38δ). The p38 MAPK family consists of highly
conserved proline-directed serine-threonine protein kinases that are activated by various
environmental stresses, growth factors and proinflammatory cytokines. MAP kinases act as an
integration point for multiple biochemical signals, and are involved in a wide variety of cellular
processes such as inflammation, apoptosis, proliferation, differentiation, transcription regulation
and development as seen in figure 4.10 (Santen, et al., 2002 and Nedunuri, et al., 2016).
The role of p38 MAPK in cancer, heart and neurodegenerative diseases was investigated and
found that this pathway was highly attractive for the development of new therapeutics strategies
to treat these pathologies (Almudena & Carmen, 2010).
84
Figure 11.10: Signaling through MAPK14 cascade and its role in the regulation of cellular functions.
MAPK14 is involved in signaling pathways triggered by a variety of stimuli such as growth factors, oxidative stress,
UV, cytokines and DNA damage. Depending on the stimulus, different receptors and intermediates (adaptors,
GTPases or kinases) are activated leading to the activation of the p38alpha MAPK cascade which is initiated by
activation of MAPKKKs, followed by activation of MAPKKs (MKK3/6/4), which in turn lead to activation of
MAPK14. Once phosphorylated, MAPK14 phosphorylates a number of cytosolic and nuclear substrates, including
transcription factors, which lead to the control of many cellular responses (Almudena & Carmen, 2010).
The fifth target was PIM1. Bullock, et al., identified quercetin as a potent inhibitor of PIM1
kinase, with a Ki value of 25 nM as determined by Isothermal Titration Calorimetry (ITC) and an
IC50 value of 43 nM in enzyme kinetic assays (Bullock, et al., 2005).
Yokoyama, et al., investigated the binding affinity between quercetin and DAPK1 and found that
quercetin exhibited DAPK1 kinase inhibitory activity with IC50 of 8.9 ± 0.90 µM (Yokoyama, et
al., 2015).
Kıyga, et al., 2020 had investigated the interaction betwwen quercetin and HSP90 and found
that the level of HSP90 was almost depleted due to high-dose quercetin (100 µM) treatment
(Kıyga, et al., 2020). Moreover, quercetin had a total interaction energy with HSP90 of -109.87
KJ/mol and a docking score of -71.27 (Singh, et al., 2015).
82
The last target was ERβ, its molecular docking energy with quercetin had a value of −106.0
KJ/mol according to Powers & Setzer, (2015) (Powers & Setzer, 2015).
4.2.2.12 Rutin
Rutin (3, 3′, 4′, 5, 7-pentahydroxyflavone-3-rhamnoglucoside) is a flavonol (as shown in table
2.2), abundantly found in many plants. Rutin, also called as rutoside, quercetin-3-rutinoside, and
sophorin. Chemically it is a glycoside comprising of flavonolic aglycone quercetin along with
disaccharide rutinose. It has demonstrated a number of pharmacological activities, including
antioxidant, cytoprotective, vasoprotective, anticarcinogenic, neuroprotective and cardioprotec-
tive activities (Ganeshpurkar, et al., 2017).
Using RTFA and Rutin as query template, we had discovered several targets that were related to
cancer, all of them were off targets except MAPK 14 as seen in table 4.13.
4.2.2.12.1 On-targets of Rutin
The only potential identified on-target was MAPK14, which was discussed in section 4.2.2.11.
Song, et al., 2018 observed that treatment with rutin (30 mg/kg/day for 3 days) reduced p38
MAPK expression compared with the spinal cord injury group (Song, et al., 2018).
Table 11.13: Pharmacological profiling for rutin using ROCS
Target Disease Tanimotto
Coefficient
Docking
Score Ref.
DAPK1 Cancer 0.882 -9.752
ERα Cancer 0.790 -8.733
HSP90-α Cancer 0.778 -14.66
CDK2 Cancer 0.777 -8.595
MAPK14 a Cancer 0.766 Song, et al., 2018
PIM1
Cancer
0.756
-12.697
CDK2: Cycline dependent kinase2, DAPK-1: Death associated protein kinase 1, ERα : Estrogen receptor alpha, HSP90-α: Heat shock protein 90 alpha, MAPK14: Mitogen activated receptor 14, PIM1: Proviral integration
site for moloney murine leukaemia virus 1.
a Has in-vitro or in-vivo evidence.
82
4.2.2.12.2 Off-targets of Rutin
The off-targets for rutin were DAPK1, ERα, HSP90-α, CDK2 and PIM1. DAPK1, ERα and
PIM1 were discussed in section 4.2.2.1 while HSP90- α and CDK2 were discussed in sections
4.2.2.6 and 4.2.2.4 respectively.
In order to validate these identified potential off-target, in-silico molecular docking simulations
were conducted to reveal the possibility of binding of Rutin with its new targets. Rutin was
successfully docked on DAPK1, ERα, HSP90-α, CDK2 and PIM1 with a relatively high good
score as seen in Table 4.13. This finding could promote further studies on these new off-targets
which could give rise to the discovery of new anticancer agents .
4.2.2.13 Rosmarinic Acid
Rosmarinic acid (RA), an ester of caffeic acid and 3, 4-dihydrophenyllactic acid (as seen in table
2.2), is a naturally occurring phenylpropanoid that is widespread in the plant kingdom. RA has
been widely investigated and has shown many remarkable biological and pharmacological
activities including antioxidant, anti-inflammatory antiviral, antibacterial, antimicrobial, photo-
protective, anticancer, antidepressant, and possible neuroprotective effects (Al-Dhabi, et al.,
2014)
By using RTFA, we had discovered several potential targets for RA that were related to cancer
and diabetes, all of them were off-targets except PPAR-γ as seen in table 4.14.
Table 11.14: Pharmacological profiling for rosmarinic acid using ROCS
Target Disease
Tanimotto
Coefficient
Docking
Score Ref.
Mcl-1 cancers. 1.057 -10.702
PPAR-γ ab
T2DM 1.006 Han, et al., 2017
CDK2 Cancer 0.983 -13.420
PDK1 Caner & T2DM 0.958 -14.355
PKACα Cancer 0.957 -15.842
PIK3CG Cancer 0.956 -11.744
CDK2:Cycline dependent kinase2, Mcl-1:Myeloid cell leukemia 1, PDK1: Phosphoinositide-dependent kinase-
1, PIK3CG: Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoform, PKACα: Protein
kinase A catalytic subunit alpha, PPAR-γ: Peroxisome proliferator activated receptor gamma.
a Has in-vitro or in- vivo evidence, b Has in-silico evidence.
81
4.2.2.13.1 On-targets of Rosmarinic acid
The only identified on target of RA was PPAR-γ which discussed in section 4.2.2.3. Han, et al.,
2017 performed a molecular docking simulation to analyze the interactions between RA and
PPAR-γ. They found that the dock score of PPAR-γ with RA had a value of 8.9589. To validate
the predicted interactions their further studies showed that RA activated PPAR-γ with IC50 value
of 55.78 μM (Han, et al., 2017).
4.2.2.13.2 Off-targets of Rosmarinic acid
The off-targets for RA, Mcl-1, CDK2, PDK1 and PIK3CG were discussed in sections 4.2.2.10.2,
4.2.2.4, 4.2.2.5.2 and 4.2.2.6 respectively.
The protein kinase A catalytic subunit alpha (PKACα) also known as protein kinase cAMP
(3′,5′-cyclic adenosine monophosphate ) activated catalytic subunit alpha or PKACA.
PKACα is a key regulatory enzyme that in humans is encoded by the PRKACA gene. PKA Cα is
a member of the AGC kinase family (protein kinases A, G, and C) of serine/threonine kinases.
PKACα exists as a tetramer comprised of a regulatory (R) subunit dimer and two catalytic (C)
subunits. Upon binding of two molecules of the second messenger cAMP to each R subunit, a
conformational change in the PKA holoenzyme occurs to release the C subunits. These active
kinases phosphorylate downstream targets to propagate cAMP responsive cell signaling events.
PKACα and the other PKA catalytic subunits (PRKACβ and PRKACγ) undergoes many cellular
functions like cell proliferations, cell cycle regulation, and survival of cells through acting on
many substrates (Turnham & Scott, 2016)
Over expression of extracellular PRKACα causes severe tumorgenesis in different organs
(prostate gland, breast, lungs and pancreas) leading to cancer (Swargam, et al., 2010).
In order to validate the identified potential off-target, in-silico molecular docking simulations
were conducted to reveal the possibility of RA binding with its new off-targets. The obtained
results revealed that RA was successfully docked within the active site of Mcl-1, CDK2, PDK1,
PKACα and PIK3CG with relatively good scores as shown in table 4.14. These findings could
promote further studies on these new off targets which could give rise to the discovery of new
anticancer/antidiabetic agents.
85
4.2.2.14 Ursolic Acid
Ursolic acid (UA), 3-beta-3-hydroxy-urs-12-ene-28-oic-acid, is a lipophilic pentacyclic triterpen-
oid (as shown in table 2.2). It is widely found naturally in the peels of fruits, as well as in many
herbs and spices. UA has been confirmed to have several biological and pharmacological effects,
such as anti-inflammatory, antitumor, antiplatelet aggregation, antiviral and anti-Mycobacterium
tuberculosis effects (Lee, et al., 2016).
Using RTFA and UA as query template, we had discovered several targets that were related to
cancer and diabetes where all of them were off-targets except ERα as seen in table 4.15.
Table 11.15: Pharmacological profiling for ursolic acid using ROCS
Target Disease Tanimotto
coefficient
Docking
Score Ref.
RPTP-γ Cancer 1.052 -6.653
CDK2 Cancer 1.051 -5.922
PPAR-γ T2DM 1.010 -5.910
ERα a cancer. 0.991 Pang, et al., 2018
Tank2 Cancer 0.983 -9.512
CDK2: Cycline dependent kinast2, ERα: Estrogen receptor alpha, PPAR-γ: Peroxisome proliferator-activated
receptor gamma, RPTP-γ: Receptor protein tyrosine phosphatase gamma, Tank2: Tankyrase.
a : Has in-vitro or in-vivo evidence.
4.2.2.14.1 On-targets of Ursolic acid
The only potential identified on-target of UA was ERα (discussed in section 4.2.2.1), Pang, et al.,
had measured the binding affinity of ERα to UA by green Polar Screen ERα Competitor Assay
and found that UA had a binding affinity with IC50 value of 977.38 ± 125.30 μM, (Pang, et al.,
2018).
4.2.2.14.2 Off- targets of Ursolic acid
To validate the UA identified potential off-target, docking simulations were conducted to detect
the possibility of binding of UA with its new off-targets. The obtained results revealed that UA
was successfully docked within the active site of these off-targets as seen in table 4.15.
86
4.3 Polypharmacology of Urtica dioica
U. dioica is the most common species of the Urticaceae family commonly known as Stinging
nettle, invasive weed, and one of the most studied medicinal plants worldwide. It can easily be
found on all temperate regions and growing in all seasons. It has been known since ancient times
for their benefits to the human health. Recently there was a rediscovery of the plant as food and
medicine because of the range of biological activities exhibited such as antirheumatic, anti-
hypertension, anti-infective, immuno-modulatory, antihyperlipidemic, antihyperglycaemic, and
allergy relief. Several studies have also reported its analgesic potential and its role as anti-
aggregating factor, as well as describing its favorable effects on treating benign prostate
hyperplasia (Grauso, et al., 2020).
The main components of U. dioica are terpenoids, phenolics, flavonoids and lignans. These
bioactive compounds have demonstrated the numerous pharmacological effects of U. dioica
especially, anticancer, antidiabetic and hypolipidemic effects. In addition, nettle roots were
largely used in the treatment of benign prostatic hyperplasia, whose activity was imputable to
their high content of lignans, which can bind to sex hormone-binding globulin, thus inhibiting
the interaction with the receptor (Xu, et al., 2019).
4.3.1 Reported anticancer, antidiabetic and hypolipidemic targets of U. dioica
Several suggested mechanisms for U dioica anticancer effects have been reported in the
literature. It was reported that U dioica activates apoptosis through the intrinsic pathway by the
increase in the caspase 3 and caspase 9, and a down regulation of anti-apoptotic Bcl2 (B-cell
lymphoma 2) (Mohammadi, et al., 2016). In addition, U dioica induced the anti-metastatic
pathway by decreasing the expression of matrix metalloproteinases 1, 9 and 13, and increased
expression of E-cadherin (Hodroj, et al., 2020). Also, U dioica reduced the activity of 5α-
reductase enzyme which is the key enzyme involved in testosterone metabolism and hormone-
dependent prostate hyperplasia and prostate cancer (Nahata & Dixit, 2012). Moreover, due to the
presence of large quantities of compounds with anti-oxidant and free radical scavenger
properties, U. dioica, is able to reduce the high level of oxidative stress present in cancerous cells
and exert a chemopreventive function (Wang, et al., 2014).
Similarly, several suggested mechanisms for U. dioica antidiabetic activity was reported and
several studies suggest that the U. dioica works as a PPAR-γ agonistic and possess a low
87
inhibitory effect against α-amylase and high inhibitory activity against α-glucosidase (Kianbakht,
et al., 2013).
Regarding to the antihyperlipidemic activity of U dioica, the suggested mechanism include the
reduction of HMG-COA reductase activity (Pourahmadi, et al., 2014).
Using RTFA, we had discovered that, U. dioica natural constituents could bind to PPAR-γ as
oleanolic acid and to matrix metalloproteinases7 (MMp7) as secoisolaricirisenol and Caffeoyl-
malic acid, as well as to pancreatic α-amylase as Caffeoylmalic acid.
As shown in table 4.16, eighteen targets related to cancer (3 of them are on target and 15 are off-
targets), six targets related to diabetes (2 of them are on targets and 4 are off targets) and one off-
target related to antihyperlipidemic activities of U. dioica were identified by the adopted RTFA
protocol.
Table 11.16: On-targets and off-targets of U.dioica
Anticancer Targets Antidiabetic Targets Hypolipidemic Targets
On-Targets Off Targets On-Targets Off Targets On-Targets Off Targets
1 MMp7 DAPK1 PPAR-γ PTP1B PTP1B
2 Caspase 3 ERα α Amylase DDP-4
3 Caspase 9 ERβ GSK-3β
4 HSP90-α PDK1
5 CDK2
6 PIM1
7 PDK1
8 CK2
9 RPTP-γ
10 Tank2
11 Mcl-1
12 PKACα
13 EGFR
14 FGFR1
15 GSK-3β
CDK2: cycline dependent kinase2, CK2: casine kinase2, DAPK1: death associated protein kinase 1, DPP-4:
dipeptityl peptidase 4, ERα: estrogen receptor alpha, ERβ: estrogen receptor beta, EGFR: epidermal growth
factor receptor, FGFR1: Fibroblast growth factor receptor1, GSK-3β : glycogen synthase kinase-3 beta, HSP90-
α: heat sock protein 90 alpha, Mcl-1:myloid cell leukemia 1, MPP7: matrix metalloproteinases7, PDK1: phosphoinositide-dependent protein kinase-1, PKACα: protein kinase A catalytic subunit alpha, PPAR-γ:
peroxisome proliferator activated receptor gamma, PTP1B: protein tyrosine phosphatase 1B, PIM1: proviral
integration site for moloney murine leukaemia virus 1, RPTP-γ : receptor type protein tyrosine phosphatase
gamma,Tank2: tankyrase2.
88
Analysis of results retrieved from RTFA highlights the importance of this approach in clarifying
the polypharmacology of U. dioica especially anticancer, antidiabetic and hypolipidemic
activities as RTFA clarify the ability of U. dioica natural constituents to bind with reported
anticancer targets (on-targets) such as caspase 3, caspase 9 and MMp7, in addition to bind with
antidiabetic targets (on-targets) as PPAR-γ and α-amylase.
4.3.2 Targets identified using U.dioica constituents as queries in RTFA
As mentioned before in section 2.3.2, the main components of U. dioica are terpenoids,
phenolics, flavonoids and lignans. From these components, ellagic acid, oleanolic acid,
quercetin, rutin and ursolic acid are present in U. dioica and S. officinalis and discussed in
sections 4.2.2.5, 4.2.2.10, 4.2.2.11, 4.2.2.12 and 4.2.2.14 respectively.
4.3.2.1 Caffeoylmalic acid
Caffeoylmalic acid(CA), a hydroxycinnamoyl-malate ester (as seen in table 2.6) existing in
various plants, is a vital antioxidant that has an important role in human health. It has shown to
be relevant for the prevention of cardiovascular disease and breast cancer. Furthermore,
caffeoylmalic acid is a distinctive compound of U. dioica (up to 1.6%), which has been widely
used for centuries in the treatment of disease and disorders, such as rheumatism, eczema,
arthritis, gout and anemia (Li, et al., 2018).
Upon applying RTFA on CA as query template, three targets related to cancer and diabetes were
identified and all of them were off-targets as seen in table 4.17.
Table 11.17: Pharmacological profiling for caffeoylmalic acid using ROCS
Target Disease Tanimoto Coefficient Docking Score
CDK2 Cancer 1.054 -12.379
HSP90-α Cancer 0.993 -13.404
PTP1B T2DM & Hyperlipidemia 0.990 -8.279
CDK2: Cycline dependent kinase2, HSP90-α :Heat shock protein 90 alpha, PTP1B: Protein tyrosine phosphatase 1B.
To validate the CA identified potential off-target, molecular docking were conducted to detect
the possibility of binding of CA with its off-targets. The obtained results revealed that CA was
successfully docked within the active site of these off-targets with relatively good scores as seen
in table 4.17. These findings could promote further studies on these off-targets which could give
rise to the discovery of new anticancer/antidiabetic/antihyperlipidemic agents.
89
4.3.2.2 Chlorogenic acid
Chlorogenic acid (CGA) (as shown in table 2.6) is one of the most available acids among
phenolic acid compounds which can be naturally found in many plants, especially U. dioica (up
to 0.5%). CGA is an important and biologically active dietary polyphenol, playing several
important and therapeutic roles such as antioxidant activity, antibacterial, hepatoprotective,
cardioprotective, anti-inflammatory, antipyretic, neuroprotective, hepatoprotective, antiobesity,
antiviral, anti-microbial, antihypertension, free radicals scavenger and a central nervous system
stimulator. In addition, it has been found that CGA could modulate lipid metabolism and glucose
in both genetically and healthy metabolic related disorders (Naveed, et al., 2018).
Using RTFA protocol, we had discovered four targets of CGA that were related to cancer, and
all of them were off-targets as shown in table 4.18.
Docking was carried out as in-silico validation for the discovered CGA off-targets. The obtained
results revealed that CGA was successfully docked within the active site of these off-targets with
relatively good scores as seen in table 4.18. These findings could promote further studies on
these new off-targets which could give rise to the discovery of new anticancer agents.
Table 11.18 : Pharmacological profiling for chlorogenic acid using ROCS
Target Disease Tanimoto Coefficient Docking Score
17β-HSD 1 Cancer 1.088 -13.439
PKACα Cancer 0.972 -15.051
CDK2 Cancer 0.951 -12.38
PIM1 Cancer 0.946 -12.411
17β-HSD1: 17 Beta Hydroxysteroid dehydrogenase 1, CDK2: cycline dependent protein kinase2, PIM1:
Proviral integration site for moloney murine leukaemia virus 1, PKACα: Protein kinase A catalytic subunit
alpha
4.3.2.3 Isolariciresinol
Isolariciresinol is a lignan that is 5, 6, 7, 8-tetrahydronaphthalen-2-ol (as seen in table 2.6). It is
widely distributed in various food plants. Although the biological functions of lignans remain
unclear, some significant pharmacological efects have been revealed, including antitumor and
antioxidative activities (Sampei, et al., 2018).
91
As we had applied our adopted approach on isolariciresinol as query template, several target
related to cancer and diabetes were identified; some of them were on-targets as ERα and ERβ
while others were off-targets as seen in table 4.19.
7.2.7.2.7 On- targets of Isolariciresinol
The first on-target identified for isolariciresinol was ERα. Isolariciresinol was found to bind to
the ERα in the position of an agonist with the Glide energy of -73.095 KJ/mol and the Glide
score of -9.78 and had good interaction with the active site residues of MET421 and MET343
with hydrogen bond distance of 3.19Ao and 3.18A
o respectively (Aishwarya, et al., 2019).
Similarly, Isolariciresinol was found to be docked to the ERβ in the position of an antagonist
with the Glide energy of -56.19 KJ/mol and the Glide score of -8.75 and had good interaction
with the active site residues of ASP258 and LEU353 with hydrogen bond distance of 3.13A° and
2.41A° respectively (Aishwarya, et al., 2019).
Table 11.19: Pharmacological profiling for isolariciresinol using ROCS
Target Disease Tanimotto
Coefficient
Docking
Score Ref.
CDK2 Cancer 1.127 -10.755
EGFR Cancer 1.126 -10.586
ERα b Cancer 1.105
Aishwarya, et al.,
2019
17β-HSD 1 Cancer 1.089 -12.596
HSP90-α Cancer 1.05 -13.00
PDK1 Cancer & T2DM 1.006 -11.592
ERβ b Cancer 1.004
Aishwarya, et al.,
2019.
FGFR1 Cancer 1.00 -11.997
17β-HSD 1: 17 Beta Hydroxysteroid dehydrogenase 1, CDK2: Cycline dependent kinase 2, EGFR: Epidermal
growth factor receptor, ERα: estrogen receptor alpha, ERβ: Estrogen receptor beta, FGFR1: Fibroblast growth
factor receptor 1, HSP90-α: Heat shock protein 90 alpha, PDK1: Phosphoinositide dependent protein kinase 1.
b: Has in-silico evidence.
4.3.2.3.2 Off-targets of Isolariciresinol
The identified potential off-targets of Isolariciresinol; CDK2, 17β-HSD1, HSP90-α, PDK1and
FGFR were mentioned previously in sections 4.2.24, 4.2.2.5.2, 4.2.2.6, 4.2.2.5.2 and 4.2.2.2
respectively.
94
The EGFR, also referred to human epidermal growth factor receptor HER1/ErbB1, belongs to a
larger family of ErbB receptors with tyrosine kinase activity. The gene symbol, ErbB, is derived
from the name of a viral oncogene to which these receptors are homologous: erythroblastic
leukemia viral oncogene. Other members of the HER family include ErbB2/HER2, ErbB3/HER3
and ErbB4/HER4. Insufficient ErbB signaling in humans is associated with the development of
neurodegenerative diseases, such as multiple sclerosis and Alzheimer's disease (Bublil & Yarden,
2007).
EGFR is frequently overexpressed and/or hyperactivated in human malignancies such as non-
small-cell lung cancer, pancreatic cancer, breast cancer, and colon cancer and therefore EGFR
inhibitors may be used in the treatment of cancers. EGFR overexpression and activation are
known to significantly impact cancer cell hallmark traits, such as increased cell survival,
proliferation and invasion as seen in figure 4.11 (Xu, et al., 2017).
Figure 11.11: Epidermal growth factor receptor (EGFR) and its downstream signaling proteins. EGF,
epidermal growth factor; EGFR, epidermal growth factor receptor; JAK, Janus kinase; STAT, signal transducer and
activator of transcription; PI3K, phosphatidylinositol 3-kinase; MAPK, mitogen-activated protein kinase; Akt,
protein kinase B; P, phosphorylation. T3; tocotrienol Arrows and perpendicular lines indicate activation/induction
and inhibition/suppression, respectively (Eitsuka,et al., 2016).
92
EGFR inhibitors can be classified as either tyrosine kinase inhibitors (TKI) (eg, erlotinib,
gefitinib): these bind to the tyrosine kinase domain in the epidermal growth factor receptor and
stop the activity of the EGFR or monoclonal antibodies (eg, cetuximab, necitumumab): these
bind to the extracellular component of the EGFR and prevent epidermal growth factor from
binding to its own receptor, therefore preventing cell division (Gerber, 2008; Liang, et al., 2014).
In-silico docking simulations were applied to detect the possibility of binding of Isolariciresinol
with its new targets. The obtained results revealed that Isolariciresinol was successfully docked
within the active site of CDK2, EGFR, 17β-HSD 1, HSP90-α, PDK1 and FGFR1 with relatively
good scores as shown in table 4.19.
The EGFR1 had a good docking score and this encouraged us to study the interaction between
Isolariciresinol and EGFR1 at molecular level as shown in figure 4.12. The figure shows a strong
network of hydrogen bonds between hydroxyl and methoxy groups of Isolariciresinol and key
amino acids in the binding site of EGFR1 including ASP855, LEU788, LEU792, GLN791 and
GLY796 similar to the co-crystallized structure. Moreover, the other aromatic rings occupy a
lipophilic cavity composed of THR854, LUE844 and VAL726.
7.2.7.7 Neoolivil
Neoolivil is the main lignan in the roots of U. dioica. Its chemical structure was shown in table
2.6. It belongs to the class of organic compounds known as 7,7'-epoxylignans. These are lignans
with a structure based on a 2,5-diaryl-3,4-dimethyltetrahydrofuran skeleton. Neoolivil have no
activity reported in the literature, but the biological activites of lignans were well established and
include antioxidant, antitumor, estrogenic, antimicrobial, and cholesterol lowering activities
(Tsopmo, et al., 2013).
Using RTFA ptotocol, we had discovered several targets of neoolivil that were related to cancer
and diabetes and all of them were off-targets as shown in table 4.20.
In-silico target fishing had led to identification of eight new off-targets for neoolivil:ERα, 17B-
HSD1, DPP-4, CDK2, Tank2, ERβ, HSP90-α and GSK-3β. All of these targets have been
discussed before except GSK-3β.
GSK3 is a multifunctional proline-directed serine/threonine kinase. It was named for its ability to
phosphorylate, and thereby inactivate glycogen synthase, a key regulatory molecule in the
synthesis of glycogen. There are two highly homologous forms of GSK3 in mammals encoded
by distinct genes, GSK3α (51 kDa) and GSK3β (47 kDa). Although GSK-3 originally was
identified to have functions in regulation of glycogen synthase, it was subsequently determined
92
to have roles in multiple normal biochemical processes as well as various disease conditions as
Alzheimer‘s disease (AD) and mood disorders, osteoporosis, atherosclerosis, cancer and cardiac
hypertrophy (Phukan, 2010).
A
B
C
Figure 11.12: A; Detailed view of docked Isolariciresinol and the corresponding interacting amino acid within
the binding site of EGFR, B; Detailed view of co-crystallized structure (1C9 , PDB code: 4I23 ) and the
corresponding interacting amino acid within the binding site of EGFR, C; Overlay view of docked
Isolariciresinol within the binding site of EGFR (Green lines refere to hydrogen bonds and black lines refere
to hydrophobic interactions).
GSK-3 is sometimes referred to as a moonlighting protein due to the multiple substrates and
processes which it controls. Frequently, when GSK-3 phosphorylates proteins, they are targeted
for degradation. GSK-3 is often considered as a component of the PI3K/PTEN/AKT/GSK-
3/mTORC1 pathway as GSK-3 is frequently phosphorylated by AKT which regulates its
91
inactivation. AKT is often active in human cancer and hence, GSK-3 is often inactivated (Duda,
et al., 2020).
Recent studies in colorectal cancer, pancreatic cancer, hepatocellular carcinoma and ovarian
cancer demonstrate that GSK-3β is involved in the process of tumorigenesis. Inhibition of the
expression and activity of GSK-3β attenuates cell proliferation and causes apoptosis in
colorectal, pancreatic and ovarian cancer cells (Wang, et al., 2008).
Table 11.20: Pharmacological profiling for neoolivil using ROCS
Target Disease Tanimoto Coefficient Docking Score
ERα Cancer 1.18 -12.452
17B-HSD 1 Cancer 1.122 -14.14
DPP-4 T2DM 1.08 -9.07
CDK2 Cancer 1.002 -9.502
Tank2 Cancer 1.00 -12.393
ERβ Cancer 0.997 -9.894
HSP90-α Cancer 0.985 -13.29
GSK-3β T2DM & Cancer 0.978 -10.639
17B-HSD1:17 Beta Hydroxysteroid dehydrogenase 1, DPP-4: Dipeptidyl peptidase 4, ERα :Estrogen receptor
alpha, ERβ: Estrogen receptor beta, GSK-3β : Glycogen synthase kinase-3 beta , HSP90-α: Heat shock protein
90 alpha, Tank2: Tankyrase 2.
Numerous GSK-3 inhibitors were developed by multiple pharmaceutical companies since the
initial characterization of the biochemical effects of GSK-3 and the association of GSK-3 with
many common immunological disorders and cancer (Duda, et al., 2020). For example, Lithium is
a well-known GSK-3 inhibitor was initially and still is used for the treatment of various
neurological disorders including bipolar disorder (manic depression). It can inhibit proliferation
of the human esophageal cancer cell lineas well as other cell lines of different tissue types by
inducing the phosphorylation of GSK-3β which can lead to inactivation of GSK-3 (Wang, et al.,
2008).
The validity of these proteins as potential targets for neoolivil was evaluated by molecular
docking simulation according to our adopted experimental procedures and the obtained results
were shown in table 4.20 and they reflected successful docking for neoolivil with all new off-
targets with relatively good scores.
DPP-4 as a potential target for neoolivil had a Tanimoto coefficient higher than 1 and a good
docking score, this had invited us to ensure the presence of possible interactions between
95
neoolivil and DPP-4 at molecular level as shown in figure 4.13. The figure shows the hydrogen
bonding between neoolivil and key amino acids at the binding site of DPP4 as GLU206,
TYR547, TYR662 and TYR666. In addition, the other aromatic rings occupy a lipophilic cavity
composed of TYP659 and PHE357.
A B
Figure 11.13: A: Detailed view of docked Neoolovil and the corresponding interacting amino acid within the
binding site of DPP4, B: Surface view of docked Neoolovil and the corresponding interacting amino acid
within the binding site of DPP-4 (Green lines refere to hydrogen bonds and black lines refere to hydrophobic
interactions).
7.2.7.4 Secoisolariciresinol
Secoisolariciresinol is a lignan, a type of phenylpropanoid (seen in table 2.6). Lignans are a
group of phytonutrients which are widely distributed in the plant kingdom. In the intestine the
gut microflora can form secoisolariciresinol from the secoisolariciresinol diglucoside. The
majority of studies demonstrate that secoisolariciresinol diglucoside has various biological
properties including anti-inflammatory, antioxidant, antimutagenic, antimicrobial, antiobesity,
antihypolipidemic and neuroprotective effects (Imran, et al., 2015).
After the application of RTFA protocol on Secoisolariciresinol as query template, we had
discovered four targets that were related to cancer and diabetes and all of them were off-targets
except ERα as shown in table 4.21.
4.3.2.5.1 On-targets of Secoisolariciresinol
ERα was the only off-target identified for secoisolariciresinol. Secoisolariciresinol was found to
96
bind to the ERα in the position of an agonist with the Glide energy of -35.144 KJ/mol and Glide
Score of -8.19 (Aishwarya, et al., 2019).
Table 11.21: Pharmacological profiling for secoisolariciresinol using ROCS
Target Disease Tanimoto
Coefficient
Docking Score Ref.
HSP90-α Cancer 0.924 -13.025
PKACα Cancer 0.908 -15.728
Tank2 Cancer 0.884 -13.192
ERα b Cancer 0.879
Aishwarya , et al.,
2019.
ERα: Estrogen receptor alpha, HSP90-α : Heat shock protein 90 alpha, PKACα: Protein kinase A catalytic
subunit alpha , Tank 2: Tankyrase 2.
b: Has in-silico evidence.
4.3.2.5.2 Off-targets of Secoisolariciresinol
For the validation of these identified potential off-target, molecular docking were applied to
reveal the possibility of binding of Secoisolariciresinol with its new off-targets. The obtained
results showed that Secoisolariciresinol was successfully docked within the active site of HSP90-
α, PKACα and Tank2 with the relatively good scores as shown in table 4.21. These findings
could promote further studies on these new off-targets which could give rise to the discovery of
new anticancer agents.
12
13
14
15
16
17
18
19
20
21
22
23
97
24
25
26 Chapter Five
Conclusion
Salvia officinalis and Urtica dioica exhibits a wide range of biological activities especially
anticancer, antidiabetic and hypolipidemic activitis as the results of our research had been
shown, these activities were attained by modulation of different biological targets.
Targets identification for S. officinalis and U. dioica is a very important step not only for full
understanding of their bioactivity and mechanisms of action, but also for the discovery and
development of new, potent and less toxic drugs for the treatment of serious diseases as cancer,
diabetes and hyperlipidemia.
Herein, the adopted ligand based target fishing approach, RTFA, facilitated the target profiling
of S. officinalis and U. dioica phytochemicals and was able to retrieve well known targets and
several new off-targets that are not reported previously as direct targets and this increases
confidence in our approach in capturing the right positive targets and in exploring the new off-
targets.
The phytochemicals of S. officinalis and U. dioica were successfully docked within active sites
of the captured off-targets as an in-silico evidence
By the application of RTFA on the phytochemicals, the concept of polypharmacology is
obviously clarified as multiple phytochemicals could affect the same target on the disease
pathway producing synergistic effect or an individual compound affecting multiple targets that
involved on the same disease.
27
28
98
29
30 Chapter 6
Recommendations
Use of natural products are safe and effective alternatives compared to unsafe drugs.
In-silico studies are rapid, time preserving and relatively accurate methods for drug discovery.
Finally, further studies on the phytochemicals of S. officinalis and U. dioica against the fished
off-targets will provide more understanding of their pharmacological properties as well as could
provide good leads for designing new more potent and safer anticancer, dantidiabetic and
hypolipidemic drugs.
99
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