Correlation of the Α-Glucosidase Inhibitory Activity to Metabolites of Tetracera Scandens Leaves Extracts Using Metabolomics and Molecular Docking Approaches

CORRELATION OF THE α-GLUCOSIDASE INHIBITORY ACTIVITY TO METABOLITES OF TETRACERA SCANDENS LEAVES EXTRACTS USING METABOLOMICS AND MOLECULAR DOCKING APPROACHES

AHMED AHMED MOHAMED NOKHALA

A thesis submitted in fulfilment of the requirement for the degree of Master in Pharmaceutical Sciences (Pharmaceutical Chemistry)

Kulliyyah of Pharmacy International Islamic University Malaysia

JANUARY 2020

ABSTRACT

α-Glucosidase inhibition is regarded as an efficient mechanism for management of the postprandial hyperglycemia associated with type 2 diabetes mellitus. The severe gastrointestinal adverse effects have been reported to affect patient`s compliance towards the synthetic α-glucosidase inhibitor drugs and have prompted many studies to discover natural alternatives with comparable efficiency and better tolerability. Tetracera scandens is a traditional medicinal plant, whose leaf has been used for the treatment of diabetes mellitus in Malaysia and other Southeast Asian countries. The α- glucosidase inhibitory potential of T. scandens leaf has not been assessed so far. Hence, this study was aimed to evaluate the α-glucosidase inhibitory potential of T. scandens leaf extracts. Moreover, it aimed to develop and validate a multivariate model to correlate the Fourier transform infrared (FT-IR) spectral fingerprint of the plant extracts to their α-glucosidase inhibitory activity. Another aim of this study was to characterize the putative α-glucosidase inhibitory metabolites of T. scandens extracts using metabolomics approach. Eventually, the affinity of the putative active metabolites towards α-glucosidase was to be predicted through molecular docking study. Different hydromethanolic extracts were prepared and assayed for their α- glucosidase inhibitory potential. The FT-IR spectra of T. scandens extracts were acquired and correlated to their corresponding α-glucosidase inhibitory IC50 values via the orthogonal partial least squares (OPLS) algorithm. Furthermore, the mass spectral data acquired via gas chromatography-mass spectrometry (GC-MS) analysis of the plant extracts was correlated to their α-glucosidase inhibitory IC50 values through an OPLS model, and the putative α-glucosidase inhibitory metabolites were suggested by the loading column plot of the developed model. Moreover, the 3D structures of the putative α-glucosidase inhibitory metabolites were further docked into the active site of Saccharomyces cerevisiae isomaltase in order to predict the ligand-enzyme interactions and affinities. The methanolic extracts of T. scandens leaf showed higher α-glucosidase inhibitory potential as compared to the aqueous ones. The developed OPLS model successfully predicted the α-glucosidase inhibitory potential of new independent T. scandens leaf samples given their fingerprint FT-IR spectra, therefore it can be used as a simple and rapid quality control tool. Moreover, the bands corresponding to the carbon-hydrogen bond (C-H), carbon-carbon double bond (C=C) and carbon-oxygen single bond (C-O) were determined to be positively correlated with the α-glucosidase inhibitory activity of the plant extracts. GC-MS based profiling of the α-glucosidase inhibitory metabolites led to the determination of 6 putative metabolites, namely, palmitic acid, 1-monopalmitin, stearic acid, emodin, catechin and β-sitosterol. Moreover, the metabolites malic acid, 4-hydroxybenzoic acid, xylitol, citric acid, D-fructose, D-glucose, D-mannose and myo-inositol were suggested to induce α-glucosidase activity. The results of the molecular docking study further supported the findings of the metabolite profiling study, since the putative α- glucosidase inhibitory metabolites showed predicted binding energies of -5.9 to -8.8, indicating moderate to high affinities. Conclusively, this study demonstrated the in vitro α-glucosidase inhibitory activity of T. scandens leaf. Furthermore, the metabolomics approach was successfully used to develop a rapid method for quality control of T. scandens leaf and to characterize its putative α-glucosidase inhibitory metabolites.

خالصة البحث IN ARAB

يعتبر تثبيط انزيم الفا جلوكوزيداز بمثابة آلية فعالة لعالج ارتفاع السكر فى الدم بعد األكل المرتبط بداء السكرى من النوع الثانى. تم اإلبالغ عن اآلثار الضارة المعوية الحادة التي أثرت على امتثال المريض لألدوية المخلقة المثبطة النزيم الفا جلوكوزيداز ودفعت العديد من الدراسات الكتشاف بدائل طبيعية ذات كفاءة مماثلة و تقبل أفضل. Tetracera scandens هو نبات طبى تقليدى، وقد استخدمت أوراقه لعالج داء السكري في ماليزيا ودول جنوب شرق آسيا األخرى. لم يتم تقييم امكانات تثبيط الفا جلوكوزيداز ألوراق T. scandens حتى األن. و بالتالى فقد هدفت هذه الدراسة الى تقييم امكانات تثبيط الفا جلوكوزيداز لمستخلصات أوراق .T scandens ، تطوير نموذج متعدد المتغيرات لربط بصمة الطيف باألشعة تحت الحمراء FT-IR للمستخلصات النباتية لنشاطها المثبط النزيم الفا جلوكوزيداز ، تحديد نواتج األيض المفترضة المثبطه النزيم الفا جلوكوزيداز باستخدام نهج االستقالب و التنبؤ بتقارب نواتج األيض النشطة المفترضة تجاه انزيم الفا جلوكوزيداز بواسطة دراسة التقارب الجزيئى. تم تحضير عدة مستخلصات مائية/ميثانولية و تم اختبار امكاناتها التثبيطية على انزيم الفا جلوكوزيداز. تم الحصول على أطياف FT-IR و أطياف الكتلة لهذه المستخلصات و تم ربط كل منها بقيم IC50 المثبطة النزيم الفا جلوكوزيداز عبر خوارزمية OPLS. تم اختبار تقارب الهياكل ثالثية األبعاد لنواتج االيض النشطة المقترحة تجاه الموقع النشط النزيم isomaltase. أظهرت المستخلصات الميثانولية لورقة T. scandens إمكانات تثبيط أعلى لاللفا جلوكوزيداز بالمقارنة مع المستخلصات المائية. لقد تنبأ نموذج OPLS باالمكانات التثبيطية اللفا جلوكوزيداز لعينات جديدة مستقلة من ورقة .T scandens باستخدام بصمة األشعة تحت الحمراء الخاصة بها، وبالتالي يمكن استخدامه كأداة بسيطة وسريعة لمراقبة الجودة. عالوة على ذلك ، تم تحديد النطاقات المقابلة لرابطة C-H و C=C و C-O لتكون مرتبطة بشكل إيجابي مع نشاط تثبيط االلفا جلوكوزيداز لمستخلصات النبات. أدى تصنيف نواتج األيض ذات التأثير المثبط النزيم الفا جلوكوزيداز الى تحديد ستة مركبات مفترضة و هى حامض النخيل ،1-مونوبالميتين ، حامض االستياريك ، إيمودين ، كاتشين و بيتا سيتوستيرول. لقد دعمت نتائج دراسة التقارب الجزيئي نتائج دراسة تصنيف نواتج األيض ، حيث أظهرت نواتج األيض المفترضة المثبطة اللفا جلوكوزيداز طاقات ربط متوقعة تتراوح بين -5.9 الى -8.8 ، مما يدل على تقارب متوسط الى عالى. بشكل قاطع ، أظهرت هذه الدراسه نشاط تثبيط الفا جلوكوزيداز ألوراق T. scandens . عالوة على ذلك ، تم استخدام نهج االستقالب بنجاح لتطوير طريقة سريعة لمراقبة الجودة ألوراق T. scandens و لتحديد المستقلبات المفترضة المثبطة النزيم الفا جلوكوزيداز.

iii

APPROVAL PAGE

I certify that I have supervised and read this study and that in my opinion, it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a thesis for the degree of Master in Pharmaceutical Sciences (Pharmaceutical Chemistry)

………………………………….. Mohammad Jamshed Siddiqui Supervisor

………………………………….. Qamar Uddin Ahmed Co-Supervisor

I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a thesis for the degree of Master in Pharmaceutical Sciences (Pharmaceutical Chemistry)

………………………………….. Alfi Khatib Internal Examiner

………………………………….. Jamia Azdina Jamal External Examiner

This thesis was submitted to the Department of Pharmaceutical Chemistry and is accepted as a fulfilment of the requirement for the degree of Master in Pharmaceutical Sciences (Pharmaceutical Chemistry)

………………………………….. Mohamed Sufian Mohd Nawi Head, Department of Pharmaceutical Chemistry

This thesis was submitted to the Kulliyyah of Pharmacy and is accepted as a fulfilment of the requirement for the degree of Master in Pharmaceutical Sciences (Pharmaceutical Chemistry)

………………………………….. Che Suraya Haji Mohd Zin Dean, Kulliyyah of Pharmacy

DECLARATION

I hereby declare that this thesis is the result of my own investigations, except where otherwise stated. I also declare that it has not been previously or concurrently submitted as a whole for any other degrees at IIUM or other institutions.

Ahmed Ahmed Mohamed Nokhala

Signature ...... Date ......

INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DECLARATION OF COPYRIGHT AND AFFIRMATION OF FAIR USE OF UNPUBLISHED RESEARCH

CORRELATION OF THE α-GLUCOSIDASE INHIBITORY ACTIVITY TO METABOLITES OF TETRACERA SCANDENS LEAVES EXTRACTS USING METABOLOMICS AND MOLECULAR DOCKING APPROACHES

I declare that the copyright holders of this thesis are jointly owned by the student and IIUM.

No part of this unpublished research may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without prior written permission of the copyright holder except as provided below

1. Any material contained in or derived from this unpublished research may be used by others in their writing with due acknowledgment.

2. IIUM or its library will have the right to make and transmit copies (print or electronic) for institutional and academic purposes.

3. The IIUM library will have the right to make, store in a retrieved system and supply copies of this unpublished research if requested by other universities and research libraries.

By signing this form, I acknowledged that I have read and understood the IIUM Intellectual Property Right and Commercialization policy.

Affirmed by Ahmed Ahmed Mohamed Nokhala

……..…………………….. ……………………….. Signature Date

vii

DEDICATION

This thesis is dedicated to my parents, my beloved wife and my lovely children for

their prayers, understanding and endurance while away

viii

ACKNOWLEDGEMENTS

Firstly, it is my utmost pleasure to dedicate this work to my dear parents and my wife, who granted me the gift of their unwavering belief in my ability to accomplish this goal: thank you for your support and patience.

I wish to express my appreciation and thanks to those who provided their time, effort and support for this project. To the members of my thesis committee, thank you for sticking with me.

Finally, a special thanks to Assist. Prof. Mohammad Jamshed Siddiqui and Assoc. Prof. Qamar Uddin Ahmed for their continuous support, encouragement and leadership, and for that, I will be forever grateful.

TABLE OF CONTENTS

Abstract ...... ii Abstract in Arabic ...... iii Approval Page ...... iv Declaration ...... vi Copyright Page ...... vii Dedication ...... viii Acknowledgements ...... ix Table of Contents ...... x List of Tables ...... xii List of Figures ...... xiii List of Abbreviations ...... xiv

CHAPTER ONE: INTRODUCTION ...... 1 1.1 Background of the Study ...... 1 1.2 Problem Statement ...... 6 1.3 Research Objectives...... 7 1.4 Research Hypotheses ...... 7 1.5 Significance of the Study ...... 8

CHAPTER TWO: LITERATURE REVIEW ...... 9 2.1 Background of Tetracera scandens ...... 9 2.1.1 Origin, Botany and Morphology ...... 9 2.1.2 Taxonomy of T. scandens ...... 10 2.1.3 Ethnobotanical Uses ...... 11 2.1.4 Medicinal Activities of T. scandens ...... 12 2.1.4.1 Antidiabetic Activity ...... 12 2.1.4.2 Other Medicinal Activities ...... 12 2.1.5 Phytochemical Compounds Isolated from T. scandens ...... 18 2.2 Pharmaceutical Values of α-Glucosidase Inhibition ...... 21 2.2.1 Postprandial Hyperglycemia ...... 21 2.2.2 α-Glucosidase Inhibition ...... 22 2.3 Plant Metabolomics ...... 24 2.3.1 Plant Metabolites ...... 24 2.3.2 Medicinal Values of Plant Metabolites ...... 25 2.3.3 Metabolomics Approach ...... 26 2.3.3.1 Sample Preparation ...... 30 2.3.3.2 Data Acquisition ...... 33 2.3.3.3 Multivariate Statistical Analysis ...... 35 2.3.4 Quality Control of Medicinal Plants Using Metabolomics Approach ...... 37

CHAPTER THREE: MATERIALS AND METHODS ...... 40 3.1 Materials ...... 40 3.1.1 Chemicals and Reagents ...... 40 3.1.2 Apparatus ...... 40

3.2 Methods ...... 41 3.2.1 Sample Preparation ...... 41 3.2.1.1 Collection of the Plant Material ...... 41 3.2.1.2 Sample Extraction ...... 41 3.2.2 In Vitro Assay of α-Glucosidase Inhibitory Activity ...... 42 3.2.3 Instrumental Analysis of T. Scandens Leaf Extracts ...... 44 3.2.3.1 FT-IR Metabolic Fingerprinting ...... 44 3.2.3.1.1 Method ...... 44 3.2.3.1.2 Data Preprocessing and Statistical Analysis ...... 44 3.2.3.1.3 Preparation of External Samples for Model Validation ...... 45 3.2.3.2 GC-MS Metabolite Profiling ...... 46 3.2.3.2.1 Derivatization Procedure...... 46 3.2.3.2.2 GC-MS Conditions ...... 46 3.2.3.2.3 Data Preprocessing and Statistical Analysis ...... 47 3.2.4 Molecular Docking...... 48

CHAPTER FOUR: RESULTS AND DISCUSSION ...... 49 4.1 Extraction Yield ...... 49 4.2 In Vitro Inhibition of α-Glucosidase Activity...... 50 4.3 FT-IR Based Metabolic Fingerprinting ...... 51 4.3.1 FT-IR Spectral Analysis ...... 51 4.3.2 Multivariate Analysis of FT-IR Data ...... 55 4.3.2.1 OPLS Model Validity ...... 56 4.3.2.2 Functional Groups Contributing to the Activity ...... 59 4.3.2.3 External Validation of the OPLS Model ...... 61 4.4 GC-MS Based Metabolite Profiling ...... 62 4.4.1 GC-MS Chromatograms of T. scandens Leaf Extracts ...... 62 4.4.2 Multivariate Data Analysis ...... 62 4.4.3 Putative α-Glucosidase Inhibitory Metabolites ...... 66

CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS ...... 73

REFERENCES ...... 77

APPENDIX I: FT-IR SPECTRA ...... 89 APPENDIX II: GC-MS CHROMATOGRAMS ...... 95 APPENDIX III: PREDICTED INTERACTIONS OF BEST DOCKED CONFORMATION – ENZYME COMPLEXES ...... 101

LIST OF TABLES

Table 2.1 Summary of the previous studies conducted on Tetracera scandens 16

Table 4.1 Extraction yield (%) of shade-dried Tetracera scandens leaf at different solvent ratios of methanol 50

Table 4.2 α-Glucosidase inhibitory IC50 values (µg/mL) of shade-dried Tetracera scandens leaf extracts 51

Table 4.3 Interpretation of the infrared spectra of Tetracera scandens leaf extracts 54

Table 4.4 Measured and predicted α-glucosidase inhibitory activity of 60% methanol extracts of Tetracera scandens leaf external samples 61

Table 4.5 Metabolites detected from the hydromethanolic extracts of Tetracera scandens leaf 65

Table 4.6 Molecular docking results of the suggested α-glucosidase inhibitory metabolites as well as the positive control (quercetin) onto the active site of Saccharomyces cerevisiae isomaltase 69

xii

LIST OF FIGURES

Figure 2.1 Tetracera scandens leaves 10

Figure 2.2 Phytochemical compounds isolated from Tetracera scandens 19

Figure 2.3 α-Glucosidase inhibitor drugs in clinical use 23

Figure 2.4 Flowchart of a typical plant metabolomics study 29

Figure 3.1 Hydrolysis of p-nitrophenyl-α-D-glucopyranoside 43

Figure 4.1 Representative FT-IR spectra of Tetracera scandens leaf extracts 53

Figure 4.2 OPLS scores scatter plot of Tetracera scandens extracts 56

Figure 4.3 Observed versus predicted IC50 values for all samples 58

Figure 4.4 Permutation plot (R2Y-intercept = 0.23 & Q2Y-intercept= -0.507) 58

Figure 4.5 Loadings line plot of the developed OPLS model 60

Figure 4.6 Representative chromatogram of the 100% methanol extract of Tetracera scandens leaf 63

Figure 4.7 Representative chromatogram of the 100% water extract of Tetracera scandens leaf 64

Figure 4.8 Scores scatter plot of the OPLS model of GC-MS data 67

Figure 4.9 Loadings column plot of the OPLS model of GC-MS data 68

Figure 4.10 Putative α-glucosidase inhibitory metabolites 72

xiii

LIST OF ABBREVIATIONS

CCl4 Carbon Tetrachloride CV Cardiovascular DM Diabetes Mellitus DMSO Dimethyl Sulfoxide FH Fasting Hyperglycemia FT-IR Fourier Transform Infrared GAE Gallic Acid Equivalents GC-MS Gas Chromatography-Mass Spectrometry LC-MS Liquid Chromatography-Mass Spectrometry MQSIC Minimum Quorum Sensing Inhibitory Concentration MSTFA N-Methyl-N-(trimethylsilyl) trifluoroacetamide MVSA Multivariate Statistical Analysis m/z Mass-to-Charge Ratio NIST National Institute of Standards and Technology NMR Nuclear Magnetic Resonance OPLS-DA Orthogonal Partial Least Squares – Discriminant Analysis PCA Principal Component Analysis PGLs Plasma Glucose Levels PLS-DA Partial Least Squares – Discriminant Analysis PNPG p-Nitrophenyl-α-D-glucopyranoside PPH Postprandial Hyperglycemia RTase Reverse Transcriptase QE Quercetin Equivalents RMSECV Root Mean Square Error of Cross-Validation RMSEE Root Mean Square Errors of Estimation XO Xanthine Oxidase VIP Variable Importance for the Projection

xiv

CHAPTER ONE

INTRODUCTION

1.1 BACKGROUND OF THE STUDY

Diabetes mellitus (DM) is a group of serious metabolic disorders characterized by persistent hyperglycemia. The abnormally high plasma glucose levels (PGLs) seen in

DM result from an absolute insulin deficiency (Type 1 DM) or from a decreased responsiveness of body organs to the secreted insulin (i.e. insulin resistance) combined with insufficient compensatory insulin secretion (Type 2 DM). It is noteworthy that type 2 is the predominant form of DM, accounting for 85 to 95% of all diabetic cases. In 2017, there were about 425 million diabetic patients in the world, and this figure is expected to rise significantly to nearly 629 million by 2045. In a similar context, the number of adult diabetic Malaysians was nearly 3.5 million in

2017 (International Diabetes Federation, 2017). Left uncontrolled, the elevated PGLs will lead to the development of long-term complications such as retinopathy that may progress to blindness, nephropathy that may end up with renal failure, as well as neuropathy with risk of foot ulcers and amputations. In addition to the cardiovascular complications which are considered the major cause of death amongst diabetics

(Ceriello et al., 2014).

Numerous drug classes have been developed to target different pathophysiologic defects in DM (Thrasher, 2017). The ultimate goal of the management of DM is to achieve a sustained state of glycemic control, which in turn slows down the progression of the disease and retards the occurrence of the life- threatening complications. Insulin replacement therapy constitutes the cornerstone in

the management of type 1 DM (Deshmukh & Jain, 2015). Management of type 2 DM involves a stepwise approach starting with diet and lifestyle modifications in addition to monotherapy with an antihyperglycemic drug, usually metformin. If the target PGL has not been achieved within 3 months, the addition of a second antihyperglycemic drug should be considered, and so on. Eventually, type 2 diabetic patients who fail to achieve satisfactory PGLs after lifestyle modifications and oral antihyperglycemic therapy should be diverted into insulin-based therapy (Thrasher, 2017).

Sulphonylureas, biguanides, meglitinides, and thiazolidinediones are examples of oral antihyperglycemic drugs in clinical use (Deepthi et al., 2017). The use of synthetic antihyperglycemic drugs is usually associated with unpleasant side effects, ranging from mild gastrointestinal upsets to severe hypoglycemia (Deshmukh & Jain, 2015).

Therefore, diabetic patients usually prefer to use natural or naturally-derived medicines instead of synthetic ones (Mustaffa et al., 2011).

Medicinal plants have been used for the management of DM for centuries, with high efficacy and good safety profiles. Being a tropical country, Malaysia constitutes a native home to a wide range of plant species. Malaysian people have relied largely on the extracts of many of these plants for the treatment of DM (Mustaffa et al., 2011).

Many studies have discussed the antihyperglycemic potential of Malaysian herbs and their underlying mechanisms (A’attiyyah et al., 2018; Bukhari et al., 2017; Jemain et al., 2011; Loh & Hadira, 2011; Muhammad et al., 2016). Tetracera scandens (Linn.)

Merr. is a Southeast Asian shrub that belongs to family Dilleniaceae. Over the years, the leaf of T. scandens has been used for the treatment of DM in the Malaysian folk medicine system. Antihyperglycemic properties of T. scandens have been explored scientifically by an in vivo study conducted by Umar et al. (2010). Moreover, Lee et al. (2009) reported the stimulant effect of the methanol extract of T. scandens

branches on the glucose-uptake activity into skeletal muscles. In addition to DM, T. scandens has been traditionally used for the management of many other illnesses including hypertension, rheumatism, hepatitis, gout and urinary disorders (Umar et al.,

2010). This variety of medical indications has attracted the attention of many researchers to this plant species, therefore many research studies have been conducted on different parts of the plant. Two studies explored the in vitro xanthine oxidase

(XO) inhibitory potential of the stem of T. scandens and resulted in the isolation of seven compounds with xanthine oxidase inhibitory properties (Nguyen et al., 2004;

Nguyen & Nguyen, 2013). Moreover, the study by Thanh et al. (2015) proved the hepatoprotective properties of T. scandens leaf and justified its traditional use for treatment of hepatitis. This activity was attributed to the antioxidant properties of the plant metabolites (Thanh et al., 2015). Furthermore, Kwon et al. (2012) reported that the ethanol extract of T. scandens leaf has strong inhibitory activity against HIV-1 reverse transcriptase.

One strategy for the treatment of type 2 DM relies on the use of a class of pseudo-carbohydrates for inhibition of a group of enzymes, called α-glucosidases, whose role is to catalyze the last step of carbohydrate digestion. α-Glucosidase inhibitors retard the release of glucose from complex carbohydrates, and that is how they blunt the increase in PGLs that occurs normally after food ingestion. Therefore, this group of drugs is indicated mainly for the management of postprandial hyperglycemia (PPH) of type 2 DM (Derosa & Maffioli, 2012; Kumar et al., 2011;

Lee et al., 2016; Proença et al., 2017; van de Laar, 2008). Despite their efficiency, α- glucosidase inhibitors in clinical use are well known for their nasty gastrointestinal side effects, which have greatly influenced the patient’s compliance towards the medication. Many screening studies have been performed recently to explore the α-

glucosidase inhibitory potential of different plant species, aiming to find natural alternatives with comparable efficiency and better tolerability (Manaharan et al.,

2012).

The past few years have witnessed tremendous developments in the field of quality evaluation of medicinal plants and phytopharmaceuticals based on the principle of metabolic fingerprinting (van der Kooy et al., 2009). Conventional approach for evaluation of the quality of medicinal plants depend on the quantitative analysis of a few major plant metabolites. However, it is believed that the bioactivity arises from the combined interactions of many metabolites, rather than from a few individual metabolites. Therefore, quality evaluation based on the metabolomics approach is believed to be more reasonable than the conventional approach. The concept involves the use of multivariate statistical tools to correlate the fingerprint FT-

IR spectra of the plant extracts to the activity. Afterwards, the developed mathematical model is validated to ensure its predictability. The validated model can be used later for prediction of the activity of new plant samples, given their fingerprint FT-IR spectra. Hence, this method is considered easier, faster and more efficient than the conventional methods (Kang et al., 2008; Khatib et al., 2017; Saleh et al., 2018; Sharif et al., 2014).

Besides being used as traditional medicines, the crude extracts of medicinal plants are considered valuable sources of drug scaffolds for drug design and optimization programs due to the incomparable diversity of the metabolites they contain. The golden procedure for characterization of the active plant metabolites relies on the activity-guided fractionation and purification approach. This approach usually involves liquid-liquid extractions, column chromatography and/or thin layer

chromatography to isolate and purify the metabolites responsible for the biological activity of the medicinal plant. However, time consumption, as well as the isolation of already known metabolites, are the main drawbacks of such approach. The great advancements of multivariate statistical analysis (MVSA) tools, as well as the rise of high throughput analytical platforms (such as GC-MS, LC-MS and NMR), has led to the emergence of metabolite profiling technique as an efficient method that determines the active metabolites in comparatively lesser time. Metabolite profiling is a time-saving method that suggests the metabolites that are positively correlated with the activity as putative active metabolites. Hence, metabolite profiling is considered as an efficient preliminary method for the prediction of the active plant metabolites in a short time. However, it lacks the ability to identify new unknown metabolites since it relies on the mass or NMR spectral data available in the databases. The plant extracts are analyzed using one of the aforementioned high throughput analytical instruments, then their activities are correlated to the acquired mass or NMR spectra via multivariate statistical tools. The metabolites that are positively correlated with the activity are further detected via the loadings column plot of the established multivariate model. The high throughput nature makes metabolite profiling an efficient screening method that helps the activity-guided fractionation approach to fulfill the high demands of the modern drug discovery policies, hoping to bring the natural products into the spotlight again as a potential source of new drug candidates

(Atanasov et al., 2015; Tebani et al., 2016; Yuliana et al., 2011).

1.2 PROBLEM STATEMENT

The leaf of T. scandens has been used by traditional practitioners for the treatment of

DM. The antidiabetic activity of the plant has been confirmed in previous studies, however, the plant has not been explored yet regarding its α-glucosidase inhibitory potential, one of the possible underlying mechanisms of the antidiabetic activity.

Furthermore, quality control of medicinal plants is a real challenge that impedes the trust in traditional medicines as the main partner to western medicine in health care.

Visual inspection and microscopic investigation may not be sufficient to discriminate between the closely related plant species. Furthermore, the targeted analysis of major metabolites requires extensive sample preparation and neglects the impact that the minor metabolites could have on the activity. Hence, alternative quality control methods that use simple sample preparation procedures and consider the impact of the whole plant metabolome on the activity are required. Moreover, the reliable results and the ability to identify new unknown metabolites make the activity-guided fractionation and purification approach the main procedure followed for characterization of the active plant metabolites. However, this approach is time and solvent-consuming and may result in the isolation of known metabolites with already known activities.

1.3 RESEARCH OBJECTIVES

1- To investigate the α-glucosidase inhibitory potential of T. scandens leaf

extracts.

2- To develop and validate a multivariate statistical model, describing the

correlation between the FT-IR fingerprint spectra of T. scandens leaf

extracts and their α-glucosidase inhibitory activities, that could be used for

quality control of T. scandens leaf.

3- Profiling of the α-glucosidase inhibitory metabolites of T. scandens leaf

extracts using GC-MS based metabolomics.

4- To study the affinities and molecular interactions within the putative

active metabolites-enzyme complexes through molecular docking.

1.4 RESEARCH HYPOTHESES

1- T. scandens leaf extracts could have a significant α-glucosidase inhibitory

potential.

2- The validated multivariate model can predict the α-glucosidase inhibitory

activity of T. scandens leaf samples given their fingerprint FT-IR spectra.

3- α-Glucosidase inhibitory metabolites of the hydromethanolic extracts of T.

scandens leaf could be determined through GC-MS metabolite profiling.

4- The affinities and possible interactions between the suggested active

metabolites and α-glucosidase can be predicted via molecular docking

using the crystal structure of Saccharomyces cerevisiae isomaltase.

1.5 SIGNIFICANCE OF THE STUDY

This study has demonstrated the in vitro inhibitory potential of the hydromethanolic extracts of T. scandens leaf against α-glucosidase, a key target enzyme in controlling

PPH of type 2 DM. Moreover, the metabolites responsible for the α-glucosidase inhibitory activity were suggested via GC-MS based metabolomics. The putative active metabolites were further docked into the active pocket of Saccharomyces cerevisiae isomaltase and the ligand-enzyme complex interactions were predicted. The low binding energies predicted by the docking software indicated high affinities of the putative active metabolites towards the active site of the enzyme. Finally, this study has successfully developed and validated an OPLS multivariate statistical model for prediction of the α-glucosidase inhibitory activity of new T. scandens leaf samples, given their FT-IR fingerprint spectra. Speed and simplicity made the FT-IR based metabolic fingerprinting a suitable tool for quality control of medicinal plants and phytopharmaceuticals.

CHAPTER TWO

LITERATURE REVIEW

2.1 BACKGROUND OF TETRACERA SCANDENS

2.1.1 Origin, Botany and Morphology

Tetracera scandens (Linn.) Merr. is a medicinal shrub that belongs to family

Dilleniaceae. It is native to Malaysia, Vietnam, Indonesia, the Philippines, India,

Southern China, Thailand, and other Southeast Asian countries. It is usually found in tropical thickets and forest margins. T. scandens has many synonyms including

Tetracera sarmentosa, Tragia scandens L., Delima sarmentosa L., Tetracera hebecarpa (DC.) Boerl, Delima hebecarpa DC and Tetracera monocarpa Blanco

(Umar et al., 2010). Similarly, it has numerous local names, with the most common is the English name, stone leaf. In Malaysia, it is usually called mempelas, akar mempelas, mempelas kasar, mempelas putih, pampan or palas (India Biodiversity

Portal; NParks Flora & Fauna Web; Vietnam Plant Data Center; Umar et al., 2010).

Furthermore, T. scandens is locally known as akosempalay in Indonesia (Muliyah et al., 2018), as malakatmon in the Philippines (Guzman & Padilla, 2017) and as day chieu in Vietnam (Lee et al., 2009).

T. scandens is an evergreen shrub that grows to 2 meters in height. In thickets, the plant usually grows as a woody climber, reaching up to 30 meters. It has simple, petiolate leaves that are arranged in opposite pairs along the stem. Moreover, the leaf blade is characterized by its ovate shape, leathery and scabrous texture, serrated margin and acute apex. The new leaves are reddish-pink in color before turning green upon maturation (NParks Flora & Fauna Web).

Figure 2.1 Tetracera scandens leaves

2.1.2 Taxonomy of T. scandens

Kingdom : Plantae

Order : Dilleniales

Family : Dilleniaceae

Genus : Tetracera

Species : scandens