INDONESIA INTERNATIONAL INSTITUTE FOR LIFE SCIENCES (i3L) AUTHOR’S NAME
STUDENT NUMBER
NAME OF FIELD SUPERVISOR NAME OF SUPERVISOSR AT I3L Rico Alexander Pratama
19010116
Dr.rer.nat Arli Aditya Parikesit, S.Si., M.Si. (Field Supervisor) Junaida Astina, S.Gz, Ph.D (EP Supervisor)
Compounds from Banana (Musa spp.) Peel Targeting PTP1B Protein
INDONESIA INTERNATIONAL INSTITUTE FOR LIFE SCIENCES (i3L)
RESEARCH REPORT
IN SILICO ANTIDIABETIC STUDY OF PHENOLIC COMPOUNDS FROM BANANA (Musa Spp.) PEEL
TARGETING PTP1B PROTEIN
By
Rico Alexander Pratama 19010116
Submitted to
i3L – Indonesia International Institute for Life Sciences School of Life Sciences
in partial fulfilment of the enrichment program for the Bachelor of Science in
Food Science and Nutrition
Research Project Supervisor: Junaida Astina, S.Gz, Ph.D
Research Project Field Supervisor: Dr.rer.nat. Arli Aditya Parikesit, S.Si., M.Si.
Jakarta, Indonesia 2022
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STATEMENT OF ORIGINALITY
submitted to
Indonesia International Institute for Life Sciences (i3L)
I, Rico Alexander Pratama, do herewith declare that the material contained in my thesis entitled:
“In Silico Antidiabetic Study of Phenolic Compounds from Banana (Musa Spp.) Peel Targeting PTP1B Protein”
is original work performed by me under the guidance and advice of my Thesis Advisor, Junaida Astina, S.Gz., Ph.D., and my Project Supervisor, Dr.rer.nat Arli Aditya Parikesit, S.Si, M.Si.
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ABSTRACT
Diabetes mellitus (DM) has been a global issue with a growing prevalence. The current treatments have made an immense progress to the treatment of DM. However, those are contraindicated for drug resistance, acute kidney toxicity, and increased risk of heart attack. On the other hand, banana (Musa spp.) peel is a major fruit waste as it comprises 40% of banana fruit while being a good source of phenolic compounds. Some studies suggested the correlation between phenolic compounds and antidiabetic activity. PTP1B (PDB ID:2NT7) is one of the novel protein targets that has been discovered and studied as a means for antidiabetic treatment. Therefore, this study aimed to screen potential phenolic compounds from banana peel as PTP1B inhibitor for antidiabetic treatment. Forty-three phenolic compounds were used as the ligands and were analyzed using QSAR, molecular docking, ADME-Tox, and molecular dynamic analysis. Eighteen ligands passed QSAR analysis and eight of them had their binding energy lower than standard, with urolithin A and chrysin had the lowest energy.
Both of them passed Lipinski’s Ro5 and had a good HIA no BBB penetration, yet their mutagenicity, carcinogenicity, and skin and eye irritability were still questionable. Molecular dynamics results showed both of them having a stable conformation towards PTP1B. Thus, this study suggests that urolithin A and chrysin are potential PTP1B inhibitor for antidiabetic treatment. Further structural improvement for drug design and experimental study are needed for the implementation of this finding
Keywords: antidiabetic, banana peel, diabetes mellitus, in silico, phenolic compound, PTP1B
ACKNOWLEDGEMENT
This research was done as the Enrichment Program (EP) for the partial fulfilment of my bachelor degree in Food Science and Nutrition at Indonesia International Institute for Life Sciences (i3L). I specifically chose this topic primarily as a tribute to my late father who has passed away due to diabetes mellitus. I really hope that this research would be able to help the scientific and general community through the study and discovery of potential antidiabetic treatment. Therefore, I am deeply grateful that this research was realized and thankful to those who made this research possible upon the completion of this report.
During my study, conducting the research, and writing this report, I found myself in a challenging road. Thankfully, I was not struggling alone and I believe the completion of this study would have not been feasible without the help, guidance, and motivations of those around me. Hence, I would like to convey my deepest gratitude for them. Praise to God the Almighty that made this research possible. My deepest gratitude and appreciation for Mr. Arli Aditya Parikesit as my project field supervisor and Ms. Junaida Astina as my project supervisor who have guided me throughout the journey of this research. Particularly to Mr. Arli for introducing me to INBIO Indonesia and giving me indispensable materials and guidance, and to Ms. Junda for helping and guiding me in the writing upon the completion of this report. I would like to thank Ms. Siti Muslimatun as the head of Food Science and Nutrition study program and Mr. Edwin Hadrian as my academic advisor that have allowed me to take this research project. Finally, I would like to convey my gratitude to my beloved family, peers, and friends who have supported me endlessly in any forms. This research has been a priceless journey and experience for me and it would have not been possible without them
Jakarta, December 21, 2022 Rico Alexander Pratama
TABLE OF CONTENT
CERTIFICATE OF APPROVAL ... ii
COPYRIGHT NOTICE ... iii
STATEMENT OF ORIGINALITY ... iv
ABSTRACT ... v
ACKNOWLEDGEMENT ... vi
TABLE OF CONTENT ... vii
LIST OF FIGURES AND TABLES ... ix
LIST OF ABBREVIATIONS ... x
I. INTRODUCTION ... 1
1.1 Background ... 1
1.2 Objective ... 3
1.3 Scope and Limitation ... 4
1.4 Significance of the Study ... 4
II. LITERATURE REVIEW ... 5
2.1 Glucose Digestion, Absorption, Metabolism, and Homeostasis ... 5
2.2 Diabetes Mellitus ... 10
2.3 Banana ... 24
2.4 In Silico Approaches to Antidiabetic Study ... 28
III. METHODOLOGY ... 33
3.1 Material ... 33
3.2 Methods ... 33
IV. RESULTS ... 38
4.1 QSAR Analysis ... 38
4.2 Molecular Docking ... 39
4.3 Drug-likeness and ADME-Tox Prediction ... 43
4.4 Molecular Dynamic Simulation ... 43
V. DISCUSSION ... 45
5.1 QSAR Analysis ... 45
4.2 Molecular Docking ... 46
4.3 Drug-likeness and ADME-Tox Prediction ... 48
4.4 Molecular Dynamic Simulation ... 52
4.5 Position of Current Study ... 53
4.6 Limitation of the Study ... 54 VI. CONCLUSION ... 55 REFERENCES ... 56
LIST OF FIGURES AND TABLES
Figure 1. Binding interaction calculated in Discovery Studio ... 32
Figure 2. Flow diagram of the methodology ... 34
Figure 3. Interactions between ligands and amino acid residues in PTP1B ... 41
Figure 4. Molecular dynamic simulation results ... 43
Table 1. Diagnosis criteria of prediabetes and diabetes (ADA, 2021) ... 18
Table 2. Classification of antidiabetic drugs, and their mechanism of action and efficacy to lower HbA1c ... 19
Table 3. Amino acid residue involved in allosteric binding of PTP1B ... 24
Table 4. Phenolic compounds found in banana peel ... 27
Table 5. Types of binding interaction and their energy ... 31
Table 6. QSAR Analysis Annotations of the Ligands ... 37
Table 7. Molecular docking results in binding energy between PTP1B and ligands ... 39
Table 8. Summary of the interaction between ligands and PTP1B amino acid residues ... 40
Table 9. Drug-likeness and ADME-Tox prediction ... 42
LIST OF ABBREVIATIONS
ADA = American Diabetes Association
ADME-Tox = Absorption, Distribution, Metabolism, Excretion, and Toxicity BBB = Blood-Brain Barrier
DM = Diabetes Mellitus
FAO = Food and Agriculture Organization HbA1c = Glycosylated Hemoglobin HIA = Human Intestinal Absorption LC50 = 50% Lethal Concentration MAC = Musa acuminata Cavendish MAD = Musa acuminata Ducasse MAL = Musa acuminata Ladyfinger MAR = Musa acuminata Red Dacca MAM = Musa acuminata Monkey
MODY = Maturity-Onset Diabetes of the Young MP = Musa paradisiaca
MW = Molecular Weight PDB = Protein Data Bank
PTP1B = Protein Tyrosine Phosphatase 1B
QSAR = Quantitative Structure–Activity Relationship RMSF = Root-Mean-Square Fluctuation
Ro5 = Rule of Five SDF = Spatial Data File SU = Sulphonylureas
T1DM = Type 1 Diabetes Mellitus T2DM = Type 2 Diabetes Mellitus WHO = World Health Organization
I. INTRODUCTION
1.1. Background
Diabetes mellitus (DM) is classified as a metabolic disease characterized by elevation of blood glucose level. There are some classifications of DM, including Type I DM (T1DM), Type II DM (T2DM), gestational diabetes, neonatal diabetes, maturity-onset diabetes of the young (MODY), and secondary causes diabetes, where T1DM and T2DM are the major contributors of DM (Sapra & Bhandari, 2022) with the prevalence of 9.5% and 6.28%, respectively (Mobasseri et al., 2020; Khan et al., 2020). Generally, DM occurs when there is an absence or reduction of insulin and/or impaired insulin signaling mechanism that leads to hyperglycemia. In T1DM, it involves the autoimmune destruction of beta cells in the pancreatic islet, which leads to depletion and total absence of insulin production (Sapra & Bhandari, 2022). Etiology of T2DM is much more complex where the insulin resistance that comes from multi factors (e.g., obesity, aging, etc.) initially causes the insulin production to increase to counter the hyperglycemia, yet over time its production decreases leading to the onset of T2DM (Goyal & Jialal, 2022).
Banana is a tropical fruit consisting of several species in the genus of Musa in Musaceae family. It is one of the most produced fruits globally with nearly 120 tones of
production in 2020 (FAO, 2022). India is the biggest producers, followed by China, Indonesia, Uganda and Brazil (FAO, 2022). In regards to its huge production size, it also comes with the large amount of fruit waste. Banana peel is one of the major fruit wastes from banana processing. It comprises nearly 40% of the banana fruit by mass (Sharma et al., 2016). This causes huge amount of waste end up in the landfill or incinerator, which would later cause further environmental problems if managed inappropriately, such as greenhouse gasses emission and toxic incomplete combustion generation. On the other side, banana peel provides a good source of fiber, protein, and phytochemicals, including phenolic compounds (Acevedo et al., 2021). Hence, there is a need to find alternative utilization of banana peel.
Phenolic compounds have gained some research interests these recent years, mainly due to their abundance in the plants as secondary metabolites and having high antioxidant capacity as well as health promoting effects. The health properties include their role as antioxidants, anti-cancer, anti-diabetes, inhibiting adipogenesis, decreasing blood pressure, and suppressing inflammatory genes (Gutiérrez-Grijalva et al, 2016). Anti-diabetic properties of phenolic compounds have been studied extensively. In vivo studies in animal models and limited human models have proven the effect of phenolic compounds to decrease hyperglycemia and improve insulin secretion and sensitivity (Aryaeian et al., 2017; Naz et al., 2019). In vitro studies showed that phenolic extracts of persimmon, finger millet, raspberry, cumin, and fig exhibit antidiabetic properties through the inhibition of α-amylase and α-glucosidase, which was further discovered that the suggested phenolic compounds were polyphenols, phenolic acids, anthocyanins, and proanthocyanidins (Asgar, 2012; Wojdyło et al., 2016; Praparatana et al., 2022). Several in silico studies on antidiabetic effect of phenolic compounds in chrysanthemum, guava, blue corn, and black bean have been conducted (Mudunuri et al., 2022; Girón-Rodríguez et al., 2019; Damián-Medina et al., 2020). It was found that phenolic compounds in anthocyanin, flavanol, flavonoid, and flavanol exhibit the highest antidiabetic potential. Although having such extensive research, study about antidiabetic potential of banana peel is still lacking. Some studies have been done using banana peel, however they either lack in the molecular mechanism or not focused on the phenolic compounds (Tongkaew et al., 2022; Genatrika et al., 2018; Navghare & Dhawale, 2017).
In silico screening is of the current interest for drug discovery and design as it is cost-
and time-effective. With regards to the in silico methods, numerous tools have been developed.
Quantitative structure–activity relationship (QSAR) analysis is a ligand-based virtual screening that investigates the relationship between chemical structure and biological activity of a compound (Bustamam et al., 2021). Molecular docking is a technique to study the interaction between two or more molecules (e.g., protein and ligand) reflected by their binding affinity (Roy
et al., 2015). Molecular dynamics is a computational prediction of the movement of atoms in the protein complex in a dynamic model that can predict the stability of the protein-ligand complex (Hollingsworth et al., 2018). Lastly, drug-likeness prediction is a method to assess the chance of a molecule to be a drug candidate related to its molecular descriptors using absorption, distribution, metabolism, excretion, and toxicity (ADME-Tox) prediction (Bickerton, 2012).
Current antidiabetic treatments have made an immense progress. However, those has been contraindicated of drug resistance, acute kidney toxicity, and increased risk of heart attack (Salehi et al., 2019). A new means to target diabetes pathogenesis has been continuously studied, including means to target novel protein targets for diabetes (Kanwal et al., 2022).
Among those, PTP1B has piqued research interest due to its role in the insulin signaling (Eleftheriou et al., 2019). It negatively regulates the insulin action and mice study has shown that the knock-out group had a lower blood glucose level and less insulin resistance (Liu et al., 2022). Thus, its inhibition is a promising target for the treatment of diabetes and was used in this study.
This study aimed to support the scientific research on the molecular mechanism of phenolic compounds found in banana peel on the emerging proteins related to diabetes, especially PTP1B. Besides, this study may also find a new candidate for drug discovery for diabetes treatment.
1.2. Objectives
This study aimed to screen phenolic compounds in banana (Musa spp.) peel as potential treatment for T2DM targeting diabetic protein, especially PTP1B. To achieve this, in silico approach using QSAR analysis, molecular docking, ADME-Tox prediction, and molecular dynamic simulation were performed.
Systematically, the objectives of this present study are,
- To virtually screen phenolic compounds in banana (Musa spp.) peel that have antidiabetic and PTP1B inhibitory property using QSAR analysis.
- To screen and calculate the binding affinity of phenolic compounds in banana (Musa spp.) peel towards PTP1B using molecular docking.
- To predict drug-likeness of phenolic compounds in banana (Musa spp.) using ADME- Tox prediction
- To simulate molecular dynamic and calculate root-mean-square fluctuation (RMSF) to the most potential phenolic compound in banana (Musa spp.) peel targeting PTP1B using molecular dynamic simulations.
1.3. Scope and limitation
This study covers the phenolic compounds in banana (Musa spp.) peel. Other bioactive compounds that present in banana (Musa spp.) peel or other phenolic compounds that present in other plants are out of the scope of this study. This study also uses novel proteins for antidiabetic target.
1.4. Significance of the Study
As a pilot study in exploring the potential mechanism of antidiabetic effect of banana peel, it can be used for new drug discovery and functional food development.
II. LITERATURE REVIEW
2.1. Glucose Digestion, Absorption, Metabolism, and Homeostasis 2.1.1. Glucose Digestion and Absorption
Carbohydrates, especially starch and disaccharides, are digested and hydrolyzed into their monomers prior to absorption. Carbohydrate digestion initially starts in the mouth where salivary α-amylase is secreted. However, this is of relatively insignificant because of its inhibition by acidic condition when the food enters the stomach. On that note, pancreatic α-amylase in the small intestines holds an important function in carbohydrate digestion. α-amylase catalyzes the hydrolysis of α-1,4 bonds, except at the ends of the molecule or beside α-1,6 branches, producing a mixture of glucose, maltose, and maltotriose. Another three enzymes, namely α- glucosidase, sucrase-isomaltase, and β-galactosidase, are also important to complete the carbohydrate digestion in the small intestines. α-glucosidase is responsible for hydrolyzing the terminal α-1,4 bonds of α-amylase digestion products into glucose.
Sucrase-isomaltase catalyzes the hydrolysis α-1,4 bonds of sucrose and maltose into glucose and fructose, as well as the hydrolysis of α-1,6 bonds of oligosaccharide digestion product from α-amylase and isomaltase. Lastly, β-galactosidase hydrolyzes lactose into glucose and galactose. However, β-galactosidase activity is only retained to a certain degree in some population (Mann & Truswell, 2017).
As the digestible carbohydrates have been hydrolyzed into monosaccharides (glucose, fructose, and galactose), they are ready to be absorbed through the intestinal enterocytes. Glucose and galactose are transported from the lumen into enterocytes through a secondary active transport of sodium-glucose co-transporter (SGLT) and subsequently to the bloodstream through facilitated diffusion by glucose transporter 2 (GLUT2). At high concentration in the lumen, transport may also be
carried out into the enterocyte through GLUT2. Fructose, however, moves into the enterocyte and the bloodstream primarily via GLUT5 (Koepsell, 2020).
2.1.2. Metabolism
In metabolism, glucose is the main sugar in the bloodstream as it is the primary source of energy in every organism, including human (Hantzidiamantis & Lappin, 2022). Other monosaccharides will be converted into intermediate molecules that can enter glycolysis pathway, stored into glycogen through glycogenolysis, or being converted into glucose through gluconeogenesis if needed. Considering the importance of glucose, it is crucial to understand the metabolism of glucose. Below are the glucose metabolism pathways according to Szablewski (2017).
1. Energy generation a) Glycolysis
It is the first metabolism pathway of energy generation by glucose.
During glycolysis, glucose is anaerobically converted into three-carbon moieties and subsequently into pyruvate. During the conversion into three-carbon moieties, two ATPs are needed. Then, each of the three- carbon molecule will result two ATPs and two NADH + H+ and converted into pyruvate.
b) Oxidative decarboxylation
After pyruvate is produced, it is then transported into mitochondria and oxidized into Acetyl Coenzyme A (Acetyl CoA). Each pyruvate will produce CO2 and NADH + H+.
c) Krebs cycle
This cycle is also known as “citric acid cycle” or tricarboxylic acid (TCA) cycle”. In the mitochondria, the Acetyl CoA will be oxidized to CO2
through series of reactions. After completely oxidized, each Acetyl CoA will
produce 2 GTP that will later be converted into ATP, 3 NADH, and 2 FADH2. These reduced molecules will be then underwent oxidative phosphorylation.
d) Oxidative phosphorylation
In this step, NADH and FADH2 will undergo oxidative phosphorylation, where their electrons are successively passed down cytochrome chains and release energy. This energy will be used for pump protons through ATP synthase, therefore producing ATPs. Each NADH and FADH2 will produce 3 and 2 ATP molecules, respectively.
2. Glycogenesis
Glycogenesis is a process which glycogen is synthesized, primarily from glucose. Glycogen itself is a glucose storage in animals, structurally similar to that of starch in plants except that it is highly branched. In a condition where blood glucose is high, they will be stored as glycogen, particularly in the liver and skeletal muscle. Molecularly, glucose-6-phosphate is converted into glucose-1-phosphate, then into uracil-diphosphate glucose (UDP). This UDP will then be incorporated into glycogen chain using several enzymes, including glycogenin, glycogen synthase, and glycogen branching enzyme.
3. Glycogenolysis
When the blood sugar level is low, glycogen in the muscle, liver, and kidney can be broken down, whose process is called glycogenolysis. Glycogen is able to be converted back into glucose-6-phosphate. In the liver and kidney, phosphate group in glucose-6-phosphate can be removed by the aid of glucose- 6-phosphatase and release free glucose back into the blood stream. In the muscle cell, however, glucose-6-phosphatase is absent. Thus, glucose-6- phosphate will be used for glycolysis instead.
4. Gluconeogenesis
When glycogen is depleted, glucose needed by the body can be replenished by gluconeogenesis, that is the synthesis of glucose from noncarbohydrate precursors (e.g., lactate, glycerol, pyruvate, and glucogenic amino acids). Gluconeogenesis predominantly occurs in the liver, yet in certain conditions (e.g., starvation and metabolic acidosis), kidney may also produce small amount of glucose. Essentially, gluconeogenesis pathway is the reverse of glycolysis. Depending on the precursors, they can enter the path through different intermediates in the glycolysis pathway, oxidative decarboxylation, or even through Krebs cycle. This anabolic process is a net energy consumer, meaning that it requires ATP to produce glucose.
5. Pentose phosphate pathway
Pentose phosphate pathway is an anabolic process where 6-carbon glucose is converted into 5-carbon sugar for the synthesis of nucleotides.
NADPH is also produced as the reduced products to be used further in other anabolic process. Pentose phosphate pathway generally has two phases:
oxidative and non-oxidative. Oxidative phase converts glucose-6-phosphate into ribose-5-phosphate and also yield NADPH from the oxidation process.
Ribose-5-phosphate is then used as the sugar backbone for nucleotide synthesis.
If the ribose-5-phosphate production exceeds the needs, non-oxidative phase will take place. In this phase, ribose-6- phosphate is rearranged into various length carbon chains, for example fructose-6-phosphate and glyceraldehyde-3- phosphate that can be used for glycolysis.
2.1.3. Homeostasis
Human body is able to regulate and maintain the blood glucose level in the range of 70 – 110 mg/dl. This is achieved by the complex interplay between organs
and hormones that monitor the blood glucose level. The glucose homeostasis is viewed according to their organs according to Lema-Pérez (2021), as the following.
1. Pancreas
There are several types of pancreatic cells, but α- and β-cells are the essentials to regulate blood glucose level. Pancreatic α-cells produce glucagon, where as β-cells produce insulin. Insulin is responsible to decrease blood glucose level by increasing glucose uptake to the cells, inhibit gluconeogenesis, promote glucose uptake and promote glucose storage via glycogenesis, lipogenesis, and protein synthesis. On the other hand, glucagon act antagonistically to insulin, that is it promotes glycogenolysis and gluconeogenesis, as well as inhibiting glycolysis and glycogen synthesis.
2. Liver
Liver is an essential to maintaining glucose homeostasis by acting as glucose storage and taking place the glucose metabolic pathways, namely glycolysis, glycogenesis, glycogenolysis, and gluconeogenesis. Hepatocytes express numerous enzymes that catalyzes these functions, and they are regulated by the hormones to keep the homeostasis.
3. Kidneys and adrenal glands
Kidneys help to maintain glucose balance primarily through glucose uptake, glucose synthesis, and glucose filtration and reabsorption. As mentioned previously that glycolysis, glycogenesis, glycogenolysis, and gluconeogenesis also occur in the kidneys.
In addition to the kidney, adrenal glands located above of the kidneys also affect the glucose homeostasis via release of cortisol. Cortisol is a glucocorticoid hormone that act in the stress mechanism. Cortisol can affect glucose metabolism in which it increases blood glucose level. Cortisol inhibits
insulin and promotes glucagon production in pancreas, increase glycogenolysis and gluconeogenesis, while inhibiting glycogenesis and decrease glucose uptake in liver, kidney, and muscle cells.
4. Gastrointestinal Tract
A key contribution of gastrointestinal tract in glucose homeostasis is the effect of incretin. Incretins are peptide hormones secreted by the small intestines in response to the presence of glucose in the digestion. The two hormones are glucose-dependent inhibitory peptide (GIP) and glucagon-like peptide 1 (GLP-1). Both hormones can promote the secretion of insulin, while at the same time, GLP-1 inhibits glucagon secretion and slows gastric emptying.
2.2. Diabetes Mellitus
Diabetes mellitus (DM) is as a metabolic disease characterized by elevation of blood glucose level resulted from absence or inadequate insulin secretion and/or defect in insulin action. The term diabetes comes from a Greek origin meaning to siphon and mellitus from a Latin origin translates as sweet. The term has been used since 250 – 300 BC and started to spread across ancient Greek, Indian, and Egyptian civilization (Sapra & Bhandari, 2022).
Although the role of pancreas and insulin in glucose homeostasis was discovered a decade ago, research and studies are still ongoing to discover more effective means to tackle this ever-growing issue. Ironically, DM is still one of the most common chronic diseases and placing the 9th cause of death with 1.5 million deaths in 2019 (Vos et al., 2020).
2.2.1. Classification and etiology
Classification of DM is crucial as it implicates the treatment strategies, however, this is still a difficult task as many patients simply do not fit into a single classification (Kharroubi & Darwish, 2015). According to Sapra and Bhandari, there are several subtypes of DM, including Type 1 DM (T1DM) Type 2 DM (T2DM), gestational diabetes, maturity onset of the young (MODY), neonatal diabetes, and other secondary cause
diabetes. Regardless, the main type of DM that are widely accepted are T1DM and T2DM which are the major contributors of DM (Sapra & Bhandari, 2022) with the prevalence of 9.5% and 6.28%, respectively (Mobasseri et al., 2020; Khan et al., 2020).
Generally, DM occurs when there is an absence or reduction of insulin and/or impaired mechanism that leads to hyperglycemia. As to overcome the challenge in classifying and diagnosing DM, several international health organizations, such as American Diabetes Association (ADA) World Health Organization (WHO) came up with the definition and diagnostic method to classify DM. It is important to note that classifying DM remains a challenge up until now since it should facilitate three primary purposes: clinical care, etio-pathology, and epidemiology. WHO in their recently published classification focused more on the clinical care, i.e. to help medical professionals to choose appropriate treatment (WHO, 2019). With that as paradigm, they classify DM into T1DM, T2DM, hybrid forms DM, other types DM, unclassified DM, and hyperglycemia during pregnancy. On the other hand, ADA (2013; 2020) put more emphasis on that the pathogenesis is of the particular importance for an effective treatment. The classification according to ADA is as following.
1. Type 1 Diabetes Mellitus (T1DM)
This type of DM is caused by β-cell destruction that leads to absolute deficiency of insulin. This classification consists of Immune-mediated DM and Idiopathic DM.
a) Immune-mediated DM
This form of DM encompasses about 5% - 10% of the total DM cases and is formerly recognized as insulin-dependent or juvenile-onset DM, resulted from cellular-mediated autoimmune destruction of pancreatic β- cell. While the exact etiology of the autoimmune destruction is not well elucidated, yet it is believed that it has strong association with HLA (human
leukocyte antigen) DR and DQ (Erlich et al., 2008; Lucifer & Weinstock, 2022). Circulating pancreatic antibodies, including islet cell cytoplasmic antibodies (ICA), and antibodies to insulin (IAA), glutamic acid decarboxylase (GAD65), insulinoma-associated 2 (IA-2), as well as zinc transporter-8 (ZnT8) mark the individual at risk or developing T1DM.
The development of immune-mediated DM is divided into 3 stages, in which all the stages require the presence of two or more types of pancreatic antibodies. Stage 1 is asymptomatic with normoglycemia; stage 2 is asymptomatic with dysglycemia (100 – 125 mg/dl glucose) or impaired glucose tolerance (140 – 199 mg/dl 2h-postprandial glucose), or hemoglobin A1c of 5.7% - 6.4%; and stage 3, where the onset of clinical symptoms occurs with hyperglycemia. Additionally, in this type of DM, the rate of pancreatic β-cell destruction varies where it is generally rapid in infants and children and slow in adults. Due to rapid destruction, children may have ketoacidosis in their first onset of clinical symptoms. Others can still maintain modest hyperglycemia and develop ketoacidosis when induced by stress of infection. Some individuals may retain some pancreatic β-cells that prevent them from ketoacidosis. At the later stage, insulin is hardly present. The occurrence of immune-mediated DM is usually happened in childhood, yet it is important to note that it could happen at any age, unlike the traditional paradigm.
As mentioned previously that immune-mediated DM is influenced by genes, namely HLA-DR and HLA-DQ, it is also related to environmental factors, such as obesity. However, the effects of these environmental factors have not been well elucidated. Although the concurrence of obesity and immune-mediated DM is uncommon, but obesity may still be its risk
factor. Furthermore, patients with this type of DM are also prone to other autoimmune disorders,
b) Idiopathic T1DM
Some T1DM have unknown etiology, i.e., the destruction of pancreatic β-cell is not caused by the autoimmunity. This type of DM is a minority of T1DM, with moth of the patients are African or Asian descends.
Hence, even though the etiology is not known, it is believed that idiopathic DM is strongly inherited without association with HLA genes due to lacks of autoimmunity evidence.
2. Type 2 Diabetes Mellitus (T2DM)
This type of DM is previously known as noninsulin-dependent or adult- onset diabetes, and is the most common type of DM which covers 90% – 95% of the cases. In this type, patients have relative insulin deficiency, depending on the cases, some do not need insulin treatment. There are various causes of T2DM, but the autoimmune destruction of pancreatic β-cell is absent.
Some of the possible etiology is obesity as it increases the risk of developing T2DM by 90 folds (Reed et al., 2021). Besides, most of the T2DM patients have overweight or obesity. With regards to obesity, having BMI >30 increases the risk exponentially (Kyrou et al., 2018). However, occurrence of normoweight and even underweight T2DM patients has been recorded. This relates to their increased fat deposition, particularly visceral fat in abdominal region (central obesity) (Reed et al., 2021). All of this is led by some environmental factors that may directly and indirectly affect obesity and T2DM, including life styles, physical activity, and nutrition (Mambiya et al., 2019).
Although environmental factors majorly affect the development of T2DM, genetics may also play an integral role. With the same exposure to the
environment, the susceptibility of the disease still varies. Moreover, the susceptibility if one or both parents have T2DM is drastically higher than otherwise healthy parents. This paradigm shapes the conclusion that hereditary may have an effect on T2DM. However, only a few genes and its variants were able to be identified even after genome wide association studies (Mambiya et al., 2019).
Pathologically speaking, at the initial state, there is a decrease in insulin response (i.e., insulin resistance). The pancreas counters this state by releasing more insulin to maintain glucose homeostasis. Over time, the insulin production is diminished, leading to T2DM (Goyal & Jialal, 2022). As most patients present with obesity and central obesity, the excess adipose tissue itself stimulate the progression of insulin resistance via several inflammatory mechanisms, and worsen the development of T2DM.
3. Other specific types of DM
This type of DM consists of several causes that lead to DM, including genetic defects of β-cell function, genetic defects in insulin action, exocrine pancreas disease-related, endocrinopathy, drug- or chemical-induced, infection, uncommon immune-mediated DM, and other genetic syndromes-associated.
a) genetic defects of β-cell
Monogenetic defects in β-cell function is known to cause some forms of DM. The most known form is maturity-onset diabetes of the young (MODY), which is characterized by onset at early age (<25 years old). In this type, insulin secretion is impaired with virtually no defect in insulin action. Mutations in 6 genetic loci has been identified to give rise to this type of DM, namely MODY 1 – 6. Diabetes diagnosed in the infants under 6-month old has been shown to be atypical for immune mediated
T1DM, and more associated with genetic defects. This neonatal DM could be transient and permanent, where the transient one is due to a defect in ZAC/HYMAI gene imprinting, while the permanent one is due to defect in the gene encoding for the Kir6.2 subunit of β-cell KATP channel.
b) genetic defects in insulin action
Some genetic defects may result in the alteration of structure and function of insulin receptor. This includes acanthosis nigricans (type A insulin resistance, Leprechaunism, Rabson-Mendenhall syndrome, and lipoatrophic DM.
c) exocrine pancreas disease-related
Any diffused injuries to the pancreas can cause diabetes. This includes infection/pancreatitis, trauma, pancreatectomy, and cancer. With the exception of cancer, all other pancreatic injuries should be extensive enough to impair the insulin secretion, while in pancreatic cancer, small portion is enough to induce DM.
d) endocrinopathy
Several hormones (e.g., glucagon, cortisol, growth hormone, epinephrine) suppress insulin actions. Therefore, excess amounts of these hormones caused by diseases altering the hormonal homeostasis will eventually cause DM. Some diseases known to cause this type of DM including acromegaly, Cushing’s syndrome, glucagonoma, pheochromocytoma, hyperthyroidism, somatostatinoma, aldosteronoma, etc.
e) drug- or chemical-induced
Some drugs may impair the insulin secretion that may lead to DM.
However, this type of DM generally occurs with pre-existing insulin
resistance. In this case, the borderline between T2DM and drug- induced DM is unclear as the sequence which one occurs first and its relative importance of β-cell dysfunction and insulin resistance are unknown. Some of the drugs and chemicals that commonly induce DM includes, Vacor, pentamidine, nicotinic acid, glucocorticoid, thyroid hormone, etc.
f) infections
Some viruses have been linked to DM through β-cell destruction. For example, DM can rise from congenital rubella. Although HLA typical in T1DM is present in some cases, in the other cases, no biomarkers for T1DM are detected. Hence the disruption of β-cell is mainly due to the viral infection. Other viruses that have been implicated with DM include coxsackievirus B, cytomegalovirus, mumps, and adenovirus.
g) uncommon immune-mediated DM
Stiff-man and anti-insulin receptor antibodies are the two categories in this type of DM. The former is an autoimmune disorder of central nervous system characterized by stiffness of axial muscles and spasms. In this syndrome, high level of GAD antibodies is detected and a third of the patient will develop DM. The latter disease can cause DM by blocking insulin receptor. However, in some cases the opposite is occurring where they act as insulin agonist. This anti- insulin receptor antibodies can be found occasionally in patients with systemic lupus erythematosus and acanthosis nigricans.
h) other genetic syndromes-associated
Some genetic syndromes have been associated with an increase incidents of DM. This includes Down syndrome, Klinefelter syndrome, Turner syndrome, Wolfram syndrome, Friedreich ataxia, Huntington chorea, etc.
4. Gestational Diabetes Mellitus (GDM)
GDM is defined as glucose intolerance of any degree whose onset is firstly detected during pregnancy. However, this definition holds certain problems in diagnosing. Many cases of GDM emerge from undiagnosed pre-pregnancy DM, hence misdiagnose them into GDM. On top of that, the pre-pregnancy and first trimester is obstructed due to lacking of consensus in diagnostic criteria.
2.2.2. Diagnosis
According to ADA (2020) diagnosing of diabetes can be divided into prediabetes and diabetes. Table 1 summarizes the diagnosis criteria of each prediabetes and diabetes in terms of fasting plasma glucose, 2-hour postprandial plasma glucose using 75-gram oral glucose, and glycated hemoglobin (A1c). Plasma glucose level is the glucose concentration in the circulating blood plasma. This measurement reflect the immediate glucose level in the blood, thus prone to the perturbation and. On the other hand, A1c is much more convenient and stable, but costly and has lower sensitivity.
Table 1. Diagnosis criteria of prediabetes and diabetes (ADA, 2020).
Diagnosis FPG 2-h PG A1c
Prediabetes ≥ 100 mg/dl
(5.6 mmol/l)
≥ 140 mg/dl (7.8 mmol/l)
5.7 – 6.4%
(39 – 47 mmol/mol)
Diabetes ≥ 126 mg/dl
(7.0 mmol/l)
≥ 200 mg/dl (11.1 mmol/l)
≥ 6.5%
(48 mmol/mol)
FPG, fasting plasma glucose; 2-h PG, 2-hour postprandial plasma glucose; A1c, glycated hemoglobin. Fasting is defined as no caloric intake for at least 8 hours. 2-h PG uses glucose load equivalent to 75 grams of anhydrous glucose dissolved in water.
2.2.3. Current treatment
DM is managed through disease management, including diet, exercise, and routine blood glucose monitor, as well as treatments that include administration of insulin and drugs (Sapra & Bhandari, 2022). In T1DM, since its primary cause is the lack of insulin, daily insulin injection remains to be the main treatment (Sapra &
Bhandari, 2022; Lucier & Weinstock, 2022). As in T2DM, many efforts in figuring out the effective treatment have been increased both in pharmacological and in non- pharmacological treatment (Goyal & Jialal, 2022). Diet and exercise remain to be the recommended non-pharmacological treatment. Pharmacologically, current drugs such as metformin and sitagliptin have been successful in impeding T2DM. However, drug resistance, acute kidney toxicity, and increased risk of heart attack resulting from those drugs may hinder their use (Salehi et al., 2019). The following are the common targets for antidiabetic drugs (Kanwal et al., 2022).
1. Insulin secretagogues
Insulin secretagogues target the stimulation of β-cell to produce more insulin. Two major classes in this category: sulphonylureas (SU) and non- sulphonylureas. SU bind to sulphonylurea receptor in β-cell and stimulate the production of insulin. They have good efficacy in reducing hemoglobin A1c, yet their major weakness is the long binding time which may resulted in prolonged insulin release. On the other hand, non-SU secretagogues work by stimulating β- cell in a shorter acting manner.
2. Insulin mimickers and sensitizers
Insulin mimickers are antidiabetic agents to lower plasma glucose by activating glucose transporter in muscle and fat cells, mimicking the effect of insulin. Meanwhile, insulin sensitizers work by enhancing the sensitivity of body cells towards insulin. One of the mechanism of actions of metformin is through
this sensitizing activity by upregulate insulin receptor expression and increase the activity of tyrosine kinase.
3. Starch and sugar blockers
Another target for antidiabetic agents is starch digestion and glucose metabolism. For example, α-glucosidase inhibition will slow down carbohydrate absorption. Dipeptidyl peptidase-4 (DPP-4) inhibition is another example where it will delay gastric emptying, increase insulin, and reduce glucagon secretion.
Sodium-glucose co-transporter-2 (SGLT2) inhibitor is a newly discovered class to prevent reabsorption of glucose in the kidney during excretion.
Table 2. Classification of antidiabetic drugs, and their mechanism of action and efficacy to lower HbA1c
From those three general classifications of target in antidiabetic drugs, furthermore they are divided based on their class (Table 2). The target for monotherapy is to reduce Hb1Ac up to 0.5% - 1.5% (Rhee et al., 2017). When the Hb1Ac is under the recommended value of less than 7%, the main focus of the therapy
Antidiabetic Drugs Mechanism and common use HbA1c reduction, %
Biguanide (metformin) decrease hepatic glucose production
1.0–2.0
Sulfonylurea (glimepiride, glibenclamide, glipizide, gliclazide)
Increase insulin secretion from pancreatic β-cells
1.0–2.0
Meglitinide (nateglinide, mitiglinide, repaglinide)
Increase insulin secretion from pancreatic β-cells, decrease postprandial hyperglycemia
0.5–1.5
PP4 inhibitor (sitagliptin,
gemigliptin, vildagliptin, linagliptin, saxagliptin, teneligliptin, alogliptin, anagliptin)
Increase postprandial incretin (GLP-1, GIP), increase glucose- dependent insulin secretion, decrease postprandial glucagon secretion, decrease postprandial hyperglycemia
0.5–1.0
Thiazolidinedione (pioglitazone, lobeglitazone)
Increase insulin sensitivity, decrease hepatic glucose production
0.5–1.4
SGLT2 inhibitor (dapagliflozin, empagliflozin, ipragliflozin)
Decrease kidney glucose reabsorption, increase glucosuria
0.5–1.0
α-Glucosidase inhibitor Decrease upper intestinal glucose absorption, decrease postprandial hyperglycemia
0.5–1.0
is shifted to postprandial glucose control (Monnier et al., 2003, as cited in Padhi et al., 2020). Metformin is set as the first line treatment, unless otherwise contraindications occur (Padhi et al., 2020). At the situations where monotherapy is unable to control the glycemic parameters in diabetic patients, combination of drugs is recommended.
As of 2022, Food and Drug Administrator (FDA) has approved 59 antidiabetic therapies (mono- and combined-therapies).
Even with a lot of options of antidiabetic therapy, until now, 99 new drugs for T2DM treatment are still ongoing in c.a. 375 clinical trials (Dahlén et al., 2022). From the list of agents being tested for clinical trials, the majority of the targets are of novel targets with 40% of the agents targeting them (Dahlén et al., 2022). The emerging targets for T2DM as described by Kanwal et al. (2022) include 11β-hydroxysteroid dehydrogenase (11β-HSD), glutamine fructose-6-phosphate amido transferase (GFAT), protein tyrosine phosphatase 1B (PTP1B), SLC16A11 gene, nephroblastoma overexpressed (CCN/NOV), forkhead box O1 (FoxO1), free fatty acid receptor 2 and 3 (FFA1/3), epoxyeicosatrienoic acids (EEAs), peroxisome proliferator-activated receptor gamma co-activator alpha (PGC-1α), peroxisome proliferator-activated receptor gamma (PPARγ), glucocorticoid receptor (GR), nuclear factor (erythroid- derived 2)-like 2 (NRF 2), and neprilysin.
Current anti-diabetic drugs, including metformin and sitagliptin, have made immense progress in the treatment of T2DM. However, drug resistance, acute kidney toxicity, and increased risk of heart attack resulting from those drugs may hinder their use (Salehi et al., 2019). Currently, research on the role of diet with respect to the T2DM management has shown that high consumption of fruits and vegetables could have positive effects on the blood glucose level in T2DM (Mellendick et al., 2018). This shift of paradigm towards natural resources in plants is due to the
provision of novel therapeutic agents from their secondary metabolites (e.g., phenolic compounds), which offer a promising alternative (Damian-Medina et al., 2019).
2.2.4. PTP1B Inhibitor
Among novel targets mentioned above, PTP1B has gain research interest as a promising target for antidiabetic treatment (Eleftheriou et al., 2019). Protein phosphatase is a crucial enzyme in regulating protein activities, in which it dephosphorylates phosphate molecules from amino acid residues, that is the opposite action of protein kinase (Powson & Scott, 2005 as cited in Tautz et al., 2013).
Even though phosphorylation of tyrosine residue only accounts for less than 2%, it is a key mechanism in the cell regulation as numerous human diseases emerge from abnormal function of protein tyrosine kinase (PTK) and protein tyrosine phosphatase (PTP) (Tautz et al., 2013). In the human PTP superfamily, PTP1B is widely recognized as an important negative regulator of various signaling cascade, including insulin pathway (Liu et al., 2022). The function of PTP1B is to dephosphorylate insulin receptor and its substrates (Yip et al., 2010 as cited in Tautz et al., 2022). The importance of PTP1B inhibition has been elucidated by animal model, in which PTP1B knock-out mice showed a lower blood glucose and insulin level as opposed to their wild-type counterparts (Haj et al., 2005 as cited in Liu et al., 2022). Therefore, its inhibition is a promising antidiabetic target, especially for T2DM.
PTP1B structure consists of 435 amino acid residues that create 3 domains: N terminal catalytic domain, regulatory domain, and C-terminal domain (Liu et al., 2022). Despite the fact that its full structure has not been solved, truncated versions of human PTP1B have been successfully crystalized (Simoncic et al., 2006 as cited in Liu et al., 2022). These crystalized proteins consist of, 298 and 321 residues, and contain the catalytic N-terminal domain that has 8 α-helices and 12 β-sheets (Liu et al., 2022).
Inhibition of PTP1B is challenging, especially due to highly conserved sequence with the other PTPs, making it difficult to obtain high specificity towards PTP1B. According to Liu et al. (2022), strategies to specifically target PTP1B can be divided into (i) binding to catalytic site and to another secondary site, (ii) binding to its allosteric site, and (iii) inhibit its expression by using antisense oligonucleotides (ASO) to reduce the expression of its mRNA. Inhibition by binding directly to PTP1B, therefore, commences with either first or second strategy, depending on the substrate used.
Catalytic site of PTP1B consist of A site along with other secondary site namely B, C, and D sites (Ala et al., 2006 as cited in Liu et al., 2022). A site is the main catalytic site of PTP1B and it is also the most accessible and polar site across the protein (Liu et al., 2022). A site is 9 Å deep (Phe182 – Cys215) and 10 Å wide (Tyr46 – Gln262). It contains the main catalytic Cys215 and phosphate binding loop on its lower half, and hydrophobic residues (Tyr46, Asp48, Val49, Phe182, Ala217, Ile219, and Gln262) on its upper half. Due to its primary role, A site inhibition has gained interest in the development. However, sole binding to A site will result poor specificity since it is highly conserved in all PTPs. Therefore, the inhibitory effect is usually achieved by means of binding along with one or more secondary sites.
Other active sites exist along with A site, namely B, C, and D sites, respectively based on their subsequent identification. B site is adjacent to A site with larger (13 x 20 Å) but shallower (<4 Å). It is lipophilic noncatalytic binding site, however, binding with this site allows for simultaneously increasing specificity and affinity. It mainly involves Tyr20, Arg24, Ala27, Asp29, Tyr 46, Asp48, Val49, Phe52, Ile219, Arg254, Met258, Gly259, and Gln262 (Ala et al., 2006; Liu et al., 2022).
C site is large, highly exposed to the solvent, extremely flat (except Lys41 and Arg47) and shares Tyr46 and Asp48 with A site. It is located near A site and
also targeted by inhibitors bound to A site. It mainly includes residue Lys36, Pro38, Lys41, Asn42, Arg45, Tyr46, Arg47, Asp48, Leu88, Pro89, and Asn90 (Ala et al., 2006;
Liu et al., 2022).
Last secondary active site is D site, which is a narrow and small pocket near A and C site, and mainly consisted of charged and polar residues. It does not possess biological activity towards insulin signaling cascade, however still has great impact on the potency and specificity. It includes residue Try46, Glu115, Lys120, Asp181, Phe182, Ser216, and Arg221 (Ala et al., 2006; Liu et al., 2022).
Allosteric inhibition of PTP1B can be achieved through different means:
(i) binding to three helices (α3-α6-α7), (ii) binding to four helices (α3-α6-α7-α9), (iii) binding to Cys121, Tyr124, and His214, (iv) 3 sites binding, and (v) binding to Leu71, Lys73, Arg79, Pro206, and Pro210 (Liu et al., 2022). Their involving amino acid residues are listed in Table 3.
Table 3. Amino acid residue involved in allosteric binding of PTP1B Allosteric Inhibiton Involving Residue Reference
α3-α6-α7 Leu192, Asn193, Phe196, Glu200,
Glu276, Phe280, Trp291 Wiesmann et al. (2004)
α3-α6-α7-α9
Site 1: Arg371, Arg373, Val375
Krishnan et al. (2014) Site 2: Val287, Lys292, Leu294,
Leu299, His310, Ile311 Cys121, Tyr124, and
His214 Cys121, Tyr124, and His214 Hansen et al. (2005)
3 Binding Sites
Site 1: Arg79, Phe196, Arg199, Glu200, Pro206, Lys237, Phe280, Ile281
Kumar et al. (2018) Site 2: Arg105, Ser146, Val155,
Arg156, Gln157, Leu172, Lys197, Glu200
Site 3: Gln78, Arg79, Ser80, Ser203, Leu204, Ser205, Pro206, His208, Val211
Leu71, Lys73, Arg79, Pro206, and Pro210
Leu71, Lys73, Arg79, Pro206, and
Pro210 Ottanà et al. (2017)
There are no available approved antidiabetic drugs targeting PTP1B. However, its development has been undertaken recently. Nonetheless, only a few inhibitors have been tested against PTP1B in the clinical trials as of now. Unfortunately, the majority was discontinued because of insufficient efficiency, low specificity, and/or notable side effects. Those inhibitors include ertiprotafib, trodusquemin, DPM-1001, JTT-551, KQ-791, TTP-814, IONIS 113715, IONIS PTP1BRx (Liu et al., 2022).
Currently, research on the role of diet with respect to the T2DM management has shown that high consumption of fruits and vegetables could have positive effects on the blood glucose level in T2DM (Mellendick et al., 2018). This shift of paradigm towards natural resources in plants is due to the provision of novel therapeutic agents from their secondary metabolites (e.g., phenolic compounds), which offer a promising alternative (Damian-Medina et al., 2019).
2.3. Banana
Banana (Musa spp.) is a tropical fruit belonging in the Musaceae family. It is one of the most produced fruits globally with nearly 120 tonnes of production in 2020. Banana is one of the most widely produced, traded, and consumed fruits in the world. There are over 1000 varieties of bananas in the world, which provide essential nutrients to populations in both producing and importing countries (FAO, 2022). Nutrient content in banana varies depending on the cultivar, harvesting age, soil, climate and weather, etc. In average, chemical composition in banana is as follows. 70.6% water, 0.89% ash, 26.86% carbohydrate, 1.67%
protein, and 0.06% fat. Aside from that, bananas also contain 114 kcal energy per 100 gr, making them a great source of energy (Hapsari & Lestari, 2016).
Not only its fruit, banana plants are arguably versatile. Almost every part of banana plants can be used in some aspects. Banana leaves for wrapper and food containers, flowers as vegetables, shoots for textiles, and trunks for paper. Bananas, including their fruit peel, also serve as medicine in the traditional use because not only nutritious in macronutrients, but
also in micronutrients in terms of vitamins and minerals. Aside from that, they also high in phytochemicals, including phenolic compounds, which have been shown to exhibit health promoting benefit (Lai et al., 2017).
With its huge production size, bananas also come with a large amount of fruit waste.
Banana peel is one of the major fruit wastes from banana processing. It comprises nearly 40%
of the banana fruit by mass (Sharma et al., 2016). This causes huge amounts of waste to end up in the landfill or incinerator, which would later cause further environmental problems if managed inappropriately, such as greenhouse gasses emission and toxic incomplete combustion generation. On the other side, banana peel provides a good source of fiber, protein, and phytochemicals, including phenolic compounds (Acevedo et al., 2021). Numerous studies have also found the health benefit of banana peel, notably its antioxidant, antimicrobial, and antidiabetic activity, especially with regards to their fiber and phytochemical contents, including phenolic compounds (Hikal et al., 2022; Wang et al., 2022).
Hence, there is a need to find alternative utilization of banana peel to reduce fruit waste and exploit its benefit.
2.2.1. Phenolic Compounds in Banana Peel
Phenolic compounds have piqued the interest of researchers in recent years, owing to their abundance in plants as secondary metabolites, high antioxidant capacity, and health-promoting effects. Antioxidants, anti-cancer, anti-diabetes, inhibiting adipogenesis, decreasing blood pressure, and suppressing inflammatory genes are among the health benefits (Gutiérrez-Grijalva et al, 2016). The anti-diabetic properties of phenolic compounds have been widely investigated. In vivo studies in animal models and limited human models have shown that phenolic compounds can reduce hyperglycemia while also improving insulin secretion and sensitivity (Aryaeian et al., 2017; Naz et al., 2019). Furthermore, studies of several phenolic acids have suggested an anti-diabetic mechanism via PPAR activation, GLUT4 activation, PI3K
activation, NF-B inhibition, and insulin secretion stimulation. In vitro studies revealed that phenolic extracts of some plants have anti-diabetic properties by inhibiting α- amylase and α-glucosidase, with the suggested phenolic compounds being polyphenols, phenolic acids, anthocyanins, and proanthocyanidins (Asgar, 2012;
Wojdyo et al., 2016; Praparatana et al., 2022).
The phenolic compounds are found relatively high in banana, including its peel (Acevedo et al., 2021). Total phenolic compounds in banana peel varies depending on the cultivar, ranging from 4.95 – 47 mg gallic acid equivalent (GAE)/ g dry matter (Vu et al., 2018). A study that compared total phenolic compounds in different fruit revealed that total phenolic compounds in banana peel was comparably higher than apricot, kiwi, dragon fruit, melon, pear, papaya, peach, pineapple, plum, pomegranate, and passion fruit peel, even though it was still lower than citrus, mango, and apple peel (Suleria et al., 2020). The phenolic compounds detected in banana peel varied across their cultivars and are summarized in Table 4 below.
Table 4. Phenolic compounds found in banana peel. Summarized from Suleria et al. (2020), Bashmil et al. (2021), and Aboul-Enein et al. (2016).
Class Compounds Species Reference
Phenolic Acids Caffeic acid MP Aboul-Enein et al. (2016) Chlorogenic acid MAC Suleria et al. (2020) Ferulic acid MAC Suleria et al. (2020) Gallic acid MAC Suleria et al. (2020) Hydroxybenzoic Acid Protocatechuic acid 4-O-
glucoside
MAC Suleria et al. (2020)
2-Hydroxybenzoic acid MAC Suleria et al. (2020) 3,4-O-Dimethylgallic acid MAL Bashmil et al. (2021) Hydroxycinnamic Acid Caffeoyl glucose MAC Suleria et al. (2020)
Cinnamic acid MP, MAC Aboul-Enein et al.
(2016), Suleria et al.
(2020)
m-coumaric acid MAC Suleria et al. (2020) Hydroxyphenylpropanoic
Acids
3-Hydroxyphenylpropionic acid
MAC Bashmil et al. (2021)
p-Coumaroyl glycolic acid MAC, MAD, MAL, MAR, MP,
Bashmil et al. (2021)
Hydroxyphenylacetic Acids
3,4-Dihydroxyphenylacetic acid
MAC, MAR
Bashmil et al. (2021)
Hydroxycoumarins Urolithin A MAC Bashmil et al. (2021) Scopoletin MP Bashmil et al. (2021) Umbelliferone MAM Bashmil et al. (2021) Anthocyanins Cyanidin 3,5-O-diglucoside MAR Bashmil et al. (2021)
Delphinidin 3-O-(6”-acetyl- galactoside)
MP, MAC, MAM
Bashmil et al. (2021)
Malvidin 3-O-(6”-acetyl- glucoside)
MAR Bashmil et al. (2021)
Flavones Chrysin MP Aboul-Enein et al. (2016)
Gardenin B MAC Suleria et al. (2020) Cirsilineol MAC Suleria et al. (2020) Chrysoeriol 7-O-glucoside MAC Bashmil et al. (2021) 6-Hydroxyluteolin 7-
rhamnoside
MAC Suleria et al. (2020)
Flavanones Hesperetin 3'-O- glucuronide
MAC Suleria et al. (2020)
Naringenin MAC Suleria et al. (2020) Neoeriocitrin MAR Bashmil et al. (2021) Flavonols 3-Methoxysinensetin MAC Suleria et al. (2020)
Isorhamnetin 3-O-glucoside 7-O-rhamnoside
MAC Bashmil et al. (2021)
Myricetin 3-O-galactoside MAC Suleria et al. (2020) Myricetin 3-O-rhamnoside MAC Suleria et al. (2020) Myricetin 3-O-rutinoside MAC Bashmil et al. (2021) Patuletin 3-O-glucosyl-(1-
>6)- [apiosyl(1->2)]- glucoside
MAR Bashmil et al. (2021)
Quercetin 3-O-xylosyl- glucuronide
MAC Bashmil et al. (2021)
Rutin MAC Suleria et al. (2020) Isoflavonoids 5,6,7,3',4'-
Pentahydroxyisoflavone
MAC Suleria et al. (2020)
Isoquercitrin MAC Suleria et al. (2020) Hydroxybenzaldehydes 4-Hydroxybenzaldehyde MAC Suleria et al. (2020) Curcuminoids Demethoxycurcumin MAC Suleria et al. (2020) Furanocoumarins Isopimpinellin MAC Suleria et al. (2020) Phenolic Terpenes Carnosic acid MAC Suleria et al. (2020)
Lignans Schisantherin A MAC Suleria et al. (2020)
Other Polyphenols Salvianolic acid B MAC Suleria et al. (2020) Note: MP: Musa paradisiaca, MAC: Musa acuminata Canvendish, MAD: Musa acuminata Ducasse, MAL:
Musa acuminata Ladyfinger, MAR: Musa acuminata Red Dacca, MAM: Musa acuminata Monkey
2.4. In Silico Approach of Antidiabetic Study
The prevalence of DM keeps increasing while the current treatment is still limited. With that in mind, research all over the world is still trying to find a better, more effective, yet safe means for the treatment of T2DM. In silico screening is of the current interest for drug discovery and design as it is cost- and time-effective. In silico approaches have been shown to be useful in estimating the biological activities of chemical compounds against a target.
Furthermore, it has been utilized to investigate binding affinities toward the target and to predict physicochemical attributes of a wide range of chemical compounds based on their molecular and structural aspects (Jabalia et al., 2021).
Some phenolic compounds in plants have shown to exhibit anti-diabetic effect via in vivo and in vitro (Aryaeian et al., 2017; Naz et al., 2019; Asgar, 2012; Wojdyo et al., 2016;
Praparatana et al., 2022). However, the underlying molecular mechanisms is yet to be elucidated. Furthermore, in silico studies of phytochemicals in plants targeting diabetic protein have been done through molecular docking and molecular dynamic. Study by Khanal et al. (2019) who conducted docking study of phytochemicals in Tinospora towards 11 target proteins, revealed that the effect of magnoflorine was potential and comparable to sitagliptin
and repaglinide. Sharma et al. (2020) docked phytochemicals in Phyllanthus emblica towards GLP1, SGLT2, and PPARγ, and showed that 7 compounds were potential. Mechchate et al.
(2021) specifically docked gentisic acid, a phenolic acid, towards PTP1B, PPARγ, and other six proteins, and their study showed that it moderately interacted with most targets. On the broader spectrum but still focus in phenolic compounds, Damián-Medina et al. (2020) did in silico study of antidiabetic activity of phenolic compounds from blue corn and black bean towards 13 proteins and revealed that four compounds highly corresponded with the proteins, mainly 11βHS, PTP1B, GFAT, PPARγ, and tyrosine kinase insulin receptor. There were lots of other works using in silico approach, indicating its robustness and reliability. To note, many of the phytochemicals indicated by those study were phenolic compounds and their interactions were indicated positive, including towards PTP1B. Nonetheless, in silico study regarding the molecular mechanism of phenolic compounds in banana peels is still lacking. Since the target proteins in the DM and their structures, as well as the ligands (phenolic compounds in banana peels) are well discovered, in silico approach to search potential treatment for T2DM is best used.
In silico study provides a critical and useful step in drug discovery, that is to screen and
identify potential drug candidates through a cost-effective means (Brogi et al., 2020). On the other hand, this rationale will also decrease the unnecessary use of animal model (in vivo) and in vitro studies by limiting the number of compounds to be tested to only those deemed as
potential candidates. Albeit robust and accurate, these in silico approaches are still predictions using computational chemistry and bioinformatics which should be proven experimentally in the next step by in vitro and in vivo studies
2.3.1. QSAR Analysis
QSAR analysis is a bioinformatics approach to quantitatively correlate the chemical structure of a compound with its biological activity or chemical reactivity (Bustamam et al., 2021). One of the online services that uses this approach to screen
