In-Silico Study of Zingiber Officinale for Its Anti-Diabetic Activity

In today’s date Diabetes is the major and most common disease condition prevailing in the     world. According to the recent studies of who About 422 million people worldwide have diabetes, the majority living in low-and middle-income countries, and 1.5 million deaths are directly attributed to diabetes each year. Both the number of cases and the prevalence of diabetes have been steadily increasing over the past few decades. The coexistent drugs have most side effect and many of them have become less responsive to this multifactorial disorder.¹  Use of computational techniques in drug discovery and development process is rapidly gaining in popularity, implementation and appreciation. Different terms are being applied to this area, including computer-aided drug design (CADD), computer-aided molecular modelling (CAMM), rational drug design, In-silico drug design, computer-aided rational drug design.² Term Computer-Aided Drug Discovery and Development (CADDD) will be employed in this overview of the area to cover the entire process.³

In a drug discovery campaign, CADD is usually used for three major purposes:

1. Filter large compound libraries into smaller sets of predicted active compounds that can be tested experimentally; 
2. Guide the optimization of lead compounds, whether to increase its affinity or optimize drug metabolism and pharmacokinetics (DMPK) properties including absorption, distribution, metabolism, excretion, and the potential for toxicity (ADMET); 
3. Design novel compounds, either by "growing" starting molecules one functional group at a time or by piecing together fragments into novel chemotypes.⁸

The National Institute of Health, defines the term ‘In-silico' as a modern word usually used to mean experimentation performed by computer and is related to the more commonly known biological terms in vivo and in vitro.9 In a more recent book, Danchin (2002) provides a quotation that offers a concise and cogent depiction of the potential of  computational tools in chemistry, biology and pharmacology: ‘Informatics is a real aid to discovery when analysing biological functions.10  In-silico virtual screening, or high-throughput virtual screening (HTVS), has yielded an excellent complement to the time-consuming and expensive experimental techniques of high-throughput screening.11 The ability to virtually screen compound libraries to improve enrichment of ligands progressed to experimental validation has provided countless lead compounds.12

Diabetes 

a. Definition

Diabetes mellitus refers to a group of diseases that affect how the body uses blood sugar (glucose). Glucose is an important source of energy for the cells that make up the muscles and tissues. It's also the brain's main source of fuel.¹⁸  In the islets of Langerhans in the pancreas, there are two main subclasses of endocrine cells: insulin-producing beta cells and glucagon secreting alpha cells. Beta and alpha cells are continually changing their levels of hormone secretions based on the glucose environment. Without the balance between insulin and glucagon, the glucose levels become inappropriately skewed. In the case of DM, insulin is either absent and/or has impaired action (insulin resistance), and thus leads to hyperglycemia.¹⁹

b. Types of diabetes mellitus 

 There are a few different types of diabetes:

• Type 1 Diabetes 
• Type 2 Diabetes

Type 1: This type is an auto immune disease in which your immune system attacks and destroys insulin-producing cells in your pancreas for unknown reasons. Up to 10% of people who have diabetes have Type 1. It’s usually diagnosed in children and young adults,  but it can develop at any age.20 Type 1 diabetes mellitus (T1D) is an autoimmune disease that leads to the destruction of insulin-producing pancreatic beta cells. Individuals with T1D require life-long insulin replacement with multiple daily insulin injections daily, insulin pump therapy, or the use of an automated insulin delivery system.21

Type 2: Type 2 diabetes occurs when your body becomes resistant to insulin, and sugar builds up in your blood. It’s the most common type—about of people living with diabetes have type 2.22 People who are middle-aged or older are most likely to get this kind of diabetes. It used to be called adult-onset diabetes or diabetes mellitus. But type 2 diabetes also affects kids and teens, mainly because of childhood obesity.23

c. Symptoms24

Symptoms of both type one and type two are common and given below:

• Urinate a lot, often at night
• Are very thirsty
• Lose weight without trying
• Are very hungry
• Have blurry vision
• Feel very tired
• Have sores that heal slowly
• Have more infections than usual
• Have numb hands or feet

D. Risk Factors²⁵

Risk factors responsible for the occurrence of diabetes are given below:

Type 1 Diabetes:

• Weak Immunity 
• Family History Type 2 Diabetes:
• Lifestyle Factor 
• Genes 
• Family History 
• Overweight (Obesity)
• Are Physically Less Active 
• Insulin Resistance 
• Impaired Glucose Tolerance

Literature Review:

A) Al-Snafi, A. E, (2024) : Zingiber officinale (Ginger) a genus classified within the family “Zingiberaceae”. This family is known because of its high content of dietary and biochemical components, especially the impacts against diabetes mellitus and inflammation. Ginger, perform the antihyperglycemic activity by causing restoration for β-cells in pancreas, providing protection for the morphology of β-cells, amelioration of the pathological injuries of kidneys, control of the concentration of pro-inflammatory cytokines, regulation of hepatic gene expression of enzymes associated with glucose metabolism, inhibition oxygen free radicles caused during glucose metabolism, translocation of GLUT4, inhibition of liver glucose production, stimulation of pancreatic insulin release, inhibition of carbohydrate metabolizing enzymes, high production of hepatic glycogen through the improvement of glycogen synthesis in the liver, and decreasing insulin resistance, and peripheral consumption of blood sugar as well as its antioxidant activity.

B) Zhijun Yang, (2023): Ginger (Zingiber officinale) belongs to the Zingiberaceae family of flowering plants, which originated in South-East Asia and has long been a popular culinary ingredient. It has been demonstrated that ginger contains a variety of nutrients, such as carbohydrates, proteins, lipids, minerals and vitamins, as well as healthpromoting phytochemicals, mainly phenolic compounds and ginger essential oil. In addition, ginger has excellent antioxidant, antibacterial, and many other biological activities. Therefore, ginger has potential applications in functional food development, aquatic products and meat preservation, and food packaging. However, the full utilization of ginger's benefits and its incorporation into food products remain largely unexplored. Therefore, this review provides a comprehensive summary of the nutritional components, phytochemical compositions, biological properties, and potential applications of ginger in the food industry.28

C) Dos Santos FAR, et al (2022) : This author said that, the antidiabetic, antiglycation, and antioxidant potentials of ethanolic extract of seeds of Brazilian Zingiber Ofiicinale fruits (PESE), a major by-product of the juice industry, and piceatannol (PIC), one of the main phytochemicals of PESE. several biological properties of passion fruits have been demonstrated using in vitro and in vivo studies, such as antioxidant, analgesic, antidepressant, sedative and anxiolytic-like, anti-inflammatory, antimicrobial, antihypertensive, hepatoprotective, and antidiabetic (peel, juice, and seed) activities. The in vitro evidence of the inhibition of alpha-amylase, alpha-glucosidase, and DPP-4 enzymes as well as antioxidant and antiglycation activities, warrants further investigation of the antidiabetic potential of Z.officinale seeds and PIC.17

D) Antoine Daina A, et al (2017) : To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which inhouse proficient methods such as the, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the loginfree website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.

E) Akram T Kharroubi, et al. (2015): This author said that, the prevalence of diabetes mellitus in diverse regions is examined, highlighting its epidemic nature. The Middle East and North Africa stand out with the highest diabetes prevalence in adults while the Western Pacific region leads in the sheer number of diagnosed adults and hosts countries with the highest diabetes prevalence. This comprehensive review delves into various diabetes classes—type 1, type 2, gestational diabetes, and other types—comparing them based on diagnostic criteria, aetiology, and genetics. Recent years have witnessed a surge in molecular genetics exploration, with a focus on mutations and single nucleotide polymorphisms in genes associated with glucose metabolism, pancreatic cell development, control, and function.27

F) V.K. Vyas, et al. (2012) : This author said that, in order to understand the binding specificity of the ligand with protein the knowledge of 3D structure is very important. With increase in the known protein structure, and the modelling software, homology modelling is rapidly becoming the method of choice for obtaining 3D coordinates of protein.  Homology modelling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins.  In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions.29

G) Xuan-Yu, et al. (June 2011) : This author said that, molecular docking plays a growing role in drug discovery. the overview investigates into various molecular docking methods, examining their evolution and usage in drug discovery. It outlines essential theories, about sampling processes and scoring functions. The discourse extends to the distinctions and effectiveness of existing docking software. Notably, accommodating flexibility in receptor structures, particularly incorporating backbone flexibility, poses a hurdle for current docking methods. A promising resolve surfaces in the form of the recently introduced Local Move Monte Carlo (LMMC) approach, tailored to address challenges in flexible receptor docking.26

H) I. M. Kapetanovic (30 January 2008) : The author highlighted the increasing use of computational power in drug discovery to expedite hit identification, streamline development processes, optimize drug properties, and address safety concerns. Term  Computer-Aided Drug Discovery and Development (CADDD) will be employed in this overview of the area to cover the entire process. Computational strategies include ligandbased and structure-based drug design, along with quantitative structure-activity relationships. Regulatory agencies and the pharmaceutical industry are collaborating to advance computational tools, aiming to improve efficiency, reduce animal testing, and enhance predictability in drug development. The trajectory suggests ongoing growth in Computer-Aided Drug Discovery and Development (CADDD) as technology progresses.2

Aim and objective

The Aim of this research work is to perform In-silico study on phytoconstituent of Zingiber officinale for antidiabetic activity and compare its results with standard drug (Metformin).

Objective of this study:

Evaluation of phytoconstituent on basis of ADMET study for selection of best suited constituent 

• To Perform Homology modelling to modify protein 
• To Perform Molecular docking on selected constituents
• To Study 2D- interaction between ligand and modified protein 
• To Compare the constituent with the standard drug

Plan of Work

• Evaluation of Phytoconstituents on Basis of Drug likeness, ADME and Toxicity 
• Preparation of Ligand 
• Preparation of Receptor
• Molecular Docking 
• Visualization Study
• Results and Discussion 
• Conclusion

MATERIALS AND METHODS

Ginger (Zingiber officinale Roscoe), which belongs to the Zingiberaceae family and the Zingiber genus, has been commonly consumed as a spice and an herbal medicine for a long tim. Ginger root is used to attenuate and treat several common diseases, such as headaches, colds, nausea, and emesis. Many bioactive compounds in ginger have been identified, such as phenolic and terpene compounds. The phenolic compounds are mainly gingerols, shogaols, and paradols, which account for the various bioactivities of ginger. In recent years, ginger has been found to possess biological activities, such as antioxidant, anti-inflammatory, antimicrobial, and anticancer activities.30 In addition, accumulating studies have demonstrated that ginger possesses the potential to prevent and manage several diseases, such as neurodegenerative diseases, cardiovascular diseases, obesity, diabetes mellitus, chemotherapy-induced nausea and  emesis, and respiratory disorders. In this review, we focus on the bioactive compounds and bioactivities of ginger, and we pay special attention to its mechanisms of action.31

Taxonomical Name*               

Kingdom: Plantae

Family: Zingiberaceae

Division: Magnoliophyta

Class: Liliopsida 

Order:  Ingiberales

Genus: Zingiber

Common Name: Ginger³²

        <a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250423171213-12.png" target="_blank">
            <img alt="Zingiber Ofiicinale Plant with Flower and Fruit.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250423171213-12.png" width="150">
        </a>
Fig No.3. Zingiber Ofiicinale Plant with Flower and Fruit

Diabetes mellitus is known as a severe metabolic disorder caused by insulin deficiency and/or insulin resistance, resulting in an abnormal increase in blood glucose. Prolonged hyperglycemia could accelerate protein glycation and the formation of advanced glycation end products (AGEs). Many research works have evaluated the antidiabetic effect of ginger and its major active constituents.³³  Additionally, 6-gingerol reduced the levels of plasma glucose and insulin in mice with high-fat diet-induced obesity. Nε-carboxymethyl-lysine (CML), a marker of AGEs, was decreased by 6-gingerol through Nrf2 activation.³⁴ In 3T3-L1 adipocytes and C2C12 myotubes, 6-paradol and 6-shogaol promoted glucose utilization by increasing AMPK phosphorylation. In addition, in a mouse model fed a high-fat diet, 6-paradol significantly reduced the level of blood glucose. In another study, 6-gingerol facilitated glucose-stimulated insulin secretion and ameliorated glucose tolerance in type 2 diabetic mice by increasing glucagon-like peptide 1 (GLP-1). Besides, 6-gingerol treatment activated glycogen synthase 1 and increased cell membrane presentation of glucose transporter type 4 (GLUT4), which increased glycogen storage in skeletal muscle. Furthermore, the consumption of ginger could reduce the levels of fasting plasma glucose, glycated hemoglobin A (HbA1C), insulin, TG, and TC in patients with type 2 diabetes mellitus (DM2). Moreover, ginger extract treatment improved insulin sensitivity in rats with metabolic syndrome, which might have been relevant to the energy metabolism improvement induced by 6-gingerol.³⁵

 a. Chemical Constituents   

9 constituents were selected from the list of 110 chemical constituent present in Zingiber officinale.

Software and Websites

List of software and websites that are used in study is given below in Table.2:

a. Evaluation of Ligand on basis of Drug Likeness, ADME and Toxicity study i. Drug Likeness study 

Drug-likeness test of phytochemicals based on the Lipinski rule using SwissADME³⁶ software.³⁷

The Lipinski rule of five state that:

• Molecular weight less than equal to 500
• Log p should be less than 5
• H bond donor and acceptor should be less than equal to 5
• TPSA should be less than 140 angstroms
• No of rotatable bonds should be less than 10

To perform In-silico studies, of the 20 phytoconstituents retrieved from Zingiber Ofiicinale should satisfy drug likeness test (determines whether particular compound could be used as drug).

ADME Study

ADME Study is carried out using the software Swissadme. It is the quick, accurate and easy to use software used for absorption, distribution, metabolism, and excretion prediction. It predicts pharmaceutically applicable properties of organic molecules individually, or in groups.³⁸ Where GI Absorption states the absorption of drug, BBB Permeation states the distribution of drug, CYP1A2 inhibitor states the protein binding, and bioavailability states the excretion of drug. All other descriptors are also stated on the software.

Toxicity study

 Toxicity study is performed to predict the possible toxicities present in the compounds based on their similarity with other compounds. For the assessment of toxicity Protox 2³⁹ software is used this software screens the compound on various toxicities such as organ toxicity, carcinogenicity, immunotoxicity, mutagenicity, cytotoxicity etc. and give the results with accuracy of 99% individually. It also provides predicted toxicity score and predicted lethal dose (LD50).⁴⁰

Preparation of ligand:

After the ADMET study, 10 constituents were selected from a large number of phytoconstituents present in passion fruit. Smiles of the phytoconstituents were retrieved from PubChem^.41 The 2D structure of the constituents were sketched on Chemsketch⁴² with the help of smiles. The structures were saved in the working folder as a mol file Using control all ligands were loaded into the Avogadro⁴³ software. And, finally converting them into the PDB format and saving them in the working folder.⁴⁴

Preparation of receptor:

Firstly, gene sequence of the required protein was retrieved from the website [NCBI national library of medicine (gene)]⁴⁵ which was further used as the template for modelling of the required protein. After that gene sequence was inputted in the required targeted sequence and then validate the provided gene sequence after that search for template and select the best suited template. Swiss Model⁴⁶ is used for homology modelling. The template selected is the enzyme with PDB id 6B1U, 6B1U is the AMPK (AMswPactivated protein kinase) enzyme and build the model whose 3d structure is available on pdb⁴⁷. The AMP-activated protein kinase (AMPK) αβγ heterotrimer regulates cellular energy homeostasis with tissue-specific isoform distribution. After the protein model is build that file of protein is opened in the discovery studio biovia⁴⁸ and the file is converted and saved in the working folder as mol file.⁴⁹

        <a href="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250423171213-11.png" target="_blank">
            <img alt="structure of AMPK enzyme.png" height="150" src="https://www.ijpsjournal.com/uploads/createUrl/createUrl-20250423171213-11.png" width="150">
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Fig No.5: 3D-structure of AMPK enzyme(6BIU)

Molecular Docking

Pyrx⁵⁰ was used to identify binding energy between ligand and macromolecule. Pyrx is the software that uses autodock vina as a default software for molecular docking. As per the inverse relation of energy and stability, the conformation with greater binding energy is less stable. The Default parameters of the software program have been applied similar to the protocol followed elsewhere. The grid box can generally be minimised or maximised as per need and presence of active pocket but in this research the grid box set on default.  As the active pocket was screened Briefly, Lamarckian Genetic Algorithm (LGA) with default atomic salvation parameters 126 Å (x, y, and z) grid box in ratio of (60:60:60) for scoring energy was set at coordinates as X =33.442; Y = 5.8935 and Z = -48.4118 with 0.375 angstroms grid points spacing as of centre.⁵¹

2d-interaction was studied using Discovery Studio Biovia software. 2d-interaction states the number of amino acids present, types of bond present between macromolecule and ligand, etc. The out files of the ligands were retrieved from the file extensions of the software. After the out files are retrieved the .PDB file of the macromolecule is opened in the biovia software along with all the out files. Then the macromolecule is copied and pasted on the out file of ligand and 2d-interaction was studied.⁵²

7. RESULTS AND DISCUSSION 

a. Drug Likeness and ADME Study:

Drug Likeness Study: The results of Drug Likeness study given in table.3.

Based on the drug likeness study it is observed that most of the constituents of Zingiber Officinale follows Lipinski rule. As it is important for all the constituents to follow Lipinski rule, for its formulation or synthesis. All the other constituents follow Lipinski rule except for certain violations. The constituents having one violation are namely, alpha Curcumene, beta Bisabolene, alpha sisquiphellandrene, farnesene.

II. ADME Study: Results of ADME study are given below in Table.4:

Based on the results of ADME study it is observed that most of the constituents of Zingiber Ofiicinale have significant with average bioavailability of 0.55 and as most of the constituents have higher absorption but some of them have lower absorption. Where as certain constituents also cross BBB permeability.

b. Toxicity study: The Results of toxicity study is given below in Table.5: 

Based on the results of toxicity study most of the drugs shows class, 4 and 5 toxicity which are safe classes but some of the constituents show toxicity of class 3 toxicity which is not suitable for administration as a formulation or as drug, because instead of giving expected results they are producing their own toxicity and are harmful for human body.   As per the combined results of drug likeness, ADME and toxicity study out of all the 9 constituents selected of Zingiber Ofiicinale all constituent follow Lipinski rule and some constituents have higher amount of toxicity present in them.   After the drug likeness, ADME and toxicity study the phytoconstituents competent are given in table 6:

Table.6. List of Chemical Constituents

Molecular Docking: Results of molecular docking are stated below:

The docking of the macromolecule modelled AMPK enzyme with chemical constituents of Zingiber Ofiicinale has been done. The table 7 shows the Binding Energy of 10 compounds including the standard. In-silico studies revealed that all the synthesized molecules show good binding affinity toward the target protein ranging from -5.7- -4.1.

d. Visualization Study

The active pocket was considered to be the site where modelled 6B1U complexes with active chemical constituents of Zingiber Ofiicinale. The 2d-interaction is generally based on the binding between ligand and macromolecule. and the bonds between ligand and macromolecule.