Potential of Aloin B Compound and its Derivatives as Type-2 Antidiabetic

: Type-2 diabetes mellitus occurs due to suboptimal insulin function (insulin resistance) or decreased insulin function. Type-2 diabetes mellitus treatment is chronic and lifelong. One of the treatments is the use of insulin and oral anti-diabetic drugs. This treatment requires a long period of time and can cause unwanted side effects. Therefore, alternative treatments are needed with minimal side effects by utilizing herbal plants containing Aloin B compounds because they have been proven to be used as antidiabetic agents. These compounds can be found from the Aloe Vera plant (Aloe Vera l.). The aim of this study was to find compounds derived from Aloin B compounds that have the most potential as anti-diabetic type-2 by inhibiting the pancreatic α-amylase enzyme (code: 1B2Y) in breaking down starch in the body. The certainty of the presence of the compound Aloin B in the flesh of the aloe plant was confirmed by the LC-MS test. This research was conducted using the Quantitative Structure-Activity Relationship (QSAR) and Molecular Bonding method. The results showed that the ID S22 compound with the IUPAC name (S)-10-amino-1,2,8-trihydroxy-6-(hydroxymethyl) -10- ((2R,3R,4S,5S,6R) -2,3, 4,5-tetrahydroxy–6(hydroxymethyl) tetrahydro-2H-piran-2-yl) anthracene-9(10H)- one is the most potent compound from Aloin B derivatives as a type-2 antidiabetic agent in the mechanism of inhibiting α-enzyme action pancreatic amylase, based on the value of R2 = 0.980, the PRESS value of the compound was 0.0004, the binding energy value was -7.07 kcal/mol, the inhibition constant was 6.58 uM and the formation of hydrogen bonds between the compound and the amino acid residues aspirin, glycine, threonine and arginine.


INTRODUCTION
Diabetes mellitus is a deadly disease. This is due to high levels of glucose in the blood as a result of the inability of the beta cells (β cells) of the pancreas to produce sufficient insulin or the use of insulin carried out by cells in the body is not effective (Berbudi et al., 2020). Around 425 million adults suffer from diabetes mellitus globally in 2017 (Tekulu et al., 2019). In Indonesia, according to the databox website, Indonesia ranks 5th with the most cases of diabetes mellitus in the world (Wiratama & Pradnya, 2022). From year to year the number of people with diabetes mellitus is increasing. Referring to data sources from the International Diabetes Federation, there were 10 million people with diabetes in 2015 in Indonesia, by 2040 it is predicted that the number of Indonesian citizens who are infected with diabetes mellitus will increase by 16.2 million Indonesians (Hana, 2020).
In general, diabetes mellitus is divided into two, namely diabetes mellitus type-1 and diabetes mellitus type-2. Type 1 diabetes mellitus occurs when an autoimmune reaction occurs with pancreatic beta cell proteins. In this type of pancreatic beta cells have been destroyed by an autoimmune process, so that insulin cannot be produced. Type-2 diabetes mellitus occurs because insulin function is not optimal (insulin resistance) or loss of insulin function. Glucose that enters the body cannot be converted into glycogen and triglycerides. Not optimal use of insulin results in high blood glucose. If not treated immediately, the pancreatic beta cells will be damaged (Organization, 2019). 9 The function of insulin itself is to regulate or control glucose that enters the body by giving signals to muscle, fat and liver cells to process glucose into glycogen and triglycerides. someone controls it (Organization, 2019).
Treatment of diabetes mellitus is chronic and lifelong. One of the treatments is the use of insulin and oral anti-diabetic drugs. This treatment requires a long period of time and can cause unwanted side effects. Therefore it is necessary to provide drugs that are effective and have minimal side effects and do not require large costs in their use or consumption. In the studies that have been carried out, several medicinal plants have been reported as having potential as anti-diabetic agents (Etxeberria et al., 2012) (Naveen & Baskaran, 2018). These plants work as antidiabetics through various mechanisms, such as increasing the quality and quantity of pancreatic β cells, accelerating the regeneration of pancreatic β cells, improving insulin action, becoming inhibitors of α-amylase and α-glucosidase enzymes and others (Anugrahini & Wahyuni, 2021 ) (Alam et al., 2019). One of the natural ingredients or plants that are widely used as traditional medicine and is believed to be able to treat various kinds of diseases is the aloe vera plant (Aloe vera.L) (Atanu et al., 2018) (Ananda & Zuhrotun, 2017).
In previous studies, it has been proven that aloe vera plants, especially the flesh of aloe vera plants, contain secondary metabolites such as flavonoids, alkaloids, quinones, saponins, tannins, glycosides, anthraquinones, and terpenoids. These compounds have pharmacological activities as anti-inflammatory, immunomodulatory, anti-bacterial, anti-fungal, antiviral, and anti-diabetic (Etxeberria et al., 2012) (Sarker & Grift, 2021) (Alejo et al., 2019). The flesh of the aloe vera plant has high properties because the ethanol extract of this plant contains glycoside and polyketide group compounds which can provide anti-diabetic effects through inhibition of the α-amylase and 10 α-glucosidase enzymes (Anugrahini & Wahyuni, 2021). Aloin B contained in the flesh extract of the aloe vera plant showed the highest inhibitory activity in the performance of the α-amylase enzyme with an IC50 of 0.34 mg/mL which was higher than acarbose (0.54 mg/mL) the positive control, indicating that the activity of the Aloe extract was related to with aloin and other anthrone compounds (Zhong et al., 2022).
Based on this research, researchers are interested in testing the antidiabetic activity of compounds modified from the aloin B structure which is a compound from the glycoside group and more precisely the anthraquinone glycoside group which has been known to have antidiabetic activity (Anugrahini & Wahyuni, 2021; Saleh Al-Sowayan & Mohammad AL-Sallali, 2023; Zhong et al., 2022). It is hoped that by modifying the structure of the aloin B compound, a compound that has greater potential in inhibiting the pancreatic α-amylase enzyme is obtained. To date, no research has been reported on the structure and activity quantitative relationship (QSAR) and molecular anchorage studies related to inhib

MATERIALS DAN METHODS Materials
The material used is the compound Aloin B which is an anthraquinone glycoside compound obtained from 100 gram extract of the flesh of the aloe vera plant (Aloe vera.L) with 200 mL of 96% ethanol solution modeled by Avogadro and the 3D molecular structure of 1B2Y protein as a protein visualization was obtained from the Protein Data Base (PDB).
The tools used were digital scales, maceration bottles, vials, Rotary Evaporator, LC-MS, in silico assessment using a laptop set with Intel Core i3-233oM specifications, 500 GB, 2 GB RAM. The program used for molecular docking is Avogadro, and Autodock Tools 1.5.6. Calculation of steric, hydrophobic, and electronic computational descriptors using NWchem, Swiss ADME, Molinsipration, and Pkcsm programs. Compound visualization using the Discovery Studio 2019 Client program. The results of the computational descriptor multilinear regression analysis using SPSS 16.0 with the backward method.

A. Determination of Aloin B Compounds in Aloe Vera Plants
Determination of the Aloin B compound in the flesh of the aloe vera plant was carried out by analyzing the results of the ethanol extract of the aloe vera plant with LC-MS. Aloe vera flesh was cleaned of skin and mucus with distilled water, then weighed with a scale of 100 grams. The aloe vera meat that has been weighed is then put into the maceration bottle. Enter the 96% ethanol solution 200 mL which has been measured first using a measuring cup and then put it into the maceration bottle which already contains the flesh of the aloe vera plant. The maceration bottle was closed and then left to stand for 3 days and 2 nights and stirring was carried out as often as possible in the maceration process.

B. Selection of Derivative Aloin B Compound
The compound selection process was carried out by following the Lipinski rules and LD 50 category 3. The compounds obtained must comply with all Lipinski rules and may only violate one of the four existing rules. The rule is that the partition coefficient of okatanol-water (log P) is in the range of -0.4 to 5.6. (if more than 5 then the compound will be more hydrophobic) Not more than 5 hydrogen bond donors. (if more then it will be difficult to bond with the target compound) Not more than 10 hydrogen bond acceptors. (if more then it will be difficult to bind to the target compound). The molecular weight is not more than 500 mg/mol. (if more then the compound will be difficult to penetrate the wall of the target compound). LD 50 category 3 is used because it is a slightly toxic category.

C. Quantitative Structure-Activity Relationship
Modeling of Aloin B derivatives makes a 3-dimensional structure of Aloin B derivatives with good shapes and configurations using Avogadro. Optimizing the geometry of all Aloin B derived compound structures that have been made previously by running geometry optimization using Avogadro. Electronic descriptor data was collected using NWchem with the B3LYP Theory DFT method and Cosmo Solvation with a base set of 6-31G* to obtain HOMO descriptor values, LUMO, ∆LUMO-HOMO, Dipole Moments, and Hydration Energy, steric descriptor data in the form of MR, Volume, Surface Area, and Topology Polar Surface Area, and hydrophobic descriptor data in the form of partition coefficient (Log P) were obtained using Swiss ADME, Molinsipration, and Pkcsm programs. The descriptor data that has been collected is then performed a multilinear regression statistical analysis using the backword method in the SPSS program to determine the physicochemical properties of each Aloin B derivative compound. This analysis is performed to obtain the theoretical activity of the aloin B compound and its derivatives using the best QSAR equation D. Molecular Docking Preparation and Optimization of Pancreatic Alpha Amylase Enzyme Protein. Download the pancreatic α-amylase enzyme protein at https://www.ncbi.nlm.nih.gov/protein and then process the separation of water molecules and non-standard ligands or residues using the Discovery Studio 2019 Client. Then the macromolecule optimization process was carried out using the Autodock Tools 1.5.6 software. Optimization includes adding hydrogen atoms and setting grid box parameters to determine the location of the ligand molecule attachment. Macromolecular optimization results are saved in .pdbqt file format. Preparation and Optimization of Ligand compounds (Test Compounds) A 3-dimensional structure of aloin B derivative compounds was created using Avogadro and saved in the .pdb file format and then optimized using Autodock Tools and saved in the .pdbqt format. Molecular anchoring using Autodock Tools software The 3D structure of aloin B derivatives with target proteins was calculated for their inhibition and binding energy values using Autogrid and Autodock via Ubuntu 20.04. Analysis and Visualization of Molecular Docking Calculation results of molecular docking can be seen from the output in .dlg file format. Selection of results by choosing a ligand that has a low binding energy. Visualization of the position of each ligand with the target protein using the Discovery Studio 2019 Client program

A. Determination of Aloin B Compounds in Aloe Vera Plants
The LC-MS test on the aloe vera plant extract that has been carried out has the following graphical results.

B. Selection of Derivative Aloin B Compound
The presence of Aloin B compound was identified by testing the results of ethanol extract of 100 g of aloe vera flesh with 200 ml of 96% ethanol solution using LC-MS. The results of the analysis of the ethanol extract of aloe vera meat found 89 compounds, and the Aloin B compound was included. The results of the analysis test also found several derivatives of the Aloin B compound. The derived compounds that were successfully identified in the LC-MS test and the results of modified compounds for replacing the active group as derivatives of the Aloin B compound will be used in the selection of drug compounds as Type-2 Antidiabetics based on The Lipinski rule was carried out using the Swiss ADME and Lethal Dose 50 (LD50) program using the Pkcsm program (Khadse et al., 2019;Zhong et al., 2022). The following is a table of compound data that was successfully obtained. From the selection results based on the Lipinski rule where the partition coefficient of okatanol-water (log P) is in the range of -0.4 to 5.6. (more than 5 then the compound will be more hydrophobic) Not more than 5 hydrogen bond donors. (if more then it will be difficult to bind to the target compound) Not more than 10 hydrogen bond acceptors. (if more then it will be difficult to bind to the target compound). The molecular weight is not more than 500 mg/mol. (if more then the compound will be difficult to penetrate the wall of the target compound). Of the four rules above, it has been agreed if a compound can be used as a medicinal ingredient by not violating more than one of these rules, and the LD 50 category 3 (Ukwenya et al., 2021). Of the 40 compounds obtained, the compounds with IDs S1 to S6 were derivatives of the Aloin B compound obtained from the test results of the aloe vera flesh extract using LC-MS, while the compounds with IDs S7 to S40 were derived compounds obtained from the modification of functional groups by placing efficient. Substitution of active groups uses hydroxy functional groups (OH) which are capable of forming hydrogen bonds with specific amino acids at the active sites of enzymes (Hilma et al., 2015; Girsang, 2020), nitrogen groups (N) as competitive inhibitors by blocking enzymatic reactions (Sofawati , 2012), and the carbonyl group (C=O) is also an inhibitor of enzymatic reactions for α-amylase enzymes (Gaspersz and Sohilait, 2019). Compounds that meet the requirements for further tests are compounds with ID S3, S8, S11, S14, S16, S17, S19, S22, S24, S28, S29, S30, S31, S33, S34. The next test was to find the descriptor values of the 15 compounds that met the requirements using the NWchem, Swiss ADME, Molinsipration, and Pkcsm programs.

C. Model analysis based on the Quantitative Structure-Activity Relationship method
Compounds that have been selected in the previous procedure were then analyzed for electronic descriptor values using NWchem with the B3LYP Theory DFT method and Cosmo Solvation with a 6-31G* basis set to obtain the descriptor values of HOMO, LUMO, ∆LUMO-HOMO, Dipole Moment, and Hydration Energy. Steric descriptor data in the form of MR, Volume, Surface Area, and Topology Polar Surface Area, and hydrophobic descriptor data in the form of partition coefficient (Log P) were obtained using the Swiss ADME, Molinsipration, and Pkcsm programs. And obtained the following data. In the analysis of the quantitative relationship between structure and activity, 10 descriptors are used. The descriptor data above will be analyzed using multilinear regression statistics using the backword method with the dependent hydrophobic descriptor, namely the partition coefficient (Log P) and the independent steric and electronic descriptors using SPSS 16   The experimental Log P regression value with Predicted Log P is 0.980, the meaning of this value is 98% of the compound explains the facts and the rest is explained by the residual variable. A good regression value is a value close to 1, and its validity can be recognized. The descriptors that influence this regression are MR, TPSA, SA, HOMO, and LUMO.

Figure 3. Graph of Log P Prediction VS Log P Experiment
From the graph above, it can be seen that the log P value indicates the strong influence of the descriptors involved, so that the best compound can be selected for validation using molecular anchoring. In addition, the PRESS (Predictive Residual Sum of Square) value can also be used to find out how well the QSAR equation has been obtained. The smaller the value (closer to 0), the better the compound is in becoming a drug candidate. The PRESS value is obtained from the squared result of the reduction between y = 0.988x -0.001 R² = 0.980 0 0

D. Molecular Docking analysis
After obtaining the results of the QSAR analysis, the next analysis is molecular docking. In molecular docking, the interaction between each compound and the target protein will be known. In this study, the protein coded 1B2Y was used. Molecular docking was performed to compare the ability of each compound to become a drug candidate. The interactions that will be studied are the bond energies that occur (Binding Energy) and the inhibition constant (Inhibition Constant). The best compound is chosen based on the low binding energy and inhibition constant values, the lower the binding energy value, the easier it is to bind to the target protein, the lower the inhibition constant value, the greater it can become an inhibitor in the target protein. Based on the table above compound number 8, with the IUPAC name (S)-10-amino-1,2,8-trihydroxy-6-(hydroxylmethyl)-10-((2R,3R,4S,5S,6R)-2,3 ,4,5-tetrahydroxy-6-(hydroxymethyl)tetrahydro-2H-pyran-2-yl) anthracen-9(10H)-one, is confirmed to have the ability as a Type-2 anti-diabetic by inhibiting the action of the amylase enzyme in breaking down starch. This happened because the compound had the smallest binding energy and inhibitor constants, namely -7.07 kcal/mol and 6.58 uM. Next, visualization was carried out between the selected compounds and the target protein using Discofery Studio Fisualizer 2019 and obtained hydrogen bonds with residues of the amino acids aspirin, glycine, threonine, and arginine. The compound with the most potential as a type-2 antidiabetic is a compound with an ID of 22. The compound has the IUPAC name (S)-10-amino-1,2,8-trihydroxy-6-(hydroxylmethyl)-10-((2R,3R,4S,5S,6R)-2,3,4,5-tetrahydroxy-6-(hydroxymethyl) tetrahydro-2H-pyran-2-yl) anthracen-9(10H)-one. This has been proven by fulfilling all validation requirements for oral drugs and type-2 antidiabetics. The results of the validation were an R2 value of 0.980, a PRESS value of a compound of 0.0004, a binding energy value of -7.07 kcal/mol, an inhibition constant of 6.58 uM and the formation of hydrogen bonds between the compound and the amino acid residues of aspirin, glycine, threonine and arginine.