In silico discovery of potential sodium–glucose cotransporter-2 inhibitors from natural products for Prevent Kidney Failure in Patients with Type 2 Diabetes

doi:10.21203/rs.3.rs-2630494/v1

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In silico discovery of potential sodium–glucose cotransporter-2 inhibitors from natural products for Prevent Kidney Failure in Patients with Type 2 Diabetes

https://doi.org/10.21203/rs.3.rs-2630494/v1

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Associated with Type 2 diabetes (T2DM), renal dysfunction contributes to an increased death rate. Consequently, it would appear that preventing the advancement of renal disease is crucial in the treatment of diabetic patients. SGLT2 inhibitors have been linked to reduced renal mortality, decreased hospitalization, and slowed the progression of renal impairment and albuminuria. The objective of this study was aimed to identify natural SGLT2 inhibitors using an in silico evaluation of the compounds of zinc database using structure-based virtual screening. Using pharmacophore modelling of the standard drug, a total of 1,1336 natural compounds that have the potential to act as SGLT2 inhibitors were identified; six of these compounds, 580, 1131, 212, 357, 822, and 306, had a similar docking affinity to the four known SGLT2 inhibitors. The top two finds, 580 and 306, were chosen due to the convenience of the pharmacokinetic characteristics from the absorption, distribution, metabolism and excretion (ADME), oral bioavailability, and parameters from molecular dynamics simulation (MD). Compound 580 was discovered as a potential treatment candidate after estimations of the metabolic processes and cardiotoxicity. This study may assist in the advancement of both in vitro and in vivo validation, as well as the development of new SGLT2 inhibitors.

Biological sciences/Drug discovery

Biological sciences/Plant sciences

In 2010, it was anticipated that almost 2 million individuals would have renal failure, and by 2030, it is expected this figure will have more than doubled ¹. The majority of kidney failures in the world are due to type 2 diabetes, accounting for more than half of all patients beginning dialysis programs in several nations ². Kidney failure brought on by type 2 diabetes poses a significant and expanding problem for patients, their families, and carers, as well as for healthcare systems and governments ³. Clinical practice guidelines advise using angiotensin-converting enzymes (ACE) inhibitors or angiotensin receptor blockers (ARBs) to treat people with type 2 diabetes who have kidney disease or are at high risk for developing it. These medications significantly reduce the likelihood of serious adverse renal outcomes in diabetics ^4–7. But the danger is still quite serious, and we urgently need innovative therapies to ease the burden of renal failure as it becomes a more common condition. A class of medications known as sodium-glucose co-transporter-2 (SGLT2) inhibitors which lower blood pressure, body weight, and albuminuria may also have direct hemodynamic effects on the kidney ⁸. As evidenced by large-scale cardiovascular outcome studies, SGLT2 inhibitors have demonstrated good results for a variety of renal outcomes including albuminuria and serum creatinine in patients with or at risk for atherosclerotic cardiovascular disease ^9–14. These studies involved patients at low risk of clinically significant renal outcomes, so kidney failure events were low, with only a few patients needing dialysis, transplants, or dying from kidney diseaseDue to the fact that these trials did not always specify kidney endpoints and were not designed specifically for renoprotective effects to be definitively observed, it was not possible to recognize acute from chronic glomerular filtration rate reductions (eGFR). A 2018 meta-analysis of cardiovascular outcome studies found that when eGFR declines, the impact of SGLT2 inhibitors on renal outcomes is weakened. Less than one-sixth of patients had eGFRs below 60 mL/min per 1.73 m2 at baseline, making it restricted to robustly assess the extent of kidney function's effect ¹³. Although the results of these studies combined indicated protection against acute renal damage ¹⁵, the safety of SGLT2 inhibitors in high-risk patients with severe kidney outcomes has not been clearly demonstrated. 2019 saw the publication of the findings of the CREDENCE trial, which was the first to accurately assess the effect of an SGLT2 inhibitor (canagliflozin) on a principal renal outcome in individuals with established diabetic kidney impairment. Canagliflozin causes lower kidney failure and cardiovascular events than placebo after a median follow-up of 2.62 years in these patients ¹³.

Nevertheless, it is unclear how SGLT2 inhibitors would affect kidney failure, specifically the requirement for dialysis, transplantation, or mortality from renal disease. Furthermore, prior research lacked sufficient power to investigate the heterogeneity of impacts on renal outcomes. We aim to conduct an in silico study to evaluate the effects of natural SGLT2 inhibitors on significant renal outcomes in type 2 diabetic patients and to establish the consistency of impact magnitude. This study evaluated the effectiveness of SGLT2 inhibitors to avoid kidney failure in type 2 diabetic patients using compounds from the ZINC database (https://zinc.docking.org/substances/subsets/natural-products).

2.1. Accurate ADME property prediction

Accurate ADME prediction prior to costly experimental procedures, such as high-throughput screening (HTS), can prevent testing on compounds that will ultimately fail; ADME prediction can also be used to focus lead optimization efforts to increase the desired features of a given drug. Using ADME predictions in the development process allows for the production of lead compounds with improved ADME properties for use in clinical trials.

Using two programs, QikProp from the Schrodinger Suite and SwissADME online server, the ADME properties of the standard drug (Empagliflozin), four chosen natural compounds (580, 1131, 212, 357, and 822), and three approved drugs as SGLT2i (Ertagliflozin, Dapagliflozin, and Canagliflozin) were calculated. Compounds 306 and 580 were placed in the range of 95% known pharmaceuticals similar to three approved drugs according to Table 1s (a-e). According to reports, the colored area in figure 1S is a favorable physicochemical region for oral bioavailability. It revealed that compounds 306 (A) and 580 (B) had higher unsaturation properties than the industry standard drug(C). Compounds 580 and 306 were reported as being soluble and poorly soluble, respectively, in comparison to the standard with moderate solubility in water. Additionally, 580 had a higher polarity than Empagliflozin. Other characteristics in figure 1S were shown to be like standard.

2.2. Docking study

The SGLT2i drug empagliflozin is used to treat type 2 diabetes ^16-23. To validate docking parameters, the co-crystallized ligand in RCSB with PDB ID: 7vsi was re-docked into the active site of the SGLT2-MAP17 complex protein. Re-docking produced essentially the same results, and the reliability of the docking procedure was demonstrated by the RMSD value (0.90) (Table 2). Four SGLT2i and two ligands were docked using the MOE program in order to acquire a comprehensive understanding of their binding mode (Table 1). The examination of the results revealed that the target compounds' docking scores were nearly similar (8.63 to -8.29 kcal/mol) to those of the lead compound, x-ray pioglitazone (-8.57 kcal/mol). Compounds 306, 580, and empagliflozin had a shared bond (H-acceptor) with GLN 457 in their active site. The H-acceptor bond related to GLN 457 for compounds 306 and 580 had better interactions with the SGLT2-MAP17 complex protein at the active site pocket (distance 3.25 and 3.28) compared to the standard compound with a distance of 2.95. Additionally, they had a pi-H bond with residues PHE_453 and PHE_98 for 306 and 580, respectively. MD studies were used to validate the results of docking. Figure 1 and table 2 depict a map of the docking of the three drugs (306, 580, and empagliflozin) in the active site of the SGLT2-MAP17 complex protein (PDB code: 7vsi).

Figure 1.

Table 1.

Table 2.

2.3. Molecular Dynamic Simulation (MD)

By using MD modeling, it was possible to better understand how well SGLT2 functioned in terms of stability, ligand-receptor interaction, and ligand binding. Using plots of root-mean-square derivations (RMSD), root-mean-square fluctuation (RMSF), and radius of gyration (Rg), the dynamic stability of secondary structures and conformational changes in protein alone were compared with the complex. After approximately 20 ns of simulation, the RMSD of the complex and the apo-form stabilized, as illustrated in figure 2. In terms of mean RMSD, apo-form and complex 306 and 580 had mean RMSD values of 0.264 ± 0.0323, 0.243 ± 0.023, and 0.220 ± 0.014 nm, respectively. The fact that SGLT2's conformational flexibility had decreased as a result of its binding to the ligand and protein was likely the cause of the complex 580's comparatively low RMSD value.

Figure 2.

Figure 3.

In addition to dynamic measurements for each atom or residue, the RMSF is macromolecularly flexible and displays any regional protein structural modifications ^24,25. Figure 3 and 4 show the RMSF plots of protein in complex and in apo-form. The apo-form and complex of 306 and 580 had average RMSFs 0.134 ± 0.063, 0.127 ± 0.0869, and 0.128 ± 0.0741 nm, respectively. Two aspects of protein structure are determined by these three systems: (a) the same RMSF dispensation, and (b) the same orientation of dynamic characteristics. The complex's residues' lower RMSF values may be explained by the fact that they are in the binding site (residues present in the active site for compound 306: Leu_283, Tyr_290, Phe_453, Asp_454, Tyr_526, His_80, and Tyr_290; residues present in the active site for compound 580: Phe_98, Val_157, Gln_457, Asn_75, Lys_321, Ser_393, and His_80). Protein flexibility did not alter noticeably due to residue binding to the ligand.

Figure 4.

The Rg represents the equilibrium conformation of the bound and unbound systems and is a protein structural compression indicator ²⁶. For the apo-form and protein-ligand complexes 306, and 580, the Rg values were 2.422 ± 0.011, 2.426 ± 0.009, and 2.440 ± 0.009 nm, respectively (Fig. 5). The protein-ligand complex's comparatively high Rg value indicates greater compaction of the complexes.

Figure 5.

The protein-ligand combination is stabilized by hydrogen bondingIn Figure 2S, the restricted ligand in the active site of the protein is shown to be responsible for the stability of this system. Empagliflozin (standard), 306, and 580 had average Hbond intermoleculars of 2.913 ± 1.1742, 0.411 ± 0.6548, and 5.980 ± 1.1352, respectively. The results showed that, compared to the standard ligand, ligand 580 formed three more hydrogen bonds with the receptor (a protein). The protein had an average of 451.251 ± 9.756 intramolecular hydrogen bonds. The hydrogen bond provides the SGLT2 secondary structure's stability (Fig. 3S). The potential energy, which remained constant over 100 ns of simulation in this work (for 306: -789401.799 ± 1106.722 and for 580: -788706.174 ± 1119.781kcal/mol) and proved the stability of the system, is a straightforward method for assessing protein-ligand interaction (fig. 4S).

Protein function depends on folded structures known as secondary structures (SS) ^27,28. VMDSS was used to calculate the secondary structure during the MD simulation. After analyzing the trajectory of SS%, the proportion of protein secondary structures was determined (Fig. 5S) ²⁹.

Different colors were used to identify secondary structures, which remained unaltered during simulation ²⁹. Following 100 ns of simulation, the secondary structure of the complex was displayed in a three-dimensional column chart (Fig. 6S (Down part)) and a pie chart (Fig. 6S (High part)). Every fifty ns, the ligand-bound protein structure was extracted from trajectories in order to analyze the simulation's results. As can be seen in snapshots from the simulation in figure 7S, the orientation of the ligand in the SGLT2 protein's active site remained constant throughout the simulation. After 100 ns of simulation, compounds 306 and 580 had two, and six hydrogen binds, respectively, according to figure 9S and table 1, while the standard ligand, empagliflozin, had four hydrogen bindings. The outcomes demonstrated that compound 580 was inserted into a matching pack, and the complex configuration was constant. Figure 8S depicts 3D images of hydrogen binding between three ligands and residues in the active site, and Figure 8S (B) depicts a 3D image of hydrogen binding between compound 580 and six residues located in the pocket. Because there is a greater connection between ligand 580 and receptor following MD compared to the standard drug, it could be inferred that 580 operates more steadily in the active site of the receptor.

2.4. Metabolic process prediction in the presence of cytochrome P450

For the two selected compounds and, empagliflozin, table 2S shows ranking, atom type and quantity, score, energy, 2DSASA, span2end, and similarity. The three atoms with the highest metabolic priority are highlighted on the left and right images of table 2S. SMARTCyp 3.0 is an online server-based application for predicting the metabolic reactivity of three well-known isoforms of cytochrome, namely Cyp3A4, Cyp2D6, and Cyp2C9. This server was applied to anticipate the metabolic sites of both the chosen compounds and empagliflozin. Each compound has an energy below 999, a span2end value less than 8, a similarity of 0 to 1, etc ³⁰. Therefore, the given data indicates that all three compounds have acceptable and comparable values to understand the metabolic reactivity towards cytochrome isoforms. The three sites with the lowest scores for the three cytochrome enzyme isoforms 3A4, 2D6, and 2C9 had the highest likelihood of metabolism, according to the data analysis using SMART Cyp 3.0. For the standard drug, empagliflozin, the most important sites with the highest likelihood of metabolism are C.14, C.16, and C.29, which have the lowest scores. Site C14 is also a key site for the creation of adducts for all three isoforms. The most well-known site of metabolism in compound 580 is atomic site C.6, which has the lowest score, and the most metabolically reactive sites in that are C.6, C.3, and C.9. In compound 306, atomic positions C.29, N.28, and C.27 are the major adduct formation sites for isoform 2D6. Also, Atomic positions C.15, N.13, C.29, and C.15, N.13, C.10 are related to major adduct formation sites of two isoforms 2C9 and 3A4 of cytochrome enzyme, respectively. The atomic site C.15 has the lowest score, making it the most likely location for the cytochrome isoforms 3A4 and 2C9 to react. Additionally, given its lower score value, the atomic site C.29 is the most likely location of reactivity for isoforms 2D6 (Table 2S).

2.5. Pred-hERG tool for cardiotoxicity prediction

Cardiotoxicity prediction is crucial to maintaining a reasonable level of safety prior to administration into the body. The pred-hERG test results show that compound 580 is not cardiotoxic and does not cause hERG blockage. Probability map results showing denser contour green lines than in standard ligand indicate cardiotoxicity for compound 306 (Table 3). By changing the structural characteristics, it is possible to create structural analogues of compound 306 that have decreased cardiotoxicity.

Table 3.

The development of renal failure is among the most serious complications of diabetic kidney disease, and it is a major cause of patient concern. In SGLT2i cardiovascular trials, exploratory results have revealed that these medicines may enhance renal outcomes, and have proven potential effects on the spectrum of albuminuria and serum creatinine-based renal outcomes in individuals with type 2 diabetes ^9-14.

Canagliflozin medication was related to a lower risk of sustained loss of kidney function, attenuated eGFR decrease, and a reduction in albuminuria in a prespecified exploratory analysis by Perkovic et al., supporting a possible renoprotective impact of this drug in persons with type 2 diabetes ⁹. In addition, empagliflozin was associated with a slower progression of kidney disease and a decreased incidence of clinically important renal events than placebo, when added to standard medication in individuals with type 2 diabetes ¹⁰. On the other hand, Nruen et al. found that the effects of canagliflozin on renal outcomes were unaffected by baseline kidney function in individuals with type 2 diabetes and a history or high risk of cardiovascular disease ¹¹. In a separate study, empagliflozin improved clinical outcomes and decreased mortality in high-risk patients with type 2 diabetes and chronic renal impairment ¹². According to a study by Mosenzon et al., dapagliflozin appears to prevent and slow the course of kidney damage in patients with type 2 diabetes, the majority of whom had retained renal function, compared to placebo ¹⁴.

In 2019, Perkovic et al. revealed the first findings of the CREDENCE trial related to the assessment of the effect of an SGLT2 inhibitor (canagliflozin) on a renal outcome in people with established diabetic kidney disease. With a median follow-up of 2.62 years, they discovered that the canagliflozin group had a lower incidence of kidney failure than the placebo group among persons with type 2 diabetes and renal illness ³¹. Moreover, Wanner et al. observed that the hemodynamic effects of empagliflozin, in conjunction with a reduction in intraglomerular pressure, may contribute to the long-term maintenance of kidney function ³².

A 2019 study demonstrated that EMPA (empagliflozin) might help reduce the course of chronic kidney disease (CKD) in individuals with T2DM and cardiovascular disease (CVD), regardless of common background drugs that change internal hemodynamics and without worsening acute renal side effects ³³. According to the meta-analysis and comprehensive review conducted by L. Neuen et al., SGLT2 inhibitors reduce the risk of renal disease-related transplantation, dialysis, or death in people with type 2 diabetes, and protect against acute kidney injury ³⁴.

In a review, Herrington et al. indicated that SGLT-2 inhibition in the kidney has the potential to reduce intraglomerular pressure by increasing sodium delivery to the macula densa. They suggested that this may explain why SGLT-2 medications lower albuminuria and appear to slow the decline of kidney function in diabetic patients ³⁵. In addition, evidence from the CREDENCE trial of canagliflozin indicated this medication's efficacy in lowering the risk of kidney failure in a population of persons with T2DM and renal illness ³⁶.

Salah et al. discovered that SGLT2i improved renal function in patients with kidney disease, regardless of the presence of T2DM and/or CKD ³⁷. A systematic review and meta-analysis conducted in 2023, which included thirteen trials investigating SGLT2i in 90,402 patients with available eGFR data, found that SGLT2i lowered the risk of renal outcomes across all estimated glomerular filtration rate (eGFR) categories ³⁸.

Throughout the process of discovering SGLT2 inhibitors, Kumar et al. drew molecular structures based on the Dioxabicyclo [3.2.1] Octane Scaffold in ChemBiodraw ultra. After conducting a molecular docking investigation, Logs and toxicity were predicted online using AlogP and Lazar, respectively, and as a result, a new lead compound with a high binding affinity was found ³⁹.

In 2021, a study was performed to identify potential SGLT-2 inhibitors among the 21 phytochemicals of Centella asiatica by in silico screening, and it was observed that the three chosen samples maintained the same level of structural stability as the references and exhibited superior oral bioavailability and drug-like properties ⁴⁰.

Kong et al. identified 113 natural SGLT2 inhibitors through docking-based virtual screening in an in silico experiment. The outcomes demonstrated that the three selected compounds stuck strongly to the SGLT2 binding site, and were suggested as possible therapeutic candidates for the treatment of heart failure after displaying valid pharmacokinetic features ⁴¹.

Until today, none in silico studies of SGLT2 inhibitors have been reported to prevent kidney failure in type 2 diabetes patients.

Conclusion

In order to prevent kidney failure in type 2 diabetes patients, our research utilized structure-based virtual screening to identify natural SGLT2 inhibitor candidates from a zinc database library. The natural chemicals were downloaded so pharmacophore modelling could be performed based on the standard drug (empagliflozin). As a result of pharmacophore modelling, 1,1336 natural compounds exhibited features similar to those of the standard ligand pharmacophore. Following a simulated screening at the SGLT2 binding site, six natural compounds with acceptable docking affinity similar to four recognized inhibitors were identified. The top two findings, 580 and 306, were selected based on the oral bioavailability and pharmacokinetic properties of the ADME. In order to determine the stability of the two docked complexes, simulations of molecular dynamics were performed in 100 ns. The post-MD examination of RMSD, RMSF, Rg, potential energy, and H-bond plots revealed that the two docked complexes and known inhibitor docked complex was stable and tightly attached to the active region of the SGLT2 protein. Moreover, based on the prediction of metabolic processes and cardiotoxicity, compound 580 looks to be a suitable therapeutic candidate. Our findings indicate that this natural chemical (580) is a strong SGLT2 inhibitor with pharmacophore features similar to those of a standard drug. In vitro and in vivo investigations are needed to determine the inhibitory activity of this compound against SGLT2 and prevent kidney failure in individuals with Type 2 Diabetes. All four proposed approaches and mechanisms (Scheme 1S, 2S, 3S, and 4S) that could encourage researchers to analyze and investigate advancements in the synthesis of compound 580 were detailed in the supplementary information.

4.1. Specifications of computer systems, computational software used, and databases

The entire computing process was carried out on a Linux machine with a Ryzen CPU (16), 1.7 GHZ, and 32 GB RAM. Additionally, the PyRx virtual screening tool, the ADME offline server of Schrodinger Maestro, the ADME online server of SwissADME, the Molecular Operating Environment (MOE), the NAMD molecular dynamics simulation program, and the SMARTCyp 3.0 and PredhERG online server, among other programs, were used for this investigation.

4.2. A summary of the compounds examined and the structure-based virtual screening results Initially, to screen the structures from the ZINC database (https://zinc.docking.org/substances/subsets/natural-products) and obtain compounds with similar pharmacophores Empagliflozin as SGLT2 inhibitor, the complex X-ray structure of SGLT2-MAP17 and empagliflozin has gotten was gotten via the RCSB database (PDB Id=7vsi). The Ligand Scott application (https://ligandscout.software.informer.com/3.0) was used to search for the pharmacophore of the standard ligand (empagliflozin). Then obtained pharmacophore was searched in the mentioned database using the MOE program (http://www.chemcomp.com), and only 1,336 of the 114,313 natural compounds in the library were extracted. Following a virtual screening using the PyRx program ^42,43, five compounds were selected due to having the lowest binding energy (about -10 kcal/mol) and the hydrogen binding number to the active site compared to the standard ligand (empagliflozin). The pharmacokinetic properties of the six desired compounds were investigated along with the standard drug empagliflozin and three approved drugs (canagliflozin, dapagliflozin, and ertugliflozin) using programs the Schrödinger (Schrodinger Release 2021.2: QikProp, Schrodinger, LLC, New York, NY, 2021) and SwissADME ^44-47 , that only two compounds (306 and 580) were in range for 95% known drugs. After examining the docking studies of the two selected compounds of the previous stage and the approved drugs utilizing Moe 2019 ^48,49, a proper conformer of the standard drug and compounds 306, and 580 was entered into a molecular simulation study via GROMACS 2018 ^50,51. Then, the prediction of cytochrome P450 mediated metabolism for the three said compounds with the use of the SMARTCyp online server ^52,53 was investigated. Finally, to maintain a reasonable level of safety prior to administration into the body, cardiotoxicity prediction was done by Pred-hERG tool ^54,55.

By reviewing several databases, including SciFinder, PubMed, Medline, Scopus, and Web of Science, the production methods of compound 580 (with names polydatin, resveratrol 3-mono-D-glucoside, trans-Piceid, resveratrol-3-beta-D-glucopyranoside, 5,4'-dihydroxystilbene-3-O-β-glucoside, resveratrol 3-O-β-D-glucopyranoside and resveratrol 3-O- α-D-glucopyranoside) was given in the supporting information (Fig. 10S).

4.2.1. Accurate ADME property prediction

A drug's chances of being successful will be greatly impacted by the ADME profile. Due to weak ADME properties, about 40% of medication candidates fail in clinical trials. These late-stage failures make a sizable contribution to the new medication development process's quickly rising costs. Most often, experiential techniques like QSAR form the foundation of ADME attributes. These approaches are very effective, applicable to a wide range of compounds, and elicit a link between structure and activity from high-quality data. Early detection of poor candidates can greatly decrease the amount of time and money spent, as well as speeds up the entire development process ^44-47. In this study, SwissADME (http://www.swissadme.ch/index.php), a free online program, and the QikProp module from the Schrodinger Suite ^56-58 were used to predict ADME properties for the standard drug Empagliflozin, four chosen natural compounds containing ZINC000096032072 (580), ZINC000253499253 (1131), ZINC000012658504 (212), ZINC000049782048 (357), and three approved drugs as SGLT2i (Ertagliflozin, Dapagliflozin, Canagliflozin).

4.2.2. Docking study

Molecular docking, one of the most used methods for drug design, was used to examine the interaction of small molecules with putative target binding sites. In order to anticipate the optimal conformations of particular compounds in the active site of an SGLT2-MAP17 complex protein, docking was carried out using MOE 2019 (www.chemcomp.com). The Protein Data Bank was used to find the crystal structure of SGLT2i with PDB ID: 7vsi and resolution 2.95. The water molecules were removed, the receptor was attached to polar hydrogen atoms according to the appropriate ionization states, and the complex was stored in the format of PDB ^59,60. ChemDraw Ultra (http://www. cambridgesoft.com) was used to draw the compound's two-dimensional structure under investigation. MOE program developed the 3D format, and the docking was accomplished utilizing the triangle matcher approach as placement and the London DG score with one hundred poses. The GBWI/WSA DG was refined using the rigid receptor approach, and five postures were used to score it ^49,59,61. For six compounds (Canagliflozin, Empagliflozin, Dapagliflozin, Ertugliflozin, and compounds 306 and 580), docking was carried out. The optimal conformation was assessed in the final stage based on the number of hydrogen bonds and the Gibbs free energy. Ultimately, the optimal docking states for compounds 306 and 580 and Empagliflozin (standard) were chosen for molecular dynamic (MD) modelling.

4.2.3. Molecular Dynamic Simulation (MD)

Based on the docking study results, the complex with the highest level of stability was chosen for molecular dynamic simulation ^47,48. The GROMACS 2018 package was used to perform molecular dynamics simulation ⁶². The system was placed in a cube with SPC-simulated water molecules inside of it in order to molecular dynamics. To balance out the system, counter ions were added. The CHARMM36 all-atomic force field was used for every simulation ^63,64. The system was equilibrated at 300K and 1 bar after energy minimization ^65,66. At 100 ns, the MD simulation was completed. RMSD, RMSF, potential energy, and Hbond intermolecular and intramolecular analyses were carried out in order to gauge the system's stability, and they were compared to a reference system. The complex's compression was also determined using the radius of gyration study. The most recent snapshot of the molecular dynamics simulation was displayed using the MOE software.

4.2.4. Metabolic process prediction in the presence of cytochrome P450

For the cytochrome isoforms CYP3A4, CYP2D6, and CYP2C9, the SMARTCyp 3.0 online server (Https://smartcyp.sund.ku.dk/mol_to_som) predicts the locations of metabolism in the molecule. Through the use of density functional theory (DFT), SMARTCyp 3.0 first determines the energies of the transition states of the atoms in a molecule. A calculation based on the fragment is then performed to determine the molecule's average power. In addition to the SMARTCyp reactivity, the method employs only two descriptors: the distance to a protonated nitrogen atom and the distance to the molecule's terminal. So, the site of metabolism can be predicted simply from the 2D structure of a molecule, without the need to calculate electrical characteristics or generate 3D structures ⁶⁷. The compounds can be submitted in SDF or MOL format using the SMARTCyp 3.0 tool, and the results are revealed in tabular form with the following parameters: score, energy, 2DSASA (Solvent Accessible Surface Area in 2D coordinates), span2end (absolute number of bonds between the target atom and the end of the molecule), N+ dist (the distance or number of bonds between the atom of interest and nitrogen) or COO- dist (the number of bonds between an atom and a carboxylic acid or its bioisostere) or relative span (The ratio of the number of bonds from the atom of interest to the end of the molecule to the size of the molecule) and similarity (indicated how similar the molecule's atom is to an atom in a fragment whose activation energies were measured using DFT). For ease of identification, the first three atoms are colored and ranked in order of increasing score. There is no chance that the compound will have metabolic sites if the energy is 999 ³⁰. A particle with a higher score is retained at the bottom of the table, while the atom with the lowest value is preserved on top. The score is the only determinant of which of the evaluated atomic locations is more likely to be the metabolic site. The possibility of being a metabolic site is inversely proportional to the score; the lower the score, the greater the likelihood of being a metabolic site. The similarity value ranges from 0 (low) to 1 (high), and if it is 0, there is no probability that an atom will function as a metabolic site. One or a score of one indicates a perfect resemblance or a high likelihood of being the location of metabolism ^67,68. Two compounds 306 and 580 with the format SDF were used for SMARTCyp. For ease of identification, only the first three atoms that, were ranked in order of increasing score or colored, were indicated in table 2S.

4.2.5. Pred-hERG tool for cardiotoxicity prediction

The protein kV 11.1, an alpha subunit of the potassium ion channel, is produced by the hERG (human ether -a-go-go-related gene). These channels are necessary for normal electrical signalling and correct operation of the human heart. Arrhythmia will ensue from the inhibition of the hERG protein, which will block the potassium ion channels. By attaching to the hERG protein, a variety of compounds have the ability to produce cardiotoxicity and stop the heart from beating normally, which may result in death. Therefore, it is essential to anticipate and comprehend the cardiotoxicity of such pharmacological compounds before they are authorized for use. Pred-hERG, a web server tool (http://www.labmol.com.br/), aids in solving such issues by foretelling the cardiotoxicity of drug-like compounds. This application is a machine learning software in which models are constructed to predict the cardiotoxicity of diverse chemicals with an accuracy, sensitivity, and specificity of approximately 89-90 percent. The anticipated results are first generated and represented as binary, multi-class, and predicted probability maps (PPM) ^55,69,70. Both selected compounds (306 and 580) were used for the Pred-HERG server in the form of SMILES and the results were exhibited in table 3.

Acknowledgment

Mashhad University of Medical Sciences provided financial support (grant number: 4011292) for this study.

Conflict of Interest

The authors declare that they have no conflicts of interest with the contents of this article.

Authors contributions:

N.SH. designed the project, wrote the main manuscript text, and prepared figures; S.H. performed molecular docking simulation experiments; M.I. and M.B. reviewed and edited the main manuscript text and F.H. managed the project.

Data availability statement:

The authors confirmed that the data supporting the study's conclusions are included in the article and its supplementary materials. Upon a reasonable request, the corresponding author will provide the raw data used to support the findings of this study.

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Table 1. Docking interactions information of the best-docked compounds at the active sites of the SGLT2-MAP17 complex protein.

pi-H binding Interactions with residues and distance (Å)	H-bonding Interactions with residues and distance (Å)	Pki	Docking score (kcal/mol)	Best ligand with the receptor
-	SER_287 (3.36), TRP_291 (3.09, ASN_75 (3.10), LYS_321(3.80)	6.33	-8.63	Canagliflozin
-	GLN_457 (3.13), GLN_457 (2.95), SER_ 287 (3.05), TRP_291 (3.15)	6.29	-8.57	Empagliflozin (Std)
TYR_290 (4.10)	GLN_457 (2.91), TRP_291 (3.04), ASN_ 75 (3.05),	6.14	-8.37	Dapagliflozin
PHE_453 (4.78)	GLN_457 (3.25), HIS_80 (3.39), SER_460 (2.85),	6.12	-8.34	306
-	SER_287 (2.89), LYS_ 321 (3.07)	6.10	-8.31	Ertugliflozin
PHE_98 (4.00)	PHE_98 (3.22), GLN_ 457 (3.28), GLY_83 (3.21),	6.09	-8.29	580

Table 2 and 3 are available in the Supplementary Files section.

Scheme 1 to 4 are available in the Supplementary Files section.

No competing interests reported.

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In silico discovery of potential sodium–glucose cotransporter-2 inhibitors from natural products for Prevent Kidney Failure in Patients with Type 2 Diabetes

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