2020 Volume 11 Issue 4

Biochemical Interaction Analysis of Natural SGLT2 Inhibitors with Alzheimer Targets: A Computational Approach

 

Ahmed Alafnan


Abstract

Alzheimer’s Disease (AD) is a progressive neurological disorder, and its association with type 2 diabetes mellitus (T2DM) has increased its intricacy to many folds. Thus, targeting both associated disorders with a single therapeutic agent appears to be promising. Anti-AD potency of a new class of anti-diabetic agents, i.e., Sodium-Glucose Co-transporters 2 (SGLT2) inhibitors have been investigated in the present study. The docking approach was applied to elucidate the interaction pattern of natural SGLT2 inhibitors (Acerogenin A, Alstiphyllanine D, ε-viniferin, Phlorizin, and Sophoraflavanone G) with AD targets. Here, Acerogenin A, ε-viniferin and Sophoraflavanone G showed better binding with almost all AD targets as compared to positive controls, except β-secretase where positive control verubecestat showed better results than these inhibitors. Also, ε-viniferin and Sophoraflavanone G have shown potency against direct amyloidβ aggregation. We state that SGLT2 inhibitors based scaffolds could be explored to develop dual therapeutic agents against T2DM and AD.

Keywords: Alzheimer’s Disease, Dual Therapy, Molecular Docking, Sodium-Glucose Co-transporters 2, Type 2 diabetes mellitus.


Introduction

Alzheimer’s Disease (AD) is an irreversible and progressive disorder of the central nervous system that is considered the major reason for dementia in the elderly population worldwide (Alharthy, et al., 2019; Abdulaziz, et al., 2019; Osman, et al., 2019; Ahmed, et al., 2018). It has been stated in the World Alzheimer’s Report 2018 (https://www.alz.co.uk/research/WorldAlzheimerReport2018.pdf.) that around 50 million individuals are currently suffering from AD and another type of dementia; however, by 2050 the number is expected to reach approximately 152 million. AD's strong association with other diseases such as type 2 diabetes mellitus (T2DM) has added to its intricacy (Rizvi et al., 2015; Caberlotto et al., 2019). T2DM is one of the focal predisposing factors for AD and in fact, reports suggest that it can increase the risk of AD by 1.6 fold (Sonnen et al., 2009; McIntosh et al., 2019). The situation for diabetes is graver than AD, around 425 million diabetic patients are reported in the statistics of the International Diabetes Federation 2017 (https://diabetesatlas.org/IDF_Diabetes_Atlas_8e_interactive_EN/.) and this might reach 629 million by 2045. The death rate for diabetes has reached one in seven seconds with 50% diabetic deaths in the age group of fewer than 60 years. Failure to treat both these debilitating diseases is a matter of great concern to the scientific community globally. Thus, researchers are trying to cope-up with the situation with alternate upcoming strategies. One such strategy is to treat T2DM with Sodium-glucose co-transporter 2 (SGLT2) inhibitors that have been approved by the FDA in the year 2013 (https://www.fda.gov/media/87579/download). SGLT2 is present in the proximal convoluted tubules of kidneys that are accountable for ~90% reabsorption of filtered glucose. Subsequently, SGLT2 inhibition will reduce the reabsorption of glucose and considered as a target for T2DM therapy (Scheen et al., 2015; Fioretto et al., 2015; Hsia et al., 2017).

In the present study, natural SGLT2 inhibitors were explored for their efficacy against AD. Even though the main targeted organs of T2DM and AD i.e., pancreas and brain are physiologically different and anatomically distant, they share some common pathophysiology (Rizvi et al., 2015; Caberlotto et al., 2019). It has been evident from various reports that pancreatic islet amyloid polypeptide could enter the brain and plausibly initiate misfolding of amyloidb, amyloid plaque formation and neuronal degradation during AD (Oskarsson et al., 2015; N Fawver et al., 2014; de la Monte et al., 2008). As T2DM could initiate the onset of AD, a treatment regime focused on dual therapeutic agents might plausibly provide a robust alternative to current unavailing medications. Five natural SGLT2 inhibitors, namely, Acerogenin A, Alstiphyllanine D, e-viniferin, Phlorizin, and Sophoraflavanone G (Sato et al., 2007; Oranje et al., 2019; Choi et al., 2016; Morita et al., 2010; Arai et al., 2010) were chosen against AD targets. AD is a multifaceted disease involving various suggested hypotheses. Among all AD hypotheses, the most accepted are cholinergic, amyloidogenic, and tau phosphorylation pathways. In this study, initially, the physiochemical properties and toxicity potential of natural SGLT2 inhibitors were checked followed by analyzing the interaction pattern of these natural inhibitors against SGLT2. Afterward, the prominent three pathways via targeting ꞌAcetylcholinesteraseꞌ, ꞌb-secretaseꞌ, ꞌAmyloidb aggregationꞌ, ꞌFyn Kinaseꞌ, ꞌGlycogen synthase kinase 2 betaꞌ and ꞌTau Tubulin Kinase 1ꞌ were covered. For all the assessments, control compounds were used for each target, such as empagliflozin for SGLT2, galantamine for acetylcholinesterase, verubecestat for b-secretase, curcumin for amyloid aggregation, leuco-methylthioninium for fyn kinase, glycogen synthase kinase 2 beta, and tau tubulin kinase 1, respectively. Interestingly, the results were quite promising and fascinating. Moreover, these predicted findings would pave the way for future dual therapeutic agents in the form of SGLT2 inhibitor scaffold for T2DM associated AD.

Materials and Methods

Sodium-glucose co-transporter 2 (SGLT2) inhibitors, control compounds, and target proteins structure retrieval 

The 3-dimensional structures of natural SGLT2 inhibitors, namely, Acerogenin A [CID: 12000158], Alstiphyllanine D [CID: 45269680], e-viniferin [CID: 5281728], Phlorizin [CID: 6072] and Sophoraflavanone G [CID: 73198] and control compounds Empagliflozin [CID: 11949646], Galantamine [CID: 9651], Verubecestat [CID: 51352361], Curcumin [CID: 969516] and Leuco-Methylthioninium [CID: 122173994] were retrieved from PubChem database. However, target protein structures of Acetylcholinesterase (AChE) [ID: 3LII], β-secretase (BACE-1) [ID: 1W51], b-turn-b-fold of Ab1-42 peptide (Ab17-42) [ID: 2BEG], Fyn Kinase (Fyn) [ID: 2DQ7], Glycogen synthase kinase 2 beta (GSK3b [ID: 1J1C] and Tau Tubulin Kinase 1(TTBK1) [ID: 4BTJ] were obtained from Protein Data Bank. However, the swiss model workspace was used to prepare the 3-D structure of the SGLT2 protein after retrieving the amino acid sequence from Uniprot [P31639].

Calculation of physicochemical properties and prediction of toxicity potential

Orisis Datawarrior property explorer tool was used (http://www.openmolecules.org/datawarrior/download.html) to check the physicochemical properties and toxicity potential of all the compounds including control. The number of hydrogen bond donors and acceptors, molecular weight, cLogP value, molecular weight, topological polar surface area, number of rotatable bonds, and the Lipinski's rule violation (Lipinski et al., 2001) were assessed using the orisis software (Table 1). Also, Absorption % was calculated according to the method of Zhao et al. (Zhao et al., 2002) by applying the formula % of Absorption = 109 ‒ (0.345 × TPSA). In the orisis data warrior tool, toxicity potential predictions are dependent on the comparative analysis of a pre-calculated set of already investigated structural molecules with the structure of tested compounds. The software predicted various aspects of toxicity such as mutagenicity, tumorigenicity, reproductive effects, and irritability of all the tested compounds (Table 2).

Molecular Docking

The protocol of MD Rizvi et al. (2013) was used to perform docking of all the tested compounds with the target protein. The MMFF94 force field was used for energy minimization of each compound followed by the addition of gasteiger partial charges. After the inclusion of non-polar hydrogen atoms, rotatable bonds were defined. AutoDock was used to add solvation parameters, hydrogen atoms, and Kollman united atom type charges. The dimensions of the grid were kept as 60 x 60 x 60 Å with points separated by 0.375 Å by using an Auto grid. To target specifically, the values of x, y and z coordinates were kept as 90.81, 83.98 and -8.04 for AChE; 73.79, 54.27 and 11.51 for BACE-1, -19.093, 25.139 and -11.588 for Fyn, 21.613, 17.277, -8.568 for GSK3b and 32.866, 38.648, -2.784 for TTBK1, respectively. For SGLT2, the grid center was kept at various well-recognized amino acid residues to dock the compounds. Default parameters and dielectric functions of AutoDock were used for van der Waals and the calculation of the electrostatic term. Docking simulation was performed using ‘Solis and Wets local search method’ and ‘Lamarckian genetic algorithm’. Other parameters of docking simulations such as orientation, initial position, and torsions were set arbitrarily. For each docking experiment, 100 different runs were applied for that was set to end after 2,500,000 energy evaluation. The population size was set as 150. Discovery Studio 2.5 (Accelrys) was used to create the final figures of AutoDock 4.2

The anti-aggregation potential of compounds against b-turn-b-fold of Ab1-42 peptide was performed by using Hex 5.1 tool.  Compound bounded Ab17-42 peptide and unbounded Ab17-42 peptide were docked with each other. The lowest E-total evaluation in Hex was based on ꞌshape onlyꞌ correlation type. The first Fourier transform (FFT) model was selected as 3D Fast Lite, the grid dimension was adjusted to 0.75, and the rest of the parameters were kept as default.

Results and Discussion

Alzheimer’s Disease (AD) and Type 2 Diabetes Mellitus (T2DM) both are intricate diseases with a strong correlation between them. There are several epidemiological, clinical, and pathophysiological pieces of evidence that validate the linkage between AD and T2DM (Rizvi et al., 2015; Caberlotto et al., 2019; Sonnen et al., 2009; McIntosh et al., 2019). Autopsy samples of T2DM patient’s brains have shown amyloid plaque accumulation, and it has been observed that insulin resistance and hyperinsulinemia lead to the building up of these amyloid plaques (Peila et al., 2002; Matsuzaki et al., 2010). Also, it has been reported that uncontrolled T2DM diabetes would increase the chances of AD by 1.6-fold (Sonnen et al., 2009; McIntosh et al., 2019). To date, no breakthrough AD treatment strategy has yet been found. Available AD drugs such as acetylcholinesterase inhibitors and N‐methyl D‐aspartate receptor antagonist offer merely symptomatic relief to AD patients. Although at present 132 anti-AD agents are under scanner via clinical trials, our past failure rate is too high (Cummings et al., 2019). To get the actual benefit, AD treatment should be started at prodromal or even pre-clinical stages. As T2DM is a prominent predisposing factor for AD, if an anti-T2DM drug scaffold would be used to develop anti-AD drugs it might provide a dual therapeutic strategy against both these linked diseases. There were several clinical trials conducted on currently approved T2DM drugs to estimate their anti-AD potential (Risner et al., 2006; Hsu et al., 2011). Unfortunately, no effective dual therapy of this kind has successfully cleared the Phase III clinical trial so far. All this information prompted us to give a try to a new class of anti-diabetic medication Sodium-glucose co-transporter 2 (SGLT2) protein inhibitors.

MD Rizvi et al. (2014) and Shaikh et al. (2016) have observed the anti-AD potential of recently FDA-approved SGLT2 inhibitors i.e., canagliflozin and dapagliflozin, respectively, and revealed some interesting findings. In the present study, natural SGLT2 inhibitors Acerogenin A, Alstiphyllanine D, e-viniferin, Phlorizin and Sophoraflavanone G (Sato et al., 2007; Oranje et al., 2019; Choi et al., 2016; Morita et al., 2010; Arai et al., 2010) were selected and tested against different AD targets to explore their dual therapeutic potential in detail. Before evaluating their anti-AD potential, the physicochemical properties and toxicity potential were checked of all these natural SGLT2 inhibitors along with control compounds using the Orisis Datawarrior tool. Table 1 shows the results of the physicochemical properties of each tested compound. It has been found that out of all-natural SGLT2 inhibitors, Acerogenin A and e-viniferin have not violated Lipinski’s rule of five with % absorption of 91.85% and 70.91%, respectively. During toxicity potential analysis (Table 2), none of the natural SGLT2 inhibitors showed toxicity except e-viniferin and Phlorizin that have high and low reproductive toxicity, respectively. However, both e-viniferin and Phlorizin have no mutagenic, tumorigenic, and irritant effects.

The main focus was to evaluate the anti-AD potential of all the tested natural SGLT2 inhibitors. As natural compounds were taken instead of known approved SGLT2 inhibitor drugs, they were tested first on SGLT2 protein rather than testing them directly on AD targets. Findings revealed that natural SGLT2 inhibitors Acerogenin A, e-viniferin, and Sophoraflavanone G showed better binding with SGLT2 than control (Empagliflozin) in terms of binding energy (∆G) and inhibition constant (Ki) (Table 3).  ∆G and Ki values for ꞌAcerogenin A-SGLT2ꞌ, ꞌe-viniferin-SGLT2ꞌ, ꞌSophoraflavanone G-SGLT2ꞌ, and ꞌEmpagliflozin-SGLT2ꞌ interaction were estimated as ꞌ-8.67kcal/mol and 444.91nMꞌ, ꞌ-10.33kcal/mol and 26.64nMꞌ, ꞌ-8.84kcal/mol and 331.37nMꞌ and ꞌ-6.27kcal/mol and 25.40mMꞌ, respectively. Structural functional studies have shown that Q457 amino acid residue of SGLT2 protein is involved directly in the reabsorption of sugar (MD Rizvi et al., 2014; Díez-Sampedro et al., 2001; Liu et al., 2009). Interestingly, in this study, all these natural SGLT2 inhibitors have bound to Q457 of SGLT2 protein (Figure 1). After revealing these promising results, they were tested on different AD targets. Most accepted AD hypotheses such as cholinergic, amyloidogenic, and tau phosphorylation were targeted to evaluate the anti-AD potential of the selected natural SGLT2 inhibitors. ꞌAcetylcholinesterase (AChE)ꞌ was selected target for cholinergic pathway and ꞌβ-secretase (BACE1)ꞌ and ꞌbeta-amyloid aggregationꞌ were

 

 

Table 1: Physicochemical properties of natural Sodium-Glucose co-transporter 2 (SGLT2) inhibitors and control compounds

Compounds

Physiochemical parameters

% of Absorption**

Topological Polar Surface Area (Å)2

Molecular Weight

cLogP***

Hydrogen Bond Donors

Hydrogen Bond Acceptors

Number of Rotatable Bonds

Lipinski’s Violation

Rule

-

-

<500

≤5

<5

<10

≤10

≤1

Acerogenin A

91.85

49.69

298.38

4.50

2

3

0

0

Alstiphyllanine D

72.69

105.23

606.67

3.20

0

11

10

2

e-viniferin

70.91

110.38

454.47

4.82

5

6

4

0

Phlorizin

47.88

177.14

436.41

0.055

7

10

7

1

Sophoraflavanone G

72.00

107.22

424.49

5.83

4

6

6

1

Empagliflozin*

71.52

108.61

450.91

1.64

4

7

6

0

Galantamine*

94.53

41.93

287.35

1.19

1

4

1

0

Verubecestat*

65.48

126.13

409.41

0.48

2

8

3

0

Curcumin*

76.89

93.06

368.38

2.94

2

6

8

0

Leuco-methylthioninium*

93.88

43.81

285.41

3.57

1

3

2

0

*Control drugs/compounds

**Percentage of Absorption (% of Absorption) was calculated by: % of Absorption= 109 ‒ [0.345 × Topological Polar Surface Area]

***Logarithm of compound partition coefficient between n-octanol and water

 

Table 2: Toxicity Potential of natural Sodium-Glucose co-transporter 2 (SGLT2) inhibitors and control compounds

Compounds

Toxicity Risks

Mutagenic

Tumorigenic

Reproductive effect

Irritant

Acerogenin A

None

None

None

None

Alstiphyllanine D

None

None

None

None

e-viniferin

None

None

High

None

Phlorizin

None

None

Low

None

Sophoraflavanone G

None

None

None

None

Empagliflozin*

None

None

None

None

Galantamine*

None

None

None

None

Verubecestat*

None

None

None

None

Curcumin*

None

None

None

None

Leuco-methylthioninium*

High

None

High

High

*Control drugs/compounds

 

Table 3: Molecular docking results of ꞌSodium glucose co-transporter 2 (SGLT2)ꞌ interaction with natural SGLT2 inhibitors and control

Compounds

Binding Energy (ΔG)

Inhibition Constant (Ki)

Acerogenin A

-8.67kcal/mol

444.91nM

Alstiphyllanine D

-6.70kcal/mol

12.27mM

e-viniferin

-10.33kcal/mol

26.64nM

Phlorizin

-3.79kcal/mol

1.67mM

Sophoraflavanone G

-8.84kcal/mol

331.37nM

Empagliflozin

-6.27kcal/mol

25.40mM

selected targets for amyloidogenic pathway, while, ꞌFyn Kinase (Fyn)ꞌ, ꞌGlycogen synthase kinase 2 beta (GSK3β)ꞌ and ꞌTau Tubulin Kinase 1 (TTBK1)ꞌ were selected targets for tau phosphorylation pathway.

 

 

Fig. 1: Amino acids involved in ꞌSodium glucose transport protein 2 (SGLT2)ꞌ interaction with natural Sodium-Glucose co-transporters 2 inhibitors and control. a) SGLT2-AcerogeninA interaction, b) SGLT2-e viniferin interaction, c) SGLT2-SophoraflavanoneG interaction and d) SGLT2-Empagliflozin interaction. The ligands are shown in ꞌstickꞌ representation.

 

Acerogenin A, e-viniferin, and Sophoraflavanone G showed promising interaction with the active site of AChE (Table 4).  e-viniferin interaction with AChE was even better than control galantamine with ∆G and Ki values as ꞌ-8.92kcal/mol and 287.74nMꞌ and ꞌ-8.69kcal/mol and 423.73nMꞌ, respectively. ∆G and Ki values for ꞌAcerogenin A-AChEꞌ and ꞌSophoraflavanone G-AChEꞌ interaction were ꞌ-8.37kcal/mol and 734.75nMꞌ and ꞌ-7.65kcal/mol and 2.45µMꞌ, respectively. Three amino acid residues, namely, S203, E334, and H447 are part of the AChE catalytic triad (Sussman et al., 1991; Remya et al.,2014). Importantly, among these three amino acids, Acerogenin A and Sophoraflavanone G showed interaction with two amino residues S203 and H447, while, e-viniferin interacted with H447 amino acid residue only (Figure 2).

 

 

Table 4: Molecular docking results of ꞌAcetylcholinesterase (AChE)ꞌ, ꞌβ-secretase (BACE1)ꞌ, ꞌFyn Kinase (Fyn)ꞌ, ꞌ ꞌGlycogen synthase kinase 2 beta (GSK3βꞌ and ꞌTau Tubulin Kinase 1(TTBK1)ꞌ interaction with natural Sodium-Glucose co-transporter 2 (SGLT2)  inhibitors and control compounds

Compounds

AChE

BACE1

Fyn

GSK3β

TTBK1

Binding Energy

(ΔG)

kcal/mol

Inhibition Constant

(Ki)

Binding Energy

(ΔG)

kcal/mol

Inhibition Constant

(Ki)

Binding Energy

(ΔG)

kcal/mol

Inhibition Constant

(Ki)

Binding Energy

(ΔG)

kcal/mol

Inhibition Constant

(Ki)

Binding Energy

(ΔG)

kcal/mol

Inhibition Constant

(Ki)

Acerogenin A

-8.37

734.75nM

-7.93

1.53mM

-7.87

1.72mM

-7.67

2.37mM

-7.90

1.61mM

Alstiphyllanine D

-5.03

205.93mM

-2.96

6.82mM

-6.78

10.64mM

-6.07

35.27mM

-4.28

724.31mM

e-viniferin

-8.92

287.74nM

-7.56

2.86mM

-8.75

387.80nM

-7.01

7.31mM

-7.60

2.69mM

Phlorizin

-2.88

7.71mM

-4.19

847.58mM

-5.70

66.91mM

-4.71

352.09mM

-4.18

860.86mM

Sophoraflavanone G

-7.65

2.45mM

-6.05

37.06mM

-5.61

77.73mM

-7.05

6.82mM

-5.99

40.67mM

Galantamine

-8.69

423.73nM

-

-

-

-

-

-

-

-

Verubecestat

-

-

-8.99

255.44nM

-

-

-

-

-

-

Leuco-methylthioninium

-

-

-

-

-6.33

23.10mM

-6.29

24.65mM

-6.11

33.46mM

 

On the other hand, AChE interaction with acetylcholine involves W86, E202, and Y337 amino acid residues (Kua et al., 2003), and interestingly, Acerogenin A, e-viniferin, and Sophoraflavanone G all showed interaction with these three important amino acids.

 

Fig. 2: Amino acids involved in ꞌAcetylcholinesterase (AChE)ꞌ interaction with natural Sodium-Glucose co-transporters 2 inhibitors and control. a) AChE-AcerogeninA interaction, b) AChE-e viniferin interaction, c) AChE-SophoraflavanoneG interaction and d) AChE-Galantamine interaction. The ligands are shown in ꞌstickꞌ representation.

 

Amyloidb accumulation and generation in the brain via the amyloid precursor pathway is associated with an enzyme known as BACE-1. To avoid or reduce the brain amyloidb load in AD patients, nowadays BACE-1 is considered a promising target (Ghosh et al., 2008). In the present study, none of the natural SGLT2 inhibitors have shown better interaction than positive control Verubecestat (Table 4). However, Acerogenin A, e-viniferin, and Sophoraflavanone G have shown somewhat hopeful interaction results against BACE-1 active site. ∆G and Ki values of ꞌAcerogenin A-BACE1ꞌ, ꞌe-viniferin-BACE1ꞌ and ꞌSophoraflavanone G-BACE1ꞌ interactions were ꞌ-7.93kcal/mol and 1.53mMꞌ, ꞌ-7.56kcal/mol and 2.86mMꞌ and ꞌ-6.05kcal/mol and 37.06mMꞌ, respectively. On the other hand, ꞌVerubecestat-BACE1ꞌ interaction ∆G and Ki values were -8.99kcal/mol and 255.44nM, respectively. Amino acid residues, L30, Y71, T72, Q73, K107, F108, W115, I118, G230, and T231 were found to be common in BACE-1 interaction with Acerogenin A, e-viniferin, Sophoraflavanone G, and positive control Verubecestat (Figure 3).

 

 

Fig. 3: Amino acids involved in ꞌsecretase (BACE1)ꞌ interaction with natural Sodium-Glucose co-transporters 2 inhibitors and control. a) BACE1-AcerogeninA interaction, b) BACE1-e viniferin interaction, c) BACE1-SophoraflavanoneG interaction and d) BACE1-Verubecestat interaction. The ligands are shown in ꞌstickꞌ representation.

 

It has been observed that D32 and D228 play an important role in selective inhibition of BACE1 (Hernández-Rodríguez et al., 2016), in this study Acerogenin A and Verubecestat showed interaction with these two amino acid residues of BACE-1 active site. Also, Rajasekhar et al. (2015) have shown that the F108 side chain of the flap pocket has interacted eminently with the BACE-1 inhibitors. In this study, all the compounds interacted with the F108 amino acid residue of the BACE-1 active site. The binding energy of BACE-1 inhibition of natural SGLT2 inhibitors was less than the positive control, although, we tried to target the amyloidogenic pathway by another approach i.e., estimating amyloidb anti-aggregation potential directly.

Amyloidb (Ab) peptide is a 39-42 amino acid residues long fragment produced by proteolytic cleavage of amyloid precursor protein with the help of BACE-1 and g-secretase enzymes (Masters et al., 1985; Kang et al., 1987). Accumulation of these fragments results in amyloid plaque formation in the brain of AD patients. A more common form of Ab peptide fragment that was observed in AD patient brain is 1-42 amino acid residues (Ab1-42) long (Jarrett et al., 1993; Burdick et al., 1992; Riek et al., 2001). Deep structural insight of Ab1-42 fragment showed that first 1 to 17 amino acid residues are not organized while 18 to 42 amino acids form a b-turn-b-fold motif with two parallel intermolecular b sheets (Lührs et al., 2005). At least two of these Ab1-42 peptides are needed to form the reiterating structural base of protofilament. Interaction patterns of b-turn-b-fold motifs have elucidated the sequence selectivity and cooperativity for Aβ fibril development. In the present study, firstly b-turn-b-fold of Ab1-42 peptide (Ab17-42) were docked with each other with the help of the Hex 5.1 docking tool to decipher the specificity towards the respective motif and to comprehend the resultant conformational adjustments the beta-strand adopt in aggregate formation. Total interaction energy (E-total) for Ab17-42 and Ab17-42 interaction were found to be -1236.83 KJ/mol (Table 5). Afterward, we estimated the E-total of compound bounded Ab17-42 with the native form of Ab17-42 for each natural SGLT2 inhibitor to observe the changes in interaction pattern. Alstiphyllanine D, e-viniferin and Sophoraflavanone G have significantly reduced the ꞌAb17-42ꞌ - Ab17-42ꞌ interaction with E-total value as -766.58KJ/mol, -724.56KJ/mol and -740.14KJ/mol, respectively. These E-total values are very near to the E-total value of positive control curcumin i.e., -760.75KJ/mol. Recently, Bibi et al. (2019) have also applied a similar approach to observe the inhibitory effect of an anticancer drug (bexarotene) on Ab17-42 aggregation.

Microtubules are stabilized by a microtubule-associated protein known as tau, and tau hyperphosphorylation is a hallmark feature of AD (Weingarten et al., 1975; Avila et al., 2004; Alonso et al., 1996; Ballatore et al., 2007). There are more than 20 kinases that participate in phosphorylation of tau protein and the majority of these kinases are involved in phosphorylating AD sites of tau (Martin et al., 2013). Each of tau phosphorylating kinase has specific phosphorylation sites and contributes to neurodegeneration associated with AD. Tau hyperphosphorylation causes abnormal tau aggregation and diminishes its affinity towards microtubules, this may lead to disruption of microtubules mediated axonal growth, signal propagation, and vesicle transport (Gendron et al., 2009; LaPointe et al., 2009). These adverse effects might aggravate the symptoms of AD. In the present study, we have chosen three potential tau phosphorylation targets i.e., Fyn, GSK 3b, and TTBK1.

 

 

Table 5: Docking results of interaction of one b-turn-b-fold of Ab1-42 peptide with another b-turn-b-fold of Ab1-42 peptide before and after binding of natural SGLT2 inhibitors

Target

Ligand

E-total (KJ/mol)

b-turn-b-fold of Ab1-42 peptide (Ab17-42)

b-turn-b-fold of Ab1-42 peptide (Ab17-42)

-1236.83

Ab17-42

Ab17-42bounded with Acerogenin A

-1226.15

Ab17-42

Ab17-42bounded with Alstiphyllanine D

-766.58

Ab17-42

Ab17-42bounded with e-viniferin

-724.56

Ab17-42

Ab17-42bounded with Phlorizin

-1058.28

Ab17-42

Ab17-42bounded with Sophoraflavanone G

-740.14

Ab17-42

Ab17-42bounded with Curcumin

-760.75

 

 

Fyn belongs to the Src family of non-receptor tyrosine kinases that directly associates with tau and phosphorylates tyrosine residues of tau protein. Its role in AD has been linked due to its participation in tau hyperphosphorylation. Thus, Fyn inhibition is considered a therapeutic intervention for AD (Nygaard et al., 2014; Nygaard et al., 2018). In this study, Acerogenin A, Alstiphyllanine D, and e-viniferin showed better interaction with the active site domain of Fyn kinase enzyme when compared with positive control Leuco-methylthioninium. ∆G and Ki values for ꞌAcerogenin-Fynꞌ, ꞌAlstiphyllanine D-Fynꞌ, ꞌe-viniferin-Fynꞌ and ꞌLeuco-methylthioninium-Fynꞌ interactions were found to be ꞌ-7.87kcal/mol and 1.72mMꞌ, ꞌ-6.78kcal/mol and 10.64mMꞌ, ꞌ-8.75kcal/mol and 387.80nMꞌ and ꞌ-6.33kcal/mol and 23.10mMꞌ, respectively (Table 4). L17, V25, A37, K39, E54, T82, G88, L137, A147, and D148 were the important amino acid residues of ꞌkinaseꞌ domain of Fyn that were commonly interacting with Acerogenin A, Alstiphyllanine D, e-viniferin and positive control Leuco-methylthioninium (Figure 4).

 

Fig. 4: Amino acids involved in ꞌFyn kinase (Fyn)ꞌ interaction with natural Sodium-Glucose co-transporters 2 inhibitors and control. a) Fyn-AcerogeninA interaction, b) Fyn-AlstiphyllanineD interaction, c) Fyn-SophoraflavanoneG interaction and d) Fyn-Leucomethylthioninium interaction. The ligands are shown in ꞌstickꞌ representation.

 

GSK3b is a serine/threonine-protein kinase, that plays a pivotal role in AD pathogenesis via activating various pathways including tau hyperphosphorylation (Hooper et al., 2008;  Kremer et al., 2011;  Avila et al., 2012). Several reports revealed that it is a part of common pathology in T2DM and AD (Gao et al., 2012). Thus, it is regarded as one of the key targets for both these linked diseases. In this study, Acerogenin A, e-viniferin, and Sophoraflavanone G interacted well with the active site of GSK3b. Interestingly molecular interactions of these natural SGLT2 inhibitors with GSK3b were better than positive control Leuco-methylthioninium (Table 4). Acerogenin A, e-viniferin, Sophoraflavanone G and Leuco-methylthioninium molecular interaction with the ꞌkinaseꞌ domain of GSK3b estimated ꞌ∆G and Kiꞌ values as ꞌ-7.67kcal/mol and 2.37mMꞌ, ꞌ-7.01kcal/mol and 7.31mMꞌ, ꞌ-7.05kcal/mol and 6.82mMꞌ and ꞌ-6.29kcal/mol and 24.65mMꞌ, respectively. Amino acid residues, namely,  I62, V70, A83, K85, Y134, V135, R141, L188, and C199 of the kinase domain of GSK3b are commonly involved in the interaction with  Acerogenin A, e-viniferin, Sophoraflavanone G, and Leuco-methylthioninium (Figure 5).

 

Fig. 5: Amino acids involved in ꞌGlycogen synthase kinase 2 beta (GSK3 GSK3b)ꞌ interaction with natural Sodium-Glucose co-transporters 2 inhibitors and control. a) GSK3 GSK3b -AcerogeninA interaction, b) GSK3 GSK3b- e viniferin interaction, c) GSK3b-SophoraflavanoneG interaction and d) GSK3b-Leucomethylthioninium interaction. The ligands are shown in ꞌstickꞌ representation.

 

Afreen et al. (2015) studied amino acid residues of GSK3b active site that was crucial for their selectivity over other kinases and found that interaction with D133, V135, and R141 was enough for active site inhibition and selectivity. Importantly, the selected natural SGLT2 inhibitors have shown interaction with V135 and R141 amino acid residues of GSK3b.

Also, it has been observed that disruption of hydration shell around V70 amino acid residue is also linked with increased activity of GSK3b inhibitors (Arfeen et al., 2015; Liang et al., 2016). In the present study, V70 amino acid residue of GSK3b active site was found to be interacting with Acerogenin A, e-viniferin, Sophoraflavanone G, and Leuco-methylthioninium. TTBK1 is a neuron-specific kinase enzyme that plays an important role in tau hyperphosphorylation and tau aggregation (Sato et al., 2006). Thus, targeting TTBK1 is considered as a potential upcoming strategy against AD. In the present study, Acerogenin A and e-viniferin were found to be better interacted with the ATP binding domain of TTBK1 as compared with positive control Leuco-methylthioninium (Table 4).

 

Fig. 6: Amino acids involved in ꞌTau Tubulin Kinase 1 (TTBK1)ꞌ interaction with natural Sodium-Glucose co-transporters 2 inhibitors and control. a) TTBK1-AcerogeninA interaction, b) TTBK1- e viniferin interaction, c) TTBK1-SophoraflavanoneG interaction and d) TTBK1-Leucomethylthioninium interaction. The ligands are shown in ꞌstickꞌ representation.

 

The ꞌ∆G and Kiꞌ values of ꞌAcerogenin A-TTBK1ꞌ, ꞌe-viniferin-TTBK1ꞌ and ꞌLeuco-methylthioninium-TTBK1ꞌ were estimated as ꞌ-7.90kcal/mol and 1.61mMꞌ, ꞌ-7.60kcal/mol, and 2.69mMꞌ and ꞌ-6.11kcal/mol and 33.46mMꞌ, respectively. However, ꞌ Sophoraflavanone G-TTBK1ꞌ interaction showed ∆G and Ki values as -5.99kcal/mol and 40.67mM, respectively. Amino acid residues, namely, I64, I72, A85, M131, L133, Q134, G135, N137, and L199 of the kinase domain of TTBK1 were found to be commonly interacting with Acerogenin A, e-viniferin, and Leuco-methylthioninium (Figure 6).  It can be safely stated from tau phosphorylation pathway inhibition results that Acerogenin A and e-viniferin were the best among all-natural SGLT2 inhibitors.

On the whole, Acerogenin A, e-viniferin, and Sophoraflavanone G were the best among all the natural SGLT2 inhibitors tested against AD targets. Acerogenin A is a cyclic diarylheptanoid isolated from Acer nikoense (Aceraceae) that is a potent SGLT2 inhibitor with 94mM IC50 against SGLT2 (Morita et al., 2010). e-viniferin is a resveratrol dimer that belongs to the stilbenoids family, and the IC50 value of e-viniferin against SGLT2 was estimated to be 110 mM (Oranje et al., 2019). On the other hand, Sophoraflavanone G is a phytoncide isolated from the Sophora flavescens (Fabaceae) family, and its IC50 value against SGLT2 was estimated to be 4.1 mM (Sato et al., 2007). In this study, these potent SGLT2 natural inhibitors were tested against various AD targets via in silico approach. They showed potential against cholinesterase, amyloidogenic, and tau phosphorylation pathways associated with AD. Further, in vitro and in vivo studies are needed to establish the dual therapeutic potential of these natural SGLT2 inhibitors. However, it has been observed that in silico findings often correlates well with wet-lab results. Nonetheless, the important structural insights of natural SGLT2 inhibitors binding to different AD targets were revealed and compared with positive control.

Conclusions

In the present study, anti-Alzheimer’s potential for natural SGLT2 inhibitors was tested to predict their dual targeting efficacy. Natural SGLT2 inhibitors, namely, Acerogenin A, Alstiphyllanine D, e-viniferin, Phlorizin, and Sophoraflavanone G were chosen for the study. Physicochemical properties, toxicity potential, and molecular interaction patterns against several targets of Alzheimer’s Disease (AD) have been revealed. Findings suggested that Acerogenin A, e-viniferin, and Sophoraflavanone G were the potent dual drug candidates for both Type 2 Diabetes Mellitus (T2DM) and AD. Acerogenin A and e-viniferin both showed no violation of the Lipinski rule and no predicted toxicity. Acerogenin A, e-viniferin, and Sophoraflavanone G all showed strong binding with the active site of AD therapeutic target enzymes. Interestingly, e-viniferin and Sophoraflavanone G bind efficiently to the interface of β-sheets with the potential of converting neurotoxic amyloidβ aggregates to nontoxic forms. In the end, it could safely state that among the SGLT2 inhibitors tested e-viniferin could provide a better scaffold to develop dual therapeutic agents against T2DM and AD shortly.

Acknowledgment

The author would like to thank and acknowledge Research Deanship, University of Hail, Hail, KSA for the financial support received through Project number (BA-1901).

Conflict of Interest

The author declares no conflict of interest related to this work.

ORCID

Ahmed Alafnan https://orcid.org/0000-0002-2473-4952

References

Abdulaziz A A, G. Dapar M L, E. Manting M M, J. Torres M A, Aranas A T, R. Mindo R A and et al. (2019), Qualitative Evaluation of the Antimicrobial, Antioxidant, and Medicinally Important Phytochemical Constituents of the Ethanolic Extracts of the Leaves of Gliricidia sepium (Jacq.) Walp, Pharmacophore, 10(4), 72-83.

Ahmed, M. Q., Alenazi, F. S., Fazaludeen, M. F., Shahid, S. M. A., & Kausar, M. A. (2018). Pathology and Management of Alzheimer's disease: A review. International Journal of Pharmaceutical Research & Allied Sciences, 7(2), 30-42.

Alharthy, N. A., & Bawazir, A. E. (2019). Effects of The Mixture Dried Figs (Ficus Carica) And ‎Olive Oil on Amnesia Model of Alzheimer's Induced by ‎Scopolamine in Male Albino Rats. Pharmacophore, 10(4), 62-71

Alonso, A.D.C., Grundke-Iqbal, I., & Iqbal, K. (1996). Alzheimer's disease hyperphosphorylated tau sequesters normal tau into tangles of filaments and disassembles microtubules. Nature medicine2(7), 783-87.

Arai, H., Hirasawa, Y., Rahman, A., Kusumawati, I., Zaini, N.C., Sato, S., Aoyama, C., Takeo, J. and Morita, H (2010). Alstiphyllanines E–H, picraline, and ajmaline-type alkaloids from Alstonia macrophylla inhibiting sodium-glucose cotransporter. Bioorganic & medicinal chemistry18(6), 2152-2158.

Arfeen, M., Patel, R., Khan, T., & Bharatam, P.V. (2015). Molecular dynamics simulation studies of GSK-3β ATP competitive inhibitors: understanding the factors contributing to selectivity. Journal of Biomolecular Structure and Dynamics33(12), 2578-93.

Avila, J., León-Espinosa, G., García, E., García-Escudero, V., Hernández, F., & DeFelipe, J. (2012). Tau phosphorylation by GSK3 in different conditions. International Journal of Alzheimer’s Disease2012, 578373.

Avila, J., Lucas, J.J., Perez, M.A.R., & Hernandez, F. (2004). Role of tau protein in both physiological and pathological conditions. Physiological Reviews84(2), 361-84.

Ballatore, C., Lee, V.M.Y., & Trojanowski, J.Q. (2007). Tau-mediated neurodegeneration in Alzheimer's disease and related disorders. Nature reviews neuroscience8(9), 663-72.

Bibi, N., Rizvi, S., Batool, A., & Kamal, M.A. (2019). Inhibitory Mechanism of An Anticancer Drug, Bexarotene Against Amyloid β Peptide Aggregation: Repurposing Via Neuroinformatics Approach. Current pharmaceutical design25(27), 2989-95.

Burdick, D., Soreghan, B., Kwon, M., Kosmoski, J., Knauer, M., Henschen, A., Yates, J., Cotman, C., and Glabe, C. (1992). Assembly and aggregation properties of synthetic Alzheimer's A4/beta-amyloid peptide analogs. Journal of Biological Chemistry267(1), 546-54.

Caberlotto, L., Nguyen, T.P., Lauria, M., Priami, C., Rimondini, R., Maioli, S., Cedazo-Minguez, A., Sita, G., Morroni, F., Corsi, M. and Carboni, L. (2019). Cross-disease analysis of Alzheimer’s disease and type-2 Diabetes highlights the role of autophagy in the pathophysiology of two highly comorbid diseases. Scientific reports9(1), 1-13.

Choi, C.I. (2016). Sodium-glucose cotransporter 2 (SGLT2) inhibitors from natural products: discovery of next-generation antihyperglycemic agents. Molecules21(9), 1136.

Cummings, J., Lee, G., Ritter, A., Sabbagh, M., & Zhong, K. (2019). Alzheimer's disease drug development pipeline: 2019. Alzheimer's & Dementia: Translational Research & Clinical Interventions5, 272-93.

De la Monte, S.M., & Wands, J.R. (2008). Alzheimer's disease is type 3 diabetes—evidence reviewed. Journal of diabetes science and technology2(6), 1101-13.

Dı́ez-Sampedro, A., Wright, E.M., & Hirayama, B.A. (2001). Residue 457 controls sugar-binding and transport in the Na+/glucose cotransporter. Journal of Biological Chemistry276(52), 49188-94.

Fioretto, P., Giaccari, A., & Sesti, G. (2015). Efficacy and safety of dapagliflozin, a sodium-glucose cotransporter 2 (SGLT2) inhibitor, in diabetes mellitus. Cardiovascular diabetology14(1), 142.

Gao, C., Hölscher, C., Liu, Y., & Li, L. (2012). GSK3: a key target for the development of novel treatments for type 2 diabetes mellitus and Alzheimer's disease. Reviews in the Neurosciences23(1), 1-11.

Gendron, T.F., & Petrucelli, L. (2009). The role of tau in neurodegeneration. Molecular neurodegeneration4(1), 13.

Ghosh, A.K., Gemma, S., & Tang, J. (2008). β-Secretase as a therapeutic target for Alzheimer’s disease. Neurotherapeutics5(3), 399-408.

Hernández-Rodríguez, M., Correa-Basurto, J., Gutiérrez, A., Vitorica, J., & Rosales-Hernández, M.C. (2016). Asp32 and Asp228 determine the selective inhibition of BACE1 as shown by docking and molecular dynamics simulations. European journal of medicinal chemistry124, 1142-54.

Hooper, C., Killick, R., & Lovestone, S. (2008). The GSK3 hypothesis of Alzheimer’s disease. Journal of neurochemistry104(6), 1433-39.

Hsia, D.S., Grove, O., & Cefalu, W.T. (2017). An update on SGLT2 inhibitors for the treatment of diabetes mellitus. Current opinion in endocrinology, diabetes, and obesity24(1), 73-79.

Hsu, C.C., Wahlqvist, M.L., Lee, M.S., & Tsai, H.N. (2011). Incidence of dementia is increased in type 2 diabetes and reduced by the use of sulfonylureas and metformin. Journal of Alzheimer's Disease24(3), 485-93.

http://www.openmolecules.org/datawarrior/download.html. Orisis Data warrior tool. Cited 04 March 2020.

https://diabetesatlas.org/IDF_Diabetes_Atlas_8e_interactive_EN/. International Diabetes Federation 2017. Cited 04 March 2020.

https://www.alz.co.uk/research/WorldAlzheimerReport2018.pdf. World Alzheimer’s Report  2018. Cited 04 March 2020.

https://www.fda.gov/media/87579/download. Food and Drug Administration (FDA) 2013. Cited 04 March 2020.

Jarrett, J.T., Berger, E.P., & Lansbury Jr, P.T. (1993). The carboxy terminus of the. beta. amyloid protein is critical for the seeding of amyloid formation: Implications for the pathogenesis of Alzheimer's disease. Biochemistry32(18), 4693-97.

Kang, J., Lemaire, H.G., Unterbeck, A., Salbaum, J.M., Masters, C.L., Grzeschik, K.H., Multhaup, G., Beyreuther, K., and Müller-Hill, B. (1987). The precursor of Alzheimer's disease amyloid A4 protein resembles a cell-surface receptor. Nature325(6106), 733-36.

Kremer, A., Louis, J.V., Jaworski, T., & Van Leuven, F. (2011). GSK3 and Alzheimer’s Disease: Facts and Fiction. Front Mol Neurosci.  4, 17.

Kua, J., Zhang, Y., Eslami, A.C., Butler, J.R., & McCammon, J.A. (2003). Studying the roles of W86, E202, and Y337 in binding of acetylcholine to acetylcholinesterase using a combined molecular dynamics and multiple docking approach. Protein Science12(12), 2675-84.

LaPointe, N.E., Morfini, G., Pigino, G., Gaisina, I.N., Kozikowski, A.P., Binder, L.I., & Brady, S.T. (2009). The amino terminus of tau inhibits kinesindependent axonal transport: implications for filament toxicity. Journal of neuroscience research87(2), 440-51.

Liang, S.H., Chen, J.M., Normandin, M.D., Chang, J.S., Chang, G.C., Taylor, C.K., Trapa, P., Plummer, M.S., Para, K.S., Conn, E.L. and LoprestiMorrow, L. et al. (2016). Discovery of a Highly Selective Glycogen Synthase Kinase3 Inhibitor (PF04802367) That Modulates Tau Phosphorylation in the Brain: Translation for PET Neuroimaging. Angewandte Chemie International Edition in English, 55(33), 9601-5.

Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews23(1-3), 3-25.

Liu, T., Krofchick, D., & Silverman, M. (2009). Effects on conformational states of the rabbit sodium/glucose cotransporter through modulation of polarity and charge at glutamine 457. Biophysical Journal96(2), 748-60.

Lührs, T., Ritter, C., Adrian, M., Riek-Loher, D., Bohrmann, B., Döbeli, H., Schubert, D. and Riek, R. (2005). 3D structure of Alzheimer's amyloid-β (1–42) fibrils. Proceedings of the National Academy of Sciences102(48), 17342-47.

Martin, L., Latypova, X., Wilson, C.M., Magnaudeix, A., Perrin, M.L., Yardin, C., & Terro, F. (2013). Tau protein kinases: involvement in Alzheimer's disease. Aging research reviews12(1), 289-09.

Masters, C.L., Simms, G., Weinman, N.A., Multhaup, G., McDonald, B.L., & Beyreuther, K. (1985). Amyloid plaque core protein in Alzheimer disease and Down syndrome. Proceedings of the National Academy of Sciences82(12), 4245-49.

Matsuzaki, T., Sasaki, K., Tanizaki, Y., Hata, J., Fujimi, K., Matsui, Y., Sekita, A., Suzuki, S.O., Kanba, S., Kiyohara, Y. and Iwaki, T. (2010). Insulin resistance is associated with the pathology of Alzheimer's disease: the Hisayama study. Neurology75(9), 764-70.

McIntosh, E.C., & Nation, D.A. (2019). Importance of treatment status in links between type 2 diabetes and Alzheimer’s disease. Diabetes care42(5), 972-79.

MD Rizvi, S., Shakil, S., Biswas, D., Shakil, S., Shaikh, S., Bagga, P., & A Kamal, M. (2014). Invokana (Canagliflozin) as a dual inhibitor of Acetylcholinesterase and Sodium-Glucose Co-Transporter 2: advancement in Alzheimer’s Disease-Diabetes Type 2 linkage via an Enzoinformatics study. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders)13(3), 447-51.

Morita, H., Deguchi, J., Motegi, Y., Sato, S., Aoyama, C., Takeo, J., Shiro, M., and Hirasawa, Y. (2010). Cyclic diarylheptanoids as Na+-glucose cotransporter (SGLT) inhibitors from Acer nikoense. Bioorganic & medicinal chemistry letters20(3), 1070-74.

N Fawver, J., Ghiwot, Y., Koola, C., Carrera, W., Rodriguez-Rivera, J., Hernandez, C., T Dineley, K., Kong, Y., Li, J., Jhamandas, J. and Perry, G. (2014). Islet amyloid polypeptide (IAPP): a second amyloid in Alzheimer's disease. Current Alzheimer Research11(10), 928-40.

Nygaard, H B. (2018). Targeting Fyn kinase in Alzheimer’s disease. Biological psychiatry83(4), 369-76.

Nygaard, H.B., van Dyck, C.H., & Strittmatter, S.M. (2014). Fyn kinase inhibition as a novel therapy for Alzheimer’s disease. Alzheimer's research & therapy6(1), 8.

Oranje, P., Gouka, R., Burggraaff, L., Vermeer, M., Chalet, C., Duchateau, G., van der Pijl, P., Geldof, M., de Roo, N., Clauwaert, F. and Vanpaeschen, T. (2019). Novel natural and synthetic inhibitors of solute carriers SGLT1 and SGLT2. Pharmacology Research & Perspectives7(4), e00504.

Oskarsson, M.E., Paulsson, J.F., Schultz, S.W., Ingelsson, M., Westermark, P., & Westermark, G.T. (2015). In vivo seeding and cross-seeding of localized amyloidosis: a molecular link between type 2 diabetes and Alzheimer's disease. The American journal of pathology185(3), 834-46.

Osman, A. S., Gad, M. H., Hareedy, A. A., Mishriki, A. A., & Rasheed, E. A. M. A. (2019). Sitagliptin attenuates cognitive impairment in the rat model of Aluminum-induced Alzheimer’s disease. Journal of Advanced Pharmacy Education & Research| Jul-Sep, 9(3), 53-61

Peila, R., Rodriguez, B.L., & Launer, L.J. (2002). Type 2 diabetes, APOE gene, and the risk for dementia and related pathologies: The Honolulu-Asia Aging Study. Diabetes51(4), 1256-62.

Rajasekhar, K., Chakrabarti, M., & Govindaraju, T. (2015). Function and toxicity of amyloid beta and recent therapeutic interventions targeting amyloid-beta in Alzheimer's disease. Chemical Communications51(70), 13434-50.

Remya, C., Dileep, K.V., Tintu, I., Variyar, E.J., & Sadasivan, C. (2014). Flavanone glycosides as acetylcholinesterase inhibitors: computational and experimental evidence. Indian journal of pharmaceutical sciences76(6), 567-70.

Riek, R., Güntert, P., Döbeli, H., Wipf, B., & Wüthrich, K. (2001). NMR studies in aqueous solution fail to identify significant conformational differences between the monomeric forms of two Alzheimer peptides with widely different plaquecompetence, Aβ (140) ox and Aβ (142) ox. European journal of biochemistry268(22), 5930-36.

Risner, M.E., Saunders, A.M., Altman, J.F., Ormandy, G.C., Craft, S., Foley, I.M., Zvartau-Hind, M.E., Hosford, D.A., and Roses, A.D. (2006). Rosiglitazone in Alzheimer’s Disease Study Group. Efficacy of rosiglitazone in a genetically defined population with mild-to-moderate Alzheimer’s disease. Pharmacogenomics J6(4), 246-54.

Rizvi, S.M.D., Shaikh, S., Waseem, S M.A., Shakil, S., Abuzenadah, A.M., Biswas, D., & Kamal, M.A. (2015). Role of anti-diabetic drugs as therapeutic agents in Alzheimer's disease. EXCLI Journal14, 684-96.

Rizvi, S.M.D., Shakil, S., & Haneef, M. (2013). A simple click by click protocol to perform docking: AutoDock 4.2 made easy for non-bioinformaticians. Excli Journal12, 831-57.

Sato, S., Cerny, R.L., Buescher, J.L., & Ikezu, T. (2006). Tautubulin kinase 1 (TTBK1), a neuronspecific tau kinase candidate, is involved in tau phosphorylation and aggregation. Journal of neurochemistry98(5), 1573-84.

Sato, S., Takeo, J., Aoyama, C., & Kawahara, H. (2007). Na+-glucose cotransporter (SGLT) inhibitory flavonoids from the roots of Sophora flavescens. Bioorganic & medicinal chemistry15(10), 3445-49.

Scheen, A.J. (2015). Pharmacodynamics, efficacy, and safety of sodium-glucose co-transporter type 2 (SGLT2) inhibitors for the treatment of type 2 diabetes mellitus. Drugs75(1), 33-59.

Shaikh, S., Rizvi, S.M.D., Shakil, S., Riyaz, S., Biswas, D., & Jahan, R. (2016). Forxiga (dapagliflozin): Plausible role in the treatment of diabetesassociated neurological disorders. Biotechnology and applied biochemistry63(1), 145-50.

Sonnen, J.A., Larson, E.B., Brickell, K., Crane, P.K., Woltjer, R., Montine, T.J., & Craft, S. (2009). Different patterns of cerebral injury in dementia with or without diabetes. Archives of neurology66(3), 315-22.

Sussman, J.L., Harel, M., Frolow, F., Oefner, C., Goldman, A., Toker, L., & Silman, I. (1991). Atomic structure of acetylcholinesterase from Torpedo californica: a prototypic acetylcholine-binding protein. Science253(5022), 872-79.

Weingarten, M.D., Lockwood, A.H., Hwo, S.Y., & Kirschner, M.W. (1975). A protein factor essential for microtubule assembly. Proceedings of the National Academy of Sciences72(5), 1858-62.

Zhao, Y.H., Abraham, M.H., Le, J., Hersey, A., Luscombe, C.N., Beck, G., Sherborne, B., & Cooper, I. (2002). Rate-limited steps of human oral absorption and QSAR studies. Pharmaceutical Research19(10), 1446-57.

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