Novel Thiazolidinedione Derivatives as effective agonists of Peroxisome Proliferator Activator Receptor-γ: A Comprehensive Study on Design, Synthesis, Molecular Docking, MD Simulations, and Antidiabetic Efficacy

doi:10.21203/rs.3.rs-3232145/v1

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Novel Thiazolidinedione Derivatives as effective agonists of Peroxisome Proliferator Activator Receptor-γ: A Comprehensive Study on Design, Synthesis, Molecular Docking, MD Simulations, and Antidiabetic Efficacy

https://doi.org/10.21203/rs.3.rs-3232145/v1

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In the quest for potential anti-diabetic agents, a novel series of thiazolidinedione derivatives (2a-j)were synthesized and characterized by spectroscopic data. These newly developed derivatives were subjected to molecular docking studies, utilizing the binding pocket of the Peroxisome proliferator-activated receptor-gamma (PPAR-γ) agonist, to understand their binding interactions. To assess the stability of the ligand-PPAR-γ complex, molecular dynamics (MD) simulations were conducted for synthesized compounds. The cytotoxicity of the compounds was evaluated in C2C12 myoblasts using the MTT assay. Additionally, the compounds were tested on C2C12 cell lines to confirm their PPAR-γ transcription activity. Furthermore, the synthesized compounds underwent evaluation for their in vivo blood glucose-lowering activity using a dexamethasone-induced diabetic rat model. Significantly, compounds 2b, 2g, and 2h demonstrated potential as PPAR-γ agonists compared to the reference drug Pioglitazone. Data analysis revealed noteworthy blood glucose-lowering effects (hypoglycaemia) for compounds 2b, 2g, and 2h (110.04±5.42, 114.52±6.32, and 118.48±5.93, respectively), as well as for the reference drug Pioglitazone (153.93±4.61), when compared to the diabetic control. Moreover, compounds 2b, 2g, and 2h exhibited significant reductions in the levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP), indicating the absence of liver damage.

Anti-diabetic

Thiazolidinedione

Molecular Docking

PPAR-gamma

Type 2 diabetes mellitus (T2DM), previously referred to as non-insulin dependent diabetes mellitus (NIDDM), has emerged as a significant global health concern, affecting more than 285 million individuals worldwide.[1] Projections suggest that by 2030, the number of diagnosed diabetes cases will increase to approximately 552 million.[2] T2DM encompasses a diverse and complex group of metabolic disorders characterized by elevated blood glucose levels due to impaired insulin secretion or insulin resistance. The development of abnormal insulin secretion or insulin resistance is influenced by various factors, including genetic defects, aging, viral infections (such as Cytomegalovirus and Coxsackie B4 virus), environmental factors, a sedentary lifestyle, and high-calorie intake.[3] T2DM also gives rise to long-term complications such as neuropathy, nephropathy, cataracts, and accelerated atherosclerosis, thereby increasing the risk of myocardial infarction.[4] The prevention and management of these complications pose significant therapeutic challenges as they are the primary causes of mortality and morbidity among individuals with diabetes.[5]

Peroxisome proliferator-activated receptor gamma (PPAR-γ) belongs to the family of peroxisome proliferator-activated receptors (PPARs) and plays a crucial role in improving insulin sensitivity while regulating the storage and breakdown of dietary fat.[6, 9] As a member of the nuclear receptor gene family, PPAR-γ is expressed in various tissues, especially the skin, brain, and adipose tissue. It governs gene expression in response to ligand binding. [8, 9] Recent research has highlighted the potential of PPAR-γ as an effective target for developing agonists to address metabolic diseases such as type 2 diabetes, atherosclerosis, and inflammatory skin conditions.[9] Furthermore, PPAR-γ agonists have shown neuroprotective effects against apoptosis induced by oxidative stress, holding promise for the treatment of chronic neurodegenerative disorders like Alzheimer's and Parkinson's disease.[10]

In the late 1990s, a class of medications called Thiazolidinediones (TZDs), also known as glitazones, emerged as a significant class of drugs for diabetes. These drugs consist of a five-membered thiazolidinedione moiety featuring carbonyl groups at positions 2 and 4.[11, 12] TZDs exhibit a broad pharmacological profile, encompassing antihyperglycemic and hypolipidemic effects, [13] as well as functioning as agonists for PPAR-γ and inhibitors of aldose reductase,[14] α-glucosidase,[15] GPR, PTP 1B.[16] They also possess anticancer properties, act as inhibitors of PI3 kinase γ and tyrosinase,[17] and demonstrate antimicrobial and antiviral activities.[18] Furthermore, TZDs exhibit antioxidant, xanthine oxidase inhibitory, anti-inflammatory, AI-2-quorum sensing inhibitory, antimalarial, β3 agonist, FFAR1 agonist,[19] PGDH inhibitory, LPS-induced NO production inhibitor,[20] and peptide deformalize inhibitory properties.[21]

Notably, PPAR-γ agonists, such as TZDs, have been shown to regulate microglial-mediated inflammatory responses and inhibit the expression of proinflammatory markers. The most commonly used PPAR-γ agonists in this class are Pioglitazone, Rosiglitazone, Troglitazone, and Ciglitazone. However, Rosiglitazone has been withdrawn from the market due to its association with myocardial infarction and heightened risks for patients, especially those with a history of cardiovascular disease. Presently, Pioglitazone has emerged as the most popular antidiabetic agent in the market, although there are concerns about its potential to induce bladder tumors. [22, 23] Therefore, there is an increasing demand for the development of more potent agents targeting PPAR-γ to ensure their clinical applicability while reducing undesirable side effects.[24]

Based on the aforementioned information, in the present work, a novel series of thiazolidinedione derivatives (2a–j) were synthesized and subjected to in silico studies, ADMET predictions, toxicity predictions followed by MD simulations and subsequently evaluated for their in vitro PPAR-γ transcription and glucose uptake capabilities. Furthermore, in vivo, studies were conducted to assess their ability to lower blood glucose levels. The objective of this research is to overcome the complications and side effects associated with previously marketed drugs.[25]

Experimental

The required chemicals and solvents were procured from Sigma Aldrich, Himedia, and SD Fine Chemicals, Mumbai, India. To assess the uniformity of the compounds, thin-layer chromatography (TLC) was performed using a solvent system consisting of n-hexane and ethyl acetate in a ratio of 2:3. The spots were visualized in the UV chamber, employing pre-coated silica GF₂₅₄ plates. For the determination of melting points, Equiptronics digital melting point apparatus was used, and the reported values are uncorrected. FT-IR spectrum (cm^− 1) was obtained using an Alpha Bruker IR spectrometer. The ¹H-NMR spectra were recorded on a Jeol ECZ 400 FT-NMR spectrometer operating at 400 MHz, while the ¹³C-NMR spectra were acquired using the same Jeol ECZ 400 FT-NMR spectrometer, operating at 100 MHz. As an internal standard, trimethylsilyl (TMS) was employed (d ppm). Mass spectra were recorded with the Shimadzu LC-MS-8030 series.

Synthesis of (Z)-5-(2-chloro-5-nitrobenzylidene)thiazolidine-2,4-dione (1)

A mixture of 2,4-thiazolidinedione (A), (0.25 mol) and 2-chloro-5-nitrobenzaldehyde (B) (0.25 mol) was dissolved in 50 mL of hot glacial acetic acid. Fused sodium acetate (1.8 g) was slowly introduced to the solution, followed by refluxing for 8 hours. After the completion of the reaction, 300 mL of water was added and stirred vigorously. The precipitate was filtered, washed with water, and subsequently recrystallized from glacial acetic acid. The yield of the product was 85%, with a melting point range of 183–185°C.[26]

Synthesis of (Z)-5-(2-chloro-5-(phenylamino) benzylidene) thiazolidine-2,4-dione derivatives(2a-j)

A solution containing 5-(4-chlorobenzylidene)-2,4-thiazolidinedione (1) (0.01 mol) and appropriate primary amine (a-j) (0.01 mol) was prepared in a mixture of potassium carbonate (0.012 mol) and acetonitrile (7 mL). The reaction mixture was refluxed for a period of 10 hrs. Upon completion of the reaction, the resulting mixture was diluted with 30 mL of ice-cold water. The product was then isolated by filtration and subsequently recrystallized from ethanol. The physical data of the compounds (2a-j) is shown in Table-1.

(Z)-5-(2-chloro-5-((2,4-dichlorophenyl) amino) benzylidene) thiazolidine-2,4-dione(2a): Yellow solid. Yield 86%, 250–252°C, FT-IR (cm^− 1): 3544 (NH), 3037 (Ar, C-H), 1749 (C = O), 1518 (C = C). ¹H-NMR (400 MHz, DMSO-d6, δ, ppm):7.353–8.008 (m, Ar-H, 7H), 10.552 (s, NH, 1H). 12.136 (s, NH, 1H). ¹³C-NMR (400MHz, DMSOd6, δ, ppm): 117.76, 120.61, 120.79, 121.60, 123.05, 123.16, 126.10, 126.14, 127.72, 128.93, 132.08, 140.38, 154.25, 156.77 and 166.32. MS (m/z): calculated for C₁₆H₉Cl₃N₂O₂S is 399.6700; found: 399.6671 (M+).

(Z)-5-(2-chloro-5-((4-fluorophenyl)amino)benzylidene)thiazolidine-2,4-dione(2b): Yellow solid. Yield 81%, 243–245°C, FT-IR (cm^− 1): 3309 (NH), 3037 (Ar, C-H), 1779 (C = O), 1564 (C = C), 824(C-F). ¹H-NMR (400 MHz, DMSO-d6, δ, ppm):7.439–7.714 (m, Ar-H, 8H), 10.374 (s, NH, 1H). 12.880 (s, NH, 1H). ¹³C-NMR (400MHz, DMSOd6, δ, ppm): 117.76, 120.62, 120.79, 121.55, 121.60, 123.05, 123.16, 126.10, 126.14, 127.72, 128.93, 132.08, 140.38, 154.25, 156.77 and 166.32. MS (m/z): calculated for C₁₆H₁₀ClFN₂O₂S is 348.7764; found: 348.7768 (M+).

(Z)-5-(2-chloro-5-((4-nitrophenyl)amino)benzylidene)thiazolidine-2,4-dione (2c): Yellow solid. Yield 74%, 234–236°C, FT-IR (cm^− 1): 3356 (NH), 3209 (Ar, C-H), 1690 (C = O), 1523 (C = C), 825(Cl). ¹H-NMR (400 MHz, DMSO-d6, δ, ppm):6.277–7.529 (m, Ar-H, 8H), 9.948 (s, NH, 1H), 12.114 (s, NH, 1H). 13C-NMR (400MHz, DMSOd6, δ, ppm): 119.76, 123.34, 124.00, 128.51, 128.59, 129.25, 130.92, 134.89, 135.06, 137.15, 148.22, 148.22, 149.81, 149.81, 165.63, 166.92, 167.04 and 167.54. MS (m/z): calculated for C₁₆H₁₀ClN₃O₄S is 375.7830; found: 375.7812(M+).

(Z)-5-(2-chloro-5-((2,4-dinitrophenyl)amino)benzylidene)thiazolidine-2,4-dione (2d): Yellow solid. Yield 64%, 219–221°C, FT-IR (cm^− 1): 3573 (NH), 3037 (Ar, C-H), 1684 (C = O), 1519 (C = C), 826(Cl). ¹H-NMR (400 MHz, DMSO-d6, δ, ppm):7.410–7.644 (m, Ar-H, 7H), 10.734 (s, NH, 1H), 12.816 (s, NH, 1H). ¹³C-NMR (400MHz, DMSOd6, δ, ppm): 119.75, 123.35, 124.00, 128.51, 128.59, 129.25, 130.92, 134.89, 135.06, 137.15, 148.22, 148.22, 149.81, 149.81, 165.63, 166.92, 167.04 and 167.53. MS (m/z): calculated for C₁₆H₉ClN₄O₆S is 420.7800; found: 420.7850(M+).

(Z)-5-(2-chloro-5-((4-methyl-2-nitrophenyl)amino)benzylidene)thiazolidine-2,4-dione (2e): Yellow solid. Yield 72%, 251–253°C, FT-IR (cm^− 1): 3332 (NH), 3039 (Ar, C-H), 1749 (C = O), 1516 (C = C), 831(Cl). ¹H-NMR (400 MHz, DMSO-d6, δ, ppm):3.891(s, CH₃, 3H) 7.304–7.725 (m, Ar-H, 7H), 10.653 (s, NH, 1H), 12.780 (s, NH, 1H). ¹³C-NMR (400MHz, DMSOd6, δ, ppm): 56.33, 125.36, 128.66, 128.72, 129.67, 130.15, 130.22, 131.24, 142.80, 143.25, 143.56, 165.36, 165.41, 165.63, 166.92, 167.04 and 167.54. MS (m/z): calculated for C₁₇H₁₂ClN₃O₄S is 389.8100; found: 389.8632(M+).

(Z)-5-(2-chloro-5-((4-chlorophenyl)amino)benzylidene)thiazolidine-2,4-dione (2f): Yellow solid. Yield 78%, 204–206°C, FT-IR (cm^− 1): 3304 (NH), 3036 (Ar, C-H), 1686 (C = O), 1523 (C = C), 826(Cl). ¹H-NMR (400 MHz, DMSO-d6, δ, ppm):7.312–7.724 (m, Ar-H, 8H), 10.652 (s, NH, 1H), 12.782 (s, NH, 1H). ¹³C-NMR (400MHz, DMSOd6, δ, ppm): 125.36, 128.66, 128.72, 129.00, 129.67, 130.15, 130.22, 131.24, 142.80, 143.25, 143.56, 165.36, 165.41, 165.63, 166.92, and 167.04. MS (m/z): calculated for C₁₆H₁₀Cl₂N₂O₂S is 365.2280; found: 365.2262(M+).

(Z)-5-(2-chloro-5-((3-chloro-2-methylphenyl)amino)benzylidene)thiazolidine-2,4-dione (2g):Yellow solid. Yield 72%, 251–253°C, FT-IR (cm^− 1): 3573 (NH), 3036 (Ar, C-H), 1747 (C = O), 1513 (C = C), 828(Cl). ¹H-NMR (400 MHz, DMSO-d6, δ, ppm):3.254(s, CH₃, 3H) 6.978–7.673 (m, Ar-H, 7H), 9.825 (s, NH, 1H), 11.953 (s, NH, 1H). ¹³C-NMR (400MHz, DMSOd6, δ, ppm): 56.34, 125.36, 128.66, 128.72, 129.00, 129.67, 130.15, 130.22, 131.24, 142.80, 143.25, 143.56, 165.41, 165.63, 166.92, 167.04 and 167.54. MS (m/z): calculated for C₁₇H₁₂ClN₃O₄S is 379.2550; found: 379.2280(M+)

(Z)-5-(2-chloro-5-((2,5-dimethylphenyl)amino)benzylidene)thiazolidine-2,4-dione (2h): Yellow solid. Yield 72%, 251–253°C, FT-IR (cm^− 1): 3332 (NH), 3039 (Ar, C-H), 1749 (C = O), 1516 (C = C), 831(Cl). ¹H-NMR (400 MHz, DMSO-d6, δ, ppm):3.321 (s, CH₃, 7H) 6.978–7.673 (m, Ar-H, 6H), 10.389 (s, NH, 1H), 11.925 (s, NH, 1H). ¹³C-NMR (400MHz, DMSOd6, δ, ppm): 30.04, 56.33, 125.36, 128.66, 128.72, 129.00, 129.67, 130.15, 130.22, 131.24, 142.80, 143.25, 143.56, 165.41, 165.63, 166.92, 167.04 and 167.54. MS (m/z): calculated for C₁₇H₁₂ClN₃O₄S is 358.8400; found: 358.8462(M+).

(Z)-5-(2-chloro-5-((3-(trifluoromethyl)phenyl)amino)benzylidene)thiazolidine-2,4-dione (2i): Yellow solid. Yield 73%, 224–226°C, FT-IR (cm^− 1): 3334 (NH), 3037 (Ar, C-H), 1690 (C = O), 1523 (C = C), 826 (Cl). ¹H-NMR (400 MHz, DMSO-d6, δ, ppm): 7.345–7.475 (m, Ar-H, 7H), 9.950 (s, NH, 1H), 12.112 (s, -NH, 1H). ¹³C-NMR (400MHz, DMSOd6, δ, ppm): 125.36, 128.66, 128.72, 129.00, 129.67, 130.15, 130.22, 131.24, 142.80, 143.25, 143.56, 165.36, 165.41, 165.63, 166.92, 167.04 and 167.43. MS (m/z): calculated for C₁₇H₁₀ClF₃N₂O₂S is 398.7842; found: 398.7829(M+).

(Z)-5-(2-chloro-5-((2,4,5-trichlorophenyl)amino)benzylidene)thiazolidine-2,4-dione (2j): Yellow solid. Yield 69%, 236–238°C, FT-IR (cm^− 1): 3356 (NH), 3036 (Ar, C-H), 1746 (C = O), 1514 (C = C), 828 (Cl). ¹H-NMR (400 MHz, DMSO-d6, δ, ppm): 7.459–7.706 (m, Ar-H, 5H), 10.374 (s, NH, 1H), 12.880 (s, NH, 1H). ¹³C-NMR (400MHz, DMSOd6, δ, ppm): 117.76, 120.61, 120.79, 121.55, 121.60, 123.05, 123.16, 123.16, 126.10, 126.14, 127.72, 128.93, 132.08, 140.38, 154.25, 156.77 and 166.32. MS (m/z): calculated for C₁₆H₈Cl₄N₂O₂S is 434.1120; found: 434.1236(M+).

Molecular Docking studies

Molecular docking studies were conducted using Glide 5.5 extra precision (XP) within the Maestro Schrodinger suite version 2022-4, running on the Linux 64 operating system. The aim was to examine the interactions and binding modes of the synthesized compounds (2a-j) with target receptors. The process involved several steps, including protein selection and preparation, receptor grid generation, ligand preparation, and docking to the receptor. To conduct molecular docking studies, the protein 2PRG (PDB ID) was obtained from the Protein Data Bank, with a resolution of 2.30 Å, and used as the receptor.

The peroxisome proliferator-activated receptor-gamma (PPAR-γ) is composed of two monomer chains, A and B. Chain B and water molecules beyond a distance of 5 Å from the ligand were removed from chain A, which contained a co-crystal ligand. Water molecules in proximity to the ligand were retained, and energy minimization was performed to refine the protein structures. The energy-minimized protein structures were utilized to create a receptor grid, with a reference drug serving as the ligand to identify binding sites within the receptor target. The ligands were prepared by converting their 2D or 3D structures into low-energy states in Maestro format using the OPLS 2005 force field. Modifications to the ligand structures were made to explore different ionization states. [27, 28]

Subsequently, the docking process was conducted using the Glide software with extra precision (XP) settings. XP descriptor information was written to generate favorable ligand poses, allowing for the determination of their spatial fit within the receptor's active site. The best-fitting poses were further evaluated, minimized, and assigned Glide scores. The Glide score, along with information on hydrogen bonds and pi-pi interactions formed with surrounding amino acids, was used to predict the binding affinities and proper alignment of the compounds within the active site of the PPAR-γ receptor. Table 2 presents the binding affinities and alignment details of these compounds at the active site of the PPAR-γ receptor.[29, 30]

Binding-free energy calculation using prime/MMGBSA approach

To estimate the binding energies and strain energies of a set of ligand molecules and a receptor molecule, Prime MM-GBSA (Molecular Mechanics – Generalized Born Model and Solvent Accessibility) was employed. This approach incorporated the OPLS_2005 force field, the VSGB solvent model,[31] and rotamer search algorithms. The Prime MM-GBSA simulation involved utilizing the Glide poses viewer file of the lead molecule to calculate the overall free energy of binding. The changes in free energy resulting from the binding process were determined using the following equations.[32]

△G_bind = G_complex – (G_protein + G_ligand)

△G_bind is the ligand binding energy, G_complexis the energy of the complex, G_protein is the energy of the receptor without the ligand, and G_ligand is the energy of the unbound ligand.

Drug likeness analysis

Drug-likeness evaluation of all the synthesized derivatives was performed using Lipinski's rule of five to identify compounds with physicochemical properties resembling potential drug candidates. The QikProp module, available in the Schrodinger suite, was utilized for this purpose. QikProp is a convenient, accurate, and user-friendly tool that can predict key physicochemical descriptors and pharmaco-kinetically relevant properties associated with Absorption, Distribution, Metabolism, and Excretion (ADME). The ADME properties of the ligand molecules were calculated using the Qikprop module (version 4.0, Schrodinger, LLC, New York, NY, 2010) during virtual screening.

Molecular Dynamics Simulations

MD simulations were performed using the Desmond software [33, 34] to investigate the interactions between the protein PPAR-γ and several ligand molecules (2b, 2g, 2h, and Pioglitazone). The simulations employed the optimized potentials for liquid simulations (OPLS)-2005 force field and the simple point charged (TIP4P) water model for solvation.[35] To ensure an adequate protein-buffer distance, an orthorhombic water box was created with a 10 Å buffer region separating the protein atoms and the box edges. Overlapping water molecules were eliminated, and the systems were neutralized with Na + ions. Energy calculations utilized the OPLS-2005 force field. The temperature was maintained at a constant 300 K, with an integration step of 2.0 fs. The stability of the PPAR-γ protein in its native motion was monitored by calculating the root mean square deviation (RMSD). The simulations were conducted for a duration of 100 ns on compounds 2b, 2g, 2h, and pioglitazone, which are selected based on Glide dock XP scores using the Maestro 12.6 Desmond panel. During the simulation studies, 1000 frames were generated and recorded at 20 ps intervals.[35]

In vitro biological evaluation

Cells and culture conditions

Sub-confluent C2C12 cells were cultured in a monolayer and plated in a growth media composed of DMEM supplemented with 10% fetal bovine serum and 1% antibiotic-antimycotic solution. To ensure continuous passage, the cultures were trypsinized using a 0.25% Trypsin-EDTA solution when the cells reached 70% confluency. The cells were then maintained in a humidified incubator at 37°C with ambient oxygen and 5% CO₂. For the experiments, cells were utilized after undergoing three consecutive passages.

Assessment of cytotoxicity by MTT assay

The cytotoxicity of test samples (2b, 2g, and 2h) against C2C12 myoblast cells was evaluated using the Methyl Thiazolyl Tetrazolium (MTT) assay. C2C12 cells were seeded onto 96-well microtitre plates at a density of 5000 cells per well and incubated at 37°C with 5% CO₂ to facilitate adherence. Once adhered, the cells were treated with varying concentrations of the test compounds, specifically 100 µM and 200 µM. The plates were then incubated further at 37°C in a 5% CO₂ atmosphere for 24 hours. After incubation, 100 µL of MTT reagent (1 mg/mL of MTT in complete DMEM) was added to each well and allowed to incubate for 4 hours. Formazan crystals formed were dissolved using 100 µL of DMSO, and the absorbance was measured at a wavelength of 570 nm using a multi-mode microplate reader (FlouSTAR Omega, BMG LabTech). [36]

Glucose Uptake Assay

C2C12 murine myoblast cells were seeded onto 24-well plates at a density of 10,000 cells per well and incubated at 37ºC with 5% CO₂. Once the cells adhered to the plates, they were treated with test samples (2b, 2g, and 2h), as well as 1 M glucose, insulin, and standard drug, and incubated for 30 minutes. To terminate glucose uptake, the cells were washed multiple times with 1 mL of ice-cold KRP buffer. The supernatant solution was collected for glucose estimation, while the cell pellet was lysed using a lysis buffer and collected for glucose estimation as well. Glucose levels in the supernatants and cell lysates from the different treatment groups were determined using commercially available kits, following the manufacturer's instructions. An automated biochemical analyzer (Mispa Plus, Agape Diagnostics, India) was used for the glucose estimation process.[37]

Human PPAR-gamma Transcription factor assay

The PPAR-γ transcription factor assay, a cell-based assay, employs a sensitive ELISA method to detect the DNA-binding activity of the PPAR-γ transcription factor. The ELISA assay was performed following the guidelines provided by the manufacturer using the RayBio™ PPAR-gamma TF Activity Assay kit.[32]

In vivo, antidiabetic activity using Dexamethasone-induced Wistar rat model

Animals

Wistar albino rats (weighing 150-200g) of either sex were used during this study. The animals were obtained one week before the experiments and kept in the laboratory for acclimatization. They were maintained at 27⁰C±2⁰C under 12hrs dark/light cycles and were housed in polypropylene cages. The animals were injected with 1mg/kg dexamethasone before the main experiments to induce Type-2 diabetes. Ethical approval for animal experiments was obtained from the IAEC (certificate reference number: NGSMIPS/IAEC/DEC-2020/219) before starting the work.

Experimental design

The three molecules, namely, 2b, 2g, and 2h were selected based on their highest docking scores for further evaluation of in vivo anti-diabetic activity screening studies. Rats were randomly divided into five groups, each consisting of 7 animals (n = 7). The animals were treated once daily for 11 days as follows: Group I (Control): Received a gavage of 1 ml/rat vehicle and a subcutaneous injection of 0.1 ml/rat normal saline. Group II (Dexa): Received a gavage of 1 ml/rat vehicle and a subcutaneous injection of dexa at a dose of 1 mg/kg. Group III (Dexa + std): Received a gavage of Pioglitazone at a dose of 10 mg/kg and a subcutaneous injection of dexa at a dose of 1 mg/kg. Group IV (Dexa + 2b): Received a gavage of 2b at a dose of 17.5 mg/kg and a subcutaneous injection of dexa at a dose of 1 mg/kg. Group V (Dexa + 2g): Received a gavage of 2g at a dose of 17.5 mg/kg and a subcutaneous injection of dexa at a dose of 1 mg/kg. Group VI (Dexa + 2h): Received a subcutaneous injection of Dexa at a dose of 1 mg/kg. Dexamethasone was administered 30 minutes after the oral gavage. At the end of the experimental period, the animals were fasted for 12 hours, and blood samples were collected for further analysis.

Blood and tissue sampling

Under the influence of isoflurane anaesthesia, blood samples were obtained by puncturing the retro-orbital plexus to assess the diabetic status of the animals through serum biochemical analyses. The collected blood samples were left to clot at room temperature and then centrifuged at 3000 rpm for 5 minutes to separate the serum. Following 12 days of isoflurane anaesthesia, the animals were euthanized. The pancreas was dissected, washed with normal saline, and dried using filter papers. The pancreatic tissues were then fixed in 10% formaldehyde for subsequent histopathological investigations.[33]

Biochemical assays

The biochemical parameters were assessed by employing standard commercial kits following the instructions provided in the manual. Quantification was performed for various biochemical parameters including total protein, triglyceride, total cholesterol, creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), LDL-C, HDL-C, VLDL-C, and glucose.

Statistical analysis

The results were presented as the mean ± standard deviation (SD) of seven animals in each group. Statistical significance among the various treatment groups was assessed using a two-way analysis of variance (ANOVA). A significance level of p < 0.05 was deemed statistically significant.

Histopathological examination

The pancreas was immersed in a 10% formalin buffer solution for fixation. Subsequently, the fixed tissues underwent standard procedures including dehydration with various concentrations of alcohol, embedding in paraffin, and preparation of 5–6 µm sections. These sections were stained with hematoxylin and eosin. Using a high-power microscope (Nikon Eclipse TS 100; Japan), the sections were examined, and photographs were captured at a magnification of 40X.

The target compounds were synthesized according to the synthetic route described in Scheme-1. The key intermediate (Z)-5-(2-chloro-5-nitrobenzylidene) thiazolidine-2,4-dione (1) was prepared by reacting 2,4-thiazolidinediones (A) and corresponding 2-chloro-5-nitrobenzaldehyde (B) using sodium acetate and glacial acetic acid medium. The title compounds (Z)-5-(2-chloro-5-nitrobenzylidene) thiazolidine-2,4-diones (2a–j) were synthesized by reacting with substituted primary amines (a-j) and compounds in acetonitrile medium in excellent yields. The Physicochemical properties of synthesized derivatives are given in Table 1. The purity of the compounds was confirmed through melting points and TLC using silica gel G plates as the stationary phase and n-hexane- ethyl acetate in a ratio of 2:3 in various proportions as the mobile phase. They were further purified by recrystallization using appropriate solvents. The formation of compounds was confirmed by ¹H-NMR, ¹3C-NMR, FT-IR, and LC-MS spectral data. The assignments of the structures of the new compounds were carried out. All the new compounds were structurally elucidated based on the spectroscopic data.

The compounds had the desired physicochemical characteristics (Table-1) having no deviations from the standard ranges. According to Lipinski's rule of five, the molecule should have a molecular weight of ≤ 500, and several hydrogen bond donors and hydrogen bond acceptors ≤ 5 and ≤ 10, respectively. Based on the data Lipinski's rule of five is obeyed by the synthesized compounds. Polar surface area (PSA) ≤ 140 Å and molecular flexibility are also considered additional criteria to determine oral bioavailability. The polar surface area (PSA) of a molecule reflects its ability to form hydrogen bonds and correlates with the number of drugs absorbed through the intestinal tract in humans. A PSA value greater than 140 Å indicates poor absorption, while 95% of the synthesized compounds had a PSA value below 140 Å, indicating favorable absorption by the digestive system.

Table 1

Physicochemical properties of compounds (2a-j)
Comp	Molecular Formula	Molecular Weight	HB Donor	HB acceptor	PSA	RO5	MP (C⁰)	Yield (%)
2a	C₁₆H₉Cl₃N₂O₂S	399.678	2	3.5	85.1	0	250–252	76
2b	C₁₆H₁₀ClFN₂O₂S	348.779	2	3.5	85.9	0	243–245	71
2c	C₁₆H₁₀ClN₃O₄S	375.786	2	4.5	131.1	0	234–236	74
2d	C₁₆H₉ClN₄O₆S	420.783	2	5.5	173.7	0	219–221	62
2e	C₁₇H₁₂ClN₃O₄S	389.812	2	4.5	128.8	0	251–253	72
2f	C₁₆H₁₀Cl₂N₂O₂S	365.233	2	3.5	86.2	0	204–206	68
2g	C₁₈H₁₅ClN₂O₂S	358.842	2	3.5	84.5	0	211–213	72
2h	C₁₇H₁₂Cl₂N₂O₂S	379.260	2	3.5	84.3	0	217–219	77
2i	C₁₇H₁₀ClF₃N₂O₂S	398.786	2	3.5	85.9	0	224–226	73
2j	C₁₆H₈Cl₄N₂O₂S	434.123	2	3.5	85.1	0	236–238	69
(HB Donor: Hydrogen bond donor; HB Aceptor: Hydrogen bond acceptor; PSA: Predicted polar surface area)

Molecular docking results

The synthesized compounds interacted with the active site of the human peroxisome proliferator-activated receptor gamma (PDB:2PRG) at the binding site groove. This interaction is significant in understanding their potential role as signal transducers in diabetes, specifically targeting PPAR-γ. The binding affinities of these derivatives were expressed as G scores (Table- 2), with binding free energy values ranging from − 12.655 to -9.730. Among the compounds, 2b, 2g, and 2h exhibited the highest binding scores, with corresponding binding energy values of -11.659, -12.655, and − 12.509, respectively. Pioglitazone, a reference compound, demonstrated a binding affinity towards the target with a binding free energy of -11.424. Notably, compound 2g, the most active compound in the dataset, formed a conventional hydrogen bond with the carbonyl group in the thiazolidinedione ring, involving interactions with Gln286, Ser289, and Hie323 residues. Figure 2 illustrates additional amino acid interactions, including Phe282, Gly284, Cys285, Ile326, Tyr327, Leu330, Leu340, Ile341, Ser342, Phe342, Phe363, Met364, Cys367, Hie449, Leu453, Leu469, and Tyr473. Similarly, compound 2h formed a hydrogen bond with the carbonyl group in the thiazolidinedione ring, engaging with Gln286, Ser289, and Hie323 residues. Other amino acid interactions involved Phe282, Cys285, Ile326, Tyr327, Leu330, Leu340, Ile341, Ser342, Phe342, Glu343, Phe363, Met364, Cys367, Hie449, Leu453, Leu469, and Tyr473. Compound 2b also exhibited a hydrogen bond with the carbonyl group in the thiazolidinedione ring, interacting with Gln286, Ser289, and Hie323 residues (Figure-2). Amino acid interactions in this case included Phe282, Gly284, Cys285, Ile326, Tyr327, Leu330, Leu340, Ile341, Ser342, Phe342, Phe363, Met364, Cys367, Hie449, Leu453, Leu469, and Tyr473. Based on the docking scores, these selected candidates demonstrated promising potential for diabetic activity.

Table 2

Molecular docking studies of compounds (2a-j) (PDB:2PRG)
Comp	Glide score	Glide EvdW	XP H Bond	Glide emodel	G-Rotatable bonds	Glide ecoul
2a	-10.741	-45.005	-10.741	-85.772	3	-12.765
2b	-11.659	-38.956	-11.659	-80.649	3	-14.676
2c	-10.214	-44.475	-10.214	-83.643	4	-9.600
2d	-10.679	-50.063	-10.679	-99.187	5	-12.149
2e	-9.730	-43.778	-9.730	-91.938	4	-15.577
2f	-10.447	-39.839	-10.447	-81.463	3	-12.772
2g	-12.655	-41.694	-12.655	-84.197	3	-14.211
2h	-12.509	-42.374	-12.509	-87.332	3	-14.411
2i	-11.314	-39.009	-11.314	-89.982	4	-17.551
2j	-10.599	-44.446	-10.599	-90.952	3	-12.946
Pioglitazone	-11.424	-46.784	-11.424	-78.391	7	-13.671

G Glide EvdW- glide van der Waals energy; XP H Bond- extra precision hydrogen bonding; Glide model- glide model energy; G Rotatable bonds- Glide Rotatable bonds; Glide ecoul- glide Coulomb energy

Binding free energy calculation by using prime/MMGBSA approach

Additionally, the molecular docking analysis was performed using MM-GBSA (Molecular Mechanics-Generalized Born Surface Area) free binding energy, employing the post-scoring approach for the PPAR-γ (PDB ID: 2PRG) target. The corresponding values are presented in Table-3. The Prime MMGBSA ∆G bind represents the calculated binding free energy. According to the MM-GBSA results, the ∆G bind values ranged from − 82.88 to -107.95 kcal/mol for the synthesized compounds. Additionally, the ∆Gvdw values, ∆Glipophilic values, and associated energies were observed to positively contribute to the overall binding energy. The stability of the docking complex can be predicted using the MMGBSA scoring function, while the docking of ligands into the binding pocket yields the Glide score and MM-GBSA free energy, indicating a higher level of stability.

Table 3

Calculation of MM-GBSA approach to compounds (2a-j)
Comp	∆G bind (Kcal/ mol)	∆G bind Coulomb	∆G bind covalently	∆G bind Vander	∆G Bind H Bond	∆G bind Lipophilic
2a	-101.48	-29.68	0.00	-50.72	-1.29	-55.84
2b	-85.81	-22.20	0.00	-45.05	-1.40	-42.05
2c	-91.12	-22.79	0.00	-52.05	-2.02	-42.14
2d	-92.28	-15.92	0.00	-55.20	-2.48	-42.00
2e	-94.64	-25.94	0.00	-52.67	-1.42	-45.90
2f	-94.18	-20.60	0.00	-48.89	-1.28	-50.28
2g	-92.82	-21.91	0.00	-50.81	-1.42	-47.82
2h	-95.56	-21.25	0.00	-50.14	-1.41	-50.06
2i	-87.92	-29.22	0.00	-48.48	-1.41	-41.99
2j	-107.95	-28.22	0.00	-51.75	-1.41	-61.02
Pioglitazone	-82.88	41.94	-0.00	-47.92	-1.50	-44.64

Δ G binds- MMGBSA free energy; ΔG binds Coulomb- Coulomb energy; ΔG binds covalent-covalent energy (internal energy); ΔG bind Vander- van der Waals energy; ΔG bind H Bond hydrogen bonding energy; ΔG bind Lipophilic- hydrophobic energy (non-polar contribution estimated by solvent accessible surface area).

Molecular dynamic simulation study

The RMSD (Root Mean Square Deviation) was employed to assess the average distance between the simulated protein-ligand complexes. Hence, RMSD values for the protein (Ca) and ligand-fitted protein were obtained from each 100 ns simulation trajectory (Figure-3). The Ca atoms in the PPAR-γ complex with compounds 2b, 2g, and 2h exhibited acceptable mean deviations (< 4 Å), which are within the range typically observed for small globular proteins.

Analyzing the RMSD plot of the 2b complex, a stable configuration was observed with an RMS deviation reaching up to 2.6 Å at 60 ns, while other regions showed lower RMSD (3.0 Å) from the initial points. The overall RMSD of 2b ranged from 1.4 ± 2.7 Å. The RMSF (Root Mean Square Fluctuation) of the protein demonstrated the highest fluctuations in the residue index 50–75, 130–140, 158–160, and 240–265 positions throughout the simulation, although most residues exhibited a decrease in RMSF below 0.8 Å.

For the 2g complex, the RMSD value of the protein remained stable up to 100 ns, and residues 75–85, 125–140, 152–154, and 245–260 showed decreased RMSF. The RMSF values ranged from 0.6 to 4.2 Å. In the case of the docked PPAR-γ-2h complex, the Ca atoms displayed a relatively stable RMS deviation averaging around 1.3–2.7 Å throughout 100 ns. The regions 75–80, 125–140, 153–155, and 245–265 exhibited low amplitude fluctuations ranging from 0.6 to 4.8 Å, even though loops are present in these regions.

Analyzing the RMSD plot of the Pioglitazone complex, initial stability was observed with an RMS deviation reaching up to 2.8 Å at 10 ns, while other regions exhibited higher RMSD (3.2 Å) compared to the initial points, with a slight deviation after 75 ns. The overall RMSD of Pioglitazone ranged from 1.2 ± 3.0 Å. The RMSF protein fluctuations were highest in the residue index 45–60, 70–80, 125–130, 140–145, 155–158, and 240–268 positions across the simulation. The alpha helices and beta strands tended to be more rigid, resulting in lower fluctuations compared to the loop regions. However, the catalytic regions and specific residues showed relatively stable behaviour with lower amplitude fluctuations.

Intermolecular contacts in molecular dynamic simulation

The Fig. 4 illustrates the preservation of intermolecular contacts during a molecular dynamic simulation. The figure visually represents various types of interactions observed in the inhibitors, such as hydrogen bonds, hydrophobic interactions, ionic interactions, and water bridges, using a stacked bar chart plot. Additionally, the simulation interactions diagram in Fig. 4 provides a detailed exploration of the specific subtypes within each interaction type.

Among the inhibitors, Compound 2b simulation demonstrated the highest score, with hydrogen bonds formed with Ser289, His323, Tyr327, Lys367, and Tyr427 residues. Additionally, water bridges involving Gln286, Leu340, and His449 were observed instead of these residues. The inhibitor also engaged in hydrophobic interactions with Cys285, Ile326, Leu330, and Phe363. The 2D interaction charts plotted in Fig. 4 depict the retention of contacts throughout the simulation for the docked complexes and compounds. Specifically, Tyr327, Leu340, and Tyr473 residues showed hydrophobic interactions of 94%, 49%, and 46%, respectively, with the -C = O and oxygen groups. Ser289 and His449 exhibited polar interactions through water bridges at 34% and 90%, respectively. Lys367 demonstrated a positive charge.

Similarly, Compound 2g simulation was identified as the best scoring, with H-bonds formed with Gln286, Ser289, His323, and Tyr327 residues. Other residues participated in water bridges instead of the aforementioned residues. Hydrophobic interactions were observed with Ile326, Met329, Leu330, Leu333, Phe363, and Met364. The 2D interaction charts in Fig. 4 illustrate the retention of contacts during the simulation. Tyr327 residue showed a hydrophobic interaction of 64% with the -C = O and oxygen groups. Gln286, Ser289, His323, and His449 exhibited polar interactions through water bridges at 40%, 44%, 33%, and 57%, respectively. Lys367 residue displayed a positive charge.

In the case of Compound 2h, interactions were observed with Cys285, Gln286, Ser289, His449, and Tyr427 residues, with other residues participating in water bridges. Hydrophobic interactions occurred with Phe282, Ile326, Tyr327, Leu330, Leu333, Val339, Ile341, Met364, and Leu469. The 2D interaction charts in Fig. 4 display the retention of contacts throughout the simulation. Cyr285 and Tyr473 residues showed hydrophobic interactions of 45% and 98%, respectively, with the -C = O and oxygen groups. Gln286, His440, His466, and Gln470 exhibited polar interactions through water bridges at 89%, 98%, 49%, and 53%, respectively. Lys367 demonstrated a positive charge.

In the case of standard Pioglitazone, H-bonds were formed with Gln286, His323, Tyr327, Lys367, His449, and Tyr473 residues. Other residues also participated in water bridges. Hydrophobic interactions occurred with Ile249, Leu255, Ile262, Leu270, Cys285, Ile326, Leu330, Ile341, Met348, and Phe363. The 2D interaction charts in Fig. 4 illustrate the retention of contacts during the simulation for the docked complex and Pioglitazone. His323, Lys367, and Tyr473 residues showed polar interactions of 72%, 87% (positive charge), and 83% (hydrophobic), respectively, with the -C = O and oxygen groups.

ADME properties of compounds (2a-j) using Qikprop

The QikProp pharmacokinetic parameters provide valuable information about various characteristics of a compound, including its molecular weight, partition coefficient, oral absorption percentage, adherence to Lipinski's Rule of An important factor governing drug metabolism and access to biological membranes is the Caco2 cell permeability (QPPCaco) and the values were observed within the range of 8.5 to 593.5 (except compound 2d). The blood/brain coefficients (Qlog B/B) determine the ability of synthesized compounds to access the central nervous system (CNS) and the predicted scores were found to be within the permissible limits. Additionally, the Madin Canine Kidney cells (QPPMDCK) serves as an imitator of the blood–brain barrier (BBB). This additional information helps in predicting BBB penetration and all the compounds possessed the recommended values (except compound 2d). The overall human oral absorption percentage for all the compounds in this study ranged from 85.8–100%. (Except compound 2c, 2d, 2e). All of these pharmacokinetic parameters, for Pioglitazone (standard), were found to be within the acceptable range defined for human use, as shown in Table 4.

Table 4

ADME properties of compounds (2a-j) using QikProp
Comp	QPPCaco	QPlogBB	QPPMDCK	% HOA
Acceptable Range	< 25 poor > 500 Great	(-3 to1.2)	< 25 poor > 500 Great	> 80% High < 25%Low
2a	519.5	-0.389	3679.3	100
2b	444.1	-0.637	994.5	93.9
2c	53.6	-1.730	55.6	72.1
2d	8.5	-2.644	7.6	54.4
2e	71.8	-1.655	76.0	76.6
2f	448.4	-0.589	1366.1	95.4
2g	525.2	-0.740	782.9	100
2h	550.2	-0.543	1505.0	100
2i	430.9	-0.561	2799.6	100
2j	593.8	-0.226	6618.9	100
Pioglitazone	725.8	-0.928	538.04	100

(QPPCaco: Predicted apparent Caco-2 cell permeability in nm/s; QPlogBB: predicted brain/blood partition coefficient; QPPMDCK: Predicted apparent MDCK cell permeability)

Cytotoxicity study

Among the series of thiazolidinedione derivatives tested, the compounds 2b, 2g, and 2h were found to be compatible with C2C12 myoblast cells at the tested concentrations of 100 µM and 200 µM. They exerted no apparent cytotoxicity and hence, these compounds were used for glucose uptake study further. The C2C12 mouse myoblast cell line is the ideal model for conducting in vitro anti-diabetic studies. This cell line provides the basis for assessing glucose metabolism, insulin resistance, insulin signalling, reactive oxygen species, and glucose transporter signalling at cellular and molecular levels. These cells do express GLUT-4 receptors and have close similarity to human skeletal muscle cells. The results of the cytotoxicity assay conducted on mouse myoblast cells are represented in Figs. 5A & 5B.

Glucose Uptake assay

A glucose uptake assay was conducted using C2C12 cells, which are skeletal muscle cells known for their relevance in studying glucose uptake activity. The impaired insulin-mediated glucose uptake in skeletal muscle is a key factor in the development of type 2 diabetes mellitus (T2DM). In this study, three compounds were tested for their potential anti-diabetic activity by assessing glucose uptake in C2C12 cells, while ensuring they remained within the cytotoxicity range. The results, depicted in Figure-6A, revealed that compounds 2b, 2g, and 2h exhibited a significant increase in glucose uptake percentage compared to the standard drugs insulin and Pioglitazone. These findings suggest that these compounds enhance glucose uptake by skeletal muscle cells, highlighting their potential therapeutic value.

Cell-based transcriptional factor assay

In this research, the PPAR-γ Transcription Factor Assay was utilized as a highly sensitive technique to detect the activity of a specific transcription factor in nuclear extracts by examining its DNA binding capability. The assay employed a 96-well ELISA format, where wells in a microtitre plate were coated with immobilized double-stranded DNA (dsDNA) sequences containing the Peroxisome proliferator response element (PPRE). A secondary antibody, conjugated with horseradish peroxidase, was introduced to generate a colorimetric signal measured at 450 nm.

In this particular investigation, three different compounds were tested for their PPAR-γ activity across a concentration range of 6.25–100µM. Pioglitazone, a known activator, was employed as a positive control (Fig. 6B). The expression of PPAR-γ increased significantly in a dose-dependent manner for all tested samples. Notably, compounds 2b, 2g, and 2h exhibited substantial PPAR-γ expression when compared to the control group. To standardize the study, a concentration of 1µM Pioglitazone was employed as a reference.

Impact of synthesized compounds on DEX-induced alteration in serum lipid profile

To evaluate the hypolipidemic effect of the synthesized compounds, the serum lipid profile was analyzed following an 11-day treatment with DEX. The results of the serum lipid profiles are presented in Figure 7. Administration of DEX led to significant increases in Glucose TC, LDL-C, HDL-c TGs, and VLDL-C compared to the control group. However, treatment with the synthesized compounds 2b, 2g, and 2h demonstrated a mitigating effect against the DEX-induced elevation in serum levels of Glucose TC, LDL-C, HDL-c TGs, and VLDL-C, respectively, when compared to the DEX group

Impact of synthesized on DEX–induced elevation in serum AST and ALT levels

At the conclusion of the experimental protocol, the levels of liver function enzymes in the serum were measured. In the sera of rats from the DEX group, there was a notable increase in serum AST and ALT levels compared to the control group. However, treatment with the synthesized compounds 2b, 2g, and 2h resulted in a reduction of these elevations in the serum levels of AST (decreased) and ALT (decreased) in comparison to the DEX group. The findings are illustrated in Figures 8A & 8B.

Histopathology of pancreatic cells

A histological investigation was conducted using bright field microscopy to assess the impact of treatment with compounds 2b, 2g, and 2h on the DEX-induced diabetic pancreas. The histopathological analysis of untreated diabetic rats revealed significant damage to pancreatic islets and the degranulation of β-cells, in contrast to the normal pancreatic histology observed in the control group (Figure 9). The control group exhibited a normal proportion of islets of Langerhans and acinar cells. However, the histological alterations observed in the diabetic rats were partially restored to a near-normal state following treatment with compounds 2b, 2g, and 2h, as well as Pioglitazone.

Classical thiazolidinediones have contributed valuable insights regarding the structural requirements for designing novel compounds that activate the PPAR-γ protein. Building upon this foundation, titled compounds were specifically designed, and their synthesis was achieved using a cost-effective synthetic approach. Extensive analysis was conducted to examine the binding mode of 2b, 2g, and 2h through molecular docking, followed by evaluating the stability of the protein-ligand complex using MD simulations. Consequently, this investigation furnished valuable information on ligand potency and predicted the agonistic mode of ligand binding to the active site of the PPARγ protein. The in silico ADMET profile was predicted for the synthesized compounds, providing insights into their pharmacokinetic and safety properties. To assess their efficacy in enhancing glucose uptake, a colorimetric GOD-POD assay was performed on C2C12 cells, alongside a PPAR- γ transcription factor assay. These assays allowed for a comparison of the glucose uptake activity of the synthesized compounds to that of the standard Pioglitazone, thus establishing a correlation between molecular docking results and glucose uptake activity. Furthermore, an in vivo anti-diabetic study was conducted, which indicated that compounds 2b, 2g, and 2h exhibited superior activity in reducing blood glucose levels compared to Pioglitazone. Further investigations are necessary to confirm the potency of the newly synthesized compounds (2b, 2g, and 2h) by in vitro and in vivo models in order to comprehensively assess their toxicity profile.

Acknowledgment

We acknowledge the support of NGSM Institute of Pharmaceutical Sciences, Nitte (Deemed to be a university), Mangalore, Karnataka, NGSM-CADD Lab for providing all the necessary infrastructure and resources to devote to this research project. I like to thank all the authors for their valuable suggestions.

Author Contributions

MGS and RBC did the conception of the work, design of the work, experimental work, and the interpretation of data and drafting of the manuscript. AP, VR, RMR, SND, and PBR contributed to the invitro analysis. SG contributed to insilico studies of the design work, and MGS, AP, and RBC reviewed the manuscript. All authors read and approved the final manuscript.

Funding None.

Data availability: Data will be made available on reasonable request

Conflict of Interest

There are no competing financial interests or personal relationships influencing the findings of this paper.

Shaw JE, Sicree RA, Zimmet PZ (2010) Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 87(1):4–14. https://doi.org/10.1016/j.diabres.2009.10.007
Hunt KJ, Schuller KL (2007) The Increasing Prevalence of Diabetes in Pregnancy. Obstet Gynecol Clin N Am 34(2):173–199. https://doi.org/10.1016/j.ogc.2007.03.002
Saltiel AR (2001) New perspectives into the molecular pathogenesis and treatment of type 2 diabetes. Cell 104(4):517–529. https://doi.org/10.1016/S0092-8674(01)00239-2
Viberti G (2005) Thiazolidinediones - Benefits on microvascular complications of type 2 diabetes. J Diabetes Complicat 19(3):168–177. https://doi.org/10.1016/j.jdiacomp.2004.04.001
Yasmin S, Capone F, Laghezza A, Piaz FD, Loiodice F, Vijayan V, Lavecchia A (2017) Novel Benzylidene Thiazolidinedione Derivatives as Partial PPARγ Agonists and their Antidiabetic Effects on Type 2 Diabetes. Sci Rep 7(1):1–17. https://doi.org/10.1038/s41598-017-14776-0
Tontonoz P, Spiegelman BM (2008) Fat and beyond: The diverse biology of PPARγ. Annu Rev Biochem 77:289–312. https://doi.org/10.1146/annurev.biochem.77.061307.091829
Willson TM, Brown PJ, Sternbach DD, Henke BR (2000) The PPARs: From orphan receptors to drug discovery. J Med Chem 43(4):527–550. https://doi.org/10.1021/jm990554g
Chaturvedi RK, Beal MF (2008) PPAR: A therapeutic target in parkinson’s disease. J Neurochem 106(2):506–518. https://doi.org/10.1111/j.1471-4159.2008.05388.x
Naim MJ, Alam O, Alam MJ, Shaquiquzzaman M, Alam MM, Naidu VGM (2018) Synthesis, docking, in vitro and in vivo antidiabetic activity of pyrazole-based 2,4-thiazolidinedione derivatives as PPAR-γ modulators. Archiv Der Pharmazie, 351(3–4). https://doi.org/10.1002/ardp.201700223
Xiong Z, Liu W, Zhou L, Zou L, Chen J (2016) Mushroom (Agaricus bisporus) polyphenoloxidase inhibited by apigenin: Multi-spectroscopic analyses and computational docking simulation. Food Chem 203:430–439. https://doi.org/10.1016/j.foodchem.2016.02.045
Kar K, Krithika U, Mithuna, Basu P, Santhosh Kumar S, Reji A, Kumar P, B. R (2014) Design, synthesis and glucose uptake activity of some novel glitazones. Bioorg Chem 56:27–33. https://doi.org/10.1016/j.bioorg.2014.05.006
Shrivastava SK, Batham A, Sinha SK, Parida TK, Garabadu D, Choubey PK (2016) Design, synthesis and evaluation of novel thiazolidinedione derivatives as anti-hyperglycemic and anti-hyperlipidemic agents. Med Chem Res 25(10):2258–2266. https://doi.org/10.1007/s00044-016-1675-y
Nazreen, S., Alam, M. S., Hamid, H., Yar, M. S., Shafi, S., Dhulap, A., … Pillai,K. K. (2014). Design, synthesis, in silico molecular docking and biological evaluation of novel oxadiazole based thiazolidine-2,4-diones bis-heterocycles as PPAR-γ agonists. European Journal of Medicinal Chemistry, 87, 175–185. https://doi.org/10.1016/j.ejmech.2014.09.010
Bhalla K, Mall R (2022) Heterocyclyl linked phenyl containing Thiazolidinediones and its Cyclic analogs as potential novel Antidiabetic agents. J Agricultural Food Chem Eng 2(1):6–15
Zhou C, Tang C, Chang E, Ge M, Lin S, Cline E, Yang L (2010) Discovery of 5-aryloxy-2,4-thiazolidinediones as potent GPR40 agonists. Bioorg Med Chem Lett 20(3):1298–1301. https://doi.org/10.1016/j.bmcl.2009.10.052
Zhou, L., Zhong, Y., Xue, M. Z., Kuang, D., Cao, X. W., Zhao, Z. J., … Wang, R. (2015).Design, synthesis and evaluation of PPAR gamma binding activity of 2-thioxo-4-thiazolidinone derivatives. Chinese Chemical Letters, 26(1), 63–68. https://doi.org/10.1016/j.cclet.2014.10.008
Pomel, V., Klicic, J., Covini, D., Church, D. D., Shaw, J. P., Roulin, K., … Rückle,T. (2006). Furan-2-ylmethylene thiazolidinediones as novel, potent, and selective inhibitors of phosphoinositide 3-kinase γ. Journal of Medicinal Chemistry, 49(13), 3857–3871. https://doi.org/10.1021/jm0601598
Ha, Y. M., Park, Y. J., Kim, J. A., Park, D., Park, J. Y., Lee, H. J., … Chung, H.Y. (2012). Design and synthesis of 5-(substituted benzylidene)thiazolidine-2,4-dione derivatives as novel tyrosinase inhibitors. European Journal of Medicinal Chemistry, 49, 245–252. https://doi.org/10.1016/j.ejmech.2012.01.019
Bahare RS, Ganguly S, Choowongkomon K, Seetaha S (2015) Synthesis, HIV-1 RT inhibitory, antibacterial, antifungal and binding mode studies of some novel N-substituted 5-benzylidine-2,4-thiazolidinediones. DARU J Pharm Sci 23(1):1–15. https://doi.org/10.1186/s40199-014-0086-1
Madhavaram M, Nampally V, Gangadhari S, Palnati MK, Tigulla P (2019) High-throughput virtual screening, ADME analysis, and estimation of MM/GBSA binding-free energies of azoles as potential inhibitors of Mycobacterium tuberculosis H37Rv. J Recept Signal Transduction 39(4):312–320. https://doi.org/10.1080/10799893.2019.1660895
Vijayakumar S, Manogar P, Prabhu S, Sanjeevkumar Singh RA (2018) Novel ligand-based docking; molecular dynamic simulations; and absorption, distribution, metabolism, and excretion approach to analyzing potential acetylcholinesterase inhibitors for Alzheimer’s disease. J Pharm Anal 8(6):413–420. https://doi.org/10.1016/j.jpha.2017.07.006
Teraiya N, Karki SS, Chauhan A (2021) Synthesis, Anticancer Evaluation and Molecular Docking of Hexahydroquinoline Derivatives as Mcl-1 Inhibitors and Apoptosis Inducers. Anti-cancer Agents Med Chem 22(11):2142–2155. https://doi.org/10.2174/1871520621666211021133558
Pattar SV, Adhoni SA, Kamanavalli CM, Kumbar SS (2020) In silico molecular docking studies and MM/GBSA analysis of coumarin-carbonodithioate hybrid derivatives divulge the anticancer potential against breast cancer. Beni-Suef Univ J Basic Appl Sci 9(1). https://doi.org/10.1186/s43088-020-00059-7
Gajiwala, K. S., Feng, J., Ferre, R., Ryan, K., Brodsky, O., Weinrich, S., … Stewart,A. (2013). Insights into the aberrant activity of mutant EGFR kinase domain and drug recognition. Structure, 21(2), 209–219. https://doi.org/10.1016/j.str.2012.11.014
Singh P, Bast F (2014) In silico molecular docking study of natural compounds on wild and mutated epidermal growth factor receptor. Med Chem Res 23(12):5074–5085. https://doi.org/10.1007/s00044-014-1090-1
Hasan MM, Khan Z, Chowdhury MS, Khan MA, Moni MA, Rahman MH (2022) In silico molecular docking and ADME/T analysis of Quercetin compound with its evaluation of broad-spectrum therapeutic potential against particular diseases. Inf Med Unlocked 29(February):100894. https://doi.org/10.1016/j.imu.2022.100894
Li J, Abel R, Zhu K, Cao Y, Zhao S, Friesner RA (2011) The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling. 2794–2812. https://doi.org/10.1002/prot.23106
Casalvieri KA, Matheson CJ, Backos DS, Reigan P (2020) Molecular docking of substituted pteridinones and pyrimidines to the ATP-binding site of the N-terminal domain of RSK2 and associated MM/GBSA and molecular field datasets. Data in Brief 29:105347. https://doi.org/10.1016/j.dib.2020.105347
Scott A, Hollingsworth, Ron O, Dror (2018) Molecular dynamics simulation for all. Neuron 99(6):1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011.Molecular
Guru A, Lite C, Freddy AJ, Issac PK, Pasupuleti M, Saraswathi NT, Arockiaraj J (2021) Intracellular ROS scavenging and antioxidant regulation of WL15 from cysteine and glycine-rich protein 2 demonstrated in zebrafish in vivo model. Developmental and Comparative Immunology, 114(October 2021). https://doi.org/10.1016/j.dci.2020.103863
Mehmood, R., Sadiq, A., Alsantali, R. I., Mughal, E. U., Alsharif, M. A., Naeem, N.,… Ahmed, S. A. (2022). Synthesis and Evaluation of 1, 3, 5-Triaryl-2-Pyrazoline Derivatives as Potent Dual Inhibitors of Urease and α-Glucosidase Together with Their Cytotoxic, Molecular Modeling and Drug-Likeness Studies. https://doi.org/10.1021/acsomega.1c06694
Issac, P. K., Karan, R., Guru, A., Pachaiappan, R., Arasu, M. V., Al-Dhabi, N. A.,… Raj, J. A. (2021). Insulin signaling pathway assessment by enhancing antioxidant activity due to morin using in vitro rat skeletal muscle L6 myotubes cells. Molecular Biology Reports, 48(8), 5857–5872. https://doi.org/10.1007/s11033-021-06580-x
Zhao Y, Zheng Q, Dakin K, Xu K, Martinez ML (2004) New Caged Coumarin Fluorophores with Extraordinary Uncaging Cross Sections Suitable for Biological Imaging Applications. (5), 4653–4663
Shittu STT, Lasisi TJ, Shittu SAS, Adeyemi A, Adeoye TJ, Alada ARA (2021) Ocimum gratissimum enhances insulin sensitivity in male Wistar rats with dexamethasone-induced insulin resistance. J Diabetes Metab Disord 20(2):1257–1267. https://doi.org/10.1007/s40200-021-00850-y
Ahmed, A., Saeed, A., Ejaz, S. A., Aziz, M., Hashmi, M. Z., Channar, P. A., … El-Seedi,H. R. (2022). Novel adamantyl clubbed iminothiazolidinones as promising elastase inhibitors:design, synthesis, molecular docking, ADMET and DFT studies. RSC Advances, 12(19), 11974–11991. https://doi.org/10.1039/d1ra09318e
Wu X, Ding H, Hu X, Pan J, Liao Y, Gong D, Zhang G (2018) Exploring inhibitory mechanism of gallocatechin gallate on a-amylase and a-glucosidase relevant to postprandial hyperglycemia. J Funct Foods 48(July):200–209. https://doi.org/10.1016/j.jff.2018.07.022
Chen, T., Zhang, Y., Liu, Y., Zhu, D., Yu, J., Li, G., … Hong, Z. (2019). miR-27a promotes insulin resistance and mediates glucose metabolism by targeting PPAR-γ-mediated PI3K/AKT signaling. Aging, 11(18), 7510–7524. https://doi.org/10.18632/aging.102263

Scheme 1 is available in the Supplementary Files section.

No competing interests reported.

supplementaryfile03082023.docx
Scheme1.png
Scheme 1: Where R= a)2,4-(Cl)₂, b)4-F, c)4-NO₂, d) 2,4-(NO₂)₂, e)2-NO₂-4-CH₃, f)4-Cl, g)2-CH₃-3-Cl, h) 2,5-(CH₃)₂, i) CF₃, j)2,4,5-(Cl)₃

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Novel Thiazolidinedione Derivatives as effective agonists of Peroxisome Proliferator Activator Receptor-γ: A Comprehensive Study on Design, Synthesis, Molecular Docking, MD Simulations, and Antidiabetic Efficacy

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Abstract

Figures

Introduction

Materials and methods

Experimental

Synthesis of (Z)-5-(2-chloro-5-nitrobenzylidene)thiazolidine-2,4-dione (1)

Synthesis of (Z)-5-(2-chloro-5-(phenylamino) benzylidene) thiazolidine-2,4-dione derivatives(2a-j)

Molecular Docking studies

Binding-free energy calculation using prime/MMGBSA approach

△Gbind = Gcomplex – (Gprotein + Gligand)

Drug likeness analysis

Molecular Dynamics Simulations

Cells and culture conditions

Assessment of cytotoxicity by MTT assay

Glucose Uptake Assay

Human PPAR-gamma Transcription factor assay

Animals

Experimental design

Blood and tissue sampling

Biochemical assays

Statistical analysis

Histopathological examination

Results and Discussion

Molecular docking results

Binding free energy calculation by using prime/MMGBSA approach

Molecular dynamic simulation study

Intermolecular contacts in molecular dynamic simulation

ADME properties of compounds (2a-j) using Qikprop

Cytotoxicity study

Glucose Uptake assay

Cell-based transcriptional factor assay

Conclusion

Declarations

References

Scheme

Additional Declarations

Supplementary Files

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△G_bind = G_complex – (G_protein + G_ligand)