In Silico Design and Identification of Novel Thiazolidinedione Derivatives as PPAR-Gamma Receptor Agonists for the Treatment of Type 2 Diabetes Mellitus

Table 1: Existing therapeutic agents used in the management of type 2 Diabetes Mellitus [5]

Despite the availability of multiple therapeutic options, limitations such as side effects, drug resistance and incomplete glycemic control necessitate the development of novel therapeutic agents.

RATIONALE FOR NOVEL THERAPEUTIC APPROACHES:

Type 2 Diabetes Mellitus has many therapeutic options, but effective long-term management has become a challenge due to limitations such as adverse effects, high cost and less patient compliance. Metformin is one of the frequently used drugs that are usually well tolerated but may cause gastrointestinal disturbances [6]. In addition, Sulfonylureas are connected with the hazards of hypoglycemia and gaining of physical weight which can negatively impact patient outcomes [7].

Furthermore, newer agents such as SGLT2 inhibitors and GLP1 receptor agonists are useful but expensive and can cause side-effects such as urinary tract infection or gastrointestinal upset [5]. These limitations underline the requirement for safer, more effective and affordable therapeutic alternatives. The Peroxisome Proliferator Activated Receptor Gamma (PPAR-Gamma) is a nuclear receptor. It plays a key role in the regulation cum direction of glucose metabolism, lipid homeostasis and insulin sensitivity. It is a hopeful beneficial target in the administration of Type 2 Diabetes Mellitus. PPAR-Gamma activation improves insulin sensitivity in peripheral tissue and as such is an attractive drug target [8].

Computational approaches such as molecular docking have emerged as powerful tools in modern drug discovery, enabling the identification and optimization of potential drug candidates by predicting their binding affinity and interaction with target proteins. Therefore, the present study aims to evaluate potential ligands targeting PPAR-Gamma using In Silico docking techniques to identify novel compounds with improved efficacy and reduced side effects.

MATERIALS AND METHODOLOGY [9-13]:

1. SOFTWARE AND WEB SERVERS:

• Protein Data Bank
• Pub Chem
• CB-DOCK2
• Auto Dock Tools
• ChemMaster

2. METHODS:

1. Selection of Receptor and Ligand preparation:

The Peroxisome Proliferator Activated Receptor Gamma (PPAR-Gamma) was selected as target for the study. The crystal structure of the ligand-binding domain (LDB) was retrieved from the RCSB Protein Data Bank (PDB) using the PDB accession code 1KNU [14]. This structure was selected because it represents the receptor in an active conformation complexed with a high affinity agonist.

Standard reference ligands, such as Pioglitazone, were retrieved from the PubChem database [15]. A series of Thizolidinedione (TZD) derivatives were designed and prepared for docking using standard energy-minimization protocols to reach their local minima [16].

Figure 1:  Human Peroxisome Proliferator Activated Receptor Gamma ligand-binding domain

(1KNU)

Table 2: Standard Compound

Sl. No.	Standard Compound	Structure
1	Pioglitazone

Table 3: List of Test Samples

Sl. No.	Test Samples	Structure
1	TEST SAMPLE 1
2	TEST SAMPLE 2
3	TEST SAMPLE 3
4	TEST SAMPLE 4
5	TEST SAMPLE 5
6	TEST SAMPLE 6
7	TEST SAMPLE 7
8	TEST SAMPLE 8
9	TEST SAMPLE 9
10	TEST SAMPLE 10
11	TEST SAMPLE 11
12	TEST SAMPLE 12
13	TEST SAMPLE 13
14	TEST SAMPLE 14
15	TEST SAMPLE 15
16	TEST SAMPLE 16
17	TEST SAMPLE 17
18	TEST SAMPLE 18
19	TEST SAMPLE 19
20	TEST SAMPLE 20
21	TEST SAMPLE 21
22	TEST SAMPLE 22
23	TEST SAMPLE 23
24	TEST SAMPLE 24
25	TEST SAMPLE 25
26	TEST SAMPLE 26
27	TEST SAMPLE 27
28	TEST SAMPLE 28
29	TEST SAMPLE 29
30	TEST SAMPLE 30

2. Molecular Docking and Virtual Screening:

Molecular docking was performed using CB-DOCK2 server to evaluate the binding affinity of the 30 designed derivatives [17]. The CB-DOCK2 algorithm was used for blind docking, which involves automatic cavity detection followed by docking with the AutoDock Vina engine. The compounds were selected based on their binding energy (Vina score) and their ability to mimic the orientation of the co-crystallized agonist found in the 1KNU structure.

Table 4: Receptor Activated Site (Coordinates)

Table 5: Molecular Docking analysis of Pioglitazone with the PPAR-Gamma Receptor (1KNU)

Sl. No.	Name of the Standard Compound	Docking Result [Binding Energy (kcal/mol)]	Molecular Docking
1	Pioglitazone	-9.4

Table 6: Molecular docking results of the sample compounds against the PPAR-Gamma Receptor (1KNU)

Sl. No.	Name of the Test Sample	Docking Result [Binding Energy (kcal/mol)]	Molecular Docking
1	Test Sample 1	-9.5
2	Test Sample 2	-8.9
3	Test Sample 3	-9.5
4	Test Sample 4	-9.5
5	Test Sample 5	-8.6
6	Test Sample 6	-9.6
7	Test Sample 7	-11.1
8	Test Sample 8	−9.5
9	Test Sample 9	−10.8
10	Test Sample 10	−8.1
11	Test Sample 11	−11.5
12	Test Sample 12	−9.5
13	Test Sample 13	−9.4
14	Test Sample 14	−9.4
15	Test Sample 15	−9.4
16	Test Sample 16	−9.3
17	Test Sample 17	−9.1
18	Test Sample 18	−9.3
19	Test Sample 19	−9.5
20	Test Sample 20	−9.0
21	Test Sample 21	−9.5
22	Test Sample 22	−9.2
23	Test Sample 23	−9.1
24	Test Sample 24	−9.4
25	Test Sample 25	−9.0
26	Test Sample 26	−8.1
27	Test Sample 27	−7.7
28	Test Sample 28	−9.1
29	Test Sample 29	−8.8
30	Test Sample 30	−9.3

3. QSAR MODEL DEVELOPMENT:

The selected 30 derivatives were subjected to Quantitative Structure Activity Relationship (QSAR) analysis using ChemMaster software [19].

Physicochemical descriptors including Molecular Weight, LogP, Hydrogen Bond Donor, Hydrogen Bond Acceptor, Molar Refractivity (MR), Rotatable Bonds were calculated for each compound.

The dataset was classified into training and test sets. Multiple Linear Regression (MLR) was used to generate the predictive model. The model’s reliability was assessed through the coefficient of determination (R²) and error metrices (RMSE, MAE) [20].

RESULTS AND DISCUSSION:

1. Physiochemical Screening and Drug-Likeness:

The 30 designed thiazolidinedione (TZD) derivatives were initially screened for their pharmacokinetic potential using Lipinski’s Rule of Five (RoF). High oral bioavailability is a prerequisite for effective Type 2 Diabetes Mellitus (T2 DM) treatments [21].

Table 7: Drug-likeness evaluation of the designed TZD derivatives

• Compliance: 93.3% (n=28) of the library demonstrated strict adherence to Ro5 parameters.
• Lipophilicity and Molecular Weight: Most compounds maintained a molecular weight under 500Da and LogP under 5.0. However , compounds 7 and 11 (MW: 581.7; LogP: 6.4) were flagged as outliners, suggesting potential challenges with passive membrane permeability [22]
• Molar Refractivity (MR): The calculated MR values (74.4 to 166.1) indicated a broad range of molecular volumes, providing a diverse dataset for subsequent QSAR modeling.

2. Quantitative Structure Activity Relationship (QSAR) Analysis :

A 2D-QSAR study was executed via ChemMaster (Version 1.3) using Multiple Linear Regression (MLR) to determine the structural drivers of activity.

Figure 2: QSAR MODEL

• Statistical Validation:

The generated model demonstrated high internal and external predictive power

• Training Set (R²): 0.911
• Test Set (R²): 0.836
• Training Set RMSE: 2.655
• Test Set RMSE: 4.419
• Training Set MAE: 2.253
• Test Set MAE: 3.448

An R² value exceeding 0.6 is considered the threshold for a reliable QSAR model; thus, our model’s 0.911 correlation indicates excellent fit [20]. The proximity of the test set R² (0.836) to the training set suggests the model is robust and avoids overfitting.

• Discussion of Molecular Descriptors:

The predictive equation is as follows:

Figure 3: Regression Equation and Validation Parameters

• Negative LogP Correlation: The negative coefficient (-3.26) suggests that as lipophilicity increases, the predicted feature value decreases. This indicates that while the PPAR-Gamma pocket is hydrophobic, excessive ‘greasiness’ may hinder the molecule’s interaction or solubility.
• Positive MR and H-Bonding: The positive coefficients for MR and Hydrogen Bond Donors (+2.13) suggest that molecular bulk and polar interactions are primary drivers for stabilization within the receptor site.

3. Molecular Docking and Binding Affinity (PDB: 1KNU):

Using CB-DOCK2, the 30 derivatives were docked into the lignd-binding domain (LBD) of the PPAR-Gamma receptor. The best performing compounds occupied the hydrophobic ‘Y-Shaped’ cavity, establishing critical hydrogen bonds with His323 and Tyr473. Stabilizing the Tyr473 residue is essential for the activation of the AF-2 surface, which facilitates the recruitment of co-activators necessary for the anti-diabetic response [22,23].

4. ADMET and Safety Profiling (Toxicity Analysis):

To address the historical safety concerns associated with glitazones (e.g., hepatotoxicity, cardiotoxicity, etc.) an in-depth ADMET profile was established for the series.

Table 8: In Silico ADMET and Safety profiling of the selected TZD derivatives

• Hepatotoxicity (DILI): DILI scores were monitored as a primary safety end point. Scores ranged significantly from 0.18 to 0.98.
• Acute Toxicity (LD₅₀): The series displayed moderate acute toxicity levels, with LD₅₀ values typically between 1.8 and 2.8.
• Cardiotoxicity (hERG): Cardiotoxic risks (hERG blocking) remained mostly under 0.50, signifying low hazards of medication-induced arrhythmia [24].

5. Lead Identification

Based on the convergence of docking scores, QSAR reliability and safety parameters, five lead compounds were identified as candidates for further synthesis:

Table 9: Top 5 Least Toxic Compounds