1. INTRODUCTION:

Obesity and its associated health conditions have emerged as significant global health concerns, in the recent statistics state that obesity has been positioned in the fifth place for being as a reason of death worldwide. Obesity is well defined by World Health Organization (WHO) as an abnormal or excessive fat accumulation that may impair health, their comment goes on to say that an inconsistency between caloric consumption and consuming is the main cause of obesity and overweight¹. Metabolic diseases associated with obesity are also common causes of mortality². Statistics taken on adults globally shows that 39% are overweight, with an additional 13% are obese. On the other hand, the Global Burden of Disease Group states that the predominance of obesity rose from 15.1% in 1980 to 20.7% in 2015. Both directly and indirectly, obesity is linked to the development of numerous health conditions that often result in impairment and a diminished quality of life. Additionally, life expectancy is lowered as a result of obesity. In 2015, the Eastern Mediterranean Region recorded 417,115 deaths related to a high body mass index (BMI), representing approximately 10% of all deaths and 6.3% of disabilities across all age groups³. The three primary components of lifestyle interventions for obesity treatment include prescribing a healthy, moderately calorie-reduced diet, promoting increased physical activity, and implementing behavioural strategies to enhance adherence to dietary and exercise recommendations⁴.

Leptin and ghrelin play a crucial role in regulating peripheral appetite. To reduce obesity prevalence and improve treatment strategies, appetite control which could be impacted by the nutrigenomics obese phenotype and generic regulation⁵. The interaction of endocrine balance and genetic factors plays a crucial role in modulating obesity. These interactions are primarily regulated by specific brain regions, with the hypothalamus and brainstem being key players. The arcuate nucleus (ARC), a vital region within the hypothalamus, receives signals that interact with distinct neuronal populations. Orexigenic peptides such as agouti-related peptide (AgRP) and neuropeptide Y (NPY) stimulate appetite, whereas cocaine and amphetamine-regulated transcript (CART) peptides and proopiomelanocortin (POMC) are responsible for appetite suppression⁶. Leptin regulates food intake, body weight and energy consumption, it controls hunger by sending signals to the brain, which may reduce appetite and improve the body's response to food⁷. On top of it’s strong growth hormone-releasing characteristics, ghrelin acts a vital role in balancing the release of prolactin (PRL) and adrenocorticotropic hormone (ACTH). It’s effect on cholesterol and glucose metabolism are equally well established, in general, ghrelin levels are lesser in obese people and greater in anorexics. The hypothalamic-pituitary axis acts as a host to the ghrelin receptor (GHSR), and it’s criticality in appetite control is made evident by the lack of negative feedback mechanism from food consumption on ghrelin release⁸.

Scientists are investigating how leptin and ghrelin interact with potential drugs to gain a better understanding of the complicated biological pathways that drive obesity. They employ a technique called molecular docking to predict how these proteins may interact with novel drugs. This technique has the potential to aid in the discovery of successful obesity treatments. The objective of this research is to identify prospective drugs that perform effectively for a wide range of people or are specifically intended for certain conditions. This will assist to develop more effective and specific obesity treatments.

The conventional pharmacological therapies, Metformin is a widely used medication for managing type 2 diabetes. Recent research suggests that it may also offer potential benefits for Anti-obesity. It has shown promise in addressing various health conditions, including obesity, by helping to reduce weight gain and promote weight loss in individuals, irrespectively if they have type 2 diabetes⁹.

Metformin’s primary adverse effects include gastrointestinal (GIT) difficulties that involve diarrhoea, nausea, vomiting, flatulence and abdominal pain¹⁰. The oral antihyperglycemic drug Vildagliptin focus to inhibit the dipeptidyl peptidase-4 (DPP-4) enzyme, and it is utilized to treat type II diabetes mellitus which has insulinotropic effects and reduced glucagon-like peptide 1 (GLP-1) release. Vildagliptin inhibits DDP-4, which stops the breakdown of intestinal hormones that stimulates insulin formation and maintain blood glucose levels, such as GLP-1 and glucose-dependant insulinotropic polypeptide (GIP). Increased GLP-1 and GIP levels eventually give rise to better glycaemic management¹¹. The most common side effects of these drugs, include hypoglycaemia and weight gain, along with overall treatment tolerance, will determine its usability¹². Rosuvastatin aids in obesity treatment by enhancing the antioxidant defence mechanism, additionally it acts as a xenobiotic, environmental pollutant, cardioprotective agent, CETP inhibitor, anti-inflammatory and antilipemic medication¹³. The most common side effects include myalgia-myopathy, myositis, elevated serum liver enzyme activities, muscle aches, weakness, stiffness, and cramps^14-15.

Conjugated linoleic acid (CLA) is a group of polyunsaturated fatty acids recognized for its metabolic and anti-obesity effects in animals¹⁶. It consists of dienoic isomers of linoleic acid and is naturally present in dairy and beef fat. Although commercial preparations contain similar proportions of c9, t11- and t10, c12-CLA isomers¹⁷. CLA has various biological characteristics that could help with diseases like asthma because of its effects on immune system function, lipid metabolism, energy management and inflammation (figure 1)¹⁷. In addition to these properties, CLA has demonstrated anticarcinogenic effects in mice and rats, while also playing a role in delaying the onset of atherosclerosis in rabbits and hamsters¹⁸. Studies in mice have shown that CLA reduces body fat and enhances insulin sensitivity ¹⁹. However, CLA-fed female mice exhibited severe lipodystrophic insulin resistance. The impact of CLA on human insulin sensitivity remains unclear²⁰. Research suggests that the t10c12 CLA isomer is primarily responsible for its anti-obesity and insulin-sensitizing properties²¹. This study provides novel insights into the potential interactions between t10c12 CLA and obesity-related receptors through in silico analysis. The analysis of binding energy, a crucial factor of evaluating the strength and stability of interactions was the main focus, it was compared with the efficacy of currently available medications to guarantee precision and dependability.

Figure 1. Diagrammatic illustration of Conjugated linolic acid’s therapeutic effects.

2. METHODOLOGY:

A wide array of in silico tools and computational platforms are available for the systematic evaluation of the physicochemical attributes, pharmacokinetic parameters, and pharmacological activities of conjugated linoleic acid (CLA). These technologies are indispensable in elucidating the therapeutic potential of CLA, particularly in the context of anti-obesity strategies. This study emphasizes the strategic relevance of leveraging computational methodologies to gain a comprehensive understanding of CLA’s bioactive properties. Molecular docking serves as a pivotal in silico technique for predicting the binding affinity between candidate molecules and their respective biological targets, offering critical insights into receptor-ligand interactions. Accordingly, this investigation extensively incorporates computational approaches to assess the pharmacokinetic behavior and biological efficacy of CLA using validated online platforms. The outcomes of this research underscore the indispensable role of in silico methods in contemporary drug discovery and development. They also provide a compelling rationale for subsequent in vitro and in vivo investigations to substantiate and broaden upon the computational predictions.

Figure 2. Diagrammatic illustration (2D and 3D) of ligand

2.1 Retrieval of ligands:

The SDF files for Conjugated linoleic acid (PubChem ID: 5282800) and reference drugs such as Metformin (PubChem ID: 4091), Rosuvastatin (PubChem ID: 446157) and Vildagliptin (PubChem ID: 6918537) were retrieved from PubChem. The SDF files were shifted into pdbqt format using the OpenBabel server. The ligands were refined by energy minimisation using precise computational techniques, enhancing accuracy and reliability for future evaluations (Table 1).

2.2 Retrieval of Protein:

The proteins were conscientious selected from the Protein Data Bank (PDB), and each one was identified by its unique PDB ID, including Ghrelin receptor (Antagonist) PDB ID:6ZYf and Leptin -R (Isoform)/OB- Gene receptor) PDB ID: 3V6O. Each protein underwent a methodical set of structural changes after the initial curation. In order to improve clarity and specificity in later investigations, water molecules were first eliminated. After that, hydrogen atoms were included to improve bonding patterns and molecular shape. Incorporating Goldman charges allowed for a precise simulation of electrostatic interactions. In order to restore the protein's structural integrity, any missing atoms were also added. Advanced computational tools, including the Auto Dock Vina Tool, were used to carry out these changes (Table 1).

2.3 Molecular docking:

The docking process begins with the identification of active sites for each protein using the CASTp web server, which aids in facilitating rigid docking. Molecular docking studies were carried out using Auto Dock Vina with a systematically optimized grid box configuration to ensure precise binding predictions. The docking results were analysed through a multi-faceted approach, utilizing PyMOL for molecular visualization and BIOVIA Discovery Studio 2021 for an in-depth examination of two-dimensional and three-dimensional molecular interactions. This evaluation encompassed both rigid docking and standard ligand-protein docking scenarios, enabling a comprehensive investigation of binding affinities, molecular conformations, and interaction stability. The study involved a detailed analysis of the spatial arrangements and intermolecular interactions between conjugated linoleic acid and the established drugs Metformin, Rosuvastatin and Vildagliptin. The primary objective was to thoroughly assess the structural characteristics and binding modes of each docking strategy, facilitating a comparative evaluation of their outcomes. This rigorous assessment enhances the understanding of the efficacy of different docking methodologies, providing valuable insights into protein-ligand interactions and the stability of resulting complexes.

2.4 Pharmacokinetic Parameter Prediction:

The canonical SMILES representation of conjugated linoleic acid (Figure 2), retrieved from the PubChem database. The canonical SMILES representation was employed to extract the ADMET profile through advanced web-based platforms. SwissADME and pkCSM, two well-known in silico methods for pharmacokinetic and drug-likeness prediction, were utilized to conduct a comprehensive evaluation of the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) parameters. pkCSM, in particular, offers a sophisticated framework for designed to predict pharmacokinetic behavior. Furthermore, the physicochemical properties and potential anti-obesity efficacy of CLA were systematically analyzed using the predictive capabilities of the pkCSM tool.

2.5 PASS Online Activity Predictions (Substance Activity Spectra Prediction):

The PASS (Prediction of Activity Spectra for Substances) online tool was employed to assess the potential biological activities of the selected compounds. This in silico approach utilizes the structural formula to assess activity spectra based on structure–activity relationship (SAR) models. As pharmacological and biological activities are critical determinants of a compound’s therapeutic relevance, these predictions provide valuable insights during the early phases of drug development. Moreover, PASS was used to anticipate potential adverse effects, offering a preliminary safety profile of the compounds. The program offers a thorough evaluation of bioactivity by forecasting more than 300 pharmacological actions and metabolic pathways. All predictions are derived solely from the chemical structure, facilitating the detection of novel drug targets and enhancing the efficiency of lead compound selection^22-23.

3. RESULTS AND DISCUSSION:

Molecular docking is a computational technique utilized to predict the optimal binding position of a ligand within a target's active site. This process involves defining the three-dimensional coordinate space of the target's binding site and evaluating the ligand's interactions within this region to form a stable complex. The significance and accuracy of binding affinity values are determined by the most negative binding energy, as a higher negative value indicates a more favorable conformation, reflecting the ligand's efficient interaction with the target's active pockets. In this study, we investigated the binding affinities of various ligands, including 10- trans,12-cis-Conjugated Linoleic Acid (CLA) as a test compound, along with Metformin, Rosuvastatin and Vildagliptin as standard drugs, against key appetite-regulating receptors: Leptin-R (3V6O) and Ghrelin antagonist (6ZYF). Ligands were retrieved from PubChem, and receptor structures were obtained from the Protein Data Bank (PDB). Molecular docking analysis were achieved using Auto Dock Vina, with ligand and protein optimizations, grid box generation, and docking analysis. Binding affinities were assessed in kcal/mol, with more negative values indicating stronger interactions with the target receptors.

Table 1: Selected Ligands and Protein for the present study

S. No	Ligand	PUB Chem ID	Protein	PDB ID
1	10- trans 12- cis Conjugated linoleic acid (Test Compound)	5282800	Leptin–R (Isoform)/OB-Gene receptor	3V6O
2	Metformin (Standard Compound)	4091	Ghrelin (Antagonist)	6ZYF
3	Vildagliptin (Standard Compound)	6918537	--	--
4	Rosuvastatin (Standard Compound)	446157	--	--

The docking results revealed that (Table 2, Figure 3) CLA exhibited a binding affinity of -4.5kcal/mol with Leptin-R (3V6O), whereas Metformin, Rosuvastatin, and Vildagliptin demonstrated affinities of -4.3 kcal/mol, -5.8kcal/mol, and -6.6kcal/mol, respectively. This suggests that CLA has a similar binding capacity to Metformin but lower affinity compared to Rosuvastatin and Vildagliptin. When interacting with the Ghrelin receptor (6ZYF), CLA exhibited (Table 3, Figure 5) a binding score of -4.6 kcal/mol, whereas Metformin, Rosuvastatin, and Vildagliptin showed affinities of -5.4 kcal/mol, -5.8kcal/mol, and -6.6kcal/mol, respectively. These results indicate a binding affinity ranking of Vildagliptin > Rosuvastatin > Metformin > CLA for both Leptin-R and Ghrelin receptors. Although CLA demonstrated a lesser binding affinity by comparing to the other compounds, its interaction with both Leptin and Ghrelin receptors suggests a potential modulatory role in metabolic regulation, warranting further investigation. Overall, the pharmacological activity ranking based on binding affinity follows the order: Vildagliptin > Rosuvastatin > Metformin > CLA for Leptin-R and Ghrelin receptors. The molecular docking analysis revealed that ligand interactions with leptin and ghrelin receptors involved an average of two hydrogen bonds per complex, as illustrated in Figure 4 and Figure 6. In these representations, hydrogen bond–donating amino acid residues are highlighted in purple, while hydrogen bond–accepting residues are depicted in green, emphasizing the critical interactions contributing to receptor ligand binding stability. These findings provide insight into the potential interactions of these compounds with appetite-regulating receptors, which could have implications for obesity and metabolic disorder treatments.

3.1 Pharmacokinetic Parameters Evaluation:

The pharmacokinetic attributes and drug-likeness profiles of the designed inhibitors, as inferred from their molecular docking affinities, identified the top-performing compounds as viable candidates for further drug development. However, the integration of key pharmacokinetic parameters specifically ADMET properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity) remains essential in the early stages of drug discovery to ensure both efficacy and safety. Accordingly, an in-depth evaluation of the selected compound’s pharmacokinetic behavior was conducted (Table 4), complemented by a comprehensive ADMET analysis utilizing various web-based predictive tools (Tables 5- 7).

Lipophilicity, a critical determinant of membrane permeability and bioavailability, was assessed using multiple computational models, including WLOGP, MLOGP, and XLOGP3 topological and atomistic implementations based on Moriguchi’s methodology. These models estimate the logarithmic partition coefficient (log Po/w) between n-octanol and water within a physiologically relevant range (-0.7 to +5.0). The consensus log Po/w was derived by averaging values across five predictive algorithms, while the SILICOS-IT model employed a hybrid approach incorporating both fragment-based and topological descriptors. Notably, all log P models indicated violations, reflecting suboptimal lipophilic balance ^24-25.

Despite exhibiting only moderate solubility in aqueous media potentially limiting its systemic distribution and absorption the compound displayed an exceptionally high predicted intestinal absorption rate of 90.9%, significantly exceeding standard benchmarks and highlighting its favorable pharmacokinetic potential. Moreover, physicochemical properties relevant to oral bioavailability, safety, and metabolic stability were assessed against Lipinski’s Rule of Five, further supporting the compound’s suitability as a drug-like molecule.

The absorption properties predicted through ADMET analysis were consistent with established pharmacokinetic thresholds, suggesting that conjugated linoleic acid (CLA) is likely to exhibit efficient gastrointestinal uptake in humans and possesses notable pharmacological potential. The predicted parameters, all within acceptable ranges, affirm the compound’s favorable physicochemical and biopharmaceutical profile. These results underscore the robustness and predictive value of in silico pharmacokinetic modeling in early-stage drug discovery, offering a reliable framework for evaluating bioavailability and drug-likeness. Importantly, CLA demonstrated a positive bioactivity score across relevant therapeutic targets, further substantiating its potential as a pharmacologically active agent. The comprehensive pharmacokinetic assessment, performed using SwissADME, highlights CLA’s promising anti-obesity activity, thereby positioning it as a compelling candidate for further exploration in metabolic disorder therapeutics.

Table 4. Pharmacokinetic Profile of Conjugated Linoleic Acid

Parameters of Pharmacokinetic	Predicted data	Units
Water solubility	-5.862	log mol/
Intestinal absorption (human)	92.329	(% Absorbed
P-glycoprotein I/II inhibitor	Absent	-
Volume of Distribution at steady state (VDss) (Human)	-0.587	log L/kg
Fraction unbound (human)	0.054	Fu
BBB permeability	-0.142	log BB
CNS permeability	-1.6	log PS
CYP2D6 inhibitior	No	-
CYP3A4 inhibitior	No	-
Total Clearance	1.933	log ml/min/kg
Max. tolerated dose (human)	-0.827	log mg/kg/day
Oral Rat Acute Toxicity (LD50)	1.429	mol/kg
Oral Rat Chronic Toxicity (LOAEL)	3.187	log mg/kg_bw/day

Table 5. CLA's physicochemical characteristics using Swiss ADME.

Parmeter Involved	Predicted data
Formula	C18H32O2
Molecular weight	280.45 g/mol
Number of heavy atoms	20
Number of aromatic heavy atoms	0
Fraction Csp3	0.72
Number of rotatable bonds	14
Number of H-bond acceptors	2
Number of H-bond donors	1
Molar Refractivity	89.46
TPSA (Topological Polar surface area)	37.30 Å²

Table 6. CLA's lipophilicity profile utilizing Swiss ADME.

Parmeter Involved	Predicted Value
iLOGP	4.13
XLOGP3	7.05
WLOGP	5.88
MLOGP	4.47
SILICOS-IT	5.77
Consensus Log Po/w	5.46

Table 7. Swiss ADME's water solubility profile of CLA.

Parmeter Involved	Predicted Value
Log S (ESOL)	-5.10
Solubility	2.25e-03mg/ml; 8.01e-06 mol/l
Class	Moderately soluble
Log S (Ali)	-7.65
Solubility	6.27e-06mg/ml; 2.24e-08 mol/l
Class	Poorly soluble
Log S (SILICOS-IT)	-4.67
Solubility	5.93e-03mg/ml; 2.11e-05 mol/l
Class	Moderately soluble

3.2 PASS Online Activity Predictions (Substance Activity Spectra Prediction):

In addition, the compounds were rigorously evaluated using advanced online platforms to predict their medicinal chemistry properties and bioactivity profiles. As summarized in Table 8, the analysis yielded predicted bioactivity scores alongside key medicinal chemistry descriptors for several synthesized compounds. The results revealed a spectrum of putative biological functions, including activity as substrates of cytochrome P450 enzymes (CYP2J and CYP2J2), as well as inhibitory roles against phosphatidylglycerophosphatase and acylcarnitine hydrolase, and potential function as mucomembranous protective agents. Conjugated linoleic acid (CLA) was included in this comprehensive assessment and demonstrated notable interaction potential with relevant biological targets. The evaluation employed probabilistic parameters Pa and Pi which are called as probability of activity and probability of inactivity respectively to estimate the likelihood of specific pharmacological effects. A Pa value exceeding 0.71 is considered indicative of a high likelihood that the compound will exhibit the predicted activity, thus serving as a critical threshold for functional relevance. These findings not only substantiate the therapeutic promise of the evaluated compounds but also reinforce the utility of in silico predictive tools in the early-stage identification of bioactive drug candidates. In this context, CLA emerged as a compound of particular interest, demonstrating a favorable bioactivity profile across multiple biological pathways.

Table 8. PASS online tool for CLA's biological activity.

(Pa) Probability of Activity	(Pi) Probability of Inactivity	Biological Activity
0.977	0.001	CYP2J substrate
0.970	0.001	CYP2J2 substrate
0.951	0.001	Phosphatidylglycerophosphatase inhibitor
0.950	0.002	Acylcarnitine hydrolase inhibitor
0.949	0.003	Mucomembranous protector

4. CONCLUSION:

This investigation leveraged a robust in silico framework to evaluate the therapeutic potential of 10-trans, 12-cis conjugated linoleic acid (CLA) in the context of obesity management. Molecular docking analyses revealed that CLA engages in stable interactions with key appetite-regulating receptors Leptin-R and the Ghrelin antagonist though its binding affinities were comparatively lower than those of benchmark pharmacological agents such as Vildagliptin and Rosuvastatin. Despite this, CLA exhibited favorable pharmacokinetic properties, including high predicted intestinal absorption and acceptable drug-likeness as per Lipinski’s criteria. Additionally, bioactivity predictions using PASS online indicated a broad spectrum of potential biological activities, underscoring its multifaceted therapeutic promise. Collectively, these findings suggest that CLA may function as a viable adjunct or lead compound in anti-obesity drug development. Nonetheless, empirical validation through rigorous in vitro and in vivo experimentation remains imperative to substantiate these mathematical insights and to delineate its mechanistic pathways. This research study reinforces the crucial function of computational techniques in accelerating early-stage drug discovery and optimizing candidate selection for metabolic disorder therapeutics.

5. AUTHOR CONTRIBUTIONS:

Magendran Rajendiran: conceptualisation, methodology, formal analysis and validation, writing original draft preparation, writing review and editing and overall supervision and project administration. Shanmugasundaram Palani: writing review and editing, overall supervision and project administration. All authors have read and agreed to the published version of the manuscript.

6. ACKNOWLEDGEMENT:

Authors thankful to the School of Pharmaceutical Sciences, Vels University India.

7. CONFLICTS OF INTEREST:

The authors declare no conflicts of interest.

8. DATA AVAILABILITY STATEMENT:

The datasets produced throughout this study, and those analysed are readily obtained from the corresponding author upon the submission of a reasonable request.

10. REFERENCES:

1. Camacho S, Ruppel A. Is the calorie concept a real solution to the obesity epidemic? Glob Health Action. 2017 Jan 9; 10(1).

2. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. New England Journal of Medicine. 2017 Jul 6; 377(1): 13–27.

3. Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of Disease 2015 study. Int J Public Health. 2018 May 3; 63(S1): 165–76.

4. Ranjit Ambad, Roshan Kumar Jha, Dhruba Hari Chandi, Saurabh Hadke. Association of Leptin in Diabetes Mellitus and Obesity. Research J. Pharm. and Tech. 2020; 13(12): 6295-6299.

5. Geary N. Control-theory models of body-weight regulation and body-weight-regulatory appetite. Appetite. 2020 Jan; 144: 104440.

6. Doo M, Kim Y. Obesity: Interactions of Genome and Nutrients Intake. Prev Nutr Food Sci. 2015 Mar 31; 20(1): 1–7.

7. De Silva A, Salem V, Long CJ, Makwana A, Newbould RD, Rabiner EA, et al. The Gut Hormones PYY3-36 and GLP-17-36 amide Reduce Food Intake and Modulate Brain Activity in Appetite Centers in Humans. Cell Metab. 2011 Nov; 14(5): 700–6.

8. Shahla O. Al-Ogaidi, Sura A. Abdulsattar, Hameed M. J. Al-Dulaimi. The Impact of Serum Leptin, Leptin Receptor and Insulin on Maternal Obesity. Research J. Pharm. and Tech. 2019; 12(7): 3569-3574.

9. Laxminarayana Kurady Bairy, Shakta Mani Satyam, Prakashchandra Shetty. An Insight on pain modulation with Gender and Obesity: A Systematic Review. Research J. Pharm. and Tech. 2020; 13(12): 6284-6290.

10. Ifana Anugraheni, Sri Andarini, Dian Handayani, Titin Andri Wihastuti. Black yeast beta glucan for insulin Resistance Prevention through IL-33, ST2 and Leptin Level: An In vivo Study of an Obesity model using Sprague dawley Rats. Research J. Pharm. and Tech. 2020; 13(12): 6077-6080.

11. Ahrén B. Dipeptidyl Peptidase-4 Inhibitors. Diabetes Care. 2007 Jun 1; 30(6): 1344–50.

12. Mathieu C. Vildagliptin: a new oral treatment for type 2 diabetes mellitus. Vasc Health Risk Manag. 2008 Dec; Volume 4: 1349–60.

13. Ansari JA, Bhandari U, Haque SE, Pillai KK. Enhancement of antioxidant defense mechanism by pitavastatin and rosuvastatin on obesity-induced oxidative stress in Wistar rats. Toxicol Mech Methods. 2012 Jan 23; 22(1): 67–73.

14. B. Lalitha Devi, N.S. Muthiah, K. Satya Narayana Murty, Sanjay Kumar. Effects of 12 weeks Treatment with Conjugated Linoleic acid Supplementation on Body Fat Mass and Lipid Metabolism in Healthy, Obese Individuals - A Pilot Study. Research J. Pharm. and Tech. 2018; 11(3): 996-1000.

15. Pressley H, Cornelio CK, Adams EN. Setmelanotide: A Novel Targeted Treatment for Monogenic Obesity. Journal of Pharmacy Technology. 2022 Dec 30; 38(6): 368–73.

16. Jeyabaskar Suganya, Viswanathan T, Mahendran Radha, Nishandhini Marimuthu. In silico Molecular Docking studies to investigate interactions of natural Camptothecin molecule with diabetic enzymes. Research J. Pharm. and Tech. 2017; 10(9): 2917-2922.

17. Pariza MW, Park Y, Cook ME. The biologically active isomers of conjugated linoleic acid. Prog Lipid Res. 2001 Jul; 40(4): 283–98.

18. Durgam VR, Fernandes G. The growth inhibitory effect of conjugated linoleic acid on MCF-7 cells is related to estrogen response system. Cancer Lett. 1997 Jun; 116(2): 121–30.

19. Park Y, Storkson JM, Albright KJ, Liu W, Pariza MW. Evidence that the trans ‐10, cis ‐12 isomer of conjugated linoleic acid induces body composition changes in mice. Lipids. 1999 Mar; 34(3): 235–41.

20. Doaa Mahdi Omran, Saad Merza Alaraji, Ali Hussein Albayati, Wathiq Essam. Relationship between Ghrelin and Leptin with Insulin Resistance in Obese Patients and Non-Obese Individuals. Research J. Pharm. and Tech. 2018; 11(1): 281-283.

21. Lalit Kumar, Ruchi Verma. Molecular docking based approach for the design of Novel Flavone Analogues as inhibitor of Beta-Hydroxyacyl-ACP Dehydratase HadAB complex. Research J. Pharm. and Tech. 2017; 10(8): 2439-2445.

22. 22 Sonal P. Kumbhar, Pratiksha A. Satpute, Akash Thombare, Shalini Shinde, N. B. Chougule. Molecular Docking and Admet Studies of Nigella sativa Plant against Osteoporosis. Asian Journal of Pharmaceutical Research. 2025; 15(2): 121-6.

23. Padmini R, Sitrarasi R, Razia M. Molecular Docking Studies of Bioactive Compounds from Allium sativum Against EML4-ALK Receptor. Research J. Pharm. and Tech. 2017; 10(11): 3741-3747.

24. Ayyakannu Arumugam Napoleon, Vijay Sharma. Molecular Docking and In-vitro anti-inflammatory evaluation of Novel Isochromen-1-one analogues from Etodolac. Research J. Pharm. and Tech. 2017; 10(8): 2446-2450.

25. Padmini R, Sitrarasi R, Razia M. Molecular Docking Studies of Bioactive Compounds from Allium sativum Against EML4-ALK Receptor. Research J. Pharm. and Tech. 2017; 10(11): 3741-3747.

Received on 16.06.2025 Revised on 10.10.2025

Accepted on 26.12.2025 Published on 01.07.2026

Available online from July 04, 2026

Research J. Pharmacy and Technology. 2026;19(7):3059-3066.

DOI: 10.52711/0974-360X.2026.00435

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

S. No	Receptor	PDB ID	Ligand	PUBCHEM	Binding Affinity score kcal/mol
1	Leptin (LEP-R Isoform/OB Gene)	3v6o	CLA	5282800	-4.5
2	Leptin (LEP-R Isoform/OB Gene)	3v6o	Metformin	4091	-4.3
3	Leptin (LEP-R Isoform/OB Gene)	3v6o	Rosuvastatin	446157	-5.8
4	Leptin (LEP-R Isoform/OB Gene)	3v6o	Vildagliptin	6918537	-6.6