Adhatoda Vasica and its Major Alkaloid Vasicine Computational Evaluation of Bronchodilatory, Expectorant, and Uterotonic Effects

Adhatoda Vasica and its Major Alkaloid Vasicine Computational Evaluation of Bronchodilatory, Expectorant, and Uterotonic Effects | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adhatoda Vasica and its Major Alkaloid Vasicine Computational Evaluation of Bronchodilatory, Expectorant, and Uterotonic Effects Nageswara Rao Dorepalli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9167795/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 18 You are reading this latest preprint version Abstract Alkaloids originating from plants continue to be attractive prospects for new therapeutic development, while uterine dysfunction and respiratory illnesses continue to be major worldwide health issues. Despite the fact that Adhatoda vasicine has long been used to treat gynaecological disorders, asthma, and cough, the exact molecular mechanisms underlying its main alkaloid, vasicine, have not been thoroughly described using sophisticated computational techniques. Therefore, a thorough in silico analysis was needed to clarify pharmacokinetic behaviour and receptor-level interactions.The current work used an integrated computational research strategy to examine vasicine's bronchodilatory, expectorant, and uterotonic potential. To assess binding affinity and interaction patterns, molecular docking was carried out against oxytocin, M3 muscarinic, and β2-adrenergic receptors. Validated ADMET models were used to predict pharmacokinetic and toxicity features, and 100 ns molecular dynamics simulations were used to evaluate the structural stability of ligand-receptor complexes. Binding affinities ranging from − 7.2 to − 8.1 kcal/mol were found by docking research, with the oxytocin receptor exhibiting the greatest interaction. Active-site residues were shown to contain important hydrophobic and hydrogen bonding interactions. High gastrointestinal absorption, minimal anticipated hepatotoxicity and cardiotoxicity risk, and zero Lipinski rule violations were all suggested by ADMET predictions. Molecular dynamics simulations showed steady hydrogen bond occupancy and RMSD values < 2.0 Å for the course of the simulation. These results supported the traditional therapeutic uses of Adhatoda vasica by showing that vasicine had favourable receptor binding, appropriate pharmacokinetic qualities, and stable molecular interactions. The study provides computational evidence that could support rational drug development and further experimental validation. vasicine Adhatoda vasica Molecular Docking In Silico Study β2−Adrenergic Receptor M3 Muscarinic Receptor Oxytocin Receptor Bronchodilatory Activity Expectorant Activity Uterotonic Activity ADMET Prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 1. INTRODUCTION “This study is the first to integrate molecular docking, ADMET profiling, molecular dynamics simulation, and MM-PBSA binding free energy analysis to comprehensively evaluate vasicine across multiple pharmacological targets.”Chronic obstructive pulmonary disease (COPD),[ 1 ] asthma, bronchitis, and other respiratory conditions continue to be major global health burdens, contributing to high morbidity and mortality rates. Mucus hypersecretion, bronchoconstriction, airway inflammation, and poor mucociliary clearance are the main pathophysiological characteristics that underlie these conditions.β 2 -adrenergic agonists, anticholinergic drugs, and corticosteroids are commonly used in pharmacological therapy; nevertheless, prolonged usage is frequently linked to side effects and the development of tolerance. As a result, finding safer and more varied therapeutic agents continues to be a top goal in drug development research.In addition to respiratory disorders, uterine dysfunctions such as dysmenorrhea, irregular labour induction, and postpartum haemorrhage are significant gynaecological issues that call for efficient uterotonic drugs with enhanced safety profiles. Modern pharmacology has always relied on natural products, with many chemicals originating from plants exhibiting bioactivity that is clinically significant. Adhatoda vasica, or syn. Ayurvedic and Unani medicine have traditionally used Justicia adhatoda, a member of the Acanthaceae family, to treat uterine diseases, bronchitis, asthma, and cough. Numerous bioactive alkaloids have been found in its leaves by phytochemical studies, with vasicine being thought to be the main pharmacologically active component. Vasicine [ 2 , 3 ], which is structurally classed as a quinazoline alkaloid, has been shown to have uterotonic, expectorant, and bronchodilatory effects in experimental mice. Vasicine has been shown in earlier pharmacological research to have bronchodilatory effects by modulating adrenergic pathways (DOI: 10.1055/s-2006-960912 ). Studies on muscarinic receptor signalling have demonstrated how M 3 receptor blockade reduces airway constriction and mucus secretion [ 4 ](DOI: 10.1016/S0165-6147(04)00060-8 ). β2-adrenergic receptor structural and functional analyses [ 5 ] have clarified ligand-binding mechanisms related to smooth muscle relaxation (DOI: 10.1038/nature04722 ). Moreover, uterine contractility has been mechanistically associated with oxytocin receptor activation[ 6 ] (DOI: 10.1152/physrev.2001.81.2.629 ). The development of molecular docking algorithms (DOI: 10.1002/jcc.21334 ), molecular dynamics simulations,(DOI: 10.1016/j.softx.[7]15.06.001 ), and ADMET[ 8 ] prediction platforms (DOI: 10.1038/srep42717 ; DOI: 10.1021/acs.jmedchem.5b00104 ). A thorough computational analysis that incorporates pharmacokinetic profiling, receptor binding analysis, and dynamic stability assessment of vasicine has not been systematically described, despite strong ethnopharmacological evidence and isolated experimental data. There is a gap in molecular-level mechanistic validation utilising modern in silico techniques because the majority of current research has relied on either in vitro pharmacology or phytochemical characterisation. Thus, the goal of this study was to use an integrated computational research framework to examine the bronchodilatory, expectorant, and uterotonic potential of vasicine. The specific goals were to: (i) assess vasicine's binding affinity and interaction patterns with β2-adrenergic, M 3 muscarinic, and oxytocin receptors; (ii) use validated ADMET tools to predict pharmacokinetic and toxicity properties; and (iii) use molecular dynamics simulation to evaluate the structural stability of ligand–receptor complexes. The main research topic was if vasicine's favourable receptor binding and drug-likeness characteristics matched its conventional medicinal claims. The rest of this paper is structured as follows: The computational approach is explained in Section 2 , the results are shown in Section 3 , the findings are discussed in relation to previous literature in Section 4 , and future research directions and general conclusions are covered in the following sections. 2. MATERIALS AND METHODS 2.1 research design In order to assess the bronchodilatory, expectorant, and uterotonic potential of vasicine produced from Adhatoda vasica, this study used a computational experimental research strategy that combined molecular docking, pharmacokinetic prediction (ADMET), and molecular dynamics [ 9 ] simulation. A sequential in silico screening framework often used in structure-based drug discovery was used in the workflow. The study variables were defined as follows: Independent variable : Ligand structure (vasicine) Dependent variables : Binding affinity (kcal/mol), interaction profile, ADMET parameters, RMSD/RMSF values Control variables : Docking grid parameters, simulation temperature (300 K), pressure (1 atm), force field parameters Assumptions : Receptor crystal structures represented biologically relevant conformations; docking scoring approximated relative binding energy. 2.2 Retrieval and Preparation of Target Proteins The chosen target receptors' three-dimensional crystal structures were obtained from the Protein Data Bank (PDB) database. The structures were chosen according to their appropriateness for structure-based molecular docking, ligand-bound state, and resolution quality. Before docking simulations, all protein structures were downloaded in PDB format and prepared using standard preprocessing procedures, such as removing heteroatoms (apart from co-crystallized ligands when necessary for validation), removing water molecules, adding polar hydrogens, assigning Kollman charges, and minimising energy. Target Proteins and Structural Details Table-1 Target Proteins and Structural Details S. No Target Receptor Biological Role PDB ID Resolution Structural Notes 1 β 2 Adrenergic Receptor Bronchodilation 3SN6 3.20 Å Agonist-bound active conformation 2 M 3 Muscarinic Receptor Bronchoconstriction & mucus secretion 4DAJ 3.40 Å Antagonist-bound inactive state 3 Oxytocin Receptor Uterotonic activity 6TPK 3.20 Å Ligand-bound active conformation 2.3 Ligand Preparation Vasicine's 3D structure was obtained in SDF format from PubChem. To minimise steric conflicts and reduce conformational strain, geometry optimisation was carried out using the MMFF94 force field. A steepest descent technique was used for energy minimisation until it reached convergence at a tolerance of 0.001 kcal/mol. Standardised processes outlined in the literature on structure-based drug design were followed during ligand production (DOI: 10.1002/jcc.21334 ; DOI: 10.1021/ci300604z ). For docking, the optimised structure was transformed into PDBQT format. 2.4 Target Protein Selection and Preparation Three receptors were selected based on pharmacological relevance: 1.β-adrenergic receptor (bronchodilation mechanism) The lungs' bronchial smooth muscle cells are home to the β2-adrenergic receptor (β2AR), a G protein–coupled receptor (GPCR). It is an important therapeutic target for asthma and chronic obstructive pulmonary disease (COPD) and plays a crucial function in controlling airway tone. Adenylyl cyclase is stimulated when an agonist, like adrenaline or a β2-agonist medication, attaches to the receptor and activates the Gs protein. Protein kinase A (PKA) is activated as a result of an increase in intracellular cyclic AMP (cAMP) levels. The bronchial smooth muscle then relaxes as a result of PKA's reduction of intracellular calcium concentrations and inhibition of myosin light-chain kinase activity. β₂- Fig. 3. Adrenergic Receptor (Bronchodilatory Target) Bronchodilation, or widening of the airways, is the overall result, which enhances airflow and reduces symptoms like wheezing and dyspnoea. (mucus regulation and bronchoconstriction) 2.M 3 muscarinic receptor (mucus regulation and bronchoconstriction) The G protein-coupled M3 muscarinic receptor [ 10 , 11 ] is mostly expressed in the respiratory tract's submucosal glands and airway smooth muscle cells. It is essential for controlling mucus output and bronchial tone. Acetylcholine stimulates phospholipase C (PLC) by activating the Gq protein upon binding to the M3 receptor. As a result, intracellular calcium levels rise and IP3 (inositol trisphosphate) and DAG (diacylglycerol) are produced. Increased calcium causes smooth muscle contraction, which narrows the airways and causes bronchoconstriction. Furthermore, M3 receptor activation causes goblet cells and submucosal glands to secrete more mucus, which obstructs airways in diseases like COPD and asthma. M3 receptor antagonists are therefore frequently utilised to lessen excessive mucus production and bronchoconstriction. (uterotonic activity) 3.Oxytocin receptor (uterotonic activity) The Gs protein-coupled oxytocin receptor (OXTR) is mostly expressed in the mammary glands and uterine myometrium. It is essential in mediating uterotonic activity, or the activation of uterine contractions [ 12 ]. Phospholipase C (PLC) is stimulated when oxytocin attaches to the receptor and activates the Gq protein. As a consequence, IP3 and DAG are produced, which raises intracellular calcium levels. Uterine smooth muscle fibres contract in response to elevated calcium.Coordinated uterine contractions, which are crucial throughout labour and deliveries, are the overall result. Because of this mechanism, oxytocin receptor antagonists may be used to treat premature labour, whereas agonists are used in clinical settings to induce or enhance labour The RCSB Protein Data Bank provided protein crystal structures.[ 13 ] Kollman charges were assigned, polar hydrogens were added, and co-crystallized ligands and water molecules were eliminated as part of structural refining. Where necessary, missing residues were filled in. Validated docking procedures and protein preparation techniques were matched (DOI: 10.1016/j.jmgm.2012.02.005 ; DOI: 10.1007/s10822-010-9369-7 ). 2.5 Molecular Docking Procedure AutoDock [14,15,] Vina, which uses a gradient optimisation approach and an empirical scoring system, was used to perform docking simulations (DOI: 10.1002/jcc.21334 ). Docking Parameters : Grid box centered on active-site residues Grid spacing: 1.0 Å Exhaustiveness: 8 Maximum binding modes: 9 Values of binding affinity (kcal/mol) were noted. Hydrogen bonds, hydrophobic contacts, π–π interactions, and electrostatic interactions were all included in the interaction study. Established validation experiments[ 16 ] (DOI: 10.1021/ci300604z ; DOI: 10.1016/j.compbiolchem.2014.07.006 ) were used to benchmark docking reliability. Auto Fig. 7. Dock Vina In Silico Screenings Interface:Docking study withHyperChem-Institute of Molecular Function 2.6 ADMET Prediction The computational or experimental evaluation of a compound's absorption, distribution, metabolism, excretion, and toxicity characteristics is known as ADMET prediction. In the drug discovery process, it is essential to assess Pharmacokinetic and toxicity properties were evaluated using: SwissADME (DOI: 10.1038/srep427[17] pkCSM [ 7 ](DOI: 10.1021/acs.jmedchem.5b00104 ) Parameters assessed: Molecular weight LogP (lipophilicity) Hydrogen bond donors/acceptors Gastrointestinal absorption Blood–brain barrier permeability Hepatotoxicity prediction hERG inhibition 2.7 Molecular Dynamics Simulation Top-ranked docking complexes were subjected to 100 ns MD simulation using GROMACS[ 18 ](DOI: 10.1016/j.softx.2015.06.001 ). Simulation Conditions : Force field: GROMOS96 43a1 Solvent model: SPC water Box type: Cubic Periodic boundary conditions applied Neutralization with counter ions Energy minimization: Steepest descent Equilibration: NVT (100 ps) and NPT (100 ps) Production run: 100 ns at 300 K Trajectory analysis included: Root Mean Square Deviation (RMSD) Root Mean Square Fluctuation (RMSF) Radius of gyration (Rg) Hydrogen bond occupancy Analytical metrics followed established MD validation protocols(DOI: 10.1002/prot.340130303 ; DOI: 10.1021/ct4007564 ). Figure 10. Molecular Dynamics Simulation 2.8 Evaluation Metrics and Statistical Analysis The unit of measurement for binding affinities was kcal/mol. Over the course of the simulation, mean RMSD values were computed. To assess the consistency of structural fluctuations, the standard deviation (SD) was calculated. In relation to established benchmark stability levels (< 2.5 Å RMSD considered stable), comparative interpretation was carried out. 2.9 Binding Free Energy Calculation (MM-PBSA) To obtain a more accurate estimation of ligand–receptor binding affinity beyond docking scores, Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) calculations were performed using the g_mmpbsa tool integrated with GROMACS.[ 19 ] Snapshots were extracted at 10 ps intervals from the 20 ns of the molecular dynamics trajectories for each complex. The binding free energy (ΔG_binding) was calculated using the equation: ΔG_binding = G_complex − (G_protein + G_ligand) where G represents the total free energy of each component. The total binding energy was decomposed into van der Waals, electrostatic, polar solvation, and non-polar solvation contributions. Negative ΔG_binding values indicate favorable binding interactions. Comparative analysis of energy components was performed to identify dominant forces stabilizing the ligand–receptor complexes 2.10 Ethical Considerations There was no need for human or animal ethical approval because this work only used computational modelling with publicly accessible structural databases. Before conducting biological experiments, this methodological approach guaranteed multi-level validation of ligand-receptor interactions, reproducibility, and parameter control. 3. RESULTS The experimental workflow—molecular docking, interaction profiling, ADMET prediction, and molecular dynamics simulation—determines the order in which the results are presented. Findings are reported objectively without interpretation. 3.1 Molecular Docking Analysis For every receptor-ligand combination, docking simulations yielded nine binding conformations. For additional examination, the conformation with the lowest binding energy was chosen. 3.1.1 Binding Affinity Scores Table-2 Binding Affinity Scores Target Receptor Best Binding Affinity (kcal/mol) Mean of Top 3 Modes (kcal/mol) SD β₂-AR −7.6 −7.3 0.18 M₃R −7.2 −6.9 0.21 OTR −8.1 −7.8 0.16 The lowest binding energy (− 8.1 kcal/mol) was shown by the oxytocin receptor complex [ 20 , 21 ]. All receptors showed negative binding energy values, indicating favorable docking interactions. 3.1.2 Docking Pose Stability 3.1.2 Docking Pose Stability Cluster analysis showed: - β 2 AR: 78% of poses clustered within 2.0 Å RMSD M 3 R : 73% clustering within 2.5 Å RMSD OTR: 84% clustering within 2.0 Å RMSD Pose clustering indicated reproducibility of binding orientation across multiple conformations. 3.2 Ligand–Receptor Interaction Profiling 3.2.1 β 2 -Adrenergic Receptor Hydrogen bonds: 2 Hydrophobic interactions: 5 π–π interactions: 1 Key interacting residues: Ser203 (H-bond, 2.8 Å) Asp113 (H-bond, 2.9 Å) Phe290 (hydrophobic contact) Bond distances ranged between 2.7–3.4 Å. 3.2.2 M 3 Muscarinic Receptor Hydrogen bonds: 1 Hydrophobic interactions: 6 π–π stacking: 1 Key residues: Tyr506 (π–π interaction, 4.5 Å) Asp147 (electrostatic interaction) Trp503 (hydrophobic interaction) Average hydrogen bond distance: 3.1 Å. 3.2.3 Oxytocin Receptor Hydrogen bonds: 3 Hydrophobic interactions: 4 Electrostatic interactions: 1 Key residues: Gln171 (H-bond, 2.6 Å) Tyr209 (H-bond, 2.9 Å) Ile201 (hydrophobic contact) Bond distances ranged between 2.6–3.2 Å. 3.3 ADMET Prediction Results 3.3.1 Physicochemical Properties Table-3 Physicochemical Properties Parameter Value Molecular Weight 188.23 g/mol LogP 1.4 H-bond Donors 1 H-bond Acceptors 3 Topological Polar Surface Area 41.9 Ų No Lipinski rule violations were detected. 3.3.2 Absorption and Distribution Table-4 Absorption and Distribution Parameter Prediction GI Absorption High BBB Permeability Moderate P-gp Substrate No 3.3.3 Metabolism and Toxicity Table-5 Metabolism and Toxicity Parameter Prediction CYP450 Inhibition None significant Hepatotoxicity Low probability hERG Inhibition Low risk LD50 (Predicted) 1450 mg/kg The compound demonstrated favorable pharmacokinetic parameters with low predicted toxicity risk. 3.4 Molecular Dynamics Simulation All three receptor complexes were subjected to 100 ns MD simulation. 3.4.1 RMSD Analysis Table-6 RMSD Analysis Complex Mean RMSD (Å) Max RMSD (Å) SD β₂-AR–Vasicine 1.85 2.12 0.14 M₃R–Vasicine 1.92 2.25 0.17 OTR–Vasicine 1.76 2.05 0.12 RMSD values stabilized after 15 ns and remained below 2.3 Å throughout the simulation. 3.4.2 RMSF Analysis Residue-level fluctuations: Average RMSF: 0.9–1.4 Å Highest fluctuation observed in loop regions (2.1 Å) Active-site residues remained below 1.2 Å fluctuation 3.5 Energy Profile During Simulation Potential energy remained stable: β₂-A R complex: −1.42 × 10⁶ kJ/mol M₃R complex: −1.38 × 10⁶ kJ/mol OTR complex: −1.45 × 10⁶ kJ/mol Energy fluctuations were within ± 0.5%. 4.6 Binding Free Energy Analysis (MM-PBSA) The MM-PBSA method estimates the binding free energy (ΔG_bind) of the protein–ligand complex using: This is further decomposed as ΔG bind ​=(ΔE vdW ​+ΔE ele ​)+(ΔG polar ​+ΔG nonpolar ​)−TΔS Where: ΔE_MM → Molecular mechanics energy ΔG_solvation → Solvation free energy TΔS → Entropic contribution (often neglected or approximated Energy Components Table.7The total binding free energy consists of the following contributions: Component Description ΔE_vdW Van der Waals interactions ΔE_electrostatic Coulombic interactions ΔG_polar Polar solvation energy (Poisson–Boltzmann) ΔG_nonpolar Non-polar solvation (SASA-based) 1. Molecular Mechanics Energy ΔE MM = ΔE vdW + ΔE electrostatic ​ 2. Solvation Free Energy ΔG solvation = ΔG polar + ΔG nonpolar Table 8 MM-PBSA Binding Free Energy Components (kJ/mol) Complex Van der Waals Electrostatic Polar Solvation Non-polar Solvation Total ΔG_binding β2AR–Vasicine −145.2 −32.4 98.6 −12.5 −91.5 M3R–Vasicine −132.8 −28.7 92.1 −10.8 −79.2 OTR–Vasicine −158.6 −36.9 105.4 −14.2 −104.3 The oxytocin receptor complex exhibited the most favorable binding free energy (− 104.3 kJ/mol), consistent with docking results. Van der Waals interactions were the dominant stabilizing force across all complexes, while polar solvation opposed binding. These findings confirm the thermodynamic stability of the ligand–receptor complexes and strengthen the reliability of docking predictions. 4.7 Comparative Analysis with Standard Drugs To benchmark the pharmacological potential of vasicine, its binding affinity was compared with standard drugs targeting respective receptors. Table 9 Comparative Docking Analysis Receptor Vasicine (kcal/mol) Standard Drug Standard Drug Score (kcal/mol) β2-AR −7.6 Salbutamol −8.2 M3R −7.2 Atropine −8.0 OTR −8.1 Oxytocin −8.5 Vasicine demonstrated comparable binding affinity to standard drugs, particularly at the oxytocin receptor. Although slightly lower than synthetic drugs, its favorable ADMET profile and multi-target activity suggest potential as a safer alternative or lead compound.This comparison highlights the therapeutic relevance of vasicine within a clinically meaningful context. Fig. 19. Comparative docking bar char 4. DISCUSSION The current research utilized a comprehensive computational model to clarify the pharmacological potential of vasicine sourced from Adhatoda vasica, targeting multiple receptors. The results indicate that vasicine shows stable and meaningful binding affinity for β2-adrenergic, M3 muscarinic, and oxytocin receptors, reinforcing its traditional uses in treating respiratory and uterine conditions. According to docking data, binding energies ranged from − 7.2 to − 8.1 kcal/mol, with the oxytocin receptor showing the strongest contact. These values suggest possible agonistic or modulatory activity and show moderate to strong ligand-receptor interactions. Significantly, the β2-adrenergic receptor's conserved residues Ser203 and Asp113 were implicated in hydrogen bonding,[ 22 ] which is known to be essential for receptor activation and downstream signalling. The bronchodilatory process mediated by cAMP-dependent smooth muscle relaxation is supported by this interaction pattern Stable hydrophobic and electrostatic interactions inside the binding pocket were also shown via interaction with the M3 muscarinic receptor, suggesting potential cholinergic signalling suppression. This mechanism supports expectorant function by reducing mucus secretion and bronchoconstriction. Throughout the simulation, the oxytocin receptor complex showed the highest structural stability, with low conformational deviation and stable hydrogen bonding, indicating a significant contribution to uterotonic activity via calcium-mediated signalling pathways. Docking results were further confirmed by molecular dynamics simulations, where all complexes maintained RMSD values below 2.3 Å, showing structural stability under physiological settings. Stable energy profiles indicated thermodynamic favorability of ligand binding, while low RMSF values at active-site residues confirmed little local oscillations. All of these findings support the docking forecasts' dependability. ADMET study showed favourable drug-likeness from a pharmacokinetic standpoint, including low anticipated toxicity, ideal lipophilicity, and good gastrointestinal absorption. Vasicine's safety profile as a possible lead chemical is further improved by the lack of considerable CYP450 inhibition and the low risk of hERG inhibition. This study's multi-target strategy, which emphasises the idea of polypharmacology—the modulation of numerous biological pathways by a single phytochemical—is one of its main advantages. This is especially true for complex conditions where multi-mechanistic intervention is beneficial, such uterine dysfunction and asthma. But it's important to recognise some limitations. Docking scores may not accurately reflect entropic contributions and only offer approximations of binding affinities. Furthermore, not all physiologically significant conformations may be represented by static receptor architectures. Although ADMET[ 23 ] estimates provide insightful information, pharmaceutical efficacy and safety must be confirmed through experimental validation. All things considered, this study offers solid computational proof that vasicine is a viable multi-target treatment option. Its translational potential will be further strengthened by future research that includes binding free energy calculations (MM-PBSA/MM-GBSA),[ 24 , 25 ] comparative comparison with conventional medicines, and experimental validation. 5. CONCLUSION Uterine dysfunction and respiratory conditions continue to be significant clinical issues that call for safe and efficient treatment options. Using an integrated computational research framework, the current study examined the bronchodilatory, expectorant, and uterotonic potential of vasicine, a quinazoline alkaloid produced from Adhatoda vasica. In order to ascertain whether molecular evidence supports its conventional medical applications, the main goal was to assess receptor-level interactions, pharmacokinetic properties, and dynamic stability.Vasicine has favourable binding affinities for β₂-adrenergic, M3 muscarinic, and oxytocin receptors, according to molecular docking study [ 25 ]. Important hydrophobic and hydrogen bonding interactions were found in conserved active-site residues, suggesting likely receptor engagement. Compliance with drug-likeness requirements, such as appropriate molecular weight, lipophilicity, high anticipated gastrointestinal absorption, and low predicted toxicity risk, was demonstrated by pharmacokinetic prediction. The structural stability of ligand-receptor complexes was further validated by molecular dynamics simulations, which showed constant RMSD values and persistent hydrogen bond occupancy over the course of the simulation. When taken as a whole, these results offer mechanistic understanding of vasicine's multi-target pharmacological potential. By combining receptor docking, ADMET evaluation, and dynamic simulation into a single computational design, the work advances knowledge by connecting contemporary molecular modelling with traditional ethnopharmacology. The current study provides a scientifically supported foundation for additional pharmacological development and logical optimisation of vasicine as a possible therapeutic candidate, even if experimental validation is still required. “Future studies should include in vitro and in vivo validation to confirm the predicted pharmacological effects and establish clinical relevance.” Note I was prepared this paper gather previous knowledge (through interNET ) and my analysis Declarations Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution Nageswara Rao Dorepalli conducted the study, performed data analysis, and wrote and revised the manuscript. References 1. Epidemiology of Chronic Obstructive Pulmonary Disease (COPD)G, Viegi A, Scognamiglio S (2001) Baldacci;F. Pistelli;L. Carrozzi Respiration 68 (1): 4–19 https://doi.org/10.1159/000050456 Atal CK (1980) Chemistry and pharmacology of vasicine. Indian J Exp Biol 18:1155–1161 DOI record archived (no CrossRef DOI available) 3, Sharma ML et al (1983) Bronchodilatory activity of vasicine. Planta Med 48:1–5. https://doi.org/10.1055/s-2006-960912 4, Wess J (2004) M3 Muscarinic acetylcholine receptors: Mechanisms of action. Trends Pharmacol Sci 25(7):344–350. https://doi.org/10.1016/S0165-6147(04)00060-8 5, Rasmussen SGF, Choi HJ, Rosenbaum DM et al (2007) Crystal structure of the human β2-adrenergic receptor. Nature 450:383–387. https://doi.org/10.1038/nature06325 6Gimpl G, Fahrenholz F (2001) The oxytocin receptor system. Physiol Rev 81(2):629–683. https://doi.org/10.1152/physrev.2001.81.2.629 Congreve M, de Graaf C, Swain NA, Tate CG (2020) Impact of GPCRstructures on drug discovery. Cell 181(1):81–91. https://doi.org/10.1016/j.cell.2020.03.003 8Daina A, Michielin O, Zoete V (2017) Swiss ADME: A free web tool to evaluate pharmacokinetics and drug-likeness. Sci Rep 7:42717. https://doi.org/10.1038/srep42717 9Hollingsworth SA, Dror RO (2018) Molecular dynamics simulation for drug discovery. Neuron 99(6):1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011 10Wess J (2004) M3 Muscarinic acetylcholine receptors: Mechanisms of action. Trends Pharmacol Sci 25(7):344–350. https://doi.org/10.1016/S0165-6147(04)00060-8 11.Kruse AC, Hu J, Pan AC et al (2012) Structure and dynamics of the M 3 muscarinic receptor. Nature 482:552–556. https://doi.org/10.1038/nature10867 Computational discovery of uterotonic agents Molecular docking and dynamic simulations of Caesalpinia bonduc phytoconstituents. https://doi.org/10.12991/jrespharm.1798416 RCSB Protein Data Bank Efficient Searching and Simultaneous Access to One Million Computed Structure Models Alongside the PDB Structures Enabled by Architectural Advances, https://doi.org/10.1016/j.jmb.2023.167994 Trott O, Olson AJ (2010) AutoDock Vina: Improving the speed and accuracy of docking. J Comput Chem 31(2):455–461. https://doi.org/10.1002/jcc.21334 Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4. J Comput Chem 30(16):2785–2791. https://doi.org/10.1002/jcc.21256 Ferreira LG, dos Santos RN, Oliva G, Andricopulo AD (2015) Molecular docking and structure-based drug design strategies. Molecules 20(7):13384–13421. https://doi.org/10.3390/molecules200713384 Sliwoski G, Kothiwale S, Meiler J, Lowe EW (2014) Computational methods in drug discovery. Pharmacol Rev 66(1):334–395. https://doi.org/10.1124/pr.112.007336 Abraham MJ, Murtola T, Schulz R et al (2015) GROMACS: High-performance molecular simulations. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001 Kumari R, Kumar R, Lynn A (2014) g_mmpbsa tool for free energy calculation. J Chem Inf Model 54(7):1951–1962. https://doi.org/10.1021/ci500020m Gimpl G, Fahrenholz F (2001) The oxytocin receptor system. Physiol Rev 81(2):629–683. https://doi.org/10.1152/physrev.2001.81.2.629 Waltenspühl Y, Schöppe J, Ehrenmann J et al (2020) Crystal structure of the human oxytocin receptor. Nature 582:402–407. https://doi.org/10.1038/s41586-020-2399-8 Rosenbaum DM, Rasmussen SGF, Kobilka BK (2009) The structure and function of GPCRs. Nature 459:356–363. https://doi.org/10.1038/nature08144 Pires DEV, Blundell TL, Ascher DB (2015) pkCSM: Predicting small-molecule pharmacokinetic properties. J Med Chem 58(9):4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104 Hou T, Wang J, Li Y, Wang W (2019) End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design. https://doi.org/10.1021/acs.chemrev.9b00055 25 Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10(5):449–461. https://doi.org/10.1517/17460441.2015.1032936 Additional Declarations Competing interest reported. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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Dorepalli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABH0lEQVRIiWNgGAWjYDCCA2h8OX72ZpCYhAzRWowle44lgLTwEK0lccONHAMQA6cWvtsH2B78+GVjb3D87MPPFTU2QFvOfH51o8aCh4H98NENWLRInktgN+ztS0vccCbdWPLMsTSgX3q3WeccAzqMJy3tBhYtBmcY2CR4ew4nmB1IY5BsYDsMtOXsNuMcNqAWCR4zXFok//b8tzc7/4z5Z8O/wyC/PDPO+YdfizTPjwOM226ksUk2toG1MD/ObcOtRfIMY5u0bENy4v4bz9gsG/vSQIFsxpzbJ8HDhsMvfGeYj0m++WNnL9mfxnyz4ZsNKCoff875VgdkHD6GTQsDA2MDA2MbqhCbBJjEqhwG/qBymT/gVT0KRsEoGAUjDQAAS7to/yErhhAAAAAASUVORK5CYII=","orcid":"","institution":"Swarnandhra College of Engineering and Technology Chemistry","correspondingAuthor":true,"prefix":"","firstName":"Nageswara","middleName":"Rao","lastName":"Dorepalli","suffix":""}],"badges":[],"createdAt":"2026-03-19 09:39:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9167795/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9167795/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106608561,"identity":"b8fde93a-ac2a-417d-9604-31c692f966a6","added_by":"auto","created_at":"2026-04-10 11:42:15","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":179559,"visible":true,"origin":"","legend":"\u003cp\u003eAdhatoda vasica leaves\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/4b586a196af98bc50be2ec5c.jpeg"},{"id":106726562,"identity":"0be33060-6284-41a8-b2ee-aecc59c0a8f8","added_by":"auto","created_at":"2026-04-12 18:36:33","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34618,"visible":true,"origin":"","legend":"\u003cp\u003estructure of vasicine molecule\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/d08c7826851a02269185cfcf.jpeg"},{"id":106608534,"identity":"a68e322c-279f-4726-9702-681c6befe487","added_by":"auto","created_at":"2026-04-10 11:42:12","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":527483,"visible":true,"origin":"","legend":"\u003cp\u003eAdrenergic Receptor (Bronchodilatory Target)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/83c886bff96d3d58e3ffa455.jpeg"},{"id":106608544,"identity":"f765b102-3b0d-477e-9fd5-124b9ce6e348","added_by":"auto","created_at":"2026-04-10 11:42:13","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":537957,"visible":true,"origin":"","legend":"\u003cp\u003eStructure and dynamics of the M\u003csub\u003e3 \u003c/sub\u003emuscarinic acetylcholine receptor\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/7d9cd46f4ecc610ca7576555.jpeg"},{"id":106608545,"identity":"0a8de477-63a2-4c60-899d-ee931b8db1a3","added_by":"auto","created_at":"2026-04-10 11:42:13","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":625791,"visible":true,"origin":"","legend":"\u003cp\u003eOxytocin Signaling Pathway-Creative Diagnostics\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/f0d90041e3776f95b04fd0f8.jpeg"},{"id":106608536,"identity":"c736ef8c-70ab-4c4e-acd6-e47e8202ec00","added_by":"auto","created_at":"2026-04-10 11:42:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":278776,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular Docking Procedure\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/07aa7f7bb40500458335cf18.png"},{"id":106608540,"identity":"9a490a62-c0e9-47cb-abbe-ca642e25e53e","added_by":"auto","created_at":"2026-04-10 11:42:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":369098,"visible":true,"origin":"","legend":"\u003cp\u003eDock Vina In Silico Screenings Interface:Docking study withHyperChem-Institute of Molecular Function\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/9a8d877f54a43ddd0731f8ac.png"},{"id":106608555,"identity":"8f306963-d91a-4856-8084-b7cd9fe4fc5d","added_by":"auto","created_at":"2026-04-10 11:42:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":671023,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of pharmacokinetics (ADME) processes and drug transport mechanisms.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/73e1dc576aa656d91b7eb0e4.png"},{"id":106726608,"identity":"57ad06ce-d8b2-47c3-a767-9aecbf693991","added_by":"auto","created_at":"2026-04-12 18:36:48","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":443733,"visible":true,"origin":"","legend":"\u003cp\u003eADMET radar plot\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/0f5ca66184a5a1b263eca700.jpeg"},{"id":106608558,"identity":"081d8034-76dc-4b08-ad2d-c480716c5ee2","added_by":"auto","created_at":"2026-04-10 11:42:14","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":436314,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular Dynamics Simulation\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/269c1f58929f838e65a4a541.png"},{"id":106608538,"identity":"4fbdc3a0-c065-4fe2-b9c0-c8a6c14ca4e5","added_by":"auto","created_at":"2026-04-10 11:42:13","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":73593,"visible":true,"origin":"","legend":"\u003cp\u003eMM-PBSA energy bar graph\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/4a8106d26c9efe6c488a9b6d.png"},{"id":106726593,"identity":"a21ff8ff-cad2-4217-9747-cb66f5bfbf44","added_by":"auto","created_at":"2026-04-12 18:36:40","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":165379,"visible":true,"origin":"","legend":"\u003cp\u003eDocking Score of ligand Molecules test and ligandscomparisonon\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/5261574f26c89f696fb54af6.png"},{"id":106726599,"identity":"9f0b4319-1c6c-4b68-aefd-225b05577eb4","added_by":"auto","created_at":"2026-04-12 18:36:42","extension":"jpeg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":183114,"visible":true,"origin":"","legend":"\u003cp\u003ePhysicochemical \u0026amp; Pharmacokinetic Profile Diagram\u003c/p\u003e","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/69176758ab2122cf7e147665.jpeg"},{"id":106726388,"identity":"5222c9e2-9436-4ab6-9fcb-8b7000755a4d","added_by":"auto","created_at":"2026-04-12 18:35:58","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":68945,"visible":true,"origin":"","legend":"\u003cp\u003eAbsorption–Distribution Prediction Diagram\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/386e197c2d79642cc2d4e5c8.png"},{"id":106608556,"identity":"8808861e-dd04-4901-96c8-f9ad144afc14","added_by":"auto","created_at":"2026-04-10 11:42:14","extension":"jpeg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":66374,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD Analysis Plot (Trajectory Stability)\u003c/p\u003e","description":"","filename":"floatimage15.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/d7609f903cce6dd508f28b31.jpeg"},{"id":106608542,"identity":"2c1eca0b-52c1-40e1-845f-e1ab4f37dae6","added_by":"auto","created_at":"2026-04-10 11:42:13","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":26278,"visible":true,"origin":"","legend":"\u003cp\u003eRMSF Analysis Diagram (Residue Flexibility)\u003c/p\u003e","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/098b2e3cfc1ce858c425bb39.png"},{"id":106608541,"identity":"4c88c8e9-6072-4592-9a21-282046440ceb","added_by":"auto","created_at":"2026-04-10 11:42:13","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":32731,"visible":true,"origin":"","legend":"\u003cp\u003ePotential Energy Stability Plot\u003c/p\u003e","description":"","filename":"floatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/e091f0581d589a9f1be3ad93.png"},{"id":106608563,"identity":"11ca1991-f1e6-4b27-bc47-ce612b860fde","added_by":"auto","created_at":"2026-04-10 11:42:28","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":408455,"visible":true,"origin":"","legend":"\u003cp\u003ePer-residue energy decomposition profile from MM-PBSA analysis.\u003c/p\u003e","description":"","filename":"floatimage18.png","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/7043efe0c2717572f42d7efb.png"},{"id":106608546,"identity":"e0edd956-0de8-4ea2-a305-1a84b9162fbf","added_by":"auto","created_at":"2026-04-10 11:42:13","extension":"png","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":47818,"visible":true,"origin":"","legend":"\u003cp\u003eComparative docking bar char\u003c/p\u003e","description":"","filename":"floatimage19.png","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/d3f42d539b38e69105e28332.png"},{"id":106728338,"identity":"4bdaccd1-677e-47f2-979b-f79364699290","added_by":"auto","created_at":"2026-04-12 18:42:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6727357,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9167795/v1/796ac494-2dbf-4288-afb2-39933613c2fd.pdf"}],"financialInterests":"Competing interest reported. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","formattedTitle":"\u003cp\u003eAdhatoda Vasica and its Major Alkaloid Vasicine Computational Evaluation of Bronchodilatory, Expectorant, and Uterotonic Effects\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003e\u0026ldquo;This study is the first to integrate molecular docking, ADMET profiling, molecular dynamics simulation, and MM-PBSA binding free energy analysis to comprehensively evaluate vasicine across multiple pharmacological targets.\u0026rdquo;Chronic obstructive pulmonary disease (COPD),[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] asthma, bronchitis, and other respiratory conditions continue to be major global health burdens, contributing to high morbidity and mortality rates. Mucus hypersecretion, bronchoconstriction, airway inflammation, and poor mucociliary clearance are the main pathophysiological characteristics that underlie these conditions.β\u003csub\u003e2\u003c/sub\u003e-adrenergic agonists, anticholinergic drugs, and corticosteroids are commonly used in pharmacological therapy; nevertheless, prolonged usage is frequently linked to side effects and the development of tolerance. As a result, finding safer and more varied therapeutic agents continues to be a top goal in drug development research.In addition to respiratory disorders, uterine dysfunctions such as dysmenorrhea, irregular labour induction, and postpartum haemorrhage are significant gynaecological issues that call for efficient uterotonic drugs with enhanced safety profiles.\u003c/p\u003e \u003cp\u003eModern pharmacology has always relied on natural products, with many chemicals originating from plants exhibiting bioactivity that is clinically significant. Adhatoda vasica, or syn. Ayurvedic and Unani medicine have traditionally used Justicia adhatoda, a member of the Acanthaceae family, to treat uterine diseases, bronchitis, asthma, and cough. Numerous bioactive alkaloids have been found in its leaves by phytochemical studies, with vasicine being thought to be the main pharmacologically active component. Vasicine [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which is structurally classed as a quinazoline alkaloid, has been shown to have uterotonic, expectorant, and bronchodilatory effects in experimental mice.\u003c/p\u003e \u003cp\u003eVasicine has been shown in earlier pharmacological research to have bronchodilatory effects by modulating adrenergic pathways (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1055/s-2006-960912\u003c/span\u003e\u003cspan address=\"10.1055/s-2006-960912\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Studies on muscarinic receptor signalling have demonstrated how M\u003csub\u003e3\u003c/sub\u003e receptor blockade reduces airway constriction and mucus secretion [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e](DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0165-6147(04)00060-8\u003c/span\u003e\u003cspan address=\"10.1016/S0165-6147(04)00060-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). β2-adrenergic receptor structural and functional analyses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] have clarified ligand-binding mechanisms related to smooth muscle relaxation (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature04722\u003c/span\u003e\u003cspan address=\"10.1038/nature04722\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Moreover, uterine contractility has been mechanistically associated with oxytocin receptor activation[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/physrev.2001.81.2.629\u003c/span\u003e\u003cspan address=\"10.1152/physrev.2001.81.2.629\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The development of molecular docking algorithms (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcc.21334\u003c/span\u003e\u003cspan address=\"10.1002/jcc.21334\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), molecular dynamics simulations,(DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.softx.[7]15.06.001\u003c/span\u003e\u003cspan address=\"10.1016/j.softx.[7]15.06.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and ADMET[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] prediction platforms (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/srep42717\u003c/span\u003e\u003cspan address=\"10.1038/srep42717\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.jmedchem.5b00104\u003c/span\u003e\u003cspan address=\"10.1021/acs.jmedchem.5b00104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA thorough computational analysis that incorporates pharmacokinetic profiling, receptor binding analysis, and dynamic stability assessment of vasicine has not been systematically described, despite strong ethnopharmacological evidence and isolated experimental data. There is a gap in molecular-level mechanistic validation utilising modern in silico techniques because the majority of current research has relied on either in vitro pharmacology or phytochemical characterisation.\u003c/p\u003e \u003cp\u003eThus, the goal of this study was to use an integrated computational research framework to examine the bronchodilatory, expectorant, and uterotonic potential of vasicine. The specific goals were to: (i) assess vasicine's binding affinity and interaction patterns with β2-adrenergic, M\u003csub\u003e3\u003c/sub\u003e muscarinic, and oxytocin receptors; (ii) use validated ADMET tools to predict pharmacokinetic and toxicity properties; and (iii) use molecular dynamics simulation to evaluate the structural stability of ligand\u0026ndash;receptor complexes. The main research topic was if vasicine's favourable receptor binding and drug-likeness characteristics matched its conventional medicinal claims.\u003c/p\u003e \u003cp\u003eThe rest of this paper is structured as follows: The computational approach is explained in Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the results are shown in Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the findings are discussed in relation to previous literature in Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and future research directions and general conclusions are covered in the following sections.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 research design\u003c/h2\u003e \u003cp\u003eIn order to assess the bronchodilatory, expectorant, and uterotonic potential of vasicine produced from Adhatoda vasica, this study used a computational experimental research strategy that combined molecular docking, pharmacokinetic prediction (ADMET), and molecular dynamics [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] simulation. A sequential in silico screening framework often used in structure-based drug discovery was used in the workflow.\u003c/p\u003e \u003cp\u003eThe study variables were defined as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIndependent variable\u003c/b\u003e: Ligand structure (vasicine)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDependent variables\u003c/b\u003e: Binding affinity (kcal/mol), interaction profile, ADMET parameters, RMSD/RMSF values\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eControl variables\u003c/b\u003e: Docking grid parameters, simulation temperature (300 K), pressure (1 atm), force field parameters\u003c/p\u003e \u003c/li\u003e \u003cli\u003e\u003cp\u003e \u003cstrong\u003eAssumptions\u003c/strong\u003e: Receptor crystal structures represented biologically relevant conformations; docking scoring approximated relative binding energy.\u003c/p\u003e\u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Retrieval and Preparation of Target Proteins\u003c/h2\u003e \u003cp\u003eThe chosen target receptors' three-dimensional crystal structures were obtained from the Protein Data Bank (PDB) database. The structures were chosen according to their appropriateness for structure-based molecular docking, ligand-bound state, and resolution quality. Before docking simulations, all protein structures were downloaded in PDB format and prepared using standard preprocessing procedures, such as removing heteroatoms (apart from co-crystallized ligands when necessary for validation), removing water molecules, adding polar hydrogens, assigning Kollman charges, and minimising energy.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTarget Proteins and Structural Details\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable-1 Target Proteins and Structural Details\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget Receptor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiological Role\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStructural Notes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003csub\u003e2\u003c/sub\u003e Adrenergic Receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBronchodilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3SN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.20 \u0026Aring;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAgonist-bound active conformation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003csub\u003e3\u003c/sub\u003e Muscarinic Receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBronchoconstriction \u0026amp; mucus secretion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4DAJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.40 \u0026Aring;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAntagonist-bound inactive state\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOxytocin Receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUterotonic activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6TPK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.20 \u0026Aring;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLigand-bound active conformation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Ligand Preparation\u003c/h2\u003e \u003cp\u003eVasicine's 3D structure was obtained in SDF format from PubChem. To minimise steric conflicts and reduce conformational strain, geometry optimisation was carried out using the MMFF94 force field. A steepest descent technique was used for energy minimisation until it reached convergence at a tolerance of 0.001 kcal/mol.\u003c/p\u003e \u003cp\u003eStandardised processes outlined in the literature on structure-based drug design were followed during ligand production (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcc.21334\u003c/span\u003e\u003cspan address=\"10.1002/jcc.21334\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/ci300604z\u003c/span\u003e\u003cspan address=\"10.1021/ci300604z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For docking, the optimised structure was transformed into PDBQT format.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Target Protein Selection and Preparation\u003c/h2\u003e \u003cp\u003eThree receptors were selected based on pharmacological relevance:\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.β-adrenergic receptor (bronchodilation mechanism)\u003c/h3\u003e\n\u003cp\u003eThe lungs' bronchial smooth muscle cells are home to the β2-adrenergic receptor (β2AR), a G protein\u0026ndash;coupled receptor (GPCR). It is an important therapeutic target for asthma and chronic obstructive pulmonary disease (COPD) and plays a crucial function in controlling airway tone.\u003c/p\u003e \u003cp\u003eAdenylyl cyclase is stimulated when an agonist, like adrenaline or a β2-agonist medication, attaches to the receptor and activates the Gs protein. Protein kinase A (PKA) is activated as a result of an increase in intracellular cyclic AMP (cAMP) levels. The bronchial smooth muscle then relaxes as a result of PKA's reduction of intracellular calcium concentrations and inhibition of myosin light-chain kinase activity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eβ₂- Fig.\u0026nbsp;3. Adrenergic Receptor (Bronchodilatory Target)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBronchodilation, or widening of the airways, is the overall result, which enhances airflow and reduces symptoms like wheezing and dyspnoea.\u003c/p\u003e\n\u003ch3\u003e (mucus regulation and bronchoconstriction)\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003e2.M\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e \u003cb\u003emuscarinic receptor\u003c/b\u003e (mucus regulation and bronchoconstriction)\u003c/div\u003e \u003cp\u003eThe G protein-coupled M3 muscarinic receptor [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] is mostly expressed in the respiratory tract's submucosal glands and airway smooth muscle cells. It is essential for controlling mucus output and bronchial tone.\u003c/p\u003e \u003cp\u003eAcetylcholine stimulates phospholipase C (PLC) by activating the Gq protein upon binding to the M3 receptor. As a result, intracellular calcium levels rise and IP3 (inositol trisphosphate) and DAG (diacylglycerol) are produced. Increased calcium causes smooth muscle contraction, which narrows the airways and causes bronchoconstriction.\u003c/p\u003e \u003cp\u003eFurthermore, M3 receptor activation causes goblet cells and submucosal glands to secrete more mucus, which obstructs airways in diseases like COPD and asthma. M3 receptor antagonists are therefore frequently utilised to lessen excessive mucus production and bronchoconstriction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e (uterotonic activity)\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003e3.Oxytocin receptor\u003c/b\u003e (uterotonic activity)\u003c/div\u003e \u003cp\u003eThe Gs protein-coupled oxytocin receptor (OXTR) is mostly expressed in the mammary glands and uterine myometrium. It is essential in mediating uterotonic activity, or the activation of uterine contractions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhospholipase C (PLC) is stimulated when oxytocin attaches to the receptor and activates the Gq protein. As a consequence, IP3 and DAG are produced, which raises intracellular calcium levels. Uterine smooth muscle fibres contract in response to elevated calcium.Coordinated uterine contractions, which are crucial throughout labour and deliveries, are the overall result. Because of this mechanism, oxytocin receptor antagonists may be used to treat premature labour, whereas agonists are used in clinical settings to induce or enhance labour\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe RCSB Protein Data Bank provided protein crystal structures.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Kollman charges were assigned, polar hydrogens were added, and co-crystallized ligands and water molecules were eliminated as part of structural refining.\u003c/p\u003e \u003cp\u003eWhere necessary, missing residues were filled in.\u003c/p\u003e \u003cp\u003eValidated docking procedures and protein preparation techniques were matched (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jmgm.2012.02.005\u003c/span\u003e\u003cspan address=\"10.1016/j.jmgm.2012.02.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10822-010-9369-7\u003c/span\u003e\u003cspan address=\"10.1007/s10822-010-9369-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Molecular Docking Procedure\u003c/h2\u003e \u003cp\u003eAutoDock \u003cb\u003e[14,15,]\u003c/b\u003e Vina, which uses a gradient optimisation approach and an empirical scoring system, was used to perform docking simulations (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcc.21334\u003c/span\u003e\u003cspan address=\"10.1002/jcc.21334\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDocking Parameters\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGrid box centered on active-site residues\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGrid spacing: 1.0 \u0026Aring;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExhaustiveness: 8\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMaximum binding modes: 9\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eValues of binding affinity (kcal/mol) were noted. Hydrogen bonds, hydrophobic contacts, π\u0026ndash;π interactions, and electrostatic interactions were all included in the interaction study.\u003c/p\u003e \u003cp\u003eEstablished validation experiments[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/ci300604z\u003c/span\u003e\u003cspan address=\"10.1021/ci300604z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.compbiolchem.2014.07.006\u003c/span\u003e\u003cspan address=\"10.1016/j.compbiolchem.2014.07.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to benchmark docking reliability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAuto Fig.\u0026nbsp;7. Dock Vina In Silico Screenings Interface:Docking study withHyperChem-Institute of Molecular Function\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 ADMET Prediction\u003c/h2\u003e \u003cp\u003eThe computational or experimental evaluation of a compound's absorption, distribution, metabolism, excretion, and toxicity characteristics is known as ADMET prediction. In the drug discovery process, it is essential to assess\u003c/p\u003e \u003cp\u003ePharmacokinetic and toxicity properties were evaluated using:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSwissADME (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/srep427[17]\u003c/span\u003e\u003cspan address=\"10.1038/srep427[17]\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003epkCSM [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e](DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.jmedchem.5b00104\u003c/span\u003e\u003cspan address=\"10.1021/acs.jmedchem.5b00104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eParameters assessed:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMolecular weight\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLogP (lipophilicity)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHydrogen bond donors/acceptors\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGastrointestinal absorption\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBlood\u0026ndash;brain barrier permeability\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHepatotoxicity prediction\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ehERG inhibition\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Molecular Dynamics Simulation\u003c/h2\u003e \u003cp\u003eTop-ranked docking complexes were subjected to 100 ns MD simulation using GROMACS[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e](DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.softx.2015.06.001\u003c/span\u003e\u003cspan address=\"10.1016/j.softx.2015.06.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSimulation Conditions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eForce field: GROMOS96 43a1\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSolvent model: SPC water\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBox type: Cubic\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePeriodic boundary conditions applied\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNeutralization with counter ions\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEnergy minimization: Steepest descent\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEquilibration: NVT (100 ps) and NPT (100 ps)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProduction run: 100 ns at 300 K\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTrajectory analysis included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRoot Mean Square Deviation (RMSD)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRoot Mean Square Fluctuation (RMSF)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRadius of gyration (Rg)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHydrogen bond occupancy\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAnalytical metrics followed established MD validation protocols(DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/prot.340130303\u003c/span\u003e\u003cspan address=\"10.1002/prot.340130303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/ct4007564\u003c/span\u003e\u003cspan address=\"10.1021/ct4007564\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;10. Molecular Dynamics Simulation\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Evaluation Metrics and Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe unit of measurement for binding affinities was kcal/mol. Over the course of the simulation, mean RMSD values were computed. To assess the consistency of structural fluctuations, the standard deviation (SD) was calculated.\u003c/p\u003e \u003cp\u003eIn relation to established benchmark stability levels (\u0026lt;\u0026thinsp;2.5 \u0026Aring; RMSD considered stable), comparative interpretation was carried out.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Binding Free Energy Calculation (MM-PBSA)\u003c/h2\u003e \u003cp\u003eTo obtain a more accurate estimation of ligand\u0026ndash;receptor binding affinity beyond docking scores, Molecular Mechanics Poisson\u0026ndash;Boltzmann Surface Area (MM-PBSA) calculations were performed using the g_mmpbsa tool integrated with GROMACS.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSnapshots were extracted at 10 ps intervals from the 20 ns of the molecular dynamics trajectories for each complex. The binding free energy (ΔG_binding) was calculated using the equation:\u003c/p\u003e \u003cp\u003eΔG_binding\u0026thinsp;=\u0026thinsp;G_complex \u0026minus; (G_protein\u0026thinsp;+\u0026thinsp;G_ligand)\u003c/p\u003e \u003cp\u003ewhere G represents the total free energy of each component. The total binding energy was decomposed into van der Waals, electrostatic, polar solvation, and non-polar solvation contributions.\u003c/p\u003e \u003cp\u003eNegative ΔG_binding values indicate favorable binding interactions. Comparative analysis of energy components was performed to identify dominant forces stabilizing the ligand\u0026ndash;receptor complexes\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Ethical Considerations\u003c/h2\u003e \u003cp\u003eThere was no need for human or animal ethical approval because this work only used computational modelling with publicly accessible structural databases.\u003c/p\u003e \u003cp\u003eBefore conducting biological experiments, this methodological approach guaranteed multi-level validation of ligand-receptor interactions, reproducibility, and parameter control.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eThe experimental workflow\u0026mdash;molecular docking, interaction profiling, ADMET prediction, and molecular dynamics simulation\u0026mdash;determines the order in which the results are presented. Findings are reported objectively without interpretation.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Molecular Docking Analysis\u003c/h2\u003e \u003cp\u003eFor every receptor-ligand combination, docking simulations yielded nine binding conformations. For additional examination, the conformation with the lowest binding energy was chosen.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Binding Affinity Scores\u003c/h2\u003e \u003cp\u003eTable-2 Binding Affinity Scores\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget Receptor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBest Binding Affinity (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean of Top 3 Modes (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ₂-AR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM₃R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe lowest binding energy (\u0026minus;\u0026thinsp;8.1 kcal/mol) was shown by the oxytocin receptor complex [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll receptors showed negative binding energy values, indicating favorable docking interactions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Docking Pose Stability\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Docking Pose Stability\u003c/h2\u003e \u003cp\u003eCluster analysis showed:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e- β\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003eAR: 78% of poses clustered within 2.0 \u0026Aring; RMSD\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003csub\u003eM 3 R\u003c/sub\u003e: 73% clustering within 2.5 \u0026Aring; RMSD\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOTR: 84% clustering within 2.0 \u0026Aring; RMSD\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003ePose clustering indicated reproducibility of binding orientation across multiple conformations.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Ligand\u0026ndash;Receptor Interaction Profiling\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 β\u003csub\u003e2\u003c/sub\u003e-Adrenergic Receptor\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHydrogen bonds: 2\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHydrophobic interactions: 5\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eπ\u0026ndash;π interactions: 1\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eKey interacting residues:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSer203 (H-bond, 2.8 \u0026Aring;)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAsp113 (H-bond, 2.9 \u0026Aring;)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePhe290 (hydrophobic contact)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBond distances ranged between 2.7\u0026ndash;3.4 \u0026Aring;.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 M\u003csub\u003e3\u003c/sub\u003e Muscarinic Receptor\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHydrogen bonds: 1\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHydrophobic interactions: 6\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eπ\u0026ndash;π stacking: 1\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eKey residues:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTyr506 (π\u0026ndash;π interaction, 4.5 \u0026Aring;)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAsp147 (electrostatic interaction)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTrp503 (hydrophobic interaction)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAverage hydrogen bond distance: 3.1 \u0026Aring;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Oxytocin Receptor\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHydrogen bonds: 3\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHydrophobic interactions: 4\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eElectrostatic interactions: 1\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eKey residues:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGln171 (H-bond, 2.6 \u0026Aring;)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTyr209 (H-bond, 2.9 \u0026Aring;)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIle201 (hydrophobic contact)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eBond distances ranged between 2.6\u0026ndash;3.2 \u0026Aring;.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.3 ADMET Prediction Results\u003c/h2\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Physicochemical Properties\u003c/h2\u003e \u003cp\u003eTable-3 Physicochemical Properties\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolecular Weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188.23 g/mol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH-bond Donors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH-bond Acceptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopological Polar Surface Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.9 \u0026Aring;\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo Lipinski rule violations were detected.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Absorption and Distribution\u003c/h2\u003e \u003cp\u003eTable-4 Absorption and Distribution\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrediction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI Absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBBB Permeability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-gp Substrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Metabolism and Toxicity\u003c/h2\u003e \u003cp\u003eTable-5 Metabolism and Toxicity\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrediction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP450 Inhibition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatotoxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow probability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehERG Inhibition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD50 (Predicted)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1450 mg/kg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe compound demonstrated favorable pharmacokinetic parameters with low predicted toxicity risk.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Molecular Dynamics Simulation\u003c/h2\u003e \u003cp\u003eAll three receptor complexes were subjected to 100 ns MD simulation.\u003c/p\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 RMSD Analysis\u003c/h2\u003e \u003cp\u003eTable-6 RMSD Analysis\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean RMSD (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax RMSD (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ₂-AR\u0026ndash;Vasicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM₃R\u0026ndash;Vasicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOTR\u0026ndash;Vasicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRMSD values stabilized after 15 ns and remained below 2.3 \u0026Aring; throughout the simulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 RMSF Analysis\u003c/h2\u003e \u003cp\u003eResidue-level fluctuations:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAverage RMSF: 0.9\u0026ndash;1.4 \u0026Aring;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHighest fluctuation observed in loop regions (2.1 \u0026Aring;)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eActive-site residues remained below 1.2 \u0026Aring; fluctuation\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Energy Profile During Simulation\u003c/h2\u003e \u003cp\u003ePotential energy remained stable:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eβ₂-A R complex: \u0026minus;1.42 \u0026times; 10⁶ kJ/mol\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eM₃R complex: \u0026minus;1.38 \u0026times; 10⁶ kJ/mol\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOTR complex: \u0026minus;1.45 \u0026times; 10⁶ kJ/mol\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eEnergy fluctuations were within \u0026plusmn;\u0026thinsp;0.5%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Binding Free Energy Analysis (MM-PBSA)\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe MM-PBSA method estimates the binding free energy (ΔG_bind) of the protein\u0026ndash;ligand complex using:\u003c/p\u003e \u003cp\u003eThis is further decomposed as\u003c/p\u003e \u003cp\u003eΔG\u003csub\u003ebind\u003c/sub\u003e​=(ΔE\u003csub\u003evdW\u003c/sub\u003e​+ΔE\u003csub\u003eele\u003c/sub\u003e​)+(ΔG\u003csub\u003epolar\u003c/sub\u003e​+ΔG\u003csub\u003enonpolar\u003c/sub\u003e​)\u0026minus;TΔS\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eΔE_MM\u003c/b\u003e \u0026rarr; Molecular mechanics energy\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eΔG_solvation\u003c/b\u003e \u0026rarr; Solvation free energy\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTΔS\u003c/b\u003e \u0026rarr; Entropic contribution (often neglected or approximated \u003cb\u003eEnergy Components\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTable.7The total binding free energy consists of the following contributions:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔE_vdW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVan der Waals interactions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔE_electrostatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoulombic interactions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔG_polar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolar solvation energy (Poisson\u0026ndash;Boltzmann)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔG_nonpolar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-polar solvation (SASA-based)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1. Molecular Mechanics Energy\u003c/h3\u003e\n\u003cp\u003eΔE\u003csub\u003eMM\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ΔE\u003csub\u003evdW\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ΔE\u003csub\u003eelectrostatic\u003c/sub\u003e​\u003c/p\u003e\n\u003ch3\u003e2. Solvation Free Energy\u003c/h3\u003e\n\u003cp\u003eΔG\u003csub\u003esolvation\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ΔG\u003csub\u003epolar\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ΔG\u003csub\u003enonpolar\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMM-PBSA Binding Free Energy Components (kJ/mol)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVan der Waals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElectrostatic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolar Solvation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-polar Solvation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal ΔG_binding\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2AR\u0026ndash;Vasicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;145.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;91.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM3R\u0026ndash;Vasicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;132.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;79.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOTR\u0026ndash;Vasicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;158.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;36.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;104.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe oxytocin receptor complex exhibited the most favorable binding free energy (\u0026minus;\u0026thinsp;104.3 kJ/mol), consistent with docking results. Van der Waals interactions were the dominant stabilizing force across all complexes, while polar solvation opposed binding.\u003c/p\u003e \u003cp\u003eThese findings confirm the thermodynamic stability of the ligand\u0026ndash;receptor complexes and strengthen the reliability of docking predictions.\u003c/p\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Comparative Analysis with Standard Drugs\u003c/h2\u003e \u003cp\u003eTo benchmark the pharmacological potential of vasicine, its binding affinity was compared with standard drugs targeting respective receptors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative Docking Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceptor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVasicine (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Drug\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Drug Score (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2-AR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSalbutamol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM3R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtropine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOxytocin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVasicine demonstrated comparable binding affinity to standard drugs, particularly at the oxytocin receptor. Although slightly lower than synthetic drugs, its favorable ADMET profile and multi-target activity suggest potential as a safer alternative or lead compound.This comparison highlights the therapeutic relevance of vasicine within a clinically meaningful context. Fig.\u0026nbsp;19. Comparative docking bar char\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe current research utilized a comprehensive computational model to clarify the pharmacological potential of vasicine sourced from Adhatoda vasica, targeting multiple receptors. The results indicate that vasicine shows stable and meaningful binding affinity for β2-adrenergic, M3 muscarinic, and oxytocin receptors, reinforcing its traditional uses in treating respiratory and uterine conditions.\u003c/p\u003e \u003cp\u003eAccording to docking data, binding energies ranged from \u0026minus;\u0026thinsp;7.2 to \u0026minus;\u0026thinsp;8.1 kcal/mol, with the oxytocin receptor showing the strongest contact. These values suggest possible agonistic or modulatory activity and show moderate to strong ligand-receptor interactions. Significantly, the β2-adrenergic receptor's conserved residues Ser203 and Asp113 were implicated in hydrogen bonding,[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] which is known to be essential for receptor activation and downstream signalling. The bronchodilatory process mediated by cAMP-dependent smooth muscle relaxation is supported by this interaction pattern\u003c/p\u003e \u003cp\u003eStable hydrophobic and electrostatic interactions inside the binding pocket were also shown via interaction with the M3 muscarinic receptor, suggesting potential cholinergic signalling suppression. This mechanism supports expectorant function by reducing mucus secretion and bronchoconstriction. Throughout the simulation, the oxytocin receptor complex showed the highest structural stability, with low conformational deviation and stable hydrogen bonding, indicating a significant contribution to uterotonic activity via calcium-mediated signalling pathways.\u003c/p\u003e \u003cp\u003eDocking results were further confirmed by molecular dynamics simulations, where all complexes maintained RMSD values below 2.3 \u0026Aring;, showing structural stability under physiological settings. Stable energy profiles indicated thermodynamic favorability of ligand binding, while low RMSF values at active-site residues confirmed little local oscillations. All of these findings support the docking forecasts' dependability.\u003c/p\u003e \u003cp\u003eADMET study showed favourable drug-likeness from a pharmacokinetic standpoint, including low anticipated toxicity, ideal lipophilicity, and good gastrointestinal absorption. Vasicine's safety profile as a possible lead chemical is further improved by the lack of considerable CYP450 inhibition and the low risk of hERG inhibition.\u003c/p\u003e \u003cp\u003eThis study's multi-target strategy, which emphasises the idea of polypharmacology\u0026mdash;the modulation of numerous biological pathways by a single phytochemical\u0026mdash;is one of its main advantages. This is especially true for complex conditions where multi-mechanistic intervention is beneficial, such uterine dysfunction and asthma.\u003c/p\u003e \u003cp\u003eBut it's important to recognise some limitations. Docking scores may not accurately reflect entropic contributions and only offer approximations of binding affinities. Furthermore, not all physiologically significant conformations may be represented by static receptor architectures. Although ADMET[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] estimates provide insightful information, pharmaceutical efficacy and safety must be confirmed through experimental validation.\u003c/p\u003e \u003cp\u003eAll things considered, this study offers solid computational proof that vasicine is a viable multi-target treatment option. Its translational potential will be further strengthened by future research that includes binding free energy calculations (MM-PBSA/MM-GBSA),[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] comparative comparison with conventional medicines, and experimental validation.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eUterine dysfunction and respiratory conditions continue to be significant clinical issues that call for safe and efficient treatment options. Using an integrated computational research framework, the current study examined the bronchodilatory, expectorant, and uterotonic potential of vasicine, a quinazoline alkaloid produced from Adhatoda vasica. In order to ascertain whether molecular evidence supports its conventional medical applications, the main goal was to assess receptor-level interactions, pharmacokinetic properties, and dynamic stability.Vasicine has favourable binding affinities for β₂-adrenergic, M3 muscarinic, and oxytocin receptors, according to molecular docking study [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Important hydrophobic and hydrogen bonding interactions were found in conserved active-site residues, suggesting likely receptor engagement. Compliance with drug-likeness requirements, such as appropriate molecular weight, lipophilicity, high anticipated gastrointestinal absorption, and low predicted toxicity risk, was demonstrated by pharmacokinetic prediction. The structural stability of ligand-receptor complexes was further validated by molecular dynamics simulations, which showed constant RMSD values and persistent hydrogen bond occupancy over the course of the simulation.\u003c/p\u003e \u003cp\u003eWhen taken as a whole, these results offer mechanistic understanding of vasicine's multi-target pharmacological potential. By combining receptor docking, ADMET evaluation, and dynamic simulation into a single computational design, the work advances knowledge by connecting contemporary molecular modelling with traditional ethnopharmacology. The current study provides a scientifically supported foundation for additional pharmacological development and logical optimisation of vasicine as a possible therapeutic candidate, even if experimental validation is still required. \u0026ldquo;Future studies should include in vitro and in vivo validation to confirm the predicted pharmacological effects and establish clinical relevance.\u0026rdquo;\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003e \u003cb\u003eI was prepared this paper gather previous knowledge (through interNET ) and my analysis\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNageswara Rao Dorepalli conducted the study, performed data analysis, and wrote and revised the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e1. Epidemiology of Chronic Obstructive Pulmonary Disease (COPD)G, Viegi A, Scognamiglio S (2001) Baldacci;F. Pistelli;L. Carrozzi\u003cem\u003eRespiration\u003c/em\u003e 68 (1): 4\u0026ndash;19\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000050456\u003c/span\u003e\u003cspan address=\"10.1159/000050456\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtal CK (1980) Chemistry and pharmacology of vasicine. Indian J Exp Biol 18:1155\u0026ndash;1161 DOI record archived (no CrossRef DOI available)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e3, Sharma ML et al (1983) Bronchodilatory activity of vasicine. Planta Med 48:1\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1055/s-2006-960912\u003c/span\u003e\u003cspan address=\"10.1055/s-2006-960912\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e4, Wess J (2004) \u003csub\u003eM3 Muscarinic\u003c/sub\u003e acetylcholine receptors: Mechanisms of action. Trends Pharmacol Sci 25(7):344\u0026ndash;350. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0165-6147(04)00060-8\u003c/span\u003e\u003cspan address=\"10.1016/S0165-6147(04)00060-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e5, Rasmussen SGF, Choi HJ, Rosenbaum DM et al (2007) Crystal structure of the human β2-adrenergic receptor. Nature 450:383\u0026ndash;387. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature06325\u003c/span\u003e\u003cspan address=\"10.1038/nature06325\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e6Gimpl G, Fahrenholz F (2001) The oxytocin receptor system. Physiol Rev 81(2):629\u0026ndash;683. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/physrev.2001.81.2.629\u003c/span\u003e\u003cspan address=\"10.1152/physrev.2001.81.2.629\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCongreve M, de Graaf C, Swain NA, Tate CG (2020) Impact of GPCRstructures on drug discovery. Cell 181(1):81\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2020.03.003\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2020.03.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e8Daina A, Michielin O, Zoete V (2017) Swiss ADME: A free web tool to evaluate pharmacokinetics and drug-likeness. Sci Rep 7:42717. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep42717\u003c/span\u003e\u003cspan address=\"10.1038/srep42717\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e9Hollingsworth SA, Dror RO (2018) Molecular dynamics simulation for drug discovery. Neuron 99(6):1129\u0026ndash;1143. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuron.2018.08.011\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2018.08.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e10Wess J (2004) \u003csub\u003eM3 Muscarinic\u003c/sub\u003e acetylcholine receptors: Mechanisms of action. Trends Pharmacol Sci 25(7):344\u0026ndash;350. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0165-6147(04)00060-8\u003c/span\u003e\u003cspan address=\"10.1016/S0165-6147(04)00060-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e11.Kruse AC, Hu J, Pan AC et al (2012) Structure and dynamics of the M\u003csub\u003e3\u003c/sub\u003e muscarinic receptor. Nature 482:552\u0026ndash;556. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature10867\u003c/span\u003e\u003cspan address=\"10.1038/nature10867\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComputational discovery of uterotonic agents Molecular docking and dynamic simulations of Caesalpinia bonduc phytoconstituents. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.12991/jrespharm.1798416\u003c/span\u003e\u003cspan address=\"10.12991/jrespharm.1798416\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRCSB Protein Data Bank Efficient Searching and Simultaneous Access to One Million Computed Structure Models Alongside the PDB Structures Enabled by Architectural Advances, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jmb.2023.167994\u003c/span\u003e\u003cspan address=\"10.1016/j.jmb.2023.167994\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrott O, Olson AJ (2010) AutoDock Vina: Improving the speed and accuracy of docking. J Comput Chem 31(2):455\u0026ndash;461. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jcc.21334\u003c/span\u003e\u003cspan address=\"10.1002/jcc.21334\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4. J Comput Chem 30(16):2785\u0026ndash;2791. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jcc.21256\u003c/span\u003e\u003cspan address=\"10.1002/jcc.21256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira LG, dos Santos RN, Oliva G, Andricopulo AD (2015) Molecular docking and structure-based drug design strategies. Molecules 20(7):13384\u0026ndash;13421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/molecules200713384\u003c/span\u003e\u003cspan address=\"10.3390/molecules200713384\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSliwoski G, Kothiwale S, Meiler J, Lowe EW (2014) Computational methods in drug discovery. Pharmacol Rev 66(1):334\u0026ndash;395. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1124/pr.112.007336\u003c/span\u003e\u003cspan address=\"10.1124/pr.112.007336\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbraham MJ, Murtola T, Schulz R et al (2015) GROMACS: High-performance molecular simulations. SoftwareX 1\u0026ndash;2:19\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.softx.2015.06.001\u003c/span\u003e\u003cspan address=\"10.1016/j.softx.2015.06.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumari R, Kumar R, Lynn A (2014) g_mmpbsa tool for free energy calculation. J Chem Inf Model 54(7):1951\u0026ndash;1962. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/ci500020m\u003c/span\u003e\u003cspan address=\"10.1021/ci500020m\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGimpl G, Fahrenholz F (2001) The oxytocin receptor system. Physiol Rev 81(2):629\u0026ndash;683. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/physrev.2001.81.2.629\u003c/span\u003e\u003cspan address=\"10.1152/physrev.2001.81.2.629\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaltensp\u0026uuml;hl Y, Sch\u0026ouml;ppe J, Ehrenmann J et al (2020) Crystal structure of the human oxytocin receptor. Nature 582:402\u0026ndash;407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-020-2399-8\u003c/span\u003e\u003cspan address=\"10.1038/s41586-020-2399-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenbaum DM, Rasmussen SGF, Kobilka BK (2009) The structure and function of GPCRs. Nature 459:356\u0026ndash;363. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature08144\u003c/span\u003e\u003cspan address=\"10.1038/nature08144\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePires DEV, Blundell TL, Ascher DB (2015) pkCSM: Predicting small-molecule pharmacokinetic properties. J Med Chem 58(9):4066\u0026ndash;4072. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jmedchem.5b00104\u003c/span\u003e\u003cspan address=\"10.1021/acs.jmedchem.5b00104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou T, Wang J, Li Y, Wang W (2019) \u003cem\u003eEnd-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.chemrev.9b00055\u003c/span\u003e\u003cspan address=\"10.1021/acs.chemrev.9b00055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e25 Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10(5):449\u0026ndash;461. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1517/17460441.2015.1032936\u003c/span\u003e\u003cspan address=\"10.1517/17460441.2015.1032936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"in-silico-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"insp","sideBox":"Learn more about [In Silico Pharmacology](https://link.springer.com/journal/40203)","snPcode":"40203","submissionUrl":"https://submission.nature.com/new-submission/40203/3","title":"In Silico Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"vasicine, Adhatoda vasica, Molecular Docking, In Silico Study, β2−Adrenergic Receptor, M3 Muscarinic Receptor, Oxytocin Receptor, Bronchodilatory Activity, Expectorant Activity, Uterotonic Activity, ADMET Prediction","lastPublishedDoi":"10.21203/rs.3.rs-9167795/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9167795/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlkaloids originating from plants continue to be attractive prospects for new therapeutic development, while uterine dysfunction and respiratory illnesses continue to be major worldwide health issues. Despite the fact that Adhatoda vasicine has long been used to treat gynaecological disorders, asthma, and cough, the exact molecular mechanisms underlying its main alkaloid, vasicine, have not been thoroughly described using sophisticated computational techniques. Therefore, a thorough in silico analysis was needed to clarify pharmacokinetic behaviour and receptor-level interactions.The current work used an integrated computational research strategy to examine vasicine's bronchodilatory, expectorant, and uterotonic potential. To assess binding affinity and interaction patterns, molecular docking was carried out against oxytocin, M3 muscarinic, and β2-adrenergic receptors. Validated ADMET models were used to predict pharmacokinetic and toxicity features, and 100 ns molecular dynamics simulations were used to evaluate the structural stability of ligand-receptor complexes.\u003c/p\u003e \u003cp\u003eBinding affinities ranging from \u0026minus;\u0026thinsp;7.2 to \u0026minus;\u0026thinsp;8.1 kcal/mol were found by docking research, with the oxytocin receptor exhibiting the greatest interaction. Active-site residues were shown to contain important hydrophobic and hydrogen bonding interactions. High gastrointestinal absorption, minimal anticipated hepatotoxicity and cardiotoxicity risk, and zero Lipinski rule violations were all suggested by ADMET predictions. Molecular dynamics simulations showed steady hydrogen bond occupancy and RMSD values\u0026thinsp;\u0026lt;\u0026thinsp;2.0 \u0026Aring; for the course of the simulation. These results supported the traditional therapeutic uses of Adhatoda vasica by showing that vasicine had favourable receptor binding, appropriate pharmacokinetic qualities, and stable molecular interactions. The study provides computational evidence that could support rational drug development and further experimental validation.\u003c/p\u003e","manuscriptTitle":"Adhatoda Vasica and its Major Alkaloid Vasicine Computational Evaluation of Bronchodilatory, Expectorant, and Uterotonic Effects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 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