BIBECHANA Vol. 22, No. 3, December 2025, 291-298 ISSN 2091-0762 (Print), 2382-5340 (Online) Journal homepage: http://nepjol.info/index.php/BIBECHANA Publisher:Dept. of Phys., Mahendra Morang A. M. Campus (Tribhuvan University)Biratnagar In silico molecular docking and ADMET analysis of the phytochemicals of Kalanchoe pinnata (Lam.) Pers. as potential modulators of the mineralocorticoid receptor against cardiovascular diseases Ram Lal (Swagat) Shrestha1,2,3, Manila Poudel2, Sujan Dhital1,2, Nirmal Parajuli 1,2, Ashika Tamang 1,2, Safal Adhikari 1,2, Shiva M.C. 1,2, Aakar Shrestha 2, Timila Shrestha 1,2, Samjhana Bharati 1,2, Binita Maharjan 1,2, Bishnu P. Marasini2,3,4,*, Jhashanath Adhikari Subin 2,5,* 1Department of Chemistry, Amrit Campus, Tribhuvan University, Lainchaur, Kathmandu 44600, Nepal 2Kathmandu Valley College, Syuchatar Bridge, Kalanki, Kathmandu 44600, Nepal 3Institute of Natural Resources Innovation, Kalimati, Kathmandu 44600, Nepal 4Nepal Health Research Council, Ministry of Health and Population, Ramshah Path, Kathmandu 44600, Nepal 5Bioinformatics and Cheminformatics Division, Scientific Research and Training Nepal P. Ltd, Bhaktapur 44800, Nepal ∗Corresponding author. Email: bishnu.marasini@gmail.com [BPM] subinadhikari2018@gmail.com [JAS] Abstract Cardiovascular diseases represent a leading global health challenge, with a pronounced impact in low and middle-income countries. The mineralocorticoid receptor (MR), a key transcrip- tion factor in cardiovascular disorders, has been linked to hypertension, heart failure, and myocardial infarction. This study aims to investigate the potential of the phytochemicals in Kalanchoe pinnata (Lam.). Pers., a plant known for its traditional medicinal uses, in modu- lating MR activity through in silico approaches. Twenty phytochemicals belonging to different classes of organic molecules from the plant were subjected to computational screening to assess their interaction with the MR (PDB ID: 5L7E). MR was treated as a flexible receptor, and molecular docking was performed in a solvated environment. The molecule astragalin among the test molecules showed a promising binding score of -9.209 kcal/mol, which is compara- ble to the native ligand's score of -9.619 kcal/mol. ADMET predictions, including toxicity classification, revealed that most of the compounds demonstrated favorable gastrointestinal ab- sorption and varying degrees of blood-brain barrier permeability. Toxicity evaluations revealed that several compounds exhibited moderate to low toxicity, with both astragalin and patuletin classified as Class 5, indicating a relatively higher level of safety compared to other phytochem- icals, and a class comparable to the native compound (Class 6). These findings suggested that phytochemicals of K. pinnata hold potential for further investigation as modulators of MR activity, with implications for drug development in cardiovascular diseases. Keywords: Flexible molecular docking, computational approach, desolvation, docking score, pharmacokinetics, drug-likeness . Article information Manuscript received: July 1, 2025; Revised: August 29, 2025; Accepted: September 11, 2025 DOI https://doi.org/10.3126/bibechana.v22i3.80853 This work is licensed under the Creative Commons CC BY-NC License. https://creativecommons. org/licenses/by-nc/4.0/ 291 http://nepjol.info/index.php/BIBECHANA bishnu.marasini@gmail.com subinadhikari2018@gmail.com https://doi.org/10.3126/bibechana.v22i3.80853 https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/ Ram Lal (Swagat) Shrestha et al./ BIBECHANA 22 (2025) 291-298 292 1 Introduction Cardiovascular (CV) disease refers to a group of conditions that affect the heart and blood vessels. This includes hypertension, heart attack, stroke, heart failure, and other heart disorders [1]. CV dis- ease is the leading cause of death worldwide, dis- proportionately affecting low- and middle-income countries more severely than high-income countries [2, 3]. The mineralocorticoid receptor (MR) is a transcription factor belonging to the steroid recep- tor family. Activation of the mineralocorticoid re- ceptor is associated with various CV system disor- ders, such as hypertension, heart failure, and my- ocardial infarction [4, 5]. The increase in MR ex- pression in the heart and blood vessels with age contributes to the higher incidence of CV diseases in the elderly [6, 7]. Enalapril and metoprolol are frequently prescribed for the treatment of CV con- ditions like hypertension and heart failure. Despite their effectiveness, these drugs may cause side ef- fects, including dizziness, headaches, weakness, a drastic decrease in white blood cell count, protein- uria, and severe allergic reactions. To address these issues, this study focuses on exploring plant-based compounds as potential alternatives [8, 9]. Medici- nal plants are recognized for containing compounds that may be valuable in treating diseases or in drug development [10, 11]. Kalanchoe pinnata (Lam.) Pers., a succulent herb, has long been utilized in traditional medicine to treat heart conditions, kid- ney stones, and cancer [12, 13]. They are used in folk medicine to relieve stomach pain and to treat gastritis, diarrhea, dysmenorrhea, liver dis- orders, fever, female infertility, genitourinary in- fections, snake and scorpion bites, leprosy, cough, asthma, kidney stones, arthritis, CV diseases, and general tiredness [14]. The plant extract exhibits antihypertensive activity as reported by various au- thors [15,16], and forms the premise for this work. Discovering new leads for a well-known target is a pivotal phase in drug development. Two pri- mary approaches are employed: experimental high- throughput screening to sift through extensive com- pound libraries for potential leads, and computa- tional methods that leverage structural data of the protein binding site [17]. To reduce the cost and time associated with extensive in vitro and in vivo experiments for identifying potential compounds in drug discovery and development, the use of in sil- ico approaches is on the rise [18–20]. This tech- nique employs a docking server to identify novel ligands for receptors with known structures, facili- tating the screening of multi-compound databases for molecules that fit a receptor's binding site, thus eliminating the initial need for wet lab experiments [21–23]. This study involves an in silico screening of compounds from K. pinnata to assess their poten- tial to modulate the MR protein and for molecular- level understanding of the relevant pathways asso- ciated with the modulation. 2 Methodology 2.1 Ligand Search Twenty phytochemicals of K. pinnata were identi- fied from the literature (Google Scholar, Research- Gate, PubMed, ScienceDirect) and are presented in Table 1 along with their PubChem CID. The compounds' structured data files (SDF) were down- loaded from the PubChem database [23]. 2.2 Protein Target Search Probable targets were identified using the Super- Pred web server (https://prediction.charite. de/) [24]. It is used for predicting the potential target proteins for a given set of ligands. A per- centage bar indicates the probability of the ligand binding to the protein. This probability is based on structural similarity, binding affinity, and other computational predictions. The protein target with the highest probability, which was commonly found among all the ligands, was chosen. Other fac- tors considered during this process included en- suring the protein resolution was below 2 Å, no mutations were present, and a native ligand was present. Eventually, a mineralocorticoid receptor target (PDB ID: 5L7E) with a resolution of 1.86 Å was chosen with a native ligand (6Q0). The structural data of the protein was retrieved from the RCSB protein bank (https://www.rcsb.org/) in PDB format.. 2.3 Ligand Optimization The native ligand (6Q0) was optimized before molecular docking by the use of the Avogadro pro- gram [25]. It is an advanced software for editing and visualizing molecules, used in computational chem- istry, molecular modeling, and bioinformatics for optimizing molecular geometries [26]. The univer- sal force field (UFF) was utilized to optimize the molecular geometries of the compounds, the con- jugate gradient method was employed, and energy convergence was adjusted to 10-8 units to reduce their structural complexities [27,28]. Alongside the native ligand, the same optimization process was applied to all the 20 compounds. After the opti- mization, it was saved in PDB format for molecular docking calculations. https://prediction.charite.de/ https://prediction.charite.de/ Ram Lal (Swagat) Shrestha et al./ BIBECHANA 22 (2025) 291-298 293 2.4 Protein Structure Optimization The PDB structure with PDB ID of 5L7E was opened in the PyMOL program. When the water molecules are removed from a protein, it is known as the holoprotein. After the native ligand is ex- tracted, the resulting structure is called the apopro- tein. The extra chain present in the target was removed, polar hydrogens were added, and it was subsequently saved in PDB format. 2.5 Molecular Docking Calculations Ligand-protein docking is an optimization problem focused on predicting the position and pose of a lig- and that achieves the lowest binding energy within the receptor's active site. Docking aids in pre- dicting the favorable binding geometries of a small molecule within a target protein's binding site and estimating the docking score of the resulting com- plex [29]. For the docking purpose, a web-based tool known as the DockThor (rigid receptor approach) (https://www.dockthor.lncc.br/) [30] was used. For this, the apo form of the protein, native ligand, and the test compounds were utilized in PDB form. Initially, to validate the molecular docking protocol, the approach used by Adhikari Subin and Shrestha (2024) was followed. The apoprotein and native lig- and were uploaded to the server, and the following parameters were applied: grid center (x: 9, y: 14, z: 11), grid size (x: 16, y: 16, z: 16), discretization: 0.17, number of evaluations: 1,000,000, population size: 750, and number of runs: 24 [31]. Subse- quently, the test compounds were docked using the same parameters as those for the native ligand. The docking score measures the strength of the interac- tion between the target and the molecule. It reflects the free energy change upon binding on a compar- ative basis, with more negative values indicating stronger interactions [30,32,33]. 2.6 Protocol Validation via RMSD Calcu- lation Root mean square deviation (RMSD) measures the average distance between atoms in a predicted model and a reference structure, assessing how well the predicted model aligns with the experimental or reference structure. Lower RMSD values indicate a better fit, reflecting a more accurate prediction of the ligand's binding mode to the target protein [34]. An RMSD value below 2.0 Å [35] is widely accepted as the benchmark for distinguishing between suc- cessful and unsuccessful reproductions of a known binding mode [29,36]. The heavy atom RMSD was calculated for the native ligand with the best bind- ing affinity. 2.7 Protein-Ligand Interactions PLIP server (https://plip-tool.biotec.tu- dresden.de/plip-web/plip/index) was used for visualizing three-dimensional representations of protein-ligand interactions [37]. 2.8 ADMET Properties and Toxicity Class Identification The in silico properties for absorption (gas- trointestinal absorption and P-glycoprotein sub- strate/inhibitor status), distribution (blood-brain barrier permeability), metabolism (cytochrome P450 inhibition or substrate status), and excretion (clearance) were evaluated using the SwissADME server (https://www.swissdock.ch/). The toxic- ity class was determined through the ProTox 3.0 server (https://tox.charite.de/protox3/). It clas- sifies compounds into six toxicity classes (1 to 6) based on their predicted lethal dose (LD50) and po- tential toxic effects. Class 1 represents the most toxic compounds, while class 6 represents the least toxic. 3 Results and Discussion 3.1 Molecular Docking Protocol Validation By superimposing the co-crystallized native ligand with the docked ligand, the heavy atom RMSD was calculated to be 1.79 Å, indicating that the dock- ing protocol was reliable and predicted the ligand's https://www.dockthor.lncc.br/ Ram Lal (Swagat) Shrestha et al./ BIBECHANA 22 (2025) 291-298 294 binding pose (Figure 1). The parameters were de- termined to be effective in capturing the presumed global minima of the protein-ligand adduct. 3.2 Binding Affinity and Strength of Inter- actions The binding affinity of -9.691 kcal/mol found by the docking of the native ligand against the target suggested a strong interaction (Table 2). The lower (more negative) the binding energy, the more sta- ble and stronger the binding between molecules [38]. Among the proposed compounds, astragalin (com- plex 1) exhibited the best binding affinity of -9.209 kcal/mol, which is the closest to that of the native ligand. Similarly, complex 2 (patuletin) demon- strated a binding affinity of -8.572 kcal/mol indi- cating slightly weaker interactions as compared to complex 1. Figure 1: Superimposition of co-crystalized native ligand 6Q0 (brown) with docked ligand (green) (Heavy atom RMSD= 1.79 Å). 3.3 Present in the Protein-Ligand Com- plexes Proteins interact with other small molecules to en- hance or inhibit biological functions. In protein- ligand interactions, a few key residues play a piv- otal role in recognizing counterparts and maintain- ing the affinity that binds the ligand to its recep- tor [39]. Identifying these key residues is essen- tial for understanding protein function, analyzing molecular interactions, and guiding future experi- mental procedures [40]. The interactions between different ligands and the target are illustrated in Figure 2. Table 2 shows that the native ligand formed hydrogen bonds with ARG817 at a distance of 3.29 Å. The distances be- tween the ligand and the amino acids reveal the proximity of their interaction within the protein binding site. The shorter the distance, the stronger the interaction. In this context, the H-bonding in- teraction involving LEU769 in complex 1, with a distance of 1.71 Å, can be considered the strongest overall. Similarly, the interaction with MET807 in complex 2 is the strongest among all residues, with a distance of just 1.92 Å. The key amino acid residues (ARG817, TRP806, LEU769) involved in the interactions between astragalin (complex 1) and the target protein are nearly identical to those ob- served in the interaction between the native ligand and the protein. This suggests that the ligand binds at the catalytic site of the protein and is therefore capable of modulating it. (Native) (1) (2) Figure 2: 3D interactive representations of the key interactions of the ligand with active site residues in protein-ligand complex (native, 1 (as- tragalin), and 2 (patuletin)). Ram Lal (Swagat) Shrestha et al./ BIBECHANA 22 (2025) 291-298 295 3.4 ADMET Analysis The ADMET data via the SwissADME server and its analysis provided key insights into the absorp- tion properties of the molecules. Molecules (tria- contane and hentriacontane) were found to be in- soluble with low gastrointestinal (GI) absorption. Their high lipophilicity (logP of 11.95 and 12.34, re- spectively) poses formulation and absorption chal- lenges. In contrast, palmitic acid displayed high GI absorption, along with moderate solubility and permeability across the blood-brain barrier (BBB), making it a promising candidate. Bersaldegenin- 3-acetate, while soluble and showing high GI ab- sorption, was not BBB permeant and had moderate lipophilicity (logP of 2.08). Most of the molecules adhered to Lipinski's Rule of Five, except astra- galin, which had a high topological polar surface area (TPSA) of 190.28 Ų and a low logP value of -0.24, making it unable to permeate the GI tract and, therefore, unlikely to enter the bloodstream [41] (Table 3). The low gastrointestinal absorption of Astragalin could potentially be improved through nanoparticle delivery, glycoside hydrolysis, or other formulation strategies such as lipid-based carriers and prodrug approaches. Palmitic acid and cardenolide were the only compounds identified as BBB permeant, indicating their ability to cross the BBB and potentially target central nervous system (CNS) disorders. However, the BOILED-Egg model, based on TPSA and logP values, revealed that 7 out of the 20 molecules did not fall within the specified range for BBB perme- ation or high gastrointestinal absorption (Supple- mentary Figure 1). This limitation suggested that these molecules may not be as effective as orally administered or CNS-targeting compounds [42]. Of the 20 molecules analyzed, 12 did not inhibit any of the major enzymes (CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4) (Supplementary Table 1). This indicates that these molecules are less likely to disrupt the metabolism of other med- ications, thereby lowering the risk of drug-drug in- teractions [43]. This is particularly significant for patients on multiple drugs, as it suggests that these 12 molecules are unlikely to lead to adverse effects associated with the buildup of other drugs metab- olized by these CYP enzymes [44]. The excretion properties of the phytocom- pounds were evaluated based on their clearance rates (mL/min/kg), revealing significant differences among the molecules. Astragalin demonstrated a low clearance rate of 3.126 mL/min/kg, suggest- ing that it remains in the body for a longer period before being eliminated. In contrast, pseudotarax- asterol and campesterol exhibited higher clearance rates of 19.75 mL/min/kg and 16.512 mL/min/kg, respectively (Supplementary Table 2). This indi- cates that they are rapidly cleared from the body, reducing the risk of accumulation and potential tox- icity. Efficient excretion minimizes the chances of toxic buildup or prolonged exposure, making these molecules less likely to cause long-term toxicity compared to those with slower clearance rates. Most of the molecules (11) were classified under Class 4 toxicity, indicating they are harmful and can lead to significant health effects at higher doses (890 mg/kg). However, they are less likely to be fatal than Classes 3, 2, and 1. Three molecules (triacontane, hentriacontane, and bersaldegenin-3- acetate) were classified as Class 3, which are toxic at moderate doses, while two were in Class 5, indi- cating they are less toxic but still harmful at very high doses. Three molecules (taraxerol, pseudo- taraxasterol, and epigallocatechin) were determined to have Class 6 toxicity, suggesting they have low or negligible toxicity and are generally safe at typ- ical exposure levels (7000 mg/kg). One molecule, identified as cardenolides, was categorized in Class 2, indicating high toxicity with potentially fatal ef- fects at relatively low doses (34 mg/kg) (Supple- mentary Table 3). This suggests that even small Ram Lal (Swagat) Shrestha et al./ BIBECHANA 22 (2025) 291-298 296 amounts could pose serious health risks, highlight- ing the need for caution in handling and poten- tial therapeutic applications. For any practical use, such as in drug development or herbal prepa- rations, rigorous dose optimization, careful moni- toring, and safety assessments would be essential to prevent toxic effects. Additionally, formulation strategies or structural modifications may be re- quired to mitigate toxicity while preserving any beneficial biological activity. Astragalin, although inactive across specific toxicity endpoints such as carcinogenicity, immunotoxicity, mutagenicity, and cytotoxicity, was classified in a safe toxicity class (Class 5). It demonstrated favorable outcomes in molecular docking studies, justifying further explo- ration. Note: Water solubility is indicated as: insol- uble (-), poorly soluble (+), moderately soluble (++), and highly soluble (+++). And rejection in the table indicates the violation of 2 rules of Lipinki. The ADMET properties of several reference drugs (enalapril, metoprolol, ramipril, and digoxin) commonly used in the management of CV diseases are shown in Supplementary Table 4. With the highest TPSA (203.06 Ų), digoxin may struggle to cross cell membranes, leading to its lower GI ab- sorption and poor BBB permeability. All the drugs, except digoxin, showed high GI absorption, suggest- ing they are effectively absorbed orally. Despite digoxin being classified as Class 1 in toxicity, it is still used for the intervention of CV diseases. The ligand we studied, astragalin, is classified as Class 5 in toxicity and has good solubility and BBB per- meability. The phytochemicals astragalin and patuletin from K. pinnata demonstrated significant binding affinity to the mineralocorticoid receptor in molec- ular docking studies, suggesting their potential as modulators. Furthermore, their ADMET profiles indicate favorable solubility, good GI absorption, and BBB permeability, along with lower toxicity levels, making them promising candidates for CV drug development. 4 Conclusion The results of this study underscore the potential of Kalanchoe pinnata phytochemicals as modula- tors of the mineralocorticoid receptor, with sig- nificant implications for CV disease management. The molecular docking analysis revealed that the phytochemical astragalin exhibited strong binding affinities close to the native ligand, suggesting its potential effectiveness in modulating MR activity. Furthermore, the ADMET analysis indicated that most of the studied compounds demonstrated fa- vorable pharmacokinetic properties, including good gastrointestinal absorption, toxicity endpoints, and acceptable toxicity levels, with several compounds falling within safe toxicity classes. Notably, astra- galin stands as a particularly promising candidate, exhibiting a strong binding affinity and low toxicity, making it a viable candidate for further investiga- tion. These findings provide a formidable founda- tion for in vitro and in vivo studies to explore the possibilities of the therapeutic potential of K. pin- nata phytochemicals in CV disease management. Ethical approval The research conducted is not related to either hu- man or animal use. 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Introduction Methodology Ligand Search Protein Target Search Ligand Optimization Protein Structure Optimization Molecular Docking Calculations Protocol Validation via RMSD Calculation Protein-Ligand Interactions ADMET Properties and Toxicity Class Identification Results and Discussion Molecular Docking Protocol Validation Binding Affinity and Strength of Interactions Present in the Protein-Ligand Complexes ADMET Analysis Conclusion