Structure-Based Molecular Docking and ADMET Evaluation of Phlorotannins From Ecklonia Cava as Potential Aldose Reductase Inhibitors for Diabetic Neuropathy

Diabetes mellitus is a chronic metabolic disorder characterized by persistent hyperglycemia resulting from defects in insulin secretion, insulin action, or both. According to the International Diabetes Federation, the global prevalence of diabetes continues to rise rapidly, with hundreds of millions of individuals affected worldwide. Long-term hyperglycemia leads to the development of several microvascular and macrovascular complications, among which diabetic neuropathy is one of the most common and debilitating conditions. Diabetic neuropathy affects nearly 50% of diabetic patients during the course of the disease and is associated with progressive nerve damage, chronic pain, sensory loss, and reduced quality of life. The pathogenesis of diabetic neuropathy is complex and multifactorial, involving metabolic, vascular, and oxidative stress-mediated mechanisms. One of the key biochemical pathways implicated in the development of diabetic complications is the polyol pathway. Under normal physiological conditions, only a small fraction of glucose is metabolized through this pathway. However, during hyperglycemia, excess intracellular glucose is converted to sorbitol by the enzyme aldose reductase. The enzyme Aldose Reductase catalyzes the NADPH-dependent reduction of glucose to sorbitol, which subsequently accumulates within cells due to its limited membrane permeability. This accumulation leads to osmotic stress, cellular swelling, oxidative damage, and impairment of nerve function, ultimately contributing to the development of diabetic neuropathy. Inhibition of aldose reductase has therefore emerged as a promising therapeutic strategy for preventing or delaying diabetic complications. Several synthetic aldose reductase inhibitors have been developed; however, many of them have shown limited clinical success due to poor pharmacokinetic properties and adverse effects. One clinically used inhibitor is Epalrestat, which has demonstrated therapeutic benefits in managing diabetic neuropathy but still presents limitations regarding efficacy and long-term safety. These challenges have prompted increasing interest in identifying natural compounds with improved safety profiles and effective inhibitory activity against aldose reductase. Marine natural products have gained significant attention as a rich source of bioactive molecules with diverse pharmacological activities. Among marine organisms, brown algae have been widely investigated for their unique secondary metabolites, particularly polyphenolic compounds known as phlorotannins. The brown seaweed Ecklonia cava is known to contain a variety of phlorotannin derivatives such as dieckol, eckol, triphloroethol A, and related compounds. These molecules possess strong antioxidant, anti-inflammatory, and antidiabetic properties and have shown potential in modulating several biological targets involved in metabolic disorders. Structure-based drug discovery approaches have become essential tools in modern pharmacological research for identifying potential inhibitors of disease-related targets. Molecular docking techniques allow the prediction of ligand binding orientation and interaction patterns within the active site of target proteins, thereby providing insights into the molecular basis of ligand–receptor interactions. Advances in computational chemistry and bioinformatics have enabled the rapid screening of natural compounds against therapeutic targets with high efficiency. Databases such as Protein Data Bank provide experimentally determined three-dimensional structures of proteins that can be utilized for in silico drug discovery studies. In this study, a structure-based computational approach was employed to evaluate the inhibitory potential of phlorotannins derived from Ecklonia cava against human aldose reductase. Molecular docking analysis was performed using SwissDock to investigate the binding affinity and interaction patterns of selected compounds within the catalytic site of aldose reductase (PDB ID: 8FH9). Furthermore, pharmacokinetic and drug-likeness properties were predicted using SwissADME, while metabolic interactions with cytochrome P450 enzymes were assessed using pkCSM. The objective of this study was to identify promising phlorotannin compounds with strong aldose reductase inhibitory potential that could serve as lead molecules for the development of novel therapeutic agents for diabetic neuropathy.

MATERIALS AND METHODS:

Protein Preparation

The three-dimensional crystal structure of human Aldose Reductase was obtained from the Protein Data Bank using the PDB ID 8FH9. The downloaded structure was carefully inspected and prepared for molecular docking studies. All non-essential molecules including crystallographic water molecules and co-crystallized ligands were removed to avoid interference with ligand binding analysis. Hydrogen atoms were added to the protein structure to stabilize the geometry and to ensure proper protonation states of amino acid residues. The prepared protein structure was then subjected to structural refinement and saved in the appropriate format for docking analysis.

Ligand Preparation

The chemical structures of selected phlorotannin compounds derived from Ecklonia cava were retrieved from the PubChem database. The compounds selected for this study included Triphloroethol A, Dieckol, Eckol, Eckstolonol, and other related phlorotannin derivatives.

The retrieved structures were downloaded in SDF format and converted into suitable three-dimensional conformations. Ligands were prepared by performing geometry optimization to achieve stable conformations and to minimize steric strain. Hydrogen atoms were added to the ligand structures, and rotatable bonds were defined to allow flexibility during the docking process.

Molecular Docking

Molecular docking analysis was performed using the online docking platform SwissDock, which is based on the EADock DSS algorithm. This platform predicts the most favorable binding orientation of ligands within the active site of the target protein by evaluating multiple conformational poses and estimating binding energies. The prepared protein structure (PDB ID: 8FH9) was uploaded as the receptor, and the optimized ligand structures were submitted individually for docking analysis. Docking simulations were carried out under blind docking conditions, allowing the ligands to explore the entire protein surface to identify potential binding pockets. SwissDock generated multiple docking clusters for each ligand based on binding energy and full fitness scores. The docking pose with the lowest estimated binding free energy (ΔG) and the most favorable interaction pattern was selected as the best binding conformation for further analysis.

Docking Interaction Analysis

The docking results obtained from SwissDock were analyzed to identify key interactions between ligands and the active site residues of aldose reductase. Interaction patterns such as hydrogen bonding, hydrophobic interactions, π–π stacking, and van der Waals interactions were examined to evaluate the stability of ligand–protein complexes. Special attention was given to residues located within the catalytic pocket of aldose reductase, including residues known to play critical roles in substrate binding and enzymatic activity. Visualization and interaction analysis helped in identifying the specific amino acid residues involved in stabilizing ligand binding within the enzyme's active site. Docking clusters generated by SwissDock were ranked according to estimated binding free energy (ΔG) and full fitness score. The docking pose with the lowest binding energy and most stable interaction pattern within the catalytic pocket was selected for further analysis.

ADMET Profiling

The pharmacokinetic and drug-likeness properties of the selected phlorotannin compounds were evaluated using SwissADME. This computational tool predicts important ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) parameters that influence the drug development process.

The predicted parameters included:

Molecular weight (MW)

Molecular weight represents the total mass of a compound expressed in g/mol and it influences drug absorption, distribution, and permeability across biological membranes.

Lipophilicity (LogP)

LogP indicates the balance between lipid solubility and water solubility of a compound and helps predict its ability to cross biological membranes.

Hydrogen bond donors and acceptors

Hydrogen bond donors and acceptors describe the ability of a molecule to form hydrogen bonds which affect solubility and interaction with biological targets.

Topological polar surface area (TPSA)

TPSA represents the surface area occupied by polar atoms such as oxygen and nitrogen and is commonly used to predict drug absorption and permeability.

Lipinski rule of five compliance

Lipinski rule of five evaluates drug likeness based on molecular weight, LogP, hydrogen bond donors and hydrogen bond acceptors to estimate oral bioavailability

Gastrointestinal GI absorption

GI absorption predicts how effectively a compound can be absorbed through the gastrointestinal tract after oral administration.

Bioavailability score

Bioavailability score indicates the probability that a compound will reach systemic circulation in sufficient concentration after oral administration.

Cytochrome P450 Profiling

Metabolic interactions of the selected compounds with major human cytochrome P450 enzymes were predicted using pkCSM. Cytochrome P450 enzymes play a crucial role in drug metabolism and can significantly influence drug safety and pharmacokinetics. The study evaluated potential inhibition of key CYP isoforms including:

CYP1A2 is an important liver enzyme responsible for the metabolism of several drugs, xenobiotics, and environmental chemicals, and its inhibition may alter drug clearance and lead to potential drug–drug interactions

CYP2C9 metabolizes many clinically important drugs including anticoagulants and anti-inflammatory agents, and inhibition of this enzyme can affect drug metabolism and increase the risk of adverse effects.

CYP2C19
is involved in the metabolism of proton pump inhibitors, antiplatelet drugs, and several antidepressants, and its inhibition may influence therapeutic efficacy and drug response.

CYP2D6
 is responsible for the metabolism of numerous cardiovascular and central nervous system drugs, and inhibition of this enzyme can lead to altered pharmacokinetics and possible drug interactions.

CYP3A4
 is one of the most abundant drug-metabolizing enzymes in the liver and intestine and is responsible for the metabolism of a large proportion of pharmaceuticals, making it a critical factor in drug metabolism studies. Prediction of CYP inhibition provides insight into possible metabolic interactions and helps assess the likelihood of drug–drug interactions during therapeutic use.

RESULTS

Molecular Docking Analysis

Three-dimensional structure of aldose reductase (PDB ID: 8FH9

Optimized three-dimensional chemical structure of phlorotannin ligand

Molecular docking results showing binding affinity values and docking poses generated during the docking process. The table displays binding energies and RMSD values for different docking modes, indicating the most stable ligand–protein interaction conformation.

Molecular docking analysis was performed to evaluate the binding affinity of selected phlorotannin compounds derived from Ecklonia cava against the enzyme Aldose Reductase using the crystal structure with PDB ID 8FH9 obtained from the Protein Data Bank. Docking simulations were conducted using SwissDock based on the EADock DSS algorithm to predict the most stable binding conformations of the ligands within the active site of the enzyme. SwissDock generated multiple docking clusters for each ligand, and the pose with the lowest estimated binding free energy was selected as the most favorable binding conformation. The docking results revealed notable differences in binding affinity among the evaluated compounds.

Surface binding pocket with ligand

EDO-A-406 Interacting chains: A

EDO-A-405 Interacting chains: A

Docking Binding Energy Analysis

The estimated binding free energies of the docked complexes are presented in Table 1.

Table 1: Molecular Docking Results of Phlorotannin Compounds with Aldose Reductase (PDB ID: 8FH9)

Among the investigated compounds, Triphloroethol A exhibited the strongest binding affinity toward aldose reductase with a binding energy of −8.333 kcal/mol, which was more favorable than that of the reference inhibitor Epalrestat (−7.639 kcal/mol). The results suggest that Triphloroethol A may form a more stable complex with the enzyme compared to other evaluated compounds.

Protein–Ligand Interaction Analysis

Detailed interaction analysis of the docked complexes revealed that the ligands bind within the catalytic pocket of aldose reductase and interact with several key amino acid residues responsible for enzymatic activity.

Triphloroethol A

Protein–Ligand Interaction Analysis

Target: Aldose Reductase

Analysis Tool: Protein–Ligand Interaction Profiler (PLIP)

Hydrogen Bond Interactions

Hydrophobic Interactions

π–π Stacking Interaction

The docked ligand exhibited multiple stabilizing interactions within the catalytic pocket of aldose reductase. Hydrogen bonding interactions were observed with Tyr48 and His110, which are known catalytic residues of the enzyme. Additionally, hydrophobic contacts with Trp20, Phe122, and Leu300 contributed to stabilization of the ligand within the binding pocket. A π–π stacking interaction with Trp111 further enhanced ligand binding affinity.

ADMET Analysis

Pharmacokinetic properties and drug-likeness parameters of the selected compounds were predicted using SwissADME to evaluate their suitability as potential therapeutic candidates.

Table 2: ADMET Properties of Selected Phlorotannins

The ADMET analysis indicated that Triphloroethol A and Eckol comply with Lipinski’s rule of five, suggesting favorable drug-likeness properties. Dieckol exhibited minor violations due to its larger molecular weight and higher number of hydrogen bond donors and acceptors. Triphloroethol A demonstrated balanced physicochemical properties including moderate lipophilicity and acceptable polar surface area, which may contribute to improved pharmacokinetic behavior.

Cytochrome P450 Profiling

Metabolic interactions with cytochrome P450 enzymes were predicted using pkCSM to evaluate the potential metabolic stability of the compounds.

Table 3: Predicted CYP450 Inhibition Profile