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СпутникиРНSpaceXРоскосмосNASAAlzheimer’s disease is a multifactorial neurodegenerative disorder involving cholinergic dysfunction, oxidative stress, and neuroinflammation. This study aimed to evaluate the neuroprotective potential of arbutinyl undecylenate through an integrated approach combining molecular modeling and nanoparticle formulation. Molecular docking and molecular dynamics simulations were performed to assess ligand interactions with acetylcholinesterase (AChE) and inducible nitric oxide synthase (iNOS). The results demonstrated that arbutinyl undecylenate exhibited favorable binding affinity toward both targets, with stable interaction profiles confirmed by RMSD, RMSF, radius of gyration, and hydrogen bonding analyses. MM-GBSA calculations further supported these findings, indicating favorable binding free energy, particularly toward iNOS. To enhance delivery, the compound was formulated into PLGA nanoparticles via emulsification. The nanoparticles exhibited a size range of approximately 176–191 nm, a polydispersity index of ~0.3, and a high entrapment efficiency (~97%). Morphological analysis confirmed the formation of spherical particles, while FTIR indicated no chemical interaction between the drug and the polymer. XRD and DSC analyses revealed a reduction in crystallinity, suggesting successful encapsulation in an amorphous or molecularly dispersed state. In conclusion, arbutinyl undecylenate demonstrates promising molecular interaction and can be effectively incorporated into PLGA nanoparticles with suitable physicochemical properties. This integrated strategy highlights the potential of combining molecular modification with nanotechnology-based delivery systems to develop neuroprotective agents. Further experimental validation is required to confirm its therapeutic applicability.
Neurodegenerative disorders, particularly Alzheimer’s disease, continue to represent a significant global health burden, not only due to their increasing prevalence but also because of their complex and multifactorial nature (Briyal et al., 2023) (Long & Holtzman, 2019) (DeTure & Dickson, 2019). Rather than being driven by a single pathological mechanism, Alzheimer’s disease is now widely understood as a networked condition involving cholinergic dysfunction, oxidative stress, mitochondrial impairment, and chronic neuroinflammation that evolve in parallel over time (Butterfield & Halliwell, 2019; DeTure & Dickson, 2019) (Lotfi et al., 2025) (Knopman et al., n.d.). Consequently, therapeutic strategies targeting a single pathway often demonstrate limited long-term efficacy, thereby necessitating the exploration of multi-target approaches (Cummings et al., 2021; DeTure & Dickson, 2019).
Among the classical therapeutic targets, acetylcholinesterase (AChE) remains one of the most extensively investigated enzymes due to its central role in regulating acetylcholine levels within the synaptic cleft (Breijyeh & Karaman, 2020; Hampel et al., 2018; Js et al., 2018). Although AChE inhibition has been widely adopted as a symptomatic treatment strategy, its clinical benefits are often modest and do not halt disease progression (Coelho et al., 2018). Furthermore, AChE has been implicated in amyloid-β aggregation through its interaction at the peripheral anionic site (PAS), thereby linking cholinergic dysfunction with amyloid pathology (Silman & Sussman, 2008; Eric, 2017).
Beyond the cholinergic system, the nitric oxide pathway has emerged as a critical contributor to neurodegenerative processes. Inducible nitric oxide synthase (iNOS) plays a key role in neuroinflammation and oxidative stress by generating excessive nitric oxide, which can react with superoxide to form reactive nitrogen species such as peroxynitrite (Pacher et al., 2026; Sessa & Fo, 2011). These reactive species are capable of inducing lipid peroxidation, protein nitration, and DNA damage, ultimately leading to neuronal dysfunction and cell death (Pacher et al., 2026) (Eric, 2017). Given this interplay, targeting both AChE and iNOS represents a rational strategy for developing more comprehensive neuroprotective agents (Zhao et al., 2024; Grabowska et al., 2025).
In parallel with target exploration, natural compounds and their derivatives have gained increasing attention due to their structural diversity and biological potential (Newman & Cragg, 2020; Atanasov et al., 2015). Arbutin, a naturally occurring glycoside, has been reported to exhibit antioxidant and cytoprotective properties. However, its relatively low lipophilicity limits its ability to effectively cross biological membranes, particularly the blood–brain barrier (BBB), which remains a major challenge in central nervous system drug delivery (Saraiva et al., 2016) (Pardridge, 2012).
To overcome this limitation, structural modification through esterification has been proposed as a strategy to enhance lipophilicity and membrane permeability without significantly altering the pharmacophoric characteristics of the parent compound (Markowicz-Piasecka et al., 2022; Bannon et al., 2024). The synthesis of arbutinyl undecylenate represents such an approach, where the introduction of a hydrophobic moiety is expected to improve both protein-binding interactions and compatibility with hydrophobic systems. Nevertheless, improving molecular interaction alone does not necessarily translate into therapeutic success. A more fundamental question emerges, not merely whether the compound can bind effectively, but whether it can be delivered efficiently to its target within a biological system.
In this context, nanoparticle-based drug delivery systems provide a promising approach to improve drug delivery to the brain. Poly (lactic-co-glycolic acid) (PLGA) nanoparticles are widely used because of their biocompatibility, biodegradability, and ability to encapsulate hydrophobic compounds (Danhier et al., 2012; Makadia & Siegel, 2011). In addition, nanoparticles sized 100–200 nm can cross the blood–brain barrier (BBB) via endocytosis, thereby enhancing the delivery of therapeutic agents to the brain (Saraiva et al., 2016; Alotaibi et al., 2021). However, although arbutin has been successfully incorporated into polymeric nanoparticles for topical and cosmetic applications, its potential for the treatment of central nervous system disorders remains underexplored (Hatem et al., 2022; Ayumi et al., 2019; Sahudin et al., 2022). This highlights a broader challenge in current research: molecular efficacy and drug delivery strategies are often studied separately rather than in an integrated manner.
Recent advances have highlighted the importance of multi-target therapeutic strategies involving acetylcholinesterase (AChE) and inducible nitric oxide synthase (iNOS). However, the development of arbutin derivatives that effectively interact with these targets while overcoming drug-delivery limitations remains limited (Ramsay et al., 2018; Kabir & Muth, 2022). To address this gap, the present study introduces arbutinyl undecylenate, a structurally modified arbutin derivative designed to improve both molecular interactions and delivery compatibility. This study integrates molecular docking, molecular dynamics simulation, and polymeric nanoparticle formulation within a unified framework to establish a dual-level optimization strategy, treating molecular design and drug delivery systems as interconnected components of neurotherapeutic development. Therefore, this study aims to investigate the molecular interactions, stability, and nanoparticle-based delivery potential of arbutinyl undecylenate, a prospective neuroprotective agent targeting AChE and iNOS.
Arbutinyl undecylenate (purity >98%) was obtained from the National Research and Innovation Agency (BRIN, Indonesia). Poly (lactic-co-glycolic acid) (PLGA; lactide: glycolide ratio 75:25, Mw 30,000–60,000 Da) was purchased from Xian Prius Biological Engineering Co., Ltd. (Shaanxi, China). Polyvinyl alcohol (PVA; 87–89% hydrolyzed, Mw 30,000–70,000 Da), ethanol (analytical grade), and ethyl acetate (analytical grade) were obtained from CV. Colorogreen (Indonesia). Distilled water was used throughout the study.
The drug-likeness properties of arbutin and arbutin undecylenate were evaluated using Lipinski’s Rule of Five to predict their potential oral bioavailability and pharmacokinetic behavior. The analysis assessed key physicochemical parameters, including molecular weight, octanol/water partition coefficient (Log P), and hydrogen bond donors and acceptors. Compounds were considered to possess favorable drug-like characteristics when meeting the following criteria: molecular weight ≤ 500 Da, Log P ≤ 5, hydrogen bond donors ≤5, and hydrogen bond acceptors ≤10. Physicochemical data for both compounds were obtained from computational chemical databases and analyzed descriptively based on Lipinski’s criteria. In addition, water solubility was evaluated further to assess the compound’s behavior in biological systems. The overall compliance with Lipinski’s Rule of Five was interpreted as an indicator of the compounds’ suitability for oral drug delivery and their potential pharmacokinetic performance.
The ligand used in this study, arbutin undecylenate, was selected based on literature evidence as a modified arbutin derivative with enhanced lipophilicity. Its two-dimensional (2D) structure was obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov) in SMILES format and converted into a three-dimensional (3D) structure. The 3D structure was geometry-optimized to obtain a stable minimum-energy conformation. Hydrogen atoms were added, and Gasteiger charges were assigned before conversion.pdbqt format using AutoDock Tools for docking compatibility. Reference ligands, including donepezil (AChE) and L-Arginine (iNOS), were prepared using the same protocol after extraction from their respective protein complexes. All ligands were visually inspected to ensure structural integrity and absence of steric clashes before docking analysis.
The three-dimensional crystal structures of acetylcholinesterase (AChE, PDB ID: 4EY7) and inducible nitric oxide synthase (iNOS, PDB ID: 1NOD) were obtained from the RCSB Protein Data Bank based on the availability of co-crystallized ligands and clearly defined active sites. Protein preparation was performed by removing native ligands, water molecules, and unnecessary heteroatoms, while native ligands were retained separately for docking validation. Polar hydrogen atoms and Gasteiger charges were then added, and all structures were converted into.pdbqt format using AutoDock Tools. The prepared proteins were subsequently inspected to ensure proper structural integrity and active site configuration prior to molecular docking analysis.
Docking validation was performed by re-docking native ligands (donepezil for AChE and L-Arginine for iNOS) into their respective binding sites using identical parameters. The accuracy was evaluated using the root-mean-square deviation (RMSD) between the re-docked and crystallographic poses, with values <2.0 Å considered acceptable. This confirms the reliability of the docking protocol for subsequent analysis (Wang et al., 2020).
Molecular docking was performed using AutoDock with validated parameters. Ligands were docked into the active sites of AChE (4EY7) and iNOS (1NOD) using the same grid configuration. Binding affinity (ΔG) was used as the primary scoring parameter, where lower values indicate more stable interactions. The best poses were selected based on the lowest binding energies and analyzed for key active-site interactions.
Ligand–protein interactions were analyzed using BIOVIA Discovery Studio Visualizer. The selected docking poses were evaluated to identify key interactions with active-site residues, including hydrogen bonds and hydrophobic contacts. Interactions similar to those of native ligands were considered indicative of potential biological activity and binding specificity.
Molecular dynamics (MD) simulations were performed to evaluate the stability and interaction behavior of ligand–protein complexes under dynamic conditions using GROMACS with the Amber ff99SB-ILDN force field. Ligand topologies were generated using ACPYPE to ensure parameter compatibility. The complexes were energy-minimized using the steepest descent algorithm, followed by equilibration under NPT conditions at 300 K and 1 atm using the Nosé–Hoover thermostat and Parrinello–Rahman barostat. Production MD simulations were conducted for 100 ns with a 2 fs time step. Long-range electrostatic interactions were calculated using the Particle Mesh Ewald (PME) method, while hydrogen-containing bonds were constrained using the LINCS algorithm under periodic boundary conditions. Trajectory analyses included root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and hydrogen-bond analysis to assess structural stability, residue flexibility, compactness, and interaction persistence throughout the simulation. In addition, binding free energy calculations were performed using the Molecular Mechanics/Poisson–Boltzmann Surface Area (MM-PBSA) method to estimate the thermodynamic favorability of ligand–protein binding through van der Waals, electrostatic, polar solvation, and nonpolar solvation energy contributions.
Preparation method
The preparation of arbutin undecylenate-loaded PLGA nanoparticles was carried out using a solvent evaporation method, involving the formation of an organic phase, emulsification into an aqueous phase, and subsequent solvent removal. The overall preparation process is illustrated in Supplementary Figure 2 (nurain thomas, 2026). Briefly, 10 mg of arbutin undecylenate and 50 mg of PLGA were dissolved in 10 mL of ethyl acetate. The resulting organic phase was added dropwise to 30 mL of an aqueous PVA solution containing 0.5% (w/v) PVA for F1, 1% (w/v) PVA for F2, or 2% (w/v) PVA for F3, with high-speed homogenization. The resulting emulsion was further stirred to remove the organic solvent, then sonicated to reduce particle size and improve dispersion. This method was selected for encapsulating lipophilic compounds and producing nanoparticles with relatively uniform size and high entrapment efficiency.
Optimization parameters
The formulation was optimized by varying the concentration of polyvinyl alcohol (PVA) as a stabilizer in the aqueous phase at 0.5%, 1%, and 2%. All other formulation components were kept constant. This variation was applied to evaluate the effect of PVA concentration on particle size, polydispersity index, zeta potential, and entrapment efficiency of the resulting nanoparticles.
Particle size, polydispersity index, and zeta potential
Particle size, polydispersity index (PDI), and zeta potential of the nanoparticles were determined using a particle size analyzer (PSA) (SZ-100-Z, Horiba, Japan). Samples were appropriately diluted with distilled water and measured at 25 °C. Each measurement was performed in triplicate, and results were expressed as mean ± standard deviation. These parameters were used to evaluate particle size distribution, homogeneity, and colloidal stability.
Scanning electron microscopy (SEM) analysis
Scanning electron microscopy (SEM) was performed to examine the surface morphology and structural characteristics of the nanoparticles using an environmental SEM (FEI Quanta 450 FEG, USA). Lyophilized samples were mounted on carbon-coated stubs and, when necessary, sputter-coated with a thin conductive layer to minimize charging effects. Images were captured under an appropriate accelerating voltage. Morphological features, including particle shape, surface texture, aggregation, and the presence of pores or structural irregularities, were analyzed to assess the effects of formulation and solvent evaporation dynamics.
Fourier-Transform infrared spectroscopy (FTIR) analysis
Fourier-transform infrared spectroscopy (FTIR) was conducted using an FTIR spectrometer (PerkinElmer Spectrum Two, USA) to identify functional groups and evaluate potential interactions between arbutin undecylenate and the PLGA matrix. Spectra of pure drug, polymer, and drug-loaded nanoparticles were recorded over the range of 4000–400 cm−1 using the KBr pellet method or attenuated total reflectance (ATR) technique. Characteristic peaks corresponding to functional groups such as hydroxyl (–OH), carbonyl (C=O), and aromatic C=C were analyzed. Changes in peak position, intensity, or band broadening were interpreted as indicators of molecular interactions or dispersion within the polymer matrix.
Differential scanning calorimetry (DSC) analysis
Thermal analysis was performed using a differential scanning calorimeter (DSC 8500, PerkinElmer Inc., USA) to investigate the thermal behavior and phase transitions of the samples. Accurately weighed samples of pure drug, PLGA, and nanoparticle formulations were sealed in aluminum pans and analyzed under a nitrogen atmosphere at a heating rate of 10 °C/min over a temperature range of 0–250 °C. Thermograms were evaluated to determine melting points, glass transition temperatures, and enthalpy changes. The disappearance or reduction of characteristic melting peaks was interpreted as evidence of amorphization and successful encapsulation.
X-ray diffraction (XRD) analysis
X-ray diffraction (XRD) analysis was performed using a diffractometer (Ultima IV, Rigaku, Japan) equipped with Cu Kα radiation at an operating voltage of 40 kV and a current of 20 mA. Samples were scanned over a 2θ range of 3°–70° at a constant scanning rate. The diffraction patterns of pure drug, PLGA, and drug-loaded nanoparticles were compared to evaluate changes in crystallinity. The reduction or disappearance of characteristic crystalline peaks in the nanoparticle formulation was considered indicative of successful drug encapsulation and transition to an amorphous state.
Lipinski’s Rule of Five is a widely used guideline for evaluating the drug-likeness of compounds, particularly their potential for oral bioavailability. This rule considers key physicochemical parameters, including molecular weight, lipophilicity (LogP), hydrogen-bond donors, and hydrogen-bond acceptors, which collectively influence absorption and permeability. Compounds that comply with these criteria are generally considered to have favorable pharmacokinetic properties. The drug-likeness properties of arbutin and arbutinyl undecylenate were evaluated based on Lipinski’s Rule of Five, as presented in supplementary Table 1 (nurain thomas, 2026).
The Lipinski’s rule of five analysis showed that both arbutin and arbutin undecylenate met all criteria without violations. Arbutin had a molecular weight of 272.25 Da, log P of 0.77, five hydrogen bond donors, and seven hydrogen bond acceptors. In comparison, arbutin undecylenate exhibited a molecular weight of 438.51 Da, a log P of 2.43, four hydrogen bond donors, and eight hydrogen bond acceptors. Both compounds were classified as having acceptable drug-likeness profiles.
The prepared protein structures were subsequently used for molecular docking analysis, followed by validation of the docking protocol and evaluation of ligand–protein interactions.
The validation of the docking protocol was performed by re-docking the native ligand into the active site of the target proteins. The results are presented in supplementary Table 2 (nurain thomas, 2026)). The RMSD values obtained were 1.039 Å for AChE (PDB ID: 4EY7) and 0.835 Å for NOS (PDB ID: 1NOD), both of which were below the acceptable threshold of 2.0 Å. These values indicate that the docking protocol reproduced the native ligand’s binding pose with good accuracy.
Following validation of the docking protocol, molecular docking analysis was conducted to evaluate the binding affinity and interaction profiles of arbutin undecylenate toward acetylcholinesterase (AChE) and nitric oxide synthase (NOS). The molecular docking results of the tested compounds against AChE and iNOS are presented in supplementary Table 3 (nurain thomas, 2026), highlighting their binding affinities and interaction profiles. Arbutin undecylenate exhibited binding energy values of −10.9 kcal/mol for AChE and − 10.8 kcal/mol for NOS. In contrast, the reference ligands donepezil and L-Arginine showed binding energies of −7.2 kcal/mol and − 4.6 kcal/mol, respectively.
Interaction analysis revealed that arbutin undecylenate engaged in multiple hydrophobic interactions with key residues in the AchE active site, including TRP286, TYR337, PHE338, and TYR341. In addition, hydrogen bonding interactions were observed, contributing to ligand stabilization within the binding pocket. For the NOS target, arbutin undecylenate interacted with residues such as TRP366 and TRP457 via hydrogen bonding and hydrophobic contacts. The reference ligand L-Arginine interacted with residues including CYS194, TRP366, MET368, ILE195, and ALA433.
These interaction patterns were consistent with the observed binding energy values, indicating stable ligand accommodation within the active sites. The detailed interaction profiles and binding orientations of arbutin undecylenate with the target proteins are presented in supplementary Figures 4,5, 6 and 7 (nurain thomas, 2026).
The RMSD profiles (supplementary Figure 8) showed that both ligand–protein complexes underwent an initial equilibration phase during the early stage of the simulation before reaching relatively stable trajectories over the 100 ns period (nurain thomas, 2026). The Arbutin undecylenate-AchE complex exhibited a noticeable increase in RMSD over the first ~20 ns, followed by stabilization to approximately 0.50–0.65 nm. In contrast, the Arbutin undecylenate-iNOS complex exhibited lower, more gradual deviations, remaining relatively stable within the range of 0.20–0.35 nm throughout the simulation. Overall, both systems maintained consistent RMSD behavior after equilibration, with the Arbutin undecylenate-iNOS complex showing comparatively lower structural deviation than the Arbutin undecylenate-AchE system.
The RMSF profiles (supplementary Figure 9) showed that most amino acid residues in both complexes exhibited relatively low fluctuation values, indicating limited structural flexibility during the simulation (nurain thomas, 2026). A notable increase in fluctuation was observed in the early residue region of the Arbutin undecylenate-iNOS complex, reaching values above 1.0 nm, followed by a relatively stable fluctuation pattern across the remaining residues. In contrast, the Arbutin undecylenate-AchE complex showed generally lower fluctuations throughout the sequence, with only minor peaks in specific regions. Overall, the observed fluctuations were primarily localized in terminal and loop regions of the protein structures.
The radius of gyration (Rg) profiles (supplementary Figure 10) showed that both ligand-protein complexes maintained relatively stable values throughout the 100 ns simulation period, with only minor fluctuations observed over time (nurain thomas, 2026). The Arbutin undecylenate-AchE complex exhibited Rg values ranging from approximately 2.28 to 2.31 nm, indicating a consistently compact structure. In comparison, the Arbutin undecylenate-iNOS complex showed slightly lower Rg values in the early phase, followed by gradual stabilization to approximately 2.24–2.29 nm. Overall, no significant structural expansion or contraction was observed in either system during the simulation.
The hydrogen bond profiles (supplementary Figure 11) showed that both ligand-protein complexes formed dynamic interactions throughout the 100 ns simulation period (nurain thomas, 2026). The Arbutin undecylenate-iNOS complex generally maintained a higher and more consistent number of hydrogen bonds, predominantly ranging from 2 to 5 over time. In contrast, the Arbutin undecylenate-AchE complex exhibited greater fluctuations, with the number of hydrogen bonds varying between 0 and 4 throughout the simulation. Despite these variations, both systems exhibited continuous hydrogen-bonding interactions, indicating sustained ligand–protein contacts throughout the simulation.
The binding free energy of the ligand-protein complexes was further evaluated using the MM-GBSA method. As shown in supplementary Figure 12, the Arbutin undecylenate-AChE complex exhibited a total binding free energy (ΔG bind) of approximately −37 kcal/mol, with the gas-phase contribution (GGAS) dominated by van der Waals (−45 kcal/mol) and electrostatic interactions (−27 kcal/mol) (nurain thomas, 2026). The solvation contribution (GSOLV) was positive, primarily due to polar solvation energy (EGB, 42 kcal/mol), whereas the nonpolar solvation term (ESURF) was negative (−7 kcal/mol). Similarly, the Arbutin undecylenate iNOS complex showed a ΔG bind of approximately −40 kcal/mol, with favorable GGAS contributions from van der Waals interactions (−50 kcal/mol) and electrostatic interactions (−30 kcal/mol), as shown in supplementary Figure 12 (nurain thomas, 2026). Although the solvation term remained unfavorable, with EGB of approximately +45 kcal/mol and ESURF of approximately −7 kcal/mol, the total binding free energy remained negative. These results indicate favorable binding of arbutin undecylenate toward both targets, with van der Waals interactions contributing most prominently to complex stabilization.
The physicochemical properties of the prepared nanoparticles were evaluated to assess their size distribution, surface characteristics, and drug-incorporation efficiency, as shown in supplementary Table 4 (nurain thomas, 2026). The average particle size of the formulations ranged from approximately 176 to 191 nm, indicating successful formation of nanoparticles at the nanoscale. The polydispersity index (PDI) was around 0.3, indicating a moderately uniform size distribution. The zeta potential values ranged from −0.8 to −3.5 mV, indicating low surface charge of the nanoparticle systems. The entrapment efficiency (%EE) was approximately 97%, indicating efficient incorporation of arbutin undecylenate into the PLGA matrix, while the drug loading (DL) was approximately 16%.
Supplementary Figure 14 presents a three-dimensional surface plot illustrating the relationship between Z-average particle size, polydispersity index (PDI), and zeta potential (mV) of arbutinyl undecylenate-loaded PLGA nanoparticles across three formulations (F1, F2, and F3) (nurain thomas, 2026). The graph provides a comprehensive visualization of how variations in particle size distribution and surface charge are associated with formulation-dependent changes in nanoparticle stability and homogeneity. Overall, this multidimensional analysis highlights the interplay between physicochemical properties that determine the quality and colloidal stability of the prepared nanoparticle systems.
The morphological characteristics of the prepared nanoparticles were examined using scanning electron microscopy (SEM), as shown in supplementary Figure 15. The images revealed that the nanoparticles were predominantly spherical, with relatively smooth surfaces. Most particles appeared well-defined, with no significant deformation observed. However, minor surface irregularities and occasional pore-like structures were observed in some particles. A slight tendency toward aggregation was also noted in certain areas, although the overall morphology remained distinguishable.
The FTIR spectra of arbutin undecylenate, PLGA, and the nanoparticle formulation are presented in supplementary Figure 16 (nurain thomas, 2026). The characteristic peaks corresponding to the functional groups of arbutin undecylenate, including hydroxyl (–OH) and carbonyl (C=O) groups, were observed in the spectrum of the pure compound. Similarly, the PLGA spectrum showed its typical absorption bands associated with ester carbonyl and C–O stretching vibrations. In the nanoparticle formulation, the characteristic peaks of both arbutin undecylenate and PLGA were retained without significant shifts in peak position. However, slight changes in peak intensity and broadening were observed in some regions. These observations indicate that the main functional groups remained intact in the nanoparticle system.
The XRD patterns of arbutin undecylenate and the nanoparticle formulation are presented in supplementary Figure 17 (nurain thomas, 2026). The pure arbutin undecylenate exhibited distinct and sharp diffraction peaks, indicating its crystalline nature. In contrast, the nanoparticle formulation showed a noticeable reduction in the intensity of these characteristic peaks, resulting in a more diffuse diffraction pattern, suggesting a partial reduction in crystallinity of arbutin undecylenate within the nanoparticle system.
The DSC thermograms of arbutin undecylenate and the nanoparticle formulation are presented in supplementary Figure 17 (nurain thomas, 2026). The pure arbutin undecylenate exhibited a distinct endothermic peak corresponding to its melting point, indicating its crystalline nature. In contrast, this characteristic peak was significantly reduced or absent in the nanoparticle formulation, suggesting that arbutin undecylenate may have transformed into a less crystalline state within the formulation.
The present study proposes an integrated framework that aligns molecular interaction with nanoparticle-based delivery, addressing a key limitation in neurotherapeutic development. Accumulating evidence indicates that the limited success of central nervous system (CNS) drug candidates is not only driven by insufficient binding affinity, but also by poor brain exposure due to physiological barriers, particularly the blood–brain barrier (BBB) (Pardridge, 2012; Banks, 2016). Therefore, simultaneous consideration of molecular binding and delivery performance is essential for effective neurotherapeutic design.
From a molecular perspective, arbutinyl undecylenate exhibited favorable binding interactions with both acetylcholinesterase (AChE) and inducible nitric oxide synthase (iNOS), supporting its potential as a multi-target-directed ligand. This is relevant given the multifactorial nature of Alzheimer’s disease, which involves cholinergic dysfunction and neuroinflammatory processes (De Strooper & Karran, 2016). However, molecular docking provides a static approximation and should not be interpreted as direct evidence of biological efficacy.
To address this limitation, molecular dynamics simulations were performed to evaluate the stability of ligand–protein complexes under dynamic conditions. The RMSD, RMSF, and hydrogen bond analyses indicated overall structural stability throughout the simulation period. In particular, the arbutinyl–iNOS complex showed lower fluctuations and more persistent hydrogen bonding, suggesting stronger interaction stability. This finding is consistent with the notion that binding stability and residence time are more predictive of pharmacological performance than binding affinity alone (Copeland, 2016; Hollingsworth & Dror, 2018).
Further, MM-GBSA analysis provided quantitative support for the binding energetics, where van der Waals interactions were the dominant contributors to complex stabilization. This suggests that hydrophobic complementarity plays a key role in ligand recognition within both target proteins. The slightly more favorable binding free energy observed for iNOS may indicate potential involvement in modulating neuroinflammatory pathways. However, given the dual physiological role of nitric oxide signaling, both excessive inhibition and overactivation must be interpreted cautiously (Radi, 2018).
A notable contribution of this study is the structural modification of arbutin through esterification, which enhances lipophilicity and improves compatibility with both molecular targets and polymeric carriers. This dual-purpose optimization reflects an emerging paradigm in drug design, where molecular engineering is aligned with formulation strategy to improve translational potential (Lipinski, 2016).
The nanoparticle formulation further supports this integrated approach. The PLGA nanoparticles exhibited particle sizes of approximately 176–191 nm, which is considered favorable for potential BBB transport via receptor-mediated or adsorptive transcytosis. In addition, high encapsulation efficiency (~97%) indicates strong affinity between the hydrophobic derivative and the polymer matrix, supporting the rationale for chemical modification to improve delivery performance.
Although relatively low zeta potential values were observed, colloidal stability may still be maintained through steric stabilization, likely provided by polyvinyl alcohol (PVA). Similar behavior has been widely reported in polymeric nanoparticle systems, where steric effects compensate for limited electrostatic repulsion (Torchilin, 2005; Danaei et al., 2018). Thus, nanoparticle stability should be interpreted as a combined effect of electrostatic and steric factors rather than solely as zeta potential.
Structural characterization using FTIR, XRD, and DSC confirmed successful encapsulation of arbutinyl undecylenate within the PLGA matrix. The absence of new chemical bonds suggests physical encapsulation without chemical degradation, while reduced crystallinity indicates conversion toward an amorphous or molecularly dispersed state, which is generally associated with improved dissolution and bioavailability (Bhugra & Pikal, 2008; Baghel et al., 2016).
Collectively, these findings support a shift toward an integrated drug development paradigm in which molecular design and delivery systems are treated as interdependent variables. This approach is increasingly emphasized in modern drug discovery, particularly for complex diseases such as Alzheimer’s disease (Scannell et al., 2012; Paul et al., 2010). In this framework, nanoparticle systems function not only as carriers but also as enabling platforms that translate molecular activity into biologically relevant delivery.
However, this study has limitations. The absence of in vitro and in vivo validation restricts direct biological interpretation, and pharmacokinetic behavior under physiological conditions remains uncharacterized. Future studies should address these limitations by incorporating experimental validation, BBB permeability assays, and in vivo neuroprotective evaluations to strengthen translational relevance.
This study demonstrates that arbutinyl undecylenate shows promising neuroprotective potential through the integration of molecular interaction analysis and nanoparticle-based delivery design. Molecular docking results indicated favorable binding affinity toward acetylcholinesterase (AChE) and inducible nitric oxide synthase (iNOS). In contrast, molecular dynamics simulations confirmed the structural stability and persistence of ligand–protein interactions throughout the simulation period. In addition, MM-GBSA analysis revealed thermodynamically favorable binding energies, primarily driven by van der Waals interactions, with the arbutinyl–iNOS complex showing slightly greater energetic stability. In parallel, the PLGA nanoparticle formulation successfully encapsulated the compound, with high entrapment efficiency and a nanoscale particle size distribution, which is considered suitable for potential blood–brain barrier transport. Physicochemical characterization further confirmed uniform morphology, preservation of functional groups, and reduced crystallinity, indicating successful incorporation into an amorphous or molecularly dispersed state. Collectively, these findings suggest that arbutinyl undecylenate may act as a multifunctional neuroprotective agent targeting both cholinergic dysfunction and neuroinflammatory pathways while benefiting from nanoparticle-mediated delivery. Importantly, this study emphasizes the need for an integrated framework that combines molecular optimization and delivery engineering rather than treating them as separate processes. However, further in vitro, pharmacokinetic, and in vivo studies are required to validate its biological efficacy, brain-targeting capability, and therapeutic potential in neurodegenerative disease models.
Ethical Approval was not required for this study.
The original contributions presented in this study are included in the article. The datasets supporting the findings of this study can be found in the ZENODO repository, under the title Study In Silico and Nanoparticle-Based Evaluation of Arbutinyl Undecylenate Targeting AChE and iNOS for Neuroprotection (nurain thomas, 2026), https://doi.org/10.5281/zenodo.20254979 License “Creative Commons Attribution 4.0 International”.
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
This project contains the following extended data:
Supplementary Figure. 1: Three-dimensional structures of target proteins and reference ligands: (A) acetylcholinesterase (AChE), (B) inducible nitric oxide synthase (iNOS), (C) donepezil, and (D) L-Arginine.
Supplementary Figure. 2: Schematic preparation of arbutin undecylenate-loaded PLGA nanoparticles.
Supplementary Figure. 3: Superimposition of native and re-docked ligands for docking validation: (1) AChE and (2) iNOS.
Supplementary Figure 4: 2D and 3D visualization of arbutin undecylenate interaction with acetylcholinesterase receptor.
Supplementary Figure 5: 2D and 3D visualization of donepezil with the acetylcholinesterase receptor.
Supplementary Figure 6: 2D and 3D visualization of arbutin undecylenate interaction with inducible nitric oxide synthase receptor.
Supplementary Figure 7: 2D and 3D visualization of L-arginine interaction with inducible nitric oxide synthase receptor.
Supplementary Figure 8: RMSD profiles of Arbutin undecylenate complexes with AChE and iNOS during 100 ns MD simulation.
Supplementary Figure 9: Residue flexibility (RMSF) of Arbutin undecylenate–AChE and –iNOS complexes.
Supplementary Figure 10: Radius of gyration (Rg) profiles of Arbutin undecylenate–AChE and iNOS complexes.
Supplementary Figure 11: Hydrogen bond profiles of Arbutin undecylenate–AChE and iNOS complexes.
Supplementary Figure 12: MM-GBSA energy contribution of arbutin undecylenate AChE complex.
Supplementary Figure 13: MM-GBSA energy contribution of arbutin undecylenate–iNOS complex.
Supplementary Figure 14: 3D surface relationship of Z-average, PDI, and zeta potential in arbutinyl undecylenate-loaded PLGA nanoparticles (F1–F3).
Supplementary Figure 15: SEM images of arbutin undecylenate-loaded PLGA nanoparticles.
Supplementary Figure 16: FTIR spectra of arbutin undecylenate and nanoparticle formulation.
Supplementary Figure 17: XRD patterns of arbutin undecylenate and its PLGA nanoparticles.
Supplementary Figure 18: DSC thermograms of arbutin undecylenate and nanoparticle formulation.
Supplementary Table 1:Drug-likeness properties of arbutin and arbutin undecylenate.
Supplementary Table 2: Re-docking validation of AChE and iNOS native ligands.
Supplementary Table 3: Molecular Docking Results of Compounds Against Target Proteins.
Supplementary Table 4: Physicochemical Characterization of Arbutinyl Undecylenate-Loaded PLGA Nanoparticles.
The authors gratefully acknowledge Universitas Negeri Gorontalo for providing academic support and research facilities that contributed to the completion of this study. The authors also sincerely thank the National Research and Innovation Agency of the Republic of Indonesia (BRIN) for its valuable support in advancing this research.