Abstract
Ixora coccinea, a medicinally important evergreen shrub, has been reported to exhibit numerous pharmacological activities, including neuroprotective effects. Hence, the current study aimed to investigate the molecular mechanisms and therapeutic potential of I. coccinea in managing Parkinson’s disease using integrated in silico and in vivo approaches. In a computational approach, bioactives of I. coccinea were screened and evaluated for their bioavailability. The potential targets regulated by bioactives were assessed via network, gene enrichment, and gene ontology analyses. Molecular docking was performed to predict the binding affinities, followed by molecular dynamics (MD) simulation. In experimental pharmacology, haloperidol was used to induce Parkinsonism in albino Wistar rats. Two doses of I. coccinea extract (200 and 400 mg/kg) were administered in conjunction with regular Syndopa. In the 21 days of treatment, rats were assessed for various neurobehavioral studies, biochemical, antioxidant and histological parameters. The network pharmacology revealed that stigmast-4-en-3-one showed the highest drug-likeness score of 0.91. Gene enrichment analysis predicted C-C motif chemokine 2, peroxisome proliferator-activated receptor alpha, and nuclear factor erythroid 2-related factor 2 (NFE2L2) as the top genes. Molecular docking revealed that NFE2L2, Catalase (CAT), and Peroxisome proliferator-activated receptor gamma (PPARG) showed the binding energy of −9.9, −9.5, and −9.3 kcal/mol, respectively. Molecular mechanics and MD simulation Poisson-Boltzmann study of surface area showed that the complex of CAT with 3-hydroxyflavone and PPARG with beta-sitosterol were most stable throughout the MD run. Further, I. coccinea ameliorated motor function, improved dopamine levels, reduced acetylcholinesterase (AChE) activity and oxidative stress, and displayed neuroprotection in the cerebral cortex region. The ethanolic extract of I. coccinea exhibited anti-Parkinson’s potential, possibly because PPARG and CAT are regulated by a variety of bioactives.
Introduction
Parkinson’s disease (PD) is a progressive neurodegenerative motor disorder marked by dopaminergic neuronal loss in the substantia nigra, causing dopamine deficiency, and intracellular inclusions containing aggregates of α-synuclein.
1
PD was first outlined by James Parkinson in his essay “An Essay of the Shaking Palsy,” describing the motor symptoms like involuntary tremors, followed by difficulty in walking, swallowing, and speech. Apart from this, PD patients also experience non-motor symptoms like cognitive impairment, sleep disturbances, and other autonomic dysfunctions.
2
It is an age-related disease and is common among people aged 50 and above. The incidence of PD increases 5–10 times from the sixth to the ninth decade of life.
3
According to the World Health Organization, the frequency of PD has doubled over the last 25 years, and as of 2019, it is thought to impact over 8.5 million individuals worldwide. The disease has greatly increased the number of deaths (0.33 million) and disability-adjusted life years globally, making it a major public health concern
4
(
Dopamine is a key neurotransmitter that regulates movement, coordination, learning, memory, and attention. 6 PD is characterized by degeneration of dopamine-producing neurones in the substantia nigra pars compacta region, in addition to other non-motor symptoms. 7 This dopamine depletion causes an imbalance in the brain’s dopaminergic and cholinergic systems, which raises acetylcholine activity and worsens motor dysfunction. 8 Additionally, abnormal dopamine metabolism generates ROS and toxic metabolites that lead to oxidative stress, mitochondrial damage, and α-synuclein aggregation, all of which contribute to neuronal death and the progression of disease. 9
Haloperidol, a common antipsychotic drug, is often used to induce Parkinsonian-like symptoms in mice due to its potent dopamine receptor antagonist action. By blocking dopamine receptors, haloperidol disrupts dopaminergic neurotransmission and increases dopamine turnover. 10 This excess dopamine travels through MAO, producing peroxide and 3,4-dihydroxyphenylacetic acid as byproducts of oxidative metabolism. Extrapyramidal Parkinson-like side effects are largely caused by oxidative stress, which is exacerbated by this buildup of hydrogen peroxide. 11
Levodopa, beta-blockers, amantadine, monoamine oxidase B inhibitors (MAO-B), and anticholinergics are examples of current PD medications that can dramatically increase dopamine levels and reverse PD symptoms, but they cannot treat the disease. 12 According to Sandhu and Crana, 13 these medications also have a number of adverse effects, including depression, respiratory issues, hallucinations, mania, convulsions, anxiety, arrhythmia, insomnia, and increased appetite. This emphasizes how critical it is to find new therapeutic agents for PD that have fewer side effects.
Since ancient times, medicinal plants and herbs have been a valuable source of medicine, as they consist of an unlimited variety of phytochemicals, continuously proven effective in preventing, treating, and ameliorating various health conditions. 14 Humans have used plants to maintain and enhance their quality of life and health for years. 9 One such plant is Ixora coccinea Linn., belonging to the family Rubiaceae, commonly known as the flame of the woods, scarlet Ixora, the jungle geranium, and red Ixora, is one such evergreen shrub. It is said to have originated in India and Sri Lanka, but it is now found growing in tropical and subtropical regions all over the world. 15 This plant is reported to have various pharmacological activities, such as antioxidative, antibacterial, gastroprotective, hepatoprotective, antidiarrheal, antinociceptive, antimutagenic, antineoplastic, chemopreventive, and anti-inflammatory activities. 16
I. coccinea leaves have been shown in earlier in vivo studies to have neuroprotective activity against neurotoxicity caused by AlCl3 and 3-nitropropionic acid in albino rats. 17 Furthermore, its potent antioxidant qualities point to possible advantages in reducing PD. 18 In Wistar rats, the ethanolic extract of I. coccinea flowers demonstrated notable anti-amnesic activity. 19 Additionally, it has been demonstrated that secondary metabolites such as flavonoids, alkaloids, tannins, and phenolic compounds have neuroprotective properties. 20
I. coccinea has neuroprotective qualities, but its extract’s role in PD is still unknown. In order to find PD-related molecular targets and pathways, this work uniquely combines network pharmacology, molecular docking, and in vivo validation. It also links computational predictions with behavioral and biochemical outcomes in a haloperidol-induced Parkinsonian model. Studies conducted in vivo, where the extract successfully reversed the Parkinsonian-like symptoms brought on by haloperidol, provide more evidence for this. These insights lay a solid framework for I. coccinea as a promising natural candidate for developing novel therapeutic approaches for PD.
Materials and Methods
Computational Pharmacology
Screening of Bioactive Components
The literature review, Chemical Entities of Biological Interest (ChEBI;
Drug-likeness and Absorption, Distribution, Metabolism, and Excretion Profile of Bioactives
To forecast the drug-likeness features of the bioactives, Molsoft (
Biological Spectra and Adverse Effect(s) of Bioactives
The PASS Online server (
Acquisition of Targets for Bioactives and PD
Using the keyword “Parkinson’s disease,” targets linked to PD were found in three different databases: the Therapeutic Target Database (TTD;
Gene Ontology, Protein Interaction Networks, and Enrichment Studies
Using STRING (
Protein-pathway-phytoconstituent Network
A network combining bioactives, proteins and pathways was established using Cytoscape version 3.7.2 (
Molecular Docking
Preparation and Optimization of Ligands
The ligands’ two- and three-dimensional molecular structures were obtained in .sdf format from the PubChem database (
Homology Modeling and Target Preparation
In order to determine if the target protein structures were available in the Protein Data Bank (RCSB;
Ligand-protein Docking
To assess their binding affinities with targets implicated in the pathophysiology of PD, all ligand molecules were docked against the target proteins using AutoDock Vina implemented in the PyRx platform (
MD Simulation
Docking analysis binding energies were used to select complexes for molecular dynamics (MD) simulation; the complexes with the lowest binding energies advanced to simulation. GROMACS version 2021 was used to perform MD simulations. 1.6 (
Principal Component Analysis, Dynamic Cross-correlation Matrix, and Free Energy Landscape
Van der Waals forces, electrostatic molecular mechanics energy, polar and non-polar solvation energies resulting from solute-solvent interactions, total gas-phase molecular mechanics energy, overall solvation energy, and the total relative binding energy were evaluated using Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) analysis. 29 A steady MD trajectory was subjected to principal component analysis (PCA) in order to investigate several molecular motion patterns. The degree of correlated or anti-correlated atomic motions between residue pairs was measured using the dynamic cross-correlation matrix (DCCM). The Bio3D package in R Studio was used for both PCA and DCCM studies. The trajectory was projected onto eigenvectors using gmx_anaeig after the covariance matrix was computed and diagonalized with mass-weighting using gmx_covar. The free energy landscape (FEL) was then created using this data using gmx_sham. 30
Experimental Pharmacology
Extraction of I. coccinea Stem
The plant I. coccinea was collected from Mangalore (12.9141° N, 74.8560° E), Karnataka, during March. The plant was officially authenticated and confirmed by Prof. Smitha Hegde, Deputy Director of Nitte University Center for Science and Research, Mangalore. I. coccinea stems were meticulously cleaned, dried in the shade, and then ground into a coarse powder using a mechanical grinder. The powder was macerated in 1400 mL of 95% v/v ethanol for 7 days, filtered through muslin cloth, and then dried in a rotary evaporator (ROTAVAP type PBV-3D). For later research, the dried extract was kept in a desiccator.
Ethical Considerations and Procurement of Experimental Animals
The research protocol was approved by the Institutional Animal Ethics Committee, with the approval number NGSMIPS/IAEC/APR-2024/418. The animal house provided healthy albino Wistar rats of both sexes that weighed between 175 and 200 grams and were between 7 and 9 weeks old. A total of 30 albino Wistar rats, weighing 170–200 g, were divided into five groups of six rats each (Table 1). The animals were kept in polypropylene plastic cages topped with stainless steel grills and maintained in standard environmental conditions of 25 ± 2°C with a 12-hour light/dark cycle. Water and food were freely available to the animals. The animals were fed a typical laboratory pellet diet designed to satisfy the nutritional needs of laboratory rats, which includes balanced amounts of proteins, carbs, fats, vitamins, and minerals. On the final day of the experiment, the animals were allowed unlimited access to water and fasted for 12 hours prior to anesthesia. The animals were acclimated to the lab prior to the studies. Animals exhibiting disease, injury, or abnormal behavioral responses during acclimatization or experimentation were excluded from the study; only healthy animals within the designated age and weight range, exhibiting no signs of illness or abnormal behavioral evaluations and data analysis were carried out by an investigator who was blind to the treatment groups. Animals were randomly assigned to experimental groups using a straightforward randomization technique. As per CCSEA guidelines, isoflurane overdose was used to euthanize the animal on completion of the study to conduct biochemical and antioxidant studies.
The Grouping Structure of the Animals.
Test for Oral Acute Toxicity
Wistar rats were used in the oral acute toxicity test in accordance with the Organization for Economic Cooperation and Development 425 (OECD 425) criteria (Guidelines, 2022). Five rats were given a single oral dosage of extract (2000 mg/kg) after fasting for 4 hours. For 4 hours, toxicity indicators were observed every 30 minutes. For 14 days, the animals were observed every day for any signs of delayed toxicity. Body weights were taken at the conclusion of the 14-day period, and any changes in body weight were noted. Rats given 2000 mg/kg of I. coccinea did not exhibit any morbidity or mortality either at the time of administration or during the observation period. Rats showed no discernible changes in body weight, organ weights, or behavior.
Neurobehavioral Studies
Behavioral Evaluation in the Open Field
The open field test uses a wooden, rectangular, black open field device to track spontaneous locomotor activity. The floor of the apparatus was divided into 16 rectangular squares, with one central square. Each animal was placed at the center of the open field apparatus and allowed to move freely for 5 minutes. The behaviors including ambulation (number of squares crossed), rearing frequency (number of times a rat stands on its hind legs), both central square activity (the number of times the animal traversed the center square) and self-grooming (the number of times the animal licked, cleaned, or scratched its body), were noted. 10
Rotarod Test
The rotarod test evaluates neuromuscular function and motor coordination in rodents. The rotarod apparatus was set to rotate at a constant speed of 10 rpm. The rod had a diameter of 2.5 cm and was positioned 25 cm above the floor to ensure a standardized testing environment. All animals underwent pre-training sessions on the rotarod before the experiment commenced to familiarize them with the apparatus. Following the administration of drugs to their respective groups, each rat was individually placed on the rotating rod and observed for 5 minutes. The fall-off time for each rat was recorded to assess motor coordination. 31
Catalepsy Test
The classic bar test was used to measure catalepsy at 15, 30, 60, 90, and 120 minutes. Animals were positioned on a table with their forelimbs on a horizontal bar (1 cm in diameter) at a height of 6 cm in order to assess catalepsy. The animals were then put on a bar 9 cm above the table. The cataleptic score was determined based on the time taken for the animal to either remove one or both of the front paws from the bar or move its head. If the animal was unable to regain its normal posture within 10 seconds, it was considered cataleptic. 32
Biochemical Estimations
Preparation of Brain Homogenate
On completion of the treatment (22nd day), brains were removed, placed in an ice bath and rinsed in ice-cold saline. Further, the brains were weighed, homogenized, and centrifuged at 10,000 g for 15 minutes. The aliquots of the supernatant were separated for biochemical and antioxidant assays. 33
Estimation of Dopamine Level
A 1000 μg/mL stock solution was made by weighing 200 mg of pure dopamine and dissolving it in 20 mL of purified water. The final concentrations of 8, 16, 24, 32, and 40 μg/mL were obtained by dividing the dopamine solution into aliquots of 0.2, 0.4, 0.6, 0.8, and 1.0 mL and transferring them into a series of 25 mL standard volumetric flasks. Similarly, supernatant of 1mL was transferred to a volumetric flask of 25 mL in order to estimate dopamine in rat brain tissues. Additionally, 1 mL of 0.02 N brominating mixture and 1 mL of 4 N hydrochloric acid (HCl) were added to each flask, and the solutions were agitated. Bromination was given 5 minutes to finish. Each flask was then filled with 1 mL of the 0.1 N potassium iodide solution, and the volume was adjusted to 25 mL using distilled water. Using pure water as the reference baseline, the resultant yellow solution was measured spectroscopically at 280 nm. The dopamine quantity in rat brain homogenate was determined using a calibration curve for the standard. 34
Estimation of Acetylcholinesterase Activity
Ellman’s technique (DTNB) was used to measure the acetylcholinesterase (AChE) activity. 35 A 0.05 mL of supernatant, 3 mL of sodium phosphate buffer (pH 8), 0.1 mL of acetylthiocholine iodide, and 0.1 mL of DTNB made up the assay combination. The absorbance was determined during 2 minutes at 30-second intervals at 412 nm using spectrophotometer (Shimadzu UV-1900 i). Micromoles of acetylthiocholine iodide hydrolyzed per minute per milligram of protein were used to express the results.
where ΔA represents the change in absorbance, Vt is the total volume of the reaction mixture in milliliters, ε is the molar extinction coefficient (M⁻¹ cm⁻¹), d is the path length of the cuvette in centimeters, Vs is the volume of the brain supernatant in milliliters, and P denotes the protein concentration in milligrams per milliliter.
Antioxidant Estimations
Total Protein Content
The protein content was determined using the biuret method with an Agappe diagnostic kit, using bovine serum albumin as the standard.
Measurement of SOD Activity
The superoxide dismutase (SOD) assay is a mixture consisting of 0.1 mL of brain supernatant, 1.2 mL of sodium pyrophosphate buffer, 0.1 mL of phenazine methosulfate, 0.3 mL of nitro blue tetrazolium, and 0.2 mL of nicotinamide adenine dinucleotide. The reaction was initiated by adding nicotinamide adenine dinucleotide and incubated at 30°C for 90 seconds. It was then terminated by the addition of 1 mL of glacial acetic acid. To extract the chromogen, the mixture was stirred vigorously with 4 mL of n-butanol and allowed to settle for 10 minutes. After centrifugation, the n-butanol layer was carefully separated, and the color intensity of the chromogen was measured spectrophotometrically at 520 nm. The SOD activity was expressed as units per milligram of protein. 36
Estimation of Catalase Activity
Catalase (CAT) was measured to evaluate the oxidative stress level in rat brain tissue. The test mixture consisted of 50 μL of 1 M Tris-HCl buffer (pH 8.0), 5 mM ethylenediaminetetraacetic acid (EDTA), 900 μL of 10 mM hydrogen peroxide (H2O2), 30 μL of distilled water, and 20 μL of brain supernatant. Spectrophotometric measurements of the H2O2 breakdown rate were made at 240 nm. According to Badoni et al., 37 CAT activity was measured in micromoles of hydrogen peroxide broken down per minute per milligram of protein.
ΔOD: Absorbance change per minute at 240 nm; V: Sample volume in milliliters; E: H2O2 extinction coefficient (0.071 mmol cm−1).
Estimation of Lipid Peroxidation
Malondialdehyde (MDA) was used as a marker of lipid peroxidation and was measured spectrophotometrically. To the brain tissue homogenate, 500 μL of phosphate buffer (pH 7.4) was added, followed by 300 μL of 30% trichloroacetic acid, 150 μL of 5 N HCl, and 300 μL of 2% (w/v) 2-thiobarbituric acid. The mixture was then heated at 90°C for 15 minutes and then cooled in ice-cold water for 30 minutes. After cooling, the pink-colored supernatant was obtained and centrifuged at 12,000 g for 10 minutes. The absorbance was then measured spectrophotometrically at 532 nm to determine the MDA concentration. 38
ΔA: Absorbance of sample - Absorbance of blank; Vreaction: Total volume of the reaction mixture in mL; ɛ: Extinction coefficient of the MDA-TBA complex (1.56 × 10⁵ M⁻¹ cm⁻¹); d: The cuvette’s path length in centimeters; Vsample: The sample’s volume in milliliters; P: The amount of protein in milligrams per milliliter.
Calculating Reduced Glutathione
Glutathione (GSH) was quantified using the DTNB method, which produces a yellow chromophore that is analyzed spectrophotometrically. To the homogenate, 500 μL of 10% trichloroacetic acid was added and then centrifuged at 2000 g for 10 minutes at 4°C to separate the proteins. 100 μL of this supernatant was mixed with 2 mL of 0.1 M phosphate buffer (pH 7.4), 0.5 mL of DTNB, and 0.4 mL of deionized water, followed by vortex mixing. Absorbance was measured at 412 nm within 15 minutes to determine the GSH concentration. 37
A: The sample’s absorbance at 412 nm; Vt is the total volume of the reaction mixture (mL); ξ is the DTNB-GSH complex’s molar extinction coefficient (typically 13,600 M−1cm−1); Vs is the volume of the sample used in the reaction (mL); and P is the protein concentration (mg/mL).
Histopathological Examination
After the rats were sacrificed, the control and treatment groups’ brains were separated using a cold phosphate-buffered saline solution and fixed in 10% formalin. The samples were examined under a microscope after being stained with eosin and hematoxylin. Cellular shrinkage, apoptosis, and neurodegeneration were evaluated. 39
Statistical Analysis
When applicable, all experimental results are displayed as mean ± SD/SEM. Statistical analysis was done using One-way or two-way analysis of variance (ANOVA), and multiple comparisons test was done using Tukey’s post hoc test. The p value of <.05 was opted as statistically significant. All statistical analyses and graphical representations were created using GraphPad Prism software (version 8.4.3; GraphPad Software Inc., USA). Every experiment was carried out in triplicate (n = 3), unless otherwise noted.
Results
Computational Pharmacology
Screening of Bioactive Components
Forty-eight bioactives were identified in the plant I. coccinea, of which 25 were from the literature review, four from ChEBI, and 19 were from IMPPAT 2.0 database.
Drug-likeness and Absorption, Distribution, Metabolism, and Excretion Profile of Bioactives
The canonical SMILES of all 48 bioactives were imported into Molsoft. Among these, 20 bioactives had a BBB score greater than 3 and were selected for further studies. Notably, 2-methoxy-4-vinylphenol, 3-hydroxyflavone, and methyl palmitate exhibited the highest BBB scores, with values of 4.65, 4.64, and 4.53, respectively. 3-hydroxyflavone possesses a positive drug-likeness score of 0.12. Apart from this, four more bioactive compounds achieved a positive drug-likeness score, with stigmast-4-en-3-one leading at 0.91. The other three compounds, that is, beta-sitosterol, ursolic acid, and oleanolic acid, scored 0.78, 0.66, and 0.37, respectively (Table 2). For compounds with a molecular weight under 500 Da and a hydrogen bond donor and acceptor count of fewer than 5 and 10, respectively, a good drug-likeness score indicates enhanced oral bioavailability through human intestinal absorption. A boiled egg model for the 20 bioactives was generated using SwissADME, illustrating their potential to penetrate the BBB, as depicted in Figure 1. It was revealed that nine bioactives successfully crossed the BBB, while six bioactives exhibited better absorption in the human intestinal tract. Two bioactives fell outside the human intestinal absorption range, and the remaining three were also out of range. Nevertheless, these bioactives were identified as non-substrates of P-glycoprotein (P-gp), suggesting improved drug absorption through the BBB. The 2D structures of BBB-crossing bioactives are shown in Figure 2. The ADME profile for 20 bioactives, as illustrated in the heat map, showed that, except for five bioactives (stigmast-4-en-3-one, beta-sitosterol, lupeol, ursolic acid, and oleanolic acid), the remaining 15 bioactives exhibited better gastrointestinal (GI) absorption. These 15 bioactives include methyl stearidonate, diisooctyl phthalate, 2-methoxy-4-vinylphenol, 3,4-dimethoxy-6-methylpyrocatechol, 4-(3-hydroxy-1-propenyl)-2-methoxy-phenol, stearic acid, oleic acid, octadecadienoic acid, palmitic acid, 3-hydroxyflavone, methyl linoleate, methyl oleate, methyl palmitate, methyl stearate, and myristic acid (Figure 3).
Drug-likeness Score of Bioactives from I. coccinea.
Boiled Egg Model for Bioactives from I. coccinea.
Biological Spectra and Adverse Effect(s) of Bioactives
Of the 20 bioactives, lupeol, methyl palmitate, methyl stearate, 3-hydroxyflavone, and beta-sitosterol showed better PD-related activities. Lupeol exhibited acetylcholine neuromuscular blocking agent activity with Pa and Pi values of 0.72 and 0.004, respectively. Methyl palmitate demonstrated antiparkinsonian and rigidity-relieving activity with Pa and Pi values of 0.642 and 0.011, respectively. Additionally, it showed other activities like tremor relieving, catechol O-methyltransferase inhibitor, and dopamine precursors. Methyl stearate was found to possess acetylcholine neuromuscular blocking agent activity with Pa (0.642) greater than pi (0.011) (Supplementary Table T1). Furthermore, the adverse effects of bioactives crossing the BBB were predicted using ADVERPred. The finding revealed that octadecadienoic acid exhibited myocardial infarction as an adverse effect with a high Pa value of 0.844. It also showed other adverse effects like hepatotoxicity and nephrotoxicity, which were found to be very common among all bioactives. Additionally, 3-hydroxyflavone, stearic acid, and myristic acid exhibited adverse effects like hepatotoxicity, nephrotoxicity and nephrotoxicity with Pa values of 0.738, 0.5, and 0.5, respectively (Table 3).
Adverse Effects of Bioactives from the Plant I. coccinea.
Low High
Screening of Targets for Bioactives and PD
Five thousand and 24 proteins associated with PD were identified from three different databases, that is, TTD, CTD, and GeneCards, with 51 of them being modulated by bioactives from I. coccinea. A total of 71 proteins were identified for all bioactive compounds. A Venn diagram for common targets associated with PD and proteins altered by the bioactives from I. coccinea was illustrated using Venny 2.1.0. A pie chart was displayed to show the distribution of different classes of targets modulated by the bioactives of I. coccinea, which may contribute to the etiology of PD (Figure 4).
(a) Venn Diagram of Active Compound Targets of I. coccinea (IC) and Related Targets of Parkinson’s Disease (PD); (b) Venn Diagram of PD Targets from CTD & TTD & GeneCards Database and Related Targets of Both IC & PD; (c) Venn Diagram of Active Compound Targets of IC, Related Targets of Both IC and PD and Targets of PD; (d) Pie Chart of Different Classes of Targets Modulated by the Bioactives of I. coccinea, Which Are Involved in the Pathogenesis of PD.
Protein–Protein Interaction Network, GO, and Enrichment Analyses
Using the STRING database, a protein interaction network (PPI) was built to investigate the relationships between potential targets (Figure 5). The PPI network consisted of 51 nodes and 229 edges, with an average node degree of 8.98, an average local clustering coefficient of 0.562, the expected number of edges was 66, and the PPI enrichment p value of < 1.0e-16. The nodes symbolize individual proteins, while the edges illustrate the interactions between these proteins. The data for GO analysis were retrieved using the DAVID (Figure 6). 22 CC were found by GO analysis, including “protein containing complex.” (GO:0032991) was found to possess a fold enrichment value of 5.98 and scored the lowest false discovery rate (FDR) of 0.004579 via the modulation of 10 observed genes that is, TOP2A, FABP1, PTPN1, AR, CASP8, CAT, MDM2, RARA, CTNNB1, NR3C1 with a gene percentage of 19.6%. Similarly, 52 MF GO terms were identified, among them “nuclear receptor activity” (GO:0004879) was exhibited to possess a fold enrichment value of 70.09 along with a low FDR of 2.92E-12 via the modulation of 10 observed genes, that is, AR, VDR, RORC, RARA, NR1H3, PPARG, PPARA, NR3C1, ESR2, PPARD, with a gene percentage of 19.60%. Likewise, 143 BP GO terms were identified, among these “long-chain fatty acid transport” (GO: 0015909) was found to possess a fold enrichment value of 145.10 and the lowest FDR of3.78E-07 by the modification of six identified genes that is, FABP1, FABP2, FABP3, FABP4, FABP5, PPARG, with a gene percentage of 11.7% (Supplementary Table T2). Further, DAVID was used to enrich the pathways linked to PD. A total of 90 pathways were obtained from KEGG and REACTOME pathways, of which 28 pathways were associated with PD. Among them, eight pathways were screened from the KEGG pathways, and 20 pathways were retrieved from the REACTOME pathways. The Nuclear Receptor transcription pathway, PPAR signaling pathway, and metabolism of lipids were found to possess the enrichment values 41.01, 23.26, and 5.35, along with the lowest FDR of 6.05E-10, 4.16E-08, and 5.55E-07, respectively (Supplementary Table T3).
Visualization of the Protein–Protein Interaction (PPI) Network of Targets from I. coccinea.
GO Analyses of Regulated Proteins by Bioactives from I. coccinea Presenting Cellular Components, Molecular Function, and Biological Processes.
Protein-pathway-phytoconstituent Network
A protein-pathway-phytoconstituent interaction map was generated for 51 proteins, 25 pathways, and 20 phytoconstituents based on degree (Figure 7). From the node table, the 19 genes with the highest degree were identified, that is, CCL2, PPARA, NFE2L2, HMOX1, VIM, CD86, PPARD, PPARG, NR1H3, FABP1, CASP8, CAT, RAC1, FABP5, FABP4, FABP3, MDM2, PRDX2, and CTNNB1. Among these, CCL2, PPARA, NFE2L2, HMOX1, and VIM were the top 5 genes, with degrees of 16, 16, 10, 10, and 9, respectively. A higher degree value denotes a protein’s more important function inside the network. Here, CCL2 scored the highest degree of 16, along with a closeness centrality value of 0.40, a topological coefficient of 0.18, and an average shortest path length of 2.46. Similarly, PPARA scored a high degree value of 16, with a closeness centrality of 0.42, a topological coefficient of 0.19, and an average shortest path length of 2.34. Likewise, NFE2L2 has scored a high degree of 10, with a closeness centrality of 0.33, and a topological coefficient of 0.22 (Supplementary Table T4).
Molecular Docking
In silico molecular docking with AutoDock Vina at PyRx revealed docking of NFE2L2, CAT, PPARG and PPARD with ursolic acid, 3-hydroxyflavone, beta-sitosterol, and beta-sitosterol showed the least binding energy of −9.9, −9.5, −9.3, and −9.3 kcal/mol, respectively (Figure 8). At least the binding energy of −9.9 kcal/mol was observed in ursolic acid with NFE2L2. Wherein, a single hydrogen bond interaction with one amino acid, that is, -OH with ALA510 and 23 van der Waals forces (GLY511, GLY464, GLY417, VAL512, VAL465, VAL418, ALA466, CYS513, VAL514, VAL561, VAL420, GLY419, VAL369, CYS368, VAL608, THR560, GLY367, ILE559, VAL606, ALA366, GLY558, LEU557, ALA607) were observed. The binding energy of 3-hydroxyflavone with CAT was found to be −9.5 kcal/mol. This showed two hydrogen bond interactions with one amino acid, that is, =O and -OH with TYR358. Further, one of the aromatic rings of 3-hydroxyflavone was involved in a pi-pi bond with the same amino acid, that is, TYR358 and a pi-alkyl interaction with ARG72. While the other two rings showed 4 π-π interactions with VAL146, ALA133, and HIS75. It showed 11 van der Waals forces (THR361, ARG365, HIS362, PHE334, ARG112, SER114, PHE132, GLY131, ARG354, VAL74, VAL73). Beta-sitosterol was identified to possess a binding energy of −9.3 kcal/mol with PPARG. No hydrogen bond interactions were observed. However, six pi bonds were observed, two at position 4, residues MET348 and ILE341, two aromatic rings possessed two pi bonds with ARG280, and one aromatic ring with an OH group attached at position 23 showed two bonds with HIS266 and PHE287. It is also observed to possess 14 van der Waals forces (LEU255, GLU259, ILE249, ILE281, GLY258, ASP260, CYS285, ARG288, SER342, GLY284, PHE264, THR268, GLU272, GLN283). Along with PPARG, beta-sitosterol showed a binding energy of −9.3 kcal/mol with PPARD. Here, no hydrogen bond was observed, but it showed to possess 12 pi bonds, that is, 2 pi bonds were observed at atom number 1 with PHE246 and CYS249 and three bonds between atom numbers 4 & 5 with HIS413, PHE291 and CYS249. While two of the aromatic rings of beta-sitosterol showed 7 pi bonds, that is, one ring showed 3 bonds with amino acids LEU303, LEU317, and CYS249 and the other showed 4 bonds with VAL312, ARG248, VAL305, and CYS249 amino acids. 12 van der Waals forces (LEU294, THR253, LYS331, ILE290, GLN250, ILE327, ILE297, ILE328, THR252, ILE213, TRP228, VAL245) were also identified (Supplementary Table T5 & T6).
2D and 3D Interaction of (a) NFE2L2 with Ursolic Acid, (b) CAT with 3-hydroxyflavone, and (c) PPARG with Beta-sitosterol When Docked in AutoDock Vina.
MD Simulation
NFE2L2 complexed with ursolic acid, CAT complexed with 3-hydroxyflavone, and PPARG complexed with beta-sitosterol and with their respective standards omaveloxolone, fomepizole, and pioglitazone were examined for their respective binding free energies and relative stability in the receptor’s active site.
Ursolic acid-NFE2L2 Complex
The ursolic acid-NFE2L2 complex’s RMSD fluctuated between ~0.8 Å and ~6.5 Å. The backbone and complex remained stable up to around 38 ns, after which the RMSD complex exhibited unstable fluctuations ranging from ~2 Å to ~6.5 Å. The omaveloxolone-NFE2L2 complex displayed RMSD fluctuation from ~0.7Å to ~2 Å throughout the MD run with a difference of ~1 Å. The RMS fluctuation of ursolic acid-NFE2L2 complex was in the range of 0.36 Å to 3.35 Å; the residue PRO384 displayed a maximum fluctuation of ~3.35 Å. However, PRO384 was not involved in the interaction with the ligand, and MET503 showed a minimum fluctuation of 0.36 Å. RMSF of omaveloxolone-NFE2L2 complex showed fluctuation in the range of 0.34 Å to 2.66 Å; residue ASP385 showed a maximum fluctuation of 2.66 Å. However, ASP385 was not involved in the ligand interaction, and VAL475 showed a minimum fluctuation of 0.34 Å. The Radius of gyration (RoG) of the ursolic acid-NFE2L2 displayed a maximum fluctuation of ~0.3 Å for both complex and backbone; the RoG complex showed an unstable fluctuation of ~0.75 Å after 50 ns. The RoG of the omaveloxolone-NFE2L2 complex displayed stable fluctuation of ~0.3 Å for both backbone and complex throughout the MD run. Additionally, Solvent accessible surface area (SASA) displayed a fluctuation in the range of ~119 to ~135 nm 2 for both ursolic acid-NFE2L2 and omaveloxolone-NFE2L2 complex. The number of hydrogen bond analyses of ursolic acid-NFE2L2 showed a maximum of three unstable hydrogen bonds. Initially, one stable bond was observed after 10 ns and later again, after 60 ns, a stable bond was formed, which broke throughout the MD run. While omaveloxolone-NFE2L2 complex showed the presence of two stable hydrogen bonds throughout the MD run. Total energy decomposition analysis by MMPBSA of ursolic acid-NFE2L2 showed MET499 to possess an energy contribution of −0.46 Kcal/mol. However, VAL465, GLY367, and VAL512 possessed energy contribution against the interaction. While the omaveloxolone-NFE2L2 complex displayed CYS368, VAL514, and VAL369 to possess energy contributions of −1.22, −1.11, and −1.03 Kcal/mol, respectively. However, VAL418 and VAL465 possessed energy contributions against the interaction (Supplementary Figure 1; Movie M1).
3-hydroxyflavone-CAT Complex
The complex 3-hydroxyflavone with CAT displayed a fluctuation in the range of ~2.2 Å to ~9.2 Å throughout the MD run with a difference of ~0.3 Å. In contrast, the CAT with its typical fomepizole varied between about 2.2 Å and around 9.9 Å. The 3-hydroxyflavone-CAT complex’s RMSF fluctuated between 0.61 Å and 13.8 Å; ARG47 had the highest RMSF of 13.8 Å, although it was not engaged in the ligand interaction, while GLY216 had the lowest variation of 0.61 Å. The RMSF of fomepizole-CAT fluctuated between 0.56 Å and 10.65 Å; the RoG of 3-hydroxyflavone-CAT showed a maximum fluctuation of ~26.9 Å, which decreased after 31 ns; PRO46 showed a maximum RMSF of 10.65 Å, although it was not involved in ligand interactions; and GLY214 showed the minimum fluctuation of 0.56 Å. However, after 5 ns of MD run, the highest variation of ~2.58 Å in the RoG of fomepizole-CAT was reduced. An increase in compactness is shown by the decrease in RoG in the 3-hydroxyflavone-CAT and fomepizole-CAT complexes. 3-hydroxyflavone-CAT’s SASA varied between about 255 and 303 nm2, whereas fomepizole-CAT’s SASA varied between approximately 250 and 285 nm2. Two of the three hydrogen bonds that could be seen were unstable. Nevertheless, the hydrogen bond’s consistency rose after 50 ns of MD run before declining after 85 ns. GLU330, VAL146, and TYR358 had energy contributions of −0.91, −0.66, and −0.58 Kcal/mol, respectively, according to the MMPBSA of 3-hydroxyflavone-CAT. ARG112, ARG72, and ARG365, on the other hand, displayed energy contributions against the interaction of 3.54, 2.47, and 0.62 Kcal/mol, respectively. However, the typical complex revealed that ARG72 had an energy contribution of −0.8 Kcal/mol (Figure 9; Movie M2).
Beta Sitosterol-PPARG Complex
Throughout the MD run, the beta-sitosterol-PPARG complex’s RMSD fluctuated between ~2 Å and ~10.2 Å. On the other hand, the pioglitazone-PPARG complex fluctuated between around 1.5 Å and 10 Å. Both complexes’ RMSD backbones and complexes varied by around 8 Å. The RMSF fluctuation ranged from 0.4 Å to 4.98 Å, with residue ILE267 showing the largest change of 4.98 Å despite not being engaged in the interaction. The residue ILE386 exhibited a 0.4 Å minimum fluctuation. Pioglitazone-PPARG’s RMSF fluctuation ranged from 0.41 Å to 3.61 Å, with PRO206 showing a high fluctuation of 3.61 Å and ILE386 showing a minimum fluctuation of 0.41 “Å.” But none of them participated in the exchange. For both complexes, the RoG showed a steady variation during the MD run. Beta-sitosterol-PPARG’s SASA varied between around 135 and 158, while the typical complex showed comparable variations in the same range. Before 20 ns, beta-sitosterol-PPARG displayed a maximum of two hydrogen bonds. One bond got fragile after that. But after 70 ns, a stable hydrogen bond was seen. During the MD run, however, the typical complex displays three stable hydrogen bonds. PHE287, GLN283, and PHE264 showed energy contributions of −1.41, −0.72, and −0.71 kcal/mol, respectively, according to the total energy decomposition using MMPBSA. On the other hand, GLU259, GLU272, and ARG280 had energy contributions that were in opposition to the interaction, with values of 2.11, 1.07, and 0.97 kcal/mol, respectively. GLU259, ILE341, and CYS285 showed energy contributions of −2.95, −1.37, and −1.31 kcal/mol, respectively, according to the typical pioglitazone-PPARG complex. LYS261 and ARG280, on the other hand, displayed energy contributions against the interaction of −1.41 and −1.20, respectively (Figure 10; Movie M3).
Principal Component, Dynamic Cross-correlation Matrix Analysis, and FEL
To elucidate the major conformational transitions during the MD simulation, PCA was performed on the backbone atoms of the protein. PCA reduces the dimensionality of the simulation data by projecting the atomic displacements onto a set of orthogonal vectors (Principal components; PC), each associated with a corresponding eigenvalue that quantifies the amount of variance explained. The first two PC1 and PC2 accounted for a substantial portion of the overall motion, explaining 37.24% and 17.09% of the total variance, respectively (Figure 9g). The 2D projection of the trajectory along PC1 and PC2 revealed distinct clustering of conformational states, indicating significant transitions in the structural ensemble over the simulation time. The frames are color-coded from blue to red, likely corresponding to early to late simulation snapshots, suggesting a directed conformational shift across the simulation timeline. A pronounced inflection (elbow) was observed after the second principal component, highlighting that the majority of the system’s essential dynamics are confined to the first few eigenvectors. The cumulative variance explained by the first four components reached approximately 68.5%, beyond which additional components contributed only marginally to the total variance (Figure 9). The projection of the MD trajectory along the PC1 and PC2 is shown in Figure 9g. PC1 and PC2 accounted for 19.29% and 9.71% of the total atomic fluctuations, respectively. The trajectory points, color-coded from blue to red, revealed a smooth transition in conformational space, suggesting stable but continuous movement of the complex toward a new equilibrium state. The absence of abrupt jumps or random clustering reflects the conformational convergence and stability of the complex during simulation. The eigen vector for beta sitosterol-PPARG complex displayed. The first five components collectively explain approximately 54.6% of the total motion, with diminishing returns from higher-ranked eigenvectors. The steep drop in variance from PC1 to PC3, followed by a gradual decline, suggests that the system’s essential dynamics are effectively described by the first few eigenvectors. The DCCM map of the β-sitosterol-PPARγ complex shows several regions of strong positive correlation, particularly along the diagonal, indicating tightly coordinated local motions within secondary structure elements. Additionally, the presence of distinct blue off-diagonal blocks suggests long-range anti-correlated movements, possibly due to allosteric effects induced by ligand binding (Figure 10).
Experimental Pharmacology
Extraction of I. coccinea Stem
The extraction of I. coccinea yielded a brownish color-died extract with a percentage yield of 13.6% (w/w). The dried extract was stored in a desiccator and dissolved in distilled water prior to use in animal experiments.
Neurobehavioral Studies
Open Field Test
Comparing the haloperidol group to the normal control, the open field test findings showed substantial behavioral impairment in all parameters, including ambulation, rearing frequency, self-grooming, and center square activity (p < .05), indicating locomotor and exploratory deficits due to haloperidol-induced Parkinsonism. The Syndopa-treated group displayed marked improvement in all four parameters in contrast to the haloperidol-treated group (p < .05). Similarly, low-dose and high-dose groups displayed significant and dose-dependent improvement in all behavioral parameters in contrast to the haloperidol-treated group (p < .05). Specifically, the high-dose group showed a more pronounced improvement compared to the low-dose group (Figure 11).
Rotarod Test
The rotarod test results revealed a marked reduction in the latency to fall in the haloperidol-treated group, in contrast to the normal control group (p < .05), indicating motor incoordination and muscle rigidity induced by haloperidol. In contrast, the Syndopa-treated group exhibited a marked improvement in fall-off time (p < .05). Likewise, I. coccinea at 200 and 400 mg/kg displayed a significant increase in latency to fall compared to the haloperidol group (p < .05). High-dose showed more pronounced improvement in latency to fall (60.40 ± 2.14; p < .05) compared to the low-dose group (44.97 ± 2.24; p < .05) (Figure 12).
Catalepsy Test
The haloperidol-induced catalepsy model showed a marked increase in cataleptic score at all time points in the haloperidol-treated group compared to the normal control group (p < .05). Administration of Syndopa significantly reduced cataleptic scores compared to the haloperidol-treated group (p < .05). Treatment with low-dose and high-dose groups displayed a marked decrease in cataleptic score in contrast to the haloperidol-treated group (p < .05), exhibiting a dose-dependent but partial reversal of catalepsy (Figure 12).
Biochemical Estimations
Estimation of Dopamine
Dopamine levels were significantly lower in the haloperidol-treated group (0.193 ± 0.008) than in the normal control group (p < .05), suggesting dopaminergic neuronal injury.
Syndopa treatment significantly restored dopamine levels to 0.716 ± 0.012 (p < .05 vs. disease). Dopamine levels were also markedly increased by I. coccinea at 200 and 400 mg/kg (p < .05). Wherein, high-dose of I. coccinea exhibited a dopamine level of 0.643 ± 0.008; p < .05, than low-dose of I. coccinea 0.466 ± 0.012; p < .05 (Figure 13).
Estimation of Ache Activity
According to the results of the AChE activity assay, there was a significant increase in enzyme activity (0.169 ± 0.001) in comparison to the normal control group (p < .05), indicating greater cholinergic dysfunction. Treatment with Syndopa significantly decreased AChE activity to 0.113 ± 0.004 (p < .05). Similarly, both low and high doses significantly reduced AChE activity, with high-dose exhibiting a marked reduction of 0.126 ± 0.003, close to the Syndopa-treated group (p < .05) (Figure 13).
Antioxidant Estimations
The oxidative stress biomarkers SOD, CAT, MDA, and GSH showed significant alterations in the haloperidol-induced PD. Comparing the haloperidol-treated group to the normal control group, there was a significant rise in MDA levels and a decrease in SOD, CAT, GSH, and total protein content (p < .05). Administration with Syndopa significantly reversed the changes induced by haloperidol (p < .05 vs. haloperidol-treated group). Syndopa significantly enhanced SOD, CAT, GSH levels, and total protein content, while decreasing MDA levels (p < .05 vs. haloperidol-treated group). Similarly, treatment with I. coccinea at 200 and 400 mg/kg exhibited marked improvement in SOD, CAT, GSH level, and total protein content, and decreased MDA level in a dose-dependent manner (p < .05 vs. Syndopa-treated group) (Figure 14).
Histopathological Examination
The brain tissue section of the normal group exhibits normal histoarchitecture with intact neurons and glial cells. No signs of neurodegeneration, cellular shrinkage, or vacuolation are observed. Neurons are clearly visible with prominent nuclei, and the neuropil appears dense and undisturbed. In the haloperidol-treated group, there were marked neurodegenerative changes observed. The section shows extensive neuronal loss, cytoplasmic vacuolization, and pyknosis in neurons. The tissue appears disorganized with disrupted neuronal morphology, indicating the neurotoxic effects of haloperidol. Treatment with Syndopa shows notable protective effects. The brain section reveals comparatively preserved neuronal architecture with minimal signs of degeneration. Mild vacuolization may be observed, but overall cellular arrangement and neuronal density are improved compared to the disease group. The section of the low-dose treated group displayed partial restoration of brain tissue architecture. Neuronal morphology appears better than in the disease group, but is not completely restored. There is moderate vacuolization, and fewer healthy neurons are visible. This suggests mild neuroprotective effects at the low dose. Significant neuroprotective effects are evident. The tissue shows well-preserved neurons with minimal vacuolization and no obvious signs of degeneration. The neuronal density and integrity appear close to the normal control group, suggesting the high dose is effective in reversing haloperidol-induced neurotoxicity (Figure 15).
Effect of Ethanolic Extract of I. coccinea on Histopathological Alterations in the Brain Tissue. (a) Normal Control; (b) Disease Group; (c) Standard Group; (d) Low-dose; (e) High-dose.
Discussion
The loss of dopaminergic neurons in the substantia nigra is the hallmark of PD, an age-dependent neurodegenerative condition. 40 The susceptibility of dopaminergic neurons in PD is known to be caused by oxidative stress, mitochondrial dysfunction, and protein mishandling, even if the control of dopaminergic neuronal transmission in PD is still unclear. 5 In the current study, we used two distinct methods: in vivo to evaluate the impact of I. coccinea against haloperidol-induced PD by analyzing neurobehaviors, biochemical, antioxidant, and histopathology of the cortex region of the brain, and in silico to predict the molecular mechanism against PD.
An established animal paradigm for evaluating possible neuroprotective medicines is haloperidol-induced Parkinsonism. 41 Haloperidol has been used in several studies to cause PD-like symptoms.10,11,40 Haloperidol successfully induced Parkinsonism-like symptoms in rats, as evidenced by motor deficits, catalepsy, and oxidative stress. 42 During the 21 days of study, the effectiveness of the ethanolic extract of I. coccinea led to the reversal of neurobehavioral impairments. The rats showed a significant improvement in the rotarod fall-off time as well as the open field test parameters of ambulation, rearing frequency, self-grooming, and central square activity. Additionally, the catalepsy test score decreased. The biochemical test assessed two important neurotransmitters that are found in the basal ganglia and are essential for motor function: dopamine and acetylcholine. 43 Administration of haloperidol caused dopamine depletion by blocking D2 receptors. 44 The biochemical estimation confirmed the enhancement of the dopamine level when compared to the haloperidol-treated group. Furthermore, AChE levels estimation reflected the reduction in the acetylcholine level in the haloperidol-induced group due to increased AChE activity, which is associated with cognitive deficits. 45 Further, haloperidol generated oxidative stress in the brain, as reflected by the increased MDA and reduced CAT, SOD, and GSH levels. On the other hand, I. coccinea therapy at both low and high doses decreased MDA while dose-dependently raising levels of CAT, SOD, GSH, and total protein. The bioactive substances found in I. coccinea may be the cause of this reduction in oxidative stress.
In the computational approach, drug-likeness properties and ADME characterization of bioactives from I. coccinea displayed 20 bioactives to cross the BBB, with 3-hydroxyflavone exhibiting the highest BBB permeability score and a positive drug-likeness profile. Similarly, 2-methoxy-4-vinylphenol showed the second-highest BBB permeability score and good GI absorption. Stigmast-4-ene-3-one, beta-sitosterol, and ursolic acid, with the highest positive drug-likeness profile, indicated their superior bioavailability. This may contribute to the neuroprotective effect shown by I. coccinea, which was evidenced in the haloperidol-induced Parkinsonism. According to research by Fu et al., 46 ursolic acid lowers oxidative stress through a variety of anti-inflammatory and antioxidant mechanisms, such as enhancing endogenous antioxidant defenses and activating the Nrf2/HO-1 pathway.46,47
Further, plant sterols like beta-sitosterol and stigmast-4-en-3-one have antioxidant and anti-inflammatory effects. Beta-sitosterol has been studied for neuroprotective effects in some neurodegenerative models. 48 Further, the results from the boiled egg model corroborated the ability of several bioactives to cross the BBB, with 3-hydroxyflavone and eight other compounds successfully crossing the barrier. The drug-likeness characteristics of a molecule help to identify the potential druggable molecules present in the plant extract, indicated by positive DLS scores. In a similar vein, the BBB permeability is a crucial metric when examining a drug’s capacity to reach the illness site, such as the brain; molecules with a BBB score higher than three are more likely to pass the BBB. Hence, in this study, we screened molecules that possessed druggable properties and could cross the BBB.
Notably, all identified bioactives were found to be non-substrates of P-gp, a critical efflux transporter known to restrict drug penetration across the BBB. This characteristic enhances the likelihood of efficient drug penetration into the brain, as the absence of P-gp substrate properties mitigates the risk of efflux-mediated exclusion. Despite their high drug-likeness scores, the ADME profile revealed that stigmast-4-en-3-one and beta-sitosterol exhibited suboptimal GI absorption. The biological spectra analysis illuminated the PD-related activities of specific bioactives, with lupeol, methyl palmitate, and methyl stearate demonstrating significant potential. The prediction of adverse effects presents a critical dimension to the assessment of bioactives. Octadecadienoic acid’s association with myocardial infarction and other toxicities raises significant concerns regarding its therapeutic viability. Similarly, hepatotoxicity and nephrotoxicity were the predicted adverse effects in compounds 3-hydroxyflavone and stearic acid, necessitating rigorous safety evaluations.
After preliminary screening of bioactives from I. coccinea, a protein–protein interaction network was constructed, followed by gene set enrichment analysis using Cytoscape. The network analysis revealed oleic acid to be a potential bioactive responsible for the activity, which may be directed via the pathway “Metabolism of lipids” by regulating PPARA and CCL2. In addition, phyto molecules like palmitic acid, myristic acid and steric acid displayed high edge count, indicating their promising potential in the treatment of PD. According to research by Ubaid et al., the camel α-LA and oleic acid compound may prevent PD via modifying SIRT1. 49
However, SIRT1 was not identified as a potential protein in our network. The anti-Parkinson potential of I. coccinea by reducing the oxidative stress induced by haloperidol may be due to oleic acid, which influences PD through multiple mechanisms by enhancing dopamine production, exhibiting neuroprotective effects in its nitrated form by inhibiting α-synuclein aggregation, while its unmodified form may exacerbate α-synuclein toxicity, highlighting the therapeutic potential of modulating oleic acid metabolism and fatty acid balance.50,51 Because of their neuroprotective qualities, peroxisome proliferator-activated receptors—especially the alpha and gamma isoforms—have become viable therapeutic targets for PD. PPARA’s participation in neuroinflammation, oxidative stress, mitochondrial function, and lipid metabolism—all of which are crucial in the pathophysiology of PD—has drawn attention to its role in the disease. Pro-inflammatory cytokines, including TNF-α, IL-1β, and IL-6, are known to be suppressed by PPARA activation, which further decreases microglial activation. Furthermore, via regulating NFR2, which in turn controls other antioxidant indicators like SOD, CAT, and GSH in ganglionic neurons, PPARA is known to contribute to the decrease of oxidative stress. According to reports, PPARA increases the beta-oxidation of fatty acids in peroxisomes and mitochondria, which eventually lowers oxidative stress and increases.
Molecular docking was performed using all the screened phyto molecules and potential proteins. Molecular docking has been identified as a robust technique to predict the interaction of ligands with targets. Molecular docking not only helps us to identify the binding affinity, but it also helps to assess the potential amino acids involved in the interaction. The molecular docking analysis revealed three complexes to possess the highest binding affinity, that is, ursolic acid-NFE2L2, 3-hydroxyflavone-CAT, and beta-sitosterol-PPARG. To confirm the interaction in real-time, MD simulations play an important role; we performed molecular simulations for all three complexes for 100 ns. The simulation results displayed the CAT-3-hydroxyflavone and PPARG-beta-sitosterol complex to be stable throughout the simulation. This indicates that PPARG and CAT are the potential genes through which the plant extract exhibits anti-Parkinsonism potential.
PPARG is a crucial regulator of PD, which modulates neuroinflammation, oxidative stress, mitochondrial activity, and dopaminergic neuron survival. It suppresses microglial activation and reduces pro-inflammatory cytokines such as TNF-α and IL-6, thereby protecting neurons from inflammatory damage. Additionally, PPARG improves energy metabolism and lowers oxidative stress by promoting mitochondrial biogenesis through PGC-1α. It enhances dopaminergic neuronal survival by regulating apoptosis-related genes. Moreover, the activation of PPARG enhances autophagy, promoting the clearance of alpha-synuclein and preventing toxic aggregation. 52
CAT is an essential antioxidant enzyme that protects dopaminergic neurons against oxidative stress-induced damage by converting hydrogen peroxide into water and oxygen, thereby preventing the buildup of ROS that causes mitochondrial dysfunction and neuronal apoptosis. 53 Research conducted by Ambani et al. demonstrated that PD patients frequently display decreased CAT activity in the substantia nigra and other brain areas impacted by neurodegeneration. The reduced CAT levels make oxidative stress worse, which causes neuronal death. 54
The modulation of these targets may contribute to the neuroprotective efficacy of I. coccinea. Activation of PPARG mitigates neuroinflammation, enhances mitochondrial function, and promotes dopaminergic neuronal survival, while the upregulation of CAT activity effectively reduces oxidative stress by detoxifying ROS. Together, these mechanisms contribute to the attenuation of neuronal damage and the preservation of motor and cognitive functions, highlighting the therapeutic potential of I. coccinea in treating PD.
Although I. coccinea demonstrated promising neuroprotective effects through integrated in vivo and in silico investigations, the present study has certain limitations. The reliance on a single experimental model, namely haloperidol-induced Parkinsonism, may not comprehensively represent the multifactorial and progressive pathology of human Parkinson’s disease. While this model effectively replicates core features such as dopaminergic dysfunction, motor impairments, and oxidative stress, it fails to fully encompass disease progression, non-motor manifestations, and clinical heterogeneity. Consequently, the translational applicability of these findings should be interpreted with caution, and further validation across multiple disease models and clinical settings is necessary.
Conclusion
The present study demonstrated the neuroprotective potential of I. coccinea against haloperidol-induced Parkinsonism through a synergistic integration of in silico prediction and in vivo validations. Computational screening predicted that compounds like oleic acid, ursolic acid, beta-sitosterol, and 3-hydroxyflavone may contribute to the neuroprotective effect owing to their high bioavailability and ability to cross the BBB. Molecular docking and MD simulation confirm the modulation of crucial targets like PPARG and CAT, further supporting their involvement in neuroprotection. Additionally, in vivo findings exhibited that the ethanolic extract of I. coccinea ameliorates the motor dysfunction, reverses the dopamine level, reduces AChE activity and oxidative stress. Together, these findings highlight that I. coccinea holds the promising therapeutic potential in the management of PD.
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Footnotes
Acknowledgements
Authors are heartily thankful to NGSM Institute of Pharmaceutical Sciences (NGSMIPS), Nitte (Deemed to be University), for providing facilities for molecular dynamic simulations.
Authors’ Contribution
Prarambh S. R. Dwivedi and Nimmy Varghese contributed to the concept and design of the study and provided supervision. Varshitha and Tanvi S were responsible for data acquisition. Varshitha, Prarambh S. R. Dwivedi, Pukar Khanal, and Tanvi S performed data analysis and interpretation. Varshitha, Prarambh S. R. Dwivedi, and Rithin R drafted the manuscript. Prarambh S. R. Dwivedi, Nimmy Varghese, and Pukar Khanal critically revised the manuscript for important intellectual content. Statistical analysis was carried out by Varshitha, Prarambh S. R. Dwivedi, Rithin R, and Tanvi S. Pukar Khanal and Rithin R provided administrative, technical, and material support. Prarambh S. R. Dwivedi and Nimmy Varghese provided final approval of the manuscript.
Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author, PSRD & NV, upon reasonable request.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethical Approval
The research protocol was approved by the Institutional Animal Ethics Committee (IAEC), NGSM Institute of Pharmaceutical Sciences (NGSMIPS), Nitte (Deemed to be University), with the approval number NGSMIPS/IAEC/APR-2024/418.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Informed Consent
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Use of Artificial Intelligence-assisted Tools:
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References
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