Comprehensive Evaluation of Methanolic Fruits Extract of Jatropha gossypifolia L.: Neuropharmacological, Cytotoxic, Anthelmintic, GC–MS Profiling, and Molecular Docking Studies

ABSTRACT

Jatropha gossypifolia L., a member of the Euphorbiaceae family, has been traditionally used in the treatment of various ailments. However, its neuropharmacological, cytotoxic, and anthelmintic potentials have not been thoroughly investigated. The methanolic fruit extract of J. gossypifolia (JGF‐ME) was evaluated for anxiolytic activity using the Elevated Plus Maze (EPM), Hole Board Test (HBT), and Light–Dark Box Test (LDT); antidepressant activity using the Forced Swimming Test (FST) and Tail Suspension Test (TST); and sedative effects through the Open Field and Hole Cross tests. Cytotoxicity was assessed via the Brine Shrimp Lethality Assay (BSLA), and anthelmintic activity was evaluated against Pheretima posthuma. GC–MS was used for phytochemical screening, followed by molecular docking and ADME/T analyses of the identified compounds. JGF‐ME exhibited a significant, dose‐dependent anxiolytic effect in EPM, HBT, and LDT. At 400 mg/kg, it significantly reduced immobility in FST and TST (p < 0.001), indicating significant antidepressant activity. The extract also exhibited notable sedative effects, as evidenced by reduced locomotor activity in the Open Field and Hole Cross tests at doses of 200 and 400 mg/kg. In BSLA, JGF‐ME displayed moderate cytotoxicity (LC₅₀ = 327.87 μg/mL) compared to colchicine (LC₅₀ = 38.81 μg/mL). It also produced a dose‐dependent anthelmintic effect by paralyzing and killing P. posthuma. Molecular docking revealed high binding affinities of the identified compounds to selected human receptors, and in silico analysis suggested acceptable drug‐likeness; experimental validation is needed. The study confirms the neuropharmacological, cytotoxic, and anthelmintic potential of J. gossypifolia extract, supporting its traditional use and therapeutic promise.

The anxiolytic properties of the methanolic fruit extract of Jatropha gossypifolia (JGF‐ME) were investigated using the Elevated Plus Maze (EPM), Hole Board Test (HBT), and Light–Dark Box Test (LDT), while its antidepressant potential was examined through the Forced Swimming Test (FST) and Tail Suspension Test (TST). Sedative effects were determined using the Open Field and Hole Cross tests. Cytotoxic activity was evaluated using the Brine Shrimp Lethality Assay (BSLA), and anthelmintic efficacy was tested against Pheretima posthuma. Phytochemical constituents were identified through GC–MS analysis, followed by molecular docking and ADME/T predictions of the detected compounds.

1. Introduction

Globally, anxiety and depression represent some of the most widespread mental health issues [1, 2]. Selective serotonin reuptake inhibitors (SSRIs) and benzodiazepines, acting indirectly on GABA_A receptors, are the main pharmacological agents prescribed to manage anxiety [3]. SSRIs are also commonly prescribed for depression [4]. However, long‐term use of benzodiazepines can lead to tolerance, and discontinuation may cause withdrawal symptoms [5]. Similarly, prolonged use of SSRIs is associated with significant side effects [6]. Insomnia is closely linked to both mental and physical health, with research showing that insomnia accompanied by short sleep duration activates the stress response system and physiological hyperarousal. These factors can harm overall health by elevating the threat of neurocognitive and cardiometabolic diseases, along with mortality [7]. Insomnia shows a stronger connection with mental health disorders than physical illnesses, and benzodiazepine receptor agonists remain the most effective medication [8, 9, 10]. Their use raises concerns due to potential side effects like habituation, tolerance, and addiction. Additionally, nonmedical issues such as drug abuse further complicate their use [11]. Given these challenges, there is a growing need for traditional medicine as an alternative treatment. Cancer is a life‐threatening disease characterized by the uncontrolled and invasive proliferation of cells, leading to the formation of tumors or malignancies in the body [12]. It ranks as the second leading cause of death globally, after heart disease [13]. Approximately 60% of anticancer drugs are derived from natural sources, such as plants, microorganisms, and marine organisms [14]. Medicinal herbs, in particular, are valued for their antioxidant and immunomodulatory properties, contributing to their anticancer effects. These drugs work by inhibiting or slowing the progression and spread of cancer. Researchers are actively exploring plant‐derived compounds to uncover novel pathways for cancer treatment. Plant‐based drugs are often preferred due to their relatively fewer side effects, and ongoing studies aim to identify key active ingredients that can effectively target various types of cancer [13].

Parasitic worms, or helminths, that inhabit the gastrointestinal (GI) tract pose a serious global health concern, affecting over a billion individuals, mainly in developing nations, through soil‐transmitted infections. These parasites also impact livestock, reducing productivity worldwide, leading to major economic losses and threatening food security [15, 16, 17]. Presently, helminth management depends largely on a small range of synthetic anthelmintic drugs. Such reliance brings challenges, including the increasing risk of drug resistance, which is already a concern in certain livestock populations [18, 19]. Additionally, the high cost of these medicines makes them less accessible to small‐scale farmers in poorer regions, and some treatments have limited effectiveness [20]. Therefore, there is a pressing demand for innovative and alternative methods to control helminth infections effectively.

Computer‐simulated screening is a key strategy for understanding the pharmacological mechanisms of phytochemicals [21]. Molecular docking plays a vital role in this process by facilitating the design and development of novel medicinal compounds through computer‐assisted drug development methods. Efficient molecular docking allows the native ligand to identify the binding site on a protein's three‐dimensional structure and interact with it through various physicochemical interactions. This technique promotes a greater understanding of molecular interactions and supports the creation of more effective therapeutic agents [22].

Natural products have attracted increasing attention worldwide due to their diverse range of bioactive components and low toxicity, making them excellent resources for the development of new therapeutic agents and the search for new drugs [23, 24, 25]. Jatropha gossypifolia, a member of the Euphorbiaceae family, is a widely distributed plant in tropical gardens and has a long history of use in traditional medicinal practices. It is used in traditional Chinese, Ayurvedic, and Thai medicine for treating conditions such as fever, pain, and dysentery [26]. In Nigeria, the fresh leaf aqueous extract is used in folk medicine to treat mouth cancer and control bleeding from the skin or nose, while the stem is used as a toothbrush for maintaining dental health [27, 28]. In India, the leaves are utilized for preventing and treating various ailments, including dysentery, eczema, diarrhea, and itches [29]. In Trinidad and Tobago, decoctions of J. gossypifolia have proven effective for wound healing, pain relief, and treating snake bites [30]. Previous studies have demonstrated that the plant exhibits potent antioxidant and antibacterial properties [31]. The plant contains bioactive compounds like phenolics, flavonoids, and alkaloids, as shown by chemical analyses [29, 30]. Additionally, Aboaba et al. [32] identified linalool, phytol, and germacrene as some of the key compounds of the leaf volatile oil in J. gossypifolia. The methanolic fruit extract of J. gossypifolia exhibited significant analgesic, anxiolytic, sedative, and antidiarrheal properties in preliminary in vivo tests [33]. Additionally, the aqueous ethanolic extract of J. gossypifolia fruit showed mild anticonvulsant activity in recent research [34]. These findings highlight the plant's healing properties and its diverse applications in traditional medicine.

This study presents a unique investigation of J. gossypifolia fruit, as it is the first comprehensive evaluation of the fruit's methanolic extract for anxiolytic, antidepressant, sedative, cytotoxic, and anthelmintic effects, combining GC–MS phytochemical profiling of the fruit extract with in vivo and in silico pharmacological validation.

2. Materials and Methods

2.1. Plant Collection and Identification

One type of Jatropha gossypifolia fruit (nearly 5 kg) was collected fresh from the Botanical Garden of the University of Chittagong. Mr. Muhammad Forkanul Hamid of the University of Chittagong's Department of Fisheries confirmed the plant's identification. The collected fruits were thoroughly cleaned and dried in the shade before being processed into a fine powder using a grinder. A voucher sample (Accession no: BFRIH‐471/SA) was stored in the Department of Fisheries, University of Chittagong, Bangladesh, for future use.

2.2. Preparation of Fruit Extract

Fruits of J. gossipifolia were separated, rinsed twice or three times with distilled water to remove dirt, and then dried at 50°C for 2 h to eliminate any moisture that remained. The dried fruits were ground in a food processor or grinder. The resulting finely powdered fruits were extracted in 100 mL of 70% methanol at room temperature for 72 h. The hydro‐alcoholic extract was then filtered through Whatman filter paper and subsequently stored at 25°C. The filtrate evaporated as the pressure was lowered at 60°C. The extract was weighed and then stored at 4°C. Here, the choice of a water–methanol solvent system for extraction was based on its ability to extract a broad range of phytochemicals with varying polarities efficiently. Methanol is highly effective at solubilizing medium‐ to low‐polarity compounds, while water enhances the extraction of polar constituents. The combination of hydrophilic (water) and organic (methanol) components allows for a more comprehensive extraction of bioactive compounds from plant material compared to single‐solvent systems. Additionally, methanol–water mixtures are widely used in natural product research due to their high extraction efficiency, reproducibility, and compatibility with downstream analytical techniques such as GC–MS, LC–MS, and various in vitro assays [35].

2.3. Drugs and Chemicals

Square Pharmaceutical Company Ltd. sent us gifts of diazepam, fluoxetine, and albendazole for research purposes. Gonoshasthaya Pharmaceuticals Ltd. in Bangladesh provided the colchicine. In this experiment, only materials and reagents of analytical quality were utilized.

2.4. Experimental Animals

Swiss albino mice were procured and maintained under standard laboratory conditions. Male mice weighing 32–42 g and in good health were purchased from the animal house at USTC, Bangladesh. For 12 h every day and 12 h every night, they were kept in a well‐ventilated chamber at 20°C ± 2°C. Throughout the experiment, the animals were housed in hygienic, roomy cages [36].

2.5. Experimental Design and Ethical Approval

Mice were randomly divided into four groups (n = 5): control (1% Tween 80, 10 mL/kg, p.o.), test groups (JGF‐ME 200 and 400 mg/kg, p.o.), and standard group (diazepam 1 mg/kg for anxiolytic and sedative, fluoxetine 25 mg/kg for antidepressant). All procedures were approved by the Institutional Animal Ethics Committee of the University of Chittagong, Bangladesh (Approval No. AERB‐FBSCU‐2024‐27), ensuring compliance with ethical standards. Mice were euthanized using diethyl ether anesthesia after the experiments, and all protocols followed the ARRIVE guidelines for humane and reproducible animal research.

2.6. Gas Chromatography–Mass Spectrometry (GC–MS) Analysis

The advantageous compounds extracted from JGF‐ME were analyzed using a Shimadzu GC–MS/MS TQ 8040 mass spectrometer employing electron impact ionization (EI). Separation occurred on a fused silica capillary column, initially heated to 50°C and then gradually raised to 300°C after sample injection at 250°C. The system functioned under a pressure of 53.5 kPa with a total flow rate of 11.0 mL/min. Identification of chemicals was confirmed through the NIST and Wiley spectral libraries. Data acquisition lasted 39 min, with the ion source temperature maintained at 230°C and the detector voltage set to 0.6 kV. This process captured m/z values ranging from 50 to 600, using a solvent delay time of 3.5 min [37].

2.7. Acute Oral Toxicity Test

Before the main study, a pilot acute toxicity test was conducted in accordance with the protocol outlined by Afrin et al. [38] to assess the safety profile of the extract. Twenty mice, fasted overnight, were randomly assigned to four groups (n = 5 per group), and each group received a single oral dose of the extract at 1, 2, 3, or 4 g/kg body weight. After dosing, the animals remained fasted for an additional 3–4 h. They were then closely observed for any signs of toxicity, including abnormalities in the eyes, mucous membranes, skin, and fur, as well as changes in respiratory and circulatory rates, and functions of the autonomic and central nervous systems. Intensive monitoring occurred during the first 30 min post‐administration, followed by observations every 24 h for 72 h, with special attention paid to the first 4 h to identify any immediate or delayed adverse effects. Since no deaths or notable toxic symptoms were observed even at the highest dose (4 g/kg), the LD₅₀ was estimated to be greater than 4 g/kg. Based on this finding, one‐tenth of the presumed LD₅₀—400 mg/kg—was chosen as the high therapeutic dose, while 200 mg/kg was selected as the moderate dose for subsequent pharmacological studies.

2.8. Anxiolytic Activity

2.8.1. Elevated Plus Maze Test (EPM)

The method outlined in Lister et al. [39, 40] has been used to calculate anxiolytic activity in the study. EPM is made up of two open and two closed arms, arranged in a plus formation and raised 50 cm from the floor. Four groups of five mice each were tested: Group I received diazepam (1 mg/kg, intraperitoneally), Group II got a control solution, and Groups III and IV were given JGF‐ME (200 and 400 mg/kg, p.o.). Following administration, each mouse was placed at the maze center in one of the closed arms. The duration spent in both open and closed arms was measured over 5 min [41].

2.8.2. Hole Board Test

The hole board has holes for animals to dip their heads, a behavior called head‐dipping that measures exploration and anxiety. Less head‐dipping indicates higher anxiety, while more suggests curiosity. Mice were placed on the board for 30 min, then head dips were counted over 5 min to assess behavior [42].

2.8.3. Light–Dark Test

The LDT is composed of two joined compartments raised 25 cm from the surface: a dark chamber (25 × 35 × 35 cm) painted black to block out light, and a lighted chamber (of identical dimensions), painted white and illuminated by a 40‐W bulb. A small floor‐level opening (7.5 × 5 cm) connects them. After 60 min of oral administration, each mouse was introduced into the center of the illuminated compartment, and its behavior was observed for 5 min [43, 44].

2.9. Antidepressant Activity

2.9.1. Forced Swimming Test (FST)

The FST was employed to assess central nervous system (CNS) depressant effects. Treatments were administered according to the experimental protocol. After 60 min, each mouse was placed individually into a transparent container (25 × 15 × 25 cm³) filled with water to a depth of 15 cm at 22°C. Behavioral responses were recorded over a 6‐min duration, where the initial 2 min allowed for acclimatization and the remaining 4 min were analyzed for immobility time [45, 46].

2.9.2. Tail Suspension Test (TST)

TST is a quick and straightforward approach to evaluating the effects of antidepressants. The total number of mice was assigned to four separate groups, each comprising five animals. Group I received the vehicle (control), Group II received fluoxetine (25 mg/kg, i.p., as the standard), and Groups III and IV received oral doses of JGF‐ME (200 and 400 mg/kg, respectively). Following treatment, the tail‐hung mice and behavior were monitored for 6 min, with immobility during the final 4 min used to evaluate antidepressant effects [42].

2.10. Sedative Activity

2.10.1. Open Field Test

Spontaneous movement and exploration were assessed using a modified open field setup (30 × 50 × 27 cm). The floor was divided into alternating black and white squares. After grouping (n = 5), mice received JGF‐ME (200/400 mg/kg, p.o.) and diazepam (1 mg/kg, i.p.). Activity was recorded at 3‐min intervals for 0, 30, 60, 90, and 120 min [47, 48].

2.10.2. Hole Cross Test

Following the procedure of Takagi et al. [49], a partitioned cage (30 × 20 × 14 cm) with a 3 cm central opening positioned 7.5 cm above the base was used. Mice received either extract, standard (i.p.), or control (p.o.) and were placed on one side. Crossings through the hole were counted for 3 min at 0, 30, 60, 90, and 120 min post‐treatment.

2.11. Anthelmintic Assay

Using the Ajaiyeoba et al. procedure [50], JGF‐ME's anthelmintic activity was evaluated with minor modifications. Pheretima posthuma is the subject of the current study. Worms (2–2.5 cm) purchased from the local Chittagong market were used to evaluate the anthelmintic activity of JGF‐ME at concentrations of 10, 25, 50, and 100 μg/mL, alongside albendazole. Double‐distilled water served as the control. Two parameters—paralysis time (no movement except on forceful shaking) and death time (complete immobility with faded color)—were recorded. Death was confirmed by immersion in warm water to ensure no signs of movement remained [51].

2.12. Cytotoxic Activity

2.12.1. Brine Shrimp Lethality Assay (BSLA)

The assay was conducted using the methodology and principles previously outlined by Meyer et al. [52], with minor adjustments. The brine shrimp lethality assay was used to assess the cytotoxic effects of various plant extracts. Artificial seawater was prepared by dissolving 38 g of NaCl in 1 L of distilled water, and then the pH was adjusted with NaOH. Brine shrimp eggs hatched in this medium yielded nauplii. Test samples were prepared by diluting with dimethyl sulfoxide (DMSO) (25–100 μg/mL), which also served as the negative control, while colchicine was used as the standard. Nauplii were placed in 5 mL of saltwater in labeled vials, and different concentrations of the samples were added. After 24 h at 25°C, the number of surviving nauplii was recorded [53, 54].

$$\begin{array}{c}\text{Mortality}\%\hfill \\ =\frac{\text{Number of naupolii taken}-\text{Number of naupolii alive}}{\text{Number of naupolii taken}}\times 100\hfill \end{array}$$

2.13. In Silico Study

2.13.1. Assessment of the Ligands' ADMET Characteristics

The GC–MS/MS analysis found 60 compounds in the JGF‐ME extract. Seventeen compounds were chosen for further investigation based on their biological activity, ADME/T properties, and pass prediction analysis. Physical and molecular properties are essential when identifying compounds as possible medications, as are pharmacokinetic components such as ADME/T (absorption, distribution, metabolism, excretion, and toxicity). The pKCSM online tool (http://biosig.unimelb.edu.au/pkcsm/) was used to analyze the characteristics of compounds discovered to be potential ligands against therapeutic targets [55]. On the SwissADME online server, Lipinski's rule of five was used to assess the compounds' potential to become medicines [56].

2.13.2. Protein Preparation and Active Site Determination

Crystal structures for selected targets were retrieved from the RCSB Protein Data Bank, including human MAO‐A enzyme (PDB: 2Z5X), serotonin reuptake transporter (PDB: 5I6X), GABA_A ion channel receptor (PDB: 6X3W), microtubule‐associated regulatory domain (PDB: 1SA0), and nuclear hormone receptor (PDB: 1ERR). The active sites of the target proteins were identified using structural information from previously published studies by Kurumbail et al., which provided critical insights into the binding regions relevant to enzymatic function and inhibitor interaction [57, 58]. The MMFF94 force field and PyRx software were used for virtual screening, minimizing the receptor protein after adding hydrogen atoms to its structure [59]. For docking analysis, the selected protein structure was saved in PDB format.

2.13.3. Molecular Docking and Post‐Docking Analysis

Hydrogens were added to the protein structure before energy minimization using the MMFF94 force field in PyRx [60]. AutoGrid generated a grid box (0.375 Å spacing) sized X: 52.06, Y: 33.57, Z: 36.64 Å. Results were analyzed using PyMOL to identify key interactions, such as cation‐π, π‐π, and hydrogen bonds, between ligands and receptors. PyMOL also provided a detailed visualization of these molecular contacts [61].

2.13.4. Toxicity Prediction by Admet SAR

The admetSAR online tool (http://lmmd.ecust.edu.cn/admetsar1/predict/) was utilized to evaluate the toxicological properties of the identified compounds, as toxicity presents a significant concern in the development of novel drugs. SwissADME was used to screen compounds from that list based on the Lipinski rule of five [59].

2.14. PASS Prediction

The PASS online tools (http://www.pharmalexpert.ru/passonline/predict.php) were used to review the PASS prediction and investigate the potential biological effects of the selected substances. The Pa and Pi values varied from 0.000 to 1.000. Biological potential is indicated when the Pa value of a compound is higher than its Pi value. Comparatively, Pa > 0.7 indicates high drug activity, Pa < 0.5 indicates weak pharmaceutical action, and 0.5 < Pa < 0.7 indicates intermediate therapeutic potential [46, 62].

2.15. Statistical Analysis

The findings were displayed as the mean ± standard error of the mean (SEM). Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS, version 16.0, IBM Corporation, New York, USA). For comparisons made after a one‐way analysis of variance (ANOVA), a post hoc Dunnett test was used. The following criteria were used to calculate the significance levels: *p < 0.05, **p < 0.01, and ***p < 0.001. These data show statistical significance in relation to the study group.

3. Results

3.1. GC–MS Analysis

The phytoconstituent‐rich methanolic extract of J. gossypifolia was analyzed using GC–MS. A total of 60 compounds were identified, each with unique phytochemical activity. The analysis revealed a diverse array of phytoconstituents, with the most abundant compounds being: Oxacycloheptadec‐8‐en‐2‐one, (8Z) (31.01%), Glycerin (25.53%), Octadecanoic acid (6.87%), 9,12‐Octadecadienoic acid (Z,Z)‐, methyl ester (4.47%), 9‐Octadecenamide, (Z)‐ (4.46%), among others. Figure 1 illustrates the chromatogram, while Table 1 lists the chemical components, including their retention times (RT), m/z values, and corresponding areas (%).

FIGURE 1.

Jatropha gossypifolia methanolic extract's GC–MS chromatogram.

TABLE 1.

Compounds identified by GC–MS analysis in the methanolic extract of J. gossypifolia.

Sl. no.	R. time	Area %	m/z	Compound name
1	3.52	0.07	28	1,2‐Hydrazinedicarboxaldehyde
2	3.557	0.38	91	Hydrazinecarbothioamide
3	3.607	0.65	87	2‐Methoxy‐3‐methyl‐butyric acid, methyl ester
4	3.806	0.29	31	Acetic acid, hydroxy‐, methyl ester
5	3.865	0.11	45	Diisopropyl ether
6	3.94	0.14	44	Cyclobutanol
7	4.072	0.28	45	Propanoic acid, 2‐hydroxy‐, methyl ester, (.+/−.)
8	4.386	0.03	58	2‐Methoxyethyl pentanoate
9	4.539	0.07	43	Propanoic acid, 2‐oxo‐, methyl ester
10	4.65	0.03	44	Hexanal
11	4.71	0.01	83	Propanenitrile, 2‐(dimethylamino)‐
12	5.182	0.12	103	Di‐sec‐butyl ether
13	6.705	0.03	57	Butanoic acid, 4‐(1,1‐dimethylethoxy)‐3‐hydroxy‐, methyl ester, (R)‐
14	6.96	0.57	43	Diglycerol
15	7.929	25.53	61	Glycerin
16	8.179	0.25	57	Dodecane
17	10.438	2.31	61	Erythritol
18	11.114	0.18	154	Phenol, 2,6‐dimethoxy‐
19	11.901	0.07	73	Glucitol, 6‐O‐nonyl‐
20	12.245	0.14	191	Phenol, 3,5‐bis(1,1‐dimethylethyl)‐
21	12.924	2.88	61	DL‐Arabinitol
22	13.295	0.17	60	β‐D‐Glucopyranoside, methyl
23	13.576	0.52	102	4‐Cyanobenzoic acid, isopropyl ester
24	13.995	0.04	57	Tridecane, 1‐iodo‐
25	14.945	0.04	57	Heptadecane, 7‐methyl‐
26	15.169	0.54	137	(E)‐4‐(3‐Hydroxyprop‐1‐en‐1‐yl)‐2‐methoxyphenol
27	15.745	0.11	124	(S,E)‐4‐Hydroxy‐3,5,5‐trimethyl‐4‐(3‐oxobut‐1‐en‐1‐yl)cyclohex‐2‐enone
28	16.181	0.05	57	Neophytadiene
29	17.145	0.07	43	Oxirane, [(hexadecyloxy)methyl]‐
30	17.48	0.6	74	Hexadecanoic acid, methyl ester
31	17.765	0.09	55	Oleic acid
32	18.048	6.5	73	n‐Hexadecanoic acid
33	18.729	0.24	167	cis‐Sinapyl alcohol
34	20.18	4.47	67	9,12‐Octadecadienoic acid (Z,Z)‐, methyl ester
35	20.925	31.01	81	Oxacycloheptadec‐8‐en‐2‐one, (8Z)‐
36	21.336	6.87	73	Octadecanoic acid
37	22.528	0.51	67	9,12‐Octadecadien‐1‐ol, (Z,Z)‐
38	22.92	0.1	69	11,13‐Dimethyl‐12‐tetradecen‐1‐ol acetate
39	23.392	0.23	57	9‐t‐Butyltricyclo[4.2.1.1(2,5)]decane‐9,10‐diol
40	23.639	0.12	57	Oxiraneoctanoic acid, 3‐octyl‐, methyl ester, cis‐
41	24.071	0.21	99	Cyclopropanebutanoic acid, 2‐[[2‐[[2‐[(2‐pentylcyclopropyl)methyl]cyclopropyl]methyl]cyclopropyl]methyl]‐, methyl ester
42	24.639	4.46	59	9‐Octadecenamide, (Z)‐
43	25.061	0.42	59	Octadecanamide
44	26.193	0.17	58	2‐Hydroxy‐4‐methoxy‐7‐methyl‐7,8,9,10,11,12,13,14‐octahydro‐6‐oxabenzocyclododecen‐5‐one
45	26.364	0.26	67	6,9,12,15‐Docosatetraenoic acid, methyl ester
46	26.745	0.15	117	Fumaric acid, 2‐butyl tetradecyl ester
47	27.082	0.34	98	Hexadecanoic acid, 2‐hydroxy‐1‐(hydroxymethyl)ethyl ester
48	29.837	2.63	67	9,12‐Octadecadienoic acid (Z,Z)‐, 2,3‐dihydroxypropyl ester
49	30.289	0.38	98	Octadecanoic acid, 2,3‐dihydroxypropyl ester
50	31.192	0.17	59	13‐Docosenamide, (Z)‐
51	31.645	0.04	69	Squalene
52	33.784	0.06	251	9(11)‐Dehydroergosteryl benzoate
53	34.968	0.13	151	γ‐Tocopherol
54	35.283	0.09	135	Cholesta‐4,6‐dien‐3‐ol, (3β)‐
55	35.595	0.06	73	β‐Sitosterol acetate
56	36.056	0.07	91	Cholesterol
57	37.83	0.34	105	5‐Cholestene‐3‐ol, 24‐methyl‐
58	38.288	0.31	55	Stigmasterol
59	38.795	0.06	271	Stigmasta‐7,25‐dien‐3‐ol, (3β,5α)‐
60	39.478	1.51	43	γ‐Sitosterol

3.2. Anxiolytic Profiling

3.2.1. Elevated Plus Maze Test

In assessing anxiolytic activity through the EPM, the methanolic extract of J. gossypifolia fruit showed a dose‐dependent effect, as evidenced by the mice being treated with two doses (200 and 400 mg/kg, p.o.). Time recorded in the open arms was increased significantly (p < 0.01) with a higher dosage compared to the lower one (142.02 ± 0.99 and 172.48 ± 1.37, respectively), compared to the control. The entries in open arms were also significantly (p < 0.01) increased at the higher dose. The standard drug diazepam also exhibited an anxiolytic potential, as evidenced by a significant enhancement in both the duration and number of open arm entries (p < 0.001) (Table 2, Figure 2).

TABLE 2.

Effect of JGF‐ME extract on the elevated plus maze test.

Treatment (mg/kg)	Time spent in open arms (s)	Entries in open arms	Time spent in close arms (s)	Entries in close arms
Control	131.27 ± 1.04	10.6 ± 0.96	168.72 ± 1.047	12.2 ± 0.94
Diazepam	224.75 ± 1.43***	14.20 ± 0.94**	75.24 ± 1.48***	2.80 ± 0.48***
JGF‐ME 200	142.02 ± 0.99	13 ± 0.57*	157.67 ± 1.06	9.2 ± 0.85**
JGF‐ME 400	172.48 ± 1.37**	13.6 ± 1.12**	127.51 ± 1.37**	5.2 ± 0.94***

Note: All values are expressed as mean ± SEM, and statistical analysis was performed using one‐way analysis of variance (ANOVA). Subsequently, n = 5 is employed for Dunnett's multiple comparison test, with *p < 0.05, **p < 0.01, and ***p < 0.001 in comparison to the control group.

FIGURE 2.

Investigating the anxiolytic potential of JGF‐ME via the Elevated Plus Maze test. All values are expressed as mean ± SEM, and statistical analysis was performed using One‐Way Analysis of Variance (ANOVA). Subsequently, n = 5 is employed for Dunnett's multiple comparison test, with **p < 0.01 and ***p < 0.001 in comparison to the control group.

3.2.2. Hole Board Test (HBT)

In the hole board test, mice administered with JGF‐ME extract at 200 and 400 mg/kg doses exhibited a dose‐dependent increase in head dips. In contrast to the control, diazepam and higher doses (400 mg/kg) of the extract showed greater head dips (p < 0.001) than the control (Figure 3).

FIGURE 3.

Investigating the anxiolytic potential of JGF‐ME via Hole Board test. All values are expressed as mean ± SEM, and statistical analysis was performed using One‐Way Analysis of Variance (ANOVA). Subsequently, n = 5 is employed for Dunnett's multiple comparison test, with **p < 0.001 in comparison to the control group.

3.2.3. Light–Dark Box Test

Diazepam and extract‐treated mice exhibited increased transitions and spent more time in the light box than the control mice. JGF‐ME (400 mg/kg) significantly prolonged light box stay (126.7 ± 2.58 s), indicating a dose‐dependent anxiolytic effect (Figure 4).

FIGURE 4.

Investigating the anxiolytic potential of JGF‐ME via the Light–Dark Box assay. All values are expressed as mean ± SEM, and statistical analysis was performed using One‐Way Analysis of Variance (ANOVA). Subsequently, n = 5 is employed for Dunnett's multiple comparison test, with **p < 0.01 and ***p < 0.001 in comparison to the control group.

3.3. Antidepressant Profiling

3.3.1. Forced Swimming Test

Both the 200 and 400 mg/kg doses of JGF‐ME significantly decreased the mice's immobility times (Table 3, Figure 5), with values of 190.2 ± 1.98 and 116 ± 2.70 s, respectively (p < 0.05 and p < 0.001), compared to the control and the standard group, which also exhibited notable antidepressant activity with a significant immobility time of 92.2 ± 1.28 s (p < 0.001).

TABLE 3.

Effect of JGF‐ME extract on forced swimming and tail suspension tests.

Group	Immobile time in seconds
	Forced swimming test	Tail suspension test
Control	215 ± 0.83	195.2 ± 4.57
Fluoxetine	92.2 ± 1.28***	56.2 ± 2.59***
JGF‐ME 200	190.2 ± 1.98*	145.4 ± 3.18**
JGF‐ME 400	116 ± 2.70***	93 ± 5.38***

Note: All values are expressed as mean ± SEM, and statistical analysis was performed using one‐way analysis of variance (ANOVA). Subsequently, n = 5 is employed for Dunnett's multiple comparison test, with *p < 0.05, **p < 0.01, and ***p < 0.001 in comparison to the control group.

FIGURE 5.

Investigating the antidepressant potential of JGF‐ME via Forced Swimming and Tail Suspension assays. All values are expressed asmean ± SEM, and statistical analysis was performed using One‐Way Analysis of Variance (ANOVA). Subsequently, n = 5 is employed for Dunnett's multiple comparison test, with *p < 0.05, **p < 0.01, and ***p < 0.001 in comparison to the control group.

3.3.2. Tail Suspension Test

Similar to the FST, mice administered 400 mg/kg doses of JGF‐ME showed a dose‐dependent, considerable reduction in immobility time compared to the control (p < 0.001), indicating that JGF‐ME extract is an effective antidepressant (Table 3, Figure 5).

3.4. Sedative Profiling

3.4.1. Open Field Test (OFT)

A drop in distance traveled in the OFT from 0 to 120 min indicated sedation in mice. JGF‐ME at 200 and 400 mg/kg significantly (p < 0.05 to < 0.001) suppressed movement up to 90 min compared to controls. Diazepam, the positive control, showed highly significant effects (p < 0.001) throughout the test (Table 4, Figure 6).

TABLE 4.

Evaluation of the sedative potential of JGF‐ME through the open field test.

Treatment group	Number of movements (mean ± SEM)
	0 min	30 min	60 min	90 min	120 min
Control	71.6 ± 0.92	63 ± 0.70	38.6 ± 1.07	37 ± 1.58	38 ± 0.70
Diazepam	62.4 ± 1.07***	56.4 ± 0.50***	28 ± 0.44***	27 ± 0.70***	20.08 ± 0.66***
JGF‐ME 200	67.4 ± 0.67**	57.4 ± 0.74**	24.6 ± 1.20***	28.8 ± 0.96***	34 ± 1.58*
JGF‐ME 400	78.2 ± 0.86***	44.4 ± 2.72***	22.6 ± 0.51***	27.8 ± 0.37***	21.4 ± 1.28***

Note: All values are expressed as mean ± SEM, and statistical analysis was performed using one‐way analysis of variance (ANOVA). Subsequently, n = 5 is employed for Dunnett's multiple comparison test, with *p < 0.05, **p < 0.01, and ***p < 0.001 in comparison to the control group.

FIGURE 6.

Investigating the sedative activity of JGF‐ME via Open Field assay. All values are expressed as mean ± SEM, and statistical analysis was performed using One‐Way Analysis of Variance (ANOVA). Subsequently, n = 5 is employed for Dunnett's multiple comparison test, with *p < 0.05, **p < 0.01, and ***p < 0.001 in comparison to the control group.

3.4.2. Hole Cross Test

Administration of JGF‐ME at a dose of 200 mg/kg showed a significant (p < 0.001, p < 0.01) reduction in the animals' movement during the hole cross test, observed consistently from the start (0 min) to the end (120 min), compared to the control. Again, the higher dose (400 mg/kg) exhibited a significant (p < 0.001) decline in mouse movement from 0 to 90 min. The standard medication diazepam also demonstrated a highly substantial sedative effect (p < 0.001) (Table 5, Figure 7).

TABLE 5.

Assessment of the sedative activity of JGF‐ME through the hole cross test.

Treatment group	Number of movements (mean ± SEM)
	0 min	30 min	60 min	90 min	120 min
Control	19.2 ± 0.73	20.60 ± 1.53	23.6 ± 0.50	15.8 ± 0.58	12.6 ± 0.81
Diazepam	11.4 ± 0.92***	9.8 ± 1.24***	7.6 ± 0.50***	6.20 ± 0.58***	2.80 ± 0.85***
JGF‐ME 200	15 ± 0.70**	11.6 ± 0.92***	9.40 ± 0.74***	7.2 ± 0.58***	5.2 ± 0.58***
JGF‐ME 400	13.2 ± 0.58***	11.2 ± 1.06***	6.8 ± 0.37***	6.6 ± 0.50***	10.6 ± 0.50

Note: All values are expressed as mean ± SEM, and statistical analysis was performed using one‐way analysis of variance (ANOVA). Subsequently, n = 5 is employed for Dunnett's multiple comparison test, with *p < 0.05, **p < 0.01, and ***p < 0.001 in comparison to the control group.

FIGURE 7.

Investigating the sedative activity of JGF‐ME via Hole Cross test. All values are expressed as mean ± SEM, and statistical analysis was performed using One‐Way Analysis of Variance (ANOVA). Subsequently, n = 5 is employed for Dunnett's multiple comparison test, with **p < 0.01 and ***p < 0.001 in comparison to the control group.

3.5. Cytotoxic Activity

3.5.1. Brine Shrimp Lethality Assay

The extract JGF‐ME showed a dose‐dependent increase in percentage mortality in the brine shrimp lethality assay. Mortality rates for both JGF‐ME and the standard compound, colchicine, were evaluated at concentrations of 250, 500, and 1000 μg/mL. The highest mortality was observed at 1000 μg/mL, with JGF‐ME causing 70% mortality and colchicine causing 90% mortality. The LC₅₀ values (concentration required to kill 50% of the population) were determined to be 327.87 μg/mL for JGF‐ME and 38.81 μg/mL for colchicine. These results indicate that, compared to the standard, the JGF‐ME extract exhibited moderate cytotoxic activity (Figure 8).

FIGURE 8.

Investigating the cytotoxic activity of JGF‐ME via the Brine Shrimp Lethality assay. JGF‐ME = J. gossypifolia fruit's methanolic extract.

3.6. Anthelmintic Activity

The paralyzing time of Pheretima posthuma was dose‐dependent when exposed to the extract, with paralysis occurring at 24.8 ± 2.52, 31.6 ± 3.01, 34.6 ± 2.52, and 53 ± 2.83 min for doses of 10, 25, 50, and 100 μg/mL, respectively. In comparison, exposure to albendazole (10 μg/mL) resulted in paralysis in 14.4 ± 1.08 min. The extract also caused the death of P. posthuma at average times of 31.2 ± 1.71, 41.2 ± 2.01, 47.6 ± 2.18, and 62.2 ± 2.08 min for the same doses (10, 25, 50, and 100 μg/mL). Albendazole at 10 μg/mL killed the parasite in 26.2 ± 1.83 min. In the control group (water), no paralysis or death was observed (Figure 9).

FIGURE 9.

Investigating the anthelmintic activity of JGF‐ME via the parasite Pheretima posthuma.

3.7. In Silico Study

3.7.1. ADME/T and Drug‐Likeness Analysis

The pharmacokinetic and drug‐likeness properties of the identified phytochemicals were evaluated to assess their therapeutic potential before docking analysis. Table 6 summarizes the pharmacokinetic characteristics of these compounds. In silico ADME/T and drug similarity studies of the compounds from J. gossypifolia fruit, conducted using pKCSM and SwissADME, produced interesting results.

TABLE 6.

In silico ADMET and drug‐likeness analysis of JGF‐ME phytochemicals.

Compound name	Lipinski rules				Lipinski's violation ≤ 1	Veber's rules		Toxicity parameters			HIA	BBB	BS
	MW (g/mol) < 500	HBA < 10	HBD < 5	Log p ≤ 5		n RB ≤ 10	TPSA ≤ 140 (Å2)	Ames toxicity	Carcinogens	AOT
Glucitol, 6‐O‐nonyl‐	308.41	6	5	3.12	0	14	110.38	NAT	NC	IV	0.9242	0.5374	0.55
Phenol, 3,5‐bis(1,1‐dimethylethyl)‐	218.29	4	1	2.7	0	7	55.76	NAT	NC	III	0.9945	0.9189	0.55
β‐D‐Glucopyranoside, methyl	194.18	6	4	1.25	0	2	99.38	NAT	NC	III	0.8373	0.6148	0.55
Hexadecanoic acid, methyl ester	270.45	2	0	4.41	1	15	26.30	NAT	NC	III	0.9881	0.9848	0.55
9,12‐Octadecadienoic acid (Z,Z)‐, methyl ester	294.47	2	0	4.41	1	15	26.30	NAT	NC	IV	0.9242	0.5374	0.55
Oxacycloheptadec‐8‐en‐2‐one, (8Z)‐	252.39	2	0	3.31	0	0	26.30	NAT	NC	II	0.9902	0.9692	0.55
9,12‐Octadecadien‐1‐ol, (Z,Z)‐	266.46	1	1	3.31	0	14	20.23	NAT	NC	III	0.9974	0.9625	0.55
9‐Octadecenamide, (Z)‐	281.48	1	1	4.1	1	15	43.09	NAT	NC	III	1	0.9972	0.55
γ‐Tocopherol	416.68	2	1	5.16	1	12	29.46	NAT	NC	III	0.9795	0.9767	0.55
β‐Sitosterol acetate	456.74	2	0	5.3	1	8	26.30	NAT	NC	III	1	0.9676	0.55
Stigmasterol	412.69	1	1	5.08	1	5	20.23	NAT	NC	I	1	0.9743	0.55
Stigmasta‐7,25‐dien‐3‐ol, (3β,5α)‐	412.69	1	1	5.04	1	6	20.23	NAT	NC	III	1	0.9888	0.55
γ‐Sitosterol	414.71	1	1	5.07	1	6	20.23	NAT	NC	I	1	0.9743	0.55
Phenol, 2,6‐dimethoxy‐	154.16	3	1	1.85	0	2	38.69	NAT	NC	III	0.9918	0.8584	0.55
Diglycerol	166.17	5	4	0.99	0	6	90.15	NAT	NC	IV	0.8615	0.5703	0.55
Erythritol	122.12	4	4	0.94	0	3	80.92	NAT	NC	IV	0.6645	0.6283	0.55
DL‐Arabinitol	152.15	5	5	1.18	0	4	101.15	NAT	NC	IV	0.6885	0.6671	0.55

Abbreviations: AOT, acute oral toxicity; BBB permeability, blood–brain barrier permeability; BS, bioavailability score; HBA, hydrogen bond acceptors; HBD, hydrogen bond donors; HIA, human intestinal absorption; MW, molecular weight; NAT, not Ames toxic; NC, not carcinogenic; nRB, number of rotatable bonds; TPSA, topological polar surface area.

3.7.2. Molecular Docking for Anxiolytic, Antidepressant, Sedative, Cytotoxic, and Anthelmintic Activities

Table 7 presents selected JGF‐ME compounds and their docking affinities for anxiolytic, antidepressant, sedative, anthelmintic, and cytotoxic activities against target proteins: human monoamine oxidase receptor (PDB: 2Z5X), serotonin transporter (PDB: 5I6X), GABA_A receptor subtype (PDB: 6X3W), tubulin regulatory complex (PDB: 1SA0), and estrogen receptor alpha (PDB: 1ERR). Docking scores, interaction details, and comparisons of the top three compounds with standard drugs are shown in Table 8 and Figures 10, 11, 12, 13, 14, emphasizing their binding affinities and molecular interactions.

TABLE 7.

Docking scores of selected JGF‐ME compounds were evaluated against target proteins—MAO‐A (PDB: 2Z5X), SERT (PDB: 5I6X), GABA_A ion channel receptor (PDB: 6X3W), tubulin‐stathmin complex (PDB: 1SA0), and estrogen receptor (PDB: 1ERR)—to explore their potential anxiolytic, antidepressant, sedative, anthelmintic, and cytotoxic activities.

Compounds	PubChem CID	Docking score (kcal/mol)
		Anxiolytic (2Z5X)	Antidepressant (5I6X)	Sedative (6X3W)	Anthelmintic (1SA0)	Cytotoxic (1ERR)
Glucitol, 6‐O‐nonyl‐	552 730	−6.9	−6.5	−4.2	−4.9	−5.7
Phenol, 3,5‐bis(1,1‐dimethylethyl)‐	70 825	−8.9	−7	−4.6	−6.4	−6.8
β‐D‐Glucopyranoside, methyl	2108	−5.6	−5.9	−4.5	−4.6	−5.7
Hexadecanoic acid, methyl ester	8181	−7.1	−6.6	−3.3	−4.8	−5.9
9,12‐Octadecadienoic acid (Z,Z)‐, methyl ester	5 284 421	−7.7	−6.9	−4.3	−5.1	−6.1
Oxacycloheptadec‐8‐en‐2‐one, (8Z)‐	5 365 703	−6	−7.7	−4.7	−6.9	−6
9,12‐Octadecadien‐1‐ol, (Z,Z)‐	5 365 682	−7.5	−6.2	−3.2	−5.3	−6
9‐Octadecenamide, (Z)‐	5 283 387	−7.8	−6.7	−3.9	−5	−6.1
γ‐Tocopherol	92 729	−10.4	−9.2	−4.9	−7	−8.6
β‐Sitosterol acetate	5 354 503	−8.9	−10.3	−5.7	−6.9	−8.7
Stigmasterol	5 280 794	−7.8	−9.8	−5.8	−7.9	−7.9
Stigmasta‐7,25‐dien‐3‐ol, (3β,5α)‐	6 321 372	−7.3	−10.4	−5.2	−7.4	−8.5
γ‐Sitosterol	457 801	−7.7	−10	−5.5	−7.5	−8.7
Phenol, 2,6‐dimethoxy‐	7041	−5.6	−5.5	−4.1	−4.7	−5.5
Diglycerol	42 953	−5	−4.2	−3.5	−3.7	−4.5
Erythritol	222 285	−4.1	−4.8	−3.4	−3.5	−4.4
DL‐Arabinitol	94 154	−4.7	−4.7	−3.6	−3.7	−4.8
Diazepam, Fluoxetine, Albendazole, Colchicine (standard)		−8.5	−9.2	−4.9	−6.2	−6.3

Note: Bold value represents the highest score among the compounds.

TABLE 8.

In silico binding affinity and nonbonding interaction of chosen phytochemicals of JGF‐ME for anxiolytic (PDB: 2Z5X), antidepressant (PDB: 5I6X), sedative (PDB: 6X3W), anthelmintic (PDB: 1SA0), and cytotoxic (PDB: 1ERR) activities.

Section number	Receptor	Compound	Binding affinity (kcal/mol)	Bond type	Amino acids
1	2Z5X	γ‐Tocopherol	−10.4	Conventional hydrogen bond	ILE180
				Pi‐Pi stacked	TYR407
				Alkyl	ARG51, CYS406, ALA448, ILE180, VAL303, LYS305, ILE23
				Pi‐alkyl	TYR69, PHE352, TYR407, TYR444
		β‐Sitosterol acetate	−8.9	Conventional hydrogen bond	GLN215
				Pi‐sigma	TYR407
				Alkyl	ILE335, ARG51, CYS406
				Pi‐alkyl	TYR69, PHE352, TYR407, TYR444
		γ‐Sitosterol	−8.9	Conventional hydrogen bond	TYR444, ASN181
				Pi‐Pi stacked	TYR407
				Alkyl	ILE180
				Pi‐alkyl	PHE352, TYR407, TYR444
		Diazepam (standard)	−8.5	Pi‐cation	TYR407
				Pi‐Pi stacked	TYR408
				Pi‐Pi T‐shaped	TYR409
				Alkyl	CYS406, MET445
				Pi‐alkyl	TYR407, TYR444, MET445
2	5I6X	Stigmasta‐7,25‐dien‐3‐ol, (3β,5α)‐	−10.4	Conventional hydrogen bond	ARG104
				Pi‐sigma	PHE335
				Alkyl	ALA169, ALA173, ILE172, LEU443
				Pi‐alkyl	ILE172, TYR95, TYR176, PHE335, PHE341
		β‐Sitosterol acetate	−10.3	Conventional hydrogen bond	ARG104
				Pi‐sigma	PHE335
				Alkyl	ILE172
				Pi‐alkyl	TYR176, PHE335, PHE341
		γ‐Sitosterol	−10	Alkyl	ILE172
				Pi‐alkyl	TYR95, TYR176, PHE335, PHE341
		Fluoxetine (standard)	−9.2	Conventional hydrogen bond	ALA96, ASP98
				Carbon hydrogen bond; halogen (fluorine)	ALA173, SER439, GLY442
				Carbon hydrogen bond	ASP98, SER336
				Halogen (fluorine)	ILE172, SER439
				Pi‐sigma	TYR95
				Pi‐Pi T‐shaped	PHE341, TYR176
				Amide‐Pi stacked	SER438, SER439
				Alkyl	ALA173, ILE172
				Pi‐alkyl	TYR176, TYR172, VAL501
3	6X3W	Stigmasterol	−5.8	Alkyl	PRO233, MET236, LEU240, LEU269
		β‐Sitosterol acetate	−5.7	Alkyl	PRO233, MET236, LEU240, LEU269
		γ‐Sitosterol	−5.5	Alkyl	PRO233, MET236, LEU240, LEU269
		Diazepam (standard)	−4.9	Carbon hydrogen bond	THR237
				Alkyl	LEU240, PRO233, LEU269
				Pi‐alkyl	PRO233
4	1SA0	Stigmasterol	−7.9	Alkyl	CYS241, LEU248, ALA250, LYS254, LEU255, ALA316, ALA354, LYS352
		γ‐Sitosterol	−7.5	Alkyl	CYS241, LEU248, ALA250, LYS254, LEU255ALA316, ALA354, LYS352
		Stigmasta‐7,25‐dien‐3‐ol, (3β,5α)‐	−7.4	Alkyl	LEU248, LYS254, ALA316, ALA354, LYS352, LEU255, LEU248, LYS352
		Albendazole (standard)	−6.2	Conventional hydrogen bond	MET259
				Carbon hydrogen bond	ASN350
				Pi‐sulfur	MET259
				Alkyl	ALA316, ALA354, ILE347, CYS241, VAL318
				Pi‐alkyl	ALA316, LYS352
5	1ERR	β‐Sitosterol acetate	−8.7	Alkyl	LEU346, ALA350, LEU525, LEU384, LEU387, MET388, ILE424, MET421, LEU428
				Pi‐alkyl	TRP383, PHE404
		γ‐Sitosterol	−8.7	Alkyl	LEU346, ALA350, LEU525, LEU384, LEU387, MET388, ILE424, MET421, LEU428
				Pi‐alkyl	TRP383
		γ‐Tocopherol	−8.6	Conventional hydrogen bond	LEU387
				Pi‐sigma	TRP383
				Pi‐Pi T‐shaped	PHE404
				Alkyl	PHE404, ALA350, LEU384, LEU525, LEU387, LEU391, LEU349, LEU387, LEU525, LEU354, LEU536
				Pi‐alkyl	TRP383, TYR526, ALA350, LEU387
		Colchicine (standard)	−6.3	Carbon hydrogen bond	ASP351
				Alkyl	LEU525, LEU536, LEU539
				Pi‐alkyl	LEU536

FIGURE 10.

Ligand–receptor binding assessment via molecular docking against the human monoamine oxidase (PDB: 2Z5X): A1. γ‐Tocopherol, A2. β‐Sitosterol acetate, A3. γ‐Sitosterol, and A4. Diazepam (Standard).

FIGURE 11.

Ligand–receptor binding assessment via molecular docking against the human serotonin transporter (PDB: 5I6X): B1. Stigmasta‐7,25‐dien‐3‐ol, (3β,5α)‐; B2. β‐Sitosterol acetate; B3. γ‐Sitosterol; and B4. Fluoxetine (Standard).

FIGURE 12.

Ligand–receptor binding assessment via molecular docking against the human GABA_A receptor alpha1‐beta2‐gamma2 subtype (PDB: 6X3W): C1. Stigmasterol, C2. β‐Sitosterol acetate, C3. γ‐Sitosterol, and C4. Diazepam (Standard).

FIGURE 13.

Ligand–receptor binding assessment via molecular docking against the tubulin–colchicine: Stathmin‐like domain complex regulator (PDB: 1SA0): D1. Stigmasterol; D2. γ‐Sitosterol; D3. Stigmasta‐7,25‐dien‐3‐ol, (3β,5α)‐; and D4. Albendazole (Standard).

FIGURE 14.

Ligand–receptor binding assessment via molecular docking against the human estrogen receptor (PDB: 1ERR): E1. β‐Sitosterol acetate, E2. γ‐Sitosterol, E3. γ‐Tocopherol, and E4. Colchicine (Standard).

3.7.2.1. Molecular Docking for Anxiolytic Activity

All compounds interacting with the human MAO enzyme (PDB: 2Z5X) were investigated. γ‐Tocopherol exhibited the most potent binding interaction (−10.4 kcal/mol), followed by β‐Sitosterol acetate and γ‐Sitosterol (both −8.9 kcal/mol). The reference drug diazepam showed a lower binding affinity (−8.5 kcal/mol) than these top compounds. Detailed docking analysis revealed that γ‐Tocopherol formed 13 contacts with short intermolecular distances, indicating its strong affinity for binding interaction at the catalytic site of the MAO‐A enzyme (Table 8, Section 1; Figure 10).

3.7.2.2. Molecular Docking for Antidepressant Activity

Antidepressant activity was demonstrated by each chemical that was successfully docked with the human serotonin transporter (PDB: 5I6X). Regarding binding strength to the target receptor, Stigmasta‐7,25‐dien‐3‐ol (3β,5α) outperformed the reference medication fluoxetine (−9.2 kcal/mol) with a binding affinity of −10.4 kcal/mol. With short intermolecular distances, Stigmasta‐7,25‐dien‐3‐ol (3β,5α) formed 11 contacts with ARG104, PHE335 (2), ALA169, ALA173, ILE172 (2), LEU443, TYR95, TYR176, and PHE341. This suggests that the material exhibits a strong affinity for the active site of the human serotonin transporter (Table 8, Section 2; Figure 11).

3.7.2.3. Molecular Docking for Sedative Activity

Each compound demonstrated notable analgesic activity with the human GABA_A ion channel receptor (PDB: 6X3W). Stigmasterol exhibited a higher binding affinity (−5.8 kcal/mol) than diazepam as the positive control (−4.9 kcal/mol). Through four interactions at short intermolecular distances, Stigmasterol interacted with key residues—PRO233, MET236, LEU240, and LEU269. The two other compounds showing significant interactions were β‐sitosterol acetate and γ‐sitosterol (Table 8, Section 3; Figure 12).

3.7.2.4. Molecular Docking for Anthelmintic Activity

The anthelmintic efficacy of the selected biological components of J. gossypifolia fruits was determined by analyzing the interactions between amino acids and the tubulin regulatory complex (PDB: 1SA0). The anthelmintic effect of each drug was associated with its affinity for the receptor. Stigmasterol (−7.9 kcal/mol) demonstrated the highest binding affinity toward the target receptor (−6.2 kcal/mol) compared to the reference drug, albendazole. Gamma‐sitosterol (−7.5 kcal/mol) and stigmasta‐7,25‐dien‐3‐ol (3β,5α) (−7.4 kcal/mol) were the second most prevalent substances (Table 8, Section 4; Figure 13).

3.7.2.5. Molecular Docking for Cytotoxic Activity

Each compound demonstrated an affinity for the human estrogen receptor (PDB: 1ERR), which correlated with its anti‐inflammatory properties. Notably, β‐Sitosterol acetate (−8.7 kcal/mol) and γ‐Sitosterol (−8.7 kcal/mol) had superior binding affinity to the target receptor compared to Colchicine (−6.3 kcal/mol) (Table 8, Section 5; Figure 14).

3.8. Pass Prediction

Seventeen compounds were selected for the pass prediction study. The results are given in Table 9.

TABLE 9.

Pass prediction of pharmacologically active compounds of JGF‐ME.

Compound name	Biological activity
	Anxiolytic		Antidepressant		Sedative		Cytotoxic		Anthelmintic
	Pa	Pi	Pa	Pi	Pa	Pi	Pa	Pi	Pa	Pi
Glucitol, 6‐O‐nonyl‐	0.208	0.138	—	—	0.232	0.034	0.340	0.045	0.268	0.050
Phenol, 3,5‐bis(1,1‐dimethylethyl)‐	0.078	0.035	—	—	0.259	0.022	0.335	0.046	0.352	0.024
β‐D‐Glucopyranoside, methyl	—	—	—	—	0.186	0.075	0.731	0.008	0.312	0.034
Hexadecanoic acid, methyl ester	0.290	0.068	0.219	0.051	0.218	0.060	0.295	0.060	0.394	0.018
9,12‐Octadecadienoic acid (Z,Z)‐, methyl ester	0.181	0.174	0.120	0.114	0.226	0.055	0.359	0.039	0.380	0.020
Oxacycloheptadec‐8‐en‐2‐one, (8Z)‐	0.307	0.058	0.158	0.084	0.168	0.106	0.257	0.079	0.318	0.032
9,12‐Octadecadien‐1‐ol, (Z,Z)‐	0.323	0.049	0.187	0.067	0.172	0.101	0.380	0.034	0.412	0.015
9‐Octadecenamide, (Z)‐	0.205	0.141	—	—	0.237	0.049	0.247	0.084	0.333	0.028
γ‐Tocopherol	0.186	0.166	—	—	—	—	0.481	0.021	0.169	0.126
β‐Sitosterol acetate	—	—	—	—	—	—	0.478	0.021	—	—
Stigmasterol	—	—	—	—	—	—	0.388	0.033	—	—
Stigmasta‐7,25‐dien‐3‐ol, (3β,5α)‐	—	—	—	—	—	—	0.359	0.039	—	—
γ‐Sitosterol	—	—	—	—	—	—	0.484	0.021	—	—
Phenol, 2,6‐dimethoxy‐	0.097	0.021	0.155	0.085	0.183	0.089	0.379	0.034	0.278	0.046
Diglycerol	0.230	0.115	0.139	0.097	0.158	0.120	0.331	0.047	0.208	0.083
Erythritol	0.295	0.065	0.210	0.055	0.392	0.004	0.327	0.049	0.244	0.061
DL‐Arabinitol	0.265	0.086	0.175	0.073	0.074	0.005	0.349	0.042	0.255	0.056

Note: Bold value represents the highest score among the compounds.

4. Discussion

Several extracts and naturally occurring compounds have drawn significant interest for their potential in preventing and treating chronic conditions. Plant extracts may contain compounds with beneficial biological attributes that combine to generate complex combinations with multi‐target effects that can simultaneously block or regulate several key targets [63]. In this regard, we explore ethnomedicine in this research to reveal the unrealized therapeutic potential of J. gossypifolia fruits, commonly known as the “Bellyache bush.”

While Jatropha species are generally known for their toxicity, several studies have reported relatively low toxicity for J. gossypifolia in both in vitro and in vivo models. Acute oral toxicity studies, particularly with the ethanolic leaf extract, have demonstrated safety in rats, with no significant adverse effects observed at high doses—classifying it as practically nontoxic in short‐term administration. However, concerns arise with chronic use; some reports indicate potential hepatotoxicity or other organ toxicities following prolonged exposure, suggesting dose‐ and duration‐dependent effects. This highlights the importance of careful dosage regulation and long‐term safety evaluation when considering J. gossypifolia for therapeutic applications [64]. A key strength of this study is the evaluation of acute oral toxicity, which demonstrated that the methanolic fruit extract of Jatropha gossypifolia (JGF‐ME) is safe at doses up to 4000 mg/kg body weight, with no mortality or observable adverse effects during the 14‐day observation period. According to OECD Guideline 423 [65], this indicates an LD₅₀ greater than 4000 mg/kg, classifying the extract as practically nontoxic. The absence of behavioral, neurological, or autonomic toxicity at such high doses underscores its wide safety margin and supports its traditional use in folk medicine. This favorable safety profile significantly enhances its therapeutic potential and highlights its suitability for further development as a herbal remedy for widespread use.

Gamma‐aminobutyric acid (GABA) is the most predominant suppressive signaling molecule in the human CNS. The GABAergic system is a viable target for novel pharmaceutical approaches to the management of anxiety [66]. GABA plays a key role in managing anxiety, vision, and motor control. Benzodiazepines enhance their calming effect by interacting with GABA_A receptors, allowing chloride ions into neurons through activated channels. The different antianxiety, sedative, or anti‐seizure effects of these medications result from this mechanism, which makes the neuron negatively charged and resistant to stimulation [67]. In our current research, we employed the EPM and HBT to investigate the anxiolytic potential of the methanolic extract of J. gossypifolia fruits. The frequency of entries into open and closed arms, as well as the duration of stay in each arm by the test animals, are indicators of the antianxiety test, EPM. The choice of diazepam as a positive control in this investigation is supported by the fact that sensitivity to drugs operates through the γ‐aminobutyric acid receptor complex [68]. The anxiolytic responses to diazepam were confirmed by the increase in open arm entrances and time spent in the open arms. The JGF‐ME extract was utilized at doses of 200 and 400 mg/kg to determine its antianxiety effects. At these dosages, the extracted dose‐dependently reduced closed‐arm entries while significantly enhancing open‐arm entries compared with the control group. Although the impact increased with increasing extract dosage, a notable antianxiety effect was observed in the whole experiment. At 200 and 400 mg/kg, J. gossypifolia exhibited considerable anxiolytic action in our research compared with the control group; however, its activity was lower than that of the reference medication, diazepam. The Hole Board test is another useful method of assessing anxiolytic action. The HBT apparatus relies mainly on the head‐dipping behavior of the experimental mice. Head‐dipping may have been elevated if the test animals respond to changes in emotional or anxiety‐related states [69]. The results demonstrated that the extract JGF‐ME showed a gradual increase in the number of head dips at both high and low doses (200 and 400 mg/kg). Compared to the negative and positive controls (diazepam), at 400 mg/kg, the extract demonstrated a significant anxiolytic effect (p < 0.001). The Light–Dark Test (LDT), similar to the EPM and HBT, is a valuable model for studying anxiety and predicting the efficacy of medications used to treat this neurological condition. The time mice spend on the light side of the box is considered a practical and reliable indicator of anxiety levels [70]. The study found that JGF‐ME at a 400 mg/kg dose increased the time spent in the lightbox to 126.7 ± 0.12 s, significantly higher (p < 0.001) than the control's 89 ± 0.05 s. Thus, the extract demonstrated strong anxiolytic effects across all three tests. The sedative effects can potentially confound the interpretation of anxiolytic activity, as reduced locomotion may reflect CNS depression rather than anxiolysis alone. To address this, we evaluated behavior using both the open field test and hole‐cross test, which assess general locomotor activity and exploratory behavior—key indicators of sedation. The results showed a significant reduction in motor activity at higher doses, suggesting a sedative component. However, increased time spent in open arms (in the elevated plus maze) and enhanced head‐dipping (in the Hole‐Board test) at specific doses indicate that the extract also exerts anxiolytic‐like effects, independent of sedation, likely mediated through GABAergic or serotonergic pathways.

For figuring out the efficiency of antidepressants, the FST and TST are widely accepted methods. This research compares immobility, a typical behavior evaluation, to human depression. The reduction of mice's durations of immobility is comparable to the drug's antidepressant potential [46]. The energy generated by mice attempting to break free from their suspension is the basis for the measurement principle. In this test, the movements were examined regarding the strength and energy they produced over time [71]. In both FST and TST, JGF‐ME extract dose‐dependently prolonged immobility time. According to the findings, the extract can be considered a potent antidepressant agent.

The sedative effects can potentially confound the interpretation of anxiolytic activity, as reduced locomotion may reflect CNS depression rather than anxiolysis alone. To address this, we evaluated behavior using both the open field test (OFT) and hole‐cross test (HCT), which assess general locomotor activity and exploratory behavior—key indicators of sedation. The results showed a significant reduction in motor activity at higher doses, suggesting a sedative component. However, increased time spent in open arms (in the elevated plus maze) and enhanced head‐dipping (in the hole‐board test) at specific doses indicate that the extract also exerts anxiolytic‐like effects, independent of sedation, likely mediated through GABAergic or serotonergic pathways. OFT and HCT are standard techniques for measuring sedative activity [72]. Anxiolytics like diazepam reduce animal movements in both HCT and OFT, indicating decreased exploratory behavior in novel surroundings. Reduced locomotion, which is a hallmark of calm and sleepiness, may be construed as less CNS excitability. In contrast, increased locomotor activity is a sign of mental alertness or attention [47]. Our research revealed that, overall, during the OFT, there was a steady decline in movement within it. During the following observation sessions (30, 60, 90, and 120 min), all tested doses of JGF‐ME (200 and 400 mg/kg) and diazepam caused significant declines in locomotor activity versus the control group.

The brine shrimp lethality assay (BSLA) is a rapid, relatively inexpensive, and straightforward bioassay for evaluating the bioactivity of plant extracts, which often correlates well with cytotoxic and antitumor effects [73]. Our extract exhibited a dose‐dependent mortality rate in the BSLB test, with an LC₅₀ value of 327.87 μg/mL, while colchicine showed an LC₅₀ value of 38.81 μg/mL. Again, the highest mortality was seen at a 1000 μg/mL concentration (70% and 90% for the extract and standard drug, respectively). Hence, the JGF‐ME can be considered a moderate cytotoxic agent, and further investigation is required for safety purposes.

The anthelmintic activity of the fruit extract was performed using P. posthuma (earthworms) because earthworms and intestinal worms, such as tapeworms, roundworms, pinworms, etc., have morphological similarities, and their mechanisms of action also exhibit physiological resemblance [74]. When compared to the standard albendazole, the JGF‐ME extract demonstrated remarkable dose‐dependent anthelmintic action in earthworms. The results of this experiment revealed that the extract triggered both paralysis and death in earthworms and that the concentration of the plant extract exhibited an inverse relationship with the estimated times for paralysis and death in earthworms.

Molecular docking is essential for analyzing ligand‐target interactions, exploring how small molecules bind to protein sites, and revealing mechanisms underlying pharmacological actions [75, 76]. In this study, molecular docking analysis was performed to gain a deeper understanding of the molecular mechanisms underlying the observed pharmacological activities of J. gossypifolia. Seventeen phytochemicals from J. gossypifolia were docked against five proteins: human MAO‐A enzyme (PDB: 2Z5X), serotonin transporter (PDB: 5I6X), GABA_A ion channel receptor (PDB: 6X3W), tubulin‐stathmin complex (PDB: 1SA0), and estrogen receptor (PDB: 1ERR) to assess their anxiolytic, antidepressant, sedative, anthelmintic, and cytotoxic potentials. The results revealed that four major compounds—γ‐Tocopherol, Stigmasta‐7,25‐dien‐3‐ol (3β,5α)‐, Stigmasterol, and β‐Sitosterol acetate—showed significant docking interactions with the target proteins associated with the activities above. Based on the convergence of in vivo pharmacological effects and in silico molecular docking results, it can be inferred that the observed anxiolytic, antidepressant, sedative, anthelmintic, and modest cytotoxic activities of the methanolic fruit extract of J. gossypifolia (JGF‐ME) are likely mediated by its top binding phytoconstituents through their strong interactions with key target proteins. The alignment between behavioral responses in animal models and predicted binding affinities supports a multi‐target mechanism of action, suggesting that these compounds synergistically contribute to the overall therapeutic potential of JGF‐ME.

We acknowledge that docking simulations primarily predict binding affinity and interaction modes but do not account for key factors such as cellular permeability, metabolic stability, off‐target effects, or dynamic protein behavior (e.g., allosteric regulation, conformational changes). Furthermore, scoring functions may produce false positives or rank inactive compounds highly due to limitations in accurately modeling solvation, entropy, and intermolecular forces.

Additionally, while a strong binding affinity (e.g., a low docking score) suggests potential interaction with a target, it does not necessarily equate to functional modulation (agonism, antagonism, or inhibition). Experimental validation through in vitro or in vivo assays remains essential to confirm biological relevance.

We have emphasized that our docking results are hypothesis‐generating, supporting the observed pharmacological effects by suggesting plausible mechanisms of action, but not proving them. These insights should guide future studies involving enzyme inhibition assays, receptor binding studies, or structural optimization.

Cytotoxicity was assessed solely using the brine shrimp lethality assay; thus, future studies should include cytotoxicity testing on cancer cell lines. Additionally, molecular dynamics simulations could be employed in subsequent research. These aspects represent the current limitations of our study.

5. Conclusion

This study demonstrates that the methanolic fruit extract of J. gossypifolia (JGF‐ME) exhibits significant antidepressant, anxiolytic, sedative, and anthelmintic activities, supported by integrated experimental and in silico evidence. Key phytoconstituents showed strong binding to neuropharmacological targets and favorable drug‐likeness profiles, corroborating their therapeutic potential.

These findings highlight the potential of JGF‐ME as a phytomedicine for the management of anxiety disorders, major depressive disorder, insomnia, and soil‐transmitted helminth infections—conditions where current therapies are limited by side effects or access. Its multi‐target profile suggests possible use as an adjunctive or alternative therapy in integrative medicine, particularly in primary care or rural health settings. Further research is needed to isolate active principles, validate mechanisms, and conduct preclinical and early‐phase clinical studies to translate these findings into evidence‐based herbal formulations.

Author Contributions

Md. Arafat: investigation, data curation, data analysis, formal analysis, software, project administration, writing. Nusrat Jahan Moon: data analysis, data curation, investigation, software, writing. Mahathir Mohammad: investigation, data curation, data analysis, software, writing. Md. Jahirul Islam Mamun: investigation, data curation, data analysis, software, writing. Jannatul Naima Meem: investigation, data curation, data analysis, software. Md. Hossain Rasel: investigation, data curation, data analysis, software, writing. Borhan Uddin: investigation, data curation, data analysis, writing. Md. Safayat Hossen Momen: investigation, data curation, data analysis, software. Md. Liakot Ali: investigation, data curation, data analysis, writing. Neamul Hoque: investigation, data curation, data analysis, writing. S. M. Moazzem Hossen: supervision, investigation, data curation, data analysis, software, writing.

Conflicts of Interest

The authors declare no conflicts of interest.

Arafat M., Moon N. J., Mohammad M., et al., “Comprehensive Evaluation of Methanolic Fruits Extract of Jatropha gossypifolia L.: Neuropharmacological, Cytotoxic, Anthelmintic, GC–MS Profiling, and Molecular Docking Studies,” Pharmacology Research & Perspectives 13, no. 6 (2025): e70185, 10.1002/prp2.70185.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.