Decoding Ocimum basilicum L.'s Medicinal Potential Through Growth Phase Profiling: A Conservation Perspective

ABSTRACT

Ocimum basilicum L. is widely documented for its medicinal properties, primarily attributed to essential oils (EOs) produced by glandular trichomes on its leaves. The present study investigates how different leaf growth phases lag, log, and stationary affect EO yield and pharmacological activity. Microscopic analysis revealed that trichome density was highest during the lag phase, while trichome size peaked was greatest in the stationary phase. EO yield was greatest in the lag phase (1.26 mL/5 g fresh weight); however, GC–MS profiling indicated that the stationary phase EO contained the highest concentrations of key bioactive compounds, notably eriodictyol and 2,5‐dioxoimidazolidin‐4‐yl urea. Bioactivity assays demonstrated that EO from the log phase exhibited the strongest DPPH radical scavenging activity (63.87 ± 2.95%), whereas the stationary phase EO showed highest total phenolic content (982.61 ± 62.45 mg GAE/g), antioxidant capacity (0.79 ± 0.01 mg EAA/g), anti‐inflammatory activity (91.89 ± 10.28%), and antidiabetic potential (95.56 ± 0.00%). Antimicrobial testing confirmed the stationary phase EO as the most potent, with the lowest MIC and MBC values against Staphylococcus aureus (6.25 and 3.125 µL/mL), Ralstonia solanacearum (12.5 and 3.125 µL/mL), and Xanthomonas oryzae (50 and 3.125 µL/mL). These findings suggest that while EO yield is highest in the lag phase, the stationary phase offers the most comprehensive pharmacological potential, making it the optimal harvest stage for medicinal applications.

1. Introduction

Global demand for herbal medicines, beauty products, nutraceuticals, healthcare items, dietary supplements, and pharmaceuticals is steadily increasing [1]. According to the World Health Organization (WHO), in developing nations approximately 70%–95% of the population relies on plants for their basic healthcare needs [2]. Historically, indigenous communities have depended on plants for nutrition and healing. The natural flora has been a valuable source for improving health and treating various diseases across cultures. Approximately 25% modern medications and numerous synthetic analogs are derived from plant‐based prototype chemicals [3]. It is well‐known that reactive oxygen species (ROS), which including hydroxyl radicals (OH·), and hydrogen peroxide (H₂O₂), contribute to oxidative stress, which is linked to diseases such as diabetes, cancer, anemia, ischemia, and neurodegenerative disorders [4]. Elevated levels of flavonoids and phenolic compounds in leaves are associated with enhanced anticancer, antioxidant, and radical‐scavenging activities [5]. Plant‐derived antioxidants play a protective role by inhibiting free radical production [6]. Proinflammatory molecules like TNF‐α and nitric oxide (NO) are often overproduced during inflammation, can combine to form peroxynitrite, causing irreversible damage to cell membranes, tissues, and leading to cell death [7].

Rise in antibiotic resistance (AMR) as a serious public health threat in recent years [8]. Adverse effects of allopathic medications contribute to approximately 8% of hospital admissions in the United States and result in around 100 000 deaths annually [9]. Microbial AMR leads to a substantial number of fatalities annually [10]. The emergence of antibiotic‐resistant bacterial strains could potentially cause up to 10 million deaths annually and incur costs of $100 trillion by 2050 [11]. AMR can impact human health in terms of preventive and therapeutic effects [8]. With growing awareness of this global issue, medicinal herbs have gained attention as promising sources for novel antibacterial agents. These medicinal herbs contain numerous chemical constituents, including alkaloids, flavonoids, and phenolic compounds, which exhibit strong antibacterial properties [12]. Over 30 000 antimicrobial molecules have been isolated from plants with more than 1340 species demonstrating antimicrobial activity (Vaou et al. 2021).

The ability of pathogenic bacteria to withstand high antibiotic concentrations is partly attributed to biofilm formation, which is linked to virulence [13]. Biofilms produced by many microbial infections pose a significant threat to human health and are difficult to eradicate [14]. More than 80% of human infections are caused by biofilms with Staphylococcus aureus being the most common species in biofilm related infections [15]. Plant essential oils (EOs) are of particular interest for their potential to combat biofilms and produce novel therapeutic constituents [16]. Plant phytochemicals, or secondary metabolites, have diverse mechanisms of action as antibiofilm agents [17].

The Lamiaceae family is widely distributed in temperate and tropical regions, comprising approximately 250 genera and 7000 species [18]. Plants of the genus Ocimum, known for their medicinal value, are highly valued for their therapeutic potential due to their phenolic chemical constituents [19]. Several Ocimum species, particularly O. basilicum L. are used for treating various diseases due to their antioxidant antifungal, and antimicrobial properties [20].

O. basilicum, commonly known as sweet basil [21], is distributed in tropical regions of central and South America Asia and Africa comprising of between 30 and 160 species [22]. It is a versatile plant known for its high concentration of fragrant EOs [23]. O. basilicum can be utilized in various forms such as dry leaves, flowers, EO, and decorative plants [21]. The EO of O. basilicum was found to contain 39 distinct components by gas chromatography–mass spectrometry (GC–MS) [24]. O. basilicum EO contains a variety of chemical compounds such as terpenes and phenylpropanoids, alcohols and aldehydes. The study found that the yield, quality, and types of phytochemicals largely depend on the extraction methods used and environmental conditions [25]. Numerous studies have documented the biological properties of O. basilicum EO [26].

Trichomes, epidermal appendages present on the upper and lower surface of plant leaves, have diverse morphologies and can be single celled or multicellular [27]. Glandular trichomes (GTs) are categorized into peltate and capitate trichomes [28], with sizes ranges from micrometers to few centimeters [29]. O. basilicum posess GTs where EO are stored (Tirillini et al. 2021). These compounds are commercially significant in pharmaceuticals, pesticides and food flavoring. GTs are involved in the synthesis, storage, and secretion of phytochemicals [30]. Basil GTs have produced EO with potential antioxidant and antimicrobial properties [31]. O. basilicum is rich in EOs, making it a focus of numerous chemical investigations (Tirillini et al. 2021). Traditional methods for extracting EOs from plants mainly include steam distillation, hydrodistillation, solvent extraction, and cold pressing [32]. Network pharmacology has emerged as novel approach for drug development and understanding drug mechanisms of action [33]. Network pharmacology methods, including chemoinformatics, bioinformatics, network biology, and pharmacology, valuable for studying and elucidating drug activities [34]. The main aim of this study was to evaluate the pharmacological activities of phytochemical constituents from EOs at different leaf growth stages (lag, log, and stationary phase) and to identify the nature, size and frequency of trichomes on the leaf surface. The study aimed to assess the pharmacological activities of phytochemical constituents in EOs at different leaf growth stages (lag, log, and stationary phase) and to analyze the trichome characteristics on the leaf surface. Sweet basil (O. basilicum L.) is a popular herb with EOs known for their antioxidant, anti‐inflammatory, antimicrobial, and antidiabetic properties. The composition of these oils changes with leaf development stages, affecting their medicinal properties. Understanding this relationship is essential for sustainable harvesting and biodiversity conservation. This research aims to determine the best harvest stage to maximize medicinal benefits while promoting conservation efforts.

2. Materials and Methods

2.1. Plant Material and Propagation

The plant used for the current study was O. basilicum L. also known as, sweet basil. Botanically authenticated plants of O. basilicum were vegetative cultivated from stem cutting of plants obtained from the Plant Genetic Resource Center, National Agriculture Research Center Islamabad in March 2024. The plants were grown in sandy loam soil (sand 50%, clay 5%, and silt 45%) with an electrical conductivity of 1.59 ds/m and organic matter of 0.48%. The soil pH range varied from 5 to 8 and the temperature range was 15°C–23°C for plant germination [35]. The leaves of O. basilicum were collected based on their developmental stages. O. basilicum immature leaves, which had an initial length of 15 mm, were measured every day from the base to the tip until they reached full maturity. When the plants had just started to grow true leaves, about 10–14 days after germination, the freshly emerging leaves (lag phase) at the top of the shoot apex were harvested. The log phase leaves, which are situated beneath the apical meristem, were harvested 20–25 days after germination. During this time, the leaves exhibited strong metabolic activity and rapid growth. When the leaves were firm and exhibited indications of complete physiological maturity, about 35–40 days following germination, the stationary phase leaves were harvested. A leaf growth curve was plotted using these data points to classify the different leaf growth stages (lag, log, and stationary phase). Voucher specimen with accession no LCWU/BOT/1335 was submitted in herbarium of Lahore College for women university for future reference.

2.2. Scanning Electron Microscopy

For anatomical studies, the leaves were examined using a scanning electron microscope (SEM) following the methodology of Zaman et al. [36]. A single‐sided razor blade was used to cut the leaf from the plant and slice it into two sections. To avoid damaging the leaves, the samples were cut with a direct and downward force instead of a slicing motion. The sections of the leaf surface were then subjected to examination under a SEM EVO LX10 to examine the trichomes.

2.3. Anatomical Studies and Light Microscopy

For the anatomical studies, leaf epidermis samples were prepared following the modified methodology of Clarke et al. [37] and Cotton et al. [38]. Leaves were dipped in a 60 % ammonia solution for 2–3 min depending on the leaf texture. The soaked leaves were boiled in water for about 5 min at 100°C. Boiled leaves were transferred to test tube containing 88% lactic acid and soaked for 10 min. Lactic acid softens the leaf tissue making peeling off easier [39]. The leaf material was peeled off using a sharp blade. The leaf epidermis was cut across the leaf and scraped away with the mesophyll cells until only the epidermal layer of the leaf remained on the slide. All debris was removed using a camel brush. The slides of both the abaxial and adaxial surfaces of the leaves were observed under a light microscope (Model: M3503DF). The slides of the abaxial and adaxial leaf surface were placed under the light microscopy (LM) to observe the trichomes. A Samsung Galaxy S24 Ultra camera was used to capture the microphotographs. A magnification power of 100× was used for the identification of anatomical features.

The measurements of quantitative morphological characteristics (numbers, length, radius, area, and perimeter of various trichomes were measured using Motic Images Plus 2.0 software [40].

2.4. Extraction of EO by Hydrodistilation

Leaves from the particular three growth phases (lag, log, and stationary) were collected and rinsed with distilled water. The leaves were partially dried at room temperature for 2–3 days. For the hydrodistillation, 5 g of leaves from each stage of growth of O. basilicum were subjected to hydro distillation in 200 mL of water in a Clevenger‐type apparatus for 4 h at 100°C.The yield of EO was calculated as the ratio between the volume of oil extracted and the weight of partially dried leaves used for oil extraction. The EO was extracted in clean vials, dehydrated over anhydrous sodium sulfate (Na₂SO₄)_, and then stored at 4°C for further analysis [41].

2.5. GC–MS Analysis

GC–MS analysis was performed on a GC TRACE‐1300 chromatograph system equipped with an auto‐sampler (AI‐1310; Thermo Scientific, Waltham, MA, USA) and a single quadrupole mass spectrometry. A Capillary column of TR‐35 MS GC Column 25 mm ID × 30 m × 25 µm, was used. The injector temperature was maintained at 280°C. The column's initial temperature was maintained at 50°C for 5 min and then programmed at 3°C/min to 240°C and 5°C/min to 300°C for 3 min. The initial sample delay time was 3.5 min. The temperature of the ion source was set at 250°C while the transfer line temperature was 300°C. Helium (99.99%) was used as carrier gas with a linear gas flow of 1.5 mL per min through split‐less injection. The injected volume was kept constant at 1 µL, and mass spectra were taken at 70 eV. Different phytochemical compounds were identified by mass spectra based on their retention time and probability as well as comparison with those in the computer library [41].

2.6. Antioxidant Activity

The antioxidant potential of EOs was evaluated by three methods; DPPH, total phenolic content (TPC), and total antioxidant content.

2.6.1. DPPH Free Radical Scavenging Assay

The free radical scavenging assay of EOs was evaluated using the1,1‐diphenyl‐2‐picryl‐hydrazyl (DPPH) assay, following the methodology of Caprari et al. [42] and Hatano et al. [43]. A 0.6 M DPPH solution was prepared in ethanol. Then 0.2 mL of plant EOs at different concentrations (50, 25, 12.5, 6.25, and 3.125 µL/mL) was added to 0.5 mL of DPPH solution in test tubes and shaken well. The mixture was placed in the dark for 30 min. The absorbance was recorded at 517 nm against a blank. DPPH served as the negative control and α‐tocopherol served as the positive control. The following formula was used to calculate the radical scavenging activity:

$$\begin{array}{ccc}& & \mathrm{Scavenging}\mathrm{activity}\left(\%\right)\hfill \\ & & =\frac{\mathrm{Absorbace}\mathrm{of}\mathrm{control}-\mathrm{Absorbance}\mathrm{of}\mathrm{sample}}{\mathrm{Absorbance}\mathrm{of}\mathrm{control}}\times 100\hfill \end{array}$$

2.6.2. TPC Assay

The TPCs in the EOs were estimated using the Folin–Ciocalteu (FC) assay of Agbo et al. [44]. Diluted concentrations of EOs (50, 25, 12.5, 6.25, and 3.125 µL/mL) were used in the experiment. In each test tube 1.25 mL of FC reagent (10%) and 0.5 mL of different concentrations of EOs were mixed. The test tubes were sealed and kept in the dark for 5 min. Then 1 mL of 75% sodium carbonate (Na₂CO₃) was added to each test tube. The mixture was incubated for 10 min at 50°C and then at room temperature for 1 h. The absorbance was recorded at 765 nm using the UV spectrophotometer against a blank without EO. The resulting data were calculated as milligrams per gram (mg/g) of gallic acid equivalents.

2.6.3. Total Antioxidant Capacity

The total antioxidant capacity (TAC) of the EOs was evaluated by the phospho‐molybdenum assay following the methodology of Jan et al. [45]. One milliliter of reagent solution (4 mM ammonium molybdate, 600 mM sulfuric acid, and 28 mM sodium phosphate 1:1:1) was added in to test tubes with 0.1 mL of different concentrations of EOs (50, 25, 12.5, 6.25, and 3.125 µL/mL), and each test tube was covered with aluminum foil. The test tubes were incubated in a water bath for 90 min at 95°C. The reaction mixture was allowed to cool at room temperature. The absorbance of the mixtures was measured at 760 nm against a blank of the reagent solution (1 mL). Butylated hydroxytoluene (BHT) served as a positive control, and each sample was run in triplicate.

$$\mathrm{Inhibition}\left(\%\right)=\frac{A_0-A_1}{A_0}\times 100$$

where A ₀ is the absorbance of the reagent solution and A ₁ is the absorbance of the sample and ascorbic acid.

2.6.4. Anti‐Inflammatory Assay

The anti‐inflammatory activity was performed following the methodology of Ameena et al. [46]. A mixture of 0.1 mL of egg albumin and 1.4 mL of phosphate‐buffered saline (PBS) (pH 6.4) were mixed in a test tube. Then, 1 mL of different concentrations of EOs (50, 25, 12.5, 6.25, and 3.125 µL/mL) were carefully added to the reaction mixtures. The test tubes were placed in a water bath for 15–20 min at 37°C, afterward, reaction mixture was incubated for 5 min at 70°C. The reaction mixture was allowed to cool at room temperature for 15 min. The absorbance of the reaction mixture was measured by a spectrophotometer at 680 nm for each concentration. Diclofenac served as the positive control whereas distilled water served as the negative control. The following formula was used to calculate the protein inhibition percentage;

$$\begin{array}{ccc}& & \mathrm{Inhibition}\left(\%\right)\hfill \\ & & =\frac{\mathrm{Absorbance}\mathrm{of}\mathrm{control}-\mathrm{Absorbance}\mathrm{of}\mathrm{sample}}{\mathrm{Absorbance}\mathrm{of}\mathrm{control}}\times 100\hfill \end{array}$$

2.6.5. Antidiabetic Assay

The antidiabetic activity of O. basilicum EOs was evaluated by the α‐amylase assay following the methodology of Ali et al. [47]. Fifty microliters of α‐amylase enzyme solution was mixed with 50 µL of different concentrations of EOs (50, 25, 12.5, 6.25, and 3.125 µL/mL) and incubated in test tubes for 10 min at 25°C. Starch solution 50 µL was added in reaction mixture and kept the test tubes for 10 min at room temperature for incubation. Then, 3, 5‐dinitrosalicylic acid (DNSA) solution (50 µL) was added to the reaction mixture and cooled at room temperature after incubation in water bath at 100°C for 5 min. For dilution, 5 mL of water was added to the test tubes and the absorbance was recorded at 540 nm wavelength against a blank without EO. Sitagliptin served as positive control. The inhibition percentage was determined using the following formula:

$$\begin{array}{ccc}& & \mathrm{Inhibition}(\%)\mathrm{of}\alpha -\mathrm{amylase}\hfill \\ & & =\frac{\mathrm{Absorbance}\mathrm{of}\mathrm{control}-\mathrm{Absorbance}\mathrm{of}\mathrm{sample}}{\mathrm{Absorbance}\mathrm{of}\mathrm{control}}\times 100\hfill \end{array}$$

2.6.6. Antimicrobial Profiling of EOs

The bacterial strains used for the study including S. aureus (ATCC23235), Ralstonia solanacearum (KC756967), and Xanthomonas oryzae (ATCC‐35933) were obtained from the first Fungal Culture Bank, Institute of Agriculture Sciences, University of Punjab, Lahore, Pakistan.

2.6.7. Agar Well Diffusion Assay

The antibacterial activity of three phases (lag, log, and stationary) of O. basilicum EOs was evaluated by the agar well diffusion method following Aftab et al. [48]. The overnight grown bacterial strains were diluted with sterile nutrient broth to obtain an optical density of 0.1 OD₅₉₅. The sterilized media was streaked on plates and the inoculum was spread on nutrient agar media plates. Sterile cork‐borers no. 2 were used to make 2 mm holes in the agar. Subsequently, 100 µL of filter sterilized O. basilicum EO from the three phases (lag, log, and stationary) was added to each hole. The EOs were allowed to absorb into the agar and then the zones of inhibition were determined in millimeters.

2.6.8. Determination of the Minimum Inhibitory Concentration and Minimum Bactericidal Concentration

Micro dilution methods were performed to determine the minimum inhibitory concentration (MIC) in 96‐well plates following the methodology of Parvekar et al. [49]. The overnight grown bacterial strains were diluted with sterile nutrient broth to obtain an optical density of 0.1 OD₅₉₅. In each well of a 96‐well plate, 100 µL of S. aureus, R. solanacearum, and X. oryzae and 100 µL of lag, log and stationary phase EOs ranging from 50 to 3.125 mg/mL were added individually. The plates were then incubated for 24 h at 37°C. The turbidity was checked and the MIC was determined as the lowest concentration at which no visible growth (turbidity) was observed. To calculate the minimal bactericidal concentration, MIC broth was spread onto the nutrient agar plates with other higher concentrations that exhibited no visible growth. The plates were incubated for 24 h at 37°C and the minimum bactericidal concentration (MBC) was determined as lowest concentration that exhibited no bacterial growth.

2.6.9. Biofilm Inhibitory Potential

The biofilm inhibitory potential of EOs was examined following the protocol of Kaur et al. [50]. One hundred milliliters of EOs along with 100 µL of autoclaved nutrient broth and 20 µL of overgrown pathogenic bacterial strains (0.1 OD₅₉₅) were placed in each well of a 96‐well plate. The plate was incubated at 37°C for 48 h to facilitate the production of biofilm in the wells. After rinsing the plate three times with autoclaved distilled water to eliminate non‐adherent cells, the wells were fixed in 200 µL of methanol for 15 min. The wells were then air dried, and 200 µL of 2% crystal violet was used to dye the fixed biofilm for 5 min and the remaining dye was rinsed with distilled water, and the dye was extracted from the adherent cells using glacial acetic acid (160 µL of 33%), and the OD₅₉₅ was measured by a microtiter plate reader. Filtered PBS was used as the control. Percentage (%) inhibition was calculated using the following formula:

$$\mathrm{Inhibition}\left(\%\right)=\frac{100-\mathrm{OD}\mathrm{of}\mathrm{sample}}{\mathrm{OD}\mathrm{of}\mathrm{control}}\times 100$$

2.6.10. Hemolysis Assay

The hemolysis assay of EOs was performed following the methodology of Ribeiro et al. [51] Sharma et al. [52]. The ethics committee of Department of Botany approved the study (DEC/LCWU/2024‐06). Human blood samples were obtained from healthy volunteers with their consent and washed in isotonic PBS to remove the buffy coat by centrifugation for 5 min at 4000 rpm. An erythrocyte suspension (10%) was prepared in PBS. 0.1 mL of EO at different concentrations (50–3.125 µL/mL) was added to 0.1 mL of erythrocyte suspension (10%) and incubated at 37°C for 3 h. Five milliliters of NaOH was added to the test tubes to terminate the reaction. The samples were centrifuged at 2500 rpm for 15 min. For the positive control or 100% hemolysis, 0.1 mL of 0.2% Triton (in PBS) was added to 0.5 mL of 10% RBC suspension. The absorbance of the supernatant was measured at 541 nm using a spectrophotometer. The hemolytic percentage was calculated using the formula:

$$\mathrm{Hemolysis}\%=\frac{\mathrm{Heamoglobin}\mathrm{ABS}}{\mathrm{Heamoglobin}100\%\mathrm{ABS}}\times 100$$

2.6.11. Statistical Analysis

Statistical analysis of the experimental data was performed using GraphPad prism. Two‐way ANOVA was used to determine the significant differences among the values. The threshold for statistical significance was set at p ≤ 0.001.

2.7. Network Pharmacology Analysis

2.7.1. Screening for Active Compounds From the GC–MS Compounds of O. basilicum

Phytochemical compounds identified through GC–MS analysis were used to evaluate the active components. These were selected based on standard criteria from network pharmacology, including oral bioavailability (OB) ≥ 30% and drug‐likeness (DL) ≥ 0.18 [53]. Additional evaluation of the active compounds was conducted using ProTox‐II, focusing on toxicity and lethal dose (LD₅₀) values. Compounds were classified into six toxicity classes: Class 1 and 2 (fatal), Class 3 (toxic), Class 4 and 5 (less harmful), and Class 6 (non‐hazardous) [54].

2.7.2. Screening of Potential Targets for O. basilicum

Targets related to the potential bioactive compounds of O. basilicum were retrieved from multiple databases, including ETCM, STRING (Search Tool for the Retrieval of Interacting Genes/Proteins; http://string.embl.de/), SymMap, and the Similarity Ensemble Approach (SEA; https://www.sogou.com/link?url=LeoKdSZoUyC9U6gWDurrLbchwv7HyEQP), using the “Homo sapiens” setting [55].

2.7.3. Screening for Potential Targets of Antioxidant

The keywords “antioxidant” and “antimicrobial” were used to search for disease‐related genes across the following databases: Online Mendelian Inheritance in Man (OMIM; https://omim.org/), Gene Cards (https://www.genecards.org/), and the NCBI Gene Database (https://pubmed.ncbi.nlm.nih.gov/29140470/). Genes that appeared more than once across these databases were recorded (see Table S1).

2.7.4. Construction of Bioactive Component–Target Network

Cytoscape 3.7.0 was used to construct a component–target network based on the bioactive compounds and their predicted targets, to better understand the mechanisms underlying O. basilicum EO treatment in relation to antioxidant activity. In this network, green rhombus nodes represent bioactive components, blue circular nodes represent predicted targets, and edges indicate the interactions between them. The “degree” value of each node, reflecting the number of connections it has within the network, was calculated using the CytoHubba plug‐in [56].

2.7.5. Protein–Protein Interaction Network Construction and Hub Genes Analysis

To elucidate the potential mechanism underlying the antioxidant activity of O. basilicum, overlapping oxidant‐related and predicted targets were identified using an online platform. A protein–protein interaction (PPI) network was constructed from these overlapping targets using the STRING database (https://string‐db.org/) with the “Homo sapiens” setting. The PPI network was analyzed in Cytoscape 3.7.0, and the topological features of each node were visualized using the “Network Analysis” plug‐in. To gain further insight into the mechanism by which the species mitigates oxidative stress, hub genes were identified based on the topological characteristics of nodes within the PPI network. The degree value of each node was calculated using the CytoHubba plug‐in, and genes with the highest degree values were designated as antioxidant hub genes for Lamiaceae species. Additional information on the target type (protein class) of these hub genes was obtained from the DisGeNET database (https://www.disgenet.org).

2.7.6. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment Analyses

The gene ontology (GO) bioinformatics tool was used to predict the functions of the identified genes. To determine the systematic functions and biological relevance of the targets, the Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.kegg.jp/) database was utilized [57].

2.7.7. Molecular Docking

Molecular docking simulations were conducted using AutoDock Vina to evaluate the binding affinity and interaction profile between the selected ligand and the target protein. The receptor structure was obtained from the Protein Data Bank (PDB) and prepared by removing water molecules, adding polar hydrogens, and assigning Gasteiger charges using AutoDock Tools. The ligand was energy‐minimized and converted into the appropriate format prior to docking

3. Results

3.1. Leaf Growth Curve

Leaf growth measurements revealed a growth curve characterized by an initial lag phase, followed by an exponential (log) growth phase, and a subsequent deceleration until the leaf reached its final size. An exponential growth model was used to illustrate this pattern, comprising three distinct stages: lag phase, log phase, and stationary phase (Figure 1). Leaves representing each of these developmental stages were selected for further analysis. The leaf length ranges corresponding to the three stages were as follows: lag phase (1–20 mm), log phase (21–38 mm), and stationary phase (39–55 mm) (Figure 2). These developmental stages were referred to as lag, log, and stationary phase leaves.

FIGURE 1.

Leaf growth curve: Lag phase, log phase, and stationary phase.

FIGURE 2.

Leaf growth stages (a) lag phase, (b) log phase, and (c) stationary phase.

3.2. SEM and LM

3.2.1. Morphology and Distribution of GTs

The leaves of O. basilicum were covered with trichomes on both the abaxial and adaxial surfaces. The current study demonstrated that frequency and morphology of trichomes vary depending on the stage of leaf growth (Figures 3 and 4).

FIGURE 3.

Abaxial surface. (a) Lag phase trichomes, (b) log phase trichomes, and (c) stationary phase trichomes.

FIGURE 4.

Adaxial surface. (a) Lag phase, (b) log phase, and (c) stationary phase trichomes.

3.2.2. Abaxial Leaf Surface

On the abaxial surface, peltate‐type GTs with a four‐celled secretory head were observed. These trichomes exhibited a swollen head due to the accumulation of EOs within the subcuticular space. A high frequency of peltate GTs (26) was recorded during the lag phase of leaf development (Figure 3a). In contrast, the number of trichomes increased to 116 in the log phase (Figure 3b) and decreased to 12 in the stationary phase (Figure 3c). Overall, the frequency of GTs declined as the leaf matured from the lag to the stationary phase. The abaxial surface consistently exhibited the highest number of GTs across all growth stages.

Trichome size varied progressively with leaf development. Peltate GTs were smaller in the lag phase (46.84 µm), increased in size during the log phase (53.75 µm), and reached their maximum size in the stationary phase (64.66 µm). This reflected consistent increase in trichome size from the lag to the stationary phase (Table 1).

TABLE 1.

Trichomes on abaxial and adaxial surface of leaf at different leaf growth phases of Ocimum basilicum (lag, log, and stationary).

Sr. no.	Growth stages	Average no. of trichomes per unit area	Average length (µm)	Average radius (µm)	Average area (µm)	Average perimeter (Squm)
Abaxial surface
1	Lag	26	46.24	24.85	1961.14	156.14
2	Log	16	53.75	26.53	2343.88	164.85
3	Stationary	12	64.66	32.58	3405.84	205.57
Adaxial surface
1	Lag	11	44.76	23.91	1845.83	150.22
2	Log	10	51.50	26.92	2282.36	169.12
3	Stationary	9	75.66	39.22	4862.14	246.44

3.2.3. Adaxial Leaf Surface

On the adaxial surface, the frequency of peltate GTs was lower compared to the abaxial surface. The highest number of trichomes (11) were observed during the lag phase (Figure 4a). Similar to the abaxial surface, the frequency of GTs decreased as the leaf matured from the lag to the stationary phase (Figure 4).

Trichome size on the adaxial surface also increased progressively with leaf development. The smallest trichomes were recorded in the lag phase (44.76 µm), with an increase in size during the log phase (51.50 µm), and the largest trichomes observed in the stationary phase (75.66 µm). This trend indicates a steady enlargement of trichomes as the leaf matures, even though their frequency declines (Table 1; Figure 5).

FIGURE 5.

(a) SEM micrographs of adaxial surface of lag phase leaves at 70× magnification power. (b) SEM micrographs of adaxial surface of lag phase leaves at 100× magnification power. (c) SEM micrographs of adaxial surface of lag phase leaves at 200× magnification power. (d) SEM micrographs of adaxial surface of log phase leaves at 70× magnification power. (e) SEM micrographs of adaxial surface of log phase leaves of Ocimum basilicum at 100× magnification power. (f) SEM micrographs of adaxial surface of log phase leaves of O. basilicum at 200× magnification power.

3.2.4. Yield of EO

The yield of EO was 1.26 mL per 5 g, 0.53 mL per 5 g, and 0.49 mL per 5 g from the lag, log, and stationary phases, respectively. Leaves from the lag phase exhibited the highest number of trichomes and produced the greatest yield of EO. The EO extracted from lag phase leaves was yellowish green, whereas the oil from the log and stationary phases appeared dark green (Table 2; Figure 6).

TABLE 2.

Biomass and essential oil of lag, log, and stationary phases of basil leaves.

Sr. no.	Plants	Biomass of plant (g)	Essential oil (mL)	Color
1	Lag phase	5	1.26	Yellowish green
2	Log phase	5	0.53	Dark green
3	Stationary phase	5	0.49	Dark green

FIGURE 6.

(a) Color of lag phase EO. (b) Color of log phase EO. (c) Color of stationary phase EO.

3.3. GC–MS Analysis of Leaf EOs

The chemical composition of O. basilicum has been extensively studied, but there is limited information on the extraction and evaluation of phytochemical compounds from leaves collected at different growth stages—lag, log, and stationary. This study aims to analyze the chemical compounds produced at these stages using GC–MS to gain insights into their antimicrobial and other bioactive properties. During the lag phase, the EO contained 16 phytochemical compounds, while the oil extracted from this phase showed the presence of 15 identifiable compounds. Major constituents in the lag phase EO included butanoic acid anhydride (4.72%), 2‐hepten‐3‐ol, 4,5‐dimethyl‐ (4.72%), linalool (1.01%), and methyl petroselinate (1.01%). The oil extracted during the log phase exhibited a different chemical profile, with major compounds such as linalool (5.01%), oleic acid (3.55%), methyl petroselinate (3.55%), isobutyric anhydride (3.01%), and linalool (3.01%). The EO obtained during the stationary phase contained nine phytochemicals, with key constituents including linalool (25.36%), gougerotin (10.39%), and (2,5‐dioxoimidazolidin‐4‐yl)urea (10.39%). Analysis revealed dynamic changes in compound concentrations across the growth stages, with certain compounds increasing or decreasing in abundance. For example, 2‐furanol, tetrahydro‐2‐methyl increased from 0.18% in the lag phase to 2.47% in the stationary phase, while linalool rose from 1.01% in the lag phase to 25.36% in the stationary phase. Overall, the study demonstrated significant variations in both the concentration and chemical composition of EOs extracted from O. basilicum leaves at different developmental stages (Table 3).

TABLE 3.

GC–MS peak report of essential oils extracted at three different leaf developmental stages.

Sr. no.	Compounds name	Lag phase	Log phase	Stationary phase
1	Linalool	1.01	5.01	25.36
2	Acetic, pentyl ester	0.34	—	—
3	Butyric acid hydrazide	0.71	0.43	2.47
4	2‐Methylpyrrolidine	0.30	2.40	—
5	2‐Methylbutyl isovalerate	0.30	2.40	—
6	Acetic acid hydrazide	0.07	—	—
7	2‐Furanmethanol, tetrahydro‐	0.25	—	2.47
8	2‐Furanol, tetrahydro‐2‐methyl‐	0.18	0.90	2.47
9	Butane, 2,2‐dimethyl‐	0.70	—	—
10	Isobutyric anhydride	0.25	3.01	—
11	Butanoic acid anhydride	4.72	0.90	—
12	Pantene, 2,3,4‐trimethyl‐	0.15	—	—
13	2‐Hepten‐3‐ol, 4,5‐dimethyl‐	4.72	—	—
14	Eriodictyol	0.09	—	1.11
15	Methyl petroselinate	1.01	3.55	—
16	Neral	—	0.01	4.27
17	Diethyl phthalate	—	1.96	—
18	1‐Heptadecyne	—	2.13	—
19	Oleic acid	—	3.55	—
20	Gougerotin	—	—	10.39
21	Limonene	—	—	1.11
22	Biurea	—	—	1.11
23	2,5‐Dioxoimidazolidin‐4‐yl)urea	—	—	10.39

3.4. TPC

Phenolic compounds are among the most abundant bioactive molecules, known for their strong antioxidant potential. They scavenge free radicals and help mitigate oxidative stress. Through GC–MS analysis of the stationary phase EO, antioxidant compounds such as eriodictyol (a flavonoid), gougerotin (a phenol), and linalool (a terpene) were identified. Phenolic compounds, due to their hydroxyl groups, are capable of neutralizing free radicals [58]. Mokdad‐Bzeouich et al. [59] reported that the presence of multiple double bonds and hydroxyl groups in eriodictyol enables it to donate electrons and stabilize free radicals. The TPC of O. basilicum EOs at three developmental phases lag, log, and stationary was determined using the FC reagent method, with absorbance measured at 760 nm. The maximum TPC was recorded in the stationary phase EO at 982.61 ± 62.45 mg GAE/g, followed by the log phase at 946.55 ± 0.00 µg GAE/g, and the lag phase at 351.33 ± 1.06 mg GAE/g at a concentration of 50 µL/mL. The stationary phase exhibited the highest TPC (Figure 7). The results indicated a statistically significant difference in TPC across all growth stages and concentrations (p < 0.0001).

FIGURE 7.

Total phenolic content of Ocimum basilicum essential oils of three phases (lag, log, and stationary) at different concentrations. Data are mean ± SD (n = 3). 50, 25, 12.5, 6.25, and 3.125 µg/mL.

3.5. Antioxidant Activity

The total antioxidant activity of EOs was evaluated by total antioxidant assay, DPPH, and TPC.

3.5.1. DPPH Free Radical Scavenging Assay

The DPPH radical scavenging activity was measured at 517 nm to assess the ability of EOs from the lag, log, and stationary phases to neutralize free DPPH radicals. GC–MS analysis of the EO from the log phase revealed the presence of methyl petroselinate, a fatty acid with antioxidant properties due to its ability to donate a proton and scavenge free radicals. Hussain et al. [60] reported that fatty acid methyl esters from O. basilicum exhibit significant antioxidant activity, as evaluated by the DPPH assay.

The free radical scavenging capacity increased with higher concentrations of EO. The maximum DPPH radical scavenging activity was 62.28 ± 2.12% for the lag phase, 63.87 ± 2.95% for the log phase, and 63.48 ± 0.23% for the stationary phase at a concentration of 50 µL/mL. The highest activity was observed during the log phase (63.87 ± 2.95%) (Figure 8). These results indicated significantly high DPPH radical scavenging activity (p < 0.0001) across all developmental stages and concentrations.

FIGURE 8.

DPPH radical scavenging % of Ocimum basilicum essential oil of three phases (lag, log, and stationary) at different concentrations. Data are mean ± SD (n = 3). Highly significant (****) (p < 0.0001). 50, 25, 12.5, 6.25, and 3.125 µg/mL.

3.5.2. TAC

The phospho‐molybdenum method was used to determine the total antioxidant activity of O. basilicum EOs at different leaf developmental stages (lag, log, and stationary). The TAC was measured at 760 nm. The EO from lag phase leaves showed a maximum antioxidant capacity of 0.75 ± 0.01 mg EAA/g, followed by 0.76 ± 0.01 mg EAA/g in the log phase, and 0.79 ± 0.01 mg EAA/g in the stationary phase at a concentration of 50 µL/mL. The highest total antioxidant content was observed in the stationary phase (0.79 ± 0.01 mg EAA/g). EOs from all developmental stages and concentrations exhibited significant TAC (p < 0.0001) (Figure 9).

FIGURE 9.

Total antioxidant capacity of Ocimum basilicum essential oils of three phases (lag, log, and stationary) at different concentrations. Highly significant (****) (p < 0.0001). 50, 25, 12.5, 6.25, and 3.125 µg/mL.

3.5.3. Anti‐Inflammatory Activity

The anti‐inflammatory potential of EOs extracted from O. basilicum leaves at different growth phases (lag, log, and stationary) was evaluated. Absorbance was measured at 680 nm. The lag phase exhibited an anti‐inflammatory activity of 86.94 ± 8.93%, the log phase showed 87.28 ± 10.51%, and the stationary phase demonstrated the highest activity at 91.89 ± 10.28%, all measured at a concentration of 50 µL/mL. The greatest anti‐inflammatory effect was observed during the stationary phase (91.89 ± 10.28%) (Figure 10). The results indicated significantly high anti‐inflammatory activity (p < 0.0001) across all developmental stages and concentrations.

FIGURE 10.

The anti‐inflammatory potential of Ocimum basilicum essential oils of three phases (lag, log, and stationary) at different concentrations. Highly significant (****), moderately significant (*** and **), less significant (*) (p < 0.0001). Data are mean ± SD (n = 3). 50, 25, 12.5, 6.25, and 3.125 µg/mL.

3.5.4. Antidiabetic Activity

EOs extracted from O. basilicum leaves at different growth phases (lag, log, and stationary) were evaluated for their antidiabetic potential using the α‐amylase enzyme inhibition method. Absorbance was measured at 540 nm. At a concentration of 50 µL/mL, the lag phase EO exhibited an inhibition rate of 93.69 ± 0.20%, followed by 95.17 ± 0.03% for the log phase, and 95.56 ± 0.00% for the stationary phase. The highest antidiabetic activity was observed during the stationary phase (95.56 ± 0.00%) (Figure 11). EOs from all developmental stages and concentrations demonstrated significant antidiabetic activity (p < 0.0001).

FIGURE 11.

The inhibition of α‐amylase (%) of Ocimum basilicum essential oils of three phases (lag, log, and stationary) at different concentrations. Highly significant (****) (p < 0.0001). Data are mean ± SD (n = 3). 50, 25, 12.5, 6.25, and 3.125 µg/mL.

4. Antibacterial Profiling of EOs

4.1. Agar Well Diffusion Assay

Antibacterial activity of EOs of lag, log and stationary phase were investigated against three bacterial strains as S. aureus, X. oryzae, and R. solanacearum by measuring the inhibition zone in mm. Against S. aureus the lag phase EO had maximum zone of inhibition (20.5 ± 0.5 mm), the maximum zone of inhibition of log phase EO was 30 ± 0.5 mm and the stationary phase EOs showed the highest inhibition zone (29 ± 0.5 mm) at 50 µL/mL (Figure 12). The maximum zone of inhibition of lag phase EO against R. solanacearum was 30 ± 0.5 mm, the log phase was 34.5 ± 0.5 mm and the stationary phase EOs was (45.5 ± 0.05 mm) at 50 µL/mL (Figure 13). The lag phase had the maximum zone of inhibition (22.5 ± 0.5 mm), log phase showed the highest zone of inhibition (27 ± 0.5 mm) and the stationary phase had a maximum zone of inhibition (37.5 ± 0.5 mm) at 50 µL/mL against X. oryzae (Figure 14). The O. basilicum EO at all stages and concentrations showed the significant zone of inhibition (p < 0.0001). The log phase revealed the highest zone of against S. aureus (30 ± 0.5 mm) however stationary phase showed the highest zone of inhibition against R. solanacearum (45.5 ± 0.05 mm), X. oryzae (58 ± 0.5 mm) (Figure 15).

FIGURE 12.

The antibacterial Ocimum basilicum EOs of three phases (lag, log, and stationary) against Staphylococcus aureus at different concentrations. Highly significant (****), moderately significant (*** and **), less significant (*) (p < 0.0001). 50, 25, 12.5, 6.25, and 3.125 µg/mL.

FIGURE 13.

The antibacterial of Ocimum basilicum essential oils of three phases (lag, log, and stationary) against Ralstonia solanacearum at different concentrations. Highly significant (****), moderately significant (*** and **), less significant (*) (p < 0.0001). 50, 25, 12.5, 6.25, and 3.125 µg/mL.

FIGURE 14.

The antibacterial activity of Ocimum basilicum essential oils of three phases (lag, log, and stationary) against Xanthomonas oryzae at different concentrations. Highly significant (****) (p < 0.0001). 50, 25, 12.5, 6.25, and 3.125 µg/mL.

FIGURE 15.

(a) Inhibition zone against Xanthomonas oryzae of stationary phase EO. (b) Inhibition zone against Staphylococcus aureus of log phase EO. (c) Inhibition zone against Ralstonia solanacearum of stationary phase EO. (d) Inhibition zone against R. solanacearum of log phase EO.

4.2. Determination of the MIC and MBC

The antibacterial activity of EOs obtained from different leaf developmental stages (lag, log, and stationary) was evaluated at various concentrations (50–3.125 µL/mL). The results showed that the MIC of the lag phase EO against S. aureus, R. solanacearum, and X. oryzae was 25 µL/mL. For the log phase EO, the MIC was 50 µL/mL, while for the stationary phase, the MIC values were 6.25, 12.5, and 50 µL/mL, respectively. These findings indicate that O. basilicum EOs from all three phases exhibited significant antibacterial activity (Table 4).

TABLE 4.

Minimal inhibitory concentration and minimal bactericidal concentration of Ocimum basilicum essential oils against Staphylococcus aureus, Xanthomonas oryzae, and Ralstonia solanacearum.

ss	S. oryzae	R. solanacearum	X. oryzae
MIC (µL/mL)
Lag	25	50	50
Log	50	50	50
Stationary	6.25	12.5	50
Gentamycin	25	50	50
MBC (µL/mL)
Lag	3.125	6.25	25
Log	12.5	12.5	12.5
Stationary	3.125	3.125	3.125
Gentamycin	3.125	6.25	50

The MBC of the lag phase EO against S. aureus, R. solanacearum, and X. oryzae was 3.125, 6.125, and 25 µL/mL, respectively. For the log phase EO, the MBC was 12.5 µL/mL, while for the stationary phase, it was 3.125 µL/mL for all three bacterial strains. Notably, the stationary phase EO showed the lowest MIC (6.25, 12.5, and 50 µL/mL) and MBC (3.125 µL/mL) values against S. aureus, R. solanacearum, and X. oryzae, respectively. The EO concentrations demonstrated significant MIC and MBC activity (p < 0.0001).

4.3. Biofilm Inhibitory Potential

Pathogenic bacteria that form biofilms pose a major threat to human health due to their ability to survive in hostile environments, including exposure to the host immune system, antibiotics, and external stressors. Antimicrobial resistance and persistent chronic infections are largely attributed to the protective properties of biofilms. In the present study, the biofilm inhibitory potential of O. basilicum EOs from different leaf developmental stages (lag, log, and stationary) was evaluated against various bacterial pathogens. EO from the stationary phase leaves exhibited the highest reduction in biofilm density, with inhibition rates of 84.39 ± 0.00% for S. aureus, 90.03 ± 0.00% for R. solanacearum, and 97.00 ± 0.00% for X. oryzae. Log phase EO showed moderate inhibitory activity, with inhibition rates of 83.37 ± 0.07%, 89.96 ± 0.00%, and 94.16 ± 0.01% against S. aureus, R. solanacearum, and X. oryzae, respectively. Lag phase EO demonstrated the lowest inhibitory activity, with inhibition rates of 74.39 ± 0.07%, 89.88 ± 0.30%, and 87.59 ± 0.03% against the same bacterial strains.

EOs from all developmental stages and concentrations exhibited significant biofilm inhibitory activity (p < 0.0001). Moreover, all EO samples showed higher biofilm inhibition potential compared to the standard antibiotic gentamicin, which exhibited inhibition rates of 41.30% for S. aureus, 64.32% for R. solanacearum, and 89.32% for X. oryzae (Figures 16, 17, 18).

FIGURE 16.

The biofilm inhibitory potential of Ocimum basilicum essential oils of three phases (lag, log, and stationary) against Staphylococcus aureus at different concentrations. Data are mean ± SD (n = 3). Highly significant (****) (p < 0.0001). 50, 25, 12.5, 6.25, and 3.125 µg/mL.

FIGURE 17.

The biofilm inhibitory potential of Ocimum basilicum essential oils of three phases (lag, log, and stationary) against Ralstonia solanacearum at different concentrations. Data are mean ± SD (n = 3). Highly significant (****), moderately significant (*** and **), less significant (*) (p < 0.0001). 50, 25, 12.5, 6.25, and 3.125 µg/mL.

FIGURE 18.

The biofilm inhibitory potential of Ocimum basilicum essential oils of three phases (lag, log and stationary) against Xanthomonas oryzae at different concentrations. Data are mean ± SD (n = 3). Highly significant (****), (p < 0.0001). Note: 50, 25, 12.5, 6.25, and 3.125 µg/mL.

4.4. Hemolytic Activity

Hemolytic activity was assessed to determine the hemolytic potential of O. basilicum EOs extracted from leaves at three developmental stages (lag, log, and stationary). The results indicated that the maximum antihemolytic activity was observed in the lag phase (0.66 ± 0.00%), followed by the log phase (0.61 ± 0.00%) and the stationary phase (0.61 ± 0.00%). Hemolytic percentage decreased with increasing EO concentration. The lowest hemolytic activity was recorded for the stationary phase (Figure 19). EOs from all stages and concentrations demonstrated significant antihemolytic activity (p < 0.0001).

FIGURE 19.

The hemolysis potential of Ocimum basilicum essential oils of three phases (lag, log, and stationary) at different concentrations. Data are mean ± SD (n = 3). Highly significant (****), moderately significant (*** and **), less significant (*) (p < 0.0001). 50, 25, 12.5, 6.25, and 3.125 µg/mL.

5. Network Pharmacology

5.1. Phytochemicals Screening

Twenty‐three phytochemical compounds identified from O. basilicum leaves were used for network pharmacology analysis. Potential active components were subsequently selected based on ADME criteria (absorption, distribution, metabolism, and excretion), resulting in the identification of two active compounds (Figure 20). These compounds were classified as nontoxic, with toxicity classes 6 and 4, respectively (Table 5). Potential infectious and antioxidant‐related targets were identified using the GeneCards, OMIM, and DisGeNET databases, yielding 15 361, 379, and 604 results, respectively. A total of 34 disease‐associated targets were identified through GeneCards, OMIM, and SwissTargetPrediction (Figure 21). These overlapping targets were considered potential therapeutic targets against infections and oxidative stress.

FIGURE 20.

Identification of two screened compounds (eriodictyol and 2,5‐dioxo‐4‐imidazolidinyl) urea).

TABLE 5.

Toxicity prediction of Ocimum basilicum two screened compounds.

FIGURE 21.

Venn diagram showing 34 common targets identified by GeneCards, Omim, and compounds Swiss.

5.2. PPI Network and Hub Genes

A protein target database was constructed using 34 screened common targets. PPIs among these targets were analyzed due to their remarkable versatility, adaptability, and specificity, which make PPIs crucial in biological processes. The strength of supporting data is represented by the thickness of the lines connecting two targets—thicker lines indicate stronger correlations (Figure 22). Upon visualization in Cytoscape, the PPI network revealed 207 edges and 34 nodes (Figure 23). The average node degree was 12, and the average local clustering coefficient was 0.724. The expected number of edges was 75, and the PPI enrichment p < 1.0e−16. The CytoHubba plug‐in in Cytoscape was used to identify hub genes, with the “degree” method selected for prediction. A higher degree value indicates stronger connectivity with other targets, suggesting potential key regulatory roles.

FIGURE 22.

Protein–protein interaction (PPI) network of the 34 common targets. Key: number of nodes = 34; number of edges = 207; average node degree = 12.2; avg. local clustering coefficient = 0.724; expected number of edges = 75; PPI enrichment p < 1.0e−16.

FIGURE 23.

Network analysis of top 10 Hubb genes, nodes showing the genes and edges showing the interaction between genes.

The top 10 hub genes identified were: AKT1 (25), HIF‐1A (24), PTGS2 (24), EGFR (22), MMP9 (22), BCL2 (21), ESR1 (19), RELA (18), PPARG (18), and TLR4 (18) (Table 6). A compound–target network was constructed between the top three hub genes (AKT1, HIF‐1A, and PTGS2) and the two screened compounds from O. basilicum (Figure 24). Analysis of this network revealed that a single active compound can influence multiple targets, and conversely, a single target can interact with several active compounds (Figures 25 and 26).

TABLE 6.

Top 10 in network string interactions short ranked by degree method.

Rank	Name	Score
1	AKT1	25
2	HIF‐1A	24
3	PTGS2	24
4	EGFR	22
5	MMP9	22
6	BCL2	21
7	ESR1	19
8	RELA	18
9	PPARG	18
10	TLR4	18

FIGURE 24.

Network of phytochemicals associated with top hub genes, hypoxia inducible factor 1 (HIF‐1A), α‐serine/threonine protein‐kinase (AKT1) and prostaglandin endoperoxidase synthase 2 (PTGS2).

FIGURE 25.

Compound–target–network between top three genes (AKTI, HIF‐1A, and PTGS2) and prevalent disease targets and compounds.

FIGURE 26.

Compound target network connecting between active compounds with their associated genes and prevalent disease targets and compounds.

5.3. GO Enrichment Analysis and KEGG Pathways

GO provides an organized and standardized framework for classifying and elucidating the roles of genes and their products. GO analysis was performed to identify gene functions at the molecular, cellular, and biological levels. The analysis revealed 228 biological processes, including positive regulation of transcription by RNA polymerase II, negative regulation of the apoptotic process, signal transduction, positive regulation of cell population proliferation, positive regulation of gene expression, cellular response to hypoxia, and positive regulation of protein phosphorylation. In total, 33 cellular components were identified, involving the cytoplasm, plasma membrane, nucleus, cytosol, extracellular space, nucleoplasm, extracellular exosome, and cell surface protein‐containing complexes. In addition, 48 molecular functions were predicted, including protein binding, identical protein binding, enzyme binding, protein homodimerization activity, ubiquitin protein ligase binding, protein kinase binding, zinc ion binding, transcription coactivator binding, and signaling receptor binding.

KEGG pathway analysis predicted 116 pathways related to oxidative stress and infectious diseases. These included PD‐L1 expression and PD‐1 checkpoint pathway in cancer, EGFR tyrosine kinase inhibitor resistance, HIF‐1 signaling pathway, prostate cancer, Chagas disease, gastric cancer, measles, proteoglycans in cancer, lipid and atherosclerosis, Kaposi sarcoma‐associated herpesvirus infection, chemical carcinogenesis via ROS, TNF signaling pathway, PI3K–Akt signaling pathway, human cytomegalovirus infection, Shigellosis, and microRNAs in cancer. These pathways are involved in various biological processes. For example, the HIF‐1 signaling pathway is essential for normal growth and development and plays a role in cancer and inflammation. The relaxin signaling pathway regulates NO levels and is implicated in benign prostatic hyperplasia (BPH) and CBFD. The PI3K–Akt signaling pathway promotes metabolism, proliferation, cell survival, growth, and angiogenesis in response to extracellular signals. The TNF signaling pathway is involved in cell proliferation, differentiation, apoptosis, immune response modulation, and inflammation (Figures 27 and 28).

FIGURE 27.

Gene ontology (GO) enrichment pathways (biological process, cellular process, and molecular function).

FIGURE 28.

Dot plot of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway.

5.4. Molecular Docking

Figure 29 illustrates the molecular interaction between a ligand and a protein, integrating both structural and chemical perspectives. The left panel presents a 3D ribbon diagram of the protein, highlighting its secondary structural elements—red α‐helices, cyan β‐sheets, and white loops—alongside the bound ligand shown in stick form, positioned within a potential active or binding site. This spatial arrangement suggests a specific conformation critical for ligand recognition and stabilization. The right panel complements this with a 2D interaction map, detailing the chemical contacts between the ligand and surrounding amino acid residues. Key interactions include conventional hydrogen bonds (e.g., GLN C:132, SER B:86), π–alkyl interactions (e.g., LEU C:129), and π–sulfur interactions, each contributing to the ligand's orientation and binding affinity. These insights are essential for understanding the protein's functional dynamics, guiding drug design, and interpreting enzymatic behavior.

FIGURE 29.

Structural and interaction analysis of protein–ligand complexes. (A–C) Ribbon diagrams of the target protein highlighting secondary structure elements—α‐helices (red), β sheets (cyan), and loops (white or green)—with bound ligands visualized in stick representation. These 3D models reveal the spatial orientation of the ligand within the active site and the overall protein architecture. (D) 2D interaction maps illustrating key molecular contacts between the ligand and surrounding amino acid residues. Conventional hydrogen bonds (green dashed lines), π‐stacking interactions (pink dashed lines), and van der Waals forces (solid green lines) are annotated, providing insight into binding specificity and stabilization mechanisms. Together, these visualizations offer a comprehensive view of the ligand's binding conformation and interaction profile, supporting structure‐based functional analysis and potential applications in drug design.

Docking results from AutoDock Vina provide a quantitative assessment of ligand binding across various sites on the target protein. Binding affinities ranged from −7.6 to −7.1 kcal/mol, indicating moderately strong interactions across all predicted poses. The top‐ranked pose, with a binding affinity of −7.6 kcal/mol, corresponds to an estimated inhibition constant (K _i) of 2.73 µM, suggesting a potentially effective interaction. As binding affinity decreased incrementally, K _i values increased, reaching up to 6.37 µM for the lowest‐ranked poses. Ligand efficiency values remained consistent across all poses, ranging from −0.36 to −0.38, reflecting stable binding energy normalized per heavy atom. These results suggest that the ligand maintains a relatively uniform binding performance across multiple conformations, with the top poses offering promising candidates for further analysis or optimization. The docking log confirmed that the simulation was conducted using appropriate grid parameters and exhaustiveness settings, ensuring reliable sampling of binding modes. Overall, the data support the ligand's potential as a lead compound for further biochemical or pharmacological evaluation. A summary table of docking results includes binding affinities, estimated inhibition constants (K _i), and ligand efficiencies for the 10 predicted binding poses (Table 7).

TABLE 7.

Binding affinity, estimated inhibition constants, and ligand efficiency of predicted poses.

Binding site	Affinity (kcal/mol)	Estimated K i (µM)	Ligand efficiency
2	−7.8	1.92	−0.37
8	−7.6	2.69	−0.36
4	−7.1	6.25	−0.34
3	−6.9	8.76	−0.33
5	−6.9	8.76	−0.33
6	−6.9	8.76	−0.33
9	−6.9	8.76	−0.33

6. Discussion

Bioactive compounds—natural molecules found in foods, plants, and other sources have garnered significant attention for their ability to enhance health and combat disease. Unlike essential macro‐ and micronutrients required for human nutrition and metabolism, bioactive substances offer unique benefits that can improve overall health and help prevent or mitigate various illnesses [61]. The aim of the present study was to examine the effect of leaf developmental stage on the phytochemical composition and pharmacological activities of O. basilicum leaf EO. Specifically, the objective was to identify the optimal leaf growth phase for harvesting leaves intended for large‐scale commercial EO production. GTs are present in approximately one‐third of angiosperm species. In this study, peltate‐type GTs with a four‐celled secretory head were observed on both the abaxial and adaxial epidermis of O. basilicum leaves across all growth phases. These trichomes are specialized structures responsible for the biosynthesis and storage of EOs and secondary metabolites. The frequency of peltate GTs was highest in lag‐phase leaves compared to log and stationary phases. This reduction may be attributed to environmental stressors such as light exposure, as stationary‐phase leaves located at the apical tips are more exposed to UV radiation than those in earlier growth stages. Martínez‐Natarén et al. [62] reported the presence of four‐celled peltate trichomes in O. campechianum, another significant species within the Ocimum genus. Similarly, Kaya et al. [63] documented four‐celled peltate trichomes in various Lamiaceae species. Bhatt et al. [64] observed GTs on both leaf surfaces of the shell bush. Werker et al. [65] noted that trichomes originate from epidermal cells and may be unicellular or multicellular. According to Saran et al. [66], peltate GTs emerge during extremely immature leaf stages. Liu et al. [67] confirmed that peltate GTs are the primary sites of EO synthesis in Mentha haplocalyx. Gang et al. [68] reported that basil leaves possess two types of GTs—peltate and capitate but in the present study, only peltate trichomes were observed, with capitate trichomes absent.

These findings align with [69] who emphasized the role of GTs in the synthesis, storage, and secretion of biochemical constituents. [70] further highlighted the diversity of GTs in terms of cell number, morphology, and metabolite profiles. The current observations also correlate with the increased pharmacological activity of EO from log and stationary‐phase leaves compared to lag‐phase leaves, as discussed below. Boukhris et al. [71] reported that EO content in O. basilicum varies with leaf maturity, with immature leaves yielding 0.66% EO compared to just 0.12% in mature leaves. In the present study, EO yield was highest in the lag phase (1.26 mL/5 g), followed by the log phase (0.53 mL/5 g), and lowest in the stationary phase (0.49 mL/5 g). Similar trends were reported by Rehman et al. [72], who found that EO content in Mentha leaves gradually declines with leaf development. During the lag phase, cellular activity is focused on division and trichome formation, enhancing EO secretion as a protective response to environmental stress. Noted that juvenile lemongrass leaves are biogenetically more active than mature leaves in EO production. This study also emphasized the regulatory role of peltate GTs in EO synthesis and accumulation in rose geranium. A significant positive correlation between trichome density and EO yield in O. basilicum was confirmed by Saran et al. [66, 73]. Rehman et al. [72] further demonstrated a relationship between leaf development and EO production across various Lamiaceae species.

In this investigation, nonvolatile constituents such as flavonoids, phenolic acids, monoterpenes, and esters were detected in EO extracted from leaves at all growth phases. The relative percentage composition of these compounds is presented in Table 5. A total of 23 chemical compounds were identified via GC–MS analysis of EO from lag, log, and stationary‐phase leaves (Table 3). Importantly, several compounds including acetic pentyl ester, butyric acid hydrazide, 2‐methyl pyrrolidine, acetic acid hydrazide, 2‐furanmethanol tetrahydro‐, 2,2‐dimethyl butane, isobutyric anhydride, butanoic acid anhydride, pantene 2,3,4‐trimethyl, 2‐haptene‐3‐ol 4,5‐dimethyl, (2,5‐dioxoimidazolidin‐4‐yl)urea, biurea, gougerotin, and diethyl phthalate are newly reported in any plant species and are documented here for the first time in O. basilicum. Four compounds 2‐methylbutyl isovalerate, tetrahydroxy‐2‐methyl‐2‐furanol, methyl petroselinate, and 1‐heptadecyne were identified for the first time in O. basilicum, although previously reported in other plant species. Kamatou et al. [74] documented 2‐methylbutyl isovalerate (4.7%) in ripe fruits of Gethyllis afra. Tetrahydro‐2‐methyl‐2‐furanol was found in pyroligneous acid synthesized from rambutan, durian, mangosteen, and langsat [75]. Methyl petroselinate was observed in Azadirachta indica from Oman (Hussain, unpublished manuscript) and in seed oil of Cuminum cyminum [76]. Millaty et al. [77] reported 1‐heptadecyne as a major phytochemical in agarwood leaf extract. In contrast, compounds such as neral, linalool, eriodictyol, and oleic acid previously reported in O. basilicum were additionally observed in the present study. Roberto et al. [78] found higher levels of eriodictyol and β‐carotene in dried basil herb compared to fresh. Nazir and Wani [79] identified unsaturated fatty acids (oleic, linoleic, and linolenic acids) across 18 basil populations. Bozin et al. reported methyl chavicol (45.8%) and linalool (24.2%) as major EO constituents.

In this investigation, oleic acid was the major constituent during the log phase, while linalool, gougerotin, and tetrahydroxyoxane‐2‐carboxylic acid were exclusive to the stationary phase. Their absence in the lag phase indicates that leaf developmental events significantly influence the chemical composition of EOs. In addition, noticeable variation in EO color across the three growth phases likely indicates both qualitative and quantitative differences in phytochemical profiles. Plants contain abundant bioactive compounds such as flavonoids, phenolic acids, and fatty acids, which exhibit antioxidant, anti‐inflammatory, and antimicrobial properties. In this investigation, the antioxidant potential of O. basilicum EO was evaluated across leaf developmental stages using DPPH, TPC, and TAA assays. ROS, generated during cellular metabolism, are associated with aging, cancer, and oxidative damage [80].

The EO extracted during the log phase demonstrated the highest DPPH radical scavenging activity (63.87 ± 2.95%) at a concentration of 50 mg/mL. GC–MS analysis revealed the presence of methyl petroselinate, a fatty acid known for its antioxidant properties due to its proton‐donating ability. These findings are consistent with Ahmed et al. [58], who reported enhanced free radical scavenging with increasing EO concentrations. Hussain et al. [60] also observed strong antioxidant activity in fatty acid methyl esters derived from O. basilicum. Gülçin et al. [81] found that ethanol extracts of basil exhibited the highest DPPH activity (67%), while aqueous extracts showed the lowest (55%). The antioxidant efficacy of O. basilicum EO is largely influenced by its chemical composition. Mokdad‐Bzeouich et al. [59] emphasized the role of hydroxyl groups and double bonds in eriodictyol for stabilizing free radicals. In the present study, EO from the stationary phase exhibited the highest TPC (982.61 ± 62.45 µg/mL at 50 µL/mL). GC–MS analysis confirmed the presence of antioxidant compounds including eriodictyol (flavonoid), gougerotin (phenol), linalool (terpene), and limonene.

Ahmed et al. [58] reported that phenolic compounds, due to their hydroxyl groups, effectively neutralize free radicals. Their study showed the highest TPC (41.3 mg PE/g) in O. basilicum EO from Minia, followed by Assiut (25.91 mg PE/g) and Benisuef (24.61 mg PE/g). The Minia EO also exhibited strong DPPH activity (IC₅₀ = 11.23 mg/mL). Rashid et al. [82] reported maximum TPC (57.751 GAE mg/100 g) in EO from Soon Valley, while lower values were recorded in the Plain Area (13.461 GAE mg/100 g) and Chakwal (19.896 GAE mg/100 g). Pakistani O. basilicum EO, rich in linalool, demonstrated antioxidant activity comparable to standard controls [60]. To date, no published data exists on the antioxidant activity of O. basilicum EO in relation to leaf developmental stages, making the present findings a novel contribution to the field.

Jan et al. [45] reported that the TAC assay is based on the reduction of phosphomolybdate ions in the presence of antioxidants, resulting in the formation of a green phosphate/Mo(V) complex. In the present study, EO extracted from the stationary phase of O. basilicum leaves exhibited the highest TAC value (0.79 ± 0.01) at 50 µL/mL. GC–MS analysis confirmed the presence of eriodictyol, a flavonoid known for its potent antioxidant properties.

TAC was found to be directly proportional to extract concentration. Islam et al. [83] documented that eriodictyol plays a multifaceted role in biological activities, including antioxidant, anti‐inflammatory, anticancer, neuroprotective, cardioprotective, and antidiabetic effects. Agarwal et al. [84] further reported a strong correlation between polyphenol and flavonoid content and the phosphomolybdate‐reducing ability of medicinal plant extracts. Rezzoug et al. [85] highlighted that basil EOs are powerful antioxidants, primarily due to their high content of oxygenated monoterpenes (92%), with linalool as the dominant compound.

The anti‐inflammatory potential of O. basilicum is attributed to its rich composition of phenols, flavonoids, glycosides, terpenoids, and steroids, all of which have been reported to exhibit anti‐inflammatory effects [86]. In the present study, EO extracted from the stationary phase of leaf development showed the highest inhibition of protein denaturation (91.89 ± 10.28%) at 50 µL/mL. GC–MS analysis of the stationary‐phase EO revealed the presence of eriodictyol (flavonoid) and pantene, 2,3,4‐trimethyl‐ (terpenoid), both known to contribute to anti‐inflammatory activity. Bayala et al. [87] emphasized that oxidative and inflammatory disorders are linked to the onset of various diseases, including cancer. Anokwah et al. [88] documented that the ability of plant extracts to inhibit thermal denaturation of proteins, such as egg albumin, is indicative of their anti‐inflammatory potential. Osei et al. [86] also reported that ethanol and hexane extracts of O. basilicum demonstrated significant anti‐inflammatory activity. Diabetes mellitus is a chronic metabolic disorder characterized by elevated blood glucose levels, which can lead to serious complications such as foot ulcers, retinopathy, nephropathy, and diabetic ketoacidosis [89]. In the present study, the antidiabetic activity of O. basilicum EO was evaluated across three leaf growth phases: lag, log, and stationary. EO extracted from the stationary phase exhibited the highest α‐amylase inhibition (95.56 ± 0.20%) at 50 µL/mL, outperforming the standard drug Acarbose.

GC–MS analysis of the stationary‐phase EO confirmed the presence of eriodictyol, a flavonoid known for its antidiabetic properties. Islam et al. [83] reported that eriodictyol exhibits a wide range of biological activities, which may explain its efficacy in this study. Malapermal et al. [90] demonstrated α‐amylase inhibitory activity in aqueous and ethanolic extracts of O. basilicum leaves. Venthodika et al. [91] further reported that O. basilicum inhibits human pancreatic α‐amylase, thereby suppressing carbohydrate digestion and moderating postprandial glucose levels. Previous studies have shown that both O. basilicum EO and extracts possess antidiabetic activity, with α‐amylase inhibition values of 79.61 ± 4.99% for EO and 64.71 ± 3.88% for aqueous extract.

EOs are known to inhibit bacterial growth and suppress the production of harmful metabolites by pathogenic microorganisms. In the present study, EO extracted from the stationary phase of O. basilicum leaves exhibited potent antimicrobial activity against S. aureus and X. oryzae, with the lowest MIC values of 6.25 and 12.5 µL/mL, respectively. This strong antibacterial potential is attributed to the phytochemical composition of the EO.

GC–MS analysis of the stationary‐phase EO revealed the presence of eriodictyol and gougerotin, both known for their antimicrobial properties. Soković et al. [92] reported that high concentrations of linalool in basil EO contribute significantly to its antibacterial activity. Xuewen et al. [93] documented that the MIC of eriodictyol against S. aureus was 512 µg/mL, suggesting that its presence, even in modest concentrations, may enhance antimicrobial efficacy when combined with other bioactives. The low MIC values observed in the stationary‐phase EO are likely due to the relatively high concentrations of gougerotin (10.39%) and eriodictyol (1.11%). In contrast, the absence of these compounds in the log‐phase EO may explain its higher MIC value (50 µL/mL) against all tested bacterial strains. Eid et al. [94] reported that basil seed EO exhibited MICs ranging from 1 to 2.3 µg/mL against various bacterial strains, including Klebsiella pneumoniae, Escherichia coli, S. aureus, Proteus mirabilis, and Pseudomonas aeruginosa. Their MIC assay also revealed potent inhibition, with MIC₅₀ and MIC₉₀ values of 3.21 and 5.36 µL/mL, respectively, for Azotobacter chroococcum and Micrococcus luteus.

In addition to planktonic bacterial inhibition, the present study evaluated the antibiofilm activity of O. basilicum EO against S. aureus, R. solanacearum, and X. oryzae. Biofilm inhibition increased proportionally with EO concentration, consistent with previous findings that antibiofilm activity is dose‐dependent [95].

The stationary‐phase EO exhibited the highest biofilm dispersal activity, with inhibition percentages of 84.39 ± 0.00% (S. aureus), 90.03 ± 0.00% (R. solanacearum), and 97.00 ± 0.00% (X. oryzae). GC–MS analysis confirmed the presence of eriodictyol and gougerotin in the stationary‐phase EO, supporting its strong antimicrobial and antibiofilm potential. Kang et al. [96] reported that peppermint EO disrupts the cell membrane integrity of S. aureus, leading to increased electrical conductivity and leakage of nucleic acids, proteins, and ATP—ultimately reducing cell viability and altering cell morphology. Similarly, Caputo et al. [97] found that myrtle EO exhibited biofilm‐inhibitory activity at maximum concentration, ranging from 26.25% (E. coli) to 66.67% (P. aeruginosa). However, its efficacy was limited against P. carotovorum and P. aeruginosa, and only marginally effective against L. monocytogenes and E. coli at lower concentrations (0.4 mg/mL).

In this study, EO extracted from the stationary phase of O. basilicum leaves demonstrated the lowest hemolytic activity (0.54 ± 0.00% at 50 µL/mL), indicating excellent biocompatibility. According to Rashid et al. [82], lower hemolytic activity enhances EO safety for food and pharmaceutical applications. Ribeiro et al. [51] classified hemolysis values between 5% and 10% as low and those above 40% as high, further validating the safety of stationary‐phase EO. These results align with Liaqat et al. [98], who reported a concentration‐dependent decrease in hemolytic activity. In addition, Abbas et al. [99] found that O. basilicum EO extracted via supercritical fluid extraction exhibited lower cytotoxicity (18.59%) compared to hydrodistillation (35.83%), reinforcing the importance of extraction method in determining EO safety and efficacy.

The complexity of herbal medicines, particularly EOs, lies in their multi‐component nature and diverse biological targets. To address this, the present study employed network pharmacology to explore the multi‐targeting potential of O. basilicum EO constituents. Guan et al. [100] described network pharmacology as an integrative approach combining pharmacology, systems biology, and computational modeling to map interactions across multiple targets and disease pathways. Jiao et al. [101] emphasized its utility in predicting pharmacological properties and elucidating mechanisms of action for complex herbal compounds. Through literature review and database mining (Swiss Target Prediction and STITCH), two key bioactive compounds eriodictyol and (2,5‐dioxoimidazolidin‐4‐yl)urea were identified. These compounds modulate targets associated with oxidative stress, inflammation, and microbial infections. Differential gene expression analysis revealed overlapping targets between plant‐derived compounds and disease‐related genes, including AKT1, HIF‐1, and PTGS2. PPI network analysis identified 10 hub genes based on degree centrality: AKT1: A serine/threonine kinase involved in cell survival, proliferation, differentiation, and apoptosis. HIF‐1α: A transcription factor essential for cellular adaptation to hypoxia, implicated in tumor angiogenesis and ischemic disease. PTGS2 (COX‐2): A rate‐limiting enzyme in prostaglandin synthesis, linked to inflammation and cancer progression.

To validate the network pharmacology findings, molecular docking was performed using AutoDock Vina. Eriodictyol showed moderately strong binding affinities to AKT1 and HIF‐1α, ranging from 7.6 to −7.1 kcal/mol. The top‐ranked pose (7.6 kcal/mol) corresponded to an estimated inhibition constant (K _i) of 2.73 µM, indicating effective interaction. As affinity decreased, K _i values rose to 6.37 µM. Ligand efficiency values remained consistent (0.36 to −0.38), suggesting stable binding across conformations. These results support eriodictyol's potential as a lead compound with multi‐targeting capabilities, particularly in pathways related to oxidative stress, metabolic regulation, and immune response. The integration of experimental assays, network pharmacology, and in silico docking provides a robust framework for understanding the therapeutic potential of O. basilicum EO. The stationary‐phase EO, enriched with eriodictyol and other bioactives, demonstrated strong antioxidant, anti‐inflammatory, antimicrobial, and antidiabetic activities, alongside a favorable safety profile. These findings position stationary‐phase O. basilicum EO as a promising candidate for use in functional foods, natural therapeutics, and integrative medicine, with potential applications in managing oxidative stress, inflammatory disorders, and metabolic diseases.

7. Conclusion

This study investigates the developmental stages of O. basilicum leaves and their influence on EO yield, composition, and pharmacological potential. GTs, the primary sites of EO biosynthesis, were observed on both abaxial and adaxial leaf surfaces. The stationary phase exhibited the highest trichome density and EO yield, containing bioactive compounds with antioxidant, anti‐inflammatory, and antimicrobial properties. EO from the stationary phase demonstrated strong antioxidant activity, potent antimicrobial performance, and minimal hemolytic activity, indicating its suitability for food and pharmaceutical applications. Molecular docking confirmed the binding affinity of key compounds to regulatory proteins, supporting their role in critical cellular pathways. Overall, the stationary phase emerged as the most biologically active and pharmacologically potent stage for EO harvesting, with promising applications in natural medicine, functional foods, and agriculture.

The study revealed that trichomes were present on both leaf surfaces, with maximum trichome number observed during the lag phase, while larger trichome size was recorded in the stationary phase. Although EO yield peaked during the lag phase, GC–MS analysis indicated higher concentrations of specific bioactive compounds in the stationary phase. EO from the log phase exhibited the highest DPPH scavenging activity, whereas EO from the stationary phase showed superior TPC, TAC, anti‐inflammatory, and antidiabetic activities. The stationary‐phase EO also demonstrated enhanced antibacterial activity compared to other stages. While the log phase showed the largest inhibition zone against S. aureus, the stationary phase EO exhibited maximum inhibition zones against S. aureus, R. solanacearum, and X. oryzae, along with the lowest MIC and MBC values across all tested bacterial strains.

Based on these findings, the stationary phase is identified as the most biologically active stage, containing specific phytochemicals responsible for diverse pharmacological activities. EO from this phase exhibited a multimodal mechanism of action, including antioxidant activity (DPPH, TPC, and TAC), anti‐inflammatory, antidiabetic, and antimicrobial effects (MIC, MBC, biofilm inhibition, and suppression of S. aureus, R. solanacearum, and X. oryzae), as well as low hemolytic activity. Network pharmacology proved to be an effective approach for modern herbal pharmaceutical research, enabling the identification of active compounds and elucidation of multitarget mechanisms. Two key bioactive compounds eriodictyol and (2,5‐dioxoimidazolidin‐4‐yl)urea, along with (2S,3R,4S,5R,6S)‐3,4,5,6‐tetrahydroxyoxane‐2‐carboxylic acid were identified through network pharmacology and confirmed via GC–MS analysis of stationary‐phase EO.

In conclusion, the stationary phase of leaf development in O. basilicum is the most pharmacologically potent, offering a rich source of bioactive compounds with therapeutic potential. These findings provide a scientific basis for targeted EO harvesting and support its application in functional foods, natural therapeutics, and integrative medicine.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File 1: cbdv70753‐sup‐0001‐SuppMat.docx

Data Availability Statement

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