Exp. Biol. Med. Experimental Biology and Medicine Exp. Biol. Med. 1535-3699 Frontiers Media S.A. 10867 10.3389/ebm.2026.10867 Original Research Metabolomics-guided identification of bioactive phytometabolites from South African plants targeting neuroblastoma Motshudi et al. 10.3389/ebm.2026.10867 Motshudi Mmei Cheryl 1 * Naidoo Clarissa Marcelle 1 Obi Chikwelu Lawrence 1 Iweriebor Benson Chucks 1 Prinsloo Earl 2 Zubair Muhammad Sulaiman 3 Mkolo Nqobile Monate 1 Department of Biology, School of Science and Technology, Sefako Makgatho Health Science University, Pretoria, South Africa Department of Biotechnology, Rhodes University, Makhanda, South Africa Department of Pharmacy, University of Tadulako, Palu, Indonesia Correspondence: Mmei Cheryl Motshudi, cheryl.motshudi@smu.ac.za 05 03 2026 2026 251 10867 19 10 2025 10 01 2026 16 02 2026 Copyright © 2026 Motshudi, Naidoo, Obi, Iweriebor, Prinsloo, Zubair and Mkolo. 2026 Motshudi, Naidoo, Obi, Iweriebor, Prinsloo, Zubair and Mkolo https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Neuroblastoma constitutes a solid tumor in pediatric populations, characterized by a dismal prognosis and a scarcity of effective therapeutic interventions. Medicinal flora from South Africa represents valuable sources of bioactive phytometabolites with potential relevance to neuroblastoma. This study employed an integrated workflow merging untargeted UPLC-MS/MS metabolomics, mitochondrial functional assays, and in silico absorption, distribution, metabolism, and excretion (ADME) prediction to systematically identify bioactive metabolites from Acorus calamus and Lippia javanica with activity against SH-SY5Y neuroblastoma cells. Cytotoxic effects were quantified utilizing the CCK-8 assay, while mitochondrial membrane potential (ΔΨm) was conducted through JC-1 flow cytometry. Untargeted UPLC-MS/MS profiling yielded metabolomic fingerprints, through PCA, PLS-DA, and OPLS-DA. ADME and drug-likeness were predicted using SWISSADME. Both plant extracts exhibited dose-dependent inhibition of SH-SY5Y cell viability, with IC50 values determined at 0.2886 μg/μL for A. calamus and 0.3066 μg/μL for L. javanica. The ΔΨm assessment indicated enhanced mitochondrial polarization (68.2% and 65.4% compared to 58.8% in untreated controls), implying modulation of mitochondrial functional status. Metabolomic profiling unveiled distinct phytochemical signatures, including flavonoids, phenolics, jasmonates, and alkaloids, exhibiting significant species-level differentiation (F = 936.71, R 2 = 0.989, p = 0.005). Notable metabolites such as isopropyl β-glucoside, 6β-hydroxymethandienone, and 7-epi-12-hydroxyjasmonic acid demonstrated favorable ADME characteristics and permeability across the blood-brain barrier. This investigation elucidates that A. calamus and L. javanica possess potential efficacy against neuroblastoma, underscoring the translational potential of African medicinal flora in pediatric oncology and necessitating further preclinical exploration.

 Acorus calamus ADME Lippia javanica metabolomics mitochondrial membrane potential National Research Foundation 10.13039/501100001321 MND210524603403 The author(s) declared that financial support was received for this work and/or its publication. This research was funded by South Africa's National Research Foundation grant number MND210524603403. section-at-acceptance Structural Biology Impact statement

This manuscript serves to investigate the medicinal flora for natural bioactive compounds as probable sources of therapeutic anticancer agents targeting SH-SY5Y neuroblastoma cells. Utilizing a multifaceted methodology that incorporates cytotoxicity assays, untargeted metabolomics, and in silico Absorption, Distribution, Metabolism, and Excretion modeling, we have identified phytometabolites exhibiting promising anticancer efficacy and have also predicted their permeability across the blood–brain barrier. The findings will offer different perspectives and insights into the metabolomic profiling and therapeutic potential of the medicinal plants, thereby enhancing the pharmacological comprehension of phytochemical agents in pediatric oncology.

This manuscript also highlights the current strategic plans for neuroblastoma treatment and approaches that could be applied in the future to improve the quality of life of patients with neuroblastoma, as well as to provide early diagnostic measures to reduce mortality rates of this fatal cancer. This information could be of great use for both clinical and scientific research.

 Introduction

Neuroblastoma (NB) is a primary cancer detected in infants; it is a predominant solid tumor commonly found in the extracranial area in children [1]. Neuroblastoma accounts for approximately 10–15% of childhood malignancies, and the primary tumour generally stems from the sympathetic chain, usually in the abdomen or in the adrenal gland [2]. Patients with neuroblastoma tend to exhibit a wide range of biological, clinical, accompanied by prognostic heterogeneity, and a fundamental prognostic trait pertaining to neuroblastoma patients is the location of the primary tumour, which can originate in the adrenal gland, abdominal/retroperitoneal area, neck, thorax, pelvis, or, less frequently, in other sites [3, 4]. Due to the heterogeneous biological nature of neuroblastoma, its prognosis, along with clinical course, differs between spontaneous regression to high-risk cases, with several neuroblastoma tumours exhibit poorly responsive to intensive multimodal therapy [5, 6]. Presently treatment strategies involve aggressive amalgamation of radiotherapy, induction chemotherapy, elevated-dose chemotherapy with autologous stem-cell rescue, surgical removal of tumors, and applications of post-consolidation methods such as immunotherapy, as well as differentiation therapy [7]. The 5-year overall survival rate for high-risk neuroblastoma individuals continues to hover around 50–60% despite these integrated multimodal approaches, and late effects are usually imminent and frequently experienced by long-term survivors [8]. Regardless of advancement in multimodal therapy, outcomes for high-risk neuroblastoma persist to be suboptimal, highlighting the necessity for alternative and complementary therapeutic strategies.

The SH-SY5Y human neuroblastoma cell line comprises an N-type catecholaminergic subclone of neuroblastoma commonly used in research areas pertaining to neuroblastoma and neurobiology [9]. SH-SY5Y cell lines convey crucial elements of human catecholaminergic systems, which encompass components such as dopamine-β-hydroxylase and tyrosine hydroxylase [10]. Induced SHSY5Y cells can be differentiated into phenotypes that resemble neurons, therefore rendering them an adaptable in vitro model [11]. However, limitations such as inadequate maturation levels of neurons and restricted functionality of network systems, irrespective of the differentiation, emphasize the necessity of cautious interpretation of pharmacological responses [12]. Changes in mitochondrial membrane potential (ΔΨm) and cellular redox balance can play context-dependent roles in cancer biology, acting as regulators of cell survival and cell death conditional on cellular state and therapeutic pressure [13]. Although moderate stabilization of mitochondrial function and redox homeostasis might permit tumour cell adaptation, excessive or dysregulated perturbation of these pathways can encourage cytotoxic or cytostatic outcomes via mitochondrial dysfunction [14, 15]. Accordingly, modulation of ΔΨm and redox-related processes has been explored as a functional vulnerability in cancer cells, also neuroblastoma, instead as a unidirectional pro-survival mechanism [16, 17].

Approximately 65% of the world’s population, along with an estimated 80% of the population in Africa as well as Asia, depends on traditional herbal remedies to cure and furthermore prevent infectious ailments and chronic diseases [18, 19]. Ethnobotanical knowledge from southern Africa has showcased many species of aromatic origin known for their anticancer, anti-inflammatory, and anti-infective properties [20]. South African based medicinal plants are rich sources of structurally diverse phytometabolites with documented biological activities, and systematic investigation of these resources might permit the discovery of candidate anti-cancer chemotypes or complementary approaches to present therapies [21–25]. Lippia javanica (Burm.f.) is a multi-branched woody shrub, that is commonly known as fever tea [9, 26]. L. javanica (Verbenaceae family) is an aromatic medicinal plant indigenous to eastern and southern Africa that has been broadly explored for its phytochemical composition and biological activities [26–30]. Previous studies have recognized phenolic, terpenoids, and flavonoids constituents, with apigenin- and luteolin-associated derivatives, which in vitro exhibit antioxidant and cytotoxic activities, supporting its importance for further assessment within a metabolomics-directed anticancer framework [31–36]. Acorus calamus Linn., (Aceraceae family), is classified as a perennial, semi-aquatic herb, that is commonly known as sweet flag {25]. A. calamus is an aromatic medicinal plant that has been extensively studied for its phytochemical diversity and biological activities, and it is frequently utilized in traditional Chinese and Indian medicine [37–41]. Notably, this plant comprises phenylpropanoid constituents for instance α- and β-asarone, for which extracts and isolated compounds have established anticancer activity in preclinical cancer models, also permitting its inclusion in metabolomics-directed anticancer investigations [42, 43]. Untargeted LC-MS metabolomics is commonly utilized to map systematic methods for enhancing quality control, distinguishing chemotypes, associating chemical properties with bioactivity of samples, and prioritizing key metabolites for further pharmacology analysis, alongside in silico ADME assessment through tools like SwissADME and the BOILED-Egg model [44–47].

Building on the above insight, while the phytochemistry and bioactivity of L. javanica and A. calamus have been previously reported in various biological contexts, their assessment within an integrated framework relating metabolomic composition to mitochondrial functional outcomes and pharmacokinetic possibility remains limited. In this study, we integrated untargeted UPLC-MS/MS metabolomics, mitochondrial membrane potential evaluation in SH-SY5Y neuroblastoma cells, and in silico ADME analysis to select metabolites with possible neuroblastoma importance.

 Materials and methods Plant collection and identification

Specimens of L. javanica and A. calamus were collected from Hartbeespoort in the North-West Province of South Africa (25.7236° S, 27.9653° E) in February 2023. Organ-matched sampling was applied to follow metabolomics reporting recommendations that highlight control of tissue-specific variability in comparative chemical analyses [48]. Species identification of both plants was authenticated by taxonomists at the National Herbarium, where voucher specimens were deposited under accession numbers NR 904 (L. javanica) and NR 905 (A. calamus). Plant material collection was completed in accordance with national regulatory requirements, under permit CF6-0234 released by the Department of Agriculture and Rural Development-Nature Conservation, South Africa (Permit Holder: Prof. Nqobile Monate Mkolo).

 Anti-cancer assay analysis Sample preparation

The L. javanica and A. calamus samples were pulverized into a powder using a mortar and pestle under liquid nitrogen. A precise weight of 0.5 g from each powdered sample was combined into a single 50 mL centrifuge tube. A total of 10 mL of buffer was added to the tube followed by extraction at a 4 °C. The extractant was filtered through a 0.45 μm filter, and the clearer supernatants were transferred into a new centrifuge tube. Crude extracts and intermediate preparations were stored at −20 °C in amber, light-protected containers to reduce degradation. Extracts were permitted to equilibrate to room temperature and were used within 1 week of preparation to allow stability and duplicability.

 SH-SY5Y cell preparation

Human neuroblastoma SH-SY5Y cells (Cellonex ™, Johannesburg South Africa) were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% (v/v) heat-inactivated fetal bovine serum, 1% penicillin-streptomycin obtained from Gibco (New York, NY, United States), and maintained at 37 °C in a humidified incubator with 5% CO2 [49]. Cells were washed by utilizing a 1 mL pipette to aspirate and discard the old medium. Gradually, 2 mL of Phosphate Buffered Saline (PBS) from Sigma-Aldrich (St. Louis, MO, United States), was poured along the edge of the T25 flask, and this flask was gently swirled to wash away any remaining medium. Cell digestion was conducted by gradually adding 1 mL of 0.25% trypsin digest solution (Sigma-Aldrich, St. Louis, MO, United States) into the T25 flask, ensuring it covered all the cells. The flask was then placed in a 37 °C CO2 incubator for 2 min. Cells were examined, using an inverted microscope, for separation and formation of a spherical shape. To stop digestion, 4 mL of cell growth medium was added to the cells. Passage was done at a ratio of 1:4, where the cells were carefully pipetted for dispensation, and a single-cell suspension was confirmed under the microscope.

 CCK-8 assay

The effect of chosen plant tissue (final concentration of 0.01–10 μg/mL) on SH-SY5Y cell viability was assessed using the CCK-8 assay according to the protocol [50]. SH-SY5Y cells were collected from an approximately 80% confluent T-25 flask using the standard trypsinization method. Afterward, 2 × 104 cells were seeded in a 96-well plate and incubated for 24 h at 37 °C. Then, the media was removed, rinsed with PBS, and the cells were treated with 100 μL of clear supernatants from the sample, while the cell medium served as the negative control. Following 24 h, 10 μL of CCK-8 was added to every well and allowed to incubate for 2 h. A microplate reader (Infinite M200, Tecan, Switzerland) scanned the 96-well plate at a wavelength of 450 nm. All CCK-8 experiments were performed utilizing three independent biological replicates, with each condition examined in technical triplicate. The subsequent formula was applied to determine the percentage of cell viability: Survival rate  % = Asample − Ab / Ac − Ab × 100

Wherein Asample, Ab, and Ac are the absorbance/Optical density of cells exposed to test reagents, absorbance/Optical density of blank, and negative control, respectively.

 Mitochondrial membrane potential detection

The mitochondrial membrane potential (ΔΨm) of SHSY-5Y cells against L. javanica and A. calamus was determined by adopting a procedure of Sakamuru and co-workers [51]. SH-SY5Y cells were seeded in a 96-well plate (2 × 104 cells per well) according to experimental groups, which included the blank control group (non-treated SH-SYSY cell line), negative control of non-treated SH-SYSY cell line for gating, and the treated SH-SYSY cell line group (L. javanica and A. calamus at 1 µg/uL). After 24 h of treatment, the cells were collected using trypsin for adherent cells to avoid over-digestion and to prevent membrane potential damage. A JC-1 working solution was prepared using a diluted JC-1 stock solution with pre-warmed (37 °C) medium, which was added to the final concentration. The JC-1 working solution was then added to the cells and incubated at 37 °C in the dark for 15 min. JC-1 solution was removed, and cells were washed 3 times with pre-cooled PBS to remove unbound dye. The cells were collected using trypsin, then centrifuged and resuspended into PBS. The set flow cytometry channels were green fluorescence, which was the FL1 channel (Ex 488 nm, Em 530/30 nm), and red fluorescence, which was the FL2 channel (Ex 488 nm, Em 585/42 nm). The red fluorescence (normal mitochondria) and green fluorescence (decreased membrane potential) were then merged for analysis. Mitochondrial membrane potential (ΔΨm) assessments were conducted utilizing three independent biological replicates, with technical triplicates for each experimental condition.

 Metabolomic characterization Chemicals and instrumentation

Multiple chemicals and reagents, such as acetonitrile (Merck, Rahway, NJ, United States), methanol (Merck, Rahway, NJ, United States), DL-o-Chlorophenylalanine (Merck, Rahway, NJ, United States), and formic acid (Merck, Rahway, NJ, United States) were utilized. Additionally, instrumentation, namely ACQUITY UPLC HSS T3 (100 × 2.1 mm × 1.8 μm), Ultimate 3000LC combined with Q Exactive MS (Thermo, Waltham, MA, United States), and Temp functional Centrifugation (Eppendorf, Enfield, CT, United States) were utilized.

 Sample preparation

The initial stages of the sample preparation comprised a freeze-drying process (lyophilization) of A. calamus and L. javanica leaf samples. The respective samples were subsequently dried and ground into powder; the resultant powder for each sample was transferred into a 5 mL homogenizing tube. A MM 400 mixer with four 5 mm diameter metal balls was utilized to mix the samples at 30 Hz. The subsequent product for each sample was added with 80% methanol at a volume of 800 µL. The resultant solution was vortexed for 30 s and sonicated thereafter for 30 min at 4 °C. Samples were stored at a temperature of −20 °C for an hour. Each sample was centrifuged thereafter at 12,000 rpm, 4 °C for 5 min. A total of 200 µL of each supernatant from the respective samples, along with 5 µL of DL-o-Chlorophenylalanine (140 μg/mL), were added to vials. The vials were then used for liquid chromatography–mass spectroscopy (LC-MS) analysis.

 Untargeted plant metabolomics analysis using UPLC-MS/MS

Separation of compounds was carried out using the Ultimate 3000LC (Thermo, Waltham, MA, United States) combined with QExactive MS (Thermo) and additionally, screened with ESI-MS [27]. The first solvent of the mobile phase comprised 0.05% formic acid in water and the second solvent was acetonitrile with a subsequent gradient elution (0–1 min, 95% A; 1–12 min, 95%–5% A; 12–13.5 min, 5% A; 13.5–13.6 min, 5–95% A; 13.6–16 min, 95% A). The mobile phase had a flow rate of 0.3 mL·min−1. The column temperature was kept at 40 °C, while the sample manager temperature was adjusted to 4 °C. The mass spectrometry settings for electrospray ionization ESI+ mode are detailed as follows: heater temperature 300 °C; sheath gas flow rate, 45 arb; auxiliary gas flow rate, 15 arb; sweep gas flow rate, 1 arb; spray voltage, 3.0 kV; capillary temperature, 350 °C; S-Lens RF level, 30%.

 Quality control samples

Extracts of the respective plants were mixed as quality control samples (QC) to evaluate the methodology and stability of the LC-MS system. Raw data for the UPLC–MS/MS were obtained from Compound Discover (3.0, Thermo) based on the m/z value and the retention time of the ion signals. To ensure consistency and efficacy of the system, multiple QC samples were formulated through a combination of identical quantities of each isolated sample. The instrument’s performance and repeatability were evaluated through pooled QC samples that were injected before the sample analysis until equilibrium was reached. The QC samples were subsequently run in positive mode. The ion characteristics of the QC samples were utilized to determine the Relative Standard Deviation (RSD). Supplementary Figure S1 of the Supplementary Material displays the percentage of RSD distribution, with a significant portion of the RSD being below 30%. This indicates that the analytical method is reliable and appropriate for application to future sample analyses.

 Identification of metabolites

Metabolite identification was performed at a putative level (MSI level 2) utilizing accurate mass measurements and MS/MS fragmentation data. Candidate metabolites were annotated by matching experimental spectra against the Human Metabolome Database 1 , ChemSpider 2 and MassBank 3 , all accessed 19 August 2024. Then manual evaluation of retention time consistency, isotope patterns, and MS/MS fragmentation spectra was done. Cross-validation across databases was achieved to enhance annotation confidence. Features detected in the ESI+ mode were cross-checked for chromatographic and spectral consistency and combined where appropriate. Redundant ions and adducts were resolved utilizing mass accuracy criteria (<5 ppm), retention-time coherence, signal intensity, and % RSD across technical replicates to reduce duplication or erroneous assignments.

 Pharmacokinetic and drug-likeness activity of differential metabolites

The properties of the ADME of significant metabolites were extrapolated through in silico methods, and their potential, along with their suitability as drug candidates, was evaluated. The SWISSADME platform 4 (accessed on 12 July 2025) was utilized for primary screening, which facilitated the high ranking of compounds that exhibited drug-like profiles and advantageous pharmacokinetic properties. Subsequently, further assessments were carried out with the assistance of recognized cheminformatics inclined tools for a more thorough evaluation of the pharmaceutical relevance of the metabolites. The parameters examined comprised physicochemical descriptors, which included molecular weight, number of heavy aromatic atoms, topological polar surface area (TPSA), molecular refractivity, number of hydrogen bonds, lipophilicity factors (logP), water solubility (logS), along other parameters were examined. Pharmacokinetic traits such as blood–brain barrier (BBB) permeation, gastrointestinal (GI) absorption, substrate specificity for P-glycoprotein (P-gp), inhibition potential essential key cytochrome P450 (CYP) isoenzymes (CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4), as well as skin permeation (logKp) were evaluated, and these aspects conjointly affect the metabolism of the potential and the likelihood of drug-to-drug interactions. Ultimately, the final analysis for drug-likeness was conducted using Lipinski, Ghose, Veber, Egan, Muegge, and bioavailability scores. These assessments provide a comprehensive view of the compounds’ appropriateness for further progression.

 Statistical analysis

In terms of anti-cancer analysis, GraphPad Prism version 8.2.0 (GraphPad Software, Inc., San Diego, CA, United States) was utilized to plot the dose-response curve and determine the IC50 of plant treatments.

While for metabolomics analysis, the raw data was collected and aligned utilizing Compound Discover (3.0, Thermo) according to the m/z values and the retention time of the ion signals. Ions from both ESI+ were combined and subsequently transferred into the SIMCA-P software (version 14.1) and Metaboanalyst version 6.0 5 (accessed on 12 August 2025) for multivariate analysis. Unsupervised principal component analysis (PCA) was achieved to evaluate intrinsic clusters and the structure of variance. Supervised partial least squares-discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) were also achieved for maximizing class segregation and assessing statistically significant metabolites. Cross-validation of these models was completed, and the variance percentages clarified through orthogonal and predictive components were verified.

Statistically significant metabolites were verified and filtered by integrating the outcomes of the Variable Importance in Projection (VIP) score greater than 1.5, a p-value less than 0.05, FDR-adjusted p < 0.05 and a fold change (FC) greater than 2.0. The R 2 and Q 2 values can explain the quality of the fitted model. R 2 shows the variance accounted for in the model and reflects the goodness of the fit. Q 2 shows the variability in the data, reflecting the model’s predictability.

 Results Anti-cancer activities

The anti-cancer activities of A. calamus and L. javanica extracts on SH-SY5Y neuroblastoma cells were assessed utilizing the CCK-8 assay. These plant extracts displayed a clear dose-dependent decrease in cell viability, as demonstrated by the sigmoidal dose-response curves (Figure 1). The estimated half maximal inhibitory concentration (IC50) values were 0.2886 μg/μL for A. calamus and 0.3066 μg/μL for L. javanica, with 0.2976 μg/μL as an overall IC50 mean. However, A. calamus displayed slightly higher effectiveness in comparison to L. javanica.

Dose-response curves for (A) A. calamus and (B) L. javanica extracts on SH-SY5Y neuroblastoma cells utilizing the CCK-8 assay.

Two side-by-side scatter plots with sigmoidal trendlines display cell viability percentage versus final concentration in micrograms per microliter. Panel A shows an IC50 value of 0.3066, and panel B shows an IC50 value of 0.2886. Both graphs indicate decreased cell viability with increasing concentration. Mitochondrial membrane potential analysis

Mitochondrial function in SH-SY5Y neuroblastoma cells was evaluated using flow cytometry and the JC-1 fluorescent probe to quantify variations in membrane potential (ΔΨm). As illustrated in Figure 2, in terms of untreated SH-SY5Y control cells, 58.8% of the population was within the Q2 gate, signifying cells with high ΔΨm (red fluorescence), although 38.3% of the population was situated in the Q3 gate, conforming to mitochondrial depolarization (green fluorescence). Following plant extract treatment, the proportion of the cells with high ΔΨm increased to 68.2% for A. calamus and 65.4% for L. javanica, implying increased mitochondrial polarization comparative to untreated controls.

Mitochondrial membrane potential (ΔΨm) analysis of SH-SY5Y neuroblastoma cells using the JC-1 fluorescent probe and flow cytometry.

Four-panel flow cytometry dot plot graphic compares control, SH-SY5Y cell lines, L. javanica treated SH-SY5Y, and A. calamus treated SH-SY5Y cell lines. Each panel displays quadrants Q1–Q4 with cell percentages, showing increased Q2 percentages upon sample treatment, suggesting increased apoptosis. X-axis shows fluorescein isothiocyanate (FITC) and y-axis shows phycoerythrin (PE). UPLC-MS/MS base peak intensity chromatograms

The UPLC-MS/MS analysis of A. calamus and L. javanica completed in ESI+ mode produced (BPI) chromatograms, a wide profile of metabolites eluting over a specified retention time frame, as shown in Figure 3.

Base peak intensity (BPI) chromatograms from (A) A. calamus and (B) L. javanica in ESI+ mode.

Two chromatogram line graphs labeled A and B display intensity versus retention time in seconds. Both show multiple sharp intensity peaks across the x-axis, with differing maximum intensities and peak patterns.

The chromatogram of A. calamus with the dominating of a particularly intense peak at ∼400 s, with a highest intensity of ∼1.2 × 1010. Subsequent moderate peaks were detected at ∼700, ∼550, ∼200, and ∼50 s. Conversely, L. javanica offered an evenly dispersed chromatographic profile with multiple peaks of medium to high intensity detected between 100s and 600s. The most intense peak occurred at ∼220 s (∼4.0 × 109). Additional prominent peaks were detected at ∼720, ∼600, ∼400, and ∼100 s.

 Multivariate analysis of <italic>A. calamus</italic> and <italic>L. javanica</italic> data

Principal Component Analysis (PCA) score model of the associated features displays complete species segregation across PC1, with a variance of 96.9% (PC2 = 1.0%). The PCA pair-plot validates that cluster variances are concentrated on the first component and also noticeable, but smaller on higher components. The dispersals of scores vary significantly for PC2: p = 0.002, PC3: p = 0.004, PC4: p = 0.004, and PC5: p = 0.002 (Figure 4A). These results signify the possession of distinct global metabolomic fingerprints of the two species, dominated by variance expressed on PC1. The observed clustering patterns were statistically significantly validated using the PERMANOVA test, consisting of an F-value of 936.71, a p-value = 0.005, and R 2 = 0.98944.

Multivariate analysis: (A) Principal Component Analysis (PCA) pairwise scatterplots, (B) Partial Least Squares Discriminant Analysis (PLS-DA) scores plot, and (C) Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) scores plot of A. calamus (green) and L. javanica (red).

Panel A contains a grid of scatter plots and density plots comparing two groups, A. calamus (green) and L. javanica (red), across principal components PC1 to PC5, with p-values indicated for group differences. Panel B displays a scatter plot of PCA scores for the two groups showing clear separation along Component 1. Panel C shows a similar scatter plot of OPLS-DA scores with the two groups clearly separated along the primary axis.

Moreover, supervised Partial Least Squares-Discriminant Analysis (PLS-DA) generated the same qualitative pattern of PCA score model with distinct separated clusters driven by component 1 (96.9%), though component 2 represented insignificant variance of 0.8% (Figure 4B). Cross-validation metrics also validate with R 2 and Q 2 values that were close to 1.0 for all 5 tested components, which displayed consistently high performance. Moreover, the permutation test additionally validated the PLS-DA model, with a noticeable statistic positioned (p < 0.01, 0/100) that was distant outside the permutation distribution (Supplementary Figure S2).

Orthogonal PLS-DA (OPLS-DA) model additionally displays variation into a single predictive component (T score [1] = 96.9%) that segregates the plant species and an orthogonal component (Orthogonal T score [1] = 0.8%) representing within the class structure (Figure 4C). The model was further validated by cross-validation, where the model generated high explanatory and predictive performance, with R 2 Y = 1.0, R 2 X = 0.969, and Q 2 = 0.999. The related p-values for the predictive component (p1) were R 2 X: 0.969; R 2 Y: 1.0; Q 2 : 0.999, whereas for the orthogonal component (o1) were R 2 X: 0.00813, R 2 Y: 0.00031, and Q 2 : 7.04 × 10-5, underscoring the model’s explanatory and predictive capability. Further validation of the model was done using a permutation test, with Q 2 and R 2 Y values of 0.969 and 0.979, respectively (Supplementary Figure S2).

 Univariate analysis of <italic>A. calamus</italic> and <italic>L. javanica</italic> data

Univariate analysis recognized the top ten metabolites with VIP >2.0, FC >2.0, p < 0.05 and FDR-adjusted p < 0.05 that significantly differentiated the clusters of A. calamus and L. javanica (Table 1). Figure 5 depicts major significant metabolites comprising Tetramethylquercetin 3-rutinoside, Isopropyl β-glucoside, and 6β-Hydroxymethandienone, altogether presenting an extreme fold of enrichment. Further significant metabolites comprised of 2-trans-O-Feruloylglucaric acid, 7-Epi-12-hydroxyjasmonic acid, and Leonoside A. Flavonoid derivatives, for instance Quercetin 3-O-glucuronide, Hesperidin methylchalcone, and Apigenin 7-O-diglucuronide were also significantly differentiated. Additionally, Hetastarch also provided a contribution to the metabolic variance. Together, these compounds underscore jasmonates, flavonoid glycosides, and phenolic derivatives as crucial, statistically significant metabolites, supporting the distinct chemical divergence amongst the two plant species. These compounds show higher abundances constantly in L. javanica (Figure 5). A detailed chemical profiling of each compound is represented in the Supplementary Table S1.

Differential metabolites distinguished from A. calamus and L. javanica in electrospray ionization ESI+ mode.

Compound name RT (min) Molecular Weight m/z HMDB_ID Formula Log2(FC) T-Test FDR Log10 (p-adj) VIP
ALKALOIDS
5-Isothiocyanatoindane 3,015 115,17 157,1334 CSID642931 C6H13NO -6,7198 4,54E-05 4,342703 1,830521
3-Indolehydracrylic acid 3,336 205,0736 206,0809 HMDB0059765 C11H11NO3 5,338881 2,95E-05 4,52994 1,634694
Hydrocotarnine 4,956 221,1048 222,1121 HMDB0033701 C12H15NO3 5,347002 4,08E-08 7,38938 1,6638
Mescaline 2,991 211,26 229,1543 HMDB0254474 C11H17NO3 5,105346 4,88E-05 4,311856 1,602307
Perlolyrine 4,972 264,0895 265,0967 HMDB0030327 C16H12N2O2 -5,99531 0,001842 2,73468 1,680894
Prodolic acid 5,075 273,33 296,1276 HMDB0256794 C16H19NO3 -6,27265 0,001789 2,747286 1,732869
CARBOHYDRATES
Isopropyl β-glucoside 3,5 222,24 245,1016 HMDB0032705 C9H18O6 10,83254 3,52E-05 4,453856 2,331036
Muramic acid 3,206 251,1014 252,1087 HMDB0003254 C9H17NO7 8,105161 9,48E-05 4,023081 2,006866
1-O-Caffeoyl-beta-glucose 5,172 342,0944 343,1016 HMDB0302440 C15H18O9 7,814332 1,86E-06 5,730844 1,977503
Cellulose, microcrystalline 2,156 370,35 393,136 HMDB0032197 C14H26O11 6,787933 8,9E-06 5,050728 1,842234
O-Desmethyltramadol glucuronide 3,259 425,50 448,1957 HMDB0060856 C21H31NO8 -6,19442 2,12E-05 4,673131 1,758105
Persicogenin 3'-glucoside 4,747 456,1638 479,1532 HMDB0041398 C23H26O11 8,080171 1,16E-06 5,934704 2,012623
Hetastarch 4,045 736,70 759,2936 HMDB0253113 C29H52O21 8,836521 2,09E-05 4,679721 2,134599
LIPIDS
4-Methyl-2-pentenoic acid 4,046 114,14 156,1018 HMDB0031561 C6H10O2 6,668955 6,85E-05 4,164162 1,833963
Undecylenic acid 4,113 184,27 226,1798 HMDB0033724 C11H20O2 7,923542 8,84E-08 7,053706 2,029285
12-Hydroxydodecanoic acid 7,277 216,32 239,1638 HMDB0002059 C12H24O3 -5,60027 1,14E-05 4,941239 1,679658
11-Dodecenoic acid 4,664 198,30 262,1797 HMDB0032248 C12H22O2 6,21621 1,46E-06 5,834954 1,771416
9-Pentadecenoic acid 10,834 240,38 263,2002 HMDB0029765 C15H28O2 -5,45158 0,000253 3,59735 1,674411
Stearidonic acid 9,676 276,40 299,1999 HMDB0006547 C18H28O2 4,57766 1,96E-07 6,707939 1,521083
MG(18:3/0:0/0:0) 8,725 352,2605 353,2678 HMDB0011570 C21H36O4 5,971525 2,8E-07 6,553359 1,761732
Prostaglandin A2 7,707 334,40 357,2028 HMDB0002752 C20H30O4 7,579842 0,000121 3,916245 1,940373
Eicosanedioic acid 10,522 342,50 365,2678 HMDB0242141 C20H38O4 6,161779 5,82E-06 5,23503 1,764398
Thromboxane B3 4,446 368,50 386,2529 HMDB0005099 C20H32O6 -6,81951 0,000823 3,084822 1,814574
MG(5-iso PGF2VI/0:0/0:0) 6,209 400,50 423,2344 HMDB0260485 C21H36O7 6,891431 1,09E-05 4,962081 1,86861
LysoPA(18:2/0:0) 4,868 434,50 457,2398 HMDB0007856 C21H39O7P 6,062893 1,42E-06 5,847397 1,754746
DG(20:4-2OH/0:0/2:0) 8,79 452,60 475,2682 HMDB0297002 C25H40O7 -8,77228 1,31E-05 4,882486 2,094746
DG(2:0/PGD2/0:0) 7,165 468,60 491,2629 HMDB0296907 C25H40O8 -5,07408 0,000151 3,820683 1,583894
DG(6 keto-PGF1alpha/2:0/0:0) 4,095 486,60 528,3155 HMDB0296912 C25H42O9 5,338498 0,000864 3,063593 1,605199
LysoPE(22:2/0:0) 10,093 533,3475 534,3547 HMDB0011522 C27H52NO7P -6,18444 0,00202 2,694655 1,568301
NITROGEN COMPOUND
1,3-Hexadien-3-amine 1,032 97,0895 98,09678 CSID67029750 C6H11N 6,805566 9,38E-06 5,027828 1,856891
2-Aminocyclohexanecarboxylic acid 1,229 143,0945 144,1017 CSID133327 C7H13NO2 5,478209 4,02E-06 5,395528 1,656544
7-Aminomethyl-7-carbaguanine 1,725 179,0804 180,0877 HMDB0011690 C7H9N5O 7,746392 4,59E-06 5,33773 2,019233
5-Fluoromethylornithine 4,624 164,18 187,0863 HMDB0245493 C6H13FN2O2 5,535211 8,43E-08 7,073976 1,674376
Succinyl proline 3,047 215,20 238,0682 CSID168469 C9H13NO5 7,81002 9,47E-08 7,02377 1,978274
erythro-4-Hydroxyarginine 3,109 190,20 229,0704 HMDB0034326 C6H14N4O3 6,393116 3,43E-07 6,464989 1,81208
Pantothenic acid 9,416 219,23 237,1402 HMDB0000210 C9H17NO5 -6,60822 0,000512 3,290701 1,785286
Phenylalanyl-Glycine 4,839 222,24 245,0917 HMDB0304788 C11H14N2O3 4,586087 6,19E-06 5,208442 1,522839
Glutaminylleucine 3,04 259,30 277,1906 HMDB0028801 C11H21N3O4 7,790423 1,82E-05 4,74028 1,971685
Glycyl-Tryptophan 3,196 261,279 279,1448 HMDB0028852 C13H15N3O3 -6,59597 0,001815 2,741203 1,589188
N-Acetyllactosamine 2,923 383,35 406,1337 HMDB0001542 C14H25NO11 6,16663 0,000286 3,544322 1,745963
NUCLEOSIDE / NUCLEOTIDE
2-Deoxy-ribono-1,4-lactone 5,407 132,0422 133,0495 HMDB0033958 C5H8O4 4,608802 2,27E-05 4,644565 1,515139
2',3'-Didehydro-2',3'-dideoxycytidine 4,434 209,0811 210,0884 HMDB0245545 C9H11N3O3 -8,76604 1,28E-05 4,892618 2,092533
Keto-3-deoxy-manno-octulosonic acid 3,079 238,19 261,0601 HMDB0244292 C8H14O8 6,750766 4,57E-06 5,340459 1,856524
5-(Hydroxymethyl)cytidine 2,821 273,0955 274,1027 CSID32720391 C10H15N3O6 4,788076 2,22E-06 5,653688 1,616127
2',3'-Dideoxyadenosine 6,718 235,24 277,1405 HMDB0245544 C10H13N5O2 -6,84114 4,18E-05 4,379233 1,84559
5-Methyldeoxycytidine 2,036 241,24 283,1396 HMDB0002224 C10H15N3O4 -8,43195 0,00119 2,924626 1,881698
3'-C-Ethynylcytidine 3,174 267,24 309,1174 HMDB0252093 C11H13N3O5 8,752045 3,19E-08 7,496043 2,107001
2'-Deoxy-5-formylcytidine 1,99 255,23 319,0993 CSID10291642 C10H13N3O5 5,632298 2,15E-06 5,666959 1,744307
Dimp 4,25 332,0524 333,0597 HMDB0006555 C10H13N4O7P 8,036495 2,38E-07 6,623169 2,006318
2-Methylguanosine 4,679 297,27 339,1407 HMDB0005862 C11H15N5O5 -5,73448 0,000162 3,789346 1,681938
OTHER
2,4-Pentadienal 10,135 82,04236 83,04964 HMDB0031597 C5H6O 7,510876 1,41E-06 5,850963 1,943115
5-Methylcytosine 6,097 125,0603 126,0676 HMDB0002894 C5H7N3O -8,4848 4,81E-06 5,317795 2,059602
xi-4-Hydroxy-4-methyl-2-cyclohexen-1-one 3,795 126,0681 127,0754 HMDB0033629 C7H10O2 8,258472 6,02E-06 5,22045 2,034825
gamma-Butyrolactone 3,78 86,09 128,0706 HMDB0000549 C4H6O2 7,403507 6,82E-06 5,166232 1,925084
Indol-2-one 3,251 131,0371 132,0444 HMDB0253466 C8H5NO 5,862663 3,16E-07 6,500593 1,718906
Methyl 2-thiofuroate 0,897 142,0115 143,0188 HMDB0037762 C6H6O2S 4,48824 1,48E-05 4,829513 1,511626
Norcamphoric acid 3,195 158,15 200,0915 CSID207959 C7H10O4 5,611922 7,34E-06 5,134455 1,720562
4-Guanidino-1-butanol 0,787 131,18 173,1396 CSID4476579 C5H13N3O -5,88882 0,001006 2,997208 1,515761
1-Oxo-1H-2-benzopyran-3-carboxaldehyde 4,626 174,0315 175,0388 HMDB0030577 C10H6O3 6,426311 1,56E-05 4,807221 1,825317
3-Hydroxysuberic acid 2,866 190,19 232,1191 HMDB0000325 C8H14O5 4,813224 4,4E-06 5,356159 1,568506
6-(2-Hydroxyethoxy)-6-oxohexanoic acid 3,717 190,19 213,0755 HMDB0061681 C8H14O5 4,69247 4,65E-06 5,332964 1,552665
N-Lactoylphenylalanine 4,391 215,1177 238,1069 HMDB0062175 C12H15NO4 5,606803 5,75E-07 6,240409 1,704328
2-(2-Furylmethyl)-1-indanol 3,905 214,26 237,0907 CSID40514807 C14H14O2 -5,2121 0,000218 3,660803 1,591246
Piperdial 6,936 250,1565 251,1638 HMDB0035798 C15H22O3 -5,12368 4,52E-06 5,345311 1,600008
1-(Ribofuranosyl)indoline 2,641 251,1153 252,1226 CSID67029342 C13H17NO4 4,946661 1,05E-05 4,977423 1,570688
2-Hydroxyacorenone 9,271 236,35 259,1664 HMDB0030916 C15H24O2 -7,68915 1,73E-06 5,761784 1,961428
Risbitin 5,609 222,32 264,1954 HMDB0302980 C14H22O2 -5,92921 0,001049 2,979129 1,703258
Linamarin 1,312 247,24 270,0944 HMDB0033699 C10H17NO6 -6,29487 0,000877 3,056864 1,759135
1-Hydroxyacorenone 7,673 250,33 273,1456 HMDB0030917 C15H22O3 -7,10849 1,82E-06 5,739609 1,887368
4-Hydroxycyclohexylcarboxylic acid 4,619 144,17 289,1637 HMDB0001988 C7H12O3 6,014314 5,86E-06 5,232144 1,73907
Pollenin A 4,254 302,042 303,0493 HMDB0303704 C15H10O7 8,76912 2,16E-07 6,664657 2,126655
9,10-DiHODE 4,767 312,40 330,2632 HMDB0010221 C18H32O4 -4,97006 0,000693 3,159427 1,553509
6β--Hydroxymethandienone 10,138 316,40 339,1922 HMDB0005832 C20H28O3 10,73503 2,66E-06 5,575855 2,317918
Bisoprolol 5,313 325,40 348,2162 HMDB0014750 C18H31NO4 5,110144 2,27E-05 4,643337 1,607036
6'-Hydroxyenterolactone 4,893 314,30 356,1482 HMDB0041697 C18H18O5 6,599311 0,00027 3,568419 1,804397
Caryoptosidic acid 2,971 392,35 415,1204 HMDB0034249 C16H24O11 6,037738 0,000224 3,649573 1,731817
Tyromycic acid 12,521 452,3278 453,3351 HMDB0035888 C30H44O3 -5,71222 4,22E-05 4,374993 1,690313
Fluocinolone 2,966 412,4 454,2062 HMDB0252347 C21H26F2O6 -5,03287 0,000493 3,307314 1,561579
Persiconin 4,285 478,1466 479,1539 HMDB0037482 C23H26O11 8,487737 1,03E-06 5,987367 2,064423
Phaseolus epsilon 3,657 526,50 544,2378 HMDB0035039 C25H34O12 7,87868 7,04E-05 4,152464 1,979816
19-Nor-5-androstenediol 9,685 276,40 553,4239 HMDB0004590 C18H28O2 7,24471 8,58E-06 5,066603 1,926738
7-Dehydrologanin tetraacetate 3,488 556,1755 557,1828 CSID391585 C25H32O14 -6,16628 0,003738 2,427367 1,709022
Procyanidin 3,823 578,1412 579,1484 HMDB0013690 C30H26O12 -7,188 0,000208 3,681349 1,883821
Desglucocheirotoxol 13,088 552,70 591,2582 HMDB0033828 C29H44O10 5,752651 2,39E-05 4,621974 1,696989
Neolicuroside 3,869 550,50 592,201 HMDB0040728 C26H30O13 8,370981 5,7E-06 5,243925 2,048135
Marmesin rutinoside 2,724 554,50 596,2325 HMDB0041413 C26H34O13 -6,94662 0,0022 2,657484 1,81168
Hesperidin methylchalcone 4,557 624,2032 625,2106 HMDB0253112 C29H36O15 9,090381 1,04E-08 7,982463 2,139523
Glucoliquiritin apioside 3,01 712,60 730,2684 HMDB0041149 C32H40O18 -6,08309 0,00112 2,950734 1,673043
Leonoside A 4,557 770,70 788,2936 HMDB0040342 C35H46O19 9,611505 1,25E-05 4,904459 2,19735
PHENOLIC
Coumarin 2,503 146,0365 147,0438 HMDB0001218 C9H6O2 5,452435 0,000132 3,879057 1,64696
3 Hydroxycoumarin 4,771 162,0314 163,0387 HMDB0002149 C9H6O3 4,910609 2,63E-07 6,580547 1,573768
4,8-Dimethyl-7-hydroxycoumarin 3,343 190,19 208,0965 CSID4512230 C11H10O3 5,339346 1,67E-05 4,77794 1,635275
Vanillactic acid 1,727 212,0682 213,0755 HMDB0000913 C10H12O5 7,713922 6,68E-07 6,175003 1,979313
Apigenin 5,177 270,0523 271,0596 HMDB0002124 C15H10O5 5,845909 7,92E-05 4,101263 1,718134
Kaempferol 4,724 286,047 287,0542 HMDB0005801 C15H10O6 7,038508 1,48E-06 5,830917 1,898751
3-Feruloyl-1,5-quinolactone 4,631 350,30 373,091 HMDB0029289 C17H18O8 7,663669 2,15E-05 4,668116 1,956575
2-trans-O-Feruloylglucaric acid 3,606 386,0841 387,0913 HMDB0302546 C16H18O11 9,917521 9,38E-06 5,027846 2,226979
Glucosyloxyanthraquinone 3,844 386,40 409,0911 CSID389045 C20H18O8 -5,23257 0,000103 3,987626 1,611837
Eriodictin 4,559 434,1204 435,1277 HMDB0037480 C21H22O10 8,417998 1,65E-10 9,781596 2,058779
Catechin 7-glucoside 3,177 452,131 453,1383 HMDB0037949 C21H24O11 -5,3561 0,003285 2,483526 1,591291
Quercetin 3-O-glucuronide 4,241 478,0739 479,0812 HMDB0029212 C21H18O13 9,420172 6,67E-08 7,175578 2,186479
Sesaminol glucoside 7,259 532,50 555,1482 HMDB0041209 C26H28O12 4,976722 7,32E-05 4,135256 1,570614
Apigenin 7-O-diglucuronide 4,027 622,1149 623,1222 HMDB0301685 C27H26O17 9,063099 3,86E-07 6,413527 2,131068
Tetramethylquercetin 3-rutinoside 4,052 666,60 708,2469 HMDB0039337 C31H38O16 11,67918 1,57E-06 5,804899 2,418616
POLYKETIDES
1(2H)-Pentalenone 2,502 118,042 119,0493 CSID66739022 C8H6O 4,673748 0,000182 3,739442 1,516804
Phthalide 2,503 134,13 157,0282 HMDB0032469 C8H6O2 8,291181 0,000232 3,634062 2,029866
Aflatoxin G2 6,708 330,0731 331,0804 HMDB0030475 C17H14O7 7,294113 0,000298 3,526371 1,900981
TERPENOIDS
Shinanolone 4,667 192,075 193,0823 HMDB0030580 C11H12O3 -5,65953 0,005135 2,289432 1,589457
4,7-Megastigmadien-9-ol 8,59 194,1668 195,1741 HMDB0038731 C13H22O -5,78981 3,46E-05 4,460445 1,700406
Vomifoliol 3,517 224,1409 225,1482 HMDB0303570 C13H20O3 6,685312 3,63E-06 5,439498 1,850491
7-Epi-12-hydroxyjasmonic acid 3,942 226,12 227,1273 HMDB0303749 C12H18O4 9,757035 2,83E-08 7,548945 2,218914
8-Hydroxygeraniol 8-O-glucoside 4,457 332.39 374,2165 HMDB0035025 C16H28O7 5,392225 0,000565 3,248129 1,621572
3beta-Hydroxy-5-cholestenal 9,848 400,60 423,3249 HMDB0060131 C27H44O2 -5,19836 1,33E-05 4,874901 1,62304
7a,12a-Dihydroxy-cholestene-3-one 10,46 416,60 439,3199 HMDB0002197 C27H44O3 -7,53264 7,79E-06 5,108677 1,956674
Limonoate a-ring-lactone 6,644 488,2015 489,2088 HMDB0302537 C26H32O9 -5,00973 0,001182 2,927482 1,561349
Cucumerin A 2,79 552,50 575,1571 HMDB0301967 C29H28O11 6,765172 0,000685 3,164562 1,819729
Cholic acid glucuronide 12,017 584,70 623,2843 HMDB0002577 C30H48O11 4,8136 9,02E-06 5,04476 1,549668
Linalool (8-hydroxydihydro-) 4,627 650,70 692,2884 HMDB0304700 C32H42O14 6,441235 4,01E-05 4,396548 1,794936

Key: Solid light green background with a very subtle gradient from a slightly lighter shade at the top to a slightly darker shade at the bottom. No objects or text are present. Highlighted top ten differential metabolites distinguished between A. calamus and L. javanica.

Differential metabolites distinguished between A. calamus and L. javanica. Molecular formulas, chemical structures, and boxplots of the top ten compounds (VIP >1.5, FC >2.0, p < 0.05, FDR-adjusted p < 0.05) are shown, with abundances in A. calamus and L. javanica.

Grid of ten panels displays chemical structures, compound names, and molecular formulas above box plots comparing concentrations in Acorus calamus and Limnophila javanica. Each compound shows a significantly higher concentration in L. javanica compared to A. calamus, with molecular structures illustrated above each plot. Pharmacokinetic and drug-likeness activity of differential metabolites

The assessment of the ten most significant metabolites from A. calamus and L. javanica was conducted through the SWISSADME platform to acquire the ADME profiles of each significant metabolite. This yields essential insights into their potential efficacy as neuroactive inhibitors targeting SH-SY5Y neuroblastoma cells (Table 2).

Physicochemical, pharmacokinetic, drug-likeness, and medicinal chemistry properties of the selected differential bioactive secondary metabolites.

Parameters Isopropyl β-glucoside Hetastarch 6β--hydroxymethandienone Hesperidin methylchalcone Leonoside A 2-trans-O-Feruloylglucaric acid Quercetin 3-O-glucuronide Apigenin 7-O-diglucuronide Tetramethylquercetin 3-rutinoside 7-Epi-12-hydroxyjasmonic acid
Physicochemical Properties
Formula C9H18O6 C29H52O21 C20H28O3 C29H36O15 C35H46O19 C16H18O11 C21H18O13 C27H26O17 C31H38O16 C12H18O4
Molecular weight 222.24 g/mol 736.71 g/mol 316.43 g/mol 624.59 g/mol 770.73 g/mol 386.31 g/mol 478.36 g/mol 622.49 g/mol 666.62 g/mol 226.27 g/mol
No. heavy atoms 15 50 23 44 54 27 34 44 47 16
No. arom. heavy atoms 0 0 0 12 12 10 16 16 12 0
Fraction Csp3 1.00 1.00 0.60 0.48 0.57 0.44 0.24 0.37 0.48 0.50
No. of rotatable bonds 0 15 6 11 13 4 4 7 13 9
No. H-bond acceptors 6 21 3 15 19 11 13 17 16 4
No. H-bond donors 0 11 0 9 11 6 8 9 8 0
Molar Refractivity 49.89 156.69 93.83 148.42 179.64 87.17 110.77 139.71 158.16 61.42
TPSA 55.38 Å2 314.83 Å2 43.37 Å2 245.29 Å2 304.21 Å2 179.28 Å2 227.58 Å2 283.34 Å2 251.36 Å2 52.60 Å2
Lipophilicity
Log Po/w (iLOGP) 3.13 2.21 4.00 2.15 2.28 0.90 1.13 0.28 3.47 3.17
Log Po/w (XLOGP3) 1.84 −7.70 5.49 −0.50 −1.60 −0.99 0.61 −0.24 0.07 2.22
Log Po/w (WLOGP) 2.05 −7.35 4.39 −1.12 −2.27 −1.61 −0.45 −1.94 −0.55 2.01
Log Po/w (MLOGP) 1.50 −7.42 3.48 −2.37 −3.75 −2.23 −2.60 −3.68 −1.99 1.94
Log Po/w (SILICOS-IT) 1.09 −6.09 4.87 −1.14 −2.61 −1.13 −1.04 −2.66 −0.61 2.53
Consensus Log Po/w 1.92 −5.27 4.45 −0.60 −1.59 −1.01 −0.47 −1.65 0.08 2.37
Water solubility
Log S (ESOL) −2.38 1.43 −4.86 −2.87 −2.92 −1.62 −3.27 −3.36 −3.35 −2.05
Solubility 9.33e-01 mg/mL; 4.20e-03 mol/L 2.00e+04 mg/mL; 2.71e+01 mol/L 4.32e-03 mg/mL; 1.37e-05 mol/L 8.36e-01 mg/mL; 1.34e-03 mol/L 9.33e-01 mg/mL; 1.21e-03 mol/L 9.24e+00 mg/mL; 2.39e-02 mol/L 2.54e-01 mg/mL; 5.32e-04 mol/L 2.75e-01 mg/mL; 4.41e-04 mol/L 2.99e-01 mg/mL; 4.49e-04 mol/L 2.03e+00 mg/mL; 8.96e-03 mol/L
Class Soluble Highly soluble Moderately soluble Soluble Soluble Very soluble Soluble Soluble Soluble Soluble
Log S (Ali) −2.62 1.83 −6.16 −4.18 −4.28 −2.29 −4.96 −5.25 −4.90 −2.96
Solubility 5.29e-01 mg/mL; 2.38e-03 mol/L 4.95e+04 mg/mL; 6.72e+01 mol/L 2.19e-04 mg/mL; 6.94e-07 mol/L 4.09e-02 mg/mL; 6.55e-05 mol/L 4.05e-02 mg/mL; 5.26e-05 mol/L 1.99e+00 mg/mL; 5.14e-03 mol/L 5.20e-03 mg/mL; 1.09e-05 mol/L 3.48e-03 mg/mL; 5.59e-06 mol/L 8.35e-03 mg/mL; 1.25e-05 mol/L 2.48e-01 mg/mL; 1.10e-03 mol/L
Class Soluble Highly soluble Poorly soluble Moderately soluble Moderately soluble Soluble Moderately soluble Moderately soluble Moderately soluble Soluble
Log S (SILICOS-IT) −1.45 5.00 −3.83 −0.22 1.04 −0.34 −1.04 0.05 −0.81 −2.55
Solubility 7.89e+00 mg/mL; 3.55e-02 mol/L 7.45e+07 mg/mL; 1.01e+05 mol/L 4.65e-02 mg/mL; 1.47e-04 mol/L 3.77e+02 mg/mL; 6.04e-01 mol/L 8.55e+03 mg/mL; 1.11e+01 mol/L 1.75e+02 mg/mL; 4.52e-01 mol/L 4.36e+01 mg/mL; 9.12e-02 mol/L 6.95e+02 mg/mL; 1.12e+00 mol/L 1.03e+02 mg/mL; 1.55e-01 mol/L 6.35e-01 mg/mL; 2.81e-03 mol/L
Class Soluble Soluble Soluble Soluble Soluble Soluble Soluble Soluble Soluble Soluble
Pharmacokinetics
GI absorption High Low High Low Low Low Low Low Low High
BBB permeant Yes No Yes No No No No No No Yes
P-gp substrate No Yes No Yes No No Yes Yes Yes No
CYP1A2 inhibitor No No No No No No No No No No
CYP2C19 inhibitor No No Yes No No No No No No No
CYP2C9 inhibitor Yes No Yes No No No No No No No
CYP2D6 inhibitor No No Yes No No No No No No No
CYP3A4 inhibitor No No Yes No No No No No No No
Log Kp (skin permeation) −6.35 cm/s −16.26 cm/s −4.33 cm/s −10.46 cm/s −12.14 cm/s −9.36 cm/s −8.78 cm/s −10.27 cm/s −10.32 cm/s −6.10 cm/s
Drug likeness
Lipinski Yes; 0 violation No; 3 violations: MW >500, NorO >10, NHorOH >5 Yes; 0 violation No; 3 violations: MW >500, NorO >10, NHorOH >5 No; 3 violations: MW >500, NorO >10, NHorOH >5 No; 2 violations: NorO >10, NHorOH >5 No; 2 violations: NorO >10, NHorOH >5 No; 3 violations: MW >500, NorO >10, NHorOH >5 No; 3 violations: MW >500, NorO >10, NHorOH >5 Yes; 0 violation
Ghose Yes No; 4 violations: MW >480, WLOGP <−0.4, MR >130, #atoms >70 Yes No; 4 violations: MW >480, WLOGP <−0.4, MR >130, #atoms >70 No; 4 violations: MW >480, WLOGP <−0.4, MR >130, #atoms >70 No; 1 violation: WLOGP <−0.4 No; 1 violation: WLOGP<−0.4 No; 3 violations: MW >480, WLOGP<−0.4, MR >130 No; 4 violations: MW >480, WLOGP <−0.4, MR >130, #atoms >70 Yes
Veber Yes No; 2 violations: Rotors >10, TPSA >140 Yes No; 2 violations: Rotors >10, TPSA >140 No; 2 violations: Rotors >10, TPSA >140 No; 1 violation: TPSA >140 No; 1 violation: TPSA >140 No; 1 violation: TPSA >140 No; 2 violations: Rotors >10, TPSA >140 Yes
Egan Yes No; 1 violation: TPSA >131.6 Yes No; 1 violation: TPSA >131.6 No; 1 violation: TPSA >131.6 No; 1 violation: TPSA >131.6 No; 1 violation: TPSA >131.6 No; 1 violation: TPSA >131.6 No; 1 violation: TPSA >131.6 Yes
Muegge Yes No; 5 violations: MW >600, XLOGP3 <−2, TPSA >150, H-acc >10, H-don >5 No; 1 violation: XLOGP3 >5 No; 4 violations: MW >600, TPSA >150, H-acc >10, H-don >5 No; 4 violations: MW >600, TPSA >150, H-acc >10, H-don >5 No; 3 violations: TPSA >150, H-acc >10, H-don >5 No; 3 violations: TPSA >150, H-acc >10, H-don >5 No; 4 violations: MW >600, TPSA >150, H-acc >10, H-don >5 No; 4 violations: MW >600, TPSA >150, H-acc >10, H-don >5 Yes
Bioavailability score 0.55 0.17 0.55 0.17 0.17 0.17 0.11 0.11 0.17 0.55
Medicinal Chemistry
PAINS 0 alert 0 alert 0 alert 1 alert: catechol_A 1 alert: catechol_A 0 alert 1 alert: catechol_A 0 alert 1 alert: catechol_A 0 alert
Brenk 1 alert: peroxide 0 alert 1 alert: isolated_alkene 2 alerts: catechol, michael_acceptor_1 2 alerts: catechol, michael_acceptor_1 1 alert: cumarine 1 alert: catechol 0 alert 3 alerts: catechol, michael_acceptor_1, more_than_2_esters 2 alerts: mchael_acceptor_1, more_than_2_esters
Leadlikeness No; 1 violation: MW 250 No; 2 violations: MW >350, Rotors >7 No; 1 violation: XLOGP3 >3.5 No; 2 violations: MW >350, Rotors >7 No; 2 violations: MW >350, Rotors >7 No; 1 violation: MW >350 No; 1 violation: MW >350 No; 1 violation: MW >350 No; 2 violations: MW >350, Rotors >7 No; 2 violations: MW < 250, Rotors >7
Synthetic accessibility 4.34 8.06 4.72 6.37 7.45 5.05 5.26 6.26 6.57 2.31
Physicochemical properties

There was a considerable amount of variability in molecular weight, polarity, and solubility of the ten-highest ranked metabolites, which was determined using the SWISSADME. Smaller molecules such as 7-epi-12-hydroxyjasmonic acid (MW 226.27 g/mol) and isopropyl β-glucoside (MW 222.24 g/mol) had a low topological polar surface area (TPSA <60 Å2) and the absence of hydrogen bond donors, indicating a pronounced potential for passive permeability across biological membranes, and these molecules also met the physicochemical criteria for drug-like entities. In contrast, more complex glycosylated flavonoids, such as apigenin 7-O-diglucuronide (MW 622.49 g/mol) and Leonoside A (MW 770.73 g/mol), exhibited slightly elevated TPSA (>250 Å2) and standout characteristics such as multiple hydrogen bond donors/acceptors, which are conventionally linked to restricted oral bioavailability.

The indices (consensus Log Po/w) for lipophilicity varied from −5.27 for hetastarch to 4.45 for 6β-hydroxymethandienone. This specifically emphasizes the solubility-permeability trade-offs across the metabolite library. Remarkably, 6β-hydroxymethandienone displayed favorable lipophilicity alongside an acceptable molecular weight, implying a significant potential for permeability; conversely, polar glucuronides (e.g., quercetin 3-O-glucuronide, consensus Log Po/w −0.47) are likely impeded in their capacity for cellular penetration.

 Water solubility

Predictions regarding water solubility indicated that hetastarch is highly soluble (>2.0 × 104 mg/mL), while 6β-hydroxymethandienone displayed suboptimal solubility (4.32 × 10-3 mg/mL). Intermediate solubility profiles were noted for flavonoid derivatives, such as hesperidin methylchalcone and tetramethylquercetin 3-rutinoside. These findings suggest that formulation strategies may be necessary for highly lipophilic compounds exhibiting poor intrinsic solubility.

 Pharmacokinetics

Predictions regarding gastrointestinal (GI) absorption depicted that 6β-hydroxymethandienone, isopropyl β-glucoside, and 7-epi-12-hydroxyjasmonic acid demonstrate high absorption potential, whereas the majority of glycosylated flavonoids exhibited low absorption due to their extensive polar surface areas. Blood-brain barrier (BBB) permeability was anticipated for the smaller hydrophobic compounds, particularly isopropyl β-glucoside, 6β-hydroxymethandienone, and 7-epi-12-hydroxyjasmonic acid, implying a potential for central nervous system (CNS) activity against SH-SY5Y neuroblastoma cells. Significantly, numerous compounds, including hesperidin methylchalcone and apigenin diglucuronide, were identified as substrates for P-glycoprotein (P-gp), suggesting a vulnerability to efflux and diminished intracellular accumulation.

Skin permeation (Log Kp) values were more favorable for lipophilic scaffolds (e.g., −4.33 cm/s for 6β-hydroxymethandienone) in comparison to highly polar compounds (−16.26 cm/s for hetastarch). The analysis of Cytochrome P450 (CYP) inhibition identified 6β-hydroxymethandienone as a multi-isoform inhibitor (CYP2C19, CYP2C9, CYP2D6, CYP3A4), indicating potential drug-drug interaction liabilities, whereas the majority of glycosides exhibited no alerts for CYP inhibition.

 Drug-likeness

Filters for Drug-likeness indicated that only isopropyl β-glucoside, 6β-hydroxymethandienone, and 7-epi-12-hydroxyjasmonic acid fully conformed to Lipinski’s rule of five, as well as Veber, Egan, and Muegge criteria, while the majority of glycosides surpassed thresholds for hydrogen bonding, molecular weight, and TPSA. Bioavailability scores corroborated these findings, revealing favorable values of 0.55 for the smaller drug-like molecules in contrast to ≤0.17 for the larger glycosides. These outcomes underscore isopropyl β-glucoside and 6β-hydroxymethandienone as promising candidates for oral bioavailability.

 Medicinal chemistry filters

The identification of Brenk alerts, as well as PAINS (Pan-Assay Interference Compounds), has highlighted various flavonoid derivatives (such as hesperidin methylchalcone, Leonoside A, and quercetin 3-O-glucuronide) that exhibit Michael acceptor motifs or catechol, thereby indicating the likelihood of assay interferences. The synthetic accessibility scores exhibited a range from 2.31 (7-epi-12-hydroxyjasmonic acid, which is readily synthesizable) to 8.06 (hetastarch, which is characterized by high complexity), reflecting considerations of feasibility for prospective lead optimization endeavors. Lead-likeness was attained solely for 6β-hydroxymethandienone, while the remaining metabolites were hindered by excessive molecular dimensions or flexibility.

Therefore, the ADME profiling has prioritized three metabolites, namely isopropyl β-glucoside, 6β-hydroxymethandienone, and 7-epi-12-hydroxyjasmonic acid, which emerge as the most promising lead-like candidates for targeting of SH-SY5Y neuroblastoma cells. Larger polar flavonoid glycosides, despite their bioactivity, may necessitate structural simplification or the implementation of advanced delivery systems to mitigate challenges related to poor absorption and permeability. Their favourable oral bioavailability, blood-brain barrier permeability, and adherence to drug-likeness criteria substantiate their potential for subsequent preclinical validation.

 Discussion

This study validates that extracts of L. javanica and A. calamus alter mitochondrial functional status and exert dose-dependent cytotoxic effects in SH-SY5Y neuroblastoma cells. The observed shifting toward increased mitochondrial membrane polarization, as evaluated by JC-1 staining, implies preservation or enrichment of mitochondrial reliability at levels of the tested exposure, rather than clear mitochondrial depolarization. Such effects are consistent with complex phytochemical concoctions in which mitochondrial responses might vary depending on exposure duration, concentration, and cellular context [14, 52].

Untargeted metabolomics analysis facilitated the chemical delineation of the phenotypic expressions of substances. With multivariate statistics verifying the distinct chemical differences between the two species, the metabolomic profiling identified a wide range of bioactive substances, including alkaloids, phenolics, flavonoid glycosides, and jasmonates. Separated and distinct groupings were exhibited by L. javanica and A. calamus in PCA, PLS-DA and OPLS-DA, with cross-validation by OPLS-DA and permutation analyses corroborating the non-random nature of the segregation of classes [53]. Given the propensity for supervised models to exhibit overfitting traits, the interpretation of data of the nearly perfect R 2 /Q 2 values with circumspection, anchoring conclusions within the framework of unsupervised PCA delineation and independent univariate filters (VIP, fold-change, p values), which is indicative of optimal methodological procedures [54]. The principal discriminative compounds identified were isopropyl β-glucoside, 6β-hydroxymethandienone, 7-epi-12-hydroxyjasmonic acid, 2-trans-O-feruloylglucaric acid, and flavonoid glycosides (e.g., conjugates of quercetin and apigenin) [55]. The identification of a jasmonate (7-epi-12-hydroxyjasmonic acid) is of significant mechanistic relevance; most jasmonates and their derivatives possess the capability to induce cancer cell apoptosis through the disassociation of hexokinase II from the mitochondria, inadvertently, disrupting metabolic processes [56, 57], and multiple research investigations have documented ROS-mediated apoptotic responses across several tumor models [57, 58]. If jasmonate-like chemistry was enriched in L. javanica, the plant may contribute to cytotoxic effects at elevated concentrations, notwithstanding the net mixture’s antioxidative behavior under the experimental conditions employed [35, 36].

The chemistry of L. javanica varies by chemotype (piperitenone-, carvone-, linalool, or myrcenone-rich), which can modulate biological effects [59]. The metabolomic analysis confirms the divergence of composition from A. calamus and focuses on phenylpropanoid, flavonoid, or jasmonate classes for fractionation [33]. The profile for A. calamus inherently invokes concerns pertaining to translation, although β-asarone and some crude extracts have the potential to exhibit antiproliferative activity both in vitro and in vivo [60–62]. β-asarone has been identified as a carcinogenic and genotoxic component in rodents as well as hepatotoxic in preclinical models; this necessitates the establishment of regulatory limits in phytomedicine as well as food products [62]. Consequently, any trajectory for the development of products must prioritize asarone-depleted fractions or alternatives devoid of asarone if the exploration of efficacy is to be conducted safely [42, 43, 62]. It is crucial to note that putative annotations of mescaline-and steroid-like compounds probable indicate structurally associated plant-derived analogues sharing common triterpenoid or phenethylamine biosynthetic scaffolds, instead of definitive identification of canonical reference molecules [48, 63].

Accordingly, SwissADME analysis was employed to provide functional prioritization of annotated metabolites. The SwissADME software was utilized alongside complementary cheminformatics principles (Lipinski, Veber, Ghose, Egan, Muegge), and in conjunction with gastrointestinal absorption/BBB predictions and alert panels (PAINS, Brenk, lead-likeness), this was done to examine the most discriminative metabolites [64–66]. Compounds such as isopropyl β-glucoside, 6β-hydroxymethandienone, and 7-epi-12-hydroxyjasmonic were identified through the internal ADME heatmap as advantageous lead-like candidates that are grounded in minimal rule infractions, bioavailability assessments, and feasible synthetic accessibility [44]. Although SwissADME’s BOILED-Egg framework serves as an ideal tool for preliminary evaluation for BBB and gastrointestinal predictions, further orthogonal verification is usually required through experimental assays such as PAMPA, MDCK/P-gp transport, and microsomal stability as described in this study [47, 67]. Within this limitation-aware framework, in silico ADME predictions still serve as a critical early-stage filter for selecting candidate phytometabolites with translational possibility in neuroblastoma research [68]. Predicted BBB permeability is mostly applicable for neuroblastoma, given the connection of paraspinal, intracranial, or central nervous system-adjacent disease locations, where incomplete tissue penetration can restrict therapeutic efficacy [44]. Moreover, predicted P-glycoprotein (P-gp) substrate status alerts the probability of active efflux, which might decrease intracellular accumulation and influence multidrug resistance [69]. Lastly, cytochrome P450 (CYP) inhibition profiles present understanding into possible metabolic liabilities and drug-drug interaction risks, which are important considerations in pediatric oncology settings where integration therapies are common [70]. In general, these parameters reinforce rational prioritization of phytometabolites for downstream optimization, while emphasizing compounds that might need structural refinement or different delivery approaches.

Natural products remain significantly important as complementary or primary scaffolds for neuroblastoma-based therapies, particularly polyphenols, which are a class of secondary metabolites known to influence apoptosis activity and autophagy, and may also specifically target neuroblastoma stem-like cell populations [71, 72]. A compilation of recent scholarly reviews has systematically outlined a variety of phytochemicals exhibiting preclinical activity against neuroblastoma and highlighted proposed combinations of probable strategies aimed at augmenting responses to standard therapeutic regimens [73]. Within this body of literature, our findings motivate a chemotype-specific follow-up: (i) to deprioritize asarone-rich fractions of A. calamus or to specifically eliminate β-asarone before experimental testing due to safety concerns [62], (ii) to assess and evaluate flavonoid conjugates for their potential to modulate redox status and kinase signaling pathways in neuroblastoma [74], and (iii) to extract jasmonate-like metabolites from L. javanica for the purpose of investigating the direct targeting of mitochondrial pathways [57].

Together, these results offer a rationale for structured future investigations that incorporate chemotype-informed isolation approaches, bioactivity-guided fractionation, and target-focused mechanistic assessment.

Future directions

Building on potential chemotypic signatures identified by incorporating univariate and multivariate metabolomic analyses, future work should prioritize targeted isolation of potential metabolites from dominant chemical classes. Bioactivity-guided fractionation methods can be critical to connect specific phytochemical fractions to functional effects studies in neuroblastoma cells and to isolate active constituents from complex extract mixtures. In silico structure-based molecular docking and molecular dynamics can be used to screen relevant molecular targets against neuroblastoma. To support rational prioritization of candidate chemotypes and guide hypothesis-driven mechanistic investigations.

 Conclusion

This study employed an integrated workflow of untargeted metabolomics, mitochondrial functional assessment, and in silico ADME to explore phytometabolites from L. javanica and A. calamus in human SH-SY5Y neuroblastoma cells. Univariate and multivariate investigations uncovered distinct chemotypic signatures, with L. javanica characterized by flavonoid- and polyphenol-enriched profiles and A. calamus was dominated by jasmonate-, phenylpropanoid- and lipophilic terpenoid-associated metabolites. Extract exposure related to dose-dependent reductions in cell viability and modulation of mitochondrial membrane potential. In silico pharmacokinetic predictions supported early-stage prioritization of selected chemotypes such as isopropyl β-glucoside, 6β-hydroxymethandienone, and 7-epi-12-hydroxyjasmonic acid. These results are preliminary and hypothesis-generating, and further mechanistic confirmation and in vivo studies are recommended to establish biological mechanisms and translational importance.

 Author contributions

Conceptualization, MM and NM; methodology, EP, NM, MZ, CO, BI, and MM; software, NM, MZ, BI, and EP; validation, CN, CO, NM, and MM; formal analysis, MM, NM, CN, MZ, and EP; investigation, MM, NM, and CN; resources, EP, MZ, CO, MM, BI, and NM; data curation, NM and MM; writing – original draft preparation, NM and MM; writing – review and editing, MM, CO, BI, EP, MZ, NM, and CN; visualization, MM, NM, BI, and CN; supervision, EP, MZ, CO, MM, BI, and NM; project administration, MM and NM; funding acquisition, MM, CO, and NM. All authors contributed to the article and approved the submitted version.

 Data availability

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

 Ethics statement

Ethical approval was not required for the studies on humans in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used.

 Acknowledgements

Research reported in this publication/article was supported by Sefako Makgatho Health Sciences University. We would also like to sincerely thank the South African Medical Research Council (SAMRC) through its Division of Research Capacity Development under the Research Capacity Development Initiative.

 Conflict of interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

 Supplementary material

The Supplementary Material for this article can be found online at: https://www.ebm-journal.org/articles/10.3389/ebm.2026.10867/full#supplementary-material

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