Startseite Naturwissenschaften Molecular insights and biological evaluation of compounds isolated from Ferula oopoda against diabetes, advanced glycation end products and inflammation in diabetics
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Molecular insights and biological evaluation of compounds isolated from Ferula oopoda against diabetes, advanced glycation end products and inflammation in diabetics

  • Muhammad Nabeel Anjum , Sumaia H. Radi , Shah Iram Niaz , Muhammad Zafar ORCID logo , Dalal Nasser Binjawhar , Sayyara Ibadullayeva ORCID logo , Sheikh Zain Ul Abidine und Adnan Amin ORCID logo EMAIL logo
Veröffentlicht/Copyright: 8. Dezember 2025
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Open Chemistry
Aus der Zeitschrift Open Chemistry Band 23 Heft 1

Abstract

Terpenoids are biologically active molecules, and a low molecular weight makes them acceptable as drug candidates. This project aimed to discover drug leads against Advance glycation end products (AGEs) and inflammation in diabetic patients from Ferula oopoda. We employed NMR, computational tools and in vitro AGEs inhibition assays and their mechanistic insights. Two terpenoid compounds namely lancerodiol p-hydroxybenzoate (1) and Iso-tschimganine (2) were isolated and analyzed using NMR analysis. The computational prediction confirmed drug likeness properties and agreeable ADMET properties. Network pharmacology revealed that the AGEs-inhibitory effect of the analyzed compounds was related to several diabetic complications. Molecular docking showed the highest free energies in Iso-Tschimganine against transcription regulators 1CX2 (ΔG −6.3 (kJ mol−1)), 4F5S (ΔG −5.9 (kJ mol−1) and 6Y3C ΔG −6.6 (kJ mol−1). A significant lowering of oxidative stress was recorded in the case of compound 1 with DPPH (24.5 μg/mL); FRAP (56.1 µg) and H2O2 (64.1 ± 2.1). Similarly, high anti-inflammatory activity was recorded in the case of compound 1 in protein denaturation (55.2 ± 1.2 %), proteinase inhibition (85.7 ± 2.8 %), and 15 Lox assay (61.6 ± 1.4 %). A moderate inhibition of AGEs was recorded with compound 2 in BSA-glucose model (61.2 ± 1.4 %). It was thus concluded that both compounds possess significant activities against AGEs and inflammation.

Keywords: Ferula species; terpenoids; network pharmacology; bioavailability; 15-lipoxygenase

1 Introduction

Plants and herbs contribute as indispensable resources for human health. The plant’s photosynthetic processes not only provide essential nutrients, but also synthesize numerous bioactive compounds that significantly contribute towards human well-being [1]. Medicinal herbs are the natural reservoir of phytoconstituents like alkaloids, tannins, resins, steroids, saponins, terpenoids, flavonoids and steroidal compounds [2], 3] that possess diverse and polyvalent therapeutic properties [4], 5]. The market of herbal pharmaceutical products is immensely growing due to fewer side effects, being inexpensive and multitargeted effects against human diseases [6], 7]. In various communities, traditional medicinal plants are prominently used for multiple disorders with characteristic examples including mint family (digestive disorders) [8] Cannabis sativa (glaucoma) [9], Ferula species (immunomodulatory effects) [10] Erythroxylon coco (local anesthetic) [11], Narcissus spp. (Alzheimer’s disease) [12], Atropa belladonna (muscle spasm) [13] and Cinchona succiruba (malaria fever) [14].

Among plants of genus Ferula, the Ferula oopoda Boiss (F. oopoda). possess significant therapeutic properties including antimicrobial, antioxidant, antimycotic, anti-inflammatory and anti-plasmodial activities [10], 15], 16]. The immense pharmacological activities of this plant is mainly attributed to the presence of phytochemicals including sesquiterpenes, flavonoids, saponins, steroids, and tannins [17], [18], [19]. The Terpenoids (monoterpenes) are a group of diverse secondary metabolites and are profusely found in plant extracts, mainly consisting of “isoprene” units [20]. These compounds are lipophilic in nature, lighter in weight and mostly found in essential oils [21]. Various classes of terpenoids have been reported including acyclic monoterpenes (geraniol and myrcene), monocyclic monoterpenes (thymol, menthol) and bicyclic monoterpenes (camphor and borneol) [22]. Most importantly, these days several “novel drug entities” with enhanced therapeutic effects are being designed and synthesized through chemical modification of monoterpenes [23]. Typical examples include ester group linkage with borneol derivatives [24], addition of an imino group to camphor [25], and introduction of a heterocyclic ring to thymol [26]. This modified structure possesses exciting biological activities compared to the parent molecule [27].

The monoterpenoids have been extracted from diverse vegetables, fruits, herbs and spices and possess multiple curative properties [28] such as antibacterial [29], antioxidant, anti-inflammatory [30], anti-viral and anticancer activities [31], 32]. In addition, researchers have documented the effectiveness of monoterpenoids against diabetes and its associated micro and macro vascular complications that occur due to AGEs [33], 34]. For instance, the p-cymene (monoterpenoid) naturally found in over 100 essential oils possesses substantial antimicrobial activity [35] and has also been found to be very effective against AGEs in combination with thymol [36]. Aucubin is another natural monoterpene isolated from Aucuba japonica, which inherently possesses AGEs inhibition potential by blocking the Methylglyoxal (MGO) [37], which is primarily responsible for the formation of AGEs associated with multiple human disorders. Likewise, catalpol, a low molecular weight terpenoid molecule obtained from Rehmannia glutinosa, has been reported to suppress the AGEs/RAGE/Nox4 signaling pathway in animal models [38].

The Maillard reaction, which excessively occurs in diabetic patients, can lead to the accumulation of AGEs in the body, particularly under conditions of persistent hyperglycemia, oxidative stress, and inflammation [39]. AGEs are recognized as actively participating in various pathological processes by modifying protein structure and altering their function [40], which thus leads to long-term diabetes complications, including cardiovascular conditions and neurodegenerative disorders [41]. The AGEs interact with specific receptors called RAGEs (receptor for AGEs) that trigger inflammation, oxidative stress and tissue damage [42]. The prevalence of AGEs in the general population has been increasing, particularly with the upsurge of diabetes and aging. Globally, it is estimated that over 400 million people are living with diabetes [43], and the World Health Organization (WHO) reports that the global prevalence of diabetes in adults was 9.3 % in 2019, and this number is anticipated to increase to 10.2 % by 2030 [44]. As the global population ages, with the proportion of people over 60 years old expected to reach 22 % by 2050, the burden of AGE - related diseases is expected to grow [45]. Studies have shown that higher AGE levels are prevalent in developed countries, where dietary patterns and lifestyle factors, including high consumption of fried and grilled foods, are common [46]. As a result, AGEs are considered “important,” focus in both public health initiatives and clinical practice, and increasing efforts aimed at addressing their role in chronic diseases across the globe is growing too. Considering the high prevalence of AGEs and the limited available therapeutic options, this study aims to evaluate the therapeutic potential of the terpenoid-rich plant F. oopoda Bioss. This investigation is primarily based on isolation, characterization of compounds, their computational analysis, network pharmacology, and molecular docking and experimental evidence. As the biological activities of the F. oopoda extract are reported in the literature, the individual compounds have not been previously explored for the management of diabetes and AGEs.

2 Materials and methods

2.1 Chemicals and reagents

Solvents used for extraction and maceration such as methanol, dichloromethane, n-hexane and ethyl acetate were purchased from Daejang, Korea. Enzyme and its substrates including Lipoxygenase (15-Lox), Linoleic acid were procured from Oxoid (United Kingdom, UK), and Follin’s Ciocalteu’s reagent was obtained from Merck (United Kingdom UK). Similarly, Casein, Bovine Serum Albumin (BSA), Trypsin, DPPH radical, Congo red, DTNB (5,5′-dithio-bis (2-nitrobenzoic acid) and NBT (4-notroblue tetrazolium) were purchased from Sigma-Aldrich (UK).

2.2 Extraction, isolation and purification

An extract of plant material was prepared by using cold maceration and fractions of diverse polarity were collected and dried. The compound isolation was accomplished by column chromatography using silica gel and Sephadex LH-20. Final purification was performed by repeated Sephadex LH-20 column chromatography, and isolated compounds were dried using nitrogen.

2.3 Spectroscopic techniques

2.3.1 Nuclear magnetic resonance spectroscopy

The 1H NMR and 13C NMR spectra of purified compounds were acquired using a Bruker ARX 400 instrument using deuterated solvents. A 5 mm dual 1H/13C probe was used for standard Bruker pulse sequences. The analysis was performed in Deuterated solvents that included CDCl3 (99.8 % D) and CD3OD (99.8 % D) (Sigma-Aldrich). Spectral visualizations were performed by Topspin® software and the online version of Modgraph software (NMR Predict version 4.8.57, Modgraph) was used for structural elucidation.

2.4 Computational assays

2.4.1 ADMET analysis, PASS and drug likeness

The drug similarity of compounds was assessed by comprehensive ADMET analysis that included the use of several online tools such as pkCSM, molsoft ADMET lab 3.0, and SWISS ADME [47].

2.4.2 Network pharmacology

2.4.2.1 Screening of targets of isolated compounds

The 2D molecular structure of both compounds along with their SMILES (Simplified Molecular Input Line Entry System), were downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the compound-target prediction was perfromed by using Swiss Target Prediction (http://www.swisstargetprediction.ch/predict.php) and superpred (https://prediction.charite.de/subpages/target_prediction.php). Further, the relevant scores with high probability were selected (≥90 %) as the screening criteria in this analysis [48].

2.4.2.2 Potential targets for AGEs and inflammation (disease genes)

Potential targets for diabetes were obtained using Genecards (https://www.genecards.org/) and OMIM (https://omim.org/), using different key words like” “Type II diabetes Mellitus”, “Inflammation in diabetes” “Advance Glycation End Products” and “RAGES or receptor for AGES”. High relevant scores were selected for GeneCards (≥0.5) as the screening criteria in all analyses. The intersected genes of target genes and ingredient target gene sets were obtained using a Venn online software mapping tool platform (https://bioinfogp.cnb.csic.es/tools.html). The intersected genes (common genes) were assumed to be potential therapeutic targets for AGEs.

2.4.2.3 Protein–protein interaction (PPI) network construction

The common targets that were identified as described above were imported into the STRING database (https://cn.string-db.org/) to generate the PPI network, using an interaction score threshold of >0.7. The Cytoscape 3.9.0 software was employed for subsequent analysis and visualization [49].

2.4.2.4 Enrichment analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to investigate the mechanisms involved through biological processes, cellular components, molecular functions, and key signaling pathways. These analyses were conducted using the DAVID database (https://davidbioinformatics.nih.gov/summary.jsp), with the species limited to “Homo sapiens.” The top 10 entries with the highest generation were visualized. The GO and KEGG results were further analyzed and visualized using an online bioinformatics platform (http://www.bioinformatics.com.cn/) and shinyGO 0.82 (https://bioinformatics.sdstate.edu/go/) [49].

2.4.3 Construction of the component-target-pathway (C-T-P) network

A “component-target-pathway” regulatory network was constructed based on the common targets between compound-disease relationships and the top predicted pathways, using Cytoscape 3.9.0 software for network construction.

2.4.4 Molecular docking

The Protein Data Bank (PDB) was used to download the protein structures (targets), whereas 3D ligand structure (compounds) was downloaded from PubChem. The CAStp was used to anticipate the active sites in the protein. and finally Auto Dock-vina version 4.0 was used to analyze how molecules docked together. To visualize the docked molecules and interaction analysis, the Ligplot+ and Discovery Studio tools were used [50].

2.5 In vitro biological activities

2.5.1 Antioxidant assay

The inhibition of oxidative stress was determined by following assays;

2.5.1.1 DPPH free radical scavenging assay

The DPPH free radical scavenging activity was determined by using a standard procedure [51]. Briefly in a 96-well microplate, a fresh DPPH solution in methanol was prepared (0.1 mM; 50 µL) and reacted with the sample (50 µL; 200–1.56 μg/mL) and placed for 30 min in a dark cabinet. Later, the absorbance was determined at 517 nm in a microplate reader (Hippo; MPP 96; Biosan Latvia). The control was prepared using the same procedure and Trolox was used as the standard. The % inhibition was calculated as;

%  inhibitation = 1 − A a A b × 100

where A a represents the absorbance of the sample and A b represents the absorbance of the control. A linear plot was used to calculate IC50 values of the tested samples.

2.5.1.2 Ferrous reducing antioxidant assay (FRAP)

The ferrous reducing antioxidant power was determined by using already set procedure [51]. Briefly, the TPTZ solution (10 mL; 10 mM), acetate buffer (100 mL; 300 mM) and FeCl3.6H2O (10 mL; 20 mM) were mixed to prepare a fresh FRAP reagent. Test samples (150 µL; 25 µg) was added to the FRAP reagent (2,850 µL) and placed in the dark for at least 35 min. Finally, absorbance was recorded at 593 nm. A Trolox calibration curve in the range of (0–250 μg/mL) was prepared and results were expressed by Ascorbic acid equivalents (AAE)/g.

2.5.1.3 Hydrogen peroxide (H2O2) scavenging activity

The free radical scavenging by H2O2 was recorded by using a standard procedure [52]. In brief the test sample or standard drug (ascorbic acid) (400 µL, different concentrations) was mixed with a H2O2 solution (600 µL; 2 mM) and the mixture was vortexed gently (1 min) and set aside undisturbed for 15 min. With the help of a UV spectrophotometer, the absorbance was recorded at 230 nm and the percentage inhibition was calculated as;

%  inhibitation = 1 − A a A b × 100

where Aa represents the absorbance of the sample and A b represents the absorbance of the control.

2.6 Anti-inflammatory assays

2.6.1 BSA denaturation assay

The protein denaturation assay was performed using a modified method [53]. Briefly, 200 μL of 1 % BSA and 20 μL of the test sample were added to 4.78 mL of phosphate-buffered saline (pH 6.4), followed by incubation at 37 °C for 15 min. The solution was then heated in a water bath at 70 °C for 5 min. Subsequently, the solution was allowed to cool to room temperature, and absorbance was measured at 660 nm using a UV–VIS spectrometer. Diclofenac sodium was used as the reference standard. The percentage inhibition was calculated using the following formula:

%  inhibitation = 1 − A a A b × 100

where Aa represents the absorbance of the sample and A b represents the absorbance of the control.

2.6.2 Proteinase inhibitory activity

The proteinase inhibitory potential was determined using a standard procedure with slight modifications [53]. In brief, an aliquot of 0.04 mL of the test sample was reacted with 0.012 mg of trypsin, pre-dissolved in 2 mL of Tris-HCl buffer (pH 7.4, 0.02 M), and incubated at 37 °C for 5 min. Casein (0.8 %, w/v, in 2 mL buffer) was then added to the reaction mixture, which was further incubated for 20 min. The reaction was terminated by adding 70 % perchloric acid, followed by centrifugation. Absorbance of the supernatant solution was measured at 210 nm. The percentage of proteinase inhibition was calculated using the following formula:

%  inhibitation = 1 − A a A b × 100

where Aa represents the absorbance of the sample and A b represents the absorbance of the control.

2.6.3 15-Lipoxygenase assay

The 15-LOX assay was performed according to a standard method [54]. In brief, varying concentrations (100 μL) of the test samples were incubated with 1 mL of 15-lipoxygenase enzyme (200 U/mL) for 5 min at 25 °C. Then, 900 μL of a 0.2 M borate buffer (pH 9) containing linoleic acid was added and changes in absorbance were monitored at 234 nm at regular intervals for 5 min. The percentage inhibition was calculated using the formula:

%  inhibitation = 1 − A a A b × 100

where Aa represents the absorbance of the sample and A b represents the absorbance of the control.

2.7 Anti-glycation (AGEs inhibition assay)

2.7.1 BSA-glucose assay

The AGEs experiment was performed using modified [55]. Briefly, buffer (Phosphate, pH 7.4) was prepared by adding NaN3 (Sodium azide; 100 mg), NaH2P4.2H2O (3.955 g) and NaOH (0.5 M). Briefly (BSA) (111 mg/mL) and glucose (1 g/mL) were dissolved in phosphate buffer [pH 7.4]. The mixture was incubated for 1 week. To prepare a positive reaction, the test compound or standard (Aminoguanidine) was incubated with the reaction mixture. Afterwards, the fluorescence was measured on a spectrofluorometer and % inhibition was calculated as;

%  inhibitation = 1 − A a A b × 100

where Aa represents the absorbance of the sample and A b represents the absorbance of the control. Same procedure was repeated for the BSA-MGO assay while replacing glucose with MGO. This experiment involved incubation of both test and control samples for 24 h.

2.7.2 β-amyloid formation

In this assay, the Congo red solution (0.139 mg/2 mL in distilled water) was freshly prepared and 25 µL of this solution was added to 25 µL of glycated sample (BSA + Glucose) and incubated at 30 °C for 20 min. After incubation, distilled water was added (1950 µL) to this mixture and absorbance was recorded using a spectrophotometer at 530 nm. Finally, the protective role of the tested samples was determined by comparing the results with the control. The analyzed samples were considered good if it has a protective effect on β-sheets of the protein (BSA) and it was estimated by comparing results with the control. The sample is considered as protective if it has an absorbance less than the control [56].

2.7.3 Carbonyl entrapment assay

The carbonyl entrapment in glycated samples was assessed using a modified method [57]. Briefly an aliquot of 10 mM solution of 2,4-dinitrophenylhydrazine (DNPH) was prepared using 2.5 M hydrochloric acid (HCl). The glycated sample (500 µL) was then incubated with an equal volume of the DNPH solution (500 µL) for 1 h at room temperature. Following incubation, protein precipitation was induced by adding 1.0 mL of 20 % trichloroacetic acid (TCA) solution. The resulting precipitate was washed with a 1:1 (v/v) mixture of ethanol and ethyl acetate (1 mL) and then dissolved in 1 mL of 6 M urea. The absorbance of final solution was measured at 365 nm. The concentration of protein carbonyl groups was determined using the molar extinction coefficient (ɛ at 365 nm = 21 mM−1 cm−1) and expressed as nM per mg of protein.

3 Results

The structures of purified compounds 1 and 2 (Figure 1) were elucidated using 1H and 13C NMR spectroscopy (Supplementary data) and structural assignment details are given as follows;

Figure 1: Structure of (a) lancerodiol p-hydroxybenzoate (4-1) (b) iso-tschimganine (4-2).
Figure 1:

Structure of (a) lancerodiol p-hydroxybenzoate (4-1) (b) iso-tschimganine (4-2).

3.1 NMR Spectroscopy

3.1.1 Lancerodiol p-hydroxybenzoate [1]

Off white crystals (10.2 mg); 1HNMR (400 MHz, Methanol-d4) δ 1.67 (m, 1H, H2a), 1.41 (m, 1H, H-2b), 2.14 (m, 1H, H-3a), 1.72 (m, 1H, H-3b), 2.54 (d, J = 11.2 Hz, 1H, H-5), 6.08 (dt, J = 11.2, 2.3 Hz, 1H, H-6), 6.24 (dq, J = 2.6, 1.4 Hz, 1H, H-7), 2.70 (d, J = 15.2 Hz, 1H, H-10a), 2.59 (d, J = 15.4 Hz, 1H, H-10b), 2.12 (m, 1H, H-11), 1.02 (d, J = 6.9 Hz, 3H, H-12), 0.85 (d, J = 6.8 Hz, 3H, H-13), 1.82 (t, J = 1.7 Hz, 3H, H14), 1.20 (s, 1H, H-15), 7.89 (d, J = 8.8 Hz, 2H, H-3′, H-7′), 6.81 (d, J = 8.8 Hz, 2H, H-4′, H-6′) 13CNMR (100 MHz, Methanol-d4) δ 42.9 (C-1), 42.5 (C-2), 32.8 (C-3), 86.9 (C-4), 54.2 (C-5), 73.4 (C-6), 140.1 (C-7), 136.0 (C-8), 203.8 (C-9), 59.5 (C-10), 37.5 (C-11), 18.6 (C-12), 17.5 (C-13), 20.9 (C-14), 21.2 (C-15), 167.1 (C-1′), 121.9 (C-2′), 132.3 (C-3′, C-7′), 116.8 (C-4′, C-6′), 162.84 (C-5′). ESI-MS (positive ionization mode); C22H28O5; m/z 372.5 g mol−1[44] (Supplementary data).

3.1.2 Iso-tschimganine [2]

Off white crystals (20.5 mg) 1H NMR (400 MHz, CDCl3) δ 0.93 (2H, d), 0.98 (1H, s), 1.23 (2H, m), 1.68 (1H, s), 1.77 (1H, m), 2.14 (2H, ddd), 2.48 (2H, m), 7.99 (2H, d), 6.9 (2H, d), 6.03 (1H, s), 3.93 (Me–OH), 13CNMR (100 MHz, CDCl3) δ 49.07 (C-1), 80.3 (C-2), 36.9 (C-3), 44.98 (C-4), 28.1 (C-5), 27.4 (C-6), 47.8 (C-7), 18.9 (C-9), 19.7 (C-10), 166.6(C-1′), 123.33 (C-2′), 131.8 (C-3′), & (C-7′), 115.16 (C-6′), 56.1 (C-4′), & 159.85 (C-5′). ESI-MS (positive ionization mode); C18H24O4 and 304.4 g/mol [44] (Supplementary data).

3.2 Computational analysis

3.2.1 ADMET and PASS analysis

Prediction of activity spectra of substances was performed at high probability levels (>70 %), and it was observed that both tested compounds are provided with multiple biological activities including Prostaglandin-E2 9-reductase inhibitor (73 % probability) anti-inflammatory (62 % probability) in case of Lancerodiol p-hydroxybenzoate (Table 6). Whereas major activities in case of Iso-Tschimganine are shown in (Tables 1 and 2).

Table 1:

PASS results of lancerodiol p-hydroxybenzoate with Pa > 0.7 (>70 % activity).

Pa Pi Activity
0.865 0.009 CYP2H substrate
0.845 0.011 Antiseborrheic
0.824 0.005 UDP-glucuronosyltransferase substrate
0.830 0.018 Testosterone 17beta-dehydrogenase (NADP+) inhibitor
0.782 0.020 Alkenylglycerophosphocholine hydrolase inhibitor
0.763 0.005 Cholesterol antagonist
0.759 0.009 Oxidoreductase inhibitor
0.738 0.007 UGT1A substrate
0.741 0.012 Apoptosis agonist
0.730 0.014 Prostaglandin-E2 9-reductase inhibitor
0.728 0.014 Fibrinolytic
0.756 0.046 CYP2C12 substrate
0.711 0.005 UGT1A4 substrate
0.708 0.043 Anti eczematic
Table 2:

PASS results of iso-tschimganine with Pa > 0.7 (>70 % activity).

Pa Pi Activity
0.892 0.003 Cardiovascular analeptic
0.885 0.004 Analeptic
0.865 0.005 Respiratory analeptic
0.859 0.004 JAK2 expression inhibitor
0.857 0.004 Antiseptic
0.856 0.010 CYP2H substrate
0.847 0.015 Testosterone 17beta-dehydrogenase (NADP+) inhibitor
0.814 0.005 UDP-glucuronosyltransferase substrate
0.805 0.003 Spasmolytic, Papaverin-like
0.804 0.017 Alkenylglycerophosphocholine hydrolase inhibitor
0.783 0.005 Spasmolytic
0.783 0.005 UGT1A substrate
0.778 0.002 Pediculicide
0.768 0.004 HMOX1 expression enhancer
0.757 0.004 UGT1A4 substrate
0.775 0.024 Antiseborrheic
0.762 0.027 Anti eczematic

Both compounds 1 and 2 show a pattern that follows Lipinski’s rule and no violations were noticed (Table 3). The ADMET profiling was performed using Molsinpiration, SWISS ADME software, and it was noticed that both tested compounds possessed a higher intestinal absorption (>90 %). In metabolism studies, compound 1 was noticed as a P-gp substrate, whereas it can act as CYT p450 enzyme series inhibitor except CYP3A4. This shows that compounds 1 and 2 may be metabolized in the liver and excreted mainly from the renal route and have little or no toxicity. All other details of ADMET profiling are given in (Table 4; Figures 2 and 3).

Table 3:

Lipinski’s properties of samples.

S. No Compound SMILES M. weight

<500Da		 Drug likeness score Log p < 5 H bond donor [5] H-bond acceptor<10 No of violations
1 Lancerodiol p-hydroxybenzoate [H]C1=C(C)C(=O)C[C@@]2(C)CC[C@@](O)(C(C)C)[C@]2([H])C1OC(=O)C1=CC=C(O)C=C1 372.19 0.74 3.96 2 5 0
2 Iso-tschimganine C(C1=CC(=C(C=C1)O)OC)(=O)O[C@@H]2C[C@@]3(CC[C@]2(C3(C)C)C)[H] 304.38 0.70 3.39 1 4 0
Table 4:

Pharmacokinetic parameters of tested compounds.

S.No Parameter Lancerodiol p-hydroxybenzoate Iso-tschimganine
1 GIT absorption 93.50 % (high) 90.83 %(High)
2 BBB permeant No Yes
3 P-gp substrate Yes No
4 CYP1A2 inhibitor No Yes
5 CYP2C19 inhibitor No Yes
6 CYP2C9 inhibitor No Yes
7 CYP2D6 inhibitor No Yes
8 CYP3A4 inhibitor Yes No
9 Log K p (skin permeation) −0.601 −4.20
10 Skin sensitization No
11 Total clearance Renal Renal
Figure 2: Bioavailability radar (A), boiled egg model (B), ADMET analysis (C) and drug likeness score (D) of lancerodiol p-hydroxybenzoate.
Figure 2:

Bioavailability radar (A), boiled egg model (B), ADMET analysis (C) and drug likeness score (D) of lancerodiol p-hydroxybenzoate.

Figure 3: Bioavailability Radar (A), boiled egg model (B), ADMET analysis (C) and drug likeness score (D) of iso-tschimganine.
Figure 3:

Bioavailability Radar (A), boiled egg model (B), ADMET analysis (C) and drug likeness score (D) of iso-tschimganine.

3.2.2 Network pharmacology

3.2.2.1 Screening of candidate targets

A total of 288 potential target were predicted by Superpred and SWISS-targection that were related to drug-target. Data from the GeneCards lead to the identification of genes related to Diabetes Mellitus type II (17,441) and AGEs-RAGE (1,809) and Inflammation in diabetes (10,480) targets with high confidence (0.55). Further, after merging and removing duplicate targets, 3,429 genes were identified. The common genes [44] were identified using a Venn diagram that were considered as target genes for Diabetes, α-glucosidase and AGEs (Figure 4).

Figure 4: Venn diagram of intersections of targets (A), PPI network (B) compound-disease network with pathway (C) compound-disease network with pathway and hub genes (E) related to diabetes mellitus, AGES-RAGES and inflammation.
Figure 4:

Venn diagram of intersections of targets (A), PPI network (B) compound-disease network with pathway (C) compound-disease network with pathway and hub genes (E) related to diabetes mellitus, AGES-RAGES and inflammation.

3.2.2.2 PPI network construction and analysis

The PPI network was constructed by using the STRING database. The minimum required interaction score” set was 0.9 (90 % confidence) (Table 5, Figure 4). A total of 44 nodes and 19 edges were noticed in the PPI network. Average node degree was 0.864 and average local clustering coefficient was 0.241 with a PPI enrichment p value of 0.003. The PPI network was built and analyzed using Cytoscape software (Version 3.10.1) (Figure 4) [32]. For Hub-Gene Identification, the cytoHubba plugin of Cytoscape (Version 3.10.1) was used, and it identified top 10 hub-genes in the network based on the “degree” method. Topological analysis showed that HSP90AB1, NFKB1, TLR4, HDAC2, CXCR4, TOP2A, CTSB, ESR2, APEX1 and KDM1A (Figure 4, Table 6). The cytoNCA plugin of Cytoscape (Version 3.10.1) was used to measure topological measures characterized the network nodes: degree, betweenness and closeness centrality results are shown in Table (Table 7).

Table 5:

Nodes and edges observed during string PPI network construction.

Parameter Count Parameter Count
Number of nodes 44 Expected number of edges 9
Number of edges 19 PPi enrichment p-value 0.00356
Average node degree 0.864
Average local clustering coefficient 0.241
Table 6:

Hub Genes data.

S.No Rank Gene name Score
1 1 HSP90AB1 16
2 2 NFKB1 11
3 3 TLR4 9
4 4 HDAC2 9
5 5 CXCR4 8
6 6 TOP2A 8
7 7 CTSB 8
8 8 ESR2 7
9 9 APEX1 6
10 10 KDM1A 5
Table 7:

Compounds and Gene’s degree and betweenness in pharmacological network.

S.No Class Degree Betweenness
1 Iso-tschimganine 44.0 1,068.4
2 Lancerodiol p-hydroxybenzoate 44.0 831.6
3 TLR4 3.0 0.45454547
4 PRCP 3.0 0.45454547
5 SLC9A1 3.0 0.45454547
6 CHRM3 3.0 0.45454547
7 METAP2 2.0 0.22727273
8 DPP9 2.0 0.22727273
9 AOC3 2.0 0.22727273
10 HSP90AB1 2.0 0.22727273
11 SCN3A 2.0 0.22727273
12 ADRB1 2.0 0.22727273
13 FPR2 2.0 0.22727273
14 CXCR4 2.0 0.22727273
15 NR3C2 2.0 0.22727273
16 GLRA1 2.0 0.22727273
17 TOP2A 2.0 0.22727273
18 FCGRT 2.0 0.22727273
19 PDGFRA 2.0 0.22727273
20 ESR2 2.0 0.22727273
21 S1PR5 2.0 0.22727273
22 CACNA1B 2.0 0.22727273
23 OPRD1 2.0 0.22727273
24 CDK5 2.0 0.22727273
25 CNR2 2.0 0.22727273
26 ACACA 2.0 0.22727273
27 FPR1 2.0 0.22727273
28 CYP3A4 2.0 0.22727273
29 HDAC8 2.0 0.22727273
30 PTGS1 2.0 0.22727273
31 SLC2A1 2.0 0.22727273
32 CHRM2 2.0 0.22727273
33 KDM1A 2.0 0.22727273
34 CSNK2B 2.0 0.22727273
35 TRIM24 2.0 0.22727273
36 PRKCD 2.0 0.22727273
37 KLF5 2.0 0.22727273
38 APEX1 2.0 0.22727273
39 NTRK3 2.0 0.22727273
40 NFKB1 2.0 0.22727273
41 CTSB 2.0 0.22727273
42 TDP1 2.0 0.22727273
43 BLM 1.0 0.0
44 HDAC2 1.0 0.0
45 CTSD 1.0 0.0
46 PSMB1 1.0 0.0
3.2.2.3 GO enrichment and KEGG pathway enrichment results

The GO enrichment and KEGG pathway with a P < 0.05 and enrichment factors >1.5 were considered significantly enriched. The GO enrichment results show a total of 51 biological processes, 31 cellular components, and 44 Molecular functions. The top 10 biological processes, cellular components, and molecular functions are shown in Figure 5. Specifically, molecular functions mainly involved protein kinase regulator activity, histone deacetylase activity, protein lysine deacetylase activity, complement receptor activity, amyloid-beta binding, G protein-coupled acetylcholine receptor activity, chromatin binding etc. Further, 22 signaling pathways were accessed from KEGG enrichment analysis, and the top 10 are illustrated in Figure 5. It was noticed that an association existed among the regulation pathway of AGEs with the pathway in Diabetic cardiomyopathy, NOD-like receptor signaling pathway, Chemical carcinogenesis activation, Sphingolipid signaling pathway and neuroactive receptor ligand-receptor interaction (Figure 6).

Figure 5: GO and KEGG pathway enrichment analysis of all intersecting targets. (A) Top 10 BP (biological process), MF (molecular function), and CC (cellular component) of GO enrichment analysis (A) enrichment results circle diagram (B), top 10 pathways of KEGG pathway enrichment analysis (C) and interaction tree (D).
Figure 5:

GO and KEGG pathway enrichment analysis of all intersecting targets. (A) Top 10 BP (biological process), MF (molecular function), and CC (cellular component) of GO enrichment analysis (A) enrichment results circle diagram (B), top 10 pathways of KEGG pathway enrichment analysis (C) and interaction tree (D).

Figure 6: Diabetic cardiomyopathy signaling pathway highlighted from KEGG pathway analysis.
Figure 6:

Diabetic cardiomyopathy signaling pathway highlighted from KEGG pathway analysis.

3.3 Molecular docking

Isolated compounds 1 and 2 were analyzed for molecular docking and interaction analysis with transcription regulators 6Y3C, 1CX2, and 4F5S. In the case of 6Y3C, compound 2 presented the highest free energy (ΔG −6.6 (kJ mol−1) followed by compound 1 (ΔG −6.2 (kJ mol−1). Similarly significant interactions with high free energies were noticed in the docking of compounds 2 (ΔG −6.3 (kJ mol−1) and compound 1(ΔG −6.0 (kJ mol−1) with transcription regulator 1CX2. Likewise in case of 4F5S, high free energy was recorded with compound 2 ((ΔG −5.9 (kJ mol−1) followed by compound 1(ΔG −5.6 (kJ mol−1) (Table 8, Figures 7–9).

Table 8:

Docking score, H and non-H-bonding interactions of Isolated compounds against selected targets.

Code Binding free energy

ΔG (kJ mol−1)		 Pose No H Bond H bond interaction residues Neighbor interacting residues
1CX2
Lancerodiol p-hydroxybenzoate −6.0 9 2 Ser38, Pro35 Tyr55, Asp53, Pro40, Cys37, Ala33, Pro160, Asn34, Val 165
Iso-tschimganine −6.3 5 2 Thr394, Asp393 Pro189, Ile430, Gln429, Pro392, Phe187
4F5S
Lancerodiol p-hydroxybenzoate −5.6 5 2 Arg143, Glu140 Leu115, Lys136, Phe36, Tyr139, Pro35, Gln33, Leu112
Iso-tschimganine −5.9 9 3 Asp108, Arg196, Tyr147 Leu103, Lys106, Ser104, His105, Lys465
6Y3C
Lancerodiol p-hydroxybenzoate −6.4 1 2 Gln370, Gln44 Pro127, Pro125, Ile137, Thr129, Thr60, Ile46, Arg61, Thr62, Asn122, Ile124Ser126
Iso-tschimganine −6.6 2 2 Arg469, Leu123 Gln44, Asn122, Ser126, Pro125, Pro127, Thr129, Arg61, Ile137, Ile46,
Figure 7: Molecular docking and interaction analysis of lancerodiol p-hydroxybenzoate (A) and iso-tschimganine (B) against 1CX2, through van der Waals’s, π-alkyl and conventional H-bonding.
Figure 7:

Molecular docking and interaction analysis of lancerodiol p-hydroxybenzoate (A) and iso-tschimganine (B) against 1CX2, through van der Waals’s, π-alkyl and conventional H-bonding.

Figure 8: Molecular docking and interaction analysis of lancerodiol p-hydroxybenzoate (A) and iso-tschimganine (B) against 4F5S through van der Waals’s, π-cation, alkyl, π-alkyl and conventional H-bonding.
Figure 8:

Molecular docking and interaction analysis of lancerodiol p-hydroxybenzoate (A) and iso-tschimganine (B) against 4F5S through van der Waals’s, π-cation, alkyl, π-alkyl and conventional H-bonding.

Figure 9: Molecular docking and interaction analysis of lancerodiol p-hydroxybenzoate (A) and iso-tschimganine (B) against 6Y3C through van der Waals’s, alkyl and conventional H-bonding.
Figure 9:

Molecular docking and interaction analysis of lancerodiol p-hydroxybenzoate (A) and iso-tschimganine (B) against 6Y3C through van der Waals’s, alkyl and conventional H-bonding.

3.4 In vitro investigations

3.4.1 Antioxidant activity

The antioxidant potential of both isolated compounds 1 (lancerodiol p-hydroxybenzoate) and 2 (Iso-tschimganine) was investigated for their capability to lower oxidative stress through diverse mechanisms including H2O2, FRAP and DPPH radical scavenging assays. The compound 1 presented significant antioxidant potential in H2O2, (64.1  ± 2.1 %), FRAP (56.1 µg AAE) and DPPH (IC50 24.5 μg/mL) assays, whereas a slightly lower inhibition was recorded in case of compound 2 in H2O2 inhibition assay (56.2 ± 2.1 %), FRAP assay (41.0 µg AAE) and DPPH radical scavenging (IC50 34.1 μg/mL) assay (Table 9).

Table 9:

Antioxidant activity of tested compounds.

Sample H2O2

% Inhibition assay		 FRAP assay (µg AAE)a DPPH assay (IC50 µg/mL)
Compound 1b 64.1 ± 2.1 % 56.1 24.5
Compound 2c 56.2 ± 2.1 % 41.0 34.1
Ascorbic acid 76 % 12.4

  1. aAscorbic acid equivalents per gram; bLancerodiol p-hydroxybenzoate; cIso-tschimganine.

3.4.2 Anti-inflammatory activity

In protein denaturation assays, highest inhibition was recorded in case of compound 1 (55.2 ± 1.2 %) followed by compound 2 (51.2 ± 1.2 %). In case proteinase inhibition assays compound 1 presented significant inhibition (85.7 ± 2.8 %), whereas as mild activity was recorded in case of compound 2 (41.5 ± 0.7 %). Similarly, 15-lipoxygenase assay revealed that compound 1 was able inhibit enzyme significantly (61.6 ± 1.4 %) compared to compound 2 (56.3 ± 2.3 %) (Figure 10).

Figure 10: 15-lipoxygenase, protein denaturation and proteinase inhibition assay of tested compounds C1 (lancerodiol p-hydroxybenzoate) and C2 (Iso-tschimganine).
Figure 10:

15-lipoxygenase, protein denaturation and proteinase inhibition assay of tested compounds C1 (lancerodiol p-hydroxybenzoate) and C2 (Iso-tschimganine).

3.4.3 Antiglycation activity

The isolated compounds (1 and 2) were further evaluated for their ability to inhibit advanced glycation in the BSA-Glucose model. It was observed that a higher inhibition was observed in the case of compound 2 (61.2  ± 1.4 %) compared to compound 1 (56.4  ± 2.1 %). Further, these compounds were investigated for mechanistic studies and it was evident that compound 1 has a more protective effect on β-sheets compared to compound 2 (Table 10). Similarly, in the carbonyl entrapment assay, compound 2 was able to entrap free carbonylic moieties more than compound 1 (Figure 11).

Table 10:

Determination of the protective role of isolated compounds against β-amyloid formation.

Sample Final mg/mL Absorbance Remarks
Compound 1a 0.1 0.065  ± 0.03 Protective
Compound 2b 0.1 0.056  ± 0.01 Moderate
Control 0.096  ± 0.21

  1. aLancerodiol p-hydroxybenzoate; bIso-tschimganine.

Figure 11: BSA-glucose and free carbonyl entrapment assay of tested C1 (lancerodiol p-hydroxybenzoate) and C2 (Iso-tschimganine).
Figure 11:

BSA-glucose and free carbonyl entrapment assay of tested C1 (lancerodiol p-hydroxybenzoate) and C2 (Iso-tschimganine).

4 Discussions

Diabetic complications present a significant global health challenge, with a high prevalence rate of diabetic complications including cardiovascular diseases (CVDs), retinopathy, nephropathy and neuropathy [58]. Amongst all, the CVDs are the most prevalent complications that generally affect individuals with diabetes at a rate 2 to 4 times higher than the general population [59]. It has been reported earlier that every one out of three diabetic individuals is likely to develop heart disease or stroke [60]. Similarly, diabetic retinopathy, a leading cause of vision impairment, affects 34 % of people with diabetes, with severe cases found in 10 % of long-term diabetic patients [61]. Furthermore, diabetic nephropathy, which is a major cause of kidney failure, affects 25–40 % of diabetics, with a significantly higher risk of progression to end-stage renal disease (ESRD) [62]. Diabetic neuropathy, a condition that contributes to 28–30 % of people with diabetes, is a leading cause of non-traumatic lower-limb amputations, which occur 10 times more often in diabetic individuals compared to the general population [63], 64]. Diabetic foot ulcers also pose a significant risk, with 1 in 10 diabetics experiencing ulcers, and 1 in 20 undergoing amputation [65]. These statistics highlight the critical need for early detection, preventive strategies and comprehensive management to mitigate the impact of diabetic complications.

In this investigation, we isolated two terpenoids, namely Lancerodiol p-hydroxybenzoate (1) and Iso-Tschimganine (2) from Ferula oopada. Terpenoids are biologically active terpenes and are well known for antimicrobial, antidiabetic, anticancer, anti-inflammatory and anti-rheumatic properties [66]. Biological activity of these compounds is mainly attributed to specialized structure and presence of diverse functional groups on different stereo-chemically important positions [67]. In computational prediction, both compound 1 and compound 2 followed Lipinski’s rule and no violations were noticed, which in fact shows high bioavailability of the tested molecules. Further, the drug likeness score of both tested compounds was within a permissible range (−1 to +1). The ADMET profiling was performed using Molsinpiration, SWISSADME software, and it was noticed that both tested compounds possessed a higher intestinal Absorption (>90 %). The compound 1 was not able to cross the blood-brain barrier. This indicates that it may not have any effect on the CNS [56]. In metabolism studies, compound 1 was noticed as a P-gp substrate, whereas it can act as CYT p450 enzyme series inhibitor except CYP3A4, showing liver-based metabolism. Further, the excretion of compound 1 was recorded from the renal route and was associated with little or no toxicity. In the case of compound 2, a good permeation was noticed, as is obvious from the boiled egg model. This indicates that Iso-Tschimganine can affect the CNS [68]. In metabolism studies, compound 2 was noticed not to act as a P-gp substrate but can act as CYT p450 enzyme series inhibitor except CYP3A4. This shows that Iso-Tschimganine may be metabolized in the liver [69]. Further, compound 2 excretion was recorded from the renal route, with little or no toxicity.

The network pharmacology of advanced glycation end products (AGEs) was accomplished through the identification of targets associated with isolated terpenoids, followed by the mapping of targets implicated in the AGEs signaling pathway. The resulting network analysis shows the potential of bioactive compounds to modulate AGEs and diabetes through complex interactions with 44 distinct proteins across various biological pathways. Protein-protein interaction (PPI) analysis identified 44 nodes and 19 edges, with key hub genes including HSP90AB1, NFKB1, TLR4, HDAC2, CXCR4, TOP2A, CTSB, ESR2, APEX1, and KDM1A, all integral to the AGEs and diabetes framework. Further analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enriched pathways indicated numerous additional pathways beyond the AGE-RAGE axis, linking the recognized genes to multiple diseases and disorders. The GO enrichment analysis verified the involvement of these biological activities in the regulatory mechanisms of anxiety. KEGG pathway analysis emphasized the crucial role of diabetic cardiomyopathy signaling pathways within the network [70]. This finding supports the hypothesis that Lancerodiol p-hydroxybenzoate [1] and Iso-tschimganine [2] may represent promising therapeutic candidates for anxiety. Outside the established signaling pathways, our investigation also highlighted the participation of several other molecular networks, including NOD-like receptor signaling, chemical carcinogenesis activation, sphingolipid signaling, and neuroactive ligand-receptor interactions. Therefore, Lancerodiol p-hydroxybenzoate and Iso-tschimganine could be regarded as potential multitarget therapeutic agents for various disorders.

Molecular docking of tested compounds was performed on molecular targets for inflammation, including 6Y3C [71], 1CX2 [72] and protein denaturation of BSA(4F5S) [73]. The molecular docking of compound 1 with transcriptional regulator gene 6Y3C revealed two strong H-bonding interactions with Gln370, Gln44 and high free energy (−6.4 ΔG (kJ mol−1). The docking interaction analysis showed van der Waals’s, π-alkyl and Conventional H-bonding of compound 1 with neighboring amino acid residues, including Pro127, Pro125, Ile137, Thr129, Thr60, Ile46, Arg61, Thr62, Asn122, Ile124 and Ser126 with pose rank 1 (Table 5, Figure 6). Likewise, compound 2 was docked with 6Y3C and strong H-bonding interactions were recorded with Arg469, Leu123, with high free energy (−6.6 ΔG (kJ mol−1) at pose rank 2. In this case, the neighboring amino acids, including Gln44, Asn122, Ser126, Pro125, Pro127, Thr129, Arg61, Ile137, Ile46 presented interaction with the molecule through van der Waal’s, π-π, Alkyl and π-alkyl bonding. Overall molecular docking results revealed that both tested molecules showed strong interaction with target molecules and thus they may have anti-inflammatory and antiglycation potential that is possibly due to strong H-bonding and hydrophobic interactions [74], 75]. The hydrophobic interactions are crucial for constancy of ligand at the interface of a protein structure [76].

In vitro anti-inflammatory experimental data showed that both tested compounds significantly protected protein from denaturation, proteinase inhibition and 15-lox inhibition. Proteins have a highly organized three-dimensional structure to perform specific functions [77]. This structure can alter on exposure to heat, strong acid, alkali, and pH, thus disturbing the orientation of protein molecule, causing denaturation [78]. Denatured protein is mainly responsible for the secretion of auto-antigen to exaggerate the inflammatory response [79]. Thus, an inhibition of protein denaturation stops the process of inflammation. In addition, proteins are naturally degraded by proteolytic enzymes present within the cell [80]. Proteolytic activity of active compounds may protect the human body from infectious, viral, and abnormal proteins. In mammals, many lipoxygenases are involved in the metabolism of eicosanoids such as prostaglandins and leukotrienes and their inhibition by terpenoids can be helpful in the management of inflammation [81], 82]. Terpenoids are believed to interfere with inflammation development through interference with the enzymatic cascade [83]. The isolated compounds were further investigated for their antioxidant potential and significant results were recorded in both compounds. The antioxidant activity of terpenoids is believed to be radical scavenging and chelating effects [84]. In earlier investigations, significant antioxidant activities were reported in other plants of Ferula genus rich in terpenoids, including Ferula cumminus extracts and Ferula assafoetida [10]. Due to significant lowering of oxidative stress, we further investigated isolated compounds for Advance glycation end products inhibition (AGEs) and a reportable inhibition of AGEs in non-oxidative mode was recorded in both compounds. Further mechanistic studies revealed a protective effect on β-sheets and free carbonyl entrapment. It was therefore evident that both compound 1 and 2 has anti AGEs potential that is attributed to β-sheets protection and free carbonylic entrapment.

5 Conclusions

The study successfully isolated two terpenoids, Lancerodiol p-hydroxybenzoate and Iso-tschimganine, from F. oopoda and evaluated their potential biological activities. Comprehensive computational analysis demonstrated that both compounds adhered to the drug-likeness criteria, exhibiting favorable bioavailability and no evidence of skin sensitization. Notably, strong interactions were observed with transcription regulators 1CX2, 4F5S, and 6Y3C. Both compounds exhibited significant antioxidant, anti-inflammatory, and anti-AGEs (Advanced Glycation End Products) activities. Based on these findings, it is concluded that Lancerodiol p-hydroxybenzoate and Iso-tschimganine have promising anti-AGEs and anti-inflammatory properties, warranting further investigation for in vivo characterization and formulation development.

Corresponding author: Adnan Amin, NPRL, Faculty of Pharmacy, Gomal University, Dera Ismail Khan, Pakistan; and Department of Life Sciences, Yeungnam University, Gyeongsan, 38541, Republic of Korea, E-mail:

Funding source: Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Award Identifier / Grant number: PNURSP2025R155

Acknowledgments

Princess Nourah bint Abdulrahman University Researchers Supporting Project number. (PNURSP2025R155), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

  1. Funding information: Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R155), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

  2. Authors’ contribution: M.N.A.; Data Curation; Formal Analysis; Investigation; Writing – Original Draft; Writing – Review & Editing; Software; S. H. R. Methodology, Investigation; Resources; Visualization; Writing – Original Draft; S. I. N. Conceptualization; Investigation Resources, Software; Validation; Writing – Original Draft; Writing – Review & Editing; M.Z, Methodology; Software; Visualization; Writing – Review & Editing, B.N.B. Methodology; Software; Visualization; Funding Acquisition Writing – Review & Editing.; S.I, Methodology; Software; Visualization; Funding Acquisition Writing – Review & Editing.; S.Z.U.A.; Investigation Resources, Software; Validation; Writing – Original Draft; Writing.; A.A.; Methodology; Project Administration; Supervision; Software; Supervision; Visualization; Writing – Review & Editing.

  3. Conflict of interests: Author declare no potential conflict of interest.

  4. Ethical approval: The conducted research is not related to either human or animals use.

  5. Data availability statement: All data generated or analysed during this study are included in this published article [and its supplementary information files].

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/chem-2025-0212).

Received: 2025-07-04
Accepted: 2025-10-27
Published Online: 2025-12-08

© 2025 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/chem-2025-0212
Schlagwörter für diesen Artikel
Ferula species; terpenoids; network pharmacology; bioavailability; 15-lipoxygenase
Creative Commons
BY 4.0

Artikel in diesem Heft

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  26. Comparative analysis of nutritional composition and bioactive properties of Chlorella vulgaris and Arthrospira platensis: Implications for functional foods and dietary supplements
  27. Growth kinetics and mechanical characterization of boride layers formed on Ti6Al4V
  28. Enhancement of water absorption properties of potassium polyacrylate-based hydrogels in CaCl2-rich soils using potassium di- and tri-carboxylate salts
  29. Electrochemical and microbiological effects of dumpsite leachates on soil and air quality
  30. Modeling benzene physicochemical properties using Zagreb upsilon indices
  31. Characterization and ecological risk assessment of toxic metals in mangrove sediments near Langen Village in Tieshan Bay of Beibu Gulf, China
  32. Protective effect of Helicteres isora, an efficient candidate on hepatorenal toxicity and management of diabetes in animal models
  33. Valorization of Juglans regia L. (Walnut) green husk from Jordan: Analysis of fatty acids, phenolics, antioxidant, and cytotoxic activities
  34. Molecular docking and dynamics simulations of bioactive terpenes from Catharanthus roseus essential oil targeting breast cancer
  35. Selection of a dam site by using AHP and VIKOR: The Sakarya Basin
  36. Characterization and modeling of kidney bean shell biochar as adsorbent for caffeine removal from aquatic environments
  37. The effects of short-term and long-term 2100 MHz radiofrequency radiation on adult rat auditory brainstem response
  38. Biochemical insights into the anthelmintic and anti-inflammatory potential of sea cucumber extract: In vitro and in silico approaches
  39. Resveratrol-derived MDM2 inhibitors: Synthesis, characterization, and biological evaluation against MDM2 and HCT-116 cells
  40. Phytochemical constituents, in vitro antibacterial activity, and computational studies of Sudanese Musa acuminate Colla fruit peel hydro-ethanol extract
  41. Chemical composition of essential oils reviewed from the height of Cajuput (Melaleuca leucadendron) plantations in Buru Island and Seram Island, Maluku, Indonesia
  42. Phytochemical analysis and antioxidant activity of Azadirachta indica A. Juss from the Republic of Chad: in vitro and in silico studies
  43. Stability studies of titanium–carboxylate complexes: A multi-method computational approach
  44. Efficient adsorption performance of an alginate-based dental material for uranium(vi) removal
  45. Synthesis and characterization of the Co(ii), Ni(ii), and Cu(ii) complexes with a 1,2,4-triazine derivative ligand
  46. Evaluation of the impact of music on antioxidant mechanisms and survival in salt-stressed goldfish
  47. Optimization and validation of UPLC method for dapagliflozin and candesartan cilexetil in an on-demand formulation: Analytical quality by design approach
  48. Biomass-based cellulose hydroxyapatite nanocomposites for the efficient sequestration of dyes: Kinetics, response surface methodology optimization, and reusability
  49. Multifunctional nitrogen and boron co-doped carbon dots: A fluorescent probe for Hg2+ and biothiol detection with bioimaging and antifungal applications
  50. Separation of sulphonamides on a C12-diol mixed-mode HPLC column and investigation of their retention mechanism
  51. Characterization and antioxidant activity of pectin from lemon peels
  52. Fast PFAS determination in honey by direct probe electrospray ionization tandem mass spectrometry: A health risk assessment insight
  53. Correlation study between GC–MS analysis of cigarette aroma compounds and sensory evaluation
  54. Synthesis, biological evaluation, and molecular docking studies of substituted chromone-2-carboxamide derivatives as anti-breast cancer agents
  55. The influence of feed space velocity and pressure on the cold flow properties of diesel fuel
  56. Acid etching behavior and mechanism in acid solution of iron components in basalt fibers
  57. Protective effect of green synthesized nanoceria on retinal oxidative stress and inflammation in streptozotocin-induced diabetic rat
  58. Evaluation of the antianxiety activity of green zinc nanoparticles mediated by Boswellia thurifera in albino mice by following the plus maze and light and dark exploration tests
  59. Yeast as an efficient and eco-friendly bifunctional porogen for biomass-derived nitrogen-doped carbon catalysts in the oxygen reduction reaction
  60. Novel descriptors for the prediction of molecular properties
  61. Synthesis and characterization of surfactants derived from phenolphthalein: In vivo and in silico studies of their antihyperlipidemic effect
  62. Turmeric oil-fortified nutraceutical-SNEDDS: An approach to boost therapeutic effectiveness of dapagliflozin during treatment of diabetic patients
  63. Analysis and study on volatile flavor compounds of three Yunnan cultivated cigars based on headspace-gas chromatography-ion mobility spectrometry
  64. Near-infrared IR780 dye-loaded poloxamer 407 micelles: Preparation and in vitro assessment of anticancer activity
  65. Study on the influence of the viscosity reducer solution on percolation capacity of thin oil in ultra-low permeability reservoir
  66. Detection method of Aristolochic acid I based on magnetic carrier Fe3O4 and gold nanoclusters
  67. Juglone’s apoptotic impact against eimeriosis-induced infection: a bioinformatics, in-silico, and in vivo approach
  68. Potential anticancer agents from genus Aerva based on tubulin targets: an in-silico integration of quantitative structure activity relationship (QSAR), molecular docking, simulation, drug-likeness, and density functional theory (DFT) analysis
  69. Hepatoprotective and PXR-modulating effects of Erodium guttatum extract in propiconazole-induced toxicity
  70. Studies on chemical composition of medicinal plants collected in natural locations in Ecuador
  71. A study of different pre-treatment methods for cigarettes and their aroma differences
  72. Cytotoxicity and molecular mechanisms of quercetin, gallic acid, and pinocembrin in Caco-2 cells: insights from cell viability assays, network pharmacology, and molecular docking
  73. Choline-based deep eutectic solvents for green extraction of oil from sour cherry seeds
  74. Green-synthesis of chromium (III) nanoparticles using garden fern and evaluation of its antibacterial and anticholinesterase activities
  75. Innovative functional mayonnaise formulations with watermelon seeds oil: evaluation of quality parameters and storage stability
  76. Molecular insights and biological evaluation of compounds isolated from Ferula oopoda against diabetes, advanced glycation end products and inflammation in diabetics
  77. Removal of cytotoxic tamoxifen from aqueous solutions using a geopolymer-based nepheline–cordierite adsorbent
  78. Unravelling the therapeutic effect of naturally occurring Bauhinia flavonoids against breast cancer: an integrated computational approach
  79. Characterization of organic arsenic residues in livestock and poultry meat and offal and consumption risks
  80. Synthesis and characterization of zinc sulfide nanoparticles and their genotoxic and cytotoxic effects on acute myeloid leukemia cells
  81. Activity of Coriandrum sativum methanolic leaf extracts against Eimeria papillata: a combined in vitro and in silico approach
  82. Special Issue on Advancing Sustainable Chemistry for a Greener Future
  83. One-pot fabrication of highly porous morphology of ferric oxide-ferric oxychloride/poly-O-chloroaniline nanocomposite seeded on poly-1H pyrrole: Photocathode for green hydrogen generation from natural and artificial seawater
  84. High-efficiency photocathode for green hydrogen generation from sanitation water using bismuthyl chloride/poly-o-chlorobenzeneamine nanocomposite
  85. Innovative synthesis of cobalt-based catalysts using ionic liquids and deep eutectic solvents: A minireview on electrocatalytic water splitting
  86. Special Issue on Phytochemicals, Biological and Toxicological Analysis of Plants
  87. Comparative analysis of fruit quality parameters and volatile compounds in commercially grown citrus cultivars
  88. Total phenolic, flavonoid, flavonol, and tannin contents as well as antioxidant and antiparasitic activities of aqueous methanol extract of Alhagi graecorum plant used in traditional medicine: Collected in Riyadh, Saudi Arabia
  89. Study on the pharmacological effects and active compounds of Apocynum venetum L.
  90. Chemical profile of Senna italica and Senna velutina seed and their pharmacological properties
  91. Essential oils from Brazilian plants: A literature analysis of anti-inflammatory and antimalarial properties and in silico validation
  92. Toxicological effects of green tea catechin extract on rat liver: Delineating safe and harmful doses
  93. Unlocking the potential of Trigonella foenum-graecum L. plant leaf extracts against diabetes-associated hypertension: A proof of concept by in silico studies
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Heruntergeladen am 10.1.2026 von https://www.degruyterbrill.com/document/doi/10.1515/chem-2025-0212/html
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