Abstract Cinnamomum tamala (Buch.-Ham.) T.Nees & Eberm., or Indian Bay Leaf, is a well-known traditional ayurvedic medicine used to treat various ailments. However, the molecular mechanism of action of Cinnamomum tamala essential oil (CTEO) against non-small cell lung cancer (NSCLC) remains elusive. The present study aims to decipher the molecular targets and mechanism of CTEO in treating NSCLC. GC-MS analysis detected 49 constituents; 44 successfully passed the drug-likeness screening and were identified as active compounds. A total of 3961 CTEO targets and 4588 anti-NSCLC-related targets were acquired. JUN, P53, IL6, MAPK3, HIF1A, and CASP3 were determined as hub genes, while cinnamaldehyde, ethyl cinnamate and acetophenone were identified as core compounds. Enrichment analysis revealed that targets were mainly involved in apoptosis, TNF, IL17, pathways in cancer and MAPK signalling pathways. mRNA expression, pathological stage, survival analysis, immune infiltrate correlation and genetic alteration analysis of the core hub genes were carried out. Kaplan-Meier overall survival (OS) curve revealed that HIF1A and CASP3 are linked to worse overall survival in Lung Adenocarcinoma (LUAD) cancer patients compared to normal patients. Ethyl cinnamate and cinnamaldehyde showed high binding energy with the MAPK3 and formed stable interactions with MAPK3 during the molecular dynamic simulations for 100 ns. The MM/PBSA analysis revealed that van der Waals (VdW) contributions predominantly account for a significant portion of the compound interactions within the binding pocket of MAPK3. Density functional theory analysis showed cinnamaldehyde as the most reactive and least stable compound. CTEO exhibited selective cytotoxicity by inhibiting the proliferation of A549 cells while sparing normal HEK293 cells. CTEO triggered apoptosis by arresting the cell cycle, increasing ROS accumulation, causing mitochondrial depolarisation, and elevating caspase-3, caspase-8 and caspase-9 levels in A549 cells. The above study provides insights into the pharmacological mechanisms of action of Cinnamomum tamala essential oil against non-small cell lung cancer treatment, suggesting its potential as an adjuvant therapy. Keywords: Cinnamomum tamala, Essential oil, Non-small cell lung cancer, Network pharmacology Graphical abstract [41]Image 1 [42]Open in a new tab 1. Introduction Lung cancer causes the highest number of cancer-related fatalities globally, with non-small cell lung cancer (NSCLC) comprising more than 85% of all cases [[43]1,[44]2]. In 2020, there were 2.2 million new cases of lung cancer and 1.8 million reported deaths worldwide (GLOBOCAN, 2020). According to the Global Cancer Observatory study, India ranks fourth globally for lung cancer mortality (GLOBOCAN, 2020). Patients with NSCLC are mostly diagnosed at advanced stages due to constraints in early detection, which results in an overall survival rate of only 10–20% [[45]3,[46]4]. Managing lung cancer involves various treatment modalities, including chemotherapy, systemic therapy, radiotherapy, and surgery [[47]5]. However, the possibility of recurrence and side effects associated with chemotherapy and radiation and emerging drug resistance underline the need for alternative effective treatment approaches [[48]6,[49]7]. Consequently, acquiring the underlying mechanisms behind NSCLC occurrence and progression is essential for developing novel anti-cancer drugs. Natural product-based therapies have been proven effective for treating cancer. Several studies have shown that patients with non-small cell lung cancer receiving herbal supplements have increased survival rates [[50]4,[51]8]. Natural herbal scaffolds based hydrogels are being developed to control the initiation and progression of cancer [[52]4,[53]9]. The members of the genus Cinnamomum are economically tree species known for their commercial spice products. Cinnamomum tamala (Buch.-Ham.) Nees, commonly known as Indian Bay Leaf, is an evergreen tree of the family Lauraceae that grows wild in tropical and subtropical areas of Asia, Australia and the Pacific islands [[54]10,[55]11]. Essential oil is one of the active ingredients of the genus Cinnamomum. In Eastern and Western countries, dried leaves of C. tamala are used as a spice and flavouring agent in various food preparations. C. tamala bark has been used for thousands of years in traditional and Western medicine. C. tamala essential oil can be found in the stem, leaves, and bark. The essential oil has widespread applications in the cosmetic, pharmaceutical, and food industries. Cinnamomum tamala bark essential oil has been reported to possess antitumor, antiparasitic, antioxidant, anti-inflammatory and antiarthritic properties [[56]12]. Several studies reveal that C. tamala exerts favourable anti-cancer effects. In vitro assay revealed Cinnamomum tamala bioactive constituents to exert cytotoxic effect against A549 (lung adenocarcinoma), MCF-7 (breast adenocarcinoma) and U-87MG (brain glioblastoma) cells [[57]13]. Cinnamomum tamala leaf constituents exhibited antiproliferative activity against A2780 human ovarian cancer cells [[58]14]. Cinnamomum tamala leaf extract showed anti-cancer activity against Ehrlich Ascites Carcinoma (EAC) bearing mice by reducing tumour growth [[59]15]. A preclinical study has shown the anticancer potential of Cinnamomum tamala (Indian Bay Leaf oil) nanoemulsion and vitamin D-encapsulated cinnamon oil nanoemulsion on lung carcinoma cells (A549) [[60]16]. Despite the claim on the anti-cancer activity of C. tamala (CTEO) essential oil, no significant study has been carried out to explain its mechanism against NSCLC. Like other herbal medicines, Cinnamomum tamala involves multiple components, targets, and pathways against cancer. However, the pharmacodynamic properties of the active components of CTEO and the key molecular targets responsible for its anti-cancer activity need to be fully explored. Recently, understanding the molecular mechanism behind the onset and development of various diseases has improved with the application of network pharmacology. Network pharmacology has been employed to analyse phytoconstituent putative targets and disease-associated pathways [[61]17]. The rationale for this research stems from the urgent need for innovative therapeutic approaches to enhance the effectiveness and safety of treating non-small cell lung cancer (NSCLC). Cinnamomum tamala, as a natural compound, represents a promising alternative that could address the limitations associated with conventional therapies. Through a combination of computational and experimental methodologies, this study aims to deepen our understanding of the molecular interactions between Cinnamomum tamala essential oil and NSCLC-related targets, as well as elucidate its potential mechanism of action. Furthermore, the utilisation of network pharmacology, molecular docking, and molecular dynamics simulations in the present study demonstrates the power of integrative approaches in drug discovery and development. Therefore, the current study aims to explore and identify bioactive constituents of Cinnamomum tamala essential oil that have the potential for treating non-small cell lung cancer. The interaction between the CTEO active constituents and the predicted targets was verified using molecular docking, density functional theory analysis and molecular dynamics approaches. In addition, in vitro experimental assays were performed to validate the anti-cancer effect of CTEO on A549 cancer cells. 2. Materials and methods 2.1. Sampling and essential oil extraction The fresh barks of C. tamala were collected in July 2022 from Chauntra, Himachal Pradesh, India (32° 0′ 52.9992″ N, 76° 44′ 9.9996″ E, 1310 m a.s.l). The plant material was authenticated by Dr. P.C. Panda, and the specimen with voucher no (2453/CBT) was deposited in the Herbarium of the same institute. The extraction of essential oil from dried bark was carried out by hydrodistillation following the protocol mentioned in European Pharmacopoeia [[62]18]. The collected essential oil was kept in dark vials at 4 °C for further analysis. 2.2. Chemical characterisation of the essential oil The chemical analysis of Cinnamomum tamala bark essential oil was performed in a Clarus 580 GC (PerkinElmer, USA) integrated with an SQ-8 MS detector. 0.1 μl of neat essential oil was injected into the system in splitless mode. Compound separation was performed on an Elite-5 MS capillary column of 30 m length, 0.25 mm i.d. and 0.25 μm thickness using helium carrier gas at 1 ml/min a flow rate. Initially, the column oven was programmed at 60 °C, then increased to 220 °C at 3 °C/min, and finally held for 7 min. The detector and ion source temperatures were set at 150 and 250 °C, respectively. The mass spectra of the constituents were recorded in the electron ionisation mode at 70 eV. The constituents were identified by matching their mass spectra with the compound library (NIST/EPA/NIH Mass spectral library, version 2.0) and comparing experimental retention indices with bibliographic literature [[63]19]. 2.3. Network pharmacology 2.3.1. Pharmacokinetic screening of phytoconstituents The phytoconstituents identified through GC-MS analysis underwent drug-like property screening using the online SwissADME web server ([64]http://www.swissadme.ch/). Screening parameters included Lipinski's rule [[65]20] and a minimum Abbott oral bioavailability (OB) score greater than 0.5. Phytoconstituents meeting both criteria were selected as bioactive constituents of CTEO. 2.3.2. Acquisition of compound and disease-related targets Compound-related putative targets were obtained from the Swiss Target Prediction (STP) ([66]http://www.swisstargetprediction.ch/) and the Comparative Toxicogenomics database ([67]https://ctdbase.org/). NSCLC-associated targets were collected from DisGenet ([68]https://www.disgenet.org/), Malacards ([69]https://www.malacards.org/), GeneCards ([70]https://www.genecards.org/) and Therapeutic Target Database ([71]https://db.idrblab.net/ttd/). Additionally, the Gene Expression Omnibus (GEO) ([72]https://www.ncbi.nlm.nih.gov/geo/) database of NCBI was used to access the mRNA expression profiles of NSCLC patients and normal patient samples by selecting the [73]GSE21933 dataset. This dataset contained 42 samples, including 21 NSCLC patients and 21 normal lung tissue samples and were analysed using the GEO2R program. The dataset was based on the [74]GPL6254 Phalanx Human OneArray platform and processed using the iDEP.96 ([75]http://bioinformatics.sdstate.edu/idep96/) online web tool. Upregulated and downregulated differentially expressed genes (DEGs) were obtained using ‘limma trend package’ with the criteria log fold change (log2FC) ≥1 and false discovery rate (FDR) value < 0.05. The differentially expressed genes heat map, venn diagram, and volcano plot were obtained using the R package. Then, Venny 2.1.0 ([76]https://bioinfogp.cnb.csic.es/tools/venny/) online tool was used to acquire the overlapping targets among compound-related targets, disease-related targets and DEGs obtained from the GEO database. These genes are considered key therapeutic targets for treating NSCLC. 2.3.3. Protein-protein interaction (PPI) network The key therapeutic targets identified by overlapping two gene datasets were deposited to the STRING database ([77]https://string-db.org/) with the species limited to “Homo sapiens”. The network containing direct and indirect protein linkages obtained with ‘high confidence’ (≥0.7) of the minimum required interaction score was then exported to Cytoscape 3.9.1 software. The Cytohubba plug-in of Cytoscape 3.9.1 was employed to perform topological parameter screening of closely interlinked genes from the network based on the degree algorithm. The obtained genes were considered hub genes. 2.3.4. Construction of compound-disease-target network The interrelationship between the bioactive constituents of CTEO and their predicted targets in the treatment of NSCLC was visualised by constructing a compound-target-disease network in Cytoscape 3.9.1. The network nodes represent the candidate compounds, while the edges represent the compound-target interactions. By employing the Cytoscape cytoNCA tool, key active constituents of CTEO were screened from the network based on the degree value. 2.3.5. GO and KEGG pathway enrichment analysis The role of critical therapeutic targets in the biological system and signalling pathways were analysed by performing Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis. The intersecting genes were imported into ShinyGO v 0.77 ([78]http://bioinformatics.sdstate.edu/go/) to predict the three GO categories (molecular functions, biological processes, cellular components) and KEGG pathways of therapeutic genes involved. The enrichment analysis cut-off value was p < 0.05 and FDR<0.01. The overlapping targets were mapped into KEGG mapper ([79]https://www.genome.jp/kegg/mapper/) to highlight the relevant pathway and analyse the distinctive molecular mechanism within that pathway. 2.3.6. External validation of the hub target genes The Gene Expression Profiling Interactive Analysis (GEPIA) webserver ([80]http://gepia.cancer-pku.cn/) was used to analyse the differential expression of hub target genes in LUAD tissues and normal lung tissues. The hub genes were imported into the GEPIA database to analyse their mRNA expression level and associated pathological stage in the Lung Adenocarcinoma (LUAD) patients and normal patients provided by the TCGA (Cancer Genome Atlas) and GTEx (Genotype Tissue Expression) dataset. The significance of hub genes on the overall survival of LUAD was assessed from the Kaplan–Meier survival plot. The Human Protein Atlas ([81]https://www.proteinatlas.org/) database was used to analzse the immune-histochemical investigation of hub genes. The protein expression of hub genes between NSCLC and normal lung tissues was compared based on staining intensity using specific antibodies. The relationship between the hub gene and immune infiltrates in the tumour microenvironment was investigated using TIMER 2.0 ([82]http://timer.cistrome.org/). 2.4. In silico analysis 2.4.1. Molecular docking The protein targets related to ‘Human’ were searched in the Uniprot database ([83]https://www.uniprot.org/). The 3D structures of the proteins were downloaded from the RCSB PDB database ([84]http://www.rcsb.org/pdb) in their PDB formats with low resolution and no mutation. The Prankweb server ([85]https://prankweb.cz/) was used to analyse the active sites of the protein into which the ligand will bind. The native conformers of proteins were refined in Biovia Discovery Studio and Galaxyrefine web by eliminating water, ligands, ions, and heteroatoms and by filling missing residues. The energy minimization addition of polar hydrogen atoms and gasteiger charges to protein structures was performed using UCSF Chimera software. In the Autodock tool, the processed PDB files of proteins were converted into PDBQT format. The three-dimensional chemical structures of selected phytoconstituents were retrieved from the PubChem database in SDF formats and saved as PDB files in the Biovia discovery studio. The PDB files of the compounds were uploaded into the PyRx software using the in-built open Babel tool of the software. The energy minimization of the ligands was performed by applying a geometry optimisation force field. Then, the energy-reduced ligands were changed to ready-to-dock PDBQT formats. After preparing the PDBQT files of receptors and ligands for docking, a grid box was generated by setting X, Y, and Z coordinate values that were identified based on the measurement of pocket-1 from the PrankWeb server so that the ligand fix into the active site residues of proteins. Finally, each protein was subjected to docking with each ligand in the Autodock vina plugin tool of Pyrx software. The visualisation of docking results was done in Biovia Discovery Studio. 2.4.2. Molecular dynamics simulations The MD simulation module created by SiBioLead LLP uses GROMACS simulation to assess the conformational deviation of docked protein-ligand complexes compared to uncomplexed apoprotein. The complexes were preprocessed by using the OPLS/AA force field [[86]21]. The complex was embedded in a triclinic-type periodic boundary box containing a simple charge point (SPC) water solvation model and 0.15 M concentration of NaCl as counterions. The systems were energy minimized using the Steepest Descent algorithm for 5000 steps and equilibrated with NVT/NPT ensembles. All simulations were performed at 300 k temperature and 1 bar pressure. Finally, the complexes were simulated with the leapfrog algorithm for 100 ns, and the simulated trajectories were interpreted using an in-built GROMACS analysis package. Root mean square fluctuation (RMSF), root mean square deviation (RMSD), solvent accessible surface area (SASA), and radius of gyration (Rg) were measured to assess the stability of molecular docking simulations [[87]22]. 2.4.3. Density functional theory analysis The chemical reactivity and stability of key active constituents of CTEO were predicted by applying density functional theory (DFT) analysis. The complete geometry optimisation of the chemical constituents was performed in Gaussian 09W software using B3LYP/6-31G (d, p) basis set. The theoretical quantum chemical parameters were calculated to assess the stability and reactivity of the constituents. The optimized chemical structures and level energy (HOMO, LUMO) of bioactive constituents were calculated using Avogadro software. 2.5. Anticancer assays 2.5.1. Cell culture A549 (Human alveolar lung adenocarcinoma cell line) and HEK-293 (Human embryonic kidney cell line) were purchased from NCCS, Pune, India. Cells were cultured in Dulbecco Modified Eagle medium (DMEM) provided with 10 % Fetal bovine serum (FBS) and 1% penicillin and streptomycin at 37 °C in a humidified incubator containing 5% (v/v) CO[2]. 2.5.2. Cell viability assay The MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay was carried out to determine the cell cytotoxicity [[88]23]. HEK-293 and A549 cells were allowed to grow in 96-well culture dishes for 24 h. The cultured cells were treated with CTEO (6.25, 12.5, 25, 50, 100 μg/ml) and incubated for 24 h in a humidified incubator. The used media was removed, and MTT reagent was added and incubated for another 3 h at 37 °C. Then, the formazan crystals were solubilized by adding 100 μl of DMSO. The absorbance was measured using an ELISA plate reader at 570 nm. The IC[50] value of the above experiment was calculated using a linear regression equation. 2.5.3. Apoptosis assay The extent of apoptosis was determined using FITC-labelled annexin V by flow cytometry as described previously with some modifications [[89]24]. A549 cells (5 × 10^5 cells/2 ml) were inoculated into culture plates and maintained at 37 °C for 24 h, followed by exposure to CTEO (6 μg/ml) for 24 h. After treatment, cells were rinsed twice with PBS (phosphate buffer saline), followed by the addition of 100 μl of staining buffer (5 μl Annexin V-fluorescein isothiocyanate + 10 μl PI) and kept for 15 min at 37 °C. The cell apoptosis rate was analysed by flow cytometry (BD FACS Calibur, BD Biosciences, CA, USA). 2.5.4. Cell cycle analysis Cell cycle phase distribution was done as described earlier [[90]25]. A549 cells (5 × 10^5 cells/2 ml) were plated into culture dishes and treated for 48 h with CTEO at an IC[50] concentration (6 μg/ml). After incubation, cells were harvested, rinsed twice with cold PBS and fixed with 1 ml of 70% ethanol at −20 °C for 30 min. The cells were then resuspended in PBS containing RNAase A (50 μg/ml) and Propidium iodide (10 μg/ml) and incubated for 15–20 min. The samples were analysed using a flow cytometer (BD FACS Calibur, BD Biosciences, CA, USA). 2.5.5. Caspase expression assay The caspase activity in A549 cells was determined as described earlier [[91]26]. A549 cells (5 × 10^5 cells/2 ml) were allowed to grow in culture plates and maintained at 37 °C for 24 h. The cells were treated with IC[50] concentration of CTEO (6 μg/ml) and incubated for 24 h. After incubation, the cells were harvested and rinsed twice with cold PBS, followed by the addition of 25 μl of 10 μM substrate solution (PhiPhiLux and CaspaLux kit; OncoImmunin, Inc., Gaithersburg, MD, USA). The cells were incubated at 37 °C for 60 min. Then, the cells were rinsed with 1X PBS and were studied using a flow cytometer (BD FACS Calibur, BD Biosciences, CA, USA). 2.5.6. Measurement of intracellular reactive oxygen species (ROS) The intracellular reactive oxygen species levels was evaluated using D2CFH-DA (2,7-dichlorofluorescein diacetate) dye as per the previously reported protocol [[92]27] with slight modifications. Briefly, A549 cells were grown in a 60-mm culture dish and kept at 37 °C for 24 h. Then, the cells were exposed to IC[50] concentration of CTEO (6 μg/ml) and again incubated for another 24 h. After treatment, the media was discarded and rinsed with PBS. The cells were resuspended in H2DCFDA (2′,7′-dichlorofluorescein diacetate) solution for 30 min. Cells were harvested, centrifuged and washed with 1X PBS. ROS generation was measured to detect DCF intensity using a microscope (Carl Zeiss LSM 880, Germany). 2.5.7. Mitochondrial membrane potential assay A549 cells were grown in culture plates and kept for 24 h in a CO[2] incubator. Then, the cells were exposed to IC[50] concentration of CTEO (6 μg/ml) and incubated for another 24 h. After treatment, the used media was discarded and rinsed with PBS. The cells were incubated with JC-1 dye, counterstained with Hoechst 33342 solution and were visualised under the microscope (Carl Zeiss LSM 880, Germany). 2.6. AO/EtBr staining Morphological observation by fluorescence microscopy was done as described earlier [[93]24]. Briefly, A549 cells (2 × 10^5 cells/2 ml) were seeded onto Petri plates and maintained in a CO[2] incubator for 24 h. Then, the cells were exposed to IC[50] concentration of CTEO (6 μg/ml) and incubated for another 24 h. Following treatment, the spent media was aspirated, and the cells were washed with PBS. Cells were harvested by trypsinisation and stained with 200 μl staining solution containing 1 ml of ethidium bromide and 20 μl of acridine orange in media for 10 min. The cell suspension was observed under a fluorescence microscope (Carl Zeiss, LSM 880, Germany). 2.7. Statistical analysis The data are expressed as mean values and standard deviation (SD) based on three replicates (n = 3) using GraphPad Prism software. 3. Results and discussion 3.1. GC-MS analysis of Cinnamomum tamala bark essential oil The C. tamala bark essential oil was pale yellowish with an oil yield of 0.35 (%v/w) on a dry weight basis. Forty-nine constituents were identified, accounting for 75.63% of the total essential oil. The major constituents were cinnamaldehyde (32.87%), δ-Cadinene (8.8%), γ-gurjunene (3.8%), α-muurolol (2.8%), γ-cadinene (2.7%) and α-muurolene (2.4%). All the identified constituents were broadly categorised into eight groups, including phenylpropanoids (34.70%), sesquiterpene hydrocarbons (29.0%), oxygenated sesquiterpenes (8.4%), benzenoids (1.8%), oxygenated monoterpenes (0.3%), monoterpene hydrocarbons (0.2%), furan (0.2%), and others (1%) ([94]Table 1). Cinnamomum cassia, an important member of the genera Cinnamomum, also contains cinnamaldehyde as the principal constituent in its bark essential oil [[95]28]. Table 1. Chemical characterisation of Cinnmamomum tamala bark essential oil. Compound RT RI[exp] RI[lit] Peak Area (%) Benzaldehyde 6.42 957 960 0.6 Acetophenone 10.03 1066 1065 0.2 Terpinolene 11.11 1095 1088 0.8 Viridene 13.63 1156 1167 0.9 Borneol 14.13 1168 1169 0.2 cis-Linalool Oxide 14.40 1175 1174 0.2 α-Terpineol 15.05 1190 1188 0.3 Cinnamaldehyde 18.48 1270 1270 32.9 Z-Methyl cinnamate 18.81 1278 1299 1.4 n-Tridecane 19.76 1300 1300 0.2 δ-Elemene 20.93 1327 1338 0.2 Cyclosativene 22.35 1360 1371 0.2 α-Copaene 22.61 1367 1376 0.3 β-Elemene 23.20 1380 1390 0.4 Ethyl cinnamate 23.61 1390 1377 1.2 Longifolene 24.42 1409 1407 0.5 6,9-Guaiadiene 25.33 1431 1444 0.2 cis-Muurola-3,5-diene 25.63 1439 1450 0.3 α-Humulene 25.87 1445 1454 0.5 γ-Gurjunene 26.71 1465 1477 3.8 α-Amorphene 26.87 1469 1484 0.9 Amorpha-4,7(11)-diene 27.57 1486 1481 0.6 α-Muurolene 27.67 1489 1500 2.4 γ-Cadinene 28.22 1502 1513 2.7 δ-Cadinene 28.48 1509 1523 8.8 cis-Calamenene 28.57 1511 1529 1.3 γ-Amorphene 28.79 1517 1495 0.2 trans-Cadina-1,4-diene 28.98 1522 1534 0.6 α-Cadinene 29.14 1526 1538 1.5 α-Calacorene 29.31 1530 1545 2.0 β-Calacorene 30.11 1550 1565 0.1 Maaliol 30.66 1564 1567 0.6 Spathulenol 30.88 1570 1578 0.2 Guaiol 31.45 1585 1600 0.3 epi-Cedrol 31.80 1594 1619 0.2 Junenol 32.08 1601 1619 0.2 α-Corocalene 32.25 1605 1623 0.9 1-epi-Cubenol 32.66 1616 1628 1.2 Hinesol 33.21 1631 1641 1.1 Valerianol 33.28 1633 1658 1.0 cis-Calamenen-10-ol 33.38 1636 1661 0.7 α-Muurolol 33.72 1645 1646 2.8 2-Hydroxydiphenyl methane 34.30 1660 1675 0.1 Amorpha-4,9-dien-2-ol 36.01 1707 1700 0.1 Sesquiterpene hydrocarbons 29 Oxygenated Sesquiterpene 8.4 Monoterpene Hydrocarbon 0.2 Oxygenated Monoterpene 0.3 Phenylpropanoid 34.7 Furan 0.2 Benzenoid 1.8 Others 1 Total Identified 75.6 [96]Open in a new tab RT: Retention time in minutes. RI[exp]: Retention indices calculated on the basis of homologous n-alkane series (C[8]–C[20]) on an Elite-5 MS column. RI[lit]: Retention indices published in literature (Adams, 2007). 3.2. Network pharmacology 3.2.1. Drug-like screening of identified compounds and acquisition of compound and disease-related targets Pharmacokinetic parameter screening is an important tool in drug discovery as it helps researchers to identify and develop safe and effective new drugs [[97]29]. Out of 49 constituents identified by GC-MS, 44 constituents were filtered as biologically active as they qualified Lipinski's rule (molecular weight <500 amu, H-bond donor <10, H-bond acceptor <5, lipophilicity value < 5) and Abbott Oral Bioavailability score (≥0.5) ([98]Table 2). Compounds that do not adhere to Lipinski Rule criteria are generally considered to have low druggability and are, therefore, unfavourable for oral consumption [[99]30]. We tried to obtain differentially expressed genes (DEGs) in NSCLC by comparing the level of gene expressions between NSCLC and normal lung tissue samples. Based on the [100]GSE21933 series of GEO dataset, 3438 differentially expressed genes were obtained, including 1894 upregulated and 1548 downregulated genes in NSCLC ([101]Fig. 1A & B). The expression pattern of these DEGs is shown in a heat map ([102]Fig. 1C). NSCLC-related targets were collected from disease-related online databases, namely GeneCards, Malacards, TTD and DisGeNET, which resulted in the identification of a total of 1417 targets ([103]Fig. 1D). After removing duplications, a total of 4588 disease-related targets in NSCLC were obtained by combining targets from Genecards, Malacards, TTD, DisGenet and DEGs from the GEO database. The targets of bioactive constituents were predicted from two public databases i.e. Swiss target prediction (STP) and the Comparative toxicogenomics database (CTD). A total of 3961 predicted targets for CTEO were retrieved from STP and CTD. The common targets that existed between them were plotted using a Venn diagram ([104]Fig. 1E). The intersection of compound targets of CTEO and disease-related targets of NSCLC included 68 targets that were considered key therapeutic targets for assessing the anti-cancer activity of CTEO ([105]Fig. 1F). Table 2. Pharmacokinetic screening of Cinnamomum tamala essential oil compounds. Compound Lipinski's rule __________________________________________________________________ Violation __________________________________________________________________ Drug likeness __________________________________________________________________ MW < 500 (g/mol) MLOGP <4.15 HBA <10 HBD <5 Benzaldehyde 106.12 1.45 1 0 0 Pass Acetophenone 120.15 1.78 1 0 0 Pass Terpinolene 136.23 3.27 0 0 0 Pass Viridene 150.26 3.56 0 0 0 Pass Borneol 154.25 2.45 1 1 0 Pass cis-Linalool Oxide 170.25 1.38 2 1 0 Pass α-Terpineol 154.25 2.3 1 1 0 Pass Cinnamaldehyde 132.16 2.01 1 0 0 Pass Z-Methyl cinnamate 162.19 2.2 2 0 0 Pass n-Tridecane 184.36 5.67 0 0 1 Pass δ-Elemene 204.35 4.53 0 0 1 Pass Cyclosativene 204.35 5.8 0 0 1 Pass α-Copaene 204.35 5.65 0 0 1 Pass β- Elemene 204.35 4.53 0 0 1 Pass Ethyl cinnamate 176.21 2.49 2 0 0 Pass Longifolene 204.35 5.65 0 0 1 Pass 6,9-Guaiadiene 204.35 4.63 0 0 1 Pass cis-Muurola-3,5-diene 204.35 4.63 0 0 1 Pass α-Humulene 204.35 4.53 0 0 1 Pass γ-Gurjunene 204.35 4.63 0 0 1 Pass α-Amorphene 204.35 4.63 0 0 1 Pass Amorpha-4,7(11)-diene 204.35 4.63 0 0 1 Pass α-Muurolene 204.35 4.63 0 0 1 Pass γ-Cadinene 204.35 4.63 0 0 1 Pass δ-Cadinene 204.35 4.63 0 0 1 Pass cis-Calamenene 202.34 5.45 0 0 1 Pass γ-Amorphene 204.35 4.63 0 0 1 Pass trans-Cadina-1,4-diene 204.35 4.63 0 0 1 Pass α-Cadinene 204.35 4.63 0 0 1 Pass α-Calacorene 200.32 5.36 0 0 1 Pass β-Calacorene 200.32 5.36 0 0 1 Pass Maaliol 222.37 3.81 1 1 0 Pass Spathulenol 220.35 3.67 1 1 0 Pass Guaiol 222.37 3.67 1 1 0 Pass epi-Cedrol 222.37 3.81 1 1 0 Pass Junenol 222.37 3.67 1 1 0 Pass α-Corocalene 200.32 5.36 0 0 1 Pass 1-epi-Cubenol 222.37 3.67 1 1 0 Pass Hinesol 222.37 3.67 1 1 0 Pass Valerianol 222.37 3.67 1 1 0 Pass cis-Calamenen-10-ol 218.33 3.46 1 1 0 Pass α-Muurolol 222.37 3.67 1 1 0 Pass 2-Hydroxydiphenyl methane 184.23 3.34 1 1 0 Pass Amorpha-4,9-dien-2-ol 220.35 3.56 1 1 0 Pass [106]Open in a new tab MW: Molecular weight, HBA: Hydrogen bond acceptor, HBD: Hydrogen bond donor, MLOGP: Moriguchi otanol-water partition coefficien Fig. 1. [107]Fig. 1 [108]Open in a new tab Acquisition of compound and disease-related targets. (A) Volcano map of the distribution of all differentially upregulated (red) and downregulated genes (blue) in [109]GSE21933 dataset. (B) Bar plot showing 1894 upregulated and 1548 downregulated genes in normal and tumour patients. (C) Heatmap of top 100 upregulated and downregulated genes. Upregulated genes are shown in red, and down regulated genes are shown in green color. (D) NSCLC-related targets obtained from Malacards, TTD, GeneCards and DisGenet databases. (E) Overlapping predictive targets of the CTEO active constituents obtained between Swiss Target Prediction (STP) and Comparative Toxicogenomics Database (CTD). (F) Venn diagram illustrating overlapping targets among CTEO (Cinnamomum tamala essential oil) targets, DEGs (Differentially Expressed Genes), and NSCLC (Non-Small Cell Lung Cancer) related targets, with a total of 68 genes. (For interpretation of the references to color in this figure legend, the reader is referred to