Therapeutic Potential of Apigenin in Combination with Docetaxel on Human Cervical Cancer Cells – In-Silico and In-Vitro Approach

Therapeutic Potential of Apigenin in Combination with Docetaxel on Human Cervical Cancer Cells – In-Silico and In-Vitro Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Therapeutic Potential of Apigenin in Combination with Docetaxel on Human Cervical Cancer Cells – In-Silico and In-Vitro Approach Deepika Bhagavatula, Lynn Clifford Dsouza, Reham I. Alagal, Asiya Nazir, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9300836/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Docetaxel is a well-established drug widely used in the treatment of malignancies. However, it is limited by its dose-associated toxicity, necessitating improved combinatorial strategies to enhance efficacy while minimizing toxicity. Here, we report for the first time the combined effect of the naturally occurring flavone apigenin with docetaxel in cervical cancer. In-silico molecular docking demonstrated that both apigenin and docetaxel mostly bind within conserved inhibitor-binding pockets of anti-apoptotic Bcl-2 family proteins and matrix metalloproteinases, sharing key interacting residues, suggesting potential cooperative inhibition. Molecular dynamics simulations further showed stable ligand–protein complexes. Drug–drug interaction prediction revealed predominantly minor interactions, supporting the safety of the combination. In-vitro, the combined sub-lethal doses of apigenin and docetaxel significantly reduced cell viability in HeLa cells, induced apoptotic nuclear morphology, and suppressed migratory capacity. Collectively, these findings provide preliminary proof-of-concept evidence that the combination of apigenin with docetaxel exerts synergistic anti-proliferative and anti-migratory effects in cervical cancer, warranting further mechanistic and in vivo investigation. Cervical cancer Apigenin Docetaxel Apoptosis Migration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction According to GLOBOCAN estimates, roughly 19.9 million new cases and 10 million mortalities were reported globally in 2022, with cervical cancer remaining the fourth leading cause of death in women [ 1 ]. Current treatments for this malignancy often involve surgery, radiotherapy, and chemotherapy; with chemotherapy being one of the most widely used approaches [ 2 ]. Many chemotherapeutic agents exert their anticancer effects by inducing programmed cell death pathways, particularly apoptosis [ 3 ]. The execution of apoptosis is tightly regulated by intracellular signaling networks, among which the BCL-2 family comprising of both pro- and anti-apoptotic members plays a pivotal role in regulating mitochondrial outer membrane permeabilization (MOMP) and cell survival. The disruption of this fine balance towards anti-apoptotic proteins such as BCL-2, BCL-W, MCL-1, and BFL-1 is associated with tumor formation [ 3 ]. The anti-apoptotic proteins primarily have 4 conserved BH domains (BH1-BH4), where BH1-BH3 regions form a hydrophobic group stabilized by the BH4 domain, which accommodates the binding and subsequent inhibition of pro-apoptotic proteins BAK and BAX, thereby preventing MOMP and apoptosis. Hence, BH3- mimetic drugs such as navitoclax (ABT-737) and venetoclax (ABT-199) have been developed to target this hydrophobic groove, displacing pro-apoptotic proteins and restoring apoptotic signaling [ 4 ]. In addition to evading apoptosis, tumor progression also depends on the ability of cancer cells to invade surrounding tissues by remodeling the extracellular matrix (ECM), a process largely mediated by matrix metalloproteinases (MMPs) and normally restrained by tissue inhibitors of metalloproteinases (TIMPs). Overexpression of MMPs is linked to cancer progression as they are involved in ECM component degradation and activation of signaling molecules that contribute to cancer cell invasion [ 5 ]. Given their role in substrate recognition, structural features within the catalytic cleft of MMP’s have emerged as key targets for inhibition. Particularly, the S1’ pocket within the catalytic cleft of MMP’s is promising, as its relatively buried nature and variability in length and amino acid composition enable selective inhibition of MMP’s. Consequently, agents such as batimastat and marimastat were developed to inhibit MMP activity and limit cancer cell migration [ 6 ]. Despite these advances, the ‘one target, one drug’ approach used by cancer chemotherapies that rely on a single molecular pathway often faces limitations such as development of resistance, toxicity, and limited efficacy due to the heterogenous nature of cancer. Therefore, combination therapy targeting multiple molecular mechanisms has been the focus of multiple studies and is thought to be more likely to achieve better treatment outcomes while allowing for lowered drug dosage and associated toxicities [ 7 , 8 ]. Cancer chemoprevention involving the use of natural, synthetic, or biological agents to prevent, delay, or reverse carcinogenesis has emerged as a promising complementary strategy [ 9 ]. Chemopreventive agents such as nonsteroidal anti-inflammatory drugs (NSAIDs), deltanoids, retinoids, and phytochemicals may enhance the efficacy of chemotherapy when used in combination regimens [ 9 ]. Flavones represent an attractive class of phytochemicals, many of which combine anticancer activity with low toxicity [ 10 ]. Apigenin, a multifunctional flavone, possesses anti-carcinogenic, antioxidant, anti-mutagenic, anti-inflammatory, anti-viral, and anti-bacterial properties [ 11 ]. Apigenin is also known to suppress cancer cell migration and invasion through suppression of matrix metalloproteinases (MMPs) [ 12 ]. Resistance to apoptosis, and activation of migration are major contributors to chemoresistance, making anti-apoptotic BCL-2 family proteins and MMPs attractive therapeutic targets [ 3 , 13 ]. Docetaxel, developed by Sanofi and approved by the FDA in 1996 as ‘Taxotere’ is a second-generation antineoplastic agent of the taxoid family used to treat breast, gastric, prostate, and lung cancer. It targets mitosis at the G2/M phase, modulates phosphorylation and inactivation of Bcl-2 protein family, and interferes with expression of cyclin - cyclin-dependent kinases (CDKs), ultimately leading to apoptosis [ 14 ]. Numerous studies delineate the synergistic effect of chemopreventives such as flavonoids in combination with docetaxel, where they act therapeutically by modulating pathways that promote chemoresistance of the anti-cancer drug, while increasing the bioavailability and stability of the drug [ 15 , 16 ]. For instance, in a study conducted by Lim et al., combining docetaxel with chrysin, a honey polyphenol, had synergistic effects on A549 human NSCLC cells where the combined treatment improved apoptosis and antiproliferation in cancer cells [ 15 ]. Similarly, another study by Bernasinska-Slomczewska et al. concluded that baicalein and baicalin synergistically enhanced the cytotoxic, genotoxic, and proapoptotic activity of docetaxel in breast cancer cells, compared to individual agents alone [ 17 ]. Treatment with apigenin has also shown sensitization of prostate cancer cells towards docetaxel treatment by targeting ABCB1 expression [ 18 ]. These findings suggest that flavone-based adjuvants may enhance the therapeutic efficacy of docetaxel chemotherapy by simultaneously modulating apoptotic signaling and tumor cell migration. However, despite growing evidence supporting flavonoid–taxane combinations, the interaction between apigenin and docetaxel in cervical cancer remains insufficiently explored. Therefore, the present study investigates the potential effects of apigenin in combination with docetaxel in cervical cancer, providing insights on the collective activity on the combination treatment strategy. Materials and Methods Selection of Target Proteins for In-Silico Analyses For this study, apoptosis-related and migration-related proteins were selected as proteins of interest (POI) based on their anti-apoptotic and migratory activity in the regulation of cancer. A total of 8 key proteins – BCL-2, BCL-w, BFL-1, and MCL-1, MMP-3, MMP-7, MMP-9, and MMP-13 were shortlisted based on literature surveys and availability in the RCSB Protein Data Bank [ 19 ]. Protein-protein interaction (PPI) networks (medium confidence, cutoff 0.4) were generated for (i) apoptosis related proteins, (ii) migration related proteins, and (iii) the combined protein set using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) Database ver. 12 ( https://string-db.org/ ) [ 20 ]. To confirm the functional relevance of the eight selected proteins, Gene Ontology (GO) enrichment analysis on biological processes was conducted to highlight cellular roles. False discovery rate (FDR) correction (medium stringency, 5%) was applied to both analyses. Protein and Ligand Structure Retrieval and Preparation High resolution (< 2.5Å) 3D structures of selected protein co-crystallized with known inhibitors were retrieved from the RCSB Protein Data Bank (PDB) website and downloaded as .pdb files. The structures were individually visualized using UCSF Chimera 1.19 [ 21 ] and cleaned by removing structurally irrelevant heteroatoms, water molecules, and native ligands. Native ligands were saved separately as a .pdb file to validate docking studies. Missing residues were restored using the SWISS-MODEL server [ 22 ]. Two ligands of interest (LOI), Apigenin (API) and Docetaxel (DTX) (Fig. 1 A, 1 B), were selected based on prior reports on apoptotic modulation. The 3D structure of ligand API (PubChem ID: 5280443) and 2D structure of ligand DTX (PubChem ID: 148124) were retrieved from the PubChem website [ 23 ] and downloaded as .sdf files. The 2D structure of DTX was converted to a 3D structure and downloaded as a .pdb file using MarvinSketch [ 24 ]. Open Babel [ 25 ] was used to convert the .sdf file of API to a .pdb file. Both structures were then visualized in UCSF Chimera 1.19 [ 21 ]. The proteins and ligands were then prepared for docking and were suitably converted to the AutoDock Vina (ADV) compatible .pdbqt format using the ADV command line [ 26 ]. Drug-drug Interaction Prediction The pharmacological interactions between LOIs were evaluated using the Prediction of Activity Spectra for Substances (PASS) method available on Way2Drug Drug interaction prediction tool, available for free at https://way2drug.com/ddi/ . Molecular structures of both compounds were submitted to predict potential Drug- Drug Interaction (DDI) mechanisms, CYP450 enzyme involvement, DDI severity, and adverse effects. Only mechanisms with probability to be active (Pa) greater than probability to be inactive (Pi) were considered relevant [ 27 ]. Individual Docking and Multiple Ligand Simultaneous Docking API and DTX were individually docked to all eight target proteins using AutoDock Vina [ 26 ]. This was followed by Multiple Ligand Simultaneous Docking (MLSD), where both LOI’s were docked simultaneously to assess the co-binding potential of both ligands. The conformation grid was set to maximize coverage to facilitate blind docking. The binding affinities (expressed as Vina Scores) were recorded for all the runs, and the docked structure with the lowest binding affinity was selected for further analyses and downloaded as a .pdb file. Molecular Interaction and Binding Site Conservation Analysis Protein-ligand interactions of docked complexes and native inhibitors were evaluated using the PLIP (Protein Ligand Interaction Profiler) server ( https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index ) [ 28 ]. .pdb files were uploaded to identify interacting residues, hydrogen, and hydrophobic contacts within the binding pockets, and comparative analyses were performed to assess binding site similarity of LOI to the protein inhibitor. Interacting residues was further evaluated using ConSurf server ( https://consurf.tau.ac.il/ ) [ 29 ] which was used to identify conserved residues within the inhibitor binding region and determine whether LOI interactions occurred at evolutionarily conserved, functionally significant sites. Energy Minimization Study All receptor-ligand complexes (protein-API, protein-DTX, and protein-API + DTX) were subjected to energy minimization using Chimera 1.16. Each structure was minimized by steepest descent and conjugate gradient minimization respectively. Final minimized energy values (in kJ/mol) were extracted from Chimera’s reply log. Molecular Simulation Study Molecular Dynamics (MD) simulations of the MLSD complexes were conducted using MyPresto ver. 5.0 [ 30 ] under the following conditions: (i) The global energy minimization was carried out with a loop limit of 5,000 using generalized Born method; (ii) The global dynamics was executed with a loop limit of 5,000,000 at a constant temperature of 300 K for 10 ns. All other parameters were maintained at default settings. The energy and temperature graphs, along with the RMSD and RMSF, were recorded for analysis. Cell culture and Drug preparation This research makes use of human cervical carcinoma HeLa cell line purchased from Addexbio (Cat. No.: C0008001) cultured in DMEM (PAN Biotech, Germany) supplemented with 10% FBS (Sigma, USA), 100X Pen-strep (Sigma, USA), and amphotericin (Sigma, USA) in a saturated atmosphere of 5% CO 2 at 37°C. DTX was acquired from RTU Technologies. Dilutions to the required drug concentrations were made directly from DTX in its original solution in injection form with a molarity of 23.2 mM. API was obtained from MedChem express (Cat. No. HY-N1201, CAS No. 520–36–5). API in its powdered form was dissolved in DMSO at a concentration of 5 mg/250 µL to obtain a concentration of 74 mM. The stock was diluted to a working concentration of 1 µM in complete media and was used to prepare various concentrations used for treatment. Cell Viability, Combination Index, and Nuclear Morphology Analysis The cell viability and drug interaction effects were evaluated using the MTT assay followed by combination index (CI) analysis. HeLa cells were seeded into a 96-well plate (10 x 10 3 cells/well) and allowed to reach ~ 80% confluency before treatment with API (1-175 µM), DTX (1-110 µM), and their combinations. Post 24-hour treatment, the MTT (0.5mg/mL) assay was performed, and the absorbance at 570 nm was measured using an ELISA reader (BioTek, USA). Experiment was performed in triplicate, and percentage cell viability was calculated as: $$\:\%\:Cell\:viability=\left(\frac{Average\:OD\:of\:individual\:test\:group}{Average\:OD\:of\:control\:group}\right)\times\:100$$ The combination index (CI) was calculated using the Chou–Talalay method to determine the nature of the drug interactions [109]: $$\:Combination\:Index\:\left(CI\right)=\:\frac{C{A}_{x}}{I{C}_{x}A}+\:\frac{C{B}_{x}}{I{C}_{x}B}$$ Where CA x and CB x represent the concentrations of drug A and B in combination achieving X% effect, and IC x A and IC x B are the individual drug concentration of A and B producing the same effect. CI values of 1 indicate synergistic, additivity, and antagonistic interactions, respectively. For nuclear morphology analysis, 3 x 10 3 cells/well were plated onto a 24 well plate, treated under the same conditions, and compared to the untreated control. Following 24 hours of treatment, the media was decanted, and the cells were washed with 1X PBS and fixed using 70% ice-cold methanol for 20 minutes, followed by staining with PI for 1–2 minutes. Excess stain was then washed using 1X PBS and the cells were visualized and imaged using a fluorescent microscope (Euromex, Holland) with Imagefocus software. Cell Migration Analysis The scratch wound and transwell migration assay was used to determine the individual and combined effect of API and DTX on the migration of cancer cells. For the wound healing assay, 5 x 10 3 cells/well were seeded into a 12-well plate and cultured until confluent. A vertical cell free line was created using a 10 µL pipette tip, followed by treatment with individual drugs or their combinations; untreated cells served as controls. Images were captured at 0 and 24 hours using an inverted microscope (Olympus Corporation). After measuring the wound width at 24 hours, the % wound closure/widening was calculated as: $$\:\%\:Cell\:wound\:closure=\:\frac{(Test-Control)}{Control}\times\:100$$ For the transwell assay, 5 x 10 3 cells/well were seeded on the insert (8µm pore size, BD Biosciences, Franklin Lakes, NJ, USA) with 1 mL FBS-free media and placed in a 24-well plate containing complete media as a chemoattractant. After 24 hours, migrated cells were fixed with absolute methanol, stained with 0.1% crystal violet, and visualized under 10X field (Olympus Corporation) to compare and capture cell migration between treated and control. The experiment was repeated thrice, and the number of migrated cells was counted using the ImageJ software. Statistical analysis All the data are expressed as means ± SD of at least three experiments. The data was statistically evaluated using one-way ANOVA followed by Dunnett’s multiple comparisons test. Significant differences were established at P ≤ 0.05. Results Apigenin and Docetaxel Combination Predicts Low DDI Risk and Predictable CYP450 Interactions The DDI results of API and DTX showed that according to ORCA classification [ 31 ] the combination was predicted to belong to DDI Severity Class II (-0.043, PASS analysis) as shown in Fig. 1 C, and Fig. 1 D, corresponding to minimal interaction risk. Moreover, the drug combination is predicted to be unlikely to cause adverse effects such as hypertension, bradycardia, and arrythmia, among others (Table 1 ). P450 mediated DDI analysis predicted that CYP3A4 and CYP2C8 may be the most likely enzymes for mediating interactions, as compared to other CYP isoforms (Fig. 1 D). Analysis of overlapping CYP450 activities predicted that DTX may act predominantly as a CYP3A4 substrate, while API might be a moderate inducer of CYP3A4 while showing minor inducer/inhibitor activity toward other CYPs. Interestingly, predicted DDI mechanisms (Table 1 ) suggest a potential pharmacokinetic effect, as predicted by its effect on intra/extra-hepatic metabolism (Pa = 0.893) and cellular transport (Pa = 0.583) rather than a pharmacodynamic effect. Table 1 Gene Ontology (GO) biological process enrichment of apoptosis and migration related proteins. Statistically significant enrichment was defined as FDR < 0.05 #Term ID Term Description Observed Gene Count Background Gene Count Strength Signal False Discovery Rate Matching Proteins in Network (Labels) GO:0022617 Extracellular matrix disassembly 4 47 2.32 2.73 1.59E-05 MMP7, MMP13, MMP3, MMP9 GO:0030574 Collagen catabolic process 4 41 2.38 2.76 1.59E-05 MMP7, MMP13, MMP3, MMP9 GO:0097192 Extrinsic apoptotic signaling pathway in absence of ligand 4 36 2.44 2.78 1.59E-05 BCL2L2, BCL2A1, MCL1, BCL2 GO:1904645 Response to amyloid-beta 4 49 2.3 2.72 1.59E-05 BCL2L2, MMP13, MMP3, MMP9 GO:0008630 Intrinsic apoptotic signaling pathway in response to DNA damage 4 75 2.12 2.43 3.71E-05 BCL2L2, BCL2A1, MCL1, BCL2 GO:2001243 Negative regulation of intrinsic apoptotic signaling pathway 4 101 1.99 2.14 0.00010 BCL2L2, MCL1, MMP9, BCL2 GO:0010941 Regulation of cell death 6 1651 0.95 0.69 0.0070 BCL2L2, BCL2A1, MMP3, MCL1, MMP9, BCL2 GO:0043066 Negative regulation of apoptotic process 5 891 1.14 0.82 0.0075 BCL2L2, BCL2A1, MCL1, MMP9, BCL2 GO:0071492 Cellular response to UV-A 2 11 2.65 1.28 0.0080 MMP3, MMP9 GO:0010243 Response to organonitrogen compound 5 963 1.11 0.78 0.0095 BCL2L2, MMP13, MMP3, MMP9, BCL2 GO:0006915 Apoptotic process 5 1041 1.07 0.73 0.0119 BCL2L2, BCL2A1, MCL1, MMP9, BCL2 GO:0009411 Response to UV 3 150 1.69 1.01 0.0134 MMP3, MMP9, BCL2 GO:2000811 Negative regulation of anoikis 2 19 2.41 1.1 0.0148 MCL1, BCL2 GO:1903209 Positive regulation of oxidative stress-induced cell death 2 23 2.33 1.02 0.0199 MMP3, MCL1 GO:0150077 Regulation of neuroinflammatory response 2 34 2.16 0.84 0.0393 MMP3, MMP9 GO:2001240 Negative regulation of extrinsic apoptotic signaling pathway in absence of ligand 2 36 2.14 0.82 0.0427 MCL1, BCL2 Enrichment Analysis Supports Coordinated Involvement of Survival and Migration Pathways The STRING database analysis of PPI of anti-apoptotic and migration proteins showed a dense interaction among BCL-2 family proteins and MMP family proteins. The combined network revealed two distinct clusters corresponding to each protein family, with limited cross-family interactions indicating that these proteins are involved in separate but complementary cancer processes rather than directly interacting with each other (Fig. 2 ). Gene Ontology (GO) enrichment analysis within STRING was used to analyze the biological roles of the selected proteins; results showed statistically significant overrepresentation of processes such as Extracellular matrix disassembly (GO:0022617) and Collagen catabolic process (GO:0030574) among others all of which are associated with proteins involved in tumor migration and invasion such as the MMP3, MMP7, MMP9, and MMP13. Simultaneously, results also showed significant enrichment of apoptosis related roles such as Extrinsic apoptotic signaling pathway in absence of ligand (GO:0097192), Intrinsic apoptotic signaling pathway in response to DNA damage (GO:0008630), and Negative regulation of intrinsic apoptotic signaling pathway (GO:2001243) thereby supporting the involvement of proteins like BCL2, BCL2L2, BCL2A1, and MCL-1 in cancer cell survival (Table 2 ). These findings show that the selected protein set is significantly associated with coordinated regulation of apoptosis evasion and activation of migration; two hallmark processes driving cancer progression. Table 2 Gene Ontology (GO) biological process enrichment of apoptosis and migration related proteins. Statistically significant enrichment was defined as FDR < 0.05. #Term ID Term Description Observed Gene Count Background Gene Count Strength Signal False Discovery Rate Matching Proteins in Network (Labels) GO:0022617 Extracellular matrix disassembly 4 47 2.32 2.73 1.59E-05 MMP7, MMP13, MMP3, MMP9 GO:0030574 Collagen catabolic process 4 41 2.38 2.76 1.59E-05 MMP7, MMP13, MMP3, MMP9 GO:0097192 Extrinsic apoptotic signaling pathway in absence of ligand 4 36 2.44 2.78 1.59E-05 BCL2L2, BCL2A1, MCL1, BCL2 GO:1904645 Response to amyloid-beta 4 49 2.3 2.72 1.59E-05 BCL2L2, MMP13, MMP3, MMP9 GO:0008630 Intrinsic apoptotic signaling pathway in response to DNA damage 4 75 2.12 2.43 3.71E-05 BCL2L2, BCL2A1, MCL1, BCL2 GO:2001243 Negative regulation of intrinsic apoptotic signaling pathway 4 101 1.99 2.14 0.00010 BCL2L2, MCL1, MMP9, BCL2 GO:0010941 Regulation of cell death 6 1651 0.95 0.69 0.0070 BCL2L2, BCL2A1, MMP3, MCL1, MMP9, BCL2 GO:0043066 Negative regulation of apoptotic process 5 891 1.14 0.82 0.0075 BCL2L2, BCL2A1, MCL1, MMP9, BCL2 GO:0071492 Cellular response to UV-A 2 11 2.65 1.28 0.0080 MMP3, MMP9 GO:0010243 Response to organonitrogen compound 5 963 1.11 0.78 0.0095 BCL2L2, MMP13, MMP3, MMP9, BCL2 GO:0006915 Apoptotic process 5 1041 1.07 0.73 0.0119 BCL2L2, BCL2A1, MCL1, MMP9, BCL2 GO:0009411 Response to UV 3 150 1.69 1.01 0.0134 MMP3, MMP9, BCL2 GO:2000811 Negative regulation of anoikis 2 19 2.41 1.1 0.0148 MCL1, BCL2 GO:1903209 Positive regulation of oxidative stress-induced cell death 2 23 2.33 1.02 0.0199 MMP3, MCL1 GO:0150077 Regulation of neuroinflammatory response 2 34 2.16 0.84 0.0393 MMP3, MMP9 GO:2001240 Negative regulation of extrinsic apoptotic signaling pathway in absence of ligand 2 36 2.14 0.82 0.0427 MCL1, BCL2 Simultaneous Docking of Apigenin and Docetaxel Exhibit Enhanced Binding Affinity Compared to Individual Ligand Docking Molecular docking was used to study the individual and simultaneous ligand interactions of API and DTX with the anti-apoptotic and migratory proteins and predict their binding positions. (Table 3 , Fig. 3 ). API, when individually docked exhibited binding affinities ranging from − 6.68 kcal/mol to -9.51 kcal/mol and bound near or within the known inhibitor cavity. In contrast, DTX bound away from the inhibitor site in proteins such as BCL-2, MCL-1, and BFL-1, but showed a slightly lower Vina Score compared to API, ranging from − 6.44 kcal/mol to -8.41 kcal/mol. In MLSD experiments, API and DTX exhibited improved binding affinities in all proteins ranging from − 9.6 kcal/mol to -12.74 kcal/mol when compared to individual ligand docking. API retained its binding site within the known-inhibitor pocket in most proteins like when API was docked individually, except in MCL-1, where the addition of DTX seems to have altered the protein in a way that none of the ligands bind to the inhibitor site. Interestingly, API and DTX both retained their initial binding domains when docked together in BCL-W, BCL-2, and MMP-3, thereby enhancing engagement and possible synergistic inhibition. Table 3 Molecular docking parameters, docking scores of API, DTX, and their combinations. Protein/ PDB ID Grid size Grid Center Energy/Vina Score (kcal/mol) API DTX API + DTX BCL-2 6QGG 45, 45, 45 -16.81, 8.26, 4.66 -7.09 -7.27 -10.3 BFL-1 3I1H 35, 41, 40 10.55, -9.26, 5.25 -6.68 -7.13 -9.6 BCL-W 7LH7 42, 44, 43 -0.90, 1.00, -0.70 -8.33 -9.31 -12.74 MCL-1 5FC4 40, 40, 40 11.56, 10.22, 11.30 -7.15 -7.29 -10.3 MMP3 4G9L 37, 34, 37 14.05, 75.84, 103.13 -9.43 -8.17 -12.14 MMP7 7WXX 35, 40, 32 -30.71, -13.96, 1.22 -7.75 -6.64 -11.09 MMP-9 1GKC 31, 34, 33 10.47, 22.2, 41.54 -9.18 -8.22 -11.13 MMP13 4JP4 35, 33, 39 25.54, 4.05, 16.28 -9.51 -7.96 -11.38 PLIP and ConSurf Analysis Reveal Key Interactions and Conserved Binding Regions for Apigenin and Docetaxel in Selected Proteins PLIP and ConSurf were used post docking to analyze the type and functionality of protein-ligand interactions formed in docked complexes of native inhibitor ligand, API, DTX, and their combination (Fig. 4 ). From Table 4 , it can be observed that API individually binds to residues like that of the known inhibitor forming key hydrophilic (3–5) and hydrogen bonds (1–4). In contrast, DTX interacts with a higher number of residues by forming multiple hydrophilic (3–6) and hydrogen bonds (3–7) due to its larger structure; however, unlike API, it only seems to target proteins such as BCL-W, MMP3, MMP7, and MMP-9 at its inhibitor site. Table 4 Comparison of binding site amino acids of protein with known inhibitors, API, DTX, and the combination of API and DTX. Bold: residues shared with known inhibitors; Yellow: conserved residues common to known inhibitors; Blue: conserved residues unique to API or DTX; Green: conserved residues unique to the API–DTX combination Protein/ PDB ID Type of bond Amino Acids involved in binding site of proteins with native inhibitor Amino Acids involved in binding site of protein with API Amino Acids involved in binding site of protein with DTX Amino Acids involved in binding site of protein with (API + DTX) Qty. Residues Qty. Residues Qty. Residues Qty. Residues BCL-2 6QGG Hydrophobic bond 8 PHE56, TYR60, MET67, VAL85, LEU89, ARG98, VAL100, PHE105 4 PHE56 , TYR60 , ARG98 , VAL100 5 ARG6, GLU31, ALA36, ILE41, GLN142 15 ARG6, GLU7, MET10, LYS11, GLU23, ALA26, VAL29, ALA36, PHE56 , TYR60 , ARG98 , VAL100 , GLU7, SER14, ASN95 Hydrogen bond 4 ASN95, TRP96, GLY97, TYR154 3 ASN95 , ARG98 , ARG98 4 ASP4, ASN5, HIS138, TRP147 4 GLU7, SER14, ASN95 , ARG98 BFL-1 3I1H Hydrophobic bond 6 VAL54, VAL58, LEU62, LYS87, VAL100, PHE105 4 VAL58 , VAL84, GLU88, PHE105 5 GLN32, PRO40, GLN48, ASN49, PHE52 10 PRO34, GLN35, PRO40, ASN49, VAL58 , VAL84, GLU88, PHE105 , GLN48, ARG98 Hydrogen bond 3 LYS87, ASN95, LYS157 2 ARG98, THR101 4 ILE33, GLN35, ARG45, GLN48 2 GLN48, ARG98 BCL-W 1ZY3 Hydrophobic bond 9 PHE52, PHE56, LEU63, LEU67, VAL81, GLU84, LEU85, TRP92, GLU156 5 GLU51 , PHE52 , VAL96, TYR105, GLU156 3 PHE56 , TRP92 , GLU156 12 LYS20, LEU21, LYS24, GLU51 , PHE52 , PHE60, LEU63, PHE78, TRP92 , VAL96, ALA107 , TYR150 Hydrogen bond 12 GLU51, ARG55, GLN66, GLU84, GLN87, ASN91, GLY93, ARG94, ASP152, GLY153, ARG159, LEU161 4 ALA48, PHE52 , TRP92 , ARG159 3 GLU51 , ARG55 , ARG94 4 ALA47, PHE60 , SER61, LEU149 MCL-1 5FC4 Hydrophobic bond 8 PHE149, PHE58, VAL79, VAL83, THR96, LEU97, PHE100, LEU65 3 VAL83 , LEU97 , PHE100 5 ARG52, GLU55, PHE103, LYS106, HIS107 13 TYR5, ARG6, LEU9, GLU10, ARG14, ARG52, GLU55, PHE103, LYS106, HIS107, ARG17, ASP48, ARG52 Hydrogen bond 7 ARG45, ARG45, VAL46, VAL46, VAL50, HIS82, ARG93 1 ARG93 7 GLN19, ASP48, ASP48 ARG52, GLU55, LYS106, LYS106 8 GLU10, ARG14, ARG17, GLN19, ASP48, ARG52, GLU55, LYS106 MMP3 4G9L (B) Hydrophobic bond 3 VAL163, VAL198, TYR223 2 LEU197, TYR223 6 TYR155, VAL163 , LEU164 , HIS166, TYR168, PHE210 8 PHE154, TYR155, LEU164 , TYR168, ALA169, HIS205 , PHE210, PRO221 Hydrogen bond 3 LEU164, ALA165, HIS205 3 LEU164 , ALA165 , PRO221 3 ALA165 , ALA167, ALA169 4 TYR155, ALA169, ASN175, HIS201 MMP7 7WXX Hydrophobic bond 3 TYR75, HIS86, PHE88 4 THR83, LEU84, TYR118, TYR144 3 LEU84, ALA119, HIS122 9 PHE6, PRO10, PHE88, ALA89, LEU94, ALA119, HIS122, THR143, TYR144 Hydrogen bond 2 ALA87, ALA89 3 THR83, GLN123, TYR141 5 ASN82, LEU84, GLN123, TYR144, GLY145 4 LEU84, ALA87 , ALA89, HIS126 MMP-9 1GKC Hydrophobic bond 1 VAL118 4 LEU83 , VAL118 , HIS121, TYR143 6 LEU83 , HIS85, PHE89, GLN81, VAL118 , HIS125 10 ASP31, LEU42, VAL46, LEU83 , ASN108, PHE116, VAL118 , HIS121, TYR143 Hydrogen bond 4 GLY81, LEU83, PRO141, TYR143 3 LEU83 , ALA84, ASN142 2 SER67, ALA84 4 LEU83 , LEU138, ARG35, ASP39 MMP13 4JP4 Hydrophobic bond 2 LEU84, HIS87 4 VAL119, HIS122 , PHE141, TYR144 5 LEU84 , VAL119, HIS122 , ILE143, TYR144 10 TYR76, PRO77, LEU84 , HIS87 , PRO90, VAL119, HIS122 , HIS126, ASP131, TYR144 Hydrogen bond 4 LEU85, ALA86, ALA88, HIS122 1 THR147 4 LEU85 , HIS122 , HIS132, PRO142 3 LEU85 , ALA86 , PRO142 The combination complexes showed an improvement in the number of binding residues per protein with 8–15 hydrophilic bonds, 2–8 hydrogen bonds, and a decent overlap of the conserved residues binding with known inhibitors. Residues that were targeted by API and DTX alone were simultaneously occupied, and several new interactions were also observed, such as LYS20, LEU21, PHE60, LEU63, and PHE78 in BCL-W, and PRO77, PRO90, and HIS126 in MMP-13. An exception to this trend is MCL-1, where neither ligand bound to the inhibitor pocket nor significant conserved residues of the protein, except for a single hydrophilic and hydrogen bond at ARG52. However, compared to API or DTX alone, simultaneous docking of API and DTX improves the binding affinities and may also be functionally relevant as it targets conserved residues of multiple apoptotic domains. Energy Minimization Reveals the Stabilizing Role of Apigenin in Ligand Binding Energy minimization was carried out for the eight POI in three different conformations - protein-API (P-A), protein-DTX (P-D), and protein-API + DTX (P-AD) to analyze the potential energy of the protein-ligand complex (Table 5 ). In all eight complexes, P-A consistently showed the lowest total energy values ranging from − 3773.21 kJ/mol (MMP-13) to -19104.54 kJ/mol (MCL-1). Except for BCL-2 and MMP-7, all P-AD complexes had lower energies compared to P-D complexes. This could suggest that the presence of API could contribute towards a more favorable binding environment for DTX, leading to synergistic effects. Table 5 Post-minimization energy, RMSD and RMSF values of API, DTX, and their combinations Protein/ PDB ID Energy Minimized (kJ/mol) RMSD (Å) RMSF (kcal/mol*Å) API DTX API + DTX BCL-2 6QGG -10268.58 -9653.08 -9607.10 5.58 39.91 BFL-1 3I1H -14965.84 -14314.97 -14513.34 5.77 30.95 BCL-W 7LH7 -16355.82 -15751.49 -15885.16 8.76 30.38 MCL-1 5FC4 -19104.54 -18676.11 -18809.45 4.25 28.95 MMP3 4G9L -4494.66 -3888.21 -3999.86 5.34 30.79 MMP7 7WXX -13032.16 -12443.14 -12314.22 4.21 30.56 MMP-9 1GKC -9246.93 -8658.06 -8757.02 7.46 30.87 MMP13 4JP4 -3773.21 -3184.43 -3333.48 6.72 31.40 Molecular Dynamics Simulation Supports Energetic Stability of MLSD Complexes Molecular dynamics simulations were carried out to evaluate the energetic behavior and structural flexibility of the P-AD complexes obtained from simultaneous docking (Fig. 5). Total energy profiles demonstrated stable energy distributions across all eight complexes throughout the 10 ns timeframe, with consistently negative potential energy values and no abrupt fluctuations, indicating energetically favorable ligand-bound systems. Temperature profiles remained centered around the target simulation temperature (~ 300 K), confirming adequate thermal control and equilibration of the simulated systems. Structural deviations were assessed using root mean square deviation (RMSD) values calculated for each P-AD complex (Table 5 ). RMSD values demonstrated varying degrees of structural deviation among the proteins, with MCL-1 (4.25 Å) and MMP-7 (4.21 Å) exhibiting the lowest RMSD values, while BCL-W/BCL2L2 (8.76 Å) and MMP-9 (7.46 Å) showed higher deviations. Intermediate RMSD values were observed for BCL2 (5.58 Å), BFL-1/BCL2A1 (5.77 Å), MMP-3 (5.34 Å), and MMP-13 (6.72 Å). Energy-weighted RMSF values ranged from 28.95 to 39.91 kcal/mol·Å across the analyzed systems. Table 6 CI of the combination treatment of API and DTX Drug Combination Concentration of API (µM) Concentration of DTX (µM) Combination Index (CI) A1D1 2.5 10 0.513 A1D2 2.5 15 0.443 A2D1 5 10 0.681 A2D2 5 15 0.447 Concurrent Administration of API and DTX Enhances Cytotoxicity and Apoptotic Morphology Simultaneous administration of API and DTX significantly enhances cytotoxicity in HeLa cells. MTT assays showed a dose-dependent reduction in cell viability after 24 hours, with IC 50 values of 75 µM for API and 72 µM for DTX, with the API value aligning closely with previously reported findings from our laboratory [ 32 ] (Fig. 6 A). Sub-lethal concentrations (API: 2.5 µM and 5 µM; DTX: 10 µM and 15 µM) brought down the cell viability to as low as 57%. The lowest concentration of DTX used individually had a viability of 78.1%, whereas its combination with API (A1D1) had a viability of 66.5%, indicating potentiation of DTX by API (Fig. 6 B). Combination index confirmed synergisms for all combinations of API and DTX, with the strongest effect at A1D2 (API 2.5 µM + DTX 15 µM) (Table 6 ). The treatments also showed morphological changes such as cell rounding, detachment, and death, which were enhanced in combination treatments (Fig. 6 C). PI staining showed a dose-dependent increase in the apoptotic index, characterized by nuclear condensation, nuclear blebbing, nuclear fragmentation, cytoplasmic spillage, and apoptotic bodies. These effects were more pronounced in cells treated with the combination of drugs as compared to individual treatments with the same concentrations (Fig. 6 D). API and DTX Inhibit the Migratory Ability of HeLa Cells The migratory ability of HeLa cells post-treatment with API, DTX, and their combinations for 24 hours was assessed using both wound healing and transwell assays. Treatment with API alone led to marginally impaired cell migration in a dose-dependent manner (wound closure A1: 80.41; A2: 32.98%), whereas treatment with DTX resulted in minimal wound closure (wound closure D1: 9.79%; D2: 1.55%). Notably, combination treatments impaired cell migration significantly, with the highest effect observed in A2D2 (wound width% = -45.36%), highlighting a synergistic anti-migratory effect. Complete closure of the wound in the control well was observed at 48 hours (Fig. 6 E, 6 F). Similarly, the transwell assay demonstrated a dose-dependent reduction in cell migration – 67.15% and 49.37% when treated with API (A1, A2) and 39.72% and 29.82% when treated with DTX (D1, D2). Concurrent treatment enhanced the anti-migratory effect, reducing the percentage of migrated cells to as low as 6.4% (A2D2) (Fig. 6 G, 6 H). Discussion Chemotherapeutic treatment of cervical cancer remains a major clinical challenge [ 33 ] due to treatment associated toxicity and the risk of chemotherapy resistance. DTX is known to exert its antitumor effect by microtubule stabilization; however, its long-term efficacy is limited due to cancer resistance mechanisms and toxicities caused due to its non-specificity [ 13 , 14 ]. These limitations provide a solid ground for exploring combination chemotherapeutic strategies that enhance efficacy of the treatment while simultaneously reducing associated toxicity [ 7 , 8 ]. The combination of DTX with various flavonoids such as chrysin, baicalein, and apigenin has shown promising results as anticancer combinations [ 15 , 17 , 34 ]. However, to the best of our knowledge, the role of the combination of API and DTX in cervical cancer cells remains unknown. In this study, we provide in-silico and in-vitro evidence that API is able to enhance the therapeutic potential of DTX in cervical cancer (HeLa) cells. STRING analysis revealed that BCL-2 family proteins and MMP’s demonstrated high degrees of interaction among themselves and formed separate clusters, with BCL-2 and MCL-1 exhibiting a few interactions with MMPs. Although these interactions do not necessarily represent direct physical binding, previous literature has reported that BCL-2 overexpression can upregulate MMP expression [ 35 ]. Gene Ontology (GO) enrichment highlighted regulation of cell death (BCL2L2, BCL2A1, MMP3, MCL1, MMP9, BCL2), extracellular matrix disassembly (MMP7, MMP13, MMP3, MMP9), and negative regulation of cell death (BCL2L2, BCL2A1, MCL1, MMP9, BCL2) among other pathways. This demonstrates that the selected proteins are involved in apoptosis evasion and extracellular matrix remodeling, which are two functionally different processes that together facilitate cancer progression. The favorable drug profile of API combined with minimal predicted DDI risk and adverse effects with DTX supports their potential as a combination in cancer therapy. Analysis of overlapping CYP450 activities predicted that DTX is mainly a substrate of the CYP3A4 enzyme, which is consistent with previously established metabolic pathway of DTX [ 36 ]. Both DTX and API were predicted to induce CYP3A4 leading to increased clearance of DTX. As CYP3A4 is often found to be overexpressed in cancers leading to DTX resistance [ 36 , 37 ], co-administration with API could both mitigate toxicity and counteract resistance through API’s anticancer effects. Moreover, the moderate substrate profile of API toward CYP3A4 further suggests limited competitive interference, reducing the likelihood of severe pharmacokinetic antagonism. Although preclinical data suggest API can enhance paclitaxel bioavailability via CYP3A and P-glycoprotein inhibition [ 38 ], our predictions indicate limited inhibitory activity, implying context-dependent effects rather than strong metabolic antagonism. DDI mechanisms predict that the interaction is predominantly pharmacokinetic, likely mediated through modulation of metabolic and transport pathways. However, the observed co-binding of API and DTX to inhibitory sites of anti-apoptotic and migratory proteins also supports the possibility of pharmacodynamic effects. This dual-interaction framework involving pharmacokinetic and pharmacodynamic modulation may collectively influence therapeutic outcome. Molecular docking was used to study the individual and simultaneous ligand interactions of API and DTX with BCL-2 family proteins and MMP’s to predict their binding positions. Multiple Ligand Simultaneous Docking (MLSD) is a computational approach that simulates binding of multiple ligands to a protein at the same time, allowing the analysis of the combined action of the ligands and potential synergistic interactions [ 39 ]. Single ligand molecular docking showed that API bound within the canonical inhibitor pocket of BCL-2 family proteins and MMP’s, while DTX bound away from the inhibitor site such as the BH4 domain in BCL-2 and the BH3 domain in MCL-1, suggesting an allosteric interaction. In contrast, the role of DTX remains undetermined in BFL-1, as it did not bind to any functionally specific region. However, in MLSD experiments, API and DTX exhibited improved binding affinities across all POI’s as seen in Table 3 with PLIP and ConSurf analysis further supporting the functional relevance of these interactions, revealing that API by itself binds similarly to the known inhibitor but engaged fewer residues due to its smaller size, while DTX showed more allosteric binding patterns. The P-AD complexes increased overall interaction density (8–15 hydrophilic and 2–8 hydrogen bonds) and engaged conserved residues from the inhibitor site and those not targeted by individual ligands, as identified by ConSurf and seen in Table 4 . This could be the result of ligand-induced conformational change, causing buried residues to resurface and interact with the ligands. As observed in Fig. 3 , residues that were targeted individually were often simultaneously occupied in BCL-2, BCL-W, and BFL-1, suggesting a non-competitive mode of binding. In the MMP family proteins (MMP-3, 7, 9, and 13) examined, API and DTX competed for the S1’ pocket, suggesting a competitive binding mode. However, MCL-1 is an exception - where neither ligand bound to the inhibitor pocket or to any functional domains of the protein, except for a single hydrophilic and hydrogen bond at ARG52 of the BH3 domain, implying limited direct functional inhibition and raising the possibility of alternative, potentially allosteric, modulation if biological inhibition is observed. Overall, compared to API or DTX alone, simultaneous docking of API and DTX improves the binding affinities and may also be functionally relevant as it targets conserved residues of multiple domains. A recent paper from our lab also examined the binding of API to BCL-2 family (BCL-2, BCL-W, BCL-XL, BFL-1, BRAG-1, and MCL-1) proteins using CBDock2, followed by PLIP interaction analysis. Although slight differences in the predicted binding site of API were observed due to different docking algorithms, API was found to bind within or near the inhibitor site [ 32 ]. Multiple emerging studies employ MLSD to identify potential synergism between dual ligands. The MLSD of compounds including Apigenin, Hesperetin, and Niazimicin A produced the maximum binding affinity of − 14.96 kcal/mol in BCL-2 protein, according to one study on the potential of Moringa oleifera leaf phytochemicals [ 40 ]. Energy minimization was performed to assess the stability of the docked complexes. Across all eight proteins, total potential energy followed a consistent trend with P-A having the lowest energies, P-D having the highest energies, and P-AD having intermediate energy values. Exceptions to this trend include BCL-2 and MMP-7, where the combination complex had a higher energy compared to individual complexes, which could be attributed to steric clashes or torsional conflicts between the ligands. The intermediate energy observed in the combination complexes suggests that the presence of API may have caused conformational changes in the protein binding pockets and reduction of steric clashes, allowing for stable binding of DTX in its binding pocket [ 41 ]. Difference in results from our previous paper involving minimization with SwissPDB Viewer of anti-apoptotic proteins with API alone may be attributed to algorithmic variations as SwissPDB Viewer performs minimization only on the protein structure, whereas Chimera minimizes the entire protein with ligand [ 32 ]. Even in MD simulations, energy and temperature profiles observed in Fig. 5 remained stable throughout 10 ns simulations, with consistent negative potential energy values and well-maintained thermal equilibration, indicating that the complexes are energetically favorable and well-equilibrated. The RMSD varied across proteins with lower deviations seen in MCL-1 (4.25 Å) and MMP7 (4.21 Å), suggesting limited structural changes, intermediate deviations seen across BFL-1 (5.77 Å), BCL-2 (5.58 Å), MMP3 (5.34 Å), and MMP13 (6.72 Å), while MMP9 (7.46 Å) and BCL-W (8.76 Å) exhibited the largest deviation, likely reflecting intrinsic flexibility in structural elements, such as the S1’ pocket, which may facilitate ligand accommodation and specificity [ 42 ]. The relatively comparable energy-weighted RMSF values across most systems indicate similar dynamic behavior under the applied simulation conditions. These results are consistent with docking scores which showed lower energy values for the dual-ligand complex, PLIP interaction analysis which showed cooperative engagement of binding site residues, and ConSurf analysis, which showed that several of these residues are evolutionarily conserved. Overall, these findings suggest that API may act as a stabilizing cofactor, enhancing the binding efficiency of DTX through synergistic interactions across anti-apoptotic and migratory targets, thereby providing strong rationale with sufficient evidence to proceed with in-vitro validation. Following these in silico observations, in-vitro validation was conducted using HeLa cells to assess the combinatorial effect of API and DTX on cellular viability and migration potential. The combination of API with DTX for 24 hours led to a dose-dependent decrease in the viability of the HeLa cells, showing synergy compared to either treatment alone. As seen in Fig. 6 A and Fig. 6 B, individual treatment of DTX at 10 µM and of API at 5 µM showed a cell viability of 89% and 87%, respectively, but their combination showed a marked decrease in cell viability, with 76.3% viable cells. All four combinations of API and DTX showed synergistic effects as their combination index was calculated to be < 1 (Table 6 ); this is visually supported by the microscopy images that show that cells treated with the combination drug showed a higher number of cells having characteristic apoptotic features, such as detachment, nuclear condensation, and observable cell death as compared to individual drug-treated cells (Fig. 6 C). This is further supported by the results obtained from the PI assay that show that HeLa cells treated with the combinations of API and DTX showed a higher percentage of cells with nuclear condensation, nuclear blebbing, nuclear fragmentation, and apoptotic bodies, all indicative of late apoptosis. In contrast, cells that were administered with API or DTX individually had fewer cells with characteristics of late apoptosis. Similarly, studies on multiple cancer cell lines such as A549, Hep3B, SKOV-3, and HeLa cells have shown that the combinational use of apigenin with paclitaxel has synergistically improved apoptosis in these cells [ 43 ]. Another study revealed that apigenin and docetaxel together sensitize prostate cancer stem cells (PCSCs) to docetaxel and induces apoptosis in PCSCs [ 44 ]. The migratory potential of HeLa cells post-treatment was also evaluated by the scratch wound and transwell migration assay (Fig. 6 E, 6 F, 6 G, 6 H). In the scratch wound assay, while API alone showed modest, dose-dependent inhibition of migration (wound closure: ~32–80%), and DTX showed minimal wound closure (wound closure: ~1–10%), their combination impaired cell migration significantly, enhancing the wound width by inhibition of cell movement and apoptotic induction, with the strongest effect observed for D2A2, which produced a wound width change of -45.36%, highlighting a synergistic effect of API and DTX in inhibiting HeLa cell migration. A similar trend was observed in the transwell migration assay, where the migratory ability of cells towards a nutrient source starkly decreased when treated with the combination of API and DTX to as low as 6.4% as compared to API (~ 49–64%) or DTX (~ 30–40%) treatment alone, suggesting possible synergism. A similar effect on the migratory ability of Prostate CSCs was observed upon treating the cells with a combination of apigenin and docetaxel [ 44 ]. Therefore, the findings of the present study provide a dual computational and experimental basis to justify the concurrent use of API with DTX in cervical cancer. STRING interaction mapping showed that the selected proteins were biologically connected, while GO enrichment showed that these proteins are significantly associated with coordinated regulation of apoptosis evasion and activation of migration. These results, along with the minimal predicted DDI between API and DTX, helped shape the direction of the in-silico and in-vitro work. The enhanced cell death and the inhibition of cell migration observed upon combined treatment with API and DTX in the assays aligned well with the in-silico results of the proteins, which showed synergism for the combined ligands as compared to individual treatment. To our knowledge, this is among the first studies to combine MLSD with experimental validation to explore API—DTX synergy against apoptosis and migration associated targets in cervical cancer cells. While the present study is limited to in vitro assessments using a single cervical cancer cell line, lacks molecular pathway validation, may not fully capture tumor microenvironment complexity, and relies partly on computational predictions that may not completely reproduce biological conditions, these limitations provide clear directions for future research. Strategies such as nanoparticle-based delivery systems [ 34 ] may boost the bioavailability of API and improve tumor specificity, while sequential treatment [ 45 ] could further improve outcomes. Overall, the combination of natural chemopreventive agents with chemotherapeutic agents has great potential and needs to be explored further as it may enhance therapeutic efficacy, overcome chemoresistance, and reduce dose-dependent toxicity, thereby improving overall treatment outcomes. The result of such synergistic pairings may enable the development of a multifaceted strategy, thereby helping overcome the shortcomings of chemotherapy. The findings of this pilot study suggest that the combination of API with DTX has therapeutic potential in cervical cancer cells that should be further explored in-vitro and in-vivo to allow for detailed evaluation of their effects. Conclusion The present study demonstrates that API can synergistically potentiate the effect of chemotherapeutic agents such as DTX by significantly enhancing apoptosis and suppressing migration in cervical cancer cells. The integration of in-silico and in-vitro validation shows coordinated multi-targeting of apoptosis and migration proteins, providing a clear biological foundation for the observed synergistic effects. These findings highlight the potential of phytochemical-chemotherapeutic combinations as a strategy to enhance treatment effectiveness while simultaneously reducing associated toxicity. Although further in-vitro and in-vivo studies are required to fully establish clinical applicability, this study provides a foundation for advancing API-DTX combination therapy and delineates the importance of multi-targeted approaches to overcome the limitations of conventional chemotherapy. Declarations Funding Statement Authors are thankful to MAHE DUBAI Internal research grant (R&DP/MAHE DUBAI/RL- 01/2024) for financial support. Author contributions DB & LCD, were responsible for investigation, data curation, methodology, formal analysis, and preparation of the original draft. A.H. contributed to the conceptualization, methodology, funding acquisition, project administration, supervision, validation, and writing—review and editing. TNH & SH were involved in data curation and visualization. R.I.A, LAA & RR, participated in formal analysis, investigation, and resource management. Ethics Statement Not Applicable Competing interests The authors declare that there is no conflict of interest. The authors declare that there are no competing interests associated with the manuscript. Dual Publication This manuscript has not been published previously and is not under consideration for publication elsewhere. Data Availability All data generated or analyzed during this study are included in this published article and its supplementary information files. Protein–protein interaction data were derived from the STRING database (https://string-db.org/cgi/input?sessionId=bKimp7reqwhD&input_page_show_search=on ) using the following query parameters: protein name: BCL2, BCL2A1, BCL2L2, MCL1, MMP3, MMP7, MMP9, MMP13; organism: Homo sapiens. Protein structures used for molecular docking were obtained from the RCSB Protein Data Bank (https://www.rcsb.org/ ) with the following accession IDs: 6QGG (BCL-2), 3I1H (BFL-1), 1ZY3 (BCL-W), 5FC4 (MCL-1), 4G9L (MMP-3), 7WXX (MMP-7), 1GKC (MMP-9), and 4JP4 (MMP-13). Ligand structures were retrieved from PubChem (https://pubchem.ncbi.nlm.nih.gov) with the following compound identifiers (CIDs): [Apigenin: 5280443; Docetaxel: 148124]. Homology models were generated using SWISS-MODEL (https://swissmodel.expasy.org/). Protein–ligand interactions were analyzed using PLIP (https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index) based on the docked complex structures generated in this study. Evolutionary conservation analysis was performed using ConSurf ( https://consurf.tau.ac.il/ ) and run Google Colab (https://colab.research.google.com/drive/1PhDXX7k12oUsV6T_xkXC3Rm9R99e7tHz) based on the protein structures described above Drug–drug interaction and related predictions were performed using the Way2Drug web server ( https://way2drug.com/ddi/ ) using SMILES representations of the compounds analyzed in this study. All datasets are publicly available from the respective databases, and no additional datasets were generated or analyzed beyond those included in this article. Clinical Trial Number Not applicable Consent to publish Not applicable Consent to participate Not applicable References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229–63. https://doi.org/10.3322/caac.21834 . 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Li H, Xiao H, Lin L, Jou D, Kumari V, Lin J, Li C. Drug Design Targeting Protein–Protein Interactions (PPIs) Using Multiple Ligand Simultaneous Docking (MLSD) and Drug Repositioning: Discovery of Raloxifene and Bazedoxifene as Novel Inhibitors of IL-6/GP130 Interface. J Med Chem. 2014;57:632–41. https://doi.org/10.1021/jm401144z . Saha A, Desai BMA, Biswas P. (2024) Multi-Ligand Simultaneous Docking Analysis of Moringa Oleifera Phytochemicals Reveals Enhanced BCL-2 Inhibition via Synergistic Action. IBIOMED 2024 - Proceedings of the 5th International Conference on Biomedical Engineering 2024 51–56. https://doi.org/10.1109/IBIOMED62485.2024.10875829 Weikl TR, Paul F. Conformational selection in protein binding and function. Protein Sci. 2014;23:1508. https://doi.org/10.1002/pro.2539 . Fabre B, Ramos A, De Pascual-Teresa B. Targeting Matrix Metalloproteinases: Exploring the Dynamics of the S1′ Pocket in the Design of Selective, Small Molecule Inhibitors. J Med Chem. 2014;57:10205–19. https://doi.org/10.1021/jm500505f . Xu Y, Xin Y, Diao Y, Lu C, Fu J, Luo L, Yin Z. Synergistic Effects of Apigenin and Paclitaxel on Apoptosis of Cancer Cells. PLoS ONE. 2011;6:e29169. https://doi.org/10.1371/journal.pone.0029169 . Erdogan S, Doganlar O, Doganlar ZB, Serttas R, Turkekul K, Dibirdik I, Bilir A. The flavonoid apigenin reduces prostate cancer CD44 + stem cell survival and migration through PI3K/Akt/NF-κB signaling. Life Sci. 2016;162:77–86. https://doi.org/10.1016/j.lfs.2016.08.019 . Sharma S, Cwiklinski K, Mahajan SD, Schwartz SA, Aalinkeel R. Combination Modality Using Quercetin to Enhance the Efficacy of Docetaxel in Prostate Cancer Cells. Cancers (Basel). 2023;15:902. https://doi.org/10.3390/cancers15030902 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor invited by journal 20 Apr, 2026 Editor assigned by journal 17 Apr, 2026 Submission checks completed at journal 15 Apr, 2026 First submitted to journal 15 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9300836","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631951381,"identity":"b8200349-037b-43f5-a397-cb0b9da906d0","order_by":0,"name":"Deepika Bhagavatula","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Deepika","middleName":"","lastName":"Bhagavatula","suffix":""},{"id":631951385,"identity":"defbfa7f-9f78-4cac-8881-8c2e8d980855","order_by":1,"name":"Lynn Clifford Dsouza","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Lynn","middleName":"Clifford","lastName":"Dsouza","suffix":""},{"id":631951391,"identity":"3409224a-79ce-48cc-8666-54e940b1c211","order_by":2,"name":"Reham I. Alagal","email":"","orcid":"","institution":"Princess Nourah Bint Abdulrahman University","correspondingAuthor":false,"prefix":"","firstName":"Reham","middleName":"I.","lastName":"Alagal","suffix":""},{"id":631951399,"identity":"14d45e06-e02b-4c2a-9804-4931fbf98af6","order_by":3,"name":"Asiya Nazir","email":"","orcid":"","institution":"Abu Dhabi University","correspondingAuthor":false,"prefix":"","firstName":"Asiya","middleName":"","lastName":"Nazir","suffix":""},{"id":631951405,"identity":"3112fe62-fad1-43c9-8853-2b370d6fc750","order_by":4,"name":"Ritu Raina","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Ritu","middleName":"","lastName":"Raina","suffix":""},{"id":631951412,"identity":"3a45ce66-6bd8-4ee6-b05b-345eb8e8e0da","order_by":5,"name":"Shafiul Haque","email":"","orcid":"","institution":"Jazan University","correspondingAuthor":false,"prefix":"","firstName":"Shafiul","middleName":"","lastName":"Haque","suffix":""},{"id":631951419,"identity":"7c3ddf1e-614b-4105-bbf6-fa62193fcbf3","order_by":6,"name":"Tarique Noorul Hasan","email":"","orcid":"","institution":"Pure Health","correspondingAuthor":false,"prefix":"","firstName":"Tarique","middleName":"Noorul","lastName":"Hasan","suffix":""},{"id":631951427,"identity":"fffcd33b-05cf-4e51-b743-30c7b6356d5d","order_by":7,"name":"Arif Hussain","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYDCCAyDCQC4BzElgsGFgkIDKsOHXYgzTkkasFgaoFgaGwwgtuADf7QOMnysKDPIMzh9/+OHhjvOJ/bObDz5gqLFh4JNuwKpF8lwCs+QZA4Nigxs5xhKJZ24nzrhzLNmA4VgaA5vMAaxaDM4AtTUY/EnccIOHQSKx7XZiw40cMwnGhsMMbBIJuLQw/2wwMEjccP744x+JbecS5xOhhU0SrOVAghnQlgNA6whokTzD2GYJ0jITqNIisS3ZeOONtGSDhGNpPLi08J1hPnyz4Y9BYh/QYTd/ttnJzruRfPDBhxobOfkZ2LUwMDA2oHAdwVygYh4c6jGBPdEqR8EoGAWjYMQAAKkvYm1qIDo+AAAAAElFTkSuQmCC","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Arif","middleName":"","lastName":"Hussain","suffix":""}],"badges":[],"createdAt":"2026-04-02 09:09:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9300836/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9300836/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108185382,"identity":"e5caa958-2218-42df-a091-ef78837f27d0","added_by":"auto","created_at":"2026-04-30 09:05:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137737,"visible":true,"origin":"","legend":"\u003cp\u003eTwo-dimensional chemical structure of (A) Apigenin (API); (B) Docetaxel (DTX); (C) PASS-based prediction of drug–drug interaction (DDI) severity class according to ORCA classification for the API–DTX combination; (D) PASS-based prediction of cytochrome P.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9300836/v1/86b3d73a91b096d88c3acd57.png"},{"id":108185392,"identity":"f4216df9-dcb5-4af1-9f95-639b4565548c","added_by":"auto","created_at":"2026-04-30 09:05:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":446607,"visible":true,"origin":"","legend":"\u003cp\u003eProtein–protein interaction (PPI) network of (A) apoptosis related proteins; (B) migration related proteins; (C) the combined protein set. The network was generated using STRING (confidence score ≥ 0.4, PPI enrichment p-value: 5.43e-11). Nodes represent proteins, and edges indicate predicted or known interactions.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9300836/v1/c43289c5dd7e79d61fe70421.png"},{"id":108185380,"identity":"95d23848-51ce-489a-936f-1c76d98252ce","added_by":"auto","created_at":"2026-04-30 09:05:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":669468,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of best binding poses of API (green) and DTX (yellow), docked individually and in combination, across eight POI. Pink, green, and blue regions correspond to binding sites of the known inhibitors, API, and DTX respectively\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9300836/v1/45d89698aacd751fd4e92447.png"},{"id":108185378,"identity":"5a888be5-614b-446c-88bb-144d6ee7eaaf","added_by":"auto","created_at":"2026-04-30 09:05:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":764176,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of bound amino acids to known inhibitors, API, DTX, and a combination of API and DTX. Yellow: ligand; Blue: Protein; Dotted line: Hydrophobic bond between protein and ligand; Blue line: Hydrogen bond between protein and ligand\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9300836/v1/19700e9bb4eb4fa774c433ae.png"},{"id":108490964,"identity":"67c27d0a-db1a-4f9d-a6c4-1ef1e45c8401","added_by":"auto","created_at":"2026-05-05 09:50:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":663738,"visible":true,"origin":"","legend":"\u003cp\u003eEnergy and Temperature graphs of P-AD complexes.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9300836/v1/e60831dd372b0fddfa8476ac.png"},{"id":108491359,"identity":"a3b59d10-c7a2-48eb-90cc-25aa44d7f390","added_by":"auto","created_at":"2026-05-05 09:53:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":891766,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of (\u003cstrong\u003eA\u003c/strong\u003e) DTX and (\u003cstrong\u003eB\u003c/strong\u003e) API–DTX combination treatment on HeLa cell viability after 24 h. (\u003cstrong\u003eC\u003c/strong\u003e) Representative morphological changes in HeLa cells following 24-hour treatment with API and DTX individually and in combination (A1: 2.5 µM; A2: 5 µM; D1: 10 µM; D2: 15 µM). (\u003cstrong\u003eD\u003c/strong\u003e) Changes in nuclear morphology of treated HeLa cells compared with the untreated controls. Yellow - large and prominent nuclei; pink - nuclear fragmentation; green - blebbing; white - apoptotic bodies. (\u003cstrong\u003eE\u003c/strong\u003e) Quantitative analysis of wound closure. Percentage wound closure was calculated relative to control; negative values indicate wound widening and strong inhibition of migration. (\u003cstrong\u003eF\u003c/strong\u003e) Representative images of scratch wound assay showing the effects of API, DTX, and their combinations (A1D1–A2D2) on HeLa cell migration after 24 h. (\u003cstrong\u003eG\u003c/strong\u003e) Quantitative analysis of migrated cells treated with API (A1, A2), DTX (D1, D2), and their combinations (A1D1–A2D2). (\u003cstrong\u003eH\u003c/strong\u003e) Representative images of Transwell migration assay showing decreased migration of HeLa cells following treatment with API, DTX, and their combinations after 24 h. Arrows indicate cells that successfully migrated through the membrane. \u0026nbsp;Data represents mean ± SD (n = 3). Statistical significance was determined by one-way ANOVA followed by Dunnett’s multiple comparisons test (*p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9300836/v1/6679e8e67ce4b0f5f5d1ce96.png"},{"id":108804486,"identity":"5dca7f2b-a658-4787-8ecd-c479ff9867b5","added_by":"auto","created_at":"2026-05-08 15:20:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3896572,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9300836/v1/2f52a19c-be00-4b39-b47d-2e6df5d6c8f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Therapeutic Potential of Apigenin in Combination with Docetaxel on Human Cervical Cancer Cells – In-Silico and In-Vitro Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to GLOBOCAN estimates, roughly 19.9\u0026nbsp;million new cases and 10\u0026nbsp;million mortalities were reported globally in 2022, with cervical cancer remaining the fourth leading cause of death in women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Current treatments for this malignancy often involve surgery, radiotherapy, and chemotherapy; with chemotherapy being one of the most widely used approaches [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Many chemotherapeutic agents exert their anticancer effects by inducing programmed cell death pathways, particularly apoptosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The execution of apoptosis is tightly regulated by intracellular signaling networks, among which the BCL-2 family comprising of both pro- and anti-apoptotic members plays a pivotal role in regulating mitochondrial outer membrane permeabilization (MOMP) and cell survival. The disruption of this fine balance towards anti-apoptotic proteins such as BCL-2, BCL-W, MCL-1, and BFL-1 is associated with tumor formation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The anti-apoptotic proteins primarily have 4 conserved BH domains (BH1-BH4), where BH1-BH3 regions form a hydrophobic group stabilized by the BH4 domain, which accommodates the binding and subsequent inhibition of pro-apoptotic proteins BAK and BAX, thereby preventing MOMP and apoptosis. Hence, BH3- mimetic drugs such as navitoclax (ABT-737) and venetoclax (ABT-199) have been developed to target this hydrophobic groove, displacing pro-apoptotic proteins and restoring apoptotic signaling [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to evading apoptosis, tumor progression also depends on the ability of cancer cells to invade surrounding tissues by remodeling the extracellular matrix (ECM), a process largely mediated by matrix metalloproteinases (MMPs) and normally restrained by tissue inhibitors of metalloproteinases (TIMPs). Overexpression of MMPs is linked to cancer progression as they are involved in ECM component degradation and activation of signaling molecules that contribute to cancer cell invasion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Given their role in substrate recognition, structural features within the catalytic cleft of MMP\u0026rsquo;s have emerged as key targets for inhibition. Particularly, the S1\u0026rsquo; pocket within the catalytic cleft of MMP\u0026rsquo;s is promising, as its relatively buried nature and variability in length and amino acid composition enable selective inhibition of MMP\u0026rsquo;s. Consequently, agents such as batimastat and marimastat were developed to inhibit MMP activity and limit cancer cell migration [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these advances, the \u0026lsquo;one target, one drug\u0026rsquo; approach used by cancer chemotherapies that rely on a single molecular pathway often faces limitations such as development of resistance, toxicity, and limited efficacy due to the heterogenous nature of cancer. Therefore, combination therapy targeting multiple molecular mechanisms has been the focus of multiple studies and is thought to be more likely to achieve better treatment outcomes while allowing for lowered drug dosage and associated toxicities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Cancer chemoprevention involving the use of natural, synthetic, or biological agents to prevent, delay, or reverse carcinogenesis has emerged as a promising complementary strategy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Chemopreventive agents such as nonsteroidal anti-inflammatory drugs (NSAIDs), deltanoids, retinoids, and phytochemicals may enhance the efficacy of chemotherapy when used in combination regimens [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Flavones represent an attractive class of phytochemicals, many of which combine anticancer activity with low toxicity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Apigenin, a multifunctional flavone, possesses anti-carcinogenic, antioxidant, anti-mutagenic, anti-inflammatory, anti-viral, and anti-bacterial properties [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Apigenin is also known to suppress cancer cell migration and invasion through suppression of matrix metalloproteinases (MMPs) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Resistance to apoptosis, and activation of migration are major contributors to chemoresistance, making anti-apoptotic BCL-2 family proteins and MMPs attractive therapeutic targets [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDocetaxel, developed by Sanofi and approved by the FDA in 1996 as \u0026lsquo;Taxotere\u0026rsquo; is a second-generation antineoplastic agent of the taxoid family used to treat breast, gastric, prostate, and lung cancer. It targets mitosis at the G2/M phase, modulates phosphorylation and inactivation of Bcl-2 protein family, and interferes with expression of cyclin - cyclin-dependent kinases (CDKs), ultimately leading to apoptosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Numerous studies delineate the synergistic effect of chemopreventives such as flavonoids in combination with docetaxel, where they act therapeutically by modulating pathways that promote chemoresistance of the anti-cancer drug, while increasing the bioavailability and stability of the drug [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For instance, in a study conducted by Lim et al., combining docetaxel with chrysin, a honey polyphenol, had synergistic effects on A549 human NSCLC cells where the combined treatment improved apoptosis and antiproliferation in cancer cells [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similarly, another study by Bernasinska-Slomczewska et al. concluded that baicalein and baicalin synergistically enhanced the cytotoxic, genotoxic, and proapoptotic activity of docetaxel in breast cancer cells, compared to individual agents alone [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Treatment with apigenin has also shown sensitization of prostate cancer cells towards docetaxel treatment by targeting ABCB1 expression [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These findings suggest that flavone-based adjuvants may enhance the therapeutic efficacy of docetaxel chemotherapy by simultaneously modulating apoptotic signaling and tumor cell migration. However, despite growing evidence supporting flavonoid\u0026ndash;taxane combinations, the interaction between apigenin and docetaxel in cervical cancer remains insufficiently explored. Therefore, the present study investigates the potential effects of apigenin in combination with docetaxel in cervical cancer, providing insights on the collective activity on the combination treatment strategy.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003eSelection of Target Proteins for\u003c/b\u003e \u003cb\u003eIn-Silico\u003c/b\u003e \u003cb\u003eAnalyses\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFor this study, apoptosis-related and migration-related proteins were selected as proteins of interest (POI) based on their anti-apoptotic and migratory activity in the regulation of cancer. A total of 8 key proteins \u0026ndash; BCL-2, BCL-w, BFL-1, and MCL-1, MMP-3, MMP-7, MMP-9, and MMP-13 were shortlisted based on literature surveys and availability in the RCSB Protein Data Bank [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Protein-protein interaction (PPI) networks (medium confidence, cutoff 0.4) were generated for (i) apoptosis related proteins, (ii) migration related proteins, and (iii) the combined protein set using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) Database ver. 12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To confirm the functional relevance of the eight selected proteins, Gene Ontology (GO) enrichment analysis on biological processes was conducted to highlight cellular roles. False discovery rate (FDR) correction (medium stringency, 5%) was applied to both analyses.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProtein and Ligand Structure Retrieval and Preparation\u003c/h2\u003e \u003cp\u003eHigh resolution (\u0026lt;\u0026thinsp;2.5\u0026Aring;) 3D structures of selected protein co-crystallized with known inhibitors were retrieved from the RCSB Protein Data Bank (PDB) website and downloaded as .pdb files. The structures were individually visualized using UCSF Chimera 1.19 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and cleaned by removing structurally irrelevant heteroatoms, water molecules, and native ligands. Native ligands were saved separately as a .pdb file to validate docking studies. Missing residues were restored using the SWISS-MODEL server [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Two ligands of interest (LOI), Apigenin (API) and Docetaxel (DTX) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), were selected based on prior reports on apoptotic modulation. The 3D structure of ligand API (PubChem ID: 5280443) and 2D structure of ligand DTX (PubChem ID: 148124) were retrieved from the PubChem website [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and downloaded as .sdf files. The 2D structure of DTX was converted to a 3D structure and downloaded as a .pdb file using MarvinSketch [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Open Babel [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] was used to convert the .sdf file of API to a .pdb file. Both structures were then visualized in UCSF Chimera 1.19 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The proteins and ligands were then prepared for docking and were suitably converted to the AutoDock Vina (ADV) compatible .pdbqt format using the ADV command line [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDrug-drug Interaction Prediction\u003c/h3\u003e\n\u003cp\u003eThe pharmacological interactions between LOIs were evaluated using the Prediction of Activity Spectra for Substances (PASS) method available on Way2Drug Drug interaction prediction tool, available for free at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://way2drug.com/ddi/\u003c/span\u003e\u003cspan address=\"https://way2drug.com/ddi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Molecular structures of both compounds were submitted to predict potential Drug- Drug Interaction (DDI) mechanisms, CYP450 enzyme involvement, DDI severity, and adverse effects. Only mechanisms with probability to be active (Pa) greater than probability to be inactive (Pi) were considered relevant [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eIndividual Docking and Multiple Ligand Simultaneous Docking\u003c/h3\u003e\n\u003cp\u003eAPI and DTX were individually docked to all eight target proteins using AutoDock Vina [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This was followed by Multiple Ligand Simultaneous Docking (MLSD), where both LOI\u0026rsquo;s were docked simultaneously to assess the co-binding potential of both ligands. The conformation grid was set to maximize coverage to facilitate blind docking. The binding affinities (expressed as Vina Scores) were recorded for all the runs, and the docked structure with the lowest binding affinity was selected for further analyses and downloaded as a .pdb file.\u003c/p\u003e\n\u003ch3\u003eMolecular Interaction and Binding Site Conservation Analysis\u003c/h3\u003e\n\u003cp\u003eProtein-ligand interactions of docked complexes and native inhibitors were evaluated using the PLIP (Protein Ligand Interaction Profiler) server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plip-tool.biotec.tu-dresden.de/plip-web/plip/index\u003c/span\u003e\u003cspan address=\"https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. .pdb files were uploaded to identify interacting residues, hydrogen, and hydrophobic contacts within the binding pockets, and comparative analyses were performed to assess binding site similarity of LOI to the protein inhibitor. Interacting residues was further evaluated using ConSurf server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://consurf.tau.ac.il/\u003c/span\u003e\u003cspan address=\"https://consurf.tau.ac.il/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] which was used to identify conserved residues within the inhibitor binding region and determine whether LOI interactions occurred at evolutionarily conserved, functionally significant sites.\u003c/p\u003e\n\u003ch3\u003eEnergy Minimization Study\u003c/h3\u003e\n\u003cp\u003eAll receptor-ligand complexes (protein-API, protein-DTX, and protein-API\u0026thinsp;+\u0026thinsp;DTX) were subjected to energy minimization using Chimera 1.16. Each structure was minimized by steepest descent and conjugate gradient minimization respectively. Final minimized energy values (in kJ/mol) were extracted from Chimera\u0026rsquo;s reply log.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Simulation Study\u003c/h2\u003e \u003cp\u003eMolecular Dynamics (MD) simulations of the MLSD complexes were conducted using MyPresto ver. 5.0 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] under the following conditions: (i) The global energy minimization was carried out with a loop limit of 5,000 using generalized Born method; (ii) The global dynamics was executed with a loop limit of 5,000,000 at a constant temperature of 300 K for 10 ns. All other parameters were maintained at default settings. The energy and temperature graphs, along with the RMSD and RMSF, were recorded for analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell culture and Drug preparation\u003c/h3\u003e\n\u003cp\u003eThis research makes use of human cervical carcinoma HeLa cell line purchased from Addexbio (Cat. No.: C0008001) cultured in DMEM (PAN Biotech, Germany) supplemented with 10% FBS (Sigma, USA), 100X Pen-strep (Sigma, USA), and amphotericin (Sigma, USA) in a saturated atmosphere of 5% CO\u003csub\u003e2\u003c/sub\u003e at 37\u0026deg;C. DTX was acquired from RTU Technologies. Dilutions to the required drug concentrations were made directly from DTX in its original solution in injection form with a molarity of 23.2 mM. API was obtained from MedChem express (Cat. No. HY-N1201, CAS No. 520\u0026ndash;36\u0026ndash;5). API in its powdered form was dissolved in DMSO at a concentration of 5 mg/250 \u0026micro;L to obtain a concentration of 74 mM. The stock was diluted to a working concentration of 1 \u0026micro;M in complete media and was used to prepare various concentrations used for treatment.\u003c/p\u003e\n\u003ch3\u003eCell Viability, Combination Index, and Nuclear Morphology Analysis\u003c/h3\u003e\n\u003cp\u003eThe cell viability and drug interaction effects were evaluated using the MTT assay followed by combination index (CI) analysis. HeLa cells were seeded into a 96-well plate (10 x 10\u003csup\u003e3\u003c/sup\u003e cells/well) and allowed to reach\u0026thinsp;~\u0026thinsp;80% confluency before treatment with API (1-175 \u0026micro;M), DTX (1-110 \u0026micro;M), and their combinations. Post 24-hour treatment, the MTT (0.5mg/mL) assay was performed, and the absorbance at 570 nm was measured using an ELISA reader (BioTek, USA). Experiment was performed in triplicate, and percentage cell viability was calculated as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\%\\:Cell\\:viability=\\left(\\frac{Average\\:OD\\:of\\:individual\\:test\\:group}{Average\\:OD\\:of\\:control\\:group}\\right)\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe combination index (CI) was calculated using the Chou\u0026ndash;Talalay method to determine the nature of the drug interactions [109]:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Combination\\:Index\\:\\left(CI\\right)=\\:\\frac{C{A}_{x}}{I{C}_{x}A}+\\:\\frac{C{B}_{x}}{I{C}_{x}B}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere CA\u003csub\u003ex\u003c/sub\u003e and CB\u003csub\u003ex\u003c/sub\u003e represent the concentrations of drug A and B in combination achieving X% effect, and IC\u003csub\u003ex\u003c/sub\u003eA and IC\u003csub\u003ex\u003c/sub\u003eB are the individual drug concentration of A and B producing the same effect. CI values of \u0026lt;\u0026thinsp;1, = 1 and \u0026gt;\u0026thinsp;1 indicate synergistic, additivity, and antagonistic interactions, respectively.\u003c/p\u003e \u003cp\u003eFor nuclear morphology analysis, 3 x 10\u003csup\u003e3\u003c/sup\u003e cells/well were plated onto a 24 well plate, treated under the same conditions, and compared to the untreated control. Following 24 hours of treatment, the media was decanted, and the cells were washed with 1X PBS and fixed using 70% ice-cold methanol for 20 minutes, followed by staining with PI for 1\u0026ndash;2 minutes. Excess stain was then washed using 1X PBS and the cells were visualized and imaged using a fluorescent microscope (Euromex, Holland) with Imagefocus software.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell Migration Analysis\u003c/h2\u003e \u003cp\u003eThe scratch wound and transwell migration assay was used to determine the individual and combined effect of API and DTX on the migration of cancer cells. For the wound healing assay, 5 x 10\u003csup\u003e3\u003c/sup\u003e cells/well were seeded into a 12-well plate and cultured until confluent. A vertical cell free line was created using a 10 \u0026micro;L pipette tip, followed by treatment with individual drugs or their combinations; untreated cells served as controls. Images were captured at 0 and 24 hours using an inverted microscope (Olympus Corporation). After measuring the wound width at 24 hours, the % wound closure/widening was calculated as:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\%\\:Cell\\:wound\\:closure=\\:\\frac{(Test-Control)}{Control}\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFor the transwell assay, 5 x 10\u003csup\u003e3\u003c/sup\u003e cells/well were seeded on the insert (8\u0026micro;m pore size, BD Biosciences, Franklin Lakes, NJ, USA) with 1 mL FBS-free media and placed in a 24-well plate containing complete media as a chemoattractant. After 24 hours, migrated cells were fixed with absolute methanol, stained with 0.1% crystal violet, and visualized under 10X field (Olympus Corporation) to compare and capture cell migration between treated and control. The experiment was repeated thrice, and the number of migrated cells was counted using the ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll the data are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of at least three experiments. The data was statistically evaluated using one-way ANOVA followed by Dunnett\u0026rsquo;s multiple comparisons test. Significant differences were established at P\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eApigenin and Docetaxel Combination Predicts Low DDI Risk and Predictable CYP450 Interactions\u003c/h2\u003e\n\u003cp\u003eThe DDI results of API and DTX showed that according to ORCA classification [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e] the combination was predicted to belong to DDI Severity Class II (-0.043, PASS analysis) as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC, and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD, corresponding to minimal interaction risk. Moreover, the drug combination is predicted to be unlikely to cause adverse effects such as hypertension, bradycardia, and arrythmia, among others (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). P450 mediated DDI analysis predicted that CYP3A4 and CYP2C8 may be the most likely enzymes for mediating interactions, as compared to other CYP isoforms (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). Analysis of overlapping CYP450 activities predicted that DTX may act predominantly as a CYP3A4 substrate, while API might be a moderate inducer of CYP3A4 while showing minor inducer/inhibitor activity toward other CYPs. Interestingly, predicted DDI mechanisms (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) suggest a potential pharmacokinetic effect, as predicted by its effect on intra/extra-hepatic metabolism (Pa\u0026thinsp;=\u0026thinsp;0.893) and cellular transport (Pa\u0026thinsp;=\u0026thinsp;0.583) rather than a pharmacodynamic effect.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGene Ontology (GO) biological process enrichment of apoptosis and migration related proteins. Statistically significant enrichment was defined as FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e#Term ID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTerm Description\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eObserved Gene Count\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBackground Gene Count\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStrength\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSignal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFalse Discovery Rate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMatching Proteins in Network (Labels)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0022617\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExtracellular matrix disassembly\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.59E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMP7, MMP13, MMP3, MMP9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0030574\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCollagen catabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.59E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMP7, MMP13, MMP3, MMP9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0097192\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExtrinsic apoptotic signaling pathway in absence of ligand\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.59E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, BCL2A1, MCL1, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:1904645\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse to amyloid-beta\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.59E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, MMP13, MMP3, MMP9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0008630\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntrinsic apoptotic signaling pathway in response to DNA damage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.71E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, BCL2A1, MCL1, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:2001243\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative regulation of intrinsic apoptotic signaling pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, MCL1, MMP9, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0010941\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegulation of cell death\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, BCL2A1, MMP3, MCL1, MMP9, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0043066\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative regulation of apoptotic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e891\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, BCL2A1, MCL1, MMP9, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0071492\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCellular response to UV-A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0080\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMP3, MMP9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0010243\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse to organonitrogen compound\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e963\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, MMP13, MMP3, MMP9, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0006915\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eApoptotic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, BCL2A1, MCL1, MMP9, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0009411\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse to UV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMP3, MMP9, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:2000811\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative regulation of anoikis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMCL1, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:1903209\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive regulation of oxidative stress-induced cell death\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMP3, MCL1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0150077\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegulation of neuroinflammatory response\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMP3, MMP9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:2001240\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative regulation of extrinsic apoptotic signaling pathway in absence of ligand\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0427\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMCL1, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eEnrichment Analysis Supports Coordinated Involvement of Survival and Migration Pathways\u003c/h2\u003e\n\u003cp\u003eThe STRING database analysis of PPI of anti-apoptotic and migration proteins showed a dense interaction among BCL-2 family proteins and MMP family proteins. The combined network revealed two distinct clusters corresponding to each protein family, with limited cross-family interactions indicating that these proteins are involved in separate but complementary cancer processes rather than directly interacting with each other (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Gene Ontology (GO) enrichment analysis within STRING was used to analyze the biological roles of the selected proteins; results showed statistically significant overrepresentation of processes such as Extracellular matrix disassembly (GO:0022617) and Collagen catabolic process (GO:0030574) among others all of which are associated with proteins involved in tumor migration and invasion such as the MMP3, MMP7, MMP9, and MMP13. Simultaneously, results also showed significant enrichment of apoptosis related roles such as Extrinsic apoptotic signaling pathway in absence of ligand (GO:0097192), Intrinsic apoptotic signaling pathway in response to DNA damage (GO:0008630), and Negative regulation of intrinsic apoptotic signaling pathway (GO:2001243) thereby supporting the involvement of proteins like BCL2, BCL2L2, BCL2A1, and MCL-1 in cancer cell survival (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings show that the selected protein set is significantly associated with coordinated regulation of apoptosis evasion and activation of migration; two hallmark processes driving cancer progression.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eGene Ontology (GO) biological process enrichment of apoptosis and migration related proteins. Statistically significant enrichment was defined as FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e#Term ID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTerm Description\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eObserved Gene Count\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBackground Gene Count\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStrength\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSignal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFalse Discovery Rate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMatching Proteins in Network (Labels)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0022617\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExtracellular matrix disassembly\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.59E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMP7, MMP13, MMP3, MMP9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0030574\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCollagen catabolic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.59E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMP7, MMP13, MMP3, MMP9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0097192\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExtrinsic apoptotic signaling pathway in absence of ligand\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.59E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, BCL2A1, MCL1, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:1904645\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse to amyloid-beta\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.59E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, MMP13, MMP3, MMP9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0008630\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntrinsic apoptotic signaling pathway in response to DNA damage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.71E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, BCL2A1, MCL1, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:2001243\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative regulation of intrinsic apoptotic signaling pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, MCL1, MMP9, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0010941\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegulation of cell death\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, BCL2A1, MMP3, MCL1, MMP9, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0043066\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative regulation of apoptotic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e891\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0075\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, BCL2A1, MCL1, MMP9, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0071492\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCellular response to UV-A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0080\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMP3, MMP9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0010243\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse to organonitrogen compound\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e963\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, MMP13, MMP3, MMP9, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0006915\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eApoptotic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBCL2L2, BCL2A1, MCL1, MMP9, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0009411\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponse to UV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMP3, MMP9, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:2000811\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative regulation of anoikis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMCL1, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:1903209\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive regulation of oxidative stress-induced cell death\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMP3, MCL1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:0150077\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegulation of neuroinflammatory response\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMMP3, MMP9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO:2001240\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative regulation of extrinsic apoptotic signaling pathway in absence of ligand\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0427\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMCL1, BCL2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003eSimultaneous Docking of Apigenin and Docetaxel Exhibit Enhanced Binding Affinity Compared to Individual Ligand Docking\u003c/h2\u003e\n\u003cp\u003eMolecular docking was used to study the individual and simultaneous ligand interactions of API and DTX with the anti-apoptotic and migratory proteins and predict their binding positions. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). API, when individually docked exhibited binding affinities ranging from \u0026minus;\u0026thinsp;6.68 kcal/mol to -9.51 kcal/mol and bound near or within the known inhibitor cavity. In contrast, DTX bound away from the inhibitor site in proteins such as BCL-2, MCL-1, and BFL-1, but showed a slightly lower Vina Score compared to API, ranging from \u0026minus;\u0026thinsp;6.44 kcal/mol to -8.41 kcal/mol.\u003c/p\u003e\n\u003cp\u003eIn MLSD experiments, API and DTX exhibited improved binding affinities in all proteins ranging from \u0026minus;\u0026thinsp;9.6 kcal/mol to -12.74 kcal/mol when compared to individual ligand docking. API retained its binding site within the known-inhibitor pocket in most proteins like when API was docked individually, except in MCL-1, where the addition of DTX seems to have altered the protein in a way that none of the ligands bind to the inhibitor site. Interestingly, API and DTX both retained their initial binding domains when docked together in BCL-W, BCL-2, and MMP-3, thereby enhancing engagement and possible synergistic inhibition.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMolecular docking parameters, docking scores of API, DTX, and their combinations.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eProtein/ PDB ID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGrid size\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGrid Center\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eEnergy/Vina Score (kcal/mol)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAPI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDTX\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAPI\u0026thinsp;+\u0026thinsp;DTX\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBCL-2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e6QGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45, 45, 45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-16.81, 8.26, 4.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-7.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-7.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-10.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBFL-1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3I1H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35, 41, 40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.55, -9.26, 5.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-6.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-7.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-9.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBCL-W\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e7LH7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42, 44, 43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.90, 1.00, -0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-8.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-9.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-12.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMCL-1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e5FC4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40, 40, 40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.56, 10.22, 11.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-7.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-7.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-10.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMMP3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e4G9L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37, 34, 37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.05, 75.84, 103.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-9.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-8.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-12.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMMP7\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e7WXX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35, 40, 32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-30.71, -13.96, 1.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-7.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-6.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-11.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMMP-9\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1GKC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31, 34, 33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.47, 22.2, 41.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-9.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-8.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-11.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMMP13\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e4JP4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35, 33, 39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.54, 4.05, 16.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-9.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-7.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-11.38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ePLIP and ConSurf Analysis Reveal Key Interactions and Conserved Binding Regions for Apigenin and Docetaxel in Selected Proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePLIP and ConSurf were used post docking to analyze the type and functionality of protein-ligand interactions formed in docked complexes of native inhibitor ligand, API, DTX, and their combination (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). From Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, it can be observed that API individually binds to residues like that of the known inhibitor forming key hydrophilic (3\u0026ndash;5) and hydrogen bonds (1\u0026ndash;4). In contrast, DTX interacts with a higher number of residues by forming multiple hydrophilic (3\u0026ndash;6) and hydrogen bonds (3\u0026ndash;7) due to its larger structure; however, unlike API, it only seems to target proteins such as BCL-W, MMP3, MMP7, and MMP-9 at its inhibitor site.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of binding site amino acids of protein with known inhibitors, API, DTX, and the combination of API and DTX. Bold: residues shared with known inhibitors; Yellow: conserved residues common to known inhibitors; Blue: conserved residues unique to API or DTX; Green: conserved residues unique to the API\u0026ndash;DTX combination\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProtein/ PDB ID\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eType of bond\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAmino Acids involved in binding site of proteins with native inhibitor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAmino Acids involved in binding site of protein with API\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAmino Acids involved in binding site of protein with DTX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAmino Acids involved in binding site of protein with (API\u0026thinsp;+\u0026thinsp;DTX)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQty.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidues\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQty.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidues\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQty.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidues\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQty.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidues\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBCL-2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6QGG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrophobic bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePHE56, TYR60, MET67, VAL85, LEU89, ARG98, VAL100, PHE105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePHE56\u003c/strong\u003e, \u003cstrong\u003eTYR60\u003c/strong\u003e, \u003cstrong\u003eARG98\u003c/strong\u003e, \u003cstrong\u003eVAL100\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eARG6, GLU31, ALA36, ILE41, GLN142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eARG6, GLU7, MET10, LYS11, GLU23, ALA26, VAL29, ALA36, \u003cstrong\u003ePHE56\u003c/strong\u003e, \u003cstrong\u003eTYR60\u003c/strong\u003e, \u003cstrong\u003eARG98\u003c/strong\u003e, \u003cstrong\u003eVAL100\u003c/strong\u003e, GLU7, SER14, \u003cstrong\u003eASN95\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrogen bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eASN95, TRP96, GLY97, TYR154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eASN95\u003c/strong\u003e, \u003cstrong\u003eARG98\u003c/strong\u003e, \u003cstrong\u003eARG98\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eASP4, ASN5, HIS138, TRP147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGLU7, SER14, \u003cstrong\u003eASN95\u003c/strong\u003e, \u003cstrong\u003eARG98\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBFL-1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3I1H\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrophobic bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVAL54, VAL58, LEU62, LYS87, VAL100, PHE105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eVAL58\u003c/strong\u003e, VAL84, GLU88, \u003cstrong\u003ePHE105\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGLN32, PRO40, GLN48, ASN49, PHE52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePRO34, GLN35, PRO40, ASN49, \u003cstrong\u003eVAL58\u003c/strong\u003e, VAL84, GLU88, \u003cstrong\u003ePHE105\u003c/strong\u003e, GLN48, ARG98\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrogen bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYS87, ASN95, LYS157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eARG98, THR101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eILE33, GLN35, ARG45, GLN48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGLN48, ARG98\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBCL-W\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1ZY3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrophobic bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePHE52, PHE56, LEU63, LEU67, VAL81, GLU84, LEU85, TRP92, GLU156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGLU51\u003c/strong\u003e, \u003cstrong\u003ePHE52\u003c/strong\u003e, VAL96, TYR105, \u003cstrong\u003eGLU156\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePHE56\u003c/strong\u003e, \u003cstrong\u003eTRP92\u003c/strong\u003e, \u003cstrong\u003eGLU156\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLYS20, LEU21, LYS24, \u003cstrong\u003eGLU51\u003c/strong\u003e, \u003cstrong\u003ePHE52\u003c/strong\u003e, PHE60, LEU63, PHE78, \u003cstrong\u003eTRP92\u003c/strong\u003e, VAL96, \u003cstrong\u003eALA107\u003c/strong\u003e, TYR150\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrogen bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGLU51, ARG55, GLN66, GLU84, GLN87, ASN91, GLY93, ARG94, ASP152, GLY153, ARG159, LEU161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALA48, \u003cstrong\u003ePHE52\u003c/strong\u003e, \u003cstrong\u003eTRP92\u003c/strong\u003e, \u003cstrong\u003eARG159\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGLU51\u003c/strong\u003e, \u003cstrong\u003eARG55\u003c/strong\u003e, \u003cstrong\u003eARG94\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALA47, \u003cstrong\u003ePHE60\u003c/strong\u003e, SER61, LEU149\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMCL-1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5FC4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrophobic bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePHE149, PHE58, VAL79, VAL83, THR96, LEU97, PHE100, LEU65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eVAL83\u003c/strong\u003e, \u003cstrong\u003eLEU97\u003c/strong\u003e, \u003cstrong\u003ePHE100\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eARG52, GLU55, PHE103, LYS106, HIS107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTYR5, ARG6, LEU9, GLU10, ARG14, ARG52, GLU55, PHE103, LYS106, HIS107, ARG17, ASP48, ARG52\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrogen bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eARG45, ARG45, VAL46, VAL46, VAL50, HIS82, ARG93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eARG93\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGLN19, ASP48, ASP48 ARG52, GLU55, LYS106, LYS106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGLU10, ARG14, ARG17, GLN19, ASP48, ARG52, GLU55, LYS106\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMMP3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4G9L\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrophobic bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVAL163, VAL198, TYR223\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLEU197, \u003cstrong\u003eTYR223\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTYR155, \u003cstrong\u003eVAL163\u003c/strong\u003e, \u003cstrong\u003eLEU164\u003c/strong\u003e, HIS166, TYR168, PHE210\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePHE154, TYR155, \u003cstrong\u003eLEU164\u003c/strong\u003e, TYR168, ALA169, \u003cstrong\u003eHIS205\u003c/strong\u003e, PHE210, PRO221\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrogen bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLEU164, ALA165, HIS205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLEU164\u003c/strong\u003e, \u003cstrong\u003eALA165\u003c/strong\u003e, PRO221\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eALA165\u003c/strong\u003e, ALA167, ALA169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTYR155, ALA169, ASN175, HIS201\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMMP7\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7WXX\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrophobic bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTYR75, HIS86, PHE88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTHR83, LEU84, TYR118, TYR144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLEU84, ALA119, HIS122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePHE6, PRO10, PHE88, ALA89, LEU94, ALA119, HIS122, THR143, TYR144\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrogen bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALA87, ALA89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTHR83, GLN123, TYR141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eASN82, LEU84, GLN123, TYR144, GLY145\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLEU84, \u003cstrong\u003eALA87\u003c/strong\u003e, ALA89, HIS126\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMMP-9\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1GKC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrophobic bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVAL118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLEU83\u003c/strong\u003e, \u003cstrong\u003eVAL118\u003c/strong\u003e, HIS121, \u003cstrong\u003eTYR143\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLEU83\u003c/strong\u003e, HIS85, PHE89, GLN81, \u003cstrong\u003eVAL118\u003c/strong\u003e, HIS125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eASP31, LEU42, VAL46, \u003cstrong\u003eLEU83\u003c/strong\u003e, ASN108, PHE116, \u003cstrong\u003eVAL118\u003c/strong\u003e, HIS121, \u003cstrong\u003eTYR143\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrogen bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGLY81, LEU83, PRO141, TYR143\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLEU83\u003c/strong\u003e, ALA84, ASN142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSER67, ALA84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLEU83\u003c/strong\u003e, LEU138, ARG35, ASP39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMMP13\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4JP4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrophobic bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLEU84, HIS87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVAL119, \u003cstrong\u003eHIS122\u003c/strong\u003e, PHE141, TYR144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLEU84\u003c/strong\u003e, VAL119, \u003cstrong\u003eHIS122\u003c/strong\u003e, ILE143, TYR144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTYR76, PRO77, \u003cstrong\u003eLEU84\u003c/strong\u003e, \u003cstrong\u003eHIS87\u003c/strong\u003e, PRO90, VAL119, \u003cstrong\u003eHIS122\u003c/strong\u003e, HIS126, ASP131, TYR144\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrogen bond\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLEU85, ALA86, ALA88, HIS122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTHR147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLEU85\u003c/strong\u003e, \u003cstrong\u003eHIS122\u003c/strong\u003e, HIS132, PRO142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLEU85\u003c/strong\u003e, \u003cstrong\u003eALA86\u003c/strong\u003e, PRO142\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe combination complexes showed an improvement in the number of binding residues per protein with 8\u0026ndash;15 hydrophilic bonds, 2\u0026ndash;8 hydrogen bonds, and a decent overlap of the conserved residues binding with known inhibitors. Residues that were targeted by API and DTX alone were simultaneously occupied, and several new interactions were also observed, such as LYS20, LEU21, PHE60, LEU63, and PHE78 in BCL-W, and PRO77, PRO90, and HIS126 in MMP-13. An exception to this trend is MCL-1, where neither ligand bound to the inhibitor pocket nor significant conserved residues of the protein, except for a single hydrophilic and hydrogen bond at ARG52. However, compared to API or DTX alone, simultaneous docking of API and DTX improves the binding affinities and may also be functionally relevant as it targets conserved residues of multiple apoptotic domains.\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003eEnergy Minimization Reveals the Stabilizing Role of Apigenin in Ligand Binding\u003c/h2\u003e\n\u003cp\u003eEnergy minimization was carried out for the eight POI in three different conformations - protein-API (P-A), protein-DTX (P-D), and protein-API\u0026thinsp;+\u0026thinsp;DTX (P-AD) to analyze the potential energy of the protein-ligand complex (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In all eight complexes, P-A consistently showed the lowest total energy values ranging from \u0026minus;\u0026thinsp;3773.21 kJ/mol (MMP-13) to -19104.54 kJ/mol (MCL-1). Except for BCL-2 and MMP-7, all P-AD complexes had lower energies compared to P-D complexes. This could suggest that the presence of API could contribute towards a more favorable binding environment for DTX, leading to synergistic effects.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePost-minimization energy, RMSD and RMSF values of API, DTX, and their combinations\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eProtein/ PDB ID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eEnergy Minimized (kJ/mol)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRMSD (\u0026Aring;)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRMSF (kcal/mol*\u0026Aring;)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAPI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDTX\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAPI\u0026thinsp;+\u0026thinsp;DTX\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBCL-2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e6QGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-10268.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-9653.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-9607.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39.91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBFL-1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3I1H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-14965.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-14314.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-14513.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBCL-W\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e7LH7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-16355.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-15751.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-15885.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMCL-1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e5FC4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-19104.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-18676.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-18809.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMMP3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e4G9L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-4494.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3888.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3999.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMMP7\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e7WXX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-13032.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-12443.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-12314.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMMP-9\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1GKC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-9246.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-8658.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-8757.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.87\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMMP13\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e4JP4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3773.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3184.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3333.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003eMolecular Dynamics Simulation Supports Energetic Stability of MLSD Complexes\u003c/h2\u003e\n\u003cp\u003eMolecular dynamics simulations were carried out to evaluate the energetic behavior and structural flexibility of the P-AD complexes obtained from simultaneous docking (Fig.\u0026nbsp;5). Total energy profiles demonstrated stable energy distributions across all eight complexes throughout the 10 ns timeframe, with consistently negative potential energy values and no abrupt fluctuations, indicating energetically favorable ligand-bound systems. Temperature profiles remained centered around the target simulation temperature (~\u0026thinsp;300 K), confirming adequate thermal control and equilibration of the simulated systems.\u003c/p\u003e\n\u003cp\u003eStructural deviations were assessed using root mean square deviation (RMSD) values calculated for each P-AD complex (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). RMSD values demonstrated varying degrees of structural deviation among the proteins, with MCL-1 (4.25 \u0026Aring;) and MMP-7 (4.21 \u0026Aring;) exhibiting the lowest RMSD values, while BCL-W/BCL2L2 (8.76 \u0026Aring;) and MMP-9 (7.46 \u0026Aring;) showed higher deviations. Intermediate RMSD values were observed for BCL2 (5.58 \u0026Aring;), BFL-1/BCL2A1 (5.77 \u0026Aring;), MMP-3 (5.34 \u0026Aring;), and MMP-13 (6.72 \u0026Aring;). Energy-weighted RMSF values ranged from 28.95 to 39.91 kcal/mol\u0026middot;\u0026Aring; across the analyzed systems.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab9\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCI of the combination treatment of API and DTX\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDrug Combination\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConcentration of API (\u0026micro;M)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConcentration of DTX (\u0026micro;M)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCombination Index (CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA1D1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.513\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA1D2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.443\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA2D1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.681\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA2D2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.447\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003eConcurrent Administration of API and DTX Enhances Cytotoxicity and Apoptotic Morphology\u003c/h2\u003e\n\u003cp\u003eSimultaneous administration of API and DTX significantly enhances cytotoxicity in HeLa cells. MTT assays showed a dose-dependent reduction in cell viability after 24 hours, with IC\u003csub\u003e50\u003c/sub\u003e values of 75 \u0026micro;M for API and 72 \u0026micro;M for DTX, with the API value aligning closely with previously reported findings from our laboratory [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Sub-lethal concentrations (API: 2.5 \u0026micro;M and 5 \u0026micro;M; DTX: 10 \u0026micro;M and 15 \u0026micro;M) brought down the cell viability to as low as 57%. The lowest concentration of DTX used individually had a viability of 78.1%, whereas its combination with API (A1D1) had a viability of 66.5%, indicating potentiation of DTX by API (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). Combination index confirmed synergisms for all combinations of API and DTX, with the strongest effect at A1D2 (API 2.5 \u0026micro;M\u0026thinsp;+\u0026thinsp;DTX 15 \u0026micro;M) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The treatments also showed morphological changes such as cell rounding, detachment, and death, which were enhanced in combination treatments (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). PI staining showed a dose-dependent increase in the apoptotic index, characterized by nuclear condensation, nuclear blebbing, nuclear fragmentation, cytoplasmic spillage, and apoptotic bodies. These effects were more pronounced in cells treated with the combination of drugs as compared to individual treatments with the same concentrations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003eAPI and DTX Inhibit the Migratory Ability of HeLa Cells\u003c/h2\u003e\n\u003cp\u003eThe migratory ability of HeLa cells post-treatment with API, DTX, and their combinations for 24 hours was assessed using both wound healing and transwell assays. Treatment with API alone led to marginally impaired cell migration in a dose-dependent manner (wound closure A1: 80.41; A2: 32.98%), whereas treatment with DTX resulted in minimal wound closure (wound closure D1: 9.79%; D2: 1.55%). Notably, combination treatments impaired cell migration significantly, with the highest effect observed in A2D2 (wound width% = -45.36%), highlighting a synergistic anti-migratory effect. Complete closure of the wound in the control well was observed at 48 hours (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE, \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eF). Similarly, the transwell assay demonstrated a dose-dependent reduction in cell migration \u0026ndash; 67.15% and 49.37% when treated with API (A1, A2) and 39.72% and 29.82% when treated with DTX (D1, D2). Concurrent treatment enhanced the anti-migratory effect, reducing the percentage of migrated cells to as low as 6.4% (A2D2) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eG, \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eH).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eChemotherapeutic treatment of cervical cancer remains a major clinical challenge [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] due to treatment associated toxicity and the risk of chemotherapy resistance. DTX is known to exert its antitumor effect by microtubule stabilization; however, its long-term efficacy is limited due to cancer resistance mechanisms and toxicities caused due to its non-specificity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These limitations provide a solid ground for exploring combination chemotherapeutic strategies that enhance efficacy of the treatment while simultaneously reducing associated toxicity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The combination of DTX with various flavonoids such as chrysin, baicalein, and apigenin has shown promising results as anticancer combinations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, to the best of our knowledge, the role of the combination of API and DTX in cervical cancer cells remains unknown. In this study, we provide in-silico and in-vitro evidence that API is able to enhance the therapeutic potential of DTX in cervical cancer (HeLa) cells.\u003c/p\u003e \u003cp\u003eSTRING analysis revealed that BCL-2 family proteins and MMP\u0026rsquo;s demonstrated high degrees of interaction among themselves and formed separate clusters, with BCL-2 and MCL-1 exhibiting a few interactions with MMPs. Although these interactions do not necessarily represent direct physical binding, previous literature has reported that BCL-2 overexpression can upregulate MMP expression [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Gene Ontology (GO) enrichment highlighted regulation of cell death (BCL2L2, BCL2A1, MMP3, MCL1, MMP9, BCL2), extracellular matrix disassembly (MMP7, MMP13, MMP3, MMP9), and negative regulation of cell death (BCL2L2, BCL2A1, MCL1, MMP9, BCL2) among other pathways. This demonstrates that the selected proteins are involved in apoptosis evasion and extracellular matrix remodeling, which are two functionally different processes that together facilitate cancer progression.\u003c/p\u003e \u003cp\u003eThe favorable drug profile of API combined with minimal predicted DDI risk and adverse effects with DTX supports their potential as a combination in cancer therapy. Analysis of overlapping CYP450 activities predicted that DTX is mainly a substrate of the CYP3A4 enzyme, which is consistent with previously established metabolic pathway of DTX [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Both DTX and API were predicted to induce CYP3A4 leading to increased clearance of DTX. As CYP3A4 is often found to be overexpressed in cancers leading to DTX resistance [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], co-administration with API could both mitigate toxicity and counteract resistance through API\u0026rsquo;s anticancer effects. Moreover, the moderate substrate profile of API toward CYP3A4 further suggests limited competitive interference, reducing the likelihood of severe pharmacokinetic antagonism. Although preclinical data suggest API can enhance paclitaxel bioavailability via CYP3A and P-glycoprotein inhibition [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], our predictions indicate limited inhibitory activity, implying context-dependent effects rather than strong metabolic antagonism. DDI mechanisms predict that the interaction is predominantly pharmacokinetic, likely mediated through modulation of metabolic and transport pathways. However, the observed co-binding of API and DTX to inhibitory sites of anti-apoptotic and migratory proteins also supports the possibility of pharmacodynamic effects. This dual-interaction framework involving pharmacokinetic and pharmacodynamic modulation may collectively influence therapeutic outcome.\u003c/p\u003e \u003cp\u003eMolecular docking was used to study the individual and simultaneous ligand interactions of API and DTX with BCL-2 family proteins and MMP\u0026rsquo;s to predict their binding positions. Multiple Ligand Simultaneous Docking (MLSD) is a computational approach that simulates binding of multiple ligands to a protein at the same time, allowing the analysis of the combined action of the ligands and potential synergistic interactions [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Single ligand molecular docking showed that API bound within the canonical inhibitor pocket of BCL-2 family proteins and MMP\u0026rsquo;s, while DTX bound away from the inhibitor site such as the BH4 domain in BCL-2 and the BH3 domain in MCL-1, suggesting an allosteric interaction. In contrast, the role of DTX remains undetermined in BFL-1, as it did not bind to any functionally specific region. However, in MLSD experiments, API and DTX exhibited improved binding affinities across all POI\u0026rsquo;s as seen in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003e with PLIP and ConSurf analysis further supporting the functional relevance of these interactions, revealing that API by itself binds similarly to the known inhibitor but engaged fewer residues due to its smaller size, while DTX showed more allosteric binding patterns. The P-AD complexes increased overall interaction density (8\u0026ndash;15 hydrophilic and 2\u0026ndash;8 hydrogen bonds) and engaged conserved residues from the inhibitor site and those not targeted by individual ligands, as identified by ConSurf and seen in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e. This could be the result of ligand-induced conformational change, causing buried residues to resurface and interact with the ligands. As observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, residues that were targeted individually were often simultaneously occupied in BCL-2, BCL-W, and BFL-1, suggesting a non-competitive mode of binding. In the MMP family proteins (MMP-3, 7, 9, and 13) examined, API and DTX competed for the S1\u0026rsquo; pocket, suggesting a competitive binding mode. However, MCL-1 is an exception - where neither ligand bound to the inhibitor pocket or to any functional domains of the protein, except for a single hydrophilic and hydrogen bond at ARG52 of the BH3 domain, implying limited direct functional inhibition and raising the possibility of alternative, potentially allosteric, modulation if biological inhibition is observed. Overall, compared to API or DTX alone, simultaneous docking of API and DTX improves the binding affinities and may also be functionally relevant as it targets conserved residues of multiple domains. A recent paper from our lab also examined the binding of API to BCL-2 family (BCL-2, BCL-W, BCL-XL, BFL-1, BRAG-1, and MCL-1) proteins using CBDock2, followed by PLIP interaction analysis. Although slight differences in the predicted binding site of API were observed due to different docking algorithms, API was found to bind within or near the inhibitor site [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Multiple emerging studies employ MLSD to identify potential synergism between dual ligands. The MLSD of compounds including Apigenin, Hesperetin, and Niazimicin A produced the maximum binding affinity of \u0026minus;\u0026thinsp;14.96 kcal/mol in BCL-2 protein, according to one study on the potential of Moringa oleifera leaf phytochemicals [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEnergy minimization was performed to assess the stability of the docked complexes. Across all eight proteins, total potential energy followed a consistent trend with P-A having the lowest energies, P-D having the highest energies, and P-AD having intermediate energy values. Exceptions to this trend include BCL-2 and MMP-7, where the combination complex had a higher energy compared to individual complexes, which could be attributed to steric clashes or torsional conflicts between the ligands. The intermediate energy observed in the combination complexes suggests that the presence of API may have caused conformational changes in the protein binding pockets and reduction of steric clashes, allowing for stable binding of DTX in its binding pocket [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Difference in results from our previous paper involving minimization with SwissPDB Viewer of anti-apoptotic proteins with API alone may be attributed to algorithmic variations as SwissPDB Viewer performs minimization only on the protein structure, whereas Chimera minimizes the entire protein with ligand [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Even in MD simulations, energy and temperature profiles observed in Fig.\u0026nbsp;5 remained stable throughout 10 ns simulations, with consistent negative potential energy values and well-maintained thermal equilibration, indicating that the complexes are energetically favorable and well-equilibrated. The RMSD varied across proteins with lower deviations seen in MCL-1 (4.25 \u0026Aring;) and MMP7 (4.21 \u0026Aring;), suggesting limited structural changes, intermediate deviations seen across BFL-1 (5.77 \u0026Aring;), BCL-2 (5.58 \u0026Aring;), MMP3 (5.34 \u0026Aring;), and MMP13 (6.72 \u0026Aring;), while MMP9 (7.46 \u0026Aring;) and BCL-W (8.76 \u0026Aring;) exhibited the largest deviation, likely reflecting intrinsic flexibility in structural elements, such as the S1\u0026rsquo; pocket, which may facilitate ligand accommodation and specificity [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The relatively comparable energy-weighted RMSF values across most systems indicate similar dynamic behavior under the applied simulation conditions. These results are consistent with docking scores which showed lower energy values for the dual-ligand complex, PLIP interaction analysis which showed cooperative engagement of binding site residues, and ConSurf analysis, which showed that several of these residues are evolutionarily conserved. Overall, these findings suggest that API may act as a stabilizing cofactor, enhancing the binding efficiency of DTX through synergistic interactions across anti-apoptotic and migratory targets, thereby providing strong rationale with sufficient evidence to proceed with in-vitro validation.\u003c/p\u003e \u003cp\u003eFollowing these in silico observations, in-vitro validation was conducted using HeLa cells to assess the combinatorial effect of API and DTX on cellular viability and migration potential. The combination of API with DTX for 24 hours led to a dose-dependent decrease in the viability of the HeLa cells, showing synergy compared to either treatment alone. As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, individual treatment of DTX at 10 \u0026micro;M and of API at 5 \u0026micro;M showed a cell viability of 89% and 87%, respectively, but their combination showed a marked decrease in cell viability, with 76.3% viable cells. All four combinations of API and DTX showed synergistic effects as their combination index was calculated to be \u0026lt;\u0026thinsp;1 (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e6\u003c/span\u003e); this is visually supported by the microscopy images that show that cells treated with the combination drug showed a higher number of cells having characteristic apoptotic features, such as detachment, nuclear condensation, and observable cell death as compared to individual drug-treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). This is further supported by the results obtained from the PI assay that show that HeLa cells treated with the combinations of API and DTX showed a higher percentage of cells with nuclear condensation, nuclear blebbing, nuclear fragmentation, and apoptotic bodies, all indicative of late apoptosis. In contrast, cells that were administered with API or DTX individually had fewer cells with characteristics of late apoptosis. Similarly, studies on multiple cancer cell lines such as A549, Hep3B, SKOV-3, and HeLa cells have shown that the combinational use of apigenin with paclitaxel has synergistically improved apoptosis in these cells [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Another study revealed that apigenin and docetaxel together sensitize prostate cancer stem cells (PCSCs) to docetaxel and induces apoptosis in PCSCs [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe migratory potential of HeLa cells post-treatment was also evaluated by the scratch wound and transwell migration assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eG, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). In the scratch wound assay, while API alone showed modest, dose-dependent inhibition of migration (wound closure: ~32\u0026ndash;80%), and DTX showed minimal wound closure (wound closure: ~1\u0026ndash;10%), their combination impaired cell migration significantly, enhancing the wound width by inhibition of cell movement and apoptotic induction, with the strongest effect observed for D2A2, which produced a wound width change of -45.36%, highlighting a synergistic effect of API and DTX in inhibiting HeLa cell migration. A similar trend was observed in the transwell migration assay, where the migratory ability of cells towards a nutrient source starkly decreased when treated with the combination of API and DTX to as low as 6.4% as compared to API (~\u0026thinsp;49\u0026ndash;64%) or DTX (~\u0026thinsp;30\u0026ndash;40%) treatment alone, suggesting possible synergism. A similar effect on the migratory ability of Prostate CSCs was observed upon treating the cells with a combination of apigenin and docetaxel [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, the findings of the present study provide a dual computational and experimental basis to justify the concurrent use of API with DTX in cervical cancer. STRING interaction mapping showed that the selected proteins were biologically connected, while GO enrichment showed that these proteins are significantly associated with coordinated regulation of apoptosis evasion and activation of migration. These results, along with the minimal predicted DDI between API and DTX, helped shape the direction of the in-silico and in-vitro work. The enhanced cell death and the inhibition of cell migration observed upon combined treatment with API and DTX in the assays aligned well with the in-silico results of the proteins, which showed synergism for the combined ligands as compared to individual treatment. To our knowledge, this is among the first studies to combine MLSD with experimental validation to explore API\u0026mdash;DTX synergy against apoptosis and migration associated targets in cervical cancer cells. While the present study is limited to in vitro assessments using a single cervical cancer cell line, lacks molecular pathway validation, may not fully capture tumor microenvironment complexity, and relies partly on computational predictions that may not completely reproduce biological conditions, these limitations provide clear directions for future research. Strategies such as nanoparticle-based delivery systems [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] may boost the bioavailability of API and improve tumor specificity, while sequential treatment [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] could further improve outcomes. Overall, the combination of natural chemopreventive agents with chemotherapeutic agents has great potential and needs to be explored further as it may enhance therapeutic efficacy, overcome chemoresistance, and reduce dose-dependent toxicity, thereby improving overall treatment outcomes. The result of such synergistic pairings may enable the development of a multifaceted strategy, thereby helping overcome the shortcomings of chemotherapy. The findings of this pilot study suggest that the combination of API with DTX has therapeutic potential in cervical cancer cells that should be further explored in-vitro and in-vivo to allow for detailed evaluation of their effects.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study demonstrates that API can synergistically potentiate the effect of chemotherapeutic agents such as DTX by significantly enhancing apoptosis and suppressing migration in cervical cancer cells. The integration of in-silico and in-vitro validation shows coordinated multi-targeting of apoptosis and migration proteins, providing a clear biological foundation for the observed synergistic effects. These findings highlight the potential of phytochemical-chemotherapeutic combinations as a strategy to enhance treatment effectiveness while simultaneously reducing associated toxicity. Although further \u003cem\u003ein-vitro\u003c/em\u003e and \u003cem\u003ein-vivo\u003c/em\u003e studies are required to fully establish clinical applicability, this study provides a foundation for advancing API-DTX combination therapy and delineates the importance of multi-targeted approaches to overcome the limitations of conventional chemotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors are thankful to MAHE DUBAI Internal research grant (R\u0026amp;DP/MAHE DUBAI/RL- 01/2024) for financial support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDB \u0026amp; LCD, were responsible for investigation, data curation, methodology, formal analysis, and preparation of the original draft. A.H. contributed to the conceptualization, methodology, funding acquisition, project administration, supervision, validation, and writing\u0026mdash;review and editing. TNH \u0026amp; SH were involved in data curation and visualization. R.I.A, \u0026nbsp;LAA \u0026amp; RR, participated in formal analysis, investigation, and resource management.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest. The authors declare that there are no competing interests associated with the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDual Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript has not been published previously and is not under consideration for publication elsewhere.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eProtein\u0026ndash;protein interaction data were derived from the STRING database (https://string-db.org/cgi/input?sessionId=bKimp7reqwhD\u0026amp;input_page_show_search=on ) using the following query parameters: protein name: BCL2, BCL2A1, BCL2L2, MCL1, MMP3, MMP7, MMP9, MMP13; organism: Homo sapiens.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eProtein structures used for molecular docking were obtained from the RCSB Protein Data Bank (https://www.rcsb.org/ ) with the following accession IDs: 6QGG (BCL-2), 3I1H (BFL-1), 1ZY3 (BCL-W), 5FC4 (MCL-1), 4G9L (MMP-3), 7WXX (MMP-7), 1GKC (MMP-9), and 4JP4 (MMP-13). Ligand structures were retrieved from PubChem (https://pubchem.ncbi.nlm.nih.gov) with the following compound identifiers (CIDs): [Apigenin: 5280443; Docetaxel: 148124]. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHomology models were generated using SWISS-MODEL (https://swissmodel.expasy.org/). Protein\u0026ndash;ligand interactions were analyzed using PLIP (https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index) based on the docked complex structures generated in this study. Evolutionary conservation analysis was performed using ConSurf ( https://consurf.tau.ac.il/ ) and run Google Colab (https://colab.research.google.com/drive/1PhDXX7k12oUsV6T_xkXC3Rm9R99e7tHz) based on the protein structures described above\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDrug\u0026ndash;drug interaction and related predictions were performed using the Way2Drug web server ( https://way2drug.com/ddi/ ) using SMILES representations of the compounds analyzed in this study.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll datasets are publicly available from the respective databases, and no additional datasets were generated or analyzed beyond those included in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. 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Cancers (Basel). 2023;15:902. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers15030902\u003c/span\u003e\u003cspan address=\"10.3390/cancers15030902\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cervical cancer, Apigenin, Docetaxel, Apoptosis, Migration","lastPublishedDoi":"10.21203/rs.3.rs-9300836/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9300836/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDocetaxel is a well-established drug widely used in the treatment of malignancies. However, it is limited by its dose-associated toxicity, necessitating improved combinatorial strategies to enhance efficacy while minimizing toxicity. Here, we report for the first time the combined effect of the naturally occurring flavone apigenin with docetaxel in cervical cancer. In-silico molecular docking demonstrated that both apigenin and docetaxel mostly bind within conserved inhibitor-binding pockets of anti-apoptotic Bcl-2 family proteins and matrix metalloproteinases, sharing key interacting residues, suggesting potential cooperative inhibition. Molecular dynamics simulations further showed stable ligand\u0026ndash;protein complexes. Drug\u0026ndash;drug interaction prediction revealed predominantly minor interactions, supporting the safety of the combination. In-vitro, the combined sub-lethal doses of apigenin and docetaxel significantly reduced cell viability in HeLa cells, induced apoptotic nuclear morphology, and suppressed migratory capacity. Collectively, these findings provide preliminary proof-of-concept evidence that the combination of apigenin with docetaxel exerts synergistic anti-proliferative and anti-migratory effects in cervical cancer, warranting further mechanistic and in vivo investigation.\u003c/p\u003e","manuscriptTitle":"Therapeutic Potential of Apigenin in Combination with Docetaxel on Human Cervical Cancer Cells – In-Silico and In-Vitro Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 09:05:33","doi":"10.21203/rs.3.rs-9300836/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-29T17:40:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227166966764431475940357086703886149610","date":"2026-04-28T14:55:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63018716099238566270636354860667920320","date":"2026-04-22T22:26:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T23:19:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-20T08:52:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T04:13:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-15T14:12:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2026-04-15T13:14:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70116493-2a91-4e55-a513-1511c58554af","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T09:05:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 09:05:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9300836","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9300836","identity":"rs-9300836","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}