Characterization of Novel Anticancer Agent using Computer-Aided Drug Discovery Processes Reveals Selective Interaction with the Epidermal Growth Factor Receptor

Characterization of Novel Anticancer Agent using Computer-Aided Drug Discovery Processes Reveals Selective Interaction with the Epidermal Growth Factor Receptor | 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 Characterization of Novel Anticancer Agent using Computer-Aided Drug Discovery Processes Reveals Selective Interaction with the Epidermal Growth Factor Receptor Bernardine Tuah, Daniel Moscoh Ayine-Tora, Jude Tetteh Quarshie, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6998508/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : The traditional drug discovery process for anticancer drugs is hindered by prolonged screening and off-target effects. To address these challenges, we screened three novel compounds for their anticancer activity and employed computer-aided drug design to evaluate lead compounds' pharmacological properties and molecular interactions. This study aimed to determine the anticancer potential of our compounds and predict the targets of the lead compound using computer-aided drug discovery processes. Method : Three synthetic compounds (compounds 1,2, and 3) were tested for cytotoxicity against four cancer cell lines. Using the STRING, protein-protein interactions were analysed, and functional enrichment analyses were performed using Metascape. Molecular docking, dynamic simulations, pharmacological ADME properties, and drug-likeness properties were assessed using GOLD, Amber 22, ADMETLab, and SWISSADME software packages. Results : Compound 2 inhibited MDA MB 468 and MDA MB 231 cell proliferation and showed good selectivity against PNT2 cells. Pathway analysis associated Compound 2 with cancer and focal adhesion pathways, identifying potential targets for EGFR, AKT1, and VEGFR2. Molecular docking revealed that Compound 2 binds to the ATP-binding activation site at Lys745 and the DFG motif at Asp855 of EGFR, as well as the ATP binding site of VEGFR2 at Cys919 and Phe918. Molecular dynamic simulations indicated that EGFR had the most energetically stable protein-ligand interactions, followed by VEGFR2 and AKT1. EGFR also showed flexible amino acid residues and strong hydrogen bond interactions. This study highlighted that Compound 2 showed significant cytotoxicity against breast cancer cell lines and is linked to cancer pathways, notably ErbB and EGFR. Compound 2 also binds to the ATP activation site and DFG motif of EGFR in docking studies and may have an energetically stable interaction with EGFR. Conclusion : This highlight Compound 2 as anticancer agent with the potential to interact with EGFR. Cancer Biology Cancer epidermal growth factor receptor (EGFR) molecular dynamics molecular docking drug discovery Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cancer is a global health concern, despite significant advances in medical and pharmaceutical research. Despite the notable success of chemotherapy, challenges such as systemic toxicity, drug resistance, off-target effects, and the heterogeneity of cancer cells hamper the utility and efficacy of the therapeutics [ 1 , 2 ]. Potential anticancer drugs fail in clinical trials owing to poor pharmacokinetic properties, lack of specificity, and adverse side effects[ 3 ]. Additionally, many drugs have off-target effects and interact with proteins that are not intended to cause side effects[ 4 ]. This lack of specificity limits the therapeutic potential of most anticancer drugs and contributes to their adverse effects[ 5 ]. Strategies such as the "network target, multi-component" approach, which applies cutting-edge technologies such as network pharmacology, molecular docking, and molecular dynamics (MD) simulation, have been adopted to transform the drug discovery process and efficiently solve traditional barriers. [ 6 , 7 ] Network pharmacology, molecular docking and MD simulations have become vital approaches to discovering new cancer drugs. These approaches help investigate the connections between biological elements to comprehensively understand diseases and identify the biological pathways, genes, and protein sets influenced by drugs[ 8 , 9 ]. The development of imatinib (Gleevec) as a target for Bcr-Abl for chronic myeloid leukaemia patients, BRAF for treating malignant melanoma, as well as antiviral drug inhibitors for HIV, influenza and SARS-CoV-2 proteins highlight the significance of utilising MD simulations for therapeutics[ 10 – 17 ]. Recent studies have also employed similar approaches to identify iridoids as KRAS G12C inhibitors, to evaluate cinnamoyl-mannopyranosides against H5N1 neuraminidase, and to assess flavonoids targeting Naegleria fowleri[ 18 – 20 ]. These studies highlight the predictive power of computational modelling for identifying bioactive candidates. Docking and MD simulations in this work characterized compound interactions with key targets, demonstrating the effectiveness of computational techniques for evaluating novel small molecules. Understanding the dynamics of these interactions aids in developing drugs that can successfully navigate cell membranes and reach their targets. the Epidermal Growth Factor Receptor (EGFR) is a transmembrane protein involved in the regulation of cell proliferation, survival, and differentiation[ 21 , 22 ]. Its overexpression and mutation are implicated in various malignancies, including non-small cell lung cancer, colorectal cancer, and glioblastoma[ 22 , 23 ]. Among various cancer types, those associated with aberrant signalling through EGFR pathway are particularly aggressive and resistant to conventional treatments[ 23 ]. Targeting both the extracellular and intracellular portions, EGFR has thus become a pivotal strategy in the fight against cancer. The extracellular domain of EGFR can be targeted by monoclonal antibodies (mAbs) such as cetuximab and panitumumab. These antibodies inhibit ligand binding, preventing receptor dimerisation and subsequent activation of downstream signalling pathways[ 24 ]. The intracellular kinase domain of EGFR can be targeted by small molecule tyrosine kinase inhibitors (TKIs) such as gefitinib, erlotinib, and osimertinib. These inhibitors block ATP binding in the tyrosine kinase domain, thereby inhibiting autophosphorylation and downstream signalling[ 25 ]. Third-generation EGFR inhibitors such as Osimertinib were developed using CADD. Dr. Michael Waring and his team at AstraZeneca employed molecular modelling to achieve high specificity for the T790M resistance mutation[ 26 ]. Following successful preclinical studies, clinical trials validated the effectiveness of osimertinib, leading to its FDA approval[ 27 ]. Computer-aided drug discovery processes have also been used to explain the potential off-target effects and drug resistance. Afatinib, an irreversible inhibitor of the EGFR tyrosine kinase, was shown to bind to different variants of EGFR using molecular dynamics simulations and covalent docking[ 28 ] Here, we aimed to discover potential anticancer compounds by screening three previously identified antileishmanial compounds for their cytotoxicity. These molecules were shown to inhibit Leishmania donovani sterol methyltransferase in silico[ 29 ]. Compound 1 (STOCK6S-84928) contains triazolopyridazine, while compounds 2 (S6S-06707) and 3 (STOCK6S-65920) are chromone-based. Although chromone-based compounds have been studied for their anticancer properties[ 30 ], there is limited data available on compounds 2 and 3. The anticancer potential of triazolopyridazine remains underexplored. Some anticancer drugs have shown antileishmanial activities, like miltefosine, amphotericin B, and sodium stibogluconate, which were originally developed for cancer treatment but became effective against Leishmaniasis[ 31 , 32 ]. These dual activities may arise from shared mechanisms, such as disrupting membrane integrity and interfering with survival pathways. Given this link, the study aimed to investigate the anticancer properties of compounds 1, 2 and 3 with known anticancer scaffolds. Of the three compounds, compound 2 showed promising anticancer cytotoxic effects. Furthermore, we aimed to employ a systematic computational approach to identify the pharmacological properties and targets of the lead compound. Three targets were identified and subjected to MD simulations to explore the dynamic behaviour and structural changes occurring in the potential target(s) when complexed with the lead compound. EGFR was later identified as the most promising target of compound 2. Materials and Methods Cancer cell lines and cell culture PC3 prostate cancer, DLD-1 colorectal cancer, MDA MB 468, and MDA-MB-231 breast cancer cell lines were obtained from ATCC. The standard human prostate cell line, PNT2, was generously provided by the Department of Chemical Pathology division at the Noguchi Memorial Institute for Medical Research. MDA MB 468, MDA-MB-231 and PC3 cancer cell lines were cultured in DMEM, while DLD-1 and PNT2 were cultured in RPMI 1640. All media were enriched with fetal bovine serum (10%) and penicillin-streptomycin-glutamine (1%) (Gibco-life Technologies, Carlsbad, CA, USA). Cells were maintained at 37°C in a humidified atmosphere with 5% CO 2 . Chemical compounds Compounds 1, 2, and 3 (Fig. 1) were purchased from Vitas M lab and DOX (D1515-10MG) was purchased from Sigma-Aldrich, St Louis, MO, USA). Cytotoxicity assay and Selectivity Index (SI) The effects of the three compounds on cell viability were determined using MTT assay. Briefly, the cells were plated in 96-well plates at 1 × 10 4 cells/well density and incubated at 37°C for 24 hours. The cells were treated with the compounds (0- 100µM) for 48 h. DOX (0–15µM) was used as a positive control. In each well, 20 µL of 2.5 mg/mL (MTT) (Sigma-Aldrich, St Louis, MO, USA) was added and incubated at 37°C for 4 hours. Subsequently, acidified isopropanol (100 µL) was added to each well and incubated at 37°C for 30 minutes. The absorbance was measured at 570 nm using a microplate reader (Varioskan™ LUX multimode, Thermo Fisher Scientific, Carlsbad, CA, USA). The percentage of cell viability was determined based on the absorbance values, and IC 50 values were later computed. The selectivity index (SI) of the most potent compound was analysed following the same procedure using PNT2 normal prostate cells in a concentration range ten times more than the concentration ranges used in the four cancer cell lines. The IC 50 of PNT2 was calculated, and the SI was computed with a good selectivity classified as SI > 3 [ 33 ]. Three independent experiments were done to determine the IC 50 of the compounds. Cancer-related genes and compound 2 target prediction Genes associated with 36 human cancers were obtained from the DisGENET ( https://www.disgenet.org/ , accessed on 8 February 2023)[ 34 ] and GeneCards ( https://www.genecards.org/ , accessed on 8 February 2023)[ 35 ] Databases. For the GeneCards database, a cut-off score > 4 was used to obtain the most probable genes. The putative targets of compound 2 were acquired from SwissTargetPrediction ( http://www.swisstargetprediction.ch/ , accessed on 9 February 2023)[ 36 ] and SuperPred ( https://prediction.charite.de/ , accessed on 9 February 2023)[ 37 ] Webservers. The targets were converted into standardised gene names based on the UniProt database ( https://www.uniprot.org/ , accessed on 9 February 2023)[ 38 ], and duplicates were removed. The cancer-related genes and targets of compound 2 were intersected with a Venn diagram using the FunRich v3.1.3 software[ 39 ] to obtain all its cancer-related targets. Protein-protein interactions (PPI) The cancer-related targets of compound 2 were uploaded into the STRING database ( https://string-db.org/ , accessed on 9 February 2023)[ 40 ] where an interactive network was generated using “ Homo sapiens ” as the screening condition. The protein-protein interaction network was loaded into Cytoscape software v3.9.1[ 41 ], where the core targets of compound 2 were identified using the CytoHubba [ 42 ] and MCODE[ 43 ] plugins. Specifically, the PPI network was analysed using nine algorithms in CytoHubba, and targets that ranked top 20 in at least 5 of the 9 algorithms were identified. MCODE plugin was used to group the targets into sub-clusters, and targets that belonged to the top 3 sub-clusters were identified. Finally, the targets identified from the CytoHubba rankings belonging to at least one MCODE sub-clusters were selected as core targets. Functional enrichment analyses The Metascape web server ( https://metascape.org/gp/index.html , accessed on 13 February 2023)[ 44 ] performed functional enrichment analyses of core compound 2 targets. The targets were annotated based on the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. In GO functional analyses, the functions of the targets were categorised into biological process, cellular component, and molecular function. The KEGG pathway plot was created through enrichment analysis. Molecular docking From the KEGG pathway analysis, the top two enriched pathways were identified. The three core proteins likely to be involved in these pathways were selected from the identified hub genes in Table 1 . Molecular docking was performed using the selected proteins. The active compound was docked to the crystal structure of EGFR (PDB: 8A27, 1.07 Å [ 45 ], AKT1 (PBD:6HHJ, 2.30 Å, [ 46 ]), and VEGFR2 (PDB:4ASE, 1.83 Å [ 47 ]) which were obtained from the Protein Data Bank (PDB) [ 48 , 49 ]. The preparation of all crystal structures for docking was conducted using the Scigress version FJ 2.6 program., i.e., hydrogen atoms were added, and the co-crystallized ligands were removed. The centre of the binding pockets, x yz for each protein, are shown in Table S1 in S Text, with a radius of 10Å. The GoldScore (GS)[ 50 ] and ChemScore (CS) [ 51 , 52 ], ChemPLP[ 53 ] and Astex statistical potential (ASP)[ 54 ] scoring functions were employed to validate the ligands' predicted binding modes and relative energies using the GOLD v5.4 software suite. Initially, the co-crystallized ligand of each protein was docked, and RMSD values were computed for the heavy atoms. The average RMSDs for each co-crystallized ligand for ASP PLP, CS, and GS underscore the robust predictive capability of the scoring functions. Detailed information on RMSD and binding scores can be found in the Tables S2 and S3 in S Text. Table 1 Cancer-related targets of compound 2 and PDB sources of 3D protein structures Gene symbol UniProt ID Target name AKT1 P31749 AKT serine/threonine kinase 1 CCND1 P24385 Cyclin D1 EGFR P00533 Epidermal growth factor receptor FYN P06241 FYN proto-oncogene GSK3B P49841 Glycogen synthase kinase 3 beta HIF1A Q16665 Hypoxia-inducible factor 1 subunit alpha HSP90AA1 P07900 Heat shock protein 90 alpha family class A member 1 HSP90AB1 P08238 Heat shock protein 90 alpha family class B member 1 JAK2 O60674 Janus kinase 2 KDR(VEGFR2) P35968 Kinase inserts domain receptor/Vascular endothelial growth factor receptor 2 MAPK1 P28482 Mitogen-activated protein kinase 1 MAPK8 P45983 Mitogen-activated protein kinase 8 PRKCB P05771 Protein kinase C beta PTK2 Q05397 Protein tyrosine kinase 2/ Focal adhesion kinase 1 SRC P12931 SRC proto-oncogene MD simulations MD simulations were performed for the three proteins: EGFR, AKT, and VEGFR2). The MD simulation was performed using the Amber 22 software[ 55 – 57 ]. The most feasible ligand configuration was employed in setting up the simulation. Initially, the ligand was prepared using an Antechamber to compute atomic point charges using the AM1-BCC charge model. The system was configured using the Leap program. The ligand and protein were subjected to GAFF and ff14SB force fields, respectively. The appropriate number of ions was added to each protein to neutralise receptor charges. The three-point transferable intermolecular potential (TIP3P) model with a 10 Å water solvate box was used to solvate the systems, eventually buffered to 150 mM. The non-bonded interaction cutoff value was set at 8.0 Å. Subsequently, the system was gradually heated to 300 K, with receptor atom constraints applied at 50 ps intervals. This was followed by a 50 ps equilibration period to attain density equilibrium. The system was then equilibrated under NPT conditions for 500 ps at a pressure of 1 atm and a temperature of 300 K. Finally, a 10-ns production stage was carried out. The Berendsen barostat and Langevin thermostat were used to keep pressure and temperature under control. Furthermore, with a time step of 0.002 ps, the shake algorithm restricted all hydrogen-involved bonds. The CPPTRAJ module was used for trajectory analysis, and VMD facilitated visualisation. To compute the binding free energy of the receptor complex, we employed both the MM-PBSA and MM-GBSA methods for overall simulated trajectories of 1000 frames (100ns). The RoG and maximum RoG (RoGmax), RMSD, and RMSF were analysed to assess the structural flexibility and stability. Lifetime hydrogen bond analysis was also computed. Drug-likeness and ADMET studies. The pharmacokinetics, structural, and physicochemical characteristics of compound 2 were predicted using ADMETlab 2.0 ( https://admetmesh.scbdd.com/ , accessed on April 18, 2023)[ 58 ] and SwissADME ( http://www.swissadme.ch/ , accessed on April 18, 2023)[ 59 ]. To evaluate its potential for absorption in the human gastrointestinal (GI) tract and its ability to traverse the blood-brain barrier (BBB), the Egan BOILED-Egg (Brain Or IntestinaL EstimateD) permeation predictive model was utilised. Furthermore, these methodologies yielded predictions regarding the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of compound 2. Statistical and Data analysis GraphPad Prism 9.3 was used to analyse cytotoxicity data and generate all the graphs. The Kruskal-Wallis test was used for statistical analysis. To determine statistical significance, p-value < 0.05 was used. The cytotoxicity tests were carried out in triplicate with three biological replicates. Mean ± SEM was used to represent IC 50 . The molecular dynamics data was analysed using Python 3.12.0. RoG, RoG max, RMSD, and RMSF values were shown as Mean ± SD. The MM-PBSA and MM-PBSA free energies were presented as Mean ± SEM. Results Cytotoxicity assay of compounds and selectivity index The impact of three small-molecule compounds on cell proliferation was assessed in various cancer cell lines and a normal cell line, with doxorubicin (DOX) as the reference anticancer drug. The goal was to determine the bioactivity of the compounds in cancer cells and evaluate the potential adverse effects of the compounds on normal cells. MDA MB 468 and MDA MB 231 breast cancer cell lines were susceptible to compound 2 in a dose-dependent manner (p = 0.0011 and p = 0.008, respectively) with IC 50 of 9.46 ± 0.80 µM and 18.30 ± 1.10 µM for MDA MB 468 and MDA MB 231 respectively. Colorectal cell line DLD1 (p-value = 0.03) and prostate cell line PC3 (p = value = 0.01) also showed mild susceptibility to compound 2 with IC 50 of 40.58 ± 1.80 µM and 46.72 ± 1.64 µM respectively (Fig. 2 ). Compounds 1 and 3 exhibited no significant cytotoxic effect on all the cell lines (Fig S1 in S Text). The selectivity index (as indicated in Table 2 ) demonstrates that compound 2 exerts a specific and targeted impact on the MDA MB 468 cell line while sparing normal cells. In contrast, PC3 displays minimal selectivity when treated with compound 2. Compound 2 exhibits a favourable selectivity index compared to DOX in breast cancer cell lines. Table 2 Compound 2 and DOX selectivity index in the cancer cell lines. CC 50 non-cancerous cell line (PNT2) IC 50 cancer cell line Selectivity index MDA MB 468 (compound 2) 40.61 9.46 4.29 MDA MB 468 (DOX) 0.88 0.72 1.22 MDA MB 231 (compound 2) 40.61 18.30 2.22 MDA MB 231 (DOX) 0.88 0.89 0.99 PC3 (compound 2) 40.61 46.72 0.87 PC3 (DOX) - - - DLD-1 (compound 2) 40.61 40.58 1.00 DLD-1 (DOX) - - - The putative targets of compound 2 are related to human cancer. 15615 cancer-related genes were identified from the DisGENET and GeneCards databases. Our Swiss Target prediction and SuperPred prediction produced 211 putative targets for compound 2 (ZINC6661981 or STK610045) (excel file; S1 and S2). After the intersection with FunRich v3.1.3, 206 cancer-related targets for compound 2 were identified (Fig S2 in S Text, excel file; S3). PPI network of compound 2 cancer-related targets A PPI network of the cancer-related targets of compound 2 was constructed in the STRING database. (Fig S2 in S Text, excel file; S3). For intuitive topological analysis, 199 nodes and 1591 edges were obtained. Eighteen (18) of the 206 compound 2 cancer-related targets were selected after screening using the CytoHubba algorithm (excel file; S4). Finally, 15 targets were selected following MCODE sub-clustering (Table 1 ; Excel file; S5). Functional enrichment analyses of core targets From functional enrichment analyses, 435 biological processes, 39 cellular components, 36 molecular functions, and 122 pathways were predicted to be associated with the anticancer activity of compound 2 (excel file; S6). The KEGG pathway analysis revealed the anticancer potential of compound 2 mainly via focal adhesion, pathways in cancer, EGFR tyrosine kinase inhibitor resistance, and ErbB signalling (Fig. 3 ). From our data, regulation via focal adhesion had the highest statistical significance, with a log(q-value) of -17, a Z-score of 35, and an enrichment score of 110. Pathways in cancer, with a log(q-value) of -14 and an enrichment score of 45, were also significantly represented in the data. A high Z-score of 23 further emphasises its relevance. The ErbB Signaling Pathway and EGFR Tyrosine Kinase Inhibitor Resistance had high enrichment scores of 190 and 200, respectively, and Z-scores of 39 and 40, respectively, indicating their significant over-representation. Molecular docking Molecular docking experiments revealed the binding mode of compound 2 to the three potential target proteins identified by functional enrichment analysis AKT1, EGFR, and VEGFR2 (the three potential target proteins identified in functional enrichment analysis). With the GOLD software suite, the four scoring functions, Piecewise Linear Potential (ChemPLP), GoldScore (GS), Astex Statistical Potential (ASP), and ChemScore (CS), were used for docking studies. The binding site of AKT1 consists of a slightly hydrophilic to neutral binding pocket, whereas EGFR and VEGFR2 consist of a hydrophobic binding pocket. For AKT1, the carbonyl end of compound 2 was fitted into the binding pocket. It formed hydrophobic contacts with cysteine (Cys 296) and isoleucine (Ile 86). The complex can be stabilised by a catalytic water molecule that forms hydrogen bonds with the backbone carbonyl oxygen of valine (Val271) and tyrosine (Tyr272). However, AKT1 did not form a classical hydrogen bond with the ligand. For EGFR and VEGFR2, the piperidine end fitted well into the binding pocket, whereas the carbonyl portion pointed outside. For EGFR, Compound 2 formed hydrophobic contacts with leucine (leu747), isoleucine (Ile759) and leucine (Leu858) and the oxygen in the benzoxazine ring formed hydrogen bonds with side chain amine of lysine (Lys745) whereas the secondary amine in the compound forms hydrogen bond with the side chain hydroxy group and the backbone amine of aspartic acid (Asp855). Compound 2 was located at the hinge region (ATP binding site) of EGFR, specifically binding to the critical ATP-binding activation site at Lys745. Compound 2 also interacted with the DFG motif, the activation loop of EGFR at Asp855. For VEGFR2, the oxygen in the benzoxazine ring formed hydrogen bonds with the backbone amine of cysteine (Cys919), which is in the ATP binding site. Compound 2 also formed hydrophobic contacts with phenylalanine (Phe918), also in the ATP binding site, and leucine (Leu840). The binding modes and the locations of the plausible binding pockets for compound 2 for each target are shown in Fig. 4 . Based on our findings, compound 2 is predicted to be an ATP competitive inhibitor. Molecular dynamics simulation The Radius of Gyration (RoG) is an important parameter that provides information on the compactness of the protein-ligand complex. EGFR maintained a consistent and compact structure during most of the simulations, with an average RoG of 38.93 ± 0.03 Å and a maximum RoG of 66.84 ± 0.42 Å. (Fig. 5 A & B) . VEGFR2 had a slightly lower average RoG of 37.09 ± 0.03 Å than EGFR. VEGFR2 had a RoGmax of 63.68 ± 0.42 Å and, like EGFR, displayed instances of more extended conformations during the simulation. RoG and RoGmax for AKT1 were 38.43 ± 0.02 Å and 66.07 ± 0.44 Å, respectively. AKT1 protein had a higher RoGmax value than EGFR protein. The significant difference between the average RoG and RoGmax observed for all proteins showed that each protein may have experienced moments of increased flexibility or conformational changes during the simulation. Owing to the low SD observed for all three proteins, it can be concluded that they remained the same size and shape throughout the simulation, implying a stable conformation. The consistent compactness may indicate that compound 2 fits snugly within the binding pocket of the proteins. The Root Mean Squared Deviation (RMSD) values for all proteins showed a similar trend: initial deviation followed by stabilisation. Throughout the simulation, the EGFR atoms deviated by about 55.11 ± 0.70 Å (Fig. 5 C) from their initial positions before stabilising around 1.5 Å, indicating only minor conformational changes after the initial frames. VEGFR2 atoms deviated from their starting positions by an average of 52.51 ± 0.52 Å and stabilised around 1.5 Å. This initial deviation is slightly less than EGFR's, implying that VEGFR2 had a somewhat more stable interaction with ligand compound 2 or had fewer conformational changes during the simulation. The RMSD of AKT1 was 54.09 ± 0.70 Å, which is between EGFR and VEGFR2. However, AKT1 stabilises near 2.5 Å, indicating that it had more significant structural deviations. The findings showed that, during the simulation, all proteins initially underwent structural changes quite different from each reference structure. However, EGFR and VEGFR2 stabilised at 1.5 Å after the initial deviation, thus converging to a stimulated structure closely resembling the reference structures. Although the AKT1 structure also stabilised within a reasonable range (2.5 Å), it showed a slightly higher deviation or flexibility level than EGFR and VEGFR2. However, the low SDs observed indicate that the RMSD values fluctuated only somewhat around the averages, suggesting a relatively stable simulation. The Root Mean Squared fluctuation (RMSF) plot revealed information about the flexibility and rigidity of different protein regions. Increased RMSF values signify increased flexibility, while decreased RMSF values indicate heightened rigidity. From the simulations (Fig. 5 D), although each protein showed unique dynamics with specific regions of flexibility, EGFR had the highest peaks compared with VEGFR2 and AKT1, indicating higher fluctuations. This showed that when EGFR is complexed with compound 2, it undergoes conformational changes. This could also mean that EGFR has certain regions that are inherently more flexible. Hydrogen bond analysis and lifetime curve plotting were performed to further understand the strength and stability of the protein-ligand complexes, with a more extended lifetime range indicating a more stable interaction (Fig. 5 E). The hydrogen bonds in the EGFR complex had longer lifetimes, with several bonds present in more than 500 counts. The hydrogen bond between compound 2-270@O2 and the solvent had the highest lifetime with an average distance of approximately 2.79 Å and an angle of 158.2°, existing for 1125 counts. Residues such as Asp155 and Thr154 formed hydrogen bonds with compound 2-270@N at an average distance of 2.96 Å and 2.94 Å, respectively, with corresponding average angles of 166.14 o and 166.36 o . For AKT1, the bond between compound 2-409@N and the solvent appeared frequently, present in over 500 counts, with an average distance of around 2.86 Å and an angle of 162.22 o . Residues such as Tyr16, Arg80, and Glu242 in AKT1 were found to form hydrogen bonds with compound 2, with an average distance range of 2.83–2.91 Å and an angle range of 142.82- 159.45 o . This shows that AKT1 has a more diverse set of hydrogen bonds with compound 2, which contributes to its overall hydrogen-bonding activity, and no single bond dominates the entire interaction. VEGFR2 exhibited the widest range of lifetimes. The bond between compound 2-308@O2 and the solvent was the most prevalent, present in 1141 counts, with an average distance of approximately 2.78 Å. The residues Lys32, Cys113, and Lys62 in VEGFR2 were identified to form hydrogen bonds with compound 2-308@O2, compound 2-308@O, and compound 2-308@N, respectively, with a distance range of 2.83–2.91 Å and an angle range of 151.15 -159.94 o . This indicated that VEGFR2 had an even more diverse set of hydrogen bonds with compound 2, and it took a larger number of unique bonds to account for most of the hydrogen bonding activity. The interaction of each protein with its specific residues indicated that these residues are likely pivotal for the binding mode of compound 2 and may play a significant role in ligand recognition or binding. The average hydrogen distance and angles for each protein also showed that the hydrogen bonds formed between compound 2 and the proteins were quite linear and had an exceptionally short distance, indicating a robust interaction. Finally, the free energy is calculated and quantitatively measured, and the binding affinity between compound 2 and proteins is measured. The binding of EGFR was energetically favourable, with a Molecular Mechanics–Poisson-Boltzmann Surface Area (MM-PBSA) value of -767.19 (Table 3 ), indicating a stable complex. Again, the Molecular Mechanics–Generalized-Born Surface Area (MM-GBSA) value of -807.34 revealed favourable binding, consistent with the MM-PBSA value. These energy values indicate compound 2 strongly binds to the EGFR protein, potentially stabilising its structure. Similarly, the free energy values calculated for VEGFR2 showed that compound 2 is bound to VEGFR2, indicating a likely stable complex. When comparing AKT1 with EGFR and VEGFR2, the free energies revealed a less favourable free energy between compound 2 and the AKT1 complex, indicating a less stable complex. Based on a thorough comparison of all three proteins using the five metrics examined, compound 2 appeared to have the highest affinity and most stable interaction with EGFR, closely followed by VEGFR2. Table 3 Free Energy Calculations for EGFR, AKT, and VEGFR2 Protein MM-PBSA (kcal/mole) MM-GBSA (kcal/mole) EGFR -767.19 ± 48.64 -807.34 ± 48.64 AKT1 -229.40 ± 46.68 -177.97 ± 46.68 VEGFR2 -729.01 ± 47.75 -772.04 ± 47.73 Drug-likeness prediction and ADMET properties. ADMETlab 2.0 webserver's server predicted that compound 2 adhered to Lipinski's Ro5 criteria[ 60 ] with one hydrogen bond donor, five hydrogen bond acceptors, a molecular weight of 406.23 Da, and an AlogP value of 4.178. The drug-likeness (QED) score prediction for compound 2 was 0.468. Compound 2 exhibited favourable pharmacokinetic properties. SwissADME BOILED-Egg model indicated how closely a compound aligns with the ideal conditions for optimal absorptionIt showed high gastrointestinal (GI) absorption with a bioavailability score of 0.55, indicating moderate oral bioavailability. The compound is also blood–brain barrier (BBB) permeant, suggesting potential effects on the central nervous system. Compound 2 was predicted to have the ability to be absorbed from the GI tract and traverse the BBB, as illustrated in Fig. 6 A. As a P-glycoprotein (P-gp) substrate, it may be subject to efflux, which can affect intracellular concentration. Regarding cytochrome P450 interactions, compound 2 is predicted to inhibit CYP2C19, CYP2C9, and CYP2D6, but not CYP1A2, highlighting a moderate potential for drug–drug interactions. Medicinal chemistry alerts are minimal, with no PAINS alerts, though two Brenk alerts (due to the presence of cumarine and polycyclic aromatic hydrocarbons) are noted. The compound is not lead-like, primarily due to a molecular weight > 350 and a logP > 3.5, but shows a manageable synthetic accessibility score of 4.37. In Fig. 6 B, the drug-likeness radar for compound 2 presented details of its compatibility with six physicochemical characteristics. Notably, the physicochemical characteristics of compound 2 fell within the pink area, as shown in Fig. 6 B. A bioavailability score of 0.55 was predicted for compound 2. In this model, compound 2 was shown to be a p-glycoprotein substrate, implying that it could be transported back into the GI lumen after absorption. The ADMET properties of compound 2 are shown in Table S4 in S text. Discussion The study was conducted to assess the anticancer potential of three synthetic compounds that had previously been identified as potentially antileishmanial. In addition, we used in silico approaches to predict the potential targets of the lead compound. Compound 2 was more potent against breast cancer cell lines using the cytotoxicity assay. The IC 50 values found for both breast cancer cell lines were lower than values obtained for antileishmanial activity (21.9 µM)[ 29 ]. This suggests compound 2 is a potential anticancer drug candidate. In the network pharmacology analysis of compound 2, fifteen hub genes were identified, primarily associated with two fundamental pathways: the focal adhesion pathway and 'pathways in cancer'. Focal adhesions, crucial for cell migration and survival, may be influenced by compound 2, impacting cancer cell invasion and metastasis. The 'Pathways in Cancer' encompass cell cycle regulation, apoptosis, and survival mechanisms. Compound 2 significantly impacted the ErbB Signalling Pathway, specifically EGFR and VEGF signalling VEGFR2 Pathways in Cancer. EGFR, a member of the ErbB family, is frequently overexpressed or mutated in several cancers, including lung, breast, colorectal, and head and neck cancers. Aberrant activation of the ErbB pathway contributes to uncontrolled cell growth, evasion of apoptosis, angiogenesis, and metastasis[ 61 ]. Targeting this pathway has become a significant therapeutic strategy, and EGFR inhibitors such as gefitinib, erlotinib, and cetuximab have shown potential in treating certain cancers, such as non-small cell lung cancer[ 62 ]. Tumours also often overexpress VEGF, promoting angiogenesis and ensuring an adequate blood supply for tumour growth[ 63 ]. VEGRF2 signalling is involved in focal adhesions, emphasising its role in endothelial cell behaviour and angiogenesis. Inhibiting VEGFR2 has been a successful strategy to impede angiogenesis and limit tumour progression. Anti-angiogenic drugs, such as bevacizumab, target VEGFR and are used in treating various cancer types, including colorectal, breast, and renal cancers[ 64 ]. From our studies, Compound 2 can potentially target both EGFR and VEGFR2 for more specific and effective treatments for patients with different types of cancer. The PI3K/AKT pathway was also highlighted as a potential target for compound 2. The PI3K/AKT pathway is frequently dysregulated in cancer and drives uncontrolled cell growth and survival. Targeting PI3K, AKT, or mTOR shows promise in cancer therapy[ 65 ]. The PI3K/AKT pathway is also involved in chemoresistance, emphasising its importance for research and therapeutic development[ 66 , 67 ]. From these studies, Compound 2 demonstrated broad, multi-targeted effects on cancer cells, impacting diverse biological processes. Molecular docking and dynamics simulations focused on AKT1, EGFR, and VEGFR2 to elucidate their interactions with compound 2, providing deeper insights. The binding mode of compound 2 to its protein targets, particularly EGFR, underwent notable conformational changes during MD simulations, significantly influencing protein-ligand interactions. These changes are pivotal in understanding the compound's efficacy and specificity. Initially, compound 2 exhibited a binding mode characterised by interactions with key residues in the ATP-binding site of EGFR, such as Lys745, a critical ATP-binding activation site of EGFR [ 68 ]. Gefitinib and Erlotinib, potent EGFR inhibitors, were also shown to interact with EGFR at the same site. This conformation was stabilised by hydrogen bonds and hydrophobic interactions, positioning the compound effectively within the active site. Compound 2 induced conformational adjustments in the EGFR binding pocket as the MD simulations progressed. The adjustments involved reorientation of side chains and slight backbone movements, resulting in a more precise fit of the ligand. This reveals compound 2 as likely an ATP-competitive inhibitor, directly competing with ATP and inhibiting the phosphorylation activity necessary for VEGFR2 and EGFR signalling. While ATP-competitive inhibitors are effective, the risk of drug resistance exists due to mutations occurring at the same ATP binding site. Additionally, this type of inhibition may impact other kinases with similar ATP-binding pockets. Notably, the interaction of compound 2 with Asp855, which is part of the DFG motif, the activation loop of EGFR, could block the activation of EGFR’s kinase domain, preventing the transmission of growth signals within the cell. During the simulation, the DFG motif (Asp855-Phe856-Gly857) activation loop also transitioned, enhancing the binding affinity of compound 2. The conformational changes increased hydrogen bonds and van der Waals interactions between compound 2 and EGFR. This led to a more stable complex, as evidenced by reduced RMSD values and lower binding free energy calculations. The enhanced stability suggests that compound 2 effectively locks EGFR in an inactive conformation, potentially inhibiting its kinase activity by preventing the transmission of growth signals within the cell. Ultimately, this inhibition strategy of compound 2 binding to both the ATP site and the DFG motif presents a robust inhibition of EGFR and holds promise for preventing cell proliferation and survival in cancers where EGFR dysregulation occurs. Likewise, for VEGFR2, compound 2 exhibited dynamic conformational changes during molecular dynamics simulations, although the effects differed from those observed with EGFR. In the VEGFR2 complex, compound 2 initially occupied the ATP-binding pocket, forming key interactions with residues such as Cys919 and Phe918. This interaction was also seen in some approved VEGFR2 inhibitors, such as sorafenib, and some previously identified novel VEGFR2[ 69 – 71 ]. As the simulation progressed, the ligand adjusted its orientation slightly to optimise hydrogen bonding and hydrophobic contacts, particularly enhancing interactions with Leu840 and surrounding residues. These subtle conformational shifts contributed to improved binding stability, as reflected in sustained hydrogen bond lifetimes and moderate reductions in RMSD, indicating a relatively stable yet flexible complex. In contrast, the AKT1–compound 2 complex displayed greater conformational variability. Compound 2 was outside the canonical ATP-binding site of AKT1 and primarily engaged in solvent-mediated hydrogen bonds with residues like Val271 and Tyr272. During simulation, the ligand's position remained less stable, and the protein exhibited more pronounced fluctuations, suggesting a weaker and less specific interaction. The AKT1 complex also showed the highest RMSD among the three targets and the least favourable binding free energy, consistent with a looser and more transient binding mode. From the MD simulation, AKT1 displayed diverse hydrogen bonds with compound 2, suggesting varied interactions contributing to its overall hydrogen bonding activity. VEGFR2 showed a balance of specific and solvent-mediated bonds, implying a more flexible binding mode, potentially making it more tolerant to ligand mutations[ 72 ] in water-mediated interactions in the AKT1 and VEGFR2 complexes, suggesting that the binding pockets are more solvent-exposed or that water molecules play an essential role in stabilising the ligand within the pocket[ 73 ]. The observed conformational change or flexibility for EGFR could be crucial to accommodate the ligand, compound 2, or to help the protein function. This conformational change in EGFR upon binding has been attributed to the extracellular domains of EGFR family members dimerising to activate the protein[ 74 ]. The high stability of interactions between compound 2 and EGFR, characterised by strong hydrogen bonds, indicates a specific binding pocket. Compound 2 formed an energetically favourable bond from the MM-PBSA and MM-GBSA calculations as well as strong and geometrically favourable hydrogen bonds with EGFR, suggesting a robust binding mechanism. This has implications for drug development because compound 2 may be a better inhibitor or modulator of EGFR and possibly VEGFR2 than AKT. This finding from the MD simulation analysis is also consistent with our KEGG pathway enrichment profile. EGFR inhibitors represent a promising strategy to combat EGFR-driven malignancies. These inhibitors bind to the EGFR protein, blocking its activation and downstream signalling. Consequently, they impede the uncontrolled growth and division of cancer cells, ultimately leading to cell death. However, existing EGFR inhibitors have limitations, including efficacy challenges and the emergence of resistance. While first-generation inhibitors like erlotinib and gefitinib show promise in treating specific EGFR-mutant cancers, resistance often develops over time[ 75 ]. Osimertinib, a third-generation irreversible EGFR-Tyrosine kinase Inhibitor (TKI), has effectively treated non-small cell lung cancer (NSCLC) in patients with EGFR T790M mutations. Unfortunately, even osimertinib faces resistance, limiting its long-term effectiveness[ 76 , 77 ]. Blueprint’s lead EGFR inhibitor, BLU-945, has shown safety in phase I trials, and fourth-generation inhibitors hold the potential for overcoming resistance mechanisms[ 78 ]. However, continuing research and developing improved EGFR inhibitors are essential to enhance therapeutic outcomes. Our study highlights compound 2 as a promising targeted therapeutic option for anticancer treatment. The dual binding mechanism of compound 2 may enhance specificity, minimising off-target effects often associated with broader ATP-competitive inhibitors[ 79 ]. Furthermore, compound 2 could prove valuable in treating cancers resistant to other EGFR inhibitors that solely target the ATP binding site. We may overcome or delay resistance by targeting multiple sites, as cancer cells would need to accumulate mutations at various locations to evade inhibition. The bioavailability score suggests that roughly 55% of the compound will be absorbed into the bloodstream after oral administration, indicating its potential for such use. This bioavailability score was consistent with that of compounds from the ZINC database screened against VEGFR2[ 80 ]. The quantitative estimate of QED score prediction for compound 2 was 0.468, indicating that compound 2 requires optimization as a lead compound for drug development[ 81 ]. However, several limitations should be considered. The molecular docking and dynamics simulations offer theoretical insights into the interactions between compound 2 and its targets. However, these predictions require validation through experimental binding assays and crystallography studies to confirm the accuracy of the predicted binding modes and interactions. Although this study employed established molecular dynamics metrics such as RMSD, RMSF, radius of gyration, hydrogen bond lifetime analysis, and MM/GBSA/MM-PBSA free energy calculations to characterize the stability and binding interactions of compound 2, additional analyses such as probability distribution function (PDF) analysis, principal component analysis (PCA) of trajectory variance, and dynamic cross-correlation (DCC) were not included. Incorporating these advanced approaches in future studies may provide deeper insight into the collective motions, correlated residue dynamics, and allosteric effects associated with ligand binding. Conclusion The present study has provided promising insights into the potential of compound 2 as an anticancer agent. Compound 2 exhibits varying affinities and stabilities with AKT1, EGFR, and VEGFR2. It binds EGFR and VEGFR2 at their ATP sites, forming stable hydrogen bonds, while binding AKT1 outside its ATP site via solvent-mediated hydrogen bonds. EGFR shows the strongest binding with low RMSD (~ 1.5 Å), compact structure (RoG ~ 38.9 Å), and favourable binding energies (MM-GBSA: -807.34 kcal/mol; MM-PBSA: -767.19 kcal/mol). The EGFR-ligand complex remains compact and stable. VEGFR2 also forms stable bonds with positive binding energy (MM-GBSA: -772.04 kcal/mol; MM-PBSA: -729.01 kcal/mol) but is less stable than EGFR. AKT1 demonstrates higher RMSD (~ 2.5 Å), weaker interactions, and less favourable binding energy (MM-GBSA: -177.97 kcal/mol; MM-PBSA: -229.40 kcal/mol). Therefore, EGFR is identified as the most favourable binding target, making it a promising candidate for further study in targeted anticancer therapy. Compound 2’s dual-site binding suggests it could be a potent and selective EGFR inhibitor. Though the study indicates compound 2's potential as an anticancer therapy, several limitations should be considered. The molecular docking and dynamics simulations offer theoretical insights into the interactions between compound 2 and its targets. However, these predictions require validation through experimental binding assays and crystallography studies to confirm the accuracy of the predicted binding modes and interactions. Also broader target profiling is needed. Future research should focus on these areas to maximise its clinical development. These findings will validate the simulation data, enhance our understanding of compound 2`s binding specificity, affinity, and availability, and aid drug development and optimisation. Declarations Acknowledgement The authors would like to acknowledge the use of high-performance computing facilities from New Zealand eScience Infrastructure (NeSI) as part of this research. The research was funded jointly by the collaborating institutions and the Ministry of Business, Innovation, and Employment Research Infrastructure program. The URL is https://www.nesi.org.nz. Authors contributions BT, RKA, ARA and KANS designed the research. BT, DMA, JTQ and KF performed all the computational analysis. BKYH, AS, BT, KF, and JNKA performed all the cytotoxicity screening. BT, DMAT, and JTQ analysed all the data. ARA, DMA and KANS validated the data. BT, DMA, JTQ and KF wrote sections of the manuscript. ARA, KANS, RKA, DP and DMA critical manuscript revision. Data availability The datasets generated and/or analysed during the current study are available in the Zenodo repository, https://zenodo.org/doi/10.5281/zenodo.10902946 Supporting information This article contains supplementary figures, Fig S1 and Fig S2, and table S1, S2, S3, S4 in file S Text and supplementary excel files; S1, S2, S3, S4, S5, S6. Funding Bernardine Tuah was supported by a WACCBIP-World Bank ACE Masters/PhD fellowship (WACCBI+NCDS; Awandare). Conflict of interests The authors declare no competing interest. Ethical Approval Ethical Approval is not applicable for this article. Statement of Human and Animal Rights This article does not contain any studies with human or animal subjects. Statement of Informed Consent There are no human subjects in this article and informed consent is not applicable. References Maleki EH, Bahrami AR, Matin MM (2023) Cancer cell cycle heterogeneity as a critical determinant of therapeutic resistance. Genes Dis 11:189–204. https://doi.org/10.1016/j.gendis.2022.11.025 Galmarini CM (2020) Why we do what we do. A brief analysis of cancer therapies. EXCLI J 19:1401–1413. https://doi.org/10.17179/excli2020-2972 DiMasi JA, Grabowski HG, Hansen RW (2016) Innovation in the pharmaceutical industry: New estimates of R&D costs. J Health Econ 47:20–33. https://doi.org/10.1016/j.jhealeco.2016.01.012 Kim D, Lee J, Lee S, Park J, Lee D (2016) Predicting unintended effects of drugs based on off-target tissue effects. Biochem Biophys Res Commun 469:399–404. https://doi.org/10.1016/j.bbrc.2015.11.095 Anand U, Dey A, Chandel AKS, Sanyal R, Mishra A, Pandey DK et al (2023) Cancer chemotherapy and beyond: Current status, drug candidates, associated risks and progress in targeted therapeutics. Genes Dis 10:1367–1401. https://doi.org/10.1016/j.gendis.2022.02.007 Lin A, Giuliano CJ, Palladino A, John KM, Abramowicz C, Yuan M, Lou et al (2019) Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials. Sci Transl Med 11:8412. https://doi.org/10.1126/SCITRANSLMED.AAW8412 Wang S, Tong Y, Ng TB, Lao L, Lam JKW, Zhang KY et al (2015) Network pharmacological identification of active compounds and potential actions of Erxian decoction in alleviating menopause-related symptoms. Chin Med 10. https://doi.org/10.1186/s13020-015-0051-z Huang DW, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37:1–13. https://doi.org/10.1093/nar/gkn923 Azmi AS (2012) Network pharmacology for cancer drug discovery: Are we there yet? Future Med Chem 4:939–941 Malkhasian AYS, Howlin BJ (2019) Automated drug design of kinase inhibitors to treat Chronic Myeloid Leukemia. J Mol Graph Model 91:52–60. https://doi.org/10.1016/j.jmgm.2019.05.014 Nagar B, Bornmann WG, Pellicena P, Schindler T, Veach DR, Miller T et al (2002) Crystal structures of the kinase domain of c-Abl in complex with the small molecule inhibitors PD173955 and imatinib (STI-571) LinkOut-more resources Other Literature Sources H1 Connect The Lens-Patent Citations Molecular Biology Databases Guide to Pharmacology Saccharomyces Genome Database. Cancer Res 62:4236–4279 Tang HSC, Chen YC (2015) Insight into molecular dynamics simulation of BRAF(V600E) and potent novel inhibitors for malignant melanoma. Int J Nanomed 10:3131–3146. https://doi.org/10.2147/IJN.S80150 Baassi M, Moussaoui M, Soufi H, Rajkhowa S, Sharma A, Sinha S et al (2023) Towards designing of a potential new HIV-1 protease inhibitor using QSAR study in combination with Molecular docking and Molecular dynamics simulations. PLoS ONE 18. https://doi.org/10.1371/journal.pone.0284539 Jenwitheesuk E, Samudrala R (2003) Improved prediction of HIV-1 protease-inhibitor binding energies by molecular dynamics simulations. BMC Struct Biol 3:1. https://doi.org/10.1186/1472-6807-3-2 . https://doi.org/ Yoosefian M, Moghani MZ, Juan A (2022) In silico evaluation of atazanavir as a potential HIV main protease inhibitor and its comparison with new designed analogs. Comput Biol Med 145:105523. https://doi.org/10.1016/j.compbiomed.2022.105523 Nichols SE, Baron R, Ivetac A, McCammon JA (2011) Predictive power of molecular dynamics receptor structures in virtual screening. J Chem Inf Model 51:1439–1446. https://doi.org/10.1021/ci200117n Peele KA, Durthi P, Srihansa T, Krupanidhi S, Sai Ayyagari V, Babu DJ et al (2020) Molecular docking and dynamic simulations for antiviral compounds against SARS-CoV-2: A computational study. Inf Med Unlocked 19:100345. https://doi.org/10.1016/j.imu.2020.100345 Saha S, Prinsa, Kawsar SMA. Natural Flavonoids as Primary Amoebic Meningoencephalitis Inhibitor: Virtual Screening, Molecular Docking, Simulation MD (2024) MMPBSA, Density Functional Theory, Principal Component, and Gibbs Free Energy Landscape Analyses. Chem Biodiv https://doi.org/10.1002/cbdv.202402521 Alamri MA, Alawam AS, Alshahrani MM, Kawsar SMA, Prinsa, Saha S (2023) Establishing the Role of Iridoids as Potential Kirsten Rat Sarcoma Viral Oncogene Homolog G12C Inhibitors Using Molecular Docking; Molecular Docking Simulation; Molecular Mechanics Poisson–Boltzmann Surface Area; Frontier Molecular Orbital Theory; Molecular Electrostatic Potential; and Absorption, Distribution, Metabolism, Excretion, and Toxicity Analysis. Molecules 28. https://doi.org/10.3390/molecules28135050 Akter S, Alhatlani BY, Abdallah EM, Saha S, Ferdous J, Hossain ME et al Exploring Cinnamoyl-Substituted Mannopyranosides: Synthesis, Evaluation of Antimicrobial Properties, and Molecular Docking Studies Targeting H5N1 Influenza A Virus. Molecules 2023;28. https://doi.org/10.3390/molecules28248001 Cao Y, Li Z, Mao L, Cao H, Kong J, Yu B et al (2019) The use of proteomic technologies to study molecular mechanisms of multidrug resistance in cancer. Eur J Med Chem 162:423–434. https://doi.org/10.1016/J.EJMECH.2018.10.001 Herbst RS (2004) Review of epidermal growth factor receptor biology. Int J Radiat Oncol Biol Phys 59:21–26. https://doi.org/10.1016/j.ijrobp.2003.11.041 Levantini E, Maroni G, Del Re M, Tenen DG (2022) EGFR signaling pathway as therapeutic target in human cancers. Semin Cancer Biol 85:253–275. https://doi.org/10.1016/J.SEMCANCER.2022.04.002 García-Foncillas J, Sunakawa Y, Aderka D, Wainberg Z, Ronga P, Witzler P et al (2019) Distinguishing Features of Cetuximab and Panitumumab in Colorectal Cancer and Other Solid Tumors. Front Oncol 9:849. https://doi.org/10.3389/FONC.2019.00849 Shaban N, Kamashev D, Emelianova A, Buzdin A (47 2023) Targeted Inhibitors of EGFR: Structure, Biology, Biomarkers, and Clinical Applications. Cells 2024. Page 13:13:47. https://doi.org/10.3390/CELLS13010047 Ward RA, Anderton MJ, Ashton S, Bethel PA, Box M, Butterworth S et al (2013) Structure- and reactivity-based development of covalent inhibitors of the activating and gatekeeper mutant forms of the epidermal growth factor receptor (EGFR). J Med Chem 56:7025–7048. https://doi.org/10.1021/JM400822Z Leighl NB, Karaseva N, Nakagawa K, Cho BC, Gray JE, Hovey T et al (2020) Patient-reported outcomes from FLAURA: Osimertinib versus erlotinib or gefitinib in patients with EGFR-mutated advanced non-small-cell lung cancer. Eur J Cancer 125:49–57. https://doi.org/10.1016/j.ejca.2019.11.006 Kannan S, Tan DSW, Verma CS (2019) Effects of Single Nucleotide Polymorphisms on the Binding of Afatinib to EGFR: A Potential Patient Stratification Factor Revealed by Modeling Studies. J Chem Inf Model 59:309–315. https://doi.org/10.1021/acs.jcim.8b00491 Sakyi PO, Kwofie SK, Tuekpe JK, Gwira TM, Broni E, Miller WA et al Inhibiting Leishmania donovani Sterol Methyltransferase to Identify Lead Compounds Using Molecular Modelling. Pharmaceuticals 2023;16. https://doi.org/10.3390/ph16030330 Raj V, Lee J (2020) 2H/4H-Chromenes—A Versatile Biologically Attractive Scaffold. Front Chem 8. https://doi.org/10.3389/fchem.2020.00623 Perez J, Fuertes M, Nguewa P, Castilla J, Alonso C (2008) Anticancer Compounds as Leishmanicidal Drugs: Challenges in Chemotherapy and Future Perspectives. Curr Med Chem 15:433–439. https://doi.org/10.2174/092986708783503221 Croft SL, Engel J Miltefosine - discovery of the antileishmanial activity of phospholipid derivatives. Trans R Soc Trop Med Hyg 2006;100. https://doi.org/10.1016/j.trstmh.2006.03.009 Duarte D, Nunes M, Ricardo S, Vale N Combination of Antimalarial and CNS Drugs with Antineoplastic Agents in MCF-7 Breast and HT-29 Colon Cancer Cells: Biosafety Evaluation and Mechanism of Action. Biomolecules 2022;12. https://doi.org/10.3390/biom12101490 Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, Ronzano F, Centeno E, Sanz F et al (2020) The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res 48:D845–D855. https://doi.org/10.1093/nar/gkz1021 Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S et al (2016) Curr Protoc Bioinf 54(130). https://doi.org/10.1002/cpbi.5 . The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses Daina A, Michielin O, Zoete V (2019) SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res 47:W357–W364. https://doi.org/10.1093/nar/gkz382 Gallo K, Goede A, Preissner R, Gohlke B-O (2022) SuperPred 3.0: drug classification and target prediction - a machine learning approach. Nucleic Acids Res 50:W726–W731. https://doi.org/10.1093/nar/gkac297 The UniProt Consortium (2021) UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 49:D480–D489. https://doi.org/10.1093/nar/gkaa1100 Pathan M, Keerthikumar S, Ang C-S, Gangoda L, Quek CYJ, Williamson NA et al (2015) FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics 15:2597–2601. https://doi.org/https://doi.org/10.1002/pmic.201400515 Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J et al (2009) STRING 8 - a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 37:D412–D416. https://doi.org/10.1093/nar/gkn760 Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303 Chin C-H, Chen S-H, Wu H-H, Ho C-W, Ko M-T, Lin C-Y (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8(Suppl 4):S11. https://doi.org/10.1186/1752-0509-8-S4-S11 Bader GD, Hogue CWV (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4:2. https://doi.org/10.1186/1471-2105-4-2 Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O et al (2019) Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 10:1523. https://doi.org/10.1038/s41467-019-09234-6 Obst-Sander U, Ricci A, Kuhn B, Friess T, Koldewey P, Kuglstatter A et al (2022) Discovery of Novel Allosteric EGFR L858R Inhibitors for the Treatment of Non-Small-Cell Lung Cancer as a Single Agent or in Combination with Osimertinib. J Med Chem 65:13052–13073. https://doi.org/10.1021/acs.jmedchem.2c00893 Uhlenbrock N, Smith S, Weisner J, Landel I, Lindemann M, Le TA et al (2019) Structural and chemical insights into the covalent-allosteric inhibition of the protein kinase Akt. Chem Sci 10:3573–3585. https://doi.org/10.1039/c8sc05212c McTigue M, Murray BW, Chen JH, Deng YL, Solowiej J, Kania RS (2012) Molecular conformations, interactions, and properties associated with drug efficiency and clinical performance among VEGFR TK inhibitors. Proc Natl Acad Sci U S A 109:18281–18289. https://doi.org/10.1073/pnas.1207759109 Berman H, Henrick K, Nakamura H (2003) Announcing the worldwide Protein Data Bank. Nat Struct Biol 10:980 Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242 Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and Validation of a Genetic Algorithm for Flexible Docking. J Mol Biol 267:727–748 Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using GOLD. Proteins 52:609–623. https://doi.org/10.1002/prot.10465 Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11:425–445 Korb O, Stützle T, Exner TE (2009) Empirical scoring functions for advanced Protein-Ligand docking with PLANTS. J Chem Inf Model 49:84–96. https://doi.org/10.1021/ci800298z Mooij WTM, Verdonk ML (2005) General and targeted statistical potentials for protein-ligand interactions. Proteins 61:272–287. https://doi.org/10.1002/prot.20588 Case DA, Cheatham TE, Darden T, Gohlke H, Luo R, Merz KM et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688. https://doi.org/10.1002/jcc.20290 Salomon-Ferrer R, Case DA, Walker RC (2013) An overview of the Amber biomolecular simulation package. Wiley Interdiscip Rev Comput Mol Sci 3:198–210. https://doi.org/10.1002/wcms.1121 Weiner PK, Kollman PA (1981) AMBER: Assisted Model Building with Energy Refinement. A General Program for Modeling Molecules and Their Interactions. J Comput Chem 2:287–303 Xiong G, Wu Z, Yi J, Fu L, Yang Z, Hsieh C et al (2021) ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res 49. https://doi.org/10.1093/nar/gkab255 Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7. https://doi.org/10.1038/srep42717 Lipinski CA (2004) Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov Today Technol 1:337–341 Lindsey S, Langhans SA (2015) Epidermal growth factor signaling in transformed cells. Int Rev Cell Mol Biol 314:1–41. https://doi.org/10.1016/bs.ircmb.2014.10.001 Yang Z, Hackshaw A, Feng Q, Fu X, Zhang Y, Mao C et al (2017) Comparison of gefitinib, erlotinib and afatinib in non-small cell lung cancer: A meta-analysis. Int J Cancer 140:2805–2819. https://doi.org/10.1002/IJC.30691 Niu G, Chen X (2010) Vascular Endothelial Growth Factor as an Anti-Angiogenic Target for Cancer Therapy. Curr Drug Targets 11:1000–1017. https://doi.org/10.2174/138945010791591395 Hsu JY, Wakelee HA (2009) Monoclonal antibodies targeting vascular endothelial growth factor: current status and future challenges in cancer therapy. BioDrugs 23:289–304. https://doi.org/10.2165/11317600-000000000-00000 Glaviano A, Foo ASC, Lam HY, Yap KCH, Jacot W, Jones RH et al (2023) PI3K/AKT/mTOR signaling transduction pathway and targeted therapies in cancer. Mol Cancer 22:138. https://doi.org/10.1186/S12943-023-01827-6 Liu R, Chen Y, Liu G, Li C, Song Y, Cao Z et al (2020) PI3K/AKT pathway as a key link modulates the multidrug resistance of cancers. Cell Death Disease 2020 11:9. https://doi.org/10.1038/s41419-020-02998-6 Kaboli PJ, Imani S, Jomhori M, Ling K-H (2021) Chemoresistance in breast cancer: PI3K/Akt pathway inhibitors vs the current chemotherapy. Am J Cancer Res 11:5155 Honegger AM, Dull TJ, Felder S, Van Obberghen E, Bellot F, Szapary D et al (1987) Point Mutation at the ATP Binding Site of EGF Receptor Abolishes Protein-Tyrosine Kinase Activity and Alters Cellular Routing. Cell 51:199–209 Bauer D, Whittington DA, Coxon A, Bready J, Harriman SP, Patel VF et al (2008) Evaluation of indazole-based compounds as a new class of potent KDR/VEGFR-2 inhibitors. Bioorg Med Chem Lett 18:4844–4848. https://doi.org/10.1016/j.bmcl.2008.07.080 Aziz MA, Serya RAT, Lasheen DS, Abdel-Aziz AK, Esmat A, Mansour AM et al (2016) Discovery of Potent VEGFR-2 Inhibitors based on Furopyrimidine and Thienopyrimidne Scaffolds as Cancer Targeting Agents. Sci Rep 6. https://doi.org/10.1038/srep24460 Liu Y, Li Y, Wang Y, Lin C, Zhang D, Chen J et al (2022) Recent progress on vascular endothelial growth factor receptor inhibitors with dual targeting capabilities for tumor therapy. J Hematol Oncol 15. https://doi.org/10.1186/s13045-022-01310-7 Sarabipour S, Ballmer-Hofer K, Hristova K VEGFR-2 conformational switch in response to ligand binding. Elife 2016;5. https://doi.org/10.7554/eLife.13876 Schiebel J, Gaspari R, Wulsdorf T, Ngo K, Sohn C, Schrader TE et al Intriguing role of water in protein-ligand binding studied by neutron crystallography on trypsin complexes. Nat Commun 2018;9. https://doi.org/10.1038/s41467-018-05769-2 Ferguson KM, Berger MB, Mendrola JM, Cho H-S, Leahy DJ, Lemmon MA (2003) EGF Activates Its Receptor by Removing Interactions that Autoinhibit Ectodomain Dimerization molecules. Crystallographic studies of several ligand-bound RTK extracellular fragments have allowed visual-ization of such dimers, including those from the Flt-1 of the complete EGF receptor extracellular region. Mol Cell 11:507–517 Pao W, Miller VA, Politi KA, Riely GJ, Somwar R, Zakowski MF et al (2005) Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PLoS Med 2:0225–0235. https://doi.org/10.1371/journal.pmed.0020073 Li Y, Mao T, Wang J, Zheng H, Hu Z, Cao P et al (2023) Toward the next generation EGFR inhibitors: an overview of osimertinib resistance mediated by EGFR mutations in non-small cell lung cancer. Cell Commun Signal 21. https://doi.org/10.1186/s12964-023-01082-8 Du X, Yang B, An Q, Assaraf YG, Cao X, Xia J Acquired resistance to third-generation EGFR-TKIs and emerging next-generation EGFR inhibitors. Innov (Camb) 2021;2. https://doi.org/10.1016/j.xinn.2021.100103 Mullard A (2022) Do fourth-generation EGFR inhibitors showcase the future of kinase inhibitors? Nat Rev Drug Discov 21:408–409. https://doi.org/10.1038/d41573-022-00091-5 Wittlinger F, Ogboo BC, Shevchenko E, Damghani T, Pham CD, Schaeffner IK et al Linking ATP and allosteric sites to achieve superadditive binding with bivalent EGFR kinase inhibitors. Commun Chem 2024;7. https://doi.org/10.1038/s42004-024-01108-3 Alamri MA, Merae Alshahrani M, Alawam AS, Paria S, Kumar Sen K, Banerjee S et al (2024) Development of newer generation Vascular endothelial growth factor Receptor-2 Inhibitors: Pharmacophore based design, virtual Screening, molecular Docking, molecular dynamic Simulation, and DFT analyses. J King Saud Univ Sci 36. https://doi.org/10.1016/j.jksus.2024.103285 Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90–98. https://doi.org/10.1038/nchem.1243 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryInformation.zip Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-6998508","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477771646,"identity":"b4b7d143-7248-4e09-8149-40be8806b472","order_by":0,"name":"Bernardine Tuah","email":"","orcid":"https://orcid.org/0000-0002-3940-346X","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Bernardine","middleName":"","lastName":"Tuah","suffix":""},{"id":477771647,"identity":"53adfbef-92ba-4dff-aff6-3752dbc648c3","order_by":1,"name":"Daniel Moscoh Ayine-Tora","email":"","orcid":"","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Moscoh","lastName":"Ayine-Tora","suffix":""},{"id":477771648,"identity":"59e03d45-b65f-4fdd-b733-3ebdb646e589","order_by":2,"name":"Jude Tetteh Quarshie","email":"","orcid":"https://orcid.org/0000-0001-5654-1859","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Jude","middleName":"Tetteh","lastName":"Quarshie","suffix":""},{"id":477771649,"identity":"fb11c202-67f2-458b-93df-295738d87464","order_by":3,"name":"Kwadwo Fosu","email":"","orcid":"https://orcid.org/0000-0003-3092-1348","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Kwadwo","middleName":"","lastName":"Fosu","suffix":""},{"id":477771650,"identity":"c2cd069b-7928-4c9a-844f-9998bad4a6fd","order_by":4,"name":"Bright Hodogbe Kwame Yayra","email":"","orcid":"","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Bright","middleName":"Hodogbe Kwame","lastName":"Yayra","suffix":""},{"id":477771651,"identity":"c89573ae-5467-4e7c-8634-9315921d5e0d","order_by":5,"name":"Jutsum Nii Kotei Amon","email":"","orcid":"","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Jutsum","middleName":"Nii Kotei","lastName":"Amon","suffix":""},{"id":477771652,"identity":"95171e65-0553-45b7-99de-92514b946b7c","order_by":6,"name":"Alberta Serwaa","email":"","orcid":"https://orcid.org/0009-0007-1466-2354","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Alberta","middleName":"","lastName":"Serwaa","suffix":""},{"id":477771653,"identity":"b3e122bf-7d26-4105-a25a-1192cf00e577","order_by":7,"name":"Diana Ahu Prah","email":"","orcid":"https://orcid.org/0000-0002-8604-7525","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Diana","middleName":"Ahu","lastName":"Prah","suffix":""},{"id":477771654,"identity":"b67fc2ab-9aab-438b-b4fc-b7d0f797d11a","order_by":8,"name":"Richard Kwamla Amewu","email":"","orcid":"https://orcid.org/0000-0002-4676-436X","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"Kwamla","lastName":"Amewu","suffix":""},{"id":477771655,"identity":"0e6c969b-ff19-48d9-a02e-94af661207e0","order_by":9,"name":"Anastasia Rosebud Aikins","email":"","orcid":"https://orcid.org/0000-0001-6028-9625","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Anastasia","middleName":"Rosebud","lastName":"Aikins","suffix":""},{"id":477771656,"identity":"0942982d-14d2-4e2e-b216-0073406dbffb","order_by":10,"name":"Kwabena Amofa Nketia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBACCQST+QCIZGxgYGAjVgtbAslaeAyI0yLZfvbwh5977siZ86/5JvmFwUZ2wwH2aw/waZHmyUuT7Hn2zNhyxttt0jIMacYbDvCUG+DTIseQY8bAc+Bw4oYbZ7dJSzAAGQd40iTwauF/Y/zxD1jLmWdALf8Ja5GWyDGQBttyvodN8gPDAaAW9mN4tUjOeGMmLXPgmbHBDTZjawaDZOOZh3nY8GqROJ9j/PHNgTtyBucPP7z5o8JOtu94+zO8WqDgAFBzAgMzOGogJDFa+A8wMP4Ac9gfEKNlFIyCUTAKRg4AAJCNUPtuNB5WAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1166-5756","institution":"University of Ghana","correspondingAuthor":true,"prefix":"","firstName":"Kwabena","middleName":"Amofa","lastName":"Nketia","suffix":""}],"badges":[],"createdAt":"2025-06-28 14:45:43","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6998508/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6998508/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85822948,"identity":"7177337f-d858-4a23-aa7b-5b0d470f6450","added_by":"auto","created_at":"2025-07-02 06:51:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98193,"visible":true,"origin":"","legend":"\u003cp\u003eThe molecular structure of the three small molecule compounds used in the anticancer screening\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6998508/v1/a481b13a9d88b5c96cb3a91e.png"},{"id":85822947,"identity":"11390011-42e9-4ea3-bf10-0111695b4016","added_by":"auto","created_at":"2025-07-02 06:51:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":543837,"visible":true,"origin":"","legend":"\u003cp\u003eCytotoxicity graph of compound 2 against four cancer cell lines: A) MDA MB 468, B) MDA MB 231, C) DLD-1, and D) PC3. The percentage cell viability was plotted against nine concentrations of 2 and DOX. The negative control (untreated cells) is represented on the graph as a percentage viability against a compound or DOX concentration of zero. See Fig S1 in S Text for compounds 1 and 3 cytotoxicity graphs.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6998508/v1/d6c1737e377c0b509e874678.png"},{"id":85822956,"identity":"7dae728b-88ca-4284-a862-f02f36573d84","added_by":"auto","created_at":"2025-07-02 06:51:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":346883,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway plot showing the functional enrichment analyses of core targets. The bigger bubble represents the enrichment of genes (high Z-score) in that pathway. The colour bubble in the plot represents the statistical significance of the pathways corresponding to the colour on the log(q-value) scale bar.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6998508/v1/0adf35f28e5c7bb99ef07beb.png"},{"id":85822952,"identity":"1065f8d1-d726-4484-8a89-53b9726ef240","added_by":"auto","created_at":"2025-07-02 06:51:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1020398,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking of compound 2 to three core targets: A) AKT1 (B) EGFR (C) VEGFR2. The top displays the protein surface; blue represents the hydrophilic regions, brown represents the hydrophobic regions, and grey represents the neutral areas. The bottom shows the binding interactions. The compound is in ball-and-stick format, while the amino acid residues are in stick form. Green lines represent hydrogen bonds, while purple lines indicate hydrophobic contact. (D) Image showing compound 2 in the hinge region of EGFR.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6998508/v1/3c8fc95e8b403f58b5c082a7.png"},{"id":85822976,"identity":"ac0495d2-d291-44d5-a142-9f2d98fdc5ab","added_by":"auto","created_at":"2025-07-02 06:52:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1020186,"visible":true,"origin":"","legend":"\u003cp\u003ePlots showing the Molecular dynamics simulations of EGFR, VEGFR2, and AKT1. Simulations were performed for approximately 1000 frames.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e RoG for EGFR, VEGFR2 and AKT1 bound to the ligand, compound 2 over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e RoG max for EGFR, VEGFR2 and AKT1 bound to the ligand, compound 2 over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003e RMSD plot showing the deviation of EGFR, VEGFR2 and AKT1 bound to compound 2 over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD.\u003c/strong\u003e RMSF plot showing the residue fluctuations of EGFR, VEGFR2 and AKT1 complexed to compound 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE.\u003c/strong\u003e The hydrogen bond lifetime plot of EGFR, VEGFR2 and AKT1 complexed with the ligand, compound 2.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6998508/v1/c7b0ef2e7c93d81641d4f1ba.png"},{"id":85822964,"identity":"e7d17de9-f3ba-49bc-a508-0712a38d0cff","added_by":"auto","created_at":"2025-07-02 06:52:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":414254,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Egan BOILED-Egg model and (B) drug-likeness radar of compound.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6998508/v1/bbaa00c0063a3f37996e7c19.png"},{"id":85825203,"identity":"1882f2f0-bb9c-4618-b096-449162b7f625","added_by":"auto","created_at":"2025-07-02 07:08:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4545110,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6998508/v1/c3b6ddc3-3988-46c4-b57c-57e6a30d2550.pdf"},{"id":85822994,"identity":"4fd3f1f0-f794-440a-8ea9-70256a9ea1d7","added_by":"auto","created_at":"2025-07-02 06:52:32","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1822236,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.zip","url":"https://assets-eu.researchsquare.com/files/rs-6998508/v1/6884fb0de9af8396d78c2973.zip"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCharacterization of Novel Anticancer Agent using Computer-Aided Drug Discovery Processes Reveals Selective Interaction with the Epidermal Growth Factor Receptor\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer is a global health concern, despite significant advances in medical and pharmaceutical research. Despite the notable success of chemotherapy, challenges such as systemic toxicity, drug resistance, off-target effects, and the heterogeneity of cancer cells hamper the utility and efficacy of the therapeutics [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Potential anticancer drugs fail in clinical trials owing to poor pharmacokinetic properties, lack of specificity, and adverse side effects[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Additionally, many drugs have off-target effects and interact with proteins that are not intended to cause side effects[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This lack of specificity limits the therapeutic potential of most anticancer drugs and contributes to their adverse effects[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Strategies such as the \"network target, multi-component\" approach, which applies cutting-edge technologies such as network pharmacology, molecular docking, and molecular dynamics (MD) simulation, have been adopted to transform the drug discovery process and efficiently solve traditional barriers. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eNetwork pharmacology, molecular docking and MD simulations have become vital approaches to discovering new cancer drugs. These approaches help investigate the connections between biological elements to comprehensively understand diseases and identify the biological pathways, genes, and protein sets influenced by drugs[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The development of imatinib (Gleevec) as a target for Bcr-Abl for chronic myeloid leukaemia patients, BRAF for treating malignant melanoma, as well as antiviral drug inhibitors for HIV, influenza and SARS-CoV-2 proteins highlight the significance of utilising MD simulations for therapeutics[\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Recent studies have also employed similar approaches to identify iridoids as KRAS G12C inhibitors, to evaluate cinnamoyl-mannopyranosides against H5N1 neuraminidase, and to assess flavonoids targeting Naegleria fowleri[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These studies highlight the predictive power of computational modelling for identifying bioactive candidates. Docking and MD simulations in this work characterized compound interactions with key targets, demonstrating the effectiveness of computational techniques for evaluating novel small molecules. Understanding the dynamics of these interactions aids in developing drugs that can successfully navigate cell membranes and reach their targets.\u003c/p\u003e \u003cp\u003ethe Epidermal Growth Factor Receptor (EGFR) is a transmembrane protein involved in the regulation of cell proliferation, survival, and differentiation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Its overexpression and mutation are implicated in various malignancies, including non-small cell lung cancer, colorectal cancer, and glioblastoma[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Among various cancer types, those associated with aberrant signalling through EGFR pathway are particularly aggressive and resistant to conventional treatments[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Targeting both the extracellular and intracellular portions, EGFR has thus become a pivotal strategy in the fight against cancer. The extracellular domain of EGFR can be targeted by monoclonal antibodies (mAbs) such as cetuximab and panitumumab. These antibodies inhibit ligand binding, preventing receptor dimerisation and subsequent activation of downstream signalling pathways[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The intracellular kinase domain of EGFR can be targeted by small molecule tyrosine kinase inhibitors (TKIs) such as gefitinib, erlotinib, and osimertinib. These inhibitors block ATP binding in the tyrosine kinase domain, thereby inhibiting autophosphorylation and downstream signalling[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Third-generation EGFR inhibitors such as Osimertinib were developed using CADD. Dr. Michael Waring and his team at AstraZeneca employed molecular modelling to achieve high specificity for the T790M resistance mutation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Following successful preclinical studies, clinical trials validated the effectiveness of osimertinib, leading to its FDA approval[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Computer-aided drug discovery processes have also been used to explain the potential off-target effects and drug resistance. Afatinib, an irreversible inhibitor of the EGFR tyrosine kinase, was shown to bind to different variants of EGFR using molecular dynamics simulations and covalent docking[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eHere, we aimed to discover potential anticancer compounds by screening three previously identified antileishmanial compounds for their cytotoxicity. These molecules were shown to inhibit \u003cem\u003eLeishmania donovani\u003c/em\u003e sterol methyltransferase in silico[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Compound 1 (STOCK6S-84928) contains triazolopyridazine, while compounds 2 (S6S-06707) and 3 (STOCK6S-65920) are chromone-based. Although chromone-based compounds have been studied for their anticancer properties[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], there is limited data available on compounds 2 and 3. The anticancer potential of triazolopyridazine remains underexplored. Some anticancer drugs have shown antileishmanial activities, like miltefosine, amphotericin B, and sodium stibogluconate, which were originally developed for cancer treatment but became effective against Leishmaniasis[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These dual activities may arise from shared mechanisms, such as disrupting membrane integrity and interfering with survival pathways. Given this link, the study aimed to investigate the anticancer properties of compounds 1, 2 and 3 with known anticancer scaffolds. Of the three compounds, compound 2 showed promising anticancer cytotoxic effects. Furthermore, we aimed to employ a systematic computational approach to identify the pharmacological properties and targets of the lead compound. Three targets were identified and subjected to MD simulations to explore the dynamic behaviour and structural changes occurring in the potential target(s) when complexed with the lead compound. EGFR was later identified as the most promising target of compound 2.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eCancer cell lines and cell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePC3 prostate cancer, DLD-1 colorectal cancer, MDA MB 468, and MDA-MB-231 breast cancer cell lines were obtained from ATCC. The standard human prostate cell line, PNT2, was generously provided by the Department of Chemical Pathology division at the Noguchi Memorial Institute for Medical Research. MDA MB 468, MDA-MB-231 and PC3 cancer cell lines were cultured in DMEM, while DLD-1 and PNT2 were cultured in RPMI 1640. All media were enriched with fetal bovine serum (10%) and penicillin-streptomycin-glutamine (1%) (Gibco-life Technologies, Carlsbad, CA, USA). Cells were maintained at 37\u0026deg;C in a humidified atmosphere with 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChemical compounds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompounds 1, 2, and 3 \u003cstrong\u003e(Fig.\u0026nbsp;1)\u003c/strong\u003e were purchased from Vitas M lab and DOX (D1515-10MG) was purchased from Sigma-Aldrich, St Louis, MO, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCytotoxicity assay and Selectivity Index (SI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effects of the three compounds on cell viability were determined using MTT assay. Briefly, the cells were plated in 96-well plates at 1 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells/well density and incubated at 37\u0026deg;C for 24 hours. The cells were treated with the compounds (0- 100\u0026micro;M) for 48 h. DOX (0\u0026ndash;15\u0026micro;M) was used as a positive control. In each well, 20 \u0026micro;L of 2.5 mg/mL (MTT) (Sigma-Aldrich, St Louis, MO, USA) was added and incubated at 37\u0026deg;C for 4 hours. Subsequently, acidified isopropanol (100 \u0026micro;L) was added to each well and incubated at 37\u0026deg;C for 30 minutes. The absorbance was measured at 570 nm using a microplate reader (Varioskan\u0026trade; LUX multimode, Thermo Fisher Scientific, Carlsbad, CA, USA). The percentage of cell viability was determined based on the absorbance values, and IC\u003csub\u003e50\u003c/sub\u003e values were later computed. The selectivity index (SI) of the most potent compound was analysed following the same procedure using PNT2 normal prostate cells in a concentration range ten times more than the concentration ranges used in the four cancer cell lines. The IC\u003csub\u003e50\u003c/sub\u003e of PNT2 was calculated, and the SI was computed with a good selectivity classified as SI\u0026thinsp;\u0026gt;\u0026thinsp;3 [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. Three independent experiments were done to determine the IC\u003csub\u003e50\u003c/sub\u003e of the compounds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCancer-related genes and compound 2 target prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenes associated with 36 human cancers were obtained from the DisGENET (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.disgenet.org/\u003c/span\u003e\u003c/span\u003e, accessed on 8 February 2023)[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e] and GeneCards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003c/span\u003e, accessed on 8 February 2023)[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] Databases. For the GeneCards database, a cut-off score\u0026thinsp;\u0026gt;\u0026thinsp;4 was used to obtain the most probable genes. The putative targets of compound 2 were acquired from SwissTargetPrediction (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003c/span\u003e, accessed on 9 February 2023)[\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e] and SuperPred (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://prediction.charite.de/\u003c/span\u003e\u003c/span\u003e, accessed on 9 February 2023)[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] Webservers. The targets were converted into standardised gene names based on the UniProt database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003c/span\u003e, accessed on 9 February 2023)[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e], and duplicates were removed. The cancer-related genes and targets of compound 2 were intersected with a Venn diagram using the FunRich v3.1.3 software[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e] to obtain all its cancer-related targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein-protein interactions (PPI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cancer-related targets of compound 2 were uploaded into the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003c/span\u003e, accessed on 9 February 2023)[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e] where an interactive network was generated using \u0026ldquo;\u003cem\u003eHomo sapiens\u003c/em\u003e\u0026rdquo; as the screening condition. The protein-protein interaction network was loaded into Cytoscape software v3.9.1[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e], where the core targets of compound 2 were identified using the CytoHubba [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e] and MCODE[\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] plugins. Specifically, the PPI network was analysed using nine algorithms in CytoHubba, and targets that ranked top 20 in at least 5 of the 9 algorithms were identified. MCODE plugin was used to group the targets into sub-clusters, and targets that belonged to the top 3 sub-clusters were identified. Finally, the targets identified from the CytoHubba rankings belonging to at least one MCODE sub-clusters were selected as core targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Metascape web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape.org/gp/index.html\u003c/span\u003e\u003c/span\u003e, accessed on 13 February 2023)[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e] performed functional enrichment analyses of core compound 2 targets. The targets were annotated based on the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. In GO functional analyses, the functions of the targets were categorised into biological process, cellular component, and molecular function. The KEGG pathway plot was created through enrichment analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the KEGG pathway analysis, the top two enriched pathways were identified. The three core proteins likely to be involved in these pathways were selected from the identified hub genes in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Molecular docking was performed using the selected proteins. The active compound was docked to the crystal structure of EGFR (PDB: 8A27, 1.07 \u0026Aring; [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e], AKT1 (PBD:6HHJ, 2.30 \u0026Aring;, [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]), and VEGFR2 (PDB:4ASE, 1.83 \u0026Aring; [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]) which were obtained from the Protein Data Bank (PDB) [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. The preparation of all crystal structures for docking was conducted using the Scigress version FJ 2.6 program., i.e., hydrogen atoms were added, and the co-crystallized ligands were removed. The centre of the binding pockets, \u003cem\u003ex\u003c/em\u003eyz for each protein, are shown in Table S1 in S Text, with a radius of 10\u0026Aring;. The GoldScore (GS)[\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e] and ChemScore (CS) [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e], ChemPLP[\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e] and Astex statistical potential (ASP)[\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e] scoring functions were employed to validate the ligands\u0026apos; predicted binding modes and relative energies using the GOLD v5.4 software suite. Initially, the co-crystallized ligand of each protein was docked, and RMSD values were computed for the heavy atoms. The average RMSDs for each co-crystallized ligand for ASP PLP, CS, and GS underscore the robust predictive capability of the scoring functions. Detailed information on RMSD and binding scores can be found in the Tables S2 and S3 in S Text.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCancer-related targets of compound 2 and PDB sources of 3D protein structures\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene symbol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUniProt ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTarget name\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\u003eAKT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP31749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAKT serine/threonine kinase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCND1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP24385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCyclin D1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP00533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEpidermal growth factor receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFYN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP06241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFYN proto-oncogene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSK3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP49841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycogen synthase kinase 3 beta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIF1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ16665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypoxia-inducible factor 1 subunit alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSP90AA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP07900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeat shock protein 90 alpha family class A member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSP90AB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP08238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeat shock protein 90 alpha family class B member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJAK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO60674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJanus kinase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDR(VEGFR2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP35968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKinase inserts domain receptor/Vascular endothelial growth factor receptor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAPK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP28482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMitogen-activated protein kinase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAPK8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP45983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMitogen-activated protein kinase 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRKCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP05771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtein kinase C beta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePTK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ05397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtein tyrosine kinase 2/ Focal adhesion kinase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP12931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRC proto-oncogene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMD simulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMD simulations were performed for the three proteins: EGFR, AKT, and VEGFR2). The MD simulation was performed using the Amber 22 software[\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]. The most feasible ligand configuration was employed in setting up the simulation. Initially, the ligand was prepared using an Antechamber to compute atomic point charges using the AM1-BCC charge model. The system was configured using the Leap program. The ligand and protein were subjected to GAFF and ff14SB force fields, respectively. The appropriate number of ions was added to each protein to neutralise receptor charges. The three-point transferable intermolecular potential (TIP3P) model with a 10 \u0026Aring; water solvate box was used to solvate the systems, eventually buffered to 150 mM. The non-bonded interaction cutoff value was set at 8.0 \u0026Aring;. Subsequently, the system was gradually heated to 300 K, with receptor atom constraints applied at 50 ps intervals. This was followed by a 50 ps equilibration period to attain density equilibrium. The system was then equilibrated under NPT conditions for 500 ps at a pressure of 1 atm and a temperature of 300 K. Finally, a 10-ns production stage was carried out. The Berendsen barostat and Langevin thermostat were used to keep pressure and temperature under control. Furthermore, with a time step of 0.002 ps, the shake algorithm restricted all hydrogen-involved bonds. The CPPTRAJ module was used for trajectory analysis, and VMD facilitated visualisation. To compute the binding free energy of the receptor complex, we employed both the MM-PBSA and MM-GBSA methods for overall simulated trajectories of 1000 frames (100ns). The RoG and maximum RoG (RoGmax), RMSD, and RMSF were analysed to assess the structural flexibility and stability. Lifetime hydrogen bond analysis was also computed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug-likeness and ADMET studies.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pharmacokinetics, structural, and physicochemical characteristics of compound 2 were predicted using ADMETlab 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://admetmesh.scbdd.com/\u003c/span\u003e\u003c/span\u003e, accessed on April 18, 2023)[\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e] and SwissADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch/\u003c/span\u003e\u003c/span\u003e, accessed on April 18, 2023)[\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e]. To evaluate its potential for absorption in the human gastrointestinal (GI) tract and its ability to traverse the blood-brain barrier (BBB), the Egan BOILED-Egg (Brain Or IntestinaL EstimateD) permeation predictive model was utilised. Furthermore, these methodologies yielded predictions regarding the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of compound 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical and Data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGraphPad Prism 9.3 was used to analyse cytotoxicity data and generate all the graphs. The Kruskal-Wallis test was used for statistical analysis. To determine statistical significance, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used. The cytotoxicity tests were carried out in triplicate with three biological replicates. Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM was used to represent IC\u003csub\u003e50\u003c/sub\u003e. The molecular dynamics data was analysed using Python 3.12.0. RoG, RoG max, RMSD, and RMSF values were shown as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. The MM-PBSA and MM-PBSA free energies were presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCytotoxicity assay of compounds and selectivity index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe impact of three small-molecule compounds on cell proliferation was assessed in various cancer cell lines and a normal cell line, with doxorubicin (DOX) as the reference anticancer drug. The goal was to determine the bioactivity of the compounds in cancer cells and evaluate the potential adverse effects of the compounds on normal cells. MDA MB 468 and MDA MB 231 breast cancer cell lines were susceptible to compound 2 in a dose-dependent manner (p\u0026thinsp;=\u0026thinsp;0.0011 and p\u0026thinsp;=\u0026thinsp;0.008, respectively) with IC\u003csub\u003e50\u003c/sub\u003e of 9.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80 \u0026micro;M and 18.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10 \u0026micro;M for MDA MB 468 and MDA MB 231 respectively. Colorectal cell line DLD1 (p-value\u0026thinsp;=\u0026thinsp;0.03) and prostate cell line PC3 (p\u0026thinsp;=\u0026thinsp;value\u0026thinsp;=\u0026thinsp;0.01) also showed mild susceptibility to compound 2 with IC\u003csub\u003e50\u003c/sub\u003e of 40.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80 \u0026micro;M and 46.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64 \u0026micro;M respectively (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Compounds 1 and 3 exhibited no significant cytotoxic effect on all the cell lines (Fig S1 in S Text). The selectivity index (as indicated in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrates that compound 2 exerts a specific and targeted impact on the MDA MB 468 cell line while sparing normal cells. In contrast, PC3 displays minimal selectivity when treated with compound 2. Compound 2 exhibits a favourable selectivity index compared to DOX in breast cancer cell lines.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCompound 2 and DOX selectivity index in the cancer cell lines.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCC\u003csub\u003e50\u003c/sub\u003e non-cancerous cell line (PNT2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIC\u003csub\u003e50\u003c/sub\u003e cancer cell line\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSelectivity index\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\u003eMDA MB 468 (compound 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMDA MB 468 (DOX)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMDA MB 231 (compound 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMDA MB 231 (DOX)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC3 (compound 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC3 (DOX)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDLD-1 (compound 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDLD-1 (DOX)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe putative targets of compound 2 are related to human cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e15615 cancer-related genes were identified from the DisGENET and GeneCards databases. Our Swiss Target prediction and SuperPred prediction produced 211 putative targets for compound 2 (ZINC6661981 or STK610045) (excel file; S1 and S2). After the intersection with FunRich v3.1.3, 206 cancer-related targets for compound 2 were identified (Fig S2 in S Text, excel file; S3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI network of compound 2 cancer-related targets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA PPI network of the cancer-related targets of compound 2 was constructed in the STRING database. (Fig S2 in S Text, excel file; S3). For intuitive topological analysis, 199 nodes and 1591 edges were obtained. Eighteen (18) of the 206 compound 2 cancer-related targets were selected after screening using the CytoHubba algorithm (excel file; S4). Finally, 15 targets were selected following MCODE sub-clustering (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Excel file; S5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment analyses of core targets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom functional enrichment analyses, 435 biological processes, 39 cellular components, 36 molecular functions, and 122 pathways were predicted to be associated with the anticancer activity of compound 2 (excel file; S6). The KEGG pathway analysis revealed the anticancer potential of compound 2 mainly via focal adhesion, pathways in cancer, EGFR tyrosine kinase inhibitor resistance, and ErbB signalling (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). From our data, regulation via focal adhesion had the highest statistical significance, with a log(q-value) of -17, a Z-score of 35, and an enrichment score of 110. Pathways in cancer, with a log(q-value) of -14 and an enrichment score of 45, were also significantly represented in the data. A high Z-score of 23 further emphasises its relevance. The ErbB Signaling Pathway and EGFR Tyrosine Kinase Inhibitor Resistance had high enrichment scores of 190 and 200, respectively, and Z-scores of 39 and 40, respectively, indicating their significant over-representation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular docking experiments revealed the binding mode of compound 2 to the three potential target proteins identified by functional enrichment analysis AKT1, EGFR, and VEGFR2 (the three potential target proteins identified in functional enrichment analysis). With the GOLD software suite, the four scoring functions, Piecewise Linear Potential (ChemPLP), GoldScore (GS), Astex Statistical Potential (ASP), and ChemScore (CS), were used for docking studies. The binding site of AKT1 consists of a slightly hydrophilic to neutral binding pocket, whereas EGFR and VEGFR2 consist of a hydrophobic binding pocket. For AKT1, the carbonyl end of compound 2 was fitted into the binding pocket. It formed hydrophobic contacts with cysteine (Cys 296) and isoleucine (Ile 86). The complex can be stabilised by a catalytic water molecule that forms hydrogen bonds with the backbone carbonyl oxygen of valine (Val271) and tyrosine (Tyr272). However, AKT1 did not form a classical hydrogen bond with the ligand. For EGFR and VEGFR2, the piperidine end fitted well into the binding pocket, whereas the carbonyl portion pointed outside. For EGFR, Compound 2 formed hydrophobic contacts with leucine (leu747), isoleucine (Ile759) and leucine (Leu858) and the oxygen in the benzoxazine ring formed hydrogen bonds with side chain amine of lysine (Lys745) whereas the secondary amine in the compound forms hydrogen bond with the side chain hydroxy group and the backbone amine of aspartic acid (Asp855). Compound 2 was located at the hinge region (ATP binding site) of EGFR, specifically binding to the critical ATP-binding activation site at Lys745. Compound 2 also interacted with the DFG motif, the activation loop of EGFR at Asp855. For VEGFR2, the oxygen in the benzoxazine ring formed hydrogen bonds with the backbone amine of cysteine (Cys919), which is in the ATP binding site. Compound 2 also formed hydrophobic contacts with phenylalanine (Phe918), also in the ATP binding site, and leucine (Leu840). The binding modes and the locations of the plausible binding pockets for compound 2 for each target are shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Based on our findings, compound 2 is predicted to be an ATP competitive inhibitor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular dynamics simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Radius of Gyration (RoG) is an important parameter that provides information on the compactness of the protein-ligand complex. EGFR maintained a consistent and compact structure during most of the simulations, with an average RoG of 38.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 \u0026Aring; and a maximum RoG of 66.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 \u0026Aring;. (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA \u003cstrong\u003e\u0026amp; B)\u003c/strong\u003e. VEGFR2 had a slightly lower average RoG of 37.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 \u0026Aring; than EGFR. VEGFR2 had a RoGmax of 63.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 \u0026Aring; and, like EGFR, displayed instances of more extended conformations during the simulation. RoG and RoGmax for AKT1 were 38.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 \u0026Aring; and 66.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44 \u0026Aring;, respectively. AKT1 protein had a higher RoGmax value than EGFR protein. The significant difference between the average RoG and RoGmax observed for all proteins showed that each protein may have experienced moments of increased flexibility or conformational changes during the simulation. Owing to the low SD observed for all three proteins, it can be concluded that they remained the same size and shape throughout the simulation, implying a stable conformation. The consistent compactness may indicate that compound 2 fits snugly within the binding pocket of the proteins.\u003c/p\u003e\n\u003cp\u003eThe Root Mean Squared Deviation (RMSD) values for all proteins showed a similar trend: initial deviation followed by stabilisation. Throughout the simulation, the EGFR atoms deviated by about 55.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70 \u0026Aring; (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC) from their initial positions before stabilising around 1.5 \u0026Aring;, indicating only minor conformational changes after the initial frames. VEGFR2 atoms deviated from their starting positions by an average of 52.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 \u0026Aring; and stabilised around 1.5 \u0026Aring;. This initial deviation is slightly less than EGFR\u0026apos;s, implying that VEGFR2 had a somewhat more stable interaction with ligand compound 2 or had fewer conformational changes during the simulation. The RMSD of AKT1 was 54.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70 \u0026Aring;, which is between EGFR and VEGFR2. However, AKT1 stabilises near 2.5 \u0026Aring;, indicating that it had more significant structural deviations. The findings showed that, during the simulation, all proteins initially underwent structural changes quite different from each reference structure. However, EGFR and VEGFR2 stabilised at 1.5 \u0026Aring; after the initial deviation, thus converging to a stimulated structure closely resembling the reference structures. Although the AKT1 structure also stabilised within a reasonable range (2.5 \u0026Aring;), it showed a slightly higher deviation or flexibility level than EGFR and VEGFR2. However, the low SDs observed indicate that the RMSD values fluctuated only somewhat around the averages, suggesting a relatively stable simulation.\u003c/p\u003e\n\u003cp\u003eThe Root Mean Squared fluctuation (RMSF) plot revealed information about the flexibility and rigidity of different protein regions. Increased RMSF values signify increased flexibility, while decreased RMSF values indicate heightened rigidity. From the simulations (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD), although each protein showed unique dynamics with specific regions of flexibility, EGFR had the highest peaks compared with VEGFR2 and AKT1, indicating higher fluctuations. This showed that when EGFR is complexed with compound 2, it undergoes conformational changes. This could also mean that EGFR has certain regions that are inherently more flexible.\u003c/p\u003e\n\u003cp\u003eHydrogen bond analysis and lifetime curve plotting were performed to further understand the strength and stability of the protein-ligand complexes, with a more extended lifetime range indicating a more stable interaction (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE). The hydrogen bonds in the EGFR complex had longer lifetimes, with several bonds present in more than 500 counts. The hydrogen bond between compound 2-270@O2 and the solvent had the highest lifetime with an average distance of approximately 2.79 \u0026Aring; and an angle of 158.2\u0026deg;, existing for 1125 counts. Residues such as Asp155 and Thr154 formed hydrogen bonds with compound 2-270@N at an average distance of 2.96 \u0026Aring; and 2.94 \u0026Aring;, respectively, with corresponding average angles of 166.14\u003csup\u003eo\u003c/sup\u003e and 166.36\u003csup\u003eo\u003c/sup\u003e. For AKT1, the bond between compound 2-409@N and the solvent appeared frequently, present in over 500 counts, with an average distance of around 2.86 \u0026Aring; and an angle of 162.22\u003csup\u003eo\u003c/sup\u003e. Residues such as Tyr16, Arg80, and Glu242 in AKT1 were found to form hydrogen bonds with compound 2, with an average distance range of 2.83\u0026ndash;2.91 \u0026Aring; and an angle range of 142.82- 159.45\u003csup\u003eo\u003c/sup\u003e. This shows that AKT1 has a more diverse set of hydrogen bonds with compound 2, which contributes to its overall hydrogen-bonding activity, and no single bond dominates the entire interaction. VEGFR2 exhibited the widest range of lifetimes. The bond between compound 2-308@O2 and the solvent was the most prevalent, present in 1141 counts, with an average distance of approximately 2.78 \u0026Aring;. The residues Lys32, Cys113, and Lys62 in VEGFR2 were identified to form hydrogen bonds with compound 2-308@O2, compound 2-308@O, and compound 2-308@N, respectively, with a distance range of 2.83\u0026ndash;2.91 \u0026Aring; and an angle range of 151.15 -159.94\u003csup\u003eo\u003c/sup\u003e. This indicated that VEGFR2 had an even more diverse set of hydrogen bonds with compound 2, and it took a larger number of unique bonds to account for most of the hydrogen bonding activity. The interaction of each protein with its specific residues indicated that these residues are likely pivotal for the binding mode of compound 2 and may play a significant role in ligand recognition or binding. The average hydrogen distance and angles for each protein also showed that the hydrogen bonds formed between compound 2 and the proteins were quite linear and had an exceptionally short distance, indicating a robust interaction.\u003c/p\u003e\n\u003cp\u003eFinally, the free energy is calculated and quantitatively measured, and the binding affinity between compound 2 and proteins is measured. The binding of EGFR was energetically favourable, with a Molecular Mechanics\u0026ndash;Poisson-Boltzmann Surface Area (MM-PBSA) value of -767.19 (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating a stable complex. Again, the Molecular Mechanics\u0026ndash;Generalized-Born Surface Area (MM-GBSA) value of -807.34 revealed favourable binding, consistent with the MM-PBSA value. These energy values indicate compound 2 strongly binds to the EGFR protein, potentially stabilising its structure. Similarly, the free energy values calculated for VEGFR2 showed that compound 2 is bound to VEGFR2, indicating a likely stable complex. When comparing AKT1 with EGFR and VEGFR2, the free energies revealed a less favourable free energy between compound 2 and the AKT1 complex, indicating a less stable complex.\u003c/p\u003e\n\u003cp\u003eBased on a thorough comparison of all three proteins using the five metrics examined, compound 2 appeared to have the highest affinity and most stable interaction with EGFR, closely followed by VEGFR2.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFree Energy Calculations for EGFR, AKT, and VEGFR2\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMM-PBSA (kcal/mole)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMM-GBSA (kcal/mole)\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\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-767.19\u0026thinsp;\u0026plusmn;\u0026thinsp;48.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-807.34\u0026thinsp;\u0026plusmn;\u0026thinsp;48.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAKT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-229.40\u0026thinsp;\u0026plusmn;\u0026thinsp;46.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-177.97\u0026thinsp;\u0026plusmn;\u0026thinsp;46.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVEGFR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-729.01\u0026thinsp;\u0026plusmn;\u0026thinsp;47.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-772.04\u0026thinsp;\u0026plusmn;\u0026thinsp;47.73\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\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug-likeness prediction and ADMET properties.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eADMETlab 2.0 webserver\u0026apos;s server predicted that compound 2 adhered to Lipinski\u0026apos;s Ro5 criteria[\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e] with one hydrogen bond donor, five hydrogen bond acceptors, a molecular weight of 406.23 Da, and an AlogP value of 4.178. The drug-likeness (QED) score prediction for compound 2 was 0.468.\u003c/p\u003e\n\u003cp\u003eCompound 2 exhibited favourable pharmacokinetic properties. SwissADME BOILED-Egg model indicated how closely a compound aligns with the ideal conditions for optimal absorptionIt showed high gastrointestinal (GI) absorption with a bioavailability score of 0.55, indicating moderate oral bioavailability. The compound is also blood\u0026ndash;brain barrier (BBB) permeant, suggesting potential effects on the central nervous system. Compound 2 was predicted to have the ability to be absorbed from the GI tract and traverse the BBB, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA. As a P-glycoprotein (P-gp) substrate, it may be subject to efflux, which can affect intracellular concentration. Regarding cytochrome P450 interactions, compound 2 is predicted to inhibit CYP2C19, CYP2C9, and CYP2D6, but not CYP1A2, highlighting a moderate potential for drug\u0026ndash;drug interactions. Medicinal chemistry alerts are minimal, with no PAINS alerts, though two Brenk alerts (due to the presence of cumarine and polycyclic aromatic hydrocarbons) are noted. The compound is not lead-like, primarily due to a molecular weight\u0026thinsp;\u0026gt;\u0026thinsp;350 and a logP\u0026thinsp;\u0026gt;\u0026thinsp;3.5, but shows a manageable synthetic accessibility score of 4.37. In Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB, the drug-likeness radar for compound 2 presented details of its compatibility with six physicochemical characteristics. Notably, the physicochemical characteristics of compound 2 fell within the pink area, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB. A bioavailability score of 0.55 was predicted for compound 2. In this model, compound 2 was shown to be a p-glycoprotein substrate, implying that it could be transported back into the GI lumen after absorption. The ADMET properties of compound 2 are shown in Table S4 in S text.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study was conducted to assess the anticancer potential of three synthetic compounds that had previously been identified as potentially antileishmanial. In addition, we used in silico approaches to predict the potential targets of the lead compound. Compound 2 was more potent against breast cancer cell lines using the cytotoxicity assay. The IC\u003csub\u003e50\u003c/sub\u003e values found for both breast cancer cell lines were lower than values obtained for antileishmanial activity (21.9 \u0026micro;M)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This suggests compound 2 is a potential anticancer drug candidate.\u003c/p\u003e \u003cp\u003eIn the network pharmacology analysis of compound 2, fifteen hub genes were identified, primarily associated with two fundamental pathways: the focal adhesion pathway and 'pathways in cancer'. Focal adhesions, crucial for cell migration and survival, may be influenced by compound 2, impacting cancer cell invasion and metastasis. The 'Pathways in Cancer' encompass cell cycle regulation, apoptosis, and survival mechanisms. Compound 2 significantly impacted the ErbB Signalling Pathway, specifically EGFR and VEGF signalling VEGFR2 Pathways in Cancer. EGFR, a member of the ErbB family, is frequently overexpressed or mutated in several cancers, including lung, breast, colorectal, and head and neck cancers. Aberrant activation of the ErbB pathway contributes to uncontrolled cell growth, evasion of apoptosis, angiogenesis, and metastasis[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Targeting this pathway has become a significant therapeutic strategy, and EGFR inhibitors such as gefitinib, erlotinib, and cetuximab have shown potential in treating certain cancers, such as non-small cell lung cancer[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Tumours also often overexpress VEGF, promoting angiogenesis and ensuring an adequate blood supply for tumour growth[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. VEGRF2 signalling is involved in focal adhesions, emphasising its role in endothelial cell behaviour and angiogenesis. Inhibiting VEGFR2 has been a successful strategy to impede angiogenesis and limit tumour progression. Anti-angiogenic drugs, such as bevacizumab, target VEGFR and are used in treating various cancer types, including colorectal, breast, and renal cancers[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. From our studies, Compound 2 can potentially target both EGFR and VEGFR2 for more specific and effective treatments for patients with different types of cancer. The PI3K/AKT pathway was also highlighted as a potential target for compound 2. The PI3K/AKT pathway is frequently dysregulated in cancer and drives uncontrolled cell growth and survival. Targeting PI3K, AKT, or mTOR shows promise in cancer therapy[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The PI3K/AKT pathway is also involved in chemoresistance, emphasising its importance for research and therapeutic development[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. From these studies, Compound 2 demonstrated broad, multi-targeted effects on cancer cells, impacting diverse biological processes.\u003c/p\u003e \u003cp\u003eMolecular docking and dynamics simulations focused on AKT1, EGFR, and VEGFR2 to elucidate their interactions with compound 2, providing deeper insights. The binding mode of compound 2 to its protein targets, particularly EGFR, underwent notable conformational changes during MD simulations, significantly influencing protein-ligand interactions. These changes are pivotal in understanding the compound's efficacy and specificity. Initially, compound 2 exhibited a binding mode characterised by interactions with key residues in the ATP-binding site of EGFR, such as Lys745, a critical ATP-binding activation site of EGFR [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Gefitinib and Erlotinib, potent EGFR inhibitors, were also shown to interact with EGFR at the same site. This conformation was stabilised by hydrogen bonds and hydrophobic interactions, positioning the compound effectively within the active site. Compound 2 induced conformational adjustments in the EGFR binding pocket as the MD simulations progressed. The adjustments involved reorientation of side chains and slight backbone movements, resulting in a more precise fit of the ligand. This reveals compound 2 as likely an ATP-competitive inhibitor, directly competing with ATP and inhibiting the phosphorylation activity necessary for VEGFR2 and EGFR signalling. While ATP-competitive inhibitors are effective, the risk of drug resistance exists due to mutations occurring at the same ATP binding site. Additionally, this type of inhibition may impact other kinases with similar ATP-binding pockets. Notably, the interaction of compound 2 with Asp855, which is part of the DFG motif, the activation loop of EGFR, could block the activation of EGFR\u0026rsquo;s kinase domain, preventing the transmission of growth signals within the cell. During the simulation, the DFG motif (Asp855-Phe856-Gly857) activation loop also transitioned, enhancing the binding affinity of compound 2. The conformational changes increased hydrogen bonds and van der Waals interactions between compound 2 and EGFR. This led to a more stable complex, as evidenced by reduced RMSD values and lower binding free energy calculations. The enhanced stability suggests that compound 2 effectively locks EGFR in an inactive conformation, potentially inhibiting its kinase activity by preventing the transmission of growth signals within the cell. Ultimately, this inhibition strategy of compound 2 binding to both the ATP site and the DFG motif presents a robust inhibition of EGFR and holds promise for preventing cell proliferation and survival in cancers where EGFR dysregulation occurs. Likewise, for VEGFR2, compound 2 exhibited dynamic conformational changes during molecular dynamics simulations, although the effects differed from those observed with EGFR. In the VEGFR2 complex, compound 2 initially occupied the ATP-binding pocket, forming key interactions with residues such as Cys919 and Phe918. This interaction was also seen in some approved VEGFR2 inhibitors, such as sorafenib, and some previously identified novel VEGFR2[\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. As the simulation progressed, the ligand adjusted its orientation slightly to optimise hydrogen bonding and hydrophobic contacts, particularly enhancing interactions with Leu840 and surrounding residues. These subtle conformational shifts contributed to improved binding stability, as reflected in sustained hydrogen bond lifetimes and moderate reductions in RMSD, indicating a relatively stable yet flexible complex. In contrast, the AKT1\u0026ndash;compound 2 complex displayed greater conformational variability. Compound 2 was outside the canonical ATP-binding site of AKT1 and primarily engaged in solvent-mediated hydrogen bonds with residues like Val271 and Tyr272. During simulation, the ligand's position remained less stable, and the protein exhibited more pronounced fluctuations, suggesting a weaker and less specific interaction. The AKT1 complex also showed the highest RMSD among the three targets and the least favourable binding free energy, consistent with a looser and more transient binding mode.\u003c/p\u003e \u003cp\u003eFrom the MD simulation, AKT1 displayed diverse hydrogen bonds with compound 2, suggesting varied interactions contributing to its overall hydrogen bonding activity. VEGFR2 showed a balance of specific and solvent-mediated bonds, implying a more flexible binding mode, potentially making it more tolerant to ligand mutations[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] in water-mediated interactions in the AKT1 and VEGFR2 complexes, suggesting that the binding pockets are more solvent-exposed or that water molecules play an essential role in stabilising the ligand within the pocket[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. The observed conformational change or flexibility for EGFR could be crucial to accommodate the ligand, compound 2, or to help the protein function. This conformational change in EGFR upon binding has been attributed to the extracellular domains of EGFR family members dimerising to activate the protein[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. The high stability of interactions between compound 2 and EGFR, characterised by strong hydrogen bonds, indicates a specific binding pocket. Compound 2 formed an energetically favourable bond from the MM-PBSA and MM-GBSA calculations as well as strong and geometrically favourable hydrogen bonds with EGFR, suggesting a robust binding mechanism. This has implications for drug development because compound 2 may be a better inhibitor or modulator of EGFR and possibly VEGFR2 than AKT. This finding from the MD simulation analysis is also consistent with our KEGG pathway enrichment profile.\u003c/p\u003e \u003cp\u003eEGFR inhibitors represent a promising strategy to combat EGFR-driven malignancies. These inhibitors bind to the EGFR protein, blocking its activation and downstream signalling. Consequently, they impede the uncontrolled growth and division of cancer cells, ultimately leading to cell death. However, existing EGFR inhibitors have limitations, including efficacy challenges and the emergence of resistance. While first-generation inhibitors like erlotinib and gefitinib show promise in treating specific EGFR-mutant cancers, resistance often develops over time[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Osimertinib, a third-generation irreversible EGFR-Tyrosine kinase Inhibitor (TKI), has effectively treated non-small cell lung cancer (NSCLC) in patients with EGFR T790M mutations. Unfortunately, even osimertinib faces resistance, limiting its long-term effectiveness[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Blueprint\u0026rsquo;s lead EGFR inhibitor, BLU-945, has shown safety in phase I trials, and fourth-generation inhibitors hold the potential for overcoming resistance mechanisms[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. However, continuing research and developing improved EGFR inhibitors are essential to enhance therapeutic outcomes. Our study highlights compound 2 as a promising targeted therapeutic option for anticancer treatment. The dual binding mechanism of compound 2 may enhance specificity, minimising off-target effects often associated with broader ATP-competitive inhibitors[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Furthermore, compound 2 could prove valuable in treating cancers resistant to other EGFR inhibitors that solely target the ATP binding site. We may overcome or delay resistance by targeting multiple sites, as cancer cells would need to accumulate mutations at various locations to evade inhibition.\u003c/p\u003e \u003cp\u003eThe bioavailability score suggests that roughly 55% of the compound will be absorbed into the bloodstream after oral administration, indicating its potential for such use. This bioavailability score was consistent with that of compounds from the ZINC database screened against VEGFR2[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. The quantitative estimate of QED score prediction for compound 2 was 0.468, indicating that compound 2 requires optimization as a lead compound for drug development[\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, several limitations should be considered. The molecular docking and dynamics simulations offer theoretical insights into the interactions between compound 2 and its targets. However, these predictions require validation through experimental binding assays and crystallography studies to confirm the accuracy of the predicted binding modes and interactions. Although this study employed established molecular dynamics metrics such as RMSD, RMSF, radius of gyration, hydrogen bond lifetime analysis, and MM/GBSA/MM-PBSA free energy calculations to characterize the stability and binding interactions of compound 2, additional analyses such as probability distribution function (PDF) analysis, principal component analysis (PCA) of trajectory variance, and dynamic cross-correlation (DCC) were not included. Incorporating these advanced approaches in future studies may provide deeper insight into the collective motions, correlated residue dynamics, and allosteric effects associated with ligand binding.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study has provided promising insights into the potential of compound 2 as an anticancer agent. Compound 2 exhibits varying affinities and stabilities with AKT1, EGFR, and VEGFR2. It binds EGFR and VEGFR2 at their ATP sites, forming stable hydrogen bonds, while binding AKT1 outside its ATP site via solvent-mediated hydrogen bonds. EGFR shows the strongest binding with low RMSD (~\u0026thinsp;1.5 \u0026Aring;), compact structure (RoG\u0026thinsp;~\u0026thinsp;38.9 \u0026Aring;), and favourable binding energies (MM-GBSA: -807.34 kcal/mol; MM-PBSA: -767.19 kcal/mol). The EGFR-ligand complex remains compact and stable. VEGFR2 also forms stable bonds with positive binding energy (MM-GBSA: -772.04 kcal/mol; MM-PBSA: -729.01 kcal/mol) but is less stable than EGFR. AKT1 demonstrates higher RMSD (~\u0026thinsp;2.5 \u0026Aring;), weaker interactions, and less favourable binding energy (MM-GBSA: -177.97 kcal/mol; MM-PBSA: -229.40 kcal/mol). Therefore, EGFR is identified as the most favourable binding target, making it a promising candidate for further study in targeted anticancer therapy. Compound 2\u0026rsquo;s dual-site binding suggests it could be a potent and selective EGFR inhibitor.\u003c/p\u003e \u003cp\u003eThough the study indicates compound 2's potential as an anticancer therapy, several limitations should be considered. The molecular docking and dynamics simulations offer theoretical insights into the interactions between compound 2 and its targets. However, these predictions require validation through experimental binding assays and crystallography studies to confirm the accuracy of the predicted binding modes and interactions. Also broader target profiling is needed. Future research should focus on these areas to maximise its clinical development. These findings will validate the simulation data, enhance our understanding of compound 2`s binding specificity, affinity, and availability, and aid drug development and optimisation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the use of high-performance computing facilities from New Zealand eScience Infrastructure (NeSI) as part of this research. The research was funded jointly by the collaborating institutions and the Ministry of Business, Innovation, and Employment Research Infrastructure program. The URL is https://www.nesi.org.nz.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBT, RKA, ARA and KANS designed the research. BT, DMA, JTQ and KF performed all the computational analysis. BKYH, AS, BT, KF, and JNKA performed all the cytotoxicity screening. BT, DMAT, and JTQ analysed all the data. ARA, DMA and KANS validated the data. BT, DMA, JTQ and KF wrote sections of the manuscript. ARA, KANS, RKA, DP and DMA critical manuscript revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the Zenodo repository, https://zenodo.org/doi/10.5281/zenodo.10902946\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupporting information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article contains supplementary figures, Fig S1 and Fig S2, and table S1, S2, S3, S4 in file S Text and supplementary excel files; S1, S2, S3, S4, S5, S6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBernardine Tuah was supported by a WACCBIP-World Bank ACE Masters/PhD fellowship (WACCBI+NCDS; Awandare).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical Approval is not applicable for this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Human and Animal Rights\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human or animal subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Informed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no human subjects in this article and informed consent is not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMaleki EH, Bahrami AR, Matin MM (2023) Cancer cell cycle heterogeneity as a critical determinant of therapeutic resistance. Genes Dis 11:189\u0026ndash;204. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gendis.2022.11.025\u003c/span\u003e\u003cspan address=\"10.1016/j.gendis.2022.11.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalmarini CM (2020) Why we do what we do. A brief analysis of cancer therapies. EXCLI J 19:1401\u0026ndash;1413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17179/excli2020-2972\u003c/span\u003e\u003cspan address=\"10.17179/excli2020-2972\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiMasi JA, Grabowski HG, Hansen RW (2016) Innovation in the pharmaceutical industry: New estimates of R\u0026amp;D costs. J Health Econ 47:20\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhealeco.2016.01.012\u003c/span\u003e\u003cspan address=\"10.1016/j.jhealeco.2016.01.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim D, Lee J, Lee S, Park J, Lee D (2016) Predicting unintended effects of drugs based on off-target tissue effects. Biochem Biophys Res Commun 469:399\u0026ndash;404. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbrc.2015.11.095\u003c/span\u003e\u003cspan address=\"10.1016/j.bbrc.2015.11.095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnand U, Dey A, Chandel AKS, Sanyal R, Mishra A, Pandey DK et al (2023) Cancer chemotherapy and beyond: Current status, drug candidates, associated risks and progress in targeted therapeutics. Genes Dis 10:1367\u0026ndash;1401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gendis.2022.02.007\u003c/span\u003e\u003cspan address=\"10.1016/j.gendis.2022.02.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin A, Giuliano CJ, Palladino A, John KM, Abramowicz C, Yuan M, Lou et al (2019) Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials. Sci Transl Med 11:8412. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/SCITRANSLMED.AAW8412\u003c/span\u003e\u003cspan address=\"10.1126/SCITRANSLMED.AAW8412\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Tong Y, Ng TB, Lao L, Lam JKW, Zhang KY et al (2015) Network pharmacological identification of active compounds and potential actions of Erxian decoction in alleviating menopause-related symptoms. Chin Med 10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13020-015-0051-z\u003c/span\u003e\u003cspan address=\"10.1186/s13020-015-0051-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang DW, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkn923\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkn923\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzmi AS (2012) Network pharmacology for cancer drug discovery: Are we there yet? Future Med Chem 4:939\u0026ndash;941\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalkhasian AYS, Howlin BJ (2019) Automated drug design of kinase inhibitors to treat Chronic Myeloid Leukemia. J Mol Graph Model 91:52\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jmgm.2019.05.014\u003c/span\u003e\u003cspan address=\"10.1016/j.jmgm.2019.05.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagar B, Bornmann WG, Pellicena P, Schindler T, Veach DR, Miller T et al (2002) Crystal structures of the kinase domain of c-Abl in complex with the small molecule inhibitors PD173955 and imatinib (STI-571) LinkOut-more resources Other Literature Sources H1 Connect The Lens-Patent Citations Molecular Biology Databases Guide to Pharmacology Saccharomyces Genome Database. Cancer Res 62:4236\u0026ndash;4279\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang HSC, Chen YC (2015) Insight into molecular dynamics simulation of BRAF(V600E) and potent novel inhibitors for malignant melanoma. Int J Nanomed 10:3131\u0026ndash;3146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/IJN.S80150\u003c/span\u003e\u003cspan address=\"10.2147/IJN.S80150\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaassi M, Moussaoui M, Soufi H, Rajkhowa S, Sharma A, Sinha S et al (2023) Towards designing of a potential new HIV-1 protease inhibitor using QSAR study in combination with Molecular docking and Molecular dynamics simulations. PLoS ONE 18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0284539\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0284539\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJenwitheesuk E, Samudrala R (2003) Improved prediction of HIV-1 protease-inhibitor binding energies by molecular dynamics simulations. BMC Struct Biol 3:1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1472-6807-3-2\u003c/span\u003e\u003cspan address=\"10.1186/1472-6807-3-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. https://doi.org/\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoosefian M, Moghani MZ, Juan A (2022) In silico evaluation of atazanavir as a potential HIV main protease inhibitor and its comparison with new designed analogs. Comput Biol Med 145:105523. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compbiomed.2022.105523\u003c/span\u003e\u003cspan address=\"10.1016/j.compbiomed.2022.105523\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNichols SE, Baron R, Ivetac A, McCammon JA (2011) Predictive power of molecular dynamics receptor structures in virtual screening. J Chem Inf Model 51:1439\u0026ndash;1446. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/ci200117n\u003c/span\u003e\u003cspan address=\"10.1021/ci200117n\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeele KA, Durthi P, Srihansa T, Krupanidhi S, Sai Ayyagari V, Babu DJ et al (2020) Molecular docking and dynamic simulations for antiviral compounds against SARS-CoV-2: A computational study. Inf Med Unlocked 19:100345. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.imu.2020.100345\u003c/span\u003e\u003cspan address=\"10.1016/j.imu.2020.100345\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaha S, Prinsa, Kawsar SMA. Natural Flavonoids as Primary Amoebic Meningoencephalitis Inhibitor: Virtual Screening, Molecular Docking, Simulation MD (2024) MMPBSA, Density Functional Theory, Principal Component, and Gibbs Free Energy Landscape Analyses. Chem Biodiv \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cbdv.202402521\u003c/span\u003e\u003cspan address=\"10.1002/cbdv.202402521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlamri MA, Alawam AS, Alshahrani MM, Kawsar SMA, Prinsa, Saha S (2023) Establishing the Role of Iridoids as Potential Kirsten Rat Sarcoma Viral Oncogene Homolog G12C Inhibitors Using Molecular Docking; Molecular Docking Simulation; Molecular Mechanics Poisson\u0026ndash;Boltzmann Surface Area; Frontier Molecular Orbital Theory; Molecular Electrostatic Potential; and Absorption, Distribution, Metabolism, Excretion, and Toxicity Analysis. Molecules 28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/molecules28135050\u003c/span\u003e\u003cspan address=\"10.3390/molecules28135050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkter S, Alhatlani BY, Abdallah EM, Saha S, Ferdous J, Hossain ME et al Exploring Cinnamoyl-Substituted Mannopyranosides: Synthesis, Evaluation of Antimicrobial Properties, and Molecular Docking Studies Targeting H5N1 Influenza A Virus. Molecules 2023;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/molecules28248001\u003c/span\u003e\u003cspan address=\"10.3390/molecules28248001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao Y, Li Z, Mao L, Cao H, Kong J, Yu B et al (2019) The use of proteomic technologies to study molecular mechanisms of multidrug resistance in cancer. Eur J Med Chem 162:423\u0026ndash;434. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.EJMECH.2018.10.001\u003c/span\u003e\u003cspan address=\"10.1016/J.EJMECH.2018.10.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerbst RS (2004) Review of epidermal growth factor receptor biology. Int J Radiat Oncol Biol Phys 59:21\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijrobp.2003.11.041\u003c/span\u003e\u003cspan address=\"10.1016/j.ijrobp.2003.11.041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevantini E, Maroni G, Del Re M, Tenen DG (2022) EGFR signaling pathway as therapeutic target in human cancers. Semin Cancer Biol 85:253\u0026ndash;275. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.SEMCANCER.2022.04.002\u003c/span\u003e\u003cspan address=\"10.1016/J.SEMCANCER.2022.04.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Foncillas J, Sunakawa Y, Aderka D, Wainberg Z, Ronga P, Witzler P et al (2019) Distinguishing Features of Cetuximab and Panitumumab in Colorectal Cancer and Other Solid Tumors. Front Oncol 9:849. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/FONC.2019.00849\u003c/span\u003e\u003cspan address=\"10.3389/FONC.2019.00849\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaban N, Kamashev D, Emelianova A, Buzdin A (47 2023) Targeted Inhibitors of EGFR: Structure, Biology, Biomarkers, and Clinical Applications. Cells 2024. Page 13:13:47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/CELLS13010047\u003c/span\u003e\u003cspan address=\"10.3390/CELLS13010047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWard RA, Anderton MJ, Ashton S, Bethel PA, Box M, Butterworth S et al (2013) Structure- and reactivity-based development of covalent inhibitors of the activating and gatekeeper mutant forms of the epidermal growth factor receptor (EGFR). J Med Chem 56:7025\u0026ndash;7048. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/JM400822Z\u003c/span\u003e\u003cspan address=\"10.1021/JM400822Z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeighl NB, Karaseva N, Nakagawa K, Cho BC, Gray JE, Hovey T et al (2020) Patient-reported outcomes from FLAURA: Osimertinib versus erlotinib or gefitinib in patients with EGFR-mutated advanced non-small-cell lung cancer. Eur J Cancer 125:49\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejca.2019.11.006\u003c/span\u003e\u003cspan address=\"10.1016/j.ejca.2019.11.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKannan S, Tan DSW, Verma CS (2019) Effects of Single Nucleotide Polymorphisms on the Binding of Afatinib to EGFR: A Potential Patient Stratification Factor Revealed by Modeling Studies. J Chem Inf Model 59:309\u0026ndash;315. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jcim.8b00491\u003c/span\u003e\u003cspan address=\"10.1021/acs.jcim.8b00491\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakyi PO, Kwofie SK, Tuekpe JK, Gwira TM, Broni E, Miller WA et al Inhibiting Leishmania donovani Sterol Methyltransferase to Identify Lead Compounds Using Molecular Modelling. Pharmaceuticals 2023;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ph16030330\u003c/span\u003e\u003cspan address=\"10.3390/ph16030330\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaj V, Lee J (2020) 2H/4H-Chromenes\u0026mdash;A Versatile Biologically Attractive Scaffold. Front Chem 8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fchem.2020.00623\u003c/span\u003e\u003cspan address=\"10.3389/fchem.2020.00623\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerez J, Fuertes M, Nguewa P, Castilla J, Alonso C (2008) Anticancer Compounds as Leishmanicidal Drugs: Challenges in Chemotherapy and Future Perspectives. Curr Med Chem 15:433\u0026ndash;439. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/092986708783503221\u003c/span\u003e\u003cspan address=\"10.2174/092986708783503221\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCroft SL, Engel J Miltefosine - discovery of the antileishmanial activity of phospholipid derivatives. Trans R Soc Trop Med Hyg 2006;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.trstmh.2006.03.009\u003c/span\u003e\u003cspan address=\"10.1016/j.trstmh.2006.03.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuarte D, Nunes M, Ricardo S, Vale N Combination of Antimalarial and CNS Drugs with Antineoplastic Agents in MCF-7 Breast and HT-29 Colon Cancer Cells: Biosafety Evaluation and Mechanism of Action. Biomolecules 2022;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/biom12101490\u003c/span\u003e\u003cspan address=\"10.3390/biom12101490\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePi\u0026ntilde;ero J, Ram\u0026iacute;rez-Anguita JM, Sa\u0026uuml;ch-Pitarch J, Ronzano F, Centeno E, Sanz F et al (2020) The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res 48:D845\u0026ndash;D855. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkz1021\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkz1021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S et al (2016) Curr Protoc Bioinf 54(130). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cpbi.5\u003c/span\u003e\u003cspan address=\"10.1002/cpbi.5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaina A, Michielin O, Zoete V (2019) SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res 47:W357\u0026ndash;W364. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkz382\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkz382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallo K, Goede A, Preissner R, Gohlke B-O (2022) SuperPred 3.0: drug classification and target prediction - a machine learning approach. Nucleic Acids Res 50:W726\u0026ndash;W731. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkac297\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkac297\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe UniProt Consortium (2021) UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 49:D480\u0026ndash;D489. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkaa1100\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkaa1100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePathan M, Keerthikumar S, Ang C-S, Gangoda L, Quek CYJ, Williamson NA et al (2015) FunRich: An open access standalone functional enrichment and interaction network analysis tool. Proteomics 15:2597\u0026ndash;2601. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1002/pmic.201400515\u003c/span\u003e\u003cspan address=\"10.1002/pmic.201400515\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J et al (2009) STRING 8 - a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 37:D412\u0026ndash;D416. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkn760\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkn760\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498\u0026ndash;2504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/gr.1239303\u003c/span\u003e\u003cspan address=\"10.1101/gr.1239303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChin C-H, Chen S-H, Wu H-H, Ho C-W, Ko M-T, Lin C-Y (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8(Suppl 4):S11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1752-0509-8-S4-S11\u003c/span\u003e\u003cspan address=\"10.1186/1752-0509-8-S4-S11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBader GD, Hogue CWV (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4:2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2105-4-2\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-4-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O et al (2019) Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 10:1523. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-019-09234-6\u003c/span\u003e\u003cspan address=\"10.1038/s41467-019-09234-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObst-Sander U, Ricci A, Kuhn B, Friess T, Koldewey P, Kuglstatter A et al (2022) Discovery of Novel Allosteric EGFR L858R Inhibitors for the Treatment of Non-Small-Cell Lung Cancer as a Single Agent or in Combination with Osimertinib. J Med Chem 65:13052\u0026ndash;13073. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jmedchem.2c00893\u003c/span\u003e\u003cspan address=\"10.1021/acs.jmedchem.2c00893\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUhlenbrock N, Smith S, Weisner J, Landel I, Lindemann M, Le TA et al (2019) Structural and chemical insights into the covalent-allosteric inhibition of the protein kinase Akt. Chem Sci 10:3573\u0026ndash;3585. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/c8sc05212c\u003c/span\u003e\u003cspan address=\"10.1039/c8sc05212c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcTigue M, Murray BW, Chen JH, Deng YL, Solowiej J, Kania RS (2012) Molecular conformations, interactions, and properties associated with drug efficiency and clinical performance among VEGFR TK inhibitors. Proc Natl Acad Sci U S A 109:18281\u0026ndash;18289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1207759109\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1207759109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerman H, Henrick K, Nakamura H (2003) Announcing the worldwide Protein Data Bank. Nat Struct Biol 10:980\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000) The Protein Data Bank. Nucleic Acids Res 28:235\u0026ndash;242\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and Validation of a Genetic Algorithm for Flexible Docking. J Mol Biol 267:727\u0026ndash;748\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using GOLD. Proteins 52:609\u0026ndash;623. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/prot.10465\u003c/span\u003e\u003cspan address=\"10.1002/prot.10465\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11:425\u0026ndash;445\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorb O, St\u0026uuml;tzle T, Exner TE (2009) Empirical scoring functions for advanced Protein-Ligand docking with PLANTS. J Chem Inf Model 49:84\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/ci800298z\u003c/span\u003e\u003cspan address=\"10.1021/ci800298z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMooij WTM, Verdonk ML (2005) General and targeted statistical potentials for protein-ligand interactions. Proteins 61:272\u0026ndash;287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/prot.20588\u003c/span\u003e\u003cspan address=\"10.1002/prot.20588\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCase DA, Cheatham TE, Darden T, Gohlke H, Luo R, Merz KM et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668\u0026ndash;1688. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jcc.20290\u003c/span\u003e\u003cspan address=\"10.1002/jcc.20290\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalomon-Ferrer R, Case DA, Walker RC (2013) An overview of the Amber biomolecular simulation package. Wiley Interdiscip Rev Comput Mol Sci 3:198\u0026ndash;210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wcms.1121\u003c/span\u003e\u003cspan address=\"10.1002/wcms.1121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiner PK, Kollman PA (1981) AMBER: Assisted Model Building with Energy Refinement. A General Program for Modeling Molecules and Their Interactions. J Comput Chem 2:287\u0026ndash;303\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong G, Wu Z, Yi J, Fu L, Yang Z, Hsieh C et al (2021) ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res 49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkab255\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkab255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep42717\u003c/span\u003e\u003cspan address=\"10.1038/srep42717\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipinski CA (2004) Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov Today Technol 1:337\u0026ndash;341\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLindsey S, Langhans SA (2015) Epidermal growth factor signaling in transformed cells. Int Rev Cell Mol Biol 314:1\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/bs.ircmb.2014.10.001\u003c/span\u003e\u003cspan address=\"10.1016/bs.ircmb.2014.10.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Z, Hackshaw A, Feng Q, Fu X, Zhang Y, Mao C et al (2017) Comparison of gefitinib, erlotinib and afatinib in non-small cell lung cancer: A meta-analysis. Int J Cancer 140:2805\u0026ndash;2819. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/IJC.30691\u003c/span\u003e\u003cspan address=\"10.1002/IJC.30691\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu G, Chen X (2010) Vascular Endothelial Growth Factor as an Anti-Angiogenic Target for Cancer Therapy. Curr Drug Targets 11:1000\u0026ndash;1017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/138945010791591395\u003c/span\u003e\u003cspan address=\"10.2174/138945010791591395\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu JY, Wakelee HA (2009) Monoclonal antibodies targeting vascular endothelial growth factor: current status and future challenges in cancer therapy. BioDrugs 23:289\u0026ndash;304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2165/11317600-000000000-00000\u003c/span\u003e\u003cspan address=\"10.2165/11317600-000000000-00000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlaviano A, Foo ASC, Lam HY, Yap KCH, Jacot W, Jones RH et al (2023) PI3K/AKT/mTOR signaling transduction pathway and targeted therapies in cancer. Mol Cancer 22:138. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/S12943-023-01827-6\u003c/span\u003e\u003cspan address=\"10.1186/S12943-023-01827-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu R, Chen Y, Liu G, Li C, Song Y, Cao Z et al (2020) PI3K/AKT pathway as a key link modulates the multidrug resistance of cancers. Cell Death Disease 2020 11:9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41419-020-02998-6\u003c/span\u003e\u003cspan address=\"10.1038/s41419-020-02998-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaboli PJ, Imani S, Jomhori M, Ling K-H (2021) Chemoresistance in breast cancer: PI3K/Akt pathway inhibitors vs the current chemotherapy. Am J Cancer Res 11:5155\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHonegger AM, Dull TJ, Felder S, Van Obberghen E, Bellot F, Szapary D et al (1987) Point Mutation at the ATP Binding Site of EGF Receptor Abolishes Protein-Tyrosine Kinase Activity and Alters Cellular Routing. Cell 51:199\u0026ndash;209\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBauer D, Whittington DA, Coxon A, Bready J, Harriman SP, Patel VF et al (2008) Evaluation of indazole-based compounds as a new class of potent KDR/VEGFR-2 inhibitors. Bioorg Med Chem Lett 18:4844\u0026ndash;4848. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bmcl.2008.07.080\u003c/span\u003e\u003cspan address=\"10.1016/j.bmcl.2008.07.080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAziz MA, Serya RAT, Lasheen DS, Abdel-Aziz AK, Esmat A, Mansour AM et al (2016) Discovery of Potent VEGFR-2 Inhibitors based on Furopyrimidine and Thienopyrimidne Scaffolds as Cancer Targeting Agents. Sci Rep 6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep24460\u003c/span\u003e\u003cspan address=\"10.1038/srep24460\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Li Y, Wang Y, Lin C, Zhang D, Chen J et al (2022) Recent progress on vascular endothelial growth factor receptor inhibitors with dual targeting capabilities for tumor therapy. J Hematol Oncol 15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13045-022-01310-7\u003c/span\u003e\u003cspan address=\"10.1186/s13045-022-01310-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarabipour S, Ballmer-Hofer K, Hristova K VEGFR-2 conformational switch in response to ligand binding. Elife 2016;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7554/eLife.13876\u003c/span\u003e\u003cspan address=\"10.7554/eLife.13876\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchiebel J, Gaspari R, Wulsdorf T, Ngo K, Sohn C, Schrader TE et al Intriguing role of water in protein-ligand binding studied by neutron crystallography on trypsin complexes. Nat Commun 2018;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-018-05769-2\u003c/span\u003e\u003cspan address=\"10.1038/s41467-018-05769-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerguson KM, Berger MB, Mendrola JM, Cho H-S, Leahy DJ, Lemmon MA (2003) EGF Activates Its Receptor by Removing Interactions that Autoinhibit Ectodomain Dimerization molecules. Crystallographic studies of several ligand-bound RTK extracellular fragments have allowed visual-ization of such dimers, including those from the Flt-1 of the complete EGF receptor extracellular region. Mol Cell 11:507\u0026ndash;517\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePao W, Miller VA, Politi KA, Riely GJ, Somwar R, Zakowski MF et al (2005) Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PLoS Med 2:0225\u0026ndash;0235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pmed.0020073\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.0020073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Mao T, Wang J, Zheng H, Hu Z, Cao P et al (2023) Toward the next generation EGFR inhibitors: an overview of osimertinib resistance mediated by EGFR mutations in non-small cell lung cancer. Cell Commun Signal 21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12964-023-01082-8\u003c/span\u003e\u003cspan address=\"10.1186/s12964-023-01082-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu X, Yang B, An Q, Assaraf YG, Cao X, Xia J Acquired resistance to third-generation EGFR-TKIs and emerging next-generation EGFR inhibitors. Innov (Camb) 2021;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.xinn.2021.100103\u003c/span\u003e\u003cspan address=\"10.1016/j.xinn.2021.100103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMullard A (2022) Do fourth-generation EGFR inhibitors showcase the future of kinase inhibitors? Nat Rev Drug Discov 21:408\u0026ndash;409. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/d41573-022-00091-5\u003c/span\u003e\u003cspan address=\"10.1038/d41573-022-00091-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWittlinger F, Ogboo BC, Shevchenko E, Damghani T, Pham CD, Schaeffner IK et al Linking ATP and allosteric sites to achieve superadditive binding with bivalent EGFR kinase inhibitors. Commun Chem 2024;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s42004-024-01108-3\u003c/span\u003e\u003cspan address=\"10.1038/s42004-024-01108-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlamri MA, Merae Alshahrani M, Alawam AS, Paria S, Kumar Sen K, Banerjee S et al (2024) Development of newer generation Vascular endothelial growth factor Receptor-2 Inhibitors: Pharmacophore based design, virtual Screening, molecular Docking, molecular dynamic Simulation, and DFT analyses. J King Saud Univ Sci 36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jksus.2024.103285\u003c/span\u003e\u003cspan address=\"10.1016/j.jksus.2024.103285\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nchem.1243\u003c/span\u003e\u003cspan address=\"10.1038/nchem.1243\" 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":true,"hideJournal":true,"highlight":"","institution":"West African Center for Cell Biology and Infectious Pathogens, University of Ghana ","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cancer, epidermal growth factor receptor (EGFR), molecular dynamics, molecular docking, drug discovery","lastPublishedDoi":"10.21203/rs.3.rs-6998508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6998508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The traditional drug discovery process for anticancer drugs is hindered by prolonged screening and off-target effects. To address these challenges, we screened three novel compounds for their anticancer activity and employed computer-aided drug design to evaluate lead compounds' pharmacological properties and molecular interactions. This study aimed to determine the anticancer potential of our compounds and predict the targets of the lead compound using computer-aided drug discovery processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e: Three synthetic compounds (compounds 1,2, and 3) were tested for cytotoxicity against four cancer cell lines. Using the STRING, protein-protein interactions were analysed, and functional enrichment analyses were performed using Metascape. Molecular docking, dynamic simulations, pharmacological ADME properties, and drug-likeness properties were assessed using GOLD, Amber 22, ADMETLab, and SWISSADME software packages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Compound 2 inhibited MDA MB 468 and MDA MB 231 cell proliferation and showed good selectivity against PNT2 cells. Pathway analysis associated Compound 2 with cancer and focal adhesion pathways, identifying potential targets for EGFR, AKT1, and VEGFR2. Molecular docking revealed that Compound 2 binds to the ATP-binding activation site at Lys745 and the DFG motif at Asp855 of EGFR, as well as the ATP binding site of VEGFR2 at Cys919 and Phe918. Molecular dynamic simulations indicated that EGFR had the most energetically stable protein-ligand interactions, followed by VEGFR2 and AKT1. EGFR also showed flexible amino acid residues and strong hydrogen bond interactions. This study highlighted that Compound 2 showed significant cytotoxicity against breast cancer cell lines and is linked to cancer pathways, notably ErbB and EGFR. Compound 2 also binds to the ATP activation site and DFG motif of EGFR in docking studies and may have an energetically stable interaction with EGFR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: This highlight Compound 2 as anticancer agent with the potential to interact with EGFR.\u003c/p\u003e","manuscriptTitle":"Characterization of Novel Anticancer Agent using Computer-Aided Drug Discovery Processes Reveals Selective Interaction with the Epidermal Growth Factor Receptor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 06:51:54","doi":"10.21203/rs.3.rs-6998508/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4d51da3e-b40c-4e3c-b913-db591bb29710","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50732691,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2025-07-02T06:51:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 06:51:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6998508","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6998508","identity":"rs-6998508","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}