In silico Multi-Target Pharmacological Profiling of Itraconazole: Molecular Docking, Binding Free Energy Estimation, and Systems Biology Insights into Potential Anti-Cancer Mechanisms

In silico Multi-Target Pharmacological Profiling of Itraconazole: Molecular Docking, Binding Free Energy Estimation, and Systems Biology Insights into Potential Anti-Cancer Mechanisms | 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 In silico Multi-Target Pharmacological Profiling of Itraconazole: Molecular Docking, Binding Free Energy Estimation, and Systems Biology Insights into Potential Anti-Cancer Mechanisms Mohd Sameer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9577042/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 Itraconazole is an approved triazole antifungal agent with documented pre-clinical anti-cancer activity. The full spectrum of human protein targets through which it may exert oncological effects has not been systematically characterized. This study presents a multi-tier in silico framework to map the pharmacological target landscape of itraconazole and investigate its potential mechanisms of anti-cancer action. Methods Molecular docking of itraconazole against the EGFR kinase domain was performed using two crystal structures (PDB IDs: 4HJO and 4FA6) with AutoDock Vina. Binding free energy was estimated using PRODIGY (approximate MM/GBSA-like approach). Target prediction was conducted with SwissTargetPrediction; protein–protein interaction networks were constructed in STRING (v11.5); and functional enrichment analysis covered Gene Ontology (GO) Biological Process, KEGG, and Reactome databases. Results Itraconazole docked into the EGFR ATP-binding cleft with affinities of − 11.0 and − 9.8 kcal/mol for 4HJO and 4FA6, respectively (RMSD < 2.0 Å). Key interacting residues included ASP1063, LYS1059, GLU1061, ASN1060, ARG1021, and GLY1058. Binding free energy estimation yielded ΔG noelec (non-electrostatic component) = − 5.43 kcal/mol. Target prediction identified eight proteins: FYN, HTR2A, SLC6A4, DRD3, CCR4, CXCR1, CYP3A4, and CYP51A1. STRING network analysis resolved two functional clusters: a serotonergic/kinase hub and a chemokine receptor pair. Pathway enrichment identified the serotonergic synapse (KEGG hsa04726; FDR = 0.00023) and biosynthesis of maresin-like specialized pro-resolving mediators (Reactome HSA-9027307; FDR = 0.025) as the most significantly enriched annotations. Conclusion Itraconazole demonstrates potential multi-target oncological activity, with computational evidence for concurrent engagement of EGFR, serotonergic, dopaminergic, chemokine, and inflammatory resolution pathways. These findings support further investigation of itraconazole as a drug repurposing candidate in oncology and provide a prioritized framework for experimental validation. itraconazole drug repurposing EGFR molecular docking network pharmacology polypharmacology systems biology cancer Figures Figure 1 Figure 2 1. INTRODUCTION Cancer accounts for approximately 10 million deaths annually and remains one of the foremost global health challenges [ 1 ]. Despite substantial advances in targeted therapy, drug resistance through compensatory pathway activation and clonal selection limits the durability of single-target agents across many solid tumor types [ 2 ]. This therapeutic constraint has renewed interest in polypharmacological strategies, wherein a single compound engages multiple oncogenic nodes simultaneously to achieve more durable disease control. The epidermal growth factor receptor (EGFR), a receptor tyrosine kinase of the ErbB family, is among the most clinically validated cancer targets. EGFR-driven activation of the PI3K/AKT and MAPK/ERK pathways sustains tumor cell proliferation, survival, angiogenesis, and metastasis across multiple malignancies, including non-small cell lung cancer, glioblastoma, and colorectal carcinoma [ 5 , 6 ]. Although first-, second-, and third-generation EGFR inhibitors have demonstrated clinical efficacy, acquired resistance — most commonly through the T790M gatekeeper mutation — frequently limits sustained responses [ 6 ]. Compounds that combine EGFR binding with additional oncogenic target engagement therefore represent a compelling strategy to address this problem. Drug repurposing — the systematic re-evaluation of approved compounds for new indications — offers a practical route to anti-cancer agents with established safety profiles and reduced development costs [ 1 , 2 ]. Itraconazole, a triazole antifungal approved in 1992 that primarily targets fungal lanosterol 14α-demethylase (CYP51A1), has emerged as a prominent repurposing candidate. Kim et al. demonstrated that itraconazole potently inhibits endothelial cell proliferation and tumor angiogenesis independently of its antifungal mechanism [ 3 ], and Aftab et al. identified suppression of the hedgehog pathway as an additional anti-cancer mechanism [ 4 ]. Phase II clinical trials have reported activity in basal cell carcinoma, prostate cancer, and non-small cell lung cancer. A comprehensive systems-level characterization of itraconazole’s broader target space in the context of cancer biology is, however, lacking. This study addresses that gap by deploying an integrated in silico pipeline encompassing AutoDock Vina-based molecular docking, PRODIGY-based binding free energy estimation (approximate MM/GBSA-like approach), SwissTargetPrediction-based target profiling, STRING network construction, and GO/KEGG/Reactome functional enrichment analysis. The combined approach aims to delineate the polypharmacological anti-cancer landscape of itraconazole and provide a structured basis for experimental follow-up. 2. MATERIALS AND METHODS 2.1 Protein Structure Preparation Crystal structures of the EGFR kinase domain were retrieved from the Protein Data Bank (PDB IDs: 4HJO and 4FA6). Both structures represent the active conformation of the kinase domain and contain co-crystallized ligands in the ATP-binding cleft. Co-crystallized ligands, water molecules, and non-standard heteroatoms were removed using UCSF Chimera. Hydrogen atoms were added under the AMBER force field, and Gasteiger partial charges were assigned. Processed structures were saved in PDBQT format for use in AutoDock Vina. 2.2 Ligand Preparation The three-dimensional structure of itraconazole (PubChem CID: 55283) was retrieved in SDF format and subjected to energy minimization under the MMFF94 force field in Open Babel, followed by further geometry optimization in Avogadro. Rotatable bonds were assigned and the ligand was exported in PDBQT format. Physicochemical properties were verified using SwissADME; itraconazole exceeds the Lipinski molecular weight threshold (705 Da) but falls within the accepted “beyond rule of five” chemical space for oral drugs with high permeability. 2.3 Molecular Docking Molecular docking was conducted using AutoDock Vina (version 1.1.2) [ 13 ], which employs a hybrid empirical scoring function and iterated local search optimization. Grid boxes were centered on the ATP-binding cleft of each EGFR structure, with dimensions of 25 × 25 × 25 Å. The exhaustiveness parameter was set to 16. Nine binding conformations were sampled per run and ranked by binding affinity. Docking protocol validation was ensured by maintaining RMSD ≤ 2.0 Å for top-ranked poses, confirming convergent sampling of a consistent binding geometry. 2.4 Protein–Ligand Interaction Analysis Residue-level interaction profiling was performed in BIOVIA Discovery Studio Visualizer 2021. Two-dimensional interaction diagrams were generated to identify hydrogen bonds, pi–cation interactions, hydrophobic (alkyl and pi-alkyl) contacts, and van der Waals forces. Residues within a 5 Å radius of the bound ligand were considered part of the binding microenvironment. 2.5 Binding Free Energy Estimation Binding free energy was estimated using PRODIGY (PROtein binDIng enerGY prediction), a knowledge-based predictor trained on protein–protein interface data that provides an approximate MM/GBSA-like estimate [ 14 ]. Importantly, PRODIGY does not perform full MM/GBSA calculations; it is a structure-trained empirical predictor. The non-electrostatic binding free energy component, ΔG noelec (non-electrostatic component), was used as the primary thermodynamic descriptor for the itraconazole–EGFR (4HJO) complex. This metric primarily reflects hydrophobic and van der Waals contributions to binding — forces that dominate in non-polar small molecules such as itraconazole. 2.6 Target Prediction The broader target space of itraconazole was profiled by submitting its canonical SMILES to SwissTargetPrediction (swisstargetprediction.ch), a machine-learning platform that predicts human protein targets based on 2D and 3D structural similarity to known bioactive compounds. The top predicted targets with probability above 0.01 were retained and classified by protein family (GPCR, kinase, cytochrome P450, transporter) for downstream network analysis. 2.7 Network Construction The eight highest-confidence predicted targets were submitted to the STRING database (version 11.5) [ 15 ] with a minimum interaction confidence threshold of 0.40. Interaction evidence was drawn from experimental data, co-expression, text mining, and curated pathway databases. Functional clusters were identified by k-means clustering within the STRING interface. 2.8 Functional Enrichment Analysis Functional enrichment analysis was conducted within STRING against Gene Ontology Biological Process (GO:BP), KEGG, and Reactome databases. Statistical significance was assessed by hypergeometric testing with Benjamini–Hochberg false discovery rate (FDR) correction, using the whole human genome as the background reference. Terms with FDR < 0.05 were considered statistically significant. 3. RESULTS 3.1 Molecular Docking Itraconazole docked into the ATP-binding cleft of EGFR with binding affinities of − 11.0 kcal/mol and − 9.8 kcal/mol for the 4HJO and 4FA6 complexes, respectively. Docking results are summarized in Table 1 . In both complexes, top-ranked poses showed RMSD values below 2.0 Å, confirming convergent sampling of a stable binding geometry within the hinge region of the kinase domain. Docked conformations are illustrated in Fig. 1 , with Panel A and Panel B corresponding to 4HJO and 4FA6, respectively. Table 1 Molecular docking results for itraconazole against the EGFR kinase domain. Protein (PDB ID) Binding Affinity (kcal/mol) Top Pose RMSD (Å) Key Interacting Residues EGFR (4HJO) −11.0 < 2.0 ASP1063, LYS1059, GLU1061, ASN1060, ARG1021, GLY1058 EGFR (4FA6) −9.8 < 2.0 ASP1063, GLU1061, LYS1059, ARG1021 3.2 Protein–Ligand Interaction Analysis Residue-level interaction analysis of the best-ranked 4HJO complex revealed a network of complementary binding contacts within the kinase hinge region. ASP1063 and GLU1061 formed direct hydrogen bonds with the triazole nitrogen and carbonyl oxygen of itraconazole, respectively. ARG1021 engaged the chlorophenyl ring through a pi–cation interaction. LYS1059, ASN1060, and GLY1058 contributed hydrophobic (alkyl and pi-alkyl) contacts and van der Waals forces that stabilized the aliphatic chain and dioxolane ring of the ligand within the hydrophobic back pocket. These contacts collectively recapitulate the pharmacophoric requirements of classical EGFR ATP-competitive inhibitors — specifically, the hinge-region hydrogen bond network and hydrophobic pocket occupancy — lending mechanistic credibility to the predicted binding mode. 3.3 Binding Free Energy Estimation Binding free energy estimation of the itraconazole–EGFR (4HJO) complex using PRODIGY (approximate MM/GBSA-like approach) yielded ΔG noelec (non-electrostatic component) = − 5.43 kcal/mol. This value, primarily reflecting hydrophobic and van der Waals contributions, is consistent with moderate but thermodynamically stable binding after solvation correction. The positive shift relative to the raw AutoDock Vina score is expected, as PRODIGY incorporates a solvation penalty not accounted for in gas-phase docking scoring functions. A ΔG noelec (non-electrostatic component) of − 5.43 kcal/mol corresponds to an estimated dissociation constant in the low micromolar range, consistent with itraconazole plasma concentrations achieved at standard therapeutic dosing. 3.4 Target Prediction and Network Analysis SwissTargetPrediction identified eight high-confidence human targets for itraconazole: serotonin receptor HTR2A, serotonin transporter SLC6A4, dopamine receptor DRD3, Src-family kinase FYN, C-C chemokine receptor CCR4, C-X-C chemokine receptor CXCR1, cytochrome P450 3A4 (CYP3A4), and lanosterol 14α-demethylase (CYP51A1). The predicted target class distribution comprised GPCRs (66.7%), cytochrome P450 enzymes (13.3%), electrochemical transporters (13.3%), and kinases (6.7%). Target annotations are summarized in Table 3 . Table 2 Predicted targets of itraconazole identified by SwissTargetPrediction. Protein Class Description Oncological Relevance FYN Kinase Src-family non-receptor tyrosine kinase PI3K/AKT, MAPK activation HTR2A GPCR 5-HT2A serotonin receptor Gq/PLC/IP3; PI3K/AKT SLC6A4 Transporter Serotonin reuptake transporter Extracellular 5-HT regulation DRD3 GPCR Dopamine D3 receptor VEGF secretion; immune modulation CCR4 GPCR C-C chemokine receptor 4 Treg recruitment; TME immunosuppression CXCR1 GPCR C-X-C chemokine receptor 1 Stemness; invasion; MDSCs CYP3A4 Cytochrome P450 Hepatic drug-metabolizing enzyme Pharmacokinetics; drug–drug interactions CYP51A1 Cytochrome P450 Lanosterol 14α-demethylase Sterol biosynthesis (antifungal target) STRING network analysis of these eight proteins produced a functionally coherent interaction map, illustrated in Fig. 2 . Two principal clusters were resolved. The first — comprising FYN, HTR2A, SLC6A4, DRD3, and CYP3A4 — constitutes a serotonergic/dopaminergic/kinase signaling hub, with multiple high-confidence edges between SLC6A4, HTR2A, and CYP3A4. The second cluster consists of CCR4 and CXCR1, linked by strong co-expression and pathway-based evidence. CYP51A1 appears as an isolated node, consistent with its primary biochemical role in sterol biosynthesis. An expanded interaction network including secondary nodes (e.g., HTR3A, HTR3B, CYP2D6, DAOA, LRTOMT) was observed in exploratory analysis but was excluded from the primary network to maintain focus on the highest-confidence predicted targets. 3.5 Functional Enrichment Analysis Functional enrichment analysis of the eight-target network identified statistically significant annotations across GO Biological Process, KEGG, and Reactome databases (Table 2 ). The top enriched GO:BP terms were serotonin receptor signaling pathway (GO:0007210; 3/38 genes; strength = 2.08; FDR = 0.0035), alkaloid catabolic process (GO:0009822; 2/3 genes; strength = 3.00; FDR = 0.0052), cellular response to dopamine (GO:1903351; 4/81 genes; strength = 1.87; FDR = 0.0035), and negative regulation of synaptic transmission (GO:0050805; 3/62 genes; strength = 1.87; FDR = 0.0099). At the pathway level, the serotonergic synapse (KEGG hsa04726; 4/108 genes; FDR = 0.00023) was the most significantly enriched annotation. The Reactome pathway for biosynthesis of maresin-like specialized pro-resolving mediators (HSA-9027307; 2/6 genes; FDR = 0.025) was also significantly enriched, implicating the inflammatory resolution machinery as an additional axis of itraconazole activity. Table 3 Functional enrichment analysis results for the itraconazole target network. Database Term / Pathway Count Strength FDR GO:BP Serotonin receptor signaling pathway (GO:0007210) 3/38 2.08 0.0035 GO:BP Alkaloid catabolic process (GO:0009822) 2/3 3.00 0.0052 GO:BP Cellular response to dopamine (GO:1903351) 4/81 1.87 0.0035 GO:BP Monoterpenoid metabolic process (GO:0016098) 2/6 2.70 0.0099 GO:BP Negative regulation of synaptic transmission (GO:0050805) 3/62 1.87 0.0099 KEGG Serotonergic synapse (hsa04726) 4/108 1.75 0.00023 Reactome Biosynthesis of maresin-like SPMs (HSA-9027307) 2/6 2.70 0.0250 4. DISCUSSION This study presents an integrated computational assessment of the multi-target pharmacological profile of itraconazole in cancer biology. The findings collectively indicate that itraconazole demonstrates potential multi-target oncological activity by simultaneously engaging receptor tyrosine kinase, neurotransmitter receptor, chemokine receptor, and lipid mediator signaling nodes. This polypharmacological mechanism may underlie the compound’s reported anti-tumor activity in pre-clinical and early clinical studies. 4.1 EGFR Binding and Kinase Inhibition The computed binding affinities of − 11.0 and − 9.8 kcal/mol for the 4HJO and 4FA6 complexes are comparable to those reported for clinically active EGFR inhibitors under similar docking conditions. Hydrogen bond interactions with ASP1063 and GLU1061 in the hinge region, and the pi–cation contact with ARG1021, correspond to canonical pharmacophoric requirements of ATP-competitive EGFR inhibitors [ 6 ]. The PRODIGY-estimated ΔG noelec (non-electrostatic component) of − 5.43 kcal/mol corroborates thermodynamically stable binding and places itraconazole in the affinity range of moderate kinase inhibitors whose cellular activity depends on intracellular accumulation and target residence time in addition to aqueous binding constants [ 14 ]. These findings are mechanistically consistent with reported suppression of EGFR signaling and its downstream PI3K/AKT and MAPK/ERK effectors in cancer cell lines treated with itraconazole [ 5 ], supporting the hypothesis that direct EGFR engagement contributes to its anti-proliferative phenotype. 4.2 Serotonergic Signaling and Cancer Proliferation The identification of HTR2A and SLC6A4 as predicted targets, together with enrichment of the serotonin receptor signaling pathway (FDR = 0.0035) and KEGG serotonergic synapse (FDR = 0.00023), points to an oncologically relevant mechanism that has received increasing attention in the literature. Serotonin acts as an autocrine and paracrine mitogen in several tumor types through HTR2A-mediated activation of the Gq/phospholipase C/IP3 cascade, which amplifies PI3K/AKT survival signaling and MAPK/ERK proliferative signaling [ 7 , 8 ]. SLC6A4, the serotonin reuptake transporter, regulates extracellular serotonin levels and thereby modulates autocrine signaling amplitude; its overexpression in colorectal, small cell lung, and breast cancers correlates with reduced apoptosis sensitivity [ 8 ]. Modulation of HTR2A and SLC6A4 by itraconazole could attenuate serotonin-driven mitogenesis in tumors expressing these targets, providing a mechanistic complement to EGFR inhibition that converges on shared PI3K/AKT and MAPK pathway nodes. 4.3 Dopaminergic Signaling and Tumor Angiogenesis DRD3 contributes to tumor biology through two principal mechanisms. Activation of D3 receptors on tumor-associated endothelial cells stimulates VEGF secretion and promotes neovascularization; D3 receptor signaling on tumor-infiltrating immune cells simultaneously modulates regulatory T cell function to dampen anti-tumor immunity [ 9 ]. The prediction of DRD3 as an itraconazole target provides a mechanistic link to the compound’s established anti-angiogenic properties [ 3 ], suggesting that the anti-vascular effect may reflect concurrent suppression of EGFR-driven VEGF transcription, DRD3-mediated VEGF secretion, and hedgehog pathway-regulated vascular remodeling [ 4 ]. This convergent multi-node anti-angiogenic activity could underlie the in vivo tumor growth inhibition reported for itraconazole. 4.4 FYN Kinase and the Src/PI3K/MAPK Axis FYN, a Src-family non-receptor tyrosine kinase, transmits oncogenic signals downstream of growth factor receptors and integrins by phosphorylating and activating PI3K — which drives AKT-mediated cell survival — and by activating the RAS/RAF/MEK/ERK axis to promote proliferation [ 7 ]. FYN overexpression has been documented in glioblastoma, melanoma, and pancreatic cancer, where it promotes invasion and metastasis. Its predicted interaction with itraconazole suggests potential co-suppression of PI3K/AKT and MAPK/ERK pathways downstream of multiple receptor inputs. Co-targeting of upstream EGFR and downstream FYN within a single compound would theoretically provide a mechanistic barrier against resistance mechanisms that arise when tumor cells upregulate Src-family kinases to bypass EGFR blockade — a clinically documented phenomenon in EGFR-inhibitor-resistant cancers. 4.5 Chemokine Receptors and Tumor Microenvironment Modulation CCR4 and CXCR1 form a distinct interaction cluster with well-characterized roles in tumor immune evasion and metastasis. CCR4 mediates recruitment of CD4 + regulatory T cells (Tregs) to the tumor microenvironment (TME) in response to CCL17 and CCL22 produced by tumor cells and tumor-associated macrophages, thereby suppressing cytotoxic anti-tumor immunity [ 10 ]. CXCR1, a receptor for interleukin-8 (CXCL8), is expressed on myeloid-derived suppressor cells and cancer stem cell populations, where it promotes stemness maintenance, invasion, and resistance to cytotoxic therapy [ 10 ]. Potential engagement of both receptors by itraconazole could reduce immunosuppression within the TME and impair metastatic dissemination, providing a systems-level rationale for observed in vivo tumor control that extends beyond direct anti-proliferative effects. 4.6 CYP3A4 and Pharmacokinetic Considerations CYP3A4, the predominant hepatic cytochrome P450 enzyme responsible for biotransformation of approximately 50% of marketed pharmaceuticals [ 11 ], is a known inhibitor substrate of itraconazole at standard antifungal doses. Its appearance as a predicted target reinforces the established clinical relevance of itraconazole-mediated CYP3A4 inhibition in polypharmacy settings. Co-administration of CYP3A4-metabolized oncology agents — including certain taxanes, vinca alkaloids, and kinase inhibitors — with itraconazole requires dose adjustment to prevent pharmacokinetic drug–drug interactions that could compromise safety or efficacy [ 11 ]. This finding underscores the importance of pharmacokinetic liability profiling in any clinical translation of an itraconazole repurposing strategy. 4.7 CYP51A1 and Antifungal Mechanism CYP51A1, lanosterol 14α-demethylase, is the established primary antifungal target of itraconazole through inhibition of ergosterol biosynthesis. Its appearance as an isolated node in the STRING network — without functionally relevant connectivity to the cancer-associated proteins in the network — is consistent with its distinct biochemical role in sterol metabolism. The antifungal and anti-cancer mechanisms of itraconazole therefore appear to operate through largely independent target sets, supporting a model in which the compound’s oncological activity is mediated by targets beyond CYP51A1. 4.8 Maresin-like SPMs and Inflammatory Resolution The enrichment of the Reactome pathway for biosynthesis of maresin-like specialized pro-resolving mediators (SPMs; FDR = 0.025) introduces a dimension of itraconazole activity not previously described in cancer biology. Maresins are docosahexaenoic acid-derived lipid mediators that actively resolve inflammation by promoting macrophage efferocytosis, inhibiting neutrophil recruitment, and suppressing pro-inflammatory cytokine production [ 12 ]. Chronic unresolved inflammation in the TME sustains angiogenesis, immune evasion, and mutagenic oxidative stress. Perturbation of the SPM biosynthetic machinery by itraconazole could shift the inflammatory balance within the TME toward resolution, potentially reducing the tumor-promoting inflammatory microenvironment. This pathway represents a novel hypothesis that requires dedicated biochemical investigation. 4.9 Polypharmacology and Drug Repurposing Implications The integrated computational evidence supports a polypharmacological model in which itraconazole demonstrates potential simultaneous activity on EGFR kinase, serotonergic and dopaminergic mitogenic receptors, Src-family kinase signaling, chemokine-mediated immune evasion, and lipid-based inflammatory resolution. These axes converge on the PI3K/AKT, MAPK/ERK, and VEGF pathways — established drivers of tumor cell proliferation, survival, and angiogenesis. The breadth of this target profile, combined with itraconazole’s established oral bioavailability and manageable safety record at antifungal doses [ 2 ], positions it as a tractable candidate for repurposing in combination oncology strategies. These computational findings are hypothesis-generating and require experimental validation before any clinical conclusions can be drawn. 5. CONCLUSION This study provides integrated computational evidence that itraconazole demonstrates potential multi-target oncological activity through concurrent engagement of EGFR, serotonergic, dopaminergic, kinase, chemokine, and lipid mediator pathways. Molecular docking confirmed high-affinity binding to the EGFR kinase domain with key hinge-region interactions. Binding free energy estimation corroborated thermodynamic stability. Target prediction and network analysis identified a pharmacologically coherent multi-node target set linked by serotonergic/kinase and chemokine clusters. Pathway enrichment connected these targets to the KEGG serotonergic synapse, PI3K/AKT- and MAPK-related biological processes, and the Reactome pro-resolving lipid mediator pathway. Collectively, these findings reframe itraconazole as a polypharmacological agent with a mechanistically coherent potential anti-cancer profile and provide a prioritized hypothesis framework to guide experimental validation of its oncological mechanisms. 6. LIMITATIONS Several limitations of this study must be acknowledged. All results derive from computational predictions and require wet-laboratory validation through in vitro binding assays, cell-based functional studies, and in vivo tumor models. Molecular dynamics simulations were not performed; the dynamic stability of the docked complexes over nanosecond-to-microsecond timescales therefore remains uncharacterized. PRODIGY was developed and validated primarily for protein–protein interfaces; its application to protein–small molecule binding free energy estimation is approximate and does not substitute for rigorous MM-PBSA or free energy perturbation calculations. SwissTargetPrediction is limited by the structural diversity of its training set and may miss targets with novel binding topologies. Finally, the clinical pharmacokinetic implications of CYP3A4 interaction must be assessed in the context of anti-tumor doses, which may differ substantially from standard antifungal dosing regimens. 7. FUTURE DIRECTIONS Future work should prioritize molecular dynamics simulations (≥ 100 ns) of the itraconazole–EGFR complexes, followed by rigorous MM-PBSA or alchemical free energy perturbation calculations to quantify binding free energy with greater accuracy. In vitro biochemical assays — including kinase inhibition assays against EGFR and FYN, and radioligand binding assays for HTR2A, DRD3, CCR4, and CXCR1 — are required to confirm the predicted interactions. Cell-based proliferation, migration, and angiogenesis assays in cancer lines with defined EGFR and GPCR expression profiles would provide functional evidence for the predicted multi-target activity. Medicinal chemistry optimization guided by the pharmacophoric contacts identified here could yield itraconazole analogs with improved kinase selectivity and reduced CYP3A4-mediated metabolic liability. Tumor microenvironment studies examining the effect of itraconazole on Treg infiltration and lipid mediator profiles in syngeneic mouse models would address the predicted immunomodulatory and inflammatory resolution mechanisms. Declarations Conflicts of Interest: The author declares no conflicts of interest. Funding: This research received no external funding. Author Contribution M.S. conceived the study, performed molecular docking and computational analyses, conducted data interpretation, prepared all figures, and wrote the manuscript. The author reviewed and approved the final version of the manuscript. Acknowledgements: The author thanks the Department of Biotechnology, SVPUAT, Meerut, for providing computational infrastructure support. Data Availability The datasets generated and/or analyzed during the current study are available from publicly accessible databases, including the Protein Data Bank (PDB) and SwissTargetPrediction. Processed data and computational results are available from the corresponding author upon reasonable request. References Chong CR, Sullivan DJ. New uses for old drugs. Nature. 2007;448(7154):645–646. Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18(1):41–58. Kim J, Tang JY, Gong R, et al. Itraconazole, a commonly used antifungal, inhibits Hedgehog pathway activity and cancer growth. Cancer Cell. 2010;17(4):388–399. Aftab BT, Dobromilskaya I, Liu JO, Rudin CM. Itraconazole inhibits angiogenesis and tumor growth in non-small cell lung cancer. Cancer Res. 2011;71(21):6764–6772. Guo S, Huang Y, Jiang Q, et al. Enhanced transcytosis of nanomedicine via EGFR signaling pathway. Mol Cancer. 2020;19(1):60. Lemmon MA, Schlessinger J. Cell signaling by receptor tyrosine kinases. Cell. 2010;141(7):1117–1134. Sriram K, Insel PA. G protein-coupled receptors as targets for approved drugs: how many targets and how many drugs? Mol Pharmacol. 2018;93(4):251–258. Carhart-Harris RL, Nutt DJ. Serotonin and brain function: a tale of two receptors. J Psychopharmacol. 2017;31(9):1091–1120. Beaulieu JM, Gainetdinov RR. The physiology, signaling, and pharmacology of dopamine receptors. Pharmacol Rev. 2011;63(1):182–217. Thomas RM, Bhatt DL, Bhatt S, et al. Chemokines, their receptors and cancer progression. Nat Rev Cancer. 2015;15(7):427–445. Zanger UM, Schwab M. Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol Ther. 2013;138(1):103–141. Dennis EA, Norris PC. Eicosanoid storm in infection and inflammation. Nat Rev Immunol. 2015;15(8):511–523. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455–461. Xue LC, Rodrigues JP, Kastritis PL, Bonvin AM, Vangone A. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics. 2016;32(23):3676–3678. Szklarczyk D, Gable AL, Nastou KC, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605–D612. 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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-9577042","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":632858039,"identity":"03c65ae7-2e28-49fa-8d13-4bb838be87f5","order_by":0,"name":"Mohd Sameer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYPCC/8xs7M0H4FwJMMIPmNn5eI4lgJk8xGrhl5PIMUDWghvIt59OfPBxD5s0m0TO58+8Ow7L2zMwH7zNw2CRh0uLwZnczYYznvEYs/G83SbNe+awYQ8DW7I1D4NEMU4tDLnbpHkOSCSzseduY5zZdpixh4HHTBqoJbEBl8P6327//eeAQX0bQ87jj0At9j0M/N/wamG4kbuNmeFAAjMbRw6DxMe2w4lAW9jwajG48XazZM+BA8xsPMfMJD6eSU/uOcxmbDnHAJ/Dcjd++AHUIt/e/PhD4g5r2/b25oc33lTU4XYYCmAEKWMG206UepiWUTAKRsEoGAVoAAArj1PCZOpVagAAAABJRU5ErkJggg==","orcid":"","institution":"Sardar Vallabhbhai Patel University of Agriculture and Technology","correspondingAuthor":true,"prefix":"","firstName":"Mohd","middleName":"","lastName":"Sameer","suffix":""}],"badges":[],"createdAt":"2026-04-30 12:10:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9577042/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9577042/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108493336,"identity":"5d5b810b-30b7-4fd6-974d-32135ddb7b31","added_by":"auto","created_at":"2026-05-05 09:59:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":474857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDocking poses of itraconazole in EGFR (PDB: 4HJO and 4FA6).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9577042/v1/b76f8809fb52feb49bb64fb8.png"},{"id":108386242,"identity":"3362f62b-99ac-4f3f-8901-2ec680418bdd","added_by":"auto","created_at":"2026-05-04 06:19:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61585,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSTRING protein–protein interaction network of predicted itraconazole targets. Network includes FYN, HTR2A, SLC6A4, DRD3, CCR4, CXCR1, CYP3A4, and CYP51A1. Edge thickness reflects interaction confidence.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9577042/v1/f86a6ad40443c5413acf6e3c.png"},{"id":109081044,"identity":"e48961d0-54c4-41c1-99a9-0c1463f86858","added_by":"auto","created_at":"2026-05-12 11:54:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":758161,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9577042/v1/8bdf69e9-9644-4f83-b77c-4af0de6f76d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"In silico Multi-Target Pharmacological Profiling of Itraconazole: Molecular Docking, Binding Free Energy Estimation, and Systems Biology Insights into Potential Anti-Cancer Mechanisms","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eCancer accounts for approximately 10\u0026nbsp;million deaths annually and remains one of the foremost global health challenges [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite substantial advances in targeted therapy, drug resistance through compensatory pathway activation and clonal selection limits the durability of single-target agents across many solid tumor types [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This therapeutic constraint has renewed interest in polypharmacological strategies, wherein a single compound engages multiple oncogenic nodes simultaneously to achieve more durable disease control.\u003c/p\u003e \u003cp\u003eThe epidermal growth factor receptor (EGFR), a receptor tyrosine kinase of the ErbB family, is among the most clinically validated cancer targets. EGFR-driven activation of the PI3K/AKT and MAPK/ERK pathways sustains tumor cell proliferation, survival, angiogenesis, and metastasis across multiple malignancies, including non-small cell lung cancer, glioblastoma, and colorectal carcinoma [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although first-, second-, and third-generation EGFR inhibitors have demonstrated clinical efficacy, acquired resistance \u0026mdash; most commonly through the T790M gatekeeper mutation \u0026mdash; frequently limits sustained responses [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Compounds that combine EGFR binding with additional oncogenic target engagement therefore represent a compelling strategy to address this problem.\u003c/p\u003e \u003cp\u003eDrug repurposing \u0026mdash; the systematic re-evaluation of approved compounds for new indications \u0026mdash; offers a practical route to anti-cancer agents with established safety profiles and reduced development costs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Itraconazole, a triazole antifungal approved in 1992 that primarily targets fungal lanosterol 14α-demethylase (CYP51A1), has emerged as a prominent repurposing candidate. Kim et al. demonstrated that itraconazole potently inhibits endothelial cell proliferation and tumor angiogenesis independently of its antifungal mechanism [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and Aftab et al. identified suppression of the hedgehog pathway as an additional anti-cancer mechanism [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Phase II clinical trials have reported activity in basal cell carcinoma, prostate cancer, and non-small cell lung cancer. A comprehensive systems-level characterization of itraconazole\u0026rsquo;s broader target space in the context of cancer biology is, however, lacking.\u003c/p\u003e \u003cp\u003eThis study addresses that gap by deploying an integrated in silico pipeline encompassing AutoDock Vina-based molecular docking, PRODIGY-based binding free energy estimation (approximate MM/GBSA-like approach), SwissTargetPrediction-based target profiling, STRING network construction, and GO/KEGG/Reactome functional enrichment analysis. The combined approach aims to delineate the polypharmacological anti-cancer landscape of itraconazole and provide a structured basis for experimental follow-up.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Protein Structure Preparation\u003c/h2\u003e \u003cp\u003eCrystal structures of the EGFR kinase domain were retrieved from the Protein Data Bank (PDB IDs: 4HJO and 4FA6). Both structures represent the active conformation of the kinase domain and contain co-crystallized ligands in the ATP-binding cleft. Co-crystallized ligands, water molecules, and non-standard heteroatoms were removed using UCSF Chimera. Hydrogen atoms were added under the AMBER force field, and Gasteiger partial charges were assigned. Processed structures were saved in PDBQT format for use in AutoDock Vina.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Ligand Preparation\u003c/h2\u003e \u003cp\u003eThe three-dimensional structure of itraconazole (PubChem CID: 55283) was retrieved in SDF format and subjected to energy minimization under the MMFF94 force field in Open Babel, followed by further geometry optimization in Avogadro. Rotatable bonds were assigned and the ligand was exported in PDBQT format. Physicochemical properties were verified using SwissADME; itraconazole exceeds the Lipinski molecular weight threshold (705 Da) but falls within the accepted \u0026ldquo;beyond rule of five\u0026rdquo; chemical space for oral drugs with high permeability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Molecular Docking\u003c/h2\u003e \u003cp\u003eMolecular docking was conducted using AutoDock Vina (version 1.1.2) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which employs a hybrid empirical scoring function and iterated local search optimization. Grid boxes were centered on the ATP-binding cleft of each EGFR structure, with dimensions of 25 \u0026times; 25 \u0026times; 25 \u0026Aring;. The exhaustiveness parameter was set to 16. Nine binding conformations were sampled per run and ranked by binding affinity. Docking protocol validation was ensured by maintaining RMSD\u0026thinsp;\u0026le;\u0026thinsp;2.0 \u0026Aring; for top-ranked poses, confirming convergent sampling of a consistent binding geometry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Protein\u0026ndash;Ligand Interaction Analysis\u003c/h2\u003e \u003cp\u003eResidue-level interaction profiling was performed in BIOVIA Discovery Studio Visualizer 2021. Two-dimensional interaction diagrams were generated to identify hydrogen bonds, pi\u0026ndash;cation interactions, hydrophobic (alkyl and pi-alkyl) contacts, and van der Waals forces. Residues within a 5 \u0026Aring; radius of the bound ligand were considered part of the binding microenvironment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Binding Free Energy Estimation\u003c/h2\u003e \u003cp\u003eBinding free energy was estimated using PRODIGY (PROtein binDIng enerGY prediction), a knowledge-based predictor trained on protein\u0026ndash;protein interface data that provides an approximate MM/GBSA-like estimate [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Importantly, PRODIGY does not perform full MM/GBSA calculations; it is a structure-trained empirical predictor. The non-electrostatic binding free energy component, ΔG\u003csub\u003enoelec\u003c/sub\u003e (non-electrostatic component), was used as the primary thermodynamic descriptor for the itraconazole\u0026ndash;EGFR (4HJO) complex. This metric primarily reflects hydrophobic and van der Waals contributions to binding \u0026mdash; forces that dominate in non-polar small molecules such as itraconazole.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Target Prediction\u003c/h2\u003e \u003cp\u003eThe broader target space of itraconazole was profiled by submitting its canonical SMILES to SwissTargetPrediction (swisstargetprediction.ch), a machine-learning platform that predicts human protein targets based on 2D and 3D structural similarity to known bioactive compounds. The top predicted targets with probability above 0.01 were retained and classified by protein family (GPCR, kinase, cytochrome P450, transporter) for downstream network analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Network Construction\u003c/h2\u003e \u003cp\u003eThe eight highest-confidence predicted targets were submitted to the STRING database (version 11.5) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] with a minimum interaction confidence threshold of 0.40. Interaction evidence was drawn from experimental data, co-expression, text mining, and curated pathway databases. Functional clusters were identified by k-means clustering within the STRING interface.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis was conducted within STRING against Gene Ontology Biological Process (GO:BP), KEGG, and Reactome databases. Statistical significance was assessed by hypergeometric testing with Benjamini\u0026ndash;Hochberg false discovery rate (FDR) correction, using the whole human genome as the background reference. Terms with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Molecular Docking\u003c/h2\u003e \u003cp\u003eItraconazole docked into the ATP-binding cleft of EGFR with binding affinities of \u0026minus;\u0026thinsp;11.0 kcal/mol and \u0026minus;\u0026thinsp;9.8 kcal/mol for the 4HJO and 4FA6 complexes, respectively. Docking results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In both complexes, top-ranked poses showed RMSD values below 2.0 \u0026Aring;, confirming convergent sampling of a stable binding geometry within the hinge region of the kinase domain. Docked conformations are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with Panel A and Panel B corresponding to 4HJO and 4FA6, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular docking results for itraconazole against the EGFR kinase domain.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (PDB ID)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinding Affinity (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTop Pose RMSD (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKey Interacting Residues\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR (4HJO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASP1063, LYS1059, GLU1061, ASN1060, ARG1021, GLY1058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR (4FA6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASP1063, GLU1061, LYS1059, ARG1021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Protein\u0026ndash;Ligand Interaction Analysis\u003c/h2\u003e \u003cp\u003eResidue-level interaction analysis of the best-ranked 4HJO complex revealed a network of complementary binding contacts within the kinase hinge region. ASP1063 and GLU1061 formed direct hydrogen bonds with the triazole nitrogen and carbonyl oxygen of itraconazole, respectively. ARG1021 engaged the chlorophenyl ring through a pi\u0026ndash;cation interaction. LYS1059, ASN1060, and GLY1058 contributed hydrophobic (alkyl and pi-alkyl) contacts and van der Waals forces that stabilized the aliphatic chain and dioxolane ring of the ligand within the hydrophobic back pocket. These contacts collectively recapitulate the pharmacophoric requirements of classical EGFR ATP-competitive inhibitors \u0026mdash; specifically, the hinge-region hydrogen bond network and hydrophobic pocket occupancy \u0026mdash; lending mechanistic credibility to the predicted binding mode.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Binding Free Energy Estimation\u003c/h2\u003e \u003cp\u003eBinding free energy estimation of the itraconazole\u0026ndash;EGFR (4HJO) complex using PRODIGY (approximate MM/GBSA-like approach) yielded ΔG\u003csub\u003enoelec\u003c/sub\u003e (non-electrostatic component)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.43 kcal/mol. This value, primarily reflecting hydrophobic and van der Waals contributions, is consistent with moderate but thermodynamically stable binding after solvation correction. The positive shift relative to the raw AutoDock Vina score is expected, as PRODIGY incorporates a solvation penalty not accounted for in gas-phase docking scoring functions. A ΔG\u003csub\u003enoelec\u003c/sub\u003e (non-electrostatic component) of \u0026minus;\u0026thinsp;5.43 kcal/mol corresponds to an estimated dissociation constant in the low micromolar range, consistent with itraconazole plasma concentrations achieved at standard therapeutic dosing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Target Prediction and Network Analysis\u003c/h2\u003e \u003cp\u003eSwissTargetPrediction identified eight high-confidence human targets for itraconazole: serotonin receptor HTR2A, serotonin transporter SLC6A4, dopamine receptor DRD3, Src-family kinase FYN, C-C chemokine receptor CCR4, C-X-C chemokine receptor CXCR1, cytochrome P450 3A4 (CYP3A4), and lanosterol 14α-demethylase (CYP51A1). The predicted target class distribution comprised GPCRs (66.7%), cytochrome P450 enzymes (13.3%), electrochemical transporters (13.3%), and kinases (6.7%). Target annotations are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredicted targets of itraconazole identified by SwissTargetPrediction.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOncological Relevance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFYN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSrc-family non-receptor tyrosine kinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePI3K/AKT, MAPK activation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHTR2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5-HT2A serotonin receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGq/PLC/IP3; PI3K/AKT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC6A4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSerotonin reuptake transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExtracellular 5-HT regulation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDopamine D3 receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVEGF secretion; immune modulation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC-C chemokine receptor 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTreg recruitment; TME immunosuppression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC-X-C chemokine receptor 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStemness; invasion; MDSCs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP3A4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCytochrome P450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHepatic drug-metabolizing enzyme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePharmacokinetics; drug\u0026ndash;drug interactions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP51A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCytochrome P450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLanosterol 14α-demethylase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSterol biosynthesis (antifungal target)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSTRING network analysis of these eight proteins produced a functionally coherent interaction map, illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Two principal clusters were resolved. The first \u0026mdash; comprising FYN, HTR2A, SLC6A4, DRD3, and CYP3A4 \u0026mdash; constitutes a serotonergic/dopaminergic/kinase signaling hub, with multiple high-confidence edges between SLC6A4, HTR2A, and CYP3A4. The second cluster consists of CCR4 and CXCR1, linked by strong co-expression and pathway-based evidence. CYP51A1 appears as an isolated node, consistent with its primary biochemical role in sterol biosynthesis. An expanded interaction network including secondary nodes (e.g., HTR3A, HTR3B, CYP2D6, DAOA, LRTOMT) was observed in exploratory analysis but was excluded from the primary network to maintain focus on the highest-confidence predicted targets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis of the eight-target network identified statistically significant annotations across GO Biological Process, KEGG, and Reactome databases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The top enriched GO:BP terms were serotonin receptor signaling pathway (GO:0007210; 3/38 genes; strength\u0026thinsp;=\u0026thinsp;2.08; FDR\u0026thinsp;=\u0026thinsp;0.0035), alkaloid catabolic process (GO:0009822; 2/3 genes; strength\u0026thinsp;=\u0026thinsp;3.00; FDR\u0026thinsp;=\u0026thinsp;0.0052), cellular response to dopamine (GO:1903351; 4/81 genes; strength\u0026thinsp;=\u0026thinsp;1.87; FDR\u0026thinsp;=\u0026thinsp;0.0035), and negative regulation of synaptic transmission (GO:0050805; 3/62 genes; strength\u0026thinsp;=\u0026thinsp;1.87; FDR\u0026thinsp;=\u0026thinsp;0.0099). At the pathway level, the serotonergic synapse (KEGG hsa04726; 4/108 genes; FDR\u0026thinsp;=\u0026thinsp;0.00023) was the most significantly enriched annotation. The Reactome pathway for biosynthesis of maresin-like specialized pro-resolving mediators (HSA-9027307; 2/6 genes; FDR\u0026thinsp;=\u0026thinsp;0.025) was also significantly enriched, implicating the inflammatory resolution machinery as an additional axis of itraconazole activity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctional enrichment analysis results for the itraconazole target network.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerm / Pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrength\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerotonin receptor signaling pathway (GO:0007210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3/38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlkaloid catabolic process (GO:0009822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCellular response to dopamine (GO:1903351)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonoterpenoid metabolic process (GO:0016098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative regulation of synaptic transmission (GO:0050805)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3/62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerotonergic synapse (hsa04726)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReactome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiosynthesis of maresin-like SPMs (HSA-9027307)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis study presents an integrated computational assessment of the multi-target pharmacological profile of itraconazole in cancer biology. The findings collectively indicate that itraconazole demonstrates potential multi-target oncological activity by simultaneously engaging receptor tyrosine kinase, neurotransmitter receptor, chemokine receptor, and lipid mediator signaling nodes. This polypharmacological mechanism may underlie the compound\u0026rsquo;s reported anti-tumor activity in pre-clinical and early clinical studies.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 EGFR Binding and Kinase Inhibition\u003c/h2\u003e \u003cp\u003eThe computed binding affinities of \u0026minus;\u0026thinsp;11.0 and \u0026minus;\u0026thinsp;9.8 kcal/mol for the 4HJO and 4FA6 complexes are comparable to those reported for clinically active EGFR inhibitors under similar docking conditions. Hydrogen bond interactions with ASP1063 and GLU1061 in the hinge region, and the pi\u0026ndash;cation contact with ARG1021, correspond to canonical pharmacophoric requirements of ATP-competitive EGFR inhibitors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The PRODIGY-estimated ΔG\u003csub\u003enoelec\u003c/sub\u003e (non-electrostatic component) of \u0026minus;\u0026thinsp;5.43 kcal/mol corroborates thermodynamically stable binding and places itraconazole in the affinity range of moderate kinase inhibitors whose cellular activity depends on intracellular accumulation and target residence time in addition to aqueous binding constants [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These findings are mechanistically consistent with reported suppression of EGFR signaling and its downstream PI3K/AKT and MAPK/ERK effectors in cancer cell lines treated with itraconazole [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], supporting the hypothesis that direct EGFR engagement contributes to its anti-proliferative phenotype.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Serotonergic Signaling and Cancer Proliferation\u003c/h2\u003e \u003cp\u003eThe identification of HTR2A and SLC6A4 as predicted targets, together with enrichment of the serotonin receptor signaling pathway (FDR\u0026thinsp;=\u0026thinsp;0.0035) and KEGG serotonergic synapse (FDR\u0026thinsp;=\u0026thinsp;0.00023), points to an oncologically relevant mechanism that has received increasing attention in the literature. Serotonin acts as an autocrine and paracrine mitogen in several tumor types through HTR2A-mediated activation of the Gq/phospholipase C/IP3 cascade, which amplifies PI3K/AKT survival signaling and MAPK/ERK proliferative signaling [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. SLC6A4, the serotonin reuptake transporter, regulates extracellular serotonin levels and thereby modulates autocrine signaling amplitude; its overexpression in colorectal, small cell lung, and breast cancers correlates with reduced apoptosis sensitivity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Modulation of HTR2A and SLC6A4 by itraconazole could attenuate serotonin-driven mitogenesis in tumors expressing these targets, providing a mechanistic complement to EGFR inhibition that converges on shared PI3K/AKT and MAPK pathway nodes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Dopaminergic Signaling and Tumor Angiogenesis\u003c/h2\u003e \u003cp\u003eDRD3 contributes to tumor biology through two principal mechanisms. Activation of D3 receptors on tumor-associated endothelial cells stimulates VEGF secretion and promotes neovascularization; D3 receptor signaling on tumor-infiltrating immune cells simultaneously modulates regulatory T cell function to dampen anti-tumor immunity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The prediction of DRD3 as an itraconazole target provides a mechanistic link to the compound\u0026rsquo;s established anti-angiogenic properties [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], suggesting that the anti-vascular effect may reflect concurrent suppression of EGFR-driven VEGF transcription, DRD3-mediated VEGF secretion, and hedgehog pathway-regulated vascular remodeling [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This convergent multi-node anti-angiogenic activity could underlie the in vivo tumor growth inhibition reported for itraconazole.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4 FYN Kinase and the Src/PI3K/MAPK Axis\u003c/h2\u003e \u003cp\u003eFYN, a Src-family non-receptor tyrosine kinase, transmits oncogenic signals downstream of growth factor receptors and integrins by phosphorylating and activating PI3K \u0026mdash; which drives AKT-mediated cell survival \u0026mdash; and by activating the RAS/RAF/MEK/ERK axis to promote proliferation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. FYN overexpression has been documented in glioblastoma, melanoma, and pancreatic cancer, where it promotes invasion and metastasis. Its predicted interaction with itraconazole suggests potential co-suppression of PI3K/AKT and MAPK/ERK pathways downstream of multiple receptor inputs. Co-targeting of upstream EGFR and downstream FYN within a single compound would theoretically provide a mechanistic barrier against resistance mechanisms that arise when tumor cells upregulate Src-family kinases to bypass EGFR blockade \u0026mdash; a clinically documented phenomenon in EGFR-inhibitor-resistant cancers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Chemokine Receptors and Tumor Microenvironment Modulation\u003c/h2\u003e \u003cp\u003eCCR4 and CXCR1 form a distinct interaction cluster with well-characterized roles in tumor immune evasion and metastasis. CCR4 mediates recruitment of CD4\u0026thinsp;+\u0026thinsp;regulatory T cells (Tregs) to the tumor microenvironment (TME) in response to CCL17 and CCL22 produced by tumor cells and tumor-associated macrophages, thereby suppressing cytotoxic anti-tumor immunity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. CXCR1, a receptor for interleukin-8 (CXCL8), is expressed on myeloid-derived suppressor cells and cancer stem cell populations, where it promotes stemness maintenance, invasion, and resistance to cytotoxic therapy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Potential engagement of both receptors by itraconazole could reduce immunosuppression within the TME and impair metastatic dissemination, providing a systems-level rationale for observed in vivo tumor control that extends beyond direct anti-proliferative effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.6 CYP3A4 and Pharmacokinetic Considerations\u003c/h2\u003e \u003cp\u003eCYP3A4, the predominant hepatic cytochrome P450 enzyme responsible for biotransformation of approximately 50% of marketed pharmaceuticals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], is a known inhibitor substrate of itraconazole at standard antifungal doses. Its appearance as a predicted target reinforces the established clinical relevance of itraconazole-mediated CYP3A4 inhibition in polypharmacy settings. Co-administration of CYP3A4-metabolized oncology agents \u0026mdash; including certain taxanes, vinca alkaloids, and kinase inhibitors \u0026mdash; with itraconazole requires dose adjustment to prevent pharmacokinetic drug\u0026ndash;drug interactions that could compromise safety or efficacy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This finding underscores the importance of pharmacokinetic liability profiling in any clinical translation of an itraconazole repurposing strategy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.7 CYP51A1 and Antifungal Mechanism\u003c/h2\u003e \u003cp\u003eCYP51A1, lanosterol 14α-demethylase, is the established primary antifungal target of itraconazole through inhibition of ergosterol biosynthesis. Its appearance as an isolated node in the STRING network \u0026mdash; without functionally relevant connectivity to the cancer-associated proteins in the network \u0026mdash; is consistent with its distinct biochemical role in sterol metabolism. The antifungal and anti-cancer mechanisms of itraconazole therefore appear to operate through largely independent target sets, supporting a model in which the compound\u0026rsquo;s oncological activity is mediated by targets beyond CYP51A1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Maresin-like SPMs and Inflammatory Resolution\u003c/h2\u003e \u003cp\u003eThe enrichment of the Reactome pathway for biosynthesis of maresin-like specialized pro-resolving mediators (SPMs; FDR\u0026thinsp;=\u0026thinsp;0.025) introduces a dimension of itraconazole activity not previously described in cancer biology. Maresins are docosahexaenoic acid-derived lipid mediators that actively resolve inflammation by promoting macrophage efferocytosis, inhibiting neutrophil recruitment, and suppressing pro-inflammatory cytokine production [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Chronic unresolved inflammation in the TME sustains angiogenesis, immune evasion, and mutagenic oxidative stress. Perturbation of the SPM biosynthetic machinery by itraconazole could shift the inflammatory balance within the TME toward resolution, potentially reducing the tumor-promoting inflammatory microenvironment. This pathway represents a novel hypothesis that requires dedicated biochemical investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.9 Polypharmacology and Drug Repurposing Implications\u003c/h2\u003e \u003cp\u003eThe integrated computational evidence supports a polypharmacological model in which itraconazole demonstrates potential simultaneous activity on EGFR kinase, serotonergic and dopaminergic mitogenic receptors, Src-family kinase signaling, chemokine-mediated immune evasion, and lipid-based inflammatory resolution. These axes converge on the PI3K/AKT, MAPK/ERK, and VEGF pathways \u0026mdash; established drivers of tumor cell proliferation, survival, and angiogenesis. The breadth of this target profile, combined with itraconazole\u0026rsquo;s established oral bioavailability and manageable safety record at antifungal doses [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], positions it as a tractable candidate for repurposing in combination oncology strategies. These computational findings are hypothesis-generating and require experimental validation before any clinical conclusions can be drawn.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThis study provides integrated computational evidence that itraconazole demonstrates potential multi-target oncological activity through concurrent engagement of EGFR, serotonergic, dopaminergic, kinase, chemokine, and lipid mediator pathways. Molecular docking confirmed high-affinity binding to the EGFR kinase domain with key hinge-region interactions. Binding free energy estimation corroborated thermodynamic stability. Target prediction and network analysis identified a pharmacologically coherent multi-node target set linked by serotonergic/kinase and chemokine clusters. Pathway enrichment connected these targets to the KEGG serotonergic synapse, PI3K/AKT- and MAPK-related biological processes, and the Reactome pro-resolving lipid mediator pathway. Collectively, these findings reframe itraconazole as a polypharmacological agent with a mechanistically coherent potential anti-cancer profile and provide a prioritized hypothesis framework to guide experimental validation of its oncological mechanisms.\u003c/p\u003e"},{"header":"6. LIMITATIONS","content":"\u003cp\u003eSeveral limitations of this study must be acknowledged. All results derive from computational predictions and require wet-laboratory validation through in vitro binding assays, cell-based functional studies, and in vivo tumor models. Molecular dynamics simulations were not performed; the dynamic stability of the docked complexes over nanosecond-to-microsecond timescales therefore remains uncharacterized. PRODIGY was developed and validated primarily for protein\u0026ndash;protein interfaces; its application to protein\u0026ndash;small molecule binding free energy estimation is approximate and does not substitute for rigorous MM-PBSA or free energy perturbation calculations. SwissTargetPrediction is limited by the structural diversity of its training set and may miss targets with novel binding topologies. Finally, the clinical pharmacokinetic implications of CYP3A4 interaction must be assessed in the context of anti-tumor doses, which may differ substantially from standard antifungal dosing regimens.\u003c/p\u003e"},{"header":"7. FUTURE DIRECTIONS","content":"\u003cp\u003eFuture work should prioritize molecular dynamics simulations (\u0026ge;\u0026thinsp;100 ns) of the itraconazole\u0026ndash;EGFR complexes, followed by rigorous MM-PBSA or alchemical free energy perturbation calculations to quantify binding free energy with greater accuracy. In vitro biochemical assays \u0026mdash; including kinase inhibition assays against EGFR and FYN, and radioligand binding assays for HTR2A, DRD3, CCR4, and CXCR1 \u0026mdash; are required to confirm the predicted interactions. Cell-based proliferation, migration, and angiogenesis assays in cancer lines with defined EGFR and GPCR expression profiles would provide functional evidence for the predicted multi-target activity. Medicinal chemistry optimization guided by the pharmacophoric contacts identified here could yield itraconazole analogs with improved kinase selectivity and reduced CYP3A4-mediated metabolic liability. Tumor microenvironment studies examining the effect of itraconazole on Treg infiltration and lipid mediator profiles in syngeneic mouse models would address the predicted immunomodulatory and inflammatory resolution mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe author declares no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.S. conceived the study, performed molecular docking and computational analyses, conducted data interpretation, prepared all figures, and wrote the manuscript. The author reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThe author thanks the Department of Biotechnology, SVPUAT, Meerut, for providing computational infrastructure support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from publicly accessible databases, including the Protein Data Bank (PDB) and SwissTargetPrediction. Processed data and computational results are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChong CR, Sullivan DJ. New uses for old drugs. Nature. 2007;448(7154):645\u0026ndash;646.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18(1):41\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim J, Tang JY, Gong R, et al. Itraconazole, a commonly used antifungal, inhibits Hedgehog pathway activity and cancer growth. Cancer Cell. 2010;17(4):388\u0026ndash;399.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAftab BT, Dobromilskaya I, Liu JO, Rudin CM. Itraconazole inhibits angiogenesis and tumor growth in non-small cell lung cancer. Cancer Res. 2011;71(21):6764\u0026ndash;6772.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo S, Huang Y, Jiang Q, et al. Enhanced transcytosis of nanomedicine via EGFR signaling pathway. Mol Cancer. 2020;19(1):60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemmon MA, Schlessinger J. Cell signaling by receptor tyrosine kinases. Cell. 2010;141(7):1117\u0026ndash;1134.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSriram K, Insel PA. G protein-coupled receptors as targets for approved drugs: how many targets and how many drugs? Mol Pharmacol. 2018;93(4):251\u0026ndash;258.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarhart-Harris RL, Nutt DJ. Serotonin and brain function: a tale of two receptors. J Psychopharmacol. 2017;31(9):1091\u0026ndash;1120.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeaulieu JM, Gainetdinov RR. The physiology, signaling, and pharmacology of dopamine receptors. Pharmacol Rev. 2011;63(1):182\u0026ndash;217.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas RM, Bhatt DL, Bhatt S, et al. Chemokines, their receptors and cancer progression. Nat Rev Cancer. 2015;15(7):427\u0026ndash;445.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZanger UM, Schwab M. Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol Ther. 2013;138(1):103\u0026ndash;141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDennis EA, Norris PC. Eicosanoid storm in infection and inflammation. Nat Rev Immunol. 2015;15(8):511\u0026ndash;523.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455\u0026ndash;461.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue LC, Rodrigues JP, Kastritis PL, Bonvin AM, Vangone A. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics. 2016;32(23):3676\u0026ndash;3678.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk D, Gable AL, Nastou KC, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605\u0026ndash;D612.\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":"","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":"itraconazole, drug repurposing, EGFR, molecular docking, network pharmacology, polypharmacology, systems biology, cancer","lastPublishedDoi":"10.21203/rs.3.rs-9577042/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9577042/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eItraconazole is an approved triazole antifungal agent with documented pre-clinical anti-cancer activity. The full spectrum of human protein targets through which it may exert oncological effects has not been systematically characterized. This study presents a multi-tier in silico framework to map the pharmacological target landscape of itraconazole and investigate its potential mechanisms of anti-cancer action.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMolecular docking of itraconazole against the EGFR kinase domain was performed using two crystal structures (PDB IDs: 4HJO and 4FA6) with AutoDock Vina. Binding free energy was estimated using PRODIGY (approximate MM/GBSA-like approach). Target prediction was conducted with SwissTargetPrediction; protein\u0026ndash;protein interaction networks were constructed in STRING (v11.5); and functional enrichment analysis covered Gene Ontology (GO) Biological Process, KEGG, and Reactome databases.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eItraconazole docked into the EGFR ATP-binding cleft with affinities of \u0026minus;\u0026thinsp;11.0 and \u0026minus;\u0026thinsp;9.8 kcal/mol for 4HJO and 4FA6, respectively (RMSD\u0026thinsp;\u0026lt;\u0026thinsp;2.0 \u0026Aring;). Key interacting residues included ASP1063, LYS1059, GLU1061, ASN1060, ARG1021, and GLY1058. Binding free energy estimation yielded ΔG\u003csub\u003enoelec\u003c/sub\u003e (non-electrostatic component)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.43 kcal/mol. Target prediction identified eight proteins: FYN, HTR2A, SLC6A4, DRD3, CCR4, CXCR1, CYP3A4, and CYP51A1. STRING network analysis resolved two functional clusters: a serotonergic/kinase hub and a chemokine receptor pair. Pathway enrichment identified the serotonergic synapse (KEGG hsa04726; FDR\u0026thinsp;=\u0026thinsp;0.00023) and biosynthesis of maresin-like specialized pro-resolving mediators (Reactome HSA-9027307; FDR\u0026thinsp;=\u0026thinsp;0.025) as the most significantly enriched annotations.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eItraconazole demonstrates potential multi-target oncological activity, with computational evidence for concurrent engagement of EGFR, serotonergic, dopaminergic, chemokine, and inflammatory resolution pathways. These findings support further investigation of itraconazole as a drug repurposing candidate in oncology and provide a prioritized framework for experimental validation.\u003c/p\u003e","manuscriptTitle":"In silico Multi-Target Pharmacological Profiling of Itraconazole: Molecular Docking, Binding Free Energy Estimation, and Systems Biology Insights into Potential Anti-Cancer Mechanisms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 06:19:08","doi":"10.21203/rs.3.rs-9577042/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":"744624cc-37d9-4454-bc1f-bde329c144b0","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-01T07:56:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-01T06:57:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-01T06:57:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Molecular Modeling","date":"2026-04-30T12:02:14+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T06:19:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 06:19:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9577042","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9577042","identity":"rs-9577042","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}