Precision Targeting Strategies in Cancer Therapy: Focusing on Synthetic Lethality with FAK Inhibition

Precision Targeting Strategies in Cancer Therapy: Focusing on Synthetic Lethality with FAK Inhibition | 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 Article Precision Targeting Strategies in Cancer Therapy: Focusing on Synthetic Lethality with FAK Inhibition Pinar Siyah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4316611/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 Synthetic lethality, involving the simultaneous deactivation of two genes, plays a critical role in disrupting vital cellular functions or prompting cell death. This study delves into the impact of synthetic lethality within cancer research, specifically examining the interplay between the Focal Adhesion Kinase (FAK) and Neurofibromin 2 (NF2) genes. While deactivating FAK or NF2 individually has minimal impact, their combined deactivation highlights the vital significance of their synthetic lethal interaction. Hence, the principal aim of this study is to direct our efforts towards the inhibition of the FAK gene, a venture of notable significance. The NF2 gene is responsible for producing Merlin, a tumor suppressor protein that is often deactivated in schwannoma, meningioma, and malignant mesothelioma. The inhibition of the FAK gene is pivotal, given its pivotal role in the synthetic lethal interplay with NF2/Merlin, promising substantial prospects for the progression of cancer treatment strategies. This investigation has the capacity to propel forward inventive therapeutic methodologies, harnessing the potential of synthetic lethal interactions within cancer cells, and forging a path towards more refined and efficacious interventions in cancer treatment. The ongoing advancements in developing new FAK inhibitors highlight the significance of this strategy in cancer treatment. Despite extensive research efforts, no FAK inhibitor has been approved for clinical use. This emphasizes the urgent need to create new FAK inhibitors with improved anti-tumor properties. The small molecule FAK inhibitor candidates identified in our study show potential for making a groundbreaking contribution in this field. Employing docking and (1ns, 10ns and 100ns) molecular dynamics (MD) simulations, we evaluated FAK inhibitor complex stability, unveiling intricate interactions. Following of molecular dynamics simulations, the MM/GBSA scores for Amprenavir, Bosutinib, Ferric derisomaltose, Flavin adenine dinucleotide, Lactulose and Tafluprost were determined to be -72,81, -71,84, -76.70, -69.09, -74.86, -65.77 kcal/mol, respectively. These molecules have been evaluated as potential candidate drugs based on these scores. This study lays a foundation for novel therapeutics, holding promise for diverse cancer treatments through our computational framework. Biological sciences/Cancer Biological sciences/Drug discovery Biological sciences/Computational biology and bioinformatics/Virtual drug screening Focal Adhesion Kinase small molecules inhibitors docking molecular dynamics computer-aided drug discovery Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Cancer ranks as the second leading cause of global mortality, surpassed only by cardiovascular diseases. The financial resources allocated for cancer treatment and pharmaceutical research impose substantial burdens on national budgets and are anticipated to increasingly constitute a significant proportion of health expenditures in the years ahead [ 1 ]. Researchers are diligently exploring diverse modalities for cancer treatment, producing a multitude of publications based on in vitro and in vivo investigations. Notably, contemporary emphasis in drug discovery has shifted towards molecular modeling studies [ 2 ]. Molecular screening facilitates the elucidation of molecular-target interactions within an in silico environment, thus enabling the reduction of the pool of drug candidate molecules prior to transitioning to in vitro and in vivo settings. Focal Adhesion Kinase (FAK) emerges as a vital protein kinase governing cell-matrix interactions, particularly influencing cell adhesion and migration [ 3 ]. Its activation is instigated through integrin signaling, playing a crucial role in cells adhering to the extracellular matrix [ 4 ]. The autophosphorylation of FAK and its subsequent conformational activation intricately regulate fundamental cellular processes including growth, proliferation, and survival through modulation of signal transduction pathways. Excessive FAK activation has been implicated in various diseases, notably cancer. Consequently, FAK inhibitors are under rigorous investigation as potential therapeutic agents in cancer management, with a specific focus on metastasis prevention [ 5 ]. The NF2 gene, responsible for encoding Merlin Protein (Moesin-Ezrin-Radixin-Like), functions as a tumor suppressor protein [ 6 ]. Merlin's normal role involves the regulation of cell growth and division, control of cell adhesion, and modulation of cellular interactions with the microenvironment. Frequent inactivation of NF2, observed in diverse cancer types such as schwannoma, meningioma, and malignant mesothelioma (MM), the latter being a highly aggressive cancer with limited treatment options [ 7 ]. NF2 gene mutations disrupt the normative functionality of Merlin protein, resulting in the manifestation of NF2 disease. Neurofibromatosis Type 2 (NF2) is characterized by a genetic predisposition causing aberrant proliferation of nervous system cells, particularly manifesting as tumor formation between Schwann cells and other cellular entities, predominantly within the brain and spinal cord. The NF2 gene mutations induce aberrations in Merlin protein regulation, instigating uncontrolled cellular proliferation and tumor development [ 8 ]. Synthetic lethality constitutes a crucial concept in contemporary cellular biology and cancer research, particularly in the development of therapeutic strategies aimed at targeting cancer cells bearing specific genetic mutations [ 9 ]. A synthetic lethal interaction arises when the disruption of either gene individually is viable, but the simultaneous disruption of both genes leads to cell death. Central to leveraging synthetic lethality in cancer therapy is the discovery and detailed understanding of robust synthetic lethal genetic interactions [ 10 ]. Traditional approaches involve the suppression or inhibition of specific genes within tumors, a methodology effective in targeting cancer cells but often associated with collateral damage to normal cells. The synthetic lethality strategy seeks to invert this paradigm by exploiting genetic interactions. By suppressing the function of a gene in normal cells without harm, a severe detriment can be induced in cancer cells harboring specific genetic mutations [ 11 ]. Emerging research underscores a synthetic lethal relationship between Focal Adhesion Kinase (FAK) and NF2/Merlin. Notably, cancer types such as schwannoma, meningioma, and malignant mesothelioma (MM) and MPM exhibit mutations in the NF2 gene, leading to impaired synthesis of the Merlin protein [ 12 ]. The underlying hypothesis posits that in instances where cancer cells recurrently experience loss of NF2/Merlin expression due to various inactivation mechanisms and mutations, the FAK protein presents a promising target for cancer therapy [ 13 – 15 ]. This study aims to identify small molecule drug candidates specifically targeting FAK in Merlin-negative tumors. Therefore, our objective is to develop a specific targeted therapy that effectively targets cancer cells without harming healthy cells. This approach ensures the development of therapeutic interventions that selectively kill cancerous tissues, offering a more precise and effective strategy for cancer therapy. The ongoing advancement of novel FAK inhibitors underscores the importance of this approach in the landscape of cancer therapeutics. Despite concerted research efforts, however, no FAK inhibitor has yet garnered clinical approval [ 16 ]. Consequently, there is an urgent and critical necessity to develop new FAK inhibitors with heightened antitumor activity to achieve more potent antitumor effects [ 17 ]. The investigation involved docking of all FDA-approved drugs and compounds under clinical investigation, totaling 3235 compounds, against the FAK protein target. Evaluation of hit molecules with the highest potential as drug candidates was facilitated through comparative analysis of their docking scores against reference compounds. Molecules with docking scores lower than − 8.00 kcal/mol were selected for further investigation. Selected top-performing molecules underwent molecular dynamics (MD) simulations of 1 nanosecond (ns), 10ns, and 100ns, culminating in the identification of optimal drug candidate molecules. Machine-learning based QSAR models, were employed to predict the therapeutic activity of these molecules. The findings contribute to advancing our understanding of potential treatments for Merlin-negative tumors through FAK inhibition. MATERIAL AND METHOD Preparation of Ligands for Docking: The ligand structures were created using the Maestro 2D Sketcher program, and they were optimized for molecular docking studies. The preparation process ensures compatibility with a physiological pH of 7.4. This involves employing the PROPKA1 algorithm [ 18 ], which predicts protonation states of ionizable groups at a given pH, and utilizing the OPLS3e2 force field [ 19 ], a set of parameters that describe the interactions between atoms in the molecular system. Furthermore, the LigPrep3 module [ 20 ] was employed to refine and preprocess the molecular structures. LigPrep3 is a tool that can generate different ionization states, tautomers, and conformers of a molecule, enhancing the accuracy and reliability of subsequent molecular docking simulations. The Epik module [ 21 ], a software engineered for rapid and accurate prediction of aqueous phase pKa values and protonation state distributions for complex, drug-like molecules, played a crucial role in this phase. It facilitated the refinement of ligands, which were systematically assessed as potential small molecule drugs, and meticulously prepared for subsequent docking procedures. Preparation of Protein for Docking: Protein preparation and docking were conducted following established protocols detailed in a prior publication [ 22 ]. The crystallographic structure of the FAK protein, identified with PDB accession number 4GU6 [ 23 ] was retrieved from the Protein Data Bank and prepared as in our previous work [ 22 ]. Utilizing the Prime module, missing side chains and regions were comprehensively completed. The protein target was elaborately prepared to undergo molecular docking and simulation studies in accordance with the physiological pH:7.4. Grid Box Generation and Molecular Docking Studies: A grid box was created using the grid center coordinates derived from the co-crystallized ligand 10N. Ligands were carefully positioned within this specified grid area on the protein target. This process was carried out meticulously using Maestro's Glide module [ 24 ], utilizing standard sensitivity settings. As a control measure, reference molecules underwent the same procedures, following identical methodologies as the ligands, ensuring methodological consistency [ 25 ]. RESULTS AND DISCUSION This study has focused on a broad collection, including 3235 FDA-approved compounds as well as those undergoing clinical evaluation. This collection was evaluated using a drug repurposing approach to identify potential drug candidates for the treatment of Merlin-negative tumors through the inhibition of the FAK protein. To achieve this, the protein and drug library were initially prepared for docking. Subsequently, a grid box was prepared on the protein (Fig. 1 ), and all ligands were docked into this grid box. This step, which involved comparative examination of docking scores in relation to reference compounds, served as an initial filter to identify molecules with the highest potential as drug candidates. Then, an attempt was made to reduce the number of candidates by performing MD/MMBG (1ns, 10ns, 100ns) analyses. Molecules with docking scores lower than − 8.00 kcal/mol were selected for detailed analysis. The reference compound bound to the FAK protein with a docking score of -10.85 kcal/mol (Fig. 2 ). Given that this compound is the co-crystallized ligand in the PDB, obtaining the highest docking score in the initial stage was quite positive and expected. Out of the 3235 FDA-approved and clinically reviewed molecules, 71 had docking scores of -8.00 kcal/mol or lower, indicating better binding affinity. This threshold was strategically chosen to ensure that only the most promising candidates, demonstrating a strong potential for interaction with the FAK protein, were advanced to the subsequent stages of the study. The docking scores are provided in Table 1 . Among the molecules we studied, Amprenavir bound to the FAK protein with a docking score of -8.91 kcal/mol, followed closely by Flavin adenine dinucleotide with − 8,47 and Lactulose with − 8.23 kcal/mol. Bosutinib, Ferric derisomaltose, and Tafluprost demonstrated good binding affinities with scores ranging between − 8.12, -8.00, -8.47, and − 8.13, respectively. These molecules were selected for their high potential binding affinity. The binding conformation of Amprenavir, which has the best docking score, to the protein is provided (Fig. 3 ). 71 molecules with docking scores of -8.00 kcal/mol or better underwent simulation, followed by MM/GBSA calculations. The MM/GBSA score of the Reference Compound was found to be -69.56 based on the 1 ns simulations. Our tested molecules exhibited significantly better scores. For example, based on the MM/GBSA analyses conducted after the 1 ns MD simulations, Flavin adenine dinucleotide yielded a score of -89.15 kcal/mol, Ferric derisomaltose scored − 82.72 kcal/mol, Lactulose scored − 71.93 kcal/mol, Bosutinib scored − 75.79 kcal/mol, Amprenavir scored − 74.84 kcal/mol, and Tafluprost scored − 72.51 kcal/mol. Consequently, the top 10 molecules with the best scores out of the 71 were selected and subjected to a 10 ns simulation. During the 10 ns simulation duration, the binding free energy scores remained relatively stable with no significant variations. Notably, Amprenavir, Bosutinib, Lactulose and Tafluprost mostly maintained their affinity with a score of -75.86, -74,38, -73,39 and − 73,13 kcal/mol respectively. Ferric derisomaltose, and Flavin adenine dinucleotide showed some changes in their binding affinities. The Reference Compound demonstrated almost stable with no significant variations, recording a score of -71.04 kcal/mol. In summary, the MM/GBSA scores after the 10 ns MD simulation were found to be nearly identical to the previous ones, indicating stability and consistency. Subsequently, 9 molecules that exhibited better scores than the − 71.04 kcal/mol of the reference compound were selected for 100 ns simulations. Based on the results of the 100 ns MD, 9 molecules that demonstrated better scores than the − 69,20 kcal/mol of the reference compound were selected for 100 ns MD and MM/GBSA analyses. Upon examining the 100 ns MD-MM/GBSA results, it was found that Amprenavir exhibited a relatively strong binding affinity with a score of -72.81 kcal/mol. Bosutinib also demonstrated robust interaction with the protein, yielding a score of -71.84 kcal/mol. Ferric derisomaltose and Flavin adenine dinucleotide showed even stronger binding affinities, recording scores of -76.70 kcal/mol and − 69.09 kcal/mol, respectively. Conversely, the reference compound exhibited a binding affinity close to and even weaker than many of these molecules with a score of -69,20 kcal/mol. Table 1 Docking scores and MM/GBSA analysis rely on 1ns, 10ns and 100ns molecular dynamics simulations. Protein-Ligand Complex Docking Score (kcal/mol) 1 ns MD-MM/GBSA (kcal/mol) 10 ns MD-MM/GBSA (kcal/mol) 100 ns MD-MM/GBSA (kcal/mol) 4GU6_Amprenavir -8,91 -74,84 -75,86 -72,81 4GU6_Bosutinib -8,12 -75,79 -74,38 -71,84 4GU6_Ferric derisomaltose -8,00 -82,72 -73,52 -76,70 4GU6_Flavin adenine dinucleotide -8,47 -89,15 -82,99 -69,09 4GU6_Lactulose -8,23 -71,93 -73,39 -74,86 4GU6_Tafluprost -8,13 -72,51 -73,13 -65,77 4GU6_Reference Compound -10,85 -69,56 -71,04 -69,20 At the specified time durations (1 ns, 10 ns, and 100 ns), the correlation between docking scores and MD-MM/GBSA results is evident. Notably, Amprenavir, Bosutinib, Ferric derisomaltose, and Lactulose have demonstrated significantly strong binding affinities. Flavin adenine dinucleotide and Tafluprost have also shown satisfactory results, closely following behind. In contrast, the reference compound exhibited weaker binding affinities compared to all other molecules in the 1 ns and 10 ns durations. After 100 ns of MD simulation, the MM/GBSA analysis indicated that Amprenavir, Bosutinib, Ferric derisomaltose, and Lactulose exhibited decreased binding affinities relative to the reference compound. The docked poses of the selected molecules and detailed docking and MM/GBSA score tables, were provided in the Supplementary Information. The alignment of docking and MD-MM/GBSA results highlights the reliability of computational approaches in predicting ligand binding affinities. Consequently, all six molecules identified in the study emerge as compelling candidates for further research, given their consistently low docking scores and stable binding interactions observed throughout the MD simulations. These findings significantly contribute to our understanding of the dynamic aspects of protein-ligand interactions and have substantial implications for the advancement of drug design and development. The identification of common amino acid residues engaged in ligand-protein interactions provides valuable insights into potential binding sites and shared mechanisms among various molecules. This knowledge is crucial for understanding the consistent aspects of ligand binding and can guide future efforts in drug design by highlighting critical amino acid residues that consistently contribute to the stability and specificity of ligand-protein complexes. This comprehensive analysis enhances our understanding of the molecular basis of ligand-protein interactions and aids in the logical development of effective therapeutics. Therefore, this study analyzed the key amino acids involved in ligand-protein interactions, in accordance with the mentioned rationale. For the reference molecule, named 10N, interactions were observed with amino acids Cys502, Glu506, Arg550, Val552, Leu553, Asp564, and Leu567 during the 100ns MD simulation, confirming our findings. Importantly, amino acids Cys502 and Asp564 were previously highlighted in the literature as crucial, which supports our results [ 16 , 26 ]. Notably, the Cys502 residue emerges as a common binding site, forming hydrogen bonds with both the reference molecule Compound 10N (Fig. 4 ), the candidate Ferric derimaltose and Bosutinib. This consistency in interaction highlights the significance of Cys502 in facilitating stable ligand binding and suggests its potential role as a key hotspot for ligand-protein interactions. Moreover, amino acid residues such as Thr503 and Asn51 exhibit recurrent involvement in the interactions. Thr503 is implicated in hydrogen bonding with Bosutinib, emphasizing its contribution to the binding affinity of the candidate drug. Similarly, Asn51 is involved in hydrogen bonding with Amprenavir, underlining its role as a critical residue in mediating ligand-protein interactions. These shared interactions across different molecules underscore the importance of specific amino acid residues in governing the binding specificity and affinity. Furthermore, the residues Asp564, Glu430, Glu506, and Cys427, identified in the interaction profile of Flavin adenine dinucleotide, appear to be significant across various ligands. Their recurrent presence indicates their pivotal role in the ligand-protein binding landscape. The interactions with water molecules also contribute to the stability of the complex, suggesting the dynamic nature of the ligand-protein interaction network involving these residues. Briefly, our in silico drug screening has demonstrated that candidate drugs consistently interact with the same amino acids, Cys502 and Asp564, as the reference molecule 10N found in the crystal structure during docking or molecular dynamics simulations. This consistent interaction pattern validates the accuracy and reliability of our in silico methodology. Considering all docking and MD simulation results, our compounds, especially Amprenavir, Bosutinib, Ferric derisomaltose, and Lactulose, outperformed the reference and are considered the most promising candidate drugs for FAK inhibition in cancer therapy. Furthermore, the other two molecules, Flavin adenine dinucleotide and Tafluprost, also showed similar potential to the reference molecule. In total, all six molecules have the potential to be promising candidates as FAK inhibitors for cancer treatment. Zhan et. al.'s research [ 27 ] utilized molecular dynamics (MD) simulations and MM-GB/SA calculations to explore combinations, revealing an enthalpy-driven mechanism. Crucial residues, particularly Cys502 and Asp564, were identified for their essential roles in forming hydrogen bonds with inhibitors, consistent with experimental observations. Moreover, Glu500 was noted to establish non-classical hydrogen bonds with each inhibitor. Stronger electrostatic interactions with PHM16 and ligand3 were exhibited by Arg426. Hydrophobic interactions were facilitated by key residues such as Ile428, Val436, Ala452, Val484, Leu501, Glu505, Glu506, Leu553, Gly563, Leu567, and Ser568. These findings hold substantial significance for the advancement of FAK inhibitors, offering valuable insights for cancer research. Particularly noteworthy are the consistently low peaks of Cys502 in all three complexes, underscoring its pivotal role in hydrogen bond interactions. Our study echoes these observations, emphasizing the importance of residues like Cys502 and Asp564, validating the significance of these interactions in our computational drug screening approach. The identified residues, coupled with hydrophobic and electrostatic interactions, contribute significantly to the comprehension of FAK inhibition, supporting the design and development of potential cancer therapeutics [ 27 ]. The alignment of key amino acids identified in the study by Zhan et. al. with the significant interaction amino acids found in our study indicates a significant alignment and consistency between the two studies. This correspondence has increased the reliability of our findings. Furthermore, our study supports the findings outlined in the research carried out by Mustafa et. al. [ 28 ] More specifically, they validate the pivotal functions of Cys502, positioned in the kinase hinge, and Asp564, found in the DFG motif within the ATP binding site of Focal Adhesion Kinase (FAK). This consistency emphasizes the importance of these residues in FAK and strengthens their significance in developing potential inhibitors for studying cancer. Our study reveals that the molecule Ferric derisomaltose interacts effectively with amino acids Ile428, Glu430, Glu500, Cys502, Glu506, Asn551, Asp564, and Leu567. The 2D interaction maps are presented in Fig. 5 A, the types of interaction bonds in Fig. 5 B, and the interaction analyses over a period of 100 ns in Fig. 5 C. These findings are supportive of previous research and suggest that Ferric derisomaltose has the potential to be a highly effective FAK inhibitor, as indicated by previous studies conducted on this molecule. Analysis of the biochemical and pharmacological properties of the leading ligands was conducted by using the MetaCore/MetaDrug platform. This tool enables the prediction of first-pass and second-pass metabolites and assesses various attributes such as reactivity, blood-brain barrier (BBB) permeability, protein binding, and water solubility. Additionally, MetaDrug employs Quantitative Structure-Activity Relationship (QSAR) models to forecast the potential toxic effects and therapeutic efficacy of the ligands under scrutiny. To determine the similarity between these ligands and those encompassed in the QSAR models, we utilized the Tanimoto Prioritization (TP) property. Further, we have previously comprehensively discussed the precision of the QSAR models in our prior studies [ 29 , 30 ]. Bosutinib, Lactulose, Flavin adenine dinucleotide, and Ferric derisomaltose compounds were predicted to exhibit anti-cancer properties. Furthermore, Amprenavir and Tafluprost showed potential for improvement. Amprenavir, a protease inhibitor approved for the therapeutic management of HIV infection by the United States Food and Drug Administration on April 15, 1999, has demonstrated efficacy with a dosing regimen of twice daily, marking a significant advancement over prior treatments necessitating administration every eight hours [ 31 ]. Beyond its primary application in HIV therapy, amprenavir has been explored both in vitro and in silico for potential utility in treating a range of diseases, including SARS-CoV-2 and various cancers [ 32 , 33 ]. Subsequent investigations have revealed that amprenavir possesses the capability to inhibit tumor cell proliferation across a diverse spectrum of cancer types, as documented [ 34 ]. Additionally, research conducted by Jiang et. al. [ 33 ], has demonstrated amprenavir's ability to induce apoptosis in MCF-7 cells in vitro and in vivo through the inhibition of ERK2 kinase activity. Esposito et. al. [ 35 ], further substantiated amprenavir's anti-cancer potential by evidencing its capacity to impede migration and proliferation in human hepatocarcinoma cell lines. Considering the cumulative findings from prior studies alongside the outcomes of current research, it is posited that amprenavir may be effectively repurposed as a Focal Adhesion Kinase (FAK) inhibitor in oncological treatments. Bosutinib, a tyrosine kinase inhibitor (TKI), was granted approval by the United States Food and Drug Administration (FDA) in September 2012 and by the European Medicines Agency (EMA) in March 2013. It is recognized for its application in the treatment of Philadelphia chromosome-positive chronic myeloid leukemia in the chronic phase (CML-CP), achieving its initial pediatric approval for this indication [ 36 ]. In a comparative study conducted among CML patients, Cortes et. al. [ 37 ] reported that patients treated with Bosutinib exhibited superior responses compared to those receiving Imatinib. Moreover, its efficacy has been investigated in various other cancer types. Singh et. al. [ 38 ] demonstrated in their study on MCF-7 cells that liposomal formulations of Bosutinib induced apoptosis in estrogen-positive cell lines. Segrelles et. al. [ 39 ] have stated that Bosutinib inhibits the growth of Head and Neck Squamous Cell Carcinoma (HNSCC) particularly by inhibiting the activity of the Epidermal Growth Factor Receptor (EGFR). Yu et. al. [ 40 ] concluded in their study with the HeLa cell line that Bosutinib could serve as a significant therapeutic agent against cervical cancer by decreasing the activity of the Src/NF-κB/survivin signaling pathway. Conversely, Watanabe et. al. [ 41 ] have reported potential adverse effects of Bosutinib, including diarrhea, hepatic toxicity, and severe lung injury. Given its more recent approval compared to Amprenavir, and notwithstanding the reported side effects, Bosutinib holds potential for repurposing as a Focal Adhesion Kinase (FAK) inhibitor, warranting cautious consideration of its application in this capacity. Ferric derisomaltose, an iron carbohydrate complex composed of ferric hydroxide and the carbohydrate derisomaltose, received regulatory approval for the treatment of iron deficiency anemia from the European Medicines Agency (EMA) in 2009 and the United States Food and Drug Administration (FDA) in January 2020. A study conducted by Kassianides et. al. [ 42 ] demonstrated that the use of ferric derisomaltose in patients with anemia is more cost-effective and efficacious compared to previous formulations, while also exhibiting minimal side effects. In a comprehensive study, Kalra et. al. [ 43 ] investigated the application of ferric derisomaltose in patients with heart failure and iron deficiency, revealing a correlation between iron supplementation and a reduced risk of cardiovascular mortality in this patient group, thereby underscoring the safety of ferric derisomaltose use. Auerbach et. al. [ 44 ] further validated the rapid amelioration of iron deficiency and the reliability of ferric derisomaltose application in their study. Concerning the potential application of ferric derisomaltose within oncological treatment paradigms, the body of research remains limited. Nevertheless, Dickson et. al. [ 45 ] have administered ferric derisomaltose to oncology patients witnessing reductions in hematological parameters, notably hemoglobin concentrations, and have documented favorable outcomes. Review of the existing literature posits ferric derisomaltose as a promising candidate for investigation as a Focal Adhesion Kinase (FAK) inhibitor. Additionally, its deployment in the context of radiotherapy merits consideration for leveraging the radiosensitizing attributes of iron, thereby highlighting its prospects as an efficacious, non-toxic, and therapeutic agent. Lactulose, synthesized through the isomerization of lactose, initially garnered approval from the United States Food and Drug Administration (FDA) in 1977 and has been recognized as one of the World Health Organization's (WHO) Essential Medicines [ 46 ]. In recent years, it has become one of the most frequently prescribed medications in the USA, primarily for the treatment of hepatic encephalopathy [ 47 , 48 ]. Moreover, emerging evidence suggests that lactulose may play a role as a pharmacotherapeutic agent in the management and prevention of type 2 diabetes through its effects on gut microbiota [ 49 ]. Research conducted by Kishor et. al. [ 50 ], has indicated that lactulose could serve as an effective Galectin inhibitor, potentially useful in targeted cancer therapy and demonstrating anticancer agent capabilities. Furthermore, Fernández et. al. [ 51 ] have shown that galacto-oligosaccharides derived from lactulose significantly reduced the incidence of Colorectal Cancer (CRC) in in vivo models. Based on these findings, we conclude that lactulose can be reliably considered for use as a Focal Adhesion Kinase (FAK) inhibitor. Tafluprost, a prostaglandin analog, received approval from the United States Food and Drug Administration (FDA) on February 13, 2013, for the treatment of ocular hypertension and glaucoma [ 52 , 53 ]. The study conducted by Papadia et. al. [ 54 ], highlights not only the efficacy of Tafluprost, which is the first prostaglandin analog without preservatives, but also its safety profile and the minimal side effects associated with its use. In recent research, Wu et. al. [ 55 ] demonstrated that Tafluprost facilitates axon regeneration through the modulation of the Zn2+-mTOR pathway. While there has been no direct investigation into the applicability of Tafluprost in cancer treatment, studies involving prostaglandins have linkedwith the initiation, progression, and metastasis of cancer [ 4 , 56 ]. Given this association, it has been concluded that the use of Tafluprost as a Focal Adhesion Kinase (FAK) inhibitor may not be appropriate. The outcomes derived from this integrated computational methodology bear considerable importance, especially concerning the advancement of therapies for Merlin-negative tumors. The discovery of potent FAK inhibitors using this approach underscores the therapeutic promise of these compounds. Furthermore, the utilization of a diverse compound library encompassing both FDA-approved medications and substances undergoing clinical scrutiny provides opportunities for drug repurposing. Repurposing established drugs presents a potentially swifter and economically efficient approach to drug innovation, particularly in oncology, where the need for rapid treatment solutions is frequently critical. CONCLUSION In conclusion, the integrated analysis of molecular docking, molecular dynamics and MM-GBSA calculations for the ligands interacting with the protein target 4GU6 provides valuable insights into the potential drug candidates. The consistent trends observed across different time durations in both docking and MM-GBSA analyses reinforce the reliability of computational approaches in predicting ligand binding affinities. Amprenavir, Bosutinib, Ferric derisomaltose, and Lactulose consistently emerges as the most promising drug candidates, exhibiting the lowest docking scores and the most negative MM/GBSA values from MD simulations. This consistent and strong binding affinity suggests Amprenavir, Bosutinib, Ferric derisomaltose, and Lactulose’s potentials as the effective therapeutic agents for the targeted protein. Flavin adenine dinucleotide and Tafluprost follow closely behind these compounds, demonstrating robust and stable binding affinities. Their stable interactions with the protein over different time scales indicate their potential as viable drug candidates. In contrast, the Reference Compound consistently displays higher docking scores and higher MM-GBSA values, indicating weaker binding affinities. This underscores the importance of utilizing computational methods to distinguish promising drug candidates from less effective ones. Overall, the collaborative use of docking and MM-GBSA analyses provides a comprehensive evaluation of ligand-protein interactions, aiding in the identification of potential drug candidates. These findings contribute to the rational design of therapeutics by highlighting molecules with the strongest and most consistent binding affinities for further experimental validation and drug development. This study significantly contributes to the field of drug design and development by providing a reliable computational model for predicting ligand binding strengths. Additionally, it enhances our understanding of protein-ligand interactions and establishes a model for future investigations in this domain. Our study sheds light on new therapeutic avenues targeting cancer through Focal Adhesion Kinase (FAK) inhibition, in a crucial field where there is currently no clinically approved FAK inhibitor and a pressing need for the development of new FAK inhibitors with antitumor properties. This work lays the foundation for promising new therapeutics for various cancer treatments while offering a general approach beyond FAK inhibitors, thereby expanding the impact in the field of drug design. This study can lead to the discovery of new therapeutic targets and the development of more effective treatment approaches. Declarations Author Contribution Pınar Siyah (P. S.), as the single author of this manuscript, conducted the study's design, data collection process, analysis, and manuscript composition independently, meticulously reviewing and approving each stage. P. S. conceptualized the study, crafted the original draft, executed the methodology and research, carefully reviewed and edited the manuscript, and determined it to be suitable for publication. Data Availability Data supporting the findings of this research are available within the manuscript and its Supplementary File. The molecular structures and docking conformations of the selected molecules have been provided as .pdb files in the Supplementary Information section. Detailed tables containing docking and MM/GBSA scores are also included in the Supplementary File. Should any raw data files be needed in an alternative format, they can be made available by the corresponding author upon a request. References Sung H, Ferlay J, Siegel RL, et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:. https://doi.org/10.3322/caac.21660 Bagchi A (2024) Molecular Modeling Techniques and In-Silico Drug Discovery. In: Mandal S (ed) Reverse Engineering of Regulatory Networks. Springer US, pp 1–11 Tan X, Yan Y, Song B, et al (2023) Focal adhesion kinase: from biological functions to therapeutic strategies. 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J Biomol Struct Dyn 36:1529–1549. https://doi.org/10.1080/07391102.2017.1329095 Zhan JY, Zhang JL, Wang Y, et al (2016) Exploring the interaction between human focal adhesion kinase and inhibitors: a molecular dynamic simulation and free energy calculations. J Biomol Struct Dyn 34:. https://doi.org/10.1080/07391102.2015.1115780 Mustafa M, Abd El-Hafeez AA, Abdelhafeez DA, et al (2021) FAK inhibitors as promising anticancer targets: Present and future directions. Future Med Chem 13 Comert Onder F, Siyah P, Durdagi S, et al (2022) Novel etodolac derivatives as eukaryotic elongation factor 2 kinase (eEF2K) inhibitors for targeted cancer therapy. RSC Med Chem 13:. https://doi.org/10.1039/d2md00105e Siyah P, Durdagi S, Aksoydan B (2023) Discovery of potential PD-L1 small molecule inhibitors as novel cancer therapeutics using machine learning-based QSAR models: A virtual drug repurposing study. Biophys J 122:144a. https://doi.org/10.1016/j.bpj.2022.11.942 Adkins JC, Faulds D, Moyle G, Hughes WT (1998) Amprenavir. Drugs 55:. https://doi.org/10.2165/00003495-199855060-00015 Halder UC (2021) Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins. Journal of Biological Research (Greece) 28:. https://doi.org/10.1186/s40709-021-00149-2 Jiang W, Li X, Li T, et al (2017) Repositioning of amprenavir as a novel extracellular signal-regulated kinase-2 inhibitor and apoptosis inducer in MCF-7 human breast cancer. Int J Oncol 50:. https://doi.org/10.3892/ijo.2017.3860 Cuneo KC, Tu T, Geng L, et al (2007) HIV protease inhibitors enhance the efficacy of irradiation. Cancer Res 67:. https://doi.org/10.1158/0008-5472.CAN-06-3684 Esposito V, Verdina A, Manente L, et al (2013) Amprenavir inhibits the migration in human hepatocarcinoma cell and the growth of xenografts. J Cell Physiol 228:. https://doi.org/10.1002/jcp.24173 Hoy SM (2024) Bosutinib: Pediatric First Approval. Pediatric Drugs 26:209–214. https://doi.org/10.1007/s40272-023-00608-4 Cortes JE, Gambacorti-Passerini C, Deininger MW, et al (2018) Bosutinib versus imatinib for newly diagnosed chronic myeloid leukemia: Results from the randomized BFORE trial. Journal of Clinical Oncology 36:. https://doi.org/10.1200/JCO.2017.74.7162 Singh P, Singh N, Mishra N, et al (2022) Functionalized bosutinib liposomes for target specific delivery in management of estrogen-positive cancer. Colloids Surf B Biointerfaces 218:. https://doi.org/10.1016/j.colsurfb.2022.112763 Segrelles C, Contreras D, Navarro EM, et al (2018) Bosutinib inhibits EGFR activation in head and neck cancer. Int J Mol Sci 19:. https://doi.org/10.3390/ijms19071824 Yu L, Guo W, Liu L, et al (2019) Bosutinib Acts as a Tumor Inhibitor via Downregulating Src/NF-κB/Survivin Expression in HeLa Cells. Anatomical Record 302:. https://doi.org/10.1002/ar.24269 Watanabe N, Takaku T, Tsukune Y, et al (2022) Bosutinib-induced lung injury: a report of two cases and literature review. Int J Hematol 115:. https://doi.org/10.1007/s12185-022-03304-0 Kassianides X, Bodington R, Bhandari S (2021) An evaluation of ferric derisomaltose as a treatment for anemia. Expert Rev Hematol 14:. https://doi.org/10.1080/17474086.2021.1858406 Kalra PR, Cleland JGF, Petrie MC, et al (2022) Intravenous ferric derisomaltose in patients with heart failure and iron deficiency in the UK (IRONMAN): an investigator-initiated, prospective, randomised, open-label, blinded-endpoint trial. The Lancet 400:. https://doi.org/10.1016/S0140-6736(22)02083-9 Auerbach M, Henry D, DeLoughery TG (2021) Intravenous ferric derisomaltose for the treatment of iron deficiency anemia. Am J Hematol 96 Dickson EA, Ng O, Keeler BD, et al (2023) The ICaRAS randomised controlled trial: Intravenous iron to treat anaemia in people with advanced cancer – feasibility of recruitment, intervention and delivery. Palliat Med 37:. https://doi.org/10.1177/02692163221145604 Wijdicks EFM (2018) Lactulose: A Simple Sugar in a Complex Encephalopathy. Neurocrit Care 28:. https://doi.org/10.1007/s12028-017-0494-4 Blei AT, Córdoba J, of the American College of Gastroenterology TPPC (2001) Hepatic Encephalopathy. Official journal of the American College of Gastroenterology | ACG 96: Ferenci P (2017) Hepatic encephalopathy. Gastroenterol Rep (Oxf) 5:138–147. https://doi.org/10.1093/gastro/gox013 Chu N, Ling J, Jie H, et al (2022) The potential role of lactulose pharmacotherapy in the treatment and prevention of diabetes. Front Endocrinol (Lausanne) 13 Kishor C, Ross RL, Blanchard H (2018) Lactulose as a novel template for anticancer drug development targeting galectins. Chem Biol Drug Des 92:. https://doi.org/10.1111/cbdd.13348 Fernández J, Moreno FJ, Olano A, et al (2018) A galacto-oligosaccharides preparation derived from lactulose protects against colorectal cancer development in an animal model. Front Microbiol 9:. https://doi.org/10.3389/fmicb.2018.02004 Swymer C, Neville MW (2012) Tafluprost: The First Preservative-Free Prostaglandin to Treat Open-Angle Glaucoma and Ocular Hypertension. Annals of Pharmacotherapy 46:. https://doi.org/10.1345/aph.1r229 Traynor K (2012) FDA approves glaucoma treatment. American Journal of Health-System Pharmacy 69 Papadia M, Bagnis A, Scotto R, Traverso CE (2011) Tafluprost for glaucoma. Expert Opin Pharmacother 12:. https://doi.org/10.1517/14656566.2011.606810 Wu S, Liu C, Tang J, et al (2024) Tafluprost promotes axon regeneration after optic nerve crush via Zn2+-mTOR pathway. Neuropharmacology 242:109746. https://doi.org/https://doi.org/10.1016/j.neuropharm.2023.109746 Wilson DJ, DuBois RN (2022) Role of Prostaglandin E2 in the Progression of Gastrointestinal Cancer. Cancer Prevention Research 15 Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile.docx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4316611","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":304289213,"identity":"1f67f88d-d1f5-468b-8882-11e44b7c56b7","order_by":0,"name":"Pinar Siyah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYDACZgjFA0SMD0BcAzCfjTgtzAYoWngI28fDJkGUFnN23oOfKxjqZAyOnz1WdaPCGihyxoDhQ9lhBnvpA1i1WDbzJUueYTjMY3AmL+12zpl0BsueHAPGGecOM/DwJWDVYgBULNnAcIDH4ECO2e3ctsMMQIYBMy+QwYPDZUAtxj8bGOp4DM6/MSvO/QfUcv6NAfNf/FrMgLYw8xjcyDFjzm0AarkBtIURjxbLZh4zywagRskbb4ylc46l81jOeFZwsOdcOg/PGRwhxn/G+GZDRZ093/kcw885NdZy5vzJGx/8KLOWY+/B4TAYqXAAIgB2zQEGPDFpAGPIN+BSMgpGwSgYBSMeAABK9FL+YNI3EgAAAABJRU5ErkJggg==","orcid":"","institution":"Bahçeşehir University","correspondingAuthor":true,"prefix":"","firstName":"Pinar","middleName":"","lastName":"Siyah","suffix":""}],"badges":[],"createdAt":"2024-04-24 08:26:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4316611/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4316611/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56964386,"identity":"11d0d12f-7efe-443c-a659-c3598bc9ec93","added_by":"auto","created_at":"2024-05-22 19:16:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24673,"visible":true,"origin":"","legend":"\u003cp\u003eThe generated grid box on the active site of the target protein, centered around the binding coordinates of the 10N ligand.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4316611/v1/067a4756f7dbcff3df2afb00.jpg"},{"id":56964405,"identity":"828a39ee-a60a-48fb-9dc5-9778e6ec3ea8","added_by":"auto","created_at":"2024-05-22 19:16:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52993,"visible":true,"origin":"","legend":"\u003cp\u003eDocking pose of the reference compound 10N at binding site on FAK protein\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4316611/v1/97bfd7033d6d879022def3be.jpg"},{"id":56964387,"identity":"ad4ae16f-094f-4452-a5b6-b8290e611f5c","added_by":"auto","created_at":"2024-05-22 19:16:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49971,"visible":true,"origin":"","legend":"\u003cp\u003eDocking pose of Amprenavir at binding site on Focal Adhesion Kinase Protein\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4316611/v1/27936e664d16a43a866507b9.jpg"},{"id":56964389,"identity":"958e6863-c7ea-4848-acce-701f8d7ef9ba","added_by":"auto","created_at":"2024-05-22 19:16:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":154454,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Two-dimensional depiction of ligand interactions at the Fak binding site with Compound 10N. (B) Analysis of the interaction types of Compound 10N and binding site residues of Fak during the MD simulations. (C) Percentage of interactions between Fak binding pocket residues and Compound 10N during the MD simulations. These results are based on statistical analysis of 100 trajectory frames obtained from 10-ns MD simulations.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4316611/v1/abacd31455e0d92d4527aabc.jpg"},{"id":56964384,"identity":"d5750d9c-67a6-417d-a646-e6d3e90084d7","added_by":"auto","created_at":"2024-05-22 19:16:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":165385,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Two-dimensional depiction of ligand interactions at the Fak binding site with Ferric derimaltose. (B) Analysis of the interaction types of Ferric derimaltose and binding site residues of Fak during the MD simulations. (C) Percentage of interactions between Fak binding pocket residues and Ferric derimaltose during the MD simulations. These results are based on statistical analysis of 100 trajectory frames obtained from 10-ns MD simulations.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4316611/v1/2f33889abcb52d0ecb7de7c5.jpg"},{"id":57665219,"identity":"b2101157-0830-4e29-9d7c-2ca05cce3842","added_by":"auto","created_at":"2024-06-04 04:50:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":846105,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4316611/v1/4a42aad1-d850-441e-90f0-503e3d37a709.pdf"},{"id":56964388,"identity":"9ca66654-b2be-4289-a755-e46db36633b9","added_by":"auto","created_at":"2024-05-22 19:16:19","extension":"docx","order_by":21,"title":"","display":"","copyAsset":false,"role":"supplement","size":31968,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4316611/v1/9e9f60cf8a6bce5b16ef3cba.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Precision Targeting Strategies in Cancer Therapy: Focusing on Synthetic Lethality with FAK Inhibition","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCancer ranks as the second leading cause of global mortality, surpassed only by cardiovascular diseases. The financial resources allocated for cancer treatment and pharmaceutical research impose substantial burdens on national budgets and are anticipated to increasingly constitute a significant proportion of health expenditures in the years ahead [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Researchers are diligently exploring diverse modalities for cancer treatment, producing a multitude of publications based on in vitro and in vivo investigations. Notably, contemporary emphasis in drug discovery has shifted towards molecular modeling studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Molecular screening facilitates the elucidation of molecular-target interactions within an in silico environment, thus enabling the reduction of the pool of drug candidate molecules prior to transitioning to in vitro and in vivo settings.\u003c/p\u003e \u003cp\u003eFocal Adhesion Kinase (FAK) emerges as a vital protein kinase governing cell-matrix interactions, particularly influencing cell adhesion and migration [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Its activation is instigated through integrin signaling, playing a crucial role in cells adhering to the extracellular matrix [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The autophosphorylation of FAK and its subsequent conformational activation intricately regulate fundamental cellular processes including growth, proliferation, and survival through modulation of signal transduction pathways. Excessive FAK activation has been implicated in various diseases, notably cancer. Consequently, FAK inhibitors are under rigorous investigation as potential therapeutic agents in cancer management, with a specific focus on metastasis prevention [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe NF2 gene, responsible for encoding Merlin Protein (Moesin-Ezrin-Radixin-Like), functions as a tumor suppressor protein [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Merlin's normal role involves the regulation of cell growth and division, control of cell adhesion, and modulation of cellular interactions with the microenvironment. Frequent inactivation of NF2, observed in diverse cancer types such as schwannoma, meningioma, and malignant mesothelioma (MM), the latter being a highly aggressive cancer with limited treatment options [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. NF2 gene mutations disrupt the normative functionality of Merlin protein, resulting in the manifestation of NF2 disease. Neurofibromatosis Type 2 (NF2) is characterized by a genetic predisposition causing aberrant proliferation of nervous system cells, particularly manifesting as tumor formation between Schwann cells and other cellular entities, predominantly within the brain and spinal cord. The NF2 gene mutations induce aberrations in Merlin protein regulation, instigating uncontrolled cellular proliferation and tumor development [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSynthetic lethality constitutes a crucial concept in contemporary cellular biology and cancer research, particularly in the development of therapeutic strategies aimed at targeting cancer cells bearing specific genetic mutations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A synthetic lethal interaction arises when the disruption of either gene individually is viable, but the simultaneous disruption of both genes leads to cell death. Central to leveraging synthetic lethality in cancer therapy is the discovery and detailed understanding of robust synthetic lethal genetic interactions [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Traditional approaches involve the suppression or inhibition of specific genes within tumors, a methodology effective in targeting cancer cells but often associated with collateral damage to normal cells. The synthetic lethality strategy seeks to invert this paradigm by exploiting genetic interactions. By suppressing the function of a gene in normal cells without harm, a severe detriment can be induced in cancer cells harboring specific genetic mutations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmerging research underscores a synthetic lethal relationship between Focal Adhesion Kinase (FAK) and NF2/Merlin. Notably, cancer types such as schwannoma, meningioma, and malignant mesothelioma (MM) and MPM exhibit mutations in the NF2 gene, leading to impaired synthesis of the Merlin protein [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The underlying hypothesis posits that in instances where cancer cells recurrently experience loss of NF2/Merlin expression due to various inactivation mechanisms and mutations, the FAK protein presents a promising target for cancer therapy [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This study aims to identify small molecule drug candidates specifically targeting FAK in Merlin-negative tumors. Therefore, our objective is to develop a specific targeted therapy that effectively targets cancer cells without harming healthy cells. This approach ensures the development of therapeutic interventions that selectively kill cancerous tissues, offering a more precise and effective strategy for cancer therapy.\u003c/p\u003e \u003cp\u003eThe ongoing advancement of novel FAK inhibitors underscores the importance of this approach in the landscape of cancer therapeutics. Despite concerted research efforts, however, no FAK inhibitor has yet garnered clinical approval [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Consequently, there is an urgent and critical necessity to develop new FAK inhibitors with heightened antitumor activity to achieve more potent antitumor effects [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe investigation involved docking of all FDA-approved drugs and compounds under clinical investigation, totaling 3235 compounds, against the FAK protein target. Evaluation of hit molecules with the highest potential as drug candidates was facilitated through comparative analysis of their docking scores against reference compounds. Molecules with docking scores lower than \u0026minus;\u0026thinsp;8.00 kcal/mol were selected for further investigation. Selected top-performing molecules underwent molecular dynamics (MD) simulations of 1 nanosecond (ns), 10ns, and 100ns, culminating in the identification of optimal drug candidate molecules. Machine-learning based QSAR models, were employed to predict the therapeutic activity of these molecules. The findings contribute to advancing our understanding of potential treatments for Merlin-negative tumors through FAK inhibition.\u003c/p\u003e"},{"header":"MATERIAL AND METHOD","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePreparation of Ligands for Docking:\u003c/h2\u003e \u003cp\u003eThe ligand structures were created using the Maestro 2D Sketcher program, and they were optimized for molecular docking studies. The preparation process ensures compatibility with a physiological pH of 7.4. This involves employing the PROPKA1 algorithm [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which predicts protonation states of ionizable groups at a given pH, and utilizing the OPLS3e2 force field [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], a set of parameters that describe the interactions between atoms in the molecular system. Furthermore, the LigPrep3 module [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] was employed to refine and preprocess the molecular structures. LigPrep3 is a tool that can generate different ionization states, tautomers, and conformers of a molecule, enhancing the accuracy and reliability of subsequent molecular docking simulations. The Epik module [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], a software engineered for rapid and accurate prediction of aqueous phase pKa values and protonation state distributions for complex, drug-like molecules, played a crucial role in this phase. It facilitated the refinement of ligands, which were systematically assessed as potential small molecule drugs, and meticulously prepared for subsequent docking procedures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePreparation of Protein for Docking:\u003c/h2\u003e \u003cp\u003eProtein preparation and docking were conducted following established protocols detailed in a prior publication [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The crystallographic structure of the FAK protein, identified with PDB accession number 4GU6 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was retrieved from the Protein Data Bank and prepared as in our previous work [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Utilizing the Prime module, missing side chains and regions were comprehensively completed. The protein target was elaborately prepared to undergo molecular docking and simulation studies in accordance with the physiological pH:7.4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGrid Box Generation and Molecular Docking Studies:\u003c/h2\u003e \u003cp\u003eA grid box was created using the grid center coordinates derived from the co-crystallized ligand 10N. Ligands were carefully positioned within this specified grid area on the protein target. This process was carried out meticulously using Maestro's Glide module [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], utilizing standard sensitivity settings. As a control measure, reference molecules underwent the same procedures, following identical methodologies as the ligands, ensuring methodological consistency [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS AND DISCUSION","content":"\u003cp\u003eThis study has focused on a broad collection, including 3235 FDA-approved compounds as well as those undergoing clinical evaluation. This collection was evaluated using a drug repurposing approach to identify potential drug candidates for the treatment of Merlin-negative tumors through the inhibition of the FAK protein. To achieve this, the protein and drug library were initially prepared for docking. Subsequently, a grid box was prepared on the protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and all ligands were docked into this grid box. This step, which involved comparative examination of docking scores in relation to reference compounds, served as an initial filter to identify molecules with the highest potential as drug candidates. Then, an attempt was made to reduce the number of candidates by performing MD/MMBG (1ns, 10ns, 100ns) analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMolecules with docking scores lower than \u0026minus;\u0026thinsp;8.00 kcal/mol were selected for detailed analysis. The reference compound bound to the FAK protein with a docking score of -10.85 kcal/mol (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Given that this compound is the co-crystallized ligand in the PDB, obtaining the highest docking score in the initial stage was quite positive and expected. Out of the 3235 FDA-approved and clinically reviewed molecules, 71 had docking scores of -8.00 kcal/mol or lower, indicating better binding affinity. This threshold was strategically chosen to ensure that only the most promising candidates, demonstrating a strong potential for interaction with the FAK protein, were advanced to the subsequent stages of the study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe docking scores are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among the molecules we studied, Amprenavir bound to the FAK protein with a docking score of -8.91 kcal/mol, followed closely by Flavin adenine dinucleotide with \u0026minus;\u0026thinsp;8,47 and Lactulose with \u0026minus;\u0026thinsp;8.23 kcal/mol. Bosutinib, Ferric derisomaltose, and Tafluprost demonstrated good binding affinities with scores ranging between \u0026minus;\u0026thinsp;8.12, -8.00, -8.47, and \u0026minus;\u0026thinsp;8.13, respectively. These molecules were selected for their high potential binding affinity. The binding conformation of Amprenavir, which has the best docking score, to the protein is provided (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e71 molecules with docking scores of -8.00 kcal/mol or better underwent simulation, followed by MM/GBSA calculations. The MM/GBSA score of the Reference Compound was found to be -69.56 based on the 1 ns simulations. Our tested molecules exhibited significantly better scores. For example, based on the MM/GBSA analyses conducted after the 1 ns MD simulations, Flavin adenine dinucleotide yielded a score of -89.15 kcal/mol, Ferric derisomaltose scored \u0026minus;\u0026thinsp;82.72 kcal/mol, Lactulose scored \u0026minus;\u0026thinsp;71.93 kcal/mol, Bosutinib scored \u0026minus;\u0026thinsp;75.79 kcal/mol, Amprenavir scored \u0026minus;\u0026thinsp;74.84 kcal/mol, and Tafluprost scored \u0026minus;\u0026thinsp;72.51 kcal/mol. Consequently, the top 10 molecules with the best scores out of the 71 were selected and subjected to a 10 ns simulation.\u003c/p\u003e \u003cp\u003eDuring the 10 ns simulation duration, the binding free energy scores remained relatively stable with no significant variations. Notably, Amprenavir, Bosutinib, Lactulose and Tafluprost mostly maintained their affinity with a score of -75.86, -74,38, -73,39 and \u0026minus;\u0026thinsp;73,13 kcal/mol respectively. Ferric derisomaltose, and Flavin adenine dinucleotide showed some changes in their binding affinities. The Reference Compound demonstrated almost stable with no significant variations, recording a score of -71.04 kcal/mol. In summary, the MM/GBSA scores after the 10 ns MD simulation were found to be nearly identical to the previous ones, indicating stability and consistency.\u003c/p\u003e \u003cp\u003eSubsequently, 9 molecules that exhibited better scores than the \u0026minus;\u0026thinsp;71.04 kcal/mol of the reference compound were selected for 100 ns simulations. Based on the results of the 100 ns MD, 9 molecules that demonstrated better scores than the \u0026minus;\u0026thinsp;69,20 kcal/mol of the reference compound were selected for 100 ns MD and MM/GBSA analyses. Upon examining the 100 ns MD-MM/GBSA results, it was found that Amprenavir exhibited a relatively strong binding affinity with a score of -72.81 kcal/mol. Bosutinib also demonstrated robust interaction with the protein, yielding a score of -71.84 kcal/mol. Ferric derisomaltose and Flavin adenine dinucleotide showed even stronger binding affinities, recording scores of -76.70 kcal/mol and \u0026minus;\u0026thinsp;69.09 kcal/mol, respectively. Conversely, the reference compound exhibited a binding affinity close to and even weaker than many of these molecules with a score of -69,20 kcal/mol.\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\u003eDocking scores and MM/GBSA analysis rely on 1ns, 10ns and 100ns molecular dynamics simulations.\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein-Ligand Complex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDocking Score (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 ns MD-MM/GBSA (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 ns MD-MM/GBSA (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100 ns MD-MM/GBSA (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4GU6_Amprenavir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-74,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-75,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-72,81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4GU6_Bosutinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-75,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-74,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-71,84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4GU6_Ferric derisomaltose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-82,72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-73,52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-76,70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4GU6_Flavin adenine dinucleotide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-89,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-82,99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-69,09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4GU6_Lactulose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8,23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-71,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-73,39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-74,86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4GU6_Tafluprost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-72,51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-73,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-65,77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4GU6_Reference Compound\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-10,85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-69,56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-71,04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-69,20\u003c/b\u003e\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\u003eAt the specified time durations (1 ns, 10 ns, and 100 ns), the correlation between docking scores and MD-MM/GBSA results is evident. Notably, Amprenavir, Bosutinib, Ferric derisomaltose, and Lactulose have demonstrated significantly strong binding affinities. Flavin adenine dinucleotide and Tafluprost have also shown satisfactory results, closely following behind. In contrast, the reference compound exhibited weaker binding affinities compared to all other molecules in the 1 ns and 10 ns durations. After 100 ns of MD simulation, the MM/GBSA analysis indicated that Amprenavir, Bosutinib, Ferric derisomaltose, and Lactulose exhibited decreased binding affinities relative to the reference compound. The docked poses of the selected molecules and detailed docking and MM/GBSA score tables, were provided in the Supplementary Information. The alignment of docking and MD-MM/GBSA results highlights the reliability of computational approaches in predicting ligand binding affinities. Consequently, all six molecules identified in the study emerge as compelling candidates for further research, given their consistently low docking scores and stable binding interactions observed throughout the MD simulations. These findings significantly contribute to our understanding of the dynamic aspects of protein-ligand interactions and have substantial implications for the advancement of drug design and development.\u003c/p\u003e \u003cp\u003eThe identification of common amino acid residues engaged in ligand-protein interactions provides valuable insights into potential binding sites and shared mechanisms among various molecules. This knowledge is crucial for understanding the consistent aspects of ligand binding and can guide future efforts in drug design by highlighting critical amino acid residues that consistently contribute to the stability and specificity of ligand-protein complexes. This comprehensive analysis enhances our understanding of the molecular basis of ligand-protein interactions and aids in the logical development of effective therapeutics. Therefore, this study analyzed the key amino acids involved in ligand-protein interactions, in accordance with the mentioned rationale. For the reference molecule, named 10N, interactions were observed with amino acids Cys502, Glu506, Arg550, Val552, Leu553, Asp564, and Leu567 during the 100ns MD simulation, confirming our findings. Importantly, amino acids Cys502 and Asp564 were previously highlighted in the literature as crucial, which supports our results [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNotably, the Cys502 residue emerges as a common binding site, forming hydrogen bonds with both the reference molecule Compound 10N (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the candidate Ferric derimaltose and Bosutinib. This consistency in interaction highlights the significance of Cys502 in facilitating stable ligand binding and suggests its potential role as a key hotspot for ligand-protein interactions. Moreover, amino acid residues such as Thr503 and Asn51 exhibit recurrent involvement in the interactions. Thr503 is implicated in hydrogen bonding with Bosutinib, emphasizing its contribution to the binding affinity of the candidate drug. Similarly, Asn51 is involved in hydrogen bonding with Amprenavir, underlining its role as a critical residue in mediating ligand-protein interactions. These shared interactions across different molecules underscore the importance of specific amino acid residues in governing the binding specificity and affinity. Furthermore, the residues Asp564, Glu430, Glu506, and Cys427, identified in the interaction profile of Flavin adenine dinucleotide, appear to be significant across various ligands. Their recurrent presence indicates their pivotal role in the ligand-protein binding landscape. The interactions with water molecules also contribute to the stability of the complex, suggesting the dynamic nature of the ligand-protein interaction network involving these residues. Briefly, our in silico drug screening has demonstrated that candidate drugs consistently interact with the same amino acids, Cys502 and Asp564, as the reference molecule 10N found in the crystal structure during docking or molecular dynamics simulations. This consistent interaction pattern validates the accuracy and reliability of our in silico methodology. Considering all docking and MD simulation results, our compounds, especially Amprenavir, Bosutinib, Ferric derisomaltose, and Lactulose, outperformed the reference and are considered the most promising candidate drugs for FAK inhibition in cancer therapy. Furthermore, the other two molecules, Flavin adenine dinucleotide and Tafluprost, also showed similar potential to the reference molecule. In total, all six molecules have the potential to be promising candidates as FAK inhibitors for cancer treatment.\u003c/p\u003e \u003cp\u003eZhan et. al.'s research [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] utilized molecular dynamics (MD) simulations and MM-GB/SA calculations to explore combinations, revealing an enthalpy-driven mechanism. Crucial residues, particularly Cys502 and Asp564, were identified for their essential roles in forming hydrogen bonds with inhibitors, consistent with experimental observations. Moreover, Glu500 was noted to establish non-classical hydrogen bonds with each inhibitor. Stronger electrostatic interactions with PHM16 and ligand3 were exhibited by Arg426. Hydrophobic interactions were facilitated by key residues such as Ile428, Val436, Ala452, Val484, Leu501, Glu505, Glu506, Leu553, Gly563, Leu567, and Ser568. These findings hold substantial significance for the advancement of FAK inhibitors, offering valuable insights for cancer research. Particularly noteworthy are the consistently low peaks of Cys502 in all three complexes, underscoring its pivotal role in hydrogen bond interactions. Our study echoes these observations, emphasizing the importance of residues like Cys502 and Asp564, validating the significance of these interactions in our computational drug screening approach. The identified residues, coupled with hydrophobic and electrostatic interactions, contribute significantly to the comprehension of FAK inhibition, supporting the design and development of potential cancer therapeutics [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The alignment of key amino acids identified in the study by Zhan et. al. with the significant interaction amino acids found in our study indicates a significant alignment and consistency between the two studies. This correspondence has increased the reliability of our findings.\u003c/p\u003e \u003cp\u003eFurthermore, our study supports the findings outlined in the research carried out by Mustafa et. al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] More specifically, they validate the pivotal functions of Cys502, positioned in the kinase hinge, and Asp564, found in the DFG motif within the ATP binding site of Focal Adhesion Kinase (FAK). This consistency emphasizes the importance of these residues in FAK and strengthens their significance in developing potential inhibitors for studying cancer. Our study reveals that the molecule Ferric derisomaltose interacts effectively with amino acids Ile428, Glu430, Glu500, Cys502, Glu506, Asn551, Asp564, and Leu567. The 2D interaction maps are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, the types of interaction bonds in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, and the interaction analyses over a period of 100 ns in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC. These findings are supportive of previous research and suggest that Ferric derisomaltose has the potential to be a highly effective FAK inhibitor, as indicated by previous studies conducted on this molecule.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of the biochemical and pharmacological properties of the leading ligands was conducted by using the MetaCore/MetaDrug platform. This tool enables the prediction of first-pass and second-pass metabolites and assesses various attributes such as reactivity, blood-brain barrier (BBB) permeability, protein binding, and water solubility. Additionally, MetaDrug employs Quantitative Structure-Activity Relationship (QSAR) models to forecast the potential toxic effects and therapeutic efficacy of the ligands under scrutiny. To determine the similarity between these ligands and those encompassed in the QSAR models, we utilized the Tanimoto Prioritization (TP) property. Further, we have previously comprehensively discussed the precision of the QSAR models in our prior studies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Bosutinib, Lactulose, Flavin adenine dinucleotide, and Ferric derisomaltose compounds were predicted to exhibit anti-cancer properties. Furthermore, Amprenavir and Tafluprost showed potential for improvement.\u003c/p\u003e \u003cp\u003eAmprenavir, a protease inhibitor approved for the therapeutic management of HIV infection by the United States Food and Drug Administration on April 15, 1999, has demonstrated efficacy with a dosing regimen of twice daily, marking a significant advancement over prior treatments necessitating administration every eight hours [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Beyond its primary application in HIV therapy, amprenavir has been explored both in vitro and in silico for potential utility in treating a range of diseases, including SARS-CoV-2 and various cancers [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Subsequent investigations have revealed that amprenavir possesses the capability to inhibit tumor cell proliferation across a diverse spectrum of cancer types, as documented [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, research conducted by Jiang et. al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], has demonstrated amprenavir's ability to induce apoptosis in MCF-7 cells in vitro and in vivo through the inhibition of ERK2 kinase activity. Esposito et. al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], further substantiated amprenavir's anti-cancer potential by evidencing its capacity to impede migration and proliferation in human hepatocarcinoma cell lines. Considering the cumulative findings from prior studies alongside the outcomes of current research, it is posited that amprenavir may be effectively repurposed as a Focal Adhesion Kinase (FAK) inhibitor in oncological treatments.\u003c/p\u003e \u003cp\u003eBosutinib, a tyrosine kinase inhibitor (TKI), was granted approval by the United States Food and Drug Administration (FDA) in September 2012 and by the European Medicines Agency (EMA) in March 2013. It is recognized for its application in the treatment of Philadelphia chromosome-positive chronic myeloid leukemia in the chronic phase (CML-CP), achieving its initial pediatric approval for this indication [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In a comparative study conducted among CML patients, Cortes et. al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] reported that patients treated with Bosutinib exhibited superior responses compared to those receiving Imatinib. Moreover, its efficacy has been investigated in various other cancer types. Singh et. al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] demonstrated in their study on MCF-7 cells that liposomal formulations of Bosutinib induced apoptosis in estrogen-positive cell lines. Segrelles et. al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] have stated that Bosutinib inhibits the growth of Head and Neck Squamous Cell Carcinoma (HNSCC) particularly by inhibiting the activity of the Epidermal Growth Factor Receptor (EGFR). Yu et. al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] concluded in their study with the HeLa cell line that Bosutinib could serve as a significant therapeutic agent against cervical cancer by decreasing the activity of the Src/NF-κB/survivin signaling pathway. Conversely, Watanabe et. al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] have reported potential adverse effects of Bosutinib, including diarrhea, hepatic toxicity, and severe lung injury. Given its more recent approval compared to Amprenavir, and notwithstanding the reported side effects, Bosutinib holds potential for repurposing as a Focal Adhesion Kinase (FAK) inhibitor, warranting cautious consideration of its application in this capacity.\u003c/p\u003e \u003cp\u003eFerric derisomaltose, an iron carbohydrate complex composed of ferric hydroxide and the carbohydrate derisomaltose, received regulatory approval for the treatment of iron deficiency anemia from the European Medicines Agency (EMA) in 2009 and the United States Food and Drug Administration (FDA) in January 2020. A study conducted by Kassianides et. al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] demonstrated that the use of ferric derisomaltose in patients with anemia is more cost-effective and efficacious compared to previous formulations, while also exhibiting minimal side effects. In a comprehensive study, Kalra et. al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] investigated the application of ferric derisomaltose in patients with heart failure and iron deficiency, revealing a correlation between iron supplementation and a reduced risk of cardiovascular mortality in this patient group, thereby underscoring the safety of ferric derisomaltose use. Auerbach et. al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] further validated the rapid amelioration of iron deficiency and the reliability of ferric derisomaltose application in their study. Concerning the potential application of ferric derisomaltose within oncological treatment paradigms, the body of research remains limited. Nevertheless, Dickson et. al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] have administered ferric derisomaltose to oncology patients witnessing reductions in hematological parameters, notably hemoglobin concentrations, and have documented favorable outcomes. Review of the existing literature posits ferric derisomaltose as a promising candidate for investigation as a Focal Adhesion Kinase (FAK) inhibitor. Additionally, its deployment in the context of radiotherapy merits consideration for leveraging the radiosensitizing attributes of iron, thereby highlighting its prospects as an efficacious, non-toxic, and therapeutic agent.\u003c/p\u003e \u003cp\u003eLactulose, synthesized through the isomerization of lactose, initially garnered approval from the United States Food and Drug Administration (FDA) in 1977 and has been recognized as one of the World Health Organization's (WHO) Essential Medicines [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In recent years, it has become one of the most frequently prescribed medications in the USA, primarily for the treatment of hepatic encephalopathy [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Moreover, emerging evidence suggests that lactulose may play a role as a pharmacotherapeutic agent in the management and prevention of type 2 diabetes through its effects on gut microbiota [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Research conducted by Kishor et. al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], has indicated that lactulose could serve as an effective Galectin inhibitor, potentially useful in targeted cancer therapy and demonstrating anticancer agent capabilities. Furthermore, Fern\u0026aacute;ndez et. al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] have shown that galacto-oligosaccharides derived from lactulose significantly reduced the incidence of Colorectal Cancer (CRC) in in vivo models. Based on these findings, we conclude that lactulose can be reliably considered for use as a Focal Adhesion Kinase (FAK) inhibitor.\u003c/p\u003e \u003cp\u003eTafluprost, a prostaglandin analog, received approval from the United States Food and Drug Administration (FDA) on February 13, 2013, for the treatment of ocular hypertension and glaucoma [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The study conducted by Papadia et. al. [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], highlights not only the efficacy of Tafluprost, which is the first prostaglandin analog without preservatives, but also its safety profile and the minimal side effects associated with its use. In recent research, Wu et. al. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] demonstrated that Tafluprost facilitates axon regeneration through the modulation of the Zn2+-mTOR pathway. While there has been no direct investigation into the applicability of Tafluprost in cancer treatment, studies involving prostaglandins have linkedwith the initiation, progression, and metastasis of cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Given this association, it has been concluded that the use of Tafluprost as a Focal Adhesion Kinase (FAK) inhibitor may not be appropriate.\u003c/p\u003e \u003cp\u003eThe outcomes derived from this integrated computational methodology bear considerable importance, especially concerning the advancement of therapies for Merlin-negative tumors. The discovery of potent FAK inhibitors using this approach underscores the therapeutic promise of these compounds. Furthermore, the utilization of a diverse compound library encompassing both FDA-approved medications and substances undergoing clinical scrutiny provides opportunities for drug repurposing. Repurposing established drugs presents a potentially swifter and economically efficient approach to drug innovation, particularly in oncology, where the need for rapid treatment solutions is frequently critical.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, the integrated analysis of molecular docking, molecular dynamics and MM-GBSA calculations for the ligands interacting with the protein target 4GU6 provides valuable insights into the potential drug candidates. The consistent trends observed across different time durations in both docking and MM-GBSA analyses reinforce the reliability of computational approaches in predicting ligand binding affinities. Amprenavir, Bosutinib, Ferric derisomaltose, and Lactulose consistently emerges as the most promising drug candidates, exhibiting the lowest docking scores and the most negative MM/GBSA values from MD simulations. This consistent and strong binding affinity suggests Amprenavir, Bosutinib, Ferric derisomaltose, and Lactulose\u0026rsquo;s potentials as the effective therapeutic agents for the targeted protein. Flavin adenine dinucleotide and Tafluprost follow closely behind these compounds, demonstrating robust and stable binding affinities. Their stable interactions with the protein over different time scales indicate their potential as viable drug candidates. In contrast, the Reference Compound consistently displays higher docking scores and higher MM-GBSA values, indicating weaker binding affinities. This underscores the importance of utilizing computational methods to distinguish promising drug candidates from less effective ones. Overall, the collaborative use of docking and MM-GBSA analyses provides a comprehensive evaluation of ligand-protein interactions, aiding in the identification of potential drug candidates. These findings contribute to the rational design of therapeutics by highlighting molecules with the strongest and most consistent binding affinities for further experimental validation and drug development. This study significantly contributes to the field of drug design and development by providing a reliable computational model for predicting ligand binding strengths. Additionally, it enhances our understanding of protein-ligand interactions and establishes a model for future investigations in this domain. Our study sheds light on new therapeutic avenues targeting cancer through Focal Adhesion Kinase (FAK) inhibition, in a crucial field where there is currently no clinically approved FAK inhibitor and a pressing need for the development of new FAK inhibitors with antitumor properties. This work lays the foundation for promising new therapeutics for various cancer treatments while offering a general approach beyond FAK inhibitors, thereby expanding the impact in the field of drug design. This study can lead to the discovery of new therapeutic targets and the development of more effective treatment approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePınar Siyah (P. S.), as the single author of this manuscript, conducted the study's design, data collection process, analysis, and manuscript composition independently, meticulously reviewing and approving each stage. P. S. conceptualized the study, crafted the original draft, executed the methodology and research, carefully reviewed and edited the manuscript, and determined it to be suitable for publication.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData supporting the findings of this research are available within the manuscript and its Supplementary File. The molecular structures and docking conformations of the selected molecules have been provided as .pdb files in the Supplementary Information section. Detailed tables containing docking and MM/GBSA scores are also included in the Supplementary File. Should any raw data files be needed in an alternative format, they can be made available by the corresponding author upon a request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21660\u003c/span\u003e\u003cspan address=\"10.3322/caac.21660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBagchi A (2024) Molecular Modeling Techniques and In-Silico Drug Discovery. In: Mandal S (ed) Reverse Engineering of Regulatory Networks. 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Cancer Prevention Research 15\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Focal Adhesion Kinase, small molecules, inhibitors, docking, molecular dynamics, computer-aided drug discovery","lastPublishedDoi":"10.21203/rs.3.rs-4316611/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4316611/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Synthetic lethality, involving the simultaneous deactivation of two genes, plays a critical role in disrupting vital cellular functions or prompting cell death. This study delves into the impact of synthetic lethality within cancer research, specifically examining the interplay between the Focal Adhesion Kinase (FAK) and Neurofibromin 2 (NF2) genes. While deactivating FAK or NF2 individually has minimal impact, their combined deactivation highlights the vital significance of their synthetic lethal interaction. Hence, the principal aim of this study is to direct our efforts towards the inhibition of the FAK gene, a venture of notable significance. The NF2 gene is responsible for producing Merlin, a tumor suppressor protein that is often deactivated in schwannoma, meningioma, and malignant mesothelioma. The inhibition of the FAK gene is pivotal, given its pivotal role in the synthetic lethal interplay with NF2/Merlin, promising substantial prospects for the progression of cancer treatment strategies. This investigation has the capacity to propel forward inventive therapeutic methodologies, harnessing the potential of synthetic lethal interactions within cancer cells, and forging a path towards more refined and efficacious interventions in cancer treatment. The ongoing advancements in developing new FAK inhibitors highlight the significance of this strategy in cancer treatment. Despite extensive research efforts, no FAK inhibitor has been approved for clinical use. This emphasizes the urgent need to create new FAK inhibitors with improved anti-tumor properties. The small molecule FAK inhibitor candidates identified in our study show potential for making a groundbreaking contribution in this field. Employing docking and (1ns, 10ns and 100ns) molecular dynamics (MD) simulations, we evaluated FAK inhibitor complex stability, unveiling intricate interactions. Following of molecular dynamics simulations, the MM/GBSA scores for Amprenavir, Bosutinib, Ferric derisomaltose, Flavin adenine dinucleotide, Lactulose and Tafluprost were determined to be -72,81, -71,84, -76.70, -69.09, -74.86, -65.77 kcal/mol, respectively. These molecules have been evaluated as potential candidate drugs based on these scores. This study lays a foundation for novel therapeutics, holding promise for diverse cancer treatments through our computational framework.","manuscriptTitle":"Precision Targeting Strategies in Cancer Therapy: Focusing on Synthetic Lethality with FAK Inhibition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-22 19:15:54","doi":"10.21203/rs.3.rs-4316611/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":"1585b7c6-2813-4b56-8e8e-e4d7365194da","owner":[],"postedDate":"May 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32122604,"name":"Biological sciences/Cancer"},{"id":32122605,"name":"Biological sciences/Drug discovery"},{"id":32122606,"name":"Biological sciences/Computational biology and bioinformatics/Virtual drug screening"}],"tags":[],"updatedAt":"2024-06-04T04:42:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-22 19:15:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4316611","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4316611","identity":"rs-4316611","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}