Targeting Multidrug Resistance Transporters ABCB1 and ABCG2 in Pancreatic Ductal Adenocarcinoma: A Bioinformatics-Driven Drug Repurposing Study

Targeting Multidrug Resistance Transporters ABCB1 and ABCG2 in Pancreatic Ductal Adenocarcinoma: A Bioinformatics-Driven Drug Repurposing Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Targeting Multidrug Resistance Transporters ABCB1 and ABCG2 in Pancreatic Ductal Adenocarcinoma: A Bioinformatics-Driven Drug Repurposing Study Durvesh Burhade, Bhavya Mishra, Avantika Harinarayanan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9610484/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 This study aimed to identify potential therapeutic candidates for pancreatic ductal adenocarcinoma (PDAC) using a bioinformatics-based drug repurposing approach. Molecular docking analysis was performed on four selected compounds to evaluate their binding affinity with the target protein. Among the tested drugs, Drug A demonstrated the highest binding affinity, indicating the most stable interaction, while the remaining compounds showed comparatively lower binding energies. These findings suggest that Drug A may serve as a promising candidate for further investigation in PDAC treatment. The study highlights the importance of computational approaches in accelerating drug discovery and identifying effective therapeutic options. Given the high drug resistance and poor prognosis associated with PDAC, such approaches are valuable in exploring alternative treatment strategies. Despite the promising results, this study has certain limitations. The findings are based solely on computational molecular docking and do not include experimental validation. The biological activity, toxicity, and pharmacokinetic properties of the compounds were not evaluated in vitro or in vivo. Therefore, further experimental studies are required to confirm the therapeutic potential of these compounds. Future studies should focus on validating these findings through in vitro cell line experiments and in vivo animal models. Additionally, investigating the effect of these compounds on PDAC-specific pathways and drug resistance mechanisms will provide deeper insights into their therapeutic potential. Cancer Biology Bioinformatics Pancreatic ductal adenocarcinoma (PDAC) Drug repurposing Molecular docking Auto Dock Vina ABC transporters Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction According to the Global Cancer Observatory 2022 report, the global burden of pancreatic cancer is high, with about 510,992 new cases reported worldwide. Among them Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, accounting for more than 90% of all pancreatic malignancies[ 16 ], and is one of the most lethal diseases, with a 5-year survival rate of less than 10%[ 20 ]. This late-stage clinical presentation inherently precludes curative surgical intervention and complicates the management of systemic progression. Historically, the cornerstones of oncology, cytotoxic chemotherapy, surgical resection, and radiotherapy have served as the standard of care; however, these modalities have failed to demonstrate a substantial impact on overall survival (OS) or long-term prognosis. Consequently, the high mortality rates associated with these refractory malignancies underscore a critical, unmet need for innovative, targeted therapeutic paradigms. Despite the transformative success of immune checkpoint blockade (ICB) in various solid tumors, the clinical application of anti-CTLA-4, anti-PD-1, and anti-PD-L1 antibodies in pancreatic cancer has yielded largely suboptimal outcomes [ 19 , 38 ]. Figure (1.1) In an ideal anti-tumor immune response, blockade of the PD-1/PD-L1 immune checkpoint using PD-L1 inhibitors restores T-cell activity, enabling recognition of tumor antigens presented via MHC–CD8 interactions and ultimately leading to effective tumor cell apoptosis. This model represents the expected therapeutic outcome in many cancers, where immune suppression is primarily mediated through PD-1/PD-L1 signaling. However, in pancreatic ductal adenocarcinoma (PDAC), this ideal scenario often fails to translate into clinical success. Despite PD-L1 inhibition, T cells frequently remain unable to effectively recognize or eliminate tumor cells, highlighting the presence of additional immunosuppressive mechanisms and a highly resistant tumor microenvironment that limits the efficacy of checkpoint blockade therapies. Multiple clinical trials have demonstrated that these monotherapies fail to elicit robust objective response rates, a phenomenon largely attributed to the highly immunosuppressive tumor microenvironment (TME) and low mutational burden characteristic of this malignancy[ 40 ]. The most common warning signs for people with this type of pancreatic cancer are losing weight without trying, feeling pain in the belly area, and having yellowing of the skin or eyes better known as jaundice[ 27 ]. Specifically, the emergence of new-onset type 2 diabetes mellitus is increasingly recognized as a potential paraneoplastic phenomenon and early harbinger of malignancy[ 37 ]. Furthermore, the presence of thromboembolic disease, often manifesting as deep vein thrombosis or pulmonary embolism, underscores the systemic hypercoagulable state frequently induced by advanced pancreatic neoplasia [ 15 ]. Surgery and chemotherapy (like gemcitabine and FOLFIRINOX, which is a mix of several drugs) can help patients with early-stage pancreatic cancer live longer. However, these treatments do not work well for patients whose cancer is in a late stage[ 46 ]. Pancreatic ductal adenocarcinoma (PDAC) has a poor outcome because it is a complex disease caused by many factors. It is difficult to detect early, and there are no easy screening methods. Most patients are diagnosed at a late stage because symptoms usually appear only after the cancer has already grown and spread to other parts of the body[ 23 ]. As mentioned earlier, the best treatment for pancreatic ductal adenocarcinoma (PDAC) is surgery followed by chemotherapy. This works mainly for patients whose cancer has not spread to nearby blood vessels or surrounding organs. Treating pancreatic cancer is very difficult because of problems at both the genetic and cellular levels. PDAC tumors have many mutations, which makes their genes unstable. This instability helps the tumor grow and become resistant to treatment. Another challenge is that PDAC is very diverse, different patients have different mutations, and even within a single tumor, there can be many variations. Targeted therapies usually work well in cancers where most patients share the same mutation, such as EGFR in lung cancer or BRAF in melanoma. However, in pancreatic cancer, there are many different mutations, and each one is found only in a small number of patients. This makes it hard to design effective targeted treatments[ 5 , 25 ]. Because multiple signaling pathways are altered, PDAC can develop many different ways to resist treatment. Although the disease is not fully understood, some key gene mutations—such as KRAS, CDKN2A (p16), TP53, and SMAD4, play an important role in tumor growth and treatment resistance. Another major issue is the presence of cancer stem cells (CSCs)[ 29 , 43 ]. These cells make up only about 0.5–1% of the tumor but are very powerful. They can renew themselves, survive harsh conditions, and resist chemotherapy. CSCs can restart tumor growth, leading to disease progression and relapse. Most current treatments do not effectively target these cells, which is one reason why therapies often fail. In addition, PDAC has a strong ability to spread (metastasize), which worsens patient outcomes. Studies have shown that even within a single tumor, there are different groups of cells (subclones) with varying abilities to spread and respond to treatment. Moreover, PDAC can spread very early, even before it is detected[ 9 , 12 , 42 ]. This early spread reduces the effectiveness of local treatments like surgery and radiation. Figure (1.2) key factors that make pancreatic ductal adenocarcinoma (PDAC) difficult to treat. Genetic instability leads to continuous mutations, while high tumor diversity results in varied cancer cell populations within the same tumor. The presence of cancer stem cells enables self-renewal and resistance to therapy, contributing to tumor recurrence. Additionally, early metastasis and multiple resistance mechanisms further limit the effectiveness of current treatments. Together, these interconnected factors create a complex tumor environment that challenges successful therapeutic intervention. Pancreatic ductal adenocarcinoma (PDAC) remains an exceptionally lethal malignancy characterized by a dismal prognosis, largely driven by a lack of reliable early-detection biomarkers, late-stage clinical presentation, and high metastatic potential[ 2 , 34 , 41 ]. Figure (1.3) Mechanism of drug resistance in pancreatic ductal adenocarcinoma (PDAC) mediated by ATP-binding cassette (ABC) transporters. Chemotherapeutic agents and targeted inhibitors enter the cancer cell through the plasma membrane; however, overexpressed ABC transporters, including ABCB1 (P-glycoprotein) and ABCG1, actively pump these drugs out of the cell using ATP hydrolysis. This efflux process significantly reduces intracellular drug accumulation, thereby limiting therapeutic efficacy. The continuous removal of drugs leads to multidrug resistance (MDR), allowing cancer cells to survive despite treatment. Collectively, this mechanism represents a major barrier to effective PDAC therapy and contributes to poor clinical outcomes. Pancreatic ductal adenocarcinoma (PDAC) is well known for its strong resistance to chemotherapy, which is a major reason for poor treatment outcomes. One of the key mechanisms behind this drug resistance is the overexpression of specific membrane proteins known as ATP-binding cassette (ABC) transporters[ 34 ]. Among these, proteins such as ABCB1 (also called P-glycoprotein) and ABCG1 play a particularly important role. These transporters act as active efflux pumps, meaning they use cellular energy (ATP) to push anticancer drugs out of the cancer cells[ 1 , 14 ]. Under normal conditions, ABC transporters help protect cells by removing toxic substances. However, in PDAC, these proteins are often overexpressed, which becomes a major problem during treatment[ 41 ]. When chemotherapy drugs enter the cancer cell, ABC transporters quickly recognize and expel them before they can reach their target and exert their therapeutic effect. As a result, the intracellular concentration of the drug remains too low to effectively kill the cancer cells[ 4 , 17 , 28 ]. This continuous efflux of drugs leads to what is known as multidrug resistance (MDR), where cancer cells become resistant not just to one drug but to multiple structurally and functionally different drugs. ABCB1 is one of the most studied transporters in this context and is strongly associated with reduced drug accumulation and decreased treatment efficacy. Similarly, ABCG1 and other members of the ABC transporter family also contribute to this resistance by enhancing the cell’s ability to eliminate therapeutic agents. Overall, the overexpression of ABC transporters significantly limits the success of chemotherapy in PDAC[ 3 , 8 , 13 , 24 , 33 ]. By actively pumping drugs out of the cells, these proteins reduce drug effectiveness and allow cancer cells to survive and continue growing. This makes PDAC particularly difficult to treat and highlights the need for new therapeutic strategies that can overcome or bypass this drug efflux mechanism. 2.1 Drug Repurposing: An Cost efficiency & friendly Approach The development of new drugs is an extremely expensive and time-consuming process[ 30 , 44 ]. It is estimated that bringing a novel drug to market requires an investment of approximately USD 2–3 billion[ 22 ]. In contrast, drug repurposing or modification of existing drugs is significantly more cost-effective, with an estimated cost of around USD 300 million. This substantial difference highlights the importance of drug repurposing as a cost-efficient strategy in the pharmaceutical industry, where financial considerations play a critical role. In addition to cost, the timeline for de novo drug development is considerably long. The process typically begins with basic research, drug discovery, and early development stages, which may take approximately 3–5 years[ 26 , 31 , 45 ]. This is followed by preclinical studies, generally lasting around 4 years. Subsequently, the drug must undergo three phases of clinical trials (Phase I, II, and III), which together may take up to 10 years depending on factors such as sample size and study design. Finally, regulatory approval, intellectual property rights (IPR), and patent processes require an additional 1–2 years. Overall, the complete drug development pipeline may take approximately 22–25 years from initial discovery to market approval[ 32 ]. Despite the extensive time, financial investment, and effort involved, the success rate of drug development remains low. It is estimated that less than 5% of candidate drugs ultimately receive approval from regulatory authorities such as the FDA[ 36 ]. These challenges further emphasize the need for alternative approaches, such as drug repurposing, to improve efficiency and reduce the cost and time associated with drug development. Stage of Drug Development Process Description Estimated Time Estimated Cost Contribution Basic Research & Discovery Target identification, validation, lead discovery 3–5 years High Preclinical Studies In vitro & in vivo testing, toxicity studies ~ 4 years High Clinical Trial – Phase I Safety testing in small group of healthy individuals 1–2 years Moderate Clinical Trial – Phase II Efficacy and dose optimization in patients 2–3 years High Clinical Trial – Phase III Large-scale validation of safety and efficacy 3–5 years Very High Regulatory Approval & Patents FDA approval, IPR, patents, commercialization 1–2 years Moderate Total (De Novo Drug Development) Complete process from discovery to market 22–25 years ~USD 2–3 Billion Table (1.1) The table summarizes the timeline and cost involved in drug development. De novo drug development is a lengthy process, taking approximately 22–25 years and costing around USD 2–3 billion, including stages such as discovery, preclinical studies, clinical trials, and regulatory approval. Despite this, the success rate remains very low. In contrast, drug repurposing significantly reduces both time (3–6 years) and cost (~ USD 300 million), making it a faster, more cost-effective approach for developing new therapies. 2.2 Pancreatic ductal adenocarcinoma & its Key Mutation Pancreatic ductal adenocarcinoma (PDAC) develops through a well-defined adenoma-to-carcinoma progression model, characterized by a stepwise transition from noninvasive preneoplastic lesions to invasive malignancy. This multistep progression highlights the gradual nature of PDAC development and the critical role of sequential genetic alterations in driving tumorigenesis[ 6 , 10 ]. Pancreatic intraepithelial neoplasms (PanINs) are the most common precursor lesions of pancreatic ductal adenocarcinoma (PDAC) and frequently contain mutations in key genes such as KRAS, CDKN2A, TP53, and SMAD4[ 18 , 21 , 32 , 35 ]. Traditionally, PDAC development has been explained by the gradual accumulation of these mutations over time. These genetic alterations may occur simultaneously rather than sequentially, possibly due to large-scale genomic events such as chromothripsis. Despite these findings, it is well established that more than 90% of pancreatic cancers carry mutations in the KRAS gene. In addition, inactivating mutations in tumor suppressor genes are very common, particularly in CDKN2A (80–95%), TP53 (50–70%), and SMAD4 (> 50%), and these alterations may occur together[ 7 , 11 , 32 , 39 ]. Furthermore, somatic mutations in DNA mismatch repair genes, such as MLH1 and MSH2, have also been reported in PDAC. Methods 3.1 Initial Screening of drugs Literature survey of cancer databases and journals help us to find these drugs, this category includes anti-diabetic, anti-helminthic, antifibrotic, inducing cell death, lipid metabolism inhibitors, etc. Table (3.1) : The flowchart outlines a systematic drug screening pipeline for PDAC, narrowing 162 initial candidates down to 4 final leads. Identification: 162 potential drugs were gathered from literature, databases & Conferences; 132 were immediately excluded for lacking potency against pancreatic cancer cells. Screening: The remaining 22 drugs were tested for stability and efficacy. 18 were removed for failing these criteria, leaving a diverse group of candidates including anti-diabetics (4), anti-fibrotics (5), and repurposed anti-infectives.- 3.2 Retrieval of chemical structures of drugs Figure (3.1) Description Given in the pre-print Legend The physicochemical and pharmacological properties of the selected compounds were obtained from the PubChem database. Metformin (CID: 4091), with the IUPAC name 3-(diaminomethylidene)-1,1-dimethylguanidine , has a molecular weight of 129.16 g/mol. It is widely used as an antidiabetic agent and is indicated as an adjunct to diet and exercise for improving glycemic control in patients with type 2 diabetes mellitus. It is available in immediate-release and extended-release formulations and is also used in combination therapies with other antidiabetic agents, including DPP-4 inhibitors, SGLT2 inhibitors, and pioglitazone. https://pubchem.ncbi.nlm.nih.gov/compound/Metformin Statine (CID: 123915), with the IUPAC name (3S,4S)-4-amino-3-hydroxy-6-methylheptanoic acid , has a molecular weight of 175.23 g/mol. It is primarily used for regulating lipid metabolism and reducing cholesterol levels. https://pubchem.ncbi.nlm.nih.gov/compound/Statine Chloroquine (CID: 2719), with the IUPAC name 4-N-(7-chloroquinolin-4-yl)-1-N,1-N-diethylpentane-1,4-diamine , has a molecular weight of 319.9 g/mol. It is indicated for the treatment of infections caused by Plasmodium species, including P. vivax , P. malariae , P. ovale , and susceptible strains of P. falciparum . Additionally, it is used for treating extraintestinal amebiasis and has off-label applications in rheumatic diseases, as well as investigational roles in viral infections. https://pubchem.ncbi.nlm.nih.gov/compound/Chloroquine Imatinib (CID: 5291), with the IUPAC name 4-[(4-methylpiperazin-1-yl)methyl]-N-[4-methyl-3-[(4-pyridin-3-ylpyrimidin-2-yl)amino]phenyl]benzamide , has a molecular weight of 493.6 g/mol. It is a targeted anticancer agent indicated for the treatment of chronic myeloid leukemia (CML) with Philadelphia chromosome positivity, acute lymphoblastic leukemia (ALL), gastrointestinal stromal tumors (GIST), and several other hematological and solid malignancies. https://pubchem.ncbi.nlm.nih.gov/compound/Imatinib 3.3 Selection of cancer protein (Biomarker) The selection of target proteins was carried out through an extensive literature survey, which revealed that members of the ATP-binding cassette (ABC) transporter family play a crucial role in pancreatic ductal adenocarcinoma (PDAC). In particular, ABCB1 and ABCG1 were identified as highly expressed proteins in PDAC tissues. These proteins are membrane-bound transporters that actively pump a wide range of drugs out of cancer cells using ATP energy. This process, known as drug efflux, significantly reduces the intracellular concentration of chemotherapeutic agents and contributes to the development of multidrug resistance (MDR). As a result, PDAC is considered highly drug-resistant, largely due to the overexpression of efflux transporters such as ABCB1 and ABCG1, which limit the effectiveness of standard treatments. Figure (3.2) The above figure represents the three-dimensional structures of key ATP-binding cassette (ABC) transporter proteins involved in pancreatic ductal adenocarcinoma (PDAC). The structure on the left corresponds to ABCB1, while the structure on the right represents ABCG1. Both proteins are membrane-bound transporters composed of multiple transmembrane helices that form a channel-like structure. The provided figure illustrates the complex structural architecture and dynamic movement of ABCB1 (P-glycoprotein), a membrane protein that functions as an ATP-driven pump to expel toxins and drugs from cells. In Panel A, we see the overall "inward-facing" conformation of the protein, which consists of two homologous halves (shaded in different reds) embedded within the plasma membrane. The gray transparent surfaces represent lipid molecules from the membrane interacting with the protein, while the Nucleotide-Binding Domains (NBDs) at the base serve as the engine room where ATP is processed to power the pump. Panel B focuses on the structural core of the transport mechanism, specifically a 3-helix bundle formed by Transmembrane helices TM4, TM6, and TM12. The figure shows that TM4 is not a single rigid rod but is split into sub-segments ( $ 4a, 4b, 4c $ ), allowing for hinge-like flexibility. The yellow dashed triangles in the "Top" and "Bottom" views highlight how these specific helices pack together to form the central pathway for drug transport. This arrangement is vital because the shifting of these specific bundles is what creates the opening and closing "gates" that move molecules across the lipid bilayer. Finally, Panel C provides a direct comparison between the experimentally determined cryo-EM structure (solid red) and the AlphaFold-predicted structure (transparent cartoon). The black arrows are the most significant part of this panel, as they indicate the large-scale conformational changes or "tilts" that the helices undergo. By overlaying these two models, the figure demonstrates that ABCB1 is a highly flexible machine; the discrepancy between the prediction and the experiment reveals the physical "stroke" of the pump. Essentially, the figure tells us that transport is achieved through the coordinated sliding and pivoting of these transmembrane helices, moving the protein from one state to another to physically push a substrate out of the cell. To further investigate their structural and functional properties, the three-dimensional structures of these proteins were retrieved from the Protein Data Bank (PDB). The structure of ABCB1 was obtained with PDB ID: 6GDI (pdb_00006gdi), and additional structural information is available under DOI: https://doi.org/10.2210/pdb9CR8/pdb . Similarly, the structure of ABCG1 was retrieved with PDB ID: 7R8C (pdb_00007r8c), with its corresponding DOI: https://doi.org/10.2210/pdb7R8C/pdb . These structures were used for further molecular docking and interaction analysis studies. 3.4 BIOINFORMATICS ANALYSIS Following the identification and validation of relevant protein biomarkers, the study progressed to the final stage of bioinformatics analysis, which involved molecular docking of the selected compounds. Molecular docking was performed to evaluate the binding interactions, stability, and affinity of the four selected drug molecules with the target protein. This step is critical in understanding the potential of these compounds as therapeutic candidates against pancreatic ductal adenocarcinoma (PDAC). The docking results provided insights into the binding modes, interaction energies, and possible inhibitory mechanisms of the selected ligands. All computational analyses in this study were conducted using open-access and widely accepted bioinformatics tools to ensure reproducibility and accessibility. Initially, MGL Tools (Molecular Graphics Laboratory Tools) were used for the preparation of both protein and ligand structures. This included essential preprocessing steps such as the removal of water molecules, addition of polar hydrogen atoms, assignment of partial charges, and conversion of file formats into PDBQT, which is required for docking studies. MGL Tools also facilitated the visualization of protein structures and the identification of active binding sites, which are crucial for accurate docking simulations. Open Babel, an open-source chemical toolbox, was utilized for the conversion and management of chemical file formats. Since ligand structures were obtained in SDF format, Open Babel was used to convert them into compatible formats such as PDB and PDBQT. Additionally, it aided in geometry optimization and ensured that the ligand structures were properly formatted and energetically favorable before docking. This step is important to maintain structural integrity and improve the reliability of docking results. AutoDock Vina was employed as the primary molecular docking software due to its high accuracy, speed, and improved scoring function. It uses a gradient optimization method to efficiently predict ligand binding conformations and calculate binding affinities. AutoDock Vina generates multiple binding poses for each ligand and ranks them based on their predicted binding energies. Lower binding energy values indicate stronger and more stable interactions between the ligand and the target protein. This allowed for the selection of the most favorable binding conformations for further analysis. Avogadro, an advanced molecular editor and visualization tool, was used for building, editing, and optimizing ligand structures prior to docking. It provides an intuitive interface for molecular modeling and allows energy minimization using force field calculations. This ensured that all ligand structures were in their most stable conformations before being subjected to docking studies. Additionally, Avogadro facilitated structural visualization, aiding in the understanding of molecular geometry and interactions. Furthermore, essential biological and chemical data were retrieved from the National Center for Biotechnology Information (NCBI) database. NCBI resources were used to obtain protein sequences, structural information, and compound-related data necessary for the study. These databases provided reliable and curated information, ensuring the accuracy of the input data used in the analysis. Overall, the integration of these computational tools enabled a comprehensive and efficient docking workflow. The use of open-access software ensured cost-effectiveness while maintaining scientific rigor. The results obtained from this bioinformatics analysis provide valuable insights into the interaction of selected drug molecules with the target protein, supporting their potential application in PDAC treatment and further experimental validation. Results 4.1 In Silico Molecular Docking and Binding Affinity Analysis To evaluate the interaction between the screened candidates and their target proteins (PDB IDs: 6GDI and 7R8C), we performed systematic molecular docking simulations using AutoDock Vina. The binding affinity, expressed as Gibbs free energy (kcal/mol), served as the primary metric for determining lead potential. Among the drugs, A emerged as the lead compound, demonstrating a superior binding affinity of -7.011 kcal/mol against the target protein. This score represents a significant high-affinity interaction, surpassing the established threshold of -6.0 kcal/mol required for stable protein-ligand complexes. Comparative analysis showed that other candidates, such as B (-5.525 kcal/mol) and C (-5.089 kcal/mol), exhibited moderate binding, while compounds like D (-4.499 kcal/mol) showed significantly lower stability. Drug Name Vina Score (kcal/mol) RMSD l.b. RMSD u.b. Torsional Constraints A -7.011 0.000 0.000 8 B -5.525 0.000 0.000 6 C -5.089 0.000 0.000 5 D -4.499 0.000 0.000 4 Table (4.1) The docking results show that Drug A has the highest binding affinity with a Vina score of − 7.011 kcal/mol, indicating the most stable interaction with the target protein. Drugs B (− 5.525 kcal/mol), C (− 5.089 kcal/mol), and D (− 4.499 kcal/mol) show comparatively lower binding affinities. All compounds have RMSD values of 0.000, indicating stable and reliable docking poses. The torsional constraints suggest that Drug A has the highest flexibility, which may contribute to better binding. Overall, Drug A appears to be the most promising candidate for further study. Figure (4.1) Represents the molecular docking results of the selected compounds visualized using ViewDock in Chimera. Each panel displays the predicted binding conformations along with corresponding Vina scores, RMSD values, and torsional parameters. The results indicate multiple binding poses for each ligand, ranked based on binding affinity. Among the compounds, one shows a more favorable (more negative) binding energy compared to others, suggesting stronger interaction with the target protein. The RMSD values reflect the stability and similarity of the docking poses, while the variation in scores highlights differences in ligand binding efficiency. Overall, the figure provides a comparative visualization of docking performance across all tested compounds. 4.2 PDAC biology & Comparison with different studies The results of this study demonstrate that the selected repurposed drugs exhibit varying binding affinities toward the target protein, which is relevant to pancreatic ductal adenocarcinoma (PDAC) progression. Among the tested compounds, Drug A showed the highest binding affinity, suggesting a stronger and more stable interaction with the target protein. This finding is significant in the context of PDAC biology, as the disease is characterized by complex signaling pathways, genetic mutations (such as KRAS, TP53, and SMAD4), and high drug resistance. One of the major challenges in PDAC treatment is drug resistance, which is often mediated by mechanisms such as overexpression of ABC transporters (e.g., ABCB1, ABCG1), dense stromal environment, and altered cellular signalling pathways. The ability of Drug A to bind effectively to the target protein suggests that it may interfere with these pathways or overcome resistance mechanisms, potentially improving therapeutic outcomes. Previous studies have shown that drug repurposing is a promising strategy in PDAC due to its cost-effectiveness and reduced development time. Compounds such as metformin and imatinib have already been explored for their anti-proliferative and anti-tumor effects in pancreatic cancer models. The present findings are consistent with these studies, supporting the idea that existing drugs can target multiple pathways involved in PDAC progression. However, the variation in binding affinities observed in this study highlights the importance of selecting compounds with optimal interaction profiles. Discussion This study aimed to identify potential therapeutic candidates for pancreatic ductal adenocarcinoma (PDAC) using a bioinformatics-based drug repurposing approach. Molecular docking analysis was performed on four selected compounds to evaluate their binding affinity with the target protein. Among the tested drugs, Drug A demonstrated the highest binding affinity, indicating the most stable interaction, while the remaining compounds showed comparatively lower binding energies. These findings suggest that Drug A may serve as a promising candidate for further investigation in PDAC treatment. The study highlights the importance of computational approaches in accelerating drug discovery and identifying effective therapeutic options. Given the high drug resistance and poor prognosis associated with PDAC, such approaches are valuable in exploring alternative treatment strategies. Despite the promising results, this study has certain limitations. The findings are based solely on computational molecular docking and do not include experimental validation. The biological activity, toxicity, and pharmacokinetic properties of the compounds were not evaluated in vitro or in vivo. Therefore, further experimental studies are required to confirm the therapeutic potential of these compounds. Future studies should focus on validating these findings through in vitro cell line experiments and in vivo animal models. Additionally, investigating the effect of these compounds on PDAC-specific pathways and drug resistance mechanisms will provide deeper insights into their therapeutic potential. Abbreviations Sr. No. Abbreviation Full Form 1 PDAC Pancreatic Ductal Adenocarcinoma 2 PanIN Pancreatic Intraepithelial Neoplasia 3 ABC ATP-Binding Cassette 4 ABCB1 ATP-Binding Cassette Subfamily B Member 1 5 ABCG1 ATP-Binding Cassette Subfamily G Member 1 6 ATP Adenosine Triphosphate 7 PDB Protein Data Bank 8 NCBI National Center for Biotechnology Information 9 SDF Structure Data File 10 RMSD Root Mean Square Deviation 11 DNA Deoxyribonucleic Acid 12 RNA Ribonucleic Acid 13 mRNA Messenger Ribonucleic Acid 14 KRAS Kirsten Rat Sarcoma Viral Oncogene 15 TP53 Tumor Protein 53 16 CDKN2A Cyclin-Dependent Kinase Inhibitor 2A 17 SMAD4 SMAD Family Member 4 18 MLH1 MutL Homolog 1 19 MSH2 MutS Homolog 2 20 CSC Cancer Stem Cell 21 FDA Food and Drug Administration 22 XR Extended Release 23 DPP-4 Dipeptidyl Peptidase-4 24 SGLT2 Sodium-Glucose Cotransporter-2 25 CML Chronic Myeloid Leukemia 26 ALL Acute Lymphoblastic Leukemia 27 GIST Gastrointestinal Stromal Tumor 28 IFN-α Interferon Alpha 29 P-gp P-glycoprotein 30 MGL Tools Molecular Graphics Laboratory Tools 31 ADT AutoDock Tools 32 Vina AutoDock Vina 33 GUI Graphical User Interface 34 CID Compound Identification Number (PubChem) 35 IUPAC International Union of Pure and Applied Chemistry 36 kcal/mol Kilocalories per Mole 37 Å Angstrom 38 DOI Digital Object Identifier 39 SAR Structure–Activity Relationship 40 QSAR Quantitative Structure–Activity Relationship References M.L. 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Kench, Precursor lesions in pancreatic cancer: morphological and molecular pathology, Pathology 43 (2011) 183–200. L. Sleire, H.E. Førde, I.A. Netland, L. Leiss, B.S. Skeie, P.Ø. Enger, Drug repurposing in cancer, Pharmacol. Res. 124 (2017) 74–91. A. De Souza, K.I. Khawaja, F. Masud, M.W. Saif, Metformin and pancreatic cancer: Is there a role?, Cancer Chemother. Pharmacol. 77 (2016) 235–242. Q. Sun, Z. Hong, C. Zhang, L. Wang, Z. Han, D. Ma, Immune checkpoint therapy for solid tumours: clinical dilemmas and future trends, Signal Transduct. Target. Ther. 8 (2023) 320. C.S. Yabar, J.M. Winter, Pancreatic Cancer, Gastroenterol. Clin. North Am. 45 (2016) 429–445. X. Yang, L. Chen, Why has immune “checkpoint” therapy failed in most clinical trials?, J. Immunother. Cancer 13 (2025) e012457. K. Young, D.J. Hughes, D. Cunningham, N. Starling, Immunotherapy and pancreatic cancer: unique challenges and potential opportunities, Ther. Adv. Med. Oncol. 10 (2018). J. Zhang, C. Wolfgang, L. Zheng, Precision Immuno-Oncology: Prospects of Individualized Immunotherapy for Pancreatic Cancer, Cancers (Basel). 10 (2018) 39. Z. Zhang, L. Zhou, N. Xie, E.C. Nice, T. Zhang, Y. Cui, C. Huang, Overcoming cancer therapeutic bottleneck by drug repurposing, Signal Transduct. Target. Ther. 5 (2020) 113. Z. Zhang, L. Zhou, N. Xie, E.C. Nice, T. Zhang, Y. Cui, C. Huang, Overcoming cancer therapeutic bottleneck by drug repurposing, Signal Transduct. Target. Ther. 5 (2020) 113. Z. Zhang, L. Zhou, N. Xie, E.C. Nice, T. Zhang, Y. Cui, C. Huang, Overcoming cancer therapeutic bottleneck by drug repurposing, Signal Transduct. Target. Ther. 5 (2020) 113. FOLFIRINOX versus Gemcitabine for Metastatic Pancreatic Cancer, New England Journal of Medicine 365 (2011) 768–769. Additional Declarations The authors declare no competing interests. Supplementary Files floatimage1.png Graphical Abstract 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-9610484","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634334587,"identity":"39f1576c-501b-4938-8ff7-bf707794bf46","order_by":0,"name":"Durvesh Burhade","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDCCA3CSsQHEMuAHkQkFpGiRBFEJBkRpgQADAzAbjxa+G7nPJD78ucMg3364gZmn4p6x8fnViR8eGDDI84sdwKpF8ka6meTMtmcMBmcSgVrOFJuZ3Xi7WQLoMMOZsxOwajG4kcZszNtwmMFAgrGBObctwcbsxtkNIC0JBrfxaPnz5zCD/AyoFuMZZzf/IKCF8TED22EGhhsQLWYG/L3b8NoieeYZ48PetsM8IL8c/nMmwVjiBu82iwQDCZx+4TuexnDgx5/DcvLtxx8+nFGRYNjff3bzzR8VNvL80ti1wAAPiDgAZkqAVUrgVY4G+A+QonoUjIJRMApGAAAAjNhjkSBWkqYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0004-8898-9737","institution":"Department of Biotechnology, Parul Institute of Technology, Parul University","correspondingAuthor":true,"prefix":"","firstName":"Durvesh","middleName":"","lastName":"Burhade","suffix":""},{"id":634334588,"identity":"8a606281-fc1e-479b-b63b-4102835e02dd","order_by":1,"name":"Bhavya Mishra","email":"","orcid":"https://orcid.org/0009-0000-5310-8216","institution":"Department of Life Sciences, Parul Institute of Applied Sciences, Parul University","correspondingAuthor":false,"prefix":"","firstName":"Bhavya","middleName":"","lastName":"Mishra","suffix":""},{"id":634334589,"identity":"73fa4890-4129-4ff4-a136-9318979ac80e","order_by":2,"name":"Avantika Harinarayanan","email":"","orcid":"https://orcid.org/0009-0008-0071-6195","institution":"Department of Life Sciences, Parul Institute of Applied Sciences, Parul University","correspondingAuthor":false,"prefix":"","firstName":"Avantika","middleName":"","lastName":"Harinarayanan","suffix":""}],"badges":[],"createdAt":"2026-05-04 16:25:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9610484/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9610484/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108809968,"identity":"41bdb65f-a201-48db-954f-f606cf4e4b41","added_by":"auto","created_at":"2026-05-08 15:56:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111812,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure (1.1):\u003c/strong\u003e In an ideal anti-tumor immune response, blockade of the PD-1/PD-L1 immune checkpoint using PD-L1 inhibitors restores T-cell activity, enabling recognition of tumor antigens presented via MHC–CD8 interactions and ultimately leading to effective tumor cell apoptosis. This model represents the expected therapeutic outcome in many cancers, where immune suppression is primarily mediated through PD-1/PD-L1 signaling. However, in pancreatic ductal adenocarcinoma (PDAC), this ideal scenario often fails to translate into clinical success. Despite PD-L1 inhibition, T cells frequently remain unable to effectively recognize or eliminate tumor cells, highlighting the presence of additional immunosuppressive mechanisms and a highly resistant tumor microenvironment that limits the efficacy of checkpoint blockade therapies.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9610484/v1/660d0e9e82a37ce75971c84e.png"},{"id":108808099,"identity":"e5d23aba-05ac-48a6-a7d5-0186ce5555c9","added_by":"auto","created_at":"2026-05-08 15:39:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140720,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure (1.2):\u003c/strong\u003e key factors that make pancreatic ductal adenocarcinoma (PDAC) difficult to treat. Genetic\u003c/p\u003e\n\u003cp\u003einstability leads to continuous mutations, while high tumor diversity results in varied cancer cell populations within the same tumor. The presence of cancer stem cells enables self-renewal and resistance to therapy, contributing to tumor recurrence. Additionally, early metastasis and multiple resistance mechanisms further limit the effectiveness of current treatments. Together, these interconnected factors create a complex tumor environment that challenges successful therapeutic intervention.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9610484/v1/f3e75f0bcd221717f24064e3.png"},{"id":108808017,"identity":"bd4790e7-2218-4e85-92b0-bd92e1d956c2","added_by":"auto","created_at":"2026-05-08 15:38:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":388748,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure (1.3):\u003c/strong\u003e Mechanism of drug resistance in pancreatic ductal adenocarcinoma (PDAC) mediated by ATP-binding cassette (ABC) transporters. Chemotherapeutic agents and targeted inhibitors enter the cancer cell through the plasma membrane; however, overexpressed ABC transporters, including ABCB1 (P-glycoprotein) and ABCG1, actively pump these drugs out of the cell using ATP hydrolysis. This efflux process significantly reduces intracellular drug accumulation, thereby limiting therapeutic efficacy. The continuous removal of drugs leads to multidrug resistance (MDR), allowing cancer cells to survive despite treatment. Collectively, this mechanism represents a major barrier to effective PDAC therapy and contributes to poor clinical outcomes\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9610484/v1/2e2c0426420fbe58b12d4348.png"},{"id":108808102,"identity":"ebe769f3-540f-4519-8a44-b761ba8db291","added_by":"auto","created_at":"2026-05-08 15:39:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1404963,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9610484/v1/2c6e3c28c2c8f5ebc308415a.png"},{"id":108808106,"identity":"cd494299-49cb-44bd-9e2f-97c602a38c79","added_by":"auto","created_at":"2026-05-08 15:39:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":37459,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure (3.1):\u003c/strong\u003e Description Given in the pre-print Legend\u003c/p\u003e\n\u003cp\u003eThe physicochemical and pharmacological properties of the selected compounds were obtained from the PubChem database.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9610484/v1/20ecc8fbe2572902c128e244.png"},{"id":108807949,"identity":"eac29a68-0425-4fe1-ad9a-7b70e32489eb","added_by":"auto","created_at":"2026-05-08 15:38:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":289332,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure (3.2):\u003c/strong\u003e The above figure represents the three-dimensional structures of key ATP-binding cassette (ABC) transporter proteins involved in pancreatic ductal adenocarcinoma (PDAC). The structure on the left corresponds to ABCB1, while the structure on the right represents ABCG1. Both proteins are membrane-bound transporters composed of multiple transmembrane helices that form a channel-like structure.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9610484/v1/b4c159c045765960c13a6745.png"},{"id":108807950,"identity":"2d3279db-ac79-4418-899a-a038cb258135","added_by":"auto","created_at":"2026-05-08 15:38:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":319659,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure (4.1):\u003c/strong\u003e Represents the molecular docking results of the selected compounds visualized using ViewDock in Chimera. Each panel displays the predicted binding conformations along with corresponding Vina scores, RMSD values, and torsional parameters. The results indicate multiple binding poses for each ligand, ranked based on binding affinity. Among the compounds, one shows a more favorable (more negative) binding energy compared to others, suggesting stronger interaction with the target protein. The RMSD values reflect the stability and similarity of the docking poses, while the variation in scores highlights differences in ligand binding efficiency. Overall, the figure provides a comparative visualization of docking performance across all tested compounds.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9610484/v1/4a5021813df5a4b8928126b2.png"},{"id":108814721,"identity":"b411801d-f046-4f8f-ad81-249594b7d164","added_by":"auto","created_at":"2026-05-08 16:19:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3264868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9610484/v1/81a5a1ce-8bf9-4958-81e4-3cd511cadb4a.pdf"},{"id":108809924,"identity":"f2621fb3-299e-4954-a3e5-53a1eb0a9125","added_by":"auto","created_at":"2026-05-08 15:56:21","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":643859,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9610484/v1/c7461986219430cf44d025d0.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eTargeting Multidrug Resistance Transporters ABCB1 and ABCG2 in Pancreatic Ductal Adenocarcinoma: A Bioinformatics-Driven Drug Repurposing Study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e According to the Global Cancer Observatory 2022 report, the global burden of pancreatic cancer is high, with about 510,992 new cases reported worldwide. Among them Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, accounting for more than 90% of all pancreatic malignancies[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and is one of the most lethal diseases, with a 5-year survival rate of less than 10%[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This late-stage clinical presentation inherently precludes curative surgical intervention and complicates the management of systemic progression. Historically, the cornerstones of oncology, cytotoxic chemotherapy, surgical resection, and radiotherapy have served as the standard of care; however, these modalities have failed to demonstrate a substantial impact on overall survival (OS) or long-term prognosis. Consequently, the high mortality rates associated with these refractory malignancies underscore a critical, unmet need for innovative, targeted therapeutic paradigms. Despite the transformative success of immune checkpoint blockade (ICB) in various solid tumors, the clinical application of anti-CTLA-4, anti-PD-1, and anti-PD-L1 antibodies in pancreatic cancer has yielded largely suboptimal outcomes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure (1.1)\u003c/strong\u003e \u003cp\u003eIn an ideal anti-tumor immune response, blockade of the PD-1/PD-L1 immune checkpoint using PD-L1 inhibitors restores T-cell activity, enabling recognition of tumor antigens presented via MHC\u0026ndash;CD8 interactions and ultimately leading to effective tumor cell apoptosis. This model represents the expected therapeutic outcome in many cancers, where immune suppression is primarily mediated through PD-1/PD-L1 signaling. However, in pancreatic ductal adenocarcinoma (PDAC), this ideal scenario often fails to translate into clinical success. Despite PD-L1 inhibition, T cells frequently remain unable to effectively recognize or eliminate tumor cells, highlighting the presence of additional immunosuppressive mechanisms and a highly resistant tumor microenvironment that limits the efficacy of checkpoint blockade therapies.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eMultiple clinical trials have demonstrated that these monotherapies fail to elicit robust objective response rates, a phenomenon largely attributed to the highly immunosuppressive tumor microenvironment (TME) and low mutational burden characteristic of this malignancy[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The most common warning signs for people with this type of pancreatic cancer are losing weight without trying, feeling pain in the belly area, and having yellowing of the skin or eyes better known as jaundice[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Specifically, the emergence of new-onset type 2 diabetes mellitus is increasingly recognized as a potential paraneoplastic phenomenon and early harbinger of malignancy[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Furthermore, the presence of thromboembolic disease, often manifesting as deep vein thrombosis or pulmonary embolism, underscores the systemic hypercoagulable state frequently induced by advanced pancreatic neoplasia [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Surgery and chemotherapy (like gemcitabine and FOLFIRINOX, which is a mix of several drugs) can help patients with early-stage pancreatic cancer live longer. However, these treatments do not work well for patients whose cancer is in a late stage[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Pancreatic ductal adenocarcinoma (PDAC) has a poor outcome because it is a complex disease caused by many factors. It is difficult to detect early, and there are no easy screening methods. Most patients are diagnosed at a late stage because symptoms usually appear only after the cancer has already grown and spread to other parts of the body[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. As mentioned earlier, the best treatment for pancreatic ductal adenocarcinoma (PDAC) is surgery followed by chemotherapy. This works mainly for patients whose cancer has not spread to nearby blood vessels or surrounding organs. Treating pancreatic cancer is very difficult because of problems at both the genetic and cellular levels. PDAC tumors have many mutations, which makes their genes unstable. This instability helps the tumor grow and become resistant to treatment. Another challenge is that PDAC is very diverse, different patients have different mutations, and even within a single tumor, there can be many variations. Targeted therapies usually work well in cancers where most patients share the same mutation, such as EGFR in lung cancer or BRAF in melanoma. However, in pancreatic cancer, there are many different mutations, and each one is found only in a small number of patients. This makes it hard to design effective targeted treatments[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Because multiple signaling pathways are altered, PDAC can develop many different ways to resist treatment. Although the disease is not fully understood, some key gene mutations\u0026mdash;such as KRAS, CDKN2A (p16), TP53, and SMAD4, play an important role in tumor growth and treatment resistance. Another major issue is the presence of cancer stem cells (CSCs)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These cells make up only about 0.5\u0026ndash;1% of the tumor but are very powerful. They can renew themselves, survive harsh conditions, and resist chemotherapy. CSCs can restart tumor growth, leading to disease progression and relapse. Most current treatments do not effectively target these cells, which is one reason why therapies often fail. In addition, PDAC has a strong ability to spread (metastasize), which worsens patient outcomes. Studies have shown that even within a single tumor, there are different groups of cells (subclones) with varying abilities to spread and respond to treatment. Moreover, PDAC can spread very early, even before it is detected[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This early spread reduces the effectiveness of local treatments like surgery and radiation.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure (1.2)\u003c/strong\u003e \u003cp\u003ekey factors that make pancreatic ductal adenocarcinoma (PDAC) difficult to treat. Genetic\u003c/p\u003e \u003c/p\u003e \u003cp\u003einstability leads to continuous mutations, while high tumor diversity results in varied cancer cell populations within the same tumor. The presence of cancer stem cells enables self-renewal and resistance to therapy, contributing to tumor recurrence. Additionally, early metastasis and multiple resistance mechanisms further limit the effectiveness of current treatments. Together, these interconnected factors create a complex tumor environment that challenges successful therapeutic intervention.\u003c/p\u003e \u003cp\u003ePancreatic ductal adenocarcinoma (PDAC) remains an exceptionally lethal malignancy characterized by a dismal prognosis, largely driven by a lack of reliable early-detection biomarkers, late-stage clinical presentation, and high metastatic potential[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure (1.3)\u003c/strong\u003e \u003cp\u003eMechanism of drug resistance in pancreatic ductal adenocarcinoma (PDAC) mediated by ATP-binding cassette (ABC) transporters. Chemotherapeutic agents and targeted inhibitors enter the cancer cell through the plasma membrane; however, overexpressed ABC transporters, including ABCB1 (P-glycoprotein) and ABCG1, actively pump these drugs out of the cell using ATP hydrolysis. This efflux process significantly reduces intracellular drug accumulation, thereby limiting therapeutic efficacy. The continuous removal of drugs leads to multidrug resistance (MDR), allowing cancer cells to survive despite treatment. Collectively, this mechanism represents a major barrier to effective PDAC therapy and contributes to poor clinical outcomes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003ePancreatic ductal adenocarcinoma (PDAC) is well known for its strong resistance to chemotherapy, which is a major reason for poor treatment outcomes. One of the key mechanisms behind this drug resistance is the overexpression of specific membrane proteins known as ATP-binding cassette (ABC) transporters[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Among these, proteins such as ABCB1 (also called P-glycoprotein) and ABCG1 play a particularly important role. These transporters act as active efflux pumps, meaning they use cellular energy (ATP) to push anticancer drugs out of the cancer cells[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Under normal conditions, ABC transporters help protect cells by removing toxic substances. However, in PDAC, these proteins are often overexpressed, which becomes a major problem during treatment[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. When chemotherapy drugs enter the cancer cell, ABC transporters quickly recognize and expel them before they can reach their target and exert their therapeutic effect. As a result, the intracellular concentration of the drug remains too low to effectively kill the cancer cells[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This continuous efflux of drugs leads to what is known as multidrug resistance (MDR), where cancer cells become resistant not just to one drug but to multiple structurally and functionally different drugs. ABCB1 is one of the most studied transporters in this context and is strongly associated with reduced drug accumulation and decreased treatment efficacy. Similarly, ABCG1 and other members of the ABC transporter family also contribute to this resistance by enhancing the cell\u0026rsquo;s ability to eliminate therapeutic agents. Overall, the overexpression of ABC transporters significantly limits the success of chemotherapy in PDAC[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. By actively pumping drugs out of the cells, these proteins reduce drug effectiveness and allow cancer cells to survive and continue growing. This makes PDAC particularly difficult to treat and highlights the need for new therapeutic strategies that can overcome or bypass this drug efflux mechanism.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eDrug Repurposing: An Cost efficiency \u0026amp; friendly Approach\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eThe development of new drugs is an extremely expensive and time-consuming process[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. It is estimated that bringing a novel drug to market requires an investment of approximately USD 2\u0026ndash;3 billion[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In contrast, drug repurposing or modification of existing drugs is significantly more cost-effective, with an estimated cost of around USD 300\u0026nbsp;million. This substantial difference highlights the importance of drug repurposing as a cost-efficient strategy in the pharmaceutical industry, where financial considerations play a critical role. In addition to cost, the timeline for de novo drug development is considerably long. The process typically begins with basic research, drug discovery, and early development stages, which may take approximately 3\u0026ndash;5 years[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This is followed by preclinical studies, generally lasting around 4 years. Subsequently, the drug must undergo three phases of clinical trials (Phase I, II, and III), which together may take up to 10 years depending on factors such as sample size and study design. Finally, regulatory approval, intellectual property rights (IPR), and patent processes require an additional 1\u0026ndash;2 years. Overall, the complete drug development pipeline may take approximately 22\u0026ndash;25 years from initial discovery to market approval[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Despite the extensive time, financial investment, and effort involved, the success rate of drug development remains low. It is estimated that less than 5% of candidate drugs ultimately receive approval from regulatory authorities such as the FDA[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These challenges further emphasize the need for alternative approaches, such as drug repurposing, to improve efficiency and reduce the cost and time associated with drug development.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage of Drug Development\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProcess Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimated Time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimated Cost Contribution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic Research \u0026amp; Discovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget identification, validation, lead discovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreclinical Studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn vitro \u0026amp; in vivo testing, toxicity studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;4 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Trial \u0026ndash; Phase I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSafety testing in small group of healthy individuals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;2 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Trial \u0026ndash; Phase II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEfficacy and dose optimization in patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026ndash;3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Trial \u0026ndash; Phase III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge-scale validation of safety and efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulatory Approval \u0026amp; Patents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFDA approval, IPR, patents, commercialization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;2 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal (De Novo Drug Development)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComplete process from discovery to market\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e22\u0026ndash;25 years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e~USD 2\u0026ndash;3 Billion\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\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable\u0026nbsp;(1.1)\u003c/strong\u003e \u003cp\u003eThe table summarizes the timeline and cost involved in drug development. De novo drug development is a lengthy process, taking approximately 22\u0026ndash;25 years and costing around USD 2\u0026ndash;3\u0026nbsp;billion, including stages such as discovery, preclinical studies, clinical trials, and regulatory approval. Despite this, the success rate remains very low. In contrast, drug repurposing significantly reduces both time (3\u0026ndash;6 years) and cost (~\u0026thinsp;USD 300\u0026nbsp;million), making it a faster, more cost-effective approach for developing new therapies.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ePancreatic ductal adenocarcinoma \u0026amp; its Key Mutation\u003c/span\u003e\u003c/h2\u003e \u003cp\u003e Pancreatic ductal adenocarcinoma (PDAC) develops through a well-defined adenoma-to-carcinoma progression model, characterized by a stepwise transition from noninvasive preneoplastic lesions to invasive malignancy. This multistep progression highlights the gradual nature of PDAC development and the critical role of sequential genetic alterations in driving tumorigenesis[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Pancreatic intraepithelial neoplasms (PanINs) are the most common precursor lesions of pancreatic ductal adenocarcinoma (PDAC) and frequently contain mutations in key genes such as KRAS, CDKN2A, TP53, and SMAD4[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Traditionally, PDAC development has been explained by the gradual accumulation of these mutations over time. These genetic alterations may occur simultaneously rather than sequentially, possibly due to large-scale genomic events such as chromothripsis. Despite these findings, it is well established that more than 90% of pancreatic cancers carry mutations in the KRAS gene. In addition, inactivating mutations in tumor suppressor genes are very common, particularly in CDKN2A (80\u0026ndash;95%), TP53 (50\u0026ndash;70%), and SMAD4 (\u0026gt;\u0026thinsp;50%), and these alterations may occur together[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, somatic mutations in DNA mismatch repair genes, such as MLH1 and MSH2, have also been reported in PDAC.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eInitial Screening of drugs\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eLiterature survey of cancer databases and journals help us to find these drugs, this category includes anti-diabetic, anti-helminthic, antifibrotic, inducing cell death, lipid metabolism inhibitors, etc.\u003c/p\u003e\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;(3.1)\u003c/b\u003e: The flowchart outlines a systematic drug screening pipeline for PDAC, narrowing 162 initial candidates down to 4 final leads. Identification: 162 potential drugs were gathered from literature, databases \u0026amp; Conferences; 132 were immediately excluded for lacking potency against pancreatic cancer cells. Screening: The remaining 22 drugs were tested for stability and efficacy. 18 were removed for failing these criteria, leaving a diverse group of candidates including anti-diabetics (4), anti-fibrotics (5), and repurposed anti-infectives.-\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eRetrieval of chemical structures of drugs\u003c/span\u003e\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure (3.1)\u003c/strong\u003e \u003cp\u003eDescription Given in the pre-print Legend\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe physicochemical and pharmacological properties of the selected compounds were obtained from the PubChem database.\u003c/p\u003e \u003cp\u003eMetformin (CID: 4091), with the IUPAC name \u003cem\u003e3-(diaminomethylidene)-1,1-dimethylguanidine\u003c/em\u003e, has a molecular weight of 129.16 g/mol. It is widely used as an antidiabetic agent and is indicated as an adjunct to diet and exercise for improving glycemic control in patients with type 2 diabetes mellitus. It is available in immediate-release and extended-release formulations and is also used in combination therapies with other antidiabetic agents, including DPP-4 inhibitors, SGLT2 inhibitors, and pioglitazone.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"ExternalRef\"\u003e \u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/compound/Metformin\u003c/span\u003e \u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/compound/Metformin\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eStatine (CID: 123915), with the IUPAC name \u003cem\u003e(3S,4S)-4-amino-3-hydroxy-6-methylheptanoic acid\u003c/em\u003e, has a molecular weight of 175.23 g/mol. It is primarily used for regulating lipid metabolism and reducing cholesterol levels.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"ExternalRef\"\u003e \u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/compound/Statine\u003c/span\u003e \u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/compound/Statine\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eChloroquine (CID: 2719), with the IUPAC name \u003cem\u003e4-N-(7-chloroquinolin-4-yl)-1-N,1-N-diethylpentane-1,4-diamine\u003c/em\u003e, has a molecular weight of 319.9 g/mol. It is indicated for the treatment of infections caused by \u003cem\u003ePlasmodium\u003c/em\u003e species, including \u003cem\u003eP. vivax\u003c/em\u003e, \u003cem\u003eP. malariae\u003c/em\u003e, \u003cem\u003eP. ovale\u003c/em\u003e, and susceptible strains of \u003cem\u003eP. falciparum\u003c/em\u003e. Additionally, it is used for treating extraintestinal amebiasis and has off-label applications in rheumatic diseases, as well as investigational roles in viral infections.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"ExternalRef\"\u003e \u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/compound/Chloroquine\u003c/span\u003e \u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/compound/Chloroquine\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eImatinib (CID: 5291), with the IUPAC name \u003cem\u003e4-[(4-methylpiperazin-1-yl)methyl]-N-[4-methyl-3-[(4-pyridin-3-ylpyrimidin-2-yl)amino]phenyl]benzamide\u003c/em\u003e, has a molecular weight of 493.6 g/mol. It is a targeted anticancer agent indicated for the treatment of chronic myeloid leukemia (CML) with Philadelphia chromosome positivity, acute lymphoblastic leukemia (ALL), gastrointestinal stromal tumors (GIST), and several other hematological and solid malignancies.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"ExternalRef\"\u003e \u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/compound/Imatinib\u003c/span\u003e \u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/compound/Imatinib\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.3 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSelection of cancer protein (Biomarker)\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eThe selection of target proteins was carried out through an extensive literature survey, which revealed that members of the ATP-binding cassette (ABC) transporter family play a crucial role in pancreatic ductal adenocarcinoma (PDAC). In particular, ABCB1 and ABCG1 were identified as highly expressed proteins in PDAC tissues. These proteins are membrane-bound transporters that actively pump a wide range of drugs out of cancer cells using ATP energy. This process, known as drug efflux, significantly reduces the intracellular concentration of chemotherapeutic agents and contributes to the development of multidrug resistance (MDR). As a result, PDAC is considered highly drug-resistant, largely due to the overexpression of efflux transporters such as ABCB1 and ABCG1, which limit the effectiveness of standard treatments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure (3.2)\u003c/strong\u003e \u003cp\u003eThe above figure represents the three-dimensional structures of key ATP-binding cassette (ABC) transporter proteins involved in pancreatic ductal adenocarcinoma (PDAC). The structure on the left corresponds to ABCB1, while the structure on the right represents ABCG1. Both proteins are membrane-bound transporters composed of multiple transmembrane helices that form a channel-like structure.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe provided figure illustrates the complex structural architecture and dynamic movement of ABCB1 (P-glycoprotein), a membrane protein that functions as an ATP-driven pump to expel toxins and drugs from cells. In Panel A, we see the overall \"inward-facing\" conformation of the protein, which consists of two homologous halves (shaded in different reds) embedded within the plasma membrane. The gray transparent surfaces represent lipid molecules from the membrane interacting with the protein, while the Nucleotide-Binding Domains (NBDs) at the base serve as the engine room where ATP is processed to power the pump. Panel B focuses on the structural core of the transport mechanism, specifically a 3-helix bundle formed by Transmembrane helices TM4, TM6, and TM12. The figure shows that TM4 is not a single rigid rod but is split into sub-segments (\u003cspan\u003e$\u003c/span\u003e4a, 4b, 4c\u003cspan\u003e$\u003c/span\u003e), allowing for hinge-like flexibility. The yellow dashed triangles in the \"Top\" and \"Bottom\" views highlight how these specific helices pack together to form the central pathway for drug transport. This arrangement is vital because the shifting of these specific bundles is what creates the opening and closing \"gates\" that move molecules across the lipid bilayer.\u003c/p\u003e \u003cp\u003eFinally, Panel C provides a direct comparison between the experimentally determined cryo-EM structure (solid red) and the AlphaFold-predicted structure (transparent cartoon). The black arrows are the most significant part of this panel, as they indicate the large-scale conformational changes or \"tilts\" that the helices undergo. By overlaying these two models, the figure demonstrates that ABCB1 is a highly flexible machine; the discrepancy between the prediction and the experiment reveals the physical \"stroke\" of the pump. Essentially, the figure tells us that transport is achieved through the coordinated sliding and pivoting of these transmembrane helices, moving the protein from one state to another to physically push a substrate out of the cell.\u003c/p\u003e \u003cp\u003eTo further investigate their structural and functional properties, the three-dimensional structures of these proteins were retrieved from the Protein Data Bank (PDB). The structure of ABCB1 was obtained with PDB ID: 6GDI (pdb_00006gdi), and additional structural information is available under DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2210/pdb9CR8/pdb\u003c/span\u003e\u003cspan address=\"10.2210/pdb9CR8/pdb\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Similarly, the structure of ABCG1 was retrieved with PDB ID: 7R8C (pdb_00007r8c), with its corresponding DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2210/pdb7R8C/pdb\u003c/span\u003e\u003cspan address=\"10.2210/pdb7R8C/pdb\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. These structures were used for further molecular docking and interaction analysis studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.4 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBIOINFORMATICS ANALYSIS\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eFollowing the identification and validation of relevant protein biomarkers, the study progressed to the final stage of bioinformatics analysis, which involved molecular docking of the selected compounds. Molecular docking was performed to evaluate the binding interactions, stability, and affinity of the four selected drug molecules with the target protein. This step is critical in understanding the potential of these compounds as therapeutic candidates against pancreatic ductal adenocarcinoma (PDAC). The docking results provided insights into the binding modes, interaction energies, and possible inhibitory mechanisms of the selected ligands.\u003c/p\u003e \u003cp\u003eAll computational analyses in this study were conducted using open-access and widely accepted bioinformatics tools to ensure reproducibility and accessibility. Initially, MGL Tools (Molecular Graphics Laboratory Tools) were used for the preparation of both protein and ligand structures. This included essential preprocessing steps such as the removal of water molecules, addition of polar hydrogen atoms, assignment of partial charges, and conversion of file formats into PDBQT, which is required for docking studies. MGL Tools also facilitated the visualization of protein structures and the identification of active binding sites, which are crucial for accurate docking simulations.\u003c/p\u003e \u003cp\u003eOpen Babel, an open-source chemical toolbox, was utilized for the conversion and management of chemical file formats. Since ligand structures were obtained in SDF format, Open Babel was used to convert them into compatible formats such as PDB and PDBQT. Additionally, it aided in geometry optimization and ensured that the ligand structures were properly formatted and energetically favorable before docking. This step is important to maintain structural integrity and improve the reliability of docking results.\u003c/p\u003e \u003cp\u003eAutoDock Vina was employed as the primary molecular docking software due to its high accuracy, speed, and improved scoring function. It uses a gradient optimization method to efficiently predict ligand binding conformations and calculate binding affinities. AutoDock Vina generates multiple binding poses for each ligand and ranks them based on their predicted binding energies. Lower binding energy values indicate stronger and more stable interactions between the ligand and the target protein. This allowed for the selection of the most favorable binding conformations for further analysis.\u003c/p\u003e \u003cp\u003eAvogadro, an advanced molecular editor and visualization tool, was used for building, editing, and optimizing ligand structures prior to docking. It provides an intuitive interface for molecular modeling and allows energy minimization using force field calculations. This ensured that all ligand structures were in their most stable conformations before being subjected to docking studies. Additionally, Avogadro facilitated structural visualization, aiding in the understanding of molecular geometry and interactions.\u003c/p\u003e \u003cp\u003eFurthermore, essential biological and chemical data were retrieved from the National Center for Biotechnology Information (NCBI) database. NCBI resources were used to obtain protein sequences, structural information, and compound-related data necessary for the study. These databases provided reliable and curated information, ensuring the accuracy of the input data used in the analysis.\u003c/p\u003e \u003cp\u003eOverall, the integration of these computational tools enabled a comprehensive and efficient docking workflow. The use of open-access software ensured cost-effectiveness while maintaining scientific rigor. The results obtained from this bioinformatics analysis provide valuable insights into the interaction of selected drug molecules with the target protein, supporting their potential application in PDAC treatment and further experimental validation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eIn Silico Molecular Docking and Binding Affinity Analysis\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eTo evaluate the interaction between the screened candidates and their target proteins (PDB IDs: 6GDI and 7R8C), we performed systematic molecular docking simulations using AutoDock Vina. The binding affinity, expressed as Gibbs free energy (kcal/mol), served as the primary metric for determining lead potential.\u003c/p\u003e \u003cp\u003eAmong the drugs, A emerged as the lead compound, demonstrating a superior binding affinity of -7.011 kcal/mol against the target protein. This score represents a significant high-affinity interaction, surpassing the established threshold of -6.0 kcal/mol required for stable protein-ligand complexes.\u003c/p\u003e \u003cp\u003eComparative analysis showed that other candidates, such as B (-5.525 kcal/mol) and C (-5.089 kcal/mol), exhibited moderate binding, while compounds like D (-4.499 kcal/mol) showed significantly lower stability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVina Score (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSD l.b.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSD u.b.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTorsional Constraints\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-7.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable\u0026nbsp;(4.1)\u003c/strong\u003e \u003cp\u003eThe docking results show that Drug A has the highest binding affinity with a Vina score of \u0026minus;\u0026thinsp;7.011 kcal/mol, indicating the most stable interaction with the target protein. Drugs B (\u0026minus;\u0026thinsp;5.525 kcal/mol), C (\u0026minus;\u0026thinsp;5.089 kcal/mol), and D (\u0026minus;\u0026thinsp;4.499 kcal/mol) show comparatively lower binding affinities. All compounds have RMSD values of 0.000, indicating stable and reliable docking poses. The torsional constraints suggest that Drug A has the highest flexibility, which may contribute to better binding. Overall, Drug A appears to be the most promising candidate for further study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure (4.1)\u003c/strong\u003e \u003cp\u003eRepresents the molecular docking results of the selected compounds visualized using ViewDock in Chimera. Each panel displays the predicted binding conformations along with corresponding Vina scores, RMSD values, and torsional parameters. The results indicate multiple binding poses for each ligand, ranked based on binding affinity. Among the compounds, one shows a more favorable (more negative) binding energy compared to others, suggesting stronger interaction with the target protein. The RMSD values reflect the stability and similarity of the docking poses, while the variation in scores highlights differences in ligand binding efficiency. Overall, the figure provides a comparative visualization of docking performance across all tested compounds.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ePDAC biology \u0026amp; Comparison with different studies\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eThe results of this study demonstrate that the selected repurposed drugs exhibit varying binding affinities toward the target protein, which is relevant to pancreatic ductal adenocarcinoma (PDAC) progression. Among the tested compounds, Drug A showed the highest binding affinity, suggesting a stronger and more stable interaction with the target protein. This finding is significant in the context of PDAC biology, as the disease is characterized by complex signaling pathways, genetic mutations (such as KRAS, TP53, and SMAD4), and high drug resistance.\u003c/p\u003e \u003cp\u003eOne of the major challenges in PDAC treatment is drug resistance, which is often mediated by mechanisms such as overexpression of ABC transporters (e.g., ABCB1, ABCG1), dense stromal environment, and altered cellular signalling pathways. The ability of Drug A to bind effectively to the target protein suggests that it may interfere with these pathways or overcome resistance mechanisms, potentially improving therapeutic outcomes.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that drug repurposing is a promising strategy in PDAC due to its cost-effectiveness and reduced development time. Compounds such as metformin and imatinib have already been explored for their anti-proliferative and anti-tumor effects in pancreatic cancer models. The present findings are consistent with these studies, supporting the idea that existing drugs can target multiple pathways involved in PDAC progression. However, the variation in binding affinities observed in this study highlights the importance of selecting compounds with optimal interaction profiles.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to identify potential therapeutic candidates for pancreatic ductal adenocarcinoma (PDAC) using a bioinformatics-based drug repurposing approach. Molecular docking analysis was performed on four selected compounds to evaluate their binding affinity with the target protein. Among the tested drugs, Drug A demonstrated the highest binding affinity, indicating the most stable interaction, while the remaining compounds showed comparatively lower binding energies. These findings suggest that Drug A may serve as a promising candidate for further investigation in PDAC treatment. The study highlights the importance of computational approaches in accelerating drug discovery and identifying effective therapeutic options. Given the high drug resistance and poor prognosis associated with PDAC, such approaches are valuable in exploring alternative treatment strategies. Despite the promising results, this study has certain limitations. The findings are based solely on computational molecular docking and do not include experimental validation. The biological activity, toxicity, and pharmacokinetic properties of the compounds were not evaluated in vitro or in vivo. Therefore, further experimental studies are required to confirm the therapeutic potential of these compounds. Future studies should focus on validating these findings through in vitro cell line experiments and in vivo animal models. Additionally, investigating the effect of these compounds on PDAC-specific pathways and drug resistance mechanisms will provide deeper insights into their therapeutic potential.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSr. No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull Form\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePDAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePancreatic Ductal Adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePanIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePancreatic Intraepithelial Neoplasia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eABC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eATP-Binding Cassette\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eABCB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eATP-Binding Cassette Subfamily B Member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eABCG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eATP-Binding Cassette Subfamily G Member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eATP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdenosine Triphosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProtein Data Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNCBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNational Center for Biotechnology Information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStructure Data File\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRMSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRoot Mean Square Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDeoxyribonucleic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRibonucleic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMessenger Ribonucleic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKRAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKirsten Rat Sarcoma Viral Oncogene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTumor Protein 53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCDKN2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCyclin-Dependent Kinase Inhibitor 2A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSMAD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSMAD Family Member 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMLH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMutL Homolog 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMSH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMutS Homolog 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer Stem Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFood and Drug Administration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eXR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtended Release\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDPP-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDipeptidyl Peptidase-4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSGLT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSodium-Glucose Cotransporter-2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChronic Myeloid Leukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAcute Lymphoblastic Leukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGastrointestinal Stromal Tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIFN-\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterferon Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP-gp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP-glycoprotein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMGL Tools\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMolecular Graphics Laboratory Tools\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eADT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAutoDock Tools\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAutoDock Vina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGUI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGraphical User Interface\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCompound Identification Number (PubChem)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIUPAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInternational Union of Pure and Applied Chemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ekcal/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKilocalories per Mole\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026Aring;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAngstrom\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e38\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDOI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDigital Object Identifier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStructure\u0026ndash;Activity Relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQSAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuantitative Structure\u0026ndash;Activity Relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eM.L. Aliru, J.E. Schoenhals, B.P. Venkatesulu, C.C. Anderson, H.B. Barsoumian, A.I. Younes, L.S. K Mahadevan, M. Soeung, K.E. Aziz, J.W. Welsh, S. Krishnan, Radiation Therapy and Immunotherapy: What is the Optimal Timing or Sequencing?, Immunotherapy 10 (2018) 299\u0026ndash;316.\u003c/li\u003e\n\u003cli\u003eA. Amedei, E. Niccolai, M. Benagiano, C. Della Bella, F. Cianchi, P. Bechi, A. Taddei, L. Bencini, M. Farsi, P. Cappello, D. Prisco, F. Novelli, M.M. D\u0026rsquo;Elios, Ex vivo analysis of pancreatic cancer-infiltrating T lymphocytes reveals that ENO-specific Tregs accumulate in tumor tissue and inhibit Th1/Th17 effector cell functions, Cancer Immunology, Immunotherapy 62 (2013) 1249\u0026ndash;1260.\u003c/li\u003e\n\u003cli\u003eY. Binenbaum, S. Na\u0026rsquo;ara, Z. Gil, Gemcitabine resistance in pancreatic ductal adenocarcinoma, Drug Resistance Updates 23 (2015) 55\u0026ndash;68.\u003c/li\u003e\n\u003cli\u003eX. Cheng, J.Y. Kim, S. Ghafoory, T. Duvaci, R. Rafiee, J. Theobald, H. Alborzinia, P. Holenya, J. Fredebohm, K.-H. Merz, A. Mehrabi, M. Hafezi, A. Saffari, G. Eisenbrand, J.D. Hoheisel, S. W\u0026ouml;lfl, Methylisoindigo preferentially kills cancer stem cells by interfering cell metabolism via inhibition of LKB1 and activation of AMPK in PDACs, Mol. Oncol. 10 (2016) 806\u0026ndash;824.\u003c/li\u003e\n\u003cli\u003eJ. Dinić, T. Efferth, A.T. Garc\u0026iacute;a-Sosa, J. Grahovac, J.M. Padr\u0026oacute;n, I. Pajeva, F. Rizzolio, S. Saponara, G. Spengler, I. Tsakovska, Repurposing old drugs to fight multidrug resistant cancers, Drug Resistance Updates 52 (2020) 100713.\u003c/li\u003e\n\u003cli\u003eM. Ducreux, A.Sa. Cuhna, C. Caramella, A. Hollebecque, P. Burtin, D. Go\u0026eacute;r\u0026eacute;, T. Seufferlein, K. Haustermans, J.L. Van Laethem, T. Conroy, D. Arnold, Cancer of the pancreas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up, Annals of Oncology 26 (2015) v56\u0026ndash;v68.\u003c/li\u003e\n\u003cli\u003eM. Ducreux, A.Sa. Cuhna, C. Caramella, A. Hollebecque, P. Burtin, D. Go\u0026eacute;r\u0026eacute;, T. Seufferlein, K. Haustermans, J.L. Van Laethem, T. Conroy, D. Arnold, Cancer of the pancreas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up, Annals of Oncology 26 (2015) v56\u0026ndash;v68.\u003c/li\u003e\n\u003cli\u003eM. Erkan, S. Hausmann, C.W. Michalski, A.A. Fingerle, M. Dobritz, J. Kleeff, H. Friess, The role of stroma in pancreatic cancer: diagnostic and therapeutic implications, Nat. Rev. Gastroenterol. Hepatol. 9 (2012) 454\u0026ndash;467.\u003c/li\u003e\n\u003cli\u003eJ. Gao, L.Z. Shi, H. Zhao, J. Chen, L. Xiong, Q. He, T. Chen, J. Roszik, C. Bernatchez, S.E. Woodman, P.-L. Chen, P. Hwu, J.P. Allison, A. Futreal, J.A. Wargo, P. Sharma, Loss of IFN-\u0026gamma; Pathway Genes in Tumor Cells as a Mechanism of Resistance to Anti-CTLA-4 Therapy, Cell 167 (2016) 397-404.e9.\u003c/li\u003e\n\u003cli\u003eR. Gupta, I. Amanam, V. 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V Bazhin, Characterization of myeloid leukocytes and soluble mediators in pancreatic cancer: importance of myeloid-derived suppressor cells, Oncoimmunology 4 (2015) e998519.\u003c/li\u003e\n\u003cli\u003eA.A. Khorana, Cancer and coagulation, Am. J. Hematol. 87 (2012).\u003c/li\u003e\n\u003cli\u003eJ. Kleeff, M. Korc, M. Apte, C. La Vecchia, C.D. Johnson, A. V. Biankin, R.E. Neale, M. Tempero, D.A. Tuveson, R.H. Hruban, J.P. Neoptolemos, Pancreatic cancer, Nat. Rev. Dis. Primers 2 (2016) 16022.\u003c/li\u003e\n\u003cli\u003eQ. Liu, Y. Li, Z. Niu, Y. Zong, M. Wang, L. Yao, Z. Lu, Q. Liao, Y. Zhao, Atorvastatin (Lipitor) attenuates the effects of aspirin on pancreatic cancerogenesis and the chemotherapeutic efficacy of gemcitabine on pancreatic cancer by promoting M2 polarized tumor associated macrophages, Journal of Experimental \u0026amp; Clinical Cancer Research 35 (2016) 33.\u003c/li\u003e\n\u003cli\u003eA. Maitra, N.V. Adsay, P. Argani, C. Iacobuzio-Donahue, A. De Marzo, J.L. 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Ther. 5 (2020) 113.\u003c/li\u003e\n\u003cli\u003eFOLFIRINOX versus Gemcitabine for Metastatic Pancreatic Cancer, New England Journal of Medicine 365 (2011) 768\u0026ndash;769.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Parul University","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":"Pancreatic ductal adenocarcinoma (PDAC), Drug repurposing, Molecular docking, Auto Dock Vina, ABC transporters ","lastPublishedDoi":"10.21203/rs.3.rs-9610484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9610484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to identify potential therapeutic candidates for pancreatic ductal adenocarcinoma (PDAC) using a bioinformatics-based drug repurposing approach. Molecular docking analysis was performed on four selected compounds to evaluate their binding affinity with the target protein. Among the tested drugs, Drug A demonstrated the highest binding affinity, indicating the most stable interaction, while the remaining compounds showed comparatively lower binding energies. These findings suggest that Drug A may serve as a promising candidate for further investigation in PDAC treatment. The study highlights the importance of computational approaches in accelerating drug discovery and identifying effective therapeutic options. Given the high drug resistance and poor prognosis associated with PDAC, such approaches are valuable in exploring alternative treatment strategies. Despite the promising results, this study has certain limitations. The findings are based solely on computational molecular docking and do not include experimental validation. The biological activity, toxicity, and pharmacokinetic properties of the compounds were not evaluated in vitro or in vivo. Therefore, further experimental studies are required to confirm the therapeutic potential of these compounds. Future studies should focus on validating these findings through in vitro cell line experiments and in vivo animal models. Additionally, investigating the effect of these compounds on PDAC-specific pathways and drug resistance mechanisms will provide deeper insights into their therapeutic potential.\u003c/p\u003e","manuscriptTitle":"Targeting Multidrug Resistance Transporters ABCB1 and ABCG2 in Pancreatic Ductal Adenocarcinoma: A Bioinformatics-Driven Drug Repurposing Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 08:12:37","doi":"10.21203/rs.3.rs-9610484/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":"4dcccbea-95b7-4822-9ba3-fdda74a50793","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67508333,"name":"Cancer Biology"},{"id":67508334,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2026-05-06T08:12:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 08:12:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9610484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9610484","identity":"rs-9610484","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}