Drug-repurposing Approach Research Articles

A High-Throughput Drug Repurposing Strategy to Treat TBX2 and/or TBX3 Dependent Cancers.

The highly homologous T-box transcription factors TBX2 and TBX3 are critical for embryonic development, and their overexpression in postnatal tissues contributes to a wide range of malignancies, including melanoma and rhabdomyosarcoma. Importantly, when TBX2 and TBX3 are depleted in cancers where they are overexpressed, the malignant phenotype is inhibited, and they have therefore been regarded as druggable targets. However, the time and costs associated with de novo drug development are challenging and result in drugs that are costly, especially for patients in low- and middle-income countries. In the current study, we therefore combined a targeted and drug repurposing approach to identify drugs that are expected to be more efficacious and cost-effective with significantly reduced side effects. A high-throughput cell-based immunofluorescence screen was performed to identify drugs in the Pharmakon 1600 drug library that can negatively regulate TBX2 and/or TBX3 levels. "Hit" drugs were validated for their effect on TBX2/TBX3 levels and cytotoxicity in TBX2/TBX3-dependent melanoma and rhabdomyosarcoma cells. To this end, immunofluorescence, western blotting, quantitative real-time PCR, and MTT cell viability assays were performed. Niclosamide, piroctone olamine, and pyrvinium pamoate, were identified as TBX2 and/or TBX3-targeting drugs, and they exhibited cytotoxicity in a TBX2/TBX3-dependent manner. Furthermore, these "Hit" drugs were shown to induce senescence and/or apoptosis. Niclosamide, piroctone olamine, and pyrvinium pamoate are promising, cost-effective therapeutic agents for the treatment of TBX2/TBX3-dependent cancers.

Antimicrobial activities of Diltiazem Hydrochloride: drug repurposing approach

Background The growing concern of antibiotic-resistant microbial strains worldwide has prompted the need for alternative methods to combat microbial resistance. Biofilm formation poses a significant challenge to antibiotic efficiency due to the difficulty of penetrating antibiotics through the sticky microbial aggregates. Drug repurposing is an innovative technique that aims to expand the use of non-antibiotic medications to address this issue. The primary objective of this study was to evaluate the antimicrobial properties of Diltiazem HCl, a 1,5-benzothiazepine Ca2+ channel blocker commonly used as an antihypertensive agent, against four pathogenic bacteria and three pathogenic yeasts, as well as its antiviral activity against the Coxsackie B4 virus (CoxB4). Methods To assess the antifungal and antibacterial activities of Diltiazem HCl, the well diffusion method was employed, while crystal violet staining was used to determine the anti-biofilm activity. The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) colorimetric assay was utilized to evaluate the antiviral activity of Diltiazem HCl against the CoxB4 virus. Results This study revealed that Diltiazem HCl exhibited noticeable antimicrobial properties against Gram-positive bacteria, demonstrating the highest inhibition of Staphylococcus epidermidis, followed by Staphylococcus aureus. It effectively reduced the formation of biofilms by 95.1% and 90.7% for S. epidermidis, and S. aureus, respectively. Additionally, the antiviral activity of Diltiazem HCl was found to be potent against the CoxB4 virus, with an IC50 of 35.8 ± 0.54 μg mL−1 compared to the reference antiviral Acyclovir (IC50 42.71 ± 0.43 μg mL−1). Conclusion This study suggests that Diltiazem HCl, in addition to its antihypertensive effect, may also be a potential treatment option for infections caused by Gram-positive bacteria and the CoxB4 viruses, providing an additional off-target effect for Diltiazem HCl.

Drug Repurposing for COVID-19 by Constructing a Comorbidity Network with Central Nervous System Disorders.

In the post-COVID-19 era, treatment options for potential SARS-CoV-2 outbreaks remain limited. An increased incidence of central nervous system (CNS) disorders has been observed in long-term COVID-19 patients. Understanding the shared molecular mechanisms between these conditions may provide new insights for developing effective therapies. This study developed an integrative drug-repurposing framework for COVID-19, leveraging comorbidity data with CNS disorders, network-based modular analysis, and dynamic perturbation analysis to identify potential drug targets and candidates against SARS-CoV-2. We constructed a comorbidity network based on the literature and data collection, including COVID-19-related proteins and genes associated with Alzheimer's disease, Parkinson's disease, multiple sclerosis, and autism spectrum disorder. Functional module detection and annotation identified a module primarily involved in protein synthesis as a key target module, utilizing connectivity map drug perturbation data. Through the construction of a weighted drug-target network and dynamic network-based drug-repurposing analysis, ubiquitin-carboxy-terminal hydrolase L1 emerged as a potential drug target. Molecular dynamics simulations suggested pregnenolone and BRD-K87426499 as two drug candidates for COVID-19. This study introduces a dynamic-perturbation-network-based drug-repurposing approach to identify COVID-19 drug targets and candidates by incorporating the comorbidity conditions of CNS disorders.

A machine learning and drug repurposing approach to target ferroptosis in colorectal cancer stratified by sex and KRAS.

The landscape of sex differences in Colorectal Cancer (CRC) has not been well characterized with respect to the mechanisms of action for oncogenes such as KRAS. However, our recent study showed that tumors from male patients with KRAS mutations have decreased iron-dependent cell death called ferroptosis. Building on these findings, we further examined ferroptosis in CRC, considering both sex of the patient and KRAS mutations, using public databases and our in-house CRC tumor cohort. Through subsampling inference and variable importance analysis (VIMP), we identified significant differences in gene expression between KRAS mutant and wild type tumors from male patients. These genes suppress (e.g., SLC7A11 ) or drive (e.g., SLC1A5 ) ferroptosis, and these findings were further validated with Gaussian mixed models. Furthermore, we explored the prognostic value of ferroptosis regulating genes and discovered sex- and KRAS-specific differences at both the transcriptional and metabolic levels by random survival forest with backward elimination algorithm (RSF-BE). Of note, genes and metabolites involved in arginine synthesis and glutathione metabolism were uniquely associated with prognosis in tumors from males with KRAS mutations. Additionally, drug repurposing is becoming popular due to the high costs, attrition rates, and slow pace of new drug development, offering a way to treat common and rare diseases more efficiently. Furthermore, increasing evidence has shown that ferroptosis inhibition or induction can improve drug sensitivity or overcome chemotherapy drug resistance. Therefore, we investigated the correlation between gene expression, metabolite levels, and drug sensitivity across all CRC primary tumor cell lines using data from the Genomics of Drug Sensitivity in Cancer (GDSC) resource. We observed that ferroptosis suppressor genes such as DHODH , GCH1 , and AIFM2 in KRAS mutant CRC cell lines were resistant to cisplatin and paclitaxel, underscoring why these drugs are not effective for these patients. The comprehensive map generated here provides valuable biological insights for future investigations, and the findings are supported by rigorous analysis of large-scale publicly available data and our in-house cohort. The study also emphasizes the potential application of VIMP, Gaussian mixed models, and RSF-BE models in the multi-omics research community. In conclusion, this comprehensive approach opens doors for leveraging precision molecular feature analysis and drug repurposing possibilities in KRAS mutant CRC.

HGTDR: Advancing drug repurposing with heterogeneous graph transformers.

Drug repurposing is a viable solution for reducing the time and cost associated with drug development. However, thus far, the proposed drug repurposing approaches still need to meet expectations. Therefore, it is crucial to offer a systematic approach for drug repurposing to achieve cost savings and enhance human lives. In recent years, using biological network-based methods for drug repurposing has generated promising results. Nevertheless, these methods have limitations. Primarily, the scope of these methods is generally limited concerning the size and variety of data they can effectively handle. Another issue arises from the treatment of heterogeneous data, which needs to be addressed or converted into homogeneous data, leading to a loss of information. A significant drawback is that most of these approaches lack end-to-end functionality, necessitating manual implementation and expert knowledge in certain stages. We propose a new solution, Heterogeneous Graph Transformer for Drug Repurposing (HGTDR), to address the challenges associated with drug repurposing. HGTDR is a three-step approach for knowledge graph-based drug repurposing: (1) constructing a heterogeneous knowledge graph, (2) utilizing a heterogeneous graph transformer network, and (3) computing relationship scores using a fully connected network. By leveraging HGTDR, users gain the ability to manipulate input graphs, extract information from diverse entities, and obtain their desired output. In the evaluation step, we demonstrate that HGTDR performs comparably to previous methods. Furthermore, we review medical studies to validate our method's top 10 drug repurposing suggestions, which have exhibited promising results. We also demonstrated HGTDR's capability to predict other types of relations through numerical and experimental validation, such as drug-protein and disease-protein inter-relations. The source code and data are available at https://github.com/bcb-sut/HGTDR and http://git.dml.ir/BCB/HGTDR.

Drug repurposing: identification of SARS-CoV-2 potential inhibitors by virtual screening and pharmacokinetics strategies.

The coronavirus disease 2019 (COVID-19) pandemic caused global health, economic, and population loss. Variants of the coronavirus contributed to the severity of the disease and persistent rise in infections. This study aimed to identify potential drug candidates from fifteen approved antiviral drugs against SARS-CoV-2 (6LU7), SARS-CoV (5B6O), and SARS-CoV-2 spike protein (6M0J) using virtual screening and pharmacokinetics to gain insights into COVID-19 therapeutics. We employed drug repurposing approach to analyze binding performance of fifteen clinically approved antiviral drugs against the main protease of SARS-CoV-2 (6LU7), SARS-CoV (5B6O), and SARS-CoV-2 spike proteins bound to ACE-2 receptor (6M0J), to provide an insight into the therapeutics of COVID-19. AutoDock Vina was used for docking studies. The binding affinities were calculated, and 2-3D structures of protein-ligand interactions were drawn. Rutin, hesperidin, and nelfinavir are clinically approved antiviral drugs with high binding affinity to proteins 6LU7, 5B6O, and 6M0J. These ligands have excellent pharmacokinetics, ensuring efficient absorption, metabolism, excretion, and digestibility. Hesperidin showed the most potent interaction with spike protein 6M0J, forming four H-bonds. Nelfinavir had a high human intestinal absorption (HIA) score of 0.93, indicating maximum absorption in the body and promising interactions with 6LU7. Our results indicated that rutin, hesperidin, and nelfinavir had the highest binding results against the proposed drug targets. The computational approach effectively identified SARS-CoV-2 inhibitors. COVID-19 is still a recurrent threat globally and predictive analysis using natural compounds might serve as a starting point for new drug development against SARS-CoV-2 and related viruses.

Abstract 4894: Multi-omic approach for drug repurposing in the poor prognosis CMS4 subtype of colon cancer

Abstract Introduction: Colon cancer (CC) is one of the deadliest malignancy in developing countries. Key drivers considered for the clinical management are RAS, BRAF alterations together with TNM staging and microsatellite status (MS). With the advancement of sequencing technologies and bioinformatic approaches, various molecular classifications of CC have been proposed. Guinney et al. proposed a transcriptomic-based molecular subtyping, the so-called four Consensus Molecular Subtypes (CMS). CMS4 patients have the worst prognosis. Biologically, CMS4, also known as mesenchymal, shows high expression of genes related to EMT, matrix remodelling, TGFb signaling, and inflammatory-related system and a peculiar enrichment in stromal cells. Thus, in the present study we are focusing on CMS4 subgroup in order to identify drugs to be repurposed in such a clinical setting through a multi-omic approach. Methods: Using TCGAbiolinks R package, we retrieved STAR Counts transcriptome profiling data, for COAD and READ. CMSclassifier was used to determine the CMS of the samples. We separated the coding genes from the long noncoding RNAs (lncRNAs) using annotation databases generated from Ensembl. We estimated the fraction of cell populations throughout the CIBERSORTx. Features selection approaches was performed both for coding genes and lncRNA. Data normalization has been carried out through variance stabilizing transformation for read count data. To identify CMS and potential biomarkers, we adopted mixOmics N-integration method. Our independent validation cohort consists of 100 patients, locally enrolled, upon local Ethical Committee approval. Features contributing to CMS4 subtype have been extracted and use to construct drug-gene interaction network. Results: To set-up the classification model the DIABLO approach was used, reaching a mean AUCROC of 0.9022. Contributing features related CMS4 subtypes included nine coding genes, six lncRNAs and, regarding CibersortX deconvolution, Macrophage M0 and M2.The drug-gene interaction network includes six subnetworks centered to ALOX5, KCNMA1, AQP9, TGFB3, THBS4 and DPYSL3. The role of TGFb pathway was confirmed, considering the contribution to classification. Interestingly, we found interactions with Non-Steroid Anti-Inflammatory Drugs (NSAIDs), such as Mesalazine. Moreover, THBSA gene, involved in cell-to-cell, cell-to-matrix and stromal response, could also be targeted. Conclusions: The results of our drug repositioning approach have been biologically corroborated considering, as mentioned above, the peculiar enrichment of CMS4 subtype for TGFb pathway and stromal cells. The chance to modulate the macrophagic activity in the tumor microenvironment through NSAIDs could be evaluated. Experimental validation on patient-derived organoids (PDOs), related to samples included to local validation cohort, is ongoing. Citation Format: Tommaso M. Marvulli, Debora Traversa, Roberta Di Fonte, Leonarda Maurmo, Amalia Azzariti, Letizia Porcelli, Claudio A. Coppola, Concetta Saponaro, Eliseo Mattioli, Francesco A. Zito, Rossella Fasano, Simona Serratì, Davide Quaresmini, Oronzo Brunetti, Stefania Tommasi, Raffaella Massafra, Simona De Summa. Multi-omic approach for drug repurposing in the poor prognosis CMS4 subtype of colon cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4894.

EKGDR: An End-to-End Knowledge Graph-Based Method for Computational Drug Repurposing.

The lengthy and expensive process of developing new drugs from scratch, coupled with a high failure rate, has prompted the emergence of drug repurposing/repositioning as a more efficient and cost-effective approach. This approach involves identifying new therapeutic applications for existing approved drugs, leveraging the extensive drug-related data already gathered. However, the diversity and heterogeneity of data, along with the limited availability of known drug-disease interactions, pose significant challenges to computational drug design. To address these challenges, this study introduces EKGDR, an end-to-end knowledge graph-based approach for computational drug repurposing. EKGDR utilizes the power of a drug knowledge graph, a comprehensive repository of drug-related information that encompasses known drug interactions and various categorization information, as well as structural molecular descriptors of drugs. EKGDR employs graph neural networks, a cutting-edge graph representation learning technique, to embed the drug knowledge graph (nodes and relations) in an end-to-end manner. By doing so, EKGDR can effectively learn the underlying causes (intents) behind drug-disease interactions and recursively aggregate and combine relational messages between nodes along different multihop neighborhood paths (relational paths). This process generates representations of disease and drug nodes, enabling EKGDR to predict the interaction probability for each drug-disease pair in an end-to-end manner. The obtained results demonstrate that EKGDR outperforms previous models in all three evaluation metrics: area under the receiver operating characteristic curve (AUROC = 0.9475), area under the precision-recall curve (AUPRC = 0.9490), and recall at the top-200 recommendations (Recall@200 = 0.8315). To further validate EKGDR's effectiveness, we evaluated the top-20 candidate drugs suggested for each of Alzheimer's and Parkinson's diseases.

Identification of Antibiotic Drugs against SARS-CoV2 Mpro: A Computational Approach for Drug Repurposing

The coronavirus pandemic has posed a significant challenge for researchers seeking to develop new compounds and repurpose existing drugs to manage this disease. It has been found that the Main protease enzyme (Mpro) is critical to the replication of the virus, making it an attractive target for drug development. Different antibiotics have been proven effective against different viruses, leading to their recommendation for COVID-19. In this study, virtual screening, pharmacokinetics, QSAR, and molecular docking techniques were used to investigate the best antibiotic drugs for COVID-19 by targeting the active and inactive conformations of the Mpro enzyme. The results of the study demonstrate that Praziquantel is a promising candidate for COVID-19 treatment. This is due to several reasons: First, Praziquantel exhibits better binding energy in both the conformations of Mpro. Second, it binds in S-3A site in native conformation and S-1B in active state. Third, Praziquantel has excellent absorption properties, strong blood-brain barrier penetration power, and reasonably good solubility. Therefore, the study nominates Praziquantel as the best option for future experimental and pre-clinical investigations for COVID-19.

Current trends and future prospects of drug repositioning in gastrointestinal oncology.

Gastrointestinal (GI) cancers comprise a significant number of cancer cases worldwide and contribute to a high percentage of cancer-related deaths. To improve survival rates of GI cancer patients, it is important to find and implement more effective therapeutic strategies with better prognoses and fewer side effects. The development of new drugs can be a lengthy and expensive process, often involving clinical trials that may fail in the early stages. One strategy to address these challenges is drug repurposing (DR). Drug repurposing is a developmental strategy that involves using existing drugs approved for other diseases and leveraging their safety and pharmacological data to explore their potential use in treating different diseases. In this paper, we outline the existing therapeutic strategies and challenges associated with GI cancers and explore DR as a promising alternative approach. We have presented an extensive review of different DR methodologies, research efforts and examples of repurposed drugs within various GI cancer types, such as colorectal, pancreatic and liver cancers. Our aim is to provide a comprehensive overview of employing the DR approach in GI cancers to inform future research endeavors and clinical trials in this field.

Computational and Experimental Drug Repurposing of FDA-Approved Compounds Targeting the Cannabinoid Receptor CB1.

The cannabinoid receptor 1 (CB1R) plays a pivotal role in regulating various physiopathological processes, thus positioning itself as a promising and sought-after therapeutic target. However, the search for specific and effective CB1R ligands has been challenging, prompting the exploration of drug repurposing (DR) strategies. In this study, we present an innovative DR approach that combines computational screening and experimental validation to identify potential Food and Drug Administration (FDA)-approved compounds that can interact with the CB1R. Initially, a large-scale virtual screening was conducted using molecular docking simulations, where a library of FDA-approved drugs was screened against the CB1R's three-dimensional structures. This in silico analysis allowed us to prioritize compounds based on their binding affinity through two different filters. Subsequently, the shortlisted compounds were subjected to in vitro assays using cellular and biochemical models to validate their interaction with the CB1R and determine their functional impact. Our results reveal FDA-approved compounds that exhibit promising interactions with the CB1R. These findings open up exciting opportunities for DR in various disorders where CB1R signaling is implicated. In conclusion, our integrated computational and experimental approach demonstrates the feasibility of DR for discovering CB1R modulators from existing FDA-approved compounds. By leveraging the wealth of existing pharmacological data, this strategy accelerates the identification of potential therapeutics while reducing development costs and timelines. The findings from this study hold the potential to advance novel treatments for a range of CB1R -associated diseases, presenting a significant step forward in drug discovery research.

Targeting Staphylococcal Cell-Wall Biosynthesis Protein FemX Through Steered Molecular Dynamics and Drug-Repurposing Approach.

Staphylococcus aureus-mediated infection is a serious threat in this antimicrobial-resistant world. S. aureus has become a "superbug" by challenging conventional as well as modern treatment strategies. Nowadays, drug repurposing has become a new trend for the discovery of new drug molecules. This study focuses on evaluating FDA-approved drugs that can be repurposed against S. aureus infection. Steered molecular dynamics (SMD) has been performed for Lumacaftor and Olaparib against staphylococcal FemX to understand their binding to the active site. A time-dependent external force or rupture force has been applied to the ligands to calculate the force required to dislocate the ligand from the binding pocket. SMD analysis indicates that Lumacaftor has a high affinity for the substrate binding pocket in comparison to Olaparib. Umbrella sampling exhibits that Lumacaftor possesses a higher free energy barrier to displace it from the ligand-binding site. The bactericidal activity of Lumacaftor and Olaparib has been tested, and it shows that Lumacaftor has moderate activity along with biofilm inhibition potential (MIC value with conc. 128 μg/mL). Pharmacokinetic and toxicology evaluations indicate that Lumacaftor has higher pharmacokinetic potential with lower toxicity. This is the first experimental report where staphylococcal FemX has been targeted for the discovery of new drugs. It is suggested that Lumacaftor may be a potential lead molecule against S. aureus.

Decoding Connectivity Map-based drug repurposing for oncotherapy

The rising global burden of cancer has driven considerable efforts into the research and development of effective anti-cancer agents. Fortunately, with impressive advances in transcriptome profiling technology, the Connectivity Map (CMap) database has emerged as a promising and powerful drug repurposing approach. It provides an important platform for systematically discovering of the associations among genes, small-molecule compounds and diseases, and elucidating the mechanism of action of drug, contributing toward efficient anti-cancer pharmacotherapy. Moreover, CMap-based computational drug repurposing is gaining attention because of its potential to overcome the bottleneck constraints faced by traditional drug discovery in terms of cost, time and risk. Herein, we provide a comprehensive review of the applications of drug repurposing for anti-cancer drug discovery and summarize approaches for computational drug repurposing. We focus on the principle of the CMap database and novel CMap-based software/algorithms as well as their progress achieved for drug repurposing in the field of oncotherapy. This article is expected to illuminate the emerging potential of CMap in discovering effective anti-cancer drugs, thereby promoting efficient healthcare for cancer patients.

Abstract 4595: STAT-3 chemical inhibition modulates decidual-like polarization in NK cells from PCa patients and restore their anti-tumor activities

Abstract Introduction. Natural Killer (NK) cells are innate lymphoid cells involved in tumour recognition/elimination. NK cells are altered in their phenotype and functions in diverse tumors, including prostate cancer (PCa). We demonstrated that PCa circulating NK cells (TANKs) acquire the pro-angiogenic/decidual-like CD56brightCD9+CD49a+ phenotype, release IL-8 and MMP-9, functionally support endothelial cell activation and secrete monocyte-recruiting/M2-like macrophage-polarizing factors. Materials and methods. Here, we characterized the phenotype of tumour infiltrating NKs (TINKs) and tumour-associated (TANKs) in PCa patients and evaluated the contribution of STAT3, as possible driver of NK cell polarization. PCa TINKs and TANKs were characterized by multicolour flow cytometry (FC) for decidual-like surface markers (CD9, CD49a) and degranulation capabilities (CD107a). STAT3 activation, was investigated in circulating PCa NK cells, by FC. Using a drug-repurposing approach with the antipsychotic agent Pimozide (a chemical inhibitor of STAT3), we modulated STAT3 activation, ex vivo, in PCa TANKs and monitored their secretome changes, by commercially available protein membrane arrays together with their capability to degranulate and produce perforin/GranzymeB. Results and discussion. We observed that PCa TINKs acquire the same CD9+CD49a+ decidual-like NK cell phenotype, as found in PCa TANKs. We detected the presence of CD56brightCD9+CD49a+ decidual-like NK cell also in peripheral blood of subjects with benign prostatic hyperplasia (BPH), but in a lower frequency, compared to those from PCa TANKs. Sera from PCa patients were enriched in IL-4, IL-6, IL-8 and IL-10, all cytokines able to activate STAT3 signalling. We detected increased phosphorylation of STAT3 in PCa TANKs, compared to NK cells from healthy controls, that was reduced following 24 hours of stimulation by Pimozide. This treatment resulted in decreased capabilities of PCa TANKs to secrete pro-angiogenic factors (IL-8, IL-6), molecules involved in monocytes recruitment/M2-like macrophage polarization (CCL-2, CCL5, GM-CSF, IL-10), together with increased degranulation, augmented secretion of (IFN-γ and TNF-α) and increased production of Perforin and Granzyme. Conclusions. Our results suggest that STAT3 inhibition can be envisaged as a potential strategy to limit the generation of pro-angiogenic/decidual-like NKs, while contributing to NK cell re-education in PCa. Citation Format: Matteo Gallazzi, Maria Teresa Palano, Martina Cucchiara, Federico Dehò, Paolo Capogrosso, Francesca Franzi, Fausto Sessa, Angelo Naselli, Lorenzo Mortara, Antonino Bruno. STAT-3 chemical inhibition modulates decidual-like polarization in NK cells from PCa patients and restore their anti-tumor activities. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4595.

In silico discovery of potent inhibitors against monkeypox’s major structural proteins

Monkeypox virus (MPXV) outbreak in non-endemic countries is a worldwide public health emergency. An enveloped double-stranded DNA virus belongs to the genus Orth poxvirus. A viral zoonotic infection known as monkeypox has been a serious risk to public health, especially in Africa. However, it has recently spread to other continents, so it might soon become a worldwide problem. There is an increased risk of transmission of the virus because there is a lack of effective treatment that cures the disease. To stop the multi-country outbreak from spreading, it is important to discover effective medications urgently. The objective of the current study is to swiftly find new treatments for the monkeypox virus using advanced computational approaches. By investigating five potential MPXV targets (DNA ligase, Palmytilated Extracellular Enveloped Virus (EEV) membrane protein, Scaffold protein D13, Thymidylate Kinase, and Viral core cysteine proteinase), this research was carried out using cutting-edge computational techniques against human monkeypox virus infection. Here we present the accurate 3D structures and their binding cavities of the selected targets with higher confidence using AlphaFold 2 and SiteMap analysis. Molecular docking and MD simulation analysis revealed the top five potential lead compounds with higher binding affinity and stability toward selected targets. Binding free energy calculations and other essential dynamics analysis supports the finding. The selected lead compounds utilizing virtual screening and drug repurposing approach reported in this study are beneficial for medical scientists and experimental biologists in drug development for the treatment of human MPXV.Communicated by Ramaswamy H. Sarma

Deep learning identification of cardiac transporters as targets for approved drugs.

We re-tasked a novel ligand-centered deep learning drug discovery method to identify molecular targets for approved drugs in the heart. This approach uses information about small-molecule effectors to map and probe the pharmacological space of functionally relevant targets in the heart. Here we studied the calcium pump (Sarcoplasmic reticulum Ca2+-ATPase, SERCA) as a proof-of-principle target to test our method. We chose SERCA because it plays a major role in the excitation-contraction-relaxation cycle in normal and pathological muscle, and it represents a major pharmacological target in the heart. We applied this method to demonstrate that SERCA is a pharmacological target for statins, a group of FDA-approved HMGCoA inhibitors used as lipid-lowering medications. We used in situ enzymatic assays and atomistic simulations to demonstrate that that these approved drugs are SERCA inhibitors at micromolar concentrations, inhibiting the pump by binding to two different effector sites. These proof-of-concept studies support the applicability of our approach for off-target identification and drug repurposing, ultimately minimizing the translational gap in drug development targeting the heart.

Drug repositioning via host-pathogen protein-protein interactions for the treatment of cervical cancer.

Integrating interaction data with biological knowledge can be a critical approach for drug development or drug repurposing. In this context, host-pathogen-protein-protein interaction (HP-PPI) networks are useful instrument to uncover the phenomena underlying therapeutic effects in infectious diseases, including cervical cancer, which is almost exclusively due to human papillomavirus (HPV) infections. Cervical cancer is one of the second leading causes of death, and HPV16 and HPV18 are the most common subtypes worldwide. Given the limitations of traditionally used virus-directed drug therapies for infectious diseases and, at the same time, recent cancer statistics for cervical cancer cases, the need for innovative treatments becomes clear. Accordingly, in this study, we emphasize the potential of host proteins as drug targets and identify promising host protein candidates for cervical cancer by considering potential differences between HPV subtypes (i.e., HPV16 and HPV18) within a novel bioinformatics framework that we have developed. Subsequently, subtype-specific HP-PPI networks were constructed to obtain host proteins. Using this framework, we next selected biologically significant host proteins. Using these prominent host proteins, we performed drug repurposing analysis. Finally, by following our framework we identify the most promising host-oriented drug candidates for cervical cancer. As a result of this framework, we discovered both previously associated and novel drug candidates, including interferon alfacon-1, pimecrolimus, and hyaluronan specifically for HPV16 and HPV18 subtypes, respectively. Consequently, with this study, we have provided valuable data for further experimental and clinical efforts and presented a novel bioinformatics framework that can be applied to any infectious disease.

Current Insights and Molecular Docking Studies of the Drugs under Clinical Trial as RdRp Inhibitors in COVID-19 Treatment.

Docking studies were performed using the Maestro 12.9 module of Schrodinger software over 70 molecules with RdRp as the target and remdesivir as the standard drug and further confirmed by simulation studies. The docking studies showed that many HIV protease inhibitors demonstrated remarkable binding interactions with the target RdRp. Protease inhibitors such as lopinavir and ritonavir are effective. Along with these, AT-527, ledipasvir, bicalutamide, and cobicistat showed improved docking scores. RMSD and RMSF were further analyzed for potent ledipasvir and ritonavir by simulation studies and were identified as potential candidates for corona disease. The drug repurposing approach provides a new avenue in COVID-19 treatment.

Deciphering the molecular mechanism and crosstalk between Parkinson's disease and breast cancer through multi-omics and drug repurposing approach

Epidemiological studies indicate a higher occurrence of breast cancer (BRCA) in patients with Parkinson's disease. However, the exact molecular mechanism is still not precise. Herein, we tested the hypothesis that this inverse comorbidity result from shared genetic and molecular processes. We conducted an integrated omics analysis to identify the common gene signatures associated with PD and BRCA. Secondly, several dysregulated biological processes in both indications were analyzed by functional enrichment methods, and significant overlapping processes were identified. To establish common regulatory mechanisms, information about transcription factors and miRNAs associated with both the disorders was extracted. Finally, disease-specific gene expression signatures were compared through LINCS L1000 analysis to identify potential repurposing drugs for PD. The potential repurposed drug candidates were then correlated with PD-specific gene signatures by Cmap analysis. In conclusion, this study highlights the shared genes, biological pathways and regulatory signatures associated with PD and BRCA with an improved understanding of crosstalk involved. Additionally, the role of therapeutics was investigated in context with their comorbid associations. These findings could help to explain the complex molecular patterns of associations between PD and BRCA.

Bioactive components of different nasal spray solutions may defeat SARS-Cov2: repurposing and in silico studies.

The recent outbreak "Coronavirus Disease 2019 (COVID-19)" is caused by fast-spreading and highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). This virus enters into the human respiratory system by binding of the viral surface spike glycoprotein (S-protein) to an angiotensin-converting enzyme2 (ACE2) receptor that is found in the nasal passage and oral cavity of a human. Both spike protein and the ACE2 receptor have been identified as promising therapeutic targets to develop anti-SARS-CoV2 drugs. No therapeutic drugs have been developed as of today except for some vaccines. Therefore, potent therapeutic agents are urgently needed to combat the COVID-19 infections. This goal would be achieved only by applying drug repurposing and computational approaches. Thus, based on drug repurposing approach, we have investigated 16 bioactive components (1-16) from different nasal spray solutions to check their efficacies against human ACE2 and SARS-CoV2 spike proteins by performing molecular docking and molecular dynamic (MD) simulation studies. In this study, three bioactive components namely ciclesonide (8), levocabastine (13), and triamcinolone acetonide (16) have been found as promising inhibitory agents against SARS-CoV2 spike and human ACE2 receptor proteins with excellent binding affinities, comparing to reference drugs such as nafamostat, arbidol, losartan, and benazepril. Furthermore, MD simulations were performed (triplicate) for 100ns to confirm the stability of 8, 13, and 16 with said protein targets and to compute MM-PBSA-based binding-free energy calculations. Thus, bioactive components 8, 13, and 16 open the door for researchers and scientist globally to investigate them against SARS-CoV2 through in vitro and in vivo analysis.