Drug Repurposing Research Articles

Comprehensive applications of the artificial intelligence technology in new drug research and development.

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

Precision Meets Repurposing: Innovative Approaches in Human Papillomavirus and Epstein-Barr Virus-Driven Cancer Therapy

Viral malignancies represent a distinct entity among cancers. Oncoviruses like the Human Papilloma Virus (HPV) and the Epstein Barr Virus (EBV) are highly potent inducers of oncogenic transformation leading to tumor development. HPV and EBV are known to be increasingly involved in the pathogenesis of various classes of cancers like cervical, head and neck, colorectal, breast, oral and anogenitial. Therapeutic vaccines directed at such oncoviruses, often fail to unleash the desired immune response against the tumor. This is largely due to the immunosuppressive microenvironment of the virus-induced tumors. Consequently, metronomic chemotherapies administered in conjunction with therapeutic viral vaccines have considerably enhanced the antitumor activity of these vaccines. Moreover, given the unique attributes of HPV and EBV-associated cancers, therapeutic agents directly targeting the oncoproteins of these viruses are still obscure. In this light, an increasing number of reports have evidenced the repurposing of drugs for therapeutic benefits in such cancers. This work delineates the significance and implications of metronomic chemotherapy and drug repurposing in HPV and EBV-associated cancers.

Two Birds with One Stone: Drug Regime Targets Viral Pathogenesis Phases and COVID-19 ARDS at the Same Time.

Severe COVID-19 or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a kind of viral pneumonia induced by infection with the coronavirus that causes ARDS. It involves symptoms that are a combination of viral pneumonia and ARDS. Antiviral or immunosuppressive medicines are used to treat many COVID-19 patients. Several drugs are now undergoing clinical studies in order to see if they can be repurposed in the future. In this study, in silico biomarker-targeted methodologies, such as target/ molecule virtual screening by docking technique and drug repositioning strategy, as well as data mining approach and meta-analysis of investigational data, were used. In silico findings of used combination of drug repurposing and high-throughput docking methods presented acetaminophen, ursodiol, and β-carotene as a three-drug therapy regimen to treat ARDS induced by viral pneumonia in addition to inducing direct antiviral effects against COVID-19 viral infection. In the current study, drug repurposing and high throughput docking methods have been employed to develop combination drug regimens as multiple-molecule drugs for the therapy of COVID-19 and ARDS based on a multiple-target therapy strategy. This approach offers a promising avenue for the treatment of COVID-19 and ARDS, and highlights the potential benefits of drug repurposing in the fight against the current pandemic.

Degarelix limits the survival of mycobacteria and granuloma formation

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb) infection, is a serious health hazard, characterized by tuberculous granuloma formation, which may facilitate bacterial survival. At the same time, the identification of multidrug-resistant and extremely drug-resistant Mtb strains, and the progressive accumulation of mutations in biological targets of frontline antimicrobials, has made TB treatments more difficult. Therefore, new and rapid drug development for TB is warranted. Recently, drug repurposing has received considerable attention. In this study, we applied the anticancer drug degarelix to anti-TB research and found that it inhibits mycobacteria survival and pathological damage in Mycobacterium marinum-infected zebrafish and Mtb-infected mice. Supplementation of degarelix matched the bactericidal activities of rifampicin (RFP) toward M. marinum in zebrafish. Mechanistically, degarelix significantly increased interferon (IFN)-γ levels in M. marinum-infected zebrafish. Degarelix had no direct anti-mycobacterial activity in vitro but significantly reduced the survival of H37Rv in macrophages. The effect of degarelix could be reversed by 3-methyladenine (3-MA), which inhibits the class III phosphatidylinositol (PI) 3 kinase required for autophagy initiation. However, no effect on later steps in autophagy could be detected. Our findings demonstrate the potential of degarelix on limiting mycobacterial survival and granuloma formation, which may generate novel TB therapeutics.

Bioinformatics Approaches in the Development of Antifungal Therapeutics and Vaccines.

Fungal infections are considered a great threat to human life and are associated with high mortality and morbidity, especially in immunocompromised individuals. Fungal pathogens employ various defense mechanisms to evade the host immune system, which causes severe infections. The available repertoire of drugs for the treatment of fungal infections includes azoles, allylamines, polyenes, echinocandins, and antimetabolites. However, the development of multidrug and pandrug resistance to available antimycotic drugs increases the need to develop better treatment approaches. In this new era of -omics, bioinformatics has expanded options for treating fungal infections. This review emphasizes how bioinformatics complements the emerging strategies, including advancements in drug delivery systems, combination therapies, drug repurposing, epitope-based vaccine design, RNA-based therapeutics, and the role of gut-microbiome interactions to combat anti-fungal resistance. In particular, we focused on computational methods that can be useful to obtain potent hits, and that too in a short period.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes in the COPDGene and ECLIPSE cohort studies

Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. We aimed to define high-risk COPD subtypes using genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics. We defined high-risk groups based on PRS and TRS quantiles by maximising differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups. We examined two high-risk omics-defined groups in non-overlapping test sets (n=1133 NHW COPDGene, n=299 African American (AA) COPDGene, n=468 ECLIPSE). We defined "high activity" (low PRS, high TRS) and "severe risk" (high PRS, high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signalling processes compared to a low-risk (low PRS, low TRS) subgroup. "High activity" but not "severe risk" participants had greater prospective FEV1 decline (COPDGene:-51mL/year; ECLIPSE:-40mL/year) and proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors. Concomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection. National Institutes of Health.

Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding.

In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topology-maintaining representations of drugs and targets, thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However, the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction, there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. We studied the drug-drug interaction (DDI) and DTI from DrugBank Version 3.0, protein-protein interaction (PPI) from the human protein reference database Release 9, drug structure similarity from Morgan fingerprints of radius 2 and calculated by RDKit, and protein sequence similarity from Smith-Waterman score. Our study consists of three major components. First, various drugs and target proteins were integrated, and a heterogeneous network was established based on a series of data sets. Second, the graph neural networks-inspired graph auto-encoding method was used to extract high-order structural information from the heterogeneous networks, thereby revealing the description of nodes (drugs and proteins) and their topological neighbors. Finally, potential DTI prediction was made, and the obtained samples were sent to the classifier for secondary classification. The performance of Graph-DTI and all baseline methods was evaluated using the sums of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC). The results indicated that Graph-DTI outperformed the baseline methods in both performance results. Compared with other baseline DTI prediction methods, the results showed that Graph-DTI had better prediction performance. Additionally, in this study, we effectively classified drugs corresponding to different targets and vice versa. The above findings showed that Graph-DTI provided a powerful tool for drug research, development, and repositioning. Graph- DTI can serve as a drug development and repositioning tool more effectively than previous studies that did not use heterogeneous network graph embedding.

One arrow two eagles: multifunctional nano-system for macrophage reprogramming and osteoclastogenesis inhibition against inflammatory osteolysis

Inflammatory osteolysis poses a significant worldwide threat to public health. However, current monotherapies, which target either the prevention of the inflammatory response or the attenuation of osteoclast (OC) formation, have limited efficacy due to the complexity of the bone immune system being overlooked. Herein, by means of modifying salmon calcitonin (sCT), a multifunctional nano-system (AuNDs-sCT) was designed to synergistically inhibit OC differentiation and reverse the inflammatory microenvironment against inflammatory osteolysis. On the one hand, AuNDs-sCT effectively restrained OC differentiation by binding to the calcitonin receptors on the surface of OC precursors, resulting in the down-regulation of OC-specific genes and proteins. The targeted capacity of AuNDs-sCT provided a more durable and precise therapeutic effect. On the other hand, AuNDs-sCT exhibited antioxidant and anti-inflammatory effects, which regulated the polarization “switch” from the pro-inflammatory M1 phenotype to the anti-inflammatory M2 phenotype in macrophages by the inhibition of NF-κB p65 phosphorylation, thereby effectively reversed the local inflammatory microenvironment. Additionally, AuNDs-sCT served as a promising fluorescent probe, enabling real-time visualization of the therapeutic process. This capability is expected to optimize drug administration and evaluate therapeutic effects. In summary, by inhibiting OC differentiation and reprogramming macrophages, AuNDs-sCT successfully realized drug repurposing and achieved the “one arrow two eagles” therapeutic strategy, which offers a synergistic and effective treatment option for the clinical management of inflammatory osteolysis.

The future of medicine: Strategic insights into drug repurposing

The future of medicine is increasingly being shaped by innovative strategies that maximize the efficacy of existing therapeutics, one of the most promising being drug repurposing. Drug repurposing involves the identification of novel therapeutic applications for approved or investigational drugs, thereby circumventing the lengthy and costly processes associated with traditional drug development. This approach not only accelerates the delivery of treatments to patients but also optimizes existing resources, making it a compelling strategy in the face of rising healthcare costs and the urgent need for novel therapies. Recent advancements in genomics, bioinformatics, and high-throughput screening have revolutionized our ability to identify potential candidates for repurposing. These technologies facilitate the exploration of drug-target interactions across various diseases, enabling researchers to uncover unexpected therapeutic potentials. For instance, well-known medications originally developed for one condition have been found effective in treating others, such as the use of thalidomide for multiple myeloma and the repurposing of antifungal agents for cancer therapy.

Leveraging regulatory pharmacist expertise to develop a framework for drug repurposing in underserved areas of the USA

The United States continues to grapple with healthcare access disparities, particularly in underserved regions. Regulatory pharmacists, with their unique expertise in pharmaceutical laws, drug development, and safety regulations, can play a vital role in drug repurposing efforts. This paper proposes a framework for utilising regulatory pharmacist expertise to develop drug repurposing strategies to meet unmet medical needs in underserved areas. We explore the regulatory hurdles, the necessity for collaborative efforts between healthcare professionals, and the potential for drug repurposing to improve healthcare outcomes in rural and socioeconomically disadvantaged communities.

The Potential of Drug Repurposing as a Rapid Response Strategy in COVID-19 Therapeutics

Drug repurposing has emerged as a promising strategy in the rapid development of effective therapeutics for COVID-19. This approach leverages existing medications, previously approved for other indications, to target the pathophysiological mechanisms of SARS-CoV-2 infection. Several drugs were tested during the COVID-19 pandemic, developed originally for other purposes and under less-than-ideal conditions. Some of the most well-known include remdesivir, an Ebola drug approved by the FDA for emergency use to treat COVID-19, and dexamethasone, a corticosteroid that reduces death associated with severe infection through immunomodulation. However, while hydroxychloroquine and ivermectin, among others, showed very meager or no benefit, it is clear that such early promise must be subjected to firm testing. Despite such promises, drug repurposing may face several inconsistent clinical outcomes, questions over safety, and the inability to address all forms of COVID-19 pathology. Key candidates identified through high-throughput screening and computational methods include antiviral agents, anti-inflammatory drugs, and those targeting host cell pathways critical for viral replication. This review discusses the efficacy and mechanisms of these repurposed drugs, highlights ongoing clinical trials, and addresses challenges such as resistance and optimal dosing. Ultimately, drug repurposing represents a crucial component of the multi-faceted response required to combat the COVID-19 pandemic effectively.

Pancreatic ductal adenocarcinoma, β-blockers and antihistamines: A clinical trial is needed.

Survival in pancreatic cancer (pancreatic ductal adenocarcinoma, PDAC) at just 5 months is the worst of all cancers. It is predicted to become the second highest cause of cancer deaths worldwide this decade and unlike most cancers, there has been little progress in improving survival in PDAC. Numerous studies including molecular and mechanistic studies, cancer biology studies and retrospective human epidemiological studies suggest that two well-known, approved drug classes - β-blockers and H1-antihistamines - may be beneficial and thus may potentially prolong life in patients with PDAC. In our opinion, the body of evidence has reached a point where the potential gains outweigh the very low risks involved in a clinical study in PDAC. We thus believe that it is now time for a clinical trial involving these two agents in PDAC patients. As a repurposing of generic drugs, this is not likely to be appealing to pharmaceutical companies and therefore is likely to require governmental, philanthropic and /or charitable organisational input. In this article, we opine and propose that an urgent clinical trial is needed to determine if repurposing these two orally administered, inexpensive, largely safe drug classes, either alone or in combination, could prolong survival in PDAC and thus improve the outcome for the 10,000 people worldwide who die from PDAC each week.

Integrated virtual screening and MD simulation study to discover potential inhibitors of mycobacterial electron transfer flavoprotein oxidoreductase.

Tuberculosis (TB) continues to be a major global health burden, with high incidence and mortality rates, compounded by the emergence and spread of drug-resistant strains. The limitations of current TB medications and the urgent need for new drugs targeting drug-resistant strains, particularly multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB, underscore the pressing demand for innovative anti-TB drugs that can shorten treatment duration. This has led to a focus on targeting energy metabolism of Mycobacterium tuberculosis (Mtb) as a promising approach for drug discovery. This study focused on repurposing drugs against the crucial mycobacterial protein, electron transfer flavoprotein oxidoreductase (EtfD), integral to utilizing fatty acids and cholesterol as a carbon source during infection. The research adopted an integrative approach, starting with virtual screening of approved drugs from the ZINC20 database against EtfD, followed by molecular docking, and concluding with molecular dynamics (MD) simulations. Diacerein, levonadifloxacin, and gatifloxacin were identified as promising candidates for repurposing against TB based on their strong binding affinity, stability, and interactions with EtfD. ADMET analysis and anti-TB sensitivity predictions assessed their pharmacokinetic and therapeutic potential. Diacerein and levonadifloxacin, previously unexplored in anti-tuberculous therapy, along with gatifloxacin, known for its efficacy in drug-resistant TB, have broad-spectrum antimicrobial properties and favorable pharmacokinetic profiles, suggesting potential as alternatives to current TB treatments, especially against resistant strains. This study underscores the efficacy of computational drug repurposing, highlighting bacterial energy metabolism and lipid catabolism as fruitful targets. Further research is necessary to validate the clinical suitability and efficacy of diacerein, levonadifloxacin, and gatifloxacin, potentially enhancing the arsenal against global TB.

Shared genetics between breast cancer and predisposing diseases identifies novel breast cancer treatment candidates.

Current effective breast cancer treatment options have severe side effects, highlighting a need for new therapies. Drug repurposing can accelerate improvements to care, as FDA-approved drugs have known safety and pharmacological profiles. Some drugs for other conditions, such as metformin, an antidiabetic, have been tested in clinical trials for repurposing for breast cancer. Here, we exploit the genetics of breast cancer and linked predisposing diseases to propose novel drug repurposing opportunities. We hypothesize that if a predisposing disease contributes to breast cancer pathology, identifying the pleiotropic genes related to the risk of cancer could prioritize drugs, among all drugs treating a predisposing disease. We aim to develop a method to not only prioritize drugs for repurposing, but also to highlight shared etiology explaining repurposing. We compile breast cancer's predisposing diseases from literature. For each predisposing disease, we use GWAS summary statistics data to identify genes in loci showing genetic correlation with breast cancer. Then, we use a network approach to link these shared genes to canonical pathways. Similarly, for all drugs treating the predisposing disease, we link their targets to pathways. In this manner, we are able to prioritize a list of drugs based on each predisposing disease, with each drug linked to a set of implicating pathways. Finally, we evaluate our recommendations against drugs currently under investigation for breast cancer. We identify 84 loci harboring mutations with positively correlated effects between breast cancer and its predisposing diseases; these contain 194 identified shared genes. Out of the 112 drugs indicated for the predisposing diseases, 74 drugs can be linked to shared genes via pathways (candidate drugs for repurposing). Fifteen out of these candidate drugs are already in advanced clinical trial phases or approved for breast cancer (OR = 9.28, p = 7.99e-03, one-sided Fisher's exact test), highlighting the ability of our approach to identify likely successful candidate drugs for repurposing. Our novel approach accelerates drug repurposing for breast cancer by leveraging shared genetics with its known predisposing diseases. The result provides 59 novel candidate drugs alongside biological insights supporting each recommendation.

Mebendazole effectively overcomes imatinib resistance by dual-targeting BCR/ABL oncoprotein and β-tubulin in chronic myeloid leukemia cells.

To target the pivotal BCR/ABL oncoprotein in chronic myeloid leukemia (CML) cells, tyrosine kinase inhibitors (TKIs) are utilized as landmark achievements in CML therapy. However, TKI resistance and intolerance remain principal obstacles in the treatment of CML patients. In recent years, drug repositioning provided alternative and promising perspectives apart from the classical cancer therapies, and promoted anthelmintic mebendazole (MBZ) as an effective anti-cancer drug in various cancers. Here, we investigated the role of MBZ in CML treatment including imatinib-resistant CML cells. Our results proved that MBZ inhibited the proliferation and induced apoptosis in CML cells. We found that MBZ effectively suppressed BCR/ABL kinase activity and MEK/ERK signaling pathway by reducing p-BCR/ABL and p-ERK levels with ABL1 targeting ability. Meanwhile, MBZ directly targeted the colchicine-binding site of β-tubulin protein, hampered microtubule polymerization and induced mitosis arrest and mitotic catastrophe. In addition, MBZ increased DNA damage levels and hampered the accumulation of ataxia-telangiectasia mutated and DNA-dependent protein kinase into the nucleus. This work discovered that anthelmintic MBZ exerts remarkable anticancer effects in both imatinib-sensitive and imatinib-resistant CML cells in vitro and revealed mechanisms underlying. From the perspective of drug repositioning and multi-target therapeutic strategy, this study provides a promising option for CML treatment, especially in TKI-resistant or intolerant individuals.

Decoding Acute Myeloid Leukemia: A Clinician’s Guide to Functional Profiling

Acute myeloid leukemia (AML) is a complex clonal disorder characterized by clinical, genetic, metabolomic, and epigenetic heterogeneity resulting in the uncontrolled proliferation of aberrant blood-forming precursor cells. Despite advancements in the understanding of the genetic, metabolic, and epigenetic landscape of AML, it remains a significant therapeutic challenge. Functional profiling techniques, such as BH3 profiling (BP), gene expression profiling (GEP), proteomics, metabolomics, drug sensitivity/resistance testing (DSRT), CRISPR/Cas9, and RNAi screens offer valuable insights into the functional behavior of leukemia cells. BP evaluates the mitochondrial response to pro-apoptotic BH3 peptides, determining a cell’s apoptotic threshold and its reliance on specific anti-apoptotic proteins. This knowledge can pinpoint vulnerabilities in the mitochondria-mediated apoptotic pathway in leukemia cells, potentially informing treatment strategies and predicting therapeutic responses. GEP, particularly RNA sequencing, evaluates the transcriptomic landscape and identifies gene expression alterations specific to AML subtypes. Proteomics and metabolomics, utilizing mass spectrometry and nuclear magnetic resonance (NMR), provide a detailed view of the active proteins and metabolic pathways in leukemia cells. DSRT involves exposing leukemia cells to a panel of chemotherapeutic and targeted agents to assess their sensitivity or resistance profiles and potentially guide personalized treatment strategies. CRISPR/Cas9 and RNAi screens enable systematic disruption of genes to ascertain their roles in leukemia cell survival and proliferation. These techniques facilitate precise disease subtyping, uncover novel biomarkers and therapeutic targets, and provide a deeper understanding of drug-resistance mechanisms. Recent studies utilizing functional profiling have identified specific mutations and gene signatures associated with aggressive AML subtypes, aberrant signaling pathways, and potential opportunities for drug repurposing. The integration of multi-omics approaches, advances in single-cell sequencing, and artificial intelligence is expected to refine the precision of functional profiling and ultimately improve patient outcomes in AML. This review highlights the diverse landscape of functional profiling methods and emphasizes their respective advantages and limitations. It highlights select successes in how these methods have further advanced our understanding of AML biology, identifies druggable targets that have improved outcomes, delineates challenges associated with these techniques, and provides a prospective view of the future where these techniques are likely to be increasingly incorporated into the routine care of patients with AML.

Enhancing the anticancer effect of metformin through nanoencapsulation: Apoptotic induction, inflammatory reduction, and suppression of cell migration in colorectal cancer cells.

Colorectal cancer (CRC) continues to be a significant health challenge, necessitating the development of efficient therapeutic strategies. Drug repurposing, which involves the use of existing medications for new purposes, presents a promising opportunity. Metformin, a widely used antidiabetic drug, has demonstrated potential anticancer effects. To enhance its efficacy, we formulated nano-metformin, metformin encapsulated within pectin nanoparticles. Our study aimed to evaluate the superiority of nano-metformin over free metformin in treating CRC. The cytotoxicity of both metformin and nano-metformin on Caco-2 CRC cells was assessed using the MTT assay, revealing a significant dose-dependent inhibition of cell growth using nano-metformin. The anti-inflammatory potential was evaluated by measuring the levels of nitric oxide and the pro-inflammatory cytokines IL-2 and IL-6 following lipopolysaccharide (LPS) induction, and the results revealed that treating LPS-induced cells with nano-metformin significantly reduced the production of these inflammatory mediators. To elucidate the mechanism of cell death, we employed an acridine orange/ethidium bromide staining assay, which revealed the enhancement of apoptotic cell death following treatment with nano-metformin. Additionally, we examined the expression of key apoptotic regulators using real-time qPCR. Nano-metformin, in particular, significantly downregulated the expression of the antiapoptotic markers Bcl-2 and Survivin while upregulating the proapoptotic caspases 3, 7, and 9. The comet assay revealed significant DNA damage induced by treatment with the nano-metformin compared with that in the free form. Moreover, nano-metformin significantly reduced the migration ability of cells. In conclusion, our work revealed the superior efficacy of our formulated nanoform over free metformin, highlighting its potential as a promising therapeutic agent for CRC treatment.

Hydralazine and Hydrazine Derivatives: Properties, Applications, and Repositioning Potential.

The investigation of new drugs is slow and costly. Drug repositioning, like with Hydralazine (HDZ), an old antihypertensive, can accelerate the process. HDZ and its hydrazonic derivatives exhibit diverse biological activities, promising for new drugs. This review explores HDZ's repositioning potential and its derivatives' applications in various biological activities. It identified 70 relevant articles through database searches. HDZ shows potential in neurology, oncology, nephrology, and gynecology, with clinical trials up to Phase III. Hydralazine-valproate, marketed in Mexico, proves effective in combination with chemotherapy. Hydrazonic derivatives offer broad applications in medicine. Studying their structure-activity relationship can enhance efficacy. This review summarizes their properties and pharmacological activities succinctly.

Abstract 4118735: Interpretable deep learning translation of GWAS findings for drug repurposing in Atrial Fibrillation

Introduction: Translating human genetic findings, such as genome-wide association studies (GWAS) to pathobiology and the discovery of therapeutic target remains a major challenge for Atrial Fibrillation (AF). We previously published a network topology-based deep learning framework to identify disease-associated genes (NETTAG). Hypothesis: By using a deep learning framework, we can efficiently identify AF risk genes, druggable targets, and candidates of repurposable drugs. Aims: To identify potential AF related genes and repurposable drug candidates using our deep learning framework. Methods: First, we collected the reported quantitative trait loci (QTLs) for AF from human heart tissues. Then, we identified the overlaps between the QTLs and the previously reported 150 AF GWAS loci in the latest meta-analysis. We previously built a comprehensive human protein-protein interactome using 18 publicly available databases, containing 351,444 unique PPIs and 17,706 proteins. Using the human protein-protein interactome and the overlaps between AF GWAS hits and QTLs, we prioritized genes and defined the genes with the top 1 % predicted score as AF risk genes (afRGs) using the NETTAG. Then, we assembled drugs from the Drugbank database relating 2,938 FDA-approved drugs or clinically investigated molecules. Using network proximity approaches to evaluate the closest distance between afRGs and a drug’s targets within the human protein-protein interactome, we computationally predicted drugs for AF using Z scores <-2.0. Results: We first collected the overlaps between AF GWAS hits and QTLs, which constituted 27 expression and 12 splicing QTLs. Via NETTAG, we identified 176 afRGs. Among the 176 predicted afRGs, 12 proteins (gene products of afRGs) have been identified as known drug targets with FDA-approved medicines. In total, 1,275 targets have been widely investigated as therapeutic targets for treating AF. Using the closest-based network proximity approach, we computationally identified 49 candidate drugs. These included drugs both reportedly potentially treating AF, such as Pioglitazone (Z=2.29), Telmisartan (Z=-2.52), Sildenafil (Z=-2.86), and those have never been reported before, such as Balsalazide (Z=-2.32). Conclusion: Using a deep learning methodology that utilized GWAS and QTL findings, we identified risk genes and repurposing drug candidates for AF.

Doxifluridine effectively kills antibiotic-resistant Staphylococcus aureus in chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality globally, often exacerbated by infections such as methicillin-resistant Staphylococcus aureus (MRSA). The rise in antibiotic-resistant strains complicates treatment and underscores the need for novel therapeutic drugs. In this paper, we further investigated the antimicrobial potential of a fluoropyrimidine anticancer drug doxifluridine against multidrug-resistant S. aureus. Determination of minimum inhibitory concentration (MIC) or minimum bactericidal concentration (MBC), monitoring of growth curve, time-kill assays, biofilm bactericidal assays, and chequerboard studies were conducted to evaluate the antibacterial efficacy of doxifluridine. Safety was assessed via hemolysis and cytotoxicity assays, and an in vivo Galleria mellonella larvae model was employed to test protective effects. Doxifluridine demonstrated significant antibacterial activity against clinical multidrug resistance (MDR) S. aureus isolates, with MIC and MBC values ranging from 0.5 to 2 µg/mL and 1 to 4 µg/mL, respectively. The results revealed doxifluridine's potent bactericidal effects within 8 hours. Moreover, doxifluridine-treated bacteria showed a substantial reduction in biofilm mass and viability. Furthermore, chequerboard assays indicated synergistic interactions between doxifluridine and other antibiotics, reducing MIC values by two- to eightfold. More importantly, safety evaluations confirmed that doxifluridine did not exhibit hemolytic toxicity or cytotoxicity. Finally, doxifluridine significantly increased the survival rate of MRSA-infected G. mellonella larvae in vivo. In brief, doxifluridine exhibited promising in vitro and in vivo antibacterial activity against MRSA, suggesting its potential as a repurposed drug for treating resistant bacterial infections in COPD patients.IMPORTANCEThe study provides robust evidence for the antibacterial efficacy of doxifluridine against Methicillin-resistant Staphylococcus aureus in chronic obstructive pulmonary disease (COPD) patients. Its rapid action, ability to disrupt biofilms, and synergistic effects with other antibiotics, combined with a favorable safety profile, highlight its potential as a repurposed therapeutic agent. Future clinical trials will be essential to confirm these findings and pave the way for its integration into clinical practice. This work not only provides candidate for tackling the management of bacterial infections in COPD but also exemplifies the potential of drug repurposing in combating antibiotic-resistant infections.