Pac Symp Biocomput. 2025;30:441-456.

ABSTRACT

Given the complexity and multifactorial nature of Alzheimer's disease, investigating potential drug-gene targets is imperative for developing effective therapies and advancing our understanding of the underlying mechanisms driving the disease. We present an explainable ML model that integrates the role and impact of gene interactions to drive the genomic variant feature selection. The model leverages both the Alzheimer's knowledge base and the Drug-Gene interaction database (DGIdb) to identify a list of biologically plausible novel gene-drug targets for further investigation. Model validation is performed on an ethnically diverse study sample obtained from the Alzheimer's Disease Sequencing Project (ADSP), a multi-ancestry multi-cohort genomic study. To mitigate population stratification and spurious associations from ML analysis, we implemented novel data curation methods. The study outcomes include a set of possible gene targets for further functional follow-up and drug repurposing.

PMID:39670388

Clin Transl Sci. 2024 Dec;17(12):e70078. doi: 10.1111/cts.70078.

ABSTRACT

In the rapidly evolving landscape of healthcare and drug development, the ability to efficiently collect, process, and analyze large volumes of real-world data (RWD) is critical for advancing drug development. This article provides a blueprint for establishing an end-to-end data and analytics pipeline in a cloud-based environment. The pipeline presented here includes four major components, including data ingestion, transformation, visualization, and analytics, each supported by a suite of Amazon Web Services (AWS) tools. The pipeline is exemplified through the CURE ID platform, a collaborative tool designed to capture and analyze real-world, off-label treatment administrations. By using services such as AWS Lambda, Amazon Relational Database Service (RDS), Amazon QuickSight, and Amazon SageMaker, the pipeline facilitates the ingestion of diverse data sources, the transformation of raw data into structured formats, the creation of interactive dashboards for data visualization, and the application of advanced machine learning models for data analytics. The described architecture not only supports the needs of the CURE ID platform, but also offers a scalable and adaptable framework that can be applied across various domains to enhance data-driven decision making beyond drug repurposing.

PMID:39670335 | DOI:10.1111/cts.70078

Front Cell Infect Microbiol. 2024 Nov 28;14:1470891. doi: 10.3389/fcimb.2024.1470891. eCollection 2024.

ABSTRACT

Antimicrobial resistance is among the greatest threats to public health globally, and drug repurposing strategies may be advantageous to addressing this problem. Mefloquine, a drug traditionally used to treat malaria, has emerged as a promising antibiotic adjuvant, due to its ability to enhance the effectiveness of conventional antibiotics against resistant bacterial strains. In this paper, we first outline the enhancement properties of mefloquine and its mechanisms of action as an adjuvant antibiotic against multidrug-resistant bacteria. Mefloquine exhibits synergistic bacteriostatic effects when combined with colistin, β-lactams, antituberculosis drugs, quinolones, and linezolid. Potential mechanisms underlying its synergistic effects include inhibition of antibiotic efflux, disruption of bacterial cell membrane integrity, and disturbance of biofilm formation. In addition, we explore the bacteriostatic effects of several mefloquine derivatives against Mycobacterium tuberculosis and some fungi. Further, we summarize the findings of recent studies on other aspects of mefloquine activity, including its antiviral and antitumor effects. Finally, the advantages and challenges of mefloquine use as an antibiotic adjuvant in combination with antibiotics for bacterial inhibition are discussed. Overall, mefloquine shows excellent potential as an antibiotic adjuvant therapy against multidrug-resistant bacteria and is a promising candidate for combination therapy; however, further studies are needed to fully elucidate its mechanism of action and address the challenges associated with its clinical application.

PMID:39669268 | PMC:PMC11634880 | DOI:10.3389/fcimb.2024.1470891

ACS Appl Mater Interfaces. 2024 Dec 12. doi: 10.1021/acsami.4c17961. Online ahead of print.

ABSTRACT

The heterogeneity of tumors and the lack of effective therapies have resulted in triple-negative breast cancer (TNBC) exhibiting the least favorable outcomes among breast cancer subtypes. TNBC is characterized by its aggressive nature, often leading to high rates of relapse, metastasis, and mortality. Niclosamide (Nic), an Food and Drug Administration-approved anthelmintic drug, has been repurposed for cancer treatment; however, its application for TNBC is hindered by significant challenges, including strong hydrophobicity, poor aqueous solubility, and low bioavailability. This study aimed to develop Nic nanoparticles (Nic NPs) using biodegradable and biocompatible polyanhydride copolymers to enhance Nic's bioavailability and therapeutic efficacy. Nic NPs effectively inhibited migration, proliferation, and clonogenicity in both murine and human TNBC cells, inducing apoptosis and suppressing STAT3 signaling. For the first time, we utilized Raman spectroscopy and Seahorse extracellular flux assays to demonstrate the metabolic responses of TNBC cells to Nic NPs, revealing significant metabolic alterations, including the inhibition of mitochondrial respiration and glycolysis. Additionally, this study is the first to explore the combination therapy of repurposed Nic with the approved chemotherapeutic agent paclitaxel in the 4T1 TNBC immunocompetent mouse model. The combination of Nic NPs and paclitaxel significantly reduced tumor growth without adversely affecting the body weight of tumor-bearing mice. In summary, these findings suggest that Nic NPs could serve as a promising component in combination therapies for the effective treatment of TNBC.

PMID:39666980 | DOI:10.1021/acsami.4c17961

Adv Sci (Weinh). 2024 Dec 12:e2406565. doi: 10.1002/advs.202406565. Online ahead of print.

ABSTRACT

Endometriosis affects over 190 million women globally, and effective therapies are urgently needed to address the burden of endometriosis on women's health. Using an artificial intelligence (AI)-driven target discovery platform, two unreported therapeutic targets, guanylate-binding protein 2 (GBP2) and hematopoietic cell kinase (HCK) are identified, along with a drug repurposing target, integrin beta 2 (ITGB2) for the treatment of endometriosis. GBP2, HCK, and ITGB2 are upregulated in human endometriotic specimens. siRNA-mediated knockdown of GBP2 and HCK significantly reduced cell viability and proliferation while stimulating apoptosis in endometrial stromal cells. In subcutaneous and intraperitoneal endometriosis mouse models, siRNAs targeting GBP2 and HCK notably reduced lesion volume and weight, with decreased proliferation and increased apoptosis within lesions. Both subcutaneous and intraperitoneal administration of Lifitegrast, an approved ITGB2 antagonist, effectively suppresses lesion growth. Collectively, these data present Lifitegrast as a previously unappreciated intervention for endometriosis treatment and identify GBP2 and HCK as novel druggable targets in endometriosis treatment. This study underscores AI's potential to accelerate the discovery of novel drug targets and facilitate the repurposing of treatment modalities for endometriosis.

PMID:39666559 | DOI:10.1002/advs.202406565

Postgrad Med J. 2024 Dec 12:qgae176. doi: 10.1093/postmj/qgae176. Online ahead of print.

ABSTRACT

BACKGROUND: The mechanisms underlying osteoarthritis (OA) remain unclear, and effective treatments are lacking. This study aims to identify OA-related genes and explore their potential in drug repositioning for OA treatment.

METHODS: Transcriptome-wide association studies (TWAS) were performed using genome-wide association studies summary data and expression quantitative trait loci data from the Genotype-Tissue Expression project. Differentially expressed genes between OA patients and healthy controls were identified using four datasets from the Gene Expression Omnibus database. Gene ontology and pathway enrichment analyses identified potential hub genes associated with OA. A network-based drug repositioning approach was applied to discover potential therapeutic drugs for OA.

RESULTS: Through TWAS and mRNA expression profiling, 7 and 167 OA-related genes were identified, respectively. From these, 128 OA-related genes were selected based on common biological processes. Using the maximal clique centrality algorithm, 10 core-related genes (JUN, VEGFA, FN1, CD44, PTGS2, STAT1, MAP 2K7, GRB2, EP300, and PXN) were identified for network-based drug repositioning. Consequently, 24 drugs were identified based on 128 OA-related genes and 23 drugs based on 10 core OA-related genes. Some identified drugs, such as dexamethasone, menadione, and hyaluronic acid, have been previously reported for OA and/or rheumatoid arthritis treatment. Network analysis also indicated that spironolactone, lovastatin, and atorvastatin may have potential in OA treatment.

CONCLUSION: This study identified potential OA-related genes and explored their roles in drug repositioning, suggesting the repurposing of existing drugs and the development of new therapeutic options for OA patients. Key message What is already known on this topic The exact pathogenesis of osteoarthritis (OA) remains unclear, and currently, there are no approved drugs that can prevent, halt, or inhibit the progression of OA. What this study adds We identified 128 OA-related genes and 10 core-related genes based on common biological processes revealed by TWAS and mRNA expression profiling. Using these genes, we discovered potential drugs for OA through the Network-based drug repositioning method. How this study might affect research, practice, or policy This study provides recommendations for repositioning existing drugs and developing new treatment options for patients with OA.

PMID:39665162 | DOI:10.1093/postmj/qgae176

Int J Biol Sci. 2024 Nov 11;20(15):6090-6113. doi: 10.7150/ijbs.99859. eCollection 2024.

ABSTRACT

Ferroptosis, a mode of cell death characterized by iron-dependent phospholipid peroxidation, has a substantial therapeutic potential for the treatment of various cancers. This study investigated the effects of trifluoperazine (TFP), an FDA-approved drug traditionally utilized for mental health disorder, on oral cancer cells, with a particular focus on the mechanisms involved in its potential anti-tumor properties. Our findings indicate that TFP significantly elevates the levels of lipid-derived reactive oxygen species (ROS) and induces ferroptotic cell death in oral cancer cells through pathways involving autophagy, the SLC7A11/GPX4 axis, and mitochondrial damage. Additionally, molecular docking analyses revealed that TFP acts as an inhibitor of GPX4. The elevated expression level of GPX4 in oral cancer biopsies was also found to correlate with a poor prognosis. Together, these results provide evidence that TFP selectively induces GPX4-mediated, autophagy-dependent ferroptosis, thereby exerting anti-cancer effects against oral cancer and preventable death.

PMID:39664583 | PMC:PMC11628333 | DOI:10.7150/ijbs.99859

Acta Pharm Sin B. 2024 Nov;14(11):5089-5090. doi: 10.1016/j.apsb.2024.09.006. Epub 2024 Sep 10.

ABSTRACT

[This corrects the article DOI: 10.1016/j.apsb.2021.12.005.].

PMID:39664436 | PMC:PMC11628776 | DOI:10.1016/j.apsb.2024.09.006

Sci Transl Med. 2024 Dec 11;16(777):eade5915. doi: 10.1126/scitranslmed.ade5915. Epub 2024 Dec 11.

ABSTRACT

Inflammatory skin disease is characterized by a pathologic interplay between skin cells and immunocytes and can result in disfiguring cutaneous lesions and systemic inflammation. Immunosuppression is commonly used to target the inflammatory component; however, these drugs are often expensive and associated with side effects. To identify previously unidentified targets, we carried out a nonbiased informatics screen to identify drug compounds with an inverse transcriptional signature to keratinocyte inflammatory signals. Using psoriasis, a prototypic inflammatory skin disease, as a model, we used pharmacologic, transcriptomic, and proteomic characterization to find that benzamil, the benzyl derivative of the US Food and Drug Administration-approved diuretic amiloride, effectively reversed keratinocyte-driven inflammatory signaling. Through three independent mouse models of skin inflammation (Rac1G12V transgenic mice, topical imiquimod, and human skin xenografts from patients with psoriasis), we found that benzamil disrupted pathogenic interactions between the small GTPase Rac1 and its adaptor NCK1. This reduced STAT3 and NF-κB signaling and downstream cytokine production in keratinocytes. Genetic knockdown of sodium channels or pharmacological inhibition by benzamil prevented excess Rac1-NCK1 binding and limited proinflammatory signaling pathway activation in patient-derived keratinocytes without systemic immunosuppression. Both systemic and topical applications of benzamil were efficacious, suggesting that it may be a potential therapeutic avenue for treating skin inflammation.

PMID:39661704 | DOI:10.1126/scitranslmed.ade5915

Int J Cancer. 2024 Dec 11. doi: 10.1002/ijc.35285. Online ahead of print.

ABSTRACT

Glucocorticoids (GCs), commonly used for anti-inflammatory and cancer treatments, have been linked to the promotion of cancer metastasis. Yet, the molecular mechanisms behind this potential remain poorly understood. Clarifying these mechanisms is crucial for a nuanced understanding and potential refinement of GC therapies in the context of cancer treatment. In HEK293T cells, co-immunoprecipitation (Co-IP) and chromatin immunoprecipitation sequencing (ChIP-seq) were used with antibodies of glucocorticoid receptor (GR) and ten-eleven translocation enzymes (TET) family proteins (TET1, TET2, TET3). Drug repositioning was performed through the Connectivity Map database, using common target genes of GR and TET2 in HEK293 and HCT116 cell lines and differentially expressed genes (DEGs) of colorectal cancer (CRC). Cell migration and invasion were tested in CRC cell lines with varying GR expression, that is, HCT116 and HT29 cell lines. Dexamethasone (Dex) treatment resulted in a significant difference in cell migration rates in two CRC cell lines with disparate GR expression levels. Co-IP and ChIP-seq analyses substantiated the interaction between GR and TET family proteins in HEK293T cells. Belinostat, the selected compound, was successfully validated for its potential to counteract the effects of GC-induced invasion in CRC cells in vitro. Transcriptomic analyses of Belinostat-treated HCT116 cells revealed down-regulation of target genes associated with cancer metastasis. This study provides valuable insights into the molecular mechanisms underlying GC-induced metastasis, introducing newly repositioned compounds that could serve as potential adjuvant therapy to GC treatment. Furthermore, it opens avenues for exploring novel drug candidates for CRC treatment.

PMID:39661335 | DOI:10.1002/ijc.35285

Rheumatology (Oxford). 2024 Dec 4:keae654. doi: 10.1093/rheumatology/keae654. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to address the lack of gene expression regulation data in synovial tissues and to identify conditionally independent genes associated with rheumatoid arthritis (RA) in the synovium, a primary target tissue for RA.

METHODS: Gene expression prediction models were built for synovial tissue using matched genotype and gene expression data from 202 subjects. Using this model, we conducted transcriptome-wide association study (TWAS), utilizing the largest RA genome-wide association study (GWAS) meta-analysis data (n = 276 020). Further analyses, including conditional and joint analysis, causal analysis, differential expression analysis and gene-set enrichment analysis, were conducted to deepen our understanding of genetic architecture and comorbidity aetiology of RA.

RESULTS: Our analysis identified eight genes associated with RA, including three novel genes: TPRA1 (PTWAS = 9.59 × 1 0 -6), HIP1 (PTWAS = 1.47 × 1 0 -5), and RP11-73E17.2 (PTWAS = 3.32 × 1 0 -7). These genes differed from those identified in previous TWAS studies using alternative tissues, may play a crucial role in the target synovial tissue. We found four genes exhibited significant causal relationships with RA and were differentially expressed in RA patients. Furthermore, we explored potential drug repurposing opportunities for these genes.

CONCLUSIONS: Our study is the first to model gene expression in synovial tissue, uncovering novel genetic determinants of RA. This advancement not only deepens our understanding of RA's genetic architecture, but also offers promising avenues for targeted therapies and drug repurposing.

PMID:39656803 | DOI:10.1093/rheumatology/keae654

Acta Crystallogr F Struct Biol Commun. 2025 Jan 1. doi: 10.1107/S2053230X24011348. Online ahead of print.

ABSTRACT

Plasmodium vivax, a significant contributor to global malaria cases, poses an escalating health burden on a substantial portion of the world's population. The increasing spread of P. vivax because of climate change underscores the development of new and rational drug-discovery approaches. The Seattle Structural Genomics Center for Infectious Diseases is taking a structure-based approach by investigating essential enzymes such as N-myristoyltransferase (NMT). P. vivax N-myristoyltransferase (PvNMT) is a promising target for the development of novel malaria treatments unlike current drugs, which target only the erythrocytic stages of the parasite. Here, the 1.8 Å resolution ternary structure of PvNMT in complex with myristoyl-CoA and IMP-1088, a validated NMT inhibitor, is reported. IMP-1088 is a validated nonpeptidic inhibitor and a ternary complex structure with human NMT has previously been reported. IMP-1088 binds similarly to PvNMT as to human NMT.

PMID:39655507 | DOI:10.1107/S2053230X24011348

Heliyon. 2024 Nov 15;10(23):e40404. doi: 10.1016/j.heliyon.2024.e40404. eCollection 2024 Dec 15.

ABSTRACT

The global outbreak of COVID-19 infection is the first pandemic the world has experienced in this 21st century. The novel coronavirus 2019 (nCoV-19) also called the SARS-CoV-2 is the reason behind the severe acute respiratory syndrome (SARS) that led to this worldwide crisis. In this current post-pandemic situation, despite having effective vaccines, the paucity of orally administrable drug molecules for such infections is a major drawback in this current scenario. Among the different viral enzymes, the SARS-CoV-2 3CLpro is an encouraging target for effective drug discovery and development. In this context, the understanding of the requirements of the small molecules at the active site and their interactions is a crucial aspect of such drug candidate development. Here in this study, structure-based pharmacophore model development and molecular docking-dependent 2D-interaction-based and 3D-field-based QSAR studies have been carried out for a set of potential SARS-CoV-2 3CLpro inhibitors. This study exposed the importance of interactions with amino acids of the active site (such as Leu167 and Gln189 amino acid residues) as well as the importance of hydrogen bond acceptor groups at the S2 and S1' pockets. The presence of hydrophobic aromatic features as well as hydrophobic contacts at the S1 and S4 pockets were also found to have a key contribution to the SARS-CoV-2 3CLpro inhibition. Moreover, the screened drug candidate Elobixibat from the structure-based virtual screening also explored promising results as evidenced in MD simulation study and thus, can be a promising drug candidate that can be repurposed to assist in the development of effective anti-SARS-CoV-2 therapy.

PMID:39654708 | PMC:PMC11626027 | DOI:10.1016/j.heliyon.2024.e40404

Mol Ther. 2024 Nov 22:S1525-0016(24)00727-5. doi: 10.1016/j.ymthe.2024.10.031. Online ahead of print.

ABSTRACT

Severe congenital neutropenia (CN) patients require life-long treatment with recombinant human granulocyte colony-stimulating factor (rhG-CSF), but some show no response. We sought to establish a therapy for CN that targets signaling pathways causing maturation arrest of granulocytic progenitors. We developed an isogenic induced pluripotent stem cell (iPSC) in vitro model of CN associated with ELANE mutations (ELANE-CN) and performed an in silico drug repurposing analysis of the transcriptomics of iPSC-generated hematopoietic stem and progenitor cells. We identified flavopiridol, a Food and Drug Administration (FDA)-approved pan-cyclin-dependent kinase inhibitor, as a potential therapeutic. Treatment with low-dose flavopiridol rescued defective granulopoiesis in primary CD34+ cells of CN patients with different inherited gene mutations in vitro and in two zebrafish CN models in vivo without any toxic effects and leading to functional granulocytes. Flavopiridol also restored granulopoiesis caused by diminished CEBPA expression, a known defective signaling molecule in CN. Thus, we described for the first time a potential therapy for CN with flavopiridol that could be potentially used to treat patients with different types of neutropenia.

PMID:39653038 | DOI:10.1016/j.ymthe.2024.10.031

Int J Technol Assess Health Care. 2024 Dec 9;40(1):e71. doi: 10.1017/S0266462324004628.

ABSTRACT

OBJECTIVES: It is vital that horizon scanning organizations can capture and disseminate intelligence on new and repurposed medicines in clinical development. To our knowledge, there are no standardized classification systems to capture this intelligence. This study aims to create a novel classification system to allow new and repurposed medicines horizon scanning intelligence to be disseminated to healthcare organizations.

METHODS: A multidisciplinary working group undertook literature searching and an iterative, three-stage piloting process to build consensus on a classification system. Supplementary data collection was carried out to facilitate the implementation and validation of the system on the National Institute of Health and Care Research (NIHR) Innovation Observatory (IO)'s horizon scanning database, the Medicines Innovation Database (MInD).

RESULTS: Our piloting process highlighted important issues such as the patency and regulatory approval status of individual medicines and how combination therapies interact with these characteristics. We created a classification system with six values (New Technology, Repurposed Technology (Off-patent/Generic), Repurposed Technology (On-patent/Branded), Repurposed Technology (Never commercialised), New + Repurposed Technology (Combinations-only), Repurposed Technology (Combinations-only)) that account for these characteristics to provide novel horizon scanning insights. We validated our system through application to over 20,000 technology records on the MInD.

CONCLUSIONS: Our system provides the opportunity to deliver concise yet informative intelligence to healthcare organizations and those studying the clinical development landscape of medicines. Inbuilt flexibility and the use of publicly available data sources ensure that it can be utilized by all, regardless of location or resource availability.

PMID:39651589 | DOI:10.1017/S0266462324004628

bioRxiv [Preprint]. 2024 Nov 26:2024.11.25.625000. doi: 10.1101/2024.11.25.625000.

ABSTRACT

Single-cell and spatial transcriptomics provide unprecedented insight into the inner workings of disease. Pharmacotranscriptomic approaches are powerful tools that leverage gene expression data for drug repurposing and treatment discovery in many diseases. Multiple databases attempt to connect human cellular transcriptional responses to small molecules for use in transcriptome-based drug discovery efforts. However, pre-clinical research often requires in vivo experiments in non-human species, which makes capitalizing on such valuable resources difficult. To facilitate the application of pharmacotranscriptomic databases to pre-clinical research models and to facilitate human orthologous conversion of non-human transcriptomes, we introduce OrthologAL. OrthologAL leverages the BioMart database to access different gene sets from Ensembl, facilitating the interaction between these servers without needing user-generated code. Researchers can input their single-cell or other high-dimensional gene expression data from any species, and OrthologAL will output a human ortholog-converted dataset for download and use. To demonstrate the utility of this application, we characterized orthologous conversion in single-cell, single-nuclei, and spatial transcriptomic data derived from common pre-clinical models, including patient-derived orthotopic xenografts of medulloblastoma, and mouse and rat models of spinal cord injury. We show that OrthologAL can convert these data types efficiently to that of corresponding orthologs while preserving the dimensional architecture of the original non-human expression data. OrthologAL will be broadly useful for applying pre-clinical, high-dimensional transcriptomics data in functional small molecule predictions using existing human-annotated databases.

PMID:39651293 | PMC:PMC11623543 | DOI:10.1101/2024.11.25.625000

medRxiv [Preprint]. 2024 Nov 27:2024.11.25.24317794. doi: 10.1101/2024.11.25.24317794.

ABSTRACT

RATIONALE: Accelerated decline in lung function contributes to the development of chronic respiratory disease. Despite evidence for a genetic component, few genetic associations with lung function decline have been identified.

OBJECTIVES: To evaluate genome-wide associations and putative downstream functionality of genetic variants with lung function decline in diverse general population cohorts.

METHODS: We conducted genome-wide association study (GWAS) analyses of decline in the forced expiratory volume in the first second (FEV 1 ), forced vital capacity (FVC), and their ratio (FEV 1 /FVC) in participants across six cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. Genotypes were imputed to TOPMed (CHARGE cohorts) or Haplotype Reference Consortium (HRC) (UK Biobank) reference panels, and GWAS analyses used generalized estimating equation models with robust standard error. Models were stratified by cohort, ancestry, and sex, and adjusted for important lung function confounders and genotype principal components. Results were combined in cross-ancestry and ancestry-specific meta-analyses. Selected top variants were tested for replication in two independent COPD-enriched cohorts.

MEASUREMENTS AND MAIN RESULTS: Our discovery analyses included 52,056 self-reported White (N=44,988), Black (N=5,788), Hispanic (N=550), and Chinese American (N=730) participants with a mean of 2.3 spirometry measurements and 8.6 years of follow-up. Functional mapping of GWAS meta-analysis results identified 361 distinct genome-wide significant (p<5E-08) variants in one or more of the FEV 1 , FVC, and FEV 1 /FVC decline phenotypes, which overlapped with previously reported genetic signals for several related pulmonary traits. Of these, 8 variants, or 20.5% of the variant set available for replication testing, were nominally associated (p<0.05) with at least one decline phenotype in COPD-enriched cohorts (White [N=4,778] and Black [N=1,118]). Using the GWAS results, gene-level analysis implicated 38 genes, including eight ( XIRP2 , GRIN2D , SATB1 , MARCHF4 , SIPA1L2 , ANO5 , H2BC10 , and FAF2 ) with consistent associations across ancestries or decline phenotypes. Annotation class analysis revealed significant enrichment of several regulatory processes for corticosteroid biosynthesis and metabolism. Drug repurposing analysis identified 43 approved compounds targeting eight of the implicated 38 genes.

CONCLUSIONS: Our multi-ancestry GWAS meta-analyses identified numerous genetic loci associated with lung function decline. These findings contribute knowledge to the genetic architecture of lung function decline, provide evidence for a role of endogenous corticosteroids in the etiology of lung function decline, and identify drug targets that merit further study for potential repurposing to slow lung function decline and treat lung disease.

PMID:39649580 | PMC:PMC11623738 | DOI:10.1101/2024.11.25.24317794

J Comput Biol. 2024 Dec 9. doi: 10.1089/cmb.2023.0427. Online ahead of print.

ABSTRACT

Developing a new drug is a long and expensive process that typically takes 10-15 years and costs billions of dollars. This has led to an increasing interest in drug repositioning, which involves finding new therapeutic uses for existing drugs. Computational methods become an increasingly important tool for identifying associations between drugs and new diseases. Graph- and hypergraph-based approaches are a type of computational method that can be used to identify potential associations between drugs and new diseases. Here, we present a drug repurposing method based on hypergraph neural network for predicting drug-disease association in three stages. First, it constructs a heterogeneous graph that contains drug and disease nodes and links between them; in the second stage, it converts the heterogeneous simple graph to a hypergraph with only disease nodes. This is achieved by grouping diseases that use the same drug into a hyperedge. Indeed, all the diseases that are the common therapeutic goal of a drug are placed on a hyperedge. Finally, a graph neural network is used to predict drug-disease association based on the structure of the hypergraph. This model is more efficient than other methods because it uses a hypergraph to model relationships more effectively than graphs. Furthermore, it constructs the hypergraph using only a drug-disease association matrix, eliminating the need for extensive amounts of data. Experimental results show that the hypergraph-based approach effectively captures complex interrelationships between drugs and diseases, leading to improved accuracy of drug-disease association prediction compared to state-of-the-art methods.

PMID:39648844 | DOI:10.1089/cmb.2023.0427

Cancer Rep (Hoboken). 2024 Dec;7(12):e70072. doi: 10.1002/cnr2.70072.

ABSTRACT

BACKGROUND: Paclitaxel (PTX) has been used as a chemotherapeutic agent for several malignancies, including breast cancer, and efforts to increase the efficiency of PTX are continuous. Previous studies have shown that the voltage-gated K+ channels are over-expressed in breast cancer cell lines; therefore, blocking this type of K+ channel reduces cell proliferation and viability.

AIMS: In this study, FDA-approved 4-aminopyridine (4-AP), a voltage-gated potassium channel blocker, was used in combination with PTX to improve the anticancer activity of PTX in MCF-7 and MDA-MB-231 cell lines.

METHODS AND RESULTS: Viability was determined with trypan blue, a clonogenic assay was performed, and the cell cycle was determined with a flow cytometer and immunochemistry. To gain an insight into the mechanism, intracellular K+ concentration, intracellular Ca2+ (calcium) concentration, and transmembrane potential measurements were made with corresponding fluorescent dyes. The apoptotic cell number was determined using Annexin /PI method by flow cytometer. Viability decreased with combination therapy and the clonogenic assay proved decreased colony formation. The apoptotic cell number was increased after treatment with the combination in both cell lines. Cell cycle measurements showed G1 arrest for both MCF-7 and MDA-MB-231 cell lines upon 4-AP treatment. PTX caused G1 arrest in MCF-7 cells and S phase arrest in MDA-MB-231 cells. Combination treatment caused S phase arrest in MCF-7 cells and S phase and G2/M phase arrest in MDA-MB-231 cells. Intracellular K+ concentration was increased after all treatments in both cell lines. Ca2+ concentration was increased significantly after combination treatment. Depolarization in the transmembrane potential was observed after all treatments in both cell lines.

CONCLUSION: Biophysical parameters like the transmembrane potential and ion fluxes have been defined in cancer progression which can provide new aspects for cancer treatments. This study shows that the combination of 4-AP with PTX is a promising alternative the mechanism of which needs further investigation considering the results obtained for Ca2+, K+, and membrane potential.

PMID:39648339 | DOI:10.1002/cnr2.70072

Biochemistry (Mosc). 2024 Nov;89(11):1868-1888. doi: 10.1134/S0006297924110026.

ABSTRACT

Substance use disorders are associated with structural and functional changes in the neuroendocrine, neuromediator, and neuromodulator systems in brain areas involved in the reward and stress response circuits. Chronic intoxication provokes emergence of somatic diseases and aggravates existing pathologies. Substance use disorders and somatic diseases often exacerbate the clinical courses of each other. Elucidation of biochemical pathways common for comorbidities may serve as a basis for the development of new effective pharmacotherapy agents, as well as drug repurposing. Here, we discussed molecular mechanisms underlying integration of visceral systems into the central mechanisms of drug dependence.

PMID:39647817 | DOI:10.1134/S0006297924110026