Mol Cell Biochem. 2024 Dec 23. doi: 10.1007/s11010-024-05192-w. Online ahead of print.

ABSTRACT

The global burden of cancer as a major cause of death and invalidity has been constantly increasing in the past decades. Monoamine oxidases (MAO) with two isoforms, MAO-A and MAO-B, are mammalian mitochondrial enzymes responsible for the oxidative deamination of neurotransmitters and amines in the central nervous system and peripheral tissues with the constant generation of hydrogen peroxide as the main deleterious ancillary product. However, given the complexity of cancer biology, MAO involvement in tumorigenesis is multifaceted with different tumors displaying either an increased or decreased MAO profile. MAO inhibitors are currently approved for the treatment of neurodegenerative diseases (mainly, Parkinson's disease) and as secondary/adjunctive therapeutic options for the treatment of major depression. Herein, we review the literature characterizing MAO's involvement and the putative role of MAO inhibitors in several malignancies, and also provide perspectives regarding the potential biomarker role that MAO could play in the future in oncology.

PMID:39714760 | DOI:10.1007/s11010-024-05192-w

Angew Chem Int Ed Engl. 2024 Dec 23:e202420547. doi: 10.1002/anie.202420547. Online ahead of print.

ABSTRACT

Microglial phagocytosis is a highly energy-consuming process that plays critical roles in clearing neurotoxic amyloid-β (Aβ) in Alzheimer's disease (AD). However, microglial metabolism is defective overall in AD, thereby undermining microglial phagocytic functions. Herein, we repurpose the existing antineoplastic drug lonidamine (LND) conjugated with hollow mesoporous Prussian blue (HMPB) as a "microglial energy modulator" (termed LND@HMPB-T7) for safe and synergistic Aβ clearance. The modified blood-brain barrier penetrating heptapeptide (T7) enables efficient transport of LND@HMPB-T7 to the AD brain. LND in LND@HMPB-T7 could fuel Aβ phagocytosis by stimulating microglial adenosine triphosphate (ATP) production, whereas HMPB with catalase and superoxide dismutase-mimicking activities substantially alleviates the mitochondrial side effects commonly associated with LND and thus further enhances ATP production. The synergism of LND and nanozyme affords a high microglial Aβ clearance efficacy without triggering mitochondrial dysfunction. In vivo experiments ascertain that LND@HMPB-T7 could synergistically promote phagocytic clearance of Aβ, relieve neuroinflammation and ameliorate cognitive function in AD mice. These findings indicate that LND@HMPB-T7 holds tremendous clinical potential as a repurposed drug for AD treatment.

PMID:39714451 | DOI:10.1002/anie.202420547

medRxiv [Preprint]. 2024 Dec 14:2024.12.13.24318996. doi: 10.1101/2024.12.13.24318996.

ABSTRACT

Recent advancements in Parkinson's disease (PD) drug development have been significantly driven by genetic research. Importantly, drugs supported by genetic evidence are more likely to be approved. While genome-wide association studies (GWAS) are a powerful tool to nominate genomic regions associated with certain traits or diseases, pinpointing the causal biologically relevant gene is often challenging. Our aim was to prioritize genes underlying PD GWAS signals. The polygenic priority score (PoPS) is a similarity-based gene prioritization method that integrates genome-wide information from MAGMA gene-level association tests and more than 57,000 gene-level features, including gene expression, biological pathways, and protein-protein interactions. We applied PoPS to data from the largest published PD GWAS in East Asian- and European-ancestries. We identified 120 independent associations with P < 5×10 -8 and prioritized 46 PD genes across these loci based on their PoPS scores, distance to the GWAS signal, and presence of non-synonymous variants in the credible set. Alongside well-established PD genes ( e.g., TMEM175 and VPS13C ), some of which are targeted in ongoing clinical trials ( i.e. , SNCA , LRRK2 , and GBA1 ), we prioritized genes with a plausible mechanistic link to PD pathogenesis ( e.g., RIT2, BAG3 , and SCARB2 ). Many of these genes hold potential for drug repurposing or novel therapeutic developments for PD ( i.e., FYN, DYRK1A, NOD2, CTSB, SV2C, and ITPKB ). Additionally, we prioritized potentially druggable genes that are relatively unexplored in PD ( XPO1, PIK3CA, EP300, MAP4K4, CAMK2D, NCOR1, and WDR43 ). We prioritized a high-confidence list of genes with strong links to PD pathogenesis that may represent our next-best candidates for disease-modifying therapeutics. We hope our findings stimulate further investigations and preclinical work to facilitate PD drug development programs.

PMID:39711693 | PMC:PMC11661345 | DOI:10.1101/2024.12.13.24318996

FEBS J. 2024 Dec 22. doi: 10.1111/febs.17370. Online ahead of print.

ABSTRACT

Japanese encephalitis virus (JEV) is the leading causative agent of viral encephalitis in India and contributes to a significant disease burden in South Asian countries. However, no antiviral treatment is available against JEV-induced encephalitis, highlighting the urgent need for novel therapeutic approaches. Repurposing or repositioning drugs was found to be more economical and practical in the current drug development scenario. The present study aimed to develop a host-directed strategy through a computational drug repurposing approach. As part of the strategy, we first generated a dynamic signature of differentially expressed JEV infection-associated proteins in mice brains through a semiquantitative proteomics approach. With the help of the Connectivity Map (CMap) analysis, we narrowed down the lists of drugs with a high negative CMap score (-70 or lower). Based on the CMap score, we chose the top three compounds (Tipifarnib, Ly303511 and MDL11939) with CMap scores of -91.83, -88.18 and -91.15, respectively. The antiviral potential of these three compounds was further compared in both JEV-infected mouse neuroblastoma cells and C57BL/6 mice. Oral administration of Ly303511 and MDL11939, alone or in combination, showed improved outcomes (e.g. delayed death, increased survival, and less viral load than Tipifarnib alone or combined). The JEV-infected mice survived upon drug treatment, effectively reducing viral load and reversing the antiviral signature. Our results highlight Ly303511 and MDL11939 as promising host-targeted inhibitors of JEV infection and pathogenesis. Moreover, our results favor the combination of Ly303511 and MDL11939 therapy to improve clinical symptoms and reduce JEV-induced damage, thus warranting inclusion in clinical studies.

PMID:39710957 | DOI:10.1111/febs.17370

Int J Biol Macromol. 2024 Dec 19:139003. doi: 10.1016/j.ijbiomac.2024.139003. Online ahead of print.

ABSTRACT

In the current study, we presented the genome sequence and taxonomic classification of the new extensively drug-resistant (XDR) Salmonella enterica serovar Typhi strain JRCGR-ST-AK02. Its genome size was found to be 4,780,534 bp, containing 4864 genes. Taxonomic classification was performed based on the Average Nucleotide Identity (ANI), Genome-to-Genome Distance Calculator (GGDC) and Average Amino Acid Identity (AAI) analysis. Pan-genome analysis revealed 34,4915 core genes, which are predominantly involved in general functions and carbohydrate metabolism. We used a subtractive genomics approach and identified the PocR protein as a drug target. Its 3D structure was built using homology modeling, and an e-pharmacophore hypothesis was created using its binding site. The pharmacophore hypothesis was screened against FDA-approved ligands library and a total of 2018 out 9392 drugs were selected for molecular docking. Cangrelor and Pentagastrin presented the highest docking scores (≥ -9.0). The binding dynamics of these promising FDA-approved drugs were further confirmed through 200 ns molecular dynamics simulation, highlighting their stable and strong interactions with the PocR protein. Our study highlights the potential of Cangrelor and Pentagastrin for repurposing against XDR Salmonella Typhi. By identifying these drugs as promising candidates, we pave the way for new treatments for XDR Salmonella Typhi infections.

PMID:39708886 | DOI:10.1016/j.ijbiomac.2024.139003

Mol Divers. 2024 Dec 21. doi: 10.1007/s11030-024-11081-7. Online ahead of print.

ABSTRACT

Prostate cancer (PC) is among the most prevalent cancers in males. It is the leading cause of death in men, in around 48 out of 185 countries. Increased androgen receptor (AR) activity is the key factor contributing to the development or progression of newly diagnosed cases of prostate cancer. Over time, numerous compounds targeting AR have been identified, presenting encouraging avenues for suppressing its hyperactivity. In our investigation, we used the GEPIA tool to study the importance of AR in the context of prostate cancer. This tool integrates the data from TCGA and GTEx in the gene expression pattern analysis and their clinical relevance. This analysis evaluates overall survival, disease-free survival, and transcripts per million (TPM) analysis of AR in PC. We performed docking and simulation for FDA-approved anticancer drugs to assess their potential interactions with the AR. We also conducted a comprehensive analysis of drugs using a quantum calculation (DFT) which provides electronic properties, chemical reactivity, and stability using the HOMO-LUMO energy gap. This study suggests that repurposed synthetic anticancer drugs could be better options for treating prostate cancer by inhibiting AR. In this work, we have shown the potential of pomalidomide, a synthetic anticancer drug, as a potential candidate for androgen-dependent PC treatment.

PMID:39708063 | DOI:10.1007/s11030-024-11081-7

Mol Med. 2024 Dec 19;30(1):245. doi: 10.1186/s10020-024-01016-1.

ABSTRACT

BACKGROUND: NLRP3 inflammasome immoderate activation results in the occurrence of various inflammatory diseases, but the clinic medications targeting NLRP3 inflammasome are still not available currently. The strategy of drug repurposing can reorient the direction of therapy, which is an indispensable method of drug research. In this study, an antimicrobial agent chlorquinaldol (CQ) was conducted to assess the effect on NLRP3 inflammasome and novel clinical value on NLRP3-driven diseases.

METHODS: The effect of CQ on NLRP3 inflammasome activation and pyroptosis was studied in mouse and human macrophages. ASC oligomerization, intracellular potassium, reactive oxygen species production, and NLRP3-ASC interaction were used to evaluate the suppression mechanism of CQ on inflammasome activation. Finally, the ameliorative effects of CQ in the model of LPS-induced peritonitis, dextran sodium sulfate (DSS)-induced colitis, and monosodium urate (MSU)-induced gouty arthritis were evaluated in vivo.

RESULTS: CQ is a highly powerful NLRP3 inhibitor that has feeble impact on the NLRC4 or AIM2 inflammasome activation in mouse and human macrophages. Further study indicated that CQ exhibits its suppression effect on NLRP3 inflammasome by blocking NLRP3-ASC interaction and hydroxyl on the benzene ring is vital for the assembly and activation of NLRP3 inflammasome. Furthermore, in vivo experiments demonstrated that administration of CQ has outstanding therapeutic action on LPS-induced peritonitis, DSS-induced colitis, and MSU-induced gouty inflammation in mice.

CONCLUSIONS: Collectively, the current study discoveries the antimicrobial agent CQ as a potentially specific NLRP3 inhibitor, and its use provides a feasible therapeutic approach for the treatment of NLRP3-driven diseases.

PMID:39701924 | DOI:10.1186/s10020-024-01016-1

PLoS One. 2024 Dec 19;19(12):e0316010. doi: 10.1371/journal.pone.0316010. eCollection 2024.

ABSTRACT

Alzheimer's disease (AD) is characterized by the aggregation of amyloid β (Aβ) peptides and the formation of plaques in the brain, primarily derived from the proteolytic degradation of amyloid precursor protein (APP). Cathepsin B (CatB) is a cysteine protease that plays a pivotal role in this process, making it a potential target for the development of anti-Alzheimer's therapies. Apart from AD, CatB is implicated in various physiological and pathological processes, including cancer. Given the critical role of CatB in these diseases, identifying effective inhibitors is of significant therapeutic interest. In this study, we employed a systematic virtual screening approach using repurposed molecules from the DrugBank database to identify potential CatB inhibitors. Primarily, we focused on binding affinities and selectivity to pinpoint potential hits against CatB. Two repurposed molecules, Lurasidone and Paliperidone, emerged as promising candidates with significant affinity for CatB. These molecules demonstrated favorable drug profiles and exhibited preferential binding to the catalytic pocket of CatB via interacting with functionally significant residues. To further explore the binding mechanism and stability of the CatB-drug complexes, molecular dynamics (MD) simulations were conducted for 500 ns. The results revealed that CatB and Lurasidone, as well as Paliperidone, form stable complexes throughout the simulation. Taken together, the findings suggest that Lurasidone and Paliperidone can act as repurposed CatB inhibitors with potential applications in the development of therapeutics against AD and other CatB-associated diseases after further validation.

PMID:39700174 | DOI:10.1371/journal.pone.0316010

ACS Nano. 2024 Dec 19. doi: 10.1021/acsnano.4c16547. Online ahead of print.

ABSTRACT

Drug repurposing refers to excavating clinically approved drugs for new clinical indications, effectively shortening the cost and time of clinical evaluation due to the established molecular structure, pharmacokinetics, and pharmacodynamics. In this sense, clinically approved Prussian blue (PB) has received considerable attention, by virtue of its unique optical, magnetic, and enzymatic performance. Nevertheless, the clinical transformation of PB-based nanodrugs remains restricted owing to their complex synthetic formulation and constrained therapeutic performance. Herein, inspired by diagnostic and therapeutic superiorities of lanthanide ions, a series of ytterbium (Yb)-containing PB nanoparticles (NPs) are synthesized in one step through interstitial Yb-doping, which aims to improve the anticancer efficacy of PB and expand the biological application orientation of Yb ions. Through a systematic comparative analysis, involving microscopic morphology, size distribution, elemental composition, raw material utilization rate, and crystal structure, Yb-enriched PB NPs with better-balanced indexes are identified as an antineoplastic drug candidate. In parallel, their anticancer mechanisms are associated with the mammalian target of rapamycin (mTOR) and adenosine monophosphate-activated protein kinase (AMPK) pathways, thus disturbing anabolism, catabolism, and homeostasis. Therefore, this study attempts to implement the concept of drug repurposing and lays the foundation for next-generation theranostic nanodrugs.

PMID:39699092 | DOI:10.1021/acsnano.4c16547

Autophagy. 2024 Dec 19. doi: 10.1080/15548627.2024.2439649. Online ahead of print.

ABSTRACT

HSPB1 [heat shock protein family B (small) member 1] and HSPB8 are essential molecular chaperones for neuronal proteostasis, as they prevent protein aggregation. Mutant HSPB1 and HSPB8 primarily harm peripheral neurons, resulting in axonal Charcot-Marie-Tooth neuropathies (CMT2). Macroautophagy/autophagy is a shared mechanism by which HSPB1 and HSPB8 mutations cause neuronal dysfunction. Autophagosome formation is reduced in mutant HSPB1-induced pluripotent stem-cell-derived motor neurons from CMT type 2F patients. Likewise, the HSPB8K141N knockin mouse model, mimicking CMT type 2 L, exhibits axonal degeneration and muscle atrophy, with SQSTM1/p62-positive deposits. We show here that mouse embryonic fibroblasts isolated from a HSPB8K141N/green fluorescent protein (GFP)-LC3 model have diminished autophagosome production under conditions of MTOR inhibition. To correct the autophagic deficits in the HSPB1 and HSPB8 models, we screened by high-throughput autophagosome quantification the repurposing Spectrum Collection library for molecules that could boost the autophagic activity above the canonical MTOR inhibition. Hit compounds were validated on motor neurons obtained by differentiation of HSPB1P182L and HSPB8K141N patient-derived induced pluripotent stem cells, focusing on autophagy induction as well as neurite network density, axonal degeneration, and mitochondrial morphology. We identified molecules that specifically stimulate autophagosome formation in the HSPB8K141N cells, without affecting autophagy flux. Two top lead compounds induced autophagy and reduced axonal degeneration, thus promoting neuronal network maturation in the CMT2 patient-derived motor neurons. Based on these findings, the phenotypical screen revealed that piplartine rescued autophagy deficiencies in both the HSPB1 and HSPB8 models, demonstrating autophagy induction as an effective therapeutic strategy for CMT neuropathies and other chaperonopathies.

PMID:39698979 | DOI:10.1080/15548627.2024.2439649

ACS Pharmacol Transl Sci. 2024 Oct 31;7(12):3780-3794. doi: 10.1021/acsptsci.4c00545. eCollection 2024 Dec 13.

ABSTRACT

Breast cancer remains one of the most prevalent and challenging cancers to treat due to its complexity and heterogenicity. Cellular processes such as metabolic reprogramming and epithelial-to-mesenchymal transition (EMT) contribute to the complexity of breast cancer by driving uncontrolled cell division, metastasis, and resistance to therapies. Strategically targeting these intricate pathways can effectively impede breast cancer progression, thereby revealing significant potential for therapeutic interventions. Among various emerging therapeutic approaches, drug repurposing offers a promising avenue for enhancing clinical outcomes. In recent years, high-throughput screening, QSAR, and network pharmacology have been widely employed to identify promising repurposed drugs. As an outcome, several drugs, such as Metformin, Itraconazole, Pimozide, and Disulfiram, were repurposed to regulate metabolic and EMT pathways. Moreover, strategies such as combination therapy, targeted delivery, and personalized medicine were utilized to enhance the efficacy and specificity of the repurposed drugs. This review focuses on the potential of targeting altered metabolism and EMT in breast cancer through drug repurposing. It also highlights recent advancements in drug screening techniques, associated limitations, and strategies to overcome these challenges.

PMID:39698277 | PMC:PMC11650739 | DOI:10.1021/acsptsci.4c00545

ACS Pharmacol Transl Sci. 2024 Nov 5;7(12):4043-4055. doi: 10.1021/acsptsci.4c00512. eCollection 2024 Dec 13.

ABSTRACT

Drug repurposing can serve an important role in rapidly discovering medicament options for emerging microbial pandemics. In this study, a pragmatic approach is demonstrated for screening and testing drug combinations as potential broad-spectrum therapies against SARS-CoV-2 and other betacoronaviruses. Rapid cell-based phenotypic small molecule screens were executed using related common-cold-causing HCoV-OC43 betacoronavirus to identify replication inhibitors from a library of drugs approved by regulatory agencies for other indications. Given the best inhibitors, an expedient checkerboard strategy then served to identify synergistic drug combinations. These combinations were then validated using more challenging assays involving SARS-CoV-2 and variants. Promising drug combinations against multiple viral variants were discovered and involved Tilorone with Nelfinavir or Molnupiravir.

PMID:39698276 | PMC:PMC11650740 | DOI:10.1021/acsptsci.4c00512

BMC Bioinformatics. 2024 Dec 18;25(1):377. doi: 10.1186/s12859-024-06009-9.

ABSTRACT

The evaluation of drug-gene-disease interactions is key for the identification of drugs effective against disease. However, at present, drugs that are effective against genes that are critical for disease are difficult to identify. Following a disease-centric approach, there is a need to identify genes critical to disease function and find drugs that are effective against them. By contrast, following a drug-centric approach comprises identifying the genes targeted by drugs, and then the diseases in which the identified genes are critical. Both of these processes are complex. Using a gene-centric approach, whereby we identify genes that are effective against the disease and can be targeted by drugs, is much easier. However, how such sets of genes can be identified without specifying either the target diseases or drugs is not known. In this study, a novel artificial intelligence-based approach that employs unsupervised methods and identifies genes without specifying neither diseases nor drugs is presented. To evaluate its feasibility, we applied tensor decomposition (TD)-based unsupervised feature extraction (FE) to perform drug repositioning from protein-protein interactions (PPI) without any other information. Proteins selected by TD-based unsupervised FE include many genes related to cancers, as well as drugs that target the selected proteins. Thus, we were able to identify cancer drugs using only PPI. Because the selected proteins had more interactions, we replaced the selected proteins with hub proteins and found that hub proteins themselves could be used for drug repositioning. In contrast to hub proteins, which can only identify cancer drugs, TD-based unsupervised FE enables the identification of drugs for other diseases. In addition, TD-based unsupervised FE can be used to identify drugs that are effective in in vivo experiments, which is difficult when hub proteins are used. In conclusion, TD-based unsupervised FE is a useful tool for drug repositioning using only PPI without other information.

PMID:39696005 | DOI:10.1186/s12859-024-06009-9

Diabetologia. 2024 Dec 19. doi: 10.1007/s00125-024-06339-6. Online ahead of print.

ABSTRACT

Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals who will progress faster to a clinical diagnosis of type 1 diabetes, and heterogeneous clinical responses in intervention trials. Classic placebo-controlled intervention trials often include monotherapies, broad participant populations and extended follow-up periods focused on clinical endpoints. While this approach remains the 'gold standard' of clinical research, efforts are underway to implement new approaches harnessing the power of artificial intelligence and machine learning to accelerate drug discovery and efficacy testing. Here, we review emerging approaches for repurposing agents used to treat diseases that share pathogenic pathways with type 1 diabetes and selecting synergistic combinations of drugs to maximise therapeutic efficacy. We discuss how emerging multi-omics technologies, including analysis of antigen processing and presentation to adaptive immune cells, may lead to the discovery of novel biomarkers and subsequent translation into antigen-specific immunotherapies. We also discuss the potential for using artificial intelligence to create 'digital twin' models that enable rapid in silico testing of personalised agents as well as dose determination. To conclude, we discuss some limitations of artificial intelligence and machine learning, including issues pertaining to model interpretability and bias, as well as the continued need for validation studies via confirmatory intervention trials.

PMID:39694914 | DOI:10.1007/s00125-024-06339-6

Antiviral Res. 2024 Dec 16:106057. doi: 10.1016/j.antiviral.2024.106057. Online ahead of print.

ABSTRACT

While the COVID-19 crisis is still ongoing, a new public health threat has emerged with recent outbreaks of monkeypox (mpox) infections in Africa. Mass vaccination is not currently recommended by the World Health Organization (WHO), and antiviral treatments are yet to be specifically approved for mpox, although existing FDA-approved drugs (Tecovirimat, Brincidofovir, and Cidofovir) may be used in severe cases or for immunocompromised patients. A first-line of defense is thus drug repurposing, which was heavily attempted against SARS-CoV-2 - albeit with limited success. This review focuses on nucleoside analogues as promising antiviral candidates for targeting of the viral DNA-dependent DNA polymerase. In contrast to broad-spectrum screening approaches employed for SARS-CoV-2, we emphasize the importance of understanding the structural specificity of viral polymerases for rational selection of potential candidates. By comparing DNA-dependent DNA polymerases with other viral polymerases, we highlight the unique features that influence the efficacy and selectivity of nucleoside analogues. These structural insights provide a framework for the preselection, repurposing, optimization, and design of nucleoside analogues, aiming to accelerate the development of targeted antiviral therapies for mpox and other viral infections.

PMID:39694420 | DOI:10.1016/j.antiviral.2024.106057

Biomed Pharmacother. 2024 Dec 17;182:117756. doi: 10.1016/j.biopha.2024.117756. Online ahead of print.

ABSTRACT

Therapy resistance in human cancers is a major limitation in Clinical Oncology. In this regard, overexpression of anti-apoptotic proteins, such as survivin, has been described in several tumors, contributing to this clinical issue. Survivin has a dual role in key cellular functions, inducing cell cycle progression and inhibiting apoptosis; thus, survivin is an attractive target for cancer therapy. Therefore, we focused on identifying and validating a novel specific, directly binding survivin inhibitor for cancer treatment and tumor sensitization to conventional proapoptotic therapies. In this work, we conducted a structure-based high-throughput virtual screening at the survivin homodimerization domain. Asenapine Maleate (AM), an approved drug for central nervous system diseases, was identified as a direct binder of the survivin homodimerization domain and it significantly affected cell viability of lung, colon, and brain cancer cell lines. Direct interaction of AM to survivin protein was corroborated by surface plasmon resonance and a specific survivin protein decrease was observed in cancer cells, compared to other inhibitors of apoptosis proteins. Therapeutic in vivo studies showed an impairment of tumor growth in AM-treated mice. Finally, a synergistic anticancer effect was detected in vitro when combined with different conventional chemotherapies, and in vivo studies showed higher antitumor effects when combined with cisplatin. Altogether, our results identify AM as a specific direct binding inhibitor of survivin, showing anticancer properties in vitro and in vivo and sensitizing effects when combined with cisplatin, opening the possibility of repositioning this approved drug for cancer treatment.

PMID:39693907 | DOI:10.1016/j.biopha.2024.117756

Comput Biol Chem. 2024 Dec 5;115:108302. doi: 10.1016/j.compbiolchem.2024.108302. Online ahead of print.

ABSTRACT

Drug Repurposing gives us facility to find the new uses of previously developed drugs rather than developing new drugs from start. Particularly during pandemic, drug repurposing caught much attention to provide new applications of the previously approved drugs. In our research, we provide a novel method for drug repurposing based on feature learning process from drug-disease-gene network. In our research, we aimed at finding drug candidates which can be repurposed under neurodegenerative diseases and glioma. We collected association data between drugs, diseases and genes from public resources and primarily examined the data related to Alzheimer's, Parkinson's and Glioma diseases. We created a Knowledge Graph using neo4j by integrating all these datasets and applied scalable feature learning algorithm known as node2vec to create node embeddings. These embeddings were later used to predict the unknown associations between disease and their candidate drugs by finding cosine similarity between disease and drug nodes embedding. We obtained a definitive set of candidate drugs for repurposing. These results were validated from the literature and CodReS online tool to rank the candidate drugs. Additionally, we verified the status of candidate drugs from pharmaceutical knowledge databases to confirm their significance.

PMID:39693851 | DOI:10.1016/j.compbiolchem.2024.108302

ACS Nano. 2024 Dec 18. doi: 10.1021/acsnano.4c10136. Online ahead of print.

ABSTRACT

The outbreak of emerging acute viral diseases urgently requires the acceleration of specialized antiviral drug development, thus widely adopting phenotypic screening as a strategy for drug repurposing in antiviral research. However, traditional phenotypic screening methods typically require several days of experimental cycles and lack visual confirmation of a drug's ability to inhibit viral infection. Here, we report a robust method that utilizes quantum-dot-based single-virus tracking and machine learning to generate unique single-virus infection fingerprint data from viral trajectories and detect the dynamic changes in viral movement following drug administration. Our findings demonstrated that this approach can successfully identify viral infection patterns at various infection phases and predict antiviral drug efficacy through machine learning within 90 min. This method provides valuable support for assessing the efficacy of antiviral drugs and offers promising applications for responding to future outbreaks of emerging viruses.

PMID:39692754 | DOI:10.1021/acsnano.4c10136

Brief Bioinform. 2024 Nov 22;26(1):bbae650. doi: 10.1093/bib/bbae650.

ABSTRACT

Drug repositioning, which involves identifying new therapeutic indications for approved drugs, is pivotal in accelerating drug discovery. Recently, to mitigate the effect of label sparsity on inferring potential drug-disease associations (DDAs), graph contrastive learning (GCL) has emerged as a promising paradigm to supplement high-quality self-supervised signals through designing auxiliary tasks, then transfer shareable knowledge to main task, i.e. DDA prediction. However, existing approaches still encounter two limitations. The first is how to generate augmented views for fully capturing higher-order interaction semantics. The second is the optimization imbalance issue between auxiliary and main tasks. In this paper, we propose a novel heterogeneous Graph Contrastive learning method with Gradient Balance for DDA prediction, namely GCGB. To handle the first challenge, a fusion view is introduced to integrate both semantic views (drug and disease similarity networks) and interaction view (heterogeneous biomedical network). Next, inter-view contrastive learning auxiliary tasks are designed to contrast the fusion view with semantic and interaction views, respectively. For the second challenge, we adaptively adjust the gradient of GCL auxiliary tasks from the perspective of gradient direction and magnitude for better guiding parameter update toward main task. Extensive experiments conducted on three benchmarks under 10-fold cross-validation demonstrate the model effectiveness.

PMID:39692448 | DOI:10.1093/bib/bbae650

MAbs. 2025 Dec;17(1):2440586. doi: 10.1080/19420862.2024.2440586. Epub 2024 Dec 17.

ABSTRACT

Antibody repurposing is the process of changing a known antibody so that it binds to a mutated antigen. One of the findings to emerge from the Coronavirus Disease 2019 (COVID-19) pandemic was that it was possible to repurpose neutralizing antibodies for Severe Acute Respiratory Syndrome, a related disease, to work for COVID-19. Thus, antibody repurposing is a possible pathway to prepare for and respond to future pandemics, as well as personalizing cancer therapies. For antibodies to be successfully repurposed, it is necessary to know both how antigen mutations disrupt their binding and how they should be mutated to recover binding, with this work describing an analysis to address the first of these topics. Every possible antigen point mutation in the interface of 246 antibody-protein complexes were analyzed using the Rosetta molecular mechanics force field. The results highlight a number of features of how antigen mutations affect antibody binding, including the effects of mutating critical hotspot residues versus other positions, how many mutations are necessary to be likely to disrupt binding, the prevalence of indirect effects of mutations on binding, and the relative importance of changing attractive versus repulsive energies. These data are expected to be useful in guiding future antibody repurposing experiments.

PMID:39690439 | DOI:10.1080/19420862.2024.2440586