J Pers Med. 2023 Nov 23;13(12):1633. doi: 10.3390/jpm13121633.

ABSTRACT

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that has a significant impact on quality of life and work capacity. Treatment of RA aims to control inflammation and alleviate pain; however, achieving remission with minimal toxicity is frequently not possible with the current suite of drugs. This review aims to summarise current treatment practices and highlight the urgent need for alternative pharmacogenomic approaches for novel drug discovery. These approaches can elucidate new relationships between drugs, genes, and diseases to identify additional effective and safe therapeutic options. This review discusses how computational approaches such as connectivity mapping offer the ability to repurpose FDA-approved drugs beyond their original treatment indication. This review also explores the concept of drug sensitisation to predict co-prescribed drugs with synergistic effects that produce enhanced anti-disease efficacy by involving multiple disease pathways. Challenges of this computational approach are discussed, including the availability of suitable high-quality datasets for comprehensive analysis and other data curation issues. The potential benefits include accelerated identification of novel drug combinations and the ability to trial and implement established treatments in a new index disease. This review underlines the huge opportunity to incorporate disease-related data and drug-related data to develop methods and algorithms that have strong potential to determine novel and effective treatment regimens.

PMID:38138860 | DOI:10.3390/jpm13121633

Medicina (Kaunas). 2023 Dec 17;59(12):2190. doi: 10.3390/medicina59122190.

ABSTRACT

Phosphodiesterase type 5 (PDE5) is pivotal in cellular signalling, regulating cyclic guanosine monophosphate (cGMP) levels crucial for smooth muscle relaxation and vasodilation. By targeting cGMP for degradation, PDE5 inhibits sustained vasodilation. PDE5 operates in diverse anatomical regions, with its upregulation linked to various pathologies, including cancer and neurodegenerative diseases. Sildenafil, a selective PDE5 inhibitor, is prescribed for erectile dysfunction and pulmonary arterial hypertension. However, considering the extensive roles of PDE5, sildenafil might be useful in other pathologies. This review aims to comprehensively explore sildenafil's therapeutic potential across medicine, addressing a gap in the current literature. Recognising sildenafil's broader potential may unveil new treatment avenues, optimising existing approaches and broadening its clinical application.

PMID:38138293 | DOI:10.3390/medicina59122190

Medicina (Kaunas). 2023 Nov 22;59(12):2059. doi: 10.3390/medicina59122059.

ABSTRACT

Background and Objectives: Gramicidin, a bactericidal antibiotic used in dermatology and ophthalmology, has recently garnered attention for its inhibitory actions against cancer cell growth. However, the effects of gramicidin on ovarian cancer cells and the underlying mechanisms are still poorly understood. We aimed to elucidate the anticancer efficacy of gramicidin against ovarian cancer cells. Materials and Methods: The anticancer effect of gramicidin was investigated through an in vitro experiment. We analyzed cell proliferation, DNA fragmentation, cell cycle arrest and apoptosis in ovarian cancer cells using WST-1 assay, terminal deoxynucleotidyl transferase dUTP nick and labeling (TUNEL), DNA agarose gel electrophoresis, flow cytometry and western blot. Results: Gramicidin treatment induces dose- and time-dependent decreases in OVCAR8, SKOV3, and A2780 ovarian cancer cell proliferation. TUNEL assay and DNA agarose gel electrophoresis showed that gramicidin caused DNA fragmentation in ovarian cancer cells. Flow cytometry demonstrated that gramicidin induced cell cycle arrest. Furthermore, we confirmed via Western blot that gramicidin triggered apoptosis in ovarian cancer cells. Conclusions: Our results strongly suggest that gramicidin exerts its inhibitory effect on cancer cell growth by triggering apoptosis. Conclusively, this study provides new insights into the previously unexplored anticancer properties of gramicidin against ovarian cancer cells.

PMID:38138162 | DOI:10.3390/medicina59122059

Mol Pharm. 2023 Dec 22. doi: 10.1021/acs.molpharmaceut.3c00964. Online ahead of print.

ABSTRACT

Drug-induced phospholipidosis (PLD) involves the accumulation of phospholipids in cells of multiple tissues, particularly within lysosomes, and it is associated with prolonged exposure to druglike compounds, predominantly cationic amphiphilic drugs (CADs). PLD affects a significant portion of drugs currently in development and has recently been proven to be responsible for confounding antiviral data during drug repurposing for SARS-CoV-2. In these scenarios, it has become crucial to identify potential safe drug candidates in advance and distinguish them from those that may lead to false in vitro antiviral activity. In this work, we developed a series of machine learning classifiers with the aim of predicting the PLD-inducing potential of drug candidates. The models were built on a high-quality chemical collection comprising 545 curated small molecules extracted from ChEMBL v30. The most effective model, obtained using the balanced random forest algorithm, achieved high performance, including an AUC value computed in validation as high as 0.90. The model was made freely available through a user-friendly web platform named AMALPHI (https://www.ba.ic.cnr.it/softwareic/amalphiportal/), which can represent a valuable tool for medicinal chemists interested in conducting an early evaluation of PLD inducer potential.

PMID:38134445 | DOI:10.1021/acs.molpharmaceut.3c00964

Bioinformatics. 2023 Dec 22:btad774. doi: 10.1093/bioinformatics/btad774. Online ahead of print.

ABSTRACT

MOTIVATION: Drug-target interaction (DTI) prediction is a relevant but challenging task in the drug repurposing field. In-silico approaches have drawn particular attention as they can reduce associated costs and time commitment of traditional methodologies. Yet, current state-of-the-art methods present several limitations: existing DTI prediction approaches are computationally expensive, thereby hindering the ability to use large networks and exploit available datasets and, the generalization to unseen datasets of DTI prediction methods remains unexplored, which could potentially improve the development processes of DTI inferring approaches in terms of accuracy and robustness.

RESULTS: In this work, we introduce GeNNius (Graph Embedding Neural Network Interaction Uncovering System), a Graph Neural Network (GNN)-based method that outperforms state-of-the-art models in terms of both accuracy and time efficiency across a variety of datasets. We also demonstrated its prediction power to uncover new interactions by evaluating not previously known DTIs for each dataset. We further assessed the generalization capability of GeNNius by training and testing it on different datasets, showing that this framework can potentially improve the DTI prediction task by training on large datasets and testing on smaller ones. Finally, we investigated qualitatively the embeddings generated by GeNNius, revealing that the GNN encoder maintains biological information after the graph convolutions while diffusing this information through nodes, eventually distinguishing protein families in the node embedding space.

AVAILABILITY: GeNNius code is available at https://github.com/ubioinformat/GeNNius.

PMID:38134424 | DOI:10.1093/bioinformatics/btad774

Trop Med Infect Dis. 2023 Nov 29;8(12):510. doi: 10.3390/tropicalmed8120510.

ABSTRACT

Toxoplasmosis is a disease that causes high mortality in immunocompromised individuals, such as AIDS patients, and sequelae in congenitally infected newborns. Despite its great medical importance, there are few treatments available and these are associated with adverse events and resistance. In this work, after screening the drugs present in the Medicines for Malaria Venture Pandemic Box, we found new hits with anti-Toxoplasma gondii activity. Through our analysis, we selected twenty-three drugs or drug-like compounds that inhibited the proliferation of T. gondii tachyzoites in vitro by more than 50% at a concentration of 1 µM after seven days of treatment. Nineteen of these compounds have never been reported active before against T. gondii. Inhibitory curves showed that most of these drugs were able to inhibit parasite replication with IC50 values on the nanomolar scale. To better understand the unprecedented effect of seven compounds against T. gondii tachyzoites, an ultrastructural analysis was carried out using transmission electron microscopy. Treatment with 0.25 µM verdinexor, 3 nM MMV1580844, and 0.25 µM MMV019724 induced extensive vacuolization, complete ultrastructural disorganization, and lytic effects in the parasite, respectively, and all of them showed alterations in the division process. Treatment with 1 µM Eberconazole, 0.5 µM MMV1593541, 1 µM MMV642550, 1 µM RWJ-67657, and 1 µM URMC-099-C also caused extensive vacuolization in the parasite. The activity of these drugs against intracellular tachyzoites supports the idea that the drugs selected in the Pandemic Box could be potential future drugs for the treatment of acute toxoplasmosis.

PMID:38133442 | DOI:10.3390/tropicalmed8120510

Pathogens. 2023 Dec 9;12(12):1433. doi: 10.3390/pathogens12121433.

ABSTRACT

Tuberculosis (TB) continues to pose a global health challenge, exacerbated by the rise of drug-resistant strains. The development of new TB therapies is an arduous and time-consuming process. To expedite the discovery of effective treatments, computational structure-based drug repurposing has emerged as a promising strategy. From this perspective, conditionally essential targets present a valuable opportunity, and the mycobactin biosynthesis pathway stands out as a prime example highlighting the intricate response of Mycobacterium tuberculosis (Mtb) to changes in iron availability. This study focuses on the repurposing and revival of FDA-approved drugs (library) as potential inhibitors of MbtA, a crucial enzyme in mycobactin biosynthesis in Mtb conserved among all species of mycobacteria. The literature suggests this pathway to be associated with drug efflux pumps, which potentially contribute to drug resistance. This makes it a potential target for antitubercular drug discovery. Herein, we utilized cheminformatics and structure-based drug repurposing approaches, viz., molecular docking, dynamics, and PCA analysis, to decode the intermolecular interactions and binding affinity of the FDA-reported molecules against MbtA. Virtual screening revealed ten molecules with significant binding affinities and interactions with MbtA. These drugs, originally designed for different therapeutic indications (four antiviral, three anticancer, one CYP450 inhibitor, one ACE inhibitor, and one leukotriene antagonist), were repurposed as potential MbtA inhibitors. Furthermore, our study explores the binding modes and interactions between these drugs and MbtA, shedding light on the structural basis of their inhibitory potential. Principal component analysis highlighted significant motions in MbtA-bound ligands, emphasizing the stability of the top protein-ligand complexes (PLCs). This computational approach provides a swift and cost-effective method for identifying new MbtA inhibitors, which can subsequently undergo validation through experimental assays. This streamlined process is facilitated by the fact that these compounds are already FDA-approved and have established safety and efficacy profiles. This study has the potential to lay the groundwork for addressing the urgent global health challenge at hand, specifically in the context of combating antimicrobial resistance (AMR) and tuberculosis (TB).

PMID:38133316 | DOI:10.3390/pathogens12121433

J Fungi (Basel). 2023 Dec 4;9(12):1166. doi: 10.3390/jof9121166.

ABSTRACT

Mucorales are a group of non-septated filamentous fungi widely distributed in nature, frequently associated with human infections, and are intrinsically resistant to many antifungal drugs. For these reasons, there is an urgent need to improve the clinical management of mucormycosis. Miltefosine, which is a phospholipid analogue of alkylphosphocholine, has been considered a promising repurposing drug to be used to treat fungal infections. In the present study, miltefosine displayed antifungal activity against a variety of Mucorales species, and it was also active against biofilms formed by these fungi. Treatment with miltefosine revealed modifications of cell wall components, neutral lipids, mitochondrial membrane potential, cell morphology, and the induction of oxidative stress. Treated Mucorales cells also presented an increased susceptibility to SDS. Purified ergosterol and glucosylceramide added to the culture medium increased miltefosine MIC, suggesting its interaction with fungal lipids. These data contribute to elucidating the effect of a promising drug repurposed to act against some relevant fungal pathogens that significantly impact public health.

PMID:38132767 | DOI:10.3390/jof9121166

Diabetes Metab Syndr. 2023 Dec 13;18(1):102921. doi: 10.1016/j.dsx.2023.102921. Online ahead of print.

ABSTRACT

BACKGROUND: Non-alcoholic steatohepatitis (NASH), a chronic liver disease, has no United States Food and Drug Administration (FDA) approved drugs for treatment.

OBJECTIVES: To examine fundamental characteristics of drug clinical trials for NASH treatment on the global clinical trials registry platform.

METHODS: Cross-sectional analysis of clinical trials with NASH as medical condition that are registered on ClinicalTrials.gov. Relevant trial entries registered before and on October 7th, 2022, were downloaded, deduplicated, and reviewed. NCT numbers, titles, locations, funder types, statuses, durations, study designs, subject information, conditions, interventions, outcome measures were extracted and analyzed.

RESULTS: Overall, 268 drug clinical trials were included in this study. Majority of the trials are conducted in United States (42.2 %). Most of the trials are funded by industry (67.9 %). The earliest initiated trials date back to 2001. Most trials are phase 2 (56.3 %), randomized (84.0 %), parallel assignment (78.7 %), and quadruple blind (40.3 %). The most concerned combined medical conditions are non-alcoholic fatty liver disease (NAFLD, 20.9 %). The most involved mechanisms of action drug categories are farnesoid X receptor (FXR) agonists and peroxisome proliferator-activated receptor (PPAR) agonists, with the most tested drugs being the FXR agonist EDP-305 and the Glucagon-like peptide-1 (GLP-1) agonist semaglutide.

CONCLUSION: Old drugs are further repurposed for testing in NASH treatment, novel drugs are developed to try to cure NASH. We expect that the drug clinical trials will accelerate the frontier of therapeutic development in NASH, bring an innovative and efficacious medication therapeutic approach to prevent the development and progression of NASH, or even reverse NASH.

PMID:38128261 | DOI:10.1016/j.dsx.2023.102921

Mem Inst Oswaldo Cruz. 2023 Dec 22;118:e230143. doi: 10.1590/0074-02760230143. eCollection 2023.

ABSTRACT

BACKGROUND: Tuberculosis (TB) is a major public health problem, which has been aggravated by the alarming growth of drug-resistant tuberculosis. Therefore, the development of a safer and more effective treatment is needed.

OBJECTIVES: The aim of this work was repositioning and evaluate histone deacetylases (HDAC) inhibitors- based drugs with potential antimycobacterial activity.

METHODS: Using an in silico pharmacological repositioning strategy, three molecules that bind to the catalytic site of histone deacetylase were selected. Pneumocytes type II and macrophages were infected with Mycobacterium tuberculosis and treated with pre-selected HDAC inhibitors (HDACi). Subsequently, the ability of each of these molecules to directly promote the elimination of M. tuberculosis was evaluated by colony-forming unit (CFU)/mL. We assessed the expression of antimicrobial peptides and respiratory burst using reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

FINDINGS: Aminoacetanilide (ACE), N-Boc-1,2-phenylenediamine (N-BOC), 1,3-Diphenylurea (DFU), reduce bacillary loads in macrophages and increase the production of β-defensin-2, LL-37, superoxide dismutase (SOD) 3 and inducible nitric oxide synthase (iNOS). While only the use of ACE in type II pneumocytes decreases the bacterial load through increasing LL-37 expression. Furthermore, the use of ACE and rifampicin inhibited the survival of intracellular multi-drug resistance M. tuberculosis.

MAIN CONCLUSIONS: Our data support the usefulness of in silico approaches for drug repositioning to provide a potential adjunctive therapy for TB.

PMID:38126492 | DOI:10.1590/0074-02760230143

Elife. 2023 Dec 21;12:RP88508. doi: 10.7554/eLife.88508.

ABSTRACT

Yes-associated protein (YAP), the downstream effector of the evolutionarily conserved Hippo pathway, promotes cellular proliferation and coordinates certain regenerative responses in mammals. Small molecule activators of YAP may, therefore, display therapeutic utility in treating disease states involving insufficient proliferative repair. From a high-throughput chemical screen of the comprehensive drug repurposing library ReFRAME, here we report the identification of SM04690, a clinical stage inhibitor of CLK2, as a potent activator of YAP-driven transcriptional activity in cells. CLK2 inhibition promotes alternative splicing of the Hippo pathway protein AMOTL2, producing an exon-skipped gene product that can no longer associate with membrane-bound proteins, resulting in decreased phosphorylation and membrane localization of YAP. This study reveals a novel mechanism by which pharmacological perturbation of alternative splicing inactivates the Hippo pathway and promotes YAP-dependent cellular growth.

PMID:38126343 | DOI:10.7554/eLife.88508

Mol Inform. 2023 Dec 20. doi: 10.1002/minf.202300284. Online ahead of print.

ABSTRACT

Tuberculosis (TB) is the second leading cause of mortality after COVID-19, with a global death toll of 1.6 million in 2021. The escalating situation of drug-resistant forms of TB has threatened the current TB management strategies. New therapeutics with novel mechanisms of action are urgently required to address the current global TB crisis. The essential mycobacterial primase DnaG with no structural homology to homo sapiens presents itself as a good candidate for drug targeting. In the present study, Mitoxantrone and Vapreotide, two FDA-approved drugs, were identified as potential anti-mycobacterial agents. Both Mitoxantrone and Vapreotide exhibit a strong Minimum Inhibitory Concentration (MIC) of ≤25µg/ml against both the virulent (M.tb-H37Rv) and avirulent (M.tb-H37Ra) strains of M.tb. Extending the validations further revealed the inhibitory potential drugs in ex-vivo conditions. Leveraging the computational high-throughput multi-level docking procedures from the pool of ~2700 FDA-approved compounds, Mitoxantrone and Vapreotide were screened out as potential inhibitors of DnaG. Extensive 200ns long all-atoms molecular dynamic simulation of DnaGDrugs complexes revealed that both drugs bind strongly and stabilize the DnaG during simulations. Reduced solvent exposure and confined motions of the active centre of DnaG upon complexation with drugs indicated that both drugs led to the closure of the active site of DnaG. From this study's findings, we propose Mitoxantrone and Vapreotide as potential anti-mycobacterial agents, with their novel mechanism of action against mycobacterial DnaG.

PMID:38123523 | DOI:10.1002/minf.202300284

PLoS One. 2023 Dec 20;18(12):e0293185. doi: 10.1371/journal.pone.0293185. eCollection 2023.

ABSTRACT

Identifying novel therapeutic agents is a fundamental challenge in contemporary drug development, especially in the context of complex diseases like cancer, neurodegenerative disorders, and metabolic syndromes. Here, we present a comprehensive computational study to identify potential inhibitors of SIRT1 (Sirtuin 1), a critical protein involved in various cellular processes and disease pathways. Leveraging the concept of drug repurposing, we employed a multifaceted approach that integrates molecular docking and molecular dynamics (MD) simulations to predict the binding affinities and dynamic behavior of a diverse set of FDA-approved drugs from DrugBank against the SIRT1. Initially, compounds were shortlisted based on their binding affinities and interaction analyses to identify safe and promising binding partners for SIRT1. Among these candidates, Doxercalciferol and Timiperone emerged as potential candidates, displaying notable affinity, efficiency, and specificity towards the binding pocket of SIRT1. Extensive evaluation revealed that these identified compounds boast a range of favorable biological properties and prefer binding to the active site of SIRT1. To delve deeper into the interactions, all-atom MD simulations were conducted for 500 nanoseconds (ns). These simulations assessed the conformational dynamics, stability, and interaction mechanism of the SIRT1-Doxercalciferol and SIRT1-Timiperone complexes. The MD simulations illustrated that the SIRT1-Doxercalciferol and SIRT1-Timiperone complexes maintain stability over a 500 ns trajectory. These insightful outcomes propose that Doxercalciferol and Timiperone hold promise as viable scaffolds for developing potential SIRT1 inhibitors, with implications for tackling complex diseases such as cancer, neurodegenerative disorders, and metabolic syndromes.

PMID:38117829 | DOI:10.1371/journal.pone.0293185

Gigascience. 2022 Dec 28;12:giad104. doi: 10.1093/gigascience/giad104.

ABSTRACT

BACKGROUND: Traditional approaches to drug development are costly and involve high risks. The drug repurposing approach can be a valuable alternative to traditional approaches and has therefore received considerable attention in recent years.

FINDINGS: Herein, we develop a previously undescribed computational approach, called DrugSim2DR, which uses a network diffusion algorithm to identify candidate anticancer drugs based on a drug functional similarity network. The innovation of the approach lies in the drug-drug functional similarity network constructed in a manner that implicitly links drugs through their common biological functions in the context of a specific disease state, as the similarity relationships based on general states (e.g., network proximity or Jaccard index of drug targets) ignore disease-specific molecular characteristics. The drug functional similarity network may provide a reference for prediction of drug combinations. We describe and validate the DrugSim2DR approach through analysis of data on breast cancer and lung cancer. DrugSim2DR identified some US Food and Drug Administration-approved anticancer drugs, as well as some candidate drugs validated by previous studies in the literature. Moreover, DrugSim2DR showed excellent predictive performance, as evidenced by receiver operating characteristic analysis and multiapproach comparisons in various cancer datasets.

CONCLUSIONS: DrugSim2DR could accurately assess drug-drug functional similarity within a specific disease context and may more effectively prioritize disease candidate drugs. To increase the usability of our approach, we have developed an R-based software package, DrugSim2DR, which is freely available on CRAN (https://CRAN.R-project.org/package=DrugSim2DR).

PMID:38116825 | DOI:10.1093/gigascience/giad104

Ther Adv Cardiovasc Dis. 2023 Jan-Dec;17:17539447231215213. doi: 10.1177/17539447231215213.

ABSTRACT

New evidence continues to accumulate regarding a significant association between excessive inflammation and dysregulated immunity (local and systemic) and the risk of cardiovascular events in different patient cohorts. Whilst research has sought to identify novel atheroprotective therapies targeting inflammation and immunity, several marketed drugs for rheumatological conditions may serve a similar purpose. One such drug, methotrexate, has been used since 1948 for treating cancer and, more recently, for a wide range of dysimmune conditions. Over the last 30 years, epidemiological and experimental studies have shown that methotrexate is independently associated with a reduced risk of cardiovascular disease, particularly in rheumatological patients, and exerts several beneficial effects on vascular homeostasis and blood pressure control. This review article discusses the current challenges with managing cardiovascular risk and the new frontiers offered by drug discovery and drug repurposing targeting inflammation and immunity with a focus on methotrexate. Specifically, the article critically appraises the results of observational, cross-sectional and intervention studies investigating the effects of methotrexate on overall cardiovascular risk and individual risk factors. It also discusses the putative molecular mechanisms underpinning the atheroprotective effects of methotrexate and the practical advantages of using methotrexate in cardiovascular prevention, and highlights future research directions in this area.

PMID:38115784 | DOI:10.1177/17539447231215213

IEEE J Biomed Health Inform. 2023 Dec 18;PP. doi: 10.1109/JBHI.2023.3344031. Online ahead of print.

ABSTRACT

The traditional drug development process requires a significant investment in workforce and financial resources. Drug repositioning as an efficient alternative has attracted much attention during the last few years. Despite the wide application and success of the method, there are still many shortcomings in the existing model. For example, sparse datasets will seriously affect the existing methods' performance. Additionally, these methods do not pay attention to the noise in datasets. In response to the above defects, we propose a semantic-enriched augmented graph contrastive learning with an adaptive denoising method, called SGCD. This method enhances data from the perspective of the embedding layer, deeply mines potential neighborhood relation-ships in semantic space, and combines similar drugs in the semantic neighborhoods into prototype comparison targets, thus effectively mitigating the impact of data sparsity on the model. Moreover, to enhance the model's robustness to noisy data, we use the adaptive denoising method, which can effectively identify noisy data in the training process. Exhaustive experiments on multiple real datasets show the effectiveness of the proposed model. The code implementation is available at https://github.com/yuhuimin11/SGCD-master.

PMID:38109249 | DOI:10.1109/JBHI.2023.3344031

Naunyn Schmiedebergs Arch Pharmacol. 2023 Dec 18. doi: 10.1007/s00210-023-02903-w. Online ahead of print.

ABSTRACT

Breast cancer and osteosarcoma are common cancers in women and children, respectively, but ideal drugs for treating patients with breast cancer or osteosarcoma remain to be found. Micafungin is an antifungal drug with antitumor activity on leukemia. Based on the notion of drug repurposing, this study aims to evaluate the antitumor effects of micafungin on breast cancer and osteosarcoma in vitro and in vivo, and to elucidate the underlying mechanisms. Five breast cancer cell lines (MDA-MB-231, BT-549, SK-BR-3, MCF-7, and 4T1) and one osteosarcoma cell line (143B) were chosen for the in vitro studies. Micafungin exerted an inhibitory effect on the viability of all cell lines, and MCF-7 cells were most sensitive to micafungin among the breast cancer cell lines. In addition, micafungin showed an inhibitory effect on the proliferation, clone formation, and migration in MCF7 and 143B cells. The inhibitory effect of micafungin on the growth of breast cancer and osteosarcoma was further confirmed with xenograft tumor mouse models. To explore the underlying mechanisms, the effect of micafungin on epithelial-mesenchymal transition (EMT) was examined. As expected, the levels of matrix metalloproteinase 9 and vimentin in MCF-7 and 143B cells were notably reduced in the presence of micafungin, concomitant with the decreased levels of ubiquitin-specific protease 7 (USP7), p-AKT, and p-GSK-3β. Based on these observations, we conclude that micafungin exerts antitumor effect on breast cancer and osteosarcoma through preventing EMT in an USP7/AKT/GSK-3β pathway-dependent manner.

PMID:38108838 | DOI:10.1007/s00210-023-02903-w

Heliyon. 2023 Nov 22;9(12):e22605. doi: 10.1016/j.heliyon.2023.e22605. eCollection 2023 Dec.

ABSTRACT

Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system with a low early diagnosis rate. Owing to the side effects, tolerance, and patient contraindications of existing therapies, effective drug treatments for HCC remain a major clinical challenge. However, using approved or investigational drugs not initially intended for cancer therapy is a promising strategy for resolving this problem because their safety have been tested in clinic. Therefore, this study evaluated differentially expressed genes between liver cancer and normal tissues in a cohort of patients with HCC from The Cancer Genome Atlas and applied them to query a connectivity map to identify candidate anti-HCC drugs. As a result, fluphenazine was identified as a candidate for anti-HCC therapy in vitro and in vivo. Fluphenazine suppressed HCC cell proliferation and migration and induced cell cycle arrest and apoptosis, possibly owing to disrupted lysosomal function, blocking autophagy flux. Additionally, in vivo studies demonstrated that fluphenazine suppresses HCC subcutaneous xenografts growth without causing severe side effects. Strikingly, fluphenazine could be used as an analgesic to alleviate oxaliplatin-induced pain as well as pain related anxiety-like behavior. Therefore, fluphenazine could be a novel liver cancer treatment candidate.

PMID:38107270 | PMC:PMC10724577 | DOI:10.1016/j.heliyon.2023.e22605

Res Sq. 2023 Dec 6:rs.3.rs-3675752. doi: 10.21203/rs.3.rs-3675752/v1. Preprint.

ABSTRACT

Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, but a comprehensive description of its genomic landscape is lacking. We report the whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project, providing the most detailed somatic mutational landscape to date. We identify new driver genes, which as well as emphasising the major role of epigenetic regulation in ccRCC highlight additional biological pathways extending opportunities for drug repurposing. Genomic characterisation identified patients with divergent clinical outcome; higher number of structural copy number alterations associated with poorer prognosis, whereas VHL mutations were independently associated with a better prognosis. The twin observations that higher T-cell infiltration is associated with better outcome and that genetically predicted immune evasion is not common supports the rationale for immunotherapy. These findings should inform personalised surveillance and treatment strategies for ccRCC patients.

PMID:38106039 | PMC:PMC10723546 | DOI:10.21203/rs.3.rs-3675752/v1

Methods. 2023 Dec 15:S1046-2023(23)00210-4. doi: 10.1016/j.ymeth.2023.12.001. Online ahead of print.

ABSTRACT

MOTIVATION: The COVID-19 pandemic has been spreading globally for four years, yet specific drugs that effectively suppress the virus remain elusive. Furthermore, the emergence of complications associated with COVID-19 presents significant challenges, making the development of therapeutics for COVID-19 and its complications an urgent task. However, traditional drug development processes are time-consuming. Drug repurposing, which involves identifying new therapeutic applications for existing drugs, presents a viable alternative.

RESULT: In this study, we construct a knowledge graph by retrieving information on genes, drugs, and diseases from databases such as DRUGBANK and GNBR. Next, we employ the TransR knowledge representation learning approach to embed entities and relationships into the knowledge graph. Subsequently, we train the knowledge graph using a graph neural network model based on TransR scoring. This trained knowledge graph is then utilized to predict drugs for the treatment of COVID-19 and its complications. Based on experimental results, we have identified 15 drugs out of the top 30 with the highest success rates associated with treating COVID-19 and its complications. Notably, out of these 15 drugs, 10 specifically aimed at treating COVID-19, such as Torcetrapib and Tocopherol, has not been previously identified in the knowledge graph. This finding highlights the potential of our model in aiding healthcare professionals in drug development and research related to this disease.

PMID:38104883 | DOI:10.1016/j.ymeth.2023.12.001