Nat Hum Behav. 2025 Jan 3. doi: 10.1038/s41562-024-02091-4. Online ahead of print.

ABSTRACT

Genome-wide association studies (GWASs) have reported multiple risk loci for schizophrenia (SCZ). However, the majority of the associations were from populations of European ancestry. Here we conducted a large-scale GWAS in Eastern Asian populations (29,519 cases and 44,392 controls) and identified ten Eastern Asian-specific risk loci, two of which have not been previously reported. A further cross-ancestry GWAS meta-analysis (96,806 cases and 492,818 controls) including populations from diverse ancestries identified 61 previously unreported risk loci. Systematic variant-level analysis, including fine mapping, functional genomics and expression quantitative trait loci, prioritized potential causal variants. Gene-level analyses, including transcriptome-wide association study, proteome-wide association study and Mendelian randomization, nominated the potential causal genes. By integrating evidence from layers of different analyses, we prioritized the most plausible causal genes for SCZ, such as ACE, CNNM2, SNAP91, ABCB9 and GATAD2A. Finally, drug repurposing showed that ACE, CA14, MAPK3 and MAPT are potential therapeutic targets for SCZ. Our study not only showed the power of cross-ancestry GWAS in deciphering the genetic aetiology of SCZ, but also uncovered new genetic risk loci, potential causal variants and genes and therapeutic targets for SCZ.

PMID:39753749 | DOI:10.1038/s41562-024-02091-4

PLoS One. 2025 Jan 3;20(1):e0316343. doi: 10.1371/journal.pone.0316343. eCollection 2025.

ABSTRACT

Epigenetic processes are the critical events in carcinogenesis. Histone modification plays a crucial role in gene expression regulation, where histone deacetylases (HDACs) are key players in epigenetic processes. Inhibiting HDACs has shown promise in modern cancer therapy. However, the non-selective nature and drug resistance of most HDAC inhibitors (HDACIs) limits their clinical use. This limitation prompts a search for isoform-selective and more effective inhibitors. Histone deacetylase 1 (HDAC1) is a member of the class I HDAC family and has emerged as a promising target in various diseases, including cancer and neurodegeneration. Drug repurposing has gained significant interest in identifying treatments for new targets, which involves finding new uses for existing drugs beyond their original medical indications. Here, we employed virtual screening of repurposed drugs from the DrugBank database to identify potential HDAC1 inhibitors. We conducted a series of analyses, including molecular docking, drug profiling, PASS evaluation, and interaction analysis. Molecular dynamics (MD) simulations and MM-PBSA analysis were also performed for 300 ns. Through these analyses, we pinpointed Alectinib, which exhibits a promising drug profile in PASS analysis and higher affinity and efficiency for HDAC1 than the reference inhibitor. MD simulations revealed that Alectinib stabilizes HDAC1 with minimal structural perturbations. The findings suggest that Alectinib holds promise as a therapeutic lead for HDAC1-associated carcinogenesis after required validation.

PMID:39752394 | DOI:10.1371/journal.pone.0316343

Alzheimers Dement. 2024 Dec;20 Suppl 1:e089356. doi: 10.1002/alz.089356.

ABSTRACT

BACKGROUND: The limited treatment options for Alzheimer's emphasizes the need to explore novel drug targets and bring new therapeutics to market. Drug repurposing is an efficient route to bring a safe and effective treatment to the clinic. Agomelatine (AGO) was identified by a high-throughput drug screening algorithm as having mechanistic potential to treat Alzheimer's. AGO is used as an atypical antidepressant and works as an MT1/MT2 receptor agonist and a 5HT2C serotonin receptor antagonist.

METHOD: The TgF344-AD rat model was used to test AGO's potential to reduce cognitive deficits and neuropathology. The TgF344-AD rat model expresses human mutant "Swedish" amyloid-precursor protein (APPsw) and a Δ exon 9 presenilin 1 (PS1ΔE9). As it presents with age-dependent progressive Alzheimer's pathology and cognitive decline it is an ideal model for investigating AGO's effect on the robust presentation of Alzheimer's. Treatment with AGO at ∼10 mg/kg body weight/day began at 5 months of age (pre-pathology) and continued until 11 months of age when cognitive testing (active place avoidance task) and tissue collection occurred. Immunohistochemistry was used to evaluate amyloid beta plaque burden. Bulk RNAsequencing was conducted to investigate AGO's effect on gene expression.

RESULT: AGO treated female TgF344-AD rats showed reduced cognitive deficits with an increased latency to first entrance in aPAT testing compared to non-treated transgenic littermates. There were no differences between the cognitive performance of AGO treated and untreated male TgF344-AD rats. Interestingly, this reduced cognitive deficit did not correlate with decreased amyloid beta pathology. RNA sequencing analysis showed that DDIT3 (CHOP) mRNA levels were downregulated in the AGO treated compared to untreated TgF344-AD females. DDIT3 is a pro-apoptotic transcription factor.

CONCLUSION: Agomelatine showed a female only reduction in cognitive deficits, which did not correlate with a decrease in amyloid beta plaque deposition. This finding paired with the decrease of DDIT3 gene expression suggests that Agomelatine has a neuroprotective mechanism that is independent of amyloid burden. Future studies will analyze neuronal loss via NeuN staining to determine if AGO prevents neuronal loss, thus supporting its ability to mitigate cognitive deficits in the TgF344-AD rat model.

PMID:39751610 | DOI:10.1002/alz.089356

Alzheimers Dement. 2024 Dec;20 Suppl 1:e089596. doi: 10.1002/alz.089596.

ABSTRACT

BACKGROUND: Microglia have been implicated as a key aspect of the pathology of Alzheimer's disease (AD). However, high microglial heterogeneities, including disease-associated microglia (DAM), tau microglia (tau-pathology related), and neuroinflammation-like microglia (NIM), hinder the development of microglia-targeted treatment.

METHOD: In this study, we integrated ∼0.7 million single-nuclei RNA (snRNA)-seq transcriptomes derived from AD patient frozen brain samples using a variational autoencoder. We used trajectory analysis to identify microglial subtypes across AD progression, including DAM, tau microglia, and NIM. We conducted transition network analysis to identify putative molecular drivers of microglial subtypes across varying severities of AD and disease progression under the human protein-protein interactome network. We prioritized candidate drugs by specifically targeting transition modules using drug-gene signature enrichment analysis and we further validated drugs using two independent real-world patient databases (MarketScan [172 million insured individuals] and INSIGHT Clinical Research Network [15 million patients]).

RESULT: We showed that tau microglia were significantly associated with synaptic processes. Compared to DAM, upregulated genes of NIM were significantly enriched with key immune pathways (e.g., toll-like receptor). We identified potential AD pathobiological regulators (e.g., SYK, CTSB, PRKCA, INPP5D, and ADAM10) in transition networks between DAM and NIM. Via network-based drug repurposing prediction by specifically targeting NIM subpopulations and real-world patient data-based validation, we identified that usage of ketorolac (anti-inflammatory medicine) is significantly associated with reduced AD incidence in both MarketScan (hazard ratio [HR] = 0.81, 95% confidence interval [CI] 0.69-0.91, p-value = 0.002 after adjusting > 400 covariates) and INSIGHT (HR = 0.83, 95% CI 0.77-0.92, p-value = 0.004 after adjusting 267 covariates) patient databases.

CONCLUSION: This study offers insights into pathobiology of AD-relevant microglial subtypes and identifies ketorolac as a potential anti-inflammatory treatment for AD.

PMID:39751502 | DOI:10.1002/alz.089596

Alzheimers Dement. 2024 Dec;20 Suppl 1:e086331. doi: 10.1002/alz.086331.

ABSTRACT

BACKGROUND: Despite recent breakthroughs, Alzheimer's disease (AD) remains untreatable. In addition, we are still lacking robust biomarkers for early diagnosis and promising novel targets for therapeutic intervention. To enable utilizing the entirety of molecular evidence in the discovery and prioritization of potential novel biomarkers and targets, we have developed the AD Atlas, a network-based multi-omics data integration platform. Through recent extensions, the AD Atlas provides a comprehensive database of high-quality multi-omics data that can be utilized for hypothesis-free ranking of molecular markers and disease modules, as well as prioritization of potential novel targets and drug repositioning candidates.

METHOD: We developed several graph-based analysis tools from proximity searches to applications of artificial intelligence that can be applied to the AD Atlas. For prioritization of potential targets and biomarkers, we derived several network-based metrics to score -omics entities for disease relevance by not only assessing evidence for a single marker but also for its functional neighborhood in the AD Atlas network. For disease module identification, we employed graph representation learning coupled with unsupervised clustering to extract functional modules as defined by the network structure. Finally, we propose an ensemble approach that enables weighted aggregation of drug repositioning predictions from both signature-based and network-based algorithms.

RESULT: We demonstrate that the AD Atlas enables complex computational analyses for target and biomarker discovery and prioritization as well as in silico drug repositioning in AD. Using the integrated scores for prioritizing single targets and biomarkers for AD, we observe significantly higher relevance scores for genes that have been nominated as promising targets by the AMP-AD consortium. We further find that extracted disease modules are enriched for specific AD-relevant biological domains and can be ranked by disease relevance using similar graph-based metrics. Finally, we demonstrate that drug repositioning candidates are significantly enriched for compounds that were or are being tested in clinical trials for AD.

CONCLUSION: High-quality, multi-omics networks, such as the AD Atlas, enable exploitation of large-scale heterogeneous data through computational applications for target, biomarker, disease module, and drug repositioning candidate discovery and prioritization.

PMID:39751427 | DOI:10.1002/alz.086331

ACS Infect Dis. 2025 Jan 3. doi: 10.1021/acsinfecdis.4c00657. Online ahead of print.

ABSTRACT

The emergence of azole resistance and tolerance in pathogenic fungi has emerged as a significant public health concern, emphasizing the urgency for innovative strategies to bolster the efficacy of azole-based treatments. Drug repurposing stands as a promising and practical avenue for advancing antifungal therapy, with the potential for swift clinical translation. This review offers a comprehensive overview of azole synergistic agents uncovered through drug repurposing strategies, alongside an in-depth exploration of the mechanisms by which these agents augment azole potency. Drawing from these mechanisms, we delineate strategies aimed at enhancing azole effectiveness, such as inhibiting efflux pumps to elevate azole concentrations within fungal cells, intensifying ergosterol synthesis inhibition, mitigating fungal cell resistance to azoles, and disrupting biological processes extending beyond ergosterol synthesis. This review is beneficial for the development of these potentiators, as it meticulously examines instances and provides nuanced discussions on the mechanisms underlying the progression of azole potentiators through drug repurposing strategies.

PMID:39749640 | DOI:10.1021/acsinfecdis.4c00657

Mol Cancer. 2025 Jan 2;24(1):2. doi: 10.1186/s12943-024-02182-w.

ABSTRACT

Metastasis remains a leading cause of cancer-related mortality, irrespective of the primary tumour origin. However, the core gene regulatory program governing distinct stages of metastasis across cancers remains poorly understood. We investigate this through single-cell transcriptome analysis encompassing over two hundred patients with metastatic and non-metastatic tumours across six cancer types. Our analysis revealed a prognostic core gene signature that provides insights into the intricate cellular dynamics and gene regulatory networks driving metastasis progression at the pan-cancer and single-cell level. Notably, the dissection of transcription factor networks active across different stages of metastasis, combined with functional perturbation, identified SP1 and KLF5 as key regulators, acting as drivers and suppressors of metastasis, respectively, at critical steps of this transition across multiple cancer types. Through in vivo and in vitro loss of function of SP1 in cancer cells, we revealed its role in driving cancer cell survival, invasive growth, and metastatic colonisation. Furthermore, tumour cells and the microenvironment increasingly engage in communication through WNT signalling as metastasis progresses, driven by SP1. Further validating these observations, a drug repurposing analysis identified distinct FDA-approved drugs with anti-metastasis properties, including inhibitors of WNT signalling across various cancers.

PMID:39748426 | DOI:10.1186/s12943-024-02182-w

Sci Rep. 2025 Jan 2;15(1):20. doi: 10.1038/s41598-024-84711-7.

ABSTRACT

The integration of drug molecular representations into predictive models for Drug Response Prediction (DRP) is a standard procedure in pharmaceutical research and development. However, the comparative effectiveness of combining these representations with genetic profiles for DRP remains unclear. This study conducts a comprehensive evaluation of the efficacy of various drug molecular representations employing cutting-edge machine learning models under various experimental settings. Our findings reveal that the inclusion of molecular representations from either PubChem fingerprints or SMILES can significantly enhance the performance of DRPs when used in conjunction with deep learning models. However, the optimal choice of drug molecular representation can vary depending on the predictive model and the specific DRP task. The insights derived from our study offer useful guidance on selecting the most suitable drug molecular representations for constructing efficient predictive models for DRPs, aiding for drug repurposing, personalized medicine, and new drug discovery.

PMID:39748003 | DOI:10.1038/s41598-024-84711-7

J Med Chem. 2025 Jan 2. doi: 10.1021/acs.jmedchem.4c02543. Online ahead of print.

ABSTRACT

Target identification is a critical stage in the drug discovery pipeline. Various computational methodologies have been dedicated to enhancing the classification performance of compound-target interactions, yet significant room remains for improving the recommendation performance. To address this challenge, we developed TarIKGC, a tool for target prioritization that leverages semantics enhanced knowledge graph (KG) completion. This method harnesses knowledge representation learning within a heterogeneous compound-target-disease network. Specifically, TarIKGC combines an attention-based aggregation graph neural network with a multimodal feature extractor network to simultaneously learn internal semantic features from biomedical entities and topological features from the KG. Furthermore, a KG embedding model is employed to identify missing relationships among compounds and targets. In silico evaluations highlighted the superior performance of TarIKGC in drug repositioning tasks. In addition, TarIKGC successfully identified two potential cyclin-dependent kinase 2 (CDK2) inhibitors with novel scaffolds through reverse target fishing. Both compounds exhibited antiproliferative activities across multiple therapeutic indications targeting CDK2.

PMID:39745279 | DOI:10.1021/acs.jmedchem.4c02543

Eur J Breast Health. 2025 Jan 1;21(1):63-73. doi: 10.4274/ejbh.galenos.2024.2024-11-2.

ABSTRACT

OBJECTIVE: The prevalence of breast cancer and gynaecological cancers is high, and these cancer types can occur consecutively as secondary cancers. The aim of our study is to determine the genes commonly expressed in these cancers and to identify the common hub genes and drug components.

MATERIALS AND METHODS: Gene intensity values of breast cancer, gynaecological cancers such as cervical, ovarian and endometrial cancers were used from the Gene Expression Omnibus database Affymetrix Human Genome U133 Plus 2.0 Array project. Using the linear modelling method included in the R LIMMA package, genes that differ between healthy individuals and cancer patients were identified. Hub genes were determined using cytoHubba in Cytoscape program. "ShinyGo 0.80" tool was used to determine the disease-specific biological KEGG pathways. Drug.MATADOR from the ShinyGo 0.80 tool was used to predict drug-target relationships.

RESULTS: The RecQ Like Helicase 4 and Family with Sequence Similarity 13 Member C genes were found to be similarly expressed in breast cancer and gynaecological cancers. Upon KEGG pathway analyses with hub genes, Drug.MATADOR analysis with hub genes related to cancer related pathways was performed. We have determined these gene/drug interactions: NBN (targeted by Hydroxyurea), EP300 (targeted by Acetylcarnitine) and MAPK14 (targeted by Salicylate and Dibutyryl cyclic AMP).

CONCLUSION: The drugs associated with hub genes determined in our study are not routinely used in cancer treatment. Our study offers the opportunity to identify the target genes of drugs used in breast and gynaecological cancers with the drug repurposing approach.

PMID:39744927 | DOI:10.4274/ejbh.galenos.2024.2024-11-2

ACS Infect Dis. 2024 Dec 31. doi: 10.1021/acsinfecdis.4c00693. Online ahead of print.

ABSTRACT

Protozoan parasite infections, particularly leishmaniasis, present significant public health challenges in tropical and subtropical regions, affecting socio-economic status and growth. Despite advancements in immunology, effective vaccines remain vague, leaving drug treatments as the primary intervention. However, existing medications face limitations, such as toxicity and the rise of drug-resistant parasites. This presents an urgent need to identify new therapeutic targets for leishmaniasis treatment. Understanding the complex life cycle of Leishmania and its survival in host macrophages can provide insights into potential targets for intervention. Current treatments, including antimonials, amphotericin B, and miltefosine, are constrained by side effects, costs, resistance, and reduced efficacy. Exploring novel therapeutic targets within the parasite's physiology, such as key metabolic enzymes or essential surface proteins, may lead to the development of more effective and less toxic drugs. Additionally, innovative strategies like drug repurposing, combination therapies, and nanotechnology-based delivery systems could enhance efficacy and combat resistance, thus improving anti-leishmanial therapies.

PMID:39737830 | DOI:10.1021/acsinfecdis.4c00693

Brief Bioinform. 2024 Nov 22;26(1):bbae696. doi: 10.1093/bib/bbae696.

ABSTRACT

Drug resistance in Mycobacterium tuberculosis (Mtb) is a significant challenge in the control and treatment of tuberculosis, making efforts to combat the spread of this global health burden more difficult. To accelerate anti-tuberculosis drug discovery, repurposing clinically approved or investigational drugs for the treatment of tuberculosis by computational methods has become an attractive strategy. In this study, we developed a virtual screening workflow that combines multiple machine learning and deep learning models, and 11 576 compounds extracted from the DrugBank database were screened against Mtb. Our screening method produced satisfactory predictions on three data-splitting settings, with the top predicted bioactive compounds all known antibacterial or anti-TB drugs. To further identify and evaluate drugs with repurposing potential in TB therapy, 15 screened potential compounds were selected for subsequent computational and experimental evaluations, out of which aldoxorubicin and quarfloxin showed potent inhibition of Mtb strain H37Rv, with minimal inhibitory concentrations of 4.16 and 20.67 μM/mL, respectively. More inspiringly, these two compounds also showed antibacterial activity against multidrug-resistant TB isolates and exhibited strong antimicrobial activity against Mtb. Furthermore, molecular docking, molecular dynamics simulation, and the surface plasmon resonance experiments validated the direct binding of the two compounds to Mtb DNA gyrase. In summary, our effective comprehensive virtual screening workflow successfully repurposed two novel drugs (aldoxorubicin and quarfloxin) as promising anti-Mtb candidates. The verification results provide useful information for the further development and clinical verification of anti-TB drugs.

PMID:39737570 | DOI:10.1093/bib/bbae696

Indian J Med Res. 2024 Nov;160(5):466-478. doi: 10.25259/IJMR_569_2024.

ABSTRACT

Background & objectives The emergence of drug resistance in leishmaniasis has remained a concern. Even new drugs have been found to be less effective within a few years of their use. Coupled with their related side effects and cost-effectiveness, this has prompted the search for alternative therapeutic options. In this study, the Computer Aided Drug Design (CADD) approach was used to repurpose already existing drugs against Leishmania major. The enzyme lanosterol 14-alpha demethylase (CYP51), in L. major, was chosen as the drug target since it is a key enzyme involved in synthesizing ergosterol, a crucial component of the cell membrane. Methods A library of 1615 FDA-approved drugs was virtually screened and docked with modeled CYP51 at its predicted binding site. The drugs with high scores and high affinity were subjected to Molecular Dynamics (MD) simulations for 100 ns. Finally, the compounds were tested in vitro using an MTT [3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide] assay against the promastigotes of L. major. Results Computational screening of FDA-approved drugs identified posaconazole and isavuconazole as promising candidates, as both drugs target the CYP51 enzyme in fungi. Molecular dynamics (MD) simulations demonstrated that both drugs form stable complexes with the target enzyme. In vitro studies of posaconazole and isavuconazole against promastigotes of L. major demonstrated significant efficacy, with IC50 values of 2.062±0.89 µg/ml and 1.202±0.47 µg/ml, respectively. Interpretation & conclusions The study showed that the existing FDA-approved drugs posaconazole and isavuconazole can successfully be repurposed for treating L. major by targeting the CYP51 enzyme, demonstrating significant efficacy against promastigotes.

PMID:39737513 | DOI:10.25259/IJMR_569_2024

Iran Biomed J. 2024 Dec 1;28(7):250.

NO ABSTRACT

PMID:39736811

Sci Rep. 2024 Dec 28;14(1):31461. doi: 10.1038/s41598-024-82967-7.

ABSTRACT

Wnt signaling is a critical pathway implicated in cancer development, with Frizzled proteins, particularly FZD10, playing key roles in tumorigenesis and recurrence. This study focuses on the potential of repurposed FDA-approved drugs targeting FZD10 as a therapeutic strategy for nasopharyngeal carcinoma (NPC). The tertiary structure of human FZD10 was constructed using homology modeling, validated by Ramachandran plot and ProQ analysis. Virtual screening of 1,094 FDA-approved drugs identified 17 potential inhibitors, with prazosin, rilpivirine, doxazosin, and nicergoline demonstrating significant cytotoxicity against NPC cells. Further molecular dynamics simulations and binding energy analyses confirmed the stable binding of these drugs to FZD10. The results suggest that these repurposed drugs could serve as promising candidates for targeted NPC therapy, warranting further investigation.

PMID:39733096 | DOI:10.1038/s41598-024-82967-7

Comput Biol Chem. 2024 Dec 25;115:108329. doi: 10.1016/j.compbiolchem.2024.108329. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Castration-resistant prostate cancer (CRPC) is caused by resistance to androgen deprivation treatment and leads to the death of patients and there is almost no chance of survival. Therefore, finding a cure to overcome CRPC is challenging and important, but discovering a new drug is very time-consuming and expensive. To overcome these problems, we used Drug repositioning (drug repurposing) strategy in this study.

METHODS: Gene expression data of CRPC and primary prostate samples were extracted from the GEO database to identify DEGs. Pathway enrichment was performed to find the role of DEGs in signaling pathways. To identify hub proteins, the PPI network was reconstructed and analyzed. drug candidates were identified and to select the most effective drug, molecular docking analysis, and molecular dynamics simulation were performed. Then MTT and qRT-PCR tests were performed to check the effectiveness of the selected drug.

RESULTS: A total of 152 upregulated DEGs and 343 downregulated DEGs were identified, and after PPI network analysis, IKBKB, SNAP23, MYC, and NOTCH1 genes were introduced as hubs. drug candidates for IKBKB were identified and by examining the results of docking screening and molecular dynamics, sulfasalazine was selected as the most effective drug. Laboratory analyses proved the effectiveness of this drug and a decrease in the expression of all target genes was observed.

CONCLUSION: In this study, IKBKB key protein were identified in CRPC, and sulfasalazine was selected as a suitable candidate for drug repositioning and its effectiveness was confirmed through tests.

PMID:39731827 | DOI:10.1016/j.compbiolchem.2024.108329

Vet Res Commun. 2024 Dec 28;49(1):59. doi: 10.1007/s11259-024-10628-1.

ABSTRACT

Camel mastitis especially caused by Staphylococcus aureus (S. aureus), is a major risk to animal health and milk production. The current investigation evaluated the antibiotic susceptibility and virulence factors of S. aureus isolates from subclinical mastitis in camels. A total of 384 milk samples were collected and submitted to isolate S. aureus. The S. aureus isolates exhibiting resistance to Penicillin and Cefoxitin disc on Kirby-Bauer disc diffusion method were considered as β-lactam resistant S. aureus (BRSA) and methicillin-resistant S. aureus (MRSA) which were further confirmed by PCR targeting blaZ and mecA genes, respectively. The results showed that S. aureus was found in 57.06% of subclinical (SCM) positive camel milk samples. A high molecular prevalence of BRSA and MRSA were found to be 48.51% and 46.53% respectively depicting that treating these infections is challenging due to their high resistance levels. The phylogenetic analysis revealed a significant resemblance of the study isolates with each other and with already reported sequences from different countries which shows the potential for the spread of pathogen. Virulence profiling of antibiotic resistance strains showed the presence of virulence markers (nuc and coag genes), intercellular adhesion genes (icaA, icaD), Panton-Valentine leukocidin (pvl) gene, and enterotoxin-producing genes including sea, seb, sec, and sed. In-vitro antibiotic susceptibility testing revealed that the most resistant antibiotic group was penicillin followed by aminoglycosides and cephalosporins. Drug repurposing analysis of different non-antibiotics for combination therapies with resistant antibiotics was done to combat the S. aureus isolates harboring the mecA and blaZ genes. The results revealed the synergistic effect of amoxicillin, sulfamethoxazole, gentamicin, and doxycycline with ketoprofen, amikacin with flunixin meglumine, and gentamicin with N-acetylcysteine (NAC) against study isolates. The current investigation provides the status of antibiotic-resistant strains and virulence factors of S. aureus in the udder of dromedary camels. The combinational therapy of resistant antibiotics with non-antibiotics provides a potential therapeutic option for the treatment of resistant strains.

PMID:39731665 | DOI:10.1007/s11259-024-10628-1

Sci Rep. 2024 Dec 28;14(1):31006. doi: 10.1038/s41598-024-82177-1.

ABSTRACT

Articular cartilage has a limited regenerative capacity, resulting in poor spontaneous healing of damaged tissue. Despite various scientific efforts to enhance cartilage repair, no single method has yielded satisfactory results. With rising drug development costs, drug repositioning has emerged as a viable alternative. This study aimed to identify a drug capable of improving cartilage defects by analyzing chondrogenesis-related microarray data from the Gene Expression Omnibus (GEO) public database. We utilized datasets GSE69110, GSE107649, GSE111822, and GSE116173 to identify genes associated with cartilage differentiation, employing StringTie for differential gene expression analysis and extracting drug data from the Drug-Gene Interaction database. Additionally, we aimed to verify the cartilage regeneration potential of the identified drug through experiments using cellular and animal models. We evaluated the effects of aripiprazole on adipose-derived mesenchymal stem cells (ADMSCs) and chondrocytes using qRT-PCR and a 3D pellet culture system. In vivo, we assessed cartilage restoration by combining aripiprazole with a scaffold and implanting it into artificially induced cartilage defects in Sprague-Dawley rats. Subsequent mRNA sequencing provided insights into the mechanistic pathways involved. Our results showed that aripiprazole significantly increased mRNA expression of COL2A1 and SOX9, markers of chondrogenesis, and promoted chondrogenic condensation in vitro. Furthermore, aripiprazole effectively enhanced cartilage regeneration in the rat model. KEGG pathway and Gene Ontology Biological Processes (GOBP) analyses of the mRNA sequencing data revealed that aripiprazole upregulated genes related to ribosomes and cytoplasmic translation, thereby facilitating chondrogenesis. In conclusion, our findings suggest that aripiprazole is a promising candidate for improving damaged cartilage, offering a novel approach to cartilage regeneration.

PMID:39730885 | DOI:10.1038/s41598-024-82177-1

PLoS One. 2024 Dec 27;19(12):e0315245. doi: 10.1371/journal.pone.0315245. eCollection 2024.

ABSTRACT

The increasing utilization of deep learning models in drug repositioning has proven to be highly efficient and effective. In this study, we employed an integrated deep-learning model followed by traditional drug screening approach to screen a library of FDA-approved drugs, aiming to identify novel inhibitors targeting the TNF-α converting enzyme (TACE). TACE, also known as ADAM17, plays a crucial role in the inflammatory response by converting pro-TNF-α to its active soluble form and cleaving other inflammatory mediators, making it a promising target for therapeutic intervention in diseases such as rheumatoid arthritis. Reference datasets containing active and decoy compounds specific to TACE were obtained from the DUD-E database. Using RDKit, a cheminformatics toolkit, we extracted molecular features from these compounds. We applied the GraphConvMol model within the DeepChem framework, which utilizes graph convolutional networks, to build a predictive model based on the DUD-E datasets. Our trained model was subsequently used to predict the TACE inhibitory potential of FDA-approved drugs. From these predictions, Vorinostat was identified as a potential TACE inhibitor. Moreover, molecular docking and molecular dynamics simulation were conducted to validate these findings, using BMS-561392 as a reference TACE inhibitor. Vorinostat, originally an FDA-approved drug for cancer treatment, exhibited strong binding interactions with key TACE residues, suggesting its repurposing potential. Biological evaluation with RAW 264.7 cell confirmed the computational results, demonstrating that Vorinostat exhibited comparable inhibitory activity against TACE. In conclusion, our study highlights the capability of deep learning models to enhance virtual screening efforts in drug discovery, efficiently identifying potential candidates for specific targets such as TACE. Vorinostat, as a newly identified TACE inhibitor, holds promise for further exploration and investigation in the treatment of inflammatory diseases like rheumatoid arthritis.

PMID:39729480 | DOI:10.1371/journal.pone.0315245

Trop Med Infect Dis. 2024 Nov 25;9(12):288. doi: 10.3390/tropicalmed9120288.

ABSTRACT

Tuberculosis (TB) is a global health challenge associated with considerable levels of illness and mortality worldwide. The development of innovative therapeutic strategies is crucial to combat the rise of drug-resistant TB strains. DNA Gyrase A (GyrA) and serine/threonine protein kinase (PknB) are promising targets for new TB medications. This study employed techniques such as similarity searches, molecular docking analyses, machine learning (ML)-driven absolute binding-free energy calculations, and molecular dynamics (MD) simulations to find potential drug candidates. By combining ligand- and structure-based methods with ML principles and MD simulations, a novel strategy was proposed for identifying small molecules. Drugs with structural similarities to existing TB therapies were assessed for their binding affinity to GyrA and PknB through various docking approaches and ML-based predictions. A detailed analysis identified six promising compounds for each target, such as DB00199, DB01220, DB06827, DB11753, DB14631, and DB14703 for GyrA; and DB00547, DB00615, DB06827, DB14644, DB11753, and DB14703 for PknB. Notably, DB11753 and DB14703 show significant potential for both targets. Furthermore, MD simulations' statistical metrics confirm the drug-target complexes' stability, with MM-GBSA analyses underscoring their strong binding affinity, indicating their promise for TB treatment even though they were not initially designed for this disease.

PMID:39728815 | DOI:10.3390/tropicalmed9120288