Adv Sci (Weinh). 2025 Jan 4:e2407812. doi: 10.1002/advs.202407812. Online ahead of print.

ABSTRACT

β-secretase (BACE1) is instrumental in amyloid-β (Aβ) production, with overexpression noted in Alzheimer's disease (AD) neuropathology. The interaction of Aβ with the receptor for advanced glycation endproducts (RAGE) facilitates cerebral uptake of Aβ and exacerbates its neurotoxicity and neuroinflammation, further augmenting BACE1 expression. Given the limitations of previous BACE1 inhibition efforts, the study explores reducing BACE1 expression to mitigate AD pathology. The research reveals that the anticancer agent 6-thioguanosine (6-TG) markedly diminishes BACE1 expression without eliciting cytotoxicity while enhancing microglial phagocytic activity, and ameliorate cognitive impairments with reducing Aβ accumulation in AD mice. Leveraging advanced deep learning-based tool for target identification, and corroborating with surface plasmon resonance assays, it is elucidated that 6-TG directly interacts with RAGE, modulating BACE1 expression through the JAK2-STAT1 pathway and elevating soluble RAGE (sRAGE) levels in the brain. The findings illuminate the therapeutic potential of 6-TG in ameliorating AD manifestations and advocate for small molecule strategies to increase brain sRAGE levels, offering a strategic alternative to the challenges posed by the complexity of AD.

PMID:39755927 | DOI:10.1002/advs.202407812

Ageing Res Rev. 2025 Jan 2:102651. doi: 10.1016/j.arr.2024.102651. Online ahead of print.

ABSTRACT

Parkinson's disease (PD) is one of the most incapacitating neurodegenerative diseases (NDDs). PD is the second most common NDD worldwide which affects approximately 1 to 2 percent of people over 65 years. It is an attractive pursuit for artificial intelligence (AI) to contribute to and evolve PD treatments through drug repositioning by repurposing existing drugs, shelved drugs, or even candidates that do not meet the criteria for clinical trials. A search was conducted in three databases Web of Science, Scopus, and PubMed. We reviewed the data related to the last years (1975-present) to identify those drugs currently being proposed for repositioning in PD. Moreover, we reviewed the present status of the computational approach, including AI/Machine Learning (AI/ML)-powered pharmaceutical discovery efforts and their implementation in PD treatment. It was found that the number of drug repositioning studies for PD has increased recently. Repositioning of drugs in PD is taking off, and scientific communities are increasingly interested in communicating its results and finding effective treatment alternatives for PD. A better chance of success in PD drug discovery has been made possible due to AI/ML algorithm advancements. In addition to the experimentation stage of drug discovery, it is also important to leverage AI in the planning stage of clinical trials to make them more effective. New AI-based models or solutions that increase the success rate of drug development are greatly needed.

PMID:39755176 | DOI:10.1016/j.arr.2024.102651

Biomed Pharmacother. 2025 Jan 3;183:117805. doi: 10.1016/j.biopha.2024.117805. Online ahead of print.

ABSTRACT

Non-alcoholic fatty liver disease (NAFLD) is a common hepatic manifestation of metabolic syndrome affecting 20-30 % of the adult population worldwide. This disease, which includes simple steatosis and non-alcoholic steatohepatitis, poses a significant risk for cardiovascular and metabolic diseases. Lifestyle modifications are crucial in the treatment of NAFLD; however, patient adherence remains challenging. As there is no specific treatment, drug repositioning is being researched as an alternative strategy. Statins, which are known for their cholesterol-lowering effects, are considered potential interventions for NAFLD. This review aimed to present the current understanding of the effects of statins on liver physiology in the context of NAFLD. The pathophysiology of NAFLD includes steatosis, inflammation, and fibrosis, which are exacerbated by dyslipidemia and insulin resistance. Statins, which inhibit 3-hydroxy-3-methylglutaryl-CoA reductase, have pleiotropic effects beyond cholesterol-lowering and affect pathways related to inflammation, fibrogenesis, oxidative stress, and microcirculation. Although clinical guidelines support the use of statins for dyslipidemia in patients with NAFLD, more studies are needed to demonstrate their efficacy in liver disease. This comprehensive review serves as a foundation for future studies on the therapeutic potential of statins in NAFLD.

PMID:39755024 | DOI:10.1016/j.biopha.2024.117805

Nat Med. 2025 Jan 3. doi: 10.1038/s41591-024-03355-2. Online ahead of print.

ABSTRACT

Obesity and type 2 diabetes are prevalent chronic diseases effectively managed by semaglutide. Here we studied the effects of semaglutide on the circulating proteome using baseline and end-of-treatment serum samples from two phase 3 trials in participants with overweight or obesity, with or without diabetes: STEP 1 (n = 1,311) and STEP 2 (n = 645). We identified evidence supporting broad effects of semaglutide, implicating processes related to body weight regulation, glycemic control, lipid metabolism and inflammatory pathways. Several proteins were regulated with semaglutide, after accounting for changes in body weight and HbA1c at end of trial, suggesting effects of semaglutide on the proteome beyond weight loss and glucose lowering. A comparison of semaglutide with real-world proteomic profiles revealed potential benefits on disease-specific proteomic signatures including the downregulation of specific proteins associated with cardiovascular disease risk, supporting its reported effects of lowering cardiovascular disease risk and potential drug repurposing opportunities. This study showcases the potential of proteomics data gathered from randomized trials for providing insights into disease mechanisms and drug repurposing opportunities. These data also highlight the unmet need for, and importance of, examining proteomic changes in response to weight loss pharmacotherapy in future trials.

PMID:39753963 | DOI:10.1038/s41591-024-03355-2

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