Search Results (2,135)

Background: Thrombotic disorders remain one of the leading causes of global mortality, necessitating the discovery of anticoagulants with broader therapeutic windows and multi-target efficacy. This study aimed to identify FDA-approved drugs capable of simultaneously inhibiting three critical nodes of the coagulation cascade: Factor X (F10), Proteinase-activated receptor 1 (PAR1) and Prothrombin (F2). Methods: High-confidence 3D structures of coagulation cascade proteins were established using AlphaFold2 and validated via MolProbity (Favored regions > 91%). A library of 1657 compounds from the Zinc database was screened using PyRx, followed by rigorous ADMET profiling to evaluate pharmacokinetic viability. The structural integrity and binding kinetics of the top candidate drugs were further analyzed through Molecular Dynamics simulation for 100 ns. Results: Virtual screening and downstream analysis identified 30 multi-target drugs. Avodart and Naldemedine were observed to have superior pharmacokinetic equilibrium. Compared to the other two drugs (Digoxin and Ledipasvir), Avodart and Naldemedine showed high affinity, higher adherence to drug likeness, lower metabolic inhibition risks and lack of acute toxicity, and were therefore the most suitable candidates. The 100 ns MD simulations revealed Avodart and Naldemedine to have the highest level of interaction stability and favorable MM-GBSA energies with Factor X, whereas Ledipasvir and Digoxin exhibited significant structural instability. Conclusions: The study proposes Avodart and Naldemedine as promising candidates for drug repurposing in antithrombotic therapy. This study provides a computational blueprint for the development of next-generation, broad-spectrum anticoagulants. Full article

Background/Objectives: Multidrug-resistant tuberculosis (MDR-TB) remains a major public health challenge in the WHO European Region, which reports the highest global proportion of rifampicin-resistant and MDR-TB cases. Whole-genome sequencing (WGS) has emerged as a key tool for improving drug-resistance detection and supporting molecular surveillance. However, the level of genomic implementation across Central and Eastern Europe (CEE) remains insufficiently characterized. This scoping review aimed to evaluate the use of WGS for MDR-TB in CEE countries and to classify implementation maturity using a predefined framework (L0–L4). Methods: A structured search of PubMed/MEDLINE and Web of Science identified original studies published in English between 2015 and 2026 reporting genomic applications in MDR-TB across 13 predefined CEE countries. Data were extracted on sequencing approaches, resistance prediction, transmission analysis, monitoring of new or repurposed drugs, bioinformatic pipelines, and programmatic integration. Countries were categorized according to a five-level maturity model based on documented capacity, scope of application, and integration into national tuberculosis programs (NTPs). Results: Twenty-eight studies were included. WGS was used in 23/28 studies (82.1%), predominantly for genomic resistance prediction (25/28). Transmission analysis was reported in 19/28 studies, with heterogeneous single nucleotide polymorphism (SNP) thresholds and clustering methodologies. Monitoring of resistance to new or repurposed drugs was described in 8/28 studies. No country achieved Level L4 (formally integrated genomic surveillance). Four countries were classified as L3 and nine as L2, while no L0 or L1 settings were identified. Conclusions: Countries in Central and Eastern Europe demonstrate increasing operational use of WGS for MDR-TB, primarily driven by clinical resistance prediction. However, the lack of formal integration into national surveillance systems highlights a persistent gap between technological adoption and structured public health implementation. Strengthening programmatic integration and methodological standardization is essential for advancing genomic surveillance of MDR-TB in the region. Full article

Background/Aim: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer and is characterized by high genetic heterogeneity and poor prognosis. TP53 is the most frequently mutated gene in LUAD and plays a critical role in tumor initiation, progression, and therapeutic resistance. The present study aimed to prioritize TP53-associated drug repurposing candidates in LUAD using an integrative in silico approach. Materials and Methods: A total of 1309 TP53-associated compounds were retrieved from the Gene2Drug database. Drug sensitivity profiles of lung adenocarcinoma cell lines were evaluated using PRISM Repurposing Public 22Q2 viability data obtained from the DepMap platform. Candidate compounds were ranked according to Gene2Drug significance scores (p < 1 × 10⁻³), and compounds with concordant sensitivity patterns in PRISM data were prioritized. Results: LUAD cell lines showed the strongest sensitivity to atropine (p = 6.83 × 10⁻⁵). Additionally, LUAD cell lines displayed consistent sensitivity signals for dropropizine (p = 8.47 × 10⁻³), terazosin (p = 1.11 × 10⁻³), morantel (p = 9.05 × 10⁻³), netilmicin (p = 8.37 × 10⁻³), altretamine (p = 9.82 × 10⁻³), and perphenazine (p = 9.58 × 10⁻³). These findings indicate that several non-oncology drugs exhibit distinct and reproducible sensitivity profiles in LUAD cell lines. Conclusions: Based on TP53-associated drug sensitivity profiles, this in silico analysis identifies atropine among the prioritized candidates, showing the strongest TP53-associated sensitivity signal in LUAD cell lines. Although experimental validation is required, the integration of independent computational datasets provides a robust framework for candidate prioritization and our findings provide a rationale for further preclinical investigation of atropine and related compounds in LUAD. Full article

Background/Objectives: Brain tumors, particularly gliomas, have high mortality and are limited in treatment options, often complicated by severe conditions, which can be fatal. Given the increasing incidence and adverse effects of current drugs, an in silico drug repurposing approach using hub gene clusters to streamline and accelerate the search for new therapies. Methods: The GSE66354, GSE68848, GSE74195, and GSE43290 datasets were used to identify DEGs using GEO2R. A gene co-expression network was constructed using the STRING PPI database. Preserved clusters revealed hub genes, which were used for GO and KEGG pathway enrichment analyses. Drug repurposing screening was performed through drug–gene interactions in DGIdb. Suggestive drugs were then validated through GSEA-CMAP and BOILED-Egg. Results: The study identified three key gene clusters that serve a role in synaptic transmission and transmembrane transport, synaptic vesicle neurotransmission, and extracellular matrix formation. Five drugs passed the drug screening, which are Gabapentin, Pyrantel, Resveratrol, Trifluoperazine, and Valproic acid. Conclusions: Valproic acid and Gabapentin are highly suggestive as candidate repurposed drugs. This study enhances our understanding of brain tumor genetics and supports the development of new immunotherapeutic strategies. Full article

Human endogenous retroviruses (HERVs) are potential driving forces of the pathophysiology of Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), linking post-infectious immune dysfunction to chronic inflammation and immune and neurocognitive dysfunction that are hallmark features of ME/CFS. Accumulating evidence from related autoimmune diseases and cancers has shown that reactivated HERVs can contribute to disease pathogenesis by amplifying immune activation through viral protein-mediated innate sensing, long terminal repeat (LTR)-driven transcription, and disrupting epigenetic silencing. HERV signatures are therefore promising biomarkers for diagnosis, patient stratification for drug-repurposing trials, and therapy monitoring. Accumulating evidence suggests a possible correlation between HERV expression and ME/CFS symptom severity, alterations in immune phenotypes, function and inflammatory gene networks. Importantly, locus-specific HERV profiling is a promising approach for distinguishing ME/CFS from overlapping or co-morbid conditions and healthy controls. Furthermore, HERV-targeted antibodies, immune modulators, epigenetic and antiviral interventions offer promise as concomitant therapeutic strategies for ME/CFS. Additional research incorporating viromics and other-omics validation, functional assays, and HERV-stratified clinical trials is now needed to realise this potential and to transform ME/CFS from a symptom-based syndrome into a mechanism-driven, treatable condition. Full article

The COVID-19 pandemic exposed significant vulnerabilities in the European Union’s health security architecture and highlighted the need for stronger coordination mechanisms for managing cross-border health threats. In response, the European Union established the Health Emergency Preparedness and Response Authority (HERA) as a central body responsible for strengthening preparedness, coordinating procurement, and supporting the development and availability of medical countermeasures. This study examines the potential role of drug repurposing as a strategic tool within the evolving EU health crisis preparedness framework. A narrative literature review and policy analysis were conducted using scientific publications indexed in PubMed and Scopus, as well as institutional and regulatory documents from the European Commission, the European Medicines Agency (EMA), and other relevant organisations. The findings indicate that drug repurposing offers important advantages during health emergencies, including shorter development timelines, lower research costs, and the possibility of leveraging existing manufacturing and regulatory infrastructures. At the same time, several challenges remain, particularly regarding regulatory coordination, intellectual property considerations, and the scalability of pharmaceutical production during periods of increased demand. The analysis suggests that drug repurposing could evolve from an ad hoc response mechanism into a more institutionalised component of EU health crisis preparedness. Integrating repurposing strategies into HERA’s threat prioritisation, regulatory coordination, and industrial preparedness mechanisms may significantly enhance the European Union’s ability to respond rapidly and effectively to future health emergencies. Full article

Background/Objectives: The rapid expansion of targeted therapies has reshaped oncology by exploiting ligand-receptor interactions (LRI) to improve treatment specificity and patient outcomes. However, the data describing these ligands remain fragmented across multiple sources, limiting accessibility for researchers and clinicians. To address this gap, we developed the OncoSolidDB, the first curated and oncology-focused bioinformatics database dedicated to ligands associated with solid malignancies. Methods: OncoSolidDB integrates and harmonizes data from reliable repositories, including ChEMBL, DrugBank and the Anti-Cancer Fund, consolidating curated structural, chemical, pharmacological, and clinical annotations along with standardized identifiers. Results: The database currently encompasses 243 ligands across 15 major solid tumor types including breast, lung, colorectal, melanoma, prostate, gastric, ovarian, cervical, bladder, esophageal, head and neck, thyroid, pancreatic, renal and liver cancer (Hepatocellular Carcinoma, HCC). Each entry is annotated by standardized identifiers (DrugBank, ChEMBL), approval year, chemical structures (SMILES strings, 2D images), and downloadable protein structure files (PDB format). Temporal coverage spans 1953–2025, enabling exploration of historical trends in oncology drug approvals. The database content is suitable for bioinformatics analysis, molecular docking, virtual screening, ligand-based modeling, and drug repurposing studies. Outputs are available through a freely accessible web interface that supports search browsing by cancer type. Conclusions: By consolidating oncology-specific ligand data into a single, structured platform, OncoSolidDB offers a valuable resource for advancing drug discovery, repurposing strategies, and the rational design of next-generation targeted therapies for solid tumors. OncoSolidDB is accessible via our Bioinformatics Research PortalEinstein. Full article

Background/Objectives: Liver diseases cause more than 2 million annual deaths globally, accounting for 4% of the total global mortality rate. Hepatic fibrosis (HF) acts as an indispensable pathological mediator in the progressive deterioration of chronic liver diseases. Thus, the identification of effective anti-fibrotic targets and rational development of corresponding therapeutic agents are expected to deliver profound clinical value for patients suffering from chronic liver disorders. Methods: An in vivo HF model was established to detect Kupffer cell (KC) polarization and periostin (POSTN) protein expression. In vitro, the CCK-8 (Cell Counting Kit-8) assay was applied to evaluate the regulatory effects of Postn-knockdown macrophages on LX-2 cell activity. Conditional knockout mice with Postn were constructed in vivo, and liver tissue samples were used for single-cell sequencing. Functional enrichment and cell differentiation prediction analyses were performed. CellChat was further utilized to characterize alterations in intercellular communication between Postn-deficient KCs and adjacent liver cells. Finally, POSTN-targeted inhibitors were screened and validated via virtual drug screening and experiments. Results: In the HF model, the M2 polarization of KCs was associated with the upregulated expression of POSTN. In contrast, in vitro Postn knockdown correlated with significantly suppressed LX-2 cell activation. Single-cell profiling suggests that Postn deficiency in Kupffer cells is linked to remodeling of the hepatic microenvironment. In drug repurposing, Rhodiosin exhibited binding affinity to POSTN and was observed to inhibit macrophage M2 polarization. Conclusions: POSTN may contribute to KC M2 polarization and be associated with remodeling of the intercellular interaction network among liver cells. Rhodiosin, as a POSTN-binding compound, shows potential for anti-hepatic fibrotic effects. Full article

Drug resistance represents one of the most critical challenges in modern medicine, undermining the efficacy of therapies across both bacterial infections and cancer. Although these conditions arise in fundamentally distinct biological systems, they are governed by shared evolutionary pressures that drive the emergence and selection of resistant populations. This narrative review provides an integrative, cross-disciplinary perspective on drug resistance, focusing on bacteria and cancer and emphasizing the shared evolutionary and molecular mechanisms underlying treatment failure in both domains. Key resistance strategies include efflux-mediated drug export, target modification, enzymatic drug inactivation, metabolic reprogramming, epigenetic and transcriptional plasticity, and protection conferred by specialized microenvironments. These processes are further reinforced by phenotypic heterogeneity, including bacterial persister cells and cancer stem-like cells, which contribute to recurrence and multidrug resistance. Collectively, these parallels define drug resistance as a convergent evolutionary phenomenon driven by adaptability under selective pressure. Recognizing these shared mechanisms reveals important translational opportunities for therapeutic intervention. Strategies such as combination therapy, drug repurposing, nanotechnology-enabled delivery systems, and host-directed approaches offer promising avenues to prevent, delay, or overcome resistance. By integrating insights from microbiology and oncology, this review proposes a unified framework for resistance biology and highlights the potential of cross-disciplinary strategies to improve treatment durability and clinical outcomes. Full article

Metastasis is a primary driver of poor outcomes in clear cell renal cell carcinoma (ccRCC), yet the role of Aquaporin-9 (AQP9) in this process remains unclear. This study aimed to investigate the function, clinical significance, and therapeutic potential of AQP9 in ccRCC. AQP9 expression was analyzed using TCGA data and validated in human tissues and cell lines via Western blot. Functional assays assessed malignant behaviors, while bioinformatics and rescue experiments explored the involvement of the JAK/STAT pathway and epithelial–mesenchymal transition (EMT). Virtual screening, molecular docking, and cellular thermal shift assays (CETSAs) were employed to identify Tedizolid as a potential AQP9 inhibitor, followed by functional validation in vitro and in a xenograft model. AQP9 was significantly upregulated in ccRCC and associated with poor prognosis. The knockdown of AQP9 suppressed proliferation, migration, invasion, and EMT, whereas its overexpression promoted these effects by activating the JAK/STAT pathway. Tedizolid bound directly to AQP9, inhibited cell viability, reversed AQP9-induced malignant phenotypes, and suppressed JAK/STAT signaling both in vitro and in vivo. In conclusion, AQP9 promotes ccRCC metastasis through the JAK/STAT-EMT axis and represents a potential prognostic biomarker and therapeutic target. Tedizolid, identified as a novel AQP9 inhibitor, offers a promising repurposed strategy for ccRCC treatment. Full article

Background/Objectives: Lung adenocarcinoma (LUAD) is molecularly heterogeneous and often requires rational drug combinations rather than single-agent therapy. Many computational repurposing methods use global signature matching or network scores, but they often treat dysregulated genes equally and optimize a single scalar objective. This study aimed to develop a contribution-aware computational framework for prioritizing repurposed multi-drug combinations that counteract LUAD driver modules; Methods: Ten LUAD driver scenarios were curated from the LUAD and non-small cell lung cancer literature and encoded as gene-level counteraction vectors across 44 unique genes. Direction-aware drug–gene interactions from the Comparative Toxicogenomics Database were processed into a weighted contribution matrix. A genetic algorithm was then used to search for small combinations of up to six drugs. The fitness function combined mean absolute error with terms for waste, mismatch, entropy, coverage, combination size, and optional cost. Orthogonal computational support was assessed using CLUE/Connectivity Map transcriptomic reversal analysis; Results: After filtering and optimization, 42 drugs and chemicals remained as candidate components across the scenarios. Increasing the combination size from one to three drugs usually reduced the mean absolute error, whereas larger combinations provided more limited gains. Compared with an MAE-only baseline, the full contribution-aware objective improved or preserved MAE in 54 of 60 scenario–drug-count comparisons. Drug and gene clustering identified interchangeable candidate groups and shared mechanisms across LUAD scenarios. CLUE-based analysis provided strong or moderate transcriptomic reversal support for several prioritized compounds; Conclusions: The proposed framework provides a transparent, scenario-based method for prioritizing repurposed drug combinations in LUAD. The results are computational and hypothesis-generating. They should guide future experimental testing, not clinical treatment decisions. Full article

Background: Oral squamous cell carcinoma (OSCC) remains a clinically challenging malignancy characterized by aggressive behavior and limited therapeutic options. Bitter taste receptors (TAS2Rs), expressed across multiple tissues and cancer types, have recently emerged as regulators of tumor biology and immune responses; however, their functional significance in OSCC remains poorly understood. Methods: Immunohistochemical analysis was performed using surgically resected human tongue OSCC specimens and a tissue microarray (TMA) cohort. In parallel, four TAS2R agonists were evaluated in SCC7 cells to assess intracellular calcium responses. RNA sequencing was conducted to analyze transcriptional changes following diphenidol treatment, and functional assays, including proliferation, migration, and apoptosis analyses, were performed in vitro. Antitumor effects were further evaluated in a syngeneic SCC7 mouse model, followed by TUNEL staining and flow cytometry to assess apoptosis and immune cell infiltration. Results: TAS2R38 expression was markedly upregulated in dysplastic and invasive OSCC lesions with predominant nuclear localization and was associated with histological grade and clinical stage, indicating an early and sustained alteration during tumor progression. Among the agonists tested, diphenidol most strongly induced IP₃-dependent intracellular Ca²⁺ elevation. RNA sequencing revealed upregulation of Il1rl1 and Lzts2. Functionally, diphenidol significantly suppressed SCC7 cell proliferation and migration and induced apoptosis in vitro. In vivo, diphenidol reduced tumor volume and weight and increased apoptotic activity. Flow cytometry demonstrated a marked reduction in tumor-infiltrating CD4⁺CD25⁺Foxp3⁺ regulatory T cells, indicating modulation of the tumor immune microenvironment. Conclusions: TAS2R activation by diphenidol suppresses tumor growth through both tumor-intrinsic mechanisms and modulation of the tumor immune microenvironment in OSCC. These findings define TAS2R-mediated calcium signaling as a novel axis linking tumor progression and immunoregulation. Given that diphenidol is a clinically approved drug with an established safety profile, our results provide a strong rationale for TAS2R-targeted drug repurposing strategies in cancer therapy. Full article

Background: The coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, remains a significant threat to global health. This continued threat is due to the emergence of new variants, the immune system’s limited ability to respond, and the limited effectiveness of available treatments for all individuals. Therefore, leveraging drug repurposing, a fast and inexpensive way to find other drugs that have already been shown to be safe and efficacious, becomes useful. This study leverages ADMET profiling, network pharmacology, and molecular docking to evaluate the repurposing of Product Nkabinde for COVID-19 treatment. Methods: ADMET analysis involving the bioactive phytochemicals of PN was evaluated for pharmacokinetic appropriateness and drug-likeness. Using topological analysis, a network of protein–protein interactions was built to identify hub genes, and predicted compound targets were intersected with COVID-19-associated genes to find shared targets. Their biological importance was characterized using functional enrichment analysis. The binding affinities of PN phytochemicals against hub proteins and SARS-CoV-2 viral proteases (Mpro and PLpro) were assessed by molecular docking using AutoDock Vina. To confirm docking accuracy, co-crystallized ligands were redocked using Schrodinger 2022-1. The multi-target therapeutic potential of PN in COVID-19 was assessed using this integrative network pharmacology and molecular docking technique. Results: Molecular docking demonstrated that PN phytochemicals displayed robust and persistent binding affinities for both viral and host targets. Oleanolic acid showed the best affinity toward Mpro (−12.9 kcal/mol vs. −8.3 kcal/mol), while quercetin-3-O-β-D-(6′-galloyl)-glucopyranoside showed better binding to PLpro (−8.4 kcal/mol vs. −6.4 kcal/mol). Procyanidin B2 toward HCK (−10.5 vs. −7.9 kcal/mol), diosgenin toward EGFR (−9.4 vs. −8.4 kcal/mol), rutin toward SRC (−10.5 vs. −7.8 kcal/mol), and pimelea factor P2 toward PIK3R1 (−11.0 vs. −8.2 kcal/mol) all showed significantly higher affinities than their corresponding co-crystallized ligands. Furthermore, procyanidin B2 demonstrated consistent binding to STAT1 and STAT3, confirming its role in modulating immune signals. Most of the PN phytochemicals show advantageous pharmacokinetic properties, including elevated anticipated gastrointestinal absorption and adherence to Lipinski’s rule of five, signifying favorable oral bioavailability and drug-like properties. Moreover, PN exhibits a remarkable multi-target binding capacity against both SARS-CoV-2 proteases and key host signaling proteins involved in immune regulation and inflammatory responses, as determined by this integrative network pharmacology and molecular docking investigation. Conclusions: PN’s prospects as a host-directed, antiviral treatment for COVID-19 are demonstrated by its coordinated modulation of the PI3K/AKT, JAK–STAT, SRC-family kinase, EGFR, and SYK pathways. These results necessitate further experimental and clinical validation, providing a solid computational basis for repurposing PN in the treatment of COVID-19. Full article

Background: Identifying potential targets for bioactive compounds is crucial for elucidating the mechanisms of action and drug development. Methods: This study presents ComTarget, a computational tool that integrates 3D molecular shape similarity analysis (based on combined 3D descriptors, C3DD) with reverse docking to predict protein targets for small molecules. ComTarget screens against a library of 4429 unique protein targets derived from 26,272 PDB complexes. Results: Validation on benchmark datasets (DEKOIS 2.0 and DUDE-Z) demonstrated that the C3DD molecular similarity calculation method effectively enriches active ligands by capturing critical 3D shape information not evident from chemical topology alone. It outperformed conventional 2D fingerprint methods and offered a favorable balance between shape sensitivity and computational efficiency, serving as a rapid pre-screening filter within the integrated workflow. For FDA-approved drugs (e.g., Imatinib, Aspirin) and natural products (e.g., Berberine). ComTarget identified targets consistent with reported therapeutic targets or putative off-targets in the literature, while also revealing potential targets aligned with the compounds’ pharmacological mechanisms. Conclusions: As a local program, ComTarget offers flexibility in computational resources customization and is freely available for polypharmacology studies, drug repurposing, and adverse reaction prediction. Full article

Artificial intelligence (AI) is increasingly used in cancer research, enabling integrative analysis of complex biomedical data to identify actionable therapeutic vulnerabilities. This review specifically examines how AI advances mechanistic cancer target discovery and translational drug development, focusing on: (1) the processing of large-scale genomics, transcriptomics, proteomics, metabolomics, single-cell profiling, spatial, and clinical datasets using machine learning (ML) and deep learning (DL) algorithms; (2) the identification of candidate biomarkers, driver genes, dysregulated pathways, tumor dependencies, and molecular targets that traditional methods often miss; (3) the integration of multi-omics data, network biology, causal inference, and systems-level modeling to refine mechanistic understanding of cancer progression and separate functional driver events from passengers; and (4) applications in drug development, including virtual screening, molecular modeling, structure-informed target validation, drug repurposing, synthetic lethality prediction, and de novo drug design, which collectively may enhance early-stage drug discovery efficiency. The review underscores that AI serves as both a predictive tool and a platform for linking molecular mechanisms to hypothesis generation, target prioritization, and rational treatment design. Challenges such as data heterogeneity, algorithmic bias, interpretability, reproducibility, regulatory requirements, and patient privacy must be addressed for robust translation and clinical use. Future directions may focus on hybrid approaches that integrate causal modeling, explainable AI, multimodal data, and experimental validation to yield mechanistically grounded, clinically actionable insights. AI-driven approaches ultimately aim to accelerate mechanism-based cancer target discovery and enable more precise, biologically informed anticancer therapies. Full article