Toxicol Rep. 2024 Dec 28;14:101879. doi: 10.1016/j.toxrep.2024.101879. eCollection 2025 Jun.

ABSTRACT

Alzheimer's Disease (AD) is one of the leading neurodegenerative diseases that affect the human population. Several hypotheses are in the pipeline to establish the commencement of this disease; however, the amyloid hypothesis is one of the most widely accepted ones. Amyloid plaques are rich in Amyloid Beta (Aβ) proteins, which are found in the brains of Alzheimer's patients. They are the spliced product of a transmembrane protein called Amyloid Precursor Protein (APP); when they enter into the amylogenic pathway, they get cleaved simultaneously by Beta and Gamma Secretase and produce Aβ protein. Appearances of Amyloid plaques are the significant clinical hallmarks of this disease. AD is mainly present in two genetically distinct forms; sporadic and familial AD. Sporadic Alzheimer's Disease (sAD) is marked by a later clinical onset of the disease, whereas, familial Alzheimer's Disease (fAD) is an early onset of the disease with mendelian inheritance. Several mutations have been clinically reported in the last decades that have shown a direct link with fAD. Many of those mutations are reported to be present in the APP. In this study, we selected a few significant mutations present in the Aβ stretch of the APP and tried to differentiate the wild-type Aβ dimers formed in sAD and the mutant dimers formed in fAD through molecular modelling as there are no structures available from wet-lab studies till date. We analysed the binding interactions leading to formations of the dimers. Our next aim was to come up with a solution to treat AD using the method of drug repurposing. For that we used virtual screening and molecular docking simulations of the already existing anti-inflammatory drugs and studied their potency in resisting the formation of Aβ dimers. This is the first such report of drug repurposing for the treatment of AD, which might pave new pathways in therapy.

PMID:39867516 | PMC:PMC11762949 | DOI:10.1016/j.toxrep.2024.101879

medRxiv [Preprint]. 2025 Jan 14:2025.01.10.25320348. doi: 10.1101/2025.01.10.25320348.

ABSTRACT

BACKGROUND: The development and approval of novel drugs are typically time-intensive and expensive. Leveraging a computational drug repurposing framework that integrates disease-relevant genetically regulated gene expression (GReX) and large longitudinal electronic medical record (EMR) databases can expedite the repositioning of existing medications. However, validating computational predictions of the drug repurposing framework remains a challenge.

METHODS: To benchmark the drug repurposing framework, we first performed a 5-method-rank-based computational drug prioritization pipeline by integrating multi-tissue GReX associated with COVID-19-related hospitalization, with drug transcriptional signature libraries from the Library of Integrated Network-Based Cellular Signatures. We prioritized FDA-approved medications from the 10 top-ranked compounds, and assessed their association with COVID-19 incidence within the Veterans Health Administration (VHA) cohort (~9 million individuals). In parallel, we evaluated in vitro SARS-CoV-2 replication inhibition in human lung epithelial cells for the selected candidates.

RESULTS: Our in silico pipeline identified seven FDA-approved drugs among the top ten candidates. Six (imiquimod, nelfinavir and saquinavir, everolimus, azathioprine, and retinol) had sufficient prescribing rates or feasibility for further testing. In the VHA cohort, azathioprine (odds ratio [OR]=0.69, 95% CI 0.62-0.77) and retinol (OR=0.81, 95% CI 0.72-0.92) were significantly associated with reduced COVID-19 incidence. Conversely, nelfinavir and saquinavir demonstrated potent SARS-CoV-2 inhibition in vitro (~95% and ~65% viral load reduction, respectively). No single compound showed robust protection in both in vivo and in vitro settings.

CONCLUSIONS: These findings underscore the power of GReX-based drug repurposing in rapidly identifying existing therapies with potential clinical relevance; four out of six compounds showed a protective effect in one of the two validation approaches. Crucially, our results highlight how a complementary evaluation-combining epidemiological data and in vitro assays-helps refine the most promising candidates for subsequent mechanistic studies and clinical trials. This integrated validation approach may prove vital for accelerating therapeutic development against current and future health challenges.

PMID:39867394 | PMC:PMC11759241 | DOI:10.1101/2025.01.10.25320348

medRxiv [Preprint]. 2025 Jan 13:2025.01.11.25320392. doi: 10.1101/2025.01.11.25320392.

ABSTRACT

RATIONALE: Individuals homozygous for the Alpha-1 Antitrypsin (AAT) Z allele (Pi*ZZ) exhibit heterogeneity in COPD risk. COPD occurrence in non-smokers with AAT deficiency (AATD) suggests inflammatory processes may contribute to COPD risk independently of smoking. We hypothesized that inflammatory protein biomarkers in non-AATD COPD are associated with moderate-to-severe COPD in AATD individuals, after accounting for clinical factors.

METHODS: Participants from the COPDGene (Pi*MM) and AAT Genetic Modifier Study (Pi*ZZ) were included. Proteins associated with FEV 1 /FVC were identified, adjusting for confounders and familial relatedness. Lung-specific protein-protein interaction (PPI) networks were constructed. Proteins associated with AAT augmentation therapy were identified, and drug repurposing analyses performed. A protein risk score (protRS) was developed in COPDGene and validated in AAT GMS using AUC analysis. Machine learning ranked proteomic predictors, adjusting for age, sex, and smoking history.

RESULTS: Among 4,446 Pi*MM and 352 Pi*ZZ individuals, sixteen blood proteins were associated with airflow obstruction, fourteen of which were highly expressed in lung. PPI networks implicated regulation of immune system function, cytokine and interleukin signaling, and matrix metalloproteinases. Eleven proteins, including IL4R, were linked to augmentation therapy. Drug repurposing identified antibiotics, thyroid medications, hormone therapies, and antihistamines as potential AATD treatments. Adding protRS improved COPD prediction in AAT GMS (AUC 0.86 vs. 0.80, p = 0.0001). AGER was the top-ranked protein predictor of COPD.

CONCLUSIONS: Sixteen proteins are associated with COPD and inflammatory processes that predict airflow obstruction in AATD after accounting for age and smoking. Immune activation and inflammation are modulators of COPD risk in AATD.

PMID:39867385 | PMC:PMC11759610 | DOI:10.1101/2025.01.11.25320392

RSC Adv. 2025 Jan 23;15(4):2298-2316. doi: 10.1039/d4ra08245a. eCollection 2025 Jan 23.

ABSTRACT

Despite significant strides in improving cancer survival rates, the global cancer burden remains substantial, with an anticipated rise in new cases. Immune checkpoints, key regulators of immune responses, play a crucial role in cancer evasion mechanisms. The discovery of immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 has revolutionized cancer treatment, with monoclonal antibodies (mAbs) becoming widely prescribed. However, challenges with current mAb ICIs, such as limited oral bioavailability, adverse effects, and high costs, underscore the need to explore alternative small-molecule inhibitors. In this work, we aimed to identify new potential ICI among all FDA-approved drugs. We employed QSAR models to predict PD-1/PD-L1 inhibition, utilizing a diverse dataset of 29 197 molecules sourced from ChEMBL, PubChem, and recent literature. Machine learning techniques, including Random Forest, Support Vector Machine, and Convolutional Neural Network, were employed for benchmarking to assess model performance. Additionally, we undertook a drug repurposing strategy, leveraging the best in silico model for a virtual screening campaign involving 1576 off-patent approved drugs. Only two virtual screening hits were proposed based on the criteria established for this approach, including: (1) QSAR probability of being active against PD-L1; (2) QSAR applicability domain; (3) prediction of the affinity between the PD-L1 and ligands through molecular docking. One of the proposed hits was sonidegib, an anticancer drug, featuring a biphenyl system. Sonidegib was subsequently validated for in vitro PD-1/PD-L1 binding modulation using ELISA and flow cytometry. This integrated approach, which combines computer-aided drug design (CADD) tools, QSAR modelling, drug repurposing, and molecular docking, offers a pioneering strategy to expedite drug discovery for PD-1/PD-L1 axis inhibition. The findings underscore the potential to identify a wider range small molecules to contribute to the ongoing efforts to advancing cancer immunotherapy.

PMID:39867321 | PMC:PMC11755407 | DOI:10.1039/d4ra08245a

ACS Omega. 2025 Jan 8;10(2):1864-1892. doi: 10.1021/acsomega.4c09114. eCollection 2025 Jan 21.

ABSTRACT

Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects movement. It occurs due to a gradual deficit of dopamine-producing brain cells, particularly in the substantia nigra. The precise etiology of PD is not fully understood, but it likely involves a combination of genetic and environmental factors. The therapies available at present alleviate symptoms but do not stop the disease's advancement. Research endeavors are currently directed at inventing disease-controlling therapies that aim at the inherent mechanisms of PD. PD biomarker breakthroughs hold enormous potential: earlier diagnosis, better monitoring, and targeted treatment based on individual response could significantly improve patient outcomes and ease the burden of this disease. PD research is an active and evolving field, focusing on understanding disease mechanisms, identifying biomarkers, developing new treatments, and improving care. In this report, we explore data from the CAS Content Collection to outline the research progress in PD. We analyze the publication landscape to offer perspective into the latest expertise advancements. Key emerging concepts are reviewed and strategies to fight disease evaluated. Pharmacological targets, genetic risk factors, as well as comorbid diseases are explored, and clinical usage of products against PD with their production pipelines and trials for drug repurposing are examined. This review aims to offer a comprehensive overview of the advancing landscape of the current understanding about PD, to define challenges, and to assess growth prospects to stimulate efforts in battling the disease.

PMID:39866628 | PMC:PMC11755173 | DOI:10.1021/acsomega.4c09114

Heliyon. 2025 Jan 3;11(1):e41688. doi: 10.1016/j.heliyon.2025.e41688. eCollection 2025 Jan 15.

ABSTRACT

Basal Cell Carcinoma (BCC) and Actinic Keratosis (AK) are prevalent skin conditions with significant health complications. The molecular mechanisms underlying these conditions and their potential shared pathways remain ambiguous despite their prevalence. Therefore, this study aims to elucidate the common molecular pathways and potential therapeutic targets for BCC and AK through comprehensive computational network analysis. Linkage analysis was performed to identify common liable genes between BCC and AK. Protein-protein interactions (PPIs), Topological properties, GO enrichment, pathway enrichment, and gene regulatory network analyses were also performed to reveal potential molecular mechanisms and pathways. Furthermore, we evaluated protein-drug interactions (PDIs) to identify potential therapeutic targets. Our analysis revealed 22 common genes between BCC and AK: TP53, EGFR, CDKN2A, MMP9, PTGS2, VDR, BCL2, MMP2, EZH2, TP63, FOXP3, MSH2, MMP14, FLG, MC1R, CDKN2B, TIMP3, TYR, SOX10, IRF4, KRT17, and NID1. PPI network analysis highlighted TP53 and EGFR as central hubs, validated using RNA-seq data. Co-expression and physical interaction analysis revealed a strong interplay between the common genes at the transcriptional and functional levels. GO analysis identified skin cancer-relevant terms: "skin development", "immune system development", and "response to radiation" as significantly enriched biological processes, while pathway enrichment analysis highlighted several cancer-related pathways enrichment. Gene regulatory network analysis revealed complex interactions between genes, miRNAs, and transcription factors, with TP53, BCL2, and EGFR playing central roles. PDI network analysis identified ibuprofen as a potential therapeutic agent targeting PTGS2 and BCL2, while other proteins VDR, MMP2, MMP9, and TYR showed interactions with multiple drugs. This computational analysis provides valuable insights into the shared molecular mechanisms of BCC and AK, revealing common pathways and potential therapeutic targets for developing novel treatment strategies and repurposing existing drugs for these prevalent skin cancers. Therefore, these findings may guide future research in understanding and developing targeted therapies for both conditions.

PMID:39866430 | PMC:PMC11761328 | DOI:10.1016/j.heliyon.2025.e41688

Ophthalmology. 2025 Jan 23:S0161-6420(25)00070-3. doi: 10.1016/j.ophtha.2025.01.016. Online ahead of print.

ABSTRACT

PURPOSE: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have risen exponentially in usage and have been shown to exert neuroprotective and anti-inflammatory effects across multiple organ systems. This study investigates whether GLP-1RAs influence the risk for age-related ocular diseases.

DESIGN: Retrospective cohort study.

SUBJECTS AND PARTICIPANTS: This study utilized an electronic health records platform of patients in the United States. Patients older than 60 years of age with at least five years of ophthalmology follow-up and medication prescription documentation were included. Patients were categorized into five medication groups: GLP-1RAs, metformin, insulin, statins, or aspirin users. Cohorts were propensity-matched on demographics and chronic health conditions using a greedy matching algorithm.

MAIN OUTCOME MEASURES: Outcomes of cataract, ocular hypertension, primary open angle glaucoma, non-exudative AMD, and exudative AMD were compared five years following initial medication prescription. We then examined earlier timepoints within the five-year period. Significance was defined as p<0.05 and HR threshold > 1.1 or < 0.9 to improve signal to noise ratio.

RESULTS: Of the 9,669 patients taking GLP-1RAs, 84.4 percent were diabetic with an average BMI of 36.2. Propensity matched cohorts demonstrated GLP-1RAs were associated with reduced hazard of non-exudative AMD compared to metformin (HR 0.68, 95%CI: 0.56-0.84), insulin (HR 0.72, 95%CI: 0.58-0.89), and statins (HR 0.7, 95%CI: 0.57-0.87). These findings were validated compared to aspirin and in an independent older cohort of patients. This significant reduction appeared after three years compared to metformin (HR 0.69, 95%CI: 0.52-0.91), insulin (HR 0.66, 95%CI: 0.5-0.87), and statins (HR 0.67, 95%CI: 0.51-0.88). Time course results were validated using independent cohorts of propensity matched patients taking medications for three years. Notably, GLP-1RAs also significantly reduced the risk of exudative AMD (HR 0.7, 95%CI: 0.58-0.84) and POAG (HR 0.58, 95% CI 0.45-0.76) compared to insulin after three years. Usage of GLP-1RAs showed no persistent significant impact on the risk of cataract formation nor ocular hypertension after five years compared other medications.

CONCLUSIONS: This study suggests GLP-1RAs may reduce the risk of multiple age-related ocular diseases and suggests the need for future prospective studies to validate these findings.

PMID:39863057 | DOI:10.1016/j.ophtha.2025.01.016

Biochim Biophys Acta Mol Basis Dis. 2025 Jan 23:167688. doi: 10.1016/j.bbadis.2025.167688. Online ahead of print.

ABSTRACT

Cancer, a heterogeneous disease, presents significant challenges for drug development due to its complex etiology. Drug repurposing, particularly through network medicine approaches, offers a promising avenue for cancer treatment by analyzing how drugs influence cellular networks on a systemic scale. The advent of large-scale proteomics data provides new opportunities to elucidate regulatory mechanisms specific to cancer subtypes. Herein, we present NetSDR, a Network-based Subtype-specific Drug Repurposing framework for prioritizing repurposed drugs specific to certain cancer subtypes, guided by subtype-specific proteomic signatures and network perturbations. First, by integrating cancer subtype information into a network-based method, we developed a pipeline to recognize subtype-specific functional modules. Next, we conducted drug response analysis for each module to identify the "therapeutic module" and then used deep learning to construct weighted drug response network for the particular subtype. Finally, we employed a perturbation response scanning-based drug repurposing method, which incorporates dynamic information, to facilitate the prioritization of candidate drugs. Applying the framework to gastric cancer, we attested the significance of the extracellular matrix module in treatment strategies and discovered a promising potential drug target, LAMB2, as well as a series of possible repurposed drugs. This study demonstrates a systems biology framework for precise drug repurposing in cancer and other complex diseases.

PMID:39862994 | DOI:10.1016/j.bbadis.2025.167688

Naunyn Schmiedebergs Arch Pharmacol. 2025 Jan 25. doi: 10.1007/s00210-025-03809-5. Online ahead of print.

ABSTRACT

As a promising candidate for tackling drug-resistant cancers, triptolide, a diterpenoid derived from the Chinese medicinal plant Tripterygium wilfordii, has been developed. This review summarizes potential antitumor activities, including the suppression of RNA polymerase II, the suppression of heat shock proteins (HSP70 and HSP90), and the blockade of NF-kB signalling. Triptolide is the first known compound to target cancer cells specifically but spare normal cells, and it has success in treating cancers that are difficult to treat, including pancreatic, breast, and lung cancers. It acts against the tolerance mechanisms, including efflux pump upregulation, epithelial-mesenchymal transition, and cancer stem cells. Triptolide modulates important cascades, including PI3K/AKT/mTOR, enhancing the efficacy of conventional therapies. Nonetheless, its clinical application is constrained by toxicity and bioavailability challenges. Emerging drug delivery systems, such as nanoparticles and micellar formulations, are being developed to address these limitations. It has strong interactions with key anticancer targets, like PARP, as determined in preclinical and computational studies consistent with its mechanism of action. Early-phase clinical trials of Minnelide, a water-soluble derivative of triptolide, are promising, but additional work is necessary to optimize dosing, delivery, and safety. This comprehensive analysis demonstrates that triptolide may constitute a repurposed precision medicine tool to overcome tolerance in cancer therapy.

PMID:39862263 | DOI:10.1007/s00210-025-03809-5

Viruses. 2025 Jan 16;17(1):117. doi: 10.3390/v17010117.

ABSTRACT

Lassa fever (LF), a viral hemorrhagic fever disease with a case fatality rate that can be over 20% among hospitalized LF patients, is endemic to many West African countries. Currently, no vaccines or therapies are specifically licensed to prevent or treat LF, hence the significance of developing therapeutics against the mammarenavirus Lassa virus (LASV), the causative agent of LF. We used in silico docking approaches to investigate the binding affinities of 2015 existing drugs to LASV proteins known to play critical roles in the formation and activity of the virus ribonucleoprotein complex (vRNP) responsible for directing replication and transcription of the viral genome. Validation of docking protocols were achieved with reference inhibitors of the respective targets. Our in silico docking screen identified five drugs (dexamethasone, tadalafil, mefloquine, ergocalciferol, and flunarizine) with strong predicted binding affinity to LASV proteins involved in the formation of the vRNP. We used cell-based functional assays to evaluate the antiviral activity of the five selected drugs. We found that flunarizine, a calcium-entry blocker, inhibited the vRNP activity of LASV and LCMV and virus surface glycoprotein fusion activity required for mammarenavirus cell entry. Consistently with these findings, flunarizine significantly reduced peak titers of LCMV in a multi-step growth kinetics assay in human A549 cells. Flunarizine is being used in several countries worldwide to treat vertigo and migraine, supporting the interest in exploring its repurposing as a candidate drug to treat LASV infections.

PMID:39861906 | DOI:10.3390/v17010117

Viruses. 2025 Jan 13;17(1):92. doi: 10.3390/v17010092.

ABSTRACT

The ongoing monkeypox (mpox) disease outbreak has spread to multiple countries in Central Africa and evidence indicates it is driven by a more virulent clade I monkeypox virus (MPXV) strain than the clade II strain associated with the 2022 global mpox outbreak, which led the WHO to declare this mpox outbreak a public health emergency of international concern. The FDA-approved small molecule antiviral tecovirimat (TPOXX) is recommended to treat mpox cases with severe symptoms, but the limited efficacy of TPOXX and the emergence of TPOXX resistant MPXV variants has challenged this medical practice of care and highlighted the urgent need for alternative therapeutic strategies. In this study we have used vaccinia virus (VACV) as a surrogate of MPXV to assess the antiviral efficacy of combination therapy of TPOXX together with mycophenolate mofetil (MMF), an FDA-approved immunosuppressive agent that we have shown to inhibit VACV and MPXV, or the N-myristoyltransferase (NMT) inhibitor IMP-1088. Both MMF and IMP-1088 drugs exhibited strong dose-dependent antiviral activity against VACV and mpox, and potent synergistic effects in conjunction with TPOXX. Our findings support combination therapy of direct-acting (TPOXX) and host-targeted (MMF and IMP-1088) antivirals as a promising approach to treat mpox and prevent the emergence and spread of TPOXX-resistant MPXV variants.

PMID:39861882 | DOI:10.3390/v17010092

Pharmaceuticals (Basel). 2025 Jan 2;18(1):43. doi: 10.3390/ph18010043.

ABSTRACT

BACKGROUND/OBJECTIVES: Septic cardiomyopathy (SCM) is a severe cardiac complication of sepsis, characterized by cardiac dysfunction with limited effective treatments. This study aimed to identify repurposable drugs for SCM by integrated multi-omics and network analyses.

METHODS: We generated a mouse model of SCM induced by lipopolysaccharide (LPS) and then obtained comprehensive metabolic and genetic data from SCM mouse hearts using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and RNA sequencing (RNA-seq). Using network proximity analysis, we screened for FDA-approved drugs that interact with SCM-associated pathways. Additionally, we tested the cardioprotective effects of two drug candidates in the SCM mouse model and explored their mechanism-of-action in H9c2 cells.

RESULTS: Network analysis identified 129 drugs associated with SCM, which were refined to 14 drug candidates based on strong network predictions, proven anti-infective effects, suitability for ICU use, and minimal side effects. Among them, acetaminophen and pyridoxal phosphate significantly improved cardiac function in SCM moues, as demonstrated by the increased ejection fraction (EF) and fractional shortening (FS), and the reduced levels of cardiac injury biomarkers: B-type natriuretic peptide (BNP) and cardiac troponin I (cTn-I). In vitro assays revealed that acetaminophen inhibited prostaglandin synthesis, reducing inflammation, while pyridoxal phosphate restored amino acid balance, supporting cellular function. These findings suggest that both drugs possess protective effects against SCM.

CONCLUSIONS: This study provides a robust platform for drug repurposing in SCM, identifying acetaminophen and pyridoxal phosphate as promising candidates for clinical translation, with the potential to improve treatment outcomes in septic patients with cardiac complications.

PMID:39861106 | DOI:10.3390/ph18010043

Pharmaceuticals (Basel). 2024 Dec 27;18(1):21. doi: 10.3390/ph18010021.

ABSTRACT

Infection with the protozoan parasite Trypanosoma cruzi causes human Chagas disease. Benznidazole (BNZ) and nifurtimox are the current drugs for the treatment; however, they induce severe adverse side effects in patients; therefore, there is a need to improve the treatment effectiveness and efficiency of these drugs for its safer use. Background/Objective: Glyburide, glipizide, and gliquidone, hypoglycemic drugs for diabetes treatment, were previously predicted to bind to dihydrofolate reductase-thymidylate synthase from T. cruzi by in silico docking analysis; they also showed antiproliferative effects against T. cruzi epimastigotes, the stage of the insect vector. In the present study, the potential parasiticidal effect of these antidiabetic drugs was tested in monotherapy and bi-therapy with BNZ in human cells in vitro and in animals. Methods: Evaluation was performed in (a) a model of in vitro infection of T. cruzi trypomastigotes using human fibroblasts as host cells and (b) in mice infected with T. cruzi. Results: The antidiabetic drugs in monotherapy showed antiparasitic effects in preventing infection progression (trypomastigotes release), with an IC50 of 8.4-14.3 µM in comparison to that of BNZ (0.26 µM) in vitro. However, in bi-therapy, the presence of just 0.5 or 1 µM of the antidiabetics decreased the BNZ IC50 by 5-10 times to 0.03-0.05 µM. Remarkably, the antidiabetic drugs in monotherapy decreased the infection in mice by 40-60% in a similar extent to BNZ (80%). In addition, the combination of BNZ plus antidiabetics perturbed the antioxidant metabolites in epimastigotes. Conclusions: These results identified antidiabetics as potential drugs in combination therapy with BNZ to treat T. cruzi infection.

PMID:39861083 | DOI:10.3390/ph18010021

Pharmaceuticals (Basel). 2024 Dec 25;18(1):14. doi: 10.3390/ph18010014.

ABSTRACT

BACKGROUND: Chemoresistance is a major obstacle in high-grade serous carcinoma (HGSC) treatment. Although many patients initially respond to chemotherapy, the majority of them relapse due to Carboplatin and Paclitaxel resistance. Drug repurposing has surfaced as a potentially effective strategy that works synergically with standard chemotherapy to bypass chemoresistance. In a prior study, using 2D cultures and two HGSC chemoresistant cell lines, it was demonstrated that combining Carboplatin or Paclitaxel with Pitavastatin or Ivermectin resulted in the most notable synergy. Acknowledging that 2D culture systems are limited in reflecting the tumor architecture, 3D cultures were generated to provide insights on treatment efficacy tests in more complex models.

OBJECTIVES: We aimed to investigate whether combining Carboplatin or Paclitaxel with Pitavastatin or Ivermectin offers therapeutic benefits in a Cultrex-based 3D model.

METHODS: Here, the cytotoxicity of Carboplatin and Paclitaxel, both alone and in combination with Pitavastatin or Ivermectin, were analyzed on two chemoresistant tumor cell lines, OVCAR8 and OVCAR8 PTX R C, in 3D cultures. Cellular viability was assessed using CellTiter-Glo® Luminescent assays. Also, it explored synergistic interactions using zero interaction potency, Loewe, Bliss independence, and High-single agent reference models.

RESULTS: Our research indicates combining chemotherapeutic drugs with Pitavastatin or Ivermectin yields significantly more cytotoxic effects than chemotherapy alone. For all the combinations tested, at least one model indicated an additive effect; however, only the combination of Paclitaxel and Ivermectin consistently demonstrated an additive effect across all chemoresistant cell lines cultured in 3D models, as well as in all four synergy reference models used to assess drug interactions.

CONCLUSIONS: Combining Paclitaxel with Ivermectin has the highest cytotoxic and the strongest additive effect for both chemoresistant cell lines compared to Paclitaxel alone.

PMID:39861076 | DOI:10.3390/ph18010014

Pharmaceuticals (Basel). 2024 Dec 25;18(1):13. doi: 10.3390/ph18010013.

ABSTRACT

Background: Aurora kinase B (AurB) is a pivotal regulator of mitosis, making it a compelling target for cancer therapy. Despite significant advances in protein kinase inhibitor development, there are currently no AurB inhibitors readily available for therapeutic use. Methods: This study introduces a machine learning-assisted drug repurposing framework integrating quantitative structure-activity relationship (QSAR) modeling, molecular fingerprints-based classification, molecular docking, and molecular dynamics (MD) simulations. Using this pipeline, we analyzed 4680 investigational and approved drugs from DrugBank database. Results: The machine learning models trained for drug repurposing showed satisfying performance and yielded the identification of saredutant, montelukast, and canertinib as potential AurB inhibitors. The candidates demonstrated strong binding energies, key molecular interactions with critical residues (e.g., Phe88, Glu161), and stable MD trajectories, particularly saredutant, a neurokinin-2 (NK2) antagonist. Conclusions: Beyond identifying potential AurB inhibitors, this study highlights an integrated methodology that can be applied to other challenging drug targets.

PMID:39861075 | DOI:10.3390/ph18010013

Pathogens. 2024 Dec 31;14(1):20. doi: 10.3390/pathogens14010020.

ABSTRACT

The development of antivirals for respiratory viruses has advanced markedly in response to the growing threat of pathogens such as Influenzavirus (IAV), respiratory syncytial virus (RSV), and SARS-CoV-2. This article reviews the advances and challenges in this field, highlighting therapeutic strategies that target critical stages of the viral replication cycle, including inhibitors of viral entry, replication, and assembly. In addition, innovative approaches such as inhibiting host cellular proteins to reduce viral resistance and repurposing existing drugs are explored, using advanced bioinformatics tools that optimize the identification of antiviral candidates. The analysis also covers emerging technologies such as nanomedicine and CRISPR gene editing, which promise to improve the stability and efficacy of treatments. While current antivirals offer valuable options, they face challenges such as viral evolution and the need for accessible treatments for vulnerable populations. This article underscores the importance of continued innovation in biotechnology to overcome these limitations and provide safe and effective treatments. Combining traditional and advanced approaches in developing antivirals is essential in order to address respiratory viral diseases that affect global health.

PMID:39860981 | DOI:10.3390/pathogens14010020

Molecules. 2025 Jan 12;30(2):274. doi: 10.3390/molecules30020274.

ABSTRACT

Predicting drug-target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel drug-target prediction model called MCF-DTI. The model utilizes the SMILES representation of drugs and the sequence features of targets, employing a multi-scale convolutional neural network (MSCNN) with parallel shared-weight modules to extract features from the drug side. For the target side, it combines MSCNN with Transformer modules to capture both local and global features effectively. The extracted features are then weighted and fused, enabling comprehensive feature representation to enhance the predictive power of the model. Experimental results on the Davis dataset demonstrate that MCF-DTI achieves an AUC of 0.9746 and an AUPR of 0.9542, outperforming other state-of-the-art models. Our case study demonstrates that our model effectively validated several known drug-target relationships in lung cancer and predicted the therapeutic potential of certain preclinical compounds in treating lung cancer. These findings contribute valuable insights for subsequent drug repurposing efforts and novel drug development.

PMID:39860144 | DOI:10.3390/molecules30020274

Molecules. 2025 Jan 10;30(2):260. doi: 10.3390/molecules30020260.

ABSTRACT

Drug development faces significant financial and time challenges, highlighting the need for more efficient strategies. This study evaluated the druggability of the entire human proteome using Fpocket. We identified 15,043 druggable pockets in 20,255 predicted protein structures, significantly expanding the estimated druggable proteome from 3000 to over 11,000 proteins. Notably, many druggable pockets were found in less studied proteins, suggesting untapped therapeutic opportunities. The results of a pairwise pocket similarity analysis identified 220,312 similar pocket pairs, with 3241 pairs across different protein families, indicating shared drug-binding potential. In addition, 62,077 significant matches were found between druggable pockets and 1872 known drug pockets, highlighting candidates for drug repositioning. We repositioned progesterone to ADGRD1 for pemphigus and breast cancer, as well as estradiol to ANO2 for shingles and medulloblastoma, which were validated via molecular docking. Off-target effects were analyzed to assess the safety of drugs such as axitinib, linking newly identified targets with known side effects. For axitinib, 127 new targets were identified, and 46 out of 48 documented side effects were linked to these targets. These findings demonstrate the utility of pocket similarity in drug repositioning, target expansion, and improved drug safety evaluation, offering new avenues for the discovery of new indications and side effects of existing drugs.

PMID:39860130 | DOI:10.3390/molecules30020260

Antibiotics (Basel). 2025 Jan 8;14(1):51. doi: 10.3390/antibiotics14010051.

ABSTRACT

Background: Multidrug-resistant bacteria cause over 700,000 deaths annually, a figure projected to reach 10 million by 2050. Among these bacteria, the ESKAPEE group is notable for its multiple resistance mechanisms. Given the high costs of developing new antimicrobials and the rapid emergence of resistance, drug repositioning offers a promising alternative. Results: This study evaluates the antibacterial activity of sertraline and paroxetine. When tested against clinical and reference strains from the ESKAPEE group, sertraline exhibited minimum inhibitory concentration (MIC) values between 15 and 126 μg/mL, while the MIC values for paroxetine ranged from 60 to 250 μg/mL. Both drugs effectively eradicated bacterial populations within 2 to 24 h and caused morphological changes, such as protrusions and cellular fragmentation, as shown by electron scanning microscopy. Regarding their mechanisms of action as antibacterials, for the first time, increased membrane permeability was detected, as evidenced by heightened dye absorption, along with the increased presence of total proteins and dsDNA in the extracellular medium of Escherichia coli ATCC2 25922 and Staphylococcus aureus ATCC 25923, and oxidative stress was also detected in bacteria treated with sertraline and paroxetine, with reduced efficiency observed in the presence of antioxidants and higher levels of oxygen-reactive species evidenced by their reaction with 6-carboxy-2',7'-dichlorodihydrofluorescein diacetate. The drugs also inhibited bacterial efflux pumps, increasing ethidium bromide accumulation and enhancing tetracycline activity in resistant strains. Conclusions: These findings indicate that sertraline and paroxetine could serve as alternative treatments against multidrug-resistant bacteria, as well as efflux pump inhibitors (EPIs), and they support further development of antimicrobial agents based on these compounds.

PMID:39858337 | DOI:10.3390/antibiotics14010051

Mol Biol Rep. 2025 Jan 24;52(1):154. doi: 10.1007/s11033-025-10264-1.

ABSTRACT

BACKGROUND: Current treatment strategies for hormone-dependent breast cancers, including adjuvant endocrine therapy, often fail due to persistence of breast cancer stem cells (brCSCs), which are significant contributors to tumor recurrence and treatment resistance. Therefore, gaining deeper insights into the molecular regulators driving breast cancer aggressiveness is important. Moreover, given the complexities and expenses involved in developing new pharmacological agents, the strategic repurposing of existing FDA-approved drugs to target these key molecular pathways presents a compelling approach for identifying novel therapeutic interventions aimed at mitigating tumor refractoriness.

METHODS: The study employs survival analysis from TCGA database, protein expression analyses alongside aldefluor assays, sphere formation efficiency tests to evaluate cellular stemness, and DCFDA analysis combined with antioxidant enzyme assays to investigate redox imbalance in brCSCs. These analyses were conducted following the genetic deletion of YAP/TAZ and pharmacological treatment with verteporfin.

RESULTS: The study demonstrated that transcriptional co-activators YAP/TAZ are significantly upregulated in chemotreated ER+ patient breast tumors and MCF-7 mammospheres, where it was found to interact with the transcription factor SOX2 within the nuclear compartment. Genetic ablation and pharmacological inhibition of YAP/TAZ markedly impaired stemness properties and disrupted redox homeostasis in the mammospheres. Additionally, treatment with verteporfin led to a substantial reduction in the frequency and viability of brCSCs, suggesting their effective eradication.

CONCLUSION: This study highlights the potential of repurposing verteporfin, an FDA-approved drug originally formulated for age-related macular degeneration, as a therapeutic agent for targeting YAP/TAZ-mediated stemness and redox balance in brCSCs, thereby reducing their viability in ER-positive breast cancers.

PMID:39853518 | DOI:10.1007/s11033-025-10264-1