Interdiscip Sci. 2025 Apr 3. doi: 10.1007/s12539-025-00698-3. Online ahead of print.

ABSTRACT

Identification of drug-target interactions (DTIs) is critical for drug discovery and drug repositioning. However, most DTI methods that extract features from drug molecules and protein entities neglect specific substructure information of pharmacological responses, which leads to poor predictive performance. Moreover, most existing methods are based on molecular graphs or molecular descriptors to obtain abstract representations of molecules, but combining the two feature learning methods for DTI prediction remains unexplored. Therefore, a new ASCS-DTI framework for DTI prediction is proposed, which utilizes a substructure attention mechanism to flexibly capture substructures of compounds at different grain sizes, allowing the important substructure information of each molecule to be learned. Additionally, the framework combines three different molecular fingerprinting information to comprehensively characterize molecular representations. A stacked convolutional coding module processes the sequence information of target proteins in a multi-scale and multi-level view. Finally, multi-modal fusion of molecular graph features and molecular fingerprint features, along with multi-modal information encoding of DTIs, is performed by the feature fusion module. The method outperforms six advanced baseline models on different benchmark datasets: Biosnap, BindingDB, and Human, with a significant improvement in performance, particularly in maintaining strong results across different experimental settings.

PMID:40178777 | DOI:10.1007/s12539-025-00698-3

Front Oncol. 2025 Mar 19;15:1450602. doi: 10.3389/fonc.2025.1450602. eCollection 2025.

ABSTRACT

AIM: Statins have been shown to improve the possibility of a pathological complete response (pCR) in patients with locally advanced rectal cancer when given in combination with neo-adjuvant chemo-radiation (NACTRT) in observational studies. The primary objective of this phase II randomized controlled trial (RCT) is to determine the impact of rosuvastatin in improving pCR rates in patients with locally advanced rectal cancer who are undergoing NACTRT. The secondary objectives are to compare adverse events, postoperative morbidity and mortality, disease-free survival (DFS), and overall survival in the two arms and to identify potential prognostic and predictive factors determining outcomes. If the study is positive, we plan to proceed to a phase III RCT with 3-year DFS as the primary endpoint.

METHODS: This is a prospective, randomized, open-label phase II/III study. The phase II study has a sample size of 316 patients (158 in each arm) to be accrued over 3 years to have 288 evaluable patients. The standard arm will receive NACTRT while the intervention group will receive 20 mg rosuvastatin orally once daily along with NACTRT for 6 weeks followed by rosuvastatin alone for 6-10 weeks until surgery. All patients will be reviewed after repeat imaging by a multidisciplinary tumor board at 12-16 weeks after starting NACTRT and operable patients will be planned for surgery. The pathological response rate, tumor regression grade (TRG), and post-surgical complications will be recorded.

CONCLUSION: The addition of rosuvastatin to NACTRT may improve the oncological outcomes by increasing the likelihood of pCR in patients with locally advanced rectal cancer undergoing NACTRT. This would be a low-cost, low-risk intervention that could potentially lead to the refinement of strategies, such as "watch and wait", in a select subgroup of patients.

CLINICAL TRIAL REGISTRATION: Clinical Trials Registry of India, identifier CTRI/2018/11/016459.

PMID:40177244 | PMC:PMC11961435 | DOI:10.3389/fonc.2025.1450602

Front Pharmacol. 2025 Mar 19;16:1565890. doi: 10.3389/fphar.2025.1565890. eCollection 2025.

NO ABSTRACT

PMID:40176905 | PMC:PMC11961997 | DOI:10.3389/fphar.2025.1565890

Adv Pharmacol. 2025;103:163-184. doi: 10.1016/bs.apha.2025.01.011. Epub 2025 Feb 16.

ABSTRACT

With the advancing technology, the way to drug discovery has evolved. The use of AI and computational methods have revolutionized the methods to develop novel therapeutics. In previous years, the methods to discover new drugs included high-throughput screening and bioassays which were labor-dependent, extremely expensive and had high probability to inaccurate results. The introduction of Computational studies has changed the process by introducing various methods to determine hit compounds and their methods of analysis. Methods such as molecular docking, virtual screening, and dynamics have changed the path to optimize and produce lead molecules. Similarly, network pharmacology also works on the identification of target proteins complex disease pathways with the help of protein-protein interactions and obtaining hub proteins. Various tools such as STRING database, cytoscape and metascape are employed in the study to construct a network between the proteins responsible for the disease progression and helps to obtain the vital target proteins, simplifying the process of drug-target identification. These approaches when employed together, results in obtaining results with better precision and accuracy which can be further validated experimentally, saving the resources and time. This chapter highlights the foundation of computational approaches in drug discovery and provides a detailed understanding of how these approaches are helping the researchers to produce novel solutions using artificial intelligence and machine learning.

PMID:40175040 | DOI:10.1016/bs.apha.2025.01.011

Adv Pharmacol. 2025;103:101-120. doi: 10.1016/bs.apha.2025.01.008. Epub 2025 Feb 6.

ABSTRACT

The lengthy and costly drug discovery process is transformed by deep learning, a subfield of artificial intelligence. Deep learning technologies expedite the procedure, increasing treatment success rates and speeding life-saving procedures. Deep learning stands out in target identification and lead selection. Deep learning greatly accelerates initial stage by analyzing large datasets of biological data to identify possible therapeutic targets and rank targeted drug molecules with desired features. Predicting possible adverse effects is another significant challenge. Deep learning offers prompt and efficient assistance with toxicology prediction in a very short time, deep learning algorithms can forecast a new drug's possible harm. This enables to concentrate on safer alternatives and steer clear of late-stage failures brought on by unanticipated toxicity. Deep learning unlocks the possibility of drug repurposing; by examining currently available medications, it is possible to find whole new therapeutic uses. This method speeds up development of diseases that were previously incurable. De novo drug discovery is made possible by deep learning when combined with sophisticated computational modeling, it can create completely new medications from the ground. Deep learning can recommend and direct towards new drug candidates with high binding affinities and intended therapeutic effects by examining molecular structures of disease targets. This provides focused and personalized medication. Lastly, drug characteristics can be optimized with aid of deep learning. Researchers can create medications with higher bioavailability and fewer toxicity by forecasting drug pharmacokinetics. In conclusion, deep learning promises to accelerate drug development, reduce costs, and ultimately save lives.

PMID:40175037 | DOI:10.1016/bs.apha.2025.01.008

Eur J Pharm Sci. 2025 Mar 31:107079. doi: 10.1016/j.ejps.2025.107079. Online ahead of print.

ABSTRACT

Hospital care is a highly complex process, requiring comprehensive documentation of all aspects of the patient journey in electronic health records. A critical component of this care is the accurate tracking of patient medications. International standards are not consistently incorporated into the electronic medication systems currently in use worldwide, and their interoperability remains an unresolved issue. We recognised the need to develop a set of standardised data elements that ensure consistent and accurate documentation. Although the medication systems studied exhibit various strengths and weaknesses and can satisfactorily document certain aspects of the medication process, none achieve the necessary level of optimal documentation. Our paper presents a new perspective on medication recording by identifying the electronic data requirements for all events in an itemized, complete, historical, and standardized manner. To address this gap, we collected, defined, and introduced the essential data elements required for the comprehensive documentation of medication sub-processes for the first time in our study. The Fast Health Interoperability Resources (FHIR) data exchange standard was employed for designing these data requirements. Our research identified and categorised 138 data elements essential for describing the complete medication process, including medication description, requests, dispensation, and administration. These data elements were divided into fundamental and supplementary categories. We developed a survey form to assess medication systems. In a pilot study, we tested the quality of 5 medication systems, currently in operation in Hungary. Our analysis assessed the accuracy of the electronic recording of medication and the correspondence of the recorded data elements with international standards. None of the systems demonstrated the ability to document medication accurately or capture all fundamental data elements. The best-performing system managed to record 63% of all fundamental data elements, while the worst-performing system managed only to document 30%. The names and the values of data elements in these systems did not comply with international standards either. The primary clinical pharmaceutical usefulness of this study was to enhance the digital documentation of medication in hospitals to meet comprehensive data recording requirements, ensure greater compliance, and improve their suitability for enriching clinical health data files, enabling real-world studies, pharmacovigilance analyses, and the identification of drug repositioning opportunities.

PMID:40174662 | DOI:10.1016/j.ejps.2025.107079

PLoS Comput Biol. 2025 Apr 2;21(4):e1012876. doi: 10.1371/journal.pcbi.1012876. Online ahead of print.

ABSTRACT

Computational approaches for drug repurposing for viral diseases have mainly focused on a small number of antivirals that directly target pathogens (virus centric therapies). In this work, we combine ideas from collaborative filtering and network medicine for making predictions on a much larger set of drugs that could be repurposed for host centric therapies, that are aimed at interfering with host cell factors required by a pathogen. Our idea is to create matrices quantifying the perturbation that drugs and viruses induce on human protein interaction networks. Then, we decompose these matrices to learn embeddings of drugs, viruses, and proteins in a low dimensional space. Predictions of host-centric antivirals are obtained by taking the dot product between the corresponding drug and virus representations. Our approach is general and can be applied systematically to any compound with known targets and any virus whose host proteins are known. We show that our predictions have high accuracy and that the embeddings contain meaningful biological information that may provide insights into the underlying biology of viral infections. Our approach can integrate different types of information, does not rely on known drug-virus associations and can be applied to new viral diseases and drugs.

PMID:40173200 | DOI:10.1371/journal.pcbi.1012876

J Med Virol. 2025 Apr;97(4):e70319. doi: 10.1002/jmv.70319.

ABSTRACT

Papaverine (PV) has been previously identified as a promising candidate in SARS-CoV-2 repurposing screens. In this study, we further investigated both its antiviral and immunomodulatory properties. PV displayed antiviral efficacy against SARS-CoV-2 and influenza A viruses H1N1 and H5N1 in single infection as well as in co-infection. We demonstrated PV's activity against various SARS-CoV-2 variants and identified its action at the post-entry stage of the viral life cycle. Notably, treatment of air-liquid interface (ALI) cultures of primary bronchial epithelial cells with PV significantly inhibited SARS-CoV-2 levels. Additionally, PV was found to attenuate interferon (IFN) signaling independently of viral infection. Mechanistically, PV decreased the activation of the IFN-stimulated response element following stimulation with all three IFN types by suppressing STAT1 and STAT2 phosphorylation and nuclear translocation. Furthermore, the combination of PV with approved COVID-19 therapeutics molnupiravir and remdesivir demonstrated synergistic effects. Given its immunomodulatory effects and clinical availability, PV shows promising potential as a component for combination therapy against COVID-19.

PMID:40171981 | DOI:10.1002/jmv.70319

Front Microbiol. 2025 Mar 18;16:1534659. doi: 10.3389/fmicb.2025.1534659. eCollection 2025.

ABSTRACT

INTRODUCTION: Streptococcus pneumoniae is a Gram-positive bacterium responsible for severe infections such as meningitis and pneumonia. The increasing prevalence of antibiotic resistance necessitates the identification of new therapeutic targets. This study aimed to discover potential drug targets against S. pneumoniae using an in silico subtractive genomics approach.

METHODS: The S. pneumoniae genome was compared to the human genome to identify non-homologous sequences using CD-HIT and BLASTp. Essential genes were identified using the Database of Essential Genes (DEG), with consideration for human gut microflora. Protein-protein interaction analyses were conducted to identify key hub genes, and gene ontology (GO) studies were performed to explore associated pathways. Due to the lack of crystal structure data, a potential target was modeled in silico and subjected to structure-based virtual screening.

RESULTS: Approximately 2,000 of the 2,027 proteins from the S. pneumoniae genome were identified as non-homologous to humans. The DEG identified 48 essential genes, which was reduced to 21 after considering human gut microflora. Key hub genes included gpi, fba, rpoD, and trpS, associated with 20 pathways. Virtual screening of 2,509 FDA-approved compounds identified Bromfenac as a leading candidate, exhibiting a binding energy of -26.335 ± 29.105 kJ/mol.

DISCUSSION: Bromfenac, particularly when conjugated with AuAgCu2O nanoparticles, has demonstrated antibacterial and anti-inflammatory properties against Staphylococcus aureus. This suggests that Bromfenac could be repurposed as a potential therapeutic agent against S. pneumoniae, pending further experimental validation. The approach highlights the potential for drug repurposing by targeting proteins essential in pathogens but absent in the host.

PMID:40170924 | PMC:PMC11958985 | DOI:10.3389/fmicb.2025.1534659

Clin Transl Med. 2025 Apr;15(4):e70290. doi: 10.1002/ctm2.70290.

ABSTRACT

Single-cell omics has emerged as a powerful tool for elucidating cellular heterogeneity in health and disease. Parallel advances in artificial intelligence (AI), particularly in pattern recognition, feature extraction and predictive modelling, now offer unprecedented opportunities to translate these insights into clinical applications. Here, we propose single-cell -omics-based Disease Predictor through AI (scDisPreAI), a unified framework that leverages AI to integrate single-cell -omics data, enabling robust disease and disease-stage prediction, alongside biomarker discovery. The foundation of scDisPreAI lies in assembling a large, standardised database spanning diverse diseases and multiple disease stages. Rigorous data preprocessing, including normalisation and batch effect correction, ensures that biological rather than technical variation drives downstream models. Machine learning pipelines or deep learning architectures can then be trained in a multi-task fashion, classifying both disease identity and disease stage. Crucially, interpretability techniques such as SHapley Additive exPlanations (SHAP) values or attention weights pinpoint the genes most influential for these predictions, highlighting biomarkers that may be shared across diseases or disease stages. By consolidating predictive modelling with interpretable biomarker identification, scDisPreAI may be deployed as a clinical decision assistant, flagging potential therapeutic targets for drug repurposing and guiding tailored treatments. In this editorial, we propose the technical and methodological roadmap for scDisPreAI and emphasises future directions, including the incorporation of multi-omics, standardised protocols and prospective clinical validation, to fully harness the transformative potential of single-cell AI in precision medicine.

PMID:40170267 | DOI:10.1002/ctm2.70290

Methods Mol Biol. 2025;2914:323-332. doi: 10.1007/978-1-0716-4462-1_22.

ABSTRACT

Selecting a fluid near an affected organ can improve the likelihood of identifying a biomarker panel from pathological tissue. Cerebrospinal fluid (CSF), in close contact with the brain, is a valuable source of biomarkers for neurological disorders due to the inaccessibility of brain tissue. Moreover, the altered CSF proteome identified in neurological diseases can facilitate the repurposing of drugs already used for other therapeutic purposes. In this context, Connectivity Map (CMap) is a valuable tool as it provides information on compounds and gene modifications that can be utilized to reverse specific pathological signatures. Analyzing CSF differential proteomics through the CMap framework offers an efficient and cost-effective approach to identifying potential novel therapies for neurodegenerative diseases.

PMID:40167927 | DOI:10.1007/978-1-0716-4462-1_22

ACS Biomater Sci Eng. 2025 Apr 1. doi: 10.1021/acsbiomaterials.5c00068. Online ahead of print.

ABSTRACT

Drug repurposing is an attractive route for finding new therapeutics for brain cancers such as glioblastoma. Local administration of drugs to brain tumors or the postsurgical resection cavity holds promise to deliver a high dose to the target site with minimal off-target effects. Drug delivery systems aim to sustain the release of the drug at the target site but typically exhibit drawbacks such as a poor safety profile, uncontrolled/rapid drug release, or poor control over synthesis parameters/material dimensions. Herein, we analyzed the antidepressant vortioxetine and showed in vitro that it causes a greater loss of viability in glioblastoma cells than it does to normal primary human astrocytes. We developed a new droplet microfluidic-based emulsion method to reproducibly produce vortioxetine-loaded poly(lactic-co-glycolic) acid (PLGA) microspheres with tight size control (36.80 ± 1.96 μm). The drug loading efficiency was around 90% when 9.1% (w/w) drug was loaded into the microspheres, and drug release could be sustained for three to 4 weeks. The vortioxetine microspheres showed robust antiglioblastoma efficacy in both 2D monolayer and 3D spheroid patient-derived glioblastoma cells, highlighting the potential of combining an antidepressant with sustained local delivery as a new therapeutic strategy.

PMID:40167528 | DOI:10.1021/acsbiomaterials.5c00068

Am J Med Genet C Semin Med Genet. 2025 Apr 1:e32138. doi: 10.1002/ajmg.c.32138. Online ahead of print.

ABSTRACT

Rare diseases impact approximately 1 in 10 people worldwide, and yet, less than 5% of all rare diseases currently have an approved treatment option available. This is due to many challenges unique to rare diseases, including small, diverse patient populations, the cost of drug development that is not proportionate to the number of patients who could potentially benefit from treatment, and difficulty with clinical trial design to validate new therapeutics. As a result, drug repurposing has become an increasingly promising option for finding treatment options for rare diseases. First described in 2018, Bachmann-Bupp Syndrome (BABS) is a rare neurodevelopmental disorder that is caused by gain-of-function variants in the ornithine decarboxylase (ODC1) gene and is characterized by developmental delay, hypotonia, and alopecia. Through collaboration and the use of a unique drug repurposing strategy, the first patient identified with BABS was treated with the repurposed drug eflornithine, also known as α-difluoromethylornithine (DFMO), in just 16 months. Currently, five additional patients with BABS are being treated with DFMO. This model of drug repurposing of an FDA-approved drug for use in another indication can serve as an example of what is possible in the scope of other rare diseases, specifically in other polyaminopathies.

PMID:40167220 | DOI:10.1002/ajmg.c.32138

Circulation. 2025 Apr 1. doi: 10.1161/CIRCULATIONAHA.121.058621. Online ahead of print.

ABSTRACT

BACKGROUND: Gene mutations are responsible for a sizeable proportion of cases of heart failure. However, the number of patients with any specific mutation is small. Repositioning of existing US Food and Drug Administration-approved compounds to target specific mutations is a promising approach to efficient identification of new therapies for these patients.

METHODS: The National Institutes of Health Library of Integrated Network-Based Cellular Signatures database was interrogated to identify US Food and Drug Administration-approved compounds that demonstrated the ability to reverse the transcriptional effects of LMNA knockdown. Top hits from this screening were validated in vitro with patient-specific induced pluripotent stem cell-derived cardiomyocytes combined with force measurement, gene expression profiling, electrophysiology, and protein expression analysis.

RESULTS: Several angiotensin receptor blockers were identified from our in silico screen. Of these, olmesartan significantly elevated the expression of sarcomeric genes and rate and force of contraction and ameliorated arrhythmogenic potential. In addition, olmesartan exhibited the ability to reduce phosphorylation of extracellular signal-regulated kinase 1 in LMNA-mutant induced pluripotent stem cell-derived cardiomyocytes.

CONCLUSIONS: In silico screening followed by in vitro validation with induced pluripotent stem cell-derived models can be an efficient approach to identifying repositionable therapies for monogenic cardiomyopathies.

PMID:40166828 | DOI:10.1161/CIRCULATIONAHA.121.058621

medRxiv [Preprint]. 2025 Mar 24:2025.03.19.25324271. doi: 10.1101/2025.03.19.25324271.

ABSTRACT

BACKGROUND: Randomized controlled trials (RCTs) provide the highest level of clinical evidence but are often limited by cost, time, and ethical constraints. Emulating RCTs using real-world data (RWD) offers a complementary approach to evaluate the treatment effect in a real clinical setting. This study aims to replicate clinical trials based on Mayo Clinic Platform (MCP) electronic health records (EHRs) and emulation frameworks. In this study, we address two key questions: (1) whether clinical trials can be feasibly replicated using the MCP, and (2) whether trial emulation produces consistent conclusions based on real clinical data compared to the original randomized controlled trials RCTs.

METHODS: We conducted a retrospective observational study with an adaption of trial emulation. To assess feasibility, we applied a refined filtering method to identify trials suitable for emulation. The emulation protocol was carefully designed on top of the original RCT protocol to balance scientific rigor and practical feasibility. To minimize potential selection bias and enhance comparability between groups, we employed propensity score matching (PSM) as a statistical adjustment method.

RESULTS: Based on our predefined search criteria targeting phase 3 trials focused on drug repurposing for heart failure patients, we initially identified 27 eligible trials. After a two-step manual review of the original eligibility criteria and extraction of the patient cohorts based on MCP visualizer, we further narrowed our selection to the WARCEF trial, as it provided an adequate sample size for the emulation within the MCP. The experiment compares the WARCEF trial and a simulation study on Aspirin vs. Warfarin. The original study (smaller sample) found no significant difference (HR = 1.016, p < 0.91). The simulation (larger sample) showed a slightly higher HR (1.161) with borderline significance (p < 0.052, CI: 0.999-1.350), suggesting a possible increased risk with Warfarin, though not conclusive.

CONCLUSION: RCT emulation enhances real-world evidence (RWE) for clinical decision-making but faces limitations from confounding, missing data, and cohort biases. Future research should explore machine learning-driven patient matching and scalable RCT emulation. This study supports the integration of RWE into evidence-based medicine.

PMID:40166580 | PMC:PMC11957179 | DOI:10.1101/2025.03.19.25324271

medRxiv [Preprint]. 2025 Mar 23:2025.03.22.25324223. doi: 10.1101/2025.03.22.25324223.

ABSTRACT

Drug development is a long and costly process, and repurposing existing drugs for use toward a different disease or condition may serve as a cost-effective alternative. As drug targets with genetic support have a doubled success rate, genetics-informed drug repurposing holds promise in translating genetic findings into therapeutics. In this study, we developed a Genetics Informed Network-based Drug Repurposing via in silico Perturbation (GIN-DRIP) framework and applied the framework to repurpose drugs for type-2 diabetes (T2D). In GIN-DRIP for T2D, it integrates multi-level omics data to translate T2D GWAS signals into a genetics-informed network that simultaneously encodes gene importance scores and a directional effect (up/down) of risk genes for T2D; it then bases on the GIN to perform signature matching with drug perturbation experiments to identify drugs that can counteract the effect of T2D risk alleles. With this approach, we identified 3 high-confidence FDA-approved candidate drugs for T2D, and validated telmisartan, an anti-hypertensive drug, in our EHR data with over 3 million patients. We found that telmisartan users were associated with a reduced incidence of T2D compared to users of other anti-hypertensive drugs and non-users, supporting the therapeutic potential of telmisartan for T2D. Our framework can be applied to other diseases for translating GWAS findings to aid drug repurposing for complex diseases.

PMID:40166562 | PMC:PMC11957187 | DOI:10.1101/2025.03.22.25324223

medRxiv [Preprint]. 2025 Mar 17:2025.03.17.25324106. doi: 10.1101/2025.03.17.25324106.

ABSTRACT

BACKGROUND: Complex diseases may share portions of their polygenic architectures which can be leveraged to identify drug targets with low off-target potential or repurposable candidates. However, the literature lacks methods which can make these inferences at scale using publicly available data.

METHODS: We introduce a Bayesian model to estimate the polygenic structure of a trait using only gene-based association test statistics from GWAS summary data and returns gene-level posterior risk probabilities (PRPs). PRPs were used to infer shared polygenicity between 496 trait pairs and we introduce measures that can prioritize drug targets with low off-target effects or drug repurposing potential.

RESULTS: Across 32 traits, we estimated that 69.5 to 97.5% of disease-associated genes are shared between multiple traits, and the estimated number of druggable genes that were only associated with a single disease ranged from 1 (multiple sclerosis) to 59 (schizophrenia). Estimating the shared genetic architecture of ALS with all other traits identified the KIT gene as a potentially harmful drug target because of its deleterious association with triglycerides, but also identified TBK1 and SCN11B as putatively safer because of their non-association with any of the other 31 traits. We additionally found 21 genes which are candidate repourposable targets for Alzheimer's disease (AD) (e.g., PLEKHA1, PPIB ) and 5 for ALS (e.g., GAK, DGKQ ).

CONCLUSIONS: The sets of candidate drug targets which have limited off-target potential are generally smaller compared to the sets of pleiotropic and putatively repurposable drug targets, but both represent promising directions for future experimental studies.

PMID:40166559 | PMC:PMC11957083 | DOI:10.1101/2025.03.17.25324106

Front Pharmacol. 2025 Mar 17;16:1537912. doi: 10.3389/fphar.2025.1537912. eCollection 2025.

ABSTRACT

BACKGROUND AND PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has devastated global health and the economy, underscoring the urgent need for extensive research into the mechanisms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral entry and the development of effective therapeutic interventions.

EXPERIMENTAL APPROACH: We established a cell line expressing human angiotensin-converting enzyme 2 (ACE2). We used it as a model of pseudotyped viral entry using murine leukemia virus (MLV) expressing SARS-CoV-2 spike (S) protein on its surface and firefly luciferase as a reporter. We screened an U.S. Food and Drug Administration (FDA)-approved compound library for inhibiting ACE2-dependent SARS-CoV-2 pseudotyped viral entry and identified several drug-repurposing candidates.

KEY RESULTS: We identified 18 drugs and drug candidates, including 14 previously reported inhibitors of viral entry and four novel candidates. Pyridoxal 5'-phosphate, Dovitinib, Adefovir dipivoxil, and Biapenem potently inhibit ACE2-dependent viral entry with inhibitory concentration 50% (IC50) values of 57nM, 74 nM, 130 nM, and 183 nM, respectively.

CONCLUSION AND IMPLICATIONS: We identified four novel FDA-approved candidate drugs for anti-SARS-CoV-2 combination therapy. Our findings contribute to the growing body of evidence supporting drug repurposing as a viable strategy for rapidly developing COVID-19 treatments.

PMID:40166473 | PMC:PMC11955658 | DOI:10.3389/fphar.2025.1537912

bioRxiv [Preprint]. 2025 Mar 18:2025.03.18.642440. doi: 10.1101/2025.03.18.642440.

ABSTRACT

Human topoisomerase III beta (TOP3B) is a type IA topoisomerase that can change the topology of DNA and RNA substrates via a phosphotyrosine covalent intermediate. TOP3B has been shown to be required for the efficient replication of certain positive-sense ssRNA viruses including Dengue. We applied molecular dynamics simulation combined with docking studies to identify potential inhibitors of TOP3B from a library comprised of drugs that are FDA-approved or undergoing clinical trials for potential drug repurposing. Topoisomerase activity assay of the top virtual screening hits showed that bemcentinib, a compound known to target the AXL receptor tyrosine kinase, can inhibit TOP3B relaxation activity. Additional small molecules that share the N5 , N3 -1 H -1,2,4-triazole-3,5-diamine moiety of bemcentinib were synthesized and tested for inhibition of TOP3B relaxation activity. Five of these molecules showed comparable IC 50 as bemcentinib for inhibition of TOP3B. However, these five molecules had less selectivity towards TOP3B inhibition versus bemcentinib when inhibition of the type IB human topoisomerase I was com-pared. These results suggest that exploration of tyrosine kinase inhibitors and their analogs may allow the identification of novel topoisomerase inhibitors.

PMID:40166181 | PMC:PMC11956937 | DOI:10.1101/2025.03.18.642440

Front Immunol. 2025 Mar 17;16:1537428. doi: 10.3389/fimmu.2025.1537428. eCollection 2025.

ABSTRACT

Pemphigoid diseases constitute a group of organ-specific autoimmune diseases characterized and caused by autoantibodies targeting autoantigens expressed in the skin and mucous membranes. Current therapeutic options are still based on unspecific immunosuppression that is associated with severe adverse events. Biologics, targeting the IL4-pathway or IgE are expected to change the treatment landscape of pemphigoid diseases. However, clinical studies demonstrated that targeting these pathways alone is most likely not sufficient to meet patient and healthcare partitioners expectations. Hence, model systems are needed to identify and validate novel therapeutic targets in pemphigoid diseases. These include pre-clinical animal models, in vitro and ex vivo model systems, hypothesis-driven drug repurposing, as well as exploitation of real-world-data. In this review, we will highlight the medical need for pemphigoid diseases, and in-depth discuss the advantages and disadvantages of the available pemphigoid disease model systems. Ultimately, we discuss how rapid translation can be achieved for the benefit of the patients.

PMID:40165962 | PMC:PMC11955494 | DOI:10.3389/fimmu.2025.1537428