bioRxiv [Preprint]. 2025 Mar 28:2025.03.24.644676. doi: 10.1101/2025.03.24.644676.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) is a complex neurodegenerative disorder with substantial molecular variability across different brain regions and individuals, hindering therapeutic development. This study introduces PRISM-ML, an interpretable machine learning (ML) framework integrating multiomics data to uncover patient-specific biomarkers, subtissue-level pathology, and drug repurposing opportunities.

METHODS: We harmonized transcriptomic and genomic data of three independent brain studies containing 2105 post-mortem brain samples (1363 AD, 742 controls) across nine tissues. A Random Forest classifier with SHapley Additive exPlanations (SHAP) identified patient-level biomarkers. Clustering further delineated each tissue into subtissues, and network analysis revealed critical "bottleneck" (hub) genes. Finally, a knowledge graph-based screening identified multi-target drug candidates, and a real-world pharmacoepidemiologic study evaluated their clinical relevance.

RESULTS: We uncovered 36 molecularly distinct subtissues, each defined by a set of associated unique biomarkers and genetic drivers. Through network analysis of gene-gene interactions networks, we highlighted 262 bottleneck genes enriched in synaptic, cytoskeletal, and membrane-associated processes. Knowledge graph queries identified six FDA-approved drugs predicted to target multiple bottleneck genes and AD-relevant pathways simultaneously. One candidate, promethazine, demonstrated an association with reduced AD incidence in a large healthcare dataset of over 364000 individuals (hazard ratios ≤ 0.43; p < 0.001). These findings underscore the potential for multi-target approaches, reveal connections between AD and cardiovascular pathways, and offer novel insights into the heterogeneous biology of AD.

CONCLUSIONS: PRISM-ML bridges interpretable ML with multi-omics and systems biology to decode AD heterogeneity, revealing region-specific mechanisms and repurposable therapeutics. The validation of promethazine in real-world data underscores the clinical relevance of multi-target strategies, paving the way for more personalized treatments in AD and other complex disorders.

PMID:40196631 | PMC:PMC11974764 | DOI:10.1101/2025.03.24.644676

Oncol Res. 2025 Mar 19;33(4):919-935. doi: 10.32604/or.2024.054537. eCollection 2025.

ABSTRACT

OBJECTIVE: Prostate cancer (PCA) is the second most widespread cancer among men globally, with a rising mortality rate. Enzyme-responsive lipid nanoparticles (ERLNs) are promising vectors for the selective delivery of anticancer agents to tumor cells. The goal of this study is to fabricate ERLNs for dual delivery of gefitinib (GF) and simvastatin (SV) to PCA cells.

METHODS: ERLNs loaded with GF and SV (ERLNGFSV) were assembled using bottom-up and top-down techniques. Subsequently, these ERLN cargoes were coated with triacylglycerol, and phospholipids and capped with chitosan (CS). The ERLNGFSV, and CS engineered ERLNGFSV (CERLNGFSV) formulations were characterized for particle size (PS), zeta potential (ZP), and polydispersity index (PDI). The biocompatibility, and cytotoxicity of the plain and GF plus SV-loaded ERLN cargoes were assessed using erythrocytes and PC-3 cell line. Additionally, molecular docking simulations (MDS) were conducted to examine the influence of GF and SV on succinate dehydrogenase (SDH), glutathione peroxidase-4 (GPX-4), and 5α-reductase (5α-RD).

RESULTS: These results showed that plain, ERLNGFSV, and CERLNGFSV cargoes have a nanoscale size and homogeneous appearance. Moreover, ERLNGFSV and CERLNGFSV were biocompatible, with no detrimental effects on erythrocytes. Treatment with GF, SV, GF plus SV, ERLNGFSV, and CERLNGFSV significantly reduced the viability of PC-3 cells compared to control cells. Particularly, the blend of GF and SV, as well as ERLNGFSV and CERLNGFSV augmented PC-3 cell death. Also, treating PC-3 cells with free drugs, their combination, ERLNGFSV, and CERLNGFSV formulations elevated the percentage of apoptotic cells. MDS studies demonstrated that GF and SV interact with the active sites of SDH, GPX-4, and 5α-reductase.

CONCLUSIONS: This study concludes that SVGF combination and ERLNs loading induce particular delivery, and synergism on PC-3 death through action on multiple pathways involved in cell proliferation, and apoptosis, besides the interaction with SDH, GPX-4, and 5α-RD. Therefore, GFSV-loaded ERLN cargoes are a promising strategy for PCA treatment. In vivo studies are necessary to confirm these findings for clinical applications.

PMID:40191728 | PMC:PMC11964872 | DOI:10.32604/or.2024.054537

ACS Omega. 2025 Mar 20;10(12):12366-12374. doi: 10.1021/acsomega.4c11382. eCollection 2025 Apr 1.

ABSTRACT

The increasing prevalence of infections byCandida hemeulonii sensu stricto, particularly due to its resistance to standard antifungal therapies, represents a significant healthcare challenge. Traditional treatments often fail, emphasizing the need to explore alternative therapeutic strategies. Drug repurposing, which reevaluates existing drugs for new applications, offers a promising path. This study examines the potential of repurposing alexidine dihydrochloride as an antifungal agent againstC. hemeulonii sensu stricto. Minimum Inhibitory Concentration (MIC) and Minimum Fungicidal Concentration (MFC) values were established using broth microdilution methods. To further assess antifungal activity, different assays were conducted, including growth inhibition, biofilm inhibition, biofilm eradication, and cell damage. Checkerboard assays were employed to study the compound's fungicidal potential and interactions with other antifungals. Additional tests, sorbitol protection assay, efflux pump inhibition, cell membrane permeability assays, and nucleotide leakage were performed. In vivo efficacy and safety were evaluated inTenebrio molitor larvae. Alexidine demonstrated fungicidal activity againstC. hemeulonii sensu stricto, with an MIC of 0.5 μg/mL. Biofilm formation was significantly inhibited, with a reduction of 78.69%. Mechanistic studies revealed nucleotide leakage, indicating membrane impact, but no significant protein leakage was detected. In vivo, alexidine displayed a favorable safety profile, with no evidence of hemolysis or acute toxicity in the T. molitor model. These findings support alexidine as a strong candidate for antifungal drug repurposing, especially for treatingC. hemeulonii sensu stricto infections. Its efficacy in inhibiting growth and biofilm formation, combined with a positive safety profile, underscores its potential for clinical development as an antifungal therapy.

PMID:40191372 | PMC:PMC11966325 | DOI:10.1021/acsomega.4c11382

RSC Adv. 2025 Apr 4;15(14):10622-10633. doi: 10.1039/d4ra09055a. eCollection 2025 Apr 4.

ABSTRACT

Peroxisome proliferator-activated receptors (PPARs) are ligand-activated nuclear receptors with a crucial regulatory role in carbohydrate and lipid metabolism and are emerging druggable targets in "metabolic syndrome" (MetS) and cancers. However, there is a need to identify ligands that can activate specific PPAR subtypes, particularly PPARβ/δ, which is less studied compared with other PPAR isoforms (α and γ). Herein, using the drug-repurposing approach, the ZINC database of clinically approved drugs was screened to target the PPARβ/δ receptor through high-throughput-virtual-screening, followed by molecular docking and molecular dynamics (MD) simulation. The top-scoring ligands were subjected to drug-likeness analysis. The hit molecule was tested in an in vitro model of NAFLD (non-alcoholic fatty liver disease). The top five ligands with strong binding affinity towards PPARβ/δ were canagliflozin > empagliflozin > lumacaftor > eprosartan > dapagliflozin. RMSD/RMSF analysis demonstrated stable protein-ligand complexation (PLC) by the top-scoring ligands with PPARβ/δ. In silico ADMET prediction analysis revealed favorable pharmacokinetic profiles of these top five ligands. Canagliflozin showed significant (P < 0.001) dose-dependent decrease in lipid accumulation and the associated oxidative stress-inflammatory response, suggesting its promising anti-steatotic potential. These outcomes pave the way for further validation and development of PPAR activity-modulating therapeutics.

PMID:40190631 | PMC:PMC11970364 | DOI:10.1039/d4ra09055a

BMC Med. 2025 Apr 7;23(1):200. doi: 10.1186/s12916-025-04022-0.

ABSTRACT

BACKGROUND: Influenza A virus (IAV) is a major cause of seasonal and global pandemics, posing serious health risks. Repositioning approved drugs offers an efficient antiviral strategy, particularly as calcium (Ca2⁺) is crucial for IAV infection, making Ca2⁺ channel blockers (CCBs) promising candidates for antiviral agents.

METHODS: The in vitro antiviral activity of cilnidipine was evaluated using MTT assays, qRT-PCR, plaque assays, and western blotting. Mechanistic studies involved time-of-addition, viral internalization, pseudovirus neutralization, and HA (hemagglutinin) syncytium assays. For in vivo analysis, BALB/c mice were intranasally infected to evaluate the effects of cilnidipine on viral titer, lung index, pulmonary inflammatory mediators, and survival rate.

RESULTS: In vitro, cilnidipine exhibits antiviral activity against IAV during the early stages of infection. It disrupts clathrin- and caveolin-mediated endocytosis to inhibit the internalization of IAV and interacts with the viral HA2 subunit to impede virus membrane fusion. Additionally, cilnidipine suppresses the PI3K-AKT and p38 MAPK pathways activated by IAV infections. In vivo, cilnidipine reduces virus titers and lung index, ameliorates lung pathology, and inhibits pulmonary inflammatory mediator expression, improving survival rates.

CONCLUSIONS: These findings highlight the promising anti-IAV properties of cilnidipine both in vitro and in vivo, suggesting its potential as a clinical agent for emergencies against influenza outbreaks.

PMID:40189517 | DOI:10.1186/s12916-025-04022-0

J Microbiol Methods. 2025 Apr 4:107125. doi: 10.1016/j.mimet.2025.107125. Online ahead of print.

ABSTRACT

Conventional clinical microbiological techniques are enhanced by the introduction of artificial intelligence (AI). Comprehensive data processing and analysis enabled the development of curated datasets that has been effectively used in training different AI algorithms. Recently, a number of machine learning (ML) and deep learning (DL) algorithms are developed and evaluated using diverse microbiological datasets. These datasets included spectral analysis (Raman and MALDI-TOF spectroscopy), microscopic images (Gram and acid fast stains), and genomic and protein sequences (whole genome sequencing (WGS) and protein data banks (PDBs)). The primary objective of these algorithms is to minimize the time, effort, and expenses linked to conventional analytical methods. Furthermore, AI algorithms are incorporated with quantitative structure-activity relationship (QSAR) models to predict novel antimicrobial agents that address the continuing surge of antimicrobial resistance. During the COVID-19 pandemic, AI algorithms played a crucial role in vaccine developments and the discovery of new antiviral agents, and introduced potential drug candidates via drug repurposing. However, despite their significant benefits, the implementation of AI encounters various challenges, including ethical considerations, the potential for bias, and errors related to data training. This review seeks to provide an overview of the most recent applications of artificial intelligence in clinical microbiology, with the intention of educating a wider audience of clinical practitioners regarding the current uses of machine learning algorithms and encouraging their implementation. Furthermore, it will discuss the challenges related to the incorporation of AI into clinical microbiology laboratories and examine future opportunities for AI within the realm of infectious disease epidemiology.

PMID:40188989 | DOI:10.1016/j.mimet.2025.107125

Pharm Dev Technol. 2025 Apr 5:1-33. doi: 10.1080/10837450.2025.2487575. Online ahead of print.

ABSTRACT

The COVID-19 pandemic exposed the fragility of today's marketed treatments for respiratory infections. As a primary site of infection, the upper airways may represent a key access route for the control and treatment for these conditions. The present study aims to explore and identify, through a patent review, the novelty of therapies for COVID-19 that use the intranasal route for drug administration. A search was carried out in Wipo and Espacenet, using the descriptors "COVID-19 OR SARS-CoV 2" AND "treatment OR therapy" AND NOT "vaccine OR immunizing" and the classification "A61K9/0043". Of the 151 patents identified, we excluded 73 duplicates, and 36 documents that meet the criteria adopted for exclusion (not nasally administered formulations, vaccines, post COVID-19 treatments, uncertain route of administration or form). We identified 78 unique patents on patent databases, of which 42 were selected for this review. The documents revealed the use of the intranasal pathway not only for drug repositioning but also for using plant-derived and biological molecules. Overall, the new formulations explore a variety of known drugs and natural products incorporated in drug carrier systems and devices for drug delivery and administration. Thus, the intranasal route remains a promising strategy for drug delivery, offering direct access to the primary infection site and warranting further exploration.

PMID:40186505 | DOI:10.1080/10837450.2025.2487575

Biofouling. 2025 Apr 4:1-18. doi: 10.1080/08927014.2025.2486250. Online ahead of print.

ABSTRACT

Urinary infections caused by Staphylococcus aureus are commonly associated with urinary catheterization and often result in severe complications. Given this problem, the objective of the study was to investigate the preventive action of promethazine (PMT) against the formation of methicillin-resistant Staphylococcus aureus (MRSA) biofilms when impregnated in urinary catheters. For this purpose, techniques such as broth microdilution, checkerboard, impregnation on urinary catheter fragments, flow cytometry assays and scanning electron microscopy were employed. PMT exhibited antimicrobial activity with Minimum Inhibitory Concentration (MIC) values ranging from 171 to 256 µg/mL, predominantly additive interaction in combination with oxacillin (OXA) and vancomycin (VAN), and a reduction in cell viability of biofilms formed and forming by methicillin-sensitive and -resistant S. aureus. Morphological alterations, damage to the membrane, and genetic material of cells treated with promethazine were also observed. The results demonstrated that PMT can be classified as a promising antimicrobial agent for use in the antibacterial coating of long-term urinary devices.

PMID:40183686 | DOI:10.1080/08927014.2025.2486250

J Virol. 2025 Apr 4:e0223424. doi: 10.1128/jvi.02234-24. Online ahead of print.

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) generally hijacks the cellular machinery of host cells for survival. However, how SARS-CoV-2 employs the host's deubiquitinase to facilitate virus replication remains largely unknown. In this study, we identified the host deubiquitinase USP22 as a crucial regulator of the expression of SARS-CoV-2 nucleocapsid protein (SARS-CoV-2 NP), which is essential for SARS-CoV-2 replication. We demonstrated that SARS-CoV-2 NP proteins undergo ubiquitination-dependent degradation in host cells, while USP22 interacts with SARS-CoV-2 NP and downregulates K63-linked polyubiquitination of SARS-CoV-2 NP, thereby protecting SARS-CoV-2 NP from degradation. Importantly, we further revealed that sulbactam, an antibiotic, can reduce USP22 protein levels, eventually promoting the degradation of SARS-CoV-2 NP in vitro and in vivo. This study reveals the mechanism by which SARS-CoV-2-encoded NP protein employs host deubiquitinase for virus survival and provides a potential strategy to fight against SARS-CoV-2 infection.IMPORTANCESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid protein (SARS-CoV-2 NP) plays a pivotal role in viral infection by binding to viral RNA, stabilizing the viral genome, and promoting replication. However, the interactions between SARS-CoV-2 NP and host intracellular proteins had not been elucidated. In this study, we provide evidence that SARS-CoV-2 NP interacts with the deubiquitinase USP22 in host cells, which downregulates SARS-CoV-2 NP ubiquitination. This reduction in ubiquitination effectively prevents intracellular degradation of SARS-CoV-2 NP, thereby enhancing its stability, marking USP22 as a potential target for antiviral strategies. Additionally, our findings indicate that sulbactam significantly decreases the protein levels of USP22, thereby reducing SARS-CoV-2 NP levels. This discovery suggests a novel therapeutic pathway in which sulbactam could be repurposed as an antiviral agent, demonstrating how certain antibiotics might contribute to antiviral treatment. This work thus opens avenues for drug repurposing and highlights the therapeutic potential of targeting host pathways to inhibit viral replication.

PMID:40183543 | DOI:10.1128/jvi.02234-24

Acta Myol. 2025 Mar;44(1):23-27. doi: 10.36185/2532-1900-1026.

ABSTRACT

OBJECTIVES: Non-dystrophic myotonias (NDM) are rare diseases due to mutations in the voltage-gated sodium (Nav1.4) and chloride (ClC-1) channels expressed in skeletal muscle fibers. We provide an up-to-date review of pharmacological treatments available for NDM patients and experimental studies aimed at identifying alternative treatments and at better understanding the mechanisms of actions.

METHODS: Literature research was performed using PubMed and ClinicalTrial.gov.

RESULTS: Today, the sodium channel blocker mexiletine is the drug of choice for treatment of NDM. Alternative drugs include other sodium channel blockers and the carbonic anhydrase inhibitor acetazolamide. Preclinical studies suggest that activators of ClC-1 channels or voltage-gated potassium channels may have antimyotonic potential.

CONCLUSIONS: An increasing number of antimyotonic drugs would help to design a precision therapy to address personalized treatment of myotonic individuals.

PMID:40183437 | DOI:10.36185/2532-1900-1026

Neuromolecular Med. 2025 Apr 3;27(1):24. doi: 10.1007/s12017-025-08847-z.

ABSTRACT

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that leads to motor neuron degeneration, muscle weakness, and respiratory failure. Despite ongoing research, effective treatments for ALS are limited. This study aimed to apply network biology and machine learning (ML) techniques to identify novel repurposed drug candidates for ALS. In this study, we conducted a meta-analysis using 4 transcriptome data in ALS patients (including motor neuron and muscle tissue) and healthy controls. Through this analysis, we uncovered common shared differentially expressed genes (DEGs) separately for motor neurons and muscle tissue. Using common DEGs as proxies, we identified two distinct clusters of highly clustered differential co-expressed cluster genes: the 'Muscle Tissue Cluster' for muscle tissue and the 'Motor Neuron Cluster' for motor neurons. We then evaluated the performance of the nodes of these two modules to distinguish between diseased and healthy states with ML algorithms: KNN, SVM, and Random Forest. Furthermore, we performed drug repurposing analysis and text-mining analyses, employing the nodes of clusters as drug targets to identify novel drug candidates for ALS. The potential impact of the drug candidates on the expression of cluster genes was predicted using linear regression, SVR, Random Forest, Gradient Boosting, and neural network algorithms. As a result, we identified five novel drug candidates for the treatment of ALS: Nilotinib, Trovafloxacin, Apratoxin A, Carboplatin, and Clinafloxacin. These findings highlight the potential of drug repurposing in ALS treatment and suggest that further validation through experimental studies could lead to new therapeutic avenues.

PMID:40180646 | DOI:10.1007/s12017-025-08847-z

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