J Bioinform Comput Biol. 2025 Mar 25:2550001. doi: 10.1142/S0219720025500015. Online ahead of print.

ABSTRACT

Drug repurposing is the process of identifying new clinical indications for an existing drug. Some of the recent studies utilized drug response prediction models to identify drugs that can be repurposed. By representing cell-line features as a pathway-pathway interaction network, we can better understand the connections between cellular processes and drug response mechanisms. Existing deep learning models for drug response prediction do not integrate known biological pathway-pathway interactions into the model. This paper presents a drug response prediction model that applies a graph convolution operation on a pathway-pathway interaction network to represent features of cancer cell-lines effectively. The model is used to identify potential drug repurposing candidates for Non-Small Cell Lung Cancer (NSCLC). Experiment results show that the inclusion of graph convolutional model applied on a pathway-pathway interaction network makes the proposed model more effective in predicting drug response than the state-of-the-art methods. Specifically, the model has shown better performance in terms of Root Mean Squared Error, Coefficient of Determination, and Pearson's Correlation Coefficient when applied to the GDSC1000 dataset. Also, most of the drugs that the model predicted as top candidates for NSCLC treatment are either undergoing clinical studies or have some evidence in the PubMed literature database.

PMID:40134346 | DOI:10.1142/S0219720025500015

Discov Oncol. 2025 Mar 26;16(1):396. doi: 10.1007/s12672-025-02067-4.

ABSTRACT

The nervous system plays a critical role in developmental biology and oncology, influencing processes from ontogeny to the complex dynamics of cancer progression. Interactions between the nervous system and cancer significantly affect oncogenesis, tumor growth, invasion, metastasis, treatment resistance, inflammation that promotes tumors, and the immune response. A comprehensive understanding of the signal transduction pathways involved in cancer biology is essential for devising effective anti-cancer strategies and overcoming resistance to existing therapies. Recent advances in cancer neuroscience promise to establish a new cornerstone of cancer therapy. Repurposing drugs originally developed for modulating nerve signal transduction represent a promising approach to target oncogenic signaling pathways in cancer treatment. This review endeavors to investigate the potential of repurposing neurological drugs, which target neurotransmitters and neural pathways, for oncological applications. In this context, it aims to bridge the interdisciplinary gap between neurology, psychiatry, internal medicine, and oncology. By leveraging already approved drugs, researchers can utilize existing extensive safety and efficacy data, thereby reducing both the time and financial resources necessary for the development of new cancer therapies. This strategy not only promises to enhance patient outcomes but also to expand the array of available treatments, thereby enriching the therapeutic landscape in oncology.

PMID:40133751 | DOI:10.1007/s12672-025-02067-4

Cell Biochem Biophys. 2025 Mar 26. doi: 10.1007/s12013-025-01725-2. Online ahead of print.

ABSTRACT

The kynurenine pathway (KP) plays a pivotal role in dampening the immune response in many types of cancer, including TNBC. The intricate involvement of tryptophan degradation via KP serves as a critical regulator in mediating immunosuppression in the tumor microenvironment. The key enzymes that facilitate this mechanism and contribute to tumor progression are indoleamine 2,3-dioxygenase (IDO1) and tryptophan 2,3-dioxygenase (TDO). Despite attempts to use navoximod as a dual-specific inhibitor, its poor bioavailability and lack of clinical efficacy have hampered its utility. To date, no FDA-approved drugs have advanced for dual targeting of these enzymes. Therefore, this study aimed to repurpose the approved drugs from the DrugBank database as novel IDO1/TDO inhibitors. Initially, 2588 FDA-approved compounds were screened by employing molecular docking and pharmacokinetic profiling. Subsequently, methods such as MM-GBSA calculations and machine learning based analysis precisely identified 20 potential lead compounds. The resultant compounds were then assessed for various toxicity endpoints and anticancer activity. The PaccMann server revealed potent anticancer activity, with sensitivities ranging from 0.203 to 24.119 μM against MDA-MB-231 TNBC cell lines. Alongside, the interaction profile with critical residues, strongly reinforced DB06292 (Dapagliflozin) as a compelling hit candidate. Finally, the reliability of the result was corroborated through a rigorous 200 ns molecular dynamics simulation, ensuring the stable binding of the hit against the target proteins. Considering the promising outcomes, we speculate that the proposed hit compound holds strong potential for the management of TNBC.

PMID:40133710 | DOI:10.1007/s12013-025-01725-2

Sci Rep. 2025 Mar 25;15(1):10298. doi: 10.1038/s41598-025-95372-5.

ABSTRACT

Drug repositioning utilizes existing drugs for new therapeutic applications, driven by the rapid increase in disease and drug-related data. However, organizing knowledge in this field and integrating the complex and scattered data from multiple systems into a cohesive knowledge network have become urgent problems to address. In this paper, we propose a drug repositioning model based on knowledge graph embedding. The model employs multivariate relational data to embed entities and relationships in a low-dimensional vector space. It also innovatively introduces the attention mechanism into translation and bilinear models, forming new models such as Attranse, Attdismult, and Attrescal. This model's feature extraction does not rely on a single approach, instead, it integrates multiple models and combines their screening results to enhance drug screening quality. The model's effectiveness was validated using COVID-19 data, yielding results consistent with 7 clinically approved drugs for COVID-19 treatment, indicating high accuracy in identifying new drug indications. The successful application of this model to COVID-19 suggests its potential for broader use in emerging infectious diseases and complex conditions, providing valuable insights for future drug development.

PMID:40133375 | DOI:10.1038/s41598-025-95372-5

Metabolites. 2025 Feb 27;15(3):159. doi: 10.3390/metabo15030159.

ABSTRACT

Background: Aging is characterized by shared cellular and molecular processes, and aging-related diseases might co-exist in a cluster of comorbidities, particularly in vulnerable individuals whose phenotype meets the criteria for frailty. Whilst the multidimensional definition of frailty is still controversial, there is an increasing understanding of the common pathways linking metabolic syndrome, cognitive decline, and sarcopenia, frequent conditions in frail elderly patients. Methods: We performed a systematic search in the electronic databases Cochrane Library and PubMed and included preclinical studies, cohort and observational studies, and trials. Discussion: Metabolic syndrome markers, such as insulin resistance and the triglyceride/HDL C ratio, correlate with early cognitive impairment. Insulin resistance is a cause of synaptic dysfunction and neurodegeneration. Conversely, fasting and fasting-mimicking agents promote neuronal resilience by enhancing mitochondrial efficiency, autophagy, and neurogenesis. Proteins acting as cellular metabolic sensors, such as SIRT1, play a pivotal role in aging, neuroprotection, and metabolic health. In AD, β-amyloid accumulation and hyperphosphorylated tau in neurofibrillary tangles can cause metabolic reprogramming in brain cells, shifting from oxidative phosphorylation to aerobic glycolysis, similar to the Warburg effect in cancer. The interrelation of metabolic syndrome, sarcopenia, and cognitive decline suggests that targeting these shared metabolic pathways could mitigate all the conditions. Pharmacological interventions, including GLP-1 receptor agonists, metformin, and SIRT 1 inducers, demonstrated neuroprotective effects in animals and some preliminary clinical models. Conclusions: These findings encourage further research on the prevention and treatment of neurodegenerative diseases as well as the drug-repurposing potential of molecules currently approved for diabetes, dyslipidemia, and metabolic syndrome.

PMID:40137124 | DOI:10.3390/metabo15030159

Metabolites. 2025 Feb 25;15(3):156. doi: 10.3390/metabo15030156.

ABSTRACT

BACKGROUND/OBJECTIVES: Neutrophil cells' lysis forms the extracellular traps (NETs) to counter the foreign body during insults to the body. Peptidyl arginine deiminase (PAD) participates in this process and is then released into the extracellular fluid with the lysed cell components. In some diseases, patients with abnormal function of PADs, especially PAD 4, tend to form autoantibodies against the abnormal citrullinated proteins that are the result of PAD activity on arginine side chains. Those antibodies, which are highly distinct in RA, are distinctly anti-citrullinated protein antibodies (ACPA). This study used an in-silico drug repurposing approach of FDA-approved medications to identify potential alternative medications that can inhibit this process and address solutions to the current limitations of existing therapies.

METHODS: We utilized Maestro Schrödinger as a computational tool for preparing and docking simulations on the PAD 4 enzyme crystal structure that is retrieved from RCSB Protein Data Bank (PDB ID: 4X8G) while the docked FDA-approved medications are obtained from the Zinc 15 database. The protein was bound to GSK 199-an investigational compound-as a positive control for the docked molecules. Preparation of the protein was performed by Schrödinger Protein Preparation Wizard tool. Binding pocket determination was performed by Glide software (Schrödinger Release 2021-3:Schrödinger, LLC., New York, NY, USA, 2021). and validation of molecular docking was carried out through the redocking of GSK 199 and superimposition. After that, standard and induced fit docking were performed.

RESULTS/CONCLUSIONS: Among the four obtained hits Pemetrexed, Leucovorin, Chlordiazepoxide, and Ioversol, which showed the highest XP scores providing favorable binding interactions. The induced-fit docking (IFD) results displayed the strong binding affinities of Ioversol, Pemetrexed, Leucovorin, Chlordiazepoxide in the order IFD values -11.617, -10.599, -10.521, -9.988, respectively. This research investigates Pemetrexed, Leucovorin, Chlordiazepoxide, and Ioversol as potential repurposing agents in the treatment of rheumatoid arthritis (RA) as they are identified as PAD4 inhibitors.

PMID:40137121 | DOI:10.3390/metabo15030156

Curr Issues Mol Biol. 2025 Feb 26;47(3):153. doi: 10.3390/cimb47030153.

ABSTRACT

Metastatic endometrial cancer continues to be a common cause of death as of 2024, even after maximal use of all currently available standard treatments. To address this problem of metastatic cancer generally in 2025, the drug repurposing movement within oncology identifies medicines in common general medical use that have clinical or preclinical experimental data indicating that they interfere with or inhibit a specific growth driving element identified in a given cancer. The drug repurposing movement within oncology also uses data from large scale in vitro screens of thousands of drugs, looking for simple empirical growth inhibition in a given cancer type. This paper outlines the data showing that five drugs from general medical practice meet these evidence criteria for inhibition of endometrial cancer growth, the EC5 regimen. The EC5 regimen uses the osteoporosis treatment drug, alendronate; the analgesic drug, celecoxib; the antifungal drug, itraconazole; the sleep aid, ramelteon; and the cholesterol lowering drug, simvastatin. Side effects seen with these drugs are usually minimal and easily tolerated by patients.

PMID:40136407 | DOI:10.3390/cimb47030153

mBio. 2025 Mar 26:e0020525. doi: 10.1128/mbio.00205-25. Online ahead of print.

ABSTRACT

Coccidioidomycosis or valley fever is a treatment-limited fungal infection endemic to the alkaline deserts of North and South America for which two classes of antifungals are typically used: the polyenes and the triazoles. In light of the limited usefulness of the echinocandins and a growing trend of azole resistance, it is essential that we identify novel antifungals. In this study, we have developed and optimized a screening methodology for identifying potential antifungals effective against Coccidioides spherule initials using a metabolic assay, used it to screen four diverse drug libraries with limited drug overlap, and established safety and efficacy data for a majority of the compounds, including the Broad Repurposing Hub, Prestwick Chemicals 1520, Selleck L8200 Anti-parasitic, and MedChemExpress CNS Penetrants libraries. Hits were defined as compounds with strong metabolic inhibition (≥70%), which were significantly different compared to the median plate readout (B-scores ≤ -3). We identified 30 promising hits and found 12 compounds exhibiting half-maximal inhibitory concentrations below 6 µM. Among these, oxethazaine, niclosamide ethanolamine, 10058-F4, niclosamide (NIC), and pentamidine isethionate showed synergy with amphotericin B, suggesting their potential use in combination therapy. Further assessment of lead compounds' effects on spherules was conducted by image flow cytometry. Additionally, we explored the potential to use an attenuated, Biosafety Level 2 containment mutant, C. posadasii ∆cts2/∆ard1/∆cts3 (∆T), as a surrogate model for drug screening. Overall, our findings provide a foundation for future research focused on screening and developing novel coccidioidomycosis treatments.IMPORTANCEThe antifungal treatment arsenal is especially limited against Coccidioides. Due to toxicity concerns, amphotericin B is generally reserved for triazole-recalcitrant infections. Recent laboratory susceptibility tests show an increase in fluconazole resistance, highlighting a need for new treatments. We have developed a large-scale metabolic screening assay under Biosafety Level 3 containment to identify existing drugs with novel activity against Coccidioides spherules. This drug-repurposing approach represents a convenient and cost-effective strategy to increase the available antifungals effective against these infections.

PMID:40135873 | DOI:10.1128/mbio.00205-25

J Biomol Struct Dyn. 2025 Mar 26:1-12. doi: 10.1080/07391102.2025.2483963. Online ahead of print.

ABSTRACT

Poly (ADP-ribose) polymerase 1 (PARP1) is a nuclear protein that plays a pivotal role in DNA repair and has emerged as a promising target for cancer therapy. Repurposing existing FDA-approved drugs for PARP1 inhibition offers an accelerated route to drug discovery. Here, we present an integrated approach to drug repurposing for PARP1 inhibition while utilizing an integrated approach involving structure-based virtual screening and molecular dynamics (MD) simulations. First, a curated library of 3648 FDA-approved drugs from DrugBank was screened to identify potential candidates capable of binding to the PARP1. Our study reveals a subset of drug molecules with favorable binding profiles and stable interactions within the PARP1 active site. The standout candidate, Nilotinib, was selected based on its drug profile and subjected to a detailed analysis, including interaction studies and 500 ns all-atom MD simulations. By integrating multiple computational approaches, we provide a rational framework for the selection of Nilotinib, demonstrating its PARP1 binding features and potential for therapeutic development after further experimentation. This study highlights the power of computational methods in accelerating drug repurposing efforts, offering an efficient strategy for identifying novel therapeutic options for PARP1-associated diseases.

PMID:40135853 | DOI:10.1080/07391102.2025.2483963

Int J Pharm. 2025 Mar 23:125499. doi: 10.1016/j.ijpharm.2025.125499. Online ahead of print.

ABSTRACT

Bio-inspired nanotopographical carriers have emerged as innovative cancer therapy strategies, mimicking natural processes to enhance targeted delivery and reduce systemic toxicity. This study presents the development of virus-like mesoporous silica nanoparticles (MSN) as a delivery platform for repurposed metformin (MTF) in a topical multi-stimuli responsive system for melanoma treatment. Metformin-loaded virus-like MSN (MTF-MSN) were fabricated and incorporated into a thermo-responsive gelling system. The particles were evaluated for morphology, zeta potential (ZP), particle size (PS), entrapment efficiency (EE%), Fourier-transform infrared (FT-IR) spectroscopy, MTT cytotoxicity assay, in vitro release, and in a melanoma in vivo model. The particles exhibited a spherical morphology, a zeta potential of +31.9 ± 1.45 mV, and a particle size of 197 ± 3.47 nm, ideal for skin penetration. MTF-MSN demonstrated significant antiproliferative activity against melanoma A375 cells, with lower IC50 values (192 μg/mL) compared to free MTF (>300 μg/mL). Sustained, pH-sensitive MTF release was observed over 48 h at pH 7.4 and 6 h at pH 5.5. In vivo studies showed enhanced anti-cancer efficacy of MTF-MSN, evidenced by elevated caspase-3 and Neurofibromin Type-1 (NF-1) levels, along with suppressed angiogenesis markers VEGF and NRAS. The MTF-MSN-treated group exhibited superior outcomes compared to free MTF and controls (p < 0.05). This innovative bio-inspired MTF-MSN hydrogel system optimizes MTF delivery for melanoma therapy, pioneering advancements in drug repurposing and nano-oncology.

PMID:40132769 | DOI:10.1016/j.ijpharm.2025.125499

JCI Insight. 2025 Mar 25:e190655. doi: 10.1172/jci.insight.190655. Online ahead of print.

ABSTRACT

BACKGROUND: The graft-vs-leukemia (GVL) effect contributes to the efficacy of allogeneic stem cell transplantation (alloSCT). However, relapse, indicative of GVL failure, is the greatest single cause of treatment failure. Based on preclinical data showing that IFN-γ is important to sensitize myeloblasts to alloreactive T cells, we performed a phase I trial of IFN-γ combined with donor leukocyte infusions (DLI) in myeloblastic malignancies that relapsed post-HLA-matched alloSCT.

METHODS: Patients with relapsed acute myeloid leukemia or myelodysplastic syndrome after alloSCT were eligible. Patients self-administered IFN-γ for 4 weeks (cohort 1) or 1 week (cohort 2), followed by DLI and concurrent IFN-γ for a total of 12 weeks. Bone marrow samples were analyzed by single-cell RNA sequencing (scRNAseq) to assess in vivo responses to IFN-γ by malignant myeloblasts.

RESULTS: IFN-γ monotherapy was well tolerated by all subjects (n=7). Treatment-related toxicities after DLI included: grade I-II graft-versus-host disease (n=5), immune effector cell-associated neurotoxicity syndrome (n=2), and idiopathic pulmonary syndrome (n=1), all of which resolved with corticosteroids. Four of 6 DLI recipients achieved minimal residual disease-negative complete remissions and full donor hematopoietic recovery. Median overall survival was 579 days (range, 97-906) in responders. ScRNAseq confirmed in vivo activation of IFN-γ response pathway in hematopoietic stem cell-like or myeloid progenitor cells after IFN-γ in analyzed samples.

CONCLUSIONS: IFN-γ was safe and well tolerated in this phase I study of IFN-γ for relapsed AML/MDS post-alloSCT, with a promising efficacy signal when combined with DLI. Larger studies are needed to formally test the efficacy of this approach.TRIAL RESGISTRATION.

CLINICALTRIALS: gov NCT04628338.

FUNDING: The research was supported by The UPMC Hillman Cancer Center Cancer Immunology and Immunotherapy Program (CIIP) Pilot Award and Cure Within Reach: Drug Repurposing Clinical Trials to Impact Blood Cancers. Recombinant IFN-gamma (Actimmune®) was donated by Horizon Therapeutics.

PMID:40131369 | DOI:10.1172/jci.insight.190655

Brief Bioinform. 2025 Mar 4;26(2):bbaf122. doi: 10.1093/bib/bbaf122.

ABSTRACT

As part of the drug repurposing process, it is imperative to predict the interactions between drugs and target proteins in an accurate and efficient manner. With the introduction of contrastive learning into drug-target prediction, the accuracy of drug repurposing will be further improved. However, a large part of DTI prediction methods based on deep learning either focus only on the structural features of proteins and drugs extracted using GNN or CNN, or focus only on their relational features extracted using heterogeneous graph neural networks on a DTI heterogeneous graph. Since the structural and relational features of proteins and drugs describe their attribute information from different perspectives, their combination can improve DTI prediction performance. We propose a relational similarity-based graph contrastive learning for DTI prediction (RSGCL-DTI), which combines the structural and relational features of drugs and proteins to enhance the accuracy of DTI predictions. In our proposed method, the inter-protein relational features and inter-drug relational features are extracted from the heterogeneous drug-protein interaction network through graph contrastive learning, respectively. The results demonstrate that combining the relational features obtained by graph contrastive learning with the structural ones extracted by D-MPNN and CNN enhances feature representation ability, thereby improving DTI prediction performance. Our proposed RSGCL-DTI outperforms eight SOTA baseline models on the four benchmark datasets, performs well on the imbalanced dataset, and also shows excellent generalization ability on unseen drug-protein pairs.

PMID:40127181 | DOI:10.1093/bib/bbaf122

Mol Divers. 2025 Mar 24. doi: 10.1007/s11030-025-11170-1. Online ahead of print.

ABSTRACT

Acetylcholinesterase inhibitors (AChEIs) are essential in the treatment of neurodegenerative disorders like Alzheimer's disease, as they prevent the breakdown of acetylcholine, thereby enhancing cognitive function. This review provides a comprehensive analysis of the structural motifs and mechanisms governing AChEI pharmacological activity, with a focus on medicinal chemistry strategies to enhance potency, selectivity, and pharmacokinetic properties. Beginning with the physiological role of acetylcholinesterase in neurological disorders, the review explores the historical evolution of AChEIs and highlights key structural interactions with catalytic, peripheral anionic, and allosteric binding sites. Advances in computational modeling, virtual screening, and structure-based drug design are discussed, alongside emerging approaches, such as multi-target-directed ligands and prodrugs. Additionally, the significance of natural products and drug repurposing in identifying novel AChEI scaffolds is emphasized, contributing to chemical diversity and innovation in drug discovery. By integrating computational tools, expansive chemical libraries, and innovative design strategies, this review identifies promising directions for developing effective AChEIs. These advancements hold great potential in addressing the multifaceted nature of neurodegenerative diseases and improving therapeutic interventions.

PMID:40126739 | DOI:10.1007/s11030-025-11170-1

Am J Med Genet B Neuropsychiatr Genet. 2025 Mar 24:e33028. doi: 10.1002/ajmg.b.33028. Online ahead of print.

ABSTRACT

Schizophrenia is a neurodevelopmental psychiatric disorder characterized by symptoms of psychosis, thought disorder, and flattened affect. Immune mechanisms are associated with schizophrenia, though the precise nature of this relationship (causal, correlated, consequential) and the mechanisms involved are not fully understood. To elucidate these mechanisms, we conducted a transcriptome-wide Mendelian randomization study using gene expression exposures from 29 human cis-eQTL data sets encompassing 11 unique immune cell types, available from the eQTL catalog. These analyses highlighted 196 genes, including 67 located within the human leukocyte antigen (HLA) region. Enrichment analyses indicated an overrepresentation of immune genes, which was driven by the HLA genes. Stringent validation and replication steps retained 61 candidate genes, 27 of which were the sole causal signals at their respective loci, thereby representing strong candidate effector genes at known risk loci. We highlighted L3HYPDH as a potential novel schizophrenia risk gene and DPYD and MAPK3 as candidate drug repurposing targets. Furthermore, we performed follow-up analyses focused on one of the candidate effectors, interferon regulatory transcription factor 3 (IRF3), which coordinates interferon responses to viral infections. We found evidence of shared genetic etiology between schizophrenia and autoimmune diseases at the IRF3 locus, and a significant enrichment of IRF3 chromatin binding at known schizophrenia risk loci. Our findings highlight a novel schizophrenia risk gene, potential drug repurposing opportunities, and provide support for IRF3 as a schizophrenia hub gene, which may play critical roles in mediating schizophrenia-autoimmune comorbidities and the impact of infections on schizophrenia risk.

PMID:40126059 | DOI:10.1002/ajmg.b.33028

Front Pharmacol. 2025 Mar 7;16:1583486. doi: 10.3389/fphar.2025.1583486. eCollection 2025.

NO ABSTRACT

PMID:40124782 | PMC:PMC11926139 | DOI:10.3389/fphar.2025.1583486

iScience. 2025 Feb 21;28(3):112081. doi: 10.1016/j.isci.2025.112081. eCollection 2025 Mar 21.

ABSTRACT

Viral genomes encode viral transcriptional regulators (vTRs) that manipulate host gene expression to facilitate replication and evade immune detection. Nevertheless, their role in non-cancerous diseases remains largely underexplored. Here, we unveiled 268 new candidate vTRs from 14 of the 20 viral families we investigated. We mapped vTRs' genome-wide binding profiles and identified their potential human targets, which were enriched in immune-mediated pathways, neurodegenerative disorders, and cancers. Through vTR DNA-binding preference analysis, 283 virus-specific and human-like motifs were identified. Prioritized Epstein-Barr virus (EBV) vTR target genes were associated with multiple sclerosis (MS), rheumatoid arthritis, and systemic lupus erythematosus. The partitioned heritability study among 19 diseases indicated significant enrichment of these diseases in EBV vTR-binding sites, implicating EBV vTRs' roles in immune-mediated disorders. Finally, drug repurposing analysis pinpointed candidate drugs for MS, asthma, and Alzheimer disease. This study enhances our understanding of vTRs in diverse human diseases and identifies potential therapeutic targets for future investigation.

PMID:40124487 | PMC:PMC11928865 | DOI:10.1016/j.isci.2025.112081

Comput Struct Biotechnol J. 2025 Mar 1;27:887-895. doi: 10.1016/j.csbj.2025.02.036. eCollection 2025.

ABSTRACT

Clostridioides difficile infection (CDI) is a major public health issue, driven by antibiotic resistance and frequent recurrence. CD2068, an ABC protein in C. difficile, is associated with drug resistance, making it a potential target for novel therapies. This study explored FDA-approved non-antibiotic drugs for their ability to inhibit CD2068 through drug screening and experimental validation. Thioridazine exhibited moderate binding affinity to CD2068 and inhibited its ATP hydrolysis activity. It also suppressed the growth of multiple C. difficile ribotypes at 64-128 µg/mL, with rapid-killing effects. When combined with sub-MIC levels of standard antibiotics, thioridazine significantly reduced bacterial growth. In a mouse CDI model, thioridazine demonstrated potential in restoring gut microbial balance and improving survival, although it did not show superiority to vancomycin. These findings suggest that thioridazine has potential as a novel therapeutic for CDI, either as an adjunct to existing antibiotics or as part of a combination therapy to combat antibiotic resistance. Further research, including replication studies and dose optimization, is needed to fully evaluate thioridazine's therapeutic potential.

PMID:40123799 | PMC:PMC11928863 | DOI:10.1016/j.csbj.2025.02.036

Chin Med. 2025 Mar 24;20(1):40. doi: 10.1186/s13020-025-01075-4.

ABSTRACT

In the past, the drug research and development has predominantly followed a "single target, single disease" model. However, clinical data show that single-target drugs are difficult to interfere with the complete disease network, are prone to develop drug resistance and low safety in clinical use. The proposal of multi-target drug therapy (also known as "cocktail therapy") provides a new approach for drug discovery, which can affect the disease and reduce adverse reactions by regulating multiple targets. Natural products are an important source for multi-target innovative drug development, and more than half of approved small molecule drugs are related to natural products. However, there are many challenges in the development process of natural products, such as active drug screening, target identification and preclinical dosage optimization. Therefore, how to develop multi-target drugs with good drug resistance from natural products has always been a challenge. This article summarizes the applications and shortcomings of related technologies such as natural product bioactivity screening, clarify the mode of action of the drug (direct/indirect target), and preclinical dose optimization. Moreover, in response to the challenges faced by natural products in the development process and the trend of interdisciplinary and multi-technology integration, and a multi-target drug development strategy of "active substances - drug action mode - drug optimization" is proposed to solve the key challenges in the development of natural products from multiple dimensions and levels.

PMID:40122800 | DOI:10.1186/s13020-025-01075-4

BMC Bioinformatics. 2025 Mar 21;26(1):86. doi: 10.1186/s12859-025-06102-7.

ABSTRACT

BACKGROUND: The Anatomical Therapeutic Chemical (ATC) classification system, proposed and maintained by the World Health Organization, is among the most widely used drug classification schemes. Recently, it has become a key research focus in drug repositioning. Computational models often pair drugs with ATC codes to explore drug-ATC code associations. However, the limited information available for ATC codes constrains these models, leaving significant room for improvement.

RESULTS: This study presents an inference method to identify highly related target proteins, structural features, and side effects for each ATC code, constructing comprehensive biological profiles. Association networks for target proteins, structural features, and side effects are established, and a random walk with restart algorithm is applied to these networks to extract raw associations. A permutation test is then conducted to exclude false positives, yielding robust biological profiles for ATC codes. These profiles are used to construct new ATC code kernels, which are integrated with ATC code kernels from the existing model PDATC-NCPMKL. The recommendation matrix is subsequently generated using the procedures of PDATC-NCPMKL. Cross-validation results demonstrate that the new model achieves AUROC and AUPR values exceeding 0.96.

CONCLUSION: The proposed model outperforms PDATC-NCPMKL and other previous models. Analysis of the contributions of the newly added ATC code kernels confirms the value of biological profiles in enhancing the prediction of drug-ATC code associations.

PMID:40119265 | DOI:10.1186/s12859-025-06102-7

Sci Rep. 2025 Mar 21;15(1):9827. doi: 10.1038/s41598-025-93509-0.

ABSTRACT

Observational studies enable causal inferences when randomized controlled trials (RCTs) are not feasible. However, integrating sensitive medical data across multiple institutions introduces significant privacy challenges. The data collaboration quasi-experiment (DC-QE) framework addresses these concerns by sharing "intermediate representations"-dimensionality-reduced data derived from raw data-instead of the raw data. Although DC-QE can estimate treatment effects, its application to medical data remains unexplored. The aim of this study was to apply the DC-QE framework to medical data from a single institution to simulate distributed data environments under independent and identically distributed (IID) and non-IID conditions. We propose a method for generating intermediate representations within the DC-QE framework. Experimental results show that DC-QE consistently outperformed individual analyses across various accuracy metrics, closely approximating the performance of centralized analysis. The proposed method further improved performance, particularly under non-IID conditions. These outcomes highlight the potential of the DC-QE framework as a robust approach for privacy-preserving causal inferences in healthcare. Broader adoption of this framework and increased use of intermediate representations could grant researchers access to larger, more diverse datasets while safeguarding patient confidentiality. This approach may ultimately aid in identifying previously unrecognized causal relationships, support drug repurposing efforts, and enhance therapeutic interventions for rare diseases.

PMID:40118898 | DOI:10.1038/s41598-025-93509-0