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

Neurotherapeutics. 2025 Mar 20:e00575. doi: 10.1016/j.neurot.2025.e00575. Online ahead of print.

ABSTRACT

Neuroblastoma is an aggressive childhood cancer that arises from the sympathetic nervous system. Despite advances in treatment, high-risk neuroblastoma remains difficult to manage due to its heterogeneous nature and frequent development of drug resistance. Drug repurposing guided by single-cell analysis presents a promising strategy for identifying new therapeutic options. Here, we aim to characterize high-risk neuroblastoma subpopulations and identify effective repurposed drugs for targeted treatment. We performed single-cell transcriptomic analysis of neuroblastoma samples, integrating bulk RNA-seq data deconvolution with clinical outcomes to define distinct malignant cell states. Using a systematic drug repurposing pipeline, we identified and validated potential therapeutic agents targeting specific high-risk neuroblastoma subpopulations. Single-cell analysis revealed 17 transcriptionally distinct neuroblastoma subpopulations. Survival analysis identified a highly aggressive subpopulation characterized by elevated UBE2C/PTTG1 expression and poor patient outcomes, distinct from a less aggressive subpopulation with favorable prognosis. Drug repurposing screening identified Adavosertib as particularly effective against the aggressive subpopulation, validated using SK-N-DZ cells as a representative model. Mechanistically, Adavosertib suppressed cell proliferation through AKT/mTOR pathway disruption, induced G2/M phase cell cycle arrest, and promoted apoptosis. Further analysis revealed UBE2C and PTTG1 as key molecular drivers of drug resistance, where their overexpression enhanced proliferation, Adavosertib resistance, and cell migration. This study establishes a single-cell-based drug repurposing strategy for high-risk neuroblastoma treatment. Our approach successfully identified Adavosertib as a promising repurposed therapeutic agent for targeting specific high-risk neuroblastoma subpopulations, providing a framework for developing more effective personalized treatment strategies.

PMID:40118716 | DOI:10.1016/j.neurot.2025.e00575

Eur J Med Chem. 2025 Mar 15;290:117516. doi: 10.1016/j.ejmech.2025.117516. Online ahead of print.

ABSTRACT

Ferroptosis has emerged as a promising therapeutic approach for a wide range of diseases. However, limited chemical diversity and poor drug-like profiles have hindered the development of effective ferroptosis inhibitors for clinical use. Herein, we identified drug-like imidazo[1,2-a]pyridine-3-amine derivatives as innovative ferroptosis inhibitors for injury-related diseases by drug scaffold repositioning strategy. Our findings established that the selected compounds exhibited high radical scavenging and effective membrane retention, thereby leading to significant suppression of lipid peroxidation and ferroptosis at nanomolar concentrations. Notably, compound C18, with low cytotoxicity and favorable pharmacokinetics properties, demonstrated remarkable in vivo neuroprotection against ischemic brain injury in mice. In conclusion, our investigations not only engender potent ferroptosis inhibitors with novel structural characteristics that warrant further development, but also serve as a valuable case study for drug repurposing in the discovery of additional ferroptosis inhibitors.

PMID:40117856 | DOI:10.1016/j.ejmech.2025.117516

HGG Adv. 2025 Mar 20:100425. doi: 10.1016/j.xhgg.2025.100425. Online ahead of print.

ABSTRACT

Haploinsufficiency of FBXO11, encoding a ubiquitin ligase complex subunit, is associated with a variable neurodevelopmental disorder. So far, the underlying nervous-system related pathomechanisms are poorly understood, and specific therapies are lacking. Using a combined approach, we established an FBXO11-deficient human stem cell-based neuronal model using CRISPR/CAS9 and a Drosophila model using tissue specific knockdown techniques. We performed transcriptomic analyses on iPSC-derived neurons and molecular phenotyping in both models. RNA-sequencing revealed disrupted transcriptional networks related to processes important for neuronal development, such as differentiation, migration and cell signaling. Consistently, we found that loss of FBXO11 leads to neuronal phenotypes such as impaired neuronal migration and abnormal proliferation/differentiation balance in human cultured neurons and impaired dendritic development and behavior in Drosophila. Interestingly, application of three different proteasome activating substances could alleviate FBXO11-deficiency-associated phenotypes in both human neurons and flies. One of these substances is the long-approved drug Verapamil, opening the possibility of drug repurposing in the future. Our study shows the importance of FBXO11 for neurodevelopment and highlights the reversibility of related phenotypes, opening an avenue for potential development of therapeutic approaches through drug repurposing.

PMID:40114442 | DOI:10.1016/j.xhgg.2025.100425

Brief Bioinform. 2025 Mar 4;26(2):bbaf117. doi: 10.1093/bib/bbaf117.

ABSTRACT

The prediction of virus-drug associations (VDAs) is crucial for drug repositioning, contributing to the identification of latent antiviral drugs. In this study, we developed a graph-based integrated Gaussian kernel similarity (GiGs) method for predicting potential VDAs in drug repositioning. The GiGs model comprises three components: (i) collection of experimentally validated VDA information and calculation virus sequence, drug chemical structure, and drug side effect similarity; (ii) integration of viruses and drugs similarity based on the above information and Gaussian interaction profile kernel (GIPK); and (iii) utilization of similarity-constrained weight graph normalization matrix factorization to predict antiviral drugs. The GiGs model enhances correlation matrix quality through the integration of multiple biological data, improves performance via similarity constraints, and prevents overfitting and predicts missing data more accurately through graph regularization. Extensive experimental results indicated that the GiGs model outperforms five other advanced association prediction methods. A case study identified broad-spectrum drugs for treating highly pathogenic human coronavirus infections, with molecular docking experiments confirming the model's accuracy.

PMID:40112339 | DOI:10.1093/bib/bbaf117

Microbiol Spectr. 2025 Mar 20:e0298724. doi: 10.1128/spectrum.02987-24. Online ahead of print.

ABSTRACT

Candida albicans filamentation and biofilm formation are key virulence factors tied to tissue invasion and antifungal tolerance. Pilocarpine hydrochloride (PHCl), a muscarinic receptor agonist, inhibits biofilm maturation, although its mechanism remains unclear. We explored PHCl effects by analyzing sphingolipid and lipid composition and proteomics in treated C. albicans SC5314 biofilms. PHCl significantly decreased polar lipid and ergosterol levels in biofilms while inducing phytoceramide and glucosylceramide accumulation. PHCl also induced reactive oxygen species and early apoptosis. Proteomic analysis revealed that PHCl treatment downregulated proteins associated with metabolism, cell wall remodeling, and DNA repair in biofilms to levels comparable to those observed in planktonic cells. Consistent with ergosterol reduction, Erg2 was found to be reduced. Overall, PHCl disrupts key pathways essential for biofilm integrity, decreasing its stability and promoting surface detachment, underscoring its potential as a versatile antifungal compound.

IMPORTANCE: Candida albicans filamentation and biofilm formation represent crucial virulence factors promoting fungus persistence and drug tolerance. The common eukaryotic nature of mammalian cells poses significant limitations to the development of new active nontoxic compounds. Understanding the mechanism underlying PHCl inhibitory activity on yeast-hypha transition, biofilm adhesion, and maturation can pave the way to efficient drug repurposing in a field where pharmaceutical investment is lacking.

PMID:40111054 | DOI:10.1128/spectrum.02987-24

Cytoskeleton (Hoboken). 2025 Mar 20. doi: 10.1002/cm.22020. Online ahead of print.

ABSTRACT

Polo-like kinase 4 (PLK4) is a centrosome-specific kinase aberrantly expressed in cancers. Drugs inhibiting its catalytic kinase domain are under clinical phase-1/2 trials in patients with different leukemia types. However, the kinase domain of PLK4 shows structural similarity with other kinases. Therefore, drugs targeting the unique C-terminal polo-box domain (PBD) of PLK4 could provide better specificity. The knowledge of domain orientation in a full-length PLK4 structure is imperative for drug discovery. In this work, we utilized ab initio and threading approaches to predict the full-length structure of human PLK4, which was employed for virtually screening the ChEMBL library. Among the hit compounds targeting the unique regions in PLK4, we identified Alectinib, which affects centrosome numbers corresponding to PLK4 levels at centrosomes. The FT-IR analysis also confirmed Alectinib interaction with the PBD. Therefore, this work identifies a chemical scaffold that could be repurposed to target the unique regions of PLK4.

PMID:40110897 | DOI:10.1002/cm.22020

Front Res Metr Anal. 2025 Mar 5;10:1509502. doi: 10.3389/frma.2025.1509502. eCollection 2025.

ABSTRACT

OBJECTIVE: Literature-based Discovery (LBD) identifies new knowledge by leveraging existing literature. It exploits interconnecting implicit relationships to build bridges between isolated sets of non-interacting literatures. It has been used to facilitate drug repurposing, new drug discovery, and study adverse event reactions. Within the last decade, LBD systems have transitioned from using statistical methods to exploring deep learning (DL) to analyze semantic spaces between non-interacting literatures. Recent works explore knowledge graphs (KG) to represent explicit relationships. These works envision LBD as a knowledge graph completion (KGC) task and use DL to generate implicit relationships. However, these systems require the researcher to have domain-expert knowledge when submitting relevant queries for novel hypothesis discovery.

METHODS: Our method explores a novel approach to identify all implicit hypotheses given the researcher's search query and expedites the knowledge discovery process. We revise the KGC task as the task of predicting interconnecting vertex embeddings within the graph. We train our model using a similarity learning objective and compare our model's predictions against all known vertices within the graph to determine the likelihood of an implicit relationship (i.e., connecting edge). We also explore three approaches to represent edge connections between vertices within the KG: average, concatenation, and Hadamard. Lastly, we explore an approach to induce inductive biases and expedite model convergence (i.e., input representation scaling).

RESULTS: We evaluate our method by replicating five known discoveries within the Hallmark of Cancer (HOC) datasets and compare our method to two existing works. Our results show no significant difference in reported ranks and model convergence rate when comparing scaling our input representations and not using this method. Comparing our method to previous works, we found our method achieves optimal performance on two of five datasets and achieves comparable performance on the remaining datasets. We further analyze our results using statistical significance testing to demonstrate the efficacy of our method.

CONCLUSION: We found our similarity-based learning objective predicts linking vertex embeddings for single relationship closed discovery replication. Our method also provides a ranked list of linking vertices between a set of inputs. This approach reduces researcher burden and allows further exploration of generated hypotheses.

PMID:40110121 | PMC:PMC11920161 | DOI:10.3389/frma.2025.1509502

Juntendo Med J. 2025 Jan 17;71(1):26-31. doi: 10.14789/ejmj.JMJ24-0026-P. eCollection 2025.

ABSTRACT

Stroke remains a leading cause of mortality and morbidity globally, posing significant challenges to healthcare systems due to its impact on Activities of Daily Living, Quality of Life, and healthcare costs. Current treatments primarily focus on acute management through thrombolytic therapy and thrombectomy, but only a limited number of patients benefit, underscoring the need for effective therapies to aid chronic stroke recovery. Despite ongoing clinical trials, cell therapy faces substantial logistical and cost-related hurdles, limiting its widespread adoption. Strategies to minimalize post-stroke sequelae emphasize preventing cerebral infarction deterioration, utilizing predictive scoring systems for focused treatment, and exploring drug repositioning. The complex interplay within the Neurovascular Unit and Oligovascular Niche highlights the role of various cell types and neurotrophic factors in stroke pathophysiology and recovery phases. Notably, microglia and astrocytes exhibit dual phenotypes ─ either inflammatory or protective ─ depending on the environment, influencing neural damage or repair processes post-stroke. Mitochondrial therapy emerges as a promising avenue, leveraging the organelles' ability to migrate between cells and mitigate inflammatory responses. Studies suggest that mitochondria transferred from astrocytes or other sources could transform inflammatory astrocytes into protective ones, thereby promoting white matter integrity and potentially reducing dementia progression associated with stroke sequelae. In conclusion, addressing stroke's multifaceted challenges requires innovative therapeutic approaches targeting inflammatory mechanisms and enhancing neuroprotection. Early detection and intervention, coupled with advancements in mitochondrial therapy and understanding intercellular interactions, hold promise for improving stroke outcomes and reducing long-term neurological complications.

PMID:40109397 | PMC:PMC11915467 | DOI:10.14789/ejmj.JMJ24-0026-P

Adv Healthc Mater. 2025 Mar 20:e2404117. doi: 10.1002/adhm.202404117. Online ahead of print.

ABSTRACT

Ovarian cancer (OC) poses a significant clinical challenge due to its high recurrence rates and resistance to standard therapies, particularly in advanced stages where recurrence is common, and treatment is predominantly palliative. Personalized treatments, while effective in other cancers, remain underutilized in OC due to a lack of reliable biomarkers predicting clinical outcomes. Accordingly, precision medicine approaches are limited, with PARP inhibitors showing efficacy only in specific genetic contexts. Drug repurposing offers a promising, rapidly translatable strategy by leveraging existing pharmacological data to identify new treatments for OC. Patient-derived polyclonal spheroids, isolated from ascites fluid closely mimic the clinical behavior of OC, providing a valuable model for drug testing. Using these spheroids, a high-throughput drug screening pipeline capable of evaluating both cytotoxicity and anti-migratory properties of a diverse drug library, including FDA-approved, investigational, and newly approved compounds is developed. The findings highlight the importance of 3D culture systems, revealing a poor correlation between drug efficacy in traditional 2D models and more clinically relevant 3D spheroids. This approach has expedited the identification of promising candidates, such as rapamycin, which demonstrated limited activity as a monotherapy but synergized effectively with standard treatments like cisplatin and paclitaxel in vitro. In combination with platinum-based therapy, Rapamycin led to significant in vitro cytotoxicity and a marked reduction in tumor burden in a syngeneic in vivo model. This proof-of-concept study underscores the potential of drug repurposing to rapidly advance new treatments into clinical trials for OC, offering renewed hope for patients with advanced disease.

PMID:40109101 | DOI:10.1002/adhm.202404117

Inflammopharmacology. 2025 Mar 19. doi: 10.1007/s10787-025-01695-0. Online ahead of print.

ABSTRACT

Sporadic Alzheimer's disease is a progressive neurodegenerative disorder affecting the central nervous system. Its main two hallmarks are extracellular deposition of aggregated amyloid beta resulting in senile plaques and intracellular hyperphosphorylated tau proteins forming neuro-fibrillary tangles. As those processes are promoted by the glycogen synthase kinase-3 enzyme, GSK3 inhibitors may be of therapeutic value in SAD. GSK3 is also inhibited by the action of insulin on insulin signaling. Insulin receptor desensitization in the brain is hypothesized to cause inhibition of insulin signaling pathway that ultimately causes cognitive deficits seen in SAD. In extant research, induction of cognitive impairment is achieved by intracerebroventricular injection of streptozotocin-a diabetogenic compound that causes desensitization to insulin receptors in the brain leading to the appearance of most of the SAD signs and symptoms. Valproic acid -a histone deacetylase inhibitor and anti-epileptic drug-has been recently studied in the management of SAD as a possible GSK3 inhibitor. Accordingly, the aim of the present study is to explore the role of multiple VPA doses on the downstream effects of the insulin signaling pathway in ICV STZ-injected mice and suggest a possible mechanism of VPA action. ICV STZ-injected mice showed deficiency in short- and long-term memory as well as increased anxiety, as established by open field test, Modified Y-maze, Morris water maze, and elevated plus maze neurobehavioral tests.

PMID:40108007 | DOI:10.1007/s10787-025-01695-0