Cancer Immunol Immunother. 2025 Feb 1;74(3):90. doi: 10.1007/s00262-025-03943-2.

ABSTRACT

Imiquimod (IMQ), a drug from aminoquinoline group, is the toll-like receptor 7 (TLR7) agonist. It acts as an immunostimulant and radio-sensitizing agent. IMQ stimulates both innate and adaptive immune response. Despite studies conducted, there are no unambiguous data showing how IMQ affects the condition of tumor blood vessels. Tumor vasculature plays the main role in tumor progression. Formation of abnormal blood vessels increases area of hypoxia which recruits suppressor cells, blocks tumor infiltration by CD8+ T lymphocytes, inhibits efficacy of chemoterapeutic drug and leads to cancer relapse. Normalization is a type of therapy targeted at abnormal tumor blood vessels. Here, we demonstrated that 50 µg of IMQ inhibits the growth of melanoma tumors more efficiently, compared to other tested doses and the control group. Dose escalation did not improve the therapeutic antitumor potential of TLR7 agonist. A dose of 50 µg of IMQ most effectively reduced tumor blood vessel density. Imiquimod normalized tumor vasculature both structurally (by reducing vessel tortuosity and increasing pericyte coverage) and functionally (by improving tumor perfusion) in a dose-dependent manner. Hypoxia regions in tumors of treated mice were significantly reduced after IMQ administration. A dose of 50 µg of IMQ had also the greatest impact on the changes in tumor-infiltrating T lymphocytes levels. TLR7 agonist inhibited angiogenesis in treated mice. Functional vascular normalization by IMQ increases the effectiveness of low dose of doxorubicin. Higher dose of IMQ showed worse effects than lower doses including decreased tumor perfusion, increased tumor hypoxia and immunosuppression. This knowledge may help to optimize the combination of the selected IMQ dose with e.g. chemotherapy or radiotherapy to elicit synergistic effect in cancer treatment. To conclude, we outline IMQ repurposing as a vascular normalizing agent. Our research results may contribute to expanding the therapeutic indications for the use of IMQ in anticancer therapy in the future.

PMID:39891776 | DOI:10.1007/s00262-025-03943-2

J Transl Med. 2025 Feb 1;23(1):57. doi: 10.1186/s12967-024-05938-6.

ABSTRACT

BACKGROUND: The traditional process of developing new drugs is time-consuming and often unsuccessful, making drug repurposing an appealing alternative due to its speed and safety. Graph neural networks (GCNs) have emerged as a leading approach for predicting drug-disease associations by integrating drug and disease-related networks with advanced deep learning algorithms. However, GCNs generally infer association probabilities only for existing drugs and diseases, requiring network re-establishment and retraining for novel entities. Additionally, these methods often struggle with sparse networks and fail to elucidate the biological mechanisms underlying newly predicted drugs.

METHODS: To address the limitations of traditional methods, we developed HEDDI-Net, a heterogeneous embedding architecture designed to accurately detect drug-disease associations while preserving the interpretability of biological mechanisms. HEDDI-Net integrates graph and shallow learning techniques to extract representative diseases and proteins, respectively. These representative diseases and proteins are used to embed the input features, which are then utilized in a multilayer perceptron for predicting drug-disease associations.

RESULTS: In experiments, HEDDI-Net achieves areas under the receiver operating characteristic curve of over 0.98, outperforming state-of-the-art methods. Rigorous recovery analyses reveal a median recovery rate of 73% for the top 100 diseases, demonstrating its efficacy in identifying novel target diseases for existing drugs, known as drug repurposing. A case study on Alzheimer's disease highlighted the model's practical applicability and interpretability, identifying potential drug candidates like Baclofen, Fluoxetine, Pentoxifylline and Phenytoin. Notably, over 40% of the predicted candidates in the clusters of commonly prescribed clinical drugs Donepezil and Galantamine had been tested in clinical trials, validating the model's predictive accuracy and practical relevance.

CONCLUSIONS: HEDDI-NET represents a significant advancement by allowing direct application to new diseases and drugs without the need for retraining, a limitation of most GCN-based methods. Furthermore, HEDDI-Net provides detailed affinity patterns with representative proteins for predicted candidate drugs, facilitating an understanding of their physiological effects. This capability also supports the design and testing of alternative drugs that are similar to existing medications, enhancing the reliability and interpretability of potential repurposed drugs. The case study on Alzheimer's disease further underscores HEDDI-Net's ability to predict promising drugs and its applicability in drug repurposing.

PMID:39891114 | DOI:10.1186/s12967-024-05938-6

Proteomics. 2025 Jan 31:e202400109. doi: 10.1002/pmic.202400109. Online ahead of print.

NO ABSTRACT

PMID:39888210 | DOI:10.1002/pmic.202400109

Front Cell Infect Microbiol. 2025 Jan 16;14:1496176. doi: 10.3389/fcimb.2024.1496176. eCollection 2024.

ABSTRACT

BACKGROUND: Drug repurposing has become a widely adopted strategy to minimise research time, costs, and associated risks. Combinations of protease inhibitors such as lopinavir and darunavir with ritonavir have been repurposed as treatments for COVID-19. Although lopinavir-ritonavir (LPV/r) and darunavir-ritonavir (DRV/r) have shown in vitro efficacy against COVID-19, the results in human studies have been inconsistent. Therefore, our objective was to compare the efficacy of LPV/r and DRV/r in COVID-19 patients admitted to a tertiary centre in Romania.

RESEARCH DESIGN AND METHODS: A clinical dataset from 417 hospitalised patients was analysed. Patients were assigned to the LPV/r, DRV/r, or control (standard-of-care) group based on clinical decisions made by the attending infectious disease specialists, aligned with national treatment protocols. Kaplan-Meier and Cox proportional hazards regression analyses were conducted to compare in-hospital mortality and to identify factors associated with clinical improvement or fatal outcomes.

RESULTS: By day 10, more patients showed improvement with LPV/r and DRV/r (p=0.03 and 0.01, respectively), but only LPV/r was associated with improved survival compared to the control group (p=0.05). Factors associated with mortality included male gender (HR: 3.63, p=0.02), diabetes (HR: 2.49, p=0.03), oxygen saturation below 90% at admission (HR: 5.23, p<0.01), high blood glucose levels (HR: 3.68, p=0.01), age (HR: 1.04, p=0.02), and more than 25% lesion extension on chest CT scan (HR: 2.28, p=0.03).

CONCLUSIONS: LPV/r, but not DRV/r, showed a survival benefit in patients hospitalised with COVID-19, but these findings deserve further investigation in a randomised clinical trial.

PMID:39885967 | PMC:PMC11779713 | DOI:10.3389/fcimb.2024.1496176

J Law Med Ethics. 2024;52(4):940-949. doi: 10.1017/jme.2024.171. Epub 2025 Jan 31.

ABSTRACT

To help academic and non-profit investigators interested in drug repurposing navigate regulatory approval processes, we compared pathways for repurposed drugs to obtain approval at EMA, UK MHRA, and the US FDA. Though we found no pathways specifically for repurposed drugs, pathways to market are available in all repurposing scenarios.

PMID:39885757 | DOI:10.1017/jme.2024.171

J Transl Med. 2025 Jan 30;23(1):133. doi: 10.1186/s12967-024-06015-8.

ABSTRACT

BACKGROUND: Psoriasis is a common chronic, recurrent, immune-mediated disease involved in the skin or joints or both. However, deeper insight into the genetic susceptibility of psoriasis is still unclear.

METHODS: Here we performed the largest multi-ancestry meta-analysis of genome-wide association study including 28,869 psoriasis cases and 443,950 healthy controls.

RESULTS: We identified 74 genome-wide significant loci for psoriasis. Of 74 loci, 32 were novel psoriasis risk loci. Across 74 loci, 801 likely causal genes are indicated and 164 causal genes are prioritized. SNP-based heritability analyses demonstrated that common variants explain 15% of genetic risk for psoriasis. Gene-set analyses and the genetic correlation revealed that psoriasis-related genes have the positive correlations with autoimmune diseases such as ulcerative colitis, inflammatory bowel diseases, and Crohn's disease. Gene-drug interaction analysis suggested that psoriasis-associated genes overlapped with targets of current medications for psoriasis. Finally, we used the multi-ancestry meta-analysis to explore drug repurposing and the potential targets for psoriasis.

CONCLUSIONS: We identified 74 genome-wide significant loci for psoriasis. Based on 74 loci, we provided new biological insights to the etiology of psoriasis. Of clinical interest, we gave some hints for 76 potential targets and drug repurposing for psoriasis.

PMID:39885523 | DOI:10.1186/s12967-024-06015-8

Mol Cell Proteomics. 2025 Jan 28:100919. doi: 10.1016/j.mcpro.2025.100919. Online ahead of print.

ABSTRACT

Understanding dysregulated genes and pathways in cancer is critical for precision oncology. Integrating mass spectrometry-based proteomic data with transcriptomic data presents unique opportunities for systematic analyses of dysregulated genes and pathways in pan-cancer. Here, we compiled a comprehensive set of datasets, encompassing proteomic data from 2,404 samples and transcriptomic data from 7,752 samples across 13 cancer types. Comparisons between normal or adjacent normal tissues (ANTs) and tumor tissues identified several dysregulated pathways including mRNA splicing, interferon pathway, fatty acid metabolism, and complement coagulation cascade in pan-cancer. Additionally, pan-cancer up- and down-regulated genes (PCUGs and PCDGs) were also identified. Notably, RRM2 and ADH1B, two genes belong to PCUGs and PCDGs, respectively, were identified as robust pan-cancer diagnostic biomarkers. TNM stage-based comparisons revealed dysregulated genes and biological pathways involved in cancer progression, among which the dysregulation of complement coagulation cascade and epithelial-mesenchymal transition are frequent in multiple types of cancers. A group of pan-cancer continuously up- and down-regulated proteins in different tumor stages (PCCUPs and PCCDPs) were identified. We further constructed prognostic risk stratification models for corresponding cancer types based on dysregulated genes, which effectively predict the prognosis for patients with these cancers. Drug prediction based on PCUPs and PCDPs as well as PCCUPs and PCCDPs revealed that small molecule inhibitors targeting CDK, HDAC, MEK, JAK, PI3K, and others might be effective treatments for pan-cancer, thereby supporting drug repurposing. We also developed web tools for cancer diagnosis, pathologic stage assessment, and risk evaluation. Overall, this study highlights the power of combining proteomic and transcriptomic data to identify valuable diagnostic and prognostic markers as well as drug targets and treatments for cancer.

PMID:39884577 | DOI:10.1016/j.mcpro.2025.100919

Pharmacol Res. 2025 Jan 28:107633. doi: 10.1016/j.phrs.2025.107633. Online ahead of print.

ABSTRACT

There is an urgent need for mechanistically novel and more efficacious treatments for schizophrenia, especially those targeting negative and cognitive symptoms with a more favorable side-effect profile. Drug repurposing-the process of identifying new therapeutic uses for already approved compounds-offers a promising approach to overcoming the lengthy, costly, and high-risk process of traditional CNS drug discovery. This review aims to update our previous findings on the clinical drug repurposing pipeline in schizophrenia. We examined studies conducted between 2018 and 2024, identifying 61 trials evaluating 40 unique repurposed drug candidates. These encompassed a broad range of pharmacological mechanisms, including immunomodulation, cognitive enhancement, and hormonal, metabolic, and neurotransmitter modulation. A notable development is the combination of the muscarinic modulators xanomeline, a compound with antipsychotic properties, and trospium, included to mitigate peripheral side effects, now approved by the FDA as the first antipsychotic drug in decades with a fundamentally novel mechanism of action. Moving beyond the traditional dopaminergic paradigm of schizophrenia, such findings highlight opportunities to improve treatment-resistant symptoms and alleviate adverse effects. Overall, the evolving drug repurposing landscape illustrates a significant shift in the rationale for schizophrenia drug development, highlighting the potential of in silico strategies, biomarker-based patient stratification, and personalized treatments that align with underlying pathophysiological processes.

PMID:39884448 | DOI:10.1016/j.phrs.2025.107633

Comput Biol Med. 2025 Jan 29;187:109729. doi: 10.1016/j.compbiomed.2025.109729. Online ahead of print.

ABSTRACT

Precision medicine aims to provide personalized therapies by analyzing patient molecular profiles, often focusing on gene expression data. However, effectively linking these data to actionable drug discovery for clinical application remains challenging. In this paper, we introduce ExPDrug, a neural network (NN) model that integrates biological pathways from transcriptomic data with a biomedical knowledge graph to facilitate pathway-based drug repurposing. ExPDrug enhances disease phenotype prediction by capturing the complex relationships between genes and pathways. Using layer-wise relevance propagation (LRP), the model interprets the contribution of each pathway using relevance scores applied in a random walk-with-restart (RWR) algorithm to prioritize potential drug candidates in the biomedical network. ExPDrug outperforms existing methods in predicting phenotypes for the three diseases and identifying drug candidates, as supported by the literature. This model offers a transformative approach for advancing precision medicine by linking transcriptomic insights directly to clinical drug repurposing, thereby potentially improving treatment strategies for complex diseases.

PMID:39884058 | DOI:10.1016/j.compbiomed.2025.109729

Arterioscler Thromb Vasc Biol. 2025 Jan 30. doi: 10.1161/ATVBAHA.124.321527. Online ahead of print.

ABSTRACT

BACKGROUND: Marfan syndrome (MFS) is an inherited disorder caused by mutations in the FBN1 gene encoding fibrillin-1, a matrix component of extracellular microfibrils. The main cause of morbidity and mortality in MFS is thoracic aortic aneurysm and dissection, but the underlying mechanisms remain undetermined.

METHODS: To elucidate the role of endothelial XOR (xanthine oxidoreductase)-derived reactive oxygen species in aortic aneurysm progression, we inhibited in vivo function of XOR either by endothelial cell (EC)-specific disruption of the Xdh gene or by systemic administration of an XOR inhibitor febuxostat in MFS mice harboring the Fbn1 missense mutation p.(Cys1041Gly). We assessed the aberrant activation of mechanosensitive signaling in the ascending aorta of Fbn1C1041G/+ mice. Further analysis of human aortic ECs investigated the mechanisms by which mechanical stress upregulates XOR expression.

RESULTS: We found a significant increase in reactive oxygen species generation in the ascending aorta of patients with MFS and Fbn1C1041G/+ mice, which was associated with a significant increase in protein expression and enzymatic activity of XOR protein in aortic ECs. Genetic disruption of Xdh in ECs or treatment with febuxostat significantly suppressed aortic aneurysm progression and improved perivascular infiltration of macrophages. Mechanistically, mechanosensitive signaling involving FAK (focal adhesion kinase)-p38 MAPK (p38 mitogen-activated protein kinase) and Egr-1 (early growth response-1) was aberrantly activated in the ascending aorta of Fbn1C1041G/+ mice, and mechanical stress on human aortic ECs upregulated XOR expression through Egr-1 upregulation. Consistently, EC-specific knockout of XOR or systemic administration of febuxostat in Fbn1C1041G/+ mice suppressed reactive oxygen species generation, FAK-p38 MAPK activation, and Egr-1 upregulation.

CONCLUSIONS: Aberrant activation of mechanosensitive signaling in vascular ECs triggered endothelial XOR activation and reactive oxygen species generation, which contributes to the progression of aortic aneurysms in MFS. These findings highlight a drug repositioning approach using a uric acid-lowering drug febuxostat as a potential therapy for MFS.

PMID:39882602 | DOI:10.1161/ATVBAHA.124.321527

Rheumatology (Oxford). 2025 Jan 29:keaf028. doi: 10.1093/rheumatology/keaf028. Online ahead of print.

ABSTRACT

OBJECTIVES: COVID-19 and systemic sclerosis (SSc) share multiple similarities in their clinical manifestations, alterations in immune response, and therapeutic options. These resemblances have also been identified in other immune-mediated inflammatory diseases where a common genetic component has been found. Thus, we decided to evaluate for the first time this shared genetic architecture with SSc.

METHODS: For this study, we retrieved genomic data from two European-ancestry cohorts: 2,597 856 individuals from The COVID-19 Host Genetics Initiative consortium, and 26 679 individuals from the largest genomic scan in SSc. We performed a cross-trait meta-analyses including >9.3 million SNPs. Finally, we conducted functional annotation to prioritize potential causal genes and performed drug repurposing analysis.

RESULTS: Our results revealed a total of 19 non-HLA pleiotropic loci, including 2 novel associations for both conditions (BMP1 and PPARG), and 12 emerging as new shared loci. Functional annotation of these regions underscored their potential regulatory role and identified potential causal genes, many of which are implicated in fibrotic and inflammatory pathways. Remarkably, we observed an antagonistic pleiotropy model of the IFN signalling between COVID-19 and SSc, including the well-known TYK2 P1104A missense variant, showing a protective effect for SSc while being a risk factor for COVID-19, along with two additional novel pleiotropic associations (IRF8 and SENP7). Finally, our findings provide new therapeutic options that could potentially benefit both conditions.

CONCLUSION: Our study confirms the genetic resemblance between susceptibility to and severity of COVID-19 and SSc, revealing a novel common genetic contribution affecting fibrotic and immune pathways.

PMID:39878951 | DOI:10.1093/rheumatology/keaf028

J Med Microbiol. 2025 Jan;74(1). doi: 10.1099/jmm.0.001943.

ABSTRACT

Introduction. Increased virulence and drug resistance in species of Candida resulted in reduced disease control and further demand the development of potent antifungal drugs.Hypothesis. The repurposing of non-antifungal drugs and combination therapy has become an attractive alternative to counter the emerging drug resistance and toxicity of existing antifungal drugs against Candida albicans and non-albicans species.Aim. This study aimed to accelerate antifungal drug development process by drug repurposing approach.Methodology. In this study, the antifungal effects of the antiviral drug, 2-adamantylamine hydrochloride (2-AM), were explored against C. albicans and C. parapsilosis. Broth microdilution measured in vitro efficacy of 2-AM, whereas reactive oxygen species (ROS) accumulation and ergosterol quantification, cell cycle and phosphatidylserine externalization studies were detailed to investigate the antifungal mode of 2-AM action.Results. Results showed that 2-AM had fungicidal action against both the strains where, 2-AM further inhibited morphogenic transitions as well. Antibiofilm action of 2-AM on C. albicans was evidenced on urinary catheters. G2/M phase arrest and apoptosis indicated ROS induced antifungal effect of 2-AM on both strains.Conclusions. Results of in vitro studies offers insight into the antifungal activity of 2-AM and may serve as an effective antifungal repurposed candidate against C. albicans and C. parapsilosis.

PMID:39878161 | DOI:10.1099/jmm.0.001943

Comput Struct Biotechnol J. 2024 Dec 25;27:307-320. doi: 10.1016/j.csbj.2024.12.020. eCollection 2025.

ABSTRACT

According to global cancer statistics for the year 2022, based on updated estimates from the International Agency for Research on Cancer, there were approximately 20 million new cases of cancer in 2022 alongside 9.7 million related deaths. Lung, breast, colorectal, gastric, and liver cancers are the most common types of cancer. Despite advancements in anticancer drugs and optimised chemotherapy regimens that have improved cure rates for malignant tumours, the presence of tumour heterogeneity has resulted in substantial variations among patients in terms of disease progression, clinical response, sensitivity to therapy, and prognosis, posing significant challenges in attaining optimal therapeutic outcomes for each patient. Here, we collected five single-cell transcriptome datasets from patients with lung, breast, colorectal, gastric, and liver cancers and constructed multiple cancer blueprints of tumour cell heterogeneity. By integrating multiple bioinformatics analyses, we explored the biological differences underlying tumour cell heterogeneity at the single-cell level and identified tumour cell subcluster-specific biomarkers and potential therapeutic drugs for each subcluster. Interestingly, although tumour cell subpopulations exhibit dramatic differences within the same cancer type and between different cancers at both the genomic and transcriptomic levels, some demonstrate similar oncogenic pathway activities and phenotypes. Tumour cell subpopulations from the five cancers listed above were classified into three major groups corresponding to different treatment strategies. The findings of this study not only focus on the differences but also on the similarities among tumour cell subpopulations across different cancers, providing new insights for individualised therapy.

PMID:39877290 | PMC:PMC11773088 | DOI:10.1016/j.csbj.2024.12.020

Front Oncol. 2025 Jan 14;14:1514120. doi: 10.3389/fonc.2024.1514120. eCollection 2024.

ABSTRACT

Drug repositioning, the practice of identifying novel applications for existing drugs beyond their originally intended medical indications, stands as a transformative strategy revolutionizing pharmaceutical productivity. In contrast to conventional drug development approaches, this innovative method has proven to be exceptionally effective. This is particularly relevant for cancer therapy, where the demand for groundbreaking treatments continues to grow. This review focuses on drug repositioning for ovarian cancer treatment, showcasing a comprehensive exploration grounded in thorough in vitro experiments across diverse cancer cell lines, which are validated through preclinical in vivo models. These insights not only shed light on the efficacy of these drugs but also expand in potential synergies with other pharmaceutical agents, favoring the development of cost-effective treatments for cancer patients.

PMID:39876896 | PMC:PMC11772297 | DOI:10.3389/fonc.2024.1514120

NPJ Digit Med. 2025 Jan 28;8(1):67. doi: 10.1038/s41746-025-01464-x.

ABSTRACT

Accurately predicting binding affinities between drugs and targets is crucial for drug discovery but remains challenging due to the complexity of modeling interactions between small drug and large targets. This study proposes DMFF-DTA, a dual-modality neural network model integrates sequence and graph structure information from drugs and proteins for drug-target affinity prediction. The model introduces a binding site-focused graph construction approach to extract binding information, enabling more balanced and efficient modeling of drug-target interactions. Comprehensive experiments demonstrate DMFF-DTA outperforms state-of-the-art methods with significant improvements. The model exhibits excellent generalization capabilities on completely unseen drugs and targets, achieving an improvement of over 8% compared to existing methods. Model interpretability analysis validates the biological relevance of the model. A case study in pancreatic cancer drug repurposing demonstrates its practical utility. This work provides an interpretable, robust approach to integrate multi-view drug and protein features for advancing computational drug discovery.

PMID:39875637 | DOI:10.1038/s41746-025-01464-x

Arch Dermatol Res. 2025 Jan 28;317(1):311. doi: 10.1007/s00403-024-03785-5.

ABSTRACT

The area of wound healing presents a promising field of interest for clinicians as well as the scientific community. A major concern for physicians is the rising number of elderly people suffering from diabetes, leprosy, tuberculosis and the associated chronic wounds. While traditional therapies target basic wound care, innovative strategies that accelerate wound healing are needed. V-RAF murine sarcoma viral oncogene homolog B1 (BRAF) inhibitors are anti-cancer drugs used primarily for melanoma. They also exhibit paradoxical activity, a phenomenon characterized by unintended activation of the Mitogen-Activated Protein Kinase (MAPK) signalling pathway leading to skin hyperproliferation. Studies have demonstrated that BRAF inhibitors can be repurposed to accelerate the healing of acute and chronic wounds by exploiting their paradoxical activity. This review evaluates studies on BRAF inhibitors by employing a systematic search strategy using databases such as PubMed, Scopus, Google Scholar, and Web of Science. Articles were screened based on relevance to the paradoxical activity of BRAF inhibitors, their mechanisms, and applications in wound healing. Evidence from in vitro, in vivo, and clinical studies demonstrates that BRAF inhibitors can enhance processes such as epithelialization and angiogenesis, essential for wound repair. This review summarizes the reports on the paradoxical activity of BRAF inhibitors, the predicted mechanisms behind the paradoxical activity, and their potential use in wound healing.

PMID:39873776 | DOI:10.1007/s00403-024-03785-5

J Chem Inf Model. 2025 Jan 27. doi: 10.1021/acs.jcim.4c02019. Online ahead of print.

ABSTRACT

The drug discovery process can be significantly accelerated by using deep learning methods to suggest molecules with druglike features and, more importantly, that are good candidates to bind specific proteins of interest. We present a novel deep learning generative model, Prot2Drug, that learns to generate ligands binding specific targets leveraging (i) the information carried by a pretrained protein language model and (ii) the ability of transformers to capitalize the knowledge gathered from thousands of protein-ligand interactions. The embedding unveils the receipt to follow for designing molecules binding a given protein, and Prot2Drug translates such instructions by using the syntax of the molecular language generating novel compounds which are predicted to have favorable physicochemical properties and high affinity toward specific targets. Moreover, Prot2Drug reproduced numerous known interactions between compounds and proteins used for generating them and suggested novel protein targets for known compounds, indicating potential drug repurposing strategies. Remarkably, Prot2Drug facilitates the design of promising ligands even for protein targets with limited or no information about their ligands or 3D structure.

PMID:39871540 | DOI:10.1021/acs.jcim.4c02019

Blood. 2025 Jan 27:blood.2024027171. doi: 10.1182/blood.2024027171. Online ahead of print.

ABSTRACT

T-cell prolymphocytic leukemia (T-PLL) is an aggressive lymphoid malignancy with limited treatment options. To discover new treatment targets for T-PLL, we performed high-throughput drug sensitivity screening on 30 primary patient samples ex-vivo. After screening over 2'800 unique compounds, we found T-PLL to be more resistant to most drug classes, including chemotherapeutics, compared to other blood cancers. Furthermore, we discovered previously not reported vulnerabilities of T-PLL. T-PLL cells exhibited a particular sensitivity to drugs targeting autophagy (thapsigargin, bafilomycin A1), nuclear export (selinexor), and inhibitor of apoptosis proteins (IAPs) (birinapant), sensitivities that were also shared by other T-cell malignancies. Through bulk and single-cell RNA-Sequencing we found these compounds to activate the toll-like-receptor (TLR) (bafilomycin A1), p53 (selinexor), and TNF-ɑ/NFκB signaling pathways (birinapant) in T-PLL cells. Focussing on birinapant for its potential in drug repurposing, we uncovered that IAP inhibitor-induced cell death was primarily necroptotic and dependent on TNF-ɑ. Through spectral flow cytometry we confirmed the absence of cleaved caspase-3 in IAP inhibitor treated T-PLL cells and show that IAP inhibition reduces the proliferation of T-PLL cells stimulated ex-vivo, while showing only a limited effect on non-malignant T-cells. In summary, our study maps the drug sensitivity of T-PLL across a broad range of targets and identifies new therapeutic approaches for T-PLL by targeting IAPs, XPO1 and autophagy, highlighting potential candidates for drug repurposing and novel treatment strategies.

PMID:39869826 | DOI:10.1182/blood.2024027171

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

ABSTRACT

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

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

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

ABSTRACT

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

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

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

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

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