Front Immunol. 2024 Dec 23;15:1431112. doi: 10.3389/fimmu.2024.1431112. eCollection 2024.

ABSTRACT

In recent decades, immunometabolism in cancers has emerged as an interesting target for treatment development. Indeed, the tumor microenvironment (TME) unique characteristics such as hypoxia and limitation of nutrients availability lead to a switch in metabolic pathways in both tumor and TME cells in order to support their adaptation and grow. Glioblastoma (GBM), the most frequent and aggressive primary brain tumor in adults, has been extensively studied in multiple aspects regarding its immune population, but research focused on immunometabolism remains limited. Here, we provide an overview of immunometabolism adaptation of myeloid cells in cancers with a specific focus on GBM and other brain tumors, before describing current therapeutic strategies targeting metabolic pathways. The main myeloid cells composing the GBM TME include tumor-associated macrophages (TAMs), which comprise both peripheral macrophages and local microglia, as well as myeloid-derived suppressor cells. The metabolic pathways involved in myeloid cell remodeling encompass the tricarboxylic acid cycle (TCA cycle), the lipid, glucose and amino acid metabolism and hypoxia. Developing treatments that target these metabolic pathways in tumor growth and its TME is a promising and increasing field. It includes both drug-repurposing and the development of innovative metabolic therapies. We finally provide an overview of all clinical trials in neuro-oncology involving treatments modifying cell metabolism and provide the preclinical rationale for both drugs already evaluated within clinical trials and potential candidates for future trials.

PMID:39763643 | PMC:PMC11700814 | DOI:10.3389/fimmu.2024.1431112

Bioinform Biol Insights. 2025 Jan 3;19:11779322241301507. doi: 10.1177/11779322241301507. eCollection 2025.

ABSTRACT

While deep learning (DL) is used in patients' outcome predictions, the insufficiency of patient samples limits the accuracy. In this study, we investigated how transfer learning (TL) alleviates the small sample size problem. A 2-step TL framework was constructed for a difficult task: predicting the response of the drug temozolomide (TMZ) in glioblastoma (GBM) cell cultures. The GBM is aggressive, and most patients do not benefit from the only approved chemotherapeutic agent TMZ. O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is the only biomarker for TMZ responsiveness but has shown limited predictive power. The 2-step TL framework was built on 3 datasets: (1) the subset of the Genomics of Drug Sensitivity in Cancer (GDSC) dataset, including miscellaneous cell cultures treated by TMZ, cyclophosphamide, bortezomib, and oxaliplatin, as the source dataset; (2) the Human Glioblastoma Cell Culture (HGCC) dataset, for fine-tuning; and (3) a small target dataset GSE232173, for validation. The latter two included specifically TMZ-treated GBM cell cultures. The DL models were pretrained on the cell cultures treated by each of the 4 drugs from GDSC, respectively. Then, the DL models were refined on HGCC, where the best source drug was identified. Finally, the DL model was validated on GSE232173. Using 2-step TL with pretraining on oxaliplatin was not only superior to those without TL and with 1-step TL but also better than 3 benchmark methods, including MGMT. The oxaliplatin-based TL improved the performance probably by increasing the weights of cell cycle-related genes, which relates to the TMZ response processes. Our findings support the potential of oxaliplatin being an alternative therapy for patients with GBM and TL facilitating drug repurposing research. We recommend that following our methodology, using mixed cancers and a related drug as the source and then fine-tuning the model with the target cancer and the target drug will enhance drug response prediction.

PMID:39763506 | PMC:PMC11700395 | DOI:10.1177/11779322241301507

J Chem Inf Model. 2025 Jan 6. doi: 10.1021/acs.jcim.4c00835. Online ahead of print.

ABSTRACT

Morphological profiling has recently demonstrated remarkable potential for identifying the biological activities of small molecules. Alongside the fully supervised and self-supervised machine learning methods recently proposed for bioactivity prediction from Cell Painting image data, we introduce here a semisupervised contrastive (SemiSupCon) learning approach. This approach combines the strengths of using biological annotations in supervised contrastive learning and leveraging large unannotated image data sets with self-supervised contrastive learning. SemiSupCon enhances downstream prediction performance of classifying MeSH pharmacological classifications from PubChem, as well as mode of action and biological target annotations from the Drug Repurposing Hub across two publicly available Cell Painting data sets. Notably, our approach has effectively predicted the biological activities of several unannotated compounds, and these findings were validated through literature searches. This demonstrates that our approach can potentially expedite the exploration of biological activity based on Cell Painting image data with minimal human intervention.

PMID:39761993 | DOI:10.1021/acs.jcim.4c00835

Cancer Med. 2025 Jan;14(1):e70567. doi: 10.1002/cam4.70567.

ABSTRACT

PURPOSE: Upper tract urothelial carcinoma (UTUC) presents a higher incidence rate in Taiwan compared to Western societies. The aim of this study is to investigate the potential of metformin in improving survival outcomes for patients with UTUC in Taiwan.

MATERIAL AND METHODS: This retrospective study included 940 patients with UTUC and type 2 diabetes from the Taiwan UTUC Collaboration Group, spanning 21 hospitals from July 1988 to September 2023. Patients were divided into two groups: those treated with metformin (n = 215) and those without metformin treatment (n = 725). Parameters analyzed included age, BMI, renal function, tumor grade and location, and pathological staging. Oncological outcomes measured were overall survival (OS), cancer-specific survival (CSS), and bladder recurrence-free survival (BRFS). Statistical analysis involved the use of Student's t-test, Mann-Whitney test, Chi-squared test, Fisher's exact test, and Cox proportional hazard regression.

RESULTS: Significant differences were observed between the two groups in BMI, preoperative creatinine, eGFR, tumor location, tumor laterality, tumor size, and pathological grade and T stage. Patients treated with metformin exhibited a lower risk of CSS (HR = 0.619; p = 0.018) and improved OS (HR = 0.713; p = 0.024), although no significant association was found with BRFS (HR = 1.034; p = 0.791). The protective effect of metformin on OS was particularly significant in patients with advanced T stage, metastasis, and high-grade tumors.

CONCLUSION: The study suggests that metformin use in UTUC patients with diabetes is associated with improved OS and CSS but not BRFS. The underlying mechanisms warrant further investigation. Repurposing metformin, a well-established and safe drug, may develop new therapeutic strategies for UTUC.

PMID:39757744 | DOI:10.1002/cam4.70567

Yakugaku Zasshi. 2025;145(1):43-47. doi: 10.1248/yakushi.24-00174-1.

ABSTRACT

Pre-eclampsia, a type of hypertensive disorders of pregnancy (HDP), is characterized by hypertension and organ dysfunction that develops or worsens after 20 weeks of gestation. Although symptomatic management using antihypertensive medications has been adopted, definitive treatments other than pregnancy termination remain unavailable to halt disease progression. Research on heme oxygenase (HO)-1, a molecule with anti-inflammatory and antioxidative properties, has shown that a pharmacological increase in placental HO-1 expression and activity may ameliorate this condition; therefore, HO-1 is a promising therapeutic target for this disorder. Medications with properties that can be used during pregnancy are strong candidates for repurposing. In this article, I discuss the potential applications of proton pump inhibitors in the prevention or treatment of preeclampsia by presenting our foundational research and subsequent observational and interventional clinical studies.

PMID:39756924 | DOI:10.1248/yakushi.24-00174-1

Biol Pharm Bull. 2025;48(1):1-5. doi: 10.1248/bpb.b24-00509.

ABSTRACT

As unexpected adverse events and successful drug repositioning have shown, drug effects are complex and include aspects not recognized by developers. How can we understand these unrecognized drug effects? Drug effects can be numerized by encompassing biological responses to drugs. For instance, the transcriptome data of cultured cells and toxicopathological images of mice treated with a compound represent the effects of the compound in vitro and in vivo, respectively. As a next step, we focused on pattern recognition, a data science framework to extract essentially important low-dimensional latent variables from high-dimensional observed data such as latent variable models. Latent variables are low-dimensional, allowing us to visualize drug effects in an easily recognizable form, such as a radar chart. This bird's-eye view of drug effects enables us to compare them with existing knowledge, potentially articulating the effects of drugs as the known knowns and known unknowns. We believe that the three-step strategy of numerization, visualization, and articulation will allow us to understand drug effects comprehensively, and we are currently verifying this approach. In this review, we will introduce these candidate studies and hope to share our interest in "pattern recognition of biological responses," the pillar of our group.

PMID:39756864 | DOI:10.1248/bpb.b24-00509

J Infect. 2025 Jan 3:106391. doi: 10.1016/j.jinf.2024.106391. Online ahead of print.

ABSTRACT

Immune checkpoint inhibitors designed to reinvigorate immune responses suppressed by cancer cells have revolutionized cancer therapy. Similarities in immune dysregulation between cancer and infectious diseases has prompted investigations into the role of immune checkpoints in infectious diseases, including the therapeutic potential of immune checkpoint blockade and drug repurposing. While most research has centered around viral infections, data for bacterial infections are emerging. This systematic review reports on the in vivo effect of immune checkpoint blockade on bacterial burden and selected immune responses in preclinical studies of bacterial infection, aiming to assess if there could be a rationale for using immunotherapy for bacterial infections. Of the 42 analyzed studies, immune checkpoint blockade reduced the bacterial burden in 60% of studies, had no effect in 28% and increased the bacterial burden in 12%. Findings suggest that the effect of immune checkpoint blockade on bacterial burden is context-dependent and in part relates to the pathogen. Further preclinical research is required to understand how the therapeutic effect of immune checkpoint blockade is mediated in different bacterial infections, and if immune checkpoint blockade can be used as an adjuvant to conventional infection management strategies.

PMID:39756696 | DOI:10.1016/j.jinf.2024.106391

Drug Discov Today. 2025 Jan 3:104286. doi: 10.1016/j.drudis.2024.104286. Online ahead of print.

ABSTRACT

Repurposing off-patent drugs can be a potential source of low-cost treatments for patients with unmet medical needs. Here, we review the proposed new European Union (EU) pharmaceutical legislation in which two articles address drug repurposing. We find certain barriers hindering the adoption of these new incentives by academic and not-for-profit stakeholders, including lack of knowledge on regulatory aspects, pharmacovigilance, and restrictions in data protection. To further empower the intended stakeholders of the legislation, these initiatives can be strengthened by creating additional scientific, regulatory, and health technology assessment (HTA) support for not-for-profit repurposers, and by determining fair data protection periods and pricing policies. To support drug repurposing, Europe should work toward a comprehensive drug-repurposing strategy that fosters the repurposing of generic, shelved, and protected drugs.

PMID:39756647 | DOI:10.1016/j.drudis.2024.104286

Pharmacol Res. 2025 Jan 3:107580. doi: 10.1016/j.phrs.2025.107580. Online ahead of print.

ABSTRACT

A significant number of patients develop chronic pain after surgery, but prediction of those who are at risk is currently not possible. Thus, prognostic prediction models that include bio-psycho, social and physiological factors in line with the complex nature of chronic pain would be urgently required. Here, we performed a translational study in male volunteers before an experimental incision injury. We determined multi-modal factors ranging from pain characteristics, psychological questionnaires to blood proteomics. Outcome measures after incision were pain intensity ratings and the extent of the area of hyperalgesia to mechanical stimuli surrounding the incision as a proxy of central sensitization. A multi-step logistic regression analysis was performed to predict outcome measures based on feature combinations using data-driven cross-validation and prognostic model development. Phenotype-based stratification resulted in the identification of low and high responders for both outcome measures. Regression analysis revealed prognostic proteomic, specific psychophysical and psychological parameters. A combinatorial set of distinct parameters enabled us to predict outcome measures with increased accuracy compared to using single features. Remarkably, in high responders, protein network analysis suggested a protein signature characteristic for low-grade inflammation. Alongside, in silico drug repurposing highlighted potential treatment options employing antidiabetic and anti-inflammatory drugs. Taken together, we present here an integrated pipeline that harnesses bio-psycho-physiological data for prognostic prediction in a translational approach. This pipeline opens new avenues for clinical application with the goal tostratify patients and identify potential new targets as well as mechanistic correlates for postsurgical pain. GERMAN CLINICAL TRIALS REGISTRY: (DRKS-ID: DRKS00016641).

PMID:39756555 | DOI:10.1016/j.phrs.2025.107580

Adv Sci (Weinh). 2025 Jan 4:e2407812. doi: 10.1002/advs.202407812. Online ahead of print.

ABSTRACT

β-secretase (BACE1) is instrumental in amyloid-β (Aβ) production, with overexpression noted in Alzheimer's disease (AD) neuropathology. The interaction of Aβ with the receptor for advanced glycation endproducts (RAGE) facilitates cerebral uptake of Aβ and exacerbates its neurotoxicity and neuroinflammation, further augmenting BACE1 expression. Given the limitations of previous BACE1 inhibition efforts, the study explores reducing BACE1 expression to mitigate AD pathology. The research reveals that the anticancer agent 6-thioguanosine (6-TG) markedly diminishes BACE1 expression without eliciting cytotoxicity while enhancing microglial phagocytic activity, and ameliorate cognitive impairments with reducing Aβ accumulation in AD mice. Leveraging advanced deep learning-based tool for target identification, and corroborating with surface plasmon resonance assays, it is elucidated that 6-TG directly interacts with RAGE, modulating BACE1 expression through the JAK2-STAT1 pathway and elevating soluble RAGE (sRAGE) levels in the brain. The findings illuminate the therapeutic potential of 6-TG in ameliorating AD manifestations and advocate for small molecule strategies to increase brain sRAGE levels, offering a strategic alternative to the challenges posed by the complexity of AD.

PMID:39755927 | DOI:10.1002/advs.202407812

Ageing Res Rev. 2025 Jan 2:102651. doi: 10.1016/j.arr.2024.102651. Online ahead of print.

ABSTRACT

Parkinson's disease (PD) is one of the most incapacitating neurodegenerative diseases (NDDs). PD is the second most common NDD worldwide which affects approximately 1 to 2 percent of people over 65 years. It is an attractive pursuit for artificial intelligence (AI) to contribute to and evolve PD treatments through drug repositioning by repurposing existing drugs, shelved drugs, or even candidates that do not meet the criteria for clinical trials. A search was conducted in three databases Web of Science, Scopus, and PubMed. We reviewed the data related to the last years (1975-present) to identify those drugs currently being proposed for repositioning in PD. Moreover, we reviewed the present status of the computational approach, including AI/Machine Learning (AI/ML)-powered pharmaceutical discovery efforts and their implementation in PD treatment. It was found that the number of drug repositioning studies for PD has increased recently. Repositioning of drugs in PD is taking off, and scientific communities are increasingly interested in communicating its results and finding effective treatment alternatives for PD. A better chance of success in PD drug discovery has been made possible due to AI/ML algorithm advancements. In addition to the experimentation stage of drug discovery, it is also important to leverage AI in the planning stage of clinical trials to make them more effective. New AI-based models or solutions that increase the success rate of drug development are greatly needed.

PMID:39755176 | DOI:10.1016/j.arr.2024.102651

Biomed Pharmacother. 2025 Jan 3;183:117805. doi: 10.1016/j.biopha.2024.117805. Online ahead of print.

ABSTRACT

Non-alcoholic fatty liver disease (NAFLD) is a common hepatic manifestation of metabolic syndrome affecting 20-30 % of the adult population worldwide. This disease, which includes simple steatosis and non-alcoholic steatohepatitis, poses a significant risk for cardiovascular and metabolic diseases. Lifestyle modifications are crucial in the treatment of NAFLD; however, patient adherence remains challenging. As there is no specific treatment, drug repositioning is being researched as an alternative strategy. Statins, which are known for their cholesterol-lowering effects, are considered potential interventions for NAFLD. This review aimed to present the current understanding of the effects of statins on liver physiology in the context of NAFLD. The pathophysiology of NAFLD includes steatosis, inflammation, and fibrosis, which are exacerbated by dyslipidemia and insulin resistance. Statins, which inhibit 3-hydroxy-3-methylglutaryl-CoA reductase, have pleiotropic effects beyond cholesterol-lowering and affect pathways related to inflammation, fibrogenesis, oxidative stress, and microcirculation. Although clinical guidelines support the use of statins for dyslipidemia in patients with NAFLD, more studies are needed to demonstrate their efficacy in liver disease. This comprehensive review serves as a foundation for future studies on the therapeutic potential of statins in NAFLD.

PMID:39755024 | DOI:10.1016/j.biopha.2024.117805

Nat Med. 2025 Jan 3. doi: 10.1038/s41591-024-03355-2. Online ahead of print.

ABSTRACT

Obesity and type 2 diabetes are prevalent chronic diseases effectively managed by semaglutide. Here we studied the effects of semaglutide on the circulating proteome using baseline and end-of-treatment serum samples from two phase 3 trials in participants with overweight or obesity, with or without diabetes: STEP 1 (n = 1,311) and STEP 2 (n = 645). We identified evidence supporting broad effects of semaglutide, implicating processes related to body weight regulation, glycemic control, lipid metabolism and inflammatory pathways. Several proteins were regulated with semaglutide, after accounting for changes in body weight and HbA1c at end of trial, suggesting effects of semaglutide on the proteome beyond weight loss and glucose lowering. A comparison of semaglutide with real-world proteomic profiles revealed potential benefits on disease-specific proteomic signatures including the downregulation of specific proteins associated with cardiovascular disease risk, supporting its reported effects of lowering cardiovascular disease risk and potential drug repurposing opportunities. This study showcases the potential of proteomics data gathered from randomized trials for providing insights into disease mechanisms and drug repurposing opportunities. These data also highlight the unmet need for, and importance of, examining proteomic changes in response to weight loss pharmacotherapy in future trials.

PMID:39753963 | DOI:10.1038/s41591-024-03355-2

Nat Hum Behav. 2025 Jan 3. doi: 10.1038/s41562-024-02091-4. Online ahead of print.

ABSTRACT

Genome-wide association studies (GWASs) have reported multiple risk loci for schizophrenia (SCZ). However, the majority of the associations were from populations of European ancestry. Here we conducted a large-scale GWAS in Eastern Asian populations (29,519 cases and 44,392 controls) and identified ten Eastern Asian-specific risk loci, two of which have not been previously reported. A further cross-ancestry GWAS meta-analysis (96,806 cases and 492,818 controls) including populations from diverse ancestries identified 61 previously unreported risk loci. Systematic variant-level analysis, including fine mapping, functional genomics and expression quantitative trait loci, prioritized potential causal variants. Gene-level analyses, including transcriptome-wide association study, proteome-wide association study and Mendelian randomization, nominated the potential causal genes. By integrating evidence from layers of different analyses, we prioritized the most plausible causal genes for SCZ, such as ACE, CNNM2, SNAP91, ABCB9 and GATAD2A. Finally, drug repurposing showed that ACE, CA14, MAPK3 and MAPT are potential therapeutic targets for SCZ. Our study not only showed the power of cross-ancestry GWAS in deciphering the genetic aetiology of SCZ, but also uncovered new genetic risk loci, potential causal variants and genes and therapeutic targets for SCZ.

PMID:39753749 | DOI:10.1038/s41562-024-02091-4

PLoS One. 2025 Jan 3;20(1):e0316343. doi: 10.1371/journal.pone.0316343. eCollection 2025.

ABSTRACT

Epigenetic processes are the critical events in carcinogenesis. Histone modification plays a crucial role in gene expression regulation, where histone deacetylases (HDACs) are key players in epigenetic processes. Inhibiting HDACs has shown promise in modern cancer therapy. However, the non-selective nature and drug resistance of most HDAC inhibitors (HDACIs) limits their clinical use. This limitation prompts a search for isoform-selective and more effective inhibitors. Histone deacetylase 1 (HDAC1) is a member of the class I HDAC family and has emerged as a promising target in various diseases, including cancer and neurodegeneration. Drug repurposing has gained significant interest in identifying treatments for new targets, which involves finding new uses for existing drugs beyond their original medical indications. Here, we employed virtual screening of repurposed drugs from the DrugBank database to identify potential HDAC1 inhibitors. We conducted a series of analyses, including molecular docking, drug profiling, PASS evaluation, and interaction analysis. Molecular dynamics (MD) simulations and MM-PBSA analysis were also performed for 300 ns. Through these analyses, we pinpointed Alectinib, which exhibits a promising drug profile in PASS analysis and higher affinity and efficiency for HDAC1 than the reference inhibitor. MD simulations revealed that Alectinib stabilizes HDAC1 with minimal structural perturbations. The findings suggest that Alectinib holds promise as a therapeutic lead for HDAC1-associated carcinogenesis after required validation.

PMID:39752394 | DOI:10.1371/journal.pone.0316343

Alzheimers Dement. 2024 Dec;20 Suppl 1:e089356. doi: 10.1002/alz.089356.

ABSTRACT

BACKGROUND: The limited treatment options for Alzheimer's emphasizes the need to explore novel drug targets and bring new therapeutics to market. Drug repurposing is an efficient route to bring a safe and effective treatment to the clinic. Agomelatine (AGO) was identified by a high-throughput drug screening algorithm as having mechanistic potential to treat Alzheimer's. AGO is used as an atypical antidepressant and works as an MT1/MT2 receptor agonist and a 5HT2C serotonin receptor antagonist.

METHOD: The TgF344-AD rat model was used to test AGO's potential to reduce cognitive deficits and neuropathology. The TgF344-AD rat model expresses human mutant "Swedish" amyloid-precursor protein (APPsw) and a Δ exon 9 presenilin 1 (PS1ΔE9). As it presents with age-dependent progressive Alzheimer's pathology and cognitive decline it is an ideal model for investigating AGO's effect on the robust presentation of Alzheimer's. Treatment with AGO at ∼10 mg/kg body weight/day began at 5 months of age (pre-pathology) and continued until 11 months of age when cognitive testing (active place avoidance task) and tissue collection occurred. Immunohistochemistry was used to evaluate amyloid beta plaque burden. Bulk RNAsequencing was conducted to investigate AGO's effect on gene expression.

RESULT: AGO treated female TgF344-AD rats showed reduced cognitive deficits with an increased latency to first entrance in aPAT testing compared to non-treated transgenic littermates. There were no differences between the cognitive performance of AGO treated and untreated male TgF344-AD rats. Interestingly, this reduced cognitive deficit did not correlate with decreased amyloid beta pathology. RNA sequencing analysis showed that DDIT3 (CHOP) mRNA levels were downregulated in the AGO treated compared to untreated TgF344-AD females. DDIT3 is a pro-apoptotic transcription factor.

CONCLUSION: Agomelatine showed a female only reduction in cognitive deficits, which did not correlate with a decrease in amyloid beta plaque deposition. This finding paired with the decrease of DDIT3 gene expression suggests that Agomelatine has a neuroprotective mechanism that is independent of amyloid burden. Future studies will analyze neuronal loss via NeuN staining to determine if AGO prevents neuronal loss, thus supporting its ability to mitigate cognitive deficits in the TgF344-AD rat model.

PMID:39751610 | DOI:10.1002/alz.089356

Alzheimers Dement. 2024 Dec;20 Suppl 1:e089596. doi: 10.1002/alz.089596.

ABSTRACT

BACKGROUND: Microglia have been implicated as a key aspect of the pathology of Alzheimer's disease (AD). However, high microglial heterogeneities, including disease-associated microglia (DAM), tau microglia (tau-pathology related), and neuroinflammation-like microglia (NIM), hinder the development of microglia-targeted treatment.

METHOD: In this study, we integrated ∼0.7 million single-nuclei RNA (snRNA)-seq transcriptomes derived from AD patient frozen brain samples using a variational autoencoder. We used trajectory analysis to identify microglial subtypes across AD progression, including DAM, tau microglia, and NIM. We conducted transition network analysis to identify putative molecular drivers of microglial subtypes across varying severities of AD and disease progression under the human protein-protein interactome network. We prioritized candidate drugs by specifically targeting transition modules using drug-gene signature enrichment analysis and we further validated drugs using two independent real-world patient databases (MarketScan [172 million insured individuals] and INSIGHT Clinical Research Network [15 million patients]).

RESULT: We showed that tau microglia were significantly associated with synaptic processes. Compared to DAM, upregulated genes of NIM were significantly enriched with key immune pathways (e.g., toll-like receptor). We identified potential AD pathobiological regulators (e.g., SYK, CTSB, PRKCA, INPP5D, and ADAM10) in transition networks between DAM and NIM. Via network-based drug repurposing prediction by specifically targeting NIM subpopulations and real-world patient data-based validation, we identified that usage of ketorolac (anti-inflammatory medicine) is significantly associated with reduced AD incidence in both MarketScan (hazard ratio [HR] = 0.81, 95% confidence interval [CI] 0.69-0.91, p-value = 0.002 after adjusting > 400 covariates) and INSIGHT (HR = 0.83, 95% CI 0.77-0.92, p-value = 0.004 after adjusting 267 covariates) patient databases.

CONCLUSION: This study offers insights into pathobiology of AD-relevant microglial subtypes and identifies ketorolac as a potential anti-inflammatory treatment for AD.

PMID:39751502 | DOI:10.1002/alz.089596

Alzheimers Dement. 2024 Dec;20 Suppl 1:e086331. doi: 10.1002/alz.086331.

ABSTRACT

BACKGROUND: Despite recent breakthroughs, Alzheimer's disease (AD) remains untreatable. In addition, we are still lacking robust biomarkers for early diagnosis and promising novel targets for therapeutic intervention. To enable utilizing the entirety of molecular evidence in the discovery and prioritization of potential novel biomarkers and targets, we have developed the AD Atlas, a network-based multi-omics data integration platform. Through recent extensions, the AD Atlas provides a comprehensive database of high-quality multi-omics data that can be utilized for hypothesis-free ranking of molecular markers and disease modules, as well as prioritization of potential novel targets and drug repositioning candidates.

METHOD: We developed several graph-based analysis tools from proximity searches to applications of artificial intelligence that can be applied to the AD Atlas. For prioritization of potential targets and biomarkers, we derived several network-based metrics to score -omics entities for disease relevance by not only assessing evidence for a single marker but also for its functional neighborhood in the AD Atlas network. For disease module identification, we employed graph representation learning coupled with unsupervised clustering to extract functional modules as defined by the network structure. Finally, we propose an ensemble approach that enables weighted aggregation of drug repositioning predictions from both signature-based and network-based algorithms.

RESULT: We demonstrate that the AD Atlas enables complex computational analyses for target and biomarker discovery and prioritization as well as in silico drug repositioning in AD. Using the integrated scores for prioritizing single targets and biomarkers for AD, we observe significantly higher relevance scores for genes that have been nominated as promising targets by the AMP-AD consortium. We further find that extracted disease modules are enriched for specific AD-relevant biological domains and can be ranked by disease relevance using similar graph-based metrics. Finally, we demonstrate that drug repositioning candidates are significantly enriched for compounds that were or are being tested in clinical trials for AD.

CONCLUSION: High-quality, multi-omics networks, such as the AD Atlas, enable exploitation of large-scale heterogeneous data through computational applications for target, biomarker, disease module, and drug repositioning candidate discovery and prioritization.

PMID:39751427 | DOI:10.1002/alz.086331

ACS Infect Dis. 2025 Jan 3. doi: 10.1021/acsinfecdis.4c00657. Online ahead of print.

ABSTRACT

The emergence of azole resistance and tolerance in pathogenic fungi has emerged as a significant public health concern, emphasizing the urgency for innovative strategies to bolster the efficacy of azole-based treatments. Drug repurposing stands as a promising and practical avenue for advancing antifungal therapy, with the potential for swift clinical translation. This review offers a comprehensive overview of azole synergistic agents uncovered through drug repurposing strategies, alongside an in-depth exploration of the mechanisms by which these agents augment azole potency. Drawing from these mechanisms, we delineate strategies aimed at enhancing azole effectiveness, such as inhibiting efflux pumps to elevate azole concentrations within fungal cells, intensifying ergosterol synthesis inhibition, mitigating fungal cell resistance to azoles, and disrupting biological processes extending beyond ergosterol synthesis. This review is beneficial for the development of these potentiators, as it meticulously examines instances and provides nuanced discussions on the mechanisms underlying the progression of azole potentiators through drug repurposing strategies.

PMID:39749640 | DOI:10.1021/acsinfecdis.4c00657

Mol Cancer. 2025 Jan 2;24(1):2. doi: 10.1186/s12943-024-02182-w.

ABSTRACT

Metastasis remains a leading cause of cancer-related mortality, irrespective of the primary tumour origin. However, the core gene regulatory program governing distinct stages of metastasis across cancers remains poorly understood. We investigate this through single-cell transcriptome analysis encompassing over two hundred patients with metastatic and non-metastatic tumours across six cancer types. Our analysis revealed a prognostic core gene signature that provides insights into the intricate cellular dynamics and gene regulatory networks driving metastasis progression at the pan-cancer and single-cell level. Notably, the dissection of transcription factor networks active across different stages of metastasis, combined with functional perturbation, identified SP1 and KLF5 as key regulators, acting as drivers and suppressors of metastasis, respectively, at critical steps of this transition across multiple cancer types. Through in vivo and in vitro loss of function of SP1 in cancer cells, we revealed its role in driving cancer cell survival, invasive growth, and metastatic colonisation. Furthermore, tumour cells and the microenvironment increasingly engage in communication through WNT signalling as metastasis progresses, driven by SP1. Further validating these observations, a drug repurposing analysis identified distinct FDA-approved drugs with anti-metastasis properties, including inhibitors of WNT signalling across various cancers.

PMID:39748426 | DOI:10.1186/s12943-024-02182-w