Molecules. 2024 Dec 16;29(24):5932. doi: 10.3390/molecules29245932.

ABSTRACT

G-quadruplexes (G4s) are distinctive four-stranded nucleic acid structures formed by guanine-rich sequences, making them attractive targets for drug repurposing efforts. Modulating their stability and function holds promise for treating diseases like cancer. To identify potential drug candidates capable of interacting with these complex DNA formations, docking studies and molecular dynamics (MDs) simulations were conducted. Our analysis revealed kanamycin's ability to bind to various G4 structures, offering valuable insights into its potential as a modulator of G4 activity. Kanamycin exhibited favorable interactions with both parallel and hybrid G4 topologies in human structures, suggesting a broader mechanism of action for aminoglycosides. These findings may also shed light on aminoglycoside-associated toxicities, indicating that their effects might extend to binding non-ribosomal RNA structures. In summary, this research highlights kanamycin's potential as a promising tool for influencing G4 dynamics, paving the way for innovative therapeutic strategies targeting G4-related pathways.

PMID:39770021 | DOI:10.3390/molecules29245932

Int J Mol Sci. 2024 Dec 18;25(24):13573. doi: 10.3390/ijms252413573.

ABSTRACT

Finding an effective treatment for T-PLL patients remains a significant challenge. Alemtuzumab, currently the gold standard, is insufficient in managing the aggressiveness of the disease in the long term. Consequently, numerous efforts are underway to address this unmet clinical need. The rarity of the disease limits the ability to conduct robust clinical trials, making in silico, ex vivo, and in vivo drug screenings essential for designing new therapeutic strategies for T-PLL. We conducted a drug repurposing analysis based on T-PLL gene expression data and identified proteasome inhibitors (PIs) as a promising new class of compounds capable of reversing the T-PLL phenotype. Treatment of ex vivo T-PLL cells with Bortezomib and Carfilzomib, two PI compounds, supported this hypothesis by demonstrating increased apoptosis in leukemic cells. The current lack of a suitable in vitro model for the study of T-PLL prompted us to perform similar experiments in the SUP-T11 cell line, validating its potential by showing an increased apoptotic rate. Taken together, these findings open new avenues for investigating the molecular mechanisms underlying the efficacy of PI in T-PLL and expand the spectrum of potential therapeutic strategies for this highly aggressive disease.

PMID:39769335 | DOI:10.3390/ijms252413573

Biomolecules. 2024 Nov 27;14(12):1515. doi: 10.3390/biom14121515.

ABSTRACT

Neutrophil-mediated inflammation is a key feature of immune-mediated chronic skin disorders, but the mechanistic understanding of neutrophil involvement in these conditions remains incomplete. Dapsone, colchicine, and tetracyclines are established drugs within the dermatologist's therapeutic armamentarium that are credited with potent anti-neutrophilic effects. Anti-neutrophilic drugs have established themselves as versatile agents in the treatment of a wide range of dermatological conditions. Some of these agents are approved for the management of specific dermatologic conditions, but most of their current uses are off-label and only supported by isolated reports or case series. Their anti-inflammatory and immunomodulatory properties make them particularly valuable in managing auto-immune bullous diseases, neutrophilic dermatoses, eosinophilic dermatoses, interface dermatitis, and granulomatous diseases that are the focus of this review. By inhibiting inflammatory pathways, reducing cytokine production, and modulating immune responses, they contribute significantly to the treatment and management of these complex skin conditions. Their use continues to evolve as our understanding of these diseases deepens, and they remain a cornerstone of dermatological therapy.

PMID:39766222 | DOI:10.3390/biom14121515

Biomolecules. 2024 Nov 24;14(12):1497. doi: 10.3390/biom14121497.

ABSTRACT

Tuberculosis (TB) is one of the leading causes of death due to infectious disease. It is a treatable disease; however, conventional treatment requires a lengthy treatment regimen with severe side effects, resulting in poor compliance among TB patients. Intermittent drug use, the non-compliance of patients, and prescription errors, among other factors, have led to the emergence of multidrug-resistant TB, while the mismanagement of multidrug-resistant TB (MDR-TB) has eventually led to the development of extensively drug-resistant tuberculosis (XDR-TB). Thus, there is an urgent need for new drug development, but due to the enormous expenses and time required (up to 20 years) for new drug research and development, new therapeutic approaches to TB are required. Host-directed therapies (HDT) could be a most attractive strategy, as they target the host defense processes instead of the microbe and thereby may prevent the alarming rise of MDR- and XDR-TB. This paper reviews the progress in HDT for the treatment of TB using repurposed drugs which have been investigated in clinical trials (completed or ongoing) and plant-derived natural products that are in clinical or preclinical trial stages. Additionally, this review describes the existing challenges to the development and future research directions in the implementation of HDT.

PMID:39766204 | DOI:10.3390/biom14121497

Cancers (Basel). 2024 Dec 20;16(24):4253. doi: 10.3390/cancers16244253.

ABSTRACT

Despite significant progress in the field of clinical oncology in terms of diagnostic and treatment methods, the results of anticancer therapy are still not fully satisfactory, especially due to limited response and high toxicity. This has forced the need for further research to finding alternative ways to improve success rates in oncological treatment. A good solution to this problem in the context of rapidly obtaining an effective drug that works on multiple levels of cancer and is also safe is the global strategy of repurposing an existing drug. Research into other applications of an existing drug enables a precise assessment of its possible mechanisms of action and, consequently, the broadening of therapeutic indications. This strategy is also supported by the fact that most non-oncological drugs have pleiotropic effects, and most of the diseases for which they were originally intended are multifactorial, which in turn is a very desirable phenomenon due to the heterogeneous and multifaceted biology of cancer. In this review, we will mainly focus on the anticancer potential of H1 antihistamines, especially the new generation that were not originally intended for cancer therapy, to highlight the relevant signaling pathways and discuss the properties of these agents for their judicious use based on the characteristic features of cancer.

PMID:39766152 | DOI:10.3390/cancers16244253

Health Inf Sci Syst. 2025 Jan 5;13(1):14. doi: 10.1007/s13755-024-00326-2. eCollection 2025 Dec.

ABSTRACT

PURPOSE: Drug repositioning, a strategy that repurposes already-approved drugs for novel therapeutic applications, provides a faster and more cost-effective alternative to traditional drug discovery. Network-based models have been adopted by many computational methodologies, especially those that use graph neural networks to predict drug-disease associations. However, these techniques frequently overlook the quality of the input network, which is a critical factor for achieving accurate predictions.

METHODS: We present a novel network-based framework for drug repositioning, named DRAW+, which incorporates noise filtering and feature extraction using graph neural networks and attention mechanisms. The proposed model first constructs a heterogeneous network that integrates the drug-disease association network with the similarity networks of drugs and diseases, which are upgraded through reduced-rank singular value decomposition. Next, a subgraph surrounding the targeted drug-disease node pair is extracted, allowing the model to focus on local structures. Graph neural networks are then applied to extract structural representation, followed by attention walking to capture key features of the subgraph. Finally, a multi-layer perceptron classifies the subgraph as positive or negative, which indicates the presence of the link between the target node pair.

RESULTS: Experimental validation across three benchmark datasets showed that DRAW+ outperformed seven state-of-the-art methods, achieving the highest average AUROC and AUPRC, 0.963 and 0.564, respectively. Moreover, DRAW+ demonstrated its robustness by achieving the best performance across two additional datasets, further confirming its generalizability and effectiveness in diverse settings.

CONCLUSIONS: The proposed network-based computational approach, DRAW+, demonstrates exceptional accuracy and robustness, confirming its effectiveness in drug repositioning tasks.

PMID:39764174 | PMC:PMC11700073 | DOI:10.1007/s13755-024-00326-2

bioRxiv [Preprint]. 2024 Dec 16:2024.12.10.627863. doi: 10.1101/2024.12.10.627863.

ABSTRACT

Benchmarking is an important step in the improvement, assessment, and comparison of the performance of drug discovery platforms and technologies. We revised the existing benchmarking protocols in our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery platform to improve utility and performance. We optimized multiple parameters used in drug candidate prediction and assessment with these updated benchmarking protocols. CANDO ranked 7.4% of known drugs in the top 10 compounds for their respective diseases/indications based on drug-indication associations/mappings obtained from the Comparative Toxicogenomics Database (CTD) using these optimized parameters. This increased to 12.1% when drug-indication mappings were obtained from the Therapeutic Targets Database. Performance on an indication was weakly correlated (Spearman correlation coefficient > 0.3) with indication size (number of drugs associated with an indication) and moderately correlated (correlation coefficient > 0.5) with compound chemical similarity. There was also moderate correlation between our new and original benchmarking protocols when assessing performance per indication using each protocol. Benchmarking results were also dependent on the source of the drug-indication mapping used: a higher proportion of indication-associated drugs were recalled in the top 100 compounds when using the Therapeutic Targets Database (TTD), which only includes FDA-approved drug-indication associations (in contrast to the CTD, which includes associations drawn from the literature). We also created compbench, a publicly available head-to-head benchmarking protocol that allows consistent assessment and comparison of different drug discovery platforms. Using this protocol, we compared two pipelines for drug repurposing within CANDO; our primary pipeline outperformed another similarity-based pipeline still in development that clusters signatures based on their associated Gene Ontology terms. Our study sets a precedent for the complete, comprehensive, and comparable benchmarking of drug discovery platforms, resulting in more accurate drug candidate predictions.

PMID:39764006 | PMC:PMC11702551 | DOI:10.1101/2024.12.10.627863

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