Sci Rep. 2024 Oct 14;14(1):23949. doi: 10.1038/s41598-024-71076-0.

ABSTRACT

Breast cancer remains a leading cause of mortality among women worldwide, with drug resistance driven by transcription factors and mutations posing significant challenges. To address this, we present ResisenseNet, a predictive model for drug sensitivity and resistance. ResisenseNet integrates transcription factor expression, genomic markers, drugs, and molecular descriptors, employing a hybrid architecture of 1D-CNN + LSTM and DNN to effectively learn long-range and temporal patterns from amino acid sequences and transcription factor data. The model demonstrated exceptional predictive accuracy, achieving a validation accuracy of 0.9794 and a loss value of 0.042. Comprehensive validation included comparisons with state-of-the-art models and ablation studies, confirming the robustness of the developed architecture. ResisenseNet has been applied to repurpose existing anticancer drugs across 14 different cancers, with a focus on breast cancer. Among the malignancies studied, drugs targeting Low-grade Glioma (LGG) and Lung Adenocarcinoma (LUAD) showed increased sensitivity to breast cancer as per ResisenseNet's assessment. Further evaluation of the predicted sensitive drugs revealed that 14 had no prior history of anticancer activity against breast cancer. These drugs target key signaling pathways involved in breast cancer, presenting novel therapeutic opportunities. ResisenseNet addresses drug resistance by filtering ineffective compounds and enhancing chemotherapy for breast cancer. In vitro studies on sensitive drugs provide valuable insights into breast cancer prognosis, contributing to improved treatment strategies.

PMID:39397003 | DOI:10.1038/s41598-024-71076-0

Chem Biol Drug Des. 2024 Oct;104(4):e14639. doi: 10.1111/cbdd.14639.

ABSTRACT

The technological revolutions in computers and the advancement of high-throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost-friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit-to-lead optimization, analyzing drug-receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.

PMID:39396920 | DOI:10.1111/cbdd.14639

Mol Psychiatry. 2024 Oct 12. doi: 10.1038/s41380-024-02777-1. Online ahead of print.

ABSTRACT

Excessive alcohol consumption is a leading cause of preventable death worldwide. To improve understanding of neurobiological mechanisms associated with alcohol use disorder (AUD) in humans, we compared gene expression data from deceased individuals with and without AUD across two addiction-relevant brain regions: the nucleus accumbens (NAc) and dorsolateral prefrontal cortex (DLPFC). Bulk RNA-seq data from NAc and DLPFC (N ≥50 with AUD, ≥46 non-AUD) were analyzed for differential gene expression using modified negative binomial regression adjusting for technical and biological covariates. The region-level results were meta-analyzed with those from an independent dataset (NNAc = 28 AUD, 29 non-AUD; NPFC = 66 AUD, 77 non-AUD). We further tested for heritability enrichment of AUD-related phenotypes, gene co-expression networks, gene ontology enrichment, and drug repurposing. We identified 176 differentially expressed genes (DEGs; 12 in both regions, 78 in NAc only, 86 in DLPFC only) for AUD in our new dataset. After meta-analyzing with published data, we identified 476 AUD DEGs (25 in both regions, 29 in NAc only, 422 in PFC only). Of these DEGs, 17 were significant when looked up in GWAS of problematic alcohol use or drinks per week. Gene co-expression analysis showed both concordant and unique gene networks across brain regions. We also identified 29 and 436 drug compounds that target DEGs from our meta-analysis in NAc and PFC, respectively. This study identified robust AUD-associated DEGs, contributing novel neurobiological insights into AUD and highlighting genes targeted by known drug compounds, generating opportunity for drug repurposing to treat AUD.

PMID:39394458 | DOI:10.1038/s41380-024-02777-1

ACS Chem Neurosci. 2024 Oct 11. doi: 10.1021/acschemneuro.4c00339. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. The pathology of AD is marked by the accumulation of amyloid beta plaques and tau protein tangles in the brain, along with neuroinflammation and synaptic dysfunction. Genetic factors, such as mutations in APP, PSEN1, and PSEN2 genes, as well as the APOE ε4 allele, contribute to increased risk of acquiring AD. Currently available treatments provide symptomatic relief but do not halt disease progression. Research efforts are focused on developing disease-modifying therapies that target the underlying pathological mechanisms of AD. Advances in identification and validation of reliable biomarkers for AD hold great promise for enhancing early diagnosis, monitoring disease progression, and assessing treatment response in clinical practice in effort to alleviate the burden of this devastating disease. In this paper, we analyze data from the CAS Content Collection to summarize the research progress in Alzheimer's disease. We examine the publication landscape in effort to provide insights into current knowledge advances and developments. We also review the most discussed and emerging concepts and assess the strategies to combat the disease. We explore the genetic risk factors, pharmacological targets, and comorbid diseases. Finally, we inspect clinical applications of products against AD with their development pipelines and efforts for drug repurposing. The objective of this review is to provide a broad overview of the evolving landscape of current knowledge regarding AD, to outline challenges, and to evaluate growth opportunities to further efforts in combating the disease.

PMID:39392435 | DOI:10.1021/acschemneuro.4c00339

Bioinformatics. 2024 Oct 11:btae598. doi: 10.1093/bioinformatics/btae598. Online ahead of print.

ABSTRACT

MOTIVATION: Target discovery is a crucial step in drug development, as it directly affects the success rate of clinical trials. Knowledge graphs (KGs) offer unique advantages in processing complex biological data and inferring new relationships. Existing biomedical KGs primarily focus on tasks such as drug repositioning and drug-target interactions, leaving a gap in the construction of KGs tailored for target discovery.

RESULTS: We established a comprehensive biomedical KG focusing on target discovery, termed TarKG, by integrating seven existing biomedical KGs, nine public databases, and traditional Chinese medicine knowledge databases. TarKG consists of 1,143,313 entities and 32,806,467 relations across 15 entity categories and 171 relation types, all centered around three core entity types: Disease, Gene, Compound. TarKG provides specialized knowledges for the core entities including chemical structures, protein sequences or text descriptions. By using different KG embedding algorithms, we assessed the knowledge completion capabilities of TarKG, particularly for disease-target link prediction. In case studies, we further examined TarKG's ability to predict potential protein targets for Alzheimer's disease (AD) and to identify diseases potentially associated with the metallo-deubiquitinase CSN5, using literature analysis for validation. Furthermore, we provided a user-friendly web server (https://tarkg.ddtmlab.org) that enables users to perform knowledge retrieval and relation inference using TarKG.

AVAILABILITY: TarKG is accessible at https://tarkg.ddtmlab.org.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:39392404 | DOI:10.1093/bioinformatics/btae598

Front Cell Infect Microbiol. 2024 Sep 26;14:1403589. doi: 10.3389/fcimb.2024.1403589. eCollection 2024.

ABSTRACT

Drug repurposing is a promising approach towards the discovery of novel treatments against Neglected Tropical Diseases, such as Leishmaniases, presenting the advantage of reducing both costs and duration of the drug discovery process. In previous work, our group developed a Machine Learning pipeline for the repurposing of FDA-approved drugs against Leishmania parasites. The present study is focused on an in vitro validation of this approach by assessing the antileishmanial effects of 10 predicted drug candidates. First, we evaluated the drugs' activity against promastigotes from two strains of L. infantum and one of L. major, which caused distinct clinical manifestations, using an MTT assay. The standard anti-Leishmania drug Amphotericin B was used as a positive control. Five molecules demonstrated anti-Leishmania effects, out of which Acebutolol, Prilocaine and Phenylephrine are described herein for the first time. When tested on promastigote growth, Acebutolol displayed IC50 values ranging from 69.28 to 145.53 µg/mL. Prilocaine exhibited IC50 values between 33.10 and 45.81 µg/mL. Phenylephrine, on the other hand, presented IC50 values >200 µg/mL. The two remaining drugs, Dibucaine and Domperidone, exhibited significantly low IC50 values varying between 0.58 and 1.05 µg/mL, and 6.30 and 8.17 µg/mL, respectively. Both compounds were previously described as anti-Leishmania agents in vivo. All five compounds demonstrated no notable cytotoxic effects on THP-1-derived macrophages at the IC50 concentrations, allowing for their testing on the intracellular form of L. major and L. infantum parasites. Interestingly, all compounds exhibited antileishmanial activity on amastigotes with enhanced IC50 values compared to the corresponding promastigotes. Noticeably, Dibucaine and Domperidone displayed IC50 values of at most 1.99 µg/mL. Acebutolol, Prilocaine and Phenylephrine showed IC50 values ranging from 13.84 to 66.81 µg/mL. Our previously published Computer-Aided repositioning pipelines of FDA-approved drugs as antileishmanial agents identified Dibucaine and Domperidone as candidates in support of previous in vivo studies. This study consolidates such findings through the in vitro validation against 2 Leishmania species, highly prevalent in Africa and Middle East, and reveals Acebutolol, Prilocaine, and Phenylephrine as novel anti-Leishmania effectors, confirming the relevance of our approach and calling for further investigations.

PMID:39391884 | PMC:PMC11464777 | DOI:10.3389/fcimb.2024.1403589

JID Innov. 2024 Jun 26;4(6):100296. doi: 10.1016/j.xjidi.2024.100296. eCollection 2024 Nov.

ABSTRACT

Drug repurposing is an attractive strategy for therapy development, particularly in rare diseases where traditional drug development approaches may be challenging owing to high cost and small numbers of patients. In this study, we used a drug identification and repurposing pipeline to identify candidate targetable drivers of disease and corresponding therapies through application of causal reasoning using a combination of open-access resources and transcriptomics data. We optimized our approach on psoriasis as a disease model, demonstrating the ability to identify known and, to date, unrecognized molecular drivers of psoriasis and link them to current and emerging therapies. Application of our approach to a cohort of tissue samples of necrobiosis lipoidica (an unrelated; rare; and, to date, molecularly poorly characterized cutaneous inflammatory disorder) identified a unique set of upstream regulators, particularly highlighting the role of IFNG and the Jak-signal transducer and activator of transcription pathway as a likely driver of disease pathogenesis and linked it to Jak inhibitors as potential therapy. Analysis of an independent cohort of necrobiosis lipoidica samples validated these findings, with the overall agreement of drug-matched upstream regulators above 96%. These data highlight the utility of our approach in rare diseases and offer an opportunity for drug discovery in other rare diseases in dermatology and beyond.

PMID:39391813 | PMC:PMC11465178 | DOI:10.1016/j.xjidi.2024.100296

Curr Med Chem. 2024 Oct 10. doi: 10.2174/0109298673334173241003060139. Online ahead of print.

ABSTRACT

Drugs are commonly utilized to diagnose, cure, or prevent the occurrence of diseases, as well as to restore, alter, or change organic functions. Drug discovery is a time-consuming, costly, difficult, and inefficient process that yields very few medicinal breakthroughs. Drug research and design involves the capturing of structural information for biological targets and small molecules as well as various in silico methods, such as molecular docking and molecular dynamic simulation. This article proposes the idea of expediting computational drug development through a collaboration of scientists and universities, similar to the Human Genome Project using machine learning (ML) strategies. We envision an automated system where readily available or novel small molecules (chemical or plant-derived), as well as their biological targets, are uploaded to an online database, which is constantly updated. For this system to function, machine learning strategies have to be implemented, and high-quality datasets and high quality assurance of the ML models will be required. ML can be applied to all computational drug discovery fields, including hit discovery, target validation, lead optimization, drug repurposing, and data mining of small compounds and biomolecule structures. Researchers from various disciplines, such as bioengineers, bioinformaticians, geneticists, chemists, computer and software engineers, and pharmacists, are expected to collaborate to establish a solid workflow and certain parameters as well as constraints for a successful outcome. This automated system may help speed up the drug discovery process while also lowering the number of unsuccessful drug candidates. Additionally, this system will decrease the workload, especially in computational studies, and expedite the process of drug design. As a result, a drug may be manufactured in a relatively short time.

PMID:39390837 | DOI:10.2174/0109298673334173241003060139

Biofouling. 2024 Oct 10:1-15. doi: 10.1080/08927014.2024.2413652. Online ahead of print.

ABSTRACT

Simvastatin had minimum inhibitory concentrations of 32 to 128 µg/mL against Klebsiella pneumoniae isolates and hindered the biofilm-formation ability of 58.54% of the isolates. It considerably diminished the bacterial cell counts in the biofilms as revealed by scanning electron microscope. Also, qRT-PCR revealed a downregulation of the biofilm genes (bcsA, wza, and luxS) by simvastatin in 48.78% of the isolates. Moreover, simvastatin has significantly improved the survival of mice and decreased the burden of bacteria in the infected lungs. Also, the histological architecture was substantially improved in the simvastatin-treated group, as the alveolar sacs and bronchioles appeared normal with minimal collagen fiber deposition. The immunohistochemical studies exposed that the TNF-α, NF-kβ, and COX-2 immunostaining considerably declined in the simvastatin-treated group. Furthermore, ELISA exposed that both IL-1β and IL-6 were considerably diminished in the lungs of the simvastatin-treated group.

PMID:39390775 | DOI:10.1080/08927014.2024.2413652

Antimicrob Agents Chemother. 2024 Oct 10:e0075624. doi: 10.1128/aac.00756-24. Online ahead of print.

ABSTRACT

Visceral leishmaniasis, caused by Leishmania infantum in New World countries, is the most serious and potentially fatal form of leishmaniasis, if left untreated. There are currently no effective prophylactic measures, and therapeutic options are limited. Therefore, we investigated whether the aromatase inhibitor letrozole (LET), which is already used to treat breast cancer, has an antileishmanial activity and/or immunomodulatory potential and therefore may be used to treat L. infantum infection. LET was active against L. infantum promastigote and amastigote life cycle stages in an in vitro infection model using human THP-1 cell-derived macrophages. In human peripheral blood leukocytes ex vivo, LET reduced the internalized forms of L. infantum by classical monocytes and activated neutrophils. Concomitantly, LET stimulated the production of IL-12/TNF-α and decreased the production of IL-10/TGF-β by peripheral blood phagocytes, while in T and B cells, it promoted the production of TNF-α/IFN-γ and decreased that of IL-10. In a murine infection model, LET significantly reduced the parasite load in the liver after just 5 days and in the spleen after 15 days. During in vivo treatment with LET, the production of TNF-α/IFN-γ also increased. In addition, the proportion of developing granulomas decreased and that of mature granulomas increased in the liver, while there was no significant change in organ architecture in the spleen. Based on these data, repositioning of LET may be promising for the treatment of visceral leishmaniasis in humans.

PMID:39387580 | DOI:10.1128/aac.00756-24

Future Cardiol. 2024 Oct 10:1-13. doi: 10.1080/14796678.2024.2411167. Online ahead of print.

ABSTRACT

This article discusses the rationale and design of the study "Methotrexate, blood pressure, and arterial function in rheumatoid arthritis". The recognition that immune activation and excess inflammation favor atherosclerosis has stimulated a significant body of research not only to identify new drugs targeting these pathways but also to repurpose (reposition) existing immunomodulatory medications as atheroprotective agents. Observational studies in patients with rheumatoid arthritis have reported that treatment with methotrexate, a traditional disease-modifying antirheumatic drug, is associated with a significantly lower risk of cardiovascular morbidity and mortality when compared with other disease-modifying antirheumatic drugs. One potential mechanism accounting for the reduced cardiovascular risk associated with methotrexate is the lowering effect on arterial blood pressure. However, such effect has only been observed in cross-sectional and observational studies. Given the established role of hypertension as a leading cardiovascular risk factor, these observations justify an intervention comparison study, the focus of this article, investigating the temporal effects of methotrexate on blood pressure and various surrogate markers of atherosclerosis in patients with rheumatoid arthritis. The results of this study might lead to the repurposing of methotrexate for cardiovascular prevention in patients with and without autoimmune disorders.Clinical Trial Registration: NCT03254589 (ClinicalTrials.gov).

PMID:39387403 | DOI:10.1080/14796678.2024.2411167

JACC Clin Electrophysiol. 2024 Sep 13:S2405-500X(24)00751-5. doi: 10.1016/j.jacep.2024.08.009. Online ahead of print.

ABSTRACT

BACKGROUND: Effective and safe pharmacological approaches for atrial defibrillation offer several potential advantages over techniques like ablation. Pharmacological therapy is noninvasive, involving no risk associated with the procedure or resulting complications. Moreover, acute drug intervention with existing drugs is likely to be low cost and broadly accessible, thereby addressing a central tenet of health equity.

OBJECTIVES: This study aims to investigate ibutilide-mediated action potential prolongation to promote use-dependent effects of flecainide on Na+ channels by reducing the diastolic interval and, consequently, drug unbinding to reduce action potential excitability in atrial tissue and terminate re-entrant arrhythmia.

METHODS: Here we utilize a modeling and simulation approach to predict the specific combinations of sodium- and potassium-channel blocking drugs to chemically terminate atrial re-entry.

RESULTS: Computational modeling and simulation show that acute application of flecainide and ibutilide is a promising example of drug repurposing that may constitute a promising combination for chemical atrial defibrillation.

CONCLUSIONS: We predict the drug concentrations that promote efficacy of flecainide and ibutilide used in combination for atrial chemical defibrillation. We also predict the potential safety pharmacology impact of this drug combination on ventricular electrophysiology.

PMID:39387743 | DOI:10.1016/j.jacep.2024.08.009

Biomed Pharmacother. 2024 Oct 9;180:117542. doi: 10.1016/j.biopha.2024.117542. Online ahead of print.

ABSTRACT

A significant hurdle in cancer treatment arises from multidrug resistance (MDR), often due to overexpression of ATP-binding cassette (ABC) transporters like ABCB1 and/or ABCG2 in cancer cells. These transporters actively diminish the efficacy of cytotoxic drugs by facilitating ATP hydrolysis-dependent drug efflux and reducing intracellular drug accumulation in cancer cells. Addressing multidrug-resistant cancers poses a significant challenge due to the lack of approved treatments, prompting the exploration of alternative avenues like drug repurposing (also referred to as drug repositioning) of molecularly targeted agents to reverse MDR-mediated by ABCB1 and/or ABCG2 in multidrug-resistant cancer cells. Epertinib, a potent inhibitor of EGFR and HER2 currently in clinical trials for solid tumors, was investigated for its potential to resensitize ABCB1- and ABCG2-overexpressing multidrug-resistant cancer cells to chemotherapeutic agents. Our findings reveal that at sub-toxic, submicromolar concentrations, epertinib restores the sensitivity of multidrug-resistant cancer cells to cytotoxic drugs in a concentration-dependent manner. The results demonstrate that epertinib enhances drug-induced apoptosis in these cancer cells by impeding the drug-efflux function of ABCB1 and ABCG2 without altering their expression. ATPase activity and molecular docking were employed to reveal potential interaction sites between epertinib and the drug-binding pockets of ABCB1 and ABCG2. In summary, our study demonstrates an additional pharmacological capability of epertinib against the activity of ABCB1 and ABCG2. These findings suggest that incorporating epertinib into combination therapy could be advantageous for a specific patient subset with tumors exhibiting high levels of ABCB1 or ABCG2, warranting further exploration.

PMID:39388999 | DOI:10.1016/j.biopha.2024.117542

Cell Rep. 2024 Oct 9;43(10):114818. doi: 10.1016/j.celrep.2024.114818. Online ahead of print.

ABSTRACT

Selective serotonin reuptake inhibitors (SSRIs) have shown promise in cancer therapy, particularly for hepatocellular carcinoma (HCC), but their molecular targets and mechanisms remain unclear. Here, we show that SSRIs exhibit significant anti-HCC effects independent of their classical target, the serotonin reuptake transporter (SERT). Using global inverse gene expression profiling, drug affinity responsive target stability assays, and in silico molecular docking, we demonstrate that citalopram targets glucose transporter 1 (GLUT1), resulting in reduced glycolytic flux. A mutant GLUT1 variant at the citalopram binding site (E380) diminishes the drug's inhibitory effects on the Warburg effect and tumor growth. In preclinical models, citalopram dampens the growth of GLUT1high liver tumors and displays a synergistic effect with anti-PD-1 therapy. Retrospective analysis reveals that SSRI use correlates with a lower risk of metastasis among patients with HCC. Our study describes a role for SSRIs in cancer metabolism, establishing a rationale for their repurposing as potential anti-cancer drugs for HCC.

PMID:39388353 | DOI:10.1016/j.celrep.2024.114818

RSC Med Chem. 2024 Sep 19. doi: 10.1039/d4md00449c. Online ahead of print.

ABSTRACT

Globally, the emergence of anti-microbial resistance in pathogens has become a serious threat to human health and well-being. Infections caused by drug-resistant microorganisms in hospitals are associated with increased morbidity, mortality, and healthcare costs. Acinetobacter baumannii is a Gram-negative bacterium belonging to the ESKAPE group and is widely associated with nosocomial infections. It persists in hospitals and survives antibiotic treatment, prompting acute infections such as urinary tract infections, pneumonia, bacteremia, meningitis, and wound-related infections. An innovation void in drug discovery and the lack of new therapeutic measures against A. baumannii continue to afflict infection control against the rising drug-resistant cases. The emergence of drug-resistant A. baumannii strains has also led to the incessant collapse of newly discovered antibiotics. Therefore exploring novel strategies is requisite to give impetus to A. baumannii drug discovery. The present review discusses the bacterial research community's efforts in the field of A. baumannii, focusing on the strategies adapted to identify potent scaffolds and novel targets to bolster and diversify the chemical space available for drug discovery. Firstly, we have discussed existing chemotherapy and various anti-microbial resistance mechanisms in A. baumannii bacterial strains. Next, we elaborate on multidisciplinary approaches and strategies that may be the way forward to combat the current menace caused by the drug-resistant A. baumannii strains. The review highlights the recent advances in drug discovery, including combinational therapy, high-throughput screening, drug repurposing, nanotechnology, and anti-microbial peptides, which are imperative tools to fight bacterial pathogens in the future.

PMID:39386059 | PMC:PMC11457259 | DOI:10.1039/d4md00449c

Front Pharmacol. 2024 Sep 25;15:1468920. doi: 10.3389/fphar.2024.1468920. eCollection 2024.

ABSTRACT

Glioblastoma (GBM) is the most commonly occurring and most aggressive primary brain tumor. Transcriptomics-based tumor subtype classification has established the mesenchymal lineage of GBM (MES-GBM) as cancers with particular aggressive behavior and high levels of therapy resistance. Previously it was show that Trihexyphenidyl (THP), a market approved M1 muscarinic receptor-targeting oral drug can suppress proliferation and survival of GBM stem cells from the classical transcriptomic subtype. In a series of in vitro experiments, this study confirms the therapeutic potential of THP, by effectively suppressing the growth, proliferation and survival of MES-GBM cells with limited effects on non-tumor cells. Transcriptomic profiling of treated cancer cells identified genes and associated metabolic signaling pathways as possible underlying molecular mechanisms responsible for THP-induced effects. In vivo trials of THP in immunocompromised mice carry orthotopic MES-GBMs showed moderate response to the drug. This study further highlights the potential of THP repurposing as an anti-cancer treatment regimen but mode of action and d optimal treatment procedures for in vivo regimens need to be investigated further.

PMID:39386028 | PMC:PMC11461351 | DOI:10.3389/fphar.2024.1468920

Sci Rep. 2024 Oct 10;14(1):23653. doi: 10.1038/s41598-024-71770-z.

ABSTRACT

The accumulation of monocyte-derived macrophages in the lung tissue during inflammation is important for the pathogenesis of fibrotic lung disease. Deficiencies in chemokine receptors CCR2 and CCR5 and their ligands, which mediate monocyte/macrophage migration, ameliorate bleomycin (BLM)-induced lung fibrosis. Disulfiram (DSF), which is used to treat alcoholism because of its aldehyde dehydrogenase (ALDH)-inhibiting effect, inhibits monocyte/macrophage migration by inhibiting FROUNT, an intracellular regulator of CCR2/CCR5 signalling. Here, we investigated the antifibrotic effect of oral DSF administration in a mouse model of BLM-induced lung fibrosis, focusing on macrophage response and fibrosis progression. The direct inhibitory activity of DSF on monocyte migration was measured using the Boyden chamber assay and compared with that of DSF-related inhibitors with different FROUNT-inhibition activities. Quantitative PCR was used to determine the expression of fibrosis-promoting genes in the lung tissue. DSF significantly suppressed macrophage infiltration into lung tissues and attenuated BLM-induced lung fibrosis. DSF and its metabolites, diethyldithiocarbamate (DDC) and copper diethyldithiocarbamate (Cu(DDC)2), inhibited monocyte migration toward the culture supernatant of primary mouse lung cells mainly comprising CCL2, whereas cyanamide, another ALDH inhibitor, did not. DSF, with higher inhibitory activity against FROUNT than DDC and Cu(DDC)2, inhibited monocyte migration most strongly. In BLM-induced fibrotic lung tissues, profibrotic factors were highly expressed but were reduced by DSF treatment. These results suggest DSF inhibits macrophage infiltration, which might be attributed to its inhibitory effect on FROUNT, and attenuates BLM-induced lung fibrosis. In addition, multiplex immunofluorescence imaging revealed reduced infiltration of S100A4+ macrophages into the lungs in DSF-treated mice and high expression of FROUNT in S100A4+ macrophages in idiopathic pulmonary fibrosis (IPF). These findings underscore the potential of macrophage-targeted therapy with DSF as a promising drug repositioning approach for treating fibrotic lung diseases, including IPF.

PMID:39384840 | DOI:10.1038/s41598-024-71770-z

Transl Psychiatry. 2024 Oct 9;14(1):428. doi: 10.1038/s41398-024-03139-9.

ABSTRACT

Structural and functional changes of the brain are assumed to contribute to excessive cocaine intake, craving, and relapse in cocaine use disorder (CUD). Epigenetic and transcriptional changes were hypothesized as a molecular basis for CUD-associated brain alterations. Here we performed a multi-omics study of CUD by integrating epigenome-wide methylomic (N = 42) and transcriptomic (N = 25) data from the same individuals using postmortem brain tissue of Brodmann Area 9 (BA9). Of the N = 1 057 differentially expressed genes (p < 0.05), one gene, ZFAND2A, was significantly upregulated in CUD at transcriptome-wide significance (q < 0.05). Differential alternative splicing (AS) analysis revealed N = 98 alternatively spliced transcripts enriched in axon and dendrite extension pathways. Strong convergent overlap in CUD-associated expression deregulation was found between our BA9 cohort and independent replication datasets. Epigenomic, transcriptomic, and AS changes in BA9 converged at two genes, ZBTB4 and INPP5E. In pathway analyses, synaptic signaling, neuron morphogenesis, and fatty acid metabolism emerged as the most prominently deregulated biological processes. Drug repositioning analysis revealed glucocorticoid receptor targeting drugs as most potent in reversing the CUD expression profile. Our study highlights the value of multi-omics approaches for an in-depth molecular characterization and provides insights into the relationship between CUD-associated epigenomic and transcriptomic signatures in the human prefrontal cortex.

PMID:39384764 | DOI:10.1038/s41398-024-03139-9

Obes Rev. 2024 Oct 9:e13848. doi: 10.1111/obr.13848. Online ahead of print.

ABSTRACT

Obesity is a major public health concern and burden on individuals and healthcare systems. Due to the challenges and limitations of lifestyle adjustments, it is advisable to consider pharmacological treatment for people affected by obesity. However, the side effects and limited efficacy of available drugs make the obesity drug market far from sufficient. Drug repurposing involves identifying new applications for existing drugs and offers some advantages over traditional drug development approaches including lower costs and shorter development timelines. This review aims to provide an overview of drug repurposing for anti-obesity medications, including the rationale for repurposing, the challenges and approaches, and the potential drugs that are being investigated for repurposing. Through advanced computational techniques, researchers can unlock the potential of repurposed drugs to tackle the global obesity epidemic. Further research, clinical trials, and collaborative efforts are essential to fully explore and leverage the potential of drug repurposing in the fight against obesity.

PMID:39384341 | DOI:10.1111/obr.13848

BMC Biol. 2024 Oct 8;22(1):226. doi: 10.1186/s12915-024-02028-3.

ABSTRACT

Drug repurposing is a promising approach in the field of drug discovery owing to its efficiency and cost-effectiveness. Most current drug repurposing models rely on specific datasets for training, which limits their predictive accuracy and scope. The number of both market-approved and experimental drugs is vast, forming an extensive molecular space. Due to limitations in parameter size and data volume, traditional drug-target interaction (DTI) prediction models struggle to generalize well within such a broad space. In contrast, large language models (LLMs), with their vast parameter sizes and extensive training data, demonstrate certain advantages in drug repurposing tasks. In our research, we introduce a novel drug repurposing framework, DrugReAlign, based on LLMs and multi-source prompt techniques, designed to fully exploit the potential of existing drugs efficiently. Leveraging LLMs, the DrugReAlign framework acquires general knowledge about targets and drugs from extensive human knowledge bases, overcoming the data availability limitations of traditional approaches. Furthermore, we collected target summaries and target-drug space interaction data from databases as multi-source prompts, substantially improving LLM performance in drug repurposing. We validated the efficiency and reliability of the proposed framework through molecular docking and DTI datasets. Significantly, our findings suggest a direct correlation between the accuracy of LLMs' target analysis and the quality of prediction outcomes. These findings signify that the proposed framework holds the promise of inaugurating a new paradigm in drug repurposing.

PMID:39379930 | DOI:10.1186/s12915-024-02028-3