Arch Microbiol. 2024 Aug 20;206(9):383. doi: 10.1007/s00203-024-04103-3.

ABSTRACT

Candida albicans has been listed in the critical priority group by the WHO in 2022 depending upon its contribution in invasive candidiasis and increased resistance to conventional drugs. Drug repurposing offers an efficient, rapid, and cost-effective solution to develop alternative therapeutics against pathogenic microbes. Alexidine dihydrochloride (AXD) and hexachlorophene (HCP) are FDA approved anti-cancer and anti-septic drugs, respectively. In this study, we have shown antifungal properties of AXD and HCP against the wild type (reference strain) and clinical isolates of C. albicans. The minimum inhibitory concentrations (MIC50) of AXD and HCP against C. albicans ranged between 0.34 and 0.69 µM and 19.66-24.58 µM, respectively. The biofilm inhibitory and eradication concentration of AXD was reported comparatively lower than that of HCP for the strains used in the study. Further investigations were performed to understand the antifungal mode of action of AXD and HCP by studying virulence features like cell surface hydrophobicity, adhesion, and yeast to hyphae transition, were also reduced upon exposure to both the drugs. Ergosterol content in cell membrane of the wild type strain was upregulated on exposure to AXD and HCP both. Biochemical analyses of the exposed biofilm indicated reduced contents of carbohydrate, protein, and e-DNA in the extracellular matrix of the biofilm when compared to the untreated control biofilm. AXD exposure downregulated activity of tissue invading enzyme, phospholipase in the reference strain. In wild type strain, ROS level, and activities of antioxidant enzymes were found elevated upon exposure to both drugs. FESEM analysis of the drug treated biofilms revealed degraded biofilm. This study has indicated mode of action of antifungal potential of alexidine dihydrochloride and hexachlorophene in C. albicans.

PMID:39162873 | DOI:10.1007/s00203-024-04103-3

Microbiol Spectr. 2024 Aug 20:e0124924. doi: 10.1128/spectrum.01249-24. Online ahead of print.

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus continues to cause severe disease and deaths in many parts of the world, despite massive vaccination efforts. Antiviral drugs to curb an ongoing infection remain a priority. The virus-encoded 3C-like main protease (MPro; nsp5) is seen as a promising target. Here, with a positive selection genetic system engineered in Saccharomyces cerevisiae using cleavage and release of MazF toxin as an indicator, we screened in a robotized setup small molecule libraries comprising ~2,500 compounds for MPro inhibitors. We detected eight compounds as effective against MPro expressed in yeast, five of which are characterized proteasome inhibitors. Molecular docking indicates that most of these bind covalently to the MPro catalytically active cysteine. Compounds were confirmed as MPro inhibitors in an in vitro enzymatic assay. Among those were three previously only predicted in silico; the boron-containing proteasome inhibitors bortezomib, delanzomib, and ixazomib. Importantly, we establish reaction conditions in vitro preserving the MPro-inhibitory activity of the boron-containing drugs. These differ from the standard conditions, which may explain why boron compounds have gone undetected in screens based on enzymatic in vitro assays. Our screening system is robust and can find inhibitors of a specific protease that are biostable, able to penetrate a cell membrane, and are not generally toxic. As a cellular assay, it can detect inhibitors that fail in a screen based on an in vitro enzymatic assay using standardized conditions, and now give support for boron compounds as MPro inhibitors. This method can also be adapted for other viral proteases.IMPORTANCEThe coronavirus disease 2019 (COVID-19) pandemic triggered the realization that we need flexible approaches to find treatments for emerging viral threats. We implemented a genetically engineered platform in yeast to detect inhibitors of the virus's main protease (MPro), a promising target to curb severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Screening molecule libraries, we identified candidate inhibitors and verified them in a biochemical assay. Moreover, the system detected boron-containing molecules as MPro inhibitors. Those were previously predicted computationally but never shown effective in a biochemical assay. Here, we demonstrate that they require a non-standard reaction buffer to function as MPro inhibitors. Hence, our cell-based method detects protease inhibitors missed by other approaches and provides support for the boron-containing molecules. We have thus demonstrated that our platform can screen large numbers of chemicals to find potential inhibitors of a viral protease. Importantly, the platform can be modified to detect protease targets from other emerging viruses.

PMID:39162260 | DOI:10.1128/spectrum.01249-24

J Neurochem. 2024 Aug 20. doi: 10.1111/jnc.16206. Online ahead of print.

ABSTRACT

Oligodendrocytes, the myelin-producing cells in the central nervous system (CNS), are crucial for rapid action potential conduction and neuronal communication. While extensively studied for their roles in neuronal support and axonal insulation, their involvement in pain modulation is an emerging research area. This review explores the interplay between oligodendrocytes, myelination, and pain, focusing on neuropathic pain following peripheral nerve injury, spinal cord injury (SCI), chemotherapy, and HIV infection. Studies indicate that a decrease in oligodendrocytes and increased cytokine production by oligodendroglia in response to injury can induce or exacerbate pain. An increase in endogenous oligodendrocyte precursor cells (OPCs) may be a compensatory response to repair damaged oligodendrocytes. Exogenous OPC transplantation shows promise in alleviating SCI-induced neuropathic pain and enhancing remyelination. Additionally, oligodendrocyte apoptosis in brain regions such as the medial prefrontal cortex is linked to opioid-induced hyperalgesia, highlighting their role in central pain mechanisms. Chemotherapeutic agents disrupt oligodendrocyte differentiation, leading to persistent pain, while HIV-associated neuropathy involves up-regulation of oligodendrocyte lineage cell markers. These findings underscore the multifaceted roles of oligodendrocytes in pain pathways, suggesting that targeting myelination processes could offer new therapeutic strategies for chronic pain management. Further research should elucidate the underlying molecular mechanisms to develop effective pain treatments.

PMID:39162089 | DOI:10.1111/jnc.16206

Endocrinology. 2024 Aug 20:bqae108. doi: 10.1210/endocr/bqae108. Online ahead of print.

NO ABSTRACT

PMID:39160063 | DOI:10.1210/endocr/bqae108

Microb Pathog. 2024 Aug 17:106863. doi: 10.1016/j.micpath.2024.106863. Online ahead of print.

ABSTRACT

Staphylococcus aureus is a bacterial pathogen that causes bloodstream infections, pneumonia, and skin abscesses and is the primary pathogen responsible for medical devices associated with biofilm infections, accounting for approximately 70% of cases. Therefore, the World Health Organization (WHO) has designated this microorganism as a top priority due to its role in causing over 20,000 bacteremia-related deaths in the US each year. The issue of pathogen resistance to antibiotics, mainly by a biofilm, further complicates these infections since biofilms render the bacterial colony impervious to antibiotics. However, many natural and synthetic substances also induce bacterial biofilm formation. Therefore, we investigated whether the most common active pharmaceutical ingredients (APIs) could induce biofilm formation in two clinical isolates of extended-spectrum beta-lactamase Staphylococcus aureus, one of them also methicillin-resistant (A2M) and two medical devices. We detected biofilm inducers, inhibitors, and destabilizers. Microbial strain, medical devices, API structure, and concentration influenced the modulatory effects of biofilm. In all devices tested, including microplates, FR18 duodenal probe, and respiratory probe, the clinic isolate methicillin-resistant S. aureus A2M exhibited lower susceptibility to biofilm formation than S. aureus A1. The anti-inflammatory acetaminophen, the hypocholesterolemic lovastatin, and the diuretic hydrochlorothiazide all induced biofilm. However, verapamil, an antihypertensive, and cetirizine, an antihistamine, inhibited biofilm on S. aureus A2M, while propranolol, another antihypertensive, inhibited biofilm on S. aureus A1. Additionally, diclofenac, an analgesic, and cetirizine destabilized the biofilm, resulting in more free bacteria and possibly making them more susceptible to external agents such as antibiotics. Nonetheless, further epidemiologic analyses and in vivo assays are needed to confirm these findings and to establish a correlation between drug use, the onset of bacterial infections in patients, and the use of medical devices. This work provides information about the probable clinical implications of drugs in patients using medical devices or undergoing surgical procedures. Inhibitory APIs could also be used as drug repurposing or templates to design new, more potent biofilm inhibitors.

PMID:39159772 | DOI:10.1016/j.micpath.2024.106863

Sci Rep. 2024 Aug 19;14(1):19133. doi: 10.1038/s41598-024-69302-w.

ABSTRACT

Type 2 diabetes (T2D) and Clear-cell renal cell carcinoma (ccRCC) are both complicated diseases which incidence rates gradually increasing. Population based studies show that severity of ccRCC might be associated with T2D. However, so far, no researcher yet investigated about the molecular mechanisms of their association. This study explored T2D and ccRCC causing shared key genes (sKGs) from multiple transcriptomics profiles to investigate their common pathogenetic processes and associated drug molecules. We identified 259 shared differentially expressed genes (sDEGs) that can separate both T2D and ccRCC patients from control samples. Local correlation analysis based on the expressions of sDEGs indicated significant association between T2D and ccRCC. Then ten sDEGs (CDC42, SCARB1, GOT2, CXCL8, FN1, IL1B, JUN, TLR2, TLR4, and VIM) were selected as the sKGs through the protein-protein interaction (PPI) network analysis. These sKGs were found significantly associated with different CpG sites of DNA methylation that might be the cause of ccRCC. The sKGs-set enrichment analysis with Gene Ontology (GO) terms and KEGG pathways revealed some crucial shared molecular functions, biological process, cellular components and KEGG pathways that might be associated with development of both T2D and ccRCC. The regulatory network analysis of sKGs identified six post-transcriptional regulators (hsa-mir-93-5p, hsa-mir-203a-3p, hsa-mir-204-5p, hsa-mir-335-5p, hsa-mir-26b-5p, and hsa-mir-1-3p) and five transcriptional regulators (YY1, FOXL1, FOXC1, NR2F1 and GATA2) of sKGs. Finally, sKGs-guided top-ranked three repurposable drug molecules (Digoxin, Imatinib, and Dovitinib) were recommended as the common treatment for both T2D and ccRCC by molecular docking and ADME/T analysis. Therefore, the results of this study may be useful for diagnosis and therapies of ccRCC patients who are also suffering from T2D.

PMID:39160196 | DOI:10.1038/s41598-024-69302-w

Chemistry. 2024 Aug 19:e202402647. doi: 10.1002/chem.202402647. Online ahead of print.

ABSTRACT

Metals have been used in medicine for centuries. However, it was not until much later that the effects of inorganic drugs could be rationalized from a mechanistic point of view. Today, thanks to the technologies available, this approach has been functionally developed and implemented. It has been found that there is probably no single biological target for the pharmacological effects of most inorganic drugs. Herein, we present an overview of some integrated and multi-technique approaches to elucidate the molecular interactions underlying the biological effects of metallodrugs. On this premise, selected examples are used to illustrate how the information obtained on metal-based drugs and their respective mechanisms can become relevant for applications in fields other than medicine. For example, some well-known metallodrugs, which have been shown to bind specific amino acid residues of proteins, can be used to solve problems related to protein structure elucidation in crystallographic studies. Diruthenium tetraacetate can be used to catalyze the conversion of hydroxylamines to nitrones with a high selectivity when bound to lysozyme. Finally, a case study is presented in which an unprecedented palladium/arsenic-mediated catalytic cycle for nitrile hydration was discovered thanks to previous studies on the solution chemistry of the anticancer compound arsenoplatin-1 (AP-1).

PMID:39158114 | DOI:10.1002/chem.202402647

Drug Discov Today. 2024 Aug 16:104138. doi: 10.1016/j.drudis.2024.104138. Online ahead of print.

ABSTRACT

Patients diagnosed with rare diseases and their and families search desperately to organize drug discovery campaigns. Alternative models that differ from default paradigms offer real opportunities. There are, however, no clear guidelines for the development of such models, which reduces success rates and raises costs. We address the main challenges in making the discovery of new preclinical treatments more accessible, using rare hereditary paraplegia as a paradigmatic case. First, we discuss the necessary expertise, and the patients' clinical and genetic data. Then, we revisit gene therapy, de novo drug development, and drug repurposing, discussing their applicability. Moreover, we explore a pool of recommended in silico tools for pathogenic variant and protein structure prediction, virtual screening, and experimental validation methods, discussing their strengths and weaknesses. Finally, we focus on successful case applications.

PMID:39154774 | DOI:10.1016/j.drudis.2024.104138

Clin Park Relat Disord. 2024 Jul 18;11:100265. doi: 10.1016/j.prdoa.2024.100265. eCollection 2024.

ABSTRACT

This review explores the intricate connections between type 2 diabetes (T2D) and Parkinson's disease (PD), both prevalent chronic conditions that primarily affect the aging population. These diseases share common early biochemical pathways that contribute to tissue damage. This manuscript also systematically compiles potential shared cellular mechanisms between T2D and PD and discusses the literature on the utilization of antidiabetic drugs as potential therapeutic options for PD. This review encompasses studies investigating the experimental and clinical efficacy of antidiabetic drugs in the treatment of Parkinson's disease, along with the proposed mechanisms of action. The exploration of the benefits of antidiabetic drugs in PD presents a promising avenue for the treatment of this neurodegenerative disorder.

PMID:39149559 | PMC:PMC11325349 | DOI:10.1016/j.prdoa.2024.100265

bioRxiv [Preprint]. 2024 Aug 10:2023.05.12.540594. doi: 10.1101/2023.05.12.540594.

ABSTRACT

While link prediction methods in knowledge graphs have been increasingly utilized to locate potential associations between compounds and diseases, they suffer from lack of sufficient evidence to explain why a drug and a disease may be indicated. This is especially true for knowledge graph embedding (KGE) based methods where a drug-disease indication is linked only by information gleaned from a vector representation. Complementary pathwalking algorithms can increase the confidence of drug repurposing candidates by traversing a knowledge graph. However, these methods heavily weigh the relatedness of drugs, through their targets, pharmacology or shared diseases. Furthermore, these methods can rely on arbitrarily extracted paths as evidence of a compound to disease indication and lack the ability to make predictions on rare diseases. In this paper, we evaluate seven link prediction methods on a vast biomedical knowledge graph for drug repurposing. We follow the principle of consilience, and combine the reasoning paths and predictions provided by path-based reasoning approaches with those of KGE methods to identify putative drug repurposing indications. Finally, we highlight the utility of our approach through a potential repurposing indication.

PMID:39149283 | PMC:PMC11326126 | DOI:10.1101/2023.05.12.540594

medRxiv [Preprint]. 2024 Aug 7:2023.03.19.23287458. doi: 10.1101/2023.03.19.23287458.

ABSTRACT

Drug repurposing - identifying new therapeutic uses for approved drugs - is often serendipitous and opportunistic, expanding the use of drugs for new diseases. The clinical utility of drug repurposing AI models remains limited because the models focus narrowly on diseases for which some drugs already exist. Here, we introduce T x GNN, a graph foundation model for zero-shot drug repurposing, identifying therapeutic candidates even for diseases with limited treatment options or no existing drugs. Trained on a medical knowledge graph, T x GNN utilizes a graph neural network and metric-learning module to rank drugs as potential indications and contraindications across 17,080 diseases. When benchmarked against eight methods, T x GNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, T x GNN's Explainer module offers transparent insights into multi-hop medical knowledge paths that form T x GNN's predictive rationales. Human evaluation of T x GNN's Explainer showed that T x GNN's predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of T x GNN's novel predictions align with off-label prescriptions clinicians make in a large healthcare system. T x GNN's drug repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop interpretable rationales.

PMID:39148855 | PMC:PMC11326339 | DOI:10.1101/2023.03.19.23287458

Nanotechnology. 2024 Aug 15. doi: 10.1088/1361-6528/ad6fa6. Online ahead of print.

ABSTRACT

Triple-negative breast cancer (TNBC) is considered the most aggressive form of breast cancer owing to the negative expression of targetable bioreceptors. Epithelial to mesenchymal transition (EMT) associated with metastatic abilities is its critical feature. As an attempt to target TNBC, nanotechnology was utilised to augment the effects of drug repurposing. Concerning that, a combination therapeutic module was structured with one of the aspects being a repurposed antihistamine, promethazine hydrochloride loaded PLGA nanoparticles. The as-synthesized nanoparticles were 217 nm in size and fluoresced at 522 nm, rendering them suitable for theranostic applications too. The second feature of the module was a common histone deacetylase inhibitor, suberoylanilide hydroxamic acid (SAHA), used as a form of pre-treatment. Experimental studies demonstrated efficient cellular internalisation and significant innate anti-proliferative potential. The use of SAHA sensitised the cells to the drug loaded nanoparticle treatment. Mechanistic studies showed increase in ROS generation, mitochondrial dysfunction followed by apoptosis. Investigations into protein expression also revealed reduction of mesenchymal proteins like vimentin by 1.90-fold; while increase in epithelial marker like E-Cadherin by 1.42-fold, thus indicating an altered EMT dynamics. Further findings also provided better insight into the benefits of SAHA potentiated targeting of tumor spheroids that mimic solid tumors of TNBC. Thus, this study paves the avenue to a more rational translational validation of combining nanotherapeutics with drug repurposing.&#xD.

PMID:39146954 | DOI:10.1088/1361-6528/ad6fa6

Sci Rep. 2024 Aug 15;14(1):18939. doi: 10.1038/s41598-024-69080-5.

ABSTRACT

Rheumatoid arthritis (RA) and arthrofibrosis (AF) are both chronic synovial hyperplasia diseases that result in joint stiffness and contractures. They shared similar symptoms and many common features in pathogenesis. Our study aims to perform a comprehensive analysis between RA and AF and identify novel drugs for clinical use. Based on the text mining approaches, we performed a correlation analysis of 12 common joint diseases including arthrofibrosis, gouty arthritis, infectious arthritis, juvenile idiopathic arthritis, osteoarthritis, post infectious arthropathies, post traumatic osteoarthritis, psoriatic arthritis, reactive arthritis, rheumatoid arthritis, septic arthritis, and transient arthritis. 5 bulk sequencing datasets and 4 single-cell sequencing datasets of RA and AF were integrated and analyzed. A novel drug repositioning method was found for drug screening, and text mining approaches were used to verify the identified drugs. RA and AF performed the highest gene similarity (0.77) and functional ontology similarity (0.84) among all 12 joint diseases. We figured out that they share the same key pathogenic cell including CD34 + sublining fibroblasts (CD34-SLF) and DKK3 + sublining fibroblasts (DKK3-SLF). Potential therapeutic target database (PTTD) was established with the differential expressed genes (DEGs) of these key pathogenic cells. Based on the PTTD, 15 potential drugs for AF and 16 potential drugs for RA were identified. This work provides a new perspective on AF and RA study which enhances our understanding of their pathogenesis. It also shed light on their underlying mechanism and open new avenues for drug repositioning studies.

PMID:39147768 | DOI:10.1038/s41598-024-69080-5

ACS Pharmacol Transl Sci. 2024 Jul 22;7(8):2391-2400. doi: 10.1021/acsptsci.4c00256. eCollection 2024 Aug 9.

ABSTRACT

As part of the NIH Helping to End Addiction Long-term (HEAL) Initiative, the National Center for Advancing Translational Sciences is dedicated to the development of new pharmacological tools and investigational drugs for managing and treating pain as well as the prevention and treatment of opioid misuse and addiction. In line with these objectives, we created a comprehensive, annotated small molecule library including drugs, probes, and tool compounds that act on published pain- and addiction-relevant targets. Nearly 3000 small molecules associated with approximately 200 known and hypothesized HEAL targets have been assembled, curated, and annotated in one collection. Physical samples of the library compounds have been acquired and plated in 1536-well format, enabling a rapid and efficient high-throughput screen against a wide range of assays. The creation of the HEAL Targets and Compounds Library, coupled with an integrated computational platform for AI-driven machine learning, structural modeling, and virtual screening, provides a valuable source for strategic drug repurposing, innovative profiling, and hypothesis testing of novel targets related to pain and opioid use disorder (OUD). The library is available to investigators for screening pain and OUD-relevant phenotypes.

PMID:39144561 | PMC:PMC11320728 | DOI:10.1021/acsptsci.4c00256

Life Sci. 2024 Aug 12:122970. doi: 10.1016/j.lfs.2024.122970. Online ahead of print.

ABSTRACT

Long COVID is a debilitating, multisystemic illness following a SARS-CoV-2 infection whose duration may be indefinite. Over four years into the pandemic, little knowledge has been generated from clinical trials. We analyzed the information available on ClinicalTrials.gov, and found that the rigor and focus of trials vary widely, and that the majority test non-pharmacological interventions with insufficient evidence. We highlight promising trials underway, and encourage the proliferation of clinical trials for treating Long COVID and other infection-associated chronic conditions and illnesses (IACCIs). We recommend several guidelines for Long COVID trials: First, pharmaceutical trials with potentially curative, primary interventions should be prioritized, and both drug repurposing and new drug development should be pursued. Second, study designs should be both rigorous and accessible, e.g., triple-blinded randomized trials that can be conducted remotely, without participants needing to leave their homes. Third, studies should have multiple illness comparator cohorts for IACCIs such as Myalgic Encephalomyelitis (ME/CFS) and dysautonomia, and screen for the full spectrum of symptomatology and pathologies of these illnesses. Fourth, studies should consider inclusion/exclusion criteria with an eye towards equity and breadth of representation, including participants of all races, ethnicities, and genders most impacted by COVID-19, and including all levels of illness severity. Fifth, involving patient-researchers in all aspects of studies brings immensely valuable perspectives that will increase the impact of trials. We also encourage the development of efficient clinical trial designs including methods to study several therapies in parallel.

PMID:39142505 | DOI:10.1016/j.lfs.2024.122970

Biomed Pharmacother. 2024 Aug 13;178:117293. doi: 10.1016/j.biopha.2024.117293. Online ahead of print.

ABSTRACT

Breast cancer is one of the most prevalent malignancies among women. Enhancing the prognosis is an effective approach to enhance the survival rate of breast cancer. Cuproptosis, a copper-dependent programmed cell death process, has been associated with patient prognosis. Inducing cuproptosis is a promising approach for therapy. However, there is currently no anti-breast cancer drug that induces cuproptosis. In this study, we repositioned the clinical drug fluphenazine as a potential agent for breast cancer treatment by inducing cuproptosis. Firstly, we utilized the Cancer Genome Atlas (TCGA) database and Connectivity Map (CMap) database to identify 22 potential compounds with anti-breast cancer activity through inducing cuproptosis. Subsequently, our findings demonstrated that fluphenazine effectively suppressed the viability of MCF-7 cells. Fluphenazine also significantly inhibited the viability of triple negative breast cancer cells MDA-MB-453 and MDA-MB-231. Furthermore, our study revealed that fluphenazine significantly down-regulated the expression of potential prognostic biomarkers associated with cuproptosis, increased copper ion levels, and reduced intracellular pyruvate accumulation. Additionally, it up-regulated the expression of FDX1 at both the mRNA and protein levels, which has been reported to play a crucial role in the induction of cuproptosis. These findings suggest that fluphenazine has the potential to be used as an anti-breast cancer drug by inducing cuproptosis. Therefore, this research provides an insight for the development of novel cuproptosis-dependent anti-cancer agents.

PMID:39142251 | DOI:10.1016/j.biopha.2024.117293

Mol Divers. 2024 Aug 14. doi: 10.1007/s11030-024-10958-x. Online ahead of print.

ABSTRACT

Rheumatoid Arthritis (RA) is a persistent autoimmune disease affecting approximately 0.5-1 percent of the world population. RA prevalence is higher in woman aged between 35 and 50 years than in age matched men, though this difference is less evident among elderly patients. The profound immune specific effects of disrupted JAK 3 (Janus kinase 3) signaling highlight the possibility of therapeutic targeting of JAK3 as a highly specific mode of immune system suppression. To address the above problem which is unendurable to patients and in the hope to cater some respite to such suffering we have targeted JAK 3 protein and JAK/STAT signaling pathway with compounds downloaded from FDA database, and performed screening of all available compounds docked against JAK3 protein. The difference between the target protein and other proteins of the same family was studied using cross docking and the compounds having higher binding affinity to JAK3 protein also showed more selectivity towards the particular protein. Density functional theory and molecular dynamics simulation study was done to study the compounds at their atomic level to know more about their drug likeliness. At the end of the study and based on our analysis we have come up with three FDA approved drugs that can be proposed as a treatment option for Rheumatoid Arthritis.

PMID:39141207 | DOI:10.1007/s11030-024-10958-x

Sci Rep. 2024 Aug 13;14(1):18772. doi: 10.1038/s41598-024-69465-6.

ABSTRACT

Computational pharmacogenomics can potentially identify new indications for already approved drugs and pinpoint compounds with similar mechanism-of-action. Here, we used an integrated drug repositioning approach based on transcriptomics data and structure-based virtual screening to identify compounds with gene signatures similar to three known proteasome inhibitors (PIs; bortezomib, MG-132, and MLN-2238). In vitro validation of candidate compounds was then performed to assess proteasomal proteolytic activity, accumulation of ubiquitinated proteins, cell viability, and drug-induced expression in A375 melanoma and MCF7 breast cancer cells. Using this approach, we identified six compounds with PI properties ((-)-kinetin-riboside, manumycin-A, puromycin dihydrochloride, resistomycin, tegaserod maleate, and thapsigargin). Although the docking scores pinpointed their ability to bind to the β5 subunit, our in vitro study revealed that these compounds inhibited the β1, β2, and β5 catalytic sites to some extent. As shown with bortezomib, only manumycin-A, puromycin dihydrochloride, and tegaserod maleate resulted in excessive accumulation of ubiquitinated proteins and elevated HMOX1 expression. Taken together, our integrated drug repositioning approach and subsequent in vitro validation studies identified six compounds demonstrating properties similar to proteasome inhibitors.

PMID:39138277 | DOI:10.1038/s41598-024-69465-6

J Chem Inf Model. 2024 Aug 13. doi: 10.1021/acs.jcim.4c00957. Online ahead of print.

ABSTRACT

Drug-Target Interaction (DTI) prediction facilitates acceleration of drug discovery and promotes drug repositioning. Most existing deep learning-based DTI prediction methods can better extract discriminative features for drugs and proteins, but they rarely consider multimodal features of drugs. Moreover, learning the interaction representations between drugs and targets needs further exploration. Here, we proposed a simple M ulti-modal G ating N etwork for DTI prediction, MGNDTI, based on multimodal representation learning and the gating mechanism. MGNDTI first learns the sequence representations of drugs and targets using different retentive networks. Next, it extracts molecular graph features of drugs through a graph convolutional network. Subsequently, it devises a multimodal gating network to obtain the joint representations of drugs and targets. Finally, it builds a fully connected network for computing the interaction probability. MGNDTI was benchmarked against seven state-of-the-art DTI prediction models (CPI-GNN, TransformerCPI, MolTrans, BACPI, CPGL, GIFDTI, and FOTF-CPI) using four data sets (i.e., Human, C. elegans, BioSNAP, and BindingDB) under four different experimental settings. Through evaluation with AUROC, AUPRC, accuracy, F1 score, and MCC, MGNDTI significantly outperformed the above seven methods. MGNDTI is a powerful tool for DTI prediction, showcasing its superior robustness and generalization ability on diverse data sets and different experimental settings. It is freely available at https://github.com/plhhnu/MGNDTI.

PMID:39137398 | DOI:10.1021/acs.jcim.4c00957

Front Chem. 2024 Jul 29;12:1392650. doi: 10.3389/fchem.2024.1392650. eCollection 2024.

ABSTRACT

Precision medicine has revolutionized modern cancer therapeutic management by targeting specific molecular aberrations responsible for the onset and progression of tumorigenesis. ROS proto-oncogene 1 (ROS1) is a receptor tyrosine kinase (RTK) that can induce tumorigenesis through various signaling pathways, such as cell proliferation, survival, migration, and metastasis. It has emerged as a promising therapeutic target in various cancer types. However, there is very limited availability of specific ROS1 inhibitors for therapeutic purposes. Exploring repurposed drugs for rapid and effective treatment is a useful approach. In this study, we utilized an integrated approach of virtual screening and molecular dynamics (MD) simulations of repurposing existing drugs for ROS1 kinase inhibition. Using a curated library of 3648 FDA-approved drugs, virtual screening identified drugs capable of binding to ROS1 kinase domain. The results unveil two hits, Midostaurin and Alectinib with favorable binding profiles and stable interactions with the active site residues of ROS1. These hits were subjected to stability assessment through all-atom MD simulations for 200 ns. MD results showed that Midostaurin and Alectinib were stable with ROS1. Taken together, the study showed a rational framework for the selection of repurposed Midostaurin and Alectinib with ROS1 inhibitory potential for therapeutic management after further validation.

PMID:39136033 | PMC:PMC11317403 | DOI:10.3389/fchem.2024.1392650