OMICS. 2023 Oct;27(10):483-493. doi: 10.1089/omi.2023.0127.

ABSTRACT

Chronic inflammation is an important contributor to tumorigenesis in many tissues. However, the underlying mechanisms of inflammatory signaling in the tumor microenvironment are not yet fully understood in various cancers. Therefore, this study aimed to uncover the gene expression signatures of inflammation-associated proteins that lead to tumorigenesis, and with an eye to discovery of potential system biomarkers and novel drug candidates in oncology. Gene expression profiles associated with 12 common cancers (e.g., breast invasive carcinoma, colon adenocarcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma) from The Cancer Genome Atlas were retrieved and mapped to inflammation-related gene sets. Subsequently, the inflammation-associated differentially expressed genes (i-DEGs) were determined. The i-DEGs common in all cancers were proposed as tumor inflammation signatures (TIS) after pan-cancer analysis. A TIS, consisting of 45 proteins, was evaluated as a potential system biomarker based on its prognostic forecasting and secretion profiles in multiple tissues. In addition, i-DEGs for each cancer type were used as queries for drug repurposing. Narciclasine, parthenolide, and homoharringtonine were identified as potential candidates for drug repurposing. Biomarker candidates in relation to inflammation were identified such as KNG1, SPP1, and MIF. Collectively, these findings inform precision diagnostics development to distinguish individual cancer types, and can also pave the way for novel prognostic decision tools and repurposed drugs across multiple cancers. These new findings and hypotheses warrant further research toward precision/personalized medicine in oncology. Pan-cancer analysis of inflammatory mediators can open up new avenues for innovation in cancer diagnostics and therapeutics.

PMID:37861711 | DOI:10.1089/omi.2023.0127

Front Med (Lausanne). 2023 Oct 4;10:1089087. doi: 10.3389/fmed.2023.1089087. eCollection 2023.

ABSTRACT

BACKGROUND: The gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities.

OBJECTIVE: This study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients.

MATERIALS AND METHODS: This observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen's kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson's correlation coefficient and Bland-Altman plots. The strength of agreement was defined as almost perfect (0.81-1.00), substantial (0.61-0.80), and moderate (0.41-0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00-0.30), low (0.30-0.50), moderate (0.50-0.70), high (0.70-0.90), and extremely high (0.90-1.00).

MEASUREMENTS AND MAIN RESULTS: The cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%.

CONCLUSION AND RELEVANCE: Our study confirms the feasibility and validity of an automated process to gather data from the EHR.

PMID:37859860 | PMC:PMC10583598 | DOI:10.3389/fmed.2023.1089087

J Cell Mol Med. 2023 Oct 19. doi: 10.1111/jcmm.18002. Online ahead of print.

ABSTRACT

Papillary thyroid carcinoma (PTC) is one of the most common endocrine carcinomas worldwide and the aetiology of this cancer is still not well understood. Therefore, it remains important to understand the disease mechanism and find prognostic biomarkers and/or drug candidates for PTC. Compared with approaches based on single-gene assessment, network medicine analysis offers great promise to address this need. Accordingly, in the present study, we performed differential co-expressed network analysis using five transcriptome datasets in patients with PTC and healthy controls. Following meta-analysis of the transcriptome datasets, we uncovered common differentially expressed genes (DEGs) for PTC and, using these genes as proxies, found a highly clustered differentially expressed co-expressed module: a 'PTC-module'. Using independent data, we demonstrated the high prognostic capacity of the PTC-module and designated this module as a prognostic systems biomarker. In addition, using the nodes of the PTC-module, we performed drug repurposing and text mining analyzes to identify novel drug candidates for the disease. We performed molecular docking simulations, and identified: 4-demethoxydaunorubicin hydrochloride, AS605240, BRD-A60245366, ER 27319 maleate, sinensetin, and TWS119 as novel drug candidates whose efficacy was also confirmed by in silico analyzes. Consequently, we have highlighted here the need for differential co-expression analysis to gain a systems-level understanding of a complex disease, and we provide candidate prognostic systems biomarker and novel drugs for PTC.

PMID:37859510 | DOI:10.1111/jcmm.18002

Curr Rev Clin Exp Pharmacol. 2023 Oct 18. doi: 10.2174/0127724328245156231008154045. Online ahead of print.

ABSTRACT

Inflammatory bowel disease (IBD), represented by Crohn's disease (CD) and ulcerative colitis (UC), is a chronic inflammatory disorder of the gastrointestinal tract (GIT) characterized by chronic relapsing intestinal inflammation, abdominal pain, cramping, loss of appetite, fatigue, diarrhoea, and weight loss. Although the etiology of IBD remains unclear, it is believed to be an interaction between genes, and environmental factors, such as an imbalance of the intestinal microbiota, changing food habits, an ultra-non-hygiene environment, and an inappropriate immune system. The development of novel effective therapies is stymied by a lack of understanding of the aetiology of IBD. The current therapy involves the use of aminosalicylates, immunosuppressants, and corticosteroids that can effectively manage symptoms, induce and sustain remission, prevent complications, modify the course of the disease, provide diverse treatment options, showcase advancements in biologic therapies, and enhance the overall quality of life. However, the efficacy of current therapy is overshadowed by a plethora of adverse effects, such as loss of weight, mood swings, skin issues, loss of bone density, higher vulnerability to infections, and elevated blood pressure. Biologicals, like anti-tumour necrosis factor agents, can stimulate an autoimmune response in certain individuals that may diminish the effectiveness of the medication over time, necessitating a switch to alternative treatments. The response of IBD patients to current drug therapy is quite varied, which can lead to disease flares that underlines the urgent need to explore alternative treatment option to address the unmet need of developing new treatment strategies for IBD with high efficacy and fewer adverse effects. Drug repurposing is a novel strategy where existing drugs that have already been validated safe in patients for the management of certain diseases are redeployed to treat other, unindicated diseases. The present narrative review focuses on potential drug candidates that could be repurposed for the management of IBD using on-target and off-target strategies. It covers their preclinical, clinical assessment, mechanism of action, and safety profiles, and forecasts their appropriateness in the management of IBD. The review presents useful insights into the most promising candidates for repurposing, like anti-inflammatory and anti-apoptotic troxerutin, which has been found to improve the DSS-induced colitis in rats, an antiosteoarthritic drug diacetylrhein that has been found to have remarkable ameliorating effects on DSS-induced colitis via anti-oxidant and anti-inflammatory properties and by influencing both apoptosis and pyroptosis. Topiramate, an antiepileptic and anticonvulsant drug, has remarkably decreased overall pathophysiological and histopathological events in the experimental model of IBD in rodents by its cytokine inhibitory action.

PMID:37859409 | DOI:10.2174/0127724328245156231008154045

Aging Cell. 2023 Oct 19:e13944. doi: 10.1111/acel.13944. Online ahead of print.

ABSTRACT

Drug repositioning strategy represents a valid tool to accelerate the pharmacological development through the identification of new applications for already existing compounds. In this view, we aimed at discovering molecules able to trigger telomere-localized DNA damage and tumor cell death. By applying an automated high-content spinning-disk microscopy, we performed a screening aimed at identifying, on a library of 527 drugs, molecules able to negatively affect the expression of TRF2, a key protein in telomere maintenance. FK866, resulting from the screening as the best candidate hit, was then validated at biochemical and molecular levels and the mechanism underlying its activity in telomere deprotection was elucidated both in vitro and in vivo. The results of this study allow us to discover a novel role of FK866 in promoting, through the production of reactive oxygen species, telomere loss and deprotection, two events leading to an accumulation of DNA damage and tumor cell death. The ability of FK866 to induce telomere damage and apoptosis was also demonstrated in advanced preclinical models evidencing the antitumoral activity of FK866 in triple-negative breast cancer-a particularly aggressive breast cancer subtype still orphan of targeted therapies and characterized by high expression levels of both NAMPT and TRF2. Overall, our findings pave the way to the development of novel anticancer strategies to counteract triple-negative breast cancer, based on the use of telomere deprotecting agents, including NAMPT inhibitors, that would rapidly progress from bench to bedside.

PMID:37858982 | DOI:10.1111/acel.13944

J Biomed Inform. 2023 Oct 17:104528. doi: 10.1016/j.jbi.2023.104528. Online ahead of print.

ABSTRACT

MOTIVATION: Drug repurposing (DR) is an imminent approach to identifying novel therapeutic indications for the available drugs and discovering novel drugs for previously untreatable diseases. Nowadays, DR has major attention in the pharmaceutical industry due to the high cost and time of launching new drugs to the market through traditional drug development. DR task majorly depends on genetic information since the drugs revert the modified Gene Expression (GE) of diseases to normal. Many of the existing studies have not considered the genetic importance of predicting the potential candidates.

METHOD: We proposed a novel multimodal framework that utilizes genetic aspects of drugs and diseases such as genes, pathways, gene signatures, or expression to enhance the performance of DR using various data sources. Firstly, the heterogeneous biological network (HBN) is constructed with three types of nodes namely drug, disease, and gene, and 4 types of edges similarities (drug, gene, and disease), drug-gene, gene-disease, and drug-disease. Next, a modified graph auto-encoder (GAE*) model is applied to learn the representation of drug and disease nodes using the topological structure and edge information. Secondly, the HBN is enhanced with the information extracted from biomedical literature and ontology using a novel semi-supervised pattern embedding-based bootstrapping model and novel DR perspective representation learning respectively to improve the prediction performance. Finally, our proposed system uses a neural network model to generate the probability score of drug-disease pairs.

RESULTS: We demonstrate the efficiency of the proposed model on various datasets and achieved outstanding performance in 5-fold cross-validation (AUC=0.99, AUPR=0.98). Further, we validated the top-ranked potential candidates using pathway analysis and proved that the known and predicted candidates share common genes in the pathways.

PMID:37858852 | DOI:10.1016/j.jbi.2023.104528

Life Sci. 2023 Oct 17:122181. doi: 10.1016/j.lfs.2023.122181. Online ahead of print.

ABSTRACT

BACKGROUND: The anti-apoptotic protein B-Cell Lymphoma 2 (Bcl-2) is a key target for the development of anti-cancer agents, as its overexpression can render cancer cells resistant to chemotherapeutic treatments.

AIMS AND OBJECTIVES: The current study has systematically evaluated a library of FDA-approved drugs for Bcl-2 inhibition using a drug repurposing strategy via in vitro, biophysical, and in-silico techniques.

MATERIALS AND METHODS: In vitro anticancer activity was performed, followed by apoptosis assay. The selected compounds were subjected to Saturation Transfer Difference Nuclear Magnetic Resonance (STD-NMR) spectroscopy, molecular docking, and molecular dynamic simulation for ligand-protein interactions.

KEY FINDINGS: In the initial screening, seventy-five (75) drugs were evaluated against the HL-60 (human blood promyelocytic leukemia) cancer cell line. Among them, paroxetine HCl, carvedilol, clomipramine HCl, and clomifene citrate showed significant anti-proliferative activity (IC50 = 9.733 ± 0.524, 11.940 ± 0.079, 12.376 ± 1.242, and 6.155 ± 0.363 μM, respectively), in comparison to the reference drug venetoclax (IC50 = 7.086 ± 0.041 μM). This indicated that the test drugs have comparable IC50 values to the standard drug. Furthermore, the drugs were able to induce apoptosis in HL-60 cells. These drugs showed interactions with Bcl-2 protein in STD-NMR analysis. Docking and MD simulation studies further supported the interaction of these drugs with Bcl-2 protein, mainly via hydrophobic contacts leading to stable drug-Bcl-2 complexes.

SIGNIFICANCE: This study, identifies paroxetine HCl, carvedilol, clomipramine HCl, and clomifene citrate as significant Bcl-2 inhibitors and needs further pre-clinical and clinical studies for potential anti-cancer agents' evaluation.

PMID:37858717 | DOI:10.1016/j.lfs.2023.122181

Sci Rep. 2023 Oct 19;13(1):17887. doi: 10.1038/s41598-023-44264-7.

NO ABSTRACT

PMID:37857641 | DOI:10.1038/s41598-023-44264-7

J Biomol Struct Dyn. 2023 Oct 19:1-12. doi: 10.1080/07391102.2023.2270086. Online ahead of print.

ABSTRACT

Emerging evidence portray the involvement of epigenomic reprogramming in the onset and progression of several malignancies, including breast cancer. Histone acetyltransferase (HAT) p300 is a critical epigenetic regulator that acts as a transcription co-activator and regulates various cellular processes. p300 is overexpressed in breast cancer and promotes cellular invasion and survival, making it a promising druggable target. In this study, the relevance of p300 in different cancer pathways was established. Virtual screening of the FDA-approved drug library was carried out using molecular docking, and the top 10 potential repurposed drugs were identified. Further, recalculation of binding free energy of drug-p300 complexes was carried out using molecular mechanics Poisson-Boltzmann and surface area (MM-PBSA) method after molecular dynamic simulation. Based on molecular dynamic simulation parameters and binding free energy analysis, two drugs, namely Netarsudil (-305.068 kJ/mol) and Imatinib (-260.457 kJ/mol), were identified as potential repurposed drugs to inhibit the activity of p300. In conclusion, these findings suggest, Netarsudil and Imatinib might be a potential repurposed drug to combat breast cancer via p300 inhibition.Communicated by Ramaswamy H. Sarma.

PMID:37855370 | DOI:10.1080/07391102.2023.2270086

Fish Shellfish Immunol Rep. 2023 Oct 8;5:100120. doi: 10.1016/j.fsirep.2023.100120. eCollection 2023 Dec 15.

ABSTRACT

Drug repurposing is a methodology of identifying new therapeutic use for existing drugs. It is a highly efficient, time and cost-saving strategy that offers an alternative approach to the traditional drug discovery process. Past in-silico studies involving molecular docking have been successful in identifying potential repurposed drugs for the various treatment of diseases including aquaculture diseases. The emerging shrimp hemocyte iridescent virus (SHIV) or Decapod iridescent virus 1 (DIV1) is a viral pathogen that causes severe disease and high mortality (80 %) in farmed shrimps caused serious economic losses and presents a new threat to the shrimp farming industry. Therefore, effective antiviral drugs are critically needed to control DIV1 infections. The aim of this study is to investigate the interaction of potential existing antiviral drugs, Chloroquine, Rimantadine, and CAP-1 with DIV1 major capsid protein (MCP) with the intention of exploring the potential of drug repurposing. The interaction of the DIV1 MCP and three antivirals were characterised and analysed using molecular docking and molecular dynamics simulation. The results showed that CAP-1 is a more promising candidate against DIV1 with the lowest binding energy of -8.46 kcal/mol and is more stable compared to others. We speculate that CAP-1 binding may induce the conformational changes in the DIV1 MCP structure by phosphorylating multiple residues (His123, Tyr162, and Thr395) and ultimately block the viral assembly and maturation of DIV1 MCP. To the best of our knowledge, this is the first report regarding the structural characterisation of DIV1 MCP docked with repurposing drugs.

PMID:37854946 | PMC:PMC10579962 | DOI:10.1016/j.fsirep.2023.100120

Cell Rep Med. 2023 Oct 17;4(10):101227. doi: 10.1016/j.xcrm.2023.101227.

ABSTRACT

Drug repositioning seeks to leverage existing clinical knowledge to identify alternative clinical settings for approved drugs. However, repositioning efforts fail to demonstrate improved success rates in late-stage clinical trials. Focusing on 11 approved kinase inhibitors that have been evaluated in 139 repositioning hypotheses, we use data mining to characterize the state of clinical repurposing. Then, using a simple experimental correction with human serum proteins in in vitro pharmacodynamic assays, we develop a measurement of a drug's effective exposure. We show that this metric is remarkably predictive of clinical activity for a panel of five kinase inhibitors across 23 drug variant targets in leukemia. We then validate our model's performance in six other kinase inhibitors for two types of solid tumors: non-small cell lung cancer (NSCLC) and gastrointestinal stromal tumors (GISTs). Our approach presents a straightforward strategy to use existing clinical information and experimental systems to decrease the clinical failure rate in drug repurposing studies.

PMID:37852183 | DOI:10.1016/j.xcrm.2023.101227

Curr Comput Aided Drug Des. 2023 Oct 17. doi: 10.2174/0115734099264119230925054833. Online ahead of print.

ABSTRACT

BACKGTOUND: The interconnection between different fields of research has gained interest due to its cutting-edge perspectives in solving scientific problems. Terminalia arjuna is indigenously used in India for curing several diseases and its pharmacological activities are being revisited in recent drug-repurposing researches.

OBJECTIVE: Efficient ultrasound-assisted extraction of phytochemicals from the bark of Terminalia arjuna is highlighted in this study. Following the optimization of the extraction process, the crude hydroethanolic extract is subjected to phytochemical profiling and an in-silico investigation on its anti-cancer property.

METHODOLOGY: A three-level four-factor Box-Behnken design is exploited to optimize four operational parameters namely extraction time, ultrasonic power, ethanol concentration (as the extracting solvent) and solute (in g) : solvent (in mL) ratio. At the optimum parametric condition, the crude extract is obtained and its GC-MS analysis is done. An analysis of network pharmacology (by constructing and visualizing biological networks using Cytoscape) combined with molecular docking reveals the potential antineoplastic targets of the crude extract.

RESULTS: The ANOVA table exhibits the significance, adequacy and reliability of the proposed second-order polynomial model with a R² value of 0.917 and adjusted R² of 0.865. Experimental results portray significant antioxidant potential of the prepared extract in its crude form. The GC-MS analysis of the crude extract predicts the extracted phytochemicals while the constructed biological networks highlight its multi-targeted activity in colorectal cancer.

CONCLUSION: The study identifies three phytochemicals viz. luteolin, β-sitosterol and arjunic acid as potent anti-cancer agents and can be extended with in-vitro and in-vivo experiments to validate the in-silico results, thus establishing lead phytochemicals in multi-targeted colorectal cancer therapies with minimal side effects.

PMID:37850546 | DOI:10.2174/0115734099264119230925054833

J Biomol Struct Dyn. 2023 Oct 18:1-12. doi: 10.1080/07391102.2023.2269280. Online ahead of print.

ABSTRACT

The identification of druggable proteins (DPs) is significant for the development of new drugs, personalized medicine, understanding of disease mechanisms, drug repurposing, and economic benefits. By identifying new druggable targets, researchers can develop new therapies for a range of diseases, leading to better patient outcomes. Identification of DPs by machine learning strategies is more efficient and cost-effective than conventional methods. In this study, a computational predictor, namely Drug-LXGB, is introduced to enhance the identification of DPs. Features are discovered by composition, transition, and distribution (CTD), composition of K-spaced amino acid pair (CKSAAP), pseudo-position-specific scoring matrix (PsePSSM), and a novel descriptor, called multi-block pseudo amino acid composition (MB-PseAAC). The dimensions of CTD, CKSAAP, PsePSSM, and MB-PseAAC are integrated and utilized the sequential forward selection as feature selection algorithm. The best characteristics are provided by random forest, extreme gradient boosting, and light eXtreme gradient boosting (LXGB). The predictive analysis of these learning methods is measured via 10-fold cross-validation. The LXGB-based model secures the highest results than other existing predictors. Our novel protocol will perform an active role in designing novel drugs and would be fruitful to explore the potential target. This study will help better to capture a more universal view of a potential target.Communicated by Ramaswamy H. Sarma.

PMID:37850427 | DOI:10.1080/07391102.2023.2269280

Sci Rep. 2023 Oct 17;13(1):17684. doi: 10.1038/s41598-023-44956-0.

ABSTRACT

Bruton's tyrosine kinase (BTK) is a non-receptor protein kinase that plays a crucial role in various biological processes, including immune system function and cancer development. Therefore, inhibition of BTK has been proposed as a therapeutic strategy for various complex diseases. In this study, we aimed to identify potential inhibitors of BTK by using a drug repurposing approach. To identify potential inhibitors, we performed a molecular docking-based virtual screening using a library of repurposed drugs from DrugBank. We then used various filtrations followed by molecular dynamics (MD) simulations, principal component analysis (PCA), and Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA) analysis to further evaluate the binding interactions and stability of the top-ranking compounds. Molecular docking-based virtual screening approach identified several repurposed drugs as potential BTK inhibitors, including Eltrombopag and Alectinib, which have already been approved for human use. All-atom MD simulations provided insights into the binding interactions and stability of the identified compounds, which will be helpful for further experimental validation and optimization. Overall, our study demonstrates that drug repurposing is a promising approach to identify potential inhibitors of BTK and highlights the importance of computational methods in drug discovery.

PMID:37848584 | DOI:10.1038/s41598-023-44956-0

Cancer Inform. 2023 Oct 14;22:11769351231202588. doi: 10.1177/11769351231202588. eCollection 2023.

ABSTRACT

The aim of this study was to utilize a computational methodology based on Gene Reversal Rate (GRR) scoring to repurpose existing drugs for a rare and understudied cancer: inflammatory breast cancer (IBC). This method uses IBC-related gene expression signatures (GES) and drug-induced gene expression profiles from the LINCS database to calculate a GRR score for each candidate drug, and is based on the idea that a compound that can counteract gene expression changes of a disease may have potential therapeutic applications for that disease. Genes related to IBC with associated differential expression data (265 up-regulated and 122 down-regulated) were collated from PubMed-indexed publications. Drug-induced gene expression profiles were downloaded from the LINCS database and candidate drugs to treat IBC were predicted using their GRR scores. Thirty-two (32) drug perturbations that could potentially reverse the pre-compiled list of 297 IBC genes were obtained using the LINCS Canvas Browser (LCB) analysis. Binary combinations of the 32 perturbations were assessed computationally to identify combined perturbations with the highest GRR scores, and resulted in 131 combinations with GRR greater than 80%, that reverse up to 264 of the 297 genes in the IBC-GES. The top 35 combinations involve 20 unique individual drug perturbations, and 19 potential drug candidates. A comprehensive literature search confirmed 17 of the 19 known drugs as having either anti-cancer or anti-inflammatory activities. AZD-7545, BMS-754807, and nimesulide target known IBC relevant genes: PDK, Met, and COX, respectively. AG-14361, butalbital, and clobenpropit are known to be functionally relevant in DNA damage, cell cycle, and apoptosis, respectively. These findings support the use of the GRR approach to identify drug candidates and potential combination therapies that could be used to treat rare diseases such as IBC.

PMID:37846218 | PMC:PMC10576937 | DOI:10.1177/11769351231202588

Comput Biol Med. 2023 Oct 12;166:107572. doi: 10.1016/j.compbiomed.2023.107572. Online ahead of print.

ABSTRACT

Cholangiocarcinoma (CCA) is a subtype of liver cancer with increasing incidence, poor prognosis, and limited treatment modalities. It is, therefore, imperative to identify novel therapeutic targets for better management of the disease. Chaperones are known to be significant regulators of carcinogenesis, however, their role in CCA remains unclear. This study aims to screen chaperones involved in CCA pathogenesis and identify drugs targeting key chaperones to improve the therapeutic response to the disease. To achieve this, first we mined the literature to create an atlas of human chaperone proteins. Next, their expression in CCA was determined by publicly available datasets of patients at mRNA and protein levels. In addition, our analysis involving protein-protein interaction and pathway analysis of eight key dysregulated chaperones revealed that they control crucial cancer-related pathways. Furthermore, topology analysis of the CCA network identified crystallin alpha-B protein (CRYAB) and prolyl-4-hydroxylase subunit 2 (P4HA2) as novel therapeutic targets for the disease. Finally, drug repurposing of 286 clinically approved anti-cancer drugs against these two chaperones performed by molecular docking and molecular dynamics simulations showed that tucatinib and regorafenib had a modulatory effect on them and could be potential inhibitors of CRYAB and P4HA2, respectively. Overall, our study, for the first time, provides insights into the pan-chaperone expression in CCA and explains the pathways that might drive CCA pathogenesis. Further, our identification of potential therapeutic targets and their inhibitors could provide new and complementary approaches to CCA treatment.

PMID:37844407 | DOI:10.1016/j.compbiomed.2023.107572

Comput Biol Chem. 2023 Oct 7;107:107968. doi: 10.1016/j.compbiolchem.2023.107968. Online ahead of print.

ABSTRACT

Predicting drug-target interaction (DTI) is a crucial step in the process of drug repurposing and new drug development. Although the attention mechanism has been widely used to capture the interactions between drugs and targets, it mainly uses the Simplified Molecular Input Line Entry System (SMILES) and two-dimensional (2D) molecular graph features of drugs. In this paper, we propose a neural network model called MdDTI for DTI prediction. The model searches for binding sites that may interact with the target from the multiple dimensions of drug structure, namely the 2D substructures and the three-dimensional (3D) spatial structure. For the 2D substructures, we have developed a novel substructure decomposition strategy based on drug molecular graphs and compared its performance with the SMILES-based decomposition method. For the 3D spatial structure of drugs, we constructed spatial feature representation matrices for drugs based on the Cartesian coordinates of heavy atoms (without hydrogen atoms) in each drug. Finally, to ensure the search results of the model are consistent across multiple dimensions, we construct a consistency loss function. We evaluate MdDTI on four drug-target interaction datasets and three independent compound-protein affinity test sets. The results indicate that our model surpasses a series of state-of-the-art models. Case studies demonstrate that our model is capable of capturing the potential binding regions between drugs and targets, and it shows efficacy in drug repurposing. Our code is available at https://github.com/lhhu1999/MdDTI.

PMID:37844375 | DOI:10.1016/j.compbiolchem.2023.107968

Mol Oncol. 2023 Oct 16. doi: 10.1002/1878-0261.13536. Online ahead of print.

ABSTRACT

Microtubule-targeting agents represent one of the most successful classes of anticancer agents. However, the development of drug resistance and the appearance of adverse effects hamper their clinical implementation. Novel microtubule-targeting agents without such limitations are urgently needed. By employing a gene expression-based drug repositioning strategy, this study identifies VU-0365114, originally synthesized as a positive allosteric modulator of human muscarinic acetylcholine receptor M5 (M5 mAChR), as a novel type of tubulin inhibitor by destabilizing microtubules. VU-0365114 exhibits a broad-spectrum in vitro anticancer activity, especially in colorectal cancer cells. A tumor xenograft study in nude mice shows that VU-0365114 slowed the in vivo colorectal tumor growth. The anticancer activity of VU-0365114 is not related to its original target, M5 mAChR. In addition, VU-0365114 does not serve as a substrate of multidrug resistance (MDR) proteins, and thus it can overcome MDR. Furthermore, a kinome analysis shows that VU-0365114 did not exhibit other significant off-target effects. Taken together, our study suggests that VU-0365114 primarily targets microtubules, offering potential for repurposing in cancer treatment, although more studies are needed before further drug development.

PMID:37842807 | DOI:10.1002/1878-0261.13536

Front Microbiol. 2023 Sep 29;14:1266261. doi: 10.3389/fmicb.2023.1266261. eCollection 2023.

ABSTRACT

Mycobacterium ulcerans causes Buruli Ulcer, a neglected infectious skin disease that typically progresses from an early non-ulcerative lesion to an ulcer with undermined edges. If not promptly treated, these lesions can lead to severe disfigurement and disability. The standard antibiotic regimen for Buruli Ulcer treatment has been oral rifampicin combined with intramuscular streptomycin administered daily for 8 weeks. However, there has been a recent shift toward replacing streptomycin with oral clarithromycin. Despite the advantages of this antibiotic regimen, it is limited by low compliance, associated side effects, and refractory efficacy for severe ulcerative lesions. Therefore, new drug candidates with a safer pharmacological spectrum and easier mode of administration are needed. Statins are lipid-lowering drugs broadly used for dyslipidemia treatment but have also been reported to have several pleiotropic effects, including antimicrobial activity against fungi, parasites, and bacteria. In the present study, we tested the susceptibility of M. ulcerans to several statins, namely atorvastatin, simvastatin, lovastatin and fluvastatin. Using broth microdilution assays and cultures of M. ulcerans-infected macrophages, we found that atorvastatin, simvastatin and fluvastatin had antimicrobial activity against M. ulcerans. Furthermore, when using the in vitro checkerboard assay, the combinatory additive effect of atorvastatin and fluvastatin with the standard antibiotics used for Buruli Ulcer treatment highlighted the potential of statins as adjuvant drugs. In conclusion, statins hold promise as potential treatment options for Buruli Ulcer. Further studies are necessary to validate their effectiveness and understand the mechanism of action of statins against M. ulcerans.

PMID:37840746 | PMC:PMC10570734 | DOI:10.3389/fmicb.2023.1266261

Naunyn Schmiedebergs Arch Pharmacol. 2023 Oct 14. doi: 10.1007/s00210-023-02722-z. Online ahead of print.

ABSTRACT

Colon cancer is one of the most common cancers and one of the main causes of death worldwide. Therefore, new treatment methods with better efficiency and fewer risks are very necessary. Mebendazole (MBZ), a drug commonly used for helminthic infections, has recently received attention as a suitable candidate for the treatment of various cancers. This study aimed to investigate, in vitro and in vivo, anticancer activity and selectivity Index of MBZ on colon cancer. HT-29 (human colorectal adenocarcinoma) and MCF-10 (non-tumorigenic epithelial) cell lines were treated with MBZ and Doxorubicin (DOX; positive control drug). IC50 values were estimated using methyl thiazole diphenyl-tetrazolium bromide (MTT) assay. We employed flow cytometry using annexin V-FITC and propidium iodide dyes. For the animal study, colon cancer was subcutaneously induced by CT26 cells (mouse colon cancer) in Bulb/C mice. The mice were treated with 0.05 of LD50, intraperitoneal, every other day for 35 days. Finally, the survival rate, tumor volume, and tumor weight were calculated. Our results demonstrated that IC50 values after 72 h for HT29 and MCF-10 cell lines were 0.29 ± 0.04 µM and 0.80 ± 0.02 µM, respectively. MBZ was more selective than DOX in inhibiting the proliferation of cancer cells compared to normal cells (2. 75 vs. 2.45). Annexin V/PI staining demonstrated that MBZ treatment at IC50 concentrations induced (78 ± 12%) apoptosis in the HT29 cancer cell line after 48 h (P ≤ 0.0001). Also, in mice bearing colon cancer, MBZ significantly reduced the tumor volume (1177 ± 1109 mm3; P ≤ 0.001) and tumor weight (2.30 ± 1.97 g; P ≤ 0.0001) compared to the negative control group (weight 12.45 ± 2.0 g; volume 7346 ± 1077). Also, MBZ increases mean survival time (MST) and increase life span (ILS) percentage in the animal study (51.2 ± 37% vs 93%, respectively). This study suggests that mebendazole strongly and selectively inhibits proliferation and induces apoptosis in colon cancer cells. It may be, accordingly, a promising drug for clinical research and application.

PMID:37837472 | DOI:10.1007/s00210-023-02722-z