Antimicrob Agents Chemother. 2022 Oct 18:e0083222. doi: 10.1128/aac.00832-22. Online ahead of print.

ABSTRACT

New anti-Entamoeba histolytica multistage drugs are needed because only one drug class, nitroimidazoles, is available for treating invasive disease, and it does not effectively eradicate the infective cyst stage. Zinc ditiocarb (ZnDTC), a main metabolite of the FDA-approved drug disulfiram, was recently shown to be highly effective against the invasive trophozoite stage. In this brief report, we show that ZnDTC is active against cysts, with similar potency to first-line cysticidal drug paromomycin.

PMID:36255253 | DOI:10.1128/aac.00832-22

Alzheimers Dement (N Y). 2022 Oct 13;8(1):e12350. doi: 10.1002/trc2.12350. eCollection 2022.

ABSTRACT

INTRODUCTION: Recent advances in generating massive single-cell/nucleus transcriptomic data have shown great potential for facilitating the identification of cell type-specific Alzheimer's disease (AD) pathobiology and drug-target discovery for therapeutic development.

METHODS: We developed The Alzheimer's Cell Atlas (TACA) by compiling an AD brain cell atlas consisting of over 1.1 million cells/nuclei across 26 data sets, covering major brain regions (hippocampus, cerebellum, prefrontal cortex, and so on) and cell types (astrocyte, microglia, neuron, oligodendrocytes, and so on). We conducted nearly 1400 differential expression comparisons to identify cell type-specific molecular alterations (e.g., case vs healthy control, sex-specific, apolipoprotein E (APOE) ε4/ε4, and TREM2 mutations). Each comparison was followed by protein-protein interaction module detection, functional enrichment analysis, and omics-informed target and drug (over 700,000 perturbation profiles) screening. Over 400 cell-cell interaction analyses using 6000 ligand-receptor interactions were conducted to identify the cell-cell communication networks in AD.

RESULTS: All results are integrated into TACA (https://taca.lerner.ccf.org/), a new web portal with cell type-specific, abundant transcriptomic information, and 12 interactive visualization tools for AD.

DISCUSSION: We envision that TACA will be a highly valuable resource for both basic and translational research in AD, as it provides abundant information for AD pathobiology and actionable systems biology tools for drug discovery.

HIGHLIGHTS: We compiled an Alzheimer's disease (AD) brain cell atlas consisting of more than 1.1 million cells/nuclei transcriptomes from 26 data sets, covering major brain regions (cortex, hippocampus, cerebellum) and cell types (e.g., neuron, oligodendrocyte, astrocyte, and microglia).We conducted over 1400 differential expression (DE) comparisons to identify cell type-specific gene expression alterations. Major comparison types are (1) AD versus healthy control; (2) sex-specific DE, (3) genotype-driven DE (i.e., apolipoprotein E [APOE] ε4/ε4 vs APOE ε3/ε3; TREM2R47H vs common variants) analysis; and (4) others. Each comparison was further followed by (1) human protein-protein interactome network module analysis, (2) pathway enrichment analysis, and (3) gene-set enrichment analysis.For drug screening, we conducted gene set enrichment analysis for all the comparisons with over 700,000 drug perturbation profiles connecting more than 10,000 human genes and 13,000 drugs/compounds.A total of over 400 analyses of cell-cell interactions against 6000 experimentally validated ligand-receptor interactions were conducted to reveal the disease-relevant cell-cell communications in AD.

PMID:36254161 | PMC:PMC9558163 | DOI:10.1002/trc2.12350

Front Cell Infect Microbiol. 2022 Sep 29;12:987683. doi: 10.3389/fcimb.2022.987683. eCollection 2022.

ABSTRACT

Porphyromonas gingivalis is a major pathogenic bacterium involved in the pathogenesis of periodontitis. Citrullination has been reported as the underlying mechanism of the pathogenesis, which relies on the interplay between two virulence factors of the bacterium, namely gingipain R and the bacterial peptidyl arginine deiminase. Gingipain R cleaves host proteins to expose the C-terminal arginines for peptidyl arginine deiminase to citrullinate and generate citrullinated proteins. Apart from carrying out citrullination in the periodontium, the bacterium is found capable of citrullinating proteins present in the host synovial tissues, atherosclerotic plaques and neurons. Studies have suggested that both virulence factors are the key factors that trigger distal effects mediated by citrullination, leading to the development of some non-communicable diseases, such as rheumatoid arthritis, atherosclerosis, and Alzheimer's disease. Thus, inhibition of these virulence factors not only can mitigate periodontitis, but also can provide new therapeutic solutions for systematic diseases involving bacterial citrullination. Herein, we described both these proteins in terms of their unique structural conformations and biological relevance to different human diseases. Moreover, investigations of inhibitory actions on the enzymes are also enumerated. New approaches for identifying inhibitors for peptidyl arginine deiminase through drug repurposing and virtual screening are also discussed.

PMID:36250046 | PMC:PMC9559808 | DOI:10.3389/fcimb.2022.987683

Front Immunol. 2022 Sep 29;13:977358. doi: 10.3389/fimmu.2022.977358. eCollection 2022.

ABSTRACT

Artificial intelligence (AI) can unveil novel personalized treatments based on drug screening and whole-exome sequencing experiments (WES). However, the concept of "black box" in AI limits the potential of this approach to be translated into the clinical practice. In contrast, explainable AI (XAI) focuses on making AI results understandable to humans. Here, we present a novel XAI method -called multi-dimensional module optimization (MOM)- that associates drug screening with genetic events, while guaranteeing that predictions are interpretable and robust. We applied MOM to an acute myeloid leukemia (AML) cohort of 319 ex-vivo tumor samples with 122 screened drugs and WES. MOM returned a therapeutic strategy based on the FLT3, CBFβ-MYH11, and NRAS status, which predicted AML patient response to Quizartinib, Trametinib, Selumetinib, and Crizotinib. We successfully validated the results in three different large-scale screening experiments. We believe that XAI will help healthcare providers and drug regulators better understand AI medical decisions.

PMID:36248800 | PMC:PMC9556772 | DOI:10.3389/fimmu.2022.977358

iScience. 2022 Sep 23;25(10):105182. doi: 10.1016/j.isci.2022.105182. eCollection 2022 Oct 21.

ABSTRACT

Cholangiocarcinoma (CCA) is rare cancer with the highest incidence in Eastern and Southeast Asian countries. Advanced CCA patients rely on chemotherapeutic regimens that offer unsatisfied clinical outcomes. We developed a comprehensive drug response profiling to investigate potential new drugs using CCA cell lines from Thai and Japanese patients against 100 approved anti-cancer drugs. We identified two major CCA subgroups that displayed unique molecular pathways from our integrative pan-omic and ligand-induced pathway activation analyses. MEK and Src inhibitors specifically killed the CCA1 subgroup without causing cytotoxicity to the normal cholangiocyte. Next, we developed the CCA45 signature to classify CCA patients based on their transcriptomic data. Our CCA45 signature could accurately predict prognosis, especially for Asian CCA patients. Our study provides a comprehensive public resource for drug repurposing in CCA and introduces analytical strategies for prioritizing cancer therapeutic agents for other rare cancer.

PMID:36248745 | PMC:PMC9563539 | DOI:10.1016/j.isci.2022.105182

Front Artif Intell. 2022 Sep 30;5:910216. doi: 10.3389/frai.2022.910216. eCollection 2022.

ABSTRACT

There are over 6,000 different rare diseases estimated to impact 300 million people worldwide. As genetic testing becomes more common practice in the clinical setting, the number of rare disease diagnoses will continue to increase, resulting in the need for novel treatment options. Identifying treatments for these disorders is challenging due to a limited understanding of disease mechanisms, small cohort sizes, interindividual symptom variability, and little commercial incentive to develop new treatments. A promising avenue for treatment is drug repurposing, where FDA-approved drugs are repositioned as novel treatments. However, linking disease mechanisms to drug action can be extraordinarily difficult and requires a depth of knowledge across multiple fields, which is complicated by the rapid pace of biomedical knowledge discovery. To address these challenges, The Hugh Kaul Precision Medicine Institute developed an artificial intelligence tool, mediKanren, that leverages the mechanistic insight of genetic disorders to identify therapeutic options. Using knowledge graphs, mediKanren enables an efficient way to link all relevant literature and databases. This tool has allowed for a scalable process that has been used to help over 500 rare disease families. Here, we provide a description of our process, the advantages of mediKanren, and its impact on rare disease patients.

PMID:36248623 | PMC:PMC9562701 | DOI:10.3389/frai.2022.910216

Front Genet. 2022 Sep 28;13:991842. doi: 10.3389/fgene.2022.991842. eCollection 2022.

ABSTRACT

Esophageal cancer (EC) remains a significant challenge globally, having the 8th highest incidence and 6th highest mortality worldwide. Esophageal squamous cell carcinoma (ESCC) is the most common form of EC in Asia. Crucially, more than 90% of EC cases in China are ESCC. The high mortality rate of EC is likely due to the limited number of effective therapeutic options. To increase patient survival, novel therapeutic strategies for EC patients must be devised. Unfortunately, the development of novel drugs also presents its own significant challenges as most novel drugs do not make it to market due to lack of efficacy or safety concerns. A more time and cost-effective strategy is to identify existing drugs, that have already been approved for treatment of other diseases, which can be repurposed to treat EC patients, with drug repositioning. This can be achieved by comparing the gene expression profiles of disease-states with the effect on gene-expression by a given drug. In our analysis, we used previously published microarray data and identified 167 differentially expressed genes (DEGs). Using weighted key driver analysis, 39 key driver genes were then identified. These driver genes were then used in Overlap Analysis and Network Analysis in Pharmomics. By extracting drugs common to both analyses, 24 drugs are predicted to demonstrate therapeutic effect in EC patients. Several of which have already been shown to demonstrate a therapeutic effect in EC, most notably Doxorubicin, which is commonly used to treat EC patients, and Ixazomib, which was recently shown to induce apoptosis and supress growth of EC cell lines. Additionally, our analysis predicts multiple psychiatric drugs, including Venlafaxine, as repositioned drugs. This is in line with recent research which suggests that psychiatric drugs should be investigated for use in gastrointestinal cancers such as EC. Our study shows that a drug repositioning approach is a feasible strategy for identifying novel ESCC therapies and can also improve the understanding of the mechanisms underlying the drug targets.

PMID:36246638 | PMC:PMC9554346 | DOI:10.3389/fgene.2022.991842

Virology. 2022 Oct 7;577:1-15. doi: 10.1016/j.virol.2022.08.008. Online ahead of print.

ABSTRACT

The nucleotide-binding pockets (NBPs) in virus-specific proteins have proven to be the most successful antiviral targets for several viral diseases. Functionally important NBPs are found in various structural and non-structural proteins of SARS-CoV-2. In this study, the first successful multi-targeting attempt to identify effective antivirals has been made against NBPs in nsp12, nsp13, nsp14, nsp15, nsp16, and nucleocapsid (N) proteins of SARS-CoV-2. A structure-based drug repurposing in silico screening approach with ADME analysis identified small molecules targeting NBPs in SARS-CoV-2 proteins. Further, isothermal titration calorimetry (ITC) experiments validated the binding of top hit molecules to the purified N-protein. Importantly, cell-based antiviral assays revealed antiviral potency for INCB28060, darglitazone, and columbianadin with EC50 values 15.71 μM, 5.36 μM, and 22.52 μM, respectively. These effective antivirals targeting multiple proteins are envisioned to direct the development of antiviral therapy against SARS-CoV-2 and its emerging variants.

PMID:36244310 | DOI:10.1016/j.virol.2022.08.008

Brief Bioinform. 2022 Oct 14:bbac434. doi: 10.1093/bib/bbac434. Online ahead of print.

ABSTRACT

MOTIVATION: Discovering the drug-target interactions (DTIs) is a crucial step in drug development such as the identification of drug side effects and drug repositioning. Since identifying DTIs by web-biological experiments is time-consuming and costly, many computational-based approaches have been proposed and have become an efficient manner to infer the potential interactions. Although extensive effort is invested to solve this task, the prediction accuracy still needs to be improved. More especially, heterogeneous network-based approaches do not fully consider the complex structure and rich semantic information in these heterogeneous networks. Therefore, it is still a challenge to predict DTIs efficiently.

RESULTS: In this study, we develop a novel method via Multiview heterogeneous information network embedding with Hierarchical Attention mechanisms to discover potential Drug-Target Interactions (MHADTI). Firstly, MHADTI constructs different similarity networks for drugs and targets by utilizing their multisource information. Combined with the known DTI network, three drug-target heterogeneous information networks (HINs) with different views are established. Secondly, MHADTI learns embeddings of drugs and targets from multiview HINs with hierarchical attention mechanisms, which include the node-level, semantic-level and graph-level attentions. Lastly, MHADTI employs the multilayer perceptron to predict DTIs with the learned deep feature representations. The hierarchical attention mechanisms could fully consider the importance of nodes, meta-paths and graphs in learning the feature representations of drugs and targets, which makes their embeddings more comprehensively. Extensive experimental results demonstrate that MHADTI performs better than other SOTA prediction models. Moreover, analysis of prediction results for some interested drugs and targets further indicates that MHADTI has advantages in discovering DTIs.

AVAILABILITY AND IMPLEMENTATION: https://github.com/pxystudy/MHADTI.

PMID:36242566 | DOI:10.1093/bib/bbac434

J Antibiot (Tokyo). 2022 Oct 14. doi: 10.1038/s41429-022-00574-y. Online ahead of print.

ABSTRACT

Phytopathogenic bacteria are a major cause of crop mortality and yield reduction, especially in field cultivation. The lack of effective chemistry agri-bactericides is responsible for challenging field prevention and treatment, prompting the development of long-lasting solutions to prevent, reduce, or manage some of the most devastating plant diseases facing modern agriculture today and in the future. Therefore, there is an urgent need to find lead drugs preventing and treating phytopathogenic bacterial infection. Drug repurposing, a strategy used to identify novel uses for existing approved drugs outside of their original indication, takes less time and investment than Traditional R&D Strategies in the process of drug development. Based on this method, we conduct a screen of 700 chemically diverse and potentially safe drugs against Xanthomonas oryzae PV. oryzae ACCC 11602 (Xoo), Xanthomonas axonopodis PV. citri (Xac), and Pectobacterium atrosepticum ACCC 19901 (Pa). Furthermore, the structure-activity relationship and structural similarity analysis of active drugs classify potent agri-bactericides into 8 lead series: salicylanilides, cationic nitrogen-containing drugs, azole antifungals, N-containing group, hydroxyquinolines, piperazine, kinase inhibitor and miscellaneous groups. MIC values were evaluated as antibacterial activities in this study. Identifying highly active lead compounds from the screening of approved drugs and comparison with the currently applied plant pathogenic bactericide to validate the bactericidal activity of the best candidates and assess if selected molecules or scaffolds lead to develop new antibacterial agents in the future. In conclusion, this study provides a possibility for the development of potent and highly selective agri-bactericides leads.

PMID:36241714 | DOI:10.1038/s41429-022-00574-y

Drug Discov Today. 2022 Oct 11:103396. doi: 10.1016/j.drudis.2022.103396. Online ahead of print.

ABSTRACT

The advancement of drug repurposing (DR) relies on scientific leadership and innovative institutions with access to knowledge. We performed a bibliometric and social network analysis (covering the period 2011-2020) to map the DR research trends and to identify innovation and knowledge leaders (IKLs). The indexing rate of DR publications has steadily increased. Major research areas included emerging viruses, cancer, methodological approaches, preclinical research, and infectious diseases. Government and academia accounted for nearly 65% of funding. Using a combination of network centrality metrics, 41 IKLs affiliated to central and pericentral institutions in the global research network were identified. These IKLs can facilitate the flow of knowledge and are best positioned to promote interactions within the network, with the potential to contribute significantly to advancing the DR field.

PMID:36241041 | DOI:10.1016/j.drudis.2022.103396

J Infect Public Health. 2022 Sep 27;15(11):1180-1191. doi: 10.1016/j.jiph.2022.09.007. Online ahead of print.

ABSTRACT

The high incidences of COVID-19 cases are believed to be associated with high transmissibility rates, which emphasizes the need for the discovery of evidence-based antiviral therapies for curing the disease. The rationale of repurposing existing classes of antiviral small molecule therapeutics against SARS-CoV-2 infection has been expected to accelerate the tedious and expensive drug development process. While Remdesivir has been recently approved to be the first treatment option for specific groups of COVID-19 patients, combinatory therapy with potential antiviral drugs may be necessary to enhance the efficacy in different populations. Hence, a comprehensive list of investigational antimicrobial drug compounds such as Favipiravir, Fidaxomicin, Galidesivir, GC376, Ribavirin, Rifabutin, and Umifenovir were computationally evaluated in this study. We performed in silico docking and molecular dynamics simulation on the selected small molecules against RNA-dependent RNA polymerase, which is one of the key target proteins of SARS-CoV-2, using AutoDock and GROMACS. Interestingly, our results revealed that the macrocyclic antibiotic, Fidaxomicin, possesses the highest binding affinity with the lowest energy value of -8.97 kcal/mol binding to the same active sites of RdRp. GC376, Rifabutin, Umifenovir and Remdesivir were identified as the next best compounds. Therefore, the above-mentioned compounds could be considered good leads for further preclinical and clinical experimentations as potentially efficient antiviral inhibitors for combination therapies against SARS-CoV-2.

PMID:36240528 | DOI:10.1016/j.jiph.2022.09.007

Front Genet. 2022 Sep 27;13:1017539. doi: 10.3389/fgene.2022.1017539. eCollection 2022.

ABSTRACT

Colorectal cancer (CRC), a common malignant tumor, is one of the main causes of death in cancer patients in the world. Therefore, it is critical to understand the molecular mechanism of CRC and identify its diagnostic and prognostic biomarkers. The purpose of this study is to reveal the genes involved in the development of CRC and to predict drug candidates that may help treat CRC through bioinformatics analyses. Two independent CRC gene expression datasets including The Cancer Genome Atlas (TCGA) database and GSE104836 were used in this study. Differentially expressed genes (DEGs) were analyzed separately on the two datasets, and intersected for further analyses. 249 drug candidates for CRC were identified according to the intersected DEGs and the Crowd Extracted Expression of Differential Signatures (CREEDS) database. In addition, hub genes were analyzed using Cytoscape according to the DEGs, and survival analysis results showed that one of the hub genes, TIMP1 was related to the prognosis of CRC patients. Thus, we further focused on drugs that could reverse the expression level of TIMP1. Eight potential drugs with documentary evidence and two new drugs that could reverse the expression of TIMP1 were found among the 249 drugs. In conclusion, we successfully identified potential biomarkers for CRC and achieved drug repurposing using bioinformatics methods. Further exploration is needed to understand the molecular mechanisms of these identified genes and drugs/small molecules in the occurrence, development and treatment of CRC.

PMID:36238159 | PMC:PMC9551025 | DOI:10.3389/fgene.2022.1017539

Molecules. 2022 Sep 30;27(19):6453. doi: 10.3390/molecules27196453.

ABSTRACT

Drug repurposing in the context of neuroimmunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that bypasses lengthy drug discovery procedures and focuses on discovering new usages for known medications. Neuroimmunological diseases, such as Alzheimer's, Parkinson's, multiple sclerosis, and depression, include various pathologies that result from the interaction between the central nervous system and the immune system. However, the repurposing of NI medications is hindered by the vast amount of information that needs mining. We previously presented Adera1.0, which was capable of text mining PubMed for answering query-based questions. However, Adera1.0 was not able to automatically identify chemical compounds within relevant sentences. To challenge the need for repurposing known medications for neuroimmunological diseases, we built a deep neural network named Adera2.0 to perform drug repurposing. The workflow uses three deep learning networks. The first network is an encoder and its main task is to embed text into matrices. The second network uses a mean squared error (MSE) loss function to predict answers in the form of embedded matrices. The third network, which constitutes the main novelty in our updated workflow, also uses a MSE loss function. Its main usage is to extract compound names from relevant sentences resulting from the previous network. To optimize the network function, we compared eight different designs. We found that a deep neural network consisting of an RNN neural network and a leaky ReLU could achieve 0.0001 loss and 67% sensitivity. Additionally, we validated Adera2.0's ability to predict NI drug usage against the DRUG Repurposing Hub database. These results establish the ability of Adera2.0 to repurpose drug candidates that can shorten the development of the drug cycle. The workflow could be download online.

PMID:36234990 | DOI:10.3390/molecules27196453

Molecules. 2022 Sep 20;27(19):6150. doi: 10.3390/molecules27196150.

ABSTRACT

The World Health Organization has put drug resistance in tuberculosis on its list of significant threats, with a critical emphasis on resolving the genetic differences in Mycobacterium tuberculosis. This provides an opportunity for a better understanding of the evolutionary progression leading to anti-microbial resistance. Anti-microbial resistance has a great impact on the economic stability of the global healthcare sector. We performed a timeline genomic analysis from 2003 to 2021 of 578 mycobacterium genomes to understand the pattern underlying genomic variations. Potential drug targets based on functional annotation was subjected to pharmacophore-based screening of FDA-approved phyto-actives. Reaction search, MD simulations, and metadynamics studies were performed. A total of 4,76,063 mutations with a transition/transversion ratio of 0.448 was observed. The top 10 proteins with the least number of mutations were high-confidence drug targets. Aminoglycoside 2'-N-acetyltransferase protein (AAC2'), conferring resistance to aminoglycosides, was shortlisted as a potential drug target based on its function and role in bait drug synergism. Gentamicin-AAC2' binding pose was used as a pharmacophore template to screen 10,570 phyto-actives. A total of 66 potential hits were docked to obtain naloxone as a lead-active with a docking score of -6.317. Naloxone is an FDA-approved drug that rapidly reverses opioid overdose. This is a classic case of a repurposed phyto-active. Naloxone consists of an amine group, but the addition of the acetyl group is unfavorable, with a reaction energy of 612.248 kcal/mol. With gentamicin as a positive control, molecular dynamic simulation studies were performed for 200 ns to check the stability of binding. Metadynamics-based studies were carried out to compare unbinding energy with gentamicin. The unbinding energies were found to be -68 and -74 kcal/mol for naloxone and gentamycin, respectively. This study identifies naloxone as a potential drug candidate for a bait drug synergistic approach against Mycobacterium tuberculosis.

PMID:36234683 | DOI:10.3390/molecules27196150

Int J Mol Sci. 2022 Oct 6;23(19):11851. doi: 10.3390/ijms231911851.

ABSTRACT

SARS-CoV-2 is the cause of the COVID-19 pandemic which has claimed more than 6.5 million lives worldwide, devastating the economy and overwhelming healthcare systems globally. The development of new drug molecules and vaccines has played a critical role in managing the pandemic; however, new variants of concern still pose a significant threat as the current vaccines cannot prevent all infections. This situation calls for the collaboration of biomedical scientists and healthcare workers across the world. Repurposing approved drugs is an effective way of fast-tracking new treatments for recently emerged diseases. To this end, we have assembled and curated a database consisting of 7817 compounds from the Compounds Australia Open Drug collection. We developed a set of eight filters based on indicators of efficacy and safety that were applied sequentially to down-select drugs that showed promise for drug repurposing efforts against SARS-CoV-2. Considerable effort was made to evaluate approximately 14,000 assay data points for SARS-CoV-2 FDA/TGA-approved drugs and provide an average activity score for 3539 compounds. The filtering process identified 12 FDA-approved molecules with established safety profiles that have plausible mechanisms for treating COVID-19 disease. The methodology developed in our study provides a template for prioritising drug candidates that can be repurposed for the safe, efficacious, and cost-effective treatment of COVID-19, long COVID, or any other future disease. We present our database in an easy-to-use interactive interface (CoviRx that was also developed to enable the scientific community to access to the data of over 7000 potential drugs and to implement alternative prioritisation and down-selection strategies.

PMID:36233149 | DOI:10.3390/ijms231911851

Int J Mol Sci. 2022 Oct 4;23(19):11746. doi: 10.3390/ijms231911746.

ABSTRACT

Reactive oxygen species (ROS) induce carcinogenesis by causing genetic mutations, activating oncogenes, and increasing oxidative stress, all of which affect cell proliferation, survival, and apoptosis. When compared to normal cells, cancer cells have higher levels of ROS, and they are responsible for the maintenance of the cancer phenotype; this unique feature in cancer cells may, therefore, be exploited for targeted therapy. Quercetin (QC), a plant-derived bioflavonoid, is known for its ROS scavenging properties and was recently discovered to have various antitumor properties in a variety of solid tumors. Adaptive stress responses may be induced by persistent ROS stress, allowing cancer cells to survive with high levels of ROS while maintaining cellular viability. However, large amounts of ROS make cancer cells extremely susceptible to quercetin, one of the most available dietary flavonoids. Because of the molecular and metabolic distinctions between malignant and normal cells, targeting ROS metabolism might help overcome medication resistance and achieve therapeutic selectivity while having little or no effect on normal cells. The powerful bioactivity and modulatory role of quercetin has prompted extensive research into the chemical, which has identified a number of pathways that potentially work together to prevent cancer, alongside, QC has a great number of evidences to use as a therapeutic agent in cancer stem cells. This current study has broadly demonstrated the function-mechanistic relationship of quercetin and how it regulates ROS generation to kill cancer and cancer stem cells. Here, we have revealed the regulation and production of ROS in normal cells and cancer cells with a certain signaling mechanism. We demonstrated the specific molecular mechanisms of quercetin including MAPK/ERK1/2, p53, JAK/STAT and TRAIL, AMPKα1/ASK1/p38, RAGE/PI3K/AKT/mTOR axis, HMGB1 and NF-κB, Nrf2-induced signaling pathways and certain cell cycle arrest in cancer cell death, and how they regulate the specific cancer signaling pathways as long-searched cancer therapeutics.

PMID:36233051 | DOI:10.3390/ijms231911746

Int J Mol Sci. 2022 Sep 28;23(19):11455. doi: 10.3390/ijms231911455.

ABSTRACT

Due to the heterogeneity of breast cancer, current available treatment options are moderately effective at best. Hence, it is highly recommended to comprehend different subtypes, understand pathogenic mechanisms involved, and develop treatment modalities. The repurposing of an old FDA approved anti-malarial drug, amodiaquine (AQ) presents an outstanding opportunity to explore its efficacy in treating majority of breast cancer subtypes. Cytotoxicity, scratch assay, vasculogenic mimicry study, and clonogenic assay were employed to determine AQ's ability to inhibit cell viability, cell migration, vascular formation, and colony growth. 3D Spheroid cell culture studies were performed to identify tumor growth inhibition potential of AQ in MCF-7 and MDAMB-231 cell lines. Apoptosis assays, cell cycle analysis, RT-qPCR assays, and Western blot studies were performed to determine AQ's ability to induce apoptosis, cell cycle changes, gene expression changes, and induction of autophagy marker proteins. The results from in-vitro studies confirmed the potential of AQ as an anti-cancer drug. In different breast cancer cell lines tested, AQ significantly induces cytotoxicity, inhibit colony formation, inhibit cell migration, reduces 3D spheroid volume, induces apoptosis, blocks cell cycle progression, inhibit expression of cancer related genes, and induces LC3BII protein to inhibit autophagy. Our results demonstrate that amodiaquine is a promising drug to repurpose for breast cancer treatment, which needs numerous efforts from further studies.

PMID:36232751 | DOI:10.3390/ijms231911455

Int J Mol Sci. 2022 Sep 20;23(19):11009. doi: 10.3390/ijms231911009.

ABSTRACT

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified as the pathogenic cause of coronavirus disease 2019 (COVID-19). The RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 is a potential target for the treatment of COVID-19. An RdRp complex:dsRNA structure suitable for docking simulations was prepared using a cryo-electron microscopy (cryo-EM) structure (PDB ID: 7AAP; resolution, 2.60 Å) that was reported recently. Structural refinement was performed using energy calculations. Structure-based virtual screening was performed using the ChEMBL database. Through 1,838,257 screenings, 249 drugs (37 approved, 93 clinical, and 119 preclinical drugs) were predicted to exhibit a high binding affinity for the RdRp complex:dsRNA. Nine nucleoside triphosphate analogs with anti-viral activity were included among these hit drugs, and among them, remdesivir-ribonucleoside triphosphate and favipiravir-ribonucleoside triphosphate adopted a similar docking mode as that observed in the cryo-EM structure. Additional docking simulations for the predicted compounds with high binding affinity for the RdRp complex:dsRNA suggested that 184 bioactive compounds could be anti-SARS-CoV-2 drug candidates. The hit bioactive compounds mainly consisted of a typical noncovalent major groove binder for dsRNA. Three-layer ONIOM (MP2/6-31G:AM1:AMBER) geometry optimization calculations and frequency analyses (MP2/6-31G:AMBER) were performed to estimate the binding free energy of a representative bioactive compound obtained from the docking simulation, and the fragment molecular orbital calculation at the MP2/6-31G level of theory was subsequently performed for analyzing the detailed interactions. The procedure used in this study represents a possible strategy for discovering anti-SARS-CoV-2 drugs from drug libraries that could significantly shorten the clinical development period for drug repositioning.

PMID:36232311 | DOI:10.3390/ijms231911009

Cells. 2022 Sep 27;11(19):3031. doi: 10.3390/cells11193031.

ABSTRACT

The incidence of allergic disease has grown tremendously in the past three generations. While current treatments are effective for some, there is considerable unmet need. Mast cells are critical effectors of allergic inflammation. Their secreted mediators and the receptors for these mediators have long been the target of allergy therapy. Recent drugs have moved a step earlier in mast cell activation, blocking IgE, IL-4, and IL-13 interactions with their receptors. In this review, we summarize the latest therapies targeting mast cells as well as new drugs in clinical trials. In addition, we offer support for repurposing FDA-approved drugs to target mast cells in new ways. With a multitude of highly selective drugs available for cancer, autoimmunity, and metabolic disorders, drug repurposing offers optimism for the future of allergy therapy.

PMID:36230993 | DOI:10.3390/cells11193031