Terazosin Stimulates Pgk1 to Remedy Gastrointestinal Disorders

Gastrointestinal disease is the most common health concern that occurs due to environmental, infectious, immunological, psychological, and genetic stress. Among them, the most frequent diseases are gastric ulcer (GU) and ulcerative colitis (UC). DSS-induced UC and ethanol-stimulated GU models resemble the pathophysiology of human gastrointestinal disease. The current study was designed to explore the anti-oxidation, anti-inflammation, anti-cell death properties of terazosin, an α-adrenergic receptor antagonist, in vivo and in vitro. Our results indicate that terazosin dramatically activates Pgk1, and upregulates glycose metabolism, evidenced by the enhanced ATP production and higher LDH enzymatic activity. Also, terazosin significantly enhances p-AKT expression and inhibits NF-κB p65 activation through abrogating the phosphorylation of IKBα, as well as lowers Caspase-1 and GSDMD expression. The findings in this study demonstrate that terazosin exhibits anti-inflammatory effects by downregulating NF-κB-GSDMD signal pathway, along with enhancing glycolysis for gastrointestinal disease treatment. Meanwhile, we also find terazosin ameliorates ethanol-induced gastric mucosal damage in mice. Collectively, as a clinical drug, terazosin should be translated into therapeutics for gastrointestinal disease soon.

Analysis of the expression patterns and clinical relevance of m6A regulators in 33 cancer types

Background: The role of N6-methyladenine (m6A) RNA methylation in a variety of biological processes is gradually being revealed. Methods: Here, we systematically describe the correlation between the expression pattern of m6A RNA methylation regulatory factors and clinical phenotype, immunity, drug sensitivity, stem cells and prognosis in more than 10,000 samples of 33 types of cancer. Results: The results show that there are significant differences in the expression of 20 m6A RNA methylation regulatory factors in different cancers, and there was a significant correlation with the analysis indicators. Conclusion: In this study, the m6A RNA methylation regulatory factor was found not only to potentially assist in stratifying the prognosis but also to predict or improve the sensitivity of clinical drug therapy.

Clinical drug response prediction from preclinical cancer cell lines by logistic matrix factorization approach

Predicting tumor drug response using cancer cell line drug response values for a large number of anti-cancer drugs is a significant challenge in personalized medicine. Predicting patient response to drugs from data obtained from preclinical models is made easier by the availability of different knowledge on cell lines and drugs. This paper proposes the TCLMF method, a predictive model for predicting drug response in tumor samples that was trained on preclinical samples and is based on the logistic matrix factorization approach. The TCLMF model is designed based on gene expression profiles, tissue type information, the chemical structure of drugs and drug sensitivity (IC 50) data from cancer cell lines. We use preclinical data from the Genomics of Drug Sensitivity in Cancer dataset (GDSC) to train the proposed drug response model, which we then use to predict drug sensitivity of samples from the Cancer Genome Atlas (TCGA) dataset. The TCLMF approach focuses on identifying successful features of cell lines and drugs in order to calculate the probability of the tumor samples being sensitive to drugs. The closest cell line neighbours for each tumor sample are calculated using a description of similarity between tumor samples and cell lines in this study. The drug response for a new tumor is then calculated by averaging the low-rank features obtained from its neighboring cell lines. We compare the results of the TCLMF model with the results of the previously proposed methods using two databases and two approaches to test the model’s performance. In the first approach, 12 drugs with enough known clinical drug response, considered in previous methods, are studied. For 7 drugs out of 12, the TCLMF can significantly distinguish between patients that are resistance to these drugs and the patients that are sensitive to them. These approaches are converted to classification models using a threshold in the second approach, and the results are compared. The results demonstrate that the TCLMF method provides accurate predictions across the results of the other algorithms. Finally, we accurately classify tumor tissue type using the latent vectors obtained from TCLMF’s logistic matrix factorization process. These findings demonstrate that the TCLMF approach produces effective latent vectors for tumor samples. The source code of the TCLMF method is available in https://github.com/emdadi/TCLMF.

Effect of Monthly Evaluations on Irrational Rates of Antimicrobial Drug Prescription in Outpatient and Emergency Departments

Reduction of the excessive rate of antibiotic prescription is needed to curb antibiotic resistance. This retrospective study was conducted to verify whether monthly evaluations of antibiotic prescriptions could improve clinical antibiotic use in outpatient and emergency departments. Every month, from July 2016 to June 2019, 25% of the antibacterial prescriptions from the outpatient and emergency departments in our hospital were randomly selected. The hospital formed an evaluation team that conducted preliminary evaluations of these prescriptions and an expert team that re-evaluated any problematic prescriptions. We analysed the irrational prescription rate, proportion of antibiotic use, and consistency between the evaluation and expert teams. At the end of the evaluation period, the utilisation rate of single antibiotics in the outpatient and emergency departments increased, the irrational prescription rate decreased, and the proportion of sold antibiotics gradually decreased. In addition, the consistency of prescription evaluation results between the evaluation and expert groups increased over time. In conclusion, monthly evaluation of antibiotic prescriptions is an effective management tool for the rational use of antibiotics in clinical practice and plays an important role in safe clinical drug use.

Identification of a Novel Delta Opioid Receptor Agonist Chemotype with Potential Negative Allosteric Modulator Capabilities

The δ-opioid receptor (δOR) holds great potential as a therapeutic target. Yet, clinical drug development, which has focused on δOR agonists that mimic the potent and selective tool compound SNC80 have largely failed. It has increasingly become apparent that the SNC80 scaffold carries with it potent and efficacious β-arrestin recruitment. Here, we screened a relatively small (5120 molecules) physical drug library to identify δOR agonists that underrecruit β-arrestin, as it has been suggested that compounds that efficaciously recruit β-arrestin are proconvulsant. The screen identified a hit compound and further characterization using cellular binding and signaling assays revealed that this molecule (R995045, compound 1) exhibited ten-fold selectivity over µ- and κ-opioid receptors. Compound 1 represents a novel chemotype at the δOR. A subsequent characterization of fourteen analogs of compound 1, however did not identify a more potent δOR agonist. Computational modeling and in vitro characterization of compound 1 in the presence of the endogenous agonist leu-enkephalin suggest compound 1 may also bind allosterically and negatively modulate the potency of Leu-enkephalin to inhibit cAMP, acting as a ‘NAM-agonist’ in this assay. The potential physiological utility of such a class of compounds will need to be assessed in future in vivo assays.

Multilevel factors predict medication adherence in rheumatoid arthritis: a 6-month cohort study

Non-adherence challenges efficacy and costs of healthcare. Knowledge of the underlying factors is essential to design effective intervention strategies.ObjectivesTo estimate the prevalence of treatment adherence in rheumatoid arthritis (RA) and to evaluate its predictors.MethodsA 6-month prospective cohort study of patients with RA selected by systematic stratified sampling (33% on first disease-modifying rheumatic drug (DMARD), 33% on second-line DMARD and 33% on biologics). The outcome measure was treatment adherence, defined by a score greater than 80% both in the Compliance Questionnaire in Rheumatology and the Reported Adherence to Medication scale, and was estimated with 95% CIs. Predictive factors included sociodemographic, psychological, clinical, drug-related, patient–doctor relationship related and logistic. Their effect on 6-month adherence was examined by multilevel logistic models adjusted for baseline covariates.Results180 patients were recruited (77% women, mean age 60.8). The prevalence of adherence was 59.1% (95% CI 48.1% to 71.8%). Patients on biologics showed higher adherence and perceived a higher medication need than the others; patients on second-line DMARDs had experienced more adverse events than the others. The variables explaining adherence in the final multivariate model were the type of treatment prescribed (second-line DMARDs OR=5.22, and biologics OR=3.76), agreement on treatment (OR=4.57), having received information on treatment adaptation (OR=1.42) and the physician perception of patient trust (OR=1.58). These effects were independent of disease activity.ConclusionTreatment adherence in RA is far from complete. Psychological, communicational and logistic factors influence treatment adherence in RA to a greater extent than sociodemographic or clinical factors.

Structure of Mycobacterium tuberculosis cytochrome bcc in complex with Q203 and TB47, two anti-TB drug candidates

Pathogenic mycobacteria pose a sustained threat to global human health. Recently, cytochrome bcc complexes have gained interest as targets for antibiotic drug development. However, there is currently no structural information for the cytochrome bcc complex from these pathogenic mycobacteria. Here, we report the structures of Mycobacterium tuberculosis cytochrome bcc alone (2.68 Å resolution) and in complex with clinical drug candidates Q203 (2.67 Å resolution) and TB47 (2.93 Å resolution) determined by single-particle cryo-electron microscopy. M. tuberculosis cytochrome bcc forms a dimeric assembly with endogenous menaquinone/menaquinol bound at the quinone/quinol-binding pockets. We observe Q203 and TB47 bound at the quinol-binding site and stabilized by hydrogen bonds with the side chains of QcrBThr313 and QcrBGlu314, residues that are conserved across pathogenic mycobacteria. These high-resolution images provide a basis for the design of new mycobacterial cytochrome bcc inhibitors that could be developed into broad-spectrum drugs to treat mycobacterial infections.