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Showing 2421 to 2424 of 5186 entries
Published on July 14, 2020
Small Molecule Enhancers of Endosome-to-Cytosol Import Augment Anti-tumor Immunity.
Abstract: Cross-presentation of antigens by dendritic cells (DCs) is critical for initiation of anti-tumor immune responses. Yet, key steps involved in trafficking of antigens taken up by DCs remain incompletely understood. Here, we screen 700 US Food and Drug Administration (FDA)-approved drugs and identify 37 enhancers of antigen import from endolysosomes into the cytosol. To reveal their mechanism of action, we generate proteomic organellar maps of control and drug-treated DCs (focusing on two compounds, prazosin and tamoxifen). By combining organellar mapping, quantitative proteomics, and microscopy, we conclude that import enhancers undergo lysosomal trapping leading to membrane permeation and antigen release. Enhancing antigen import facilitates cross-presentation of soluble and cell-associated antigens. Systemic administration of prazosin leads to reduced growth of MC38 tumors and to a synergistic effect with checkpoint immunotherapy in a melanoma model. Thus, inefficient antigen import into the cytosol limits antigen cross-presentation, restraining the potency of anti-tumor immune responses and efficacy of checkpoint blockers.
Published on July 14, 2020
Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing.
Abstract: Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Various studies exist about the molecular mechanisms of viral infection. However, such information is spread across many publications and it is very time-consuming to integrate, and exploit. We develop CoVex, an interactive online platform for SARS-CoV-2 host interactome exploration and drug (target) identification. CoVex integrates virus-human protein interactions, human protein-protein interactions, and drug-target interactions. It allows visual exploration of the virus-host interactome and implements systems medicine algorithms for network-based prediction of drug candidates. Thus, CoVex is a resource to understand molecular mechanisms of pathogenicity and to prioritize candidate therapeutics. We investigate recent hypotheses on a systems biology level to explore mechanistic virus life cycle drivers, and to extract drug repurposing candidates. CoVex renders COVID-19 drug research systems-medicine-ready by giving the scientific community direct access to network medicine algorithms. It is available at https://exbio.wzw.tum.de/covex/.
Published on July 13, 2020
High Throughput Virtual Screening to Discover Inhibitors of the Main Protease of the Coronavirus SARS-CoV-2.
Abstract: We use state-of-the-art computer-aided drug design (CADD) techniques to identify prospective inhibitors of the main protease enzyme, 3CL(pro) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19. From our screening of over one million compounds including approved drugs, investigational drugs, natural products, and organic compounds, and a rescreening protocol incorporating enzyme dynamics via ensemble docking, we have been able to identify a range of prospective 3CL(pro) inhibitors. Importantly, some of the identified compounds had previously been reported to exhibit inhibitory activities against the 3CL(pro) enzyme of the closely related SARS-CoV virus. The top-ranking compounds are characterized by the presence of multiple bi- and monocyclic rings, many of them being heterocycles and aromatic, which are flexibly linked allowing the ligands to adapt to the geometry of the 3CL(pro) substrate site and involve a high amount of functional groups enabling hydrogen bond formation with surrounding amino acid residues, including the catalytic dyad residues H41 and C145. Among the top binding compounds we identified several tyrosine kinase inhibitors, which include a bioflavonoid, the group of natural products that binds best to 3CL(pro). Another class of compounds that decently binds to the SARS-CoV-2 main protease are steroid hormones, which thus may be endogenous inhibitors and might provide an explanation for the age-dependent severity of COVID-19. Many of the compounds identified by our work show a considerably stronger binding than found for reference compounds with in vitro demonstrated 3CL(pro) inhibition and anticoronavirus activity. The compounds determined in this work thus represent a good starting point for the design of inhibitors of SARS-CoV-2 replication.
Published on July 12, 2020
A possible strategy to fight COVID-19: Interfering with spike glycoprotein trimerization.
Abstract: The recent release of COVID-19 spike glycoprotein allows detailed analysis of the structural features that are required for stabilizing the infective form of its quaternary assembly. Trying to disassemble the trimeric structure of COVID-19 spike glycoprotein, we analyzed single protomer surfaces searching for concave moieties that are located at the three protomer-protomer interfaces. The presence of some druggable pockets at these interfaces suggested that some of the available drugs in Drug Bank could destabilize the quaternary spike glycoprotein formation by binding to these pockets, therefore interfering with COVID-19 life cycle. The approach we propose here can be an additional strategy to fight against the deadly virus. Ligands of COVID-19 spike glycoprotein that we have predicted in the present computational investigation, might be the basis for new experimental studies in vitro and in vivo.
Published on July 9, 2020
Hybrid phenotype mining method for investigating off-target protein and underlying side effects of anti-tumor immunotherapy.
Abstract: BACKGROUND: It is of utmost importance to investigate novel therapies for cancer, as it is a major cause of death. In recent years, immunotherapies, especially those against immune checkpoints, have been developed and brought significant improvement in cancer management. However, on the other hand, immune checkpoints blockade (ICB) by monoclonal antiboties may cause common and severe adverse reactions (ADRs), the cause of which remains largely undetermined. We hypothesize that ICB-agents may induce adverse reactions through off-target protein interactions, similar to the ADR-causing off-target effects of small molecules. In this study, we propose a hybrid phenotype mining approach which integrates molecular level information and provides new mechanistic insights for ICB-associated ADRs. METHODS: We trained a conditional random fields model on the TAC 2017 benchmark training data, then used it to extract all drug-centric phenotypes for the five anti-PD-1/PD-L1 drugs from the drug labels of the DailyMed database. Proteins with structure similar to the drugs were obtained by using BlastP, and the gene targets of drugs were obtained from the STRING database. The target-centric phenotypes were extracted from the human phenotype ontology database. Finally, a screening module was designed to investigate off-target proteins, by making use of gene ontology analysis and pathway analysis. RESULTS: Eventually, through the cross-analysis of the drug and target gene phenotypes, the off-target effect caused by the mutation of gene BTK was found, and the candidate side-effect off-target site was analyzed. CONCLUSIONS: This research provided a hybrid method of biomedical natural language processing and bioinformatics to investigate the off-target-based mechanism of ICB treatment. The method can also be applied for the investigation of ADRs related to other large molecule drugs.
Published on July 9, 2020
Oxytetracycline Pharmacokinetics After Intramuscular Administration in Cows with Clinical Metritis Associated with Trueperella Pyogenes Infection.
Abstract: Systemic therapy with oxytetracycline is often used for treatment of clinical metritis although data about its penetration into the uterus and uterine secretion are lacking. Uterine secretions and milk from six cows with clinical metritis were collected for microbiological assay. The animals were treated intramuscularly with long-acting oxytetracycline (20 mg/kg) and samples of plasma, milk and uterine secretions were collected for determination of the antibiotic concentrations by HPLC-PDA analysis. Pharmacokinetics of the antibiotic and in silico prediction of its penetration into the uterus were described. Trueperella pyogenes with MIC values of 16-64 microg mL(-1) was isolated (n of cows = 4) from uterine secretions. Oxytetracycline showed fast absorption and penetration in the uterine secretions and milk. No change of withdrawal time for milk was necessitated in cows with clinical metritis. Maximum levels in uterine secretions and predicted concentrations of oxytetracycline in the uterus were lower than MIC values. Systemic administration of long-acting oxytetracycline did not guarantee clinical cure and was not a suitable choice for treatment of clinical metritis associated with Trueperella pyogenes. The appropriate approach to antibiotic treatment of uterine infections of cows requires knowledge on penetration of the antibiotics at the site of infection and sensitivity of pathogens.
Published on July 7, 2020
DrugSniper, a Tool to Exploit Loss-Of-Function Screens, Identifies CREBBP as a Predictive Biomarker of VOLASERTIB in Small Cell Lung Carcinoma (SCLC).
Abstract: The development of predictive biomarkers of response to targeted therapies is an unmet clinical need for many antitumoral agents. Recent genome-wide loss-of-function screens, such as RNA interference (RNAi) and CRISPR-Cas9 libraries, are an unprecedented resource to identify novel drug targets, reposition drugs and associate predictive biomarkers in the context of precision oncology. In this work, we have developed and validated a large-scale bioinformatics tool named DrugSniper, which exploits loss-of-function experiments to model the sensitivity of 6237 inhibitors and predict their corresponding biomarkers of sensitivity in 30 tumor types. Applying DrugSniper to small cell lung cancer (SCLC), we identified genes extensively explored in SCLC, such as Aurora kinases or epigenetic agents. Interestingly, the analysis suggested a remarkable vulnerability to polo-like kinase 1 (PLK1) inhibition in CREBBP-mutant SCLC cells. We validated this association in vitro using four mutated and four wild-type SCLC cell lines and two PLK1 inhibitors (Volasertib and BI2536), confirming that the effect of PLK1 inhibitors depended on the mutational status of CREBBP. Besides, DrugSniper was validated in-silico with several known clinically-used treatments, including the sensitivity of Tyrosine Kinase Inhibitors (TKIs) and Vemurafenib to FLT3 and BRAF mutant cells, respectively. These findings show the potential of genome-wide loss-of-function screens to identify new personalized therapeutic hypotheses in SCLC and potentially in other tumors, which is a valuable starting point for further drug development and drug repositioning projects.
Published on July 7, 2020
In Silico ADME/Tox Profiling of Natural Products: A Focus on BIOFACQUIM.
Abstract: Natural products continue to be major sources of bioactive compounds and drug candidates not only because of their unique chemical structures but also because of their overall favorable metabolism and pharmacokinetic properties. The number of publicly accessible natural product databases has increased significantly in the past few years. However, the systematic ADME/Tox profile has been reported on a limited basis. For instance, BIOFACQUIM was recently published as a public database of natural products from Mexico, a country with a rich source of biomolecules. However, its ADME/Tox profile has not been reported. Herein, we discuss the results of an in-depth in silico ADME/Tox profile of natural products in BIOFACQUIM and other large public collections of natural products. It was concluded that the absorption and distribution profiles of compounds in BIOFACQUIM are similar to those of approved drugs, while the metabolism profile is comparable to that in the other natural product databases. The excretion profile of compounds in BIOFACQUIM is different from that of the approved drugs, but their predicted toxicity profile is comparable. This work further contributes to the deeper characterization of natural product collections as major sources of bioactive compounds with therapeutic potential.
Published on July 6, 2020
Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).
Abstract: Drug-induced liver injury (DILI) is one the most unpredictable adverse reactions to xenobiotics in humans and the leading cause of postmarketing withdrawals of approved drugs. To date, these drugs have been collated by the FDA to form the DILIRank database, which classifies DILI severity and potential. These classifications have been used by various research groups in generating computational predictions for this type of liver injury. Recently, groups from Pfizer and AstraZeneca have collated DILI in vitro data and physicochemical properties for compounds that can be used along with data from the FDA to build machine learning models for DILI. In this study, we have used these data sets, as well as the Biopharmaceutics Drug Disposition Classification System data set, to generate Bayesian machine learning models with our in-house software, Assay Central. The performance of all machine learning models was assessed through both the internal 5-fold cross-validation metrics and prediction accuracy of an external test set of compounds with known hepatotoxicity. The best-performing Bayesian model was based on the DILI-concern category from the DILIRank database with an ROC of 0.814, a sensitivity of 0.741, a specificity of 0.755, and an accuracy of 0.746. A comparison of alternative machine learning algorithms, such as k-nearest neighbors, support vector classification, AdaBoosted decision trees, and deep learning methods, produced similar statistics to those generated with the Bayesian algorithm in Assay Central. This study demonstrates machine learning models grouped in a tool called MegaTox that can be used to predict early-stage clinical compounds, as well as recent FDA-approved drugs, to identify potential DILI.
Published on July 6, 2020
Drug-target interaction prediction using semi-bipartite graph model and deep learning.
Abstract: BACKGROUND: Identifying drug-target interaction is a key element in drug discovery. In silico prediction of drug-target interaction can speed up the process of identifying unknown interactions between drugs and target proteins. In recent studies, handcrafted features, similarity metrics and machine learning methods have been proposed for predicting drug-target interactions. However, these methods cannot fully learn the underlying relations between drugs and targets. In this paper, we propose anew framework for drug-target interaction prediction that learns latent features from drug-target interaction network. RESULTS: We present a framework to utilize the network topology and identify interacting and non-interacting drug-target pairs. We model the problem as a semi-bipartite graph in which we are able to use drug-drug and protein-protein similarity in a drug-protein network. We have then used a graph labeling method for vertex ordering in our graph embedding process. Finally, we employed deep neural network to learn the complex pattern of interacting pairs from embedded graphs. We show our approach is able to learn sophisticated drug-target topological features and outperforms other state-of-the-art approaches. CONCLUSIONS: The proposed learning model on semi-bipartite graph model, can integrate drug-drug and protein-protein similarities which are semantically different than drug-protein information in a drug-target interaction network. We show our model can determine interaction likelihood for each drug-target pair and outperform other heuristics.
Showing 2421 to 2424 of 5186 entries