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Showing 1741 to 1744 of 5186 entries
Published on May 5, 2021
Prospective Drug Candidates as Human Multidrug Transporter ABCG2 Inhibitors: an In Silico Drug Discovery Study.
Abstract: Breast cancer resistance protein (ABCG2) is a human ATP-binding cassette (ABC) that plays a paramount role in multidrug resistance (MDR) in cancer therapy. The discovery of ABCG2 inhibitors could assist in designing unprecedented therapeutic strategies for cancer treatment. There is as yet no approved drug targeting ABCG2, although a large number of drug candidates have been clinically investigated. In this work, binding affinities of 181 drug candidates in clinical-trial or investigational stages as ABCG2 inhibitors were inspected using in silico techniques. Based on available experimental data, the performance of AutoDock4.2.6 software was first validated to predict the inhibitor-ABCG2 binding mode and affinity. Combined molecular docking calculations and molecular dynamics (MD) simulations, followed by molecular mechanics-generalized Born surface area (MM-GBSA) binding energy calculations, were then performed to filter out the studied drug candidates. From the estimated docking scores and MM-GBSA binding energies, six auspicious drug candidates-namely, pibrentasvir, venetoclax, ledipasvir, avatrombopag, cobicistat, and revefenacin-exhibited auspicious binding energies with value < -70.0 kcal/mol. Interestingly, pibrentasvir, venetoclax, and ledipasvir were observed to show even higher binding affinities with the ABCG2 transporter with binding energies of < -80.0 kcal/mol over long MD simulations of 100 ns. The stabilities of these three promising candidates in complex with ABCG2 transporter were demonstrated by their energetics and structural analyses throughout the 100 ns MD simulations. The current study throws new light on pibrentasvir, venetoclax, and ledipasvir as curative options for multidrug resistant cancers by inhibiting ABCG2 transporter.
Published on May 4, 2021
GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds.
Abstract: The current study describes the construction of various ligand-based machine learning models to be used for drug-repurposing against the family of G-Protein Coupled Receptors (GPCRs). In building these models, we collected > 500,000 data points, encompassing experimentally measured molecular association data of > 160,000 unique ligands against > 250 GPCRs. These data points were retrieved from the GPCR-Ligand Association (GLASS) database. We have used diverse molecular featurization methods to describe the input molecules. Multiple supervised ML algorithms were developed, tested and compared for their accuracy, F scores, as well as for their Matthews' correlation coefficient scores (MCC). Our data suggest that combined with molecular fingerprinting, ensemble decision trees and gradient boosted trees ML algorithms are on the accuracy border of the rather sophisticated deep neural nets (DNNs)-based algorithms. On a test dataset, these models displayed an excellent performance, reaching a ~ 90% classification accuracy. Additionally, we showcase a few examples where our models were able to identify interesting connections between known drugs from the Drug-Bank database and members of the GPCR family of receptors. Our findings are in excellent agreement with previously reported experimental observations in the literature. We hope the models presented in this paper synergize with the currently ongoing interest of applying machine learning modeling in the field of drug repurposing and computational drug discovery in general.
Published on May 3, 2021
Cannabidiol inhibits the skeletal muscle Nav1.4 by blocking its pore and by altering membrane elasticity.
Abstract: Cannabidiol (CBD) is the primary nonpsychotropic phytocannabinoid found in Cannabis sativa, which has been proposed to be therapeutic against many conditions, including muscle spasms. Among its putative targets are voltage-gated sodium channels (Navs), which have been implicated in many conditions. We investigated the effects of CBD on Nav1.4, the skeletal muscle Nav subtype. We explored direct effects, involving physical block of the Nav pore, as well as indirect effects, involving modulation of membrane elasticity that contributes to Nav inhibition. MD simulations revealed CBD's localization inside the membrane and effects on bilayer properties. Nuclear magnetic resonance (NMR) confirmed these results, showing CBD localizing below membrane headgroups. To determine the functional implications of these findings, we used a gramicidin-based fluorescence assay to show that CBD alters membrane elasticity or thickness, which could alter Nav function through bilayer-mediated regulation. Site-directed mutagenesis in the vicinity of the Nav1.4 pore revealed that removing the local anesthetic binding site with F1586A reduces the block of INa by CBD. Altering the fenestrations in the bilayer-spanning domain with Nav1.4-WWWW blocked CBD access from the membrane into the Nav1.4 pore (as judged by MD). The stabilization of inactivation, however, persisted in WWWW, which we ascribe to CBD-induced changes in membrane elasticity. To investigate the potential therapeutic value of CBD against Nav1.4 channelopathies, we used a pathogenic Nav1.4 variant, P1158S, which causes myotonia and periodic paralysis. CBD reduces excitability in both wild-type and the P1158S variant. Our in vitro and in silico results suggest that CBD may have therapeutic value against Nav1.4 hyperexcitability.
Published on May 3, 2021
The Coronavirus Network Explorer: mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function.
Abstract: BACKGROUND: Leveraging previously identified viral interactions with human host proteins, we apply a machine learning-based approach to connect SARS-CoV-2 viral proteins to relevant host biological functions, diseases, and pathways in a large-scale knowledge graph derived from the biomedical literature. Our goal is to explore how SARS-CoV-2 could interfere with various host cell functions, and to identify drug targets amongst the host genes that could potentially be modulated against COVID-19 by repurposing existing drugs. The machine learning model employed here involves gene embeddings that leverage causal gene expression signatures curated from literature. In contrast to other network-based approaches for drug repurposing, our approach explicitly takes the direction of effects into account, distinguishing between activation and inhibition. RESULTS: We have constructed 70 networks connecting SARS-CoV-2 viral proteins to various biological functions, diseases, and pathways reflecting viral biology, clinical observations, and co-morbidities in the context of COVID-19. Results are presented in the form of interactive network visualizations through a web interface, the Coronavirus Network Explorer (CNE), that allows exploration of underlying experimental evidence. We find that existing drugs targeting genes in those networks are strongly enriched in the set of drugs that are already in clinical trials against COVID-19. CONCLUSIONS: The approach presented here can identify biologically plausible hypotheses for COVID-19 pathogenesis, explicitly connected to the immunological, virological and pathological observations seen in SARS-CoV-2 infected patients. The discovery of repurposable drugs is driven by prior knowledge of relevant functional endpoints that reflect known viral biology or clinical observations, therefore suggesting potential mechanisms of action. We believe that the CNE offers relevant insights that go beyond more conventional network approaches, and can be a valuable tool for drug repurposing. The CNE is available at https://digitalinsights.qiagen.com/coronavirus-network-explorer .
Published on May 3, 2021
Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease.
Abstract: BACKGROUND: Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. METHODS: In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. RESULTS: We applied our computational framework to prioritize novel putative target genes for Alzheimer's disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). CONCLUSIONS: Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.
Published on May 1, 2021
Multi-omics data integration and network-based analysis drives a multiplex drug repurposing approach to a shortlist of candidate drugs against COVID-19.
Abstract: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is undeniably the most severe global health emergency since the 1918 Influenza outbreak. Depending on its evolutionary trajectory, the virus is expected to establish itself as an endemic infectious respiratory disease exhibiting seasonal flare-ups. Therefore, despite the unprecedented rally to reach a vaccine that can offer widespread immunization, it is equally important to reach effective prevention and treatment regimens for coronavirus disease 2019 (COVID-19). Contributing to this effort, we have curated and analyzed multi-source and multi-omics publicly available data from patients, cell lines and databases in order to fuel a multiplex computational drug repurposing approach. We devised a network-based integration of multi-omic data to prioritize the most important genes related to COVID-19 and subsequently re-rank the identified candidate drugs. Our approach resulted in a highly informed integrated drug shortlist by combining structural diversity filtering along with experts' curation and drug-target mapping on the depicted molecular pathways. In addition to the recently proposed drugs that are already generating promising results such as dexamethasone and remdesivir, our list includes inhibitors of Src tyrosine kinase (bosutinib, dasatinib, cytarabine and saracatinib), which appear to be involved in multiple COVID-19 pathophysiological mechanisms. In addition, we highlight specific immunomodulators and anti-inflammatory drugs like dactolisib and methotrexate and inhibitors of histone deacetylase like hydroquinone and vorinostat with potential beneficial effects in their mechanisms of action. Overall, this multiplex drug repurposing approach, developed and utilized herein specifically for SARS-CoV-2, can offer a rapid mapping and drug prioritization against any pathogen-related disease.
Published on May 1, 2021
Therapeutic Targeting of Nemo-like Kinase in Primary and Acquired Endocrine-resistant Breast Cancer.
Abstract: PURPOSE: Endocrine resistance remains a major clinical challenge in estrogen receptor (ER)-positive breast cancer. Despite the encouraging results from clinical trials for the drugs targeting known survival signaling, relapse is still inevitable. There is an unmet need to discover new drug targets in the unknown escape pathways. Here, we report Nemo-like kinase (NLK) as a new actionable kinase target that endows previously uncharacterized survival signaling in endocrine-resistant breast cancer. EXPERIMENTAL DESIGN: The effects of NLK inhibition on the viability of endocrine-resistant breast cancer cell lines were examined by MTS assay. The effect of VX-702 on NLK activity was verified by kinase assay. The modulation of ER and its coactivator, SRC-3, by NLK was examined by immunoprecipitation, kinase assay, luciferase assay, and RNA sequencing. The therapeutic effects of VX-702 and everolimus were tested on cell line- and patient-derived xenograft (PDX) tumor models. RESULTS: NLK overexpression endows reduced endocrine responsiveness and is associated with worse outcome of patients treated with tamoxifen. Mechanistically, NLK may function, at least in part, via enhancing the phosphorylation of ERalpha and its key coactivator, SRC-3, to modulate ERalpha transcriptional activity. Through interrogation of a kinase profiling database, we uncovered and verified a highly selective dual p38/NLK inhibitor, VX-702. Coadministration of VX-702 with the mTOR inhibitor, everolimus, demonstrated a significant therapeutic effect in cell line-derived xenograft and PDX tumor models of acquired or de novo endocrine resistance. CONCLUSIONS: Together, this study reveals the potential of therapeutic modulation of NLK for the management of the endocrine-resistant breast cancers with active NLK signaling.
Published on May 1, 2021
Deep learning integration of molecular and interactome data for protein-compound interaction prediction.
Abstract: MOTIVATION: Virtual screening, which can computationally predict the presence or absence of protein-compound interactions, has attracted attention as a large-scale, low-cost, and short-term search method for seed compounds. Existing machine learning methods for predicting protein-compound interactions are largely divided into those based on molecular structure data and those based on network data. The former utilize information on proteins and compounds, such as amino acid sequences and chemical structures; the latter rely on interaction network data, such as protein-protein interactions and compound-compound interactions. However, there have been few attempts to combine both types of data in molecular information and interaction networks. RESULTS: We developed a deep learning-based method that integrates protein features, compound features, and multiple types of interactome data to predict protein-compound interactions. We designed three benchmark datasets with different difficulties and applied them to evaluate the prediction method. The performance evaluations show that our deep learning framework for integrating molecular structure data and interactome data outperforms state-of-the-art machine learning methods for protein-compound interaction prediction tasks. The performance improvement is statistically significant according to the Wilcoxon signed-rank test. This finding reveals that the multi-interactome data captures perspectives other than amino acid sequence homology and chemical structure similarity and that both types of data synergistically improve the prediction accuracy. Furthermore, experiments on the three benchmark datasets show that our method is more robust than existing methods in accurately predicting interactions between proteins and compounds that are unseen in training samples.
Published in April 2021
A small molecule compound berberine as an orally active therapeutic candidate against COVID-19 and SARS: A computational and mechanistic study.
Abstract: The novel coronavirus disease, COVID-19, has grown into a global pandemic and a major public health threat since its breakout in December 2019. To date, no specific therapeutic drug or vaccine for treating COVID-19 and SARS has been FDA approved. Previous studies suggest that berberine, an isoquinoline alkaloid, has shown various biological activities that may help against COVID-19 and SARS, including antiviral, anti-allergy and inflammation, hepatoprotection against drug- and infection-induced liver injury, as well as reducing oxidative stress. In particular, berberine has a wide range of antiviral activities such as anti-influenza, anti-hepatitis C, anti-cytomegalovirus, and anti-alphavirus. As an ingredient recommended in guidelines issued by the China National Health Commission for COVID-19 to be combined with other therapy, berberine is a promising orally administered therapeutic candidate against SARS-CoV and SARS-CoV-2. The current study comprehensively evaluates the potential therapeutic mechanisms of berberine in preventing and treating COVID-19 and SARS using computational modeling, including target mining, gene ontology enrichment, pathway analyses, protein-protein interaction analysis, and in silico molecular docking. An orally available immunotherapeutic-berberine nanomedicine, named NIT-X, has been developed by our group and has shown significantly increased oral bioavailability of berberine, increased IFN-gamma production by CD8+ T cells, and inhibition of mast cell histamine release in vivo, suggesting a protective immune response. We further validated the inhibition of replication of SARS-CoV-2 in lung epithelial cells line in vitro (Calu3 cells) by berberine. Moreover, the expression of targets including ACE2, TMPRSS2, IL-1alpha, IL-8, IL-6, and CCL-2 in SARS-CoV-2 infected Calu3 cells were significantly suppressed by NIT-X. By supporting protective immunity while inhibiting pro-inflammatory cytokines; inhibiting viral infection and replication; inducing apoptosis; and protecting against tissue damage, berberine is a promising candidate in preventing and treating COVID-19 and SARS. Given the high oral bioavailability and safety of berberine nanomedicine, the current study may lead to the development of berberine as an orally, active therapeutic against COVID-19 and SARS.
Published in April 2021
Network model-based screen for FDA-approved drugs affecting cardiac fibrosis.
Abstract: Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and alpha-SMA expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFbeta expression and MMP2-driven TGFbeta activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drug-target mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.
Showing 1741 to 1744 of 5186 entries