Publications Search
Explore how scientists all over the world use DrugBank in their research.
Showing 1851 to 1854 of 5186 entries
Published on March 10, 2021
A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19.
Abstract: In order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrodinger software. Six carrier candidates were derived by the proposed method that could find molecules meeting the predefined conditions using the molecular structure and its functional group positional information. Then, just one compound named glycyrrhizin was selected as a candidate for drug delivery through the Schrodinger software. Using glycyrrhizin, nafamostat mesilate (NM), which is known for its efficacy, was converted into micelle nanoparticles (NPs) to improve drug stability and to effectively treat COVID-19. The spherical particle morphology was confirmed by transmission electron microscopy (TEM), and the particle size and stability of 300-400 nm were evaluated by measuring DLSand the zeta potential. The loading of NM was confirmed to be more than 90% efficient using the UV spectrum.
Published on March 6, 2021
A hijack mechanism of Indian SARS-CoV-2 isolates for relapsing contemporary antiviral therapeutics.
Abstract: Coronavirus disease (COVID-19) rapidly expands to a global pandemic and its impact on public health varies from country to country. It is caused by a new virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is imperative for relapsing current antiviral therapeutics owing to randomized genetic drift in global SARS-CoV-2 isolates. A molecular mechanism behind the emerging genomic variants is not yet understood for the prioritization of selective antivirals. The present computational study was aimed to repurpose existing antivirals for Indian SARS-CoV-2 isolates by uncovering a hijack mechanism based on structural and functional characteristics of protein variants. Forty-one protein mutations were identified in 12 Indian SARS-CoV-2 isolates by analysis of genome variations across 460 genome sequences obtained from 30 geographic sites in India. Two unique mutations such as W6152R and N5928H found in exonuclease of Surat (GBRC275b) and Gandhinagar (GBRC239) isolates. We report for the first time the impact of folding rate on stabilizing/retaining a sequence-structure-function-virulence link of emerging protein variants leading to accommodate hijack ability from current antivirals. Binding affinity analysis revealed the effect of point mutations on virus infectivity and the drug-escaping efficiency of Indian isolates. Emodin and artinemol suggested herein as repurposable antivirals for the treatment of COVID-19 patients infected with Indian isolates. Our study concludes that a protein folding rate is a key structural and evolutionary determinant to enhance the receptor-binding specificity and ensure hijack ability from the prevalent antiviral therapeutics.
Published on March 5, 2021
Prediction of lncRNA-disease associations via an embedding learning HOPE in heterogeneous information networks.
Abstract: Uncovering additional long non-coding RNA (lncRNA)-disease associations has become increasingly important for developing treatments for complex human diseases. Identification of lncRNA biomarkers and lncRNA-disease associations is central to diagnoses and treatment. However, traditional experimental methods are expensive and time-consuming. Enormous amounts of data present in public biological databases are available for computational methods used to predict lncRNA-disease associations. In this study, we propose a novel computational method to predict lncRNA-disease associations. More specifically, a heterogeneous network is first constructed by integrating the associations among microRNA (miRNA), lncRNA, protein, drug, and disease, Second, high-order proximity preserved embedding (HOPE) was used to embed nodes into a network. Finally, the rotation forest classifier was adopted to train the prediction model. In the 5-fold cross-validation experiment, the area under the curve (AUC) of our method achieved 0.8328 +/- 0.0236. We compare it with the other four classifiers, in which the proposed method remarkably outperformed other comparison methods. Otherwise, we constructed three case studies for three excess death rate cancers, respectively. The results show that 9 (lung cancer, gastric cancer, and hepatocellular carcinomas) out of the top 15 predicted disease-related lncRNAs were confirmed by our method. In conclusion, our method could predict the unknown lncRNA-disease associations effectively.
Published on March 5, 2021
LncRNA-mRNA co-expression analysis discovered the diagnostic and prognostic biomarkers and potential therapeutic agents for myocardial infarction.
Abstract: Currently, the role of lncRNA in myocardial infarction (MI) is poorly understood. 17 co-expression modules were determined, specifically, the greenyellow, saddlebrown, grey60, royalblue, lightgreen, white, and pink modules were specifically expressed in the acute phase of MI, and brown, darkred, and royalblue, while greenyellow modules were specifically expressed in MI compared with CAD. 12 time-dependent of lncRNA/mRNA clusters with consistent expression trends were also identified. MI-associated modules were mainly enriched to immune, cell cycle, and metabolic pathways. We further obtained a network of 1816 lncRNA-mRNAs with higher expression correlations among these lncRNAs by analyzing the topological properties of the network. Herein, lncRNA RP11-847H18.2 and KLHL28, SPRTN, and EPM2AIP1 were determined as gene markers specifically expressed in MI, and they demonstrated a high predictive performance for MI diagnosis and prognosis. Three drugs, namely, Calcium citrate, Calcium Phosphate, and Calcium phosphate dihydrate, were identified as potential precursors of MI. Finally, gene and lncRNA diagnostic models were developed based on these genes and lncRNAs, with their AUCs averaged above 0.89 in both training and validation datasets. The findings of this study improve the diagnosis and prognosis of MI and personalized treatment of MI.
Published on March 2, 2021
Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics.
Abstract: Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2(+)TMPRSS2(+) cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.
Published on March 1, 2021
Metal-Free Electrochemical Synthesis of Sulfonamides Directly from (Hetero)arenes, SO2 , and Amines.
Abstract: Sulfonamides are among the most important chemical motifs in pharmaceuticals and agrochemicals. However, there is no methodology to directly introduce the sulfonamide group to a non-prefunctionalized aromatic compound. Herein, we present the first dehydrogenative electrochemical sulfonamide synthesis protocol by exploiting the inherent reactivity of (hetero)arenes in a highly convergent reaction with SO2 and amines via amidosulfinate intermediate. The amidosulfinate serves a dual role as reactant and supporting electrolyte. Direct anodic oxidation of the aromatic compound triggers the reaction, followed by nucleophilic attack of the amidosulfinate. Boron-doped diamond (BDD) electrodes and a HFIP-MeCN solvent mixture enable selective formation of the sulfonamides. In total, 36 examples are demonstrated with yields up to 85 %.
Published in February 2021
Investigating the protective effect of tanshinone IIA against chondrocyte dedifferentiation: a combined molecular biology and network pharmacology approach.
Abstract: Background: Osteoarthritis (OA) is a common degenerative disease with multifactorial etiology. The dedifferentiation of chondrocytes can accelerate the progress of OA. Tanshinone IIA (TIIA) has been widely used to treat OA for many years and has proved to be effective in inhibiting chondrocyte dedifferentiation. Until now, the precise mechanism of TIIA's effect against dedifferentiation has not been well understood. Methods: The targets of TIIA were explored from public databases using various methods. The related targets of OA were obtained from the GeneCards database and the Online Mendelian Inheritance in Man (OMIM) database. The potential targets and signaling pathways were determined using protein-protein interaction (PPI), Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Cell viability, proliferation, and metabolic activity were analyzed in vitro. The effects of TIIA on chondrocyte dedifferentiation were evaluated by assessing morphological changes, glycosaminoglycan (GAG) production, and messenger RNA (mRNA) levels of cartilage-related genes. After 48 hours of culture in medium with 100 mug/mL TIIA, chondrocytes/hydrogel spheres were implanted to repair cartilage defects in a rat model. The harvested specimens were examined with hematoxylin and eosin (H&E) staining and immunohistochemistry to evaluate cartilage regeneration. Results: The results showed that there were 28 genes potentially interacting in the TIIA-chondrocyte dedifferentiation network, and nine hub genes were identified. In vitro experiments showed an inhibitory effect of TIIA on chondrocyte dedifferentiation. The proliferation and viability of chondrocytes were promoted by TIIA at a concentration of 100-200 mug/mL, but inhibited by TIIA at 400 mug/mL. Furthermore, the histology results showed that chondrocyte/hydrogel spheres pre-treated with TIIA had better cartilage repair. Conclusions: This study revealed a systematic network pharmacology approach and provided a basis for the future study of TIIA as an effective treatment for cartilage regeneration. Moreover, in vitro and in vivo results confirmed the protective effects of TIIA against chondrocyte dedifferentiation.
Published in February 2021
Network Pharmacology-Based Exploration of Synergistic Mechanism of Guanxin II Formula (II) for Coronary Heart Disease.
Abstract: OBJECTIVE: To study the pharmacological mechanism of Guanxin II formula (II) for treatment of coronary heart disease (CHD). METHODS: A network pharmacology-based method was utilized. First candidate compounds, targets of GX II were collected using PharmMapper, BATMAN-TCM, DrugBank and SwissTargetPrediction, and targets on CHD were mined from GeneCards, DisGenet, DrugBank and GEO. Afterwards, the big hub compounds and targets were chosen in the candidate compounds-direct therapeutic targets on the CHD (C-T) network and the direct therapeutic targets on the CHD (T-D) network. Furthermore, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were performed to identify the enriched terms. Finally, a molecular docking simulation strategy was adopted to verify the binding capacity between the big hub compounds and big hub targets on CHD. RESULTS: First, 114 candidate compounds were selected with the following criteria: OB30%, DL0.18, and HL 4 h. Then, 1,035 targets of GX II were gathered, while 928 targets on CHD were collected. Afterwards, 196 common targets of compound targets and therapeutic targets on CHD were defined as direct therapeutic targets acting on CHD. In addition, the contribution index (CI) in the C-T network was calculated, and 4 centrality properties, including degree, betweenness, closeness and coreness, in the T-D network, 4 big hub compounds, and 6 big hub targets were eventually chosen. Furthermore, the GO and KEGG analysis indicated that GX II acted on CHD by regulating the reactive oxygen species metabolism, steroid metabolism, lipid metabolism, inflammatory response, proliferation, differentiation and apoptosis. The docking results manifested excellent binding capacity between the 4 big hub compounds and 6 big hub targets on CHD. CONCLUSION: This network pharmacology-based exploration revealed that GX II might prevent and inhibit the primary pathological processes of CHD.
Published in February 2021
Patient-tailored design for selective co-inhibition of leukemic cell subpopulations.
Abstract: The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.
Published in February 2021
A wealth of discovery built on the Human Genome Project - by the numbers.
Abstract:
Showing 1851 to 1854 of 5186 entries