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Showing 961 to 964 of 5186 entries
Published in February 2022
Curation of an international drug proprietary names dataset.
Abstract: A drug dataset containing international proprietary names is essential for researchers investigating different drugs from different countries worldwide. However, many websites on the internet offer free access for a single drug searching service to identify international drug trade names, but not for a list of drugs to be searched and identified. Therefore, it will be problematic if the researcher has a list of hundreds or thousands of drug trade names to be identified. In this project, we have created an International Drug Dictionary (IDD) by curating collected drug lists from open access websites belonging to official drug regulatory agencies, official healthcare systems, or recognized scientific bodies from 44 countries around the world in addition to the European public assessment reports (EPAR) and the DRUGBANK vocabulary published in the public domain. Researchers interested in pharmacovigilance, pharmacoepidemiology, or pharmacoeconomics can benefit from this dataset, especially when identifying lists of proprietary drug names, particularly of multi-national origin. To enhance its adaptability, we also mapped the IDD to the standardized drug vocabulary RxNorm. The IDD can also be used as a tool for mapping international drug trade names to RxNorm. Each drug entity in the IDD mapped to a unique identification number for each entity called Atom Unique Identifier (RXAUI) from RxNorm.
Published in February 2022
Identifying the molecular basis of Jinhong tablets against chronic superficial gastritis via chemical profile identification and symptom-guided network pharmacology analysis.
Abstract: Chronic superficial gastritis (CSG) is a common disease of the digestive system that possesses a serious pathogenesis. Jinhong tablet (JHT), a traditional Chinese medicine (TCM) prescription, exerts therapeutic effects against CSG. However, the molecular basis of its therapeutic effect has not been clarified. Herein, we employed ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q/TOF-MS) based chemical profile identification to determine the chemical components in JHT. Further, we applied network pharmacology to illustrate its molecular mechanisms. A total of 96 chemical constituents were identified in JHT, 31 of which were confirmed using reference standards. Based on the bioinformatics analysis using the symptom-guided pharmacological networks of "chi," "blood," "pain," and "inflammation," and target screening through the interaction probabilities between compounds and targets, matrix metalloproteinase 2 (MMP2), dopamine d2 receptor (DRD2), and Aldo-keto reductase family 1 member B1 (AKR1B1) were identified as key targets in the therapeutic effect exhibited by JHT against CSG. Moreover, according to the inhibitory activities presented in the literature and binding mode analysis, the structural types of alkaloids, flavonoids, organic acids, including chlorogenic acid (10), caffeic acid (13), (-)-corydalmine (33), (-)-isocorypalmine (36), isochlorogenic acid C (38), isochlorogenic acid A (41), quercetin-3-O-alpha-l-rhamnoside (42), isochlorogenic acid B (47), quercetin (63), and kaempferol (70) tended to show remarkable activities against CSG. Owing to the above findings, we systematically identified the chemical components of JHT and revealed its molecular mechanisms based on the symptoms associated with CSG.
Published in February 2022
Knowledge-based approaches to drug discovery for rare diseases.
Abstract: The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics data of relevance to rare diseases and provide a perspective on the effectiveness of machine learning (ML) and biomedical knowledge graph mining in rare disease drug discovery. We illustrate the power of these methodologies using a chordoma case study. We expect that a broader application of knowledge graph mining and artificial intelligence (AI) approaches will expedite the discovery of viable drug candidates against both rare and common diseases.
Published in February 2022
PPDTS: Predicting potential drug-target interactions based on network similarity.
Abstract: Identification of drug-target interactions (DTIs) has great practical importance in the drug discovery process for known diseases. However, only a small proportion of DTIs in these databases has been verified experimentally, and the computational methods for predicting the interactions remain challenging. As a result, some effective computational models have become increasingly popular for predicting DTIs. In this work, the authors predict potential DTIs from the local structure of drug-target associations' network, which is different from the traditional global network similarity methods based on structure and ligand. A novel method called PPDTS is proposed to predict DTIs. First, according to the DTIs' network local structure, the known DTIs are converted into a binary network. Second, the Resource Allocation algorithm is used to obtain a drug-drug similarity network and a target-target similarity network. Third, a Collaborative Filtering algorithm is used with the known drug-target topology information to obtain similarity scores. Fourth, the linear combination of drug-target similarity model and the target-drug similarity model are innovatively proposed to obtain the final prediction results. Finally, the experimental performance of PPDTS has proved to be higher than that of the previously mentioned four popular network-based similarity methods, which is validated in different experimental datasets. Some of the predicted results can be supported in UniProt and DrugBank databases.
Published on February 28, 2022
Effects and Mechanisms of Rhus chinensis Mill. Fruits on Suppressing RANKL-Induced Osteoclastogenesis by Network Pharmacology and Validation in RAW264.7 Cells.
Abstract: Rhus chinensis Mill. fruits are a kind of widely distributed edible seasoning, which have been documented to possess a variety of biological activities. However, its inhibitory effect on osteoclast formation has not been determined. The objective of this study was to evaluate the effect of the fruits on osteoclast differentiation of RAW264.7 cells, induced by receptor activator of nuclear factor-kappaB ligand (RANKL) and to illuminate the potential mechanisms using network pharmacology and western blots. Results showed that the extract containing two organic acids and twelve phenolic substances could effectively inhibit osteoclast differentiation in RANKL-induced RAW264.7 cells. Network pharmacology examination and western blot investigation showed that the concentrate essentially decreased the expression levels of osteoclast-specific proteins, chiefly through nuclear factor kappa-B, protein kinase B, and mitogen-activated protein kinase signaling pathways, particularly protein kinase B alpha and mitogen-activated protein kinase 1 targets. Moreover, the extract likewise directly down regulated the expression of cellular oncogene Fos and nuclear factor of activated T-cells cytoplasmic 1 proteins. Citric acid, quercetin, myricetin-3-O-galactoside, and quercetin-3-O-rhamnoside were considered as the predominant bioactive ingredients. Results of this work may provide a scientific basis for the development and utilization of R. chinensis fruits as a natural edible material to prevent and/or alleviate osteoporosis-related diseases.
Published in February 2022
Cinnamic Aldehyde, the main monomer component of Cinnamon, exhibits anti-inflammatory property in OA synovial fibroblasts via TLR4/MyD88 pathway.
Abstract: Cinnamon is a wildly used traditional Chinese herbal medicine for osteoarthritis (OA) treatment, but the underlying mechanism remains ambiguous. The purpose of this study is to explore the mechanism of cinnamic aldehyde (CA), a bioactive substance extracted from Cinnamon, on synovial inflammation in OA. A total of 144 CA-OA co-targeted genes were identified by detect databases (PubChem, HIT, TCMSP, TTD, DrugBank and GeneCards). The results of GO enrichment analysis indicated that these co-targeted genes have participated in many biological processes including 'inflammatory response', 'cellular response to lipopolysaccharide', 'response to drug', 'immune response', 'lipopolysaccharide-mediated signalling pathway', etc. KEGG pathway analysis showed these co-targeted genes were mainly enriched in 'Toll-like receptor signalling pathway', 'TNF signalling pathway', 'NF-kappa B signalling pathway', etc. Molecular docking demonstrated that CA could successfully bind to TLR2 and TLR4. The results of in vitro experiments showed no potential toxicity of 10, 20 and 50 muM/L CA on human OA FLS, and CA can significantly inhibit the inflammation in LPS-induced human FLS. Further experimental mechanism evidence confirmed CA can inhibited the inflammation in LPS-induced human OA FLS via blocking the TLR4/MyD88 signalling pathway. Our results demonstrated that CA exhibited strong anti-inflammation effect in OA FLS through blocking the activation of TLR4/MyD88 signalling pathway, suggesting its potential as a hopeful candidate for the development of novel agents for the treatment of OA.
Published on February 28, 2022
Designing Novel Compounds for the Treatment and Management of RET-Positive Non-Small Cell Lung Cancer-Fragment Based Drug Design Strategy.
Abstract: Rearranged during transfection (RET) is an oncogenic driver receptor that is overexpressed in several cancer types, including non-small cell lung cancer. To date, only multiple kinase inhibitors are widely used to treat RET-positive cancer patients. These inhibitors exhibit high toxicity, less efficacy, and specificity against RET. The development of drug-resistant mutations in RET protein further deteriorates this situation. Hence, in the present study, we aimed to design novel drug-like compounds using a fragment-based drug designing strategy to overcome these issues. About 18 known inhibitors from diverse chemical classes were fragmented and bred to form novel compounds against RET proteins. The inhibitory activity of the resultant 115 hybrid molecules was evaluated using molecular docking and RF-Score analysis. The binding free energy and chemical reactivity of the compounds were computed using MM-GBSA and density functional theory analysis, respectively. The results from our study revealed that the developed hybrid molecules except for LF21 and LF27 showed higher reactivity and stability than Pralsetinib. Ultimately, the process resulted in three hybrid molecules namely LF1, LF2, and LF88 having potent inhibitory activity against RET proteins. The scrutinized molecules were then subjected to molecular dynamics simulation for 200 ns and MM-PBSA analysis to eliminate a false positive design. The results from our analysis hypothesized that the designed compounds exhibited significant inhibitory activity against multiple RET variants. Thus, these could be considered as potential leads for further experimental studies.
Published in February 2022
Chromosome-scale assembly and whole-genome sequencing of 266 giant panda roundworms provide insights into their evolution, adaptation and potential drug targets.
Abstract: Helminth diseases have long been a threat to the health of humans and animals. Roundworms are important organisms for studying parasitic mechanisms, disease transmission and prevention. The study of parasites in the giant panda is of importance for understanding how roundworms adapt to the host. Here, we report a high-quality chromosome-scale genome of Baylisascaris schroederi with a genome size of 253.60 Mb and 19,262 predicted protein-coding genes. We found that gene families related to epidermal chitin synthesis and environmental information processes in the roundworm genome have expanded significantly. Furthermore, we demonstrated unique genes involved in essential amino acid metabolism in the B. schroederi genome, inferred to be essential for the adaptation to the giant panda-specific diet. In addition, under different deworming pressures, we found that four resistance-related genes (glc-1, nrf-6, bre-4 and ced-7) were under strong positive selection in a captive population. Finally, 23 known drug targets and 47 potential drug target proteins were identified. The genome provides a unique reference for inferring the early evolution of roundworms and their adaptation to the host. Population genetic analysis and drug sensitivity prediction provide insights revealing the impact of deworming history on population genetic structure of importance for disease prevention.
Published in February 2022
Identification of SARS-CoV-2 Papain-like Protease (PLpro) Inhibitors Using Combined Computational Approach.
Abstract: In the current pandemic, finding an effective drug to prevent or treat the infection is the highest priority. A rapid and safe approach to counteract COVID-19 is in silico drug repurposing. The SARS-CoV-2 PLpro promotes viral replication and modulates the host immune system, resulting in inhibition of the host antiviral innate immune response, and therefore is an attractive drug target. In this study, we used a combined in silico virtual screening for candidates for SARS-CoV-2 PLpro protease inhibitors. We used the Informational spectrum method applied for Small Molecules for searching the Drugbank database followed by molecular docking. After in silico screening of drug space, we identified 44 drugs as potential SARS-CoV-2 PLpro inhibitors that we propose for further experimental testing.
Published on February 25, 2022
Nanotechnological approaches for pentamidine delivery.
Abstract: Pentamidine (PTM), which is a diamine that is widely known for its antimicrobial activity, is a very interesting drug whose mechanism of action is not fully understood. In recent years, PTM has been proposed as a novel potential drug candidate for the treatment of mental illnesses, myotonic dystrophy, diabetes, and tumors. Nevertheless, the systemic administration of PTM causes severe side effects, especially nephrotoxicity. In order to efficiently deliver PTM and reduce its side effects, several nanosystems that take advantage of the chemical characteristics of PTM, such as the presence of two positively charged amidine groups at physiological pH, have been proposed as useful delivery tools. Polymeric, lipidic, inorganic, and other types of nanocarriers have been reported in the literature for PTM delivery, and they are all in different development phases. The available approaches for the design of PTM nanoparticulate delivery systems are reported in this review, with a particular emphasis on formulation strategies and in vitro/in vivo applications. Furthermore, a critical view of the future developments of nanomedicine for PTM applications, based on recent repurposing studies, is provided. Created with BioRender.com.
Showing 961 to 964 of 5186 entries