A New Prospect for the Treatment of Nephrotic Syndrome Based on Network Pharmacology Analysis

Varghese, Rini; Majumdar, Anuradha

doi:10.1016/j.crphys.2021.12.004

Cited by 8 publications

(2 citation statements)

References 69 publications

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“…In light of this, we present our interactive dashboard, StarGazer, which aims to address these challenges by integrating three different datatypes (i.e., disease-target association, target druggability, and target protein-protein interaction) into a novel scoring system, utilizing real-time API calls and Python-based Streamlit technology. While these types of datasets have been used for numerous repositioning studies separately ( Liu et al, 2014 ; Khaladkar et al, 2017 ; Hermawan et al, 2020 ; Wijetunga et al, 2020 ; Adikusuma et al, 2021 ; Attique et al, 2021 ; Ghoussaini et al, 2021 ; Portelli et al, 2021 ; Tan et al, 2021 ; Varghese and Majumdar, 2022 ; Zhao et al, 2022 ), StarGazer represents the first ever integration of the PheWAS catalog, Open Targets, STRING and Pharos, all of which are well-curated, well-studied, open access databases. Furthermore, computational repositioning studies focus largely on singular diseases, phenotypes or drugs, but StarGazer is equipped for flexible investigation into any of the 1,844 phenotypes and traits within the dashboard.…”

Section: Introductionmentioning

confidence: 99%

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit

et al. 2022

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Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.

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Section: Introductionmentioning

confidence: 99%

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit

et al. 2022

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“…Network pharmacology systematically studies the interaction between “drug-target-pathway-disease” based on the integration of bioinformatics, molecular biology, and public database information. Varghese and Majumdar proposed network pharmacology as an initial inherent approach in identifying the repurposing and synergism of drug candidates for NS treatment [ 11 ]. Some previous studies have identified the efficacy and underlying mechanisms of TCMs treating NS via network pharmacology [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Network Pharmacology and In Vivo Experimental Validation to Uncover the Renoprotective Mechanisms of Fangji Huangqi Decoction on Nephrotic Syndrome

Yin

Mao

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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Background. Fangji Huangqi decoction (FHD) is a traditional Chinese medicine formula that has the potential efficacy for nephrotic syndrome (NS) treatment. This study aims to explore the effects and underlying mechanisms of FHD against NS via network pharmacology and in vivo experiments. Methods. The bioactive compounds and targets of FHD were retrieved from the TCMSP database. NS-related targets were collected from GeneCards and DisGeNET databases. The compound-target and protein-protein interaction networks were constructed by Cytoscape 3.8 and BisoGenet, respectively. GO and KEGG analyses were performed by the DAVID online tool. The interactions between active compounds and hub genes were revealed by molecular docking. An NS rat model was established to validate the renoprotective effects and molecular mechanisms of FHD against NS in vivo. Results. A total of 32 hub genes were predicted to play essential roles in FHD treating NS. Eight main bioactive compounds of FHD had the good affinity with 9 hub targets (CCL2, IL-10, PTGS2, TNF, MAPK1, IL-6, CXCL8, TP53, and VEGFA). The therapeutic effect of FHD on NS was closely involved in the regulation of inflammation and PI3K-Akt pathway. In vivo experiments confirmed the renoprotective effect of FHD on NS, evidenced by reducing the levels of proteinuria, serum creatinine, blood urea nitrogen, and inflammatory factors in NS rats. The PI3K activator 740Y-P weakened the effects of FHD against NS. Furthermore, FHD downregulated the levels of PTGS2, MAPK1, IL-6, and p-Akt in NS rats. Conclusions. FHD alleviates kidney injury and inflammation in NS by targeting PTGS2, MAPK1, IL-6, and PI3K-Akt pathway.

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Decreased GM3 correlates with proteinuria in minimal change nephrotic syndrome and focal segmental glomerulosclerosis

et al. 2022

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A New Prospect for the Treatment of Nephrotic Syndrome Based on Network Pharmacology Analysis

Cited by 8 publications

References 69 publications

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit

Network Pharmacology and In Vivo Experimental Validation to Uncover the Renoprotective Mechanisms of Fangji Huangqi Decoction on Nephrotic Syndrome

Decreased GM3 correlates with proteinuria in minimal change nephrotic syndrome and focal segmental glomerulosclerosis

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