Antagonistic Effect of Jiawei Shengjiang San on a Rat Model of Diabetic Nephropathy: Related to EGFR/MAPK3/1 Signaling Pathway
May 10th, 2024
Here, we present a protocol describing network pharmacology and molecular docking techniques to explore the mechanism of action of Jiawei Shengjiang San (JWSJS) in treating diabetic nephropathy.
We are exploring how traditional san or traditional can tackle diabetic candidate disease. To address our questions, we study the cell sampling and the cell in candidate cells. Traditional herbs help manage diabetic candidates But the mechanism of action is not clear.
We explore network pharmacology and the molecular techniques to explore jiawei Shengjiang sans mechanism of action. Our approach is like or JPS for finding the good stuff. In herbal remedies, we can easily predict the active components and targets of jiawei Shengjiang san and construct a drug component target network, which is faster and gives us a clearer picture than any trial and error method.
To begin open the traditional Chinese medicine systems pharmacology database and analysis platform. Enter the medicinal composition for jiawei Shengjiang san or JWSJS and apply the screening criteria. Then using the database, retrieve the targets corresponding to the ingredients.
Obtain the active ingredients of and in the traditional Chinese medicine and chemical composition databases. After selecting compounds with chemical abstract service numbers, download 2D structured diagrams of active ingredients from pub chem. With Swiss ADME.
Screen the components with high gastrointestinal absorption as drug similarity whose two items or more were yes. Import these into the database for target protein prediction in the uni prod set the status as reviewed and species as human and standardize the targets. Search diabetic nephropathy or DN through various databases and obtain the targets.
After combining and deduplicating using our 4.2.0 software screen the common targets of JWSJS and DN to draw Venn diagrams. Import the active ingredients and potential targets of JWSJS into cyto scape 3.8.0 software to build a drug ingredient target network diagram that visualizes the connection between drugs, ingredients, targets, and diseases. To analyze the intersecting genes in string platform, set the species to homo sapiens and the minimum required interaction score to more than 0.9 for constructing the network.
Using a multiple protein analysis mode using cyto scape 3.8.0 analyze the network topologically and calculate the between ness centrality, closeness centrality, degree centrality, iGen, vector centrality, local average connectivity based method and network centrality values for each node. Then obtain the IDs of the intersection targets with org.hs.eg. db package using the cluster profiler org.hs.eg.
db enrich plot and GG plot two packages perform enrichment analysis screen for functional gene ontology enrichment analysis on the top 10 biological hits with a corrected P value less than 0.05 and select top 30 pathways with the highest enrichment for Kyoto Encyclopedia of genes and genomes analysis. Search in the pub chem database to obtain the SDF file of the 2D structure of JWSJS core components using chem bio 3D Ultra 14.0 software. Generate and optimize its 3D structure.
Save it in mold two format to use it as a ligand file. Find and download the PDB format of the 3D structure of the core targets from the research collaboratory for structural bioinformatics protein data bank database. Using PIMOL 2.4.0 software.
Remove water molecules and ligands from the protein structure and save it as PDB receptor file. Import the receptor protein file into AUTODOC Tools, 1.5.7 software for hydrogenation and convert both receptor protein and small molecule ligand into PD BQT format. Set active pocket for receptor protein with spacing coefficient set to one.
Using autodoc vena, 1.2.0 for molecular docking calculate the binding energy, visualize the docking results using PIMOL 2.3.0 and lid plot Plot 2.2.5 software. Conduct molecular dynamic or MD investigations on a trio of the most promising derived ligand complexes and use A SPC aqueous environment with 0.15 molar sodium chloride. Initiate an energy minimization phase for 100 picoseconds using Desmond default parameters.
Ensure stable temperature and pressure of 26.85 degrees Celsius and 1.01325 bar across all production systems through the nose Hoover chain and Martina Tobias Klein methodologies. Execute the MD simulation for 100 nanoseconds with a time stamp of two femtoseconds recording the atomic coordinates at every 100 picoseconds. Open the simulation interactions diagram or SID module and load the simulation result data files into it.
Once the analysis is complete, view and interpret the results within the SID module interface. Inject the model group animal with an intraperitoneal injection of Streptozotocin. After 72 hours, test the blood sugar levels through tail vein blood sampling.
Observe the histopathological changes in the rat kidney to confirm the successful DN model. Then administer appropriate drugs to the rat via oral gavage once. daily, collect blood samples from the anesthetized rat's abdominal aorta.
Finally, after euthanizing the rat collect the kidneys for biochemical and microscopic analyses. The periodic acid shift staining showed that the model group had increased glomerular volumes, thickened basement membrane, and increased mass angio matrix compared to the normal group. These pathological manifestations were significantly alleviated in each drug administered group transmission electron microscopy indicated that the glomerular basement membrane in the model group was thickened versus the normal group.
Microscopic manifestations of rats in each administration group were relieved to varying extents versus the model group. Compared to the model group, there was a significant reduction in the expression of PEGFR, PMAPK 31 and Bax and upregulation of BCL2 expression in rat kidney tissues of each dosing group to varying degrees.
Overview
This article presents a protocol that utilizes network pharmacology and molecular docking techniques to investigate the mechanism of action of Jiawei Shengjiang San (JWSJS) in treating diabetic nephropathy. The study aims to clarify how traditional herbal remedies can manage diabetic conditions.
Key Study Components
Area of Science
- Neuroscience
- Pharmacology
- Diabetology
Background
- Diabetic nephropathy is a common complication of diabetes.
- Traditional herbs have been used to manage diabetes, but their mechanisms are often unclear.
- Jiawei Shengjiang San is a traditional remedy under investigation.
- Network pharmacology offers a systematic approach to explore herbal mechanisms.
Purpose of Study
- To explore the active components of Jiawei Shengjiang San.
- To construct a drug component-target network for JWSJS.
- To provide insights into the treatment of diabetic nephropathy.
Methods Used
- Network pharmacology techniques.
- Molecular docking methods.
- Cell sampling from candidate cells.
- Analysis of active components and their targets.
Main Results
- Identification of key active components in JWSJS.
- Construction of a comprehensive drug component-target network.
- Insights into the mechanism of action of JWSJS.
- Faster predictions of efficacy compared to traditional methods.
Conclusions
- Network pharmacology is effective for exploring herbal remedies.
- Jiawei Shengjiang San shows potential in treating diabetic nephropathy.
- Further studies are needed to validate these findings.
Frequently Asked Questions
Executive Industry Relevance
Network pharmacology and molecular docking enable systematic de-risking of herbal compound mechanisms in disease-relevant models, supporting predictive confidence in early-stage target validation. This approach clarifies the multi-target actions of Jiawei Shengjiang San (JWSJS) in diabetic nephropathy, informing portfolio triage and translational continuity. Integrating computational and in vivo validation accelerates mechanistic insight for complex therapeutic candidates.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables interrogation of therapeutic hypotheses by mapping compound-target-disease networks.
- Supports biological de-risking through identification of intersecting targets and pathways.
- Facilitates predictive confidence by linking active herbal components to validated disease mechanisms.
- Prioritizes targets such as EGFR, MAPK1, and MAPK3 for further mechanistic study.
Screening & Assay Development
- Prepares validated compound-target networks for downstream screening workflows.
- Standardizes target selection using quantitative network topology and enrichment analyses.
- Enables reproducible molecular docking and dynamics simulations for compound assessment.
- Supports scalable evaluation of multi-component herbal mixtures in disease models.
Translational & Preclinical Research
- Aligns disease-relevant pathways (e.g., EGFR/MAPK3/1) with translational biomarker strategies.
- Provides continuity from computational prediction to in vivo validation in preclinical models.
- Informs risk-adjusted advancement decisions by linking molecular effects to phenotypic outcomes.
- De-risks progression of complex natural products through mechanistic clarity.
Pipeline & Workflow Integration
This workflow integrates computational target prediction, network analysis, and in vivo validation from early discovery through preclinical research.
- Discovery Biology: Supports hypothesis testing and pathway clarification via network pharmacology and enrichment analysis.
- Screening: Delivers assay-ready target lists and quantitative docking outputs for compound evaluation.
- Analytics: Provides statistical and topological metrics for comparing compound-target interactions.
- Translational Research: Connects molecular mechanism to preclinical efficacy and biomarker modulation.
- Enterprise Reuse: Establishes a reusable computational-experimental pipeline for multi-component therapeutics.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in target validation.
- Operational Value: Standardizes multi-step workflows and enhances reproducibility across teams.
- Strategic Value: Enables informed go/no-go decisions and capital-efficient portfolio management.
- Portfolio Impact: Supports risk-adjusted prioritization and advancement of complex therapeutic candidates.
Implementation Considerations
- Requires expertise in network pharmacology, molecular docking, and in vivo disease modeling.
- Demands access to cheminformatics, bioinformatics, and molecular simulation infrastructure.
- Necessitates cross-team standardization of data formats and analysis pipelines.
- Adaptation may be needed for different compound classes or disease models.
- Interpretation of multi-target effects must be grounded in robust statistical and biological validation.
