This study was supported by the Innovation Training Program of Beijing University of Chinese Medicine (No: 202110026036).

All the network pharmacology procedures were carried out in accordance with the Guidelines for Network Pharmacology Evaluation Methods18. All the experimental procedures were performed in accordance with the laboratory management regulations of the Beijing University of Chinese Medicine.
1. Network pharmacological prediction
<ol>
	<li>Selection of active components
	<ol>
		<li>Open the HERB database (http://herb.ac.cn)19, and use &#34;Gualou&#34; (the Chinese name for Trichosanthes kirilowii Maxim) and &#34;Zhebeimu&#34; (the Chinese name for Bulbus Fritillariae thunbergii) as keywords to obtain the components of the two drugs. Download the list and the canonical SMILES structures of the related components of the two drugs.</li>
		<li>Determine whether the obtained component is the active component.
		<ol>
			<li>Include as active components those that have oral bioavailability (OB) and drug-like (DL) values in the HERB group database (i.e., components with OB &#8805; 30% and DL &#8805; 0.18)20,21.</li>
			<li>If the component has no OB and DL values, input the component into the Swiss ADME database (http://www.swissadme.ch/index.php)22&#160;to obtain the information on each component. Include components with &#8220;high&#8221; GI absorption and at least two &#8220;Yes&#8221; DL values as active components.&#8203;</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Target prediction of the active components
	<ol>
		<li>Open the HERB database (http://herb.ac.cn). Search and copy the canonical SMILES structures of the active components.</li>
		<li>Open the SEA (Similarity ensemble approach, http://sea.bkslab.org) database23. Paste the canonical SMILES structures of the active components into the search box, and click on Try SEA to obtain the Target key, Target name, P-Value, and MaxTC of each active component.</li>
		<li>Copy the data into a spreadsheet, and use the filtering function of the spreadsheet file to filter the targets of the active components by Target key (Human, P &#60; 0.05, and MaxTC &#62; 0.5).</li>
		<li>Copy all the targets to a spreadsheet, and remove the duplicates to obtain the drug targets.</li>
	</ol>
	</li>
	<li>Prediction of disease targets
	<ol>
		<li>Open the GeneCards database (https://www.genecards.org)24&#160;and Online Mendelian Inheritance in Man database&#160;(OMIM,&#160;https://www.omim.org)25&#160;and use Lung Adenocarcinoma as the keyword to obtain the disease targets of the lung adenocarcinoma.</li>
		<li>Download the spreadsheets of the disease targets. Delete the repeated targets to obtain the LUAD targets.</li>
	</ol>
	</li>
	<li>Construction of a drug-component-disease-target network
	<ol>
		<li>Copy the LUAD-related targets and drug targets into the same column in a new spreadsheet. Use the Data - Identify Duplicates function in the toolbar to obtain intersection targets of the LUAD-related targets and the Trichosanthes-Fritillaria thunbergii active component-related targets.</li>
		<li>Open Cytoscape 3.8.0. Click on File in the menu bar, and then select Import &#62; Network from File to import the spreadsheet file. Optimize the size and color of the network nodes through the style bar in the left control panel.</li>
		<li>Use the Analyze Network function for the network topology analysis. Click on Tools in the menu bar, and then select Analyze Network. On the Table panel, click on the Degree in the title bar to arrange the components by degree in descending order. Take the top ten components and targets as the main active components and core targets.</li>
	</ol>
	</li>
	<li>Construction of the PPI network and screening of the core proteins
	<ol>
		<li>Open the STRING database (https://cn.string-db.org/)26. Paste the text-format list of the potential targets of&#160;Trichosanthes-Fritillaria thunbergii against LUAD into the List of Names dialog box. Select Homo sapiens in Organisms, and click on the SEARCH &#62; CONTINUE buttons.</li>
		<li>When the results are available, click on Settings, and tick High Confidence (0.700) in Basic Settings &#62; Minimum Required Interaction Score. Tick Hide Disconnected Nodes in the Network in Advanced Settings, and then click on the UPDATE button.</li>
		<li>Click on Exports in the title bar, and download the short tabular text of the PPI relationship in TSV format.</li>
		<li>Open Cytoscape 3.8.0. Click on File &#62; Import &#62; Network from File to import the TSV format file for visual analysis.</li>
		<li>Use the Network Analyzer function to conduct the topological analysis. Optimize the size and color of the network nodes through the style bar in the left control panel.</li>
	</ol>
	</li>
	<li>KEGG enrichment analysis
	<ol>
		<li>Open the Metascape (https://metascape.org/)27 platform. Paste the text-format list of the potential therapeutic targets into the dialog box, and then click on the&#160;Submit button. Tick H. sapiens in both Input as Species and Analysis as Species, and then click on the Custom Analysis button. Select Enrichment, tick only the KEGG Pathway, and then click on Enrichment Analysis. After the progress bar reaches 100%, click on the Analysis Report Page orange button for the enrichment results.</li>
		<li>Click on All in One Zip File for the download of the enrichment result, and then open the _ FINAL_GO.csv file in the Enrichment_GO folder to obtain the result.</li>
		<li>Open R software (https://cran.r-project.org/). Type install.package (&#34;ggplot2&#34;) and library (ggplot2) in R for the installation of the ggplot2 R package. Press Enter to run the KEGG visualization program.</li>
	</ol>
	</li>
</ol>
2. Experimental verification
<ol>
	<li>Drug preparation 
	NOTE: Fritillaria thunbergii Miq and Trichosanthes kirilowii Maxim were purchased from Dongzhimen Hospital, affiliated with the Beijing University of Chinese Medicine.
	<ol>
		<li>Mix 50 g of Fritillaria thunbergii Miq and 50 g of Trichosanthes kirilowii Maxim together. Soak the mixture in 1 L of distilled water for 20 min, and then decoct the mixture at 100 &#176;C under normal pressure for 1 h.</li>
		<li>Filter the decoction to obtain a filtrate with a double layer of sterile medical gauze, and then use 80 &#181;m filter paper to further filter the extract. Repeat the above operation three times.</li>
		<li>Mix the filtrate together to obtain an approximately 1.2 L decoction. Place the solution in the flask of a rotary evaporator. Set the rotation speed to 50 rpm with a temperature of 37 &#176;C. Mix and condense the decoctions into an extract in the rotary evaporator for 6 h to yield a viscous liquid.</li>
		<li>Turn on the vacuum freeze-drying mechanism to precool the temperature to &#8722;40 &#176;C. Put the obtained extract into the material tray, and place it into the cold trap for freezing.</li>
		<li>When the cold trap temperature reaches &#8722;50 &#176;C and the material has been frozen for 2 h, transfer the frozen materials to a drying rack placed in the cold trap. Turn on the vacuum pump to initiate the lyophilization process. Take out the freeze-dried powder, and store this in the refrigerator at &#8722;20 &#176;C for later use.</li>
	</ol>
	</li>
	<li>Cell cultivation
	<ol>
		<li>Configure DMEM complete medium with 89% DMEM basic medium, 10% fetal bovine serum, and 1% penicillin-streptomycin.</li>
		<li>After removing the A549 cells from liquid nitrogen, incubate the cells in a 37 &#176;C water bath, and stir until the medium in the vial melts.</li>
		<li>Add a fourfold volume of DMEM complete medium into the melted cells. Centrifuge the cells (4 &#176;C, 679 x g, 5 min), and discard the supernatant.</li>
		<li>Resuspend the precipitated cells with 6 mL of DMEM complete medium, plate them in a T25 culture flask, and incubate the flask in a cell incubator at 37 &#176;C, 5% CO2.</li>
	</ol>
	</li>
	<li>Detection of cell viability
	<ol>
		<li>Digest the logarithmic growth-phase A549 cells with 1 mL of 0.25% trypsin for 1 min at 37&#176;C. Add 1 mL of DMEM complete medium to neutralize the trypsin, and gently blow it to promote cell shedding. Centrifuge the mixture to obtain the cell pellet (4 &#176;C, 192 x g, 5 min). Resuspend the obtained cells using DMEM complete medium.</li>
		<li>Add the cell suspension to a hemocytometer, and count using an automated cell counter. Dilute it to 5 x 104 cells/mL using DMEM complete medium.</li>
		<li>Dissolve 1 g of Trichosanthes-Fritillaria thunbergii water extract in 10 mL of PBS solution, and filter-sterilize it through a 0.22 &#181;m filter. Dilute the mixture to 90 mg/mL, 80 mg/ml, 70 mg/mL, 60 mg/mL, 50 mg/mL, 40 mg/mL, 30 mg/mL, 20 mg/mL, and 10 mg/mL using PBS.</li>
		<li>Plate the diluted cells on 96-well plates with 100 &#181;L per well. After cell adherence, add 1 &#181;L of Trichosanthes-Fritillaria thunbergii water extracts of different concentrations to adjust the concentration of each well to 900 &#181;g/mL, 800 &#181;g/mL, 700 &#181;g/mL, 600 &#181;g/mL, 500 &#181;g/mL, 400 &#181;g/mL, 300 &#181;g/mL, 200 &#181;g/mL, and 100 &#181;g/mL.</li>
		<li>Discard the original medium after 24 h of culture, and add 100 &#181;L of DMEM basic medium for further incubation for 2 h at 37 &#176;C, 5% CO2.</li>
		<li>After the above treatment, add 20 &#181;L of MTS solution (Table of Materials), and incubate the cells for another 1 h at 37 &#176;C, 5% CO2.</li>
		<li>Transfer the incubated mixture to a different plate. Measure the absorbance (OD) at a 490 nm wavelength using a microplate reader. Calculate the cell viability using the following formula: 
		Viability (%) = 100 &#215; (OD of the treated sample &#8722; OD of the medium)/(OD of the control sample &#8722; OD of the medium). 
		NOTE: A minimum concentration close to IC50 is defined as a high dose. The concentration at which the cell proliferation begins to be inhibited is defined as a low-dose concentration, and the intermediate value is defined as a medium-dose concentration for subsequent experimental studies. In this study, 400 &#181;g/mL, 600 &#181;g/mL, and 800 &#181;g/mL were used as the low, medium, and high doses.</li>
	</ol>
	</li>
	<li>Drug intervention and sample collection 
	NOTE: Cell growth was plotted against time to determine the log phase.
	<ol>
		<li>Dilute the logarithmic growth-phase A549 cells to 5 x 105 cells/mL. Add 2 mL of the cell suspension to a 6-well plate, and grow for 12 h.</li>
		<li>Repeat step 2.3.3 to obtain 80 mg/mL, 60 mg/mL, and 40 mg/mL PBS dilutions of Trichosanthes-Fritillaria thunbergii water extract. Add 20 &#181;L of the PBS solution, 20 &#181;L of the 40 mg/mL Trichosanthes-Fritillaria thunbergii water extract, 20 &#181;L of the 60 mg/mL Trichosanthes-Fritillaria thunbergii water extract, and 20 &#181;L of the 80 mg/mL Trichosanthes-Fritillaria thunbergii water extract to the blank control group, low concentration group, medium concentration group, and high concentration group, respectively. Discard the supernatant after 24 h of intervention, and clean the cells with PBS three times. 
		NOTE: The concentrations of the blank control group, low concentration group, medium concentration group, and high concentration group of Trichosanthes-Fritillaria thunbergii water extract were 0 &#181;g/mL, 400 &#181;g/mL, 600 &#181;g/mL, and 800 &#181;g/mL, respectively.</li>
		<li>Add 250 &#181;L of RIPA buffer (containing 1% PMSF and 1% phosphatase inhibitor) to each well, and lyse the cells for 30 min. Collect the lysate for centrifugation (4 &#176;C, 6,714 x g, 10 min), and obtain the supernatant.</li>
	</ol>
	</li>
	<li>Western blotting
	<ol>
		<li>Detect the protein concentration of the samples by the BCA method according to the manufacturer&#39;s instructions. Use RIPA buffer to adjust the concentration of each sample to be consistent.</li>
		<li>Mix 40 &#181;L of the protein sample with 10 &#181;L of 5x loading buffer, and boil for 5 min at 100 &#176;C. Centrifuge (4 &#176;C, 6,714 x g, 10 min) the mixture to obtain the supernatant for subsequent experiments.</li>
		<li>Isolate the proteins by gel electrophoresis using 120 V vertical electrophoresis.</li>
		<li>Prepare an electrical &#34;sandwich&#34; (sponge - filter paper - gel - PVDF membrane - filter paper - sponge). Soak the transfer apparatus in an ice bath, and transfer the protein onto PVDF membranes at 70 V for 60 min.</li>
		<li>Use 100 mL of TBST solution (0.05% [v/v] Tween-20, 10 mM Tris, 150 mM NaCl, pH 7.5) and 5 g of skimmed milk powder to configure 5% milk. Pre-dilute the AKT and p-AKT antibodies with the antibody diluent in a ratio of 1:1,000.</li>
		<li>Block the PVDF membrane in 5% skimmed milk powder for 1 h with shaking. After blocking, discard the blocking milk, and add the diluted primary antibody for overnight incubation at 4 &#176;C.</li>
		<li>After removing the primary antibody, use TBST solution to wash the PVDF membrane five times for 5 min each.</li>
		<li>Pre-dilute the secondary antibody with the antibody diluent in a ratio of 1:5,000. Add the secondary antibody, and incubate at room temperature (RT) for 1.5 h. After removing the secondary antibody, use TBST solution to wash the PVDF membrane five times for 5 min each.</li>
		<li>Detect the protein using a chemiluminescence detection system.</li>
	</ol>
	</li>
</ol>
3. Molecular docking
<ol>
	<li>Open the UniProt database (https://www.uniprot.org)28. Type the target symbols into the search box, and click on the&#160;SEARCH button. In the Structure subheading, click on Download for the 3D structures of the protein targets.</li>
	<li>Open the RCSB PDB database (https://www.rcsb.org/pdb/home/sitemap.do)29.&#160;Type the name of the protein macromolecules into the search box. Download the protein macromolecule structures in PDB format.</li>
	<li>Download the installation package of UCSF Chimera 1.16 software (https://vina.scripps.edu/)&#160;and AutoDock Vina (https://vina.scripps.edu/). Install and open the UCSF Chimera 1.16 software. Click on&#160;File &#62; Open to display the receptor protein.</li>
	<li>Click on Tools &#62; Structure Editing &#62; Dock Prep, and tick Delete Solvent, Add Hydrogens, and Add Charges in the popover to remove water and add hydrogenation and balance charges. Click on Write Mol2 File to save the receptor protein in mol2 format. Perform the same step on the ligand.</li>
	<li>Click on File &#62; Open to display the mol2-format ligand in the UCSF Chimera 1.16 software. In Tools &#62; Surface/Binding Analysis &#62; AutoDock Vina, set the Receptor bar to the name of the receptor protein and the Ligand bar to the name of the ligand. Enter a value in the box behind Center and Size to adjust the newly developed space, making it possible to fully encompass the ligand and the receptor.</li>
	<li>Click on OK for the molecular docking virtual screening to obtain the optimal location for ligand binding to the receptor. Record the binding energy value at the optimal position.</li>
</ol>
4. Statistical analysis
<ol>
	<li>Open SPSS 26.0 statistical software. Click on File &#62; Import Data for data loading.</li>
	<li>Present the experimental data as mean &#177; standard deviation.</li>
	<li>Compare multiple groups using a one-way ANOVA. 
	NOTE: P &#60; 0.05 was considered statistically significant in this work.</li>
</ol>

The authors have no conflicts of interest to declare.

Generally, a complete network pharmacology study includes the identification of active components from databases, the acquisition of targets corresponding to active components and diseases, the construction of a drug-component-disease-target network, and the prediction of core targets and pathways. The association between active components and core proteins (molecular docking) is preliminarily predicted by computer technology, and the final verification is conducted using an experiment.
The se...

Lung cancer refers to malignant tumors originating from the lung bronchial mucosa, including squamous cell carcinoma, adenocarcinoma, large cell carcinoma, and small cell carcinoma1. Lung adenocarcinoma (LUAD) is the most common type of lung cancer, accounting for about 40% of the total lung cancer cases2. Most patients are diagnosed at an advanced stage or have remote metastasis and, thus, lose the opportunity of surgery3. In current clinical treatment, concurrent chemoradiotherapy is the most common strategy for treating LUAD, but its application is limited due to serious adverse reactions4.
Traditional Chinese medicine (TCM) can effectively relieve the clinical symptoms of LUAD patients and reduce the adverse reactions caused by radiotherapy and chemotherapy and has, thus, become a research hotspot5,6,7. In traditional Chinese medicine, lung cancer belongs to the category of &#34;lung accumulation&#34; and &#34;pulmonary petrous&#34;. The deficiency of Qi and the interaction of phlegm, stasis, and poison are important in the pathogenesis of lung cancer. Therefore, tonifying Qi and eliminating phlegm and blood stasis are the main clinical treatment8 methods for lung cancer according to TCM theory9. Trichosanthes kirilowii Maxim (Gualou) and Fritillaria thunbergii Miq (Zhebeimu) represent a common drug pair in treating lung cancer, and this combination has the effects of clearing heat and reducing phlegm10,11,12. However, its mechanism of action is still unclear, and further research needs to be conducted.
Network pharmacology is a comprehensive method based on the theory of systems biology and multidirectional pharmacology that aims to reveal complex network relationships between multiple drugs and diseases13. Traditional Chinese prescriptions have the characteristics of being multi-component and multi-target, meaning they are very suitable for the study of network pharmacology14,15. Recently, network pharmacology has emerged as a powerful approach in the study of TCM formulas and has become a research hotspot16,17.
However, to the best of our knowledge, all of the research on network pharmacology are presented as text. Presenting this technology through video will greatly reduce the learning threshold and facilitate the promotion of this technology, which is one of the advantages of this article. In this study, we took Trichosanthes-Fritillaria thunbergii against lung adenocarcinoma as an example to carry out network pharmacology prediction and experimental validation.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>0.25% trypsin-EDTA</td>
			<td>Gibco</td>
			<td>R001100</td>
			<td></td>
		</tr>
		<tr>
			<td>A549&#160;cell line</td>
			<td>Procell</td>
			<td>CL-0016</td>
			<td></td>
		</tr>
		<tr>
			<td>AKT antibody</td>
			<td>CST</td>
			<td>4691S</td>
			<td></td>
		</tr>
		<tr>
			<td>BCA Protein Assay Kit</td>
			<td>Solarbio</td>
			<td>PC0020</td>
			<td></td>
		</tr>
		<tr>
			<td>Chemiluminescence detection system</td>
			<td>Shanghai Qinxiang Scientific Instrument Factory</td>
			<td>ChemiScope 6100</td>
			<td></td>
		</tr>
		<tr>
			<td>Dulbecco&#39;s modified eagle medium (DMEM)</td>
			<td>Solarbio</td>
			<td>11995</td>
			<td></td>
		</tr>
		<tr>
			<td>Enhanced chemiluminescence (ECL) kit&#160;</td>
			<td>ABclonal</td>
			<td>RM00021</td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal bovine serum</td>
			<td>ScienCell</td>
			<td>0025</td>
			<td></td>
		</tr>
		<tr>
			<td>HRP Goat Anti-Rabbit IgG (H+L)</td>
			<td>ABclonal</td>
			<td>AS014</td>
			<td></td>
		</tr>
		<tr>
			<td>MTS assay kit</td>
			<td>Promega</td>
			<td>G3580</td>
			<td></td>
		</tr>
		<tr>
			<td>p-AKT antibody</td>
			<td>CST</td>
			<td>6040S</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin streptomycin</td>
			<td>Gibco</td>
			<td>C14-15070-063</td>
			<td></td>
		</tr>
		<tr>
			<td>Phenylmethanesulfonyl fluoride (PMSF)</td>
			<td>Solarbio</td>
			<td>P0100</td>
			<td></td>
		</tr>
		<tr>
			<td>Phosphatase inhibitor</td>
			<td>Beyotime</td>
			<td>P1081</td>
			<td></td>
		</tr>
		<tr>
			<td>Phosphate buffered saline (PBS)</td>
			<td>Solarbio</td>
			<td>P1020</td>
			<td></td>
		</tr>
		<tr>
			<td>Polyvinylidene difluoride (PVDF) membranes</td>
			<td>Millipore</td>
			<td>ISEQ00010</td>
			<td></td>
		</tr>
		<tr>
			<td>RIPA lysis solution</td>
			<td>Solarbio</td>
			<td>R0010</td>
			<td></td>
		</tr>
		<tr>
			<td>Rotary evaporator</td>
			<td>Shanghai Yarong Biochemical Instrument Factory</td>
			<td>RE52CS-1</td>
			<td></td>
		</tr>
		<tr>
			<td>Vacuum freeze-drying mechanism</td>
			<td>Ningbo Scientz Biotechnology</td>
			<td>SCIENTZ-10</td>
			<td></td>
		</tr>
		<tr>
			<td>&#946;-Actin antibody</td>
			<td>ABclonal</td>
			<td>AC026</td>
			<td></td>
		</tr>
	</tbody>
</table>

network pharmacology prediction and experimental validation of trichosanthes-fritillaria thunbergii action mechanism against lung adenocarcinoma

We aimed to study the mechanism of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma (LUAD) based on network pharmacology and experimental verification. The effective components and potential targets of Trichosanthis and Fritillaria thunbergii were collected by high-throughput experiment and reference-guided (HERB) database of traditional Chinese medicine and a similarity ensemble approach (SEA) database, and the LUAD-related targets were queried by the GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. A drug-component-disease-target network was constructed by Cytoscape software. Protein-protein interaction (PPI) network, gene ontology (GO) function, and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were conducted to obtain core targets and key pathways. An aqueous extract of Trichosanthes-Fritillaria thunbergii and A549 cells were used for the subsequent experimental validation. Through the HERB database and literature search, 31 effective compounds and 157 potential target genes of Trichosanthes-Fritillaria thunbergii were screened, of which 144 were regulatory targets of Trichosanthes-Fritillaria thunbergii in the treatment of lung adenocarcinoma. The GO functional enrichment analysis showed that the mechanism of action of Trichosanthes-Fritillaria thunbergii against lung adenocarcinoma is mainly protein phosphorylation. The KEGG pathway enrichment analysis suggested that the treatment of lung adenocarcinoma by Trichosanthes-Fritillaria thunbergii mainly involves the PI3K/AKT signaling pathway. The experimental validation showed that an aqueous extract of Trichosanthes-Fritillaria thunbergii could inhibit the proliferation of A549 cells and the phosphorylation of AKT. Through network pharmacology and experimental validation, it was verified that the PI3K/AKT signaling pathway plays a vital role in the action of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma.

A total of 31 Trichosanthes-Fritillaria thunbergii-related active components were identified, including 21 Trichosanthes and 10 Fritillaria thunbergia components, as well as 144 corresponding targets. Overall, 9,049 and 67 LUAD-related genes were extracted from the GeneCards database and the OMIM database, respectively. After deleting duplicated genes, 9,057 genes related to LUAD were identified. The intersection of the LUAD-related genes and Trichosanthes-Fritillaria thunbergii active...

Watch this Scientific Journal Video about Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma at JoVE.com

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

This study reveals the mechanism of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma based on network pharmacology and experimental verification. The study also demonstrates that the PI3K/AKT signaling pathway plays a vital role in the action of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma.

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

We aimed to study the mechanism of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma (LUAD) based on network pharmacology and ...

network-pharmacology-prediction-experimental-validation-trichosanthes

Research

JoVE Journal

Medicine

1.1K Views.  Dongzhimen Hospital affiliated to Beijing University of Chinese Medicine. This study reveals the mechanism of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma based on network pharmacology and experimental verification. The study also demonstrates that the PI3K/AKT signaling pathway plays a vital role in the action of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma.

Video: Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

An Experimental Paradigm for the Prediction of Post-Operative Pain (PPOP)

Many women undergo cesarean delivery without problems, however some experience significant pain after cesarean section. Pain is associated with negative short-term and long-term effects on the mother. Prior to women undergoing surgery, can we predict who is at risk for developing significant postoperative pain and potentially prevent or minimize its negative consequences? These are the fundamental questions that a team from the University of Washington, Stanford University, the Catholic University in Brussels, Belgium, Santa Joana Women's Hospital in S&atilde;o Paulo, Brazil, and Rambam Medical Center in Israel is currently evaluating in an international research collaboration. The ultimate goal of this project is to provide optimal pain relief during and after cesarean section by offering individualized anesthetic care to women who appear to be more 'susceptible' to pain after surgery.
A significant number of women experience moderate or severe acute post-partum pain after vaginal and cesarean deliveries. 1 Furthermore, 10-15% of women suffer chronic persistent pain after cesarean section. 2 With constant increase in cesarean rates in the US 3 and the already high rate in Brazil, this is bound to create a significant public health problem. When questioning women's fears and expectations from cesarean section, pain during and after it is their greatest concern. 4 Individual variability in severity of pain after vaginal or operative delivery is influenced by multiple factors including sensitivity to pain, psychological factors, age, and genetics. The unique birth experience leads to unpredictable requirements for analgesics, from 'none at all' to 'very high' doses of pain medication. Pain after cesarean section is an excellent model to study post-operative pain because it is performed on otherwise young and healthy women. Therefore, it is recommended to attenuate the pain during the acute phase because this may lead to chronic pain disorders. The impact of developing persistent pain is immense, since it may impair not only the ability of women to care for their child in the immediate postpartum period, but also their own well being for a long period of time.
In a series of projects, an international research network is currently investigating the effect of pregnancy on pain modulation and ways to predict who will suffer acute severe pain and potentially chronic pain, by using simple pain tests and questionnaires in combination with genetic analysis. A relatively recent approach to investigate pain modulation is via the psychophysical measure of Diffuse Noxious Inhibitory Control (DNIC). This pain-modulating process is the neurophysiological basis for the well-known phenomenon of 'pain inhibits pain' from remote areas of the body. The DNIC paradigm has evolved recently into a clinical tool and simple test and has been shown to be a predictor of post-operative pain.5 Since pregnancy is associated with decreased pain sensitivity and/or enhanced processes of pain modulation, using tests that investigate pain modulation should provide a better understanding of the pathways involved with pregnancy-induced analgesia and may help predict pain outcomes during labor and delivery. For those women delivering by cesarean section, a DNIC test performed prior to surgery along with psychosocial questionnaires and genetic tests should enable one to identify women prone to suffer severe post-cesarean pain and persistent pain. These clinical tests should allow anesthesiologists to offer not only personalized medicine to women with the promise to improve well-being and satisfaction, but also a reduction in the overall cost of perioperative and long term care due to pain and suffering. On a larger scale, these tests that explore pain modulation may become bedside screening tests to predict the development of pain disorders following surgery.

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

Diffuse noxious inhibitory control, temporal summation and wound hyperalgesia testing are demonstrated in the obstetric patient. These tests evaluate inhibitory and excitatory mechanisms of pain processing and are here utilized to evaluate endogenous analgesia at different time-points during pregnancy and the peripartum period to help reveal individual s risk for persistent pain.

Many women undergo cesarean delivery without problems, however some experience significant pain after cesarean section. Pain is associated with negative ...

Experimental Human Pneumococcal Carriage

Experimental human pneumococcal carriage (EHPC) is scientifically important because nasopharyngeal carriage of Streptococcus pneumoniae is both the major source of transmission and the prerequisite of invasive disease. A model of carriage will allow accurate determination of the immunological correlates of protection, the immunizing effect of carriage and the effect of host pressure on the pathogen in the nasopharyngeal niche. Further, methods of carriage detection useful in epidemiologic studies, including vaccine studies, can be compared.
Aim
We aim to develop an EHPC platform that is a safe and useful reproducible method that could be used to down-select candidate novel pneumococcal vaccines with prevention of carriage as a surrogate of vaccine induced immunity. It will work towards testing of candidate vaccines and descriptions of the mechanisms underlying EHPC and vaccine protection from carriage1. Current conjugate vaccines against pneumococcus protect children from invasive disease although new vaccines are urgently needed as the current vaccine does not confer optimal protection against non-bacteraemic pneumonia and there has been evidence of serotype replacement with non-vaccine serotypes2-4.
Method
We inoculate with S. pneumoniae suspended in 100 &mu;l of saline. Safety is a major factor in the development of the EHPC model and is achieved through intensive volunteer screening and monitoring. A safety committee consisting of clinicians and scientists that are independent from the study provides objective feedback on a weekly basis.
The bacterial inoculum is standardized and requires that no animal products are inoculated into volunteers (vegetable-based media and saline). The doses required for colonization (104-105) are much lower than those used in animal models (107)5. Detecting pneumococcal carriage is enhanced by a high volume (ideally &gt;10 ml) nasal wash that is relatively mucus free. This protocol will deal with the most important parts of the protocol in turn. These are (a) volunteer selection, (b) pneumococcal inoculum preparation, (c) inoculation, (d) follow-up and (e) carriage detection.
Results
Our current protocol has been safe in over 100 volunteers at a range of doses using two different bacterial serotypes6. A dose ranging study using S. pneumoniae 6B and 23F is currently being conducted to determine the optimal inoculation dose for 50% carriage. A predicted 50% rate of carriage will allow the EHPC model to have high sensitivity for vaccine efficacy with small study numbers.

Experimental human pneumococcal carriage offers a natural model of carriage and a potential model for use in vaccine development. This technique is valuable yet complex and involves clinical risk by introducing a pathogen into a human. We have developed a detailed protocol.

Experimental human pneumococcal carriage (EHPC) is scientifically important because nasopharyngeal carriage of Streptococcus pneumoniae is both the major ...

Evaluation of Lung Metastasis in Mouse Mammary Tumor Models by Quantitative Real-time PCR

Metastatic disease is the spread of malignant tumor cells from the primary cancer site to a distant organ and is the primary cause of cancer associated death 1. Common sites of metastatic spread include lung, lymph node, brain, and bone 2. Mechanisms that drive metastasis are intense areas of cancer research. Consequently, effective assays to measure metastatic burden in distant sites of metastasis are instrumental for cancer research. Evaluation of lung metastases in mammary tumor models is generally performed by gross qualitative observation of lung tissue following dissection. Quantitative methods of evaluating metastasis are currently limited to ex vivo and in vivo imaging based techniques that require user defined parameters. Many of these techniques are at the whole organism level rather than the cellular level 3-6. Although newer imaging methods utilizing multi-photon microscopy are able to evaluate metastasis at the cellular level 7, these highly elegant procedures are more suited to evaluating mechanisms of dissemination rather than quantitative assessment of metastatic burden. Here, a simple in vitro method to quantitatively assess metastasis is presented. Using quantitative Real-time PCR (QRT-PCR), tumor cell specific mRNA can be detected within the mouse lung tissue.

This protocol describes how to use quantitative Real-time PCR (QRT-PCR) to detect tumor cell specific mRNA representing metastasis within the mouse lung tissue.

Metastatic disease is the spread of malignant tumor cells from the primary cancer site to a distant organ and is the primary cause of cancer associated ...

Laser-Induced Action Potential-Like Measurements of Cardiomyocytes on Microelectrode Arrays for Increased Predictivity of Safety Pharmacology

Life-threatening drug-induced cardiac arrhythmia is often preceded by prolonged cardiac action potentials (AP), commonly accompanied by small proarrhythmic membrane potential fluctuations. The shape and time course of the repolarizing fraction of the AP can be pivotal for the presence or absence of arrhythmia.
Microelectrode arrays (MEA) allow easy access to cardiotoxic compound effects via extracellular field potentials (FP). Although a powerful and well-established tool in research and cardiac safety pharmacology, the FP waveform does not allow to infer the original AP shape due to the extracellular recording principle and the resulting intrinsic alternating current (AC) filtering.
A novel device, described here, can repetitively open the membrane of cardiomyocytes cultivated on top of the MEA electrodes at multiple cultivation time points, using a highly focused nanosecond laser beam. The laser poration results in transforming the electrophysiological signal from FP to intracellular-like APs (laser-induced AP, liAP) and enables the recording of transcellular voltage deflections. This intracellular access allows a better description of the AP shape and a better and more sensitive classification of proarrhythmic potentials than regular MEA recordings. This system is a revolutionary extension to the existing electrophysiological methods, permitting accurate evaluation of cardiotoxic effect with all advantages of MEA-based recordings (easy, acute, and chronic experiments, signal propagation analysis, etc.).

The combination of laser poration and microelectrode arrays (MEA) allows action potential-like recordings of cultivated primary and stem cell-derived cardiomyocytes. The waveform shape provides superior insight into test compounds' mode of action than standard recordings. It links patch-clamp and MEA readout to further optimize cardio safety research in the future.

Life-threatening drug-induced cardiac arrhythmia is often preceded by prolonged cardiac action potentials (AP), commonly accompanied by small ...

Behavioral and Network Pharmacology-Based Analyses for the Traditional Mongolian Medicine Zadi-5 in a Rat Model of Depression

Zadi-5 is a traditional Mongolian medicine that is widely used for the treatment of depression and symptoms of irritation. Although the therapeutic effects of Zadi-5 against depression have been indicated in previously reported clinical studies, the identity and impact of the active pharmaceutical compounds present in the drug have not been fully elucidated.&#160;This study used network pharmacology to predict the drug composition and identify the therapeutically active compounds in Zadi-5 pills. Here, we established a rat model of chronic unpredicted mild stress (CUMS) and conducted an open field test (OFT), Morris water maze (MWM) analysis, and sucrose consumption test (SCT) to investigate the potential therapeutic efficacy of Zadi-5 in depression. This study aimed to demonstrate Zadi-5&#39;s therapeutic effects for depression and predict the critical pathway of the action of Zadi-5 against the disorder. The vertical and horizontal scores (OFT), SCT, and zone crossing numbers of the fluoxetine (positive control) and Zadi-5 groups were significantly higher (P &#60; 0.05) than those of the CUMS group rats without treatment. According to the results of network pharmacology analysis, the PI3K-AKT pathway was found to be essential for the antidepressant effect of Zadi-5.

The present protocol describes a method for the behavioral test validation and bioinformatical prediction of the therapeutic efficacy of Zadi-5, a traditional Mongolian medicine, in depression.

Zadi-5 is a traditional Mongolian medicine that is widely used for the treatment of depression and symptoms of irritation. Although the therapeutic ...

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Hyperlipidemia has become a leading risk factor for cardiovascular diseases and liver injury worldwide. Fructus Phyllanthi (FP) is an effective drug against hyperlipidemia in Traditional Chinese Medicine (TCM) and Indian Medicine theories, however the potential mechanism requires further exploration. The present research aims to reveal the mechanism of FP against hyperlipidemia based on an integrated strategy combining network pharmacology prediction with metabolomics validation. A high-fat diet (HFD)-induced mice model was established by evaluating the plasma lipid levels, including total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Network pharmacology was applied to find out the active ingredients of FP and potential targets against hyperlipidemia. Metabolomics of plasma and liver were performed to identify differential metabolites and their corresponding pathways among the normal group, model group, and intervention group. The relationship between network pharmacology and metabolomics was further constructed to obtain a comprehensive view of the process of FP against hyperlipidemia. The obtained key target proteins were verified by molecular docking. These results reflected that FP improved the plasma lipid levels and liver injury of hyperlipidemia induced by a HFD. Gallic acid, quercetin, and beta-sitosterol in FP were demonstrated as the key active compounds. A total of 16 and six potential differential metabolites in plasma and liver, respectively, were found to be involved in the therapeutic effects of FP against hyperlipidemia by metabolomics. Further, integration analysis indicated that the intervention effects were associated with CYP1A1, AChE, and MGAM, as well as the adjustment of L-kynurenine, corticosterone, acetylcholine, and raffinose, mainly involving tryptophan metabolism pathway. Molecular docking ensured that the above ingredients acting on hyperlipidemia-related protein targets played a key role in lowering lipids. In summary, this research provided a new possibility for preventing and treating hyperlipidemia.

The present protocol describes an integrated strategy for exploring the key targets and mechanisms of Fructus Phyllanthi against hyperlipidemia based on network pharmacology prediction and metabolomics verification.

Hyperlipidemia has become a leading risk factor for cardiovascular diseases and liver injury worldwide. Fructus Phyllanthi (FP) is an effective drug ...

Salidroside's Mechanism in Suppressing MCF-7 Cell Migration and Proliferation

Exploring the Pharmacological Action and Molecular Mechanism of Salidroside in Inhibiting MCF-7 Cell Proliferation and Migration

Salidroside (Sal) contains anti-carcinogenic, anti-hypoxic, and anti-inflammatory pharmacological activities. However, its underlying anti-breast cancer mechanisms have been only incompletely elucidated. Hence, this protocol intended to decode the potential of Sal in regulating the PI3K-AKT-HIF-1&#945;-FoxO1 pathway in the malignant proliferation of human breast cancer MCF-7 cells. First, the pharmacological activity of Sal against MCF-7 was evaluated by CCK-8 and cell scratch assays. Moreover, the resistance of MCF-7 cells was measured by migration and Matrigel invasion assays. For cell apoptosis and cycle assays, MCF-7 cells were processed in steps with annexin V-FITC/PI and cell cycle-staining detection kits for flow cytometry analyses, respectively. The levels of reactive oxygen species (ROS) and Ca2+ were examined by DCFH-DA and Fluo-4 AM immunofluorescence staining. The activities of Na+-K+-ATPase and Ca2+-ATPase were determined using the corresponding commercial kits. The protein and gene expression levels in apoptosis and the PI3K-AKT-HIF-1&#945;-FoxO1 pathway were further determined using western blot and qRT-PCR analyses, respectively. We found that Sal treatment significantly restricted the proliferation, migration, and invasion of MCF-7 cells with dose-dependent effects. Meanwhile, Sal administration also dramatically forced MCF-7 cells to undergo apoptosis and cell cycle arrest. The immunofluorescence tests showed that Sal observably stimulated ROS and Ca2+ production in MCF-7 cells. Further data confirmed that Sal promoted the expression levels of pro-apoptotic proteins, Bax, Bim, cleaved caspase-9/7/3, and their corresponding genes. Consistently, Sal intervention prominently reduced the expression of the Bcl-2, p-PI3K/PI3K, p-AKT/AKT, mTOR, HIF-1&#945;, and FoxO1 proteins and their corresponding genes. In conclusion, Sal can be used as a potential herb-derived compound for treating breast cancer, as it may reduce the malignant proliferation, migration, and invasion of MCF-7 cells by inhibiting the PI3K-AKT-HIF-1&#945;-FoxO1 pathway.

Author Spotlight: Exploring Salidroside's Molecular Mechanisms in Breast Cancer Treatment 

The present protocol describesa comprehensive strategy for evaluating the pharmacological action and mechanism of salidroside in inhibiting MCF-7 cell proliferation and migration.

Salidroside (Sal) contains anti-carcinogenic, anti-hypoxic, and anti-inflammatory pharmacological activities. However, its underlying anti-breast cancer ...

Network Pharmacology and GEO Dataset Analysis of SDG in Anus Eczema Treatment

The Efficacy and Underlying Pathway Mechanisms of ShiDuGao Treatment for Anus Eczema Based on GEO Datasets and Network Pharmacology

Anus eczema is a chronic and recurrent inflammatory skin disease affecting the area around the anus. While the lesions primarily occur in the anal and perianal skin, they can also extend to the perineum or genitalia. ShiDuGao (SDG) has been found to possess significant reparative properties against anal pruritus, exudation control, moisture reduction, and skin repair. However, the genetic targets and pharmacological mechanisms of SDG on anal eczema have yet to be comprehensively elucidated and discussed. Consequently, this study employed a network pharmacological approach and utilized gene expression omnibus (GEO) datasets to investigate gene targets. Additionally, a protein-protein interaction network (PPI) was established, resulting in the identification of 149 targets, of which 59 were deemed hub genes, within the "drug-target-disease" interaction network.
The gene function of SDG in the treatment of perianal eczema was assessed through the utilization of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. Subsequently, the anti-perianal eczema function and potential pathway of SDG, as identified in network pharmacological analysis, were validated using molecular docking methodology. The biological processes associated with SDG-targeted genes and proteins in the treatment of anus eczema primarily encompass cytokine-mediated responses, inflammatory responses, and responses to lipopolysaccharide, among others. The results of the pathway enrichment and functional annotation analyses suggest that SDG plays a crucial role in preventing and managing anal eczema by regulating the Shigellosis and herpes simplex virus 1 infection pathways. Network pharmacology and GEO database analysis confirms the multi-target nature of SDG in treating anal eczema, specifically by modulating TNF, MAPK14, and CASP3, which are crucial hub targets in the TNF and MAPK signaling pathways. These findings provide a clear direction for further investigation into SDG's therapeutic mechanism for anal eczema while highlighting its potential as an effective treatment approach for this debilitating condition.

Author Spotlight: Exploring ShiDuGao's Multi-Target Approach in Anus Eczema Treatment

This investigative effort sought to elucidate the mechanism of topical drug administration using a synergistic integration of network pharmacology and gene expression omnibus (GEO) datasets. This article evaluated the feasibility, target, and mechanism of ShiDuGao (SDG) in treating anus eczema.

Anus eczema is a chronic and recurrent inflammatory skin disease affecting the area around the anus. While the lesions primarily occur in the anal and ...

Validating Xanthatin-Keap1 Interaction with Docking and CETSA

Protein Target Prediction and Validation of Small Molecule Compound

Proteins are fundamental to human physiology, with their targets being crucial in research and drug development. The identification and validation of crucial protein targets have become integral to drug development. Molecular docking is a computational tool widely utilized to investigate protein-ligand binding, especially in the context of drug and protein target interactions. For the experimental verification of the binding and to access the binding of the drug and its target directly, the cellular thermal shift assay (CETSA) method is used. This study aimed to integrate molecular docking with CETSA to predict and validate interactions between drugs and vital protein targets. Specifically, we predicted the interaction between xanthatin and Keap1 protein as well as its binding mode through molecular docking analysis, followed by verification of the interaction using the CETSA assay. Our results demonstrated that xanthatin could establish hydrogen bonds with specific amino acid residues of Keap1 protein and reduce the thermostability of Keap1 protein, indicating that xanthatin could directly interact with Keap1 protein.

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

The experiment used here shows a method of molecular docking combined with cellular thermal shift assay to predict and validate the interaction between small molecules and protein targets.

Proteins are fundamental to human physiology, with their targets being crucial in research and drug development. The identification and validation of ...

Study of Experimental Organ Donation Models for Lung Transplantation

Experimental models are important tools for understanding the etiological phenomena involved in various pathophysiological events. In this context, different animal models are used to study the elements triggering the pathophysiology of primary graft dysfunction after transplantation to evaluate potential treatments. Currently, we can divide experimental donation models into two large groups: donation after brain death and donation after circulatory arrest. In addition, the deleterious effects associated with hemorrhagic shock should be considered when considering animal models of organ donation. Here, we describe the establishment of three different lung donation models (post-brain death donation, post-circulatory death donation, and post-hemorrhagic shock donation) and compare the inflammatory processes and pathological disorders associated with these events. The objective is to provide the scientific community with reliable animal models of lung donation for studying the associated pathological mechanisms and searching for new therapeutic targets to optimize the number of viable grafts for transplantation.

The present study shows the establishment of three different lung donation models (post-brain death donation, post-circulatory death donation, and post-hemorrhagic shock donation). It compares the inflammatory processes and pathological disorders associated with these events.

Experimental models are important tools for understanding the etiological phenomena involved in various pathophysiological events. In this context, ...