A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus

Abstract

Complex disease is a cascade process which is associated with functional abnormalities in multiple proteins and protein-protein interaction (PPI) networks. One drug one target has not been able to perfectly intervene complex diseases. Increasing evidences show that Chinese herb formula usually treats complex diseases in the form of multi-components and multi-targets. The key step to elucidate the underlying mechanism of formula in traditional Chinese medicine (TCM) is to optimize and capture the important components in the formula. At present, there are several formula optimization models based on network pharmacology has been proposed. Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. How to select the key group of effective components (KGEC) from the formula of TCM based on the optimal space which link pathogenic genes and drug targets is a bottleneck problem in network pharmacology. To address this issue, we designed a novel network pharmacological model, which takes Lang Chuang Wan (LCW) treatment of systemic lupus erythematosus (SLE) as the case. We used the weighted gene regulatory network and active components targets network to construct disease-targets-components network, after filtering through the network attribute degree, the optimization space and effective proteins were obtained. And then the KGEC was selected by using contribution index (CI) model based on knapsack algorithm. The results show that the enriched pathways of effective proteins we selected can cover 96% of the pathogenetic genes enriched pathways. After reverse analysis of effective proteins and optimization with CI index model, KGEC with 82 components were obtained, and 105 enriched pathways of KGEC targets were consistent with enriched pathways of pathogenic genes (80.15%). Finally, the key components in KGEC of LCW were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the KGEC in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.

Revealing mechanism of Caulis Sargentodoxae for the treatment of ulcerative colitis based on network pharmacology approach

“…This network-based approach can explain the combination rules and network regulation effects of herbal formulas [ 21 ]. Network pharmacology strategies were successfully used to reveal mechanism of Lang Chuang Wan in treating systemic lupus erythematosus [ 22 ] and Yizhiqingxin formula on Alzheimer’s disease [ 23 ].…”

Exome-Wide Search for Genes Associated With Central Nervous System Inflammatory Demyelinating Diseases Following CHIKV Infection: The Tip of the Iceberg

“…The most significant GO terms were related to regulation of chemokine production (GO:0032642; p = 2.75E-4), endocytosis (GO:0030100; p = 8.87E-4), acute inflammatory response (GO:0006953; p = 2.41E-3) and leukocyte activation involved in inflammatory response (GO:0002269; p = 2.51E-3). They all reflect mechanisms of viral infection, endocytosis and immune mediated inflammatory response (Lindenbach and Rice, 2003 ; Gao et al, 2020 ). Interestingly, we also found evidence for microglial cell activation (GO:0001774; p = 2.51E-3) only in patients with ADEM after CHIKV, which is involved in immune regulation and CNS autoimmune disease (Dheen et al, 2007 ; O'Loughlin et al, 2018 ).…”

Analysis of Molecular Mechanism of Erxian Decoction in Treating Osteoporosis Based on Formula Optimization Model

“…ADME characteristics, including OB, MW, Caco-2, and DL, were used to screen bioactive molecules. Based on previous reports [ 35 – 37 ], we selected OB > 30%, MW < 500 Da [ 38 ], Caco‐2 > 0.4 [ 39 ], and DL > 0.14 [ 28 ] as screening criteria for active compounds. These criteria will also be incorporated with ADME screening in future research.…”

“…Then, component characteristics were retrieved from TCMSP, including molecular weight (MW), oral bioavailability (OB), Caco-2 permeability (Caco-2), and DL (drug-likeness). [35][36][37], we selected OB > 30%, MW < 500 Da [38], Caco-2 > 0:4 [39], and DL > 0:14 [28] as screening criteria for active compounds. These criteria will also be incorporated with ADME screening in future research.…”