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

Complex diseases are cascade process which are associate with functional abnormalities in multiple proteins and protein interaction networks. One drug one target has not been able to intervene well with disease. More and more 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 action mechanism of traditional Chinese medicine (TCM) is to optimize the important components in the formula of TCM. At present, there are several models of formula Therapeutic Mechanisms of Lang Chuang Wan for SLE optimization 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 construction of functional optimization space based on relationship between disease-causing 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 have 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 selected the KGEC use contribution index (CI) model based on a reversed strategy. The results show that the enriched pathways of effective proteins we selected can cover 96% of the disease-causing genes enriched pathways. After reverse analysis of effective proteins and optimization with CI index model, 82 KGEC were obtained, and 105 enriched pathways of the targets of 82 KGEC were consistent with enriched pathways of pathogenic genes (80.15%). 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.