A Network Pharmacology‐Based Approach to Investigate the Novel TCM Formula against Huntington’s Disease and Validated by Support Vector Machine Model

Abstract

Several pathways are crucial in Huntington's disease (HD). Based on the concept of multitargets, network pharmacology-based analysis was employed to find out related proteins in disease network. The network target method aims to find out related mechanism of efficacy substances in rational design way. Traditional Chinese medicine prescriptions would be used for research and development against HD. Virtual screening was performed to obtain drug molecules with high binding capacity from traditional Chinese medicine (TCM) database@Taiwan. Quantitative structure-activity relationship (QSAR) models were conducted by MLR, SVM, CoMFA, and CoMSIA, constructed to predict the bioactivities of candidates. The compounds with high-dock score were further analyzed compared with control. Traditional Chinese medicine reported in the literature could be the training set provided for constructing novel formula by SVM model. We tried to find a novel formula that can bind well with these targets at the same time, which indicates our design could be highly related to the HD. Additionally, the candidates would validate by a long-term molecular dynamics (MD) simulation, 5 microseconds. Thus, we suggested the herbs Brucea javanica, Holarrhena antidysenterica, Dichroa febrifuga, Erythrophleum guineense, etc. which contained active compounds might be a novel medicine formula toward Huntington's disease.

Network Pharmacology-Based Prediction of the Active Compounds, Potential Targets, and Signaling Pathways Involved in Danshiliuhao Granule for Treatment of Liver Fibrosis

“…The pharmacodynamic mechanisms of TCM against liver fibrosis have multiple levels and multiple targets and pay attention to the characteristics of overall regulation [10]. A new approach that analyzes TCM with network pharmacology may be a reliable way to overcome disease [49]. Cumulating data have shown that network pharmacology can reveal the interactions between multiple targets of compounds present in Chinese herbal medicines [50].…”

Network Pharmacology Approach for Medicinal Plants: Review and Assessment

“…Currently, there are no treatments that can slow or stop the progression of the disease. Dai et al [ 233 ] also employed the same network pharmacology-based methodology to explore a novel herbal formula against Huntington’s disease, which was then further validated by a support vector machine model. The authors demonstrated that Brucea javanica , Dichroa febrifuga , E. micranthum Harms , Erythrophleum guineense , Holarrhena antidysenterica , and Japanese Ardisia Herb contained active compounds that might be a novel medicine formula for Huntington’s disease.…”

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

“…Each created data set may indicate the quantitative variation of between 5,000 and 10,000 experimental indices (transcripts, proteins, or metabolites), with perhaps 100s-1000s of these being statistically significant. In this context the ability of an individual scientist to appreciate the connectivity between these factors, which likely represents the true biomedical and pharmacological meaning of the data, is profoundly limited without the assistance of machine-based clustering and annotation (Chen et al, 2018;Dai et al, 2018;Lee et al, 2018;Lin et al, 2018;Lim and Xie, 2019). While the intrinsic depth of such data streams is a tremendous analytical advance for the study of complex drug activities, a major hurdle for the clinical translation of such data are the pace of advanced data management and investigational platform development.…”

“…In addition to the application of data deconvolution to identify key factors within complex networks, graphbased pipelines have been used to define drug-signaling pathway association analytics. For example, Dai et al (2018) defined a computational process, integrative graph regularized matrix factorization, to enhance the drug-induced signaling cascade classification and prioritization. Integrative graph regularized matrix factorization employs graph regularization to encode data geometrical information and prevent possible overfitting in the prediction of the association of specific therapeutic agents with the strongest associated signaling paradigm.…”