Search

Unlike single-target Western medicines, traditional Chinese medicines (TCMs) exhibit diverse curative effects against multiple diseases through their “multicomponent” and “multitarget” manifestations. However, the material basis of the major therapeutic diseases and TCM underlying molecular mechanisms remain to be challenged. In the current study, we applied, for the first time, an integrated strategy that combines network pharmacology and experimental evaluation and explored and demonstrated the underlying possible mechanisms of a classic TCM formula, Huanglian Jiedu Decoction (HLJD), in the treatment of cerebral ischemia. First, the herb compound, protein compound, and GO-BP and KEGG pathways were constructed to predict the material basis of HLJD in the treatment of cerebral ischemia and explore the underlying molecular mechanisms. Network pharmacology analysis showed that HLJD treats cerebral ischemia mainly through its anti-inflammatory effect. We used molecular docking to verify that HLJD components have good binding activities to the arachidonic acid pathway enzymes, cyclooxylipase-2 (COX-2) and 5-lipoxygenase (5-LOX). Next, based on the prediction by the network pharmacology analysis, the rat model of middle cerebral artery occlusion (MCAO) was established to verify the efficacy of HLJD. The results showed that HLJD reduces the degree of brain injury in MCAO rats, probably by inhibiting the expression of the 5-LOX pathway and inflammatory response. In conclusion, our study demonstrates the effectiveness of integrating network pharmacology with an experimental study for material basis of the major therapeutic diseases and the underlying molecular mechanisms of TCM.

Lianpu drink (LPD) is a traditional Chinese medicine (TCM) formula for the treatment of chronic gastritis (CG), and its clinical effects have been effectively verified. However, due to the complexity of the chemical composition of TCM formulas, its mechanism of action has not yet been clearly explained. Many studies have shown that the principal drugs in the TCM formula play a major therapeutic role. Therefore, in this study, the principal drugs Coptidis Rhizoma (CR) and Magnolia officinalis Rehd. et Wils. (MOR) in LPD were used as the main research objects to predict the mechanism of LPD on CG. We contrasted a “compounds-targets-diseases” network and screened the putative targets of CR and MOR in LPD related to CG, respectively. Furthermore, common targets of CR and MOR related to CG were selected as candidate targets. In this study, the specific target proteins of CR, MOR, and CG were combined by protein-protein interaction (PPI) to construct a pharmacological network of “components-targets-diseases.” In addition, we investigated the effects of CR and MOR on the TNF signaling pathway, which mediated the remission of CG. This study preliminarily revealed that CR and MOR play a key role in the treatment of CG. Animal experiments also showed that CR and MOR could significantly improve CG by inhibiting MKK6/p38 and RIP/p38 pathway.

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.

Ban-Xia-Xie-Xin-Tang (BXXXT) is a classical formula from Shang-Han-Lun which is one of the earliest books of TCM clinical practice. In this work, we investigated the therapeutic mechanisms of BXXXT for the treatment of multiple diseases using a network pharmacology approach. Here three BXXXT representative diseases (colitis, diabetes mellitus, and gastric cancer) were discussed, and we focus on in silico methods that integrate drug-likeness screening, target prioritizing, and multilayer network extending. A total of 140 core targets and 72 representative compounds were finally identified to elucidate the pharmacology of BXXXT formula. After constructing multilayer networks, a good overlap between BXXXT nodes and disease nodes was observed at each level, and the network-based proximity analysis shows that the relevance between the formula targets and disease genes was significant according to the shortest path distance (SPD) and a random walk with restart (RWR) based scores for each disease. We found that there were 22 key pathways significantly associated with BXXXT, and the therapeutic effects of BXXXT were likely addressed by regulating a combination of targets in a modular pattern. Furthermore, the synergistic effects among BXXXT herbs were highlighted by elucidating the molecular mechanisms of individual herbs, and the traditional theory of “Jun-Chen-Zuo-Shi” of TCM formula was effectively interpreted from a network perspective. The proposed approach provides an effective strategy to uncover the mechanisms of action and combinatorial rules of BXXXT formula in a holistic manner.

A traditional Chinese medicine (TCM) formula network including 362 TCM formulas was built by using complex network methodologies. The properties of this network were analyzed including network diameter, average distance, clustering coefficient, and average degree. Meanwhile, we built a TCM chemical space and a TCM metabolism room under the theory of chemical space. The properties of chemical space and metabolism room were calculated and analyzed. The properties of the medicine pairs in “eighteen antagonisms and nineteen mutual inhibitors,” an ancient rule for TCM incompatibility, were studied based on the TCM formula network, chemical space, and metabolism room. The results showed that the properties of these incompatible medicine pairs are different from those of the other TCM based on the analysis of the TCM formula network, chemical space, and metabolism room. The lines of evidence derived from our work demonstrated that the ancient rule of TCM incompatibility, “eighteen antagonisms and nineteen mutual inhibitors,” is probably scientifically based.

Because of the complexity of the components in Traditional Chinese Medicine formula (TCM formula), it is still a challenge to identify its effective components, to elucidate the mechanism of the components, and to discover the relationship between components and therapy objectives. In this paper, a method called directed TCM grammar systems (dTGS) for effective component identification was proposed using entity grammar systems (EGS) as the theoretical framework. The component-disease relationship of a TCM formula (i.e., Bai-Hu decoction plus Wasting-Thirsting formula, BHDWT) and one disease (i.e., type 2 diabetes mellitus) treated with it was studied, and the effective component groups (ECGs) were identified. 19 compounds were found acting on 20 proteins in type 2 diabetes mellitus (T2D) disease network, and 15 compounds were determined as the candidate effective components. Results indicated that this method can be used to identify the effective components and provide an innovative way to elucidate the molecular mechanism of TCM formulas.

Huang-Lian-Jie-Du-Tang (HLJDT) is a classic TCM formula to clear “heat” and “poison” that exhibits antirheumatic activity. Here we investigated the therapeutic mechanisms of HLJDT at protein network level using bioinformatics approach. It was found that HLJDT shares 5 target proteins with 3 types of anti-RA drugs, and several pathways in immune system and bone formation are significantly regulated by HLJDT’s components, suggesting the therapeutic effect of HLJDT on RA. By defining an antirheumatic effect score to quantitatively measure the therapeutic effect, we found that the score of each HLJDT’s component is very low, while the whole HLJDT achieves a much higher effect score, suggesting a synergistic effect of HLJDT achieved by its multiple components acting on multiple targets. At last, topological analysis on the RA-associated PPI network was conducted to illustrate key roles of HLJDT’s target proteins on this network. Integrating our findings with TCM theory suggests that HLJDT targets on hub nodes and main pathway in the Hot ZENG network, and thus it could be applied as adjuvant treatment for Hot-ZENG-related RA. This study may facilitate our understanding of antirheumatic effect of HLJDT and it may suggest new approach for the study of TCM pharmacology.

At the molecular level, it is acknowledged that a TCM formula is often a complex system, which challenges researchers to fully understand its underlying pharmacological action. However, module detection technique developed from complex network provides new insight into systematic investigation of the mode of action of a TCM formula from the molecule perspective. We here proposed a computational approach integrating the module detection technique into a 2-class heterogeneous network (2-HN) which models the complex pharmacological system of a TCM formula. This approach takes three steps: construction of a 2-HN, identification of primary pharmacological units, and pathway analysis. We employed this approach to study Shu-feng-jie-du (SHU) formula, which aimed at discovering its molecular mechanism in defending against influenza infection. Actually, four primary pharmacological units were identified from the 2-HN for SHU formula and further analysis revealed numbers of biological pathways modulated by the four pharmacological units. 24 out of 40 enriched pathways that were ranked in top 10 corresponding to each of the four pharmacological units were found to be involved in the process of influenza infection. Therefore, this approach is capable of uncovering the mode of action underlying a TCM formula via module analysis.