Traditional Chinese Medicine (TCM), the world’s oldest known herbal medicine, has long been used in several Asian countries. Emerging evidence has demonstrated their effectiveness and safety in the treatment of various diseases, ranging from mild flu to severe cancers. A great number of patients have benefited by receiving TCM therapies.

However, due to the complex chemical compositions of TCM, which could produce highly complex compound-protein binding interactions, the molecular mechanisms underlying the pharmacological actions of TCM still remain poorly understood. Specifically, it is difficult to identify and isolate the effective constituents from the mixture of many compounds, even the therapeutic targets are known. Secondly, it is unclear whether the therapeutic effects are due to different individual constituent working independently on the therapeutic target or working together on a single or multiple target(s). Whichever the case is, it is unlikely to quantify the therapeutic contribution of a single compound in TCM.

The development of complex network models and relevant analytical methods have paved a way to the study of TCMs’ pharmacological characteristics. Through such approaches, a complex system made up of interactive components or nodes can be established and visualized as an interconnected network. A number of computational algorithms could subsequently be used to analyze the global properties and local features of the whole system.

The application of complex network analyses in the field of life science or health science are often termed “Systems Biology” or “Systems Biomedicine” [1]. “Network Pharmacology” is a commonly used term to describe a study that involves the analyses of a particular drug or a chemical compound. In such biomedical or molecular systems, the nodes of the network could be biomolecules (e.g., proteins, genes, or RNAs), diseases, or signaling pathways; whilst the edges in such systems could be the relationship between different nodes. For example, an edge between two proteins indicates a binding interaction; an edge between a drug and a protein indicates a drug-target therapeutic relationship. Protein-protein interaction networks [2], drug-target interaction networks [3] and compound-protein interaction networks [4,5] are commonly seen (bio)molecular networks in systems biology or network pharmacology studies. The development of these new research methods enabled the multi-target mechanical studies, and systematic studies of multi-genic diseases such as cancers and other complicated diseases.

Compound Kushen Injection (CKI) is a TCM preparation, indicating for various types of solid tumors. Previous studies have explored the mechanisms of CKI in treating cancers [6-8]. For instance, using computational methods, Meng et al. identified the potential therapeutic targets and active signaling pathway (PI3K-Akt) in patients with lung cancer who received CKI therapy [6]. Wang et al. reviewed the active ingredients of CKI as well as their molecular mechanisms in anti-inflammation and inhibition of cancer cell proliferation [7]. However, due to the complex chemical compositions of CKI, previously published studies were unable to identify its therapeutic mechanisms caused by the synergy effects of multiple ingredient compounds.

Recently, Liang et al. focused on the treatment of lung cancer with CKI, using the network pharmacology approach to characterize the synergistic effects of multi-compounds treating against multi-targets [2]. Specifically, in this study, the authors firstly constructed a target-pathway-target network and a protein-protein interaction network with a special attention to the modularity properties (also known as the community feature in graph theory). A topological analysis using Louvain algorithm (i.e., a sort of network community/cluster detection method) [9-12] and the shortest path computing method were conducted subsequently to identify the potential active constituents with similar functional propensity and relevant potential therapeutic target groups. Through a series of the aforementioned network topological and association analyses, naringenin (derived from Baituling), kurarinone and isoxanthohumol (derived from Kushen) were determined to be the active ingredients of CKI in treating lung cancer [2].

There are several works that could be done on top of Liang et al.’s study [2] to further improve the research quality. Firstly, the binding interactions between active ingredients of CKI and the corresponding therapeutic targets of lung cancer could be further validated. Computational methods such as 3-dimensional molecular docking analysis is advised to validate the compound-protein binding interactions. Second, the predictive results can be further validated by in vivo and in vitro studies should laboratory-based experimental resources are available.

Systems biology and network pharmacology have provided powerful analytic tools, which have been adopted by a number of studies to characterize the component features in a complex system [3-8]. To enhance the research quality of to a higher level, it is suggested to integrate current systems biology and network pharmacology research methods with big data and other state-of-the-art techniques such as high throughput sequencing, machine learning and multi-omics. An important trend for future systems biology and network pharmacology research will be integrated with multi-omic big datasets. Supposedly, the availability of lung cancer’s genetic/protein expression data should enable the studies into protein-protein interaction networks. Such resultant networks can provide more in-depth insights into the molecular status of a living system compared with the theoretical networks.

Although TCM are considered by some people as an alternative or complementary treatment option, it is indeed a promising pharmacological intervention and a rich resource pool for discovering anti-cancer agents. With the development of complex network analysis and network pharmacology, it is expected that more detailed TCM therapeutic mechanisms will be elucidated. It is also hoped that more effective and safe anti-cancer pharmacotherapies can be developed based on the outcomes of TCM network pharmacology studies.