Network pharmacology: Changes the treatment mode of "one disease-one target" in cancer treatment

Abstract

The article concluded that network pharmacology provides new ideas and insights into the molecular mechanism of traditional Chinese medicine (TCM) treatment of cancer. TCM is a new choice and hot spot in the field of cancer treatment. We have also previously published studies on TCM and network pharmacology. In this letter, we summarize the new paradigm of network pharmacology in cancer treatment mechanisms.

Key Words: Network pharmacology; Cancer; Multicomponent; Multitarget; Therapy

Core Tip: Cancer cannot be effectively treated against a single gene target because it involves the synergy of different gene groups. Network pharmacology provides strong evidence of "multicomponent-multitarget" synergistic effects for the treatment of cancer with Chinese medicine.

TO THE EDITOR

We read with great interest the article recently published on the World Journal of Gastrointestinal Oncology[1]. The authors suggest that gastric cancer (GC) is the third most common malignancy in terms of both morbidity and mortality. At the same time, due to the difficulty in early diagnosis of GC, the current therapeutic methods are limited[2]. Although some molecular targeted drugs can be used for certain patients with overexpression or activation of certain targets, the scope of application is still very limited. Traditional Chinese medicine (TCM) is designed on the basis of the integrity of the human body and has the characteristics of multicomponent, multitarget and multilevel action[3], which can improve the clinical symptoms and quality of life in GC patients, prolong the survival time of patients, and reduce the toxic side effects caused by radiotherapy and chemotherapy. The authors revealed the possible mechanism by which the TCM Xiaojianzhong decoction improves GC by the synergistic effect of multiple targets through network pharmacology.

In recent years, the incidence and mortality of cancer have been rising, and traditional treatments, including chemotherapy, radiotherapy and surgery, are often unable to effectively control the progression of cancer. In the past few decades, the development of cancer treatment has focused on the development of drugs for a single target, that is, the "one disease-one target" model[4]. However, this strategy has been limited in addressing the complex pathological mechanisms of cancer. Cancer is a multifactorial, multistep, and multigene mutation-driven disease that involves abnormal activation or inhibition of multiple biological pathways[5]. Single-target drugs often cannot effectively address the diversity and complexity of cancer.

LIMITATIONS OF THE TRADITIONAL “ONE DISEASE-ONE TARGET” MODEL

The traditional drug development model is usually based on a single relationship between drug targets and diseases, which means that each disease is directly related to one or a few targets and that drugs regulate the progression of the disease by acting on these targets. However, the pathogenesis of cancer involves multiple signalling pathways and complex gene-environment interactions. Therefore, single-target drugs often fail to achieve the expected results in clinical applications, especially in the face of cancer heterogeneity and drug resistance[6]. First, the heterogeneity of cancer means that even for the same type of cancer, there are significant differences in gene expression profiles and pathological characteristics between patients. This heterogeneity means that a certain targeted drug is effective for some patients but may be ineffective or even harmful to others. Second, tumor cells are highly adaptable, and long-term use of a single targeted drug often leads to drug resistance, which gradually weakens or even makes the treatment ineffective. In addition, cancer involves abnormalities in multiple biological processes, such as proliferation, apoptosis, metabolism, and immune escape, and these processes cannot be fully inhibited by regulating a single target.

THE RISE OF NETWORK PHARMACOLOGY

In response to these challenges in cancer treatment, network pharmacology has been developed. Network pharmacology is a research method based on systems biology and network science that aims to understand the impact of drugs on the entire biological system by analyzing the complex interactions between drugs, genes, targets and diseases. Unlike the traditional "one disease-one target" model, network pharmacology focuses on the overall regulation of multiple targets and biological pathways and uses multi omics data and computational models to identify potential multitarget drugs or drug combinations, thereby formulating more precise and comprehensive treatment strategies[7,8]. Network pharmacology reveals complex disease mechanisms, especially multiple pathway regulatory mechanisms in cancer, by constructing a "disease-gene-target-drug" network. It uses computational models and big data analysis to connect targets, pathways, drugs, and diseases to form a multidimensional interactive pharmacological network. On the basis of this model, researchers can screen for potential multitarget drugs and even design treatments that can regulate multiple pathways simultaneously, thereby improving the accuracy and efficacy of cancer treatment.

APPLICATION OF NETWORK PHARMACOLOGY IN CANCER THERAPY

In cancer treatment, network pharmacology can provide new ideas and tools for the development of personalized treatment plans. Through network analysis, researchers can identify key regulatory nodes and core pathways in cancer, thereby developing multifunctional drugs that act on multiple targets. These drugs can not only regulate the multiple pathway mechanisms of cancer but also reduce the drug resistance caused by the failure or mutation of a single target[9-11]: (1) Development of multitarget drugs: Unlike single-target drugs, multitarget drugs can act on multiple interrelated targets or signalling pathways at the same time. Through network pharmacology, researchers can discover key target combinations that are closely related to cancer pathological mechanisms, facilitating the design of more effective drugs. For example, some natural products or compound drugs may regulate multiple signalling pathways at the same time, and the screening and development of these compounds can be achieved through network pharmacology; (2) Optimization of drug combinations: In addition to developing new multitarget drugs, network pharmacology can also be used to optimize existing drug combinations. By analyzing the interactions between different drugs and their impact on cancer pathology networks, we can identify drug combinations with synergistic effects, thereby improving efficacy and reducing side effects; and (3) Precision medicine and personalized treatment: Network pharmacology can provide personalized treatment options for cancer patients. By analyzing multi omics data such as patient genomes, transcriptomes, and proteomes, researchers can construct personalized disease networks and identify patient-specific pathological pathways and key targets. This personalized network analysis lays the foundation for precision medicine, allowing each patient to receive a tailored treatment plan to maximize the treatment effect.

SHORTCOMINGS AND FUTURE OF NETWORK PHARMACOLOGY

Although network pharmacology has shown great potential in cancer treatment, it still faces some challenges. First, network pharmacology requires a large amount of multi omics data support, and the integration of different types of omics data is still difficult. Second, the construction and analysis of complex network models require advanced computational tools and algorithms, which places high demands on the technical level of researchers. In addition, although multitarget drugs and combination therapies are expected to improve therapeutic effects, how to reduce their potential side effects and toxicity still needs further study. In the future, with the advancement of omics technology and the accumulation of data, network pharmacology is expected to play a more important role in cancer treatment. In particular, with the introduction of artificial intelligence and big data analysis technology, network pharmacology will be able to predict the mechanism of action and clinical effects of drugs more accurately, thereby leading to more breakthroughs in cancer treatment.

CONCLUSION

As an emerging research paradigm, network pharmacology has changed the traditional "one disease-one target" treatment model and provided a new perspective for the multitarget and multi pathway treatment of cancer. By integrating multiomics data and systems biology methods, network pharmacology can not only improve the efficiency of drug development but also promote the development of personalized medicine, thereby providing cancer patients with more accurate and effective treatment options. In the future, with the further development of technology, network pharmacology will play an increasingly important role in cancer treatment.