The challenges and opportunities of traditional Chinese medicines against COVID-19: a way out from a network perspective

Abstract

The purpose of this perspective is to provide insights into the current challenges and opportunities of applying network pharmacology (NP) to illustrate the effectiveness of traditional Chinese medicines (TCMs) against the coronavirus disease 2019 (COVID-19). Emerging studies have indicated that the progression of COVID-19 is associated with hematologic and immunologic responses in patients, and TCMs may fight against COVID-19 regarding the two aspects. However, the underlying mechanisms remain largely unclear [1]. This perspective is intended as a brief report derived from our previous experience in investigating the efficacy of TCMs, via conventional reductionism-based research methods, holistic NP, systems biology, or “omics” research, and prevailing big data analysis. The main idea of this perspective is outlined in Fig. 1. The widespread and long-lasting pandemic has caused tens of millions of human lives infected and more than one million claimed. Although self-quarantine and the roll-out of vaccines as biological shields have effectively slowed down the COVID-19 infection rate, therapeutic strategies for COVID-19 must still be discovered. Several attempts have been made to treat COVID-19, directly by inhibiting or killing the coronavirus SARS-COV-2, as artemisinin does the plasmodium, or indirectly by preventing virus-induced complications. From the perspective of drug development, it is of importance to better understand the pathogenesis and delineated risk factor of COVID-19. Several studies have suggested that the COVID-19 is possibly impacted by the conditions of dysregulated immune systems or hypercoagulable states [2, 3]. This dysfunction may further cause immunothrombosis which is the symptom of deep vein thrombosis and multiple thrombi in the vessels of lungs, kidneys, or other organs. Immunothrombosis has been found as one of the major causes of death due to COVID-19 [4–6]. The coagulation pathways have long been regarded as separate entities from pathways that regulate innate immune responses to infections. However, it has been indicated that a remarkable degree of interplay exists between the immune and the coagulation systems [7], both of which involve complicated molecular mechanisms. Our previous network-based investigation also suggested that the protein targets involved in thrombosis pathways are highly associated with neuroactive−immune−metabolism/endocrine regulation [8]. With regard to COVID-19, a closer interaction between coagulation and immune system dysfunction may be one of the causes of death due to this disease. According to “Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia” in China [9], TCMs have been recommended, and the use of TCMs for COVID-19 treatment has achieved satisfactory effects [10, 11]. TCMs possibly act by preventing thrombosis and regulating the immune systems. Although these TCM therapies have achieved promising results, their exact mechanisms are restricted by explanations derived from reductionist methodologies, which focus on “one drug–one target”. To this end, the presented “multi drug–multi target” therapeutic action mode inspires a new analytical approach by using networks to investigate TCMs. Such an approach is based on a schematic representation of the interactions among various entities. Additionally, owing to the rapid development of bioinformatics, NP, which combines network analysis and bioinformatics tools with polypharmacology, allows the illustration of TCMs comprehensively at the interactome levels. In recent years, NP analysis has gained impetus as a powerful tool for understanding TCMs, as well as a novel paradigm for promoting TCM-based drug discovery. Various research models have been developed and applied in the past decade. A brief review of NP-based research on TCMs summarized the major types of network models that have been applied, including protein–protein interaction networks, pathway/signaling transduction networks, herb–herb interaction networks, and multi-layer networks [12]. As one of the most widely used models, multi-layer networks involve the depiction/visualization of multiple levels of interactions among herbs, compounds (ingredients), targets, pathways (bioprocesses), and diseases (functions, or effects). This model has sometimes been supplemented with network topological analysis. These pioneering explorations have paved the way for NP-based research on TCMs. However, this endeavor is replete with challenges, which mainly derive from the insufficiency of data. The insufficiency of data can greatly affect the accuracy of