Pharmacological Mechanism of NRICM101 for COVID-19 Treatments by Combined Network Pharmacology and Pharmacodynamics

Abstract

Symptom treatments for Coronavirus disease 2019 (COVID-19) infection and Long COVID are one of the most critical issues of the pandemic era. In light of the lack of standardized medications for treating COVID-19 symptoms, traditional Chinese medicine (TCM) has emerged as a potentially viable strategy based on numerous studies and clinical manifestations. Taiwan Chingguan Yihau (NRICM101), a TCM designed based on a medicinal formula with a long history of almost 500 years, has demonstrated its antiviral properties through clinical studies, yet the pharmacogenomic knowledge for this formula remains unclear. The molecular mechanism of NRICM101 was systematically analyzed by using exploratory bioinformatics and pharmacodynamics (PD) approaches. Results showed that there were 434 common interactions found between NRICM101 and COVID-19 related genes/proteins. For the network pharmacology of the NRICM101, the 434 common interacting genes/proteins had the highest associations with the interleukin (IL)-17 signaling pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Moreover, the tumor necrosis factor (TNF) was found to have the highest association with the 30 most frequently curated NRICM101 chemicals. Disease analyses also revealed that the most relevant diseases with COVID-19 infections were pathology, followed by cancer, digestive system disease, and cardiovascular disease. The 30 most frequently curated human genes and 2 microRNAs identified in this study could also be used as molecular biomarkers or therapeutic options for COVID-19 treatments. In addition, dose-response profiles of NRICM101 doses and IL-6 or TNF-α expressions in cell cultures of murine alveolar macrophages were constructed to provide pharmacodynamic (PD) information of NRICM101. The prevalent use of NRICM101 for standardized treatments to attenuate common residual syndromes or chronic sequelae of COVID-19 were also revealed for post-pandemic future.

Keywords: NRICM101; biomarker; pharmacodynamics; pharmacogenomics; protein–protein interaction; traditional Chinese medicine.

Figure 1

Compositions of the TCM formula: Taiwan Chingguan Yihau (NRICM101).

Figure 2

Venn diagram of NRICM101-10 herbs interacting chemicals. NRICM101 chemicals were found to interact with 218 and 181 unique chemicals, where 89 of them were in common.

Figure 3

The four-way Venn diagram of interacted genes, and 434 of them were in common. A Venn diagram was constructed to identify overlapping genes in different gene sets. The four areas represent different related gene sets, where A represents BATMAN-TCM chemicals related 7522 gene/protein from CTD, B is TCM database@TW chemicals related 7483 gene/protein from CTD, C is COVID-19 related 1043 gene/protein from GeneCards, and D is COVID-19 related 9884 gene/protein from CTD. The cross areas indicate the overlapping common genes.

Figure 4

(a) Network pharmacology of herbs–chemicals–targets. (b) Gene ontology analyses of (i) biological process, (ii) cellular component, (iii) molecular function, and (iv) KEGG pathway enrichment analysis. (c) Ontology enrichment clustering network. (d) The PPI networks of the 30 most frequently curated genes/proteins. Network pharmacology of herbs–chemicals–targets relationships that contained the associations among herbs, targets, and chemical compounds of NRICM101. The pink nodes represent the module genes/proteins and the blue nodes represent the chemicals of herbs in NRICM101. GO term and KEGG pathway enrichment analysis of (i) biological process, (ii) cellular component, (iii) molecular function, and (iv) KEGG pathway for the target 434 genes. The gene ratios refer to the ratio of enriched genes to all target genes, and counts refer to the number of enriched genes. A complex clustering network was generated by the target 434 gene sets. It was visualized by Cytoscape with “force-directed” layout and with edge bundled for clarity. Terms with a similarity score > 0.3 were linked by an edge (the thickness of the edge represents the similarity score). One term from each cluster was selected to have its term description as shown in labels. The enrichment network visualization was shown with the intra-cluster and inter-cluster similarities of enriched terms. Cluster annotations were shown in color code. The PPI networks of the 30 most frequently curated genes/proteins of 434 gene set were further analyzed with the highest confidence interaction score of 0.9, evidence of network edges, 30 nodes, and 90 edges. The expected number of edges was 24, the average node degree was 6, and significant PPI enrichment p-value < 1.0 × 10⁻¹⁶.

Figure 4

(a) Network pharmacology of herbs–chemicals–targets. (b) Gene ontology analyses of (i) biological process, (ii) cellular component, (iii) molecular function, and (iv) KEGG pathway enrichment analysis. (c) Ontology enrichment clustering network. (d) The PPI networks of the 30 most frequently curated genes/proteins. Network pharmacology of herbs–chemicals–targets relationships that contained the associations among herbs, targets, and chemical compounds of NRICM101. The pink nodes represent the module genes/proteins and the blue nodes represent the chemicals of herbs in NRICM101. GO term and KEGG pathway enrichment analysis of (i) biological process, (ii) cellular component, (iii) molecular function, and (iv) KEGG pathway for the target 434 genes. The gene ratios refer to the ratio of enriched genes to all target genes, and counts refer to the number of enriched genes. A complex clustering network was generated by the target 434 gene sets. It was visualized by Cytoscape with “force-directed” layout and with edge bundled for clarity. Terms with a similarity score > 0.3 were linked by an edge (the thickness of the edge represents the similarity score). One term from each cluster was selected to have its term description as shown in labels. The enrichment network visualization was shown with the intra-cluster and inter-cluster similarities of enriched terms. Cluster annotations were shown in color code. The PPI networks of the 30 most frequently curated genes/proteins of 434 gene set were further analyzed with the highest confidence interaction score of 0.9, evidence of network edges, 30 nodes, and 90 edges. The expected number of edges was 24, the average node degree was 6, and significant PPI enrichment p-value < 1.0 × 10⁻¹⁶.

Figure 5

The herb–meridian disease network of NRICM101. Dots in red and green represent herbs and diseases, respectively. The NRICM101 includes 10 herbs of Scutellaria Root (heat-clearing and damp-drying medicine), Heartleaf Houttuynia (heat-clearing and detoxifying drugs), Mongolian Snakegourd Fruit (heat-clearing and phlegm-resolving medicine), Indigowoad Root (heat-clearing and detoxifying drugs), Magnolia Bark (dampness medicine), Peppermint Herb (Diffuse wind-heat medicine), Fineleaf Nepeta (post apothecary), Mulberry Leaf (heat-clearing medicine), Saposhnikovia Root (Healing wind, dispersing muscle surface wind evil medicine), and Baked Licorice Root (Qi tonic). The NRICM101 has the functions of detoxification and dampness, clearing heat and relieving asthma, and enhancing the body’s immunity.

Figure 6

Pharmacodynamics analysis of relationships between cytokine expressions of (A) IL-6 and (B) TNF-α (%) and dilution folds of NRICM101. Dose–response profiles of NRICM101 with different dilution folds and corresponding cytokine inhibitions (IL-6 or TNF-α expression) in cell cultures of murine alveolar macrophages were established by adapting the experimental data [10]. A three-parameter Hill model was applied to fit the experimental data describing the relationship between the dilution folds of NRICM101 and increments of IL-6 or TNF-α expression (%) [10].