Protection against COVID-19 injury by qingfei paidu decoction via anti-viral, anti-inflammatory activity and metabolic programming

Abstract

Qingfei Paidu decoction (QFPD), a multi-component herbal formula, has been widely used to treat COVID-19 in China. However, its active compounds and mechanisms of action are still unknown. Firstly, we divided QFPD into five functional units (FUs) according to the compatibility theory of traditional Chinese medicine. The corresponding common targets of the five FUs were all significantly enriched in Go Ontology (oxidoreductase activity, lipid metabolic process, homeostatic process, etc.), KEGG pathways (steroid biosynthesis, PPAR signaling pathway, adipocytokine signaling pathway, etc.), TTD diseases (chronic inflammatory diseases, asthma, chronic obstructive pulmonary Disease, etc.), miRNA (MIR183), kinase (CDK7) and TF (LXR). QFPD contained 257 specific targets in addition to HCoV, pneumonia and ACE2 co-expression proteins. Then, network topology analysis of the five components-target-pathway-disease networks yielded 67 active ingredients. In addition, ADMET estimations showed that 20 compounds passed the stringent lead-like criteria and in silico drug-likeness test with high gastrointestinal absorption and the median lethal dose (LD50 > 1600 mg/kg). Moreover, 4 specific ingredients (M3, S1, X2 and O2) and 5 common ingredients (MS1, MX16, SX1, WO1 and XO1) of QFPD presented good molecular docking score for 2019-nCov structure and non-structure proteins. Finally, drug perturbation of COVID-19 network robustness showed that all five FUs may protect COVID-19 independently, and target 8 specifically expressed drug-attacked nodes which were related to the bacterial and viral responses, immune system, signaling transduction, etc. In conclusion, our new FUNP analysis showed that QFPD had a protection effect on COVID-19 by regulating a complex molecular network with safety and efficacy. Part of the mechanism was associated with the regulation of anti-viral, anti-inflammatory activity and metabolic programming.

Keywords: Anti-inflammatory; Anti-viral; COVID-19; Functional units of network pharmacology; Metabolic programming; Qingfei paidu decoction.

Graphical abstract

Fig. 1

Venn diagram of the five formulae’ active compounds and targets. A: compounds, B: targets.

Fig. 2

Bubble plot of the GO analysis of the five formulae’ targets.

Fig. 3

Bubble plot of the KEGG/TTD analysis of the five formulae’ targets. A: KEGG, B: TTD.

Fig. 4

The miRNA, kinase and TF analysis of the five formulae’ targets by WebGestalt. Chord plot showing the five formulae’ targets present in the represented enriched miRNA, kinase and TF terms. Outer ring shows miRNA/kinase/TF term and log2 enrichment ratio (left) or five formulae grouping (right). Chords connect miRNA/kinase/TF term with formulae groups. A: miRNA, B: kinase, C: TF.

Fig. 5

KEGG analysis of MCODE modules. MCODE analysis was performed after the construction of the five formulae’ targets PPI; then, KEGG analysis was conducted on the MCODE modules. A: MXSG, B: Others, C: WLS, D: SGMH, E: XCH.

Fig. 6

The component-target-pathway-disease network. Purple polygons: PubChem ID of QFPD compounds; blue pentagrams: QFPD targets; yellow circles: KEGG pathway; red square: Therapeutic Target Database (TTD) disease term, green square: Online Mendelian Inheritance in Man (OMIN) disease term. A: MXSG, B: SGMH, C: WLS, D: XCH, E: Others. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 7

Chemical properties statistics of hub components in the formulae. A: Molecular weight, B: rotatable bond count, C: H-bond acceptors count, D: H-bond donors count, E: topological polar surface area (TPSA), F: leadlikeness violations, G: pharmacokinetic and toxicity evaluated parameters of 20 leadlikeness compounds by pkCSM; green = good, yellow = tolerable, red = bad. Caco2: Caco-2 Permeability,HIA: Intestinal Absorption (Human), Skin: Skin Permeability, VDss: volume of distribution, FU: Fraction Unbound (Human), BBB: Blood Brain Barrier permeability, CNS: Central Nervous System permeability,TC: Total Clearance, OCT2: Renal Organic Cation Transporter 2, AMES: AMES toxicity, MTDD: Maximum Tolerated Dose (Human), hERG I/II: hERG I and II Inhibitors, LD50: Oral Rat Acute Toxicity (LD50), HT: Hepatotoxicity, SS: Skin Sensitisation, MT: Minnow toxicity. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 8

Schematic (3D and 2D) representation that molecular model of specific compounds of each formulae with COVID-19 proteins. A: M3 and E protein [ion channel], B: M3 and nsp13 [Helicase NCB site], C: S1 and nsp13 [Helicase ADP site], D: S1 and PLpro, E: X2 and Mpro, F: O2 and Mpro. M: MXSG, S: SGMH, X: XCH, O: Others.

Fig. 9

Schematic (3D and 2D) representation that molecular model of common compounds of the five formulae with COVID-19 proteins. A: MS1 and N protein NCB site, B: MS1 and nsp14 [ExoN], C: MX16 and nsp15 [endoribonuclease], D: SX1 and nsp14 [N7-MTase], E: SX1 and nsp15 [endoribonuclease], F: WO1 and nsp16 [2′-O-MTase], G: WO1 and nsp12 [RdRp without RNA], H: XO1 and nsp12 [RdRp with RNA]. MS: MXSG and SGMH, MX: MXSG and XCH, SX: SGMH and XCH, WO: WLS and Others, XO: XCH and Others.

Fig. 10

ACE2 and CD147 expression across tissues and co-expression genes. A: Radar plot of ACE2 and CD147 expression across 53 tissues. The expression values were converted to base-2 logarithm. Red triangle and square mean the top 5 expression tissues. B: UpSet plot of proteins among QFPD, HCoV (Host_protein), pneumonia, ACE2 co-expression genes (ACE2_database), CD147 co-expression genes (CD147_database), and ACE2 co-expression genes in colonic epithelial cells (ACE2_colonic). The horizontal bar graph at the bottom left shows the total number of proteins for each group set. Circles and vertical lines in the x-axis mark the corresponding data sets being compared. The vertical bar graph at the top quantitates the number of proteins in the comparisons. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 11

Evaluation of the effect of QFPD on the robustness disturbance of COVID-19 network. Blue normal distribution: drug attack on random networks as a null distribution for the permutation test. Red vertical line: the disturbance rate of the drug to the real disease network. Fist row: MXSG, second row: SGMH, third row: XCH, fourth row: WLS, fifth row: Others. Fist column: average connectivity, second row: average length of shortest path, third row: connection centrality, fourth row: closeness centrality. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 12

Disturbance analysis of QFPD for COVID-19 network. A: Venn diagram of the five formulae’ attacked targets. B: Formula-attacked target-KEGG pathway network; green square: formula, yellow diamond: attacked target, red circle: KEGG pathways. The bigger the size of the nodes is, the higher the degree is. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)