Abstract

   Non-steroidal anti-inflammatory drugs (NSAIDs) showed promising
   clinical efficacy toward COVID-19 (Coronavirus disease 2019) patients
   as potent painkillers and anti-inflammatory agents. However, the
   prospective anti-COVID-19 mechanisms of NSAIDs are not evidently
   exposed. Therefore, we intended to decipher the most influential NSAIDs
   candidate(s) and its novel mechanism(s) against COVID-19 by network
   pharmacology. FDA (U.S. Food & Drug Administration) approved NSAIDs (19
   active drugs and one prodrug) were used for this study. Target proteins
   related to selected NSAIDs and COVID-19 related target proteins were
   identified by the Similarity Ensemble Approach, Swiss Target
   Prediction, and PubChem databases, respectively. Venn diagram
   identified overlapping target proteins between NSAIDs and COVID-19
   related target proteins. The interactive networking between NSAIDs and
   overlapping target proteins was analyzed by STRING. RStudio plotted the
   bubble chart of the KEGG (Kyoto Encyclopedia of Genes and Genomes)
   pathway enrichment analysis of overlapping target proteins. Finally,
   the binding affinity of NSAIDs against target proteins was determined
   through molecular docking test (MDT). Geneset enrichment analysis
   exhibited 26 signaling pathways against COVID-19. Inhibition of
   proinflammatory stimuli of tissues and/or cells by inactivating the RAS
   signaling pathway was identified as the key anti-COVID-19 mechanism of
   NSAIDs. Besides, MAPK8, MAPK10, and BAD target proteins were explored
   as the associated target proteins of the RAS. Among twenty NSAIDs,
   6MNA, Rofecoxib, and Indomethacin revealed promising binding affinity
   with the highest docking score against three identified target
   proteins, respectively. Overall, our proposed three NSAIDs (6MNA,
   Rofecoxib, and Indomethacin) might block the RAS by inactivating its
   associated target proteins, thus may alleviate excessive inflammation
   induced by SARS-CoV-2.

   Subject terms: Biotechnology, Computational biology and bioinformatics,
   Genetics, Molecular biology, Health care, Medical research

Introduction

   An initial outbreak of pneumonia caused by unknown etiology was first
   reported at Wuhan in Hubei Province, China, and alerted to the World
   Health Organization (WHO) by the Wuhan Municipal Health Commission on
   31 December 2019^[28]1. Later, the infectious disease experts detected
   severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can
   rapidly transmit from person to person through interaction or
   respiratory droplets^[29]2. As a consequence of its tremendous spread
   globally, WHO announced a changing level from epidemic to pandemic
   disease (COVID-19) on March 11, 2020^[30]3. Although the symptoms were
   identical to pneumonia, however, many COVID-19 patients showed no
   physical sign, thus transmitting the virus to others, as silently
   spread^[31]4.

   Due to a reliable vaccine's unavailability, clinicians utilize
   antiviral drugs and NSAIDs as a significant viable option for COVID-19
   patients^[32]5. A recent study has reported that the use of NSAIDs is
   safe for COVID-19 treatment without exposing specific adverse
   effects^[33]6. Though there is a lack of evidence whether combined
   NSAIDs treatment could worsen COVID-19 symptoms^[34]7, but researchers
   suggested that anti-inflammatory therapies might suppress the fatal
   cytokine storm of COVID-19 patients^[35]8. Additionally, WHO announced
   that no evidence of unwanted side effects was reported, particularly
   the risk of death with NSAIDs' administration in COVID-19
   patients^[36]9.

   NSAIDs are commonly used to treat diverse anti-inflammatory symptoms
   due to their excellent therapeutic efficacy^[37]10. For example, some
   evidence suggests that NSAIDs are associated with mitigating
   depression, bladder function recovery, reduction of psychiatric events,
   and a decrease in cancer risk, all of which connected directly to
   anti-inflammatory effects^[38]11. Most NSAIDs are known as inhibitors
   of COX-1 (Cyclooxygenase-1) or/and COX-2 (Cyclooxygenase-2) involved in
   the synthesis of prostaglandin and thromboxanes^[39]12. Recently, both
   COX-1 and COX-2 are expressed in inflamed tissues constitutively, and
   Indomethacin inhibited the two forms of COX effectively^[40]13. Also,
   Indomethacin is a potent NSAIDs against rheumatoid arthritis, used as
   an immune response enhancer against HIV (Human Immunodeficiency Virus),
   inhibiting harmful immune response induced by COX-2^[41]13,[42]14.
   Generally, COX-1 is expressed in most normal cells, and COX-2 is
   induced by an abundance of physiological stimulus^[43]15. An animal
   test demonstrated that the inhibition of COX-1 up-regulates COX-2
   expression level, results in the prevention of aggravated effects
   against inflammatory response^[44]16. It was reported that the
   administration of dual COX-1/2 inhibitors slightly diminished viral DNA
   replication but could not induce viral DNA cleavage^[45]17. Hence,
   COX-1/2 inhibitors are involved in host immune responses while cannot
   take effect on pathogens. However, one potential drug is Indomethacin,
   which possesses both anti-inflammatory and antiviral properties. Its
   antiviral potentiality was first identified in 2006 during the outbreak
   of SARS-CoV^[46]18, and subsequent attribution was also observed
   against SARS-CoV-2^[47]19. A study on canine coronavirus (in vitro)
   revealed that Indomethacin could significantly suppress virus
   replication, thus protecting host cell from virus-induced damage. A
   similar antiviral effect was also observed during in vivo assessment
   where an average anti-inflammatory dose was found very
   effective^[48]19,[49]20. Although many NSAIDs may have possible
   therapeutic interventions against COVID-19, but lack of scientific
   evidence has limited their broad application to COVID-19 patients.
   Hence, we aimed to identify the most influential NSAIDs and their
   mechanism(s) against COVID-19 through network pharmacology.

   Network pharmacology can decode the mechanism(s) of drug action with an
   overall viewpoint^[50]21, which focuses on pattern changing from
   "single protein target, single drug" to "multiple protein targets,
   multiple drugs"^[51]22,[52]23. Currently, network pharmacology has been
   extensively utilized to explore multiple targets and unknown additional
   mechanism(s) against diverse diseases^[53]24. In this research, network
   pharmacology was applied to investigate the most potent NSAIDs and
   their novel mechanisms of action against COVID-19. Firstly, FDA
   approved NSAIDs (nineteen active drugs and one prodrug) were selected
   via using public websites. The selected NSAIDs and COVID-19 related
   target proteins were also identified using public databases. Next, the
   extracted overlapping target proteins were discovered as target
   proteins for analyzing anti-COVID-19 properties. Finally, pathway
   enrichment analysis was performed to reveal the mechanism(s) of the
   most potent NSAIDs against COVID-19. Figure [54]1 shows the overall
   workflow.

Figure 1.

   [55]Figure 1
   [56]Open in a new tab

   Workflow of network pharmacology analysis of NSAIDs against COVID-19.

Results

Information of NSAIDs

   A total of nineteen NSAIDs and one prodrug (FDA approved) were
   selected. Table [57]1 displayed the NSAIDs' chemical information and
   TPSA (Topological Polar Surface Area). Among the selected NSAIDs,
   nineteen NSAIDs were found as an active drug, and one "nabumetone" was
   a prodrug, and its metabolite form is 6-methoxy-2-naphthylacetic acid
   (6MNA). Figure [58]2 exhibited the chemical structure of these NSAIDs.

Table 1.

   A list of NSAIDs (19 active drugs and one prodrug) approved by FDA and
   TPSA (Å^2) values.
   No Drug name PubChem CID Mechanism of action TPSA (< 140 Å^2)
   1 Flubiprofen 3394 Nonselective COX inhibitor 37.30
   2 Ibuprofen 3672 Nonselective COX inhibitor 37.30
   3 Indomethacin 3715 Nonselective COX inhibitor 68.53
   4 Ketorolac 3826 Nonselective COX inhibitor 59.30
   5 Mefenamic acid 4044 Nonselective COX inhibitor 49.33
   6 Piroxicam 54,676,228 Nonselective COX inhibitor 107.98
   7 Diflunisal 3059 Prostaglandin synthesis inhibitor 57.53
   8 Fenoprofen 3342 Prostaglandin synthesis inhibitor 46.53
   9 Naproxen 156,391 Prostaglandin synthesis inhibitor 46.53
   10 Sulindac 1,548,887 Prostaglandin synthesis inhibitor 73.58
   11 Tolmetin 5509 Prostaglandin synthesis inhibitor 59.30
   12 Ketoprofen 3825 Selective COX-1 inhibitor 54.37
   13 Oxaprozin 4614 Selective COX-1 inhibitor 63.33
   14 Celecoxib 2662 Selective COX-2 inhibitor 86.36
   15 Rofecoxib 5090 Selective COX-2 inhibitor 68.82
   16 Valdecoxib 119,607 Selective COX-2 inhibitor 94.57
   17 Diclofenac 3033 Selective COX-2 inhibitor 49.33
   18 Etodolac 3308 Selective COX-2 inhibitor 62.32
   19 Meloxicam 54,677,470 Selective COX-2 inhibitor 136.22
   20^a 6MNA 32,176 Selective COX-2 inhibitor 46.53
   [59]Open in a new tab

   ^a6MNA (Active form) of Nabumetone (Prodrug); TPSA (Topological Polar
   Surface Area).

Figure 2.

   [60]Figure 2
   [61]Open in a new tab

   Structure of 19 NSAIDs and 1 prodrug. (1) Celecoxib (2) Diclofenac (3)
   Diflunisal (4) Etodolac (5) Fenoprofen (6) Flubiprofen (7) Ibuprofen
   (8) Indomethacin (9) Ketoprofen (10) Ketorolac (11) Mefenamic acid (12)
   Meloxicam (13) Naproxen (14) Oxaprozin (15) Piroxicam (16) Rofecoxib
   (17) Sulindac (18) Tolmetin (19) Valdecoxib *(20): Nabumetone (prodrug
   of 6MNA). The three NSAIDs in box line are the most potent NSAIDs
   candidates against COVID-19.

NSAIDs connected to the 781 target proteins or COVID-19 targeted proteins

   A total of 781 NSAIDs related proteins were identified (Supplementary
   Table [62]S1) through two public databases (SEA and STP). Figure [63]3A
   showed the overlapping target proteins (228 target proteins) selected
   from the two databases (Supplementary Table [64]S2). The number of 466
   COVID-19 targeted proteins was identified from the PubChem database
   (Supplementary Table [65]S3). Figure [66]3B illustrated that the final
   26 overlapping target proteins were selected between 228 overlapped
   target proteins and 466 COVID-19 targeted proteins (Supplementary Table
   [67]S4).

Figure 3.

   Figure 3
   [68]Open in a new tab

   (A) Overlapping target proteins (228 target proteins) of NSAIDs related
   target proteins identified from SEA (529 target proteins) and STP (480
   target proteins). (B) Overlapping target proteins (26 target proteins)
   between NSAIDs related 228 overlapped target proteins and COVID-19
   related 466 target proteins.

Protein–protein interaction (PPI) networks and KEGG pathway enrichment
analysis

   Figure [69]4 demonstrated that the final 26 overlapping target proteins
   were selected by STRING, which represents 26 nodes and 78 edges.
   According to the KEGG pathway enrichment analysis, 13 target proteins
   were connected to 26 signaling pathways (False Discovery Rate < 0.05).
   Table [70]2 showed the description of 26 signaling pathways.
   Figure [71]5 displayed that 13 out of the final 26 overlapping target
   proteins were strongly associated with 26 signaling pathways against
   COVID-19; moreover, the RAS signaling pathway with the lowest Rich
   factor was identified as a hub signaling pathway.

Figure 4.

   [72]Figure 4
   [73]Open in a new tab

   Protein protein interaction (PPI) networks with 26 nodes and 78 edges
   in NSAIDs against COVID-19 via STRING analysis. Node: the number of
   units; Edge: the number of interactions between the units.

Table 2.

   Target proteins in 26 signaling pathways enrichment related to
   COVID-19.
   KEGG ID & description Target proteins Rich factor False discovery rate
   hsa04014: Ras signaling pathway MAPK8, MAPK10, BAD 0.013157895 0.0071
   hsa04921: Oxytocin signaling pathway PTGS2, PPP1CA 0.013422819 0.0294
   hsa04010: MAPK signaling pathway MAPK8, MAPK10, MAPK14, CASP3
   0.013651877 0.0016
   hsa04310: WNT signaling pathway MAPK8, MAPK10 0.013986014 0.0276
   hsa04022: cGMP -PKG signaling pathway ENDRA, BAD, PPP1CA 0.01875 0.003
   hsa04064: NF-kappa B signaling pathway CXCL8, PTGS2 0.021505376 0.0136
   hsa04926: Relaxin signaling pathway MAPK8, MAPK10, MAPK14 0.023076923
   0.0018
   hsa04068: FoxO signaling pathway MAPK8, MAPK10, MAPK14 0.023076923
   0.0018
   hsa04071: Sphingolipid signaling pathway MAPK8, MAPK10, MAPK14
   0.025862069 0.0014
   hsa04910: Insulin signaling pathway MAPK8, MAPK10, BAD, PPP1CA
   0.029850746 0.00013
   hsa04621: NOD-like receptor signaling pathway MAPK8, MAPK10, MAPK14,
   CXCL8, CASP1 0.030120482 0.0000197
   hsa04024: cAMP signaling pathway ENDRA, MAPK8, BAD, MAPK10, PPP1CA,
   PPARA 0.030769231 0.00000373
   hsa04912: GnRH signaling pathway MAPK8, MAPK10, MAPK14 0.034090909
   0.00072
   hsa04722: Neurotrophin signaling pathway MAPK8, MAPK10, MAPK14, BAD
   0.034482759 0.00000853
   hsa04012: ErbB signaling pathway MAPK8, MAPK10, BAD 0.036144578 0.00062
   hsa04620: Toll-like receptor signaling pathway MAPK8, MAPK10, MAPK14,
   CXCL8 0.039215686 0.0000581
   hsa03320: PPAR signaling pathway PPARA, PPARG, FABP2 0.041666667
   0.00044
   hsa04920: Adipocytokine signaling pathway MAPK8, MAPK10, PPARA
   0.043478261 0.00041
   hsa04917: Prolactin signaling pathway MAPK8, MAPK10, MAPK14 0.043478261
   0.00041
   hsa04664: Fc epsilon RI signaling pathway MAPK8, MAPK10, MAPK14
   0.044776119 0.00039
   hsa04668: TNF signaling pathway MAPK8, MAPK10, MAPK14, CASP3, PTGS2
   0.046296296 0.00000413
   hsa04370: VEGF signaling pathway MAPK8, MAPK10, BAD 0.050847458 0.00029
   hsa04933: AGE-RAGE signaling pathway in diabetic complications MAPK8,
   MAPK10, MAPK14, CXCL8, CASP1 0.051020408 0.00000373
   hsa04622: RIG-I-like receptor signaling pathway MAPK8, MAPK10, MAPK14,
   CXCL8 0.057142857 0.0000197
   hsa04657: IL-17 signaling pathway MAPK8, MAPK10, MAPK14, CXCL8, CASP3,
   PTGS2 0.065217391 0.000000135
   hsa05120: Epithelial cell signaling in Helicobacter pylori infection
   MAPK8, MAPK10, MAPK14, CXCL8, CASP3 0.075757576 0.000000954
   [74]Open in a new tab

Figure 5.

   [75]Figure 5
   [76]Open in a new tab

   Bubble chart of 26 signaling pathways related to the occurrence and
   progression of COVID-19.

A signaling pathway-target protein—NSAID (STN) network analysis

   Figure [77]6 revealed 26 signaling pathways—13 target proteins—19
   NSAIDs networks (58 nodes and 194 edges). Among 20 NSAIDs, Diflunisal
   has no association with signaling pathways against COVID-19.

Figure 6.

   [78]Figure 6
   [79]Open in a new tab

   Signaling pathway-Target protein-NSAID (STN) networks. Green circle:
   signaling pathway; Yellow square: target protein; Orange triangle:
   NSAID.

   The nodes indicated a total number of signaling pathways, target
   proteins, and NSAIDs. The edges represented the relationships of the
   three components. The STN relationship suggested that the network might
   be potential therapeutic efficacy against COVID-19. The STN network
   displayed that 13 target proteins associated with 26 signaling pathways
   built by a size map. Among the 13 target proteins, both MAPK8 and
   MAPK10 have the highest degree (22), followed by MAPK14 (15), BAD (7),
   and CXCL8 (7). Additionally, among the 19 NSAIDs, Diclofenac has the
   greatest degree (10), followed by 6MNA (9), Fenoprofen (8), and
   Sulindac (8).

MDT of 3 target proteins and 15 NSAIDs associated with RAS signaling pathway

   From the SEA and STP databases, it was revealed that MAPK8 was
   associated with three NSAIDs (6MNA, Mefenamic acid, and Etodolac),
   MAPK10 was related to twelve NSAIDs (Mefenamic acid, Naproxen,
   Tolmetin, Fenoprofen, Ketorolac, Ketoprofen, Ibuprofen, Flurbiprofen,
   Oxaprozin, Sulindac, Diclofenac, and Rofecoxib), BAD was involved with
   two NSAIDs (6MNA and Indomethacin). The MDT was performed to evaluate
   NSAIDs' binding affinity on target three proteins (MAPK8, MAPK10, and
   BAD) connected directly to the RAS signaling pathway. The MDT score of
   three NSAIDs on MAPK8 (PDB ID: 4YR8) was analyzed in the "Homo Sapiens"
   mode. Table [80]3 indicated that 6MNA (− 7.1 kcal/mol) revealed the
   highest binding energy, followed by Mefenamic acid (− 6.4 kcal/mol),
   and Etodolac (− 6.3 kcal/mol) on MAPK8 (PDB ID: 4YR8). Figure [81]7A
   exhibited the MDT of the 6MNA-MAPK8 (PDB ID: 4YR8) complex with the
   highest binding affinity. The MDT score of twelve NSAIDs on MAPK10 (PDB
   ID: 3TTJ) was analyzed in the "Homo Sapiens" mode. Table [82]4
   indicated that Rofecoxib (− 7.5 kcal/mol) exposed the highest binding
   energy, followed by Sulindac (− 7.4 kcal/mol), Oxaprozin
   (− 7.1 kcal/mol), Ketorolac (− 7.1 kcal/mol), Flubiprofen
   (− 6.9 kcal/mol), Tolmetin (− 6.7 kcal/mol), Diclofenac
   (− 6.7 kcal/mol), Fenoprofen (− 6.5 kcal/mol), Ketoprofen
   (− 6.5 kcal/mol), Mefenamic acid (− 6.4 kcal/mol), Naproxen
   (− 6.1 kcal/mol), Ibuprofen (− 5.6 kcal/mol) on MAPK10 (PDB ID: 3TTJ).
   Figure [83]7B presented the MDT of the Rofecoxib—MAPK10 (PDB ID: 3TTJ)
   complex with the highest binding affinity. The MDT score of two NSAIDs
   on BAD (PDB ID: 1G5J) was analyzed in the "Homo Sapiens" mode.
   Figure [84]7C exhibited the MDT of the Indomethacin—BAD (PDB ID: 1G5J)
   complex with the highest binding affinity. Table [85]5 showed that
   Indomethacin (− 7.1 kcal/mol) was the highest binding energy, followed
   by 6MNA (− 6.8 kcal/mol) on BAD (PDB ID: 1G5J). Figure [86]8 depicted
   that MAPK-6MNA complex and MAPK10-Rofecoxib complex inhibit AP1 induced
   inflammatory responses; simultaneously, blocked AP1 inhibits cytokine
   productions. As an upstream region, the BAD-Indomethacin complex also
   interrupts cytokine production against COVID-19.

Table 3.

   Binding energy and interactions of potential three NSAIDs on MAPK8 (PDB
   ID:4YR8).
   Protein Ligand PubChem ID Symbol Binding energy (kcal/mol) Hydrogen
   bond interactions Hydrophobic interactions
   Amino acid residue Amino acid residue
   4YR8 6MNA 32,176 M1 − 7.1 Lys-218 Pro-221, Gly-199
   Pro-254, Phe215
   Cys-216, Gln-253
   Pro-210, Lys-218
   Glu-217, Lys-225
   Mefenamic acid 4044 M2 − 6.4 Glu-217 Trp-222, Val-211
   Arg-208, Cys-216
   Asn-193, Lys-218
   Pro-211
   Etodolac 3308 M3 − 6.3 n/a Tyr-202, Lys-203
   Met-200, Gly-201
   Pro-221, Lys-218
   Lys-251, Ser-307
   Ala-306
   [87]Open in a new tab

Figure 7.

   [88]Figure 7
   [89]Open in a new tab

   Molecular docking interaction between best docked NSAIDs and target
   proteins. (A) 6MNA on MAPK 8 (PDB ID:4YR8) (B) Rofecoxib on MAPK 10
   (PDB ID:3TTJ). (C) Indomethacin on BAD (PDB ID: 1G5J).

Table 4.

   Binding energy and interactions of potential twelve NSAIDs on MAPK10
   (PDB ID: 3TTJ).
   Protein Ligand PubChem ID Symbol Binding energy (kcal/mol) Hydrogen
   bond interactions Hydrophobic Interactions
   Amino acid residue Amino acid residue
   3TTJ Mefenamic acid 32,176 R1 − 6.4 Arg-107 Asp-207, Gln-75
   Leu-206, Lys-93
   Asn-194, Asp-207
   Naproxen 4044 R2 − 6.1 Asn-194, Lys-93 Arg-107, Asp-189
   Val-225, Lys-191
   Gln-75, Gly-73
   Tolmetin 3308 R3 − 6.7 Asn-194, Asp-189 Lys-106, Leu-210
   Ala-211, Arg-110
   Arg-230, Lys-191
   Arg-107, Thr-103
   Fenoprofen 3342 R4 − 6.5 Lys-93, Lys-191 Ser-193, Ser-72
   Asn-194 Val-78, Gly-73
   Gln-75, Ala-74
   Arg-107
   Ketorolac 3826 R5 − 7.1 Glu-111, Arg-107 Asp-207, Leu-206
   Asn-194, Lys-93 Gln-75, Ser-193
   Ser-72, Val-78
   Gly-73
   Ketoprofen 3825 R6 − 6.5 Lys-93, Asn-194 Val-78, Leu-206
   Ser-193 Arg-107, Gln-75
   Gly-73
   Ibuprofen 3672 R7 − 5.6 Lys-93, Ser-193 Leu-206, Ala-74
   Asn-194 Gly-73, Gln-75
   Val-78
   Flubiprofen 3394 R8 − 6.9 Lys-191, Asp-189 Lys-93, Val-78
   Asn-194 Gly-73, Arg-107
   Gln-75
   Oxaprozin 4614 R9 − 7.1 Asn-194, Arg-107 Asp-189, Thr-103
   Lys-191 Ser-217, Val-225
   Arg-230
   Sulindac 1,548,887 R10 − 7.4 Asn-152 Arg-107, Asn-194
   Lys-93, Ser-72
   Gly-73, Ser-193
   Ala-74
   Diclofenac 3033 R11 − 6.7 Asn-194 Ser-72, Gly-73
   Ser-193, Gln-75
   Arg-107, Lys-93
   Leu-206, Val-78
   Gly-71
   Rofecoxib 5090 R12 − 7.5 Asn-194, Lys-191 Asp-189, Arg-230
   Ser-217 Thr-203, Leu-210
   Gly-209, Ala-211
   Arg-107
   [90]Open in a new tab

Table 5.

   Binding energy and interactions of potential two NSAIDs on BAD (PDB ID:
   1G5J).
   Protein Ligand PubChem ID Symbol Binding energy (kcal/mol) Hydrogen
   bond interactions Hydrophobic interactions
   Amino acid residue Amino acid residue
   1G5J 6MNA 32,176 B1 − 6.8 Trp-173, His-181 Arg-169, Tyr-124
   Phe-127, Tyr-177
   Thr-176
   Indomethacin 3715 B2 − 7.1 Asp-180 Arg-169, Tyr-124
   Phe-127, Val-131
   Glu-128, His-181
   Thr-176, Trp-173
   [91]Open in a new tab

Figure 8.

   [92]Figure 8
   [93]Open in a new tab

   Anti-inflammation mechanisms of promising NSAIDs against COVID-19.

Discussion

   STN networking analysis demonstrated that the therapeutic effect of
   NSAIDs against COVID-19 was directly related to 26 signaling
   pathways—13 target proteins—19 NSAIDs. The results of the KEGG pathway
   enrichment analysis of 13 target proteins suggested that 26 signaling
   pathways were associated with the occurrence and development of the
   COVID-19 symptoms. The relationships of 26 signaling pathways with
   COVID-19 symptoms were succinctly discussed as follows. PPAR
   (Peroxisome Proliferator-Activated Receptor) signaling pathway: a
   report shows that PPARγ (Peroxisome Proliferator-Activated
   Receptor-gamma), PPARα (Peroxisome Proliferator-Activated
   Receptor-alpha), and PPARβ/δ (Peroxisome Proliferator-Activated
   Receptor-beta/delta) agonists have anti-inflammatory and
   immunomodulatory functions^[94]25. MAPK (Mitogen-Activated Protein
   Kinase) signaling pathway: The mechanisms of p38 MAPK inactivation
   might be an effective therapy against the SARS infected cells^[95]26.
   Additionally, MAPK stimulates cytokine production such as IL-10
   (Interleukin 10), TNF-α (Tumor Necrosis Factor-Alpha), IL-4
   (Interleukin 4), and IFN-γ (Interferon-gamma)^[96]27. It is evident
   that MAPK inhibitor can alleviate inflammatory responses against
   COVID-19. ErbB (Erythroblastic Leukemia Viral Oncogene Homolog)
   signaling pathway: ErbB signaling reduces the proinflammatory
   activation in cardiac cells^[97]28. RAS (Renin-Angiotensin System)
   signaling pathway: The inhibition of ACE (Angiotensin Converting
   Enzyme) connected to RAS signaling pathway could reduce tissue damage
   in COVID-19 patients^[98]29. Thus, the study indicates that blocking of
   the RAS signaling pathway can reduce inflammatory response level.
   cGMP-PKG (Cyclic GMP-Protein Kinase G) signaling pathway: the
   activation of cGMP-PKG signaling inhibits inflammatory response in the
   prostate, and also decreases CCL5 (C–C Motif Chemokine Ligand 5)
   released in CD8 ^+ T cells (Cluster of Differentiation 8 T
   cells)^[99]30. cAMP (Cyclic Adenosine Monophosphate) signaling pathway:
   the elevation of cAMP leads to diverse cellular effects, such as airway
   smooth muscle relaxation, repressed effects on cellular inflammation,
   and immune responses^[100]31. NF-κB (Nuclear Factor
   kappa-light-chain-enhancer of activated B cells) signaling pathway:
   activation of the NF-κB signaling pathway gives rise to the
   inflammation induced by the SARS-CoV infection. In contrast, NF-κB
   inhibitors are the potential antivirals, even against SARS-CoV and can
   also contribute to other pathogenic human coronaviruses^[101]32. FOXO
   (Forkhead box protein O1) signaling pathway: decrease of FOXO3
   (Forkhead box protein O3) in T cells inhibits apoptosis, enhances
   multifunction of CD8 cells, and elevates viral control^[102]33.
   Sphingolipid signaling pathway: Sphingolipids play a vital role to
   protect the lung from damages, and its control may give a good
   therapeutic efficacy^[103]34. WNT (Wingless/Integrated) signaling
   pathway: WNT signaling involves with the prime inflammatory pathways
   like intestinal inflammation. Also, the understanding mechanism of WNT
   ligands and cytokines manifest new treatment strategies for chronic
   colitis and other inflammatory diseases^[104]35. VEGF (Vascular
   Endothelial Growth Factor) signaling pathway: a report suggested that
   the activation of ACE2 (Angiotensin-Converting Enzyme 2) inhibits VEGFA
   (Vascular Endothelial Growth Factor A), which elevates vascular
   permeability and severity of endothelial damage^[105]36. TLR (Toll-like
   receptor) signaling pathway: Toll-like receptors (TLRs) play a pivotal
   role in the innate immune system and contribute to defending host cells
   by recognizing PAMPs (Pathogen-Associated Molecular Patterns) induced
   by various microbes^[106]37. TLRs' activation triggers an array of
   responses resulting in the expression of different cytokines and
   chemokines, phagocytosis, and even apoptotic case activation to induce
   programmed cell death^[107]38. NOD-like receptor (NLR) signaling
   pathway: Nod-like receptors (NLRs) have been revealed as the major
   microbial signals that take part in the universal immune responses to
   infection and contribute to the prevention of infections^[108]39.
   RIG-I-like receptor (RLR) signaling pathway: RIG-I-like receptors
   (RLRs) play a vital role in the pathogen sensor of RNA virus infection,
   enhancing the antiviral immunity by sensing foreign RNA^[109]40. IL-17
   (Interleukin-17) signaling pathway: IL-17 receptor inhibitors are
   widely used to ameliorate the inflammatory acuteness to date.
   Furthermore, it is a potential target to suppress severe inflammation
   induced by COVID-19^[110]41. Fc epsilon RI signaling pathway: Fc
   epsilon RI interconnecting causes mast cell degranulation and synthesis
   of proinflammatory mediators^[111]42. TNF (Tumor Necrosis Factor)
   signaling pathway: TNF deficit is associated with dysfunctional
   secretion of inflammatory cytokine, leading to lung pathology and death
   during respiratory poxvirus infection, and thus TNF is a very
   significant element for regulating inflammation^[112]43. Neurotrophin
   signaling pathway: COVID-19 causes severe brain damage and destruction
   of the central nervous system derived from
   neurotrophin^[113]44,[114]45. Insulin signaling pathway:
   Obesity-oriented insulin resistance is associated with the induction of
   proiifnnflammatory macrophage, leads to inflammation of adipose
   tissue^[115]46. GnRH (Gonadotropin-Releasing Hormone) signaling
   pathway: BBB (Blood Brain Barrier) disrupted by a viral infection,
   lymphocytes (B and T cells), monocytes, and granulocytes can penetrate
   in the brain parenchyma, which induces inflammation, resulting in
   dysregulation of GnRH neurons. Additionally, the inflammation of GnRH
   neurons inhibits GnRH transport through proinflammatory cytokines by
   impairing the cytoskeleton^[116]47. Prolactin signaling pathway: HIV
   patients have greater prolactin quantity compared to others. Besides,
   prolactin is regarded as a cytokine to stimulate the immune
   system^[117]48,[118]49. Adipocytokine signaling pathway: Adipocytokines
   stimulate inflammation and disrupt immune response, which induces
   proinflammation in RA (Rheumatoid Arthritis) patients, leading to the
   development of bone damage^[119]50. Oxytocin signaling pathway:
   oxytocin interrupts proinflammatory cytokines' production by
   inactivating the eIF-2α–ATF4 (Eukaryotic Initiation Factor-2
   alpha-Activating Transcription Factor 4) pathway^[120]51. Relaxin
   signaling pathway: relaxin inhibitors are good therapeutic targets to
   suppress inflammation caused by airway dysfunction^[121]52. AGE-RAGE
   (Advanced Glycation End product-Receptor of Advanced Glycation End
   product) signaling pathway in diabetic complications: The binding of
   AGE to its receptor RAGE can trigger cytokine production, thus, can
   cause tissue damages, while the blockage of AGE-RAGE can effectively
   ameliorate the inflammation^[122]53. Epithelial cell signaling in
   Helicobacter pylori infection: Helicobacter pylori interrupts T and B
   cell signaling to work the immune system. It is apparent that COVID-19
   patients with Helicobacter pylori might be vulnerable to inflammatory
   responses^[123]54.

   Generally, SARS-CoV-2 invades the lungs and throat, induces excessive
   inflammation, which causes cytokines' secretion, resulting in severe
   complications like acute respiratory failure, pneumonia, and acute
   liver injury^[124]55. The leading cause is that the downregulation of
   ACE2 results in an angiotensin-II (Ang II) increase, which might spur
   the progression of COVID-19 through activated RAS^[125]56.

   It was discovered that ACE2 is the functional receptor for the
   SARS-CoV-2 to trigger an infection in the lung alveolar epithelial
   cells. The internalization of the virus leads to downregulating the
   ACE2 on the host cell surface that could cause the elevation and
   demotion of Ang II and angiotensin 1–7 (Ang 1–7), respectively. Such an
   imbalance between these angiotensins may induce deleterious effects in
   the lung and heart. Thus, the SARS-CoV-2 affects humans through this
   mechanism^[126]57–[127]60. Therefore, RAS blockade may restore the RAS
   balance by reducing the deleterious effects associated with Ang
   II^[128]61. Recent evidence showed that RAS inhibitors might be a
   promising target for relieving acute-severe pneumonia caused by the
   COVID-19^[129]62.

   Interestingly, our study identified that the three target proteins
   (MAPK8, MAPK10, and BAD) are mainly associated with the RAS signaling
   pathway. MAPK8 and MAPK10 are members of the MAPK family which are the
   key mediators of inflammation, vasoconstriction, and thrombosis.
   Besides, overwhelming heart and lung injury in COVID-19 infection might
   be due to the overactivation of MAPK^[130]63. Therefore, these
   proteins' inactivation can also be a viable strategy for relieving
   COVID-19 induced organ injury. In addition, disposal of inflammatory
   cells by promoting cell death can be an innovative approach to control
   excessive inflammation. In this regard, the anti-apoptotic Bcl-2 gene's
   inhibition can also be a potential target to lessen
   inflammation^[131]64,[132]65. Our findings also explored that MAPK8
   MAPK10 and BAD proteins are related to three, 12, and two NSAIDs,
   respectively. During the molecular docking analysis, 6MNA, Rofecoxib,
   and Indomethacin revealed promising binding affinity along with the
   highest docking score against MAPK8, MAPK10 and BAD proteins,
   respectively. The result suggested that the three NSAIDs' key mechanism
   against COVID-19 might be to inhibit inflammation of lung cells by
   inactivating the RAS signaling pathway, and blockers of MAPK8, MAPK10
   and BAD might suppress cytokine storm. Among various NSAIDs,
   Indomethacin is a current drug of interest to clinicians. Primary care
   physicians (New York) reported that Indomethacin had been prescribed to
   a large number of COVID-19 patients and observed quick recovery from
   cough, pain, and other symptoms. Such improvements and well-being
   benefits were not evident in the case of ibuprofen and
   hydroxychloroquine implementation ( Little^[133]66). Notably, many
   researchers previously reported varying degrees of Indomethacin
   antiviral activity against herpesvirus^[134]67, pseudorabies
   virus^[135]68, cytomegalovirus^[136]69, hepatitis B virus^[137]70,
   vesicular stomatitis virus^[138]71, rotavirus ^[139]72, and canine
   coronavirus^[140]18. In contrast, 6MNA (an active metabolite of
   Nabumetone) and Rofecoxib are also potential anti-inflammatory drugs,
   but studies disclosed that they are less potent than
   Indomethacin^[141]73,[142]74. A clinical study recently demonstrated
   that Indomethacin has potent anti-inflammatory (decrease in IL-6) and
   antiviral efficacy; taking SRF (Sustained Release Formulation) with
   75 mg twice a day achieved full effects in 3 days for patients infected
   by COVID-19^[143]75.

   Hence, such compelling outcomes indicate that Indomethacin can be
   considered alone or in combination for antiviral therapy, which may
   assist in combating human coronavirus (SARS-CoV-2). However, there are
   also some limitations to our analysis. This study has provided a
   predictive viewpoint of NSAIDs' mechanism against COVID-19 through
   public databases. Thus, further experimental results should be
   validated to achieve the reliability of predicted outputs through in
   vitro and in vivo followed by NGS (Next Generation Sequencing)
   technique. Finally, our analysis did not consider the target gene
   expression level practically after treating the selected NSAIDs (6MNA,
   Rofecoxib, and Indomethacin), which should be considered and
   implemented in the future. The proposed (three) NSAIDs against COVID-19
   might be significant for clinical application, mainly depending on the
   genetic, ethnic, and underlying diseases associated with the
   therapeutic method.

Conclusion

   This study suggests that 6MNA, Rofecoxib, and Indomethacin are the most
   potent NSAIDs against COVID-19. The basis of this research is an
   understanding of how these NSAIDs (which stimulates anti-inflammatory
   processes against COVID-19) work against COVID-19 patients. That
   scientific evidence informs the selection of NSAIDs, in turn, provides
   for clinical design against COVID-19. Our research suggests that
   BAD-Indomethacin's inhibition with the other two hub proteins,
   MAPK8-6MNA, MAPK10-Rofecoxib might play cumulative actions by
   inactivating the RAS signaling pathway against COVID-19. Most recently,
   the efficacy of Indomethacin against COVID-19 has been approved
   clinically. Our study presents that Indomethacin is a potent
   therapeutic candidate to relieve COVID-19 symptoms, which is in line
   with the many previous studies. However, further clinical trial on
   Indomethacin should be warranted in COVID-19 patients to slow down the
   progression of SARS-CoV-2 and mitigate the severity.

Materials and methods

NSAIDs linked to selected proteins or COVID-19 related proteins

   FDA (U.S. Food & Drug Administration) approved NSAIDs (nineteen active
   drugs and one prodrug) were used in this study. Based on SMILES,
   targeted gene(s) of NSAIDs were identified through Similarity Ensemble
   Approach (SEA) ([144]http://sea.bkslab.org/)^[145]76 and Swiss Target
   Prediction (STP) ([146]http://www.swisstargetprediction.ch/)^[147]77
   with the "Homo sapience" mode^[148]78. Additionally, COVID-19 targeted
   proteins were identified by retrieving COVID-19 in PubChem
   ([149]https://pubchem.ncbi.nlm.nih.gov/). Also, the topological polar
   surface area (TPSA) value identified by SwissADME is included to verify
   the NSAIDS' cell permeability; particularly, its permeability is
   typically limited when the TPSA value is more than 140 Å^2^[150]79. The
   final overlapping proteins between NSAIDs and COVID-19—targeted
   proteins were identified and visualized by Venny 2.1
   ([151]https://bioinfogp.cnb.csic.es/tools/venny/).

PPI network between NSAIDs and COVID-19 targeted proteins

   The protein–protein interaction network (PPI) between NSAIDs and
   COVID-19 targeted proteins were selected by STRING
   ([152]https://string-db.org/)^[153]80 and finally plotted by RStudio.

Signaling pathway enrichment analysis of overlapping proteins via KEGG
database

   KEGG database provides correlation of target proteins and signaling
   pathways through functional annotation^[154]81. A bubble chart of
   signaling pathways associated with COVID-19 infection plotted by
   RStudio. The bubble chart demonstrates a hub signaling pathway (Lowest
   rich factor) between NSAIDs and COVID-19 related proteins.

The construction of STN network

   The Signaling pathway(s)—Target protein(s)—NSAIDs were used to
   construct a signaling pathway—target protein—NSAID (STN) network. In
   this STN network, different colors and shapes (nodes) stand for the
   signaling pathways (green circle), target proteins (yellow square), and
   NSAIDs (orange triangles). Gray lines (edges) indicated the interaction
   of signaling pathways—target proteins—NSAIDs.

   The STN networks were utilized to construct a size map, based on degree
   of values. In this network, green circles (nodes) represented signaling
   pathways; yellow squares (nodes) represented target proteins, and
   orange triangles (nodes) represented NSAIDs; its size represented
   degree value. The size of yellow squares stands for the number of
   connectivity with signaling pathways; the size of orange triangles
   stands for the number of connectivity with target proteins. The merged
   networks were constructed by using RStudio.

Preparation for MDT of NSAIDs

   The ligand molecules were converted .sdf from PubChem into .pdb format
   using Pymol, and the ligand molecules were converted into .pdbqt format
   through Autodock.

Preparation for MDT of target proteins

   Three target proteins of a hub signaling pathway, i.e. MAPK8 (PDB ID:
   4YR8), MAPK10 (PDB ID: 3TTJ), and BAD (PDB ID: 1G5J), were identified
   by RCSB PDB ([155]https://www.rcsb.org/). The proteins selected as .pdb
   format converted into .pdbqt format via Autodock
   ([156]http://autodock.scripps.edu/).

NSAIDs- target protein(s) docking

   The ligand molecules were docked with target proteins utilizing
   autodock4 by setting-up 4 energy range and 8 exhaustiveness as default
   to obtain 10 different poses of ligand molecules^[157]82. The active
   site's grid box size was x = 20.973 Å, y = 25.96 Å and z = 41.239 Å.
   The 2D binding simulation was identified via LigPlot + v.2.2
   ([158]https://www.ebi.ac.uk/thornton-srv/software/LigPlus/). After
   docking, NSAIDs of the lowest binding energy (highest affinity) were
   selected to visualize the 3D docking simulation in Pymol.

Supplementary Information

   [159]Supplementary Information 1.^ (269.1KB, pdf)
   [160]Supplementary Information 2.^ (129.1KB, pdf)
   [161]Supplementary Information 3.^ (34.2KB, pdf)
   [162]Supplementary Information 4.^ (96.9KB, pdf)
   [163]Supplementary Information 5.^ (6.9KB, pdf)

Acknowledgements