4.1 Ligand and protein preparation
A total number of 6834 minimised conformers of the drug molecules were regenerated in a ‘ready-to-dock’ ligprep.out file, while the minimized receptors with binding pocket configurations were prepared in glide_grid.zip files for docking.
4.2 Analysis of receptor active cavity and grid box generation
In line with the set parameters in Maestro, five active sites (hot-spots for ligand binding) were predicted as sites 1-5 with site score, size, Dscore and volume for each receptor in decreasing order of site score (Table 1). The site with the highest predicted Dscore was selected as main target of each receptor. Therefore, site 2, site 1 and site 3 were selected for PDBs 6M3M, 6VSB and 6M17 respectively.
Table 1: Predicted site map parameters for receptor targets
PDB Code
|
Site
|
Site score
|
Size
|
DScore
|
Volume
|
6M3M
|
1
|
1.034
|
621
|
1.005
|
2561.18
|
|
2
|
1.017
|
194
|
1.006
|
618.03
|
|
5
|
0.605
|
24
|
0.599
|
71.00
|
|
3
|
0.595
|
24
|
0.562
|
66.20
|
|
4
|
0.558
|
23
|
0.494
|
92.95
|
6VSB
|
3
|
1.076
|
836
|
1.020
|
2627.38
|
|
1
|
1.044
|
1069
|
1.046
|
3653.29
|
|
5
|
1.029
|
368
|
1.004
|
1187.44
|
|
2
|
1.014
|
1097
|
1.031
|
3739.73
|
|
4
|
0.996
|
566
|
0.938
|
1439.57
|
6M17
|
3
|
1.029
|
180
|
1.042
|
633.52
|
|
4
|
1.024
|
165
|
0.976
|
640.04
|
|
2
|
1.012
|
186
|
1.011
|
658.56
|
|
1
|
1.012
|
335
|
0.956
|
1383.66
|
|
5
|
0.991
|
111
|
1.034
|
337.99
|
The x, y and z grid box coordinated of selected site of each receptor target as well as the potentially active residues for interaction with the drug conformers are presented (Table 2). The site with the highest Dscore in the crystal structure of the N-terminal NTD nucleocapsid phosphoprotein of SARS-COV-2 is site 2 (blue and red cartoon region, Fig. 1A) with the active residue backbones (green labels, Fig. 1B) identifiable within the unique drug targeting site of the NTD terminal (residue numbers 46-174) of the aligned structures of the nucleocapsid proteins of the three CoV strains (Fig. 1C). The residues of SARS-CoV-2 receptors smartly aligned in sequence with the residues of SARS-CoV-1 and MERS-CoV of the same NTD target. Furthermore, the N-terminal NTD SARS-CoV-2 (PDB 6M3M) has 100% homology identity with the resolved structure (PDB 6VYO) (supplementary Fig. 1) whose inhibitors have been demonstrated to be potent against similar targets of other CoV strains. Similarly, the active site of the crystal structure of the viral S glycoprotein of SARS-CoV-2 (PDB 6VSB) (red and blue cartoon, Fig. 1D) reveals the proposed active residues (green labels, Fig. 1E), whose residual sequence aligned smoothly with the resolved active sites for the same target in MERS-CoV (PDB 5W9H) and SARS-CoV-1 (PDB 6CRX) (Fig. 1F). These further validate the possibility of similar inhibition mechanisms by the drug conformers across different coronavirus strains as previously demonstrated [55, 56], and also support the accuracy in the configuration of the binding sites of the receptor targets. The active RBD site as a target for blocking the host enzyme, ACE2 during infection is mapped (blue and red cartoon, Fig. 1G) with the most probable residues for interaction (green labels, Fig. 1H) traceable to the predictions in Table 2.
Table 2: Predicted binding site properties of the selected structures
PDB code
|
Site score
|
Size
|
DScore
|
Coordinates
|
Predicted binding site residues numbers
|
X
|
Y
|
Z
|
6M3M
(Site 2)
|
1.016662
|
194
|
1.005915
|
8.08
|
-7.11
|
-30.57
|
49, 50, 53, 54, 74, 75, 76, 77, 78, 79, 80, 83, 113, 116, 117, 118, 120, 123, 124, 125, 126, 127, 141, 145, 146, 147, 148, 149, 150, 151, 154, 155, 156, 157, 158, 159, 160, 161
|
6VSB
(Site 1)
|
1.044
|
1069
|
1.046
|
226.32
|
229.16
|
206.72
|
661, 662, 663, 675, 678, 679, 697, 698, 699, 700, 703, 704, 705, 706, 707, 708, 709, 711, 712, 713, 725, 727, 728, 759, 762, 763, 765, 766, 768, 769, 770, 773, 774, 776, 777, 779, 780, 781, 783, 784, 785, 786, 787, 789, 792, 793, 794, 795, 796, 797, 873, 888, 889, 891, 892, 893, 894, 895, 946, 947, 950, 951, 953, 954, 957, 958, 961, 962, 965, 1003, 1005, 1006, 1007, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1026, 1027, 1028, 1030, 1031, 1034, 1039, 1041, 1042, 1043, 1044, 1045, 1064, 1072, 1074, 1075, 1304, 1309, 1310
|
6M17
|
1.029
|
180
|
1.042
|
146.60
|
201.25
|
222.83
|
85, 91, 92, 94, 95, 98, 99, 101, 102, 103, 104, 130, 131, 139, 140, 169, 170, 171, 172, 194, 195, 196, 202, 203, 205, 206, 208, 209, 210, 212, 219, 392, 395, 396, 397, 398, 511, 514, 562, 563, 564, 565, 566, 688, 689, 702, 905
|
4.3 Protein preparation and receptor grid generation
The active binding pocket grid box of each minimized receptor was generated (in glide grid zip file) for ready-to-dock conformation.
4.4 Molecular docking
The glide XP glide scores, glide energy and binding poses of the nine most interacted drugs across the targets, N-terminal of the RBD of SARS-CoV-2 (PDB 6M3M), S-protein of SARS-CoV-2 (PDB 6VSB) and human ACE2 enzyme (PDB 6M17) are presented presented in Table 3-5. The most strongly interacted drug molecules are buried deep within the binding pocket cavity of each receptor shown by surface structures (Fig. 2). The binding poses to show the bonded and non-bonded residues with the drug molecules within the active site of each receptor as well as the nature of interactions are displayed in Fig. 3-5.
For convenience, the drugs are annotated with numeric identities as given below:
Drug: 1 - S9349 D-(+)-Raffinose pentahydrate
2 - S4768 Melibiose
3 - S3950 Maltitol
4 - S5282 Lactitol monohydrate
5 - S3925 (-)-Epicatechin gallate
6 - S4704 D-(+)-Cellobiose
7 - Drug_Repurposing:2797
8 - Rutin DAB10
9 - S5453 Hyperoside
R1 - Remdesivir
R2 - Ribavirin
Table 3: Docking and molecular dynamic binding free energy of selected drugs with PDB: 6M3M
Drug
|
Docking scores
|
Molecular dynamics energy components
|
XP gscore
|
Glide energy
(kcal/mol)
|
EE
|
VW
|
GE
|
GBS
|
GBT
|
TS
|
MMGB(SA) (kcal/mol)
|
1
|
-16.20
|
-71.96
|
-23.20
|
-54.66
|
-77.86
|
35.45
|
-42.40
|
14.60
|
-27.80
|
2
|
-12.88
|
-56.47
|
-14.49
|
-41.61
|
-56.10
|
25.07
|
-31.04
|
15.72
|
-15.32
|
3
|
-12.85
|
-53.03
|
-18.01
|
-42.72
|
-60.73
|
27.19
|
-33.54
|
16.20
|
-17.34
|
4
|
-12.80
|
-50.92
|
-11.68
|
-37.63
|
-49.31
|
24.29
|
-25.02
|
13.96
|
-11.06
|
5
|
-11.98
|
-56.52
|
-16.56
|
-58.07
|
-74.63
|
30.94
|
-43.69
|
15.78
|
-27.91
|
6
|
-11.84
|
-47.11
|
-17.89
|
-35.33
|
-53.22
|
28.36
|
-24.86
|
12.14
|
-12.72
|
7
|
-11.83
|
-62.03
|
-18.17
|
-65.30
|
-83.47
|
34.88
|
-48.59
|
16.20
|
-32.39
|
8
|
-11.81
|
-66.16
|
-16.95
|
-67.36
|
-84.31
|
36.83
|
-47.48
|
19.57
|
-27.91
|
9
|
-11.41
|
-65.26
|
-26.16
|
-53.41
|
-79.56
|
39.25
|
-40.32
|
10.82
|
-29.50
|
R1
|
-10.27
|
-72.05
|
-20.34
|
-62.61
|
-82.95
|
36.80
|
-46.15
|
21.88
|
-24.27
|
R2
|
-9.06
|
-41.66
|
-7.75
|
-36.76
|
-44.50
|
15.01
|
-29.49
|
14.12
|
-15.37
|
XP = Extra Precision; EE = Electrostatic energy; VW = van der Waals contribution; GE = total gas-phase energy; GBS = GB contribution to solvation; GBT = GB total; MMGB(SA) = final binding free energy. The table is arranged in increasing order of the XP docking scores. For complete table, please refer to the supplementary file.
Considering the inhibition potentials against the viral N-terminal RNA-binding nucleocapsid (PDB: 6M3M) (Table 3), all the drugs including the references interacted with residues within the therapeutic target, NTD region, residue number 46-174 (Fig.1C) of the receptor [18], an indication of inhibitory actions as proposed. Drug 1 has the least XP score followed by 2, while 9 has the least. In comparison to the reference drugs, R1 and R2, the selected drugs interacted more strongly as indicated by lower glide XP scores. The binding poses (Fig. 3) reveal occupation of the same volume by the selected drugs to the references in favour of common H-bond interactions with similar amino acid residues within the active cavity of the receptor. This predicts conformity in their systemic inhibitory actions. Drug R1 shows H-bond interactions to Thr A149, Thr D77 and Asn D155 through OH and NH groups respectively similar to R2 with Thr A149 and Asn B123 through OH groups. The least scored drug 1 interacted with the five amino acid residues through H-bonds, Thr A149, Thr D77, Asn D76, Asn D 78 and Asn B127 all through OH groups while the least scored 9 has four H-bond interactions with Thr A149, Asn D76, Thr A50 and Asn B127, three to OH groups and one to carbonyl O. While all the drugs including the references interacted with Thr A149 through H-bonding and in groups with other residues within the N-terminal NTD, RNA binding domain as resolved [18, 57], only drugs 1, 5 and 9 interacted with Asn B 127, and no bonded interaction occurred with Trp 53, Ser 79, Hie 146 and Ile 147 along the protein residual chains. The selected ligands displayed similar interactions to the amino acid residues mostly in folds of H-bonding compared to the reference drugs and this favours similar but stronger potential inhibition mechanisms against the mutation and replication of the SARS-CoV-2 RNA along the NTD region.
Table 4: Docking and molecular dynamic binding energy of selected drugs with PDB: 6VSB
Drug
|
Docking scores
|
Molecular dynamics energy components
|
XP gscore
|
Glide energy
(kcal/mol)
|
EE
|
VW
|
GE
|
GBS
|
GBT
|
TS
|
MMGB(SA) (kcal/mol)
|
1
|
-12.17
|
-61.25
|
-17.39
|
-68.39
|
-85.78
|
31.49
|
-54.29
|
17.02
|
-37.27
|
2
|
-11.66
|
-53.05
|
-15.21
|
-47.20
|
-62.42
|
25.81
|
-36.61
|
19.17
|
-17.44
|
7
|
-11.67
|
-66.16
|
-6.81
|
-65.76
|
-72.57
|
22.47
|
-50.10
|
20.09
|
-30.01
|
4
|
-11.30
|
-60.16
|
-10.59
|
-45.53
|
-56.12
|
17.66
|
-38.46
|
19.98
|
-18.48
|
3
|
-10.74
|
-52.90
|
-13.42
|
-43.91
|
-57.33
|
22.80
|
-34.52
|
19.21
|
-15.31
|
8
|
-10.62
|
-66.47
|
-13.56
|
-69.13
|
-82.70
|
28.64
|
-54.05
|
19.55
|
-34.50
|
5
|
-10.46
|
-58.68
|
-11.97
|
-62.72
|
-74.70
|
23.08
|
-51.62
|
23.12
|
-28.50
|
9
|
-10.22
|
-61.32
|
-30.63
|
-64.94
|
-95.56
|
36.95
|
-58.61
|
20.20
|
-38.41
|
6
|
-9.97
|
-49.80
|
-9.44
|
-44.82
|
-54.26
|
18.70
|
-35.56
|
23.28
|
-12.28
|
R2
|
-9.10
|
-43.38
|
-8.08
|
-40.48
|
-48.56
|
14.37
|
-34.19
|
14.45
|
-19.74
|
R1
|
-6.05
|
-57.95
|
-6.82
|
-80.16
|
-86.98
|
19.45
|
-67.52
|
19.39
|
-48.13
|
XP = Extra Precision; EE = Electrostatic energy; VW = van der Waals contribution; GE = total gas-phase energy; GBS = GB contribution to solvation; GBT = GB total; MMGB(SA) = final binding free energy. The table is arranged in increasing order of the XP docking scores. For complete table, please refer to the supplementary file.
The XP docking scores (Table 4) represent the potentials of the selected drugs to inhibit the priming of the SARS-CoV-2 S-protein (PDB 6VSB) to the human ACE2 in comparison with reference drugs in the clinical trial of COVID-19 treatment, remdesivir and ribavirin. Drug 1 shows the best potential in terms of XP docking score followed by 2, while the least is observed with 6. However, all the nine selected drugs displayed better interactions, an indication of a more promising potential than the reference drugs. They occupy almost same volume within the binding sub-pocket of the receptor as the references, mostly through H-bond interactions (Fig. 2) and this favours similarity in biological functions. From the binding poses (Fig. 4), R1 interacted through H-bonds as Asn B1023 and Thr C1027 through H-bonding to OH and NH group respectively, R2 show H-bonds through Glu A780, Lys C947, Glu C1017 and Asn A1023, and π – cation to Arg A1019 using OH and NH groups, the best docked drug 1 indicates five H-bond interactions through Asn A1023, Asn B1023, Thr C1027, Arg A1039 and Arg B1039 engaging OH in all. Almost all the drugs including the references interacted with residues number Asn 1023, Thr 1027 and Arg 1039 across different chains, no bonded interaction occurred with Leu 727, Ala 1016, Ser 1021 and Leu 1024 across the residual chains. Interestingly, all the selected drugs including the references interacted with residues within the most important therapeutic targets of the receptor: residues 705-771 in the upstream helix (UH) region, 772-921 in the fusion peptide (FP) region, 922-982 heptad repeat 1 (HR1) region and 983-1028 the central helix (CH) region of the complex spike glycoprotein of SARS-CoV-2 [14, 58]. The selected drugs mostly interacted with more active amino acid residues along various residual chains within the binding pocket of the receptor, an indication of a more promising potential to inhibit the viral S-protein along the therapeutic regions and prevent SARS-CoV-2 infusion to the host than the reference drugs.
Table 5: Docking and molecular dynamic binding energy of selected drugs with PDB: 6M17
Drug
|
Docking scores
|
Molecular dynamics energy components
|
XP gscore
|
Glide energy
(kcal/mol)
|
EE
|
VW
|
GE
|
GBS
|
GBT
|
TS
|
MMGB(SA) (kcal/mol)
|
8
|
-12.11
|
-76.45
|
-16.48
|
-65.97
|
-82.45
|
32.25
|
-50.20
|
37.11
|
-13.09
|
1
|
-11.62
|
-60.54
|
-19.18
|
-54.59
|
-73.77
|
31.10
|
-42.66
|
33.13
|
-9.53
|
5
|
-10.19
|
-53.03
|
-8.76
|
-54.38
|
-63.13
|
20.65
|
-42.49
|
31.02
|
-11.47
|
2
|
-9.82
|
-44.95
|
-18.34
|
-45.03
|
-63.37
|
28.26
|
-35.11
|
32.53
|
2.58
|
4
|
-9.75
|
-53.50
|
-21.37
|
-37.37
|
-58.73
|
27.74
|
-31.00
|
31.72
|
0.72
|
7
|
-9.58
|
-63.51
|
-25.63
|
-71.44
|
-97.07
|
36.44
|
-60.64
|
37.70
|
-22.94
|
3
|
-9.20
|
-45.03
|
-17.38
|
-39.56
|
-56.95
|
24.62
|
-32.33
|
30.17
|
-2.16
|
6
|
-8.91
|
-44.35
|
-21.07
|
-41.48
|
-62.54
|
28.53
|
-34.01
|
28.68
|
-5.33
|
9
|
-8.26
|
-42.84
|
-14.47
|
-44.81
|
-59.28
|
27.97
|
-31.31
|
36.02
|
4.71
|
R2
|
-5.96
|
-36.28
|
-8.23
|
-25.67
|
-33.90
|
15.11
|
-18.79
|
23.02
|
4.23
|
R1
|
-4.64
|
-60.84
|
-2.61
|
-45.70
|
-48.31
|
14.08
|
-34.23
|
30.65
|
-3.58
|
XP = Extra Precision; EE = Electrostatic energy; VW = van der Waals contribution; GE = total gas-phase energy; GBS = GB contribution to solvation; GBT = GB total; MMGB(SA) = final binding free energy. For details, please refer to the supplementary file.
One of the major targets in the vaccination against SARS-CoV-2 infection is the human host cell protease, ACE2. The result (Table 5) shows XP scores as representation of the higher potentials of the selected drugs to block the RBD structure of human ACE2 (PDB 6M17) in comparison with R1 and R2, reference drugs. Drug 8 has the highest potential in terms of glide XP score followed by 1, while the least is observed in 9. Interestingly, all the selected nine drugs have lower glide XP scores than the references. The surface and binding poses (Fig. 2 and 5) indicate that all the drugs and the references occupied the same volume and interacted through H-bonding with amino acid residues within the active sub-cavity of the RBD as expected to indicate therapeutic potentials [14]. This could form basis for their similar biological functions. However, as R1 show three different H-bonding interactions to Asn 194, Glu 208 and Asn 210 through OH and NH groups, R2 like the selected drugs possesses four H-bond interactions, three through OH to Asp 206, Glu 208 and Lys 562 while the last occur through NH to Asn 210. Drug 8 with the least XP score interacted through six H-bonds (same as drug 1) between OH at different chemical environment with Glu 98, Tyr 202, Gly 205, Asp 206, Glu 208 and Lys 562 while 9 with the highest score possesses five H-bond interactions through OH to Leu 95, Asp 206, Glu 208, Asn 210 and Lys 652. Residues such as Lys 562, Asn 210 and Glu 208 seems most important at the RBD of the ACE2 receptor as all the drugs including the references strongly interact with them, however, no bonded interactions were formed between the selected drugs and some residues including Ala 99, Tyr 196, Trp 203, Val 209, Leu 392, Gly 395, Ser 511 and Arg 514. All the nine drugs show stronger interactions with similar and more amino acids residues within the catalytic and RBD sites of the ACE2 receptor than the references, an indication of similar but stronger potentials to interfere with the complex (S-protein RBD-ACE2) forming loop region and prevent the infusion of SARS-CoV-2 into the host [19, 42].
4.5 Molecular dynamic simulations
The binding free energies and molecular dynamic trajectories in terms of statistics of H-bond, RMSD, RMSF, Rg and Q plots of the ligand-receptor complex systems of the nine selected drugs are presented in Table 3-5, Fig. 6-8 and supplementary Fig. 2-4.
Molecular dynamics simulation has become an indispensable structural and biophysical tool for an extensive study of biochemical ligand-receptor interactions [59]. From Table 3, the total binding free energy, MMGB(SA) is a sum of GBT and TS which comprehensively quantify the interactions that exist between the small drug molecules and the larger biological target through various conformations. The statistics of H-bond (Fig. 6A) indicates that drugs 8, 5, 1 and 9 possess the highest number of hydrogen bond interactions in descending order, then others along the simulation period. This accounts for their respective higher binding free energy than drugs 2, 4, 6 and R2 whose number of H-bond interaction occur at lower rates. The RMSD plot (Fig. 6B) shows that the receptor backbone initially experienced slight atomic deviation between 2.0 - 3.5Å and become equilibrated around 1500 ps till the end of simulation with an insignificant deviation of <1.0Å. This indicate that the 3000 ps selected for the simulation is adequate especially since the longer simulations do not necessarily influence the binding free energy and dynamic conformations [60]. The selected drugs mostly entered the equilibrium conditions earlier and maintained it throughout the simulation periods with insignificant deviations in the order 7<9<1<5<8<R1. The dynamic thermal motion paths, transient channels and mean square isotropic displacements which allow the ligands to enter the internal cavities of the receptor are shown by B-factor or temperature factor and RMSF plots (Supplementary Fig. 2C-D). Although all the selected drugs including the references undergo very insignificant fluctuations, however the least is observed in drug 1 and highest in R2. These indicate a good thermostability in the drug-receptor systems within the period of simulation. The compactness and stability of the drug-receptor complexes to further probe the structural activity were revealed by the radius of gyration (Rg) plot (Supplementary Fig. 2E). The Rg is influenced by folding state of proteins in a complex with ligands during simulation and it favours drugs 1, 5, 7, 8 and 9 than others including the references. The network of native contacts (Supplementary Fig. 2F) which captures the transition state between the ligands and the receptor with a folding free energy barrier reveal more thermostability of the complexes. A system involving an unfolding protein is indicated by large changes in Q. Thus, selected drugs show better contact with the receptor in folding state along the simulation periods than the references, indicating the flexibility of the receptor to allow stable complexes with the drugs. Cumulatively, drugs 1, 5, 7, 8 and 9 especially show better inhibitory interactions, stability and potent biological functionalities against the target receptor for SARS-CoV-2 RNA replication than R1 and R2.
Drugs 1, 8 and 9 display higher numbers of H-bond interactions throughout the period of simulation while the least is observed with R2 (Fig. 7A) consistently with their binding free energy values (Table 4). The RMSD plot (Fig. 7B) indicates that the selected drugs, the references as well as the receptor backbone form stable systems with little or no deviation of atom/residue throughout the period of simulation. In spite of this, the least fluctuation is observed in 1 and 2 while R1 and R2 produced the largest during the thermodynamic simulation as shown (Supplementary Fig. 3C-D). The receptor enters into a folding state with drugs 1 and 8 the most during the simulation while the least is observed with R1 and R2 (Supplementary Fig. 3E), although all the drugs including the references favour the folding of the receptor except 4 as indicated (Supplementary Fig. 3F). On the average, the selected drugs display virtual inhibitory potentials, stability and bio-applicability in complex with the receptor representing the viral spike glycoprotein.
The plot of statistics of H-bond interactions throughout the simulation trajectory (Fig. 8A) indicates that drugs 7, 8 and 9 have the highest number while drug 4 show the least consistently with their binding free energy scores (Table 5). Others considerably compete with R1 and R2. The RMSD plot (Fig. 8B) reveals that the receptor backbones become stable around 1300 ps, and only undergoing an insignificant fluctuation of <1Å till the end of simulation while the drugs including the references form stable systems almost throughout the period of simulation (Supplementary Fig. 4C-D). Although the compactness, protein folding state and stability (Supplementary Fig. 4E-F) favour all the drugs including the references in complex with the receptor, however a little deviation is observed in R2. Summarily the drugs virtually interact more strongly and form stable systems with the receptor, an indication of potential abilities to actively block the human ACE2 against SARS-CoV-2 infection in better forms than the reference drugs.
4.6 Biochemical analysis of the antiviral activities of the selected drugs
The selected drugs coincidentally have been experimented for efficacy against some virulent viral strains through anti-CoV/antiviral activities and the results (Table 6) indicate potent inhibition. From the biochemical analysis, the drugs demonstrate strong inhibitory activity in vitro and in vivo experimental models including clinical trials against some coronaviruses and other virulent viral strains, corroborating the theoretical findings presented above. Their mechanisms of antiviral actions include prevention of viral infusion, disruption of viral E proteins, inhibition of prenylation, disruption of the viral RNA for replication, inhibition of viral DNA and suppression of enteroviruses [50–54]. This phenomenon is reasonably feasible due to some degrees of genomic similarities that exist among active pathogenic sites of the viral strains [61]. Advantages of repurposing already approved drugs for this quest include the availability of physico-chemical and pharmacodynamics information as well as documented activity data relevant to the study, additionally supporting the promising inhibition in real biological system.
Table 6: Biochemical in vitro and in vivo experimental results on the inhibition of the selected drugs against CoVs/other viral strains
Drug
|
Experimental protocol
|
Result
|
Ref.
|
1
|
Inactivation, flocculation and removal of SVHR strain in osmolyte solution of the drug.
|
>90% flocculation and removal of SVHR occurred at concentration range of 0.3-1.0 M.
|
[50]
|
5
|
Inhibition assay of SARS-CoV-1 using quantum dots-conjugated oligonucleotide system
|
Dose-dependent inhibition >40% at 0.05 µg/mL observed with IC50 = 0.05 µg/mL.
|
[51]
|
7
|
Clinical trial on patients infected with HDV by administration of 200/400 mg in two doses for 28 days.
|
HDV-RNA declined by 0.73 log and 1.54 log at low and high doses respectively. This is significantly higher than the effect of placebo.
|
[52]
|
8
|
In vitro inhibitory assay on Vero cells infected with IBV;
In vitro inhibitory assay on murine CoV
|
Inhibits the viral replication in dose-dependents up to 4-6-folds titer reduction at 0.004 g/mL of crude extract rich in drug 8.
Inhibition of murine CoV occur at concentration range of 15.63-500 µg/mL
|
[51, 53]
|
9
|
In vitro inhibitory assay on human hepatoma Hep G2.2.15 cells, HBeAg and HBsAg.
In vivo inhibitory assay on DHBV-DNA-infected duckling model at doses 0.05 g/Kg/day and 0.10 g/Kg/day
|
Significant inhibition with TC50 = 0.115g/L and maximum TC0 = 0.05g/L. The IC50s for HBeAg and HBsAg are 0.012 g/L and 0.015 g/L after 4-day, 0.009 g/L and 0.011 g/L after 8-day treatment respectively. Great reduction in DHBV-DNA (p<0.01) with mean percentage of viral DNA inhibition as 56.24% and 60.94% for 0.05 g/Kg/day and 0.10 g/Kg/day respectively.
|
[54]
|
4.7 Identification of the selected drugs to their current applications and natural sources
The information presented in Table 7 indicate that the five (5) selected drugs (Scheme 1) are either currently approved for clinical trials or in use for treatment of some ailments. More interestingly, they are commercially available and mostly traceable to vastly available natural products. This study thus, demonstrates their dual applicability potentials.
Table 7: Selected drugs, their current clinical applications and natural sources
Drug
|
Current application
|
Sources
|
1
|
In clinical trial for cardiovascular disease
|
Eucalyptus spp, Myrtaceae, cotton seed meal
|
5
|
Investigated for treatment of hypertension and pre-diabetes
|
Camellia sinensis (green tea plant)
|
7
|
In trial study for treating solid tumour, lung cancer and leukemia
|
Synthetic
|
8
|
Used to treat capillary fragility
|
Forsythia, Hydrangea, Viola, buckwheat, tobacco
|
9
|
Hepatoprotective and metabolic agent
|
Agrimonia eupatoria, Arctostaphylos uva-ursi, Hypericum perforatum, Crataegus laevigata, Crataegus monogyna, Fagopyrum esculentum, Houttuynia cordata, Polygonum multiflorum, Tussilago farfara, Rosa canina, Rumex acetosa, Artemisia capillaris
|
Similar to other previously reported study [40–43], the application of computational tools such as molecular docking and molecular dynamics simulation have afforded fast and cheap demonstration of multi-target directed inhibition mechanisms against SARS-CoV-2 through the viral N-terminal NTD nucleocapsid phosphoprotein for translation and replication, the viral S protein along its various therapeutic targets for infusion and the human ACE2 to reception. The molecular docking protocols reveal that the drugs show stronger inhibition potentials against the targets than remdesivir and ribavirin, drugs in clinical trials. The molecular dynamics simulations to approximately mimic the real biological system also indicate better binding affinity, biostability and biofunctionalities in favour of the selected drugs than the references. Interestingly, the drugs have activity history which corroborate the findings from the virtual studies. The selected drugs were also traced to abundant natural resources for global accessibility. The protocols reveal that the identified drugs possess stronger multi-target inhibition potentials against the SARS-CoV-2 pathogenic targets, similarly to some previous studies where multi-target inhibition mechanisms have been demonstrated in favour of some bioactive agents against cancer-related targets/pathways to overcome incessant resistance to drugs [62, 63].