Understanding binding affinity is key to appreciation of the intermolecular interactions driving biological processes, structural biology and structure-function relationships. It is also measured as part of the drug discovery process to help design drugs that bind their targets selectively and specifically. Binding affinity is the strength of the binding interaction between a single biomolecule (e.g. protein or DNA) to its ligand/binding partner-drug or inhibitor. Binding affinity is typically measured and reported by the dissociation constant-KD, the smaller the KD value, the greater the binding affinity of the ligand for its target. [11]. Binding energy is released when a drug molecule associates with a target leading to a lowering of the overall energy of the complex. The release in binding energy also compensates for any transformation of the ligand from its energy minimum to its bound conformation with the protein [12,13].
With respect to the structure-activity relationship (SAR) of these molecules, as summarized in Table 1, the removal of one ethyl group from the terminal nitrogen atom of CQ increased the binding affinity of the molecule with a characteristic binding energy of - 4.5 Kcal/mol (CQ) to - 4.7 Kcal/mol (C–383). Further hydroxylation of the ethyl group led to a further increase in binding affinity with a corresponding binding energy of - 5.7 Kcal/mol (H–139). This can also be seen in the binding interaction of C–136 (Fig. 3d), where the hydrogens of the terminal amino group participated in hydrogen-bonding. The 3D view of binding conformation of H–372, H–156, and C–136 to the active site residues of SARS-CoV–2–6W63.pdb showing hydrogen-bond interactions is shown in Fig. 4. The complete conversion of the two alkyl groups attached to the terminal nitrogen of HCQ to alcoholic groups also led to an increase in binding affinity with a binding energy of –5.9 Kcal/mol (H–156). Also, the removal of C–11 along with the amino group attached to C–9 and C–10 led to an increase in binding affinity of the molecule H–372 (–6.0 Kcal/mol). All the designed molecules have a synthetic accessibility-SA of less than 3. A compound’s SA is a very important aspect of computer-aided drug design since in some cases computer-designed compounds/molecules cannot be synthesized [14]. It is often reported within the range of 1 (very easy to synthesize) and 10 (difficult to synthesize).
Due to technical limitations, Table 1 is provided in the Supplementary Files section.
The LogP values (Table 2) of all the molecules are within the range of 2 and less than 5 which indicate compounds of intermediate polarity, good balance between aqueous and lipid solubility, good absorption and distribution. The logP value of a compound, which is the logarithm of its partition coefficient between n-octanol and water i.e. log (Coctanol/Cwater), is a well-established measure of the compound’s hydrophilicity. Low hydrophilicity, and therefore high logP values poor absorption or permeation. It has been shown for compounds to have reasonable probability of been well absorbed their logP values must not be greater than 5.0 [15].
The designed molecules possess up to 60% activity on the G-protein-coupled receptors (GPCRs) (Table 2) specifically on the family A (rhodopsin-like receptors) as obtained on the Swiss-Target platform. Presently, there are over four hundred (400) drug molecules i.e. approximately 34% of all FDA approved drugs, that act on more than 100 unique targets of GPCRs. Generally, GPCRs are among the most numerous groups of transmembrane proteins of the mammalian genome. Till date, about 800 of these proteins have been identified in humans [16]. The relevance of their manifold functions has made them therapeutically attractive as shown by the fact that they are the targets of over 30% of united states Food and Drug Administration-approved drugs [17]. Two analogues of HCQ (H–139 and H–156) are substrates of the permeability-glycoprotein (P-gp) which implies that these molecules will undergo less pharmacokinetic-related drug-drug interactions and will also be easily cleared from the human system. The knowledge about compounds being substrate or non-substrate of P-gp is key to appraise active efflux through biological membranes, for instance from the git wall to the lumen or from the brain [18]. One important role of the P-gp is to protect the CNS from xenobiotics [19].
Table2: Pharmacokinetic properties and Toxicity Prediction
S/N
|
Name
|
LogP
|
B.A Score
|
G.I Absorption
|
BBB
|
GPCRs (%)
|
P-gp
|
LD50 (mg/Kg)
|
T.C
|
1
|
CQ
|
4.15
|
0.55
|
High
|
Yes
|
60.0
|
No
|
311
|
4
|
2
|
HCQ
|
3.37
|
0.55
|
High
|
Yes
|
53.3
|
No
|
1240
|
4
|
3
|
C-136
|
2.83
|
0.55
|
High
|
Yes
|
60.0
|
No
|
750
|
4
|
4
|
H-139
|
2.77
|
0.55
|
High
|
Yes
|
53.3
|
Yes
|
1240
|
4
|
5
|
H-156
|
2.88
|
0.55
|
High
|
Yes
|
53.3
|
Yes
|
1240
|
4
|
6
|
C-189
|
3.49
|
0.55
|
High
|
Yes
|
60.0
|
No
|
311
|
4
|
7
|
H-140
|
3.36
|
0.55
|
High
|
Yes
|
60.0
|
No
|
750
|
4
|
8
|
C-383
|
3.57
|
0.55
|
High
|
Yes
|
53.3
|
No
|
311
|
4
|
9
|
H-7715
|
3.67
|
0.55
|
High
|
Yes
|
60.0
|
No
|
750
|
4
|
10
|
H-97
|
3.36
|
0.55
|
High
|
Yes
|
53.3
|
No
|
750
|
4
|
11
|
H-368
|
3.32
|
0.55
|
High
|
Yes
|
26.7
|
No
|
200
|
3
|
12
|
H-372
|
4.8
|
0.55
|
High
|
Yes
|
40.0
|
No
|
416
|
4
|
13
|
H-369
|
2.95
|
0.55
|
High
|
Yes
|
53.3
|
No
|
750
|
4
|
14
|
H-347
|
3.12
|
0.55
|
High
|
Yes
|
53.3
|
No
|
750
|
4
|
The interaction of the molecules with CYP450 isoforms and kinase is as presented in Table 3. The HCQ analogue (H–156) stands out as it inhibits only one isoform of CYP450 i.e. CYP2D6 and as earlier mentioned, H–156 is also a substrate of P-gp. The knowledge about interaction of molecules with CYP450 is essential because it plays a major role in drug elimination through metabolic biotransformation. It’s being documented that both CYP and P-gp can synergistically process small molecules to improve the protection of tissues and most therapeutic molecules are substrate of five major isoforms (Table 3). Inhibition of these major isoforms is certainly one major cause of pharmacokinetic-related drug-drug interactions leading to toxic or other unwanted adverse effects due to lower clearance and accumulation of the drug or its metabolite. It is therefore important for drug discovery to predict the propensity with which the molecule will cause significant drug interactions through inhibition of CYPs and to determine which isoforms are affected. [20,21]
Table 3: Enzyme activity
|
CYP450 Isoforms Inhibitors
|
S/N
|
Name
|
Kinase Inhibitor
|
CYP1A2
|
CYP2C19
|
CYP2C9
|
CYP2D6
|
CYP3A4
|
1
|
CQ
|
0.38
|
Yes
|
No
|
No
|
Yes
|
Yes
|
2
|
HCQ
|
0.44
|
Yes
|
No
|
No
|
Yes
|
No
|
3
|
C-136
|
0.46
|
Yes
|
Yes
|
No
|
Yes
|
Yes
|
4
|
H-139
|
0.46
|
Yes
|
No
|
No
|
Yes
|
Yes
|
5
|
H-156
|
0.43
|
No
|
No
|
No
|
Yes
|
No
|
6
|
C-189
|
0.41
|
Yes
|
No
|
No
|
Yes
|
Yes
|
7
|
H-140
|
0.33
|
Yes
|
No
|
No
|
Yes
|
No
|
8
|
C-383
|
0.40
|
Yes
|
Yes
|
No
|
Yes
|
Yes
|
9
|
H-7715
|
0.44
|
Yes
|
No
|
No
|
Yes
|
Yes
|
10
|
H-97
|
0.52
|
Yes
|
No
|
No
|
Yes
|
Yes
|
11
|
H-368
|
0.04
|
Yes
|
No
|
No
|
Yes
|
Yes
|
12
|
H-372
|
-0.01
|
Yes
|
Yes
|
No
|
Yes
|
Yes
|
13
|
H-369
|
0.55
|
Yes
|
No
|
No
|
Yes
|
Yes
|
14
|
H-347
|
0.49
|
Yes
|
No
|
No
|
Yes
|
Yes
|