2.1. Design of analogues and in silico-study
All analogues were designed on the basis of bioisosteric rules and/or provided by the "SwissBioisoster" database. They are listed in Tables 1, 2 and 3.
Bioisosteric approaches are widely used as effective tools in rational drug design processes. The design of bioisosteres frequently introduces changes at the structural level, affecting key properties in target recognition such as molecular size, topology, electronic distribution, polarizability, polarity, lipophilicity, and pKa. These effects can be beneficial or detrimental, including improved efficacy, selectivity, changes in physical properties, reduction in metabolism and elimination, modification of toxicophores, etc. Some substitutions are simple. Others are more complex and are based on equivalence between scaffolds considered as "mimetics". Complex bioisosteres would thus likely be different in terms of electronic distribution, physicochemical properties, steric and topological effects. Among recent bioisosteric replacements, deuteration, which involves replacing hydrogen with deuterium, improves the chemical stability of a molecule, especially in cases where molecules have a chiral center that racemizes in vivo. Deuteration also reduces metabolism by Cytochrome P450 and aldheyde oxidase enzymes. Fluorination is commonly used to modulate metabolism or target activity, as the C-F bond is strong and resistant to metabolic cleavage. Fluorine is also used to modulate the pKa of a basic nitrogen. Replacing an OH group with an NH2 group results in significant changes in the acid-base properties of the molecule. The replacement of an oxygen atom with a sulfur atom in an ether-oxide yields thioethers, which are less polar than the corresponding oxygen-containing compounds. The rules for replacement between chemical groups have led to the generation of analogues that are highly similar to the parent molecule. A Tanimoto similarity score of 0.85 or greater is considered a reliable indicator of similar activity between two compounds. However, the reliability of this score depends on various factors, such as the size of the database of molecules and the type of structures used for analysis [6–8].
All of the designed bioisosteric analogues (compounds 2–30, except 19, 20, 21 and 22) showed a Tanimoto score lower than 0.85. These four compounds correspond to remdesivir-like compounds designed based on a non-rational approach. Interestingly, these analogues had the lowest Tanimoto scores (0.4000 and 0.5806, respectively). These results indicate that the geometrical similarity with the MOL is no longer maintained, suggesting that the approach used to design these analogues may not be optimal for developing effective treatments for COVID-19.
2.2. Results of the Molecular Docking
In total, we investigated 30 structural analogs through molecular docking studies. The validation of the docking protocol was performed in two parts: docking of the co-crystallized ligand with the target receptor and comparison of the chemical interactions between the best docking pose and the co-crystallized ligand. The results of the 5 trials were reproducible (standard deviation σ = 0.048 and variance σ2 = 0.0024). The mean of the 5 trials was − 6.76 kcal/mol, with an RMSD ≤ 2 Å, indicating that the docking was successful overall [9, 10]. The RMSD plays a fundamental role in comparing different conformations of the same ligand with respect to a given receptor. As the docking software provides different ligand poses, it is particularly important to evaluate them using RMSD calculations. The RMSD is divided into rmsd/lb (RMSD lower bound) and rmsd/ub (RMSD upper bound). For the analysis of our results, we considered the rmsd/ub. Indeed, the lower this value, the better the alignment between the two structures. In addition, we used rmsd/ub because it examines each atom in one conformation with the same atom in another conformation, ignoring any symmetry [11].
To validate the docking method, a comparison of the chemical interactions produced by the co-crystallized MOL (PDB file 7OZV) and those produced after docking the MOL (10,563 residues) was performed. Similarity was observed between the MOL-target chemical interactions produced by docking and those existing in the MOL co-crystallized with RdRp. We detected in common: hydrogen bonds, electrostatic bonds involving the attraction between two charges of opposite sign, in this case the N11 and N13 nitrogen atoms carrying a partial positive charge (resulting from electron delocalization in the urea motif) and the COO− of the aspartic acid residue; conventional carbon-hydrogen bonds; low energy Van der Waals type bonds (Figs. 2, 3 and 4). Additionally, docking showed the presence of Pi-alkyl type interactions, involving the aromatic amino acid residue tyrosine. The MOL was bound to the finger subdomain of RdRp by 5 hydrogen bonds, with residues THR B 859, SER B 735, SER A 316, and GLN A 314. Furthermore, it formed an electrostatic bond with ASP B 737 and a carbon-hydrogen bond with VAL B 736. Both results presented a similar binding free energy with an average of -6.76 kcal/mol. This average belongs to the predefined reference range of binding energy ranging from − 5 to -15 kcal/mol, already used in the validation of a docking protocol by Shah and al.. Our docking score is similar to the study of Patil and al. [12], where the MOL presented a score of -7.3 kcal/mol (versus − 6.8 kcal/mol in our case). This difference of -0.5 can be explained by the difference in the docking software used, the nature of the downloaded PDB file, and finally the ligand interaction site at the target level. The same study also demonstrated an energy of -6.9 kcal/mol for the interaction between remdesivir and RdRp. This score is almost identical to that of the MOL in our study. It can be concluded that both antivirals have the same stability towards the target in question.
The best result was obtained with analogs 7 and 9, which have the lowest score and an RMSD ≤ 2 Å. Analogue 20 has the highest score, namely − 4.7 kcal/mol, but its RMSD remains ≤ 2 Å. Note that these results do not allow us to correlate docking and Tanimoto score results, since correct scores were obtained with structures that are not similar to the MOL (compound 20), and vice versa. Figure 5 summarizes the docking results.
The fluorinated analogues 7 and 9 showed the best docking results with a score lower than that of MOL (Fig. 6). Figures 7, 8, and 9 show the different interactions identified with these analogues.
These two analogues have in common the presence of a fluorine atom. Fluorine, being strongly electronegative, generates a new interaction with the target residues, called "halogen bonding." Indeed, the fluorine atom on the cycle at position 9 of analogue 7 created this bond with the GLN A: 292 residue of the target. This same fluorine interacted with the ARG A: 735 residue of the target by generating a hydrogen bond.
Fluorine can interact with both polar and hydrophobic groups within proteins. Polar interactions involve hydrogen bond donors (NH, polarized Cα-H bond, water bound to proteins, lateral residues), or hydrophobic interactions with lipophilic lateral chains. Orthogonal multipolar interactions can occur with carbonyl groups, amide functions (Asn and Gln), guanidine groups (Arg), and sulfur bridges (Cys) [13–16]. Some of these interactions were highlighted by the docking in our study. In conclusion, the presence of the fluorine atom contributed to improving the affinity with the target since the free binding energy decreased in both cases. Finally, it is worth noting that introducing fluorine on the methylene showed slightly better results than those obtained by introducing fluorine on the cytosine nucleus.
2.3. In silico prediction of structural analogues' druglikeness
The druglikeness of our molecules was predicted using the website ADME-SWISS: http://www.swissadme.ch/, based on Lipinski's Rule of Five, which states that if chemical molecules meet certain parameters, they have a good probability of being orally administered.
The MOL, the 5 deuterated analogues, analogues 6 and 7, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, and 23–30 all comply with Lipinski's rule, with 0 violations. They are all potentially good candidates for oral drugs. However, they all violate Veber's rule, with a TPSA > 140 A°. TPSA is an important parameter used to estimate the drug's bioavailability. If TPSA exceeds the value of 140 A°, poor intestinal absorption is possible. We can therefore expect poor intestinal absorption for the MOL and the 5 deuterated analogues. However, clinical studies have shown that the MOL is well absorbed orally and has linear pharmacokinetics between doses of 50 to 1,600 mg [17]. In conclusion, the violation of a single parameter, such as TPSA, does not necessarily mean that the drug will not be well absorbed orally.
Analogues 8 and 9 do not violate any of these rules. They can be good candidates for oral drugs. Analogues 15, 17, 20, and 22 all violate Lipinski's rule and at least one violation of Veber's rule. The violation always concerns TPSA, which is greater than 140. They are unlikely to be good candidates for oral drugs. Finally, it should be noted that the strict application of Lipinski's pre-filtering rule can easily lead to the exclusion of molecules of interest. For example, many marketed drugs do not comply with Lipinski's rules, such as atorvastatin and cyclosporine [18, 19].