In Silico Screening and Pharmacological Evaluation of Fluoroquinolones: Lead-Optimization Studies


 Fluoroquinolones are broad-spectrum antibiotics regularly used to treat eyes, urinary tracts, and respiratory infections. Transcriptional regulator (TcaR) enzyme plays an important role in the formation of biofilms in Staphylococcus epidermidis. These biofilms are important for the protection of bacteria from the host immune system. By using computer-aided drug design techniques, we investigate the molecular interactions and pharmacological properties of selected fluoroquinolones on Staphylococcus epidermidis TcaR. We identified the hit molecules through molecular docking and pharmacological evaluation of the selected dataset on TcaR. The hit molecules were used as the leads for further optimization studies to design novel pharmacophore analogs against Staphylococcus epidermidis TcaR. The newly designed lead-optimized molecules have shown good binding energies and fitness scores with improved pharmacological properties.


Introduction
Fluoroquinolones are potential antibiotic drugs of both Gram-negative and positive bacteria: they could be used to treat respiratory and urinary tract infections (Lesher et al., 1962;Robson 1992). The rst synthetic quinolone, nalidixic acid, was discovered in 1962 by Lesher  activities. Also, Fluoroquinolones can be used for treating Mycobacterium tuberculosis as a part of multidrug medication (Chhabra et al., 2012). Nowadays, uoroquinolones have been predominantly used to control diseases caused by plant pathogenic fungi and bacteria (Huang 2004). In the last two decades, several antibiotic uoroquinolones have been designed, developed, and commercialized: for example, cipro oxacin, moxi oxacin (Wise et al., 1983), geni oxacin (Hong et al., 1997), nor oxacin (Koga et al., 1980), and spar oxacin (Miyamoto et al., 1990). Unfortunately, many bacteria such as Gram-positive bacteria (Staphylococcus aureus and Staphylococcus epidermidis), Gram-negative bacteria (Campylobacter organisms) and Mycobacterium tuberculosis develop resistance to these drugs (Appelbaum et al., 2000). Recently, the rates of uoroquinolone resistance in Campylobacter species have been signi cantly increased and exceeded 85% (Sproston et al., 2018). In order to overcome this drug resistance, it is urgently desired to design and develop novel pharmacophore analogs of uoroquinolones with enhanced pharmacological properties and binding a nities to a speci c target protein/enzyme. The evaluation of binding a nity between ligands and the selected target protein would facilitate the synthesis of the most promising compounds (Drlica et al., 2014;Ferreira et al., 2015).
Herein, by using the computer-aided drug design techniques, we investigate the molecular interactions and pharmacological analyses (physicochemical properties, ADMET) of the selected uoroquinolones on S. epidermidis, which frequently instigates infection in immunocompromised people or those after mutilation to the epithelium. Predicting the binding a nity of the ligand to a speci c target is an important step of the drug design process. Molecular docking is a successful technique to identify the best conformation of a ligand to the speci c target (Ferreira et al., 2015). We performed a molecular docking analysis of the selected dataset by utilizing two well-known docking programs: Genetic Optimization for Ligand Docking (GOLD) (Jones et al., 1997) and AutoDock vina (Trott et al., 2010). The pharmacological analysis of the selected dataset revealed that they have some toxic properties such as irritant, tumorigenic and low drug-likeness (DL) scores. Hence, we have designed new structural/pharmacophore analogs with enhanced DL scores. We further performed a molecular docking analysis on the speci c target, S. epidermidis transcriptional regulator (TcaR). The TcaR plays an important role in the bio lm production of bacteria which is essential for bacteria to protect themselves from the host immune system and thereby to improve their resistance to antibiotic chemotherapy (Stewart et al., 2001). The bio lm tolerance is of major clinical signi cance, because the most of the bacterial infections involves the formation of bio lm (Fux et al., 2005). The present molecular docking results revealed that the designed molecules have a good binding a nity with the speci c target S. epidermidis TcaR.

Computational Details And Methodology
We took a set of fourteen uoroquinolones having antibacterial inhibitory activities (Ravi Kumar et al., 2014) (Table S1)  We carried out the pharmacological analysis on the dataset to reveal structural characteristics (H-bond donors/acceptors, lipophilicity, molecular weight, volume, topological polar surface area), physicochemical, and ADMET properties. The molecules satisfying Lipinski's rule of ve (Lipinski 2004) have a good bioavailability in the metabolic process of the organism and therefore are more likely to be eligible for oral medications. The pharmacological analysis of these molecules was carried out by using Molinspiration (http://www.molinspiration.com/ ) and OSIRIS property explorer (Mabkhot et al., 2016) online tools.
In the molecular docking studies on Staphylococcus epidermidis TcaR (PDB ID: 3KP4), we predicted the molecular interaction between ligand and target protein by using well-validated docking programs: GOLD and AutoDock vina. The ligand-target complex results, analysis, and interaction images were generated by using Discovery studio visualization software (Discovery Studio visualizer 2012, http://www.accelrys.com/ ). The crystal structure of Staphylococcus epidermis TcaR was retrieved from the RCSB Protein databank (http://www.rcsb.org ) at a resolution of 2.84 Å and the active site analysis was done by using SPDBV Software (Guex et al., 2006).

SAR and ADMET analysis
We performed the in silico analysis of 14 molecules to understand their structural features required to interact with the selected target by using ADMET and binding a nity prediction tools. We calculated the physicochemical and ADMET properties of the selected dataset by using Molinspiration and Osiris property explorer. All molecules satis ed Lipinski's rule of ve and have shown positive enzyme inhibitor constant but the DL scores were very poor (Table 1 and 2). The poor DL scores represented that these molecules are highly toxic and not a good pharmacophore. We therefore moved to design novel pharmacophore analogs with improved pharmacological properties.

Molecular Docking Analysis
We have performed molecular docking simulations on S. epidermidis TcaR to understand the enzymeligand interaction at the molecular level and to nd a suitable orientation of each ligand within the active site. The tness scores obtained from the GOLD program were high for active molecules when compared to those of least active and inactive molecules. In the docking results of the selected dataset, the tness scores and binding energies did not correlate with the inhibitory activity of the molecules whereas the hydrophilic character (H-bond score) of molecules played an essential role and also exhibited a good correlation with their inhibitory activities. The most active molecules 7a, 7b, and 7g have shown the highest protein-ligand H-bonding scores 6.27, 6.73, and 6.40, tness score of 56.33, 57.47, and 56.90, and binding energies of -8.7, -9.0, and -9.2 kcal/mol (Table 3) respectively. The inactive molecules had low protein-ligand H-bonding scores except molecule 7o. Accordingly, a signi cant correlation has been found between the protein-ligand H-bond score and the inhibitory activity for the selected dataset. . The π cloud of the quinolone ring was involved in two π-π stacked interactions (BL 3.88 Å, 3.95 Å) and one π -cation interaction (BL 3.66 Å) with HIS42 residue. The present molecular docking analysis is helped us to understand how each substituent affects the binding a nity with the target.

Lead Optimization Studies
The initial pharmacological analysis of the selected dataset has shown poor DL properties. Therefore, we have carried out lead optimization studies by considering the most active inhibitors (7a, 7b, and 7g) as leads to develop novel molecules with improved pharmacological properties. We designed forty new pharmacophore analogs ( Figure S1) by substituting various functional groups at different positions (1st, 6th, and 7th ) of the basic skeleton of quinolone. Also, we analyzed the importance of each substituent and how the substituent enhances their medicinal values in the basic skeleton by using the in silico tools.

SAR and ADMET analysis of designed molecules
The physicochemical properties and in silico drug-relevant properties of designed molecules are summarized in Tables S2 and S3. The designed molecules satis ed Lipinski's rule of ve, a rule of thumb to evaluate the drug-likeness of a molecule. The lipophilicity values of the designed molecules were less than ve. The number of the H-bond donors and acceptors were not more than ve and 10, respectively. The molecular weight was less than ve hundred Dalton. The designed molecules have shown a positive enzyme inhibition constant, signifying that the molecules act as enzyme inhibitors.
The molecules of our design have shown signi cantly higher DL scores (except Mol37 to Mol40 over the selected dataset. The positive DL scores (4.83 to 7.20) of these molecules con rm that these pharmacophore analogs qualify as potential commercial drugs. Molecules Mol36, Mol35, Mol32, Mol24, and Mol33 have shown the highest DL scores of 7.20, 6.86, 6.37, 6.34, and 6.33, respectively.
Interestingly, the in silico ADMET predictions indicated that the loss of one -COOH group (at 1st position) and the substitution of uorine with chlorine can increase the DL properties and reduce the toxicity risks comparatively.

Molecular Docking Analysis of designed molecules
As per the docking results, all the designed molecules have shown good tness scores (<50) (Table S4) against the target. In the selected dataset, the H-bond score has shown a very good correlation with the inhibitory activity. Therefore, molecules Mol1, Mol2, Mol4, Mol9, Mol17, Mol24, Mol34, and Mol35 with the highest H-bond scores were taken to be the most active inhibitors of the target.
We have drawn in Figure 4 the binding conformation of the best candidate molecule Mol34 in the active site of S. epidermis TcaR. The molecule (binding energy of -10.6 kcal/mol, H-bond score of 7.98, and tness score of 62.95) took a conformation that ts well in the entire groove of the binding site of S. epidermis TcaR. The carboxyl and keto groups of Mol34 played a signi cant role in the binding by forming three H-bond interactions with the active site residues. The carboxyl group had one H-bond interaction with the ARG110 (BL 2.48 Å) and the keto group had two H-bond interactions with ASN20 (BL 2.71 Å, 2.06 Å). We observed two π-π stacked interactions with HIS42 (BL 4.79 Å, 4.22 Å) and several hydrophobic π-alkyl interactions with VAL63 (BL 4.89 Å), ALA67 (BL 4.53 Å), ALA38 (BL 5.12 Å, 3.75 Å), ALA24 (BL 4.34 Å, 3.36 Å), and LEU27 (BL 5.15 Å). In addition, Mol34 formed a π-cation interaction with ARG71(BL 3.97 Å) and C-H interaction with ASN20 (BL 3.47 Å).

Conclusion
We completed a comprehensive in silico analysis to design and develop novel pharmacophores of uoroquinolones with improved pharmacological properties and binding a nities. By utilizing a series of computational approaches, we identi ed the hit molecules against S. epidermis TcaR for a selected dataset. All the molecules of the dataset have satis ed Lipinski's rule of ve but their drug-like scores were very poor. Hence, we have carried out the structure-based lead optimization of selected active molecules (7a, 7b, and 7g) to design and develop novel pharmacophore analogs with enhanced pharmacological properties, binding a nities, and docking scores. We have designed a total of 40 molecules and considered them for the in silico analysis. Among 40 molecules designed, ve molecules namely Mol4, Mol9, Mol24, Mol34, and Mol35 have shown positive enzyme inhibitor value, good drug-like scores, and the highest H-bond scores along with good binding a nities. The designed molecules are considered to be the best antibacterial inhibitors of S. epidermidis TcaR over the selected dataset.
Declarations Figure 1 Molecular docking conformation of molecule 7a in the active site of S. epidermis TcaR. Drawn as broken lines (green: H-bond; pink: π -π stacked; orange: π -cation; light pink: alkyl) are the molecular interactions between molecule and protein active site.

Figure 2
Molecular docking conformation of molecule 7b in the active site of S. epidermis TcaR. Drawn as broken lines (green: H-bond; pink: π -π stacked; orange: π -cation; light pink: alkyl) are the molecular interactions between molecule and protein active site.

Figure 3
Molecular docking conformation of molecule 7g in the active site of S. epidermis TcaR. Drawn as broken lines (green: H-bond; pink: π -π stacked; orange: π -cation; light pink: alkyl) are the molecular interactions between molecule and protein active site.