VIRTUAL DESIGN OF NOVEL OF ORALLY BIOAVAILABLE PIPERAZINE INHIBITORS OF ENOYL-ACYL CARRIER PROTEIN REDUCTASE OF MYCOBACTERIUM TUBERCULOSIS WITH FAVORABLE PHARMACOKINETIC PROFILES

Koffi Charles Kouman¹, Affiba Florance Kouassi¹, Yves Kily Hervé Fagnidi^1,2, 

Issouf Fofana¹, Koffi N’Guessan Placide Gabin Allangba^1,3,4,5, Mélalie Kéïtaépouse Guego¹, 

Eugene Megnassan^1,6,7,8,9

¹Applied Fundamental Physics Laboratory (LPFA), Nangui Abrogoua University, Ivory Coast. ²Science and Technology Training and Research Unit, Alassane Ouattara University, Ivory Coast. ³Laboratoryof Environmental Sciences and technologies, Jean Lorougnon Guédé University, Ivory Coast. ⁴Laboratory of Biophysics and Nuclear Medicine (LBNM), Félix Houphouët-Boigny University, Ivory Coast. ⁵Department of Medical Physics, University of Trieste and International Centre for Theoretical Physics (ICTP), Trieste, Italy. ⁶Laboratory of Structural and Theoretical Organic Chemistry, Félix Houphouët-Boigny University, Ivory Coast. ⁷International Center for Theoretical Physics, ICTP-UNESCO, Coastal Road 11, I-34151 Trieste, Italy. ⁸International center for applied research and sustainable technology, SK-84104 Bratislava. ⁹Laboratory of Crystallography-Molecular Physics, Félix Houphouët-Boigny University, Ivory Coast.

Abstract

Background: During the previous decade, Anti-tuberculosis therapies were confronted with drug-resistant strains. We report here virtual design and evaluation of novel piperazine (PPZ) as inhibitors of InhA-Mt endowed with favorable predicted pharmacokinetic profiles.

Method: Three-dimensional (3D) models of InhA-PPZx complexes were prepared by in situ modifications of the crystal structure of InhA-PPZ1 (Protein Data Bank (PDB) entry code: 1P44), the reference compound of a training set of 12 PPZs with known experimental inhibitory potencies (IC₅₀^exp). First, in the search for active conformation of the PPZ 1-12, we built a gas phase quantitative structure-activity relationships (QSAR) model, linearly correlating the computed enthalpy of the InhA-PPZ complex formation and the pIC₅₀^exp. Finally, the VCL filtered by Lipinski’s rule-of-five was screened by the PH4 model and the potencies of the new PPZ analogs obtained were evaluated with the initial QSAR and their pharmacokinetic profile was evaluated.

Results: The VCL of 310,500 PPZs s was filtered down to 19,044 analogs by the Lipinski’s rule. The five-point PH4 screening retained 50 new potent PPZs with predicted inhibitory potencies IC₅₀^pre up to 100-times better than that of PPZ1 (IC₅₀^exp=160 nM). Predicted pharmacokinetic profile of the new analogs showed enhanced cell membrane permeability, side effect and high human oral absorption compared to current anti-tuberculosis candidates

Conclusions: Combining molecular modeling and PH4 in virtual screening of the VL resulted in the proposed novel potent antituberculotic agent candidates with favorable pharmacokinetic profiles.

Keywords: ADME, InhA inhibitors, Piperazine, pharmacophore, QSAR, tuberculosis, virtual screening.  

INTRODUCTION

Tuberculosis is as old as humanity itself. It has afflicted kings and queens, poets and politicians, revolutionaries and writers, activists and actors. Most of its victims, however, are poor, marginalised or malnourished, and the out-of-pocket costs associated with treating TB expose them to financial hardship or drive them further into poverty. TB is the definitive disease of deprivation¹. Turning the tide on TB means screening and treatment for those it strikes, and preventing it by addressing its drivers and developing a new vaccine. Only by working together can we turn the tide against this ancient killer" is the position of the Dr. Tedros Adhanom Ghebreyesus, Director-General of World Health Organization according the 2024 WHO report².

We can prevent and curate tuberculosis disease. However in 2023, TB probably returned to being the world’s leading cause of death from a single infectious agent, following 3 years in which it was replaced by coronavirus disease (COVID-19), and caused almost twice as many deaths as HIV/AIDS. More than 10 million people continue to fall ill with TB every year and the number has been rising since 2021⁸. All member states of the United Nations (UN) and World Health Organization (WHO) adopted the urgency action to end the global tuberculosis epidemic by 2030⁴. Despite the increasing worldwide incidence of TB and the threat for the public health, no novel antituberculotic drugs have been introduced into clinical practice over the past decades. Bedaquiline, a new antimycobacterial, approved at the end of 2012² inhibits adenosine 5′-triphosphate (ATP)-synthase of MTb with good clinical efficacy against multiple resistant strains. However, this drug has cardiovascular side effects³. It is therefore imperative to diversify the mycobacterial drug targets in order to fight the increasing incidence of drug-resistant strains of MTb. The oxidoreductase activity of enoyl-acyl carrier protein reductase (InhA or ENR) plays a key role in the type-II fatty-acid synthesis (FAS-II system) of MTb. This essential enzyme catalyzes the elongation cycle of biosynthesis of mycolic acid, which is a vital component of the mycobacterial cell wall⁴. InhA represents a validated drug target of antituberculotic agents and has been indicated in the Special Programme for Research & Training in Tropical Diseases of WHO (TDR) targets database as an attractive pharmacological target for design of new drug candidates^5,6. “The term totally resistant (TDR-TB) has emerged to mean infection with a strain resistant to all first- and second-line drugs”^[i]. Isoniazid prodrug (INH), is a first-line medication used for prevention and treatment of TB. The INH is a prodrug that must be activated by the bacterial catalase-peroxidase enzyme (KatG) which couples the isonicotinic acyl with the reduced form of nicotinamide adenine dinucleotide (NADH) to form an isonicotinic acyl-NADH complex. The complex binds tightly to InhA, blocks the natural substrate and hinders the action of fatty acid synthase, which hampers the synthesis of mycolicacid⁷. Compounds that can therefore directly inhibit InhA without the need for activation by KatG are of major interest in the fight against multidrug-resistant tuberculosis (MDR-TB), extensively drug-resistant tuberculosis (XDR-TB) and drug-resistant total tuberculosis (TDR-TB)”⁷. Several research groups were working on the discovery of InhA inhibitors not requiring KatG activation which were based on various scaffolds: triclosan⁹, diphenyl ether^10,11, pyrrolidinecarboxamide¹¹, arylamide deriva-tives¹¹  benzamide derivatives with Tyr158 'out' conformation and interaction with the Phe41 and Arg43 pocket instead of the stacking with Phe97⁷, thiadiazole-based InhA inhibitors⁹ and Piperazine derivatives with Tyr158 'out' conformation and interaction with NADH cofactor⁸. All displaying intermediate inhibitory potencies. These studies indicated that a potent InhA inhibitor should be a relatively long molecule, which binds to the InhA next to the NADH cofactor binding site. This inhibitor should also contain a bulky group that selectively fits into a hydrophobic pocket of InhA constituted by residues Met155, Pro193, Ile215, Leu217, Leu218 and Trp222 that is located near to a larger solvent accessible cavity¹⁵. The main aim of this work was to design novel potent 1-(9H-fluoren-9-yl)-piperazine (PPZs) based on a series of 12 (training set) and 3 (validation set) nanomolar inhibitors with observed inhibitory potencies as low as IC₅₀^exp=160 nM¹⁶. Starting with in situ modification of the crystal structure of InhA-PPZ1 complex (PDB: 1P44) we have elaborated a QSAR model which correlated Gibbs free energies of InhA-PPZ_x complex formation with the potencies IC₅₀^exp and determined the active conformation of PPZs bound at the active site of InhA of Mt (MM-PB complexation approach). Based on this active conformation we have formulated 3D QSAR pharmacophore of InhA inhibition (PH4). Large virtual library of compounds sharing the PPZ scaffold has been generated and in silico screened with the PH4. The screening yielded virtual hits that exhibited predicted inhibitory potencies IC₅₀^pre more than 100 times lower than the most active training set compound PPZ1. Finally, the hits underwent complexation simulations for evaluation of the predicted inhibitory activity for the best analogues and for calculation of their ADMET profile. 

MATERIAL AND METHODS

Structural studies and bioassays (IC₅₀^exp) of our studied piperazines derivatives (PPZ) InhA inhibitors were taken from literature¹⁷. The efficacy range of inhibitory concentrations (160≤ IC₅₀^exp ≤ 51000 nM), allows us to realize QSAR models. The whole series of 15PPZs were divided into a training (TS) and a validation (VS) sets of 12 and 3PPZs respectively¹⁸.

Model building

The whole complex (E:I), with free InhA (E) and inhibitor (I) was resolved to a reliability factor of 1.7 Å containing the Genz-10850  (PPZ1) bound to InhA (Whose crystallographic data entry code 1P44^12,13 from Discovery Studio 2.5 software⁸. Virtual design plan to result in new PPZ analogs with higher predicted activity is presented in scheme1. The structures (E and E:I complexes) were at the neutral pH=7 and neutral N- and C-terminal residues, all protonizable and ionizableaminoacids being charged, without any crystallographic water molecules. The inhibitors were built into the 1P44 structure by in situ replacing derivative groups of the PPZ1 moiety followed by systematic conformational search of the replacing group coupled with a careful energy minimization of the modified inhibitor and surrounding InhA active site residues^20-35.

Molecular mechanics

Modeling of inhibitors, InhA, and E-I complexes was carried out by molecular mechanics using CFF97 force field²⁷ as described earlier²⁸.

Conformational Search

For conformational research we recommend reading the following articles^18,21.

Solvation Gibbs free energies

As for the free solvation energy has been described perfectly by the following articles²⁴.

Calculation of Binding Affinity and QSAR Model

The calculation of binding affinity expressed as complexation GFE has been described fully earlier²⁵.

Interaction energy

For interaction energy refer to the full description we reported formerly²⁸.

Pharmacophore generation

Bound conformations of inhibitors taken from the models of E-I complexes were used for constructing of 3D-QSAR pharmacophore (PH4) by means of Catalyst HypoGen algorithm³¹ implemented in Discovery Studio³² as described earlier²⁶.

Virtual library generation

The virtual library generation was performed as described earlier¹⁸.

ADME properties

The drug-likeness selection criterion served to focus the initial virtual library as described earlier¹⁷.

Pharmacophore-based library searching

The pharmacophore model (PH4) described in Section “Pharmacophore generation” and derived from the bound conformations of PPZs at the active site of InhA served as library searching tool as described earlier²².

Inhibitory potency prediction

The conformer with the best mapping on the PH4 pharmacophore in each cluster of the focused library subset was used for  calculation and IC50 preestimation (virtual screening) by the complexation QSAR model as described earlier²¹.

RESULTS

Training and validation sets

The training set of 12 PPZs and validation set of another 3 analogs (Table 1) were selected from a heterogeneous series of InhA inhibitors with known experimentally determined inhibitory activities originating from a single laboratory²³. First, 1-(9H-fluoren-9-yl) -piperazine derivatives were synthesized bearing modifications around the carbonyl hydrogen bond acceptor. Using 1-(9H-fluoren-9-yl)-piperazine (3), compounds 2 and 4 - 8 were synthesized in low to good yields. Further, direct sulfonylation was used for the synthesis of benzenesulfonyl derivatives 5a - b to obtain the desired products²⁵. The whole series was obtained by variations at five positions of the substituents on the phenyl rings in Table 1. The experimental half-maximal inhibitory concentrations (160 ≤ IC₅₀^exp ≤ 51000 nM)²⁸ cover a sufficiently wide concentration range for the construction of a reliable QSAR model.

One descriptor QSAR models

Each of the 12training set (TS) and 3 validation set (VS) InhA- PPZx complexes (Table 1), was prepared by in situ modification of the refined template crystal structure (PDB entry code 1P44²² of the complex InhA-PPZ1 as described in the Methods section. Further the relative Gibbs free energy of the InhA-PPZx complex formation (∆∆G_com) was computed for each of the 15 optimized enzyme-inhibitor complexes. Table 2 lists computed values of ∆∆G_com and its components as defined earlier²⁵ for the TS and VS of piperazine²⁷. The QSAR model explained variation in the PPZs experimental inhibitory potencies (pIC₅₀^exp=–log₁₀(IC₅₀^exp)²⁸ by correlating it with computed GFE ∆∆G_com through linear regression (eq. B), Table 2. In search for a better insight into the binding affinity of PPZs towards MtInhA, the enthalpy of complexation in gas phase ∆∆H_MM was analyzed by correlating it with the pIC₅₀^exp. The validity of this linear correlation (for statistical data of the regression see Table 3, eq. A) allowed assessment of the significance of inhibitor-enzyme interactions (∆∆H_MM) without considering solvent effect and loss of entropy of the inhibitor upon binding to the enzyme. This correlation explained about 88% of the pIC₅₀^exp data variation and showed the role not negligible of the enthalpic contribution to the binding affinity of the ligand to active site. Likewise, the more advanced descriptor, namely the GFE of the InhA-PPZx complex formation containing all components: ∆∆HMM, ∆∆TSvib and ∆∆Gsol, has been evaluated (see Table 3 for statistical data, eq. B). Certainly the strong relationship between the 3D model of inhibitor binding and the observed inhibitory potencies of the PPZs was explained by the relatively high values of the regression coefficient R², leave-one-out cross-validated regression coefficient R²xv and Fischer F-test of the correlation suggest²⁹.Therefore, the active bound conformation of the PPZs at the InhA binding site enabled definition of the PH4 pharmacophore and structural information derived from the 3D models of InhA-PPZx complexes can be expected to lead to reliable prediction of InhA inhibitory potencies for new PPZs analogs based on the QSAR model B, Table 3.

The statistical data confirmed validity of the correlation equations (A) and (B) plotted on Figure 1. The ratio pIC₅₀^pre/pIC₅₀^exp≅ 1 (the pIC₅₀^pre values were estimated using correlation eq. B, Table 3) calculated for the validation set PPV13-15 documents the substantial predictive power of the complexation QSAR model from Table 2. Thus, the regression equation B (Table 3) and computed ∆∆G_com GFEs can be used for prediction of inhibitory potencies IC₅₀^pre against MtInhA for novel PPZ analogs, provided that they share the same binding mode as the training set piperazine PPZ1-12.

Binding mode of PPZs

In the crystal structure of InhA –PPZ1²³ the substitution at R-groups of the piperazine derivative scaffold of the inhibitor sits in a hydrophobic cavity of the active-site surrounded by side chains of predominantly nonpolar residues: Pro 99, Gly 104, Met103, Tyr158, Phe149 and Met161, Met 98, Ala 198. According to the structures of PPZ8 and PPZ5 studied by docking simulations, the presence of the methyl group at the C2 position ofthe aryl moiety causes steric hindrance due to the restricted space in the cavity formed by Met103, Tyr158, and Met161 residues of MtInhA. In contrast to the recently reported methyl thiazoles that interact with MtInhA in a “Tyr158-out” binding mode direct inhibitors such as pyrrolidine carboxamides⁸ and piperazine-indole derivatives⁷ have explored polar interactions involving a ribose hydroxyl, the Tyr158 hydroxyl and a hydrogen bond acceptor in the compounds.

Interaction Energy

Another key structural information was provided by the interaction energy (IE, ΔE_int) diagram obtained for four training set inhibitor. IE breakdown to contributions from InhA active site residue is helpful for the choice of relevant R-groups which could improve the binding affinity of PPZ analogs to the MtInhA and the subsequently enhance the inhibitory potency. A comparative analysis of computed IE for training set PPZs (Figure 3) divided into three classes,(highest (PPZ1), moderate (PPZ5 and PPZ8) and lowest (PPZ12) activity) has been carried out to identify the residues for which the contribution to binding affinity could be increased. However, the comparative analysis showed about the same level of IE contributions from active site residues for all three classes of inhibitors.

Therefore, no suggestions of suitable substitutions able to improve the binding affinity as we previously reported for thymine-like inhibitors of Mt thymidine monophosphate kinase design, could be proposed³⁷. Since specific substitutions could not be proposed we have adopted a combinatorial approach to novel PPZ analogs design and in silico screened a virtual library of 310,500PPZanalogs with help of the PH4 pharmacophore of InhA inhibition derived from the complexation QSAR model. As we can see from the IE analysis (Figure 3) the TS and VS piperazine³⁶ do not show significant interaction energies with the residues of hydrophobic pocket surrounding aromatic ring.

3D-QSAR Pharmacophore Model

The interaction generation protocol in Discovery Studio molecular modeling program³⁸ provides the pharmacophore features of the active site of a protein. InhA predominantly displays hydrophobic features at the active site³⁹. InhA substrate-competitive inhibitors design often exploits the pocket flexibility because of the high mobility of the Tyr158, Phe149 side chains and the substrate-binding loop (Thr196-Gly208)⁴⁰

Generation and validation of 3D-QSAR pharmacophore

InhA inhibition 3D-QSAR pharmacophore was generated from the active conformation of 12 TS PPZ1-12 and assessed by 3 VS PPV13-15 covering a large range of experimental activity (160 - 51000 nM) spanning more than two orders of magnitude. The generation process was carried out into three main steps: constructive, subtractive and optimization³⁹ as described earlier⁴⁰. During the constructive phase, PPZ1 alone was retained as the lead because only the activity of PPZ1 fulfilled the threshold criterion (IC₅₀^exp ≤ 5/4 × 160 nM) and used to generate the starting PH4 feature. In the of the threshold criterion subtractive phase, compounds for which: IC₅₀^exp > 160 × 10^3.5 nM =505 968 nM were considered inactive. Considering this criterion, none of the training set PPZx was inactive and no starting PH4 features were removed. Finally, during the optimization phase the score of the pharmacophoric hypotheses was improved. Hypotheses were scored according to errors in activity estimates from regression and complexity via a simulated annealing approach. At the end of the optimization, the top scoring 10 of pharmacophore hypotheses were kept, all displaying five points features.To be statistically significant a hypothesis has to be as close as possible to the fixed cost and as far as possible from the null cost. For the set of 10 hypotheses the difference ∆ ≥ 506.68, which attests high quality of the pharmacophore model. The standard indicators such as the RMSD between the hypotheses ranged from 1.75 to 2.60and the squared correlation coefficient (R²) falls to an interval from 0.98 to 0.96. The first PH4 hypothesis with the closest cost (51.9) to the fixed one (32.01), best RMSD and R² was retained for further analysis. The statistical data for the set of hypotheses (costs, RMSD, R²) are listed in Table 4. The configuration cost (9.65 for all hypotheses) far below 17 confirms this pharmacophore as a reasonable one.The regression equation for pKi_exp vs. pK_i^pre estimated from Hypo1: =0.9961 × + 0,0226 (n=12. R²=0.97. R²_xv=0.96. F-test=294. σ=0.18, α > 98%) is also plotted on Figure 4 (E). Therefore, the PH4 is good potentially to choice the new PPZ analogs. These parameters are in accordance with the OECD QSAR guidelines⁵. To assess the predictive power of the pharmacophore model, we calculated the ratio between the activities predicted by the PH4 model and those observed experimentally (pIC₅₀^pre/pIC₅₀^exp) for the validation set (PPV13-15). The following ratios were obtained PPV13: 0.95, PPV14: 1.08 and PPV15: 1.07.

All ratios were close to one, demonstrating the strong predictive power of this regression for the optimal PH4 model. Another evaluation of hypothesis 1 is the mapping of the PH4 binding mode in the 3D QSAR (Figure 4) of the most active PPZ1. We can carry out computational design and selection of new PPZ analogs with elevated inhibitory potencies against MtInhA based on a strategy using the noticeable presence of the hydrophobic features included in the best pharmacophore model.

Virtual Screnning

In silico screening of a virtual (combinatorial) library can lead to hit identification as it was shown in our previous works on inhibitors design³⁸.

   Virtual library

An initial virtual library (VL) was generated by substitutions small fragments (R₁ to R₅) on the aromatic ring of the PPZ scaffold based on structural information gathered³⁹ (Table 5). During the virtual library enumeration all R-groups listed in Table 5 were attached to in positions R₁ to R₅ of the PPZ scaffold to form a combinatorial library of the size: 

R₁ × R₂ ×R₃×R₄×R₅= 6×23×30×15×5=310,500 analogs.

This initial diversity library was generated from building blocks (chemicals) listed in the databases of available chemicals^[i]. To design a more focused library of a reduced size and increased content of drug-like molecules, we have introduced a set of filters and penalties such as the Lipinski rule-of-five, which helped to select smaller number of suitable PPZs that could be submitted to in silico screening. This focusing has reduced the size of the initial library to 19,044 analogs, 6% of its original number size.

Virtual screening of library of PPZs

The focused library of 19044 analogs was further screened for molecular structures matching the 3D-QSAR PH4 pharmacophore model Hypo1 of InhA inhibition. 60 PPZs mapped to at least 4 pharmacophoric features, 50 of which mapped to at least 5 features of the pharmacophore. These best fitting analogs (PH4 hits) then underwent complexation QSAR model screening. 

The computed GFE of InhA-PPZx complex formation, their components and predicted half-maximal inhibitory concentrations IC₅₀^pre calculated from the correlation equation B (Table 3), are listed in Table 6.

Analysis of novel PPZs inhibitors

The design of virtual library of novel analogs was guided by structural information retrieved from the PPZx active conformation and was used for the selection of appropriate substituents at position R₁, R₂, R₃, R₄ and R₅). In order to identify which substituents, lead to new inhibitor candidates with the highest predicted potencies towards the InhA of Mt, we have prepared histograms of the frequency of occurrence of R₁, R₂, R₃, R₄ and R₅ among the 50 best fit PH4 hits (Figure 6). The histograms show that the R₁groups 1 and 4 were represented respectively with the highest frequency of occurrence (10) and (27) among the 50 hits; the R₂ groups:  12 (5); 22 (6); 20 (11); R₃ groups :  7, 29, (6) and 21 (9); R₄ groups : 1 (5) and 2, 7 (4) and 12, 13 (6) and 8 (7);  R5 groups : 1(12) and 2(7) and 3 (30). The top ten scoring virtual hits namely analogs are: 1-21-7-2-4 (IC₅₀^pre=2100 pM), 2-12-7-12-4 (4510 pM), 2-20-29-7-4 (360 pM), 4-1-7-9-4 (1010  pM), 4-8-8-1-4 (910 pM), 4-9-6-8-4 (700 pM), 4-8-27-12-1 (4460 pM), 4-6-29-7-4 (1130 pM), 4-15-7-6-1(6980 pM) and 4-20-9-13-4 (7810 pM). These analogues include mostly the following substituents at R₁ position: 1, 2, 4; R₂ position: 21, 12, 20, 18, 9, 6, 15; R₃ position : 7, 29, 8, 6, 27, 7, 9; R4 groups : 2, 12, 7, 9, 1, 8, 6, 13; R5  groups :  4, 1.

Due to amino acid composition of the larger hydrophobic pocket, all the R-groups display preferences for shorter aliphatic building blocks as showed the Table 5. 

The substitutions in R₁ to R₅ positions of PPZs led to an overall increase of affinity of InhA binding as exemplified by the inhibitory potencies of majority of new designed analogs. The best designed benzamide PPZ 2-20-29-7-4 displays predicted half-minimal inhibitory concentration of IC₅₀^pre=360 pM that is more than 100-times lower than that of the most active compound of the TS, namely the PPZ1 with IC₅₀^exp=160 nM, Figure 5.

ADME profiles of designed PPZs

Pharmacokinetic profile obtained of InhA inhibitors still requires increased research. Presented in Table 7, the ADME of our new analogs, were described earlier by the QikProp program²⁴ taken from the method of Jorgensen⁸. The fundamental principles of this method are described previously³⁹. Our best analogs are compared with that of drugs used on the market to treat tuberculosis disease (Table 7).

DISCUSSION

A training set of 12 PPZs and validation set of 3 PPZs (Table1) were selected from a heterogeneous   series of InhA inhibitors for which experimentally determined inhibitory activities were available from a single laboratory³⁵. Both were obtained by synthesize from the 1-(9H-fluoren-9-yl)-piperazine. The first one is from the synthesis bearing modifications around the carbonyl hydrogen bond acceptor using 1-(9H-fluoren-9-yl)-piperazine and the second group were from direct sulfonylation. The whole series was obtained by substitutions at five positions of the aromatic ring of the other side of the sulfonyl and carbonyl surrounded by hydrophobic residues constituted of residues constituted of residues Pro 99, Gly 104, Met 103, Tyr 158, Phe 149 and Met 161, Met 98, Ala 198 of Genz-10850 (PPZ1) as shown in Table1. Their experimental inhibitory concentrations IC₅₀^exp30 cover a concentration range sufficiently wide to serve well for building of a reliable QSAR model of InhA inhibition. Accord to the PPZ8 According to the structures of PPZ8 and PPZ5 studied, the presence of the methyl group at the C2 position of the aryl moiety causes steric hindrance due to the restricted space in the cavity formed by Met103, Tyr158, and Met161 residues of MtInhA⁴¹. In order to identify structural modifications of the aromatic ring leading to increased binding affinity of PPZs to InhA of MTb we have carried out detailed analysis of interactions in a series of InhA-PPZs complexes with help of the complexation QSAR model. The first step of this analysis aimed at obtaining insight into InhA active-site interactions by performing the interaction energy breakdown into contributions from individual residues filling the hydrophobic pocket displayed on Figure 2 for the most active inhibitor PPZ1, moderate active (PPZ5 and PPZ8) and the lowest (PPZ12) activity, Table 2⁴³. Figure 3 showed that there no too difference concerning the interaction Energy (IE) between the three classes of activity. To obtain the best analogs PPZ we proceed by a combinatory library. As the pocket containing the R₁ groups and formed by Met103, Tyr158, and Met161is too small we put their short substituents. We noticed that the analogs dominated by small fragment like halogenure and hydroxyd have the most frequency of occurrence. The convenable size of the fragment chosed lead to obtain the best analogs considerably most actif than the most active of the training and the validation sets. The most four virtual active analogs were PPZ 2-20-29-7-4 (IC₅₀^pre=360 pM), PPZ 4-9-6-8-4(IC₅₀^pre=700 pM), PPZ 4-8-8-1-4 (IC₅₀^pre= 910 pM) and PPZ 4-1-7-9-4 (IC₅₀^pre=1010 pM) with favorable predicted pharmacokinetic profile than the older currently used drugs. We can suggest them for synthesis.

CONCLUSIONS AND RECOMMANDATION

In this work the crystallographic structure of the InhA-PPZ1 (1P44) complex and the structural properties of the Piperazine derivatives identified by MarianeRotta et al.²² as a potential antituberculosis agent and whose target is InhA enabled us to develop a QSAR complexation model capable of explaining more than 92% of the variation in the experimental inhibitory activity of Piperazine derivatives by the Gibbs free energy of formation of the InhA-PPZx complex. Following this QSAR model, we obtained a 3D-QSAR PH4 pharmacophore model using a training set of 12 PPZs and a validation set of 3 PPZs with known inhibitory activities⁴². The visual analysis and calculation of the interactions between InhA and PPZs in the active site of the enzyme guided us in the design of a virtual combinatorial library of new PPZ analogs with a substitution on the scaffold at the position R₁ to R₅ on the aromatic ring. The virtual library obtained was first focused by considering Lipinski's five rule and then screened by the 3D QSAR pharmacophore identified during chemical space exploration around R₁to R₅ positions novel PPZs analogsby the QSAR complexation modelwith predicted picomolarMtInhA inhibitory potencies PPZ 2-20-29-7-4 (IC₅₀^pre=360  pM), PPZ 4-9-6-8-4(IC₅₀^pre=700 pM), PPZ 4-8-8-1-4 (IC₅₀^pre=910 pM) and PPZ 4-1-7-9-4 (IC₅₀^pre=1010 pM) all display also favorable pharmacokinetic profiles compared to current antituberculotics. We believe that they are worth synthesizing and evaluating.

ACKNOWLEDGEMENTS

The authors would like to thank the Laboratory of Fundamental and Applied Physics at NANGUI ABROGOUA University, in Côte d’Ivoire, for providing the facilities necessary for this work.

AUTHOR’S CONTRIBUTION

Kouman C: performed the complexation study, PH4 pharmacophore generation, interaction energy analysis, the PH4-based VL searching and the analogues evaluation. Kouassi F: established the first preliminary complexation model in order to confirm the feasibility of this work. Fagnidi H:performed the VL generation and focusing and wrote original draft, methodology, investigation. Kéita M: editing, review. Fofana I: performed the VL generation and focusing. Allangba G: formal analysis. Megnassan E: writing, review, and editing, data curation. All authors read and approved the final manuscript for publication.

DATA AVAILABILITY

Data will be available on request to anyone from the correspondence author.

CONFLICT OF INTERESTS

The authors declared no conflict of interests

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