croniconmohit kumar, mohd salman and sisir nandi* division of pharmaceutical chemistry, global...

CroniconO P E N A C C E S S EC MICROBIOLOGY

Research Article

QSAR of Imidazo[1,2-a]pyridine Ethers as Mycobacterial Adenosine Triphosphate Synthase Inhibitors against Tuberculosis

Mohit Kumar, Mohd Salman and Sisir Nandi*

Division of Pharmaceutical Chemistry, Global Institute of Pharmaceutical Education and Research, Affiliated to Uttarakhand Technical Uni-versity, Kashipur, India

*Corresponding Author: Sisir Nandi, Division of Pharmaceutical Chemistry, Global Institute of Pharmaceutical Education and Research, Affiliated to Uttarakhand Technical University, Kashipur, India.

Citation: c., et al. “QSAR of Imidazo[1,2-a]pyridine Ethers as Mycobacterial Adenosine Triphosphate Synthase Inhibitors against Tuber-culosis”. EC Microbiology 8.6 (2017): 280-293.

Received: May 25, 2017; Published: June 28, 2017

AbstractTuberculosis (TB) is one of the major health issues in the universe. There are nearly 10 million new cases of tuberculosis of

which 1.8 million are died each year as per the report of World Health Organization (WHO). MTB infection may produce multi drug resistance (MDR) and MTB with HIV may produce extensively drug resistance (XDR) tuberculosis which is very difficult to control. Incredible research has been carried out to treat MDR-TB and XDR-TB. It was reported that Imidazo[1,2-a]pyridine ethers (IPE) having selective and potent Inhibitory activities of Mycobacterial Adenosine Triphosphate synthesis was given for the treatment of MDR-TB. Therefore, an attempt has been made in the present study to quantify the essential structural features of IPE through quantitative structure activity relationship (QSAR) utilizing theoretical molecular descriptors. The developed models were validated statistically. Such validated QSAR model with acceptable parameters capture important descriptors responsible for producing bio-chemical mechanism of IPE compounds against ATP synthase target. Such model can be used for further modeling of IPE congeneric compounds. Presence of most important parameter ETA_ BetaP explains electronic features of the molecules related to the molecular size. An increase in the molecular size of the molecule may produce more affinity towards the target. Electronegativity is a measure of the tendency of an atom to attract a bonding pair of electrons. In this case electronegativity is an urgent parameter to produce lead like compound. Presence of more electronegative group may the potency of the deliberated compounds. The hydrophobic substitu-ent is more favored in the ring for potency improvement.

Keywords: M. tuberculosis; Imidazo[1,2-a]pyridine Ethers; Inhibitors of Mycobacterial Adenosine Triphosphate Synthesis; QSAR, Theo-retical Molecular Descriptors, Anti-Tubercular Drug Design

AbbreviationsMtb: M. tuberculosis; MDR-TB: Multi Drug Resistance Tuberculosis; XDR-TB: Extensively Drug Resistance Tuberculosis; IPE: Imidazo[1,2-a]pyridine Ethers; QSAR: Quantitative Structure Activity Relationship; ATP: Adenosine triphosphate

IntroductionTuberculosis (TB) is a mortal infectious disease that can affect one or more parts of the body including lungs, lymph nodes, meninges,

urinary genital tract, bone and joints. It is caused by a mycobacterial organism, the Mycobacterium tuberculosis or tubercle bacillus. De-spite BCG (Bacillus Calmette−Guerin) vaccine and the combined Directly Observed Therapy Short (DOTS) course with first-line including isoniazid (H), ethambutol (E), rifampicin (R), pyrazinamide (Z) for the 2 months followed by a continuation phase of 4 months treatment with isoniazid and rifampin or second-line antibiotics chemotherapy proposed by WHO [1-4]. If patients take prescribed medicines, TB can be cured and prevented. Missing of any prescribed doses and interrupted treatment may produce emergence of multidrug-resistant (MDR) strains of Mtb and Mycobacterium-Avium Complex tuberculosis co-infected with HIV may produce extensively drug resistance

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Citation: Sisir Nandi., et al. “QSAR of Imidazo[1,2-a]pyridine Ethers as Mycobacterial Adenosine Triphosphate Synthase Inhibitors against Tuberculosis”. EC Microbiology 8.6 (2017): 280-293.

(XDR) tuberculosis which badly attacks on the vulnerable immune system of the patients [5-6]. The global survey for the period 2002 - 2004 has found 20% TB isolates to be MDR, out of which 2% were XDR. The XDR-TB is virtually untreatable; mortality is high, particularly among HIV positive patient [7]. To treat relapse and interrupted cases of MDR-TB, initial phase all 5 first line drugs including isoniazid (H), ethambutol (E), rifampicin (R), pyrazinamide (Z) and streptomycin (S) are given for 2 months followed by 4 drugs (HRZE) for an-other month continued by 5 months treatment with H, R and E [8]. Depending on the patient’s condition, 12-24 months long combination therapy is given for complete eradication of MDR-TB. But extensive combination treatment with 2nd line drugs is not so safe. Therefore, researchers are devoted to discover new least toxic tuberculocidal chemical entities to reduce the duration of treatment and to improve the efficacy of TB treatment as well as to make an impact on the global war against tuberculosis.

Treatment of multidrug-resistant tuberculosis (MDR-TB) cases is challenging because only two new anti-tubercular drugs were ap-proved and released within last 50 years time phase. Bedaquiline (TMC207) having diarylquinoline scaffold which is being in the phase IIb trials and showed tremendous activity against MDR-TB. Delamanid (OPC-67683) is the first drug of the nitroimidazole class to enter clinical practice. Similarly, to bedaquiline results of phase IIb studies showed increased sputum-culture conversion at 2 months and bet-ter final treatment outcomes in patients with MDR-TB [9]. It acts by inhibiting the biosynthesis of methoxy mycolic acid and ketomycolic acid which are mycobacterial cell wall components. Bedaquiline inhibits mycobacterial ATP synthase with a high selectivity index. The successful clinical trials of bedaquiline have resulted in an increased exploration to identify additional inhibitors of mycobacterial ATP synthase. Several attempts made in this direction have resulted in the identification of a few compounds which inhibit the process of ATP synthesis in mycobacteria [10-11]. Potent mycobacterial ATP synthesis inhibitors including 2,4-diaminoquinazolines and aminopyrazo-lopyrimidines were studied [12].

Q203, an imidazopyridine compound, which is currently in phase I clinical trials, has robust activity against latent Mtb and is a promis-ing option for addition to MDR-TB treatment regimens. It targets the cytochrome b subunit (QcrB) of the cytochrome bc1 complex. This complex is an essential component of the respiratory electron transport chain of ATP synthesis. Q203 causes a rapid depletion of intracel-lular ATP at an IC50 of 1.1 nM and interrupts ATP homeostasis in dormant Mtb at an IC50 of 10 nM. Both of these values are better than bedaquiline’s measures, and they explain Q203’s excellent killing profile in chronic Mtb infection models. Therefore, imidazopyridine scaffold was selected as a potent template for the generation of new hits against MDR-TB [13]. The imidazopyridine QcrB inhibitors [30] were discovered around the same time by several groups applying phenotypic MIC screening approaches of pharmaceutical company screening collections [14].

In order to shortlist specific and selective inhibitors of mycobacterial ATP synthesis pathway, prioritized hit compounds were de-signed by Tantry et al. and screened through high throughput screening considering imidazo[1,2-a]pyridine Ethers as scaffold. Further hit triage included structure activity relationship (SAR) studies to check the potential for chemical diversification and confirmation of activity through synthesis of hits and testing of biological activity against mycobacterial tubercular ATP synthase [15]. But there is no QSAR reported considering this data set. Therefore, it is our target to explore essential structural requirements of IPE derivatives utiliz-ing computed descriptors for further modeling of highly active hybrid inhibitors mycobacterial adenosine triphosphate synthesis against tuberculosis.

Computational Methods

Anti-tubercular Activity Data

A total number of 22 Imidazo[1,2-a]pyridine ether derivatives (Table 1) showing maximum inhibitory effect on mycobacterial adenos-ine triphosphate synthesis has been collected from the literature [15]. These compounds were tested in the mycobacterial ATP synthesis assay (Myc_ATPS IC50) and in the anti-mycobacterial assay (M. tuberculosis MIC) for assessing the potency. A number of IPE compounds was generated by incorporating various substitutions on imidazo[1,2-a]pyridine followed by optimization of the ether side chain. It was observed that IPE scaffold formulated by fusion of imidazo and pyridine rings in the position of bridgehead nitrogen in [1,2-a] format are


QSAR of Imidazo[1,2-a]pyridine Ethers as Mycobacterial Adenosine Triphosphate Synthase Inhibitors against Tuberculosis282

essential for producing both ATP synthesis inhibition and M. tuberculosis MIC. Target of IPE compounds is to inhibit mycobacterial ATP synthase which is a ubiquitous enzyme in energy metabolism due to its involvement in the generation of sufficient amount of ATP and/or in generating a proton motive force (PMF) in mycobacteria during adverse conditions of low oxygen environment and nutrient deficiency.



284

Table 1: Biological activity data.

Computation structural predictors

All the structures of 22 Imidazo[1,2-a] pyridine ethers derivatives were drawn using 2D Chemdraw. The drawn structures were then converted into 3D modules and the geometries of all compounds were fully optimized using MM2 force field considering the default conversion procedure implemented in Chem3D Ultra [16]. All the 3D modules were taken into consideration for the computation of struc-tural predictors (Table 2) by using PaDEL Descriptor Computation software [17]. PaDEL Descriptor is an open source molecular property calculation freeware readily available at http://padel.nus.edu.sg/software/padeldescriptor. It can calculate a number of 1875 descrip-tors including 1444 1D, 2D descriptors and 431 three dimensional (3D) descriptors and 12 types of fingerprints (total 16092 bits). The descriptors and fingerprints are calculated using The Chemistry Development Kit. Structural predictors are the theoretical molecular descriptors which are the numerical representation of molecule, obtained by the principles of graph theory to molecular structure. It en-codes molecular architecture and quantifies such aspects of molecular structure as size, shape, symmetry, complexity, branching, cyclist, stereo electronic character, etc. and plays a crucial role in QSAR and molecular design [18-21].

http://padel.nus.edu.sg/software/padeldescriptor

285



286





287



289

Table 2: Descriptors used in current study.

Statistical model generation

A total number of 1875 descriptors were computed and sooner than the QSAR model development, the descriptor set is reduced into 990. Descriptors with perfectly constant and highly inter-correlated descriptors were removed considering variance and correlation coefficient cut-off values of 0.0001 and 0.99 using V-WSP algorithm [22] incorporated into vWSP module of NanoBRIDGES software [23]. As the number of structural predictors greatly exceeds the number of compounds, selection of important predictors is necessary for the QSAR modeling. Genetic algorithm-multiple linear regression (GA-MLR) has been used for the development of QSAR model considering reduced predictors data after variable selection by genetic algorithm method.

Genetic algorithm is a stochastic optimization method based on the principles of Darwin’s evolution theory. It is a very powerful tool to explore many solutions to a large problem space [24]. In the first step, a different combination of genes (chromosomes) of initial popu-lation is generated. These genes may be numerically encoded as molecular descriptors. The fitness of each chromosome, which reflects the quality of the solution, is evaluated. Fitness function is calculated by considering the following default parameters as modeled in NanoBridges software: Total number of iterations = 100, equation length = 5, crossover probability = 1, mutation probability = 0.5, initial number of equations generated = 100, number of best equation selected=20, smoothing parameters (LOF calculations) = 10. A population of 100 different random combinations of the calculated molecular descriptors is generated. A QSAR model is developed based on each parent combination of descriptors for the entire data set using MLR. Fitness function of each model is formulated in term of Q2

Loo or R2 where, Q2

Loo represents cross-validated R2. Values of Q2Loo and R2 are calculated by the standard equation [25].

R2 and Q2 of a model are calculated byR2 = 1- [∑ (Yobs - Ycalc)2 / ∑(Yobs – Ῡ)2] and Q2

Loo = 1- [∑ (Yobs – Ypred)2 / ∑(Yobs – Ῡ)2]

Where Yobs and Ypred indicate observed and predicted activity values, respectively, and Ῡ indicates mean activity value of training mol-ecules. A model is considered acceptable when the value of Q2

Loo exceeds 0.5.

R2pred = 1- [∑ (Ypred test – Ytest)2 / ∑(Ytest – Ῡtraining)2]

where, Ypred test and Ytest indicate predicted and observed activity values respectively of the test set compounds and training indicates mean of observed activity values of the training set. For a predictive QSAR model, the value of R2

pred should be more than 0.5 [26].

Where, r2 and r2o are squared correlation coefficient between the observed (Y axis) and predicted (X axis) activity values of the test set

with and without intercept, respectively. r2m value must be greater than 0.5 to have a significant model [27].

Result and Discussion

Pivotal role of ATP synthase is to generate ATP and proton motive force for producing virulence viability of mycobacteria tuberculosis. IPE class of compounds can inhibit ATP synthase. Therefore, in the present study a number of 2D-QSAR models have been developed for the IPE compounds utilizing 1d, 2D and 3D descriptors solely computed from the structures of Imidazo[1,2-a]pyridine ethers by the state-of-the-art chemometric approach . The impact of the different computed descriptors on Mycobacterium Adenosine Triphosphate (ATP) synthesis inhibition has been captured by the various validated training QSAR modeled parameters. A number of training models were generated by dividing the data set into different test and training set. Test and training set variations are done taking 20 - 37% and 63 - 80% of the total observation. The training models were validated by predicting anti-tubercular activities of the test set IPE compounds. In this study, three models showing best results in terms of Mycobacterial Adenosine Triphosphate (ATP) synthesis inhibition were reported in Table 3. Test set of model 1 consists of compound number 1, 9, 11, 12, 13, 14, 17, 20 and it can produce R2, Q2

Loo, R2pred and r2

m (test) values of 0.971, 0.936, 0.741 and 0.708 respectively.

Model-1Log (MIC) = -21.91132(+/-1.72248) + 0.69188(+/-0.0624) LipoaffinityIndex +11.51192(+/-1.17571) ETA_BetaP - 0.03735(+/-

0.01559) RDF65e - 0.10612(+/-0.07916) C1SP2

N=14, R2= 0.971, Q2Loo= 0.936, R2

pred= 0.741, r2m (test):0.708

Parameters Physical interpretationLipoaffinity Index Lipoaffinity index

ETA_BetaP A measure of electronic features of the molecule relative to molecular sizeRDF65e Radical distribution function- 6.5/ weighed by atomic Sanderson electronegativityC1SP2 Doubly hound carbon bound to one other carbon

Model-2Log (MIC) = 2.75479(+/-0.66055) - 0.12433(+/-0.23141) VPC-4 -11.78435(+/-0.90713) GATS1i +0.00525(+/-0.00053) ATS6e +

10.46725(+/-1.3293) GATS1e

N=16, R2= 0.960, Q2Loo= 0.913, R2

pred= 0.759, r2m (test): 0.545.

Parameters Physical interpretationVPC-4 Valence path cluster, order 4ATS6e Broto-Moreau autocorrelation of lag 6 weighted by Sanderson electronegativity

GATS1e Geary autocorrelation of lag 1 weighted by Sanderson electronegativityModel-3

Log (MIC) = -40.71622(+/-3.60928) + 5.65877(+/-0.58453) piPC2 - 0.88177(+/-0.04376) nHBAcc + 7.0347(+/-1.23272) Sp-Max8_Bhe - 0.11478(+/-0.10545) VP-2

N=15, R2=0.983, Q2Loo= 0.961, R2

pred= 0.743, r2m (test): 0.623.



290

Further, external predictability of the generated QSAR models was scrutinized by calculating modified r2 (r2m), average modified r2 ( )

and delta modified r2 (∆r2m) respectively which are given as

r2m = r2 (1 - |√(r2-r0

2 |)

Parameters Physical interpretationpiPC2 Pathcount descriptor

nHBAcc Number of hydrogen bond acceptors (using CDK HBondAcceptorCountDescriptor algorithm)SpMax8_Bhe Burden modified eigenvalues

VP-2 Valence path, order 2

Table 3: Best QSARs on IPE compounds showing Mycobacterial Adenosine Triphosphate (ATP) synthesis inhibition.

Test set of model 2 consists of compound number 2, 4, 7, 12, 13 and 14. This model can explain a predict 96% and 91.3% of variances for the inhibition of mycobacterial ATP synthase of the studied compounds. This model can also produce rm

2 value of 0.545 and 75.9% external test set predictability whereas test set of model 3 comprises of 31% of the total data excluding compound number 9 in compare to test data of model 1. It produces R2, Q2

Loo, R2pred and r2

m (test) values of 0.983, 0.961, 0.743 and 0.623 respectively.

ConclusionFollowing predictors including Lipoaffinity index, ETA_BetaP, GATS1e, piPC2 and SpMax8_Bhe with positive regression coefficients

as given in Table 4 are significant descriptors captured in the training QSAR models 1-3 responsible for the inhibition of Mycobacterium adenosine triphosphate (ATP) synthase.

S.no. Name of important descriptors

Physical interpretation Regression coefficient

1 Lipoaffinity index Lipoaffinity index 0.691882 ETA_BetaP A measure of electronic features of the molecule

relative to molecular size11.51192

3 GATS1e Geary autocorrelation of lag 1 weighted by Sanderson electronegativity

10.46725

4 SpMax8_Bhe Burden modified eigen values 7.03475 piPC2 Path count descriptor 5.65877

Table 4: Important descriptors.

In Table 4, it was predicted that ETA_BetaP contributes highest positive inhibition towards ATP synthesis. It is a measure of electronic features of the molecule relative to molecular size. It has electronic and electrostatic effect by producing charge surface area in the mol-ecule. So, presence of some groups like F, Br, CF3 that may increase electronic and electrostatic charge relative to molecular size are favor-able at IPE nucleus for deriving active congeners. The possible reason behind this is that fluorine or bromine atom has more electronega-tivity and its van der waals volume is more, so ligand binding affinity toward receptor surface cavity will be more. Geary autocorrelation of lag 1 weighted by Sanderson electronegativity (GATS1e) and Burden modified eigen values (SpMax8_Bhe) with positive regression coefficients indicate the relative propensity of electron cloud of an atom or molecule which may further produce aromaticity. Therefore, substitution of more aromatic groups like pyridyl in the IPE scaffold may increase inhibition towards proton motive force of mycobacte-ria. It is proved by fusion of pyridyl moiety with the imidazole in the position of bridgehead nitrogen in [1,2-a] format. piPC2 denotes path count descriptor that signifies total number of fragments in the molecule. Increase in the fragmentation or branching may increase mo-lecular surface area in the substituent which may increase the biological activity. The hydrophobic and aromatic substituent as denoted by the presence of Lipoaffinity index in the modeled parameter is more favored in the scaffold to produce more potent congeners. In this way, modeled parameters pave direction for the design of the more potent congeneric IPE compounds. The above study can schemati-cally represent the following desired property of the highly active molecule towards inhibition of mycobacterial ATP synthase (Figure 1).



N

N

R3

OR2

R1

Essential ScaffoldAromaticity

and Electron cloud

F, Br, CF3 (Electronegativity and increase binding affinity towards ATP

synthase)

F, Br, CF3 (Electronegativity and increase binding affinity towards ATP

synthase)

More branched ether moeity withhydrophobic and Aromatic substituents are favourable

Figure 1: Crucial features for the design of highly active congeneric ligands.

Acknowledgements

SN wish to express sincere gratitude to Dr. Anil Kumar Saxena, Chairman, GIPER and Ex-Chief Scientist and Head, Medicinal and Pro-cess Chemistry Division, CSIR Central drug Research Institute, India for constructive suggestions on realistic QSARs in this work. Authors are sincerely thankful to Professor Kunal Roy, Drug Theoretics and Chemoinformatics Lab, Department of Pharmaceutical Technology, Jadavpur University, India for providing “NanoBRIDGES” software. Mohit acknowledges Mr. Rajeshwar KK Arya, Department of Pharma-ceutical Sciences, Kumaun University, Bhimtal Campus, India, for the evaluation of this dissertation.

Conflict of InterestDeclare if any financial interest or any conflict of interest exists.

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