Identification of some novel pyrazolo[1,5-a]pyrimidine derivatives as InhA inhibitors through pharmacophore-based virtual screening and molecular docking

Article abstract of DOI:10.1080/07391102.2018.1465852

The InhA inhibitors play key role in mycolic acid synthesis by preventing the fatty acid biosynthesis pathway. In this present article, Pharmacophore modelling and molecular docking study followed by in silico virtual screening could be considered as effective strategy to identify newer enoyl-ACP reductase inhibitors. Pyrrolidine carboxamide derivatives were opted to generate pharmacophore models using HypoGen algorithm in Discovery studio 2.1. Further it was employed to screen Zinc and Minimaybridge databases to identify and design newer potent hit molecules. The retrieved newer hits were further evaluated for their drug likeliness and docked against enoyl acyl carrier protein reductase. Here, novel pyrazolo[1,5-a]pyrimidine analogues were designed and synthesized with good yields. Structural elucidation of synthesized final molecules was perform through IR, MASS, ¹H-NMR, ¹³C-NMR spectroscopy and further tested for its in vitro anti-tubercular activity against H37Rv strain using Microplate Alamar blue assay (MABA) method. Most of the synthesized compounds displayed strong anti-tubercular activities. Further, these potent compounds were gauged for MDR-TB, XDR-TB and cytotoxic study.

Full text of DOI:10.1080/07391102.2018.1465852

Journal of Biomolecular Structure and Dynamics
ISSN: 0739-1102 (Print) 1538-0254 (Online) Journal homepage: http://www.tandfonline.com/loi/tbsd20
Identification of some novel pyrazolo[1,5-
a]pyrimidine derivatives as InhA inhibitors
through pharmacophore-based virtual screening
and molecular docking.
Palmi Modi, Shivani Patel & Mahesh T Chhabria
To cite this article: Palmi Modi, Shivani Patel & Mahesh T Chhabria (2018): Identification of some
novel pyrazolo[1,5-a]pyrimidine derivatives as InhA inhibitors through pharmacophore-based
virtual screening and molecular docking. , Journal of Biomolecular Structure and Dynamics, DOI:
10.1080/07391102.2018.1465852
To link to this article: https://doi.org/10.1080/07391102.2018.1465852
Accepted author version posted online: 17
Apr 2018.
Submit your article to this journal
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=tbsd20

1
Publisher: Taylor & Francis
Journal: Journal of Biomolecular Structure and Dynamics
DOI: http://doi.org/10.1080/07391102.2018.1465852
Identification of some novel pyrazolo[1,5-a]pyrimidine derivatives as InhA
inhibitors through pharmacophore-based virtual screening and molecular
docking.
Palmi Modia,b, Shivani Patela,c, Mahesh T Chhabriaa*
a,*Department of Pharmaceutical Chemistry, L. M. College of Pharmacy, Ahmedabad –
380009, Gujarat, India
bDepartment of Pharmacy, Dharmsinh Desai University, Nadiad – 387001,Gujarat, India
cDivision of biological and life sciences, Ahmedabad University, Ahmedabad – 380009,
Gujarat, India
*Corresponding author
Dr. Mahesh Chhabria
Principal,
L. M. College of Pharmacy, Ahmedabad
Gujarat, India
E-mail: mahesh.chhabria@rediffmail.com
Abstract:
The InhA inhibitors play key role in mycolic acid synthesis by preventing the fatty acid
biosynthesis pathway. In this present article Pharmacophore modelling and molecular
docking study followed by in silico virtual screening could be considered as effective strategy

2
to identify newer enoyl-ACP reductase inhibitors. Pyrrolidine carboxamide derivatives were
opted to generate pharmacophore models using HypoGen algorithm in Discovery studio 2.1.
Further it was employed to screen Zinc and Minimaybridge databases to identify and design
newer potent hit molecules. The retrieved newer hits were further evaluated for their drug
likeliness and docked against enoyl acyl carrier protein reductase. Here, novel pyrazolo[1,5-
a]pyrimidine analogues were designed and synthesized with good yields. Structural
elucidation of synthesized final molecules was perform through IR, MASS, 1H-NMR, 13C-
NMR spectroscopy and further tested for its in-vitro anti-tubercular activity against H37Rv
strain using Microplate Alamar blue assay (MABA) method. Most of the synthesized
compounds displayed strong anti-tubercular activities. Further, these potent compounds were
gauged for MDR-TB, XDR-TB and cytotoxic study.
Key words:
Enoyl acyl carrier protein reductase (InhA); pharmacophore modelling; virtual screening;
molecular docking
Abbreviations:
TB; Tuberculosis; MDR-TB; Multi-Drug Resistant Tuberculosis; XDR-TB; Extensively
Drug Resistant Tuberculosis; InhA; Enoyl acyl carrier protein reductase; PDB; Protein Data
Bank; RMSD; Root Mean Square Deviation; RMSF; Root Mean Square Fluctuations; HBA;
Hydrogen Bond Acceptor; RA; Ring Aromatic; HY; Hydrophibic; ADMET; Adsorption,
Distribution, Metabolism, Disstribution, Toxicity.

3
Introduction
Tuberculosis (TB) is an airborne infectious disease caused by different species of
Mycobacterium tuberculosis. It is one of the greatest health hitches with gradually increasing
the mortality and morbidity (Tsukamura et al., 1978). Although effective chemotherapy has
been placed over 50 years, world health organisation has declared tuberculosis “a global
health emergency” which is the leading cause of death (Dye et al., 2009). Worldwide
estimated 9.2 million people are infected with tuberculosis among them 4.9 million were
men, 3.2 million women and 1.0 million children (Williams et al., 2005). Also 1.2 million
new TB cases are living with HIV (Yang et al., 2006). There is one death from TB every 15
second (over two million per year). Now days, drug resistance strain of mycobacterium
species has narrowed down the conventional anti-tuberculosis therapy. Development of
resistance towards the first line drugs such as isoniazid (INH) and rifampicin is also
significant concern (Banerjee et al., 1994). Thus, there is an urgent need to develop newer
therapeutic entities with different mechanism of action as an anti-tubercular agent.
Pathogenicity of Mycobacterium tuberculosis is linked with its peculiar nature of cell
envelope which possesses two fatty acid synthase (FAS) pathways FAS-I and FAS-II. These
pathways are involved for synthesis of mycolic acids. Mycolic acids, the key constituent for
cell wall biosynthesis with 60-90 carbons of α-alkyl β-hydroxyl fatty acids (Takayama et al.,
2005). Enoyl-ACP reductase (InhA) of Mycobacterium tuberculosis participates by blocking
the fatty acid biosynthesis pathway (FAS II) (Asselineau et al., 1950). For the synthesis of
mycolic acid it utilises the NADH cofactor as a hydrogen bond donor to reduce the trans
double bond conjugated to the carbonyl group of long chain fatty acid (Zhang et al., 1993 and
Quemard et al., 1995). Substantial number of strains are unaffected to INH develop mutation
in KatG, a catalase-peroxidase enzyme with twofold activities peroxidase oxidizing to form
INH-NAD adduct at the 4th position and catalase (Zhang et al., 1992 and Zhao et al., 2006).
As there are limited numbers of drugs available to treat tuberculosis, there is a crucial
requirement to develop medicinally useful enoyl acyl carrier protein reductase (InhA)
inhibitors as newer anti-tubercular drug. So, it is a target of choice for the expansion of newer
anti-tubercular agents.
Recently in the past, several efforts have been made to discover novel InhA inhibitors
combined with in-silico virtual screening, molecular dynamics simulations and quantum
chemical (Mohsin et al., 2017) as well in-vitro analysis against Mycobacterium tuberculosis
H37Rv strain (Mohsin et al., 2017). But, only a few of them have shown the prominent
response to in-vitro anti-tubercular study. Based on the requirement for the potent anti-

4
tubercular agents, we have applied combination of ligand based pharmacophore modelling
and structure based molecular docking study to identify newer InhA inhibitors. Ligand based
pharmacophore strategy was applied with novel class of pyrolidine carboxamides as InhA
inhibitors (He et al., 2007). The best generated pharmacophore model was used to screen
database which retrieved structurally companionable potential hits. On the basis of virtual
screening we have designed newer class of pyrazolo[1,5-a]pyrimidine analogues. This newer
analogues were further evaluated for their drug likeness properties using QikProp
(Schrodinger Inc., USA). Molecular docking study was carried out using co-crystal structure
of enoyl-ACP reductase (PDB Code: 2H7M) to identify the essential binding modes. Anti-
tubercular activity of these synthesized compounds was performed through Microplate
Alamar Blue Assay (MABA) Method. Potent hit molecules from preliminary screening were
further opted for MDR-TB, XDR-TB and cytotoxicity study.
Materials and methods
Pharmacophore modelling
Dataset
Series of Pyrrolidine carboxamide derivatives with known biological activity were used as a
dataset to generate the pharmacophore model (He et al., 2006). One of the important criteria
for generation of pharmacophore model is that the activity data must be derived from the
same bioassay method, represented in same term and have wide range in biological activity.
Selected pyrrolidine carboxamide series has all these characteristics and thus selected for this
study. Structural diversity and biological activity with the 5-6 orders of magnitude were also
important criteria for the selection of the training set and test set (Table S1, Supplementary
information). Compounds which do not have activity in real number were omitted (Shalini et
al., 2017).
Generation of the pharmacophore model
This approach signifies the 3D pharmacophore design including chemical features derived
from the datasets of molecules which were previously reported as InhA inhibitors. In present
study, discovery studio 2.1 was used to construct and visualise the pharmacophore models
(He et al., 2006). All the Structures were optimised using CHARMm force field. Pooling
algorithm was employed to find out the best conformations. The “Best conformer generation”
method was carried with energy cut off 29kcal/mole and maximum 255 conformers were
generated.
Generation of 3D pharmacophore model was carried out in following three steps:

5
a) Selection of training set molecules with the activity span of 5-6 orders of magnitude to
generate 3D structures; b) Generation of the pharmacophore by selecting appropriate
pharmacophoric features. The pharmacophore was generated using HypoGen module by
considering the important features of enoyl-ACP reductase inhibitors. Hydrogen bond
acceptor (HBA), ring aromatic (RA) and hydrophobic (HY) were the well-defined features
for the generation of the pharmacophore model. The minimum numbers of features were
varied from 0 to 5 to achieve a statistically significant model; and c) Conformation search
was carried out to find novel entities form the databases (Rao et al., 2008). Further to achieve
final pharmacophore model selected features were refined and trimmed manually
accompanying the important residues with “Edit and cluster pharmacophore features”
protocol in the tool panel.
Pharmacophore validation
Pharmacophore validation was performed to check whether generated models are adequate to
predict the active compounds or not. It was collectively performed by evaluating the cost
analysis: fixed cost, total cost, null cost, configuration cost, Fischer randomization (known as
Y-scrambling) and external predictability of the model (ability to predict biological activity
of the test set compounds). In the cost analysis, fixed cost is the cost of theoretically ideal
pharmacophore model with representation of the simplest model to fit all the data perfectly.
Null cost presumes no statistically significant structure in the data and the experimental
activities are normally distributed around their mean (Mason et al., 2001). The weight cost,
error cost and configuration cost is sum of the total cost. The weight cost (sum of feature
weight) increases in a Gaussian form which should be deviated from its ideal value 2.0
(Mason et al., 2001). The error cost value increases with the increase in root mean square
difference between estimated and measured activity. The configuration cost is a fixed cost
signifies the complexity and entropy of the total assumed pharmacophoric space which
should always be less than 17 (Santo et al., 2005). For the statistical relevance of the model
Fischer’s randomization test was employed to revalidate the model. Activities of the training
set molecules were reassigned using Fischer’s randomization at statistical significance levels
of 95% to generate 19 random spread sheets. Further, the predictability of the model was
checked by 17 test set molecules and minimized using CHARMm forcefield in the Discovery
Studio. Mapping of the test set molecules was performed with “Ligand pharmacophore
Mapping” protocol using “Best Mapping only” and rigid “Fitting Method”. Rigid “Fitting
Method was applied for the test set molecules to achieve defined estimated biological activity
(IC50 in µM) by Hypo 1.

6
Virtual screening
Virtual screening is an efficient tool used to find chemically diverse and potential leads. Two
databases: MiniMaybridge and ZINC database were screened by employing validated
pharmacophore model as 3D query (Funk et al., 2004). The pharmacophore search results
were sorted according to fit value. The “Maximum omitted features” was set to 1. Hit
compounds with higher fit values and better estimated biological activity IC50 in µM
predicted using Hypo 1 model were subjected for their drug likeness properties using
QikProp module.
Identification of the best protein for docking
Six co-crystallised enoyl-ACP reductase proteins (PDB IDs: 2H7I, 2NSD, 2H7M, 2B36,
3FNG, 2H7L) are available with different resolutions and co-crystal structure in the protein
data bank. All 3D protein structure has 100% sequence similarity. So, before carrying out the
molecular docking protocol it is necessary to identify the best protein for further study based
on RMSD value between docked confirmation of ligand and co-crystal ligand also having
lower binding energies. The crystal ligands were grouped and redocked to verify essential
hydrogen bonding interactions with Tyr158 and NAD+. These ligands were Cross dock
against the listed protein to identify variable binding poses with significantly differing
binding energies using Glide. The average RMSD was calculated with respect to each protein
ligand pose. Structural alignment was also performed with co-crystalline structures. Here
alignment of all the PDBs have been performed to identify the exact binding mode of the co-
crystal structure to PDB. The X-ray structure with their exact binding modes and reasonable
low binding energies compared to other structures is further selected for the molecular
docking study. Structural alignment of 2H7I and 2H7M proteins Figure 1 and their alignment
score was also calculated Table 1. On the basis of lower RMSD and good alignment score
2H7M protein was further employed for molecular docking study.

7
Figure 1 Crystal structures of 2H7M and 2H7I overlaid on each other. Conformational
changes of aliphatic side chains on binding of different ligands are evident. Protein residues
in lines, ligands in ball and sticks.
Table 1 Alignment score and RMSDs of different X-ray crystal structures of InhA
protein
Sr. No.
PDB Codes
Alignment Score
RMSD
1
2
3
4
5
3FNG-2B36
2B36-2H7L
2H7L-2H7I
2H7I-2H7M
2H7M-2NSD
0.036
0.020
0.01
0.943
0.715
0.136
0.118
0.682
0.01
0.020
Protein preparation and grid generation
Protein structure was prepared using the Protein Preparation Wizard in Maestro (Maestro
v.10.1). Default settings were used, except that missing side chains were added, water
molecules were removed from the crystal structure beyond 5 Ǻ. Exhaustive sampling was
done to refine the protein structure in order to obtain better H-bond assignment. Receptor grid

8
was generated using “Glide’s Receptor Grid Generation” module with the default parameter
of van der Waals scaling factor and charge cut off. A centred dimension cubic box of 10 Ǻ ×
10 Ǻ × 10 Ǻ was generated with the help of OPLS 2005 force field at the active site of the
co-crystalline ligand.
Molecular Docking
Extra precision (XP) docking was carried out with flexible ligands to identify the essential
amino acid residual interaction (Irwin et al., 2012). Van der Waals scaling factor and partial
charge cut off was selected for ligand atoms (Maestro v10.1 Glide, 2009). Energy-minimized
poses of the virtual hit ranked by Glide score. Minimum 10 poses were generated for each
ligand (Podlogar et al., 2000). Molecular docking via computer aided-drug design approach
plays a vital role in the designing of potential ligands with compatible binding site of desired
targeted molecules. Co-crystallised enoyl-ACP reducates proteins (PDB IDs: 2H7M) were
selected and extracted from protein data bank (PDB http://www.rcsb.org/pdb) (Sivakumar et
al., 2007). To refine the protein default settings were applied structural hydrogen atoms were
added and water molecules were removed beyond 5Ǻ followed by minimization with OPLS
2005 force field. Further the crystal ligand was extracted and “Glide’s Receptor Grid
Generation” protocol was applied to generate the receptor grid with cubic box of 10 Ǻ × 10 Ǻ
× 10 Ǻ dimensions at the active site of the co-crystalline ligand (Halgren et al., 2004).
Predicted designed NCE’s (6a-6i) surviving to the Lipinski’s rule of five were docked using
Glide Standard precision (SP) protocol to study the predicted essential amino acid residual
interaction (Maestro v10.1, Schrodinger, LLC, NEW YORK, NY).
Chemistry
All Laboratory grade solvents and chemicals were purchased from commercial suppliers,
Spectrochem Pvt. Ltd., Rankem India Ltd. and Sigma Aldrich. Thin layer chromatography
was performed for monitoring the completion of the reaction. Melting points were obtained
by open capillary method on VEEGO VMP-D model and are uncorrected. Infrared (IR)
spectra were recorded in KBr using FT-IR 8400S Shimadzu Fourier Transform
spectrophotometer. Mass spectra were recorded on Perkin-Elmer LC-MS PE Sciex API/65.
1H NMR spectra and 13C NMR were taken on Bruker Advance 400 spectrophotometer and
the chemical shifts are given as parts per million (ppm) downfield from tetramethylsilane
(TMS) as internal standard.
Synthesis
General procedure for the synthesis of 2-cyano-N-(substituted phenyl)acetamide (2)

9
Different substituted amines (0.0468 mol) was added to ethyl cyanoacetate (0.0468 mol) in a
round bottom flask and refluxed for 8 hours using DMF as a solvent. Completion of the
reaction was monitored by TLC. After the completion of the reaction, mixture was poured
into ice-cold water. The solid obtained was filtered and dried. The crude product was
recrystallized from ethanol to yield pure crystalline solid.
General procedure for the synthesis of 2-cyano-N-(substituted phenyl)-
3,3bis(methylthio)acrylamide (3)
To the solution of potassium hydroxide (0.01046mol, 0.59 g) in 5 ml water, 2-cyano-N-
(substituted phenyl)acetamide (0.00523 mol, 1 g) was added with continuous stirring and
cooled at 0-5oC. To this solution 15 ml of DMF was added, and carbon disulphide
(0.00523mol, 0.397 g) was added drop wise. Reaction mixture was allowed to stirr for 30
minutes and dimethylsulphate (0.01046mol, 1.34 g) was added drop wise. The reaction
mixture was stirred for 2 hours and further poured into ice-water mixture. The solid obtained
was filtered, washed with water and dried. Recrystallized from methanol yielded colorless
crystalline product.
General procedure for the synthesis of 2-cyano-3-(methylthio)-N-substituted phenyl-3-
(substituted phenylamino)acrylamide (4)
2-cyano-N-(substituted phenyl)-3,3bis(methylthio)acrylamide (1.0 g, 0.003 mol) was
dissolved in 10 ml IPA. Different substituted aniline (0.67 g, 0.003 mol) was added to the
reaction mixture and the mixture was refluxed for 14-15 hours. The reaction was monitored
by TLC. After completion of the reaction, mixture was poured into ice cold water. Solid
obtained was filtered and dried. Recrystallized it from IPA yielded crystalline product.
General procedure for the synthesis of 5-amino-N-substituted phenyl-3-(substituted
phenylamino)-1H-pyrazole-4-carboxamide (5)
To the solution of 2-cyano-3-(methylthio)-N-substituted phenyl-3-(substituted
phenylamino)acrylamide (1g, 0.0104 mol) in 20 ml ethanol hydrazine hydrate (0.62 ml,
0.0104 mol) was added. The reaction mixture was refluxed for 3-4 hours and completion of
the reaction was monitored by TLC. After completion of the reaction, ethanol was distilled
off until the solid was obtained. Solid was filtered and dried. Recrystallized it from ethanol to
yield pure crystalline product.
General procedure for the synthesis of 7-hydroxy-5-methyl-N- substituted phenyl-2-
(substituted phenylamino)pyrazolo[1,5-a]pyrimidine-3-carboxamide (6)
To the crystalline solid product of 5-amino-N-substituted phenyl-3-(substituted
phenylamino)-1H-pyrazole-4-carboxamide (1g, 0.001029 mol) ethyl aceto acetate (0.7 ml,

10
0.01029 mol) was added and refluxed in oil bath for 1 hours. Completion of the reaction was
monitored by TLC. Solid separated out from the reaction mixture was filtered. Recrystallized
from chloroform to yielded pure crystalline product.
Spectral Characteristics
N-(3-chlorophenyl)-7-hydroxy-2-((4-methoxyphenyl)amino)-5-methylpyrazolo[1,5-
a]pyrimidine-3-carboxamide (6a)
M.P. 262-264°C; Yield, 82%; IR spectra (KBr, cm-1) 3353 (-NH str.), 1660 (-CO-NH str.),
1245 (-C-N str.), 813 (P-substituted), 775 (-C-Cl str.); Calculated (M/Z): 407.1; Obtained
MS: 407.1 (M), 409. 4 (M+2); 1H NMR (400 MHz, DMSO-d6, δ, ppm): δ 12(s, 1H, OH-Ar.,
D2O exchangeable), δ 9.8(s, 1H, C-O-NH-Ar., D2O exchangeable), δ 8.8 (s, 1H, NH-Ar.,
D2O exchangeable), δ 5.5-8.1(m, 7H,-CH-Ar.), δ 5.7(s, 1H,-CH-Ar.), δ 2.3(s, 3H, -CH3-Ar),
δ 2.2(s, 3H, -CH3-Ar); 13C NMR (400 MHz, DMSO-d6, δ, ppm): 162.20, 154.96, 153.71,
140.18, 138.86, 136.17, 132.80, 130.22, 125.61, 123.06, 122.91, 122.70, 120.08, 119.05,
115.47, 98.33, 87.14, 20.45, 12.92
2-((3-chloro-4-fluorophenyl)amino)-7-hydroxy-N-(4-methoxyphenyl)-5
methylpyrazolo[1,5-a]pyrimidine-3-carboxamide (6b)
M.P. 268-271°C; Yield, 67%; IR spectra (KBr, cm-1) 3390 (-NH str.), 1616 (-CO-NH str.),
1215 (-C-N str.), 1163 (-C-F str.), 1037 (-C-O-C), 867 (D-substituted), 833 (P-substituted),
773 (-C-Cl str.);Calculated (M/Z): 441.1; Obtained MS: 442.9 (M+1); 1H NMR (400 MHz,
DMSO-d6, δ, ppm): δ 12(s, 1H, OH-Ar., D2O exchangeable), δ 9.8(s, 1H, C-O-NH-Ar., D2O
exchangeable), δ 8.8 (s, 1H, NH-Ar., D2O exchangeable), δ 5.5-8.1(m, 8H,-CH-Ar.), δ 5.7(s,
1H,-CH-Ar.), δ 2.3(s, 3H, -CH3-Ar); 13C NMR (400 MHz, DMSO-d6, δ, ppm):161.22,
155.57, 154.82, 153.49, 153.21, 147.20, 140.30, 134.42, 131.59, 122.56, 118.60, 114.04,
113.61, 98.14, 87.33, 55.15, 18.79
2-((4-fluorophenyl)amino)-7-hydroxy-N-(4-methoxyphenyl)-5-methylpyrazolo[1,5-
a]pyrimidine-3-carboxamide (6c)
M.P. 276-278°C; Yield, 56%; IR spectra(KBr, cm-1) 3402 (-NH str.), 1616 (-CO-NH str.),
1218 (-C-N str.), 1081 (-C-F str.), 1041 (-C-O-C), 829 (P-substituted); Calculated (M/Z):
407.1; Obtained MS: 408.5 (M+1); 1H NMR (400 MHz, DMSO-d6, δ, ppm): δ 11.9(s, 1H,
OH-Ar., D2O exchangeable), δ 9.9(s, 1H, C-O-NH-Ar., D2O exchangeable), δ 8.8 (s, 1H,
NH-Ar., D2O exchangeable), δ 6.9-7.7(m, 8H,-CH-Ar.), δ 5.7(s, 1H,-CH-Ar.), δ 3.7(s, 3H, -
CH3-Ar), δ 2.5(s, 3H, -CH3-Ar); 13C NMR (400 MHz, DMSO-d6, δ, ppm): 161.22, 155.57,
154.82, 153.49, 153.21, 147.20, 140.30, 134.42, 131.59, 122.56, 118.60, 114.04, 113.61,
98.14, 87.33, 55.15, 18.79

11
2-((3-chlorophenyl)amino)-7-hydroxy-N-(4-methoxyphenyl)-5-methylpyrazolo[1,5-
a]pyrimidine-3-carboxamide (6d)
M.P. 210-213°C; Yield, 72%; IR spectra (KBr, cm-1) 3305 (-NH str.), 1658 (-CO-NH str.),
1253 (-C-N str.), 1041 (-C-O-C), 819 (P-substituted), 773 (-C-Cl str.); Calculated (M/Z):
423.1; Obtained MS: 424.2 (M+1), 426. 4(M+2); 1H NMR (400 MHz, DMSO-d6, δ, ppm): δ
12(s, 1H, OH-Ar., D2O exchangeable), δ 9.5(s, 1H, C-O-NH-Ar., D2O exchangeable), δ 8.7
(s, 1H, NH-Ar., D2O exchangeable), δ 6.9-7.6(m, 8H,-CH-Ar.), δ 5.7(s, 1H,-CH-Ar.), δ 2.5(s,
3H, -CH3-Ar), δ 2.3(s, 3H, -CH3-Ar); 13C NMR (400 MHz, DMSO-d6, δ, ppm): 162.20,
155.57, 154.82, 153.49, 153.21, 147.20, 140.30, 134.42, 131.59, 122.56, 118.60, 114.04,
113.61, 98.14, 87.33, 55.15, 18.79
2-((3-chloro-4-fluorophenyl)amino)-7-hydroxy-5-methyl-N-phenylpyrazolo[1,5-
a]pyrimidine-3-carboxamide (6e)
M.P. 281-282°C; Yield, 48%; IR spectra (KBr, cm-1) 3375 (-NH str.), 1628 (-CO-NH str.),
1232 (-C-N str.), 1049 (-C-F str.), 900 (D-substituted), 819 (P-substituted), 765 (-C-Cl str.);
Calculated (M/Z): 411.09; Obtained MS: 411.1 (M+1); δ 11.9(s, 1H, OH-Ar., D2O
exchangeable), δ 9.7(s, 1H, C-O-NH-Ar., D2O exchangeable), δ 8.8(s, 1H, NH-Ar., D2O
exchangeable), δ 6.9-7.6(m, 8H,-CH-Ar.), δ 5.7(s, 1H,-CH-Ar.), δ 2.3(s, 3H, -CH3-Ar); 13C
NMR (400 MHz, DMSO-d6, δ, ppm): 161.22, 154.81, 152.42, 150.09, 140.48, 139.94,
138.71, 128.90, 128.72, 128.51, 128.47, 123.81, 123.54, 120.75, 118.97, 118.66, 98.16,
88.05, 18.85, 16.71
7-Hydroxy-N-(4-methoxyphenyl)-2-((4-methoxyphenyl)amino)-5-methylpyrazolo[1,5-
a]pyrimidine-3-carboxamide (6f)
M.P. 280-282°C; Yield, 83%; IR spectra (KBr, cm-1) 3363 (-NH str.), 1662 (-CO-NH str.),
1244 (-C-N str.), 1037 (-C-O-C), 825 (P-substituted); Calculated (M/Z): 389.1; Obtained MS:
390.5 (M+1), 391.5 (M+2); 1H NMR (400 MHz, DMSO-d6, δ, ppm): δ 11.9(s, 1H, OH-Ar.,
D2O exchangeable), δ 9.7(s, 1H, C-O-NH-Ar., D2O exchangeable), δ 8.5(s, 1H, NH-Ar., D2O
exchangeable), δ 6.8-7.6(m, 9H,-CH-Ar.), δ 5.7(s, 1H,-CH-Ar.), δ 3.7(s, 3H, -CH3-Ar), δ
2.3(s, 3H, -CH3-Ar); 13C NMR (400 MHz, DMSO-d6, δ, ppm): 161.22, 154.81, 152.42,
150.09, 140.48, 139.94, 138.71, 128.90, 128.72, 128.51, 128.47, 123.81, 123.54, 120.75,
118.97, 118.66, 98.16, 88.05, 55.15, 18.85
7-hydroxy-2-((4-methoxyphenyl)amino)-5-methyl-N-phenylpyrazolo[1,5-a]pyrimidine-
3-carboxamide (6g)
M.P. 284-283°C; Yield, 78%; IR spectra(KBr, cm-1) 3371 (-NH str.), 1635 (-CO-NH str.),
1211 (-C-N str.), 1059 (-C-F str.), 829 (P-substituted); Calculated (M/Z): 395.1; Obtained

12
MS: 396.2 (M+1); 1H NMR (400 MHz, DMSO-d6, δ, ppm): δ 11.9(s, 1H, OH-Ar., D2O
exchangeable), δ 9.9(s, 1H, C-O-NH-Ar., D2O exchangeable), δ 9.7(s, 1H, NH-Ar., D2O
exchangeable), δ 6.4-9.2(m, 8H,-CH-Ar.), δ 5.8(s, 1H,-CH-Ar.), δ 2.5(s, 3H, -CH3-Ar); 13C
NMR (400 MHz, DMSO-d6, δ, ppm): 161.38, 157.71, 155.36, 154.74, 152.61, 150.01,
140.67, 140.37, 137.43, 132.76, 130.15, 123.02, 119.93, 118.86, 118.75, 118.68, 115.27,
115.05, 98.27, 87.73, 18.85
5-amino-N-(4-fluorophenyl)-3-((4-fluorophenyl)amino)-1H-pyrazole-4-carboxamide
(6h)
M.P. 297-300°C; Yield, 63%; IR spectra (KBr, cm-1) 3365 (-NH str.), 1662 (-CO-NH str.),
1222 (-C-N str.), 1026 (-C-O-C), 867 (D-substituted), 813 (P-substituted); Calculated (M/Z):
417.1; Obtained MS: 418.2 (M+1); 1H NMR (400 MHz, DMSO-d6, δ, ppm): δ 11.7(s, 1H,
OH-Ar., D2O exchangeable), δ 9.0(s, 1H, C-O-NH-Ar., D2O exchangeable), δ 8.9(s, 1H, NH-
Ar., D2O exchangeable), δ 6.8-7.5(m, 7H,-CH-Ar.), δ 5.8(s, 1H,-CH-Ar.), δ 3.7(s, 3H, -
OCH3-Ar), δ 2.4(s, 3H, -CH3-Ar.), δ 2.3(s, 6H, -CH3-Ar.); 13C NMR (400 MHz, DMSO-d6, δ,
ppm): 161.79, 155.70, 153.71, 138.99, 136.17, 131.38, 125.68, 122.86, 122.67, 122.56,
115.24, 113.72, 55.20, 20.47, 18.81, 12.91
5-amino-N-(2,4-dimethylphenyl)-3-((2-methoxyphenyl)amino)-1H-pyrazole-4-
carboxamide (6i)
M.P. 199-203°C; Yield, 67%; IR spectra (KBr, cm-1) 3305 (-NH str.), 1660 (-CO-NH str.),
1255 (-C-N str.), 1099 (-C-F str.), 871 (D-substituted), 813 (P-substituted), 775 (-C-Cl str.);
Calculated (M/Z): 445.02; Obtained MS: 445.2 (M), 447.3 (M+2), 449.3 (M+4); 1H NMR
(400 MHz, DMSO-d6, δ, ppm): δ 11.9(s, 1H, OH-Ar., D2O exchangeable), δ 9.9(s, 1H, C-O-
NH-Ar., D2O exchangeable), δ 8.5(s, 1H, NH-Ar., D2O exchangeable), δ 6.8-7.9(m, 7H,-CH-
Ar.), δ 5.7(s, 1H,-CH-Ar.), δ 3.5(s, 3H, -CH3-Ar), δ 2.3(s, 6H, -CH3-Ar.); 13C NMR (400
MHz, DMSO-d6, δ, ppm): 161.50, 154.89, 154.43, 152.96, 152.01, 149.93, 140.63, 138.35,
136.03, 136.00, 129.24, 129.08, 122.00, 120.91, 118.95, 118.77, 117.28, 116.69, 98.29,
87.34, 20.28, 18.94
Biological Screening
In vitro anti-tubercular activity
Newly synthesized 7-hydroxy-5-methyl-N- substituted phenyl-2-(substituted
phenylamino)pyrazolo[1,5-a]pyrimidine-3-carboxamide (6a-6i) derivatives were screened for
in-vivo anti-mycobacterial activity against H37Rv strain using Micro plate Alamar Blue
Assay method (Niu et al., 2013). Streptomycin, Pyrazinamide, and Ciprofloxacin were used
as standard drugs. This methodology is non-toxic, uses thermally stable reagents and showed

13
good correlation with the proportional and BACTEC radiometric method. To minimize
evaporation of medium from wells during incubation sterile deionized water was added to all
outer perimeter of 96 wells plate. To that 100 μL of the Middle Brook 7H9 broth and serial
dilutions of final drug concentrations tested were 100 to 0.2 μg/mL and incubated at 37ºC for
five days. To this freshly prepared mixture of 25μL Alamar Blue reagent and 10% tween 80
was added and again incubated for 24 hrs. A blue color in the well was inferred as no
bacterial growth, pink color was recorded as growth and MIC defined as lowest concentration
which prevents color change from blue to pink.
MDR-TB and XDR-TB study
Potent compounds 6b, 6d and 6h from in vitro anti-tubercular screening method further opted
for MDR-TB and XDR-TB study using Lowenstein-Jensen medium (L. J. medium) on
H37Rv strain (Maria at al., 2007). Susceptibility of the compounds checked against
multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) strains.
Eggs were broken aseptically and the solution was filtered through a sterile muslin cloth. To
that solution 4 mL of sterilized malachite green solution is added. This mineral salt solution
consist of potassium phosphate (4.0 g), magnesium sulphate (0.4 g), magnesium citrate (1.6
g), asparagine (6.0 g), glycerol (20 mL) and distilled water to make up the volume up to 1000
mL. The contents were mixed well to form a uniform medium. Synthesized targeted
compounds were dissolved in 10 mL dimethyl sulfoxide (DMSO) and an aliquot 0.8 mL of
each concentration was transferred into different McCartney bottles. To this, 7.2 mL of L. J.
medium was added and mixed well. Isoniazid was considered as a reference standard for the
comparison of anti-tubercular activity. The bottles were incubated at 75˚C - 80˚C for 3 days
for solidification and sterilization.
MTT Cytotoxicity activity
Viable cells are able to reduce the yellow MTT (Tetrazolium bromide) under tetrazolium ring
cleavage into a purple-blue water insoluble formazan product which precipitates in the
cellular cytosol and can be dissolved after cell-lysis. Whereas cells are being dead following
a toxic damage, cannot transform MTT (Stover et al., 2000). This formation production is
proportionate to the viable cell number and inversely proportional to the degree of
cytotoxicity. Vero Cell line was obtained from National Centre for Cell Sciences (NCCS),
Pune. Anti-tuberculosis active compounds (with MIC < 12.5μg/mL) 6b, 6d and 6h were
further evaluated for its toxicity against mammalian Vero cell line. After 72 h of exposure,
viability was assessed on the basis of cellular conversion of MTT into a formazan product
using the Promega Cell Titer 96 non-radioactive cell proliferation assay. These results are

14
important as these compounds with their increased cytoliability are much attractive in the
development of new chemical entities for the treatment of TB. This is primarily due to the
fact that the eradication of TB requires a lengthy course of treatment, and the need for an
agent with a high margin of safety becomes a primary concern.
Results and Discussion
Pharmacophore model
Ligands with resemble conformation and selective biological activity were used to develop
reliable pharmacophore model. With the information of training set molecules ten hypotheses
(named as Hypo 1 to Hypo 10) were generated. The details of the generated models along
with its statistical and cost values are presented in Table 2. Hypo 1, a four point
pharmacophore model with two features (1 HBA and 3 HY) Figure 2 showed good
correlation (r2 = 0.85) and low RMSD value of 1.64. The null cost (consideration of cost
without any features) is 230, and fixed cost (cost of a perfect hypothesis) is 94 and
configuration cost is 14.15. The greater the difference between null cost and total cost value
shows the more statistically significant and reliable hypothesis (He et al., 2006). The
difference between the null cost and fixed cost should be ˃ 60 bits. It was encouraging to see
that this difference is 95.26 for model Hypo 1, which signifies 90% chance of accurate
correlation in the data. Configuration cost ˂ 17 (14.15 for generated model) indicates that the
generated models are not by chance correlation (He et al., 2006).
Table 2. Cost analysis and statistical characteristics of the generated pharmacophore
models
Hypo No.
Features
Total cost Δ cost
Correlation RMS deviation
1
2
3
4
5
6
7
8
9
10
HBA,HY,HY,HY 135.4
HBA,HY,HY,HY 136.1
HBA,HY,HY,HY 137.1
HBA,HY,HY,HY 141.2
95.26
94.55
93.63
89.42
87.93
83.15
86.07
82.96
81.1
0.850
0.850
0.846
0.830
0.820
0.800
0.800
0.800
0.800
0.790
1.64
1.66
1.67
1.75
1.79
1.87
1.86
1.87
1.88
1.92
HBA,HY,HY
142.7
HBA,HY,HY,HY 147.5
HBA,HY,HY,HY 147.6
HBA,HY,HY,HY 147.7
HBA,HY,HY,HY 149.5
HBA,HY,HY
149.8
80.87

15
Null Cost: 230.641, Fixed Cost: 94.35, Configuration Cost: 14.15
Figure 2 The common feature pharmacophore model (Hypo 1) with distance constrains.
Green: Hydrogen Bond Acceptor (A), Cyan: Hydrophobic (HY).
Cross validation with test set molecules
Besides cost analysis, the generated models were further evaluated for their ability to predict
the activity of test set molecules by internal and external cross validation method. The entire
test set (*) molecules were mapped on Hypo 1 shown in (Table S1, Supplementary
information). The best pharmacophore model further validated using Fischer randomization
test and leave-one-out method.
Fischer randomization
The generated pharmacophore model was cross validated to check its prediction power
towards the active molecules. By using the Fischer’s randomization method, experimentally
observed activity of the training set molecules were randomly scrambled and further
rearranged to generate the HypoGen models with previously chosen parameters when
generating the original pharmacophore model. The randomization result shows that none of
the generated hypothesis has higher cost value and good correlation compared to Hypo 1.
This value recommends 95 % chance for the true correlation (He et al., 2006).
Leave-one-out method
Leave-one-out method is used to cross-validate the generated pharmacophore model. Here
generated pharmacophore was recomputed by omitting one compound at a time from the
training set (Rao et al., 2008). This method demonstrates the correlation coefficient of Hypo1
does not depend on a single individual compound. If the parallel one compound is missing
then hypothesis can predict the activity appropriately. This confirms for Hypo1 the

16
correlation co-efficient does not depend on a single particular compound from the training
set.
Database screening
The generated HypoGen model (Hypo 1) was employed to screen Zinc and MiniMaybridge
databases by adopting feature shape query. Newer hits were identified with optimum fit
values. The retrieved hits shown in Figure 3 were extracted based on their fit value and
predicted IC50 values. Here circles in the figure represent both individual and clubbed
pyrazole and pyrimidine heterocyclic rings act as anti-tubercular potential. So, on the basis of
structural modification from the virtual screening we designed newer series of clubbed
pyrazolo[1,5-a]pyrimidine analogues as potential anti-tubercular agent.
Figure 3 The retrieved hits on the basis of virtual screening of the ZINC and MiniMaybridge
databases

17
Molecular Docking
Molecular docking is a part of the structure based drug design approach conveying
knowledge of enzyme-inhibitor complex with respect to binding affinity and binding mode
using Glide module of Schrodinger (Maestro v10.1, Schrodinger, LLC, NEW YORK, NY)
(Sinosh et al., 2018). The cross validation of docking protocol exhibited that 2H7M protein
retains the native binding interactions with lowest binding energy in comparison to other
crystal structures of protein. The binding interactions of primary hits were also studied by
performing docking analysis against 2H7M protein. The docking results with predicted IC50
values of pyrazolo[1,5-a]pyrimidine analogues surviving to the Lipinski’s rule of five (6a-6i)
are summarized in Table 3.
Table 3. Results of the database screening and docking of the identified potent
compounds
Compound Name/Code Estimated IC50 (µM) by Hypo 1 Glide Score
Co-Crystal ligand
2.9
-8.400
-8.774
-9.901
-8.471
-10.841
-7.991
-7.621
-9.480
-10.675
-6.937
6a
6b
6c
6d
6e
6f
0.002
0.598
0.067
0.299
0.121
0.153
0.166
0.261
0.084
6g
6h
6i
All the docked ligands occupy the identical interactions with hydrophobic cavity Met103,
Phe149, Pro156 Tyr158, Met161, Met199, Ile202 and Leu218 and side chain π-π residual
interaction with Phe149, Gly14 and Gly96. Also some of the compounds shows hydrogen
bond network with the catalytic residue Lys165. Topmost hit molecules (6a-6i) amongst
predicted pyrazolo[1,5-a]pyrimidine analogues were further synthesized and evaluated for its
anti-tubercular potential.

18
Analysis of complexes
Amongst all synthesised analogues we have selected some of the most potent inhibitors 6b,
6d and 6h possess -9.901, -10.841 and -10.675 docking score and MIC 3.12, 0.8 and 0.8
µg/ml respectively which has been discussed in detail below. Compound 2-((3-chloro-4-
fluorophenyl)amino)-7-hydroxy-N-(4-methoxyphenyl)-5-methylpyrazolo[1,5-a]pyrimidine-
3-carboxamide (6b) Figure 4(a) phenyl ring showed hydrogen bonding interaction with
Lys165 and hydrophobic interaction with important key amino acid residues Ile21, Met103,
Met147, Phe149, Pro156, Tyr158, Met199, Ile 202 and Leu218 which indicates the summary
of all the forces interacted between the ligands and InhA enzyme. Hydrogen bonds are
important performers and responsible for its specificity towards targeted enzyme [28]. Here
two water molecules takes part for hydrogen bonding which increases the ligand binding
affinity towards the protein. The compound 2-((3-chlorophenyl)amino)-7-hydroxy-N-(4-
methoxyphenyl)-5-methylpyrazolo[1,5-a]pyrimidine-3-carboxamide (6d) Figure 4(b) also
suggest hydrogen bond interaction through oxygen with Lys165 and major important
hydrophobic interacting residues are Ile21, Met103, Met147, Phe149, Pro156, Tyr158,
Lys165, Ile194, Met199, Leu218, Ile202 and Ile215. Also compounds 5-amino-N-(2,4-
dimethylphenyl)-3-((2-methoxyphenyl)amino)-1H-pyrazole-4-carboxamide (6h) Figure 4(c)
have molecular interactions with Ile21, Met103, Met147, Phe149, Tyr158, Pro156, Lys 165,
Ala191, Ile194, Ile202, Leu218 also one water molecule incorporating which indicates same
residue comparing to co-crystalline structure which could be further potent novel M.
tuberculosis inhibitor. These findings suggest that the newly designed compounds may be
considered as potential targets for anti-tubercular drug discovery.

19

20
Figure 4 Docking conformation of the most potent inhibitors (a) 6b, (b) 6d and (c) 6h with
binding pocket of InhA enzyme (PDB : 2H7M)
In-silico ADME prediction:
Good efficiency with an acceptable ADMET profile is the important criteria for the
accomplishment of a drug. The identified potential leads of Pyrazolo[1,5-a]pyrimidine
derivatives (6a-6i) through molecular docking study were further screened for in-silico
ADMET prediction to check its druggability using QikProp module in Schrodinger. This
predicts several significant physical descriptors like molecular weight, octanol/water partition
coefficient, aqueous solubility, QPPcaco for cell permeability, brain/blood partition
coefficient, QPlogHERG for blockage of HERG K+ channels, metab for number of metabolic
reactions, QPlogKhsa for Prediction of binding affinity to human serum albumin and
percentage human oral absorption and mutagenicity compared to the properties of a particular
known drugs. The results are shown in Table 4. Small molecules that comply with Lipinski’s
rule of five were further opted for the synthesis.

21
Table 4. ADMET properties of synthesized compounds (6a-6i)
%
QPLog
Khsa
Co
Log P
o/w
QPP
Caco
Log
BB
QPlog
MW
Log S
Metab
Oral
abs
100
mp.
HERG
407.11 4.684 -6.802 1359.021
441.10 4.433 -6.75 1005.631
-6.995
-6.986
-7.004
-6.939
-7.032
6
5
6
6
6
5
6
5
0.539
0.562
0.468
0.404
0.288
0.438
0.328
0.398
6a
6b
6c
6d
6e
6f
-0.68
-0.76
-0.57
-0.51
-.1.82
-0.39
-0.91
-0.77
100
100
100
100
100
100
100
407.14 4.13
-6.193 1004.219
423.11 4.364 -6.561 1004.788
411.09 4.261 -6.383 1196.565
389.15 4.178 -6.335 1361.929
395.12 3.876 -5.779 1005.099
417.18 3.876 -5.833 1359.021
445.02 4.493 -6.75 1005.631
-7.012
6g
6h
6i
-6.893
-7.025
-0.52 -7.028
6
0.536
100
Molecular weight (MW)-acceptable limit is <500 Da; Log P-predicted octanol/water partition
coefficient with range-(2.0 to 6.5); Log S-estimated solubility limit is value greater than -4;
QPPcaco-predictes Caco-2 cell permeability model for the gut-blood barrier in nm/s, where
limit is <25 is considered poor and >500 is considered excellent; QPlogHERG-Predictes IC50
value for blockage of HERG K+ channels range (>-5); metab-Number of likely metabolic
reactions range (1-8); QPlogKhsa-Prediction of binding to human serum albumin. Range (–
1.5 to 1.5); Percentage human oral absorption-this gives the predicted human oral absorption
on 0-100% scale.
Chemistry
The synthetic approach for the derivatives of 7-hydroxy-5-methyl-N- substituted phenyl-2-
(substituted phenylamino)pyrazolo[1,5-a]pyrimidine-3-carboxamide (6a-6i) is summarised in
the synthetic scheme 1. Nucleophilic addition reaction occurs between different substituted
amines and ethyl cyano acetate (1) for the synthesis of 2-cyano-N-
(substitutedphenyl)acetamide (2a-2f). Further protonation of the carbonyl carbon of 2-cyano-
N-(substitutedphenyl)acetamide (2a-2f) will occur and undergo for nucleophilic addition
reaction with the help of carbon disulphide and dimethyl sulphate to form the S, S acetal

22
(Ketals) derivatives (3a-3f). These acetal derivatives (3a-3f) reacts different substituted
amines for the formation of Keten S, N- acetal (4a-4i). Further this S, N- acetal (4a-4i) will
undergo for ring closure strategy with the help of hydrazine hydrate for the formation of 5-
amino-N-substituted phenyl-3-(substituted phenylamino)-1H-pyrazole-4-carboxamide (5a-
5i). Again these pyrazole derivatives (5a-5i) condensed with ethyl aceto acetate to from final
product 7-hydroxy-5-methyl-N- substituted phenyl-2-(substituted phenylamino)pyrazolo[1,5-
a]pyrimidine-3-carboxamide (6a-6i). All the synthesised final compounds (6a-6i) were
purified and confirmed with IR, MASS, 1HNMR, 13CNMR spectroscopy. The IR spectrum of
final compounds shows strong -OH stretching appear near to 3400 cm-1 and amidic carbonyl
stretching at 1660 cm-1 which is absent in intermediate (5a-5i). Here appearance of OH group
broadening in the IR spectrum confirms the formation of the condensed final pyrazolo[1,5-
a]pyrimidine. The 1H spectrum of intermediate compound (5a-5i) displays absence of the
aromatic OH proton peak which is present in the final compounds nearer to δ 11.3 which
confirms the formation of final cyclization product.
O
KOH,
O
NH2
CN
S
DMS
CS2
R N
O
CN
R
H
R N
NC
H
S
OC2H5
0 °C,
Stirring
DMF,

(2a-2o)
(1)
(3a-3o)
2a
, R=4-OCH3-C6H
2b
, R=2,4-CH3-C6H4
, R=-H-C6H6
2c
2d
, R=4-F-C6H6
2e
, R=3-Cl, 4-F-C6H4
2f
, R=3-Cl-C6H5
OH
O
R'
NH
N
HN
H2N
Ethyl aceto
acetate
CN
S
R'
NH
N
NH2 NH2
N
R
HN
NH-C6H5-R'

EtoH, 
N
HN
R'
NH
R
IPA, 
O
NH
O
R
(5a-5z)
(4a-4z)
(6a-6z)
6a
, R=3-Cl-C6H5,
R'=4-CH3-C6H5
, R=4-OCH3-C6H5, R'=3-Cl, 4-F-C6H4
, R=4-OCH3-C6H5, R'=4-F-C6H5
6b
6c
6d
, R=4-OCH3-C6H5, R'=3-Cl-C6H5
6e
, R=-C6H5,
R'=3-Cl, 4-F-C6H4
6f
, R=-C6H5,
, R=4-F-C6H5,
R'=4-OCH3-C6H5
R'=4-F-C6H5
6g
6h
, R=2,4-CH3-C6H4, R'=2-OCH3-C6H5
, R=3-Cl, 4-F-C6H4, R'=3-Cl-C6H5
6i
Scheme 1 synthetic approach for the derivatives of 7-hydroxy-5-methyl-N- substituted
phenyl-2-(substituted phenylamino)pyrazolo[1,5-a]pyrimidine-3-carboxamide (6a-6i)

23
Anti-tubercular studies
Newer series of pyrazolo[1,5-a]pyrimidine (6a-6i) derivatives were synthesized and
evaluated for its anti-tubercular potential against Mycobacterium tuberculosis H37Rv strain
by Micro plate Alamar Blue Assay (MABA) method. All the synthesized compounds possess
good to moderate inhibitory activity compared to standard drugs Pyrazinamide, Ciprofloxacin
and streptomycin. In vitro anti-tubercular activity with MIC levels of the titled compounds
(6a–6i) are presented in Table 5. Amongst screened all the synthesized compounds 6d (R= 4-
OCH3, R’= 3-Cl) and 6h (R= 2,4-CH3, R’= 2-OCH3) demonstrates an excellent activity
against M. tuberculosis with MIC of 0.8 µg/mL. Also one of the compound 6b (R= 4-OCH3,
R’= 3-Cl 4-F) have shown MIC 3.12 µg/mL which is as active as standard drug Pyrazinamide
and Ciprofloxacin. One of the compound 6g (R= 4-F, R’= 4-F) showed MIC 6.25 μg/mL, as
potent as standard drug streptomycin. However compounds 6a (R= 3-Cl, R’= 4-CH3), 6c (R=
4-OCH3, R’= 4-F) and 6f (R= NH, R’= 4-OCH3) exhibited good anti-tubercular activity with
MIC 12.5 µg/mL but starting to loss the potency. Other remaining compounds 6e (R= -NH,
R’= 3-Cl 4-F) and 6i (R= 3-Cl 4-F, R’= 3-Cl) possess anti-tubercular activity with MIC 25
µg/mL. Identified newer potent compounds 6b, 6d and 6h further opted for MDR-TB, XDR-
TB and cytotoxicity study.
Table 5. Anti-mycobacteraial activity of pyrrazolo[1,5-a]pyrimidine (6a-6i)
Sample
code
6a
6b
6c
6d
6e
R
R’
MIC(µg/ml)
3-Cl
4-CH3
3-Cl 4-F
4-F
3-Cl
3-Cl
12.5
3.12
12.5
0.8
4-OCH3
4-OCH3
4-OCH3
NH
25
6f
NH
4-F
4-OCH3
4-F
2-OCH3
3-Cl
12.5
6.25
0.8
6g
6h
6i
2,4-CH3
3-Cl 4-F
Pyrazinamide
Ciprofloxacin
Streptomycin
25
3.12
3.12
6.25
MDR-TB and XDR-TB

24
From the Synthesized newer designed analogues some of the compounds 6b, 6d and 6h
demonstrate the potent anti-tubercular activity against M. tuberculosis H37Rv strain via
MABA method. Further they were assayed by Lowenstein-Jensen medium (L.J. medium) to
check their susceptibility against multidrug resistant Tb (MRD-TB) and extensively drug-
resistant TB (XDR-TB). The results are summarized in Table 6.
Table 6. MDR-TB and XDR-TB strain using Lowenstein-Jensen medium and
Cytotoxicity study on Vero cell line
Sr.
Cytotoxicity
IC50 (µg/mL)
R
R’
MIC (µg/mL)
H37Rv
No.
MDR-TB
12.5
XDR-TB
>100
50
6b
6d
6h
4-OCH3
4-OCH3
2,4-CH3
Isoniazide
3-Cl 4-F 3.12
3-Cl 0.8
5.4
1.6
7.3
2-OCH3 0.8
0.5
3.2
12.5
11.02
ND
6.25
50
ND: Not determined
IC50 values are the mean of triplicate study
Cytotoxicity assay
The cellular transformation of MTT [3-(4,5-dimethylthiazo-2-yl)-2,5-diphenyl-tetrazolium
bromide] is a pale yellow substrate which converted into a formazan a dark blue product,
computed by colorimetric methods. Some of the selected potent anti-tubercular compounds of
6b, 6d and 6h were further evaluated for cytotoxic activity against a mammalian Vero cell
followed by the determination of IC50 Table 6. Among all the synthesized compounds 6b has
lowest IC50 5.4 μg/mL on mammalian VERO cell line. Also, compounds 6d and 6h shows
effect on cancer cell line with IC50 values around 7.3 μg/mL and 11.2 μg/mL respectively.
Conclusion
With the emergence of MDR-TB, XDR-TB and recent cases of TDR-TB, discovery of newer
anti-tubercular drug is currently driving the resurgence. Enoyl acyl carrier protein (ACP)
reductase plays critical role in the biosynthesis of mycobacterial cell wall, which is the
promising target for tuberculosis. In the present work, we have designed and synthesized a
series of 7-hydroxy-5-methyl-N- substituted phenyl-2-(substitutedphenylamino)pyrazolo[1,5-

25
a]pyrimidine-3-carboxamide (6a-6i) derivatives via pharmacophore-based virtual screening
and molecular docking studies. This studies also suggests compounds 6b, 6d and 6h
interacted more efficiently with InhA enzyme. Looking at the bioactivity of final potent
nucleus, the synthesized derivatives 6b, 6d and 6h showed potent anti-tuberculosis activities
which were found to be comparable to the reference standard drugs with less toxicity. The
potent assumption opens up new potential targeted molecules where discovery will yield
novel anti-tuberculosis agents.
Acknowledgments
The authors acknowledge Oxygen healthcare, Changodar and Saurashtra University, Rajkot
for providing analytical instrumental data and Maratha Mandal’s NGH Institute of Dental
Sciences and Research Centre, Belguam, Karnataka, India for biological screening facilities.
Conflicts of interest
The authors declare no conflict of interest about this article.
Supporting Information
The training and test set molecules to generate the pharmacophore models as well 1H NMR
and 13C NMR spectra have been provided for all the final compounds.

26
References
Asselineau, J., Lederer, E. (1950). Structure of the Mycolic acid of Mycobacteria.
Nature, 166, 782-783.
Banerjee, A., Dabnau, E., Quemard, A., Balasubramanian, V., Um, K. S., Wilson, T., Collins,
D., De, L. G., Jacobs, W. R. (1994). InhA, a gene encoding a target for isoniazid and
ethionamide in Mycobacterium tuberculosis. Science, 263, 227-230.
Dye, C., Lonnroth, K., Jaramilo, E., Williams, B. G., Raviglione, M. (2009). Trends in
tuberculosis incidence and their determinants in 134 countries. Bull. World Health
Organ, 87, 683-691.
Funk, O. F., Kettmann, V., Drimal, J., Langer, T. (2004). Chemical Function Based
Pharmacophore Generation of Endothelin-A Selective Receptor Antagonists. J Med
Chem, 47, 2750-2760.
Halgren, A., Murphy, B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., Banks, J.
L. (2004). Glide: A new approach for rapid, accurate docking and scoring 2.
Enrichment factors in database screening. J Med Chem, 47, 1750-1759.
He, X., Alion, A., Ortiz, D. E., Montellano, P. R. (2007). Inhibition of the Mycobacterium
tuberculosis enoyl acyl carrier protein reductase InhA by arylamides. Bioorg Med
Chem., 15, 6649-6658.
He, X., Allian, A., Stroud, R., Ortiz, D. E., Montellano, P. R. (2006). Pyrrolidine
carboxamides as a novel class of inhibitors of enoyl acyl carrier protein reductase
from Mycobacterium tuberculosis. J Med Chem., 49, 6308-6323.
Irwin, J., Sterling, T., Mysinger, M., Bostand, S., Coleman, G. (2012). ZINC: A free tool to
discover chemistry for biology. J of Chem Info & Modelling, 52, 1757-1768.
Maria, C. S., Marcus, V. N., DeSouza, A. C. P., Marcelle, D. L., Rasnisb, B., Cristina, M.,
Nogneira, M., Peralta, A. (2007). Evaluation of anti-Tubercular activity of nicotinic
and isoniazid analogues. ARKIVOC, 15, 181-191.
Maestro v10.1 (2009) Glide, Schrodinger, LLC, New York.
Mason, J. S., Good, A. C., Martin, E. J. (2001). 3D pharmacophore in Drug discovery. Curr
Pharm Des., 7, 567-597.
Mohsin, Y. L., Mohd., A., Vivek, K. G., Prakash, C. J. (2017). Identification of
Mycobacterium tuberculosis enoyl-acyl carrier proteinreductase inhibitors: A
combined in-silico and in-vitro analysis. J Mol Graph Model., 76, 172–180.
Mohsin, Y. L., Anu M., Mohd. A., Prakash, C. J. (2017). Identification of InhA inhibitors: A

27
combination of virtual screening, molecular dynamics simulations and quantum
chemical studies. J Biomol Struct Dyn., DOI:10.1080/07391102.2017.1372313.
Niu, M., Dong, F., Tang, S., Fida, G., Qin, J., Qui, J., Liu, K., Gao, W., Gu, Y. (2013).
Pharmacophore Modeling and Virtual Screening for the Discovery of New type 4
cAMP Phosphodiesterase (PDE4) Inhibitors. PLOS ONE, 8(12), e82360.
Podlogar, B. L., Ferguson, D. M. (2000). QSAR and CoMFA: a perspective on the practical
application to drug discovery. Drug Des Discov, 17, 4-12.
Quemard, A., Blanchard, J. S., Jacobs, W. R., Sacchetini, J. C. (1995). Crystal structure and
function of the isoniazid target of Mycobacterium tuberculosis. Science, 267, 1638-
1641.
Rao, G. S., Vijaykrishnan, R., Kumar, M. (2008). Structure based design of novel classof
potent inhibitors of InhA, the enoyl acyl carrier protein reductase from
Mycobacterium tuberculosis: A Computer modelling approach. Chem. Biol. Drug.
Des., 72, 444-449.
Santo, D., Fermeglia, M., Ferrone, M. (2005). Simple but Highly Effective Three-
Dimensional Chemical-Feature-Based Pharmacophore Model for Diketo Acid
Derivatives as Hepatitis C Virus RNA-Dependent RNA Polymerase Inhibitors. J Med
Chem, 48, 6304-6314.
Shalini, S., Maaged, A., Dharmarajan, S., Lalitha, G. (2017). Discovery of novel inhibitors
of Mycobacterium tuberculosis MurG: homology modelling, structure based
pharmacophore, molecular docking, and molecular dynamics simulations. J Biomol
Struct Dyn.,DOI: 10.1080/07391102.2017.1384398.
Sinosh, S., Meghna, M., Nikhil, B. (2018). Screening of potential lead molecules against
prioritised targets of multi-drug-resistant-Acinetobacter baumannii – insights from
molecular docking, molecular dynamic simulations and in vitro assays. J Biomol
Struct Dyn., DOI: 10.1080/07391102.2018.1451387.
Sivakumar, P. M., Seenivasan, S. P., Kumar, V., Doble, M. (2007). Synthesis,
antimycobacterial activity evaluation, and QSAR studies of chalcone derivatives.
Bioorg Med Chem Lett, 17, 1695-1700.
Stover, C.K., Warrener, P., Van Devanter, D.R., Sherman, D.R., Arain, T.M., Langhorne,
M.H. and Anderson, S.W. (2000). A Small-Molecule Nitroimidazopyran Drug
Candidate for the Treatment of Tuberculosis. Nature, 405, 962-966.
Takayama, K., Wang, C., Besra, G. S. (2005). Pathway to synthesis and processing of
mycolic acids in Mycobacterium tuberculosis. Clin Microbil Rev, 18, 81-101.

28
Tsukamura, M., Wang, C., Besra, S. (1978). A comparison of the time of in vivo resistance
development of tubercle bacilli to Rifampicin, Kanamycin, Ethionamide,
Lividomycin, and Enviomycin (Tuberactinomycin-N) in patients with chronic cavitary
Tuberculosis. Kekkaku (Tuberculosis), 53, 495-498.
Williams, P., Floyd, K., Dye, C., Elzinga, G., Raviglione, M. (2005). Tuberculosis control in
the era of HIV. Nat. Rev. Immunol, 5, 819-826.
Yang, Y., Xu, S., Zhang, M., Jin, R., Zhang, L., Bao, J., Wang, H. (2006). Purification and
characterization of a functionally active Mycobacterium tuberculosis pyrroline-5-
carboxylate reductase. Protein Expr Purif, 45, 241-248.
Zhang, Y., Garbe, T., Young, D. (1993). Transformation with katG restores isoniazid-
sensitivity in Mycobacterium tuberculosis isolates resistant to a range of drug
concentrations. Mol Microbiology, 3, 521-524.
Zhang, Y., Heym, B., Allen, B., Young, D., Cole, S. (1992). The catalase-peroxidase gene
and isoniazid resistance of Mycobacterium tuberculosis. Nature, 283, 591593.
Zhao, X., Yu, H., Yu, S., Wang, F., Sacchettini, J. C., Magliozzo, R. S. (2005). Hydrogen
Peroxide-Mediated Isoniazid Activation Catalyzed by Mycobacterium
tuberculosis Catalase−Peroxidase (KatG) and Its S315T. Mutant Biochemistry, 45,
4131-4140.
Supplementary material
Table S1 Structures of the training and test set of molecules and their residual values
Compd. Chemical Structures
IC50
Predicted
Activity
42.84
Residual
(µM)
34.88
1
-7.96
O
NH
N
O
O

29
2*
0.89
1.91
-1.02
-2.95
-4.12
3.97
O
N
NH
O
O
O
O
Br
3
16.79
19.744
O
N
N
N
NH
4*
5.55
9.67
O
NH
5
56.02
52.047
O
NH
Cl
Cl
6*
7*
56.5
0.97
45.23
5.514
11.27
-4.54
O
N
NH
O
Cl
Cl
O
N
NH
O
Cl
CH3