Synthesis, characterization, PASS prediction and in silico ADME studies of ester and ether linked 1,4-disubstituted 1,2,3-triazoles derivatives via click approach

Article abstract of DOI:10.14233/ajchem.2020.22748

In the present investigation, we focused our interest on the synthesis of pharmacophoric units (quinoline and 1,2,3-triazole) linked through ester (3a-b) and (substituted aromatic ring and 1,2,3-triazole) linked through an ether (3c-h). The synthesis invo

Full text of DOI:10.14233/ajchem.2020.22748

Asian Journal of Chemistry; Vol. 32, No. 8 (2020), 1857-1864
A
SIAN
J
OURNAL OF HEMISTRY
C
https://doi.org/10.14233/ajchem.2020.22748
Synthesis, Characterization, PASS Prediction and in silico ADME Studies of Ester and
Ether Linked 1,4-Disubstituted 1,2,3-Triazoles Derivatives via Click Approach
1
1,
1
1
1
G. KRISHNASWAMY , P. RAGHURAM SHETTY , B. ROOPA , SALMA BANU , H.J. PRERITHA ,
1
1
1,
1
1,2,*,
B.S. RAJESHWARI , M. RAVIKUMAR , K. PRUTHVIRAJ , D.B. ARUNA KUMAR and S. SREENIVASA
1
2
Department of Studies and Research in Organic Chemistry, Tumkur University, Tumakuru-572103, India
Deputy Adviser, National Assessment and Accreditation Council, Nagarbhavi, Bengaluru-560072, India
*Corresponding author: E-mail: drsreenivasa@yahoo.co.in
Received: 21 January 2020; Accepted: 25 March 2020;
Published online: 27 July 2020;
AJC-19958
In the present investigation, we focused our interest on the synthesis of pharmacophoric units (quinoline and 1,2,3-triazole) linked
through ester (3a-b) and (substituted aromatic ring and 1,2,3-triazole) linked through an ether (3c-h). The synthesis involves multiple
sequence of reactions viz. diazotization reaction followed by nucleophilic substitution and finally Cu(I)-catalyzed alkyne azide cycloaddition
1
13
reaction (CuAAC). The assigned structures of the compound were confirmed by H & C NMR and mass spectrometry. Prediction of
activity spectra for substances (PASS) training set for the synthesized compounds were carried out using PASS software. Interestingly,
PASS prediction of the compounds (3a-h) showed that the compounds are more potent as anti-inflammatory (Pa < 0.65) compared to
antibacterial (Pa < 0.33) as well as antifungal agents (Pa < 0.35). Furthermore, these compounds were subjected to in silico ADMETox
evaluation.All the compounds were found to pass theADME evaluation and only few compounds passed the predicted toxicity evaluation.
This work could be used as an initial approach in identifying potential novel molecules with promising activity and low toxicity.
Keywords: 1,2,3-Triazole, 1,3-Dipolar cycloaddition, AromaticAzides, Prediction of activity spectra for substances, Druglikeness.
also serves as key synthetic intermediates in many industrial
applications such as agrochemicals [18-20], corrosion inhi-
bitors [21,22], additives [23,24], supramolecular chemistry
[25,26], dendrimers, polymers [27,28], liquid crystals [29,30],
photostabilizers [31,32], pigments [33,34] and metal chelators
[35,36]. Recent reports demonstrated that 1,2,3-traizole incor-
porated quinoline derivatives have wide variety of pharmaco-
logical applications like 8-trifluoromethyl quinoline based 1H-
1,2,3-triazole derivatives (I) are found to be moderate anti-
microbial agents [37].A series of 2-quinoline-1H-1,2,3-triazole
hybrids (II) exhibited inhibition activity against M. tuberculosis
H37Rv strain [38]. The 6-methoxy-2-methylquinoline-1H-
1,2,3-triazole core having amides (III) or sulphonamides (IV)
exhibited promising antitubercular activity [39].
INTRODUCTION
Heterocyclic moieties are the most important pharmacolo-
gically active structural units and attracted a lot of attention
among the medicinal chemist and biologist who are involved
in the process of developing new drug candidates [1]. Among
the heterocyclic moieties quinoline and its derivatives form a
class of such a molecular moiety because of their wide variety
of pharmacological activities such as antimalarial (quinine,
chloroquine), chemotherapeutic activity (topotecan), antituber-
cular activity (mefloquine, moxifloxacin) etc. On the other
hand azoles are most promising five membered nitrogen conta-
ining aromatic hetereocycles. Recent literature report identified
that among the azoles (imidazole, pyrazole, triazole, tetrazole
and pentazole), triazole containing three nitrogen compounds
were the most recently studied in particular 1,2,3-traizoles [2-
Considering the individual biological and medicinal
importance of quinoline and 1,2,3-triazoles, we wanted to
explore novel chemical entities based on quinoline and triazole
moieties towards their biological significance. Hence, in the
present study we focused our interest on the synthesis, charac-
7
]. These heterocycles have been well exploited for many
medicinal scaffolds exhibiting anti-HIV, anticancer, anti-
infective and antimicrobial activities [8-17]. These moieties
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1
858 Krishnaswamy et al.
Asian J. Chem.
ice bath.After 30 min, NaNO
2
(0.496 g, 0.0072 mol) dissolved
N N
N
in 5 mL of water was added drop wise and stir it for 30 min.
Then NaN (0.468 g, 0.0072 mol) dissolved in 5 mL of water
3
N
was carefully added dropwise to the above solution.After com-
plete addition, the resulting mixture was stirred at room temp-
erature for 1 h. The completion of conversion was monitored
by disappearance of starting material in TLC. After completion
of reaction, dilute the reaction mixture with 10 mL of diethyl
ether. The organic layer was washed with water followed by
brine solution and dried over anhydrous Na SO . The organic
O
O
N
N
N
S
O
(
II)
N
(
CF3
I)
O
O
O
NH
2
4
N
N
N
N
N
H
layer was evaporated under reduced pressure to get solid azide
derivatives (2a-b).
N
N
O
O
General procedure for the preparation of (ester and
ether linked 1,4-disubstituted 1,2,3-triazoles (3a-h): Com-
pound (1a-d) (0.2 g, 0.0012 mol), aryl azides (2a-b) (0.2 g,
N
N
III)
(IV)
(
0
.0012 mol) were added to mixture of 3:1 (THF:H
taken in 100 mL round bottom flask and kept for stirring at
room temperature. Then add CuSO ·5H O (10 mol %) and
2
O) solvent
terization and in silico assessment of drugability of 1,2,3-triazole
derivatives (3a-h). One group of compounds was quinoline
and 1,2,3-triazoles linked through ester linkage and another
one substituted aromatic ring and 1,2,3-triazoles linked via ether
linkage. Additionally, computational evaluation of antimicro-
bial and anti-inflammatory activities andADMETox evaluation
were carried out.
4
2
sodium ascorbate (20 mol %) allow the resulting reaction mixture
to stir at lab temperature for 24 h. The progress of the reaction
was monitored using TLC.After completion of reaction, dilute
the reaction mixture with 10 mL of ethyl acetate. The organic
layer washed with water, brine and dried over anhydrous Na
2
SO .
4
The organic layer was evaporated under vacuum to residue
further triturating with chloroform and hexane resulted in the
desired solid products1,2,3-triazoles (3a-h).
EXPERIMENTAL
The organic solvents and chemicals were purchased from
SD Fine, Spectrochem, Sigma Aldrich and standard commer-
cial sources are used without further purification. Melting point
ranges of solid compounds were determined in open capillary
tubes using a hot stage apparatus. Progress of the reactions
was monitored by TLC using Merck silica gel 60 F254 precoated
on aluminium backed plates. FTIR spectra were recorded using
Spectral data
Prop-2-ynyl-2-phenylquinoline-4-carboxylate (1a):
Yield: 90%, m.p.: 90-92 °C m.f. (m.w.): C19
H
13NO
2
(287.31);
): 8.53-8.51 (d, 1H, J =
.0 Hz, Ar-H), 8.42 (s, 1H, Ar-H), 8.25-8.23 (d, 2H, J = 8.0
1
H NMR (400 MHz, δ ppm, DMSO-d
6
8
1
13
Hz, Ar-H), 8.16-8.14 (d, 1H, J = 6.0 Hz, Ar-H), 7.86-7.82 (t,
1H, Ar-H), 7.72-7.68 (t, 1H, Ar-H), 7.57-7.51 (m, 3H, Ar-H),
Shimadzu ATR spectrophotometer. H and C NMR spectra
1
were recorded onAgilent (400 MHz, H NMR) and (100 MHz,
1
3
5.136-5.130 (d, 2H, J = 2.4 Hz, -O-CH
C NMR (100 MHz, δ ppm, DMSO-d ): 165.65, 156.32,
2
), 3.71 (s, 1H, ≡C-H).
6
C NMR) spectrometers using deuteriated solvents (CDCl
or DMSO-d ) and Tetramethylsilane (TMS) as an internal stan-
dard. The Mass spectra were recorded using Waters Alliance
795 separation module and the Waters Micromass LCT mass
detector.
General procedure for the synthesis of o-propargylated
3
1
3
6
1
1
48.92, 138.24, 136.06, 131.02, 130.65, 130.51, 129.56,
28.73, 127.78, 125.46, 123.59, 119.99, 79.15, 78.59, 54.06;
2
Mass: m/z: 288.01.
1
-Chloro-4-(prop-2-ynyloxy) benzene (1b):Yield: 90%,
1
m.p.: NA (oil). m.f. (m.w.): C
9
H ClO (166.6); H NMR (400
7
derivatives (1a-d): 4-Chloro phenol (or) 5-substituted salicyal-
dehyde (or) 2-phenyl quinoline-4-carboxylic acid (0.1 g, 0.001
MHz, δ ppm, DMSO-d
6
): 7.34-7.31 (d, 2H, J = 8.0 Hz, Ar-H),
7
.00-6.97 (d, 2H, J = 8.0 Hz, Ar-H), 4.78-4.77 (d, 2H, J = 3.0
mol) and K
bottom flask containing DMF (5 mL). To this propargyl bromide
0.089 mL, 0.001 mol) was added drop-wise and resulting
2
CO
3
(0.13 g, 0.001 mol) were taken in a round
13
Hz, -O-CH
2
), 3.55-3.54 (t, 1H, ≡C-H). C NMR (100 MHz, δ
): 162.89, 156.62, 129.80, 125.60, 117.25,
9.48, 78.95, 56.33.
-Bromo-2-(prop-2-ynyloxy) benzaldehyde (1c):Yield:
0%, m.p.: 93-95 °C (lit. [40] 89-91 °C). m.f. (m.w.): C10 Br
): 10.25 (s,
ppm, DMSO-d
7
6
(
mixture was stirred at lab temperature for 12-18 h. The comple-
tion of conversion was monitored by disappearance of starting
material inTLC.After completion of reaction, dilute the reaction
mixture with 10 mL of ethyl acetate. The organic layer was
washed with water followed by brine solution and dried over
SO . The organic layer was evaporated under
reduced pressure to get product of propynyloxy benzene and
benzaldehyde derivatives (1a-d).
5
9
H O
7 2
1
(
239.07); H NMR (400 MHz, δ ppm, DMSO-d
6
1
7
H, CHO), 7.88-7.85 (dd, 1H, J = 8.0 & 5.0 Hz, Ar-H), 7.77-
.76 (d, 1H, J = 6.0 Hz, Ar-H), 7.30-7.28 (d, 2H, J = 5.0 Hz,
anhydrous Na
2
4
Ar-H), 5.02-5.01 (d, 2H, J = 3.0 Hz, -O-CH ), 3.71-3.70 (t,
2
1
H, ≡C-H).
-Nitro-2-(prop-2-ynyloxy) benzaldehyde (1d): Yield:
0%, m.p.: 90-92 °C (lit. [40] 89-91 °C). m.f. (m.w.): C10 NO
): 10.29 (s,
H, CHO), 8.53-8.50 (dd, 1H, J = 8.0 & 5.0 Hz, Ar-H), 8.42-
5
General procedure for the preparation of substituted
azido benzene derivatives (2a-b): Substituted anilines (0.5
g, 0.0036 mol) were added to 10 mL of 2.4 N HCl taken in
9
H
7
4
1
(
1
205.17); H NMR (400 MHz, δ ppm, DMSO-d
6
1
00 mL round bottom flask and cooled to below 10 °C in an

Vol. 32, No. 8 (2020)
Studies of Ester and Ether Linked 1,4-Disubstituted 1,2,3-Triazoles Derivatives via Click Approach 1859
1
8
.41 (d, 1H, J = 6.0 Hz, Ar-H), 7.51-7.49 (d, 2H, J = 5.0 Hz,
Ar-H), 5.16-5.15 (d, 2H, -O-CH ), 3.75-3.74 (t, 1H, ≡C-H).
C NMR (100 MHz, δ ppm, DMSO-d ): 188.12, 163.76,
41.82, 131.08, 124.90, 124.12, 115.68, 80.42, 78.10, 57.92.
1-(4-Nitrophenyl)-1H-1,2,3-triazol-4-yl) methyl-2-
phenylquinoline-4-carboxylate (3a):Yield: 60%, m.p.: 172-
m.f. (m.w.): C18
ppm, DMSO-d
H
14BrN
3
O
3
(400.23); H NMR (400 MHz, δ
2
6
): 10.34 (s, 1H, CHO), 9.16 (s, 1H, triazole-
H), 8.18-8.16 (d, 1H, J = 6.0 Hz, Ar-H), 8.12-8.10 (d, 2H, J =
6.0 Hz, Ar-H), 7.87-7.86 (d, 2H, J = 6.0 Hz, Ar-H), 7.49-7.47
1
3
6
1
(
(d, 2H, J = 8.0 Hz, Ar-H), 5.64 (s, 2H, CH
2
), 2.47 (s, 3H,
13
CH
3
). C NMR (100 MHz, δ ppm, DMSO-d
6
): 195.20, 189.30,
1
1
78 °C. m.f. (m.w.): C25
δ ppm, DMSO-d ): 9.21 (s, 1H, triazole-H), 8.58-8.56 (d, 1H,
J = 8.0 Hz, Ar-H), 8.46 (s, 1H, Ar-H), 8.42-8.40 (d, 2H, J =
.0 Hz, Ar-H), 8.24-8.14 (m, 6H, Ar-H), 7.86-7.82 (t, 1H, Ar-
H), 7.71-7.68 (t, 1H, Ar-H), 7.56-7.50 (m, 3H, Ar-H), 5.70 (s,
H N O
17 5 4
(451.43); H NMR (400 MHz,
161.60, 148.98, 144.74, 140.69, 138.51, 134.00, 130.26,
129.67, 128.75, 124.92, 121.80, 115.54, 61.91, 28.42. Mass:
m/z: 400.26 (M+), 402.23 (M+2).
6
8
5-Nitro-2-((1-(4-nitrophenyl)-1H-1,2,3-triazol-4-yl)
methoxy) benzaldehyde (3g):Yield: 40%, m.p.: 242-244 °C.
1
3
1
2
1
1
1
H, CH
2
); C NMR (100 MHz, δ ppm, DMSO-d
6
): 166.06,
m.f. (m.w.): C16
DMSO-d
H
11
N O
5 6
(369.29); H NMR (400 MHz, δ ppm,
): 10.37 (s, 1H, CHO), 9.24 (s, 1H, triazole-H), 8.50-
56.33, 148.92, 147.34, 143.95, 141.28, 138.27, 136.36, 130.99,
30.62, 132.46, 129.54, 128.67, 127.78, 126.06, 125.62, 124.28,
23.67, 121.27, 120.59, 59.16; Mass: m/z: 452.04.
6
8.42 (d, 1H, J = 6.0 Hz, Ar-H), 8.32-8.30 (d, 2H, J = 6.0 Hz,
Ar-H), 7.88-7.86 (d, 2H, J = 6.0 Hz, Ar-H), 7.71-7.69 (d, 2H,
1
3
(
1-(4-Acetylphenyl)-1H-1,2,3-triazol-4-yl) methyl-2-
J = 8.0 Hz, Ar-H), 5.58 (s, 2H, CH
2
). C NMR (100 MHz, δ
phenylquinoline-4-carboxylate (3b):Yield: 70%, m.p.: 201-
ppm, DMSO-d ): 188.54, 164.60, 148.98, 143.74, 141.69,
6
1
2
04 °C. m.f. (m.w.): C27
δ ppm, DMSO-d ): 9.06 (s, 1H, triazole-H), 8.15-8.13 (d, 1H,
J = 8.0 Hz, Ar-H), 8.07-8.05 (d, 2H, J = 8.0 Hz, Ar-H), 7.34-
H N O
20 4 3
(448.47); H NMR (400 MHz,
137.51, 132.00, 131.26, 126.67, 124.75, 123.92, 123.80, 123.74,
63.63. Mass: m/z: 369.45.
6
5-Nitro-2-((1-(4-acetylphenyl)-1H-1,2,3-triazol-4-yl)
7
.32 (d, 2H, J = 8.0 Hz, Ar-H), 7.10-7.07 (d, 2H, J = 10.0 Hz,
methoxy) benzaldehyde (3h):Yield: 45%, m.p.: 255-257 °C.
13
1
Ar-H), 5.24 (s, 2H, CH
MHz, δ ppm, DMSO-d
2
), 2.61 (s, 3H, CH
3
). C NMR (100
m.f. (m.w.): C18
DMSO-d
H
14
N O
4 5
(366.33); H NMR (400 MHz, δ ppm,
6
): 193.24, 165.47, 155.75, 148.32,
6
): 10.37 (s, 1H, CHO), 9.21 (s, 1H, triazole-H), 8.18-
146.78, 143.34, 140.69, 137.65, 135.89, 135.61, 130.44, 130.06,
129.87, 128.97, 128.10, 127.19, 125.50, 124.84, 123.70, 122.97,
119.42, 58.56, 26.42. Mass: m/z: 448.15.
8.16 (d, 1H, J = 6.0 Hz, Ar-H), 8.12-8.10 (d, 2H, J = 6.0 Hz,
Ar-H), 7.87-7.86 (d, 2H, J = 6.0 Hz, Ar-H), 7.49-7.47 (d, 2H,
13
J = 8.0 Hz, Ar-H), 5.58 (s, 2H, CH
2
), 2.45 (s, 3H, CH ). C
3
4
-((4-Chlorophenoxy) methyl-1-(4-nitrophenyl)-1H-
NMR (100 MHz, δ ppm, DMSO-d ): 193.50, 188.25, 157.36,
6
1
C
d
,2,3-triazole (3c):Yield: 80%, m.p.: 130-132 °C. m.f. (m.w.):
147.40, 144.88, 141.35, 135.69, 129.91, 126.16, 124.73,
125.40, 123.97, 121.31, 117.19, 61.86, 29.30. Mass: m/z:
367.24.
1
15
H
11
N O
4 3
Cl(330.73); H NMR (400 MHz, δ ppm, DMSO-
): 9.14 (s, 1H, triazole-H), 8.44-8.42 (d, 1H, J = 8.0 Hz, Ar-
H), 8.22-8.20 (d, 2H, J = 10.0 Hz, Ar-H), 7.35-7.32 (d, 2H, J
10.0 Hz, Ar-H), 7.10-7.07 (d, 2H, J = 10.0 Hz, Ar-H), 5.25
6
RESULTS AND DISCUSSION
=
(
1
1
13
s, 2H, CH
2
). C NMR (100 MHz, δ ppm, DMSO-d
6
): 157.36,
Quinoline-4-carboxylic acid (or) 4-chloro phenol (or) 5-
substituted salicyaldehyde underwent nucleophilic displace-
ment reaction with propargyl bromide in presence of base at
lab temperature to give o-propargylated quinoline-4-carboxylic
acid (1a), benzene and salicyaldehyde derivatives (1b-d).
Further, o-propargylated derivatives (1a-d) underwent Cu(I)-
catalyzed Huisgen 1,3-dipolar cycloaddition reaction with
substituted azido benzene derivatives (2a-b) at lab temperature
afforded corresponding ester and ether linked 1,4-disubsti-
tuted-1,2,3-triazole derivatives (3a-h), respectively in good
yield as depicted in Scheme-I. The assigned structures of the
intermediates and triazole derivatives were confirmed by their
47.40, 144.88, 141.35, 129.91, 126.16, 125.40, 123.97, 121.31,
17.19, 61.86. Mass: m/z: 330.96.
1
-(4-(4-((4-Chlorophenoxy) methyl)-1H-1,2,3-triazol-
1
-yl) phenyl) ethanone (3d): Yield: 80%, m.p.: 195-197 °C.
1
m.f. (m.w.): C17
ppm, DMSO-d ): 9.06 (s, 1H, triazole-H), 8.15-8.13 (d, 1H, J
Hz, Ar-H), 8.07-8.05 (d, 2H, J = 8.0 Hz, Ar-H), 7.34-7.32
d, 2H, J = 8.0 Hz, Ar-H), 7.10-7.07 (d, 2H, J = 10.0 Hz, Ar-
H
14
N
3
O Cl (327.76); H NMR (400 MHz, δ
2
6
=
(
13
H), 5.24 (s, 2H, CH
2
), 2.61 (s, 3H, CH ). C NMR (100 MHz,
3
δ ppm, DMSO-d
30.65, 129.90, 125.36, 123.67, 120.44, 117.17, 61.91, 27.42.
Mass: m/z: 328.65.
6
): 197.49, 157.40, 144.58, 140.09, 137.06,
1
1
13
physical characterization data and spectral studies viz. H & C
NMR and mass analysis.
5
-Bromo-2-((1-(4-nitrophenyl)-1H-1,2,3-triazol-4-yl)
methoxy) benzaldehyde (3e):Yield: 45%, m.p.: 253-255 °C.
Computational evaluation of antimicrobial and anti-
inflammatory activities: Prediction of activity spectra for
substances (PASS) (http://www.way2drug.com/) is software
for the creation of SAR models based on MNA descriptors
and modified Bayesian algorithm. PASS approach can be applied
to so called “drug-like” substances.
1
m.f. (m.w.): C16
ppm, DMSO-d
H), 8.47-8.45 (d, 1H, J = 6.0 Hz, Ar-H), 8.26-8.24 (d, 2H, J =
.0 Hz, Ar-H), 7.87-7.85 (d, 2H, J = 8.0 Hz, Ar-H), 7.50-7.47
H
11
N
4
O
4
Br (403.19); H NMR (400 MHz, δ
6
): 10.34 (s, 1H, CHO), 9.22 (s, 1H, triazole-
6
13
(
d, 2H, J = 8.0 Hz, Ar-H), 5.50 (s, 2H, CH
2
). C NMR (100
MHz, δ ppm, DMSO-d
6
): 191.00, 162.60, 148.98, 144.74,
40.69, 138.51, 134.00, 130.26, 128.67, 124.75, 122.92, 121.80,
PASS programme software is used for the prediction of
biological activity spectra of organic molecules on the basis
of their structural formula. PASS result spectrum of a compound
is designated as Probable activity (Pa) and Probable inactivity
(Pi). Interpreting the prediction results is related to novelty of
1
1
16.74, 65.63. Mass: m/z: 403.17 (M+), 405.12 (M+2).
-Bromo-2-((1-(4-acetylphenyl)-1H-1,2,3-triazol-4-yl)
methoxy) benzaldehyde (3f):Yield: 50%, m.p.: 235-237 °C.
5

1
860 Krishnaswamy et al.
O OH
Asian J. Chem.
compounds (3a-h) were more potent as anti-inflammatory
compared to antibacterial as well as antifungal agents.
ADMETox evaluation: By applying computational
methods, the various physicochemical features and pharmaco-
kinetic descriptors were calculated through the online web tool
SwissADME [42]. Whereas, in silico toxicity evaluation was
carried out using an online server ProTox-II [43], that gives
predicted oral toxicity, cytotoxicity, mutagenicity, carcino-
genicity, hepatotoxicity and immunotoxicity values for 1,2,3-
triazole derivatives (3a-h).
N
(i)
R2
N
O
O
N
O
O
N
N
Drug-likeness, bioavailability and synthetic accessibility:
Drug-likeness is a quantitative parameter that measures a com-
pound’s oral bioavailability.Abbot bioavailability score predicts
the chance of a compound to have at least 10% oral bioavailability
in rat or measurable Caco-2 cell line permeability experiment
using a model for human intestinal absorption of drugs [39].
The parameters considered to measure the score are lipophi-
licity (–0.7 < XLOGP3 < 5.0), molecular weight (MW) (150
N
[
1a]
[3a-b]
(ii)
R2
N3
[2a-b]
R
R1
R
R1
O
O
N
R2
N N
[
1b-d]
–1
–1
2
g mol < MW < 500 g mol ), polarity (20 Å < TPSA < 130
2
[3c-h]
Å ), solubility (0 < log S (ESOL) < 6), saturation (0.25 <
3
Fraction Csp < 1) and flexibility (0 < of rotatable bonds < 9).
(
i)
This semi-quantitative rule-based score defines the compounds
into four probability score classes i.e. 11%, 17%, 55% and
R
R1
R = -Cl, -Br, -NO2
8
5% [44,45]. The acceptable probability score is 55% which
R = -H, -CHO
1
indicates that it passed the rule of five. All the synthesized
compounds (3a-h) showed a score of 55%, indicating good
bioavailability (Table-2).
OH
R = 4-NO , 4-COCH
2
2
3
Scheme-I: Synthetic route for the preparation of ester and ether linked
,4-disubstituted 1,2,3-triazoles derivatives (3a-h) via Click
approach; Reagents and condition: (i) Propargyl bromide,
CO , DMF, room temperature, 12-18 h; (ii) CuSO ·5H O,
NaAs, THF-H O, room temperature
1
The drug-likeness scores were also calculated by consi-
dering (miLog P, TPSA, nAtoms, nON, nOHNH, rotb & MW)
based on Lipinski’s, Ghose and Veber rule for the prediction
of bioactivity score were carried out. The results of these pre-
diction showed that the compounds obeyed Lipinski’s, Ghose
and Veber rule except compound (3g) which violates Veber
rule (Table-3). Further, synthetic accessibility of the (3a-h)
was assessed to quantify the complexity of the molecular struc-
ture. The results showed that the score were in the range of
2.53-3.38 revealed that the compounds does not have complex
synthetic route as tabulated in Table-3.
K
2
3
4
2
2
the analyzed compounds. For example, if Pa > 0.7, the chances
of finding experimental activity are rather high but the comp-
ounds found may be close structural analogs of known drugs.
If we select in the range 0.5 < Pa < 0.7, the chances for detecting
experimental activity will be lower but the compounds will be
less similar to known pharmaceutical agents. For Pi < Pa <
0.5, the chances of detecting experimental activity will be even
lower, but if the prediction is confirmed, the compound found
may prove a parent compound for a new chemical class for
the biological activity examined [41]. PASS prediction of
compounds (3a-h) were 0.22 < Pa < 0.33 in antibacterial, 0.23
The compound’s aqueous and non-aqueous solubility
influences the absorption and is an important factor in view of
the drug development process. Lipophilicity is the effective
solubility of a compound into the non-aqueous medium and
correlated to various models of drug properties such as adsorp-
tion, distribution, metabolism and toxicity. The mean predicted
<
Pa < 0.35 in antifungal and 0.33 < Pa < 0.62 in anti-
inflammatory (Table-1). These predictive results showed that
TABLE-1
PREDICTED BIOLOGICAL ACTIVITY OF SYNTHESIZED COMPOUNDS (3a-h)
Antibacterial Antifungal
Anti-inflammatory
Compound
Probable to be
active (Pa)
Probable to be
inactive (Pi)
Probable to be
active (Pa)
Probable to be
inactive (Pi)
Probable to be
Probable to be
inactive (Pi)
active (Pa)
0.524
0.622
0.501
0.610
–
0.332
0.342
0.382
3
3
a
b
0.297
0.264
0.257
0.219
0.317
0.281
0.326
0.328
0.061
0.076
0.079
0.102
0.054
0.067
0.051
0.050
–
–
–
–
0.050
0.027
0.056
0.029
–
0.135
0.127
0.105
3
c
0.236
0.235
0.356
0.359
0.303
0.329
0.114
0.115
0.061
0.060
0.080
0.070
3
d
3
e
3
f
3
3
g
h

Vol. 32, No. 8 (2020)
Compound
Studies of Ester and Ether Linked 1,4-Disubstituted 1,2,3-Triazoles Derivatives via Click Approach 1861
TABLE-2
PHYSICO-CHEMICAL PROPERTIES OF SYNTHESIZED COMPOUNDS (3a-h)
3
m.f.
m.w.
XLOGP3
1.97
1.82
3.30
3.16
2.83
2.69
4.49
4.34
Fraction Csp
0.06
log S (ESOL)
-3.37
3
3
a
b
C
16
C
18
C
15
C
17
C
16
C
18
C
25
C
27
H
H
H
H
H
H
H
H
11
14
11
14
11
14
17
20
N O
5
6
369.29
366.33
330.73
327.76
403.19
400.23
451.43
448.47
N O
4
0.11
0.07
0.12
0.06
0.11
0.04
0.07
-3.26
-4.19
-4.08
-4.23
-4.12
-5.59
-5.48
5
3
c
N O
4
3
Cl
Cl
Br
Br
3
d
N O
3
2
3
e
N O
4
4
3
f
N O
3
3
3
3
g
h
N O
5
4
N O
4
3
TABLE-3
TABLE-4
DRUG LIKENESS, BIOACTIVITY AND SYNTHETIC ACCESSI-
PREDICTED ABSORPTION PARAMETERS
BILITY SCORE OF SYNTHESIZED COMPOUNDS (3a-h)
OF SYNTHESIZED COMPOUNDS (3a-h)
Bioactivity
score
Synthetic
accessibility
Consensus
log Po/w
Consensus
log S
Compound Lipinski Ghose Veber
Compound
Solubility class
3
a
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
0.55
0.55
0.55
0.55
0.55
0.55
0.55
0.55
3.35
3.38
2.63
2.53
2.79
2.78
2.81
2.86
3a
3b
3c
3d
3e
3f
3.58
4.14
2.41
3.16
2.23
2.96
1.00
1.60
-5.59
-5.48
-4.19
-4.08
-4.23
-4.12
-3.37
-3.26
Moderately soluble
Moderately soluble
Moderately soluble
Moderately soluble
Moderately soluble
Moderately soluble
Soluble
3b
3
c
3d
3
3
e
f
3g
3g
3h
3h
Yes
Soluble
lipophilicity values are known as the consensus log Po/w is
used to decide the non-aqueous solubility.A molecule is more
soluble if the consensus log Po/w values is more negative.
Results showed that compounds (3a-h) were not soluble in
non-aqueous medium (Table-4). Some drugs have to be highly
water solubility to deliver active ingredient and to estimate
this qualitative estimation of solubility log S scale was used:
if log S < –10 poorly soluble, < –6 moderately soluble, < –4
soluble, < –2 very soluble and < 0 highly soluble. Based from
this predictive model, compounds (3a-f) were predicted to be
moderately soluble and compounds (3g-h) are predicted to be
water soluble (Table-4).
intestinal epithelial cells, blood capillary wall, glomerulus,
restrictive organ barriers (e.g. blood-brain-barrier) and the
target cell. The brain or intestinal estimated permeation method
(BOILED-Egg) is proposed as an accurate predictive model
that works by computing the lipophilicity and polarity of small
molecules. The white region indicates passive gastrointestinal
absorption and yellow region indicates passive brain permea-
tion. The derivatives (3a) and (3g) have low GI absorption as
shown in Fig. 1, while compounds (3d) and (3f) have BBB
permeant as tabulated in Table-5. A compound being blood-
brain permeant there is a possibility of causing harmful toxi-
cants in the brain and blood stream when metabolized. The
remaining compounds were predicted to be non blood-brain
penetrates. A molecule is said to be less skin permeant if the
After absorption of drug by the system, it encounters various
membrane barriers such as hepatocyte membrane, gastro-
7
6
7
6
5
4
5
Molecule 1
O
N
O
4
3
2
1
0
3
2
Molecule 1
O O
N
N
N
N
O
O
O
1
O
H
N
0
N
N
N
-
1
-1
-2
-3
-4
N
O
-2
O
-3
-4
0
20
40
60
80 100 120 140 160 180 200
TPSA
0
20
40
60
80 100 120 140 160 180 200
TPSA
Fig. 1. BOILED Egg image of compounds 3a and 3g

1
862 Krishnaswamy et al.
Asian J. Chem.
TABLE-5
PREDICTED DISTRIBUTION
PARAMETERS OF THE COMPOUNDS (3a-h)
at which the test subjects die upon exposure to it [38]. The
toxicity class ranges from 1 to 6 as shown below:
Class I: fatal if swallowed (LD50 ≤ 5)
Compound
GI absorption
Low
BBB permeant
log Kp (cm/s)
-5.87
Class II: fatal if swallowed (5 < LD50 ≤ 50)
3a
No
No
No
Yes
No
Yes
No
No
Class III: toxic if swallowed (50 < LD50 ≤ 300)
Class IV: harmful if swallowed (300 < LD50 ≤ 2000)
3b
High
High
High
High
High
Low
High
-5.95
-5.97
-6.06
-6.75
-6.83
-7.15
-7.24
3c
Class V: may be harmful if swallowed (2000 < LD50
000)
Class VI: non-toxic (LD50 > 5000)
≤
3d
5
3e
3
f
The results showed that the synthesized compounds were
predicted to be harmful if swallowed and belongs to class IV
3g
3h
(
Table-7).
value of log Kp is more negative. From the predicted results,
compounds (3g) and (3h) were found to be the least skin
permeant (Table-5).
Metabolism plays an important role in the bioavailability
of drugs as well as drug-drug interactions. It is also important
to have a better understanding if a certain compound is a subs-
trate or non-substrate of the certain proteins. The permeability
glycoprotein (P-gp) is an important protein in assessing active
efflux through biological membranes and cytochrome P450
TABLE-7
PREDICTED LD AND TOXICITY
50
CLASS OF THE COMPOUNDS (3a-h)
Compound
Predicted LD (mg/kg)
Toxicity class
50
3a
495
4
4
4
4
4
4
4
4
3b
495
494
400
494
1000
494
3c
3d
3e
(CYP) enzymes as they involved in drug elimination through
3
f
metabolic transformation. Both these can process small mole-
cules synergistically to enhance the protection of tissues and
organisms. Inhibition of these isoenzymes may result in unwan-
ted adverse side-effects by lowering the solubility and the accu-
mulation of the drug or its metabolites. Hence the compounds
3g
3h
400
The ProTox online server also predicts four toxicological
endpoints such as cytotoxicity, mutagenicity, carcinogenicity
and immunotoxicity. Results suggested that all the compounds
were predicted to be non-immunotoxic (Table-8). Immuno-
toxic chemicals are known to alter the correct functioning of
immune system by B cell growth inhibition. Moreover, the
organ toxicity, specifically hepatotoxicity was predicted to
evaluate if the compound will cause liver dysfunction. Results
showed that the compounds (3a-d) were non-hepatotoxic and
remaining compounds (3e-f) are hepatotoxic. Moreover, comp-
ounds (3a-c), (3e) and (3g-h) were predicted to be a mutagenic
compound (Table-8). This means that it can possibly cause
alteration of a genetic material, such as the DNA of an organism
(
3a-h) were evaluated to determine whether the compound can
act as substrate or an inhibitor of P-gp and CYPs. All comp-
ounds are found to be non-substrates of P-gp. The compounds
(
compounds are predicted to be substrates of CYP2C19 and
CYP2C19. All compounds are predicted to be CYP2D6 non
substrates and the compounds 3b, 3e and 3h were found to be
potential substrates for CYP3A4 (Table-6).
Toxicity evaluation is initially used to determine the
compound’s toxicity as a fast and an inexpensive method to
reduce the number of compounds to be sent later for further
testing. in silico toxicity evaluation could not act as absolute
answer for the compound’s toxicity evaluation and it should
always be accompanied by in vitro and in vivo experiments to
verify the biological activities beyond the capability of these
computational approaches. Here, the synthesized compounds
were subjected to an in silico toxicity evaluation using Pro-Tox.
The LD50 is defined as the median lethal dose of a compound
3c-f) presented were found to be substrates of CYP1A2. All
[
42].
Conclusion
In the present investigation, synthesis of pharmacophoric
units (quinoline and 1,2,3-triazole) linked through ester and
(substituted aromatic ring and 1,2,3-triazole) linked through
an ether were carried out in good yield. The assigned structures
TABLE-6
PREDICTED METABOLISM PARAMETERS OF THE COMPOUNDS (3a-h)
Compound
P-gp
No
No
No
No
No
No
No
No
CYP1A2 inhibitor
CYP2C19 inhibitor
CYP2C9 inhibitor CYP2D6 inhibitor CYP3A4 inhibitor
3
3
a
b
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
Yes
No
No
Yes
No
3
c
Yes
Yes
Yes
Yes
No
3
d
3
e
3
f
3
3
g
h
No
Yes
No

Vol. 32, No. 8 (2020)
Compound
Studies of Ester and Ether Linked 1,4-Disubstituted 1,2,3-Triazoles Derivatives via Click Approach 1863
TABLE-8
PREDICTED ACTIVITY OF THE COMPOUNDS ON TOXICITY ENDPOINTS (3a-h)
Hepatotoxicity
Inactive
Inactive
Inactive
Inactive
Active
Carcinogencity
Active
Immunotoxicity
Inactive
Mutagencity
Active
Active
Active
Inactive
Active
Cytotoxicity
Inactive
Inactive
Inactive
Inactive
Active
3
3
a
b
Inactive
Active
Inactive
Active
Inactive
Inactive
Inactive
Inactive
3
c
3
d
3
e
3
f
Active
Active
Active
Inactive
Active
Active
Inactive
Inactive
Inactive
Inactive
Active
Active
Inactive
Inactive
Inactive
3
3
g
h
1
of the compound were confirmed by multi nuclear NMR ( H
10. S. Cocklin, H. Gopi, B. Querido, M. Nimmagadda, S. Kuriakose, C.
Cicala, S. Ajith, S. Baxter, J.Arthos, J. Martin-Garcia and I.M. Chaiken,
J. Virol., 81, 3645 (2007);
13
&
C) and Mass spectrometry. PASS prediction of the comp-
ounds (3a-h) showed that the compounds are more potent as
anti-inflammatory compared to antibacterial which in turn
more potent than antifungal agents. Furthermore, these comp-
ounds were subjected to in silico ADMETox evaluation. All
the compounds were found to pass the ADME evaluation and
few compounds passed the predicted toxicity evaluation. From
this work an initial approach in identifying potential novel
molecules with promising activity with low toxicity was carried
out and paves the way for further investigation and develop-
ment.
https://doi.org/10.1128/JVI.01778-06
11. A. Brik, J. Alexandratos,Y.C. Lin, J.H. Elder, A.J. Olson,A. Wlodawer,
D.S. Goodsell and C.-H. Wong, ChemBioChem, 6, 1167 (2005);
https://doi.org/10.1002/cbic.200500101
2. W.J. Yu, Q. Rao, M. Wang, Z. Tian, D. Lin, X.R. Liu and J.X. Wang,
Leuk. Res., 30, 575 (2006);
1
https://doi.org/10.1016/j.leukres.2005.08.028
13. L.B. Peterson and B.S.J. Blagg, Bioorg. Med. Chem. Lett., 20, 3957
2010);
(
https://doi.org/10.1016/j.bmcl.2010.04.140
1
1
4. M. Nahrwold, T. Bogner, S. Eissler, S. Verma and N. Sewald, Org.
Lett., 12, 1064 (2010);
https://doi.org/10.1021/ol1000473
5. J. Doiron, A.H. Soultan, R. Richard, M.M. Toure, N. Picot, R. Richard,
M. Cuperlovic-Culf, G.A. Robichaud and M. Touaibia, Eur. J. Med.
Chem., 46, 4010 (2011);
ACKNOWLEDGEMENTS
The authors are thankful to Tumkur University, Tumakuru
for providing the laboratory facility to carry out this research/
dissertation work.
https://doi.org/10.1016/j.ejmech.2011.05.074
1
1
1
1
6. I. Fichtali, M. Chraibi, F.E. Aroussi, A. Ben-Tama, E. M. E. Hadrami,
K. F. Benbrahim and S. E. Stiriba, Der. Pharm. Chem., 8, 236 (2016).
7. B. F. Abdel-Wahab, H. A. Mohamed and G. E. A. Awad, Eur. Chem.
Bull., 4, 106 (2015).
8. D. Dheer, V. Singh and R. Shankar, Bioorg. Chem., 71, 30 (2017);
https://doi.org/10.1016/j.bioorg.2017.01.010
CONFLICT OF INTEREST
The authors declare that there is no conflict of interests
regarding the publication of this article.
9. T.F. Borgati, R.B.Alves, R.R. Teixeira, P.F. Rossimiriam, T.G. Perdigão,
S.F. Silva, A.A. Santos and A.J.O. Bastidas, J. Braz. Chem. Soc., 24,
9
53 (2013);
REFERENCES
https://doi.org/10.5935/0103-5053.20130121
0. R.A. Silva, E.D. Quintela, G.M. Mascarin, J.A.F. Barrigossi and L.M.
Lião, Sci. Agric., 70, 152 (2013);
https://doi.org/10.1590/S0103-90162013000300003
1. Q. Ma, S. Qi, X. He, Y. Tang and G. Lu, Corros. Sci., 129, 91 (2017);
https://doi.org/10.1016/j.corsci.2017.09.025
2
1
2
.
.
R. Reddyrajula and U. Dalimba, ChemistrySelect, 4, 2685 (2019);
https://doi.org/10.1002/slct.201803946
A. Ayati, S. Emami and A. Foroumadi, Eur. J. Med. Chem., 109, 380
2
2
(
2016);
2. L. Wang, M.J. Zhu, F.C. Yang and C.W. Gao, Int. J. Corros., 2012,
https://doi.org/10.1016/j.ejmech.2016.01.009
I. Briguglio, S. Piras, P. Corona, E. Gavini, M. Nieddu, G. Boatto and
A. Carta, Eur. J. Med. Chem., 97, 612 (2015);
https://doi.org/10.1016/j.ejmech.2014.09.089
K.R.A. Abdellatif and R.B. Bakr, Bioorg. Chem., 78, 341 (2018);
https://doi.org/10.1016/j.bioorg.2018.03.032
R.J. Herr, Bioorg. Med. Chem., 10, 3379 (2002);
https://doi.org/10.1016/S0968-0896(02)00239-0
L. Dymiñska, Bioorg. Med. Chem., 23, 6087 (2015);
https://doi.org/10.1016/j.bmc.2015.07.045
J.A. Maertens, Clin. Microbiol. Infect., 10, 1 (2004);
https://doi.org/10.1111/j.1470-9465.2004.00841.x
F.D.C. Da Silva, M.C.B.V. De Souza, I.I.P. Frugulhetti, H.C. Castro,
S.L.D.O. Souza, T.M.L. De Souza, D.Q. Rodrigues, A.M.T. Souza,
P.A. Abreu, F. Passamani, C.R. Rodrigues and V.F. Ferreira, Eur. J.
Med. Chem., 44, 373 (2009);
5
73964 (2012);
3
.
https://doi.org/10.1155/2012/573964
2
2
3. T.Y.B. Leung, M. Kang, B.F. Corry and A.A. Gewirth, J. Electrochem.
Soc., 147, 3326 (2000);
https://doi.org/10.1149/1.1393902
4. K.S. McNeill and D.A. Cancilla, Bull. Environ. Contam. Toxicol., 82,
4
5
6
7
8
.
.
.
.
.
2
65 (2009);
https://doi.org/10.1007/s00128-008-9626-z
25. B. Schulze and U.S. Schubert, Chem. Soc. Rev., 43, 2522 (2014);
https://doi.org/10.1039/c3cs60386e
26. Y. Hua and A.H. Flood, Chem. Soc. Rev., 39, 1262 (2010);
https://doi.org/10.1039/b818033b
27. M. Arseneault, C. Wafer and J.F. Morin, Molecules, 20, 9263 (2015);
https://doi.org/10.3390/molecules20059263
28. D. Astruc, L. Liang, A. Rapakousiou and J. Ruiz, Acc. Chem. Res., 45,
630 (2012);
https://doi.org/10.1016/j.ejmech.2008.02.047
9
.
M.J. Giffin, H. Heaslet, A. Brik, Y.C. Lin, G. Cauvi, C.-H. Wong, D.E.
McRee, J.H. Elder, C.D. Stout and B.E. Torbett, J. Med. Chem., 51,
https://doi.org/10.1021/ar200235m
29. D. Srividhya, S. Manjunathan, S. Thirumaran, C. Saravanan and S.
Senthil, J. Mol. Struct., 927, 7 (2009);
6263 (2008);
https://doi.org/10.1021/jm800149m
https://doi.org/10.1016/j.molstruc.2009.01.035

1
864 Krishnaswamy et al.
Asian J. Chem.
3
0. H. Gallardo, F. Ely, A. Bortoluzzi and G. Conte, Liq. Cryst., 32, 667
2005);
39. K.D. Thomas, A.V. Adhikari, I.H. Chowdhury, E. Sumesh and N.K.
Pal, Eur. J. Med. Chem., 46, 2503 (2011);
(
https://doi.org/10.1080/02678290500139732
1. N.A. Evans, Aust. J. Chem., 34, 691 (1981);
https://doi.org/10.1071/CH9810691
https://doi.org/10.1016/j.ejmech.2011.03.039
40. M.J. Khoshkholgh, S. Balalaie, R. Gleiter and F. Rominger,
Tetrahedron, 64, 10924 (2008);
https://doi.org/10.1016/j.tet.2008.08.056
41. V. Poroikov, D. Filimonov, A. Lagunin, T. Gloriozova andA. Zakharov,
SAR QSAR Environ. Res., 18, 101 (2007);
https://doi.org/10.1080/10629360601054032
42. A. Daina, O. Michielin and V. Zoete, Sci. Rep., 7, 42717 (2017);
https://doi.org/10.1038/srep42717
43. P. Banerjee, A.O. Eckert, A.K. Schrey and R. Preissner, Nucleic Acids
Res., 46(W1), W257 (2018);
3
3
3
3
3
3
2. P. M. Miladinova and T. N. Konstantinova, J. Chem. Tech. Metall., 50,
229 (2015).
3. K.M. Rajalekshmi, C.A. Jaleel, M.M. Azooz and R. Panneerselvam,
Adv. Biol. Res., 3, 117 (2009).
4. I.A. Khalil, E.I. Mercer and Z.X. Wang, Plant Sci., 66, 21 (1990);
https://doi.org/10.1016/0168-9452(90)90164-J
5. K. Uzun, E. Cevik and M. Senel, Am. J. Chem, 1, 16 (2012);
https://doi.org/10.5923/j.chemistry.20110101.03
6. G. Zhang, Y. Wang, X. Wen, C. Ding and Y. Li, Chem. Commun., 48,
https://doi.org/10.1093/nar/gky318
2
979 (2012);
44. R.B. van Breemen and Y. Li, Expert Opin. Drug Metab. Toxicol.,, 1,
175 (2005);
https://doi.org/10.1039/c2cc18023e
3
3
7. B. Garudachari, A.M. Isloor, M.N. Satyanarayana, H.K. Fun and G.
Hegde, Eur. J. Med. Chem., 74, 324 (2014);
https://doi.org/10.1016/j.ejmech.2014.01.008
8. K.K. Kumar, S.P. Seenivasan, V. Kumar and T.M. Das, Carbohydr.
Res., 346, 2084 (2011);
https://doi.org/10.1517/17425255.1.2.175
45. S. Alam and F. Khan, Sci. Rep., 8, 5524 (2018);
https://doi.org/10.1038/s41598-018-23768-7
46. M.N. Drwal, P. Banerjee, M. Dunkel, M.R. Wettig and R. Preissner,
Nucleic Acids Res., 42(W1), W53 (2014);
https://doi.org/10.1016/j.carres.2011.06.028
https://doi.org/10.1093/nar/gku401