Synthesis, biological evaluation, and molecular modeling studies of chiral chloroquine analogues as antimalarial agents

Article abstract of DOI:10.1128/AAC.02347-17

In a focused exploration, we designed, synthesized, and biologically evaluated chiral conjugated new chloroquine (CQ) analogues with substituted piperazines as antimalarial agents. In vitro as well as in vivo studies revealed that compound 7c showed potent activity (in vitro 50% inhibitory concentration, 56.98 nM for strain 3D7 and 97.76 nM for strain K1; selectivity index in vivo [up to at a dose of 12.5 mg/kg of body weight], 3,510) as a new lead antimalarial agent. Other compounds (compounds 6b, 6d, 7d, 7h, 8c, 8d, 9a, and 9c) also showed moderate activity against a CQ-sensitive strain (3D7) and superior activity against a CQ-resistant strain (K1) of Plasmodium falciparum. Furthermore, we carried out docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of all in-house data sets (168 molecules) of chiral CQ analogues to explain the structure-activity relationships (SAR). Our new findings specify the significance of the H-bond interaction with the side chain of heme for biological activity. In addition, the 3D-QSAR study against the 3D7 strain indicated the favorable and unfavorable sites of CQ analogues for incorporating steric, hydrophobic, and electropositive groups to improve the antimalarial activity.

Full text of DOI:10.1128/AAC.02347-17

AAC Accepted Manuscript Posted Online 17 September 2018
Antimicrob. Agents Chemother. doi:10.1128/AAC.02347-17
Copyright © 2018 American Society for Microbiology. All Rights Reserved.
1
2
Synthesis, Biological Evaluation and Molecular Modeling Studies
of Chiral Chloroquine Analogues as Antimalarial Agents
3
4
Srinivasarao Kondaparlaa†, Utsab Debnatha†, Awakash Sonib, Vasantha Rao Dolaa, Manish
Sinhaa, Kumkum Srivastavab, Sunil K. Purib and Seturam B. Katti*a
5
6
aMedicinal and Process Chemistry Division, CSIR-Central Drug Research Institute,
Lucknow 226031, India
7
8
bParasitology Division, CSIR-Central Drug Research Institute, Sector-10, Jankipuram
Extension, Sitapur Road, Lucknow-226031, India
9
10
11
† These two authors contributed equally to this work
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Abstract: In a focused exploration, we have designed synthesized and biologically evaluated
chiral conjugated new chloroquine (CQ) analogues with substituted piperazines as
antimalarial agents. In vitro as well as in vivo studies revealed that compound 7c showed
potent activity [for in vitro IC50= 56.98nM (3D7), 97.76nM (K1); for in vivo (up to at the
dose of 12.5 mg/kg); SI = 3510] as a new lead of antimalarial agent. Other compounds 6b,
6d, 7d, 7h, 8c, 8d, 9a and 9c are also showing moderate activity against CQ-sensitive (3D7)
strain and superior activity against resistant (K1) strain of P. falciparum. Furthermore, we
have carried out docking and 3D-QSAR studies of all in-house data sets (168 molecules) of
chiral CQ analogues to explain the structure activity relationships (SAR). Our new findings
specified the significance of H-bond interaction with the side chain of heme for biological
activity. In addition, the 3D-QSAR study against 3D7 strain indicated the favourable and
unfavourable sites of CQ analogues for incorporating steric, hydrophobic and electropositive
groups to improve the antimalarial activity.
Key-words: Piperazines, CQ-sensitive strain-3D7, Heme binding assay, In-vitro & in-vivo
assay, Docking, 3D-QSAR.
29
30
Corresponding author: Tel: +91-522-2772454, Fax; +91-522-2771941 Email address:
setu_katti@yahoo.com, (S. B. Katti)
31
32
1

33 1. Introduction
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
Malaria is a parasitic disease with high mortality and morbidity which sternly
influence the socio-economic development of affected countries. [1]. It is one of the most
prevalent parasitic disease, affecting nearly 300 million people around the globe annually [2].
Five species of Plasmodium (P. falciparum, vivax, ovale, malariae and knowlesi) are known
to infect humans and P. falciparum causes the most severe form of infection. In erythrocytic
stage, malaria parasite utilizes host haemoglobin for its growth and proliferation and convert
toxic heme into non-toxic insoluble crystalline form called hemozoin [3]. However, in the
infected erythrocytes, the mechanism of the conversion of heme to hemozoin through
biochemical factors is not fully understood [4]. The inhibition of heme to hemozoin
conversion in parasite is one of the most effective ways to treat malaria. Among the
antimalarial agents quinoline scaffold have immense significance as forming a complex with
heme in its monomer and µ-oxo dimer forms. Also, one of the 4-aminoquinoline based
derivatives (Chloroquine; CQ) is most widely used for malaria treatment. [5-6]. Nevertheless,
the widespread resistance of P. falciparum to CQ and artemisinin class of antimalarials has
hampered the efforts to combat this deadly disease [7-8]. The resistance is a consequence of
decreased accumulation of the drug in the food vacuole of the parasite owing to enhanced
efflux and reduced uptake [9]. Therefore, it is an urgent need to modify the structure of CQ
for the development of alternative drug like molecules that can circumvent the problem of P.
falciparum resistance.
The SAR studies on CQ analogues revealed that compounds having shorter chain
length are found to be active against CQ-resistant parasite strains [9-11]. By considering this
scenario a number of research groups have synthesized new short chain analogues of 4-
aminoquinolines, which were found considerably more potent than CQ against the resistant
strain of P. falciparum in vitro [12]. However, Roepe et al. recently concluded that these
observations only hold good for quinoline derivatives that contain a diethyl substituent on the
terminal nitrogen [13]. It is believed that N-dealkylation reduces lipid solubility of these
drugs and their derivatives leading to reduced antiplasomdial activity facilitating drug
resistance [14]. Towards this objective, present researchers are putting efforts to modify the
existing chemical moieties to get new leads for antimalarial activity. Previously from our
group, we have investigated 4-aminoquinolines, synthesized by modulating the lipophilicity
and basicity of lateral side chain attached to the 4-amino group of the CQ side chain [15-21].
Many of these analogues were found to form a complex with hematin and inhibit the β-
2

66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
hematin formation, suggesting that this class of compounds act on a heme dimerization
target. Also, many of these analogues have shown significant activity against CQ-resistant
parasites.
Encouraged by these findings, we thought that fine-tuning of the terminal side chain
with N-methylpiperazine could modulate the antimalarial activity [22]. Furthermore, from our
laboratory several chiral chloroquine and its analogues were developed which showed
excellent in vitro activity as compared to CQ [23]. As a part of our antimalarial drug
discovery programme, the above mentioned results offered an ideal opportunity for better
designing of molecules with chirally defined architecture and tailored pendant groups.
Therefore, we have designed target specific compounds through a rational method. This
rational method was used with special emphasis on avoiding metabolic N-dealkylation by
incorporating bulkier substituents at adjacent part of the side chain and was evaluated for
their antimalarial activity.
It has been seen that majority of analogues were found to have excellent activities
against CQ-sensitive parasite strain 3D7. Nevertheless, no 3D-QSAR study has been reported
earlier with this in-house data set of compounds. Therefore, we have focused on 3D-QSAR
study of all in-house chiral chain based CQ analogues to explain the biological activity
against 3D7 strain. Hence, in the present work we have developed our new findings through
docking and 3D-QSAR study to get an insight about the importance of chiral chains in CQ
analogues. Presence of H-bond interactions with heme in conjunction with favourable steric,
hydrophobic and electropositive groups pave a novel way to design more potential analogues
having anti-malarial activity against the CQ-resistant parasite strain.
88 2. Materials & Methods
89
90
91
92
93
94
95
96
97
98
2.1 Synthesis
Synthesis of compounds (6a-6d, 7a-7h, 8a-8d, 9a-9d, 10a, 10b, 11a-11c and 12a)
Synthesis of 6a-6d, 7a-7h, 8a-8d, 9a-9d, 10a, 10b, 11a-11c and 12a involves
following steps starting from the preparation of the Boc-amino acids (Figure 1). Compounds
1a-1g was converted to corresponding Boc derivatives 2a-2g in quantitative yields. The Boc
protected amino acids were converted to the corresponding methyl esters by using
K2CO3/MeI in DMF solvent. Further, these esters were reduced to alcohols using sodium
borohydride [24]. Boc amino alcohols were subjected to mesylation to afford 5a-5g in good
yields. The mesylated products were treated with N-substituted piperazines under nitrogen
atmosphere in acetonitrile to get the desired compounds in good yields. Further, Boc
3

99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
deprotection was accomplished using 20% HCl/Dioxane at room temperature in quantitative
yields as corresponding hydrochloride salt. Hydrochloride salts were converted to free bases
using triethylamine. Finally the intermediates (6AD-6DD, 7AD-7CD, 7ED-7GD, 8AD-8DD,
9AD-9DD, 10AD-10BD, 11AD-11CD and 12AD) obtained were fused to 4,7-
dichloroquinoline and/or 4-chloro-7-(trifluoromethyl)quinoline in the presence of phenol to
obtain the title compounds [25]. All compounds were purified by the silica gel column
1
chromatography and characterized by mass spectrometry, HRMS, H-NMR and 13C-NMR
spectroscopy.
2.2 Biological Methods
2.2.1. In vitro antiplasmodial assay
The compounds were evaluated for antiplasmodial activity against 3D7 (CQ- sensitive)
and K1 (CQ-resistant) strains of Plasmodium falciparum using Malaria SYBR Green I
nucleic acid staining dye based fluorescence (MSF) assay as mentioned by Kondaparla et al.
[26]. The stock (10 mM) solution was prepared in DMSO and test dilutions were prepared in
culture medium (RPMI-1640-FBS). Chloroquine-diphosphate was used as reference drug.
For assessment of antimalarial activity 50μl of culture medium was dispensed in 96 well plate
followed by addition of 50 μL of highest concentration (˂ 0.5% of DMSO) of test compounds
(in duplicate wells) in row B. Subsequently two-fold serial dilutions were prepared in culture
medium and finally 50 µL of 2.0 % parasitized cell suspension containing 0.8 % parasitaemia
(Asynchronous culture containing more than 80% ring stages) was added to each well except
4 wells in row ‘A’ received non parasitized erythrocyte suspension. The plates were
incubated at 37 °C in CO2 incubator in an atmosphere of 5% CO2 and air mixture for 72 h.
After 72 h 100 µL of lysis buffer containing 2x concentration of SYBR Green-I (Invitrogen)
was added to each well and incubated for one hour at 37°C. The plates were examined at
485±20 nm of excitation and 530±20 nm of emission for relative fluorescence units (RFUs)
per well using the fluorescence plate reader (FLX800, BIOTEK). Data was transferred into a
graphic programme (EXCEL) and IC50 values were obtained by Logit regression analysis of
dose response curves using pre-programmed Excel spreadsheet. Three replicates were carried
out for each compound.
128
129
130
2.2.2 Determination of hematin 4-aminoquinoline derivatives association constant.
131
132
Association constant for hematin and 4-aminoquinoline derivative complex formation were
determined by spectrometric titration procedure in aqueous DMSO at pH-7.5[27]. In this
4

133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
assay condition, hematin is strictly in monomeric state and interpretation of the results is not
complicated by the need to consider hematin disaggregation process. Association constant
calculated in this technique is a good refection of the interaction that would occur in the
acidic food vacuole of the parasite and pH-7.5 improves the stability of hematin solutions and
quality of the data.
2.2.3. In vitro inhibition of β-hematin formation assay
The ability of the 4-aminoquinoline derivatives to inhibit β-hematin formation was
induced by 1-oleoyl-rac-glycerol. Spectroscopic measurements were done using UV
spectrophotometer at 405 nm λmax and at pH 5 [28]. The IC50 values obtained from the assay
are expressed as percent inhibition relative to β-hematin formation in a drug free control. The
IC50 for the compounds were obtained from the sigmoidal dose-response curves using non-
linear regression curve fitting analyses with GraphPad Prism v.3.00 software [29].
2.2.4. In vitro assay for evaluation of cytotoxic activity
Cytotoxicity of the compounds was carried out using Vero cell line (C1008; Monkey
kidney fibroblast) following the method as mentioned in Sinha et al. [23]. The cells were
incubated with compound-dilutions for 72 h and MTT was used as reagent for detection of
cytotoxicity, 50% cytotoxic concentration (CC50) was determined using the nonlinear
regression analysis of dose response curves using the pre-programmed Excel spreadsheet.
Selectivity index (SI) was calculated as:
SI = CC50 / IC50
2.2.5. In vivo antimalarial assay
The in vivo drug response was evaluated in Swiss mice infected with P. yoelii (N-67
strain) which is innately resistant to CQ [30]. The mice (22±2 g) were inoculated with 1x106
parasitized RBC on day 0 and treatment was administered to a group of five mice from day 0-
3, once daily. The aqueous suspensions of compounds were prepared with a few drops of
Tween 80. The efficacy of test compounds was evaluated at 100 mg/kg/day and the required
daily dose was administered in 0.5 mL volume via oral route. Parasitemia levels were
recorded from thin blood smears at regular intervals of four days throughout the period of
experiment. The mean value determined for a group of five mice was used to calculate the
percent suppression of parasitemia with respect to the untreated control group. Treatment was
considered curative when no parasites were detected till day 28. Mice surviving at day 28
were inoculated with 1x106 P. yoelii parasitized RBC to monitor their susceptibility to
rechallenge [31]. Mice treated with CQ served as reference controls.
2.3 Molecular Modeling study:
5

167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
2.3.1 Database Preparation
Molecular docking study was carried out for in-house CQ analogues by targeting heme.
SYBYL-X 1.3 (Tripos Inc, St. Louis, MO, USA) modeling package [32] was used for
molecular modeling studies. To execute the docking experiment, in-house database was
prepared containing 168 CQ derivatives including recently synthesized molecules which are
reported in this paper. All compounds were drawn by using sketch module in sybyl-X 1.3[33].
Structures were minimized further by adding Gasteiger-Huckel charges along with distance-
dependent dielectric and the Powell conjugate gradient algorithms with a convergence criterion
of 0.001 kcal/mol. All prepared structures were put into a new database and finally aligned
with most active molecule (M96) of the present database by 'Fit Atom' method.
2.3.2 Protomol based docking
Docking study of synthesized compounds was performed using the Surflex-Dock
module with standard protocols in SYBYL-X 1.3 [34]. Since, heme is the primary target of CQ
analogues, we have extracted the heme from the protein of P. falciparum (PDB ID: 4D6U) to
continue the docking experiment [35]. Additionally, CQ was taken as a reference to validate
our docking experiment. Extracted heme was further prepared through Surflex-Dock protocol.
Hydrogen atoms were added to the heme to define the correct configuration and tautomeric
states. Gasteiger-Huckel charges were also added to it followed by energy-minimizations using
the Tripos force fields along with distance-dependent dielectric and the Powell conjugate
gradient algorithms with a convergence criterion of 0.001 kcal/mol. After the preparation of
heme, automated protomol generation process was used to identify the grid for docking. The
docking algorithm implemented in SURFLEX uses an idealized active site ligand called a
protomol. This protomol is made up of probes representing sites of potential hydrogen bonds
and favorable hydrophobic interactions with docking target.
Molecular docking of 168 molecules was performed by placing the molecules including
the reference compound CQ into the protomol based grid by an empirical scoring function to
score the ligand and protomol guided docking. Finally, the protomol-based method and
empirically derived scoring function (e.g. Total score, crash score, polar etc.) were used to
calculate the binding affinities. The scoring function comprised hydrophobic, polar, repulsive,
entropic, crash and salvation terms. Total score is expressed in -log (Kd) units to represent total
binding affinities. Higher total score represents good binding affinity. Crash is the degree of
inappropriate penetration by the ligand into the protein and of interpenetration (self-clash)
between ligand atoms that are separated by rotatable bonds. A smaller crash value (near to
zero) indicates a better ability to exclude the false positives screened. Polar represents the
6

201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
contribution of the polar interactions to the total score. The polar score is useful for excluding
docking results that make no hydrogen bonds.
2.3.3. Data set preparation for 3D-QSAR
All docked molecules were further used to prepare a 3D-QSAR model to identify
important features for antiplasmodial activity. The in-house database of 168 molecules
(Table 1) along with their in vitro antiplasmodial activity (P. falciparum 3D7 strain) in the
form of logarithm of the inverse of inhibitory concentration (-logIC50) were used. For the
purpose of CoMFA/CoMSIA analysis dataset has been randomly divided into training set
(111 compounds) and test set (57 compounds) (Table 3).
2.3.4. Molecular alignment
The 3D-structure building and all modelling studies were performed using the
SYBYL program package, version 7.3 [36] on a silicon graphics fuel workstation with IRIX
6.5 operating system. In this study conformations of all molecules (168) were taken from
previous docking experiment. Out of this, one best conformation (based on Total Score) of
compound M96 was identified to execute molecular alignment of all molecules. Before
alignment energy minimizations were performed on docked molecules to add charges using
the Tripos force field [37] with a distance-dependent dielectric function and the Powell
conjugate gradient algorithm with a convergence criterion of 0.05 kcal/mol. Partial atomic
charges were calculated using the Gastieger–Huckel method. The most potent compound
M96 was chosen as template molecule to fit the remaining training and test compounds by
using the ‘‘database align’’ function in SYBYL. The reference atoms in the compound M96
were used for alignment via "fit atom" method which is exposed in Figure 3.
2.3.5. CoMFA and CoMSIA analysis
In deriving the CoMFA and CoMSIA descriptor fields, a 3D cubic lattice with grid
spacing of 1Å and extending to 4Å units beyond the aligned molecules in all directions was
created to encompass the aligned molecules. CoMFA descriptors were calculated using an sp3
carbon probe atom with a van der Waals radius of 1.52Å and a charge of +1.0 to generate
steric (Lennard–Jones 6–12 potential) field energies and electrostatic (Coulombic potential)
fields with a distance-dependent dielectric at each lattice point. Steric and electrostatic fields
generated were scaled by the CoMFA-Standard method in SYBYL with default cut-off
energy of 30 kcal/mol. The minimum column filtering was set to 2.0 kcal/mol to improve the
signal-to-noise ratio by omitting those lattice points whose energy variation was below this
threshold. CoMSIA similarity indices were derived according to Klebe et al. [38] with the
7

234
235
same lattice box as was used for the CoMFA calculations. CoMSIA similarity indices (AF)
for a molecule j with atoms i at a grid point q were calculated using Eq. (1) as follows:
236
(1)
237
238
239
240
241
242
243
244
245
246
247
248
The CoMSIA method involves computation of five different physicochemical
properties (k) namely steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen
bond acceptor as molecular similarity indices (Eq. 1). The computational use of Gaussian
type distance-dependent function was performed between the grid point q and each atom i in
the molecule. A default value of 0.3 was used as the attenuation factor (α). In CoMSIA, the
steric indices are related to the third power of the atomic radii; the electrostatic descriptors
are derived from partial atomic charges; the hydrophobic fields are derived from atom based
parameters [39].
2.3.6. PLS analysis
The CoMFA and CoMSIA 3D-QSAR models were derived using the PLS regression
procedure of SYBYL [40]. The predictive ability of the model was measured in terms of
cross-validated r2 (r2cv or Q2) as shown in equation 2.
249
(2)
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
Where Ypredicted, Yobserved, and Ymean are predicted, actual, and mean values of the target
property (pIC50), respectively. The number of components leading to the lowest standard
error of prediction (SEP) were used as optimum number of components (ONC) to generate
the final PLS regression models. The models were validated through the boot strapping
analysis for 100 runs and the cross-validation analysis (leave-half-out and leave 20% out;
each 50 runs) [40]. The CoMFA and CoMSIA equations were plotted as contour maps to
express the percentage contribution of respective fields to the activity. This was done by
considering the field energies at each lattice point as a multiple of the regression coefficient
and corresponding standard deviation. Here, the contour maps help in identifying the
important regions where changes may affect the binding preference and thereby facilitate the
recognition of key features contributing to the interactions between the ligand and the active
site of a receptor. Furthermore, the developed CoMFA and CoMSIA models were validated
by predicting the activity of the external test set compounds. A model with predictive r2 value
(r2pred) more than 0.5 may be considered as statistically significant.
3. Results and Discussion
3.1. In vitro antiplasmodial activity
8

266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
All compounds synthesized in the present study 6a-6d, 7a-7h, 8a-8d, 9a-9d, 10a-10b,
11a-11c and 12a were evaluated for antiplasmodial activity against the 3D7-chloroquine
sensitive (CQ-S) and K1-chloroquine resistant (CQ-R) strains of P. falciparum according to
the procedure reported by Kondaparla et al. [26]. Selected compounds (6b, 6d, 7c, 7h, 8c, 8d,
9a and 9c), showing superior activity than CQ against the K1 strain, were evaluated for in
vivo activity against the innately chloroquine resistant P. yoelii (N-67 strain) in Albino mice
of Swiss strain. All tested compounds displayed moderate activity with IC50 value ranging
from 22.61 to >5000 nM against 3D7 strain. In all tested compounds, two compounds (7d
&7h) showed promising activity with an IC50 of 24.55 and 22.61nM, and three compounds
(6a, 7c & 9a) with IC50 values ranging from 56.98 nM to 108.39 nM showed moderate
activity. Moreover, eight compounds of the series were discerned to be more active (IC50
46.50 to 161.80 nM) than CQ (IC50 = 255 nM) against the CQ resistant strain of P.
falciparum. Three compounds 6a, 7e and 9b exhibited IC50 values 276.90, 352.30 and 362.28
nM than the CQ, respectively. Moreover, low resistance index was exhibited by majority of
the synthesized compounds (Table 1). In vitro biological activity data of the synthesized
compounds are summarized as shown in Figure 2.
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
The structure activity relationship studies on these compounds suggested that the
activities were greatly influenced by the type of substitutions at 4th position of piperazine
moiety, structural diversity in the amino acid side chain and trifluoromethyl substitution at C-
7 position of the quinoline ring. More importantly, substitution at the piperazine moiety
showed remarkable influence on the antiplasmodial activity.
The results presented in Table1 indicate that the compounds having ethyl, benzyl
and piperonyl group at the 4th position of piperazine (irrespective of amino acid side chain)
exhibited mild inhibition against the 3D7 strain and superior activity against the K1 strain of
P. falciparum as compared to CQ. However, compound 7c showed 2.6 fold more activity
than CQ against the K1 strain with IC50 value 97.76 nM . Further, replacement of chlorine in
7c with trifluoromethyl gave the compound 7d (IC50 = 797.67 nM) with many fold decrease
in the activity against the K1 strain. This decrease in the activity may be due to increase in
the hydropobicity. Again, the activity was increased substantially against the K1 strain in the
case of compound 7h (IC50 = 46.52 nM (K1)) which is having piperonyl methyl substitution
in the side chain. It is important to note that this compound was found 5.5 fold more active
than CQ against the K1 strain. When the amino acid side chain was changed to the
hydrophobic benzyl group it furnished the compounds with considerable decrease in the
9

299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
activity against the K1 strain of P. falciparum. However, compound 8c (IC50 = 70.82 nM)
with similar substitution as in the case of compound 7c (IC50 = 97.76 nM) exhibited slight
increase in the activity against the K1 strain. Similar activity profile was observed with
piperonyl methyl substitution in the case of compound 8d (IC50 = 89.0 nM). Moreover,
compound 6d (IC50 = 46.50 nM) having glycine at 4th position of quinoline ring showed
better activity than the compound 9a (IC50 = 71.39 nM) having alanine at the same position.
More importantly, these two compounds exhibited 5.5 fold and 3.6 fold superior activities
respectively, than CQ against the K1 strain of P. falciparum. The chance of parasite to
develop resistance to a particular class of compounds has been calculated as a ratio of IC50 in
CQ-Resistance vs CQ-Sensitive strains called resistance factor (RI). Therefore, smaller
resistance factor of the given compound corresponds to less chance of developing resistance.
Interestingly, the majority of tested compound in this series exhibited low resistance factor
between 0.08 and 32.49 as compared to 51 for CQ. Furthermore, the better antiplasmodial
activity has been accounted in compounds of present study against both CQ-S and CQ-R
strains in comparison to the earlier reported antimalarial activity from our laboratory. In the
light of current study, tested compounds seem to suggest further lead optimization to obtain
active molecule against drug resistant parasites.
3.2. In vitro inhibition of β-hematin formation
Newly synthesised 4-aminoquinolines (6a-6d, 7a-7h, 8a-8d, 9a-9d, 10a-10b, 11a-11c
and 12a) were evaluated for their mode of action using the reported method [19] and the
results are given in Table 1. The heme binding assay results revealed that all synthesised 4-
AQ’s derivatives interact with the heme. Further, association constant was observed in the
range of 4.23-7.81. These results indicate that interaction between quinoline ring and the
porphyrin ring system might be π–π stacking interaction. These results are complemented by
the inhibition of β-hematin formation which also showed good IC50 values found in the range
of 0.21–0.83 µM. In the present series the most active compound 7h exhibited IC50 0.21 µM
against the CQ-R strain.
3.3. In vitro Cytotoxicity
MTT assay in VERO cell line was used to determine the cytotoxicity of all synthesized
compounds (6a-6d, 7a-7h, 8a-8d, 9a-9d, 10a-10b, 11a-11c and 12a) (Table 1). The
selectivity index (SI) of nearly all compounds in series is good and ranging between 32.77
and 3671.69. Compounds 7c, 7h and 9a of the series showed enhanced antiplasmodial
activities against K1 strain and also exhibit good selectivity SI values 3510, 582.04 and
117.94, respectively. However, compounds 6a, 9b and 12a displaying promising activities
10

333
334
335
336
337
338
against the K1 strain also showed considerable SI values 979.10, >1580.52 and 1093.10
respectively. Moreover within series of compounds, compounds 7d and 7h showed
significant activity against the 3D7 strain with good SI value 3671.69 and 582.04,
respectively. In general, with promising activity against the K1 strain, less cytotoxic effect
and fairly high selectivity index, most of the 4-aminoquinoline derivatives of this series are
very healthy candidate for further lead optimization.
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
3.4. In vivo antimalarial activity
The in vivo antimalarial activity was assessed against inherently chloroquine resistant P.
yoelii (N-67 strain) in Albino mice of Swiss strain. Compounds (6b, 6d, 7c, 7h, 8c, 8d, 9a,
9c) with IC50 ranging between 31.19 nM and 252.28 nM were selected based on in vitro
antiplasmodial activity. In the beginning, the in vivo activities of selected compounds were
examined at the dose of 100mg/kg for four consecutive days, once daily via oral route.
Parasitaemia reduction and survival of animals were recorded until day 28 post-infection
(Table 2).
A dose of 100 mg/kg compounds 6b, 6d, 8d, 9a and 9c showed 100% parasitaemia
suppression on day 4 but no mice of the group was cured up to day 28 of treatment. Further,
compound 8c was administered at two different doses such as 100 and 50 mg/kg,
respectively. At a dose of 100 mg/kg it displayed 100% parasitaemia suppression on day 4
with 100% survival and curative rates up to day 28. Whereas, at a dose of 50 mg/kg exhibited
50% parasitaemia suppression on day 4 with 40% survival rate up to day 28 of treatment.
Moreover, compound 7c one of the potent compounds in the series tested initially at a dose of
100 mg/kg, showed 100% survival as well as curative rates up to day 28. When it was
administered at lower doses, 50, 25, 12.5 and 6.25 mg/kg, respectively, it displayed 100%
parasitaemia suppression on day 4 with 100% curative rate up to day 28 at doses of 50, 25
and 12.5 mg/kg, respectively. While, at a dose of 6.25 mg/kg showed 100% parasitaemia
suppression on day 4 but none of the mice survived. Re-inoculation with infective inoculum
post day 28 showed that all the cured group mice developed fulminating parasitemia and
succumbed to infection thus validating absence of any latent parasites in these animals. In
contrast surviving animals which were not designated as cured survived the re-challenge
(Table 2).
3.5. Molecular docking studies
11

365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
Recent evidence strongly leans towards interaction of CQ analogues with the surface
of the hemozoin crystal, rather than free heme [41-42]. Understandably, this is difficult to
make a model of hemozoin structure for docking. Therefore, the in-house database created in
the present study (168 molecules) was used to dock against the heme instead of hemozoin,
(extracted from PDB ID: 4D6U) to investigate the probable interactions between CQ
analogues and the heme. Docking analysis of these molecules clearly showed the importance
of total score, crash score and polar scores (Table 4).
Most of the compounds showed moderate to good binding scores with the heme.
Compounds M64, M96, M97 and M100 confirmed good binding affinity with the heme due
to strong H-bond interactions with free carboxylic group of the heme moiety (Figure 4)
whereas compounds M32, M158, M165 showed poor binding affinity towards the heme due
to improper binding conformations. This is in agreement with the observed biological
activity.
Additionally, excellent electrostatic and hydrophobic (π-π stacking) interactions
between quinoline ring and the porphyrin ring of the heme moiety contribute significantly to
improve the binding affinities (Figure 5, Table 5) which are directly linear to their biological
activity against the 3D7 strain (Table 1 & 4). Furthermore from the above observation, it is
concluded that compounds with more bulky groups in side chains (compound M153, M161
and M165) poorly bind with the heme which is reflected through their maximum crash score
value and unfavourable docked conformations due to the lack of π-π stacking interactions. In
addition, docking analysis further revealed that low polar score may reduce the formation of
good heme-ligand complex (compound M37, M153 and M157) leading to reduced activity
(Figure 4). These results revealed the importance of the side chain modifications to improve
the binding affinity of CQ analogues towards heme.
3.6. CoMFA and CoMSIA Statistical analysis:
The best possible 3D-QSAR model is derived from the PLS statistical data of CoMFA
and CoMSIA for CQ analogues which is shown in Table 6. The optimal value of Q2 (> 0.5)
was used as criteria to find out new significant model. The ideal CoMFA model has been
recognized with five PLS components along with cross-validated Q2 value of 0.641, non-
cross validated r2 value of 0.924 and r2
= 0.656 (Table 6). In the case of CoMSIA study,
pred
five components were established to ideally express the anti-malarial activity of the
compounds. It has revealed a cross-validated Q2 of 0.680 and a non-cross validated r2 of
0.885. In CoMSIA also good predictive value is identified (r2pred = 0.608) with test sets.
12

398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
The 3D-QSAR model derived from this experiment has shown that all five fields of CoMSIA
do not equally play a vital role to explain the activity. Therefore, single as well as multiple
groupings of CoMSIA fields have been measured to choose the important fields with best
cross-validated Q2 value. Finally, from this experiment most important fields responsible for
activity are recognized, namely steric, electrostatic and hydrophobic fields (Figure 6).
3D models of CoMFA and CoMSIA are found to be effective in bootstrapping and other
cross-validation experiments (Table 6). Moreover, derived model predicted the test set of
compounds satisfactorily. The scattered graphical plots of observed vs predicted values of
biological activities of training and test groups resulting from the CoMFA and CoMSIA
study are revealed in Figure 7.
3.7. Contour map analysis
3.7.1. CoMFA Steric
The CoMFA steric contour maps of CQ derivatives against 3D7 strain are exposed in Figure
7. Here, green and yellow colour contours correspondingly specified the steric favoured as
well as unfavoured regions for the activity. Green contour present at linker region (Figure 8)
identify the sterically preferred groups in this area to get improved anti-malarial activity as
observed in the compound M96. In addition, green contour close to the end of linker site
(Figure 8) indicates the positivity of this location for steric groups. All active analogues
(Compounds M64, M96, M98 and M126) satisfy this criteria with different steric groups such
as tert-butyl, methyl piperazine and methyl piperidine moieties. Similarly, yellow contours
are situated at C-7 position of the quinoline ring (Figure 8). Another yellow contour has
enclosed more bulky groups at linker region (Compounds M32, M34 and M68 Figure 8).
This speaks in favour of least steric groups at C-7 position.
3.7.2. CoMFA Electrostatic
The electrostatic contour map build up for CQ derivatives from CoMFA model is shown in
Figure 8. In this, a large blue colour contour adjacent to the electropositive 4-amino of
quinoline group indicate the presence of electron rich nitrogen which may increase the
activity (Compounds M60, M62, M96 and M97). Additionally another small blue contour
near to the linker site specifies its requirement for electropositive groups. Apart from that, a
red colour contours are observed near to linker site in the case of less active compounds
(Figure 8, Compound M146, M149 and M161). Therefore, electronegative groups can be
replaced at linker region with electropositive moieties to improve their activity. Moreover, a
blue contour is positioned in front of quinoline moiety representing the prominence of
electropositive groups for the biological activity.
13

432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
3.7.3. CoMSIA Steric:
In CoMSIA, the favourable areas for steric groups are indicated in green contour and the
disfavoured regions are represented in yellow colour contour. At this point, two large green
contours occurred in proximity to linker (chain) sides indicating the fitness for large groups in
these positions. The presence of tert-butyl, methyl piperazine and like moieties appears to be
favourable for good activity (e.g. compounds M64, M96 and M97). The substituted
piperazine moiety has fulfilled the steric requirements of the green contour parallel to linker
region (Figure 9, compound M96). Most of the active compounds in the dataset contribute to
this feature (e.g. compounds M96, M100 and M109). Besides this, two yellow big contours
were found over the green contour suggesting the presence of large groups in this area which
can diminish the activity (compounds M32, M158 and M165).
3.7.4. CoMSIA Electostatic
In this electrostatic map blue colour contours specified the electropositive favourable area
and red colour contours pointed out electronegative favourable sites. The blue contour existed
close to the linker region (Figure 9) which suggested that electropositive groups are suitable
for the activity (compounds M60, M62, M96 and M97). In case of active compounds, this
position is fulfilled by methyl ethyl amine and methyl piperazine groups. Incorporation of
alkyl chains in this electropositive region improves the activity but red contour map near to
this region drastically reduces the biological activity (compounds M146, M149 and M161).
3.7.5. CoMSIA Hydrophobic
The CoMSIA hydrophobic contour maps are shown in Figure 9. In these contours, yellow
colour specified hydrophobic favourable regions whereas gray colour specified for
hydrophobic unfavourable sites. The yellow contour near to the linker position showed the
positive site for hydrophobic groups (compounds M63, M64, M96 and M100). In highly
active compounds dimethyl amine, tert-butyl, methyl piperazine and other related groups
were incorporated in this region. Perhaps, the hydrophobic part of these groups along with
other characteristics may be appropriate for this position. Apart from this, a large gray
contour and a small gray contour occupied the central area of linker region and C-7 position
of the quinoline ring, respectively. This denoted an adverse environment for hydrophobic
moieties in the surrounding area of mid part of the linker position as well as C-7 position of
the quinoline ring system (compounds M32, M108 and M167).
4. Conclusion
14

465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
In summary, we have synthesised a new series of chiral 4-aminoquinoline derivatives
in order to search for more potent molecules active against both in vitro and in vivo strains of
P. falciparum. Among all synthesised compounds, eight compounds (6b, 6d, 7c, 7h, 8c, 8d,
9a and 9c) exhibited excellent antimalarial activity against the K1 strain as compared to the
CQ and these compounds were also found to be active against P. yoelii mouse model in vivo.
It may be inferred from the above mentioned results that the less hindered amino acids
(namely Gly, Ala and Leu) at 4th position of the quinoline ring and ethyl substituted
piperazine in the side chain exhibited potent activity as a new lead and requires further
optimization for drug development. The docking as well as 3D-QSAR studies were
performed to make SAR on our in-house 4-aminoquinoline derivatives data set. After
exploration, it is clear that H-bond as well as hydrophobic π-π stacking interactions between
quinoline ring and porphyrin moiety of the heme is crucial for antimalarial activity. Also, the
CoMFA/CoMSIA outcomes mostly pointed out the influence of steric, hydrophobic and
electropositive environment creating groups adjacent to the linker attached to the quinoline
moiety for their potential activity against P. falciparum. The steric field around the linker site
is most crucial to enhance the biological activity. The end part of the linker region is suitable
for steric, hydrophobic and electropositive groups as well. Apart from that, C-7 position of
quinoline ring specified the significance of minimum hydrophobicity and least steric
influence for the activity. The present in silico experiments with the newly synthesized
compounds offered support to the biological activities. Therefore, it is expected that the
present study will provide a new way to design and synthesize novel CQ analogues having
antimalarial activity against drug resistant malaria paasite.
Acknowledgements
S. Kondaparla and U. Debnath are thankful to the CSIR, New Delhi, for a Senior Research
Fellowship. We thank the director of CSIR-CDRI for support and the SAIF division for the
spectral data. Authors are also thankful to Dr. R K Rawal for his advice. CSIR-CDRI
Communication no. is 9731.
Conflict of interest
The authors declare that no competing interests exist.
15

499
500
501
502
References:
1.
Schlitzer, M. Malaria chemotherapeutics part I: History of antimalarial drug
development, currently used therapeutics, and drugs in clinical development. Chem.
Med. Chem. 2007, 2, 944-86.
503
2.
3.
World Health Organization’s World Malaria Report 2016; www.who.int
504
505
506
Dola, V. R.; Kondaparla, S.; Katti, S. B. Recent developments in the side chain
modified 4-aminoquinolines as antimalarial agents. Chem. Biolo. Interface. 2014, 4,
206-22.
507
508
509
4.
O'Neill, P. M.; Bray, P. G.; Hawley, S. R.; Ward, S. A.; Park, B. K. 4-
Aminoquinolines--past, present, and future: a chemical perspective. Pharmacol Ther
1998, 77, 29-58.
510
511
5.
6.
Rodrigues, T.; Moreira, R.; Lopes, F. New hope in the fight against malaria? Future
Med Chem 2011, 3, 1-3.
512
513
514
Read, J. A.; Wilkinson, K. W.; Tranter, R.; Sessions, R. B.; Brady, R. L., Chloroquine
binds in the cofactor binding site of Plasmodium falciparum lactate dehydrogenase.
The J. Biol. Chem 1999, 274 (15), 10213-8.
515
516
517
518
7.
8.
9.
Hawley, S. R.; Bray, P. G.; Mungthin, M.; Atkinson, J. D.; O'Neill, P. M.; Ward, S.
A. Relationship between antimalarial drug activity, accumulation, and inhibition of
heme polymerization in Plasmodium falciparum in vitro. Antimicrob Agents
Chemother 1998, 42, 682-6.
519
520
521
522
Bray, P. G.; Howells, R. E.; Ritchie, G. Y.; Ward, S. A. Rapid chloroquine efflux
phenotype in both chloroquine-sensitive and chloroquine-resistant Plasmodium
falciparum. A correlation of chloroquine sensitivity with energy-dependent drug
accumulation. Biochem Pharmacol 1992, 44, 1317-24.
523
524
525
De, D.; Krogstad, F. M.; Byers, L. D.; Krogstad, D. J. Structure-activity relationships
for antiplasmodial activity among 7-substituted 4-aminoquinolines. J Med Chem
1998, 41, 4918-26.
16

526
527
528
10.
11.
Burgess, S. J.; Kelly, J. X.; Shomloo, S.; Wittlin, S.; Brun, R.; Liebmann, K.; Peyton,
D. H. Synthesis, structure-activity relationship, and mode-of-action studies of
antimalarial reversed chloroquine compounds. J Med Chem 2010, 53, 6477-89.
529
530
531
532
533
Ridley, R. G.; Hofheinz, W.; Matile, H.; Jaquet, C.; Dorn, A.; Masciadri, R.; Jolidon,
S.; Richter, W. F.; Guenzi, A.; Girometta, M. A.; Urwyler, H.; Huber, W.; Thaithong,
S.; Peters, W. 4-aminoquinoline analogs of chloroquine with shortened side chains
retain activity against chloroquine-resistant Plasmodium falciparum. Antimicrob
Agents Chemother 1996, 40, 1846-54.
534
535
536
12.
13.
O'Neill, P. M.; Ward, S. A.; Berry, N. G.; Jeyadevan, J. P.; Biagini, G. A.; Asadollaly,
E.; Park, B. K.; Bray, P. G. A medicinal chemistry perspective on 4-aminoquinoline
antimalarial drugs. Curr Top Med Chem 2006, 6, 479-507.
537
538
539
540
Natarajan, J. K.; Alumasa, J. N.; Yearick, K.; Ekoue-Kovi, K. A.; Casabianca, L. B.;
de Dios, A. C.; Wolf, C.; Roepe, P. D. 4-N-, 4-S-, and 4-O-chloroquine analogues:
influence of side chain length and quinolyl nitrogen pKa on activity vs chloroquine
resistant malaria. J Med Chem 2008, 51, 3466-79.
541
542
14.
15.
Kaur, K.; Jain, M.; Reddy, R. P.; Jain, R. Quinolines and structurally related
heterocycles as antimalarials. Eur J Med Chem 2010, 45, 3245-64.
543
544
545
Solomon, V. R.; Haq, W.; Srivastava, K.; Puri, S. K.; Katti, S. B. Synthesis and
antimalarial activity of side chain modified 4-aminoquinoline derivatives. J Med
Chem 2007, 50, 394-8.
546
547
16.
17.
Solomon, V. R.; Puri, S. K.; Srivastava, K.; Katti, S. B. Design and synthesis of new
antimalarial agents from 4-aminoquinoline. Bioorg Med Chem 2005, 13, 2157-65.
548
549
550
Solomon, V. R.; Haq, W.; Smilkstein, M.; Srivastava, K.; Puri, S. K.; Katti, S. B. 4-
Aminoquinoline derived antimalarials: synthesis, antiplasmodial activity and heme
polymerization inhibition studies. Eur J Med Chem 2010, 45, 4990-6.
551
552
553
554
555
18.
Dola, V. R.; Soni, A.; Agarwal, P.; Ahmad, H.; Raju, K. S.; Rashid, M.; Wahajuddin,
M.; Srivastava, K.; Haq, W.; Dwivedi, A. K.; Puri, S. K.; Katti, S. B. Synthesis and
Evaluation of Chirally Defined Side Chain Variants of 7-Chloro-4-Aminoquinoline
To Overcome Drug Resistance in Malaria Chemotherapy. Antimicrob Agents
Chemother 2017, 61.
17

556
557
558
19.
20.
21.
22.
Kondaparla, S.; Soni, A.; Manhas, A.; Srivastava, K.; Puri, S. K.; Katti, S. B.
Synthesis and antimalarial activity of new 4-aminoquinolines active against drug
resistant strains. RSC Adv 2016, 6, 105676-89.
559
560
561
Sinha, M.; Dola, V. R.; Agarwal, P.; Srivastava, K.; Haq, W.; Puri, S. K.; Katti, S. B.
Antiplasmodial activity of new 4-aminoquinoline derivatives against chloroquine
resistant strain. Bioorg Med Chem 2014, 22, 3573-86.
562
563
564
Kondaparla, S.; Agarwal, P.; Srivastava, K.; Haq, W.; Puri, S. K.; Katti, S. B. Design,
synthesis and in vitro antiplasmodial activity of 4-aminoquinolines containing
modified amino acid conjugates. Med Chem Res 2016, 25, 1148-62.
565
566
567
568
Ryckebusch, A.; Deprez-Poulain, R.; Maes, L.; Debreu-Fontaine, M. A.; Mouray, E.;
Grellier, P.; Sergheraert, C. Synthesis and in vitro and in vivo antimalarial activity of
N1-(7-chloro-4-quinolyl)-1,4-bis(3-aminopropyl)piperazine derivatives. J Med Chem
2003, 46, 542-57.
569
570
571
23.
24.
Sinha, M.; Dola, V. R.; Soni, A.; Agarwal, P.; Srivastava, K.; Haq, W.; Puri, S. K.;
Katti, S. B. Synthesis of chiral chloroquine and its analogues as antimalarial agents.
Bioorg Med Chem 2014, 22, 5950-60.
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
Soal, A.; Oyamada, H.; Takase, M. The preparation of N-protected amino alcohols
and N-protected peptide alcohol by reduction of the corresponding esters with sodium
borohydride. An improved procedure involving a slow addition of a small amount of
methanol. Bull Chem Soc Jpn, 1984, 57, 2327-28.
25.
26.
Kenyon, RL.; Wiesner, JA.; Kwartler, CE. Chloroquine manufacture. Ind Eng Chem
1949, 41, 654-62.
Kondaparla, S.; Soni, A.; Manhas, A.; Srivastava, K.; Puri, S. K.; Katti, S. B.
Antimalarial activity of novel 4-aminoquinolines active against drug resistant strains.
Bioorg Chem 2017, 70, 74-85.
27.
28.
Pagola, S.; Stephens, W. P.; Bohle, D. S.; Kosar, A. D.; Madsen, S. K. The structure
of malaria pigment β-haematin. Nature 2000, 404, 307-10.
Tripathi, A. K.; Khan, S. I.; Walker, L. A.; Tekwani, B. L. Spectrophotometric
determination of de novo hemozoin/beta-hematin formation in an in vitro assay. Anal
Biochem 2004, 325, 85-91.
29.
GraphPad Prism, version 3.0, GraphPad Software, 10855 Sorrento Valley Rd.
18

587
#203, San Diego, CA 92121, 1999.
588
589
590
591
30.
31.
Cheng, J.; Zeidan, R.; Mishra, S.; Liu, A.; Pun, S. H.; Kulkarni, R. P.; Jensen, G. S.;
Bellocq, N. C.; Davis, M. E. Structure-function correlation of chloroquine and
analogues as transgene expression enhancers in nonviral gene delivery. J Med Chem
2006, 49, 6522-31.
592
593
594
Gumila, C1.; Ancelin, ML.; Delort, AM.; Jeminet, G.; Vial, HJ.; Characterization of
the potent in vitro and in vivo antimalarial activities of ionophore compounds.
Antimicrob Agents Chemother. 1997, 41, 523-29.
595
596
32.
33.
Tripos Associates: SYBYL Molecular Modelling Software, version X. St. Louis, MO:
Tripos Associates; 2009. Technical tips online. Available at http://www.tripos.com.
597
598
599
Verma, S; Debnath, U.; Agarwal, P.; Srivastava, K.; Prabhakar, Y.S. In Silico
exploration for new antimalarials: Arylsulfonyloxy acetimidamides as Prospective
Agents. J Chem Inf Model. 2015, 55, 1708-19.
600
34.
35.
SYBYL, version 7.3, 2006, Tripos Associates, St. Louis, MO, USA.
601
602
603
604
Capper, M. J.; O'Neill, P.M.; Fisher, N.; Strange, R.W.; Moss, D.; Ward, S.A.; Berry,
N.G.; Lawrenson, A. S.; Hasnain, S. S.; Biagini, G. A.; Antonyuk, S. V. Antimalarial
4(1H)-Pyridones Bind to the Qi Site of Cytochrome Bc1. Proc.Natl.Acad.Sci.USA,
2015,112,755-60.
605
606
36.
37.
Clark, M.; Cramer, R. D.; Opdenbosch, N.V. Validation of the general-purpose tripos
5.2 force field. J Comput Chem 1989, 10, 982-12.
607
608
609
Klebe, G.; Abraham, U.; Mietzner, T. Molecular similarity indices in a comparative
analysis (CoMSIA) of drug molecules to correlate and predict their biological activity.
J Med Chem 1994, 37, 4130-46.
610
611
38.
39.
Klebe, G. The use of composite crystal-field environments in molecular recognition
and the de novo design of protein ligands. J Mol Biol 1994, 237, 212-35.
612
613
Stahle, L.; Wold, S. Multivariate data analysis and experimental design in biomedical
research. Prog Med Chem 1988, 25, 291-338.
19

614
615
616
40.
41.
42.
Debnath, U.; Verma, S.; Jain, S.; Katti, S. B.; Prabhakar, Y. S. Pyridones as NNRTIs
against HIV-1 mutants: 3D-QSAR and protein informatics. J Comput Aided Mol Des
2013, 27, 637-54.
617
618
619
Schwedhelm, K.; Horstmann, M.; Faber, J.; Reichert, Y.; Buchner, M.; Bringmann,
G.; Faber, C. Spin State of Chloroquine-Heme Complexes: Formation of a Hemin
Tetramer Adduct. The Open Spectroscopy Journal, 2008, 2, 10-18.
620
621
622
623
624
Solomonov, I.; Osipova, M.; Feldman, Y.; Baehtz, C.; Kjaer, K.; Robinson, I. K.;
Webster, G. T.; McNaughton, D.; Wood, B. R.; Weissbuch, I.; Leiserowitz, L. Crystal
Nucleation, Growth, and Morphology of the Synthetic Malaria Pigment β-Hematin
and the Effect Thereon by Quinoline Additives:ꢀ The Malaria Pigment as a Target of
Various Antimalarial Drugs. J. Am. Chem. Soc., 2007, 129, 2615–2627
625
626
Captions
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
Table 1
Biological and biophysical data of the synthesized compounds.
Table-2
In vivo antiplasmodial activity of selected compounds against CQ resistant P. yoelii (N-67)
in Albino mice of Swiss strain.
Table-3
Distribution pattern of 4-aminoquinoline derivatives in training and test sets
Table 4
Binding scores (Total Score, Crash Score & Polar Score) of selected molecules from the in
house database.
Table 5
Different types of non h-bond interactions between heme and CQ analogue (M96).
Table 6
Goodness of fit of CoMFA and CoMSIA model for the antiplasmodial activities of
chloroquine (CQ) analogues against 3D7 strain.
Figure 1
Synthesis of compounds (6a-6d, 7a-7h, 8a-8d, 9a-9d, 10a, 11a-11c, and 12a)
20

645
Figure 2
646
647
648
Histogram showing the number of compounds evaluated against P. falciparum in the present
study.
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
Figure 3
General structures of the all synthesized compounds. The asterisk marked the atoms which
are common to all structures and used for the CoMFA/CoMSIA alignment.
Figure 4
Molecular docking of most active (‘A’: M96, ‘B’: M64 and ‘C’: M62) and less active (‘D’:
M32, ‘E’: M158 and ‘F’: M165) compounds with heme. Red colour (---) indicate h-bond
interaction.
Figure 5
Non h-bond interactions between heme and M96. Yellow colour (---) indicate electrostatic
interactions whereas pink colours (---) indicate hydrophobic interactions.
Figure 6
A graphical Plots of Q2 values of CoMSIA models which indicated single as well as multiple
field combinations for CQ analogues as antiplasmodial inhibitors against the 3D7 strain. In
this plot, x-axis indicate the fields of CoMSIA with single letters i.e. A (HB-Acceptor), D
(HB-donor), E (electrostatic), H (hydrophobic) and S (steric). The field combinations which
led to the model with ideal Q2 (y-axis) is marked with circle.
Figure 7
The scatter plots of experimental pIC50 values versus predicted pIC50 values derived from
CoMFA/CoMSIA training (filled box) and the test set (triangle) of CQ derivatives for
Plasmodium falciparum 3D7 strain.
Figure 8
Two different contour maps including Steric field contour maps (A&B), & electrostatic field
contour maps(C&D) of CoMFA 3D-QSAR are appeared for CQ analogues as antiplasmodial
inhibitors against 3D7 strain. In this figure, the poses of compounds M96 (active) & M32
(inactive) are used to demonstrate the contour maps. Colour code specified favourable &
unfavourable regions. Here, green contours indicate sterically favourable regions and yellow
contours shows sterically unfavourable regions for the biological activity (A&B). Again, blue
contours indicate electropositive regions & red contours pointed out the electronegative
regions (C&D).
678
21

679
Figure 9
680
681
682
683
684
685
686
687
688
689
Three different contour maps including Steric field contour maps (A&B), electrostatic field
contour maps(C&D) and hydrophobic contour maps (E&F) of CoMSIA 3D-QSAR are
appeared for CQ analogues as antiplasmodial inhibitors against 3D7 strain. In this figure, the
poses of compounds M96 (active) & M32 (inactive) are used to demonstrate the contour
maps. Colour code specified favourable & unfavourable regions. Here, green contours
indicate sterically favourable regions and yellow contours shows sterically unfavourable
regions for the biological activity (A&B). Similarly, blue contours indicate electropositive
regions & red contours pointed out the electronegative regions (C&D). Again, yellow
contours designate hydrophobic favourable regions whereas white contours indicate
hydrophobic unfavourable regions for the activity (E&F).
690
691
692
Table 1 Biological and biophysical data of the synthesized compounds.
Cmpd.No
IC50(nM)a
Resistance factorb
SIc
LogKd
IC50(µM)e
3D7 K1
108.39 276.90
899.23 161.80
2.54
0.17
979.10 5.85±0.03 0.38±0.02
6a (M144)
6b(M146)
69.6
61.5
4.72±0.02 0.41±0.01
6.33±0.03 0.69±0.03
4.23±0.03 0.23±0.01
993.50
4231
4.26
0.08
6c(M149)
6d(M161)
7a
521.20 46.50
123.53 >1000
153.90 >1000
43.28
>8.09
>6.49
174.70 6.63±0.02 0.73±0.01
292.90 6.43±0.01 0.69±0.02
7b
22

56.98
97.76
1.73
3510.0 4.41±0.02 0.27±0.01
3671.69 5.52±0.03 0.79±0.03
7c(M140)
7d(M164)
7e(M141)
7f(M142)
7g(M143)
7h(M165)
8a(M150)
8b(M151)
8c(M152)
24.55 797.67
460.05 352.30
32.49
0.76
90.90
76.60
6.71±0.02 0.44±0.02
6.31±0.01 0.45±0.02
857.68 412.40
211.10 452.80
0.48
2.14
2.05
1.10
1.00
0.16
153.50 6.53±0.02 0.48±0.03
582.04 4.32±0.02 0.21±0.02
724.03 6.82±0.01 0.51±0.01
22.61
46.52
1100
>1000
>5000 >5000
427.89 70.82
ND
7.81±0.02 0.73±0.03
4.51±0.01 0.23±0.01
4.48±0.02 0.29±0.02
66.81
61.47
757.38
89.0
0.11
0.65
2.87
8d(M153)
9a(M154)
108.10 71.39
126.54 362.28
117.94 4.53±0.03 0.26±0.01
>1580.52 5.98±0.03 0.42±0.03
9b(M155)
9c(M156)
404.80 84.01
0.20
112.69 4.42±0.02 0.27±0.01
23

538.59 783.73
4150.08 >5000
1.45
1.20
40.34
ND
5.12±0.03 0.48±0.02
6.97±0.03 0.81±0.03
9d(M157)
10a(M158)
596.52 528.72
>5000 >5000
3099.19 >5000
0.88
1.00
1.61
32.77
ND
6.23±0.02 0.49±0.01
7.13±0.02 0.83±0.02
6.92±0.03 0.79±0.03
10b(M160)
11a(M159)
ND
11b(M162)
11c(M163)
12a(M145)
2659
>5000
1.88
4.44
ND
6.96±0.03 0.75±0.02
113.76 502.10
1093.10 5.84±0.02 0.49±0.03
5
255
51
8983
5.52±0.02 0.17±0.02
CQ
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
a IC50 (nM): Concentration corresponding to 50% growth inhibition of the parasite; bResistance factor (RI) =
c
IC50 (K1)/IC50 (3D7); Selectivity index (SI): (CC50 for cytotoxicity to vero cells/IC50(3D7) for antiplasmodial
activity);d 1:1 (compound: Hematin) complex formation in 40% aqueous DMSO, 20 mM HEPES buffer, pH 7.5
e
at 25 ˚C (data are expressed as mean ± SD from at least three different experiments in triplicate); The IC50
represents the millimolar equivalents of test compounds, relative to hemin, required to inhibit β-hematin
formation by 50% (data are expressed as mean ± SD from at least three different experiments in triplicate).ND:
Not done.
24

709
710
Table 2: In vivo antiplasmodial activity of selected compounds against CQ resistant P.
yoelii (N-67) in Albino mice of Swiss strain.
Compoun
d
Dose (mg/kg [x
indicated time])
(p.o)c
%
Survivala
Cureb
Survival
after
rechallenged
suppression
on day 4
100 x 4 days
100 x 4 days
100
100
0/5
2/5
0/5
0/5
-
6b(M146)
6d(M161)
7c(M140)
2/5
100 x 4 days
50 x 4 days
100
100
100
100
100
5/5
5/5
5/5
5/5
2/5
5/5
5/5
5/5
5/5
0/5
0/5
0/5
0/5
0/5
2/5
25 x 4 days
12.5 x 4 days
6.25 x 4 days
100 x 4 days
50 x 4 days
25 x 4 days
100 x 4 days
50 x 4 days
100 x 4 days
100
100
100
100
50
5/5
5/5
0/5
5/5
2/5
3/5
5/5
5/5
0/5
5/5
0/5
0/5
0/5
0/5
7h(M165)
8c(M152)
0/5
2/5
3/5
100
8d(M153)
9a(M154)
9c(M156)
100 x 4 days
100 x 4 days
100
100
99.0
2/5
1/5
5/5
0/5
0/5
0/5
2/5
1/5
5/5
20 x 4 days
CQ
711
712
713
714
715
716
717
718
aNumber of mice that survived untill day 28 post-infection/total number of mice in the group.
bNumber of mice without parasitaemia (cured) through day 28 post-infection/total number of mice in
the group.
cp.o.,oral.
dMice suvivng till day 28 were inoculated with 1x106 P yoelii parasitized erythrocytes to monitor
susceptibility to rechallenge.
25

719
Table 3: Distribution pattern of 4-aminoquinoline derivatives in training and test sets
Dataset Compounds Activity (pIC50) distribution
Min. Max. Avg.
SD
111
57
5.30
5.30
8.33
8.48
6.85 0.87
6.84 0.86
Training
Test set
720
721
722
723
724
725
726
Table 4: Binding scores (Total Score, Crash Score & Polar Score) of selected molecules
from the in house database.
Cmpd.Code Total Score Crash Score Polar
M32
M62
1.1368
1.6703
1.6657
1.9119
4.2653
2.2337
2.8436
2.3441
2.6098
2.2171
3.1888
2.3102
-0.4986
-0.8912
-0.4918
-0.9051
-0.5227
-0.5357
-1.0254
-0.6026
-0.7997
-0.6227
-0.8168
-0.9484
1.4845
0.4415
1.4768
1.4469
1.4153
1.3241
0
M64
M87
M96
M97
M100
M113
M114
M134
M158
M165
1.4784
1.512
1.2771
2.0496
0
727
26

728
729
Table 5: Different types of non h-bond interactions between heme and CQ analogue
(M96).
No. of
Distance (Å)
Category
Types
non h-bond interactions
1
2
3
4
5
6
7
3.39
4.11
4.72
5.00
3.78
4.32
4.32
Electrostatic
Electrostatic
Hydrophobic Pi-Pi Stacked
Hydrophobic
Hydrophobic
Hydrophobic
Hydrophobic
Pi-Cation
Pi-Anion
Pi-Alkyl
Pi-Alkyl
Pi-Alkyl
Pi-Alkyl
730
731
732
733
Table 6: Goodness of fit of CoMFA and CoMSIA model for the antiplasmodial activities of
chloroquine (CQ) analogues against 3D7 strain.
3D7
PLS Statistics CoMFA CoMSIA
Model
Model
r2 nCVa
0.924
0.237
463.97
0.641
0.596
0.656
5
0.885
0.353
114.714
0.680
0.574
0.608
5
SEEb
Ftestc
r2 CVd
SEPe
r2 predf
PLS
components
Contribution
steric
electrostatic
hydrophobic
H-bond
donor
0.627
0.373
¯
-
0.452
0.207
0.341
-
-
-
H-bond
acceptor
r2 bootg
SEE booth
r2 LHOi
SD LHOj
r2 5cvk
0.946
0.237
0.479
0.073
0.532
0.045
0.933
0.263
0.535
0.051
0.564
0.039
SD 5cvl
734
735
736
737
a The predictable values correlation coefficient. For all models optimal number of PLS components are five.
b Standard error of estimate
c Ratio of r2 explained to unexplained = r2/(1 - r2)
d Leave-one-out Predicted cross-validated correlation coefficient
27

738
739
740
741
742
743
744
745
746
e Standard error of prediction
f External predicted correlation coefficient for test set of compounds
g Average of correlation coefficient for 100 samplings using bootstrapped method
h Average standard error of estimate for 100 samplings using bootstrapped method
i Average cross-validated correlation coefficient for 50 runs using leave-half-out (LHO) group
j Standard deviation of average cross-validated correlation coefficient for 50 runs
k Average cross-validated correlation coefficient for 50 runs using five cross-validation group
l Standard deviation of average cross-validated correlation coefficient for 50 runs.
747
748
O
O
R
O
O
R
R
O
O
R
c
749
b
a
d
HN
R1
H
H
O
HN
R1
HN
R1
H
H2N
a-
3
3
a-
1
a-
2
g
a-
4 4
g
1
2
g
g
750
R
N
R
N
NH
N
751
R2
O
R
R
e
f
N
g
N
O S
O
g
HN
R1
NH2
N
N
NH
R1
R2
R2
R3
6
a-
5
5
752
-
,
,
-
,
-
6AD 6DD 7AD 7CD 7ED
,
,
a-
6d
a-
7h
a-
a-
d
9
,
7
,
-
-
-
8
,
8
-
,
-
,
7
H
G
8AD 8DD 9AD
6A 6D 7A 7C 7E
,
,
-
-
-
-
7
8A 8D 9A
9DD 10AD 10BD 11AD
a
a-
c
,
a
G
,
,
9d 10 10b 11 11 12
,
,
-
-
11C
D 12AD
9D 10A 10B 11A
,
,
,
=
or om oun s
R3 CF3 f
an
C
d
p
11C 12A
,
753
7d
7h
d
ere
=
R3
n o e
Wh
R1
R
H CH CH CH CH
CH CH C H CH CH S CH CH C H CH i
d
l
;
;
,
,
,
,
,
,
(
)
3 2
(
)
2( )
B
F 2
3
3
2
5
2
3
2
6
5
2
=
oc
=
or
Cl C
3
754
R2
m
o
C
d
N
p
m
o
d
N
C
p
R
H
m
o
N
C
d
R
p
755
756
757
758
759
760
761
N
a e
7
6
,
a
a a a a
3
2 4 5
, , ,
1
N
,
H
a-
6
6d
c
7b
c
b
1 b 11b
8
9
0
,
,
,
,
1b 2b 3b 4b 5b
,
,
,
,
H
H
2C
H
C
C
(
a
-
7 7h
)
H
2C
H
H
C
( )
3 2
C
3 2
c
,
a
0
c
11 12
, ,
a
c
c c c c
3
2 4 5
, , ,
H
C
H5
2 C6
7
7d
8
9
b 1
,
,
,
1
,
H
2 C6
H5
a
-
-
C
8
9
8d
a
a
a
7
8
9
d 11
,
,
,
H
C
a
1d 2d 3d 4d 5d
9d
,
,
,
,
H
C
3
3
O
a-
10 10b
- n o e
2 i d l
H
C
e
,
e e
3
2 4
, ,
e e
5
,
- n o e
H2 i
6b
,
7f
1
C
d
l
O
a-
c
CH2CH S CH
2( )
11 11
1f 2f 3f 4f 5f
,
,
,
,
H
C
H
H
2C S C
2( )
3
3
O
a
d
8 9
,
7
,
g
7h
,
6d
,
O
1
2
,
g
3
4 5
, ,
g g g
,
g
H
H
C
2H5
C
)
C
a
12
(
H
H
2H5
C
)
C
C
(
3
3
c
6
O
O
762
763
764
765
766
Figure 1 Synthesis of compounds (6a-6d, 7a-7h, 8a-8d, 9a-9d, 10a, 10b, 11a-11c and 12a);
Reagents and Conditions: (a) (Boc)2O, NaOH/Dioxane, 0°C, 2-3 h; (b) K2CO3, DMF, MeI, 0°C, 6-
8h;(c) NaBH4, MeOH, THF, 65°C, 1h; (d) Triethyl amine, Methane sulphonyl chloride, THF, 0 °C, 45
min; (e) N-Substituted piperazines, Triethyl amine, acetonitrile, N2 atm, r.t, 40 h; (f) 20% HCl/dioxane
1h; (g) 4,7-dichloroquinoline, and/or 4-chloro-7-(trifluoromethyl)quinoline, Phenol, 160°C, 4-6 h.
28

767
768
769
770
771
772
773
774
775
776
777
778
779
780
Figure 2: Histogram showing the number of compounds evaluated against P. falciparum in
the present study.
R'
N
R'
N
R
R
R
X
R'
HN
X
a
R''
HN
N
n
n
R''
HN
X
n
*
*
*
*
R''
*
*
*
*
*
*
*
*
*
*
*
n =
*
*
1 2
=
,
*
Y
l
C
F
C
,
3
*
*
N
*
*
N
=
Y
*
Y
l
F
C
,
C
Y
*
n =
X
X
3 H
0 1 2 3
, , ,
*
N
*
y
Y
=
=
*
C O C
,
2
=
=
H
C C O
,
2
=
ar
a
rou s
lk l
y g
R
H
lk l
l
i
,
,
,
y
p
=
H
,
a
an ar
a
rou s
lk l
y g
p
R
lk l
y
d
l
y
=
=
-me
eraz ne a
am nes
i
R' R''
N
t
h l
i
lk l
y
,
y p p
=
=
oam es a
ar
am nes.
a
i
rou s -su s u e
b tit t d
eraz nes
er ne su s u e er nes
idi i idi
b tit t d
R' R'' Thi id lk l
l
lk
y g
N
i
i
i
,
,
,
,
,
,
y
y
p
p p
p p
p p
rro ne mor o ne a
lidi h li
lk l
,
y
,
py
p
781
782
Figure 3: General structures of the all synthesized compounds. The asterisk marked the
783
784
785
786
787
788
789
790
791
atoms which are common to all structures and used for the CoMFA/CoMSIA alignment.
29

792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
Figure 4: Molecular docking of most active (‘A’: M96, ‘B’: M64 and ‘C’: M62) and less
active (‘D’: M32, ‘E’: M158 and ‘F’: M165) compounds with heme. Red colour (---) indicate
h-bond interaction.
Figure 5: Non h-bond interactions between heme and M96. Yellow colour (---) indicate
electrostatic interactions whereas pink colours (---) indicate hydrophobic interactions.
30