Exploring clotrimazole-based pharmacophore: 3D-QSAR studies and synthesis of novel antiplasmodial agents

Article abstract of DOI:10.1016/j.bmcl.2015.09.007

We report herein the generation and validation of a 3D-QSAR model based on a set of antimalarials previously described by us and characterized by a clotrimazole-based pharmacophore. A novel series of derivatives was synthesized and showed activity against

Full text of DOI:10.1016/j.bmcl.2015.09.007

Accepted Manuscript
Exploring clotrimazole-based pharmacophore: 3D-QSAR studies and synthesis
of novel antiplasmodial agents
Simone Brogi, Margherita Brindisi, Bhupendra P. Joshi, Salvatore Sanna
Coccone, Silvia Parapini, Nicoletta Basilico, Ettore Novellino, Giuseppe
Campiani, Sandra Gemma, Stefania Butini
PII:
S0960-894X(15)30042-1
DOI:
http://dx.doi.org/10.1016/j.bmcl.2015.09.007
BMCL 23088
Reference:
Bioorganic & Medicinal Chemistry Letters
To appear in:
Received Date:
Revised Date:
Accepted Date:
30 July 2015
2 September 2015
4 September 2015
Please cite this article as: Brogi, S., Brindisi, M., Joshi, B.P., Coccone, S.S., Parapini, S., Basilico, N., Novellino,
E., Campiani, G., Gemma, S., Butini, S., Exploring clotrimazole-based pharmacophore: 3D-QSAR studies and
synthesis of novel antiplasmodial agents, Bioorganic & Medicinal Chemistry Letters (2015), doi: http://dx.doi.org/
10.1016/j.bmcl.2015.09.007
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Bioorganic & Medicinal Chemistry Letters
Exploring clotrimazole-based pharmacophore: 3D-QSAR studies and synthesis
of novel antiplasmodial agents
Simone Brogi,a,b,c Margherita Brindisi,a,b,c Bhupendra P. Joshi,a,b Salvatore Sanna Coccone,a,b Silvia
Parapini,a,c,d Nicoletta Basilico,a,c,e Ettore Novellino,a,f Giuseppe Campiani,*,a,b,c Sandra Gemma,*,a,b,c and
Stefania Butinia,b,c
aEuropean Research Centre for Drug Discovery and Development (NatSynDrugs), Università degli Studi di Siena, via Aldo Moro 2, 53100 Siena, Italy
bDipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena, via Aldo Moro 2, 53100 Siena, Italy
cCentro Interuniversitario di Ricerche sulla Malaria (CIRM), University of Perugia, Perugia, Italy
dDipartimento di Scienze Farmacologiche e Biomolecolari, Universita’ di Milano, Via Pascal 36, 20133 Milan, Italy
eDipartimento di Scienze Biomediche, Chirurgiche e Odontoiatriche, Università di Milano, Via Pascal, 36, 20133, Milan, Italy
fDipartimento di Farmacia, University of Naples Federico II, Via D. Montesano 49, 80131, Naples, Italy
ARTICLE INFO
ABSTRACT
Article history:
Received
We report herein the generation and validation of a 3D-QSAR model based on a set of
antimalarials previously described by us and characterized by
a clotrimazole-based
Revised
Accepted
Available online
pharmacophore. A novel series of derivatives was synthesized and showed activity against
Plasmodium falciparum chloroquine-sensitive (CQ-S) and chloroquine-resistant (CQ-R) strains.
Gratifyingly, compounds 35a-c showed interesting activity against P. falciparum CQ-R strains
with improved predicted physico-chemical properties.
Keywords:
Malaria
2009 Elsevier Ltd. All rights reserved.
Plasmodium falciparum
Clotrimazole
3D-QSAR
Ligand-based
Despite the recent progresses in the number of new chemical
entities under development as antimalarials, morbidity and
mortality due to malaria infections are still very high worldwide.
According to WHO, 1.2 billion people are at high risk of
infection. Moreover, 198 million cases of malaria occurred in
2013 and the disease led to 584,000 deaths.1 The problem is
heavier in African regions, where an estimated 90% of all malaria
deaths occur, and youth mortality (for children younger than 5
years) accounts for 78% of all deaths.1 Additionally, the spread
of Plasmodium drug-resistance makes ineffective almost all
known current antimalarial drugs, including the artemisinin
derivatives that represent the mainstay of antimalarial
combination therapy (ACT). Indeed, the efficacy of ACT is
falling down rapidly in South East Asia where treatment failure
has been recently associated with triple mutants of P.
falciparum.2-4 The mutations are present in the kelch-13 propeller
domain (k13 C580Y), and are associated with additional
mutations MAL10 and MAL13. These triple mutants had a
recrudescence failure rate five times higher than that of patients
without the triple mutation.5 Certainly, the outbreak of
artemisinin resistance could lead to a worrisome scenario. In this
context, novel therapeutics are urgently needed for halting the
spread of resistant P. falciparum strains.
approaches targeting various Plasmodium systems.6-13 In one of
our research lines, we focused our attention on clotrimazole
(CLT)-based antimalarials and we proved that this scaffold is a
valuable opportunity to develop efficacious antimalarial
drugs.14,15 The rationale of our investigation was that the
antimycotic drug CLT (1, Figure 1) is able to form complexes
with free Fe(III)-protoporphyrin, thereby inhibiting its
crystallization into β-hematin,16 and exhibits micromolar
antimalarial activity in vitro (W2, IC50 = 0.49 μM, Table 1).17 We
previously exploited the scaffold of CLT for developing a novel
class of antimalarials characterized by potent antiplasmodial
activity against both chloroquine-sensitive (CQ-S) and
chloroquine-resistant (CQ-R) P. falciparum strains, and by
antimalarial activity against P. chabaudi after oral
administration.14,15
In an effort to develop novel therapeutics for fighting malaria,
our research group is involved in several ways, with different
Figure 1. Chemical scaffolds of the compounds used to derive the 3D-
QSAR model (R1, R2, Ar and X are defined in Table 1).
*To whom correspondence should be addressed: Sandra Gemma:
gemma@unisi.it; Giuseppe Campiani: campiani@unisi.it

Table 1. Compounds used for developing 3D-QSAR model and their experimental (IC50 (nM) on W2 and pIC50) and predicted
activities (pIC50) as evaluated by the model.
Cpd
R1a
R2a
Ar a
Xa
IC50 W2
(nM)
W2 Observed W2 Predicted
pIC50
pIC50
1 (CLT)c
490
6.31
5.35
6.10
5.26
2b
Et
Et
-
4,460
3b
4c
-(CH2)4-
-
3,050
1,700
1,030
1,130
>10,000d
100
5.52
5.77
5.99
5.94
5.00
7.00
7.22
5.42
5.59
5.73
5.88
5.39
6.39
6.25
7.26
7.08
5.49
5.70
6.09
5.76
5.04
7.08
6.55
5.97
5.75
5.94
5.72
5.70
6.11
6.74
6.91
6.56
2-Cl
3-Cl
4-Cl
2-Cl
3-Cl
4-Cl
4-Cl
4-Cl
4-
H
H
H
H
5c
6c
7c
8b
9b
59
10b
11c
12b
13c
14c
15c
16c
17b
18c
3,830
2,570
1,840
1,293
4,109
411
OMe
2-Cl
H
H
H
2-Cl
2-Cl
4-Cl
4-Cl
4-Cl
561
55.4
82.0
19b
4-Cl
26.1
7.58
7.79
20b
21b
22c
23b
24c
25b
3-Cl
3-Cl
3-Cl
3-Cl
3-Cl
4-Cl
4,267
314
5.37
6.50
6.99
6.46
6.23
5.45
5.51
6.45
6.91
6.38
6.42
5.73
101
349
587
3,571

26b
27c
4-Cl
H
177
148
6.75
6.83
6.37
6.59
28b
4-F
66.9
7.17
7.45
29c
30b
4-F
4-F
379
6.42
5.46
6.10
5.36
3,485
31b
32b
2-Cl
Cl
H
>10,000d
>10,000d
5.00
5.00
5.17
4.83
-
-
33c
34c
-
-
5,034
1,808
5.30
5.74
5.55
6.06
-
-
aR1, R2, Ar and X refer to general structures reported in Figure 1; bcompounds included in the training set; ccompounds included in the test set; dduring the
calculation a value of 10,000 was arbitrarily chosen.
Based on the CLT derivatives previously synthesized (general
structures are reported in Figure 1 and detailed in Table 1), we
3D model hypotheses, we selected the one (AHPRR.309)
represented in Figure 2. The 3D model hypothesis is
characterized by the following 5 features: one hydrogen-bond
acceptor feature (A), two aromatic rings (R), one positive
ionizable feature (P) and one hydrophobic function (H). These
features recapitulate the structural requirements for interacting
with heme group, as observed through the superposition between
the pharmacophore and 19 (Figure 2A). In particular, the H-
bonds acceptor feature (A) is critical for the interaction with
metal charged moiety of the heme group, while additional
hydrophobic interactions with the protoporphyrin-IX ring can be
maximized by matching all non-polar features (R and H). The
AHPRR pharmacophore hypothesis was then employed as
alignment rule to derive the 3D-QSAR model. An exhaustive
conformational search for each ligand used in this study was
performed employing MacroModel23 (see supplementary
experimental procedure for further details). This step was crucial
to increase both the quality of the alignment for the molecules
chosen to generate the 3D-QSAR model and the reliability of the
computational tool.24 In this step the compounds were randomly
subdivided (50%) in training and test sets (Table 2).
describe herein the development of
a three-dimensional
quantitative structure-activity relationship (3D-QSAR) model.
The computational tool was generated by means of Phase
software18 using a pharmacophore-based alignment,19-21 that links
the biological activity of specific compounds with the presence
of pivotal functional groups. In the work here presented, since the
compounds taken into consideration displayed similar potencies
against both CQ-S (D10) and CQ-R (W2) P. falciparum strains,
the biological data taken into consideration were related to the
activity against the CQ-R P. falciparum strain W2. We validated
the model in silico, and we synthesized novel derivatives that
were evaluated for their capability to inhibit P. falciparum
growth in CQ-S D10 and CQ-R W2 strains.
As the first step to develop the 3D-QSAR model, we
generated a common features pharmacophore taking into account
the most active compounds included in the data set (34
compounds).14,15 Remarkably, the set of compounds selected for
the development of 3D-QSAR model spanned four orders of
magnitude with reference to P. falciparum CQ-R strain growth
inhibition (from 26.1 nM of compound 19 to > 10 μM of
compound 7, 31 and 32) (Figure 1 and Table 1). All the
compounds taken into consideration were synthesized and tested
as racemates. However, since their mode of action is likely to
involve interaction with the free heme group (as previously
reported for the reference compound clotrimazole16), no
stereoselective mode of interaction may be predicted or expected.
Consequently, for the generation of the 3D-QSAR model, the (R)
enantiomer only was used.6,22
Models containing up to three PLS factors were generated
(statistical parameters are reported in Table 2)
As aforementioned, highly active compounds showing IC50
values in the nanomolar range (9, 17-19, 28) (Figure 1 and Table
1) were used to generate the common pharmacophore
hypotheses, that were subsequently scored and ranked by Phase
(further details about pharmacophore hypothesis generation are
provided in the Supplementary Material). Among the generated
Figure 2. (A) Superposition of pharmacophore model AHPRR.309 with the
most active ligand in the set (19). (B) Features of pharmacophore obtained by
Phase are as follows: one hydrogen-bond acceptor (A; represent by red
vector), two aromatic groups (R; orange ring), one positive ionizable (P;
represented by blue sphere) and one hydrophobic function (H; represented by
green sphere). (B) Inter-features distances of the hypothesis.

Table 2. Statistical parameters of the 3D-QSAR model.
Database of Useful Decoys: Enhanced (DUD-E) web server
(http://dude.docking.org access date May 2015). This procedure
is largely used to assess the ability of computational models to
discriminate between active or inactive molecules.25-27 Starting
from active compounds presented herein (9, 17-19, 28; Figure 1
and Table 1), and from two sets of active molecules previously
developed and tested under the same experimental conditions,8,9
PL
S
r2a
SDb
Fc
Pd
RMSE
Q2f
R-
e
Pearson
g
0.45
2
0.68
3
12.
4
0.003
1
0.384
0.333
0.266
0.54
4
0.738
0.844
0.886
1
2
3
0.73
6
0.49
0
19. 8.85e-
05
35. 1.55e-
06
0.65
5
6
0.89
2
0.32
6
0.78
1
6
ar2: value of r2 of the regression. bSD: standard deviation of the regression.
e
cF: variance ratio. dP: significance level of variance ratio. RMSE: root-mean-
f
square error. Q2: value of Q2 for the predicted activities. gR: R-Pearson,
correlation between the predicted and observed selectivity index values for
the test set.
Figure 4. Superposition of highly active compounds 17 (A) and 19 (B)
with the 3D-QSAR model. Superposition of the 3D-QSAR model with two of
the less active compounds 31 (C) and 32 (D).
Table 3. EF and GH scores obtained by the application of 3D-
QSAR model in a database screening.
Parameters
Total molecules in database (D)
Total number of actives in database (A)
Total hits (Ht)
Values
2259
26
Figure 3. Scatter plot for the predicted (phase predicted activity) and
observed (experimental activity) pIC50 values (M) as calculated by the 3D-
QSAR model applied to the training set (cyan) and test set compounds
(orange).
30
Active hits (Ha)
21
The model with three PLS factors was preferred and selected.
The high correlation and cross-validated correlation coefficients
(r2 = 0.892 and Q2 = 0.781, respectively) together with the high
Pearson R value (R-Pearson = 0.886), are indicative of a close
correspondence between predicted and actual IC50 values, and
hence of a model with robust predictive power and significance.
% Yield of actives (YA)
% Ratio of actives (RA)
Enrichment factor (EF)
Goodness of hit score (GH)
70
80.7
60.8
0.72
A scatter plot of experimental versus predicted activities was
generated (Figure 3), and IC50 values were reliably predicted for
both training and test set molecules (Table 1). These features,
along with the low RMSE value and the small variance ratio (P)
supported the significance of the approach.
we selected a total of 26 actives (A, Table 3). From this latter set
(which includes 21 unrelated compounds not used for developing
the present model), DUD-E generated 2233 decoys. Next, after
minimization and conformational analysis, we combined active
and inactive molecules (2259 compounds referred as D in Table
3), and using the 3D-QSAR model, we performed a virtual
screening campaign (see Supplementary Material for further
details). The results of the screening (Table 3) showed that the
top thirty ranked compounds were considered as hits (Ht) (about
1% of database). This value could represent a suitable number of
molecules to be purchased after a virtual screening campaign.
Among these, 21 compounds (Ha) belonged to the set of 26
known inhibitors. Accordingly, the calculated EF was 60.8,
which means that it is 60.8 times more probable to identify active
compounds from databases than expected by chance. The
calculated GH score value of 0.72, greater than 0.5, indicates a
good reliability of the model, suggesting that it could be
The 3D-QSAR results were visualized using 3D plots of the
crucial volume elements occupied by ligands. The 3D plot
representation of the whole model, superimposed to derivatives
17, 19, 31, and 32 is shown in Figure 4 (Panels A-D,
respectively). In this representation blue and red cubes specify
positive and negative coefficients, respectively. These are
volumes in which the occupying atoms of the ligand cause an
increase (blue cubes) or a decrease (red cubes) of activity.
Notably, compounds 17 and 19 (Figure 4A and B, respectively)
as well as highly active compounds, mainly sit in the blue
regions, while the less active derivatives such as 31 and 32
(Figure 4C and D, respectively) mainly fit in the red regions.
In order to assess the predictive power of the developed 3D-
QSAR model, we have used a decoys set generated by means of

successfully employed in virtual screening studies to find novel
chemical entities against P. falciparum (the output of the virtual
screening is provided as Supplementary Material, Figure S2).
depth explore the R2 region. Consequently, we decided to
synthesize novel compounds bearing various R2 functional
groups (35a-i,36,37, Scheme 1 and Table 3).
In our previous studies14,15 we coupled protonatable groups at
R1 with various heterocycles as Ar substituents, but we did not in
Scheme 1. Synthesis of compounds 35a-i, 36 and 37.a
aReagents and conditions: (a) i. Mg, 39a-f, THF, reflux, 1 h; ii. 38a-d, THF, reflux, 8 h; (b) NBS, AIBN, CCl4, reflux, 3 h; (c) NaH, imidazole, N,N-DMF, 0 °C,
1 h; (d) i. SOCl2, DCM, 40 °C, 4 h; ii. imidazole, Et3N, MeCN, reflux, 4 h; (f) BBr3, DCM, 0 °C, 3 h.
In the novel molecules synthesized and here described, we
coupled novel R2 substituents with protonatable groups at R1. The
R1 substituents match the P group of the AHPRR and are
characterized by various degrees of protonatability. In particular,
we introduced pyrrolidine, morpholine, piperazine and imidazole
heterocyles with one methylene spacer from the aromatic ring.
From our model, it is evident that the blue area around Ar group
is quite large and introduction of apolar and polar substituents is
tolerated. The in silico analysis of the best-predicted compounds
revealed a good matching with the developed pharmacophore
model and a satisfactory activity prediction (Table 3 and Figure
5). Moreover, for compounds 35a-i, 36 and 37, a general
improvement of predicted cell permeability (LogP, MDCK and
Caco-2 models) and solubility (LogS), according to QikProp
output has been observed (Table S1 and Table S2 for reference
and synthesized compounds, respectively).
3D-QSAR results (Figure 5 for compounds 35a,b) and
satisfactory predicted physico-chemical properties.
Figure 5. Superposition of 35a (A) and 35b (B) with the 3D-QSAR
model.
Table 3. Compounds designed by the application of 3D-
QSAR model, experimental activities against D10 and W2 P.
falciparum strains (IC50, nM for D10 and W2 and pIC50 for W2),
and predicted activities (pIC50) as evaluated by the model.
The synthesis of compounds 35a-i, 36 and 37 is described in
Scheme 1. Benzophenone derivatives 38a-d were prepared as
previously described14,15 and reacted with the Grignard reagents
prepared in situ from 39a-e. The resulting carbinols 40a-g and 41
were submitted to a chlorination reaction in the presence of
thionyl chloride. The chlorine atom was subsequently displaced
using imidazole in the presence of a base. This procedure
furnished compounds 35a-h, while compound 35i was obtained
after HCl-mediated deprotection. Compound 41 was also
brominated and then treated with the imidazole sodium salt
leading to intermediate 43, in turn converted into final compound
35h. Phenol derivatives 36 and 37 were prepared from the
corresponding 3-methoxy-analogues 35b,e by using boron
trifluoride in DCM at low temperature. When the same reaction
was conducted on the 4-methoxy derivative 35f, the quinone 44
was the only product isolated from the reaction mixture.
W2
W2
Cpd
R1
R2
D10
W2
Observed Predicted
IC50 (nM)a
pIC50
pIC50
6.95
6.84
pyrrolidin-
1-yl
pyrrolidin-
1-yl
4-Cl
128
177
244
112
118
122
35a
35b
35c
6.93
6.91
6.76
7.16
3-OMe
3,4-
OCH2O-
pyrrolidin-
1-yl
6.15
5.91
6.08
6.61
5.93
6.11
3,4-
OCH2O-
1,038
705
35d
35e
35f
morpholin-
1-yl
3-OMe
1,582 1219
morpholin-
1-yl
The biological results on derivatives 35a-i, 36 and 37 are
listed in Table 3. Interestingly, compounds 35a-c displayed a
good inhibitory profile against CQ-R strain, consistent with the
4-OMe
751
829
morpholin-
1-yl

S.; Teja-isavadharm, P.; Pichyangkul, S.; Phann, S. T.; Balasubramanian, S.;
Juliano, J. J.; Meshnick, S. R.; Chour, C. M.; Prom, S.; Lanteri, C. A.; Lon,
C.; Saunders, D. L. Lancet Infect. Dis. 2015, 15, 683.
5.96
6.14
6.56
6.57
5.82
6.24
5.72
6.89
6.53
6.15
4-F
H
1,459 1,084
35g
35h
35i
36
morpholin-
1-yl
6.
Brindisi, M.; Gemma, S.; Kunjir, S.; Di Cerbo, L.; Brogi, S.;
365
448
138
716
278
269
imidazol-3-
yl
Parapini, S.; D'Alessandro, S.; Taramelli, D.; Habluetzel, A.; Tapanelli, S.;
Lamponi, S.; Novellino, E.; Campiani, G.; Butini, S. Med. Chem. Commun.
2015, 6, 357.
3,4-
OCH2O-
piperazin-4-
yl
7.
Gemma, S.; Brogi, S.; Patil, P. R.; Giovani, S.; Lamponi, S.;
Cappelli, A.; Novellino, E.; Brown, A.; Higgins, M. K.; Mustafa, K.; Szestak,
T.; Craig, A. G.; Campiani, G.; Butini, S.; Brindisi, M. RSC Adv. 2014, 4,
4769.
3-OH
pyrrolidin-
1-yl
8.
Gemma, S.; Camodeca, C.; Brindisi, M.; Brogi, S.; Kukreja, G.;
Kunjir, S.; Gabellieri, E.; Lucantoni, L.; Habluetzel, A.; Taramelli, D.;
Basilico, N.; Gualdani, R.; Tadini-Buoninsegni, F.; Bartolommei, G.;
Moncelli, M. R.; Martin, R. E.; Summers, R. L.; Lamponi, S.; Savini, L.;
Fiorini, I.; Valoti, M.; Novellino, E.; Campiani, G.; Butini, S. J. Med. Chem.
2012, 55, 10387.
3-OH
1,089 1,497
37
morpholin-
1-yl
aIC50 values are the mean of at least three determinations in duplicate.
Standard errors were all within 10% of the mean.
9.
Gemma, S.; Camodeca, C.; Sanna Coccone, S.; Joshi, B. P.;
Bernetti, M.; Moretti, V.; Brogi, S.; Bonache de Marcos, M. C.; Savini, L.;
Taramelli, D.; Basilico, N.; Parapini, S.; Rottmann, M.; Brun, R.; Lamponi,
S.; Caccia, S.; Guiso, G.; Summers, R. L.; Martin, R. E.; Saponara, S.;
Gorelli, B.; Novellino, E.; Campiani, G.; Butini, S. J. Med. Chem. 2012, 55,
6948.
In summary, we generated a novel 3D-QSAR model and we
described a series of CLT-based antimalarials. All tested
compounds showed inhibitory activity against CQ-R and CQ-S
P. falciparum strains in the low micromolar range. Among them,
three compounds (35a-c) showed an interesting activity profile
against CQ-R P. falciparum strain as well as against the CQ-S
strain, when compared to the reference compound CLT (1).
Consequently, this experimental validation will allow us to use
the developed 3D-QSAR model to perform a virtual screening
campaign to search for novel derivatives sharing the CLT-based
pharmacophore.
10.
Gemma, S.; Giovani, S.; Brindisi, M.; Tripaldi, P.; Brogi, S.;
Savini, L.; Fiorini, I.; Novellino, E.; Butini, S.; Campiani, G.; Penzo, M.;
Blackman, M. J. Bioorg. Med. Chem. Lett. 2012, 22, 5317.
11.
Gemma, S.; Kunjir, S.; Coccone, S. S.; Brindisi, M.; Moretti, V.;
Brogi, S.; Novellino, E.; Basilico, N.; Parapini, S.; Taramelli, D.; Campiani,
G.; Butini, S. J. Med. Chem. 2011, 54, 5949.
12.
Giovani, S.; Penzo, M.; Brogi, S.; Brindisi, M.; Gemma, S.;
Novellino, E.; Blackman, M. J.; Campiani, G.; Butini, S. Bioorg. Med. Chem.
Lett. 2014, 24, 3582.
13.
Giovani, S.; Penzo, M.; Butini, S.; Brindisi, M.; Gemma, S.;
Novellino, E.; Campiani, G.; Blackman, M. J.; Brogi, S. RSC Adv. 2015, 5,
22431.
14.
Acknowledgments
Gemma, S.; Campiani, G.; Butini, S.; Kukreja, G.; Coccone, S. S.;
Joshi, B. P.; Persico, M.; Nacci, V.; Fiorini, I.; Novellino, E.; Fattorusso, E.;
Taglialatela-Scafati, O.; Savini, L.; Taramelli, D.; Basilico, N.; Parapini, S.;
Morace, G.; Yardley, V.; Croft, S.; Coletta, M.; Marini, S.; Fattorusso, C. J.
Med. Chem. 2008, 51, 1278.
The authors wish to thank NatSynDrugs and MIUR for
financial support.
15.
Gemma, S.; Campiani, G.; Butini, S.; Kukreja, G.; Joshi, B. P.;
Persico, M.; Catalanotti, B.; Novellino, E.; Fattorusso, E.; Nacci, V.; Savini,
L.; Taramelli, D.; Basilico, N.; Morace, G.; Yardley, V.; Fattorusso, C. J.
Med. Chem. 2007, 50, 595.
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Graphical Abstract
Exploring clotrimazole-based pharmacophore: 3D-QSAR
studies and synthesis of novel antiplasmodial agents
Leave this area blank for abstract info.
Simone Brogi, Margherita Brindisi, Bhupendra P. Joshi, Salvatore Sanna Coccone, Silvia Parapini, Nicoletta
Basilico, Ettore Novellino, Giuseppe Campiani,* Sandra Gemma,* and Stefania Butini