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Antimicrobial Agents and Chemotherapy, October 2000, p. 2771-2776, Vol. 44, No. 10
0066-4804/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.
Anti-Toxoplasma Activities of 24 Quinolones and
Fluoroquinolones In Vitro: Prediction of Activity by Molecular
Topology and Virtual Computational Techniques
Rafael
Gozalbes,1,2
Monique
Brun-Pascaud,3
Ramon
Garcia-Domenech,4
Jorge
Galvez,4
Pierre-Marie
Girard,5
Jean-Pierre
Doucet,2 and
Francis
Derouin1,*
Laboratoire de Parasitologie-Mycologie,
Faculté de Médecine Lariboisière Saint-Louis,
Université Paris 7, 75006 Paris,1
Institut de Topologie et de Dynamique des Systèmes
(ITODYS), Université Paris 7, 75005 Paris,2 Inserm E9933, Hôpital
Bichat, 75018 Paris,3 and Service des
Maladies Infectieuses, Hôpital Rothschild, 75571 Paris Cedex
12,5 France, and Unidad de
Investigación en Diseño de Fármacos y Conectividad
Molecular, Departamento de Química-Física, Facultad
de Farmacia, Universidad de Valencia, 46100 Burjassot (Valencia),
Spain4
Received 26 June 2000/Returned for modification 21 July
2000/Accepted 26 July 2000
 |
ABSTRACT |
The apicoplast, a plastid-like organelle of Toxoplasma
gondii, is thought to be a unique drug target for quinolones. In
this study, we assessed the in vitro activity of quinolones against T. gondii and developed new quantitative structure-activity
relationship models able to predict this activity. The
anti-Toxoplasma activities of 24 quinolones were examined
by means of linear discriminant analysis (LDA) using topological
indices as structural descriptors. In parallel, in vitro 50%
inhibitory concentrations (IC50s) were determined in tissue
culture. A multilinear regression (MLR) analysis was then performed to
establish a model capable of classifying quinolones by in vitro
activity. LDA and MLR analysis were applied to virtual structures to
identify the influence of each atom or substituent of the quinolone
ring on anti-Toxoplasma activity. LDA predicted that 20 of
the 24 quinolones would be active against T. gondii. This
was confirmed in vitro for most of the quinolones. Trovafloxacin,
grepafloxacin, gatifloxacin, and moxifloxacin were the quinolones most
potent against T. gondii, with IC50s of 0.4, 2.4, 4.1, and 5.1 mg/liter, respectively. Using MLR analysis, a good
correlation was found between measured and predicted IC50s (r2 = 0.87, cross-validation
r2 = 0.74). MLR analysis showed that the
carboxylic group at position C-3 of the quinolone ring was not
essential for anti-Toxoplasma activity. In contrast,
activity was totally dependent on the presence of a fluorine at
position C-6 and was enhanced by the presence of a methyl group at C-5
or an azabicyclohexane at C-7. A nucleophilic substituent at C-8 was
essential for the activity of gatifloxacin and moxifloxacin.
| |
INTRODUCTION |
The discovery of a novel organelle
in apicomplexan parasites and its characterization in Toxoplasma
gondii offers new opportunities for pharmacological research on
several protozoa of major medical importance (16). This
organelle, the apicoplast, is a plastid-like structure which was
probably acquired by secondary endosymbiosis from a green alga
(15). The function of the apicoplast is still not clear, but
the presence of this procaryotic structure within T. gondii
presents a unique therapeutic target. Fichera and Roos showed that
several antibiotics, such as azithromycin and ciprofloxacin, could
inhibit DNA replication within the apicoplast and thus inhibited Toxoplasma growth (6). That study confirmed
the previously well-known effect of macrolides on T. gondii
but also revealed fluoroquinolones as candidate
anti-Toxoplasma drugs. However, other studies performed in
vitro and in vivo failed to confirm the activity of ciprofloxacin
against T. gondii and showed that, among the
fluoroquinolones, only trovafloxacin and some of its derivatives
inhibited Toxoplasma growth at micromolar concentrations (9, 10). Better knowledge of the structure-activity
relationships of quinolones against T. gondii is thus needed.
The aims of this work were (i) to assess quinolone activity against
T. gondii by using a previously described model of virtual prediction (8) and by testing the inhibitory effects of 24 quinolones and fluoroquinolones in vitro, (ii) to establish
quantitative structure-activity relationship (QSAR) models based on
molecular topology and multilinear regression (MLR) analysis in order
to predict the 50% inhibitory concentrations (IC50s) of
quinolones for T. gondii, and (iii) to identify the basic
chemical structures responsible for the anti-T. gondii
activity of quinolones by using atom level topological indices and by
testing computer-generated virtual structures of quinolones (2,
13).
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MATERIALS AND METHODS |
The 24 quinolones studied were cinoxacin, enoxacin, flumequin,
nalidixic acid, norfloxacin, oxolinic acid, pipemidic acid, piromidic
acid, sparfloxacin, temafloxacin, trovafloxacin (Sigma Aldrich, Paris,
France), ciprofloxacin, moxifloxacin (Bayer Pharma), irloxacin
(Laboratorios Dr. Esteve), grepafloxacin (Glaxo Wellcome), gatifloxacin
(Grünenthal), levofloxacin, ofloxacin (Hoechst Marion Roussel),
rufloxacin (Mediolanum Farmaceutici), lomefloxacin (Monsanto Searle),
clinafloxacin (Parke-Davis), fleroxacin (Roche), pefloxacin (Roger
Bellon), and acroxacin (Sanofi Winthrop).
Assessment of quinolone anti-Toxoplasma activity by
LDA.
We used a mathematical model previously described for virtual
identification of anti-T. gondii drugs (8).
Briefly, linear discriminant analysis (LDA) is a pattern recognition
method which provides a classification model based on the combination
of variables that best predicts the category (active or inactive) to
which a given compound belongs. The independent variables in this study were topological indices (TIs) that were calculated for each drug, and
the discrimination property was in vitro anti-T. gondii
activity. Two LDA equations (T1 and T2) were
obtained. Equation T1 discriminates drugs that are active
against T. gondii (T1 > 0) from any other drug with no antiprotozoal activity (T1 < 0).
Equation T2 separates anti-Toxoplasma drugs
(T2 > 0) from antiprotozoals with no
anti-Toxoplasma activity (T2 < 0). Both
equations were reliably predictive of in vitro activity, as more than
90% of the drugs included in the test groups have been correctly
classified by their anti-Toxoplasma activity (8).
In vitro assessment of quinolone anti-Toxoplasma
activity.
Stock solutions of each drug were prepared at 2 mg/ml in
dimethyl sulfoxide, and serial dilutions were then prepared in
distilled water.
In vitro studies were performed with the virulent RH strain of T. gondii, which was maintained in mice by intraperitoneal passage
every 2 days. For each experiment, tachyzoites were collected from the
peritoneal cavity and then resuspended in physiological saline. Tissue
culture and drug tests were carried out using MRC5 fibroblasts as
previously described (3), with minor modifications. Briefly,
confluent monolayers prepared in 96-well tissue culture plates were
inoculated with 2,000 fresh tachyzoites. After 4 h, drugs at
various concentrations were added to the culture medium and the plates
were incubated for a further 72 h. Each drug was tested at 10 concentrations ranging from 0.01 to 200 mg/liter (final concentration
in the culture). Each concentration was tested in eight replicate wells
and in two replicate culture plates. Each culture plate comprised eight
negative control wells (without T. gondii) and eight
positive control wells (without a drug). After incubation, the plates
were examined microscopically for cytopathic effects and then fixed
with cold methanol for 5 min. Toxoplasma growth was assessed
by enzyme-linked immunoassay directly on fixed cultures by using a
peroxidase-labeled monoclonal antibody directed against the T. gondii SAG-1 surface protein. After addition of the substrate,
spectrophotometric readings were performed at a wavelength of 405 nm
with blanking on the negative control wells. For each well, the results
were expressed as optical density values. The optical density values
were plotted as a function of the logarithm of the concentration, and a
linear regression model was used to summarize the concentration-effect
relationship and to determine the IC50 (3).
MLR.
The 24 quinolones were characterized by using a set of
145 TIs specific for each molecule (11). We used topological
descriptors provided by the MOLCONN-Z software, version 3.50 (L. H. Hall, Eastern Nazarene College, Quincy, Mass.), and
especially the Kier & Hall connectivity indexes (up to 10th order). We
also calculated some descriptors as charge indexes (7) using
the Etopo 11 software developed in our research unit.
The calculated TIs were related by MLR to the observed
IC
50s of the 24 quinolones to predict the IC
50s
of new quinolones.
MLR was performed with the 9R module of the BMDP
program (W. J.
Dixon, BMDP Statistical Software, University of
California, Berkeley),
which estimates regression equations for best
subsets of predictor
variables and provides detailed residual analysis.
The lower Mallows
Cp was used to identify the best subsets.
Mallows'
Cp =
RSS/
s2 
(
n 2
p'), where
RSS is the residual sum of squares for the
best subset being tested,
p' is the number of independent
variables
in the subset (including the intercept),
n is the
number of cases,
and
s2 is the residual mean
square based on the regression using all
independent variables
(
14).
Topological superposition with atomic E-state indices to identify
basic pharmacophore structures.
Usual structural descriptions
based on topological descriptors are based mainly on the whole
molecule, and the QSAR models thereby obtained therefore offer little
insight into drug pharmacophores. We therefore used of a new kind of
topological descriptor
E-state indicesspecific for each atom
(12). Briefly, the E-state index of a given atom reflects
its electronic and topological features, taking into account the
interaction with the rest of the molecule, particularly the
relationship between valence and sigma electrons. These topological
indices were related to the anti-T. gondii activities of the
24 quinolones. We focused on atoms which are always present in the
basic structure of quinolones. The E-state indices were calculated for
each atom in the set (numbered as shown in Fig. 1) and were related by
MLR to the IC50s of the 24 quinolones.
Virtual computational screening.
Computational screening was
used to determine the influence of quinolone substituents on
anti-Toxoplasma activity and to help select new quinolones
with improved efficacy. Virtual structures were designed by omission or
substitution of radical R1, R5, R6, R7, or R8 on the most active
quinolones tested (trovafloxacin, grepafloxacin, gatifloxacin, and
moxifloxacin). Figure 1 shows the general
quinolone structure and radical numbers. The TIs were calculated, and
LDA and MLR equations were used to determine their activity or
inactivity and IC50s, respectively.
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FIG. 1.
General quinolone structure. X and Y can be carbon or
nitrogen atoms, and the R1, R5, R6, R7, and R8 groups can be very
diverse structures. The basic structure selected to study the
pharmacophoric structure by using E-state indices is indicated by
italic letters, representing the atoms studied.
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RESULTS |
Anti-Toxoplasma activities of quinolones determined by
LDA and in vitro tests.
The T1 and T2
equations obtained by LDA were applied to the 24 quinolone structures
(Table 1). Among the 24 quinolones
tested, 20 had positive T1 values, indicating theoretical
activity against T. gondii. T2 values were
negative for 21 of the 24 compounds, indicating that the
anti-Toxoplasma effects of these drugs cannot be
distinguished from general antiprotozoan activity. Most of the
quinolones tested had growth-inhibitory activity in vitro, although
some were effective only at high concentrations. IC50s ranged from 0.4 mg/liter for trovafloxacin to >100 mg/liter for cinoxacin and levofloxacin (Table 2).
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TABLE 2.
Structures of the 24 quinolones studied, in order of
decreasing experimental and calculated IC50s
against T. gondii
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|
MLR analysis.
From the results of in vitro testing of the 24 quinolones, the MLR technique was used to establish a QSAR model able
to correlate the chemical structures with in vitro activity. In this
analysis, levofloxacin and ofloxacin were not considered because they
have the same plane formula, implying the same TIs. Preliminary
analysis also led us to remove cinoxacin, as its particular structure
and experimental IC50 (200 mg/liter) resulted in an
incorrect model.
The best equation obtained with the remaining 21 quinolones was log
(1/IC
50) =
6.1 + 0.3
G2 0.6
G3 9.3
J4 + 18.1
J4v + 0.3
PR1. The statistical
parameters were as follows:
r2 = 0.87
(cross-validation
r2
[
r2cv] = 0.74), Mallows'
Cp = 6.0, standard error = 0.24, and
P <
0.0001.
The principal selected descriptors were charge indexes
(
G2,
G3,
J4, and
J4v), which take into account the
distribution of intramolecular
charges at different topological
distances (
7). Their presence
is logical because of the
multiple points of union to the basic
quinolone structure and the
presence of heteroatoms such as N
and F. The other selected index was
PR1, which reflects the degree
of ramification of the
structure (
PR1 increases with ramification,
and the
IC
50 falls as a
result).
The experimental and calculated IC
50s of the 24 quinolones
are presented, together with their structures, in Table
2. The
correlation is represented graphically in Fig.
2.
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FIG. 2.
Comparison between experimental (y axis) and
calculated (x axis) log (1/IC50) values for 24 quinolones. IC50s were determined in vitro by culture and
calculated by MLR analysis.
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|
From both the experiments and the mathematical model, four
fluoroquinolones emerged as more active than the other compounds,
as
their IC
50s were below 10 mg/liter. Trovafloxacin was the
most
active drug, with experimental and calculated IC
50s
below 0.5
mg/liter, followed by grepafloxacin, gatifloxacin, and
moxifloxacin.
To further validate the predictive model, the MLR equation was also
applied to 11 trovafloxacin analogs whose structures and
IC
50s were recently published (
10). Although the
technique used
to determine the IC
50 was slightly different
from that used in
our study, very good agreement was obtained for 10 of
11 compounds
between our predicted IC
50s and those obtained
experimentally
by Khan et al. (
10).
Identification of pharmacophoric structure.
To identify the
basic quinolone atoms which contribute the most to
anti-Toxoplasma activity, fourteen E-state indices
representing all of the atoms in the basic structure (designated as
shown in Fig. 1) were related to the IC50s of the 24 quinolones. The best equation was IC50 = 871.5 + 55.4 S (b) 72.5 S (h) + 4.3 S (k) 24.7 S (l). The statistical parameters were n = 22, r2 = 0.74, and
r2cv = 0.42.
The statistical parameters indicate that the atomic position only
partially correlates with the IC
50. The mean values (and
ranges) of the indices were 1.39 (0.90 to 3.85) for S (
b),
12.37
(11.91 to 12.78) for S (
h), 0.13 (
1.02 to 4.24) for
S (
k), and
0.40 (
0.28 to 1.42) for S (
l). This
equation reveals the atomic
positions which most contribute to lowering
of the calculated
IC
50. The most contributory structures
were the carbonyl group
represented
by (
h)
and position 2, both of which are basic quinolone structures,
and, to a lesser extent,
positions 6 and 7, which usually bear
a fluorine atom and a
substitutive radical, respectively. The
carboxylic group at position 3 did not appear in the
equation.
Virtual computational screening of some analogs of the most active
quinolones was used to identify the influence of radicals
on
anti-
Toxoplasma activity. As the LDA and MLR equations were
reliably predictive of in vitro activity, they were then applied
to
virtual structures derived from the four quinolones most active
against
T. gondii, i.e. trovafloxacin, grepafloxacin, gatifloxacin,
and moxifloxacin. Several major substituents (R1, R5, R6, R7,
and R8)
were removed or replaced, and the in vitro anti-
Toxoplasma activities of these virtual compounds were then estimated by LDA
and
MLR
analysis.
LDA resulted in positive T
1 values and negative
T
2 values for almost all of the virtual quinolones tested,
showing that the
basic quinolone structure accounts for
anti-
Toxoplasma and antiprotozoan
activity.
MLR analysis yielded estimated IC
50s of the virtual
compounds (Table
3). The results revealed
the importance of the substituents
at R5, R6, and R7. The presence of a
fluorine at R6 was crucial,
as its omission from the four most active
quinolones resulted
in an increase in the IC
50 to >100
mg/liter. When the R5 methyl
group was removed from grepafloxacin,
there was a 15-fold increase
in the IC
50; when it was added
to the structure of trovafloxacin,
gatifloxacin or moxifloxacin, the
IC
50 fell 5- to 7-fold. At R7,
we replaced the original
groups with several substituents that
are present in other quinolones.
With trovafloxacin, all of the
changes resulted in significant loss of
activity. In contrast,
with grepafloxacin, gatifloxacin, and
moxifloxacin, replacement
of the original R7 substituent with an
azabicyclohexane group
resulted in a 10- to 12-fold increase in
activity, showing the
importance of this radical in
anti-
Toxoplasma activity. Similarly,
we found that the
presence of a nucleophilic substituent at R8
was important for the
activity of gatifloxacin and moxifloxacin,
as its omission resulted in
a three- to ninefold increase in the
IC
50. The presence of
a cyclopropyl or 2,4-difluorophenyl at R1
was associated with better
activity than was that of a methyl,
ethyl, or
t-butyl
radical.
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TABLE 3.
Computational screening of MLR function applied to
virtual quinolones derived from trovafloxacin, grepafloxacin,
gatifloxacin, and moxifloxacin
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| |
DISCUSSION |
The results of this study confirm that quinolones are active
against T. gondii (6, 9, 10) and show that this
activity can be predicted using molecular topology methods. The LDA
model, which we have previously used to identify antiprotozoan and
anti-Toxoplasma drugs (8), showed that 20 of the
24 quinolones or fluoroquinolones studied were predicted to be active
against T. gondii. When chemical structures defined by TIs
were entered into an equation which distinguished
anti-Toxoplasma drugs from other antiprotozoan drugs, the
values were negative for 21 of the 24 compounds, indicating that the
anti-Toxoplasma efficacy of these drugs could not be distinguished from general antiprotozoan activity. Despite the fact
that four quinolones were misclassified with T1 and three were
misclassified with T2 (probably due to the heterogeneity of the
database used to build the model), these results suggest that, beside
their anti-Toxoplasma activity, quinolones are also effective against other protozoa. This supports the hypothesis that
several protozoa, and more specifically those belonging to the phylum
Apicomplexa, have a quinolone target in common. It is also
in keeping with several reports on the in vitro and in vivo activities
of some fluoroquinolones against Plasmodium falciparum (4).
The predicted activity of quinolones against T. gondii was
confirmed in vitro. An inhibitory effect was found with most of the
quinolones tested, although sometimes only at high concentrations. Twenty of the 24 quinolones had IC50s above 10 mg/liter;
this may explain why Khan et al. (9), who used
concentrations below 10 mg/liter, found that ciprofloxacin, fleroxacin,
ofloxacin, temafloxacin, and tosufloxacin were not active. However,
like those authors, we found that trovafloxacin was highly active, with
an IC50 of 0.4 mg/liter. We also found that another three fluoroquinolones (grepafloxacin, gatifloxacin, and moxifloxacin) potently inhibited Toxoplasma growth, with IC50s
below 5 mg/liter. These results indicate that only a few quinolones are
candidates for the treatment of toxoplasmosis and that more effective
compounds need to be developed.
To identify more active quinolones, the experimental IC50s
were related to a large number of TIs by using the MLR method. The
equation thus obtained accurately matched experimental and calculated
IC50s (r2 = 0.87), and the very
good predictive capacity of the model was confirmed by the
cross-validation test (r2cv = 0.74). Furthermore, when we examined 11 trovafloxacin analogs whose IC50s had not been determined in our laboratory, very
good agreement was observed between our predicted IC50s of
10 compounds and those determined experimentally by Khan et al.
(10).
The LDA and MLR models were then used to identify the pharmacophoric
structures responsible for the anti-Toxoplasma activity of
quinolones. Two complementary approaches were used to examine the
respective roles of each atom in the quinolone ring and that of the
substituent radicals. We first used new atomic E-state indices which
provide information on the electronic and topological structure at the
atomic level (12). The regression equation revealed that the
C-14 position of the quinolone ring markedly contributed to lowering of
the calculated IC50. This reflects the importance of the
carbonyl position in anti-Toxoplasma activity. Surprisingly,
the carboxyl group, which is essential for gyrase binding in bacteria
(1, 5), did not appears in the MLR equation, suggesting that
this group is not so crucial for anti-Toxoplasma activity.
Next, virtual modifications of trovafloxacin, grepafloxacin,
gatifloxacin, and moxifloxacin were submitted to LDA and MLR analysis
to investigate the influence of different radicals on anti-Toxoplasma activity. The presence of a fluorine at R6
was fundamental for anti-Toxoplasma activity, as its
omission resulted in a total lack of activity. The presence of a
cyclopropyl or a 2,4-difluorophenyl group at R1 appeared to be related
to the anti-Toxoplasma activity of the four quinolones
tested, as other substituents resulted in an increase in the
IC50. The presence of an R8 substituent was important for
the activity of grepafloxacin and moxifloxacin. All changes in the R7
radical resulted in lower activity. It is of note that changing the
3'-aminopyrrolidinyl substituent on trovafloxacin resulted in a marked
loss of activity; in fact, this structure is that of tosufloxacin,
which has been reported to be inactive in vitro (10), thus
further validating the predictive value of our QSAR models. Finally, we
showed the importance of a methyl group at C-5 and an aza-bicyclohexane
at R7, as their presence or addition markedly enhanced
anti-Toxoplasma activity.
In conclusion, the combination of LDA using topological descriptors and
MLR statistical analysis can contribute to the design of new quinolones
with improved anti-Toxoplasma activity and possibly identify
drugs with a broader spectrum of antiprotozoan activity. Computational
screening of thousands of virtual molecules using this method in a
search for optimal substitutions is readily feasible and is far less
costly than combinatory chemistry and in vitro screening.
| |
ACKNOWLEDGMENTS |
R. Gozalbes is indebted to the association Ensemble contre le
SIDA and the French Ministry of Foreign Affairs for their financial support of this work, which was conducted in the Parasitology-Mycology Laboratory and ITODYS, University of Paris VII, Paris, France. The
members of the Molecular Connectivity & Drug Design Research Unit are
grateful for the support given by Generalitat Valenciana through the
project GV99-91-1-12.
We thank Bayer Pharma, Esteve, Glaxo Wellcome, Grünenthal,
Hoechst Marion Roussel, Mediolanum Farmaceutici, Monsanto Searle, Parke-Davis, Roche, Roger Bellon, and Sanofi Winthrop Laboratories for
supplying compounds. We thank David Young for reviewing the manuscript.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Laboratoire de
Parasitologie-Mycologie, Lariboisière St-Louis, Faculté de
Médecine, 15 rue de l'Ecole de Médecine, 75006 Paris,
France. Phone: 33 1 43 29 65 25. Fax: 33 1 43 29 51 92. E-mail:
paracord{at}wanadoo.fr.
| |
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Antimicrobial Agents and Chemotherapy, October 2000, p. 2771-2776, Vol. 44, No. 10
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Copyright © 2000, American Society for Microbiology. All rights reserved.
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Abstract
Introduction
