![[Feedback]](/icons/banner/feedbackACT.gif)
![[For Subscribers]](/icons/banner/subscriberACT.gif)
![[Archive]](/icons/banner/archiveACT.gif)
![[Search]](/icons/banner/searchACT.gif)
![[Contents]](/icons/banner/tocACT.gif)
Previous Article | Next Article 
Antimicrobial Agents and Chemotherapy, October 2000, p. 2764-2770, Vol. 44, No. 10
0066-4804/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.
Prediction of Quinolone Activity against Mycobacterium
avium by Molecular Topology and Virtual Computational
Screening
Rafael
Gozalbes,1,2
Monique
Brun-Pascaud,3
Ramon
García-Domenech,4
Jorge
Gálvez,4
Pierre-Marie
Girard,5
Jean-Pierre
Doucet,2 and
Francis
Derouin1,*
Laboratoire de Parasitologie-Mycologie, Faculté de
Médecine Lariboisière, Hôpital 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,
Spain4
Received 2 February 2000/Returned for modification 10 May
2000/Accepted 28 June 2000
 |
ABSTRACT |
We conducted a quantitative structure-activity relationship study
using a database of 158 quinolones previously tested against Mycobacterium avium-M. intracellulare complex in order to
develop a model capable of predicting the activity of new quinolones
against the M. avium-M. intracellulare complex in vitro.
Topological indices were used as structural descriptors and were
related to anti-M. avium-M. intracellulare complex activity
by using the linear discriminant analysis (LDA) statistical technique.
The discriminant equation thus obtained correctly classified 137 of the
158 quinolones, including 37 of a test group of 44 randomly chosen
compounds. This model was then applied to 24 quinolones, including
recently developed fluoroquinolones, whose MICs were subsequently
determined in vitro by using the Alamar blue microplate assay; the
biological results confirmed the model's predictions. The MICs of
these 24 quinolones were then treated by multilinear regression (MLR)
to establish a model capable of classifying them according to their in
vitro activities. Using this model, a good correlation between measured
and predicted MICs was found (r2 = 0.88; r2cv
[cross-validation correlation] = 0.82). Moxifloxacin, sparfloxacin, and gatifloxacin were the most potent against the M. avium- M. intracellulare complex, with MICs of 0.2, 0.4, and
0.9 µg/ml, respectively. Finally, virtual modifications of these
three drugs were evaluated in LDA and MLR models in order to determine
the importance of different substituents in their activity. We conclude that the combination of molecular-topology methods, LDA, and MLR provides an excellent tool for the design of new quinolone structures with enhanced activity.
| |
INTRODUCTION |
Mycobacteria belonging to the
Mycobacterium avium-M. intracellulare complex are
responsible for opportunistic infections in immunocompromised patients,
especially those with HIV infection (29), and there is a
need for new drugs that can be used for treatment and prophylaxis.
Quinolones are good candidates because of their broad
antibacterial spectrum, which includes atypical mycobacteria
(16, 22, 25, 30), together with their good tissue
distribution and intracellular concentration (2).
Ciprofloxacin and sparfloxacin are the only quinolones currently
used against M. avium-M. intracellulare complex infection,
but the incidence of strains resistant to these compounds is
increasing, and there is a need for new derivatives.
In addition to in vitro and in vivo tests, which are time-consuming for
the M. avium-M. intracellulare complex, powerful
methodologies for drug design and drug database screening and selection
are now available (7, 19). Equation systems linking
structure and activity (QSAR studies) are particularly relevant, and
application of the mathematical models thereby obtained to large
libraries of computer-generated compounds is known as virtual
computational screening (5, 24). An important feature in
this method is the use of good structural descriptors that are
representative of the molecular features responsible for the relevant
biological activity; a very useful technique for describing molecular
structure is molecular topology, a two-dimensional QSAR method which
takes into account the internal atomic arrangement of compounds. The structure of each molecule is represented by specific subsets of
topological indices (TIs). Klopman et al. first developed
computer-based predictive models to characterize anti-M.
avium-M. intracellulare complex activity (20, 21,
23). The aim of this study was to develop new QSAR models, based
on TIs, statistical linear discriminant analysis (LDA), and multilinear
regression (MLR) in order to predict the in vitro activity and
MICs of quinolones against the M. avium-M. intracellulare complex.
| |
MATERIALS AND METHODS |
The study involved a number of steps, which are described in the
following paragraphs.
Obtaining structural descriptors by molecular topology.
A
database of 158 quinolones with known anti-M. avium-M.
intracellulare complex activities has been built up from several articles by Klopman et al. (20, 21, 23). Each quinolone was
characterized by a set of 145 TIs specific to each molecule. We used
the topological descriptors provided by MOLCONN-Z software, version
3.50 (L. H. Hall, Eastern Nazarene College, Quincy, Mass.), especially the Kier and Hall connectivity indices (up to the 10th order) and the electrotopological indexes (17, 18). We also calculated some descriptors as charge indices (11) using
Etopo 11, a computer software developed in our research unit.
Statistical treatment: LDA.
On the basis of the data of
Klopman et al. (20, 21, 23), the quinolones studied here
were separated into active and inactive compounds according to their
MICs around a cutoff of 32 µg/ml. In accordance with the studies of
Klopman et al., this cutoff was selected since it allowed a clear
differentiation between active and inactive drugs. LDA was then applied
to these two groups (except for 44 quinolones reserved as a test group)
in order to obtain a mathematical equation linking structural
descriptors and activity. LDA is a pattern recognition method providing
a classification model based on the combination of variables that best
predicts the category or group to which a given compound belongs. The
independent variables in this study were the calculated TIs, and the
discrimination property was anti-M. avium-M. intracellulare complex activity. The software used for the LDA study was the BMDP 7M
package (W. J. Dixon, BMDP Statistical Software, University of
California, Berkeley), which randomly chooses the compounds reserved
for the test set. The variables used to compute the linear classification functions are chosen in stepwise manner: at each step
the variable that makes the larger contribution to the separation of
the groups is entered into the discriminant equation (or the variable
that makes the smallest contribution is removed). The method used to
select the descriptors was based on the F-Snedecor parameter, which
allows the assessment of relative importance among the candidate
variables (8, 15). The classification criterion was the
minimal Mahalanobis distance, which is the distance of each case to the
mean of all cases used in the regression equation (15). The
quality of the discriminant equation was evaluated using Wilk's
U-statistical parameter, a multivariate analysis of variance that tests
the equality of group means for the variable(s) in the discriminant
equation (15).
PDDs.
Pharmacological distribution diagrams (PDDs) were
constructed to determine the intervals of the equation in which the
probability of finding active compounds increases (9). PDDs
are histograms of calculated values of the mathematical functions in
which expectancies appear on the ordinate axis. For an arbitrary
interval of values of a given function, we can define an expectancy of
activity as Ea = a/(i + 1), where a is the number of active compounds in the interval divided by the total number of active compounds and
i is the number of inactive compounds in the interval
divided by the total number of inactive compounds. The expectancy of
inactivity is defined in a symmetrical way as Ei = i/(a + 1). This representation provides
good visualization of the regions of minimum overlap and selects
regions in which the probability of finding improved compounds is maximum.
Pharmacological tests.
Twenty-four quinolones that had not
been used for the LDA analysis were evaluated with the model in order
to establish their potential activities against the M. avium-M.
intracellulare complex. These quinolones were selected as having
structures representative of the main quinolone structures, including
the most recently developed fluoroquinolones (3, 6). In
parallel, in vitro studies were performed with the MO-1 strain of
M. avium, which was isolated from the blood of a patient
with AIDS and which has already been used for in vitro drug
susceptibility studies (27). We used the Alamar blue
susceptibility test described by Collins and Franzblau (4).
This technique uses an oxidation-reduction dye which is an indicator of
cell growth and/or viability. The inoculum was prepared from a
subculture of M. avium in 10 ml of Middlebrook 7H9 broth
containing 0.2% (vol/vol) glycerol, 0.05% (vol/vol) Tween 80, and
10% albumin-dextrose-catalase (ADC) (Difco Laboratories, Detroit,
Mich.). The cultures were used after 7 days of incubation at 37°C
with 5% CO2. Initial drug dilutions were prepared in the
appropriate medium, and subsequent twofold dilutions were made in 0.1 ml of 7H9 broth (plus glycerol and ADC) in 96-well microplates. Control
wells consisted of medium alone (0.2 ml) and bacteria alone (0.1 ml of
medium plus 0.1 ml of bacteria; final density, 2 × 105 CFU/well). Bacteria (0.1 ml) were added to the wells
containing the drug dilutions. Each test was run in duplicate, and
control experiments were done with the solvent alone. After 4 days of culture at 37°C with 5% CO2, 20 µl of 10× Alamar blue
solution (Interchim, Montluçon, France, or Alamar Biosciences
Inc., Sacramento, Calif.) was added to one medium control well and one
bacterial control well, and the plates were further incubated for
24 h. When the color changed in the bacterial well (from the blue
oxidized form to the pink reduced form), Alamar blue was added to the
other wells and plates were incubated for 24 h. Results were read
on a spectrophotometer at wavelengths of 540 and 620 nm using an automatic plate reader (Labsystems Multiskan RC, Helsinki, Finland) interfaced to a Macintosh computer. Percent inhibition was calculated as (1 
mean test well optical density [OD]/mean bacterial well OD) × 100. The MIC was the lowest drug concentration yielding at
least 90% inhibition. Results obtained in preliminary experiments with
anti-M. avium-M. intracellulare complex antibiotics
(rifampin, clarithromycin, rifabutin) and quinolones (ofloxacin and
sparfloxacin) were in agreement with those obtained by Collins and
Franzblau (4).
MLR.
A correlation between the calculated and observed MICs
of the 24 quinolones tested was obtained by MLR in order to predict the
MICs of new quinolones. The MLR was performed with the 9R module of the
BMDP program, 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 in accordance with
the equation Mallows' Cp = RSS/s2 (n 2p'), 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 (26).
Virtual computational screening.
Computational screening was
used to determine the influence of quinolone substituents on
anti-M. avium-M. intracellulare complex activity. Virtual
structures obtained by omission or substitution of radicals R1, R6, R7,
or R8 on the three most active quinolones were designed (Fig.
1). Their TIs were calculated, and LDA
and MLR equations were applied to predict anti-M. avium-M.
intracellulare complex activity or inactivity and MICs.
View larger version (10K):
[in this window]
[in a new window]
|
FIG. 1.
General structure of the quinolones. X and Y can be
carbon or nitrogen atoms, and the R1, R5, R6, R7, and R8 groups can be
very diverse structures.
|
|
| |
RESULTS |
LDA.
Using a MIC cutoff of 32 µg/ml, the following equation
(M), selected by LDA, classified the compounds as active
against M. avium-M. intracellulare complex if M
was >0 and inactive if M was <0: M = 2.6 + 20.13
ch 12.94c + 42.54cv + 25.66ch 2.2G3v + 2.4G4v. Statistical parameters were
as follows: n = 114, F = 30.79, Wilk's
U = 0.37. The indices 4c and
4cv represent the
quaternary ramifications, 3ch and
6ch reflect the presence of cycles of three
and six atoms, respectively, and G3v and
G4v furnish information about the transfer of
intramolecular charges between atoms separated by distances of 3 and 4, respectively (13). The 3ch index
made a marked contribution to the positivity of the equation,
reflecting the role of the cyclopropyl substituent at N-1 to
anti-M. avium-M. intracellulare complex activity.
Sixty-one of 77 quinolones with cyclopropyl substitutions were active
in vitro, and all of them had M values that were >0 (except PD135739).
The values of the discriminant function M and the
corresponding prediction of activity or inactivity are presented in
Table 1 for the training group and in
Table 2 for the test group. In the
training group (114 compounds), 51 of the 57 active compounds (89.5%)
and 49 of the 57 inactive compounds (86.0%) were correctly classified. In the test group, which comprised 44 randomly chosen quinolones, 23 of the 27 active compounds (85.2%) and 14 of the 17 inactive compounds (82.4%) were correctly classified. Overall, the
rate of correct classification was 86.7%.
A good example of the discriminating capacity of the model was the
result obtained with two quinolones which have the same molecular
weight and large structural similarities but different anti-M.
avium-M. intracellulare complex activities. The M
function was 1.77 for PD139586, which is active in vitro, and 0.75
for PD138362, which is inactive (Fig. 2).
The pharmacological activity distribution diagrams (Fig.
3) show that for M values
between 1 and 1.5 the classification of the drugs is uncertain,
because of marked overlap of M values of several active and
inactive drugs. In contrast, the highest activity expectancy occurred
at M values >1.5. As our principal objective was to define
a clear-cut difference between active and inactive quinolones, we
assumed that quinolones with M values >1.5 were active and
those with M values <1 were inactive. Quinolones with
M values between 1 and 1.5 were considered nonclassified.
View larger version (85K):
[in this window]
[in a new window]
|
FIG. 3.
(a) Activity distribution diagram of anti-M.
avium activity in the training group (114 compounds), obtained
after LDA statistical treatment. (b) Activity distribution diagram of
anti-M. avium activity in the test group (44 compounds
selected at random), obtained after LDA statistical treatment.
E, expectancy of activity or inactivity; white bars, active
quinolones; black bars, inactive ones.
|
|
When these criteria were applied to the 158 quinolones, only 2 of
50 compounds in the training group (PD125639 and PD127275) and 3 of 23 compounds in the test group (PD129626, PD135305, and PD141494) had
M values >1.5 even though they were inactive in vitro. This
meant that a cutoff of M = 1.5 yielded overall rates of
correct classification of quinolones predicted as active of 96.0% in
the training group and 87.0% in the test group.
Predicted and observed activities of 24 commercial quinolones.
The activities of 24 quinolones that had not been used to calculate the
mathematical model were estimated, and the results were compared to
those subsequently obtained in vitro (Alamar blue tests). Sixteen
compounds were classified (Table 3).
Assuming that a drug was inactive if it had a MIC of >10 µg/ml, 15 quinolones were correctly classified as active or inactive. The seven
compounds that were predicted as active showed in vitro activity at low concentrations (between 0.2 and 5.4 µg/ml). Among the quinolones that
were predicted as inactive, only lomefloxacin was misclassified.
MLR and virtual computational screening.
Based on the results
of in vitro MICs, we used the MLR statistical technique to define a
mathematical model able to correlate experimental and calculated MICs.
The best correlation was obtained using the following equation: log
(1/MIC) = 7.6 + 1.05p 2.09p + 0.30v. Statistical parameters
were as follows: r2 = 0.88 (r2cv [cross-validation
correlation] = 0.82), Mallows' Cp = 4.0, standard error = 0.35, P (significance) < 0.0001.
Experimental and calculated MICs of the 24 quinolones are presented,
together with their structures, in Table
4, and their correlation is represented
graphically in Fig. 4. The most-active quinolones were moxifloxacin, sparfloxacin, and gatifloxacin, with MICs
of 0.2, 0.4, and 0.9 µg/ml, respectively, suggesting that the best
structural characteristics for anti-M. avium-M. intracellulare complex activity are a quinoline basic nucleus, a
fluorine atom at R6, a cyclopropyl group at N-1, and nucleophilic groups (F or OCH3) at R8.
View larger version (13K):
[in this window]
[in a new window]
|
FIG. 4.
Correlation between experimental (y-axis) and
calculated (x-axis) log (1/MIC) values for 24 quinolones.
MICs were determined in vitro by culture and calculated by MLR
analysis.
|
|
As the LDA and MLR equations were reliably predictive of in vitro
activity, they were then applied to virtual structures derived from the
three quinolones most active against M. avium-M.
intracellulare complex (moxifloxacin, sparfloxacin, and
gatifloxacin) by removing or substituting significant radicals. Initial
analysis by LDA showed the crucial importance of the N-1 position, as
omission of the cyclopropyl radical implied LDA values that were <0
for the three drugs, i.e., anti-M. avium-M. intracellulare
complex inactivity. The other changes were less determinant, yielding positive LDA values, and a more accurate analysis was conducted with
the MLR technique (Table 5). Replacement
of the N-1 cyclopropyl by tert-butyl or 2,4-difluorophenyl
radicals implied similar or lower MICs. Deletion of R6 resulted in a
three- to fivefold increase in the MIC but not in a complete loss of
activity. Piperazine or an equivalent group on R7 was determinant for
anti-M. avium-M. intracellulare complex activity, as its
replacement by a simple pyrrolidinyl or 3'-amino-pyrrolidinyl radical
resulted in predicted MICs 2 to 18 times higher. Similarly, the
presence of R8 seemed essential for activity, because its
omission implied a very large increase in the MIC in every case.
View this table:
[in this window]
[in a new window]
|
TABLE 5.
Predicted MICs by computational screening and MLR
function on virtual quinolones derived by modification of moxifloxacin,
sparfloxacin, and gatifloxacin
|
|
| |
DISCUSSION |
Molecular topology is a very useful tool for describing molecular
structures and has been used for efficient analysis of QSAR data
(10). This method has proven its utility for the prediction of diverse physical, chemical, and biological properties in different groups of compounds (14) and has also been used with success for the design of new drugs (10, 12, 13). In previous
studies Klopman et al. developed a MULTICASE fragment approach that was used to analyze the structure-activity relationships of quinolones against mycobacteria and to discriminate between active and inactive compounds (23). Using LDA, we have confirmed that
molecular-topology methods can reliably discriminate between drugs
according to their in vitro activities. In addition we show that MICs
of active compounds can also be predicted using an MLR model.
LDA on a panel of 158 active and inactive quinolones correctly
classified 96 and 87% of the quinolones predicted as active in the
training group and test group, respectively, showing the excellent
predictive capacity of the model. When LDA was applied to 24 commercial
quinolones that had not been used to define the model and whose MICs
were subsequently determined in vitro, 16 quinolones were assigned to
the active or inactive group and 15 showed the expected activity or
inactivity. Eight quinolones were not classified because of
intermediate M values from the LDA equation, but it should
be noted that these drugs also had intermediate in vitro activities.
The most important result was the correct prediction of seven active
quinolones which had low in vitro MICs, and specially the correct
prediction of three of them (moxifloxacin, sparfloxacin, and
gatifloxacin) which had MICs of <1 µg/ml.
In addition to the ability of a model to discriminate between active
and inactive compounds, we considered it important that a model be able
to predict the effective concentration of quinolones against M. avium. For this purpose, an MLR analysis was developed. It was
applied to the in vitro values of the 24 quinolones and defined an
equation which accurately correlated experimental and calculated MICs
(r2 = 0.88). Furthermore, the very good
predictive capacity was confirmed by a cross-validation test
(r2cv = 0.82). In conclusion,
we consider that a combination of structural description by using TIs
and statistical treatment by LDA and MLR can reliably select
new quinolones effective against the M. avium-M.
intracellulare complex. The obtained prediction models can readily
be applied to large databases of quinolones to identify active
structures and to estimate MICs.
In addition, the combination of LDA and MLR proved useful to
investigate the influence of different quinolone radicals on anti-M. avium-M. intracellulare complex activity. The role
of several radicals in quinolone activity has already been
demonstrated by Klopman et al. (20, 23), but using
MLR allowed estimates of their influence on MICs. Virtual
structures were designed from modifications of moxifloxacin,
sparfloxacin, and gatifloxacin, and their TIs were calculated. A first
study with LDA was sufficiently determinant to show the importance of a
cyclopropyl group at the N-1 position, which is present on the three
above-mentioned quinolones, as its omission resulted in negative
M values, implying total anti-M. avium-M.
intracellulare inactivity. Using MLR as a complement to LDA
allowed the identification of other structural changes that did not
result in negative M values (and thus could not be discriminated by LDA) but that had a significant impact on
anti-M. avium-M. intracellulare complex activity, as they
induced marked changes in MICs determined with the MLR equation.
Specifically, tert-butyl and 2,4-difluorophenyl radicals in
the N-1 position produced predicted MICs as good as or better than
experimental MICs of moxifloxacin, gatifloxacin, and sparfloxacin,
confirming the experiments previously performed by Klopman
(20) to test the effectiveness of these groups. A loss of
activity was found when the R6 position was not occupied by an F atom,
confirming the importance of this radical, which is suspected to be
involved in cellular penetration. Nevertheless, its influence could be less than that on other bacteria, since some activity was found against
the M. avium-M. intracellulare complex. Anti-M.
avium-M. intracellulare complex activity was theoretically
decreased by a pyrrolidinyl group or a 3'-amino-pyrrolidinyl
group relative to the original groups in R7: a
3'-methyl-piperazinyl group (gatifloxacin), a
3',5'-dimethyl-piperazinyl group (sparfloxacin), and a
pyrrolidinyl group attached to a six-member ring (moxifloxacin).
Finally, the determining role of nucleophilic substituents such as F
and OCH3 radicals on R8 was demonstrated, as its
suppression resulted in a very marked increase in estimated MICs. These
results may reflect a unique characteristic of the anti-M.
avium-M. intracellulare complex activity of quinolones compared to
their activity against other bacteria, in which the influence of the R7
and R8 positions is less marked.
On the basis of these results, these predictive models might prove
useful in the design of new quinolones with improved anti-M. avium-M. intracellulare complex activity, providing considerable cost savings relative to in vitro and in vivo experimental models (1).
| |
ACKNOWLEDGMENTS |
R. Gozalbes is indebted to the association Ensemble contre le
SIDA and the French Ministry of Foreign Affairs for their financial support for this work conducted in the Parasitology-Mycology Laboratory and ITODYS, at University Paris-7, Paris, France. The members of
the Molecular Connectivity and Drug Design Research Unit are grateful for the support by Generalitat Valenciana through project GV99-91-1-12.
We thank Bayer Pharma, Esteve, Glaxo Wellcome,
Grünenthal, Hoechst-Marion-Roussel, Mediolanum Farmaceutici, Monsanto Searle, Parke-Davis, and Sanofi Winthrop laboratories for supply of compounds and David D. Young for reviewing the manuscript.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Laboratoire de
Parasitologie, 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.
| |
REFERENCES |
| 1.
|
Brun-Pascaud, M.,
P. Rajagopalan-Levasseur,
F. Chau,
G. Bertrand,
L. Garry,
F. Derouin, and P. M. Girard.
1998.
Drug evaluation of concurrent Pneumocystis carinii, Toxoplasma gondii, and Mycobacterium avium complex infections in a rat model.
Antimicrob. Agents Chemother.
42:1068-1072[Abstract/Free Full Text].
|
| 2.
|
Bryskier, A.
1993.
Fluoroquinolones: mechanisms of action and resistance.
Int. J. Antimicrob. Agents
2:151-184.
|
| 3.
|
Bryskier, A., and J. F. Chantot.
1995.
Classification and structure-activity relationships of fluoroquinolones.
Drugs
49(Suppl. 2):16-28.
|
| 4.
|
Collins, L., and S. Franzblau.
1997.
Microplate Alamar blue assay versus BACTEC 460 system for high-throughput screening of compounds against Mycobacterium tuberculosis and Mycobacterium avium.
Antimicrob. Agents Chemother.
41:1004-1009[Abstract].
|
| 5.
|
de Julián-Ortiz, J. V.,
J. Gálvez,
C. Muñoz-Collado,
R. García-Domenech, and C. Gimeno-Cardona.
1999.
Virtual Combinatorial Syntheses and Computational Screening of new potential anti-Herpes compounds.
J. Med. Chem.
42:3308-3314[CrossRef][Medline].
|
| 6.
|
Domagala, J. M.
1994.
Structure-activity and structure-side-effect relationships for the quinolone antibacterials.
J. Antimicrob. Chemother.
33:685-706[Abstract/Free Full Text].
|
| 7.
|
Doucet, J. P., and J. Weber.
1996.
Computer-aided molecular design: theory and applications.
Academic Press, London, United Kingdom.
|
| 8.
|
Draper, N. R., and H. Smith.
1981.
Applied regression analysis, 2nd ed., p. 101-102.
John Wiley & Sons, Inc., New York, N.Y.
|
| 9.
|
Gálvez, J.,
R. García-Domenech,
C. de Gregorio Alapont,
J. V. de Julián-Ortiz, and L. J. Popa.
1996.
Pharmacological distribution diagrams: a tool for de novo drug design.
J. Mol. Graphics
14:272-276[CrossRef][Medline].
|
| 10.
|
Gálvez, J.,
R. García-Domenech,
J. V. de Julián-Ortiz, and R. Soler.
1994.
Topological approach to drug design.
J. Chem. Inf. Comput. Sci.
32:272-284[CrossRef].
|
| 11.
|
Gálvez, J.,
R. García,
M. T. Salabert, and R. Soler.
1994.
Charge indexes. New topological descriptors.
J. Chem. Inf. Comput. Sci.
34:520-525[CrossRef].
|
| 12.
|
Gozalbes, R.,
J. Gálvez,
R. García-Domenech, and F. Derouin.
1999.
Molecular search of new active drugs against Toxoplasma gondii.
SAR QSAR Environ. Res.
10:47-60[Medline].
|
| 13.
|
Gozalbes, R.,
J. Gálvez,
A. Moreno, and R. García-Domenech.
1999.
Discovery of new antimalarial compounds by use of molecular connectivity techniques.
J. Pharm. Pharmacol.
51:111-117[CrossRef][Medline].
|
| 14.
|
Gozalbes, R.,
J. V. de Julián-Ortiz,
G. M. Antón-Fos,
J. Gálvez, and R. García-Domenech.
2000.
Prediction of chromatographic properties of organophosphorus insecticides by molecular connectivity.
Chromatographia
51:331-337[CrossRef].
|
| 15.
|
Jennrich, R., and P. Sampson.
1979.
Stepwise discriminant analysis, p. 339-357.
In
W. J. Dixon (ed.), BMD biomedical computer programs. University of California Press, Berkeley.
|
| 16.
|
Johnson, S. M., and G. D. Roberts.
1987.
In vitro activity of ciprofloxacin and ofloxacin against the M. avium-intracellulare complex.
Diagn. Microbiol. Infect. Dis.
7:89-91[CrossRef][Medline].
|
| 17.
|
Kier, L. B., and L. H. Hall.
1986.
Molecular connectivity in structure-activity analysis, p. 225-246.
John Wiley & Sons, Letchworth, England.
|
| 18.
|
Kier, L. B., and L. H. Hall.
1990.
An electrotopological-state index for atoms in molecules.
Pharm. Res.
7:801-807[CrossRef][Medline].
|
| 19.
|
Kleinberg, M. L., and L. A. Wanke.
1995.
New approaches and technologies in drug design and discovery.
Am. J. Health-Syst. Pharm.
52:1323-1336.
|
| 20.
|
Klopman, G.,
D. Fercu,
T. E. Renau, and M. R. Jacobs.
1996.
N-1-tert-Butyl-substituted quinolones: in vitro anti-Mycobacterium avium activities and structure-activity relationship studies.
Antimicrob. Agents Chemother.
40:2637-2643[Abstract].
|
| 21.
|
Klopman, G.,
J.-Y. Li,
S. Wang,
A. J. Pearson,
K. Chang,
M. R. Jacobs,
S. Bajaksouizian, and J. J. Ellner.
1994.
In vitro anti-Mycobacterium avium activities of quinolones: predicted active structures and mechanistic considerations.
Antimicrob. Agents Chemother.
38:1794-1802[Abstract/Free Full Text].
|
| 22.
|
Klopman, G.,
S. Wang,
M. R. Jacobs,
S. Bajaksouizian,
K. Edmonds, and J. J. Ellner.
1993.
Anti-Mycobacterium avium activity of quinolones: in vitro activities.
Antimicrob. Agents Chemother.
37:1799-1806[Abstract/Free Full Text].
|
| 23.
|
Klopman, G.,
S. Wang,
M. R. Jacobs, and J. J. Ellner.
1993.
Anti-Mycobacterium avium activity of quinolones: structure-activity relationship studies.
Antimicrob. Agents Chemother.
37:1807-1815[Abstract/Free Full Text].
|
| 24.
|
Lahana, R.
1997.
Virtual combinatorial chemistry.
Sci. Am.
241:56-58. (In French.)
|
| 25.
|
Leysen, D. C.,
A. Haemers, and S. R. Pattyn.
1989.
Mycobacteria and the new quinolones.
Antimicrob. Agents Chemother.
33:1-5[Free Full Text].
|
| 26.
|
Mallows, C. L.
1973.
Some comments on Cp.
Technometrics
15:661-675[CrossRef].
|
| 27.
|
Perronne, C.,
A. Gikas,
C. Truffot-Pernot,
J. Grosset,
J. L. Vildé, and J. J. Pocidalo.
1991.
Activities of sparfloxacin, azithromycin, temafloxacin, and rifapentine compared with that of clarithromycin against multiplication of Mycobacterium avium complex within human macrophages.
Antimicrob. Agents Chemother.
35:1356-1359[Abstract/Free Full Text].
|
| 28.
|
Yajko, D. M.,
P. S. Nassos, and W. K. Hadley.
1987.
Broth microdilution testing of susceptibilities to 30 antimicrobial agents of Mycobacterium avium strains from patients with acquired immune deficiency syndrome.
Antimicrob. Agents Chemother.
31:1579-1584[Abstract/Free Full Text].
|
| 29.
|
Young, L. S.,
C. B. Inderlied,
O. G. W. Berlin, and M. J. Gottlied.
1986.
Mycobacterial infections and AIDS patients, with an emphasis on the Mycobacterium avium complex.
Rev. Infect. Dis.
8:1024-1033[Medline].
|
| 30.
|
Young, L. S.,
O. G. W. Berlin, and C. B. Inderlied.
1987.
Activity of ciprofloxacin and other fluorinated quinolones against mycobacteria.
Am. J. Med.
82(Suppl. 4A):23-26[CrossRef][Medline].
|
Antimicrobial Agents and Chemotherapy, October 2000, p. 2764-2770, Vol. 44, No. 10
0066-4804/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.
This article has been cited by other articles:
-
Mahmoudi, N., Garcia-Domenech, R., Galvez, J., Farhati, K., Franetich, J.-F., Sauerwein, R., Hannoun, L., Derouin, F., Danis, M., Mazier, D.
(2008). New Active Drugs against Liver Stages of Plasmodium Predicted by Molecular Topology. Antimicrob. Agents Chemother.
52: 1215-1220
[Abstract]
[Full Text]
-
Mahmoudi, N., de Julian-Ortiz, J.-V., Ciceron, L., Galvez, J., Mazier, D., Danis, M., Derouin, F., Garcia-Domenech, R.
(2006). Identification of new antimalarial drugs by linear discriminant analysis and topological virtual screening. J Antimicrob Chemother
57: 489-497
[Abstract]
[Full Text]
-
Garcia-Garcia, A., Galvez, J., de Julian-Ortiz, J. V., Garcia-Domenech, R., Munoz, C., Guna, R., Borras, R.
(2005). Search of Chemical Scaffolds for Novel Antituberculosis Agents. J Biomol Screen
10: 206-214
[Abstract]
-
Garcia-Garcia, A., Galvez, J., de Julian-Ortiz, J.-V., Garcia-Domenech, R., Munoz, C., Guna, R., Borras, R.
(2004). New agents active against Mycobacterium avium complex selected by molecular topology: a virtual screening method. J Antimicrob Chemother
53: 65-73
[Abstract]
[Full Text]
Top
Abstract
Introduction
