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Antimicrobial Agents and Chemotherapy, June 1998, p. 1454-1458, Vol. 42, No. 6
0066-4804/98/$04.00+0
Copyright © 1998, American Society for Microbiology. All rights reserved.
Quantitative Structure-Activity Relationship
Studies of a Series of Sulfa Drugs as Inhibitors of Pneumocystis
carinii Dihydropteroate Synthetase
Theresa
Johnson,1
Ikhlas A.
Khan,2,3
Mitchell A.
Avery,1,2,3,*
Juliana
Grant,4 and
Steven R.
Meshnick4,*
Department of Medicinal
Chemistry,1
Research Institute of Pharmaceutical
Sciences,2 and
National Center for the
Development of Natural Products,3 University of
Mississippi, University, Mississippi 38677, and
Department of
Epidemiology, School of Public Health, University of Michigan, Ann
Arbor, Michigan 481094
Received 29 December 1997/Returned for modification 5 February
1998/Accepted 14 March 1998
 |
ABSTRACT |
Sulfone and sulfanilamide sulfa drugs have been shown to inhibit
dihydropteroate synthetase (DHPS) isolated from Pneumocystis carinii. In order to develop a pharmacophoric model for this
inhibition, quantitative structure-activity relationships (QSAR) for
sulfa drugs active against DHPS have been studied. Accurate 50%
inhibitory concentrations were collected for 44 analogs, and other
parameters, such as partition coefficients and molar refractivity, were
calculated. Conventional multiple regression analysis of these data did
not provide acceptable QSAR. However, three-dimensional QSAR provided by comparative molecular field analysis did give excellent results. Upon removal of poorly correlated analogs, a data set of 36 analogs, all having a common NHSO2 group, provided a cross-validated
r2 value of 0.699 and conventional
r2 value of 0.964. The resulting pharmacophore
model should be useful for understanding and predicting the binding of
DHPS by new sulfa drugs.
| |
INTRODUCTION |
Pneumocystis carinii can
be considered a protozoan organism, but it shares many characteristics
with fungi (5). As one of the more common AIDS-defining
illnesses, P. carinii pneumonia is often the ultimate cause
of morbidity for immunocompromised individuals. As such, treatment and
long-term prophylaxis of P. carinii pneumonia continue to be
active areas of study. Currently, the most effective treatment is
co-trimoxazole, a synergistic combination of the sulfanilamide
sulfamethoxazole and the dihydrofolate reductase inhibitor
trimethoprim. The use of co-trimoxazole, however, produces adverse side
effects in 20 to 57% of patients, resulting in discontinuation of
treatment (12). Other combination treatments, including
pyrimethamine and either sulfadiazine or sulfadoxine, are also plagued
with side effects which limit their use and give rise to the need for
more effective treatments (13).
The antimetabolite sulfa drugs function by inhibiting dihydropteroate
synthetase (DHPS), an enzyme catalyzing a crucial step in the
biosynthesis of tetrahydrofolate and, ultimately, nucleotides. The lack
of a mechanism for uptake of preformed folate in P. carinii and the absence of a DHPS pathway in mammals make this enzyme an ideal
target for drug therapy. As noted in a recent paper by Hong et al.
(7), of the many sulfa drugs that have been synthesized, few
have been tested against P. carinii. Given that sulfa drugs differ in their ability to elicit adverse effects, an exploration of
the structure-activity relationships (SAR) of a variety of sulfa drugs
could lead to more effective treatments. To aid in this exploration as
well as to gain further insight into the SAR of these compounds, we
have performed quantitative SAR (QSAR) studies using a conventional
QSAR methodology as well as comparative molecular field analysis
(CoMFA) (4). CoMFA is a powerful three-dimensional (3-D)
QSAR method based on the observation that receptor-ligand interactions
are largely shape dependent and noncovalent. In a CoMFA analysis, each
molecule is defined by a set of steric and electrostatic points in
space (8, 9). These representations are stored and then
mathematically correlated with activity data by partial least-squares
(PLS) analysis. A pharmacophoric model can be derived in 3-D space by
using cross-validation in this analysis, and the resulting extensive
equation is subjected to statistical analysis to establish the
predictive ability of the model as indicated by a cross-validated
r2 value of greater than 0.5. Additional output
from the statistical analysis includes a measure of the reproducibility
of the model (conventional r2 value) as well as
standard error and an F value. Visual output of the QSAR equation is
provided by contour plots, which indicate to the viewer those areas
about a template molecule that are predicted to improve or decrease
potency. Hypothetical structures are designed on the basis of these
contour plots, and their activities can be predicted, leading to a
cycle of synthesis, testing, and refinement of the pharmacophoric
model.
| |
MATERIALS AND METHODS |
Data set.
The in vitro sulfa drug activities used in
this study were previously determined by Hong et al. using P. carinii DHPS in a cell-free system (7). Fourteen
compounds (no. 1 to 14) with originally reported 50% inhibitory
concentrations (IC50s) of >10 µM were reexamined in the
laboratory by the previously reported procedure (7) (with
exceptions noted below), and the resultant data are reported in Tables
1 and 2.
The sulfa drugs used in the study by Hong et al. included sulfones and
aryl sulfanilamides with structural variations as follows: (i) the
nature of the amide aryl group, (ii) the substituent type and
substitution pattern of the amide aryl group, and (iii) the
substitution on the 4-aminoaryl ring.
Testing of sulfa compounds.
DHPS assays were carried out on
compounds 1 to 14 as previously described by Hong et al.
(7). The enzyme assay buffer contained 40 mM Tris-HCl (pH
8.2), 5 mM MgCl2, 10 mM dithiothreitol, 66 nM
p-aminobenzoic acid (PABA; made as a mixture of 16 nM
[3H]PABA and 50 nM unlabelled PABA), and 100 µM freshly
prepared reduced 6-hydroxymethylpterin pyrophosphate. The reaction was initiated by the addition of Spodoptera frugiperda 9 cell
lysates containing 4 U of enzyme (1 U being the amount of enzyme
required to catalyze the production of 1 pmol of 7,8-dihydropteroate
per h at 37°C). After 1-h incubations, the reactions were stopped by
adding 300 µl of 1 M citrate-phosphate buffer, pH 3.8. Using a
modified ether extraction method, the radioactive
7,8-dihydropteroate formed was separated from unreacted
[3H]PABA and the radioactivity was measured in a
scintillation counter.
To determine the IC
50s, stock solutions of each sulfa drug
were prepared in dimethyl sulfoxide (DMSO) and then diluted to
100 and
500 µM in water. As opposed to the previous assay conditions,
the
DMSO concentration was sometimes as high as 6%. These high
concentrations of DMSO had no effect on enzyme activity. These
data
were pooled with earlier inhibition data and analyzed by
linear
regression to generate IC
50s as reported previously
(
7).
Compound NSC74428-i (no. 35) was dropped from all
analyses due
to the observance of a negative correlation between the
drug concentration
and inhibition.
Computational approach.
Calculations were performed on a
Silicon Graphics Indigo 2 workstation equipped with an Impact
processor. CoMFA and structure generation were executed by the Tripos
Associates SYBYL version 6.2 molecular modeling package with a QSAR
module (15). Conformational searches were performed with the
MacroModel program (3), and conventional QSAR was performed
with Tsar software provided by the Oxford Molecular Modeling Group
(11). The default SYBYL, MacroModel, and Tsar settings were
used unless otherwise noted.
Conventional QSAR studies.
Using the Tsar suite of programs,
QSAR studies were performed on the original data set of 44 molecules.
The dependent variable was defined as the inverse log of the
IC50 calculated to three significant figures. Two
independent variables were incorporated into this QSAR study. The first
was the partition coefficient (log P), a quantitative measurement of
the hydrophobicity of a molecule calculated by summing the log P
contributions of the individual fragments of a compound. These standard
fragment values came from the Tsar fragment database and are based on a
library of compounds whose log P values had been previously measured by the partitioning of the molecule between a nonpolar and a polar solvent
(most commonly octanol and water) (6).
Molar refractivity, the second independent variable, provides a measure
of the inherent steric properties of a molecule and
is also calculated
by a summation of the individual-substituent
contributions retrieved
from the Tsar database. The substituent
values were derived from a
library of compounds whose molar refractivities
were experimentally
calculated from their corresponding refractive
indices, molecular
weights, and densities. Both independent-variable
values were generated
by Tsar, and regression analysis was performed
to furnish the
correlation coefficient,
r2.
Molecular conformational analysis.
Due to the lack of
structural data available for a sulfa drug inhibitor-DHPS complex and
the inherent flexibility of the sulfa drugs, conformational analysis of
sulfamethoxypyridazine (7) was performed using MacroModel.
The geometry of the sulfanilamides was first optimized with the MM3
force field, using the dielectric constant of water to simulate aqueous
solvation and an energy gradient convergence criterion of 0.005 kcal/mol. A Monte Carlo sampling method conformational search was then
performed using 1,000 steps. All compounds within 100 kcal/mol of the
global minimum were reported. The global minimum and the 10 lowest-energy conformations were then imported into Sybyl and compared
by root-mean-square (RMS) fit analysis to each other and to the
crystal structure of sulfamethoxypyridazine (2) to determine
the reasonability of the structures generated. Least-squares fit
analysis of all non-H atoms of the global minima with each of the 10 most similar conformations resulted in RMS values of less than 0.20 Å,
indicating that these lowest-energy conformations were all highly
similar. The crystal structure data presented two different
conformations of sulfamethoxypyridazine per unit cell. One of the
conformations (molecule A of reference 2) was
similar to within 0.463 Å (RMS value) of the global minimum structure
determined by MacroModel when a least-squares fit analysis of all non-H
atoms was performed.
The remaining compounds were then constructed, using the global minimum
of sulfamethoxypyridazine as a template. Gasteiger-Hückel
charges
were calculated for each compound. Any remaining portion
of the
sulfamethoxypyridazine template for each compound was defined
as an
aggregate, and the newly formed fragments of the molecular
geometry
were optimized by using the Tripos force field with a
0.001-kcal/mol
energy gradient convergence termination point.
The aggregate was then
deleted, and the entire molecular geometry
was optimized by using a
0.05-kcal/mol energy gradient convergence.
As noted by Cramer et al. (
4), the alignment rule is crucial
to the development of a reliable CoMFA model. To obtain a
good
alignment, Multifit, a flexible-fit protocol executed in
SYBYL, was
then performed between each molecule and the structure
of
sulfamethoxypyridazine at the eight points labeled in Fig.
1. To minimize the possibility of
molecular distortions, the Multifit
spring constant was reduced to 10 kcal/(mol)(Å)
2.
The use of conformational searches in conjunction with flexible data
sets which lack active-conformation data and the use
of multiple
conformations have been previously documented by Nicklaus
et al.
(
10) and Tong et al. (
14). The use of multiple
conformations
of an ambiguous molecule allows the less-active conformer
to be
eliminated from the data set, thereby improving the predictive
correlation coefficient. Thus, for the compounds
difluorodinitrophenylsulfone
(no. 2), NSC355394-h (no. 13),
NSC403439-f, and sulfaquinoxoline
(
7), for which different
conformations can result from a 180°
rotation about the aryl-amide
bond (rotation 1 in Fig.
1), two
conformations (A and B) were placed in
the data set. The less-active
conformation was then deleted from the
data set after the initial
analysis had been performed.
CoMFA generation and analysis.
Although the more-active
sulfa drugs have a sulfonamide N-H which is ionized at physiological
pH, CoMFA was performed on the data set of 47 aligned compounds (43 plus 4 conformation isomers) in their nonionized forms. A lattice
spacing of 2.0 Å in the x, y, and z
directions that extended 5.0 Å beyond the extremities of all of the
compounds was used. An sp3 carbon atom with a +1 charge was
used as a probe atom in the calculation of interaction energies at each
of the lattice points. The lattice point energies were then used in the
QSAR analysis to generate the steric and electrostatic fields.
A PLS analysis was performed on the data set with cross-validation
using 5 components and 10 groups. The dependent variable
for the PLS
analysis was the inverse log of the IC
50 calculated
to
three significant figures. The process of deleting each compound
once
from the analysis, recalculating the model, and predicting
the excluded
compound leads to the generation of residual values.
A residual is thus
the actual activity minus the predicted activity,
and those compounds
with large residuals are identified as outliers.
The less-active conformations of the four compounds with A and B
conformations were deleted to give a final data set of 43
compounds.
The full data set less the four unfavorable conformations
was defined
as D1 (see Table
3).
A PLS analysis was then performed once again, using the leave-one-out
cross-validation technique to determine the optimal
number of
components. A final PLS analysis was performed with
the optimal number
of components without validation to give the
conventional
r2 value as well as generate the steric and
electrostatic fields.
As noted above, the active form of the sulfa drug involves ionization
at the amide position. To best model this effect, the
N-sulfonamide-substituted compounds Ro55615 (no. 3), Ro72844
(
7),
and Ro52928 (
7) and the sulfone compounds
(no. 1, no. 2, and
dapsone [
7]) were deleted from the
data set. Although Ro211182
(no. 4) is predicted to have activity
similar to experimental
values (see Table
4), it was nonetheless
deleted because the
quaternary center adjacent to the amide would have
had an abnormal
effect on the ionization process. A PLS analysis was
also performed
on this data set of 39 (36 plus 3 conformational isomer)
compounds,
with the 3 less-active conformations being deleted as before
to
give a total data set of 36 compounds. This data set was called
D2
(see Table
3).
| |
RESULTS |
Conventional QSAR studies.
Regression analysis of DHPS
IC50s versus Tsar-calculated log P and molar refractivity
data resulted in an r2 value of only 0.142, indicative of a poor correlation between the lipophilicity of the
compounds and their activity. This lack of correlation has been noted
previously for sulfanilamide data sets. However, a strong correlation
of activity with the ionization constant (pKa) of
sulfanilamides is widely accepted, and several QSAR studies have been
developed (1). Since pKa data for our compounds
were not available, no further conventional QSAR studies were
performed. Calculation of pKa values remains a possibility for future studies.
CoMFA.
The results for the two data sets of the CoMFA studies
are summarized in Table 3. For the first
data set (D1) of 43 compounds, a cross-validated
r2 value of 0.488 and a conventional
r2 value of 0.952 were obtained. Deletion of the
sulfone and N-disubstituted compounds gave data set D2 and resulted in
increases in the cross-validated and a conventional
r2 values to 0.699 and of 0.964, respectively. A
comparison of the actual and calculated IC50s for the
compounds deleted from the D1 data set can be seen in Table
4.
| |
DISCUSSION |
The compounds deleted to give the D2 data set included the
N-methoxy-substituted compound Ro55615 (no. 3). As seen in Table 2,
this compound was completely inactive in the assay system used,
although it closely resembles the highly active compound Ro43476
(7). In contrast, the N-amide acetyl-substituted compounds Ro72844 and Ro52928 (7) were some of the most potent
compounds in this study. This discrepancy can be explained by commonly
accepted SAR for sulfanilamides (1). Only compounds which
can form an ionized species at the amide position have good activity as
sulfa drugs. Thus, while compound no. 3 cannot undergo ionization,
Ro72844 and Ro52928 are readily hydrolyzed to the ionizable
-HN-SO2-species under even slightly basic conditions. To
test the possibility that hydrolysis of Ro72844 and Ro52928 occurs
during the enzyme binding assay, the latter compound was exposed to
weakly basic solutions (pH, ~9). A rapid disappearance of Ro52928 was
observed, and sulfamethoxazole was noted as the only organic product.
However, when placed in the buffered enzyme-free assay system at pH
8.2, hydrolysis of Ro52928 and Ro72844 was not observed. It seems
highly likely, nonetheless, that these compounds are converted to their corresponding sulfanilamides under the conditions of the assay, and it
may even be possible that an allosteric site on DHPS is effecting this
hydrolysis.
Since it seemed likely that the active compounds Ro72844 and Ro52928
would be better represented in the CoMFA as their corresponding monosubstituted sulfanilamides, and since at least in one case the
hydrolysis product was a compound already included in the study
(sulfamethoxazole), they were deemed redundant and deleted from the
data set. In addition, the sulfone compounds, which lack the sulfamide
functionality, were deleted due to their inability to ionize as
sulfanilamides.
An examination of the residual values for the nonionizable compounds
provides further evidence supporting the removal of these compounds to
give the D2 data set. As can be seen in Table 4, the predicted
activities of the sulfone compounds differ from their observed
activities by as much as 0.8 U. Because the original IC50
data was input as 
log IC50s, this would result in a
predicted activity that was incorrect by as much as 1 order of
magnitude. A similar result was also seen with the disubstituted
sulfanilamides.
The improvement in the cross-validated correlation coefficient of the
QSAR resulting from deletion of these six compounds from the data set
is consistent with the ionization theory and indicates that the D2
model designed from N-ionizable compounds describes the pharmacophore
of sulfanilamides more accurately than does the D1 model.
CoMFA fields.
To better visualize the CoMFA fields, the CoMFA
contours were generated for the D1 and D2 PLS analyses; they are shown
in Fig. 2. In the electrostatic contour
plots (Fig. 2A and C), the red polyhedra represent regions on the drug
molecule at which a negative charge is predicted to result in lower
IC50s (i.e., greater drug potency). The red regions
themselves might be imagined as electropositive groups within the
receptor. The blue polyhedra represent regions in which a positive
charge on the drug would have a beneficial effect on IC50s.
The blue contours are, of course, complementary to this and may be
treated loosely as electron-rich groups within the active site of the
receptor.
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|
FIG. 2.
CoMFA electrostatic and steric contour plots from the D1
(A and B) and D2 (C and D) PLS analyses. Sulfamethoxypyridazine is
shown within the contours for reference. The electrostatic contour
plots (A and C) show contours (color coded in red) corresponding to
regions where electron-rich groupings are predicted to improve
activity, while electron-poor groups within the blue electrostatic
contours are predicted to improve activity. The steric contour plots (B
and D) are shown separately in yellow and green. The green steric
contours show where added steric bulk is predicted to be beneficial for
activity, while the yellow contour indicates regions where steric bulk
is negatively correlated with activity.
|
|
The steric contour plots (Fig.
2B and D) likewise show regions in which
increased steric bulk would likely enhance activity
by lowering the
IC
50 (green polyhedra). The yellow polyhedra indicate
areas
in which steric bulk is predicted to decrease binding. The
structure of
sulfamethoxypyridazine is shown for reference in
the views. Thus, green
polyhedra might represent hydrophobic cavities
within the receptor in
which there is room for hydrophobic groups
on a ligand, and the yellow
polyhedra could be interpreted as
being areas that are already occupied
by the receptor and would
thus prohibit effective drug binding.
As can be seen in Fig.
2, the electrostatic CoMFA fields (contours A
and C) for D1 and D2 are closely related. A large, blue,
positive-charge-favored region is observed on one face of the
4-aminoaryl portion of the molecule. This region corresponds to
the
R
1 group of the 4-aminoaryl-substituted compounds, such as
no. 6 to 14 in Table
2, which contain electron-rich groups at
the
R
1 position and demonstrate a relative lack of activity.
The
presence of electronegative R
1 groups in a blue contour
region,
which prefers to have electropositive groups, is consistent
with
the observed loss of activity. Conversely, a red,
negative-charge-favored
region over the opposite face of the
sulfonamide aromatic ring
of the molecules could be interpreted as a
interaction with
an electropositive group in the receptor, such as
a
-stacking
interaction. The other red contour, occurring about the
nonsulfanilamide
aromatic ring, corresponds well with the
electronegative N's of
the methoxypyridazine structure (contour C).
The steric CoMFA fields of D1 and D2 appear to be slightly different.
Although a yellow, sterically unfavorable contour is
localized around
the 4-aminoaryl group in both D1 and D2, two
much larger, green,
sterically favorable contours encompass both
ends of the molecule in
the D1 model and appear to extend down
most of the length of the
molecule. This could be due to the sulfone
compounds, which did not
overlay properly with the sulfanilamides
and were thus deleted from the
D2 data set. In addition, a small,
green, sterically favorable contour
appears over the N of the
sulfanilamide in the D1 model. This would
directly coincide with
the disubstituted sulfanilamides, which were
determined to most
likely undergo ionization in the assay system and,
as such, were
not included in the D2 dataset. As with the electrostatic
fields,
the yellow, sterically unfavorable region corresponds with the
R
1 substitution of the
p-amino-substituted
sulfanilamides in Table
2 as well as NSC52105-s (
7). It is
generally accepted that
p-amino-substituted sulfanilamides
are only active if the R
1 group
can be readily hydrolyzed
in vivo or in cell culture (
1). While
the use of a cell-free
system allows for greater ease of testing
of a large number of sulfa
drugs, hydrolysis of the
p-amino sulfanilamides
in vitro is
unlikely, and therefore analogs which would otherwise
be active in
whole-cell assays would not be detected.
In conclusion, the steric and electrostatic features of a series of 43 sulfa drugs have been correlated with DHPS IC
50 data
by a
3-D QSAR approach, CoMFA. CoMFA is a technique which has
gained
widespread acceptance in recent years as a tool for novel-drug
development (
8,
9). Two sulfa drug pharmacophore models
were
examined; the first contained analogs bearing substituents
on the
sulfanilamide N atom, while the second did not incorporate
these
analogs. The CoMFA model containing the N-substituted sulfanilamides
was less accurate due to an underprediction of activity of several
of
these sulfanilamides. Deletion of these N-substituted sulfanilamides
resulted in higher cross-validation values, and the reasonableness
of
their exclusion was further supported by the likelihood of
their being
hydrolyzed to active compounds already included in
the model. This
model is consistent with accepted SAR of sulfanilamides
in that only
acidic drugs with an ionizable sulfanilamide proton
are expected to be
active.
Bulky N substituents at the
para position of the anilide
ring detract substantially from DHPS binding, a finding that is
reflected
in the CoMFA contour plots in this region. According to
reported
SAR, these types of compounds would be expected to be active
in
vivo only if they were capable of hydolysis to the parent anilide
(
1). The lack of intrinsic activity (receptor binding) of
R
1-substituted
anilides (e.g., NSC52105-s and compounds 6 to 12) indicates that
they do not undergo hydrolysis under the
conditions of the assay.
For the design of potent inhibitors of
P. carinii DHPS by CoMFA,
no bulky modifications should be
attempted at the
para position
of the anilide ring.
With the many sulfa drugs already synthesized and described in the
literature, this CoMFA study can be used as a preliminary
screen for
drugs with potential for improved activity toward
P. carinii
DHPS. Future studies will be directed toward the refinement
of this
model by the screening of additional compounds for inclusion
in the
QSAR analysis.
| |
ACKNOWLEDGMENTS |
We acknowledge Yu-long Hong, who developed the PC DHPS screen.
Support of modeling efforts was provided in part by the Molecular Modeling Laboratory of the National Center for the Development of
Natural Products at the University of Mississippi.
Financial support was provided by the National Institutes of Health
(grant 31775 to S.R.M.).
| |
FOOTNOTES |
*
Corresponding author. Mailing address for Mitchell A. Avery: Department of Medicinal Chemistry, 421 Fraser Hall, School of Pharmacy, University of Mississippi, University, MS 38677. Phone: (601)
232-5880. Fax: (601) 232-5638. E-mail: mavery{at}olemiss.edu. Mailing address for Steven R. Meshnick: Department of Epidemiology, School of Public Health, University of Michigan, 109 Observatory St., Ann Arbor, MI 48109. Phone: (734) 747-2406. Fax: (734)
764-3192. E-mail: meshnick{at}umich.edu.
| |
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Abstract
Introduction
