SCIENTIFIC PAPER
LIPOPHILICITY AND ANTIFUNGAL ACTIVITY OF SOME
2-SUBSTITUTED BENZIMIDAZOLE DERIVATIVES
SANJA O. PODUNAVAC-KUZMANOVIĆ, DRAGOLJUB D. CVETKOVIĆ
1 Department of Applied and Engineering Chemistry, Faculty of Technology,
University of Novi Sad, Bul. Cara Lazara 1, 21000 Novi Sad, Serbia
Received 29.03.2010. Revised 23.07.2010. Accepted 30.07.2010.
Corresponding author: [email protected]
2
Abstract
In the present paper, the antifungal activity of some 2-methyl and 2-
aminobenzimidazole derivatives was evaluated against yeast Saccharomyces cerevisiae.
The tested compounds displayed in vitro antifungal activity and minimum inhibitory
concentration (MIC) was determined for all compounds. The partition coefficients of the
studied compounds were measured by the shake flask method (logP) and by theoretical
calculation (ClogP). The logP values were compared and the relationships between the
logP values and antifungal activities were investigated. The mathematical models have
been developed as a calibration models for predicting the antifungal activity of this
class of compounds. The quality of models was validated by leave one out (LOO)
technique as well as by the calculation of statistical parameters for the established
models. The results of the present study may be useful for the designing of new more
potent benzimidazole derivatives against yeast Saccharomyces cerevisiae.
Keywords: Benzimidazole, antifungal activity, lipophilicity, quantitative structure-
activity relationship, Saccharomyces cerevisiae.
3
Because of their excellent activities, benzimidazole and its derivatives have a long
history as antimicrobial agents. Several thousands of benzimidazole analogs have been
synthesized and screened for pharmacological activity. They are of wide interest
because of their diverse biological activity and clinical applications. These heterocyclic
systems have different activities as they can act as bacteriostats or bactericides, as well
as fungicides [1-5] and they are present in numerous antiparasitic, antitumoral and
antiviral drugs [6-7]. Also, some of them exhibit appreciable antiprotozoal activity [8].
They were confirmed to have a moderate in vitro anti-HIV activity [9]. The success
with this group of molecules stimulated the search for new biologically active
derivatives. Understanding the role of chemical structure on biological activity is very
important. Predictions of biological and physicochemical properties of molecules based
on their structures are the fundamental and most interesting objectives of chemistry.
The opinion that there exists a close relationship between bulk properties of
compounds and the molecular structure of those compounds is rooted in chemistry. This
idea allows one to provide a clear connection between the macroscopic and the
microscopic properties of matter, and thus has been firmly established as one of the
central foundations of chemistry. Therefore, it is the one of the aims of chemistry to
identify relationships between molecular structure and physico-chemical properties and
then to quantify them [10].
A large number of research studies are needed to analyze the pharmacophore present
in these compounds using the Three Dimensional QSAR (quantitative structure-activity
relationship) methods [11-12]. The physicochemical properties predicted from structure
are helpful in the search for new molecules of similar or increased biological activity.
QSAR studies enable the investigators to establish reliable quantitative structure-
activity relationships, to derive a QSAR model and predict the activity of novel
molecules prior to their synthesis [13-16]. These studies reduce the trial- and error
element in the design of compounds by establishing mathematical relationships between
physical, chemical, biological, or environmental activities of interest and measurable or
computable parameters such as physicochemical, electronic, topological, or
stereochemistry. 3D-QSAR methodology has been successfully used to generate models
for various chemotherapeutic agents.
4
Progress in the use of QSAR methods has shown the importance of the hydrophobic
or lipophilic nature of biologically active molecules. The lipophilicity modifies the
penetration of bioactive molecules through the apolar cell membranes. This property is
usually characterized by partition coefficient (logP), which is essentially determined
from distribution studies of the compound between an immiscible polar and non-polar
solvent pair. To measure logP by the conventional shake flask technique [17] is difficult
and time resuming. It is complicated to determine logP for substances that are not very
soluble in water or can not be detected by conventional techniques. Instead of
measuring the logP values by equilibrium methods, partition chromatographic data can
be determined.
One of the most frequently used methods for lipophilicity measuring is reversed-
phase thin-layer chromatography (RP TLC) [18-22]. Lipophilicity can be expressed in
terms of many different descriptors (logP, π, f, logkw, RM, RM0) obtained experimentally
or calculated. Most frequently used experimental parameters are retention constants,
RM0 (RP TLC) and logkw (RP HPLC), whilst the calculated factor is logP. Samples of
pure compounds are not always available, so it is important to develop QSAR methods
that can efficiently predict biological activity by using theoretical descriptors computed
from the chemical structure. logP is also used in many environmental studies to
determine the environmental fate of chemicals. By knowing the exact values for this
parameter, it is possible to predict the inhibitory activity of a drug.
In continuation of our studies on inhibitory activities of benzimidazole derivatives
[23-28], in the present work we examined the activity of different substituted 2-methyl
and 2-aminobenzimidazoles against yeast Saccharomyces cerevisiae and studied the
quantitative effect of lipophilicity on antifungal activity. The main objective was to
establish a quantitative lipophilicity-inhibitory activity relationships and derive a high-
quality model which would link the lipophilicity of these compounds with their
inhibitory activity.
5
EXPERIMENTAL
Matherial and Methods
The structures of the benzimidazoles tested in this study are presented in Table 1.
All the compounds, except (1) and (8) were synthesized by a general procedure
described by Vlaović [29]. 2-methylbenzimidazole (1) and 2-aminobenzimidazole (8)
were of analytical reagent grade, commercially available.
Table 1.
Antifungal Investigations
All the benzimidazole derivatives were tested for their in vitro growth inhibition
activity against yeast Saccharomyces cerevisiae (ATCC 24860). For the evaluation of
the antifungal activities of the samples, agar disc diffusion method was used as
described by NCCLS [30].
The strains were grown on Sabouraud Dextrose slants for 24 hours at 25 oC and
checked for purity. After incubation the cells were washed from the surface of agar and
suspended in a sterile physiological solution. The number of cells in 1 cm3 of
suspension for inoculation, measured by Mc Farland nefelometer, was 1107 cfu cm-3.
The 1 cm3 of this suspensions was homogenized with 9 cm3 of melted (45 oC)
Sabouraud Dextrose Agar and poured into Petri dishes. On the surface of the agar the
6mm diameter sterile paper discs (Hi Media, Mumbai, India) were put and impregnated
with 10-3 cm3 of samples. The plates were incubated for 24-47 hours at 25 oC, and the
diameter of the resulting inhibition zone (including the disc) was measured (in mm).
The evaluation of the antifungal activities of the samples was carried out in three
repetitions.
Minimum inhibitory concentration (MIC) was determined by the agar dilution
method according to guidelines established by the NCCLS standard M7-A5 [31]. The
MIC of the tested benzimidazoles is defined as the lowest concentration of the
compound at which no growth of the strain is observed in time and under specified
6
experimental conditions. Stock solutions of the compounds were prepared in
dimethylformamide (DMF). Further dilutions were performed with distilled water. The
inoculated plates were then incubated at 35 oC for 16-20 h. A control (using DMF
without any test compound) was included for each organism. It was determined that the
solvent had no activity against any of the test microorganisms. The negative logarithms
of molar MICs (log1/cMIC) were determined and used for further calculations.
Molecular Modelling and Calculations of Lipophilicity Parameters
Molecular modelling studies were performed using CS Chem-Office Software
version 7.0 (Cambridge software) running on a P-III processor [32]. All molecules were
constructed by using Chem Draw Ultra 7.0 and saved as the template structures. For
every compound, the template structure was suitably changed considering its structural
features, copied to Chem 3D 7.0 to create a 3-D model and, finally, the model was
cleaned up and subjected to energy minimization using molecular mechanics (MM2).
The minimization was executed until the root mean square (RMS) gradient value
reached a value smaller than 0.1 kcal/molA. The Austin Model-1 (AM-1) method was
used for re-optimization until the RMS gradient attained a value smaller than 0.0001
kcal/molA using MOPAC. The lowest energy structure was used for each molecule to
calculate ClogP values by using ChemDraw Ultra 7.0. (Table 2).
Also, for all the compounds the lipophilicity parameters, logP values, were
experimentally determined by shake flask method. Partition coefficients (P) for
benzimidazoles investigated between n-octanol and phosphate buffer were determined
at 25 oC. Before the partitioning of benzimidazoles, the buffer (0.15moldm-3, pH=7.4)
and n-octanol (99%, Sigma, USA) were saturated with each other. Benzimidazoles were
dissolved in ethanol (96%, Zorka, Serbia) at a concentration of 2mgcm-3 to give the
stock solution. Calibration was done in exactly the same manner as the partitioning,
except that n-octanol was not used. The amounts of the sample were chosen so that
absorbance (=252nm) of 0.1 to 0.8 was measured. Partitioning experiments were
performed in the systems n-octanol/phosphate buffer 1:20, 1:30, 1:70 and 1:80 (V/V).
All solutions were pipetted into glass vials; the n-octanol and stock solution were added
with a microliter sxringe. The phases were shaken together on a mechanical shaker
7
(Viggo, Sweden) for 30 minutes, centrifuged (Rotofix, Switzerland) at 2500rpm for 20
min to afford complete phase separation, and n-octanol phase was removed. Absorbance
of the buffer phase was measured using Shimadzu UV/VIS spectrophotometer (Japan)
at 252nm. logP values were calculated using Eq. 1:
oltanocn
buffer
V
V
x
xylogPlog
(1)
where: P - partition coefficient; y - total mass of benzimidazole derivative (mg); X -
mass of benzimidazole derivative in the buffer phase after partitioning (mg); Vbuffer -
volume of phosphate buffer (cm3); Vn-octanol - volume of n-octanol (cm3). Each
experimental logP value is the average of five determinations (Table 2)..
Table 2.
Statistical Methods
The complete regression analysis was carried out by PASS 2005, GESS 2006,
NCSS Statistical Softwares [33].
RESULTS AND DISCUSSION
The results of antifungal studies of benzimidazoles tested against Saccharomyces
cerevisiae are summarized in Table 3. As indicated, all the compounds show antifungal
activities against the tested yeast. Consequently, compounds with high log1/cMIC are the
best antifungals. The MICs were compared with Ketoconazole and Amphotericin B
which were screened under similar conditions as reference drugs.
Table 3.
8
In order to identify the effect of lipophilicity on the inhibitory activity, QSAR
studies of title compounds were performed. A set of benzimidazoles consisting of 14
molecules was used for multilinear regression model generation. The reference drugs
were not included in model generation as they belong to a different structural series. An
attempt has been made to find structural requirement for inhibition of Saccharomyces
cerevisiae using QSAR Hansch approach on benzimidazole derivatives. To obtain the
quantitative effects of the lipophilicity parameter of benzimidazole derivatives on their
antifungal activity, QSAR analysis with logP was operated.
It is obtained that the correlation between MICs and shake flask logP, as well as
correlation between MICs and ClogP were equally significant. In this approach, two
mathematical models were derived (Table 4). It is noteworthy that both the models were
derived using entire data set of compounds (n=14) and no outliers were identified. The
F-value obtained in Models.(1-2) is found statistically significant at 99% level since all
the calculated F values are higher as compared to tabulated values. It is apparent from
the data that fitting equations improve when resorting to second order polynomial.
Table 4.
It is well known that there are three important components in any QSAR study:
development of models, validation of models and utility of developed models.
Validation is a crucial aspect of any QSAR analysis 34. The statistical quality of the
resulting models, as depicted in Table 4, is determined by r, s, and F 35-37.
For the testing the validity of the predictive power of selected models the LOO
technique was used. The developed models were validated by the calculation of the
following statistical parameters: PRESS, SSY, SPRESS, r2CV, and r2
adj (Table 5).
Table 5.
These parameters were calculated from the following equations.
2)(PRESS calcobs YY (1)
9
2)(SSY meanobs YY (2)
n
PRESSSPRESS (3)
SSY
PRESS12
CV r (4)
1
1)(1 22
adj pn
nrr (5)
Where, Yobs, Ycalc and Ymean are observed, calculated and mean values; n, number of
compounds; p, number of independent parameters.
PRESS is an acronym for prediction sum of squares. It is used to validate a
regression model with regards to predictability. To calculate PRESS, each observation
is individually omitted. The remaining n-1 observations are used to calculate a
regression and estimate the value of the omitted observation. This is done n times, once
for each observation. The difference between the actual Y value, yobs, and the predicted
Y, ycalc, is called the prediction error. The sum of the squared prediction errors is the
PRESS value. The smaller PRESS is, the better the predictability of the model. Its value
being less than SSY points out that the model predicts better than chance and can be
considered statistically significant. SSY are the sums of squares associated with the
corresponding sources of variation. These values are in terms of the dependent variable,
y.
The PRESS value above can be used to compute an r2CV statistic, called r2 cross
validated, which reflects the prediction ability of the model. This is a good way to
validate the prediction of a regression model without selecting another sample or
splitting data. It is very possible to have a high r2 and a very low r2CV. When this occurs,
it implies that the fitted model is data dependent. This r2CV ranges from below zero to
above one. When outside the range of zero to one, it is truncated to stay within this
range. Adjusted r-squared (r2adj) is an adjusted version of r2. The adjustment seeks to
remove the distortion due to a small sample size.
In many cases r2CV and r2
adj is taken as a proof of the high predictive ability of
QSAR models. A high value of these statistical characteristic (>0.5) is considered as a
10
proof of the high predictive ability of the model. But, recent reports have proved the
opposite 38. Although, the low value of r2CV for the training set can indeed serve as an
indicator of a low predictive ability of a model, the opposite is not necessarily true.
Indeed, the high r2CV does not imply automatically a high predictive ability of the
model. Thus, the high value of LOO r2CV is the necessary condition for a model to have
a high predictive power, but it is not a sufficient condition.
The only way to estimate the true predictive power of a model is to test its ability to
predict accurately the biological activities of compounds. In order to verificate the
predictive power of the developed model, predicted log1/cMIC values of benzimidazoles
investigated were calculated by using models (1) and (2) and compared with the
experimental values (Table 3). The data presented in Table 3 show that the observed
and the estimated activities are very close to each other. The residual activity (difference
between experimentally observed log (1/cMIC) and QSAR calculated log (1/cMIC)) is less
than equal to 0.192. Further, the plot of predicted log1/cMIC values against the observed
log1/cMIC values also proves the usefulness of the derived models. (Figure 1).
Figure 1.
In order to investigate the existence of a systemic error in developing the QSAR
models, the residuals of predicted log1/cMIC were plotted against the observed log1/cMIC
values (Figure 2). The propagation of the residuals on both sides of the zero axis
indicates that no systemic error in the development of regression models exists, as
suggested by Jalali-Heravi and Kyani [39].
Figure 2.
The results of this investigation indicate that the antifungal activity exhibited by the
tested compounds is governed by the partition coefficient, logP. Lipophilicity as a
physicochemical parameter has an important effect on inhibitory activity and this
parameter is usually related to pharmacological activity [40,41]. LogP is a measure of
hydrophobicity which is important for the penetration and distribution of the drug, but
11
also for the interaction of drug with receptors. To conclude, results of this study indicate
that both shake flask logP and ClogP are equaly suitable for prediction of partition
coefficient.
CONCLUSIONS
QSAR analysis was performed to estimate the quantitative effects of the
lipophilicity parameter, logP, of the different substituted 2-methyl and 2-
aminobenzimidazole derivatives on their antifungal activity against Saccharomyces
cerevisiae. For all the studied compounds the lipophilicity parameters were
experimentally determined by shake flask method (logP) as well as by theoretical
calculation (ClogP). Accurate mathematical models were developed for predicting the
inhibitory activity of some benzimidazole derivatives. The validity of the models has
been established by the determination of suitable statistical parameters. The established
model was used to predict inhibitory activity of the benzimidazoles investigated and
close agreement between experimental and predicted values was obtained. The low
residual activity and high cross-validated r2 values (r2CV) observed indicated the
predictive ability of the developed QSAR model. It indicates that the antifungal activity
of series of 2-methyl and 2-aminobenzimidazole derivatives can be successfully
modeled using different molecular descriptors. The results of this study indicate that
both shake flask logP and ClogP are equaly suitable for prediction of partition
coefficient.
Acknowledgment
These results are part of the project No. 142028, supported by the Ministry of
Science and Technological Development of the Republic of Serbia.
12
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15
Table 1. The structures of the compounds examined
N
N
R1
R2
Compound R1 R2
1 CH3 H
2 CH3 C6H5–CH2
3 CH3 4–CH3–C6H4–CH2
4 CH3 4–Cl–C6H4–CH2
5 CH3 C6H5–CO
6 CH3 4–CH3–C6H4–CO
7 CH3 4–Cl–C6H4–CO
8 NH2 H
9 NH2 C6H5–CH2
10 NH2 4–CH3–C6H4–CH2
11 NH2 4–Cl–C6H4–CH2
12 NH2 C6H5–CO
13 NH2 4–CH3–C6H4–CO
14 NH2 4–Cl–C6H4–CO
16
Table 2. Experimentally determined and calculated lipophilicity descriptors
Compound Shake flask logP ClogP
1 1.53 1.48
2 3.51 3.45
3 4.01 3.94
4 4.07 4.01
5 3.38 3.33
6 3.87 3.81
7 3.95 3.89
8 1.05 0.99
9 3.02 2.96
10 3.50 3.44
11 3.59 3.52
12 2.90 2.84
13 3.35 3.32
14 3.45 3.39
17
Table 3. Antifungal screening summary
log1/cMIC predict. Residuals Compound log1/cMICexp.
Model 1 Model 2 Model 1 Model 2
1 4.525 4.406 4.421 0.119 0.104
2 4.325 4.411 4.413 -0.086 -0.088
3 3.277 3.439 3.456 -0.162 -0.179
4 3.313 3.296 3.289 0.017 0.024
5 4.577 4.599 4.589 -0.022 -0.012
6 3.602 3.750 3.747 -0.148 -0.145
7 3.637 3.576 3.571 0.061 0.066
8 3.425 3.479 3.468 -0.054 -0.043
9 4.854 4.982 4.989 -0.128 -0.135
10 4.579 4.426 4.428 0.153 0.151
11 4.615 4.581 4.599 0.034 0.016
12 4.88 5.065 5.072 -0.185 -0.192
13 4.604 4.639 4.603 -0.035 0.001
14 4.638 4.501 4.504 0.137 0.134
Ketoconazole 4.628 - -
Amphotericin 4.869 - -
18
Table 4. Best mathematical models for the prediction of antifungal activity.
Model Coefficient n r s F
Intercept 0.1890
Shake flask logP 3.9568 1
Shake flask logP2 -0.7846
14 0.9697 0.0257 86.7954
Intercept 0.3835
ClogP 3.8997 2
ClogP2 -0.7918
14 0.9706 0.0249 89.5498
19
Table 5. Cross-validation parameters
Model PRESS SSY PRESS/SSY SPRESS r2CV r2
adj
1 0.4350 4.7381 0.0918 0.1762 0.9082 0.9296
2 0.4152 4.7381 0.0876 0.1722 0.9124 0.9316
20
3.2 3.6 4.0 4.4 4.8 5.2
3.2
3.6
4.0
4.4
4.8
5.2
r=0.9823
Model 1
Observed log (1/cMIC
)
Pre
dic
ted
log
(1/
c MIC
)
3.2 3.6 4.0 4.4 4.8 5.2
3.2
3.6
4.0
4.4
4.8
5.2
r=0.9821
Model 2
Observed log (1/cMIC
)
Pre
dic
ted
log
(1/
c MIC
)
a)
b)
Figure 1. Plots of predicted versus experimentally observed antifungal activity against
Saccharomyces cerevisiae
21
3.2 3.6 4.0 4.4 4.8 5.2
-0.2
-0.1
0.0
0.1
0.2Model 1
Observed log (1/cMIC
)
Res
idu
e
3.2 3.6 4.0 4.4 4.8 5.2
-0.2
-0.1
0.0
0.1
0.2Model 2
Observed log (1/cMIC
)
Res
idu
e
a)
b)
Figure 2. Plots of the residual values against the experimentally observed log1/cMIC
values