Study of hydrophobic properties of biologically active open analogues of flavonoids

Article abstract of DOI:10.1016/j.jmgm.2012.07.009

Hydrophobicity can either be determined experimentally or predicted by means of commercially available programs. In the studies concerning biological activities of pyrazine analogues of chalcones, 3-(2-hydroxyphenyl)-1-(pyrazin-2- yl)prop-2-en-1-ones were more potent than the corresponding 3-(4-hydroxyphenyl)- 1-(pyrazin-2-yl)prop-2-en-1-ones. As the difference in lipophilicity may be a factor responsible for the difference in the potency, R_M values of the compounds were determined by RP-TLC and compared with log P values calculated by various commercially available programs. Important discrepancies were found between experimental and computational lipophilicity data. Therefore, we have tried to find a reliable method for calculating R_M values from in silico derived molecular parameters. The R_M values obtained with the chromatographic system consisting of Silufol UV 254 plates impregnated with silicon oil as the stationary phase and acetone-citrate buffer (pH = 3) 50:50 (v/v) as the mobile phase correlated well with van der Waals volumes (V_W) and hydration energies (ΔGH 2O) derived of molecular models calculated on RHF/AM1 level.

Full text of DOI:10.1016/j.jmgm.2012.07.009

Journal of Molecular Graphics and Modelling 39 (2013) 61–64
Contents lists available at SciVerse ScienceDirect
Journal of Molecular Graphics and Modelling
journal homepage: www.elsevier.com/locate/JMGM
Study of hydrophobic properties of biologically active open analogues of
flavonoids
Veronika Opletalováa, Petr Kastnera, Marta Kucˇerová-Chlupácˇováa, Karel Palátb,∗
a Department of Pharmaceutical Chemistry and Drug Control, Charles University in Prague, Faculty of Pharmacy in Hradec Kralove, 500 05 Hradec Kralove, Czech Republic
b Department of Inorganic and Organic Chemistry, Charles University in Prague, Faculty of Pharmacy in Hradec Kralove, 500 05 Hradec Kralove, Czech Republic
a r t i c l e i n f o
a b s t r a c t
Article history:
Hydrophobicity can either be determined experimentally or predicted by means of commercially
available programs. In the studies concerning biological activities of pyrazine analogues of chalcones,
3-(2-hydroxyphenyl)-1-(pyrazin-2-yl)prop-2-en-1-ones were more potent than the corresponding 3-
(4-hydroxyphenyl)-1-(pyrazin-2-yl)prop-2-en-1-ones. As the difference in lipophilicity may be a factor
responsible for the difference in the potency, RM values of the compounds were determined by RP-TLC
and compared with log P values calculated by various commercially available programs. Important dis-
crepancies were found between experimental and computational lipophilicity data. Therefore, we have
tried to find a reliable method for calculating RM values from in silico derived molecular parameters. The
RM values obtained with the chromatographic system consisting of Silufol UV 254 plates impregnated
with silicon oil as the stationary phase and acetone–citrate buffer (pH = 3) 50:50 (v/v) as the mobile phase
Received 21 November 2011
Received in revised form 3 July 2012
Accepted 30 July 2012
Available online 30 October 2012
Keywords:
Hydroxylated 1-pyrazin-2-ylpropen-1-ones
Lipophilicity
RP-TLC
Molecular models
correlated well with van der Waals volumes (VW) and hydration energies (ꢀGH O) derived of molecular
2
models calculated on RHF/AM1 level.
© 2012 Elsevier Inc. All rights reserved.
1. Introduction
three carbon, ␣,␤-unsaturated carbonyl system. Biological effects
of both naturally occurring chalcones and their synthetic con-
The discovery and development of a new drug is a long, difficult
and expensive process. Industry’s R&D investment has increased
by 12% on overage year-on-year since 1970. It takes almost 1.8
billion US$ over a period of 13.5 years to develop a new drug
[1]. Theoretical models for predicting absorption, distribution,
metabolism and excretion (ADME) properties and also safety
and toxicity of potential drugs play important roles in the drug
development process [2–5].
Lipophilicity is the key factor in many ADME processes involv-
ing membrane transport, but it also influences the interaction with
metabolizing enzymes and transporters [6,7,8]. The logarithm of
the partition coefficient between n-octanol and water (log P) has
often been used to represent molecular lipophilicity. The dominant
role of octanol–water partition coefficients in the pharmacoki-
netic processes, as well as in ligand–macromolecule interactions
triggered further research on the development of rapid methods
for their experimental assessment, among them chromatographic
techniques [9–12], as well as of suitable calculation methods
[13–18].
geners have recently been reviewed [19]. As a part of studies
aimed at finding novel biologically active pyrazine derivatives a
number of ring substituted 3-phenyl-1-(pyrazin-2-yl)prop-2-en-
1-ones was prepared in our laboratory. The compounds (see Fig. 1)
were studied as potential antifungal [20–22], antimycobacterial
[20–22] and platelet-antiaggregatory agents [23]. An influence
of these compounds on photosynthetic processes was studied as
well [20–22]. In all types of bioassays, 3-(2-hydroxyphenyl)-1-
(pyrazin-2-yl)prop-2-en-1-ones (1a–1e) were more potent than
the corresponding 3-(4-hydroxyphenyl)-1-(pyrazin-2-yl)prop-2-
en-1-ones (2a–2e). As the difference in lipophilicity could be
responsible for the higher potency of 2-OH derivatives, log P val-
ues of the compounds were calculated by means of commercially
available programs. However, the results obtained with various
programs differed significantly. Therefore we tried to find a more
reliable method for the evaluation of lipophilicity, and the results
of these efforts are reported in the present paper.
2. Materials and methods
Chalcones (1,3-diphenylprop-2-en-1-ones) are open analogues
of flavonoids in which the two aromatic rings are joined by a
2.1. Synthesis of model compounds
Model
3-(2-hydroxyphenyl)-1-(pyrazin-2-yl)prop-2-en-1-
∗
ones 1a–1e and 3-(4-hydroxyphenyl)-1-(pyrazin-2-yl)prop-
2-en-1-ones 2a–2e were prepared by the Claisen-Schmidt
Corresponding author.
E-mail address: palat@faf.cuni.cz (K. Palát).
1093-3263/$ – see front matter © 2012 Elsevier Inc. All rights reserved.
http://dx.doi.org/10.1016/j.jmgm.2012.07.009

62
V. Opletalová et al. / Journal of Molecular Graphics and Modelling 39 (2013) 61–64
Fig. 1. Studied compounds.
˚
condensation of the corresponding acetylpyrazines with 2-
hydroxybenzaldehyde and 4-hydroxybenzaldehyde, respectively
[20].
[29] and solvent probe radius 1.4 A. Hydration energy (ꢀGH O) was
2
calculated by the method published by Ooi et al. [30].
Correlation and regression analyses of the QSAR study were
run on a PC computer using the Microsoft Excel program. Multiple
regression analyses, which involve finding the best fit of depend-
ent variable (experimental hRM value) to a linear combination of
independent variables (descriptors), were performed by the least
squares method. In the equations, the figures in the parentheses are
the standard errors of the regression coefficients, n is the number
of compounds, r is the multiple correlation coefficient, F is the sig-
nificance test (F-test), and s is the standard error of estimate. F test
values are for all equations statistically significant at the 1% level of
probability of error.
2.2. Calculations of log P by means of commercially available
programs
ACD/logP v. 1.0 (Advanced Chemistry Development Inc., Toronto,
Kanada), HyperChem v. 6.03 (HyperCube Inc., Gainesville, USA) and
ChemBioDraw Ultra v. 11.0 (CambridgeSoft Corporation, Cambridge,
USA) that automatically generate log P from the structure were
used. log P calculated with ACD/logP v. 11.02 were found in Chemical
Abstracts [24]. The results are given in Table 1.
3. Results and discussion
2.3. Evaluation of lipophilicity by RP-TLC
Commercially available programs generating log P from the
structure have been widely used recently [16–18]. However, the
results are dependent on the size of the training set and the
algorithm used for calculation and may not be reliable, espe-
cially in the case of ortho-substituted compounds where possible
intramolecular interactions affecting lipophilicity must be taken
in account [31–36]. In the present paper, ACD/logP v. 1.0 and v.
11.02 (Advanced Chemistry Development Inc., Toronto, Canada),
HyperChem v. 6.03 (HyperCube Inc., Gainesville, USA) and ChemBio-
Draw Ultra v. 11.0 (CambridgeSoft Corporation, Cambridge, USA)
were used to calculate log P values of hydroxylated 3-phenyl-1-
(pyrazin-2-yl)prop-2-en-1-ones. Table 1 shows that according to
ACD/logP v. 1.0, 2-OH derivatives (1a–1e) are less lipophilic than
the corresponding 4-OH derivatives (2a–2e), whilst the version
11.02 indicated that 2-OH chalcones (1b–1e) are more lipophilic
than their 4-OH counterparts (2b–2e). Suprisingly, this does not
apply for compound 1a and 2a. HyperChem and ChemBioDraw Ultra
showed no differences in the lipophilicity of the two series. Chem-
BioDraw Ultra enables to calculate C log P, i.e. the n-octanol/water
partition coefficient based on established chemical interactions.
However, based on this value no difference in lipophilicity of the
two series was observed either.
We have therefore decided to verify the results experimen-
tally by measuring of hRM values which characterize the molecular
lipophilicity [37] on reversed phase thin-layer chromatography
(RP-TLC). Chromatographic systems were chosen on the basis of
literature data [32,38]. Citrate buffer (pH = 3) [39] was used to sup-
press dissociation of the phenolic groups. The results (hRMA and
hRMB in Table 1) clearly show that 2-OH derivatives (1a–1e) are
more lipophilic than the corresponding 4-OH derivatives (2a–2e).
Hence, some intramolecular interactions that are not reflected by
commercially available programmes must occur in the molecules
of the studied compounds, and the definite conclusions about their
lipophilicity should be preferably done on experimental data.
Although RP-TLC is relatively inexpensive, it still requires a
great deal of highly accurate laboratory work. The expression
of lipophilicity by properties of molecules that can be obtained
Two chromatographic systems were used to determine
hRM values (hRM = RM × 100 and RM can be calculated as:
RM = log(1/RF − 1)):
A: stationary phase – Celufol plates (Kavalier, Votice) impregnated
by development in 5% solution of octan-1-ol in diethyl ether;
mobile phase – acetone + citrate buffer (pH = 3) 50:50 (v/v) satu-
rated with octan-1-ol.
B: stationary phase – Silufol plates UV 254 (Kavalier, Votice)
impregnated by immersion in 5% solution of silicone oil (Lukoil
M 200, Lucˇební závody, Kolín) in diethyl ether; mobile phase –
acetone + citrate buffer (pH = 3) 50:50 (v/v)
Studied compounds were dissolved in methanol (1 mg/ml) and
2 ␮l was applied on the plate with an interval 1.3–1.4 cm between
circular spots using micropipette. The starting line was 1.5 cm from
the lower edge of the plate. The plates were developed over a path
of 12.0 cm in a normal chamber (16 cm × 16 cm × 7 cm) at room
temperature, previously equilibrated for 2 h. After development
the plates were dried in a gentle stream of air and the spots were
visualized in ꢁ = 254 nm UV light by means of a Camag UV lamp.
An arithmetic average was calculated from three independent TLC
measurements.
2.4. Generating hRM values from quantum-chemical calculations
Quantum-chemical calculations were run on a PC computer
using software HyperChem v. 6.03 (HyperCube Inc., Gainesville,
USA). The models of compounds were formed on RHF/AM1 level
(Austin Model 1 [25–27]). The most stable conformations of unsub-
stituted compounds 1a and 2a were optimized by conformational
analysis using random variation of dihedral angles. The other
derivatives were designed on the basis of these models. Van der
Waals volume (VW) and surface (SW) and solvent accessible vol-
ume (VSA) and surface (SSA) were calculated using the grid method
described by Bodor et al. [28] using the atomic radii of Gavezotti

V. Opletalová et al. / Journal of Molecular Graphics and Modelling 39 (2013) 61–64
63
Table 1
Molecular parameters and lipophilicity of compounds 1a–1e and 2a–2e.
Compound log P ACD
log P ACD
11.02
log P
HyperChem
log P
ChemBio
Draw
C log P
ChemBio
Draw
hRM
A
hRM
B
ꢀGH
VW [Å3] VSA [Å3] SW [Å2] SSA [Å2] hRMCalc.
O
1.0
[kcal2/mol]
1a
2a
1b
2b
1c
2c
1d
2d
1e
2e
1.61
2.36
3.30
4.05
3.48
4.23
3.66
4.41
3.13
3.88
1.102
1.153
3.793
3.240
3.975
3.422
4.159
3.605
3.628
3.074
2.56
2.56
4.12
4.12
3.94
3.94
4.01
4.01
3.61
3.61
0.94
0.94
3.07
3.07
2.88
2.88
2.97
2.97
2.55
2.55
1.30066
1.30066
3.12667
3.12667
3.25666
3.25666
3.38667
3.38667
2.85767
2.85767
−86
−79
19
−33
−37
12
9
9
2
12
5
3
−9
−10.9
−12.84
−7.41
−9.35
−7.6
205.08
204.98
271.76
271.77
272.23
272.52
272.43
272.73
255.67
255.86
688.1
242.99
243.03
320.52
320.86
321.35
322.59
324.00
325.34
303.25
304.85
435.45 −32
440.67 −38
690.25
880.24
883.57
888.69
892.00
900.08
904.26
846.42
849.46
535.53
539.24
545.39
545.98
552.77
557.46
523.42
530.05
12
5
−10
−2
11
5
11
5
2
−5
−16
7
−9.53
−7.56
−9.5
−10
−31
−52
−8
−9.94
from models calculated by quantum-chemical methods is another
possibility. In the present study, experimental hRM values were cor-
related with van der Waals volume (VW), solvent accessible volume
(VSA), van der Waals surface (SW), solvent accessible surface (SSA
)
and hydration energy (ꢀGH O). The models of the studied com-
2
pounds were formed on the semi-empirical level, which should
be sufficiently accurate for this purpose, and the volumes or the
surfaces of the molecules were calculated from the most stable con-
formation with the lowest energy. The best fit was found between
hRMB and van der Waals volume (VW), especially in the combina-
tion with the hydration energy (ꢀGH O). The correlation coefficient
for this combination of parameters i2s 0.991. Slightly worse results
were obtained when the solvent accessible volume VSA (with the
´
Fig. 2. Compounds used for the validation.
˚
solvent probe radius 1.4 A) or surfaces (SW, SSA) of the calculated
molecules were used as independent variables.
Estimating lipophilicity of chalcones using the relationship
expressed by Eq. (1) thus represents a suitable and even better alter-
native to some common algorithms based on fragment addition
methods.
Table 2
Validation of the regression equation (1).
Compound
ꢀGH [kcal/mol]
VW [Å3]
hRMCalc
hRMB
O
2
3a
3b
3c
−12.66
−7.62
−4.47
205.30
223.17
290.37
−38
−37
−14
32
The following relationships were derived:
−12
30
hRMB = 0.47( 0.06)VW + 3.4( 0.9)ꢀGH O − 91( 21)
2
(1)
n = 10 s = 2.735 r = 0.991 F = 94
Then we calculated their in silico models and obtained values of
hRMB = 0.64( 0.06)VW − 170( 10)
hydration energy (ꢀGH O) and van der Waals volume (VW). These
2
(2)
parameters were used for predicting of hRMCalc values which are in
good conformity with numbers (hRMB) obtained from the reversed
phase thin-layer chromatography (Table 2).
n = 10 s = 4.570 r = 0.971 F = 133
hRMA = 1.2( 0.2)VW − 330.0( 50)
(3)
n = 10 s = 14.800 r = 0.920 F = 44
4. Conclusions
hRMB = 0.15( 0.02)VSA + 3.8( 1.0)ꢀGH O − 92( 25)
2
(4)
Our paper shows different methods for the description
of hydrophobic properties of compounds by alternative ways
where we used calculated parameters from computed models of
molecules on the semi-empirical level which is possible to obtain
in relatively short time on present-day PC computers. In our calcu-
lation we succeeded the best result (Eq. (1)) with the combination
of van der Waals volume (VW) and the hydration energy (ꢀGH O).
But in our series of compounds also one parameter equations 2(2),
(3), (6) and (8) give good correlation with relatively exact predic-
tion of lipophilic parameters of possible new compounds. In these
equations van der Waals volume (VW) or molecular surfaces (either
van der Waals or solvent accessible) were used as the computed
parameter which describe the molecule.
n = 10 s = 3.216 r = 0.988 F = 139
hRMB = 0.39( 0.05)SW + 3.6( 0.9)ꢀGH O − 86( 22)
2
(5)
n = 10 s = 2.928 r = 0.990 F = 169
hRMB = 0.54( 0.05)SW − 170( 20)
(6)
n = 10 s = 4.900 r = 0.967 F = 115
hRMB = 0.25( 0.05)SSA + 4.4( 1.2)ꢀGH O − 95( 32)
2
(7)
n = 10 s = 3.87511 r = 0.98205 F = 95
hRMB = 0.38( 0.05)SSA − 200( 20)
(8)
n = 10 s = 6.295 r = 0.945 F = 67
Acknowledgements
The equation with the highest correlation coefficient (1) was
then used to generate hRMCalc. For the results see Table 1.
In the end of our study we tried to validate our best model (Eq.
(1)). We measured hRM values for three novel compounds with the
similar structure (Fig. 2).
This study is a part of the research project No. MSM 0021620822
of the Ministry of Education of the Czech Republic and it was finan-
cially supported by the Grant No. IGA NS10367-3.

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V. Opletalová et al. / Journal of Molecular Graphics and Modelling 39 (2013) 61–64
[22] V. Opletalova, M. Pour, J. Kunes, V. Buchta, L. Silva, K. Kralova, D. Meltrova,
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