Похідні цитизину як нові антибактеріальні агенти проти Escherichia coli: in silico та in vitro дослідження
QSAR analysis of a 5143 compounds set of previously synthesized compounds tested against multi-drug resistant (MDR) clinical isolate Escherichia coli strains was done by using Online Chemical Modeling Environment (OCHEM).The predictive ability of the regression models was tested through cross-valida...
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V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine
2021
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Ukrainica Bioorganica Acta| _version_ | 1871193557833875456 |
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| author | Hodyna, Diana M. Kovalishyn, Vasyl V. Blagodatnyi, Volodymyr M. Bondarenko, Svitlana P. Mrug, Galyna P. Frasinyuk, Mykhaylo S. Metelytsia, Larysa O. |
| author_facet | Hodyna, Diana M. Kovalishyn, Vasyl V. Blagodatnyi, Volodymyr M. Bondarenko, Svitlana P. Mrug, Galyna P. Frasinyuk, Mykhaylo S. Metelytsia, Larysa O. |
| author_institution_txt_mv | [
{
"author": "Diana M. Hodyna",
"institution": "V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the NAS of Ukraine, 1 Murmanska St., Kyiv, 02094, Ukraine "
},
{
"author": "Vasyl V. Kovalishyn",
"institution": "V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the NAS of Ukraine, 1 Murmanska St., Kyiv, 02094, Ukraine "
},
{
"author": "Volodymyr M. Blagodatnyi",
"institution": "V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the NAS of Ukraine, 1 Murmanska St., Kyiv, 02094, Ukraine "
},
{
"author": "Svitlana P. Bondarenko",
"institution": "National University of Food Technologies, 68 Volodymyrska St., Kyiv, 01601, Ukraine "
},
{
"author": "Galyna P. Mrug",
"institution": "V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the NAS of Ukraine, 1 Murmanska St., Kyiv, 02094, Ukraine"
},
{
"author": "Mykhaylo S. Frasinyuk",
"institution": "V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the NAS of Ukraine, 1 Murmanska St., Kyiv, 02094, Ukraine"
},
{
"author": "Larysa O. Metelytsia",
"institution": "V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the NAS of Ukraine, 1 Murmanska St., Kyiv, 02094, Ukraine"
}
] |
| author_sort | Hodyna, Diana M. |
| baseUrl_str | https://bioorganica.com.ua/index.php/journal/oai |
| collection | OJS |
| datestamp_date | 2026-07-19T14:56:53Z |
| description | QSAR analysis of a 5143 compounds set of previously synthesized compounds tested against multi-drug resistant (MDR) clinical isolate Escherichia coli strains was done by using Online Chemical Modeling Environment (OCHEM).The predictive ability of the regression models was tested through cross-validation, giving coefficient of determination q2=0.72-0.8. The validation of the models using an external test set proved that the models can be used to predict the activity of newly designed compounds with reasonable accuracy within the applicability domain (q2=0.74-0.8). The models were applied to screen a virtual chemical library of cytisine derivatives, which was designed to have antibacterial activity. The QSAR modeling results allowed to identify a number of cytisine derivatives as effective antibacterial agents against antibiotic-resistant E. coli strains. Seven compounds were selected for synthesis and biological testing. In vitro investigation of the selected cytisine derivatives have shown that all studied compounds are potential antibacterial agents against MDR E. coli strains |
| doi_str_mv | 10.15407/bioorganica2021.02.023 |
| first_indexed | 2025-07-17T12:19:31Z |
| format | Article |
| fulltext |
ISSN 1814-9758. Ukr. Bioorg. Acta, 2021, Vol. 16, N 2
UDC 615.28+547.814+547.83+547.94+004.942
DOI: https://doi.org/10.15407/bioorganica2021.02.023
23
RESEARCH ARTICLE
Cytisine derivatives as new anti-Escherichia coli agents: in silico and
in vitro studies
Diana M. Hodyna1, Vasyl V. Kovalishin1, Volodymyr M. Blagodatnyi1, Svitlana P. Bondarenko2,
Galyna P. Mrug1, Mykhaylo S. Frasinyuk1, Larysa O. Metelytsia1*
1 V. P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the NAS of Ukraine, 1 Murmanska St., Kyiv, 02094, Ukraine
2 National University of Food Technologies, 68 Volodymyrska St., Kyiv, 01601, Ukraine
Abstract: QSAR analysis of a 5143 compounds set of previously synthesized compounds tested against multi-drug resistant (MDR)
clinical isolate Escherichia coli strains was done by using Online Chemical Modeling Environment (OCHEM).The predictive ability of the
regression models was tested through cross-validation, giving coefficient of determination q2 = 0.72-0.8. The validation of the models
using an external test set proved that the models can be used to predict the activity of newly designed compounds with reasonable accuracy
within the applicability domain (q2 = 0.74-0.8). The models were applied to screen a virtual chemical library of cytisine derivatives, which
was designed to have antibacterial activity. The QSAR modeling results allowed to identify a number of cytisine derivatives as effective
antibacterial agents against antibiotic-resistant E. coli strains. Seven compounds were selected for synthesis and biological testing. In vitro
investigation of the selected cytisine derivatives have shown that all studied compounds are potential antibacterial agents against MDR
E. coli strains.
Keywords: cytisine derivatives; QSAR; Escherichia coli; antibacterial activity.
Introduction
In modern conditions, when the therapeutic effectiveness
of known antibiotics becomes limited due to the growth of
resistance of pathogenic bacteria, research aimed at the
search and development of new antibacterial drugs is of
particular importance [1, 2]. Modern in silico and in vitro
screening methods promise the successful discovery of new
biologically active compounds, including the antibacterial
type of action, but their further clinical testing can be quite
long.
Therefore, one approach to the problem decision of new
antibiotics is the so-called repurposing of known chemical
compounds, which have already demonstrated, along with
Received:
Revised:
Accepted:
Published online:
27.08.2021
08.09.2021
29.09.2021
30.12.2021
Corresponding author. Tel.: +380-95-870-6014;
e-mail: metelitsa@bpci.kiev.ua (L. O. Metelytsia)
ORCID: 0000-0002-9876-6076
the main activity, a wide range of pharmacological
effects [3, 4]. Screening of such compounds can be one
effective way to detect novel antibacterials.
It is known that natural products as well as their
derivatives play a significant role in the discovery of new
biologically active compounds in the different areas of the
lifetime especially in the pharmacology due to a wide range
of their biological properties. They demonstrate the
сholinolitic, nootropic, antiviral, anticancer, hemostatic,
anti-inflammatory, antiarrhythmic and antioxidant and
cytotoxic activity [5, 6]. In the small number of studies
carried out to date, such compounds have shown promise in
treating bacterial infections.
Cytisine is one of the most promising in terms of
possible modification and creation of new biologically
active substances [7, 8]. Chemical modifications of cytisine
have large potential prospects. Among the various
derivatives of cytisine, compounds are constantly found
with other types of biological activity that are not
characteristic of itself (antispasmodic, antiarrhythmic,
hepatoprotective, analgesic, cholinergic, insecticidal,
antioxidant, etc.), which attracts attention and encourages
the synthesis and study of its new derivatives [9, 10]. Small
© Metelytsia L. O. et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Ukrainica Bioorganica Acta
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ISSN 1814-9758. Ukr. Bioorg. Acta, 2021, Vol. 16, N 2
24
doses of cytisine strongly arouse breathing and increase
blood pressure. In the form of a 1.5% aqueous solution
(Cityton), the alkaloid is used in medicine in cases of
asphyxia and intoxication. Currently, more than 1000
cytisine derivatives of various structures are known,
methods of their synthesis are described and
pharmacological activity being studied.
The current paper presents the results of the study of
cytisine derivatives as antibacterials by QSAR method and
experimental testing against antibiotic-resistant E. coli
strains.
Results and Discussion
Chemistry
Synthesis of cytisine derivatives (Figure 1) was achieved
by linking of the alkaloid with chromone or 2H-benzofuran-
3-one derivatives using methylene, ethylene or
1,3-propylene linker. Synthesis of desired isoflavone
derivative 1a [11], isoxazole 2 [12] or pyrazole 3 [13] was
reported early.
Chromone-cytisine hybrid 1b was synthesized by
alkylation of 2,3-dimethylchromone (4a) [14] with
epichlorohydrin in N,N-dimethylacetamide in presence of
K2CO3 with subsequent regioselective ring-opening reaction
of epoxide 5a with cytisine. The similar aurone derivative
1c [15] was synthesized starting from aurone 4c using
described above procedure (Scheme 1).
Mannich reaction of (2Z)-6-hydroxy-2-(pyridin-3-
ylmethylene)-2,3-dihydro-1-benzofuran-3-one (4c) [16] or
4’-chloro-7-hydroxy-5-methoxyisoflavone (4d) [17] with
cytisine and paraformaldehyde in presence of
4-(dimenthylamino)pyridine (DMAP) led to formation of
flavonoid-cytisine hybrids 6a,b containing methylene group
as linker between flavonoid and alkaloid moeties (Scheme
2).
Figure 1. Structures of cytisine derivatives 1a, 2, and 3.
Scheme 1. Reagents and conditions: a) epichlorohydrin, K2CO3, N,N-dimethylacetamide, 65-70 °C, 5-10 h; b) cytisine, MeCN, Bu4N+I-,
80 °C 10-12 h.
D. M. Hodyna, V. V. Kovalishin et al.
25
Scheme 2. Reagents and conditions: a) cytisine, (CH2O)n, DMAP, i-PrOH, 80 °C, 10-12 h.
QSAR modeling
The initial dataset of 5143 compounds with activity
against E. coli was split by chance into training (3780) and
test (1363) sets. The regression models built by the Trans-
CNN [18], ASNN [19], and RFR [20] methods (see Table
1) calculated the best performances. For this analysis
E-state [21], ALOGPS [22], CDK2 [23], descriptors were
included in the best models for all methods. The results are
summarized in Table 1 and the performances of individual
models are shown in Figure S1 of the Supplementary
materials.
The q2 values were 0.72-0.8 and 0.74-0.8 for training and
test sets, respectively. Other statistical parameters of the
models are summarized in Table 1 as well as in Figure S1
of the Supplementary materials. A consensus model, which
is an average of all three models, obtained the best
performance. It was used to provide a quantitative
evaluation of activity of compounds against E. coli as
described in the Experimental sections. The variances of
individual predictions of the consensus model were used to
calibrate the prediction errors and estimate their
applicability domain [24].
Evaluation activity of new compounds
A virtual database of drug-like cytisine derivatives was
generated based on available synthetic blocks and reactions.
It included 26 compounds with different substitution
patterns (see Supplementary materials, Table S1). These
compounds were screened using the consensus model
against E. coli. The 11 compounds predicted as most active
within the applicability domain (i.e., compounds with MIC
< 50µM) were selected for further evaluation (see also
Supplementary materials, Table S1). The next analysis was
to examine the toxic effects (mutagenicity,
tumorigenicity, irritation and reproductive
effectiveness) of the studied compounds using the
DataWarrior 5.5 program [25]. As a result of this
analysis, only 7 compounds were selected for synthesis
and testing. The synthetic feasibility of the compounds was
evaluated by organic chemists, and all seven compounds
were synthesized and tested for their antibacterial
activity against E. coli. (see Table 2 and Table S1, in
Supplementary materials).
Table 1. Statistical coefficients of the regression models.
Method Training Seta Test Seta
R2 q2 RMSE R2 q2 RMSE
Trans-CNN 0.80 ± 0.01 0.80 ± 0.01 0.48 ± 0.01 0.8 ± 0.02 0.8 ± 0.02 0.48 ± 0.02
ASNNb 0.73 ± 0.01 0.72 ± 0.01 0.58 ± 0.01 0.74 ± 0.02 0.74 ± 0.02 0.57 ± 0.03
RFRb 0.76 ± 0.01 0.75 ± 0.01 0.55 ± 0.01 0.78 ± 0.02 0.77 ± 0.02 0.53 ± 0.02
Consensusc
0.79 ± 0.01 0.79 ± 0.01 0.34 ± 0.01 0.80 ± 0.02 0.79 ± 0.01 0.33 ± 0.01
aThe training and test sets included 3780 and 1363 molecules, respectively. The cross-validation results are reported for the training set;
bASNN and RFR models developed by using E-state, ALOGPS and CDK2 descriptors;
cConsensus model was built by averaging outputs of all three models.
R2 – square of correlation coefficient; q2 – coefficient of determination; RMSE – Root Mean Squared Error.
ISSN 1814-9758. Ukr. Bioorg. Acta, 2021, Vol. 16, N 2
26
Biology testing
In vitro antimicrobial activity results by measuring the
zone diameter of growth inhibition of studied cytisine
derivatives tested against pathogenic E. coli strains are
shown in Table 2.
Table 2. In vitro activity of cytisine derivatives against
E. coli strains by the diameter of growth inhibition zones.
Compd
Zone diameter of growth inhibition of
E. coli strains, mm
E. coli
ATCCa
E. coli
CRBRb
E. coli
MDRc
1a 16 14 8
1b 18 16 16
1с 17 17 10
2 15 13 na
3 19 15 10
6a 14 17 na
6b 18 15 9
na - no activity.
aAmerican Type Culture Collection (strain 25922).
bCarbenicillin resistant clinical isolate of hemolytic E. coli strain.
cAmpicillin, Ceftazidime, Ofloxacin, Kanamycin, Ceftriaxone resistant
E. coli clinical isolate.
The results presented in Table 1 show that all studied
cytisine derivatives exhibited antibacterial activity against
E. coli ATCC and E. coli CRBR strains with diameters of
inhibition zones in the range of 13-19 mm. MDR E. coli
strain has demonstrated the least sensitivity to all
compounds (except compound 1b with inhibition zone of
16 mm).
Thus it is worth to note the activity of compound 1b
which showed high antibacterial properties against all
E. coli strains. Moreover compounds 1c, 3, and 6b
possessed the high antibacterial effect against E. coli ATCC
and E. coli CRBR strains.
Conclusions
A number of predictive regression models based on
different machine learning techniques were built using the
OCHEM platform. The created models demonstrated good
stability, robustness, and predictive power. Our results
demonstrated that designed and synthesized seven
compounds were found to be active against the E. coli
ATCC and E. coli CRBR strains. These compounds can be
perspective antibacterial against MDR E. coli clinical
isolate due to future structural optimization.
Experimental section
Data
The data for our analysis were obtained from multiple
publications and uploaded into the On-line Chemical
Database and Modeling Environment (OCHEM) [26].The
structure of compounds, their antibacterial activity and the
literary source of all data are freely available on the
OCHEM website. The initial dataset of 5143 consisted of
diverse chemical series with minimum inhibitory
concentration (MIC) values of the molecules ranging from
1.94 nM to 260 mM against the E. coli ATCC 25922 strain.
MICs were converted into log(1/MIC) values and were used
as the target variable to develop regression models.
Machine-learning methods
Well-known machine-learning methods such as
Transformer Convolutional Neural Network (Trans-CNN)
[18], Associative Neural Networks (ASNNs) [19] and
Random Forest (RFR) [20] were used to build QSAR
models.
Transformer Convolutional Neural Network (Trans-
CNN). The Trans-CNN method uses the internal
representation of molecules based on their SMILES
notation for extracting information-rich real-value
embedding during the encoding process and uses them for
further QSAR-oriented blocks to model biological activity
[18]. The Transformer-CNN architecture usually requires a
few tens iterations to converge for new tasks. The method
developed predicts the endpoint based on an average of
individual prognosis for a batch of augmented SMILES
belonging to the same molecule. The deviation within the
batch can serve as a measure of a confidence interval of the
prognosis, whereas the possibility to canonize SMILES can
be used for deriving applicability domains of models.
Associative Neural Network (ASNN). ASNN represents a
combination of an ensemble of the Feed-Forward
Backpropagation Neural Networks and the k-Nearest
Neighbors (kNN) method [19]. While neural networks build
an ensemble of global models, kNN provides a local
correction of the global model set. This combination
corrects the bias of the neural network ensemble and
increases its accuracy. The ASNN was trained by
SuperSAB [27]. The number of input neurons corresponded
to the amount of analyzed descriptors. The neural network
weight coefficients were initialized with random values
within [-0.5; +0.5] for each network in the ensemble. The
bias neuron was also presented in both the input and hidden
layer of nodes. The ensemble includes 100 neural networks,
which were developed using the default parameters
provided by OCHEM.
Random Forest (RFR). The random forest is a recursive
partition ensemble method consisting of a set of decision
trees, each of which is built using a bootstrap replica of the
training set and randomly selected subsets of descriptors.
The random forest makes predictions by majority votes of
the individual trees. Random Forest calculates predictions
by using a majority vote of the individual trees. This is a
D. M. Hodyna, V. V. Kovalishin et al.
27
high-dimensional non-parametric method that operates
quickly on large datasets [20].
Descriprors.The OCHEM supports multiple software
packages for calculation of diverse molecular descriptors. In
this study, we used E-state indices [21], AlogPS [22] and
CDK2 [16] packages, which were frequently top-performing
descriptors according to our previous studies. The electro-
topological state indices are 2D descriptors that combine both
electronic and topological characteristics of the analyzed
compounds [21]. AlogPS estimates lipophilicity and
solubility of chemical compounds while electrotopological
descriptors describe their electronic and topological
characteristics [22]. CDK descriptors (3D) are calculated by
the CDK Descriptors Engine and include 204 molecular
descriptors such as topological, geometrical, constitutional,
electronic, and hybrid descriptors [23].
Descriptor preprocessing. The unsupervised filtering of
descriptors was used. Descriptors with fewer than two unique
variables or with a coefficient of variance, less than 0.01 were
excluded. Moreover, descriptors with a pairwise non-
parametric Pearson’s correlation coefficient R > 0.95 were
grouped. Additionally, the Unsupervised Forward Selection
(UFS) method [28] was used to select a representative non-
redundant set for model development.
Model validation. Two validation protocols were used.
First of all, the initial data were split by chance into training
and test sets. For the training set five-fold cross-validation
with variable selection in each step of the analysis was used
to estimate accuracy of models for the training set [29]. To
avoid incorrect estimation of the models due to over-fitting
by the variable selection, the OCHEM repeats the cross-
validation step for all steps of model development.
Estimation of prediction accuracy. The OCHEM estimates
the applicability domain and the accuracy for each prediction
[24]. We used two criteria to access the goodness of fitting:
the squared correlation coefficient R2 and the coefficient of
determination q2. In addition, we used root mean square
error (RMSE) and the Mean Absolute Error (MAE)
statistics to estimate the errors in predictions [26]. A
detailed description of used machine-learning methods, all
selected descriptors, and validation procedures can be found
in the OCHEM manual [30].
Chemistry
1H spectra were recorded on Varian 400 (400 MHz)
spectrometers in CDCl3 [residual CHCl3 (δH = 7.26 ppm) as
internal standard] Melting points were determined in open
capillary tubes using Buchi B-535 apparatus and were
uncorrected. Mass spectra were obtained using an Agilent
1100 spectrometer using APCI (atmospheric-pressure
chemical ionization).
2,3-Dimethyl-7-(oxiran-2-ylmethoxy)-4H-chromen-4-one
(5a) synthesized as previously was described procedure
[31].
Yield 73%; mp 112-114 °C. 1H NMR (400 MHz, CDCl3)
δ 8.10 (d, J 8.9 Hz, 1H), 6.95 (dd, J 8.9, 2.4 Hz, 1H), 6.80
(d, J 2.4 Hz, 1H), 4.34 (dd, J 11.1, 2.9 Hz, 1H), 4.00 (dd,
J 11.1, 5.9 Hz, 1H), 3.44-3.34 (m, 1H), 2.95 (dd, J 4.9, 4.1
Hz, 1H), 2.80 (dd, J 4.9, 2.6 Hz, 1H), 2.39 (s, 3H), 2.04 (s,
3H). LC/MS (APCI) m/z 247.0 [M+H]+. Anal. calcld. for
C14H14O4: C, 68.28; H. 5.73. Found: C, 68.53; H, 5.99.
(2Z)-2-(3,4-Dimethoxybenzylidene)-6-(oxiran-2-ylmetho-
xy)-1-benzofuran-3(2H)-one (5b) synthesized as previously
was described procedure [31].
Yield 81%; mp 165-167 °C. 1H NMR (400 MHz, CDCl3)
δ 7.70 (d, J 8.9 Hz, 1H), 7.50-7.43 (m, 2H), 6.93 (d,
J 8.3 Hz, 1H), 6.79 (s, 1H), 6.77-6.75 (m, 2H), 4.39 (dd,
J 11.1, 2.8 Hz, 1H), 4.03 (dd, J 11.1, 5.9 Hz, 1H), 3.97 (s,
3H), 3.94 (s, 3H), 3.44-3.38 (m, 1H), 2.98-2.92 (m, 1H),
2.80 (dd, J 4.8, 2.6 Hz, 1H). LC/MS (APCI) m/z 335.2
[M+H]+. Anal. calcld. for C20H18O6: C, 67.79; H, 5.12.
Found: C, 67.53; H, 5.40.
(1S,5R)-3-{3-[(2,3-Dimethyl-4-oxo-4H-chromen-7-yl)-
oxy]-2-hydroxypropyl}-1,2,3,4,5,6-hexahydro-8H-1,5-me-
thanopyrido[1,2-a][1,5]diazocin-8-one (1b) synthesized as
previously was described procedure [31].
Yield 83%; mp 173-175 °C. 1H NMR (400 MHz, CDCl3)
δ 7.91 (d, J 8.9 Hz, 1H), 7.51-7.38 (m, 1H), 7.06 (d,
J 2.2 Hz, 1H), 7.03-6.95 (m, 1H), 6.40 (dd, J 8.8, 4.7 Hz,
1H), 6.30 (dd, J 7.1, 1.4 Hz, 1H), 3.93-3.85 (m, 4H), 3.18-
2.85 (m, 3H), 2.70-2.61 (m, 1H), 2.56-2.41 (m, 5H), 2.38 (s,
3H), 2.05-1.85 (m, 2H), 1.80 (s, 3H). LC/MS (APCI) m/z
437.2 [M+H]+. Anal. calcld. for C25H28N2O5: C, 68.79;
H, 6.47; N, 6.42. Found: C, 69.03; H, 6.22; N, 6.70.
(1S,5R)-3-(3-{[(2Z)-2-(3,4-Dimethoxybenzylidene)-3-
oxo-2,3-dihydro-1-benzofuran-6-yl]oxy}-2-hydroxypropyl)-
1,2,3,4,5,6-hexahydro-8H-1,5-methanopyrido[1,2-a][1,5]-
diazocin-8-one (1c) synthesized as previously was
described procedure [31].
Yield 77%; mp 124-126 °C. 1H NMR (400 MHz, CDCl3)
δ 7.68 (dd, J 8.4, 1.2 Hz, 1H), 7.53-7.46 (m, 2H), 7.29-7.20
(m, 1H), 6.95 (d, J 8.8 Hz, 1H), 6.80 (s, 1H), 6.72-6.64 (m,
2H), 6.45 (d, J 9.0 Hz, 1H), 6.03-5.93 (m, 1H), 3.99 (s, 3H),
3.95 (s, 3H), 3.93-3.85 (m, 4H), 3.18-2.85 (m, 3H), 2.70-
2.61 (m, 1H), 2.56-2.41 (m, 5H), 2.01-1.80 (m, 2H). LC/MS
(APCI) m/z 545.2 [M+H]+. Anal. calcld. for C31H32N2O7:
C, 68.37; H, 5.92; N, 5.14. Found: C, 68.21; H, 5.12;
N, 5.02.
(1S,5R)-3-{[(2Z)-6-Hydroxy-3-oxo-2-(pyridin-3-ylmethy-
lene)-2,3-dihydro-1-benzofuran-7-yl]methyl}-1,2,3,4,5,6-
hexahydro-8H-1,5-methanopyrido[1,2-a][1,5]diazocin-8-
one (6a) synthesized as previously was described procedure
[32].
Yield 63%; mp 219-221 °C. 1H NMR (400 MHz, CDCl3)
δ 9.37 (s, 1H), 9.03 (d, J 8.5 Hz, 1H), 8.93 (d, J 5.7 Hz,
1H), 8.13 (dd, J 8.2, 5.7 Hz, 1H), 7.75 (d, J 8.5 Hz, 1H),
7.48 (t, J 8.0 Hz, 1H), 7.06 (s, 1H), 6.95 (d, J 8.5 Hz, 1H),
6.49 (d, J 9.0 Hz, 1H), 6.41 (d, J 7.0 Hz, 1H), 4.49 (d,
J 14.1 Hz, 1H), 4.43 (d, J 14.1 Hz, 1H), 4.11 (d, J 15.8 Hz,
1H), 3.93-3.82 (m, 1H), 3.77-3.65 (m, 1H), 3.65-3.55 (m,
1H), 3.51-3.30 (m, 3H), 2.79-2.65 (m, 1H), 2.02-1.78 (m,
2H). LC/MS (APCI) m/z 442.2 [M+H]+. Anal. calcld. for
ISSN 1814-9758. Ukr. Bioorg. Acta, 2021, Vol. 16, N 2
28
C26H23N3O4: C, 70.74; H, 5.25; N, 9.52. Found: C, 70.93;
H, 5.48; N, 9.31.
(1S,5R)-3-{[3-(4-Chlorophenyl)-7-hydroxy-5-methoxy-4-
oxo-4H-chromen-8-yl]methyl}-1,2,3,4,5,6-hexahydro-8H-
1,5-methanopyrido[1,2-a][1,5]diazocin-8-one (6b) synthe-
sized as previously was described procedure [32].
Yield 66%; mp 186-188 °C. 1H NMR (400 MHz, CDCl3)
δ 7.72 (s, 1H), 7.45 (d, J 8.1 Hz, 2H), 7.38-7.31 (m, 3H),
6.56 (d, J 9.1 Hz, 1H), 6.27 (s, 1H), 6.05 (d, J 6.8 Hz, 1H),
4.19 (d, J 15.6 Hz, 1H), 3.97-3.81 (m, 5H), 3.76 (d, J 14.4
Hz, 1H), 3.22-3.03 (m, 3H), 2.65-2.41 (m, 3H), 2.07-1.86
(m, 2H). LC/MS (APCI) m/z 505.0 [M+H]+. Anal. calcld.
for C28H25ClN2O5: C, 66.60; H, 4.99; N, 5.55. Found:
C, 66.32; H, 5.18; N, 5.79.
Biology
The antimicrobial activity of the cytisine derivatives was
evaluated in vitro against E. coli ATCC 25922 (American
Type Culture Collection) strain, E. coli CRBR
(Carbenicillin resistant clinical isolate of hemolytic E. coli)
strain and MDR E. coli strain (Ampicillin, Ceftazidime,
Ofloxacin, Kanamycin, Ceftriaxone resistant) received from
the Museum of Microbial Culture Collection of the Shupyk
National Healthcare University of Ukraine. Antimicrobial
properties were determined by the disc diffusion method in
Mueller-Hinton agar [33]. A final inoculum concentration
of 1*105 colony-forming unit (CFU) per mL was
established using a 0.5 McFarland turbidity standard. The
subsequent dilution of 0.02 ml of the tested compounds was
applied on standard paper disks (6 mm) which were placed
on the agar plate.
The compound content on a disk was 5.0 μM. The
activity of tested compounds was identified by measuring
the zone diameter of the growth inhibition, which indicates
the degree of susceptibility or resistance of bacterial
pathogens against the test compounds.
Notes
Supplementary Materials: Supplementary materials
can be found at https://bioorganica.com.ua/index.php
/journal/issue/archive.
The authors declare no conflict interest.
Author contributions. D. M. H.: conceptualization,
investigation of bioactivity, results analysis; supervision,
writing most of the manuscript. V. V. K.: QSAR
investigation, results analysis, writing; V. M. B.:
investigation; S. P. B.: synthesis of compounds; G. P. M.:
synthesis of compounds, formal analysis; M. S. F.:
conceptualization, writing, results analysis; V. S. B.:
conceptualization, supervision; L. O. M.: conceptualization,
supervision, writing - review & editing.
References
1. Laws, M.; Shaaban, A.; Rahman, K.M. Antibiotic resistance
breakers: current approaches and future directions. FEMS
Microbiology Reviews. 2019, 43, 490-516.
2. Pietsch, F.; O'Neill, A. J.; Ivask, A.; Jenssen, H.; Inkinen, J.; Kahru,
A.; Ahonen, M.; Schreiber, F. Selection of resistance by
antimicrobial coatings in the healthcare setting. J. Hosp. Infect. 2020,
106, 115-125.
3. Ahmad, S.; Sahar, Q.; Raza, K. Translational bioinformatics methods
for drug discovery and drug repurposing. Translational
Bioinformatics in Healthcare and Medicine. 2021, 13, 127-139.
4. Doan, T. L.; Pollastri, M.; Walters, M. A.; Georg, G. I. The Future of
Drug Repositioning: Old Drugs, New Opportunities. Annu. Rep.
Med. Chem. 2011, 46, 385-401.
5. Cahlíková, L.; Breiterová, K.; Opletal, L. Chemistry and Biological
Activity of Alkaloids from the Genus Lycoris (Amaryllidaceae).
Molecules. 2020, 25, 4797-4817.
6. Thawabteh, A.; Juma, S.; Bader, M.; Karaman, D.; Scrano, L.; Bufo,
S. A.; Karaman, Rafik. The Biological Activity of Natural Alkaloids
against Herbivores, Cancerous Cells and Pathogens. Toxins (Basel).
2019, 11, 656-679.
7. Mineur, Y. S.; Einstein, E. B.; Bentham, M. P.; Wigestrand, M. B.;
Blakeman, S.; Newbold, S. A.; Picciotto, M. R. Expression of the
5-HT1A serotonin receptor in the hippocampus is required for social
stress resilience and the antidepressant-like effects induced by the
nicotinic partial agonist cytisine. Neuropsychopharmacology. 2015,
40, 938-946.
8. Gotti, C.; Clementi, F. Cytisine and cytisine derivatives. More than
smoking cessation aids. Pharmacological Research. 2021, 170,
105700.
9. Kulakov, I. V.; Nurkenov, O. A. Synthesis and Biological Activity of
the Derivatives of Alkaloid Cytisine. Chemistry for Sustainable
Development. 2012, 20, 237-250.
10. Metelytsia, L. O.; Trush, M. M.; Kovalishyn V. V., Hodyna, D. M.;
Kachaeva, M. V.; Brovarets, V. S.; Pilyo, S. G.; Sukhoveev, V. V.;
Tsyhankov, S. A.; Blagodatnyi, V. M.; Semenyuta, I. V. 1,3-Oxazole
derivatives of cytisine as potential inhibitors of glutathione reductase
of Candida spp.: QSAR modeling, docking analysis and experimental
study of new anti-Candida agents. Comput. Biol. Chem. 2021, 90,
107407.
11. Bondarenko, S. P.; Makarenko, O. G.; Vinogradova, V. I.; Frasinyuk,
M. S. Synthesis of 7-(N-12-cytisinylpropoxy)isoflavones. Chem. Nat.
Compd. 2020, 56, 1040-1043.
12. Bondarenko, S. P.; Frasinyuk, M. S.; Vinogradova, V. I.; Khilya, V.
P. Synthesis of 4-aryl-5-[2-hydroxy-4-(2-cytisin-12-yletho-
xy)phenyl]isoxazoles. Chem. Nat. Compd. 2016, 52, 463-467.
13. Bondarenko, S. P.; Frasinyuk, M. S.; Vinogradova, V. I.; Khilya, V.
P. Synthesis of 4-aryl-3-[2-hydroxy-4-(2-cytisin-12-yletho-
xy)phenyl]-pyrazoles. Chem. Nat. Compd. 2014, 50, 889-891.
14. Mrug, G. P.; Biletska, I. M.; Bondarenko, S. P.; Sviripa, V. M.;
Frasinyuk, M. S. Trifluoroacetylation of 2-methyl- and 2-
ethylchromones: a convenient access to 2-trifluoroacetonyl
chromones. ChemistrySelect 2019, 4, 11506-11510.
15. Popova, A. V.; Frasinyuk, M. S.; Bondarenko, S. P.; Zhang, W.; Xie,
Y.; Martin, Z. M.; Cai, X.; Fiandalo, M. V.; Mohler, J. L.; Liu, C.;
Watt, D. S.; Sviripa, V. M. Efficient synthesis of aurone Mannich
bases and evaluation of their antineoplastic activity in PC-3 prostate
cancer cells. Chem. Pap. 2018, 72, 2443-2456.
16. WO Patent No WO2019144009 A1. Semisynthetic aurones and
methods of use thereof / Blackburn, J. S.; Frasinyuk, M.; Liu, C.;
Xie, Y.; Watt, D. S. Patent appl. No PCT/US2019/014299
18.01.2019. Publ. 25.07.2019.
17. Mrug, G. P.; Bondarenko, S. P.; Khilya, V. P.; Frasinyuk, M. S.
Synthesis and aminomethylation of 7-hydroxy-5-
methoxyisoflavones. Chem. Nat. Compd. 2013, 49, 235-241.
18. Karpov, P.; Godin, G.; Tetko, I.V. Transformer-CNN: Swiss knife
for QSAR modeling and interpretation. J. Cheminform. 2020, 12, 17.
19. Tetko, I. V. Associative neural network. Methods Mol. Biol. 2008,
458, 185-202.
20. Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5-32.
21. Hall, L. H.; Kier, L. B. Electrotopological State Indexes for Atom
Types - a Novel Combination of Electronic, Topological, and
Valence State Information, J. Chem. Inf. Comput. Sci. 1995, 35,
1039-1045.
22. Tetko, I. V.; Tanchuk, V. Y.; Kasheva, T. N.; Villa, A. E. P.
Estimation of Aqueous Solubility of Chemical Compounds Using E-
State Indices. J. Chem. Inf. Comput. Sci. 2001, 41, 1488-1493.
D. M. Hodyna, V. V. Kovalishin et al.
29
23. Chemistry Development Kit. [Internet]. Available from:
https://cdk.github.io (accessed on September 08, 2021).
24. Sushko, Y.; Novotarskyi, S.; Körner, R.; Pandey, A. K.; Kovalishyn,
V. V.; Prokopenko, V. V.; Tetko I. V. Applicability domain for in
silico models to achieve accuracy of experimental measurements. J.
Chemom. 2010, 24, 202-208.
25. DataWarrior. [Internet]. Available from:
https://openmolecules.org/datawarrior (accessed on September 08,
2021).
26. Sushko, I.; Novotarskyi, S.; Korner, R.; Pandey, A. K.; Rupp, M.;
Teetz, W.; Brandmaier, S.; Abdelaziz, A.; Prokopenko, V. V.;
Tanchuk, V. Y.; Todeschini, R.; Varnek, A.; Marcou, G.; Ertl, P.;
Potemkin, V.; Grishina, M.; Gasteiger, J.; Schwab, C.; Baskin, I. I.;
Palyulin, V. A.; Radchenko, E. V., Welsh, W. J.; Kholodovych, V.;
Chekmarev, D.; Cherkasov, A.; Aires-de-Sousa, J.; Zhang, Q. Y.;
Bender, A.; Nigsch, F.; Patiny, L.; Williams, A.; Tkachenko, V.;
Tetko, I.V. Online chemical modeling environment (OCHEM): web
platform for data storage, model development and publishing of
chemical information. J. Comput. Aided Mol. Des. 2011, 25, 533-
554.
27. Tollenaere, T. SuperSAB: Fast Adaptive Back Propagation with
Good Scaling Properties. Neural Networks. 1990, 3, 561-573.
28. Whitley, D. C.; Ford, M.G.; Livingstone D. J. Unsupervised forward
selection: a method for eliminating redundant variables. J. Chem. Inf.
Comput. Sci. 2000, 40, 1160-1168.
29. Tetko, I. V.; Sushko, I.; Pandey, A. K.; Zhu, H.; Tropsha, A.; Papa,
E.; Oberg, T.; Todeschini, R.; Fourches, D.; Varnek, A. Critical
assessment of QSAR models of environmental toxicity against
Tetrahymena pyriformis: focusing on applicability domain and
overfitting by variable selection. J. Chem. Inf. Model. 2008, 48,
1733-1746.
30. OCHEM docs. [Internet]. Available from:
http://docs.ochem.eu/display/MAN (accessed on September 08,
2021).
31. Bondarenko, S. P.; Frasinyuk, M. S.; Khilya, V. P. New Aloperine-
Isoflavone Conjugates. Chem. Nat. Compd. 2016, 52, 615-619.
32. Bondarenko, S. P.; Frasinyuk, M. S.; Vinogradova, V. I.; Khilya, V.
P. Synthesis of cytisine derivatives of flavonoids. 2.
Aminomethylation of 7-hydroxyisoflavones. Chem. Nat. Compd.
2011, 47, 604-607.
33. Bauer, A. W.; Kirby, W. M.; Sherris, J. C.; Turck, M. Antibiotic
susceptibility testing by a standardized single disk method. Am. J.
Clin. Pathol., 1966, 45, 493-496.
Похідні цитизину як нові антибактеріальні агенти проти Escherichia coli: in silico та
in vitro дослідження
Д. М. Година1, В. В. Ковалішин1, В. М. Благодатний1, С. П. Бондаренко2, Г. П. Мруг1,
М. С. Фрасинюк1, Л. О. Метелиця1*
1 Інститут біоорганічної хімії та нафтохімії ім. В.П. Кухаря НАН України, вул. Мурманська, 1, Київ, 02094, Україна
2 Національний університет харчових технологій, вул. Володимирська, 68, Київ, 01601, Україна
Резюме: QSAR аналіз, який базувався на основі набору з 5143 раніше синтезованих сполук з активністю проти культури Escherichia coli із
множинною лікарською стійкістю (MDR), був проведений за допомогою Онлайн платформи хімічного моделювання (OCHEM). Передбачувана
здатність регресійних моделей була перевірена шляхом перехресної перевірки, коефіцієнт детермінації якої становив q2 = 0,72-0,8. Перевірка
моделей з використанням зовнішнього тестового набору підтвердила використання моделей для прогнозування активності нових розроблених
сполук із достатньою точністю в межах області застосування (q2 = 0,74-0,8). QSAR-моделі були використані для скринінгу віртуальної хімічної
бібліотеки похідних цитизину, які володіють антибактеріальною активністю. Результати QSAR-моделювання дозволили ідентифікувати ряд
похідних цитизину як ефективних антибактеріальних засобів проти антибіотикорезистентних штамів E. coli. Для синтезу та біологічного
тестування було відібрано сім сполук. Іn vitro дослідження синтезованих похідних цитизину показали, що всі сполуки є потенційними
антибактеріальними засобами проти мультирезистентних штамів E. coli.
Ключові слова: похідні цитизину; QSAR; Escherichia coli; антибактеріальна активність.
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| id | oai:ojs2.bioorganica.com.ua:article-23 |
| institution | Ukrainica Bioorganica Acta |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-20T01:00:37Z |
| publishDate | 2021 |
| publisher | V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine |
| record_format | ojs |
| resource_txt_mv | bioorganicacomua/b7/319581d79a94791a623119a91bb14eb7.pdf |
| spelling | oai:ojs2.bioorganica.com.ua:article-232026-07-19T14:56:53Z Cytisine derivatives as new anti-Escherichia coli agents: in silico and in vitro studies Похідні цитизину як нові антибактеріальні агенти проти Escherichia coli: in silico та in vitro дослідження Hodyna, Diana M. Kovalishyn, Vasyl V. Blagodatnyi, Volodymyr M. Bondarenko, Svitlana P. Mrug, Galyna P. Frasinyuk, Mykhaylo S. Metelytsia, Larysa O. cytisine derivatives QSAR Escherichia coli antibacterial activity похідні цитизину QSAR Escherichia coli антибактеріальна активність QSAR analysis of a 5143 compounds set of previously synthesized compounds tested against multi-drug resistant (MDR) clinical isolate Escherichia coli strains was done by using Online Chemical Modeling Environment (OCHEM).The predictive ability of the regression models was tested through cross-validation, giving coefficient of determination q2=0.72-0.8. The validation of the models using an external test set proved that the models can be used to predict the activity of newly designed compounds with reasonable accuracy within the applicability domain (q2=0.74-0.8). The models were applied to screen a virtual chemical library of cytisine derivatives, which was designed to have antibacterial activity. The QSAR modeling results allowed to identify a number of cytisine derivatives as effective antibacterial agents against antibiotic-resistant E. coli strains. Seven compounds were selected for synthesis and biological testing. In vitro investigation of the selected cytisine derivatives have shown that all studied compounds are potential antibacterial agents against MDR E. coli strains QSAR аналіз, який базувався на основі набору з 5143 раніше синтезованих сполук з активністю проти культури Escherichia coli із множинною лікарською стійкістю (MDR), був проведений за допомогою Онлайн платформи хімічного моделювання (OCHEM). Передбачувана здатність регресійних моделей була перевірена шляхом перехресної перевірки, коефіцієнт детермінації якої становив q2 = 0,72-0,8. Перевірка моделей з використанням зовнішнього тестового набору підтвердила використання моделей для прогнозування активності нових розроблених сполук із достатньою точністю в межах області застосування (q2 = 0,74-0,8). QSAR-моделі були використані для скринінгу віртуальної хімічної бібліотеки похідних цитизину, які володіють антибактеріальною активністю. Результати QSAR-моделювання дозволили ідентифікувати ряд похідних цитизину як ефективних антибактеріальних засобів проти антибіотикорезистентних штамів E. coli. Для синтезу та біологічного тестування було відібрано сім сполук. Іn vitro дослідження синтезованих похідних цитизину показали, що всі сполуки є потенційними антибактеріальними засобами проти мультирезистентних штамів E. coli V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry of the National Academy of Sciences of Ukraine 2021-12-27 Article Article application/pdf application/pdf https://bioorganica.com.ua/index.php/journal/article/view/23 10.15407/bioorganica2021.02.023 Ukrainica Bioorganica Acta; Vol. 16 No. 2 (2021): Ukrainica Bioorganica Acta; 23-29 Ukrainica Bioorganica Acta; Том 16 № 2 (2021): Ukrainica Bioorganica Acta; 23-29 1814-9766 1814-9758 10.15407/bioorganica2021.02 en https://bioorganica.com.ua/index.php/journal/article/view/23/27 https://bioorganica.com.ua/index.php/journal/article/view/23/28 Copyright (c) 2021 Diana M. Hodyna, Vasyl V. Kovalishyn, Volodymyr M. Blagodatnyi, Svitlana P. Bondarenko, Galyna P. Mrug, Mykhaylo S. Frasinyuk, Larysa O. Metelytsia https://creativecommons.org/licenses/by/4.0 |
| spellingShingle | похідні цитизину QSAR Escherichia coli антибактеріальна активність Hodyna, Diana M. Kovalishyn, Vasyl V. Blagodatnyi, Volodymyr M. Bondarenko, Svitlana P. Mrug, Galyna P. Frasinyuk, Mykhaylo S. Metelytsia, Larysa O. Похідні цитизину як нові антибактеріальні агенти проти Escherichia coli: in silico та in vitro дослідження |
| title | Похідні цитизину як нові антибактеріальні агенти проти Escherichia coli: in silico та in vitro дослідження |
| title_alt | Cytisine derivatives as new anti-Escherichia coli agents: in silico and in vitro studies |
| title_full | Похідні цитизину як нові антибактеріальні агенти проти Escherichia coli: in silico та in vitro дослідження |
| title_fullStr | Похідні цитизину як нові антибактеріальні агенти проти Escherichia coli: in silico та in vitro дослідження |
| title_full_unstemmed | Похідні цитизину як нові антибактеріальні агенти проти Escherichia coli: in silico та in vitro дослідження |
| title_short | Похідні цитизину як нові антибактеріальні агенти проти Escherichia coli: in silico та in vitro дослідження |
| title_sort | похідні цитизину як нові антибактеріальні агенти проти escherichia coli: in silico та in vitro дослідження |
| topic | похідні цитизину QSAR Escherichia coli антибактеріальна активність |
| topic_facet | cytisine derivatives QSAR Escherichia coli antibacterial activity похідні цитизину QSAR Escherichia coli антибактеріальна активність |
| url | https://bioorganica.com.ua/index.php/journal/article/view/23 |
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