Synthesis and hybrid sar property modeling of novel cholinesterase inhibitors?

Article abstract of DOI:10.3390/ijms22073444

A library of novel 4‐{[(benzyloxy)carbonyl]amino}‐2‐hydroxybenzoic acid amides was designed and synthesized in order to provide potential acetyl‐ and butyrylcholinesterase (AChE/BChE) inhibitors; the in vitro inhibitory profile and selectivity index were

Full text of DOI:10.3390/ijms22073444

International Journal of
Molecular Sciences
Article
Synthesis and Hybrid SAR Property Modeling of Novel
†
Cholinesterase Inhibitors
Jiri Kos *, Violetta Kozik , Dominika Pindjakova , Timotej Jankech 3,4, Adam Smolinski 5
1
,
2
3
,
Sarka Stepankova 6 , Jan Hosek , Michal Oravec , Josef Jampilek
7
8
1,3
and Andrzej Bak *
2,
1
Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research
Institute, Palacky University, Slechtitelu 27, 78371 Olomouc, Czech Republic; josef.jampilek@gmail.com
Department of Chemistry, University of Silesia, Szkolna 9, 40007 Katowice, Poland; violetta.kozik@us.edu.pl
Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University, Ilkovicova 6,
2
3
8
4215 Bratislava, Slovakia; pindjakova.dominika@gmail.com (D.P.); timotej.jankech@gmail.com (T.J.)
4
5
6
NT-LAB o.z., Teplicka 35, 92101 Piestany, Slovakia
GiG Research Institute, Pl. Gwarkow 1, 40166 Katowice, Poland; smolin@gig.katowice.pl
Department of Biological and Biochemical Sciences, Faculty of Chemical Technology, University of Pardubice,
Studentska 573, 53210 Pardubice, Czech Republic; sarka.stepankova@upce.cz
Department of Pharmacology and Toxicology, Veterinary Research Institute, Hudcova 296/70,
7
6
2100 Brno, Czech Republic; hosek.jan@vri.cz
8
Global Change Research Institute CAS, Belidla 986/4a, 60300 Brno, Czech Republic; oravec.m@czechglobe.cz
Correspondence: jiri.kos@upol.cz (J.K.); andrzej.bak@us.edu.pl (A.B.)
*
†
This article is sincerely dedicated to the memory of Dr. Milan Hutta, a Professor of Analytical Chemistry at
the Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava
(
Slovakia), a long-time head of the department, a versatile scientist and especially a rare person of
ꢀ
ꢁꢂꢀꢃꢄꢅꢆꢇ
ꢀ
ꢁꢂꢃꢄꢅꢆ
outstanding character who left us these days due to the consequences of disease COVID-19 caused by the
coronavirus SARS-CoV-2.
Citation: Kos, J.; Kozik, V.;
Pindjakova, D.; Jankech, T.; Smolinski, A.;
Stepankova, S.; Hosek, J.; Oravec, M.;
Jampilek, J.; Bak, A. Synthesis and
Hybrid SAR Property Modeling of
Novel Cholinesterase Inhibitors. Int.
J. Mol. Sci. 2021, 22, 3444.
Abstract: A library of novel 4-{[(benzyloxy)carbonyl]amino}-2-hydroxybenzoic acid amides was de-
signed and synthesized in order to provide potential acetyl- and butyrylcholinesterase (AChE/BChE)
inhibitors; the in vitro inhibitory profile and selectivity index were specified. Benzyl(3-hydroxy-4-
{[2-(trifluoromethoxy)phenyl]carbamoyl}phenyl)carbamate was the best AChE inhibitor with the in-
hibitory concentration of IC50 = 36.05
µM in the series, while benzyl{3-hydroxy-4-[(2-methoxyphenyl)
https://doi.org/10.3390/ijms22073444
carbamoyl]phenyl}-carbamate was the most potent BChE inhibitor (IC50 = 22.23
µM) with the highest
selectivity for BChE (SI = 2.26). The cytotoxic effect was evaluated in vitro for promising AChE/BChE
inhibitors. The newly synthesized adducts were subjected to the quantitative shape comparison
with the generation of an averaged pharmacophore pattern. Noticeably, three pairs of fairly similar
fluorine/bromine-containing compounds can potentially form the activity cliff that is manifested
formally by high structure–activity landscape index (SALI) numerical values. The molecular docking
study was conducted for the most potent AChE/BChE inhibitors, indicating that the hydropho-
bic interactions were overwhelmingly generated with Gln119, Asp70, Pro285, Thr120, and Trp82
Academic Editor: Sotiris
K Hadjikakou
Received: 24 February 2021
Accepted: 25 March 2021
Published: 26 March 2021
aminoacid residues, while the hydrogen bond (HB)-donor ones were dominated with Thr120.
π-
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
stacking interactions were specified with the Trp82 aminoacid residue of chain A as well. Finally,
the stability of chosen liganded enzymatic systems was assessed using the molecular dynamic simu-
lations. An attempt was made to explain the noted differences of the selectivity index for the most
potent molecules, especially those bearing unsubstituted and fluorinated methoxy group.
Keywords: 4-aminosalicylanilides; carbamate synthesis; lipophilicity; cholinesterase inhibition;
CoMSA; molecular docking; similarity-activity landscape index
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
1. Introduction
As is known, cholinesterase (ChE) lyses choline-related esters that can serve as choliner-
gic neurotransmitters [1]. Roughly speaking, two groups of serine hydrolases are present in
4
.0/).
Int. J. Mol. Sci. 2021, 22, 3444. https://doi.org/10.3390/ijms22073444
https://www.mdpi.com/journal/ijms

Int. J. Mol. Sci. 2021, 22, 3444
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the human nervous system: acetyl-based (AChE) and butyryl-based (BChE) cholinesterases,
respectively [
CNS) with dysfunction of perception/cognition and dementia symptoms is characteris-
tic for Alzheimer’s disease (AD) [ ]. Following the cholinergic hypothesis, the potential
cholinesterase inhibitors (ChEIs) are postulated to reduce the psychosomatic AD disor-
ders [ ]. In fact, the competitive and non-competitive ChEIs are used as marketed drugs in
2]. Unfortunately, the progressive degeneration of the central nervous system
(
3
4
the AD pharmacotherapy (e.g., galanthamine, rivastigmine, tacrine, donepezil) in order to
diminish the disease symptoms [5–8].
Diversely substituted amide (-CONH-) or carbamate (-OCONH-) fragments can be
incorporated as privileged structural motifs (scaffolds) in the design of the potential anti-
ChE agents [
9
–
12]. As a matter of fact, the amide/carbamate-based groups are present as
15]. Thus,
building blocks or linkers in vast number of clinically implemented drugs [13
–
the amide/carbamate-like anti-AD derivatives appear to be an attractive synthetic and
modeling target as well.
The pharmacological property profiling and potency modeling strategies are com-
monly utilized to escort the synthetic efforts and nominate the prospective candidates in
the hit
→
lead
→
seed
→
drug race [16]. SAR-guided (structure–activity) exploration of the
descriptor-based feature space seems pivotal at the crucial decision-making phases of drug
discovery [17]. Hence, in silico computation of the ADMET-tailored properties (Absorption,
Distribution, Metabolism, Excretion, Toxicity) is expected to minimize the probability of
drugs’ late attrition [18]. As a matter of fact, a range of meaningful computer-supported
approaches have been proposed to restrict the values of structural and/or physicochemical
descriptors to the ADMET-friendly property space [19]. Basically, in silico-assisted approx-
imation of the host–guest intermolecular recognition patterns can be dichotomized into
the ligand-based and structure-related methodologies. In practice, the collaborative fu-
sion of direct (receptor-dependent) and indirect (receptor-independent) procedures seems
advisable in namely consensus approach for searching of promising drug molecule [20].
Conceptually, the mapping of the binding/active site in the pharmacophore-guided study
is based on the simple tenet of substituent interchangeability and complementarity in the
congeneric (structurally related) series of compounds. It is assumed that similar size, shape,
and charge distributions should exert a comparable impact on the binding affinity and phar-
macological profile, respectively [21]. Practically, the similarity-driven methodologies have
been engaged in the comparable molecular shape analysis (e.g., CoMSA), where machine
learning techniques are coupled with descriptor elimination/selection algorithms in order
to approximate roughly the complex biological reality [22]. Despite some obstacles, the
distance-related similarity investigations between structurally-alike molecules contribute
significantly in the multidimensional quantitative (mD-Q) SAR practice [23]. Moreover,
the numerical estimation of the ADMET-friendly features conjugated with desirable drug
affinity (structure–activity landscape index, SALI) produces the SAR landscape, where the
smooth/flat (homogenous) areas are striated with sharp (heterogeneous) activity cliffs [24].
Obviously, the clever management of SALI-related information can provide hints, which
might be incorporated at the synthetic stage in order to modulate pharmacological effects
and/or demolish unwanted side effects [25].
The relatedness of a ligand steric/electronic/lipophilic pattern can be also mediated
by a complementary target surface using the protein-based docking procedures, where the
(
bio)effector-binding mode is determined according to the atomic coordinates of both host
and guest molecules [26]. Despite drugs being basically engineered to fit tightly into the
active interface of the enzyme/receptor, the binding goodness evaluation is continually
questionable due to the lack of truly selective scoring functions [27]. Since the ligand–
enzyme interaction pattern is dependent on their conformations and relative orientations
(
poses), it seems advisable to investigate the guest–host system stability using molecular
dynamic simulations (MDs) [28].
The primary objective of the presented study was the design, synthesis, and in vitro
specification of the anti-AChE/BChE profile for the new set of ring-substituted benzyl[4-

Int. J. Mol. Sci. 2021, 22, 3444
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(
arylcarbamoyl)phenyl-3-hydroxy]carbamates carrying different functionalized aryl side
chains. Moreover, the structured in silico computation of ADMET-friendly property was
performed to estimate the intermolecular similarity, lipophilicity, and inhibitory potential,
respectively. The newly synthesized adducts were subjected to the quantitative shape
comparison with the generation of an averaged pharmacophore pattern. The molecular
docking approach was employed for the most potent AChE/BChE inhibitors in order to
procure an additional comprehension of the binding mode. Finally, the stability of some
fused ligand–enzyme systems was evaluated using the molecular dynamic simulations.
An attempt was made to explain the observed variations of the selectivity index for the
potent molecules.
2. Results and Discussion
2.1. Design and Synthesis
The synthesis of the carbamates of 4-aminosalicylanilides was carried out in two steps
(
see Scheme 1). The primary amino moiety of 4-aminosalicylic acid was protected by a reaction
with benzyl chloroformate in an alkaline medium to form 4-{[(benzyloxy)carbonyl]amino}-
-hydroxybenzoic acid (CbzPAS) that was subsequently condensed with an appropriate
2
substituted anilines using phosphorus trichloride in dry chlorobenzene under microwave
conditions to give a series of investigated benzyl carbamates of 4-aminosalicylanilides.
Scheme 1. Synthesis of 4-{[(benzyloxy)carbonyl]amino}-2-hydroxybenzoic acid (CbzPAS) and corresponding anilides 1–32.
◦
Reagents and conditions: (
0 min.
a
) MeOH, NaHCO , room temperature, 24 h; (
b
) PCl , chlorobenzene, microwave reactor, 130 C,
3
3
3
The lipophilicity of all the target compounds was investigated by means of RP-HPLC
determination of capacity factors k with a subsequent calculation of log k. The retention
times of individual compounds were determined under isocratic conditions with methanol
as an organic modifier in the mobile phase using end-capped non-polar C18 stationary RP
columns. All the results are shown in Figure 1 and Table 1.
Figure 1. Structure of ring-substituted benzyl[4-(arylcarbamoyl)phenyl-3-hydroxy]carbamates 1–32.

Int. J. Mol. Sci. 2021, 22, 3444
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Table 1. Structure of ring-substituted benzyl [4-(arylcarbamoyl)phenyl-3-hydroxy]carbamates 1–
32, experimentally determined log k and in vitro acetyl- and butyrylcholinesterase (AChE and
BChE) inhibitory concentration (IC ) in comparison with starting 4-{[(benzyloxy)carbonyl]amino}-
5
0
2
-hydroxybenzoic acid (CbzPAS) and standards rivastigmine (RIV) and galanthamine (GLT). ChE
inhibitions are expressed as mean ± SD (n = 3 experiments).
IC50 (µM)
Comp.
R
log k
SI 1
AChE
BChE
1
2
3
4
5
6
7
8
9
H
2-OCH3
3-OCH3
4-OCH3
3-CH3
2-F
3-F
4-F
2-Cl
3-Cl
0.1160
0.3368
0.1280
0.1176
0.2725
0.3577
0.2303
0.3924
0.5188
0.6411
0.6399
0.4318
0.4477
0.7408
0.6227
0.4705
0.1912
0.4012
-0.1205
0.4072
0.5497
0.6138
0.8125
0.6664
0.9084
0.7366
0.9667
-0.0131
0.5169
0.9360
0.3679
0.4427
–
65.17 ± 2.24
50.14 ± 1.57
69.26 ± 2.00
57.49 ± 0.08
70.23 ± 1.70
50.07 ± 0.06
66.60 ± 0.14
47.37 ± 0.61
41.86 ± 0.15
61.62 ± 1.74
78.20 ± 1.58
56.42 ± 1.26
70.67 ± 0.57
71.42 ± 5.04
36.05 ± 1.48
62.42 ± 1.42
79.23 ± 1.27
58.31 ± 0.20
64.31 ± 4.74
67.36 ± 5.24
88.78 ± 1.42
99.14 ± 0.20
76.74 ± 0.74
57.99 ± 5.83
50.62 ± 0.38
94.89 ± 0.97
59.35 ± 3.38
49.87 ± 0.09
62.27 ± 0.75
94.88 ± 1.78
93.09 ± 1.80
65.92 ± 2.81
58.08 ± 1.04
50.10 ± 3.08
4.0 ± 0.13
175.18 ± 5.85
22.23 ± 0.53
361.80 ± 8.12
153.98 ± 6.09
167.46 ± 10.21
70.84 ± 5.61
197.62 ± 1.36
105.82 ± 0.43
140.06 ± 0.35
203.54 ± 7.49
138.67 ± 6.86
33.59 ± 0.71
309.25 ± 8.06
136.95 ± 21.94
44.31 ± 0.87
199.81 ± 7.00
180.31 ± 7.04
157.34 ± 3.11
42.95 ± 2.13
208.34 ± 5.30
247.56 ± 14.95
141.20 ± 7.12
117.57 ± 1.11
185.06 ± 6.13
143.08 ± 3.69
261.65 ± 5.10
145.68 ± 2.65
31.03 ± 2.11
183.71 ± 0.75
174.98 ± 10.47
57.11 ± 1.72
56.01 ± 0.58
136.85 ± 21.80
19.95 ± 0.31
7.96 ± 0.59
0.37
2.26
0.19
0.37
0.42
0.71
0.34
0.45
0.30
0.30
0.56
1.68
0.23
0.52
0.81
0.31
0.44
0.37
1.50
0.32
0.36
0.70
0.65
0.31
0.35
0.36
0.41
1.61
0.34
0.54
1.63
1.18
0.42
2.51
0.50
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
3
3
3
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
5
6
7
8
9
0
1
2
4-Cl
2-CF3
3-CF3
4-CF3
2-OCF3
2,3-F
2,4-F
2,5-F
2,6-F
3,5-F
2,3-Cl
2,5-Cl
2,6-Cl
3,4-Cl
3,5-Cl
2,4-Br
3,5-CF3
2,4,6-F
3,4,5-F
2,4,5-Cl
2,4,6-Cl
2,4,6-Br
–
CbzPAS
RIV
GLT
–
–
–
–
1
SI (index selectivity) = IC50 (AChE)/IC50 (BChE). Greater selectivity to BChE in bold.
2.2. Probability-Guided Pharmacophore Mapping
All synthesized ring-substituted benzyl[4-(arylcarbamoyl)phenyl-3-hydroxy]-carbamates
1–32 were tested to specify their in vitro inhibitory profile toward AChE and BChE targets and
were compared with marketed drugs rivastigmine (RIV), galanthamine (GLT), and the pattern
acid CbzPAS. Despite different mechanisms of action, RIV and GLT are the 2nd generation of
ChEIs that pseudo-irreversibly inhibit AChE and BChE [8,14], respectively. Table 1 reports
the IC50 values ( M) that express the quantitative measure of inhibitor concentration needed
µ
to reduce the biological process by 50%.
Benzyl(3-hydroxy-4-{[2-(trifluoromethoxy)phenyl]carbamoyl}phenyl)carbamate (15
)
showed the best AChE inhibition (IC50 = 36.05
methoxyphenyl)carbamoyl]phenyl}carbamate (
IC50 = 22.23 M). In addition, compound demonstrated the highest selectivity index for
µ
M), while benzyl{3-hydroxy-4- [(2-
2
) was the most potent BChE inhibitor
(
µ
2

Int. J. Mol. Sci. 2021, 22, 3444
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BChE (SI = 2.26). In addition, carbamates 12 (R = 2-CF ) and 28 (R = 2,4,6-F) showed BChE
3
inhibition IC50 values of 33.59 and 31.03 µM, respectively and SI 1.68 and 1.61, respectively.
A comprehensive visualization tool for the investigation of SAR trends was provided
in the form of the structure–activity landscape indexes (SALI), where the planar image
of molecular similarity is augmented with molecular activity profile [29]. Basically, the
detection of the continuity areas and/or activity cliffs depends largely on the accessibility
of structurally related compounds (chemotypes) with noticeable variations in the activity
profile. The distribution of the Tanimoto coefficients was analyzed for the triangular
T3
matrix revealing that the majority of compounds are characterized by pretty high
2×32
similarity metric (0.85
marked as fairly similar with T = 0.93. Obviously, for structurally related molecules
e.g., stereoisomers T 1), SALI infinity; therefore it is recommended to replace such
≥
T
≥
0.75). Interestingly, the potent compounds 2 and 15 are
(
→
→
values by the largest SALI values [30]. The symmetrical grayscaled heatmap of SALI values
for the congeneric series of analyzed compounds is illustrated in Figure 2a, where axes
correspond to molecules sorted according to the increasing pBChE inhibitory potential
(∆pBChE = 1.2), respectively. Accordingly, the white spot of the heatmap represent the
highest numerical values of SALI parameters, while the black ones specify the minimal
ones. In fact, molecule 15 is characterized by low values of SALI indexes with the remaining
compounds in the dataset, while the most potent compound is accompanied by gray
2
pots of the inactive molecules (positional isomers) and 1 (unsubstituted analogue),
3,
4
respectively. The structural modifications that considerably affect the molecular activity
profile are illustrated as the neighborhood plot in Figure 2b, where the structural relatedness
between pairs of compounds is shown in the function of the activity difference and color-
coded according to SALI values. Noticeably, three pairs of fairy similar fluorine/bromine-
containing compounds (17 vs. 19 and 28
, 26 vs. 32) can potentially form the activity cliff
that is manifested formally by high SALI numerical values (see Figure 2b). In consequence,
further dense samplings of the indicated SAR-variations (T > 0.95 &
reasonable for the investigated set of BChE inhibitors.
∆pBChE > 1) seem
Figure 2. Gray-scaled structure–activity landscape index SALI plane with molecules 1–32 ordered according to increasing
BChE inhibitory potential (a) and neighborhood plot (b).
The crucial distributions of the electronic/steric/lipophilic properties of the guest–host
composition can be specified by the systematic sampling of the functional group relatedness
with the introduction of the pharmacophore concept in QSAR studies [31]. In reality, the
spatial arrangement of pharmacophoric features is determined for the homogeneous

Int. J. Mol. Sci. 2021, 22, 3444
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ensemble of molecules that share the common structural pattern (chemotype), because the
enhancement of the SAR applicability domain (AD) for a non-congeneric set of structures
is a rather elusive and enigmatic operation. Moreover, the prediction of the modeled
property is firmly dependent on the molecule training and test separation. On the other
hand, there are no strictly defined rules for splitting the set of compounds into training/test
subfamilies in order to maintain the model robustness as well as its predictive power.
Hence, the recurrent and interchangeable training/test subset division was proposed for
the probability-driven pharmacophore mapping based on the stochastic model validation
(SMV) algorithm [32]. Despite the CPU-intense (Central Processing Unit) SAR calculations,
the whole pool of systematically produced training/test subpopulations (ratio 3:1) was
8
7
investigated in CoMSA modeling of the BChE inhibition potency (
C
≈
10 models).
32
The frequency distribution in the test set for preferable statistic parameters of model
2
2
robustness (
q
> 0.6 and
q
> 0.5) indicted that the potent compound 2 is noticeably
cv
test
overrepresented in contrast to molecule 15. Surprisingly, the preferential selection of the
active compounds ( and ) and inactive ones ( 13 23 and 26) that are basically ortho-
2
6
3,
,
and meta-substituted analogues resulted in the generation of the robust models with the
acceptable predictive power for the test set. Finally, the selection-oriented pharmacophore
pattern was generated based on the robust models following the principles of IVE (iterative
variable elimination-partial least squares)-PLS algorithm that is described elsewhere [33].
The graphical representation of the descriptor-based areas that contribute (un)favorably
into CoMSA models is shown in Figure 3. The relative contribution of the surface/charge
descriptors is weighted by the corresponding regression coefficient indicating the regions
of the positive (bright color) and negative (dark color) impact on the inhibitory potency
(see Figure 3a). Moreover, the four possible combinations of the charge and the mean
regression coefficients are introduced in Figure 3b.
Noticeably, a diverse set of steric and electrostatic features was indicated on the
averaged surface for the enzyme-selective ligands; therefore, the direct translation of
the pharmacophore-based areas of the space into the complementary pseudoreceptor
model that potentially harbors putative inhibitors is a fairy complicated task. The spatial
pattern plotted in Figure 3a indicates the favorable steric contributions of areas that spread
uniformly over the 4-{[(benzyloxy)carbonyl]amino}-2-hydroxybenzoic acid skeleton and
aniline-like substituent as well. Not surprisingly, the positively charged areas of hydrogen
atoms directly attached to nitrogen atoms and the negatively charged oxygen atoms of
carbonyl groups were depicted as the favorable contributors to the inhibitory potency of
the investigated compounds (see Figure 3b). The specified atoms can potentially form
the hydrogen bonds with the hypothetical receptor aminoacid residue in the enzyme
active site. Unfortunately, the provided pharmacophoric pattern does not explain the
observed variations in selectivity of molecule 2 and 15 toward A/BChE enzymes. In fact,
no pharmacophoric areas were specifically pointed out as dominant ones to increase the
inhibitory potency; however, the consensus 3D-QSAR modeling provides the spatial map
of steric/electronic features of the ligand-site composition.
2.3. Similarity-Driven Property Assesment
The distance-oriented property evaluation was conducted using the Principal Compo-
nent Analysis (PCA) and Hierarchical Clustering Analysis (HCA) on the pool of 2819 de-
scriptors derived from Dragon 6.0 software. The descriptor-based data were organized
into a matrix X3
with rows presenting molecules (objects) and columns representing
2×2819
numerical variables (parameters), respectively. The resulting dataset was centered and stan-
dardized, since the calculated parameters varied considerably. The number of significant
principal components (PCs) was determined according to the percentage of the modeled
variance. The first four PCs accounted for 77.6% of the total data variance, while the first
three PCs described 72.42%. The respective scoreplots with the projection of molecules
1–
32 and color-coded with the corresponding molecular weight (MW) on plane PC1 vs.
PC2 are presented in Figure 4a,b.

Int. J. Mol. Sci. 2021, 22, 3444
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Figure 3. Three-dimensional (3D) pattern specified by comparable molecular shape analysis (CoMSA)
IVE (iterative variable elimination-partial least square)-PLS procedure for 24/8 training/test sam-
plings of potential BChE inhibitors. Colors code the contribution (
a) and four possible combinations
of mean charge descriptor and correlation coefficient (b). The potent compound 2 is plotted as a
reference molecule.
Figure 4. Projection of compounds 1–32 (a) with corresponding molecular weights (b) on plane defined by PC1 and PC2.
Colors code molecular weight (MW) values.

Int. J. Mol. Sci. 2021, 22, 3444
8 of 27
The dissimilarities of molecules 12
–
14, 27 with trifluoromethyl substituent(s) (-CF3)
and compound 15 with trifluromethoxy group (-OCF ) from the remaining ones, are
3
observed along the first principal component (PC1 > 45). Moreover, objects
methoxy-based (-OCH ) positional isomers and molecule with methyl substituent (-CH3)
are also clustered together along the second principal component (PC2 < 20). Interestingly,
the lower weighted molecules (MW < 400) are located in the negative area of the plane
2–4 that are
5
3
−
(
PC1 < 0 and PC2 < 0).
Moreover, the exploratory analysis of the descriptor-driven data structure was per-
formed by tracing the (dis)similarities between objects (molecules) in the multidimensional
variable space in order to reveal its clustering tendency. Due to its hierachical nature, the
HCA method basically leads to sub-optimal clustering of objects that is mainly dependent
on the procedure employed for clusters’ linkage [34]. The interpretability of the extracted
data structure is not obvious in the high-dimensional variable space; therefore, the findings
are presented in the form of dendogram constructed on the Euclidean-based distance and
the Ward linkage algorithm, where the OX axis presents the sequence of objects/parameters
and the OY axis specifies the dissimilarity [35]. The dendogram shown in Figure 5a proved
our previous PCA findings (see Figure 4a), where at least three sub-clusters (A,B,C) can
be distinguished—molecules 12–15 and 27 (blue lines) differ noticeably from the rest of
the objects (red lines), respectively. The HCA dendogram was augmented with a color-
coded map of the experimental activity and lipophilicity data in the logarithmic scale as
presented in Figure 5b. As a matter of fact, a simultaneous interpretation of the dendogram
objects (sorted according to the Ward linkage method) and the colored map of the empirical
data revealed that there is no clear relationship between the investigated data. Roughly
speaking, the molecules grouped in cluster D are characterized by relatively high values of
the log k parameter when compared to the remaining objects.
Figure 5. Dendogram of compounds
1–32 in descriptor-based space (a) and color map of experimental
activity and lipophilicity values in logarithmic scale (b).

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From the user’s perspective, the non-trivial question arises of how to single out the
best-suited method or combination of methods for clogP estimation of new compounds. In
order to investigate the lipophilic profile of the analyzed molecules, the experimentally
specified log k parameters were compared with the calculated lipophilicity coefficients
(clogP) estimated using a set of in silico predictors, e.g., clogP, Molinspiration, Osiris,
HyperChem 7.0, Sybyl-X, MarvinSketch 15, ACD/ChemSketch 2015, Dragon 6.0, Kowwin,
XlogP3, ChemDraw, and ACD/Percepta programs (see Table S1 in the Supplementary Ma-
terials). Moreover, the selected physicochemical properties of rivastigmine, galanthamine,
and compound 2 were predicted by ACD/Percepta 2012 (see Table S2 in Supplementary
Materials). The corresponding clogP estimators deduced by the group of alternative pro-
grams were cross-compared with the experimental potency values and inter-correlated
with each other, as illustrated in Figure 6. Relatively good correlations (r = 0.77) between
the generated clogP values using Sybyl-X program and the empirical log k were recorded.
The averaged clogP values over the set of programs produced the value of r = 0.73, whereas
the median resulted in r = 0.70 with the experimental data.
Figure 6. Matrix of linear correlation coefficients between empirical log k and clogP calculated by
various programs.
On the whole, a pretty high inter-correlation was observed among the programs for
lipophlicity specification (see Figure 6); however, some variations in clogP values resulted
probably from different computational algorithms (atom/fragment- or descriptor-based)
implemented in the software and/or training data engaged at the training stage—models
are as good as the modeling data used. Additionally, the iterative variable elimination
procedure (IVE-PLS) was applied on the integrated clogP matrix (X3
) and log k
2 × 13
parameter indicating Sybyl-X and XlogP3 estimators as significant contributors to the
linear quantitative structure-property relationship QSPR model in namely consensus
lipophilicity determination [36]. Consequently, the averaged values of the indicated clogP
estimators were correlated with log k values resulting in r = 0.80, because not only the best
inter-correlated logP estimators were chosen. Similarly, the same programs were specified
with the stepwise procedure implemented in Matlab environment.
2.4. Docking and Molecular Dynamics Simulations
The pharmacophore-driven (ligand-based) SAR approaches can be coupled with the
site-directed (structure-based) docking procedures that position a potential bio-effector

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molecule within a protein binding/active. The complementary host–guest binding mode
is deduced based on the target spatial arrangement of atoms with the engagement of
feature/descriptor-matching algorithms, where the ligand property space is correspond-
ingly mapped to the receptor steric, electrostatic, and/or lipophilic patterns. Unfortunately,
it is still not clear how to directly relate the pharmacological or ADMET effects to enthalpi-
cally and/or enthropically favorable ligand–receptor modes and scoring descriptors. On
the other hand, in silico attempts to reconstruct the drug–protein interactions using the
molecular docking are commonly accepted as a complimentary approach to the traditional
mD-QSAR ligand-based methods [37].
The structural geometries of human acetyl-/butyrylcholinesterase enzymes (A/BChE)
in a complex with the commercially accessible drug molecules (e.g., galanthamine and
rivastigmine) are being currently intensively scrutinized. The crystallographic struc-
ture of BChE determined at a sophisticated resolution of 2.6 Å in the ligand-containing
state (holo) with the rivastigmine (RIV) analogue [3-[(1~{R})-1-(dimethylamino)ethyl]-4-
oxidanyl-phenyl]~{N}-ethyl-~{N}-methyl-carbamate was retrieved from the Protein Data
Bank repository (PDB code: 6eyf). Subsequently, the marketed drugs and the potential
anti-BChE population (IC50 < 150) were docked in the active site of the enzymatic chain
A using AutoDock Vina software in order to collate the binding mode of the analyzed
analogues with the drug–enzyme interacting pattern [38]. The resulting molecular confor-
mations and orientations (poses) of the potentially active compounds revealed mainly three
types of non-binding interactions: the hydrogen bond (HB), hydrophobic, and π-stacking
interactions, respectively. The graphical illustrations of the relevant ligand–enzyme in-
teractions were produced for the entire population of the active compounds (IC50 < 150)
using Schrödinger Maestro software and Protein–Ligand Interaction Profiler (PLIP) and
compared with the commercial drug molecules (GLT and RIV) [39]. As a matter of fact,
the hydrophobic interactions were overwhelmingly generated (70%) with Gln119 (20%),
Asp70 (13.7%), Pro285 (13.7%), Thr120 (12.7%), and Trp82 (9.5%) aminoacid residues, while
the HB-donor ones were dominated (50%) with Thr120, which confirms our previous
findings [1,2]. π-stacking interactions were specified with Trp82 aminoacid residue of chain
A as well. The planar (2D) and spatial (3D) binding pattern distribution for marketed drugs
and the most active anti-BChE molecule 2 are presented in Figures 7 and 8, respectively.
Figure 7. Planar comparison of binding RIV (a), GLT (b), and the most potent BChE inhibitor 2 (c) generated by the
Maestro program.
The hydroxyl group of Thr120 seems to be the key structural motif in the formation of
hydrogen bond (HBD) with hydroxyl oxygen of molecule (see Figure 8c). The -stacking
interaction was revealed between the substituted anilide-based ring and Trp82, respectively.
Unfortunately, the replacement of hydrogen atoms in the methoxy group of molecule with
2
π
2
fluorine in the trifluoromethoxy substituent in compound 15 does not change noticeably
the enzyme–ligand binding pattern; however the additional hydrogen bond (HBA) is
generated using the oxygen (acceptor) of Asn68 carbonyl group and nitrogen (donor)

Int. J. Mol. Sci. 2021, 22, 3444
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of molecule 15. The molecular electrostatic potentials (MESP) as well as the lipophilic
potentials determined on Connolly surface of molecule
2 and 15 were determined. In fact,
the interrelation was reported previously between electrostatic and lipophilic potentials,
because the molecular charge distribution affects the lipophilic property [40]. Noticeably,
the electron-rich substituents correspond well to higher lipophilic potential values, as
illustrated in Figure 9. Not surprisingly, the variations of charge distribution in molecule
and 15 have a visible impact on the lipophilic patterns, especially in the regions of –OCH3
and –OCF groups as indicated by brow colors (see Figure 9).
2
3
Figure 8. Spatial comparison of binding RIV (a), GLT (b), and the most potent BChE inhibitor 2 (c) generated by the
PLIP program.
Figure 9. Lipophilic potential distribution on Connolly surface of molecule 2 (a) and 15 (b) with the residues of BChE active site.
The molecular dynamic simulations (MDs) can supply fruitful knowledge regarding
the host–guest complex stability as well as the potential mechanism of ligand action in the
active site of the enzyme [41]. The liganded state (holo) of the BChE enzyme docked with
the most potent molecule 2 was applied as the input structure in the MD simulations. Taking
into account the technical feasibility (computational time and resource constraints), the CPU-
intense MD calculations were conducted with the involvement of the flexible ligand and the
rigid target according to the procedure implemented in Sybyl-X 2.2.1 program. Contrarily, the
internal macromolecular motions (atom trajectories) and the system energy variations were
investigated based on the reduced molecular dynamics (RedMD) algorithm. Accordingly, in
the RedMD strategy, each aminoacid/nucleotide is expressed by a singular spherical particle

Int. J. Mol. Sci. 2021, 22, 3444
12 of 27
(
‘bead’) centered on a C
α
or P atom with the mass that corresponds to the total mass of a given
aminoacid or nucleotide [42–44]. The Berendsen algorithm was engaged in RedMDs on the
NVT ensemble (constant number, volume, temperature) in order to control temperature. The
initial atom velocities were assigned at 293K; however, the system was then heated, and the
corresponding trajectories were specified at the temperature of 300K. The total length of the
RedMDs was arranged to 20 ns with a 10 fs time step, respectively. The enzyme fluctuations of
the total energy during the RedMD simulations are illustrated in Figure 10a. In fact, relatively
small variations of the root mean square deviation (RMSD) values indicated the enzyme
stability, as shown in Figure 10b.
Figure 10. Total energy (a) and root mean square deviation (RMSD) (b) variations enzyme ‘bead’ atoms in RedMD simulations.
Noticeably, the side chains of compound 2 and 15 have an important impact on the
resulting variations of RMSD values recorded for heavy atoms during MDs, as depicted in
Figure 11. It seems that trifluoromethoxy substituent of molecule 15 is characterized by
lower spatial flexibility compared to the methoxy group of compound 2. Obviously, –OCF3
is considerably heavier and can generate greater steric hindrance in comparison to –OCH3.
The most electronegative element fluorine can interact with the surrounding aminoacid
residues that stiffen the ligand-enzyme system, as mirrored in Figure 11.
Figure 11. Variations of RMSD parameter of molecules 2 and 15.

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2
.5. In Vitro Cell Viability Assay
To evaluate the potential cytotoxic effect, the preliminary in vitro screening of the
antiproliferative activity of the most effective AChE/BChE inhibiting compounds and
was performed. The human monocytic leukemia THP-1 cell line was selected for this
purpose, as we described previously [45]. Nor neither 28 show negative effect on relative
2
28
2
cell viability up to the concentration of 30
at this concentration. It makes compounds
µ
M. The relative cell viability was around 100%
and 28 interesting for further pharmacological
2
evaluation and allows them to become lead compounds.
3. Materials and Methods
3.1. General Methods
All reagents were purchased from Merck (Sigma-Aldrich, St. Louis, MO, USA) and
Alfa (Alfa-Aesar, Ward Hill, MA, USA). Reactions were performed using a Anton-Paar
Monowave 50 microwave reactor (Graz, Austria). The melting points were determined on a
Kofler hot-plate apparatus HMK (Franz Kustner Nacht KG, Dresden, Germany) and are
uncorrected. Infrared (IR) spectra were recorded on Nicolet iS5 IR spectrometer (Thermo
Scientific, West Palm Beach, FL, USA). The spectra were obtained by the accumulation of
−
1
−1
1
13
2
56 scans with 2 cm resolution in the region of 4000–450 cm . All H- and C-NMR
1
spectra were recorded on a JEOL JNM-ECA 600II device (600 MHz for H and 150 MHz
for C, JEOL, Tokyo, Japan) in dimethyl sulfoxide-d (DMSO-d ). H and C chemical
13
1
13
6
6
shifts (δ) are reported in ppm. The purity of the final compounds was checked by the HPLC
separation module Agilent 1200 series equipped with a DAD SL (Agilent Technologies, Santa
Clara, CA, USA). The detection wavelength 210 nm was chosen with operational conditions
described in Section 3.3. The solvent peaks in the (blank) chromatogram were deducted
from the peaks in the chromatogram of the sample solution. The purity of individual
compounds was determined based on the area of peaks in the chromatogram of the sample
solution. High-resolution mass spectra were measured using a high-performance liquid
®
chromatograph Dionex UltiMate 3000 (Thermo Scientific, West Palm Beach, FL, USA)
coupled with an LTQ Orbitrap XL TM Hybrid Ion Trap-Orbitrap Fourier Transform Mass
Spectrometer (Thermo Scientific) equipped with a HESI II (heated electrospray ionization)
source in the positive mode.
3.2. Synthesis
4-{[(Benzyloxy)carbonyl]amino}-2-hydroxybenzoic acid (CbzPAS) [46]: 4-Aminosylicylic
acid (6.0 g, 39.2 mM) was disolved in methanole (64 mL) at ambient temperature, and benzyl
chloroformate (6.8 mL, 47.6 mM) was added dropwise. The reaction mixture was stirred at
room temperature for the time period of 24 h. Then, the solvent was removed to dryness
under reduced pressure. Crude product was dissolved in the mixture of ethyl acetate (200 mL)
and 1M hydrochloric acid (200 mL). Layers were divided. The water layer was additionally
extracted with ethyl acetate (3
×
50 mL). Afterwards, organic layers were collected and dried
with magnesium sulfate. Then, the solvent was removed to dryness under reduced pressure.
◦
−1
Yield 71%; Mp 219–220 C; IR (ATR, cm ): 1727, 1623, 1589, 1538, 1440, 1383, 1307, 1270,
1
6
249, 1220, 1204, 1185, 1165, 1104, 1056, 1028, 998, 976, 905, 871, 774, 767, 760, 725, 691, 669,
1
56, 639, 620, 595, 573, 547, 509, 466, 453; H-NMR (DMSO-d ),
δ: 11.39 (brs, 1H), 10.13 (s, 1H),
6
7.69 (d, J = 8.7 Hz, 1H), 7.46–7.32 (m, 5H), 7.16 (d, J = 2.1 Hz, 1H), 6.99 (dd, J = 8.8 Hz, 2.2 Hz,
1H), 5.17 (s, 2H); 13C-NMR (DMSO-d6),
δ: 171.65, 162.20, 153.05, 145.72, 136.23, 131.09, 128.48,
128.25, 128.17, 109.28, 106.96, 104.72, 66.14.
3
.2.1. General Procedure Used to Synthesize the Carbamates 1–32
-{[(Benzyloxy)carbonyl]amino}-2-hydroxybenzoic acid (0.6 mmol; 0.2 g) was sus-
pended in dry chlorobenzene (6 mL) at ambient temperature and phosphorus trichloride
0.3 mM, 0.5 eq.), and the corresponding substituted aniline (0.6 mM, 1 eq.) was added
dropwise. The reaction mixture was transferred to the microwave reactor, where the syn-
4
(
◦
◦
thesis was performed (30 min, 130 C). Then, the mixture was cooled to 40 C, and then the

Int. J. Mol. Sci. 2021, 22, 3444
14 of 27
solvent was removed to dryness under reduced pressure. The residue was washed with
hydrochloride acid and water. The crude product was recrystallized from ethanol.
(
1) Benzyl[3-hydroxy-4-(phenylcarbamoyl)phenyl]carbamate (1)
◦
−1
Yield: 69%; Mp 170–172 C; HPLC purity: 98.31%; IR (ATR, cm ): 3326, 3033, 1742,
1
1
701, 1635, 1618, 1536, 1498, 1446, 1434, 1328, 1298, 1279, 1219, 1191, 1142, 1097, 1076, 1062,
1
029, 967, 850, 763, 750, 692, 598, 564, 508, 464; H-NMR (DMSO-d ),
δ: 12.14 (s, 1H), 10.24
6
(
s, 1H), 10.09 (s, 1H), 7.93 (d, J = 8.3 Hz, 1H), 7.67 (d, J = 8.3 Hz, 2H), 7.45—7.35 (m, 7H), 7.23
d, J = 2.8 Hz, 1H), 7.13 (t, J = 6.9 Hz, 1H), 7.03 (dd, J = 8.3 Hz, J = 2.8 Hz, 1H), 5.18 (s, 2H);
(
1
3
C-NMR (DMSO-d6),
δ: 167.10, 160.54, 153.65, 144.68, 138.66, 136.86, 130.22, 129.26, 129.03,
1
28.77, 128.71, 124.62, 121.60, 111.43, 109.52, 105.88, 66.59; HR-MS: C H O N [M + H]+
21
19
4
2
calcd 363.1339 m/z, found 363.1337 m/z.
(
2) Benzyl{3-hydroxy-4-[(2-methoxyphenyl)carbamoyl]phenyl}carbamate (2)
◦
−1
Yield: 68%; Mp 159–162 C; HPLC purity: 98.24%; IR (ATR, cm ): 3350, 2966, 1737,
1
9
607, 1578, 1556, 1486, 1376, 1284, 1210, 125,2 1219, 1206, 1189, 1142, 1094, 1052, 1028, 963,
1
38, 926, 912, 840, 822, 792, 743, 691, 601, 571, 531, 496; H-NMR (DMSO-d ),
δ
: 11.74 (s, 1H)
,
6
10.66 (s, 1H), 10.06 (s, 1H), 8.35 (d, J =8.2 Hz, 1H), 7,91 (d, J = 8.2 Hz, 1H), 7.45–7.43 (m, 2H),
7
5
.42–7.39 (m, 2H), 7.37–7.34 (m, 2H), 7.08–7.06 (m, 2H), 7.01–6.99 (m, 1H), 6.95–6.93 (m, 1H),
.17 (s, 2H), 3.87 (s, 3H); 13C-NMR (DMSO-d6)
δ: 163.33; 157.09; 153.13; 148.66; 143.60; 136.38;
1
6
31.36; 128.48; 128.19; 128.14; 128.00; 123.68; 120.57; 120.29; 113.04; 110.93; 109.64; 105.20;
+
5.99; 55.98; HR-MS: C H O N [M + H] calcd 393.1445 m/z, found 393.1442 m/z.
22
21
5
2
(
3) Benzyl{3-hydroxy-4-[(3-methoxyphenyl)carbamoyl]phenyl}carbamate (3)
◦
−1
Yield: 48%; Mp 196–198 C; HPLC purity: 98.13%; IR (ATR, cm ): 3381, 2967, 2881,
1
1
741, 1609, 1583, 1741, 1609, 1583, 1519, 1457, 1434, 1409, 1308, 1291, 1259, 1214, 1200, 1163,
1
092, 1052, 967, 872, 842, 763, 749, 715, 697, 685, 599, 577, 492; H NMR (DMSO-d )
δ
: 12.08
6
(
s, 1H), 10.20 (s, 1H), 10.08 (s, 1H), 7.91 (d, J = 8.8 Hz, 1H), 7.46–7.34 (m, 6H), 7.28–7.21
m, 3H), 7.03 (dd, J = 8.8 Hz, J = 2.2 Hz, 1H), 6.72–6.89 (m, 1H), 5.17 (s, 2H), 3.76 (s, 3H),
C NMR (100 MHz, DMSO-d6) δ: 166.47, 159.85, 159.48, 153.11, 144.14, 139.36, 136.32,
(
1
3
1
5
29.74. 129.50, 128.49, 128.23, 128.16, 113.14, 111.02, 109.55, 109.01, 106.64, 105.34, 66.07,
+
5.07; HR-MS: C H O N [M + H] calcd 393.1440 m/z, found 393.1442 m/z.
22
21
5
2
(
4) Benzyl{3-hydroxy-4-[(4-methoxyphenyl)carbamoyl]phenyl}carbamate (4)
◦
−1
Yield: 65%; Mp 183–186 C; HPLC purity: 98.84%; IR (ATR, cm ): 3431, 3308, 1740,
1
7
967, 1620, 1572, 1510, 1418, 1365, 1309, 1244, 1199, 1140, 1065, 1033, 960, 845, 829, 762,
1
36, 729, 695, 680, 533, 520, 462; H-NMR (DMSO-d ),
δ: 12.34 (s, 1H), 10.15 (s, 1H), 10.07
6
(
(
(
1
[
s, 1H), 7.93 (d, J = 8.9 Hz, 1H), 7.57 (d, J = 8.9 Hz, 2H), 7.45–7.34 (m, 5H), 7.20 (s, 1H), 7.03
1
3
dd, J = 8.9 Hz, J = 1.4 Hz, 1H), 6.94 (d, J = 8.9 Hz, 2H), 5.18 (s, 2H), 3.75 (s, 3H); C NMR
100 MHz, DMSO-d6) : 166.71, 160.49, 155.98, 153.13, 144.10, 136.34, 130.94, 129.26, 128.49,
28.24, 128.17, 122.95, 113.84, 110.45, 108.84, 105.40, 66.07, 55.20; HR-MS: C H O N
δ
2
2
21
5
2
+
M + H] calcd 393.1445 m/z, found 393.1442 m/z.
(
5) Benzyl{3-hydroxy-4-[(3-methylphenyl)carbamoyl]phenyl}carbamate (5)
◦
−1
Yield: 50%; Mp 167–170 C; HPLC purity: 98.51%; IR (ATR, cm ): 3326, 3186, 3032,
1
8
702, 1636, 1613, 1570, 1535, 1492, 1433, 1363, 1300, 1244, 1199, 1178, 1136, 1109, 1070, 967,
1
53, 768, 730, 696, 685, 674, 634, 546, 442; H-NMR (DMSO-d ),
δ: 12.17 (br s, 1H), 10.18
6
(
s, 1H), 10.08 (s, 1H), 7.94 (d, J = 8.9 Hz, 1H), 7.51 (s, 1H) 7.48–7.47 (m, 1H), 7.45–7.43 (m, 2H),
7
6
.42–7.39 (m, 2H), 7.37–7.34 (m, 1H) 7.25–7.23 (m, 2H), 7,03 (dd, J = 8.9 Hz, J = 2.1 Hz, 1H),
.95 (d, J = 7.6 Hz, 1H), 5.17 (s, 2H), 2.31 (s, 3H); 13C-NMR (DMSO-d6)
δ
: 166.49, 160.00,
1
53.13, 144.14, 138.06, 137.95, 136.33, 129.69, 128.56, 128.49, 128.24, 128.17, 124.79, 121.56,
+
1
18.21, 110.90, 109.01, 105.37, 66.07, 21.14; HR-MS: C H O N [M + H] calcd 377.1496
22
21
4
2
m/z, found 377.1491 m/z.

Int. J. Mol. Sci. 2021, 22, 3444
15 of 27
(
6) Benzyl{4-[(2-fluorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (6)
◦
−1
Yield: 68%; Mp 173–176 C; HPLC purity: 98.48%; IR (ATR, cm ): 3319, 2981, 2970,
883, 1699, 1645, 1618, 1591, 1566, 1535, 1487, 1458, 1435, 1361, 1338, 1287, 1277, 1244, 1198,
2
1
4
185, 1153, 1136, 1089, 1064, 1032, 969, 926, 839, 785, 790, 744, 735, 695, 674, 616, 599, 570, 531,
1
63, 432; H-NMR (DMSO-d ),
δ: 12.00 (s, 1H), 10.54 (d, J = 1.4 Hz, 1H), 10.11 (s, 1H), 8.19
6
(
td, J = 7.9 Hz, J = 1.4 Hz, 1H), 7.92 (d, J = 8.2 Hz, 1H), 7.45–7.43 (m, 2H), 7.42–7.39 (m, 2H)
,
7
.37–7.35 (m, 2H), 7.31 (ddd, J = 11.2 Hz, J = 8.1 Hz, J = 1.4 Hz, 1H), 7.21 (td, J = 7.6 Hz,
1
3
J = 1.4 Hz, 1H), 7.17–7.15 (m, 1H), 7.02 (dd, J = 8.9 Hz, J = 2.1 Hz, 1H), 5.18 (s, 2H); C-NMR
DMSO-d6) : 164.49, 158.08, 153.29 (d, J = 244.2 Hz), 153.12, 144.14, 136.33, 130.94, 128.48,
28.22, 128.16, 126.29 (d, J = 10.1 Hz), 124.97 (d, J = 7.2 Hz), 124.64 (d, J = 2.9 Hz), 123.25,
(
δ
1
1
3
+
15.25 (d, J = 18.8 Hz), 111.70, 109.63, 105.19, 66.05; HR-MS: C H O N F [M + H] calcd
81.1245 m/z, found 381.1243 m/z.
21
18
4
2
(
7) Benzyl{4-[(3-fluorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (7)
◦
−1
Yield: 63%; Mp 195–196 C; HPLC purity: 98.52%; IR (ATR, cm ): 3428, 3326, 3038,
732, 1618, 1604, 1534, 1492, 1437, 1386, 1329, 1302, 1279, 1253, 1201, 1177, 1154, 1109, 1082,
1
1
1
061, 1029, 991, 980, 948, 889, 858, 839, 773, 762, 733, 695, 677, 635, 597, 547, 520, 466; H
NMR (DMSO-d6),
dt, J = 11.6 Hz, J =2.2 Hz, 1H), 7.45–7.34 (m, 7H), 7.26 (d, J = 2.0 Hz, 1H), 7.03 (dd, J = 8.7 Hz
J = 2.1 Hz, 1H), 6.97–6.93 (m, 1H), 5.17 (s, 2H), 13C NMR (DMSO-d6),
: 166.46, 162.09
d, J = 241.3 Hz), 159.63, 153.13, 144.29, 140.07 (d, J = 10.1 Hz), 136.33, 130.33 (d, J = 8.7 Hz),
δ: 11.92 (s, 1H), 10.36 (s, 1H), 10.09 (s, 1H), 7.89 (d, J = 8.8 Hz, 1H), 7.69
(
,
δ
(
1
29.98, 128.49, 128.25, 128.18, 116.49 (d, J = 2.3 Hz), 111.16, 110.39 (d, J = 20.2 Hz), 109.14,
+
1
07.56 (d, J = 26.0 Hz), 105.35, 66.09; HR-MS: C H O N F [M + H] calcd 381.1245 m/z,
21
18
4
2
found 381.1242 m/z.
(
8) Benzyl{4-[(4-fluorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (8)
◦
−1
Yield: 59%; Mp 170–173 C; HPLC purity: 98.49%; IR (ATR, cm ): 3410, 3326, 2988,
2
900, 1722, 1613, 1601, 1531, 1508, 1455, 1434, 1406, 1393, 1322,1278, 1258, 1195, 1157, 1093,
1
1
080, 1054, 1028, 976, 892, 866, 826, 775, 762, 732, 694, 667, 635, 587, 511, 466, 444; H-NMR
(
DMSO-d6),
J = 8.2 Hz, J = 4.8 Hz, 2H), 7.45–7.35 (m, 5H), 7.22–7.19 (m, 3H), 7.03 (d, J = 8.2 Hz, 1H),
.17 (s, 2H); 13C-NMR (DMSO-d6)
: 166.66, 160.12, 158.61 (d, J = 241.3 Hz), 153.13, 144.20,
δ: 12.12 (s, 1H), 10.27 (s, 1H), 10.08 (s, 1H), 7.91 (d, J = 8.9 Hz, 1H), 7.69 (dd,
5
δ
1
36.33, 134.45 (d, J = 2.9 Hz), 129.58, 128.51, 128.25, 128.18, 123.07 (d, J = 8.7 Hz), 115.33 (d,
+
J = 23.1 Hz), 110.69, 108.99, 105.37, 66.08; HR-MS: C H O N F [M + H] calcd 381.1245
21
18
4
2
m/z, found 381.1242 m/z.
(
9) Benzyl{4-[(2-chlorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (9)
◦
−1
Yield: 52%; Mp 156–158 C; HPLC purity: 98.79%; IR (ATR, cm ): 3419, 2981, 2970,
883, 1749, 1620, 1596, 1585, 1532, 1458, 1440, 1417, 1382, 1366, 1301, 1278, 1263, 1196, 1158,
2
1
5
141, 1127, 1109, 1068, 1049, 1036, 980, 950, 865, 849, 834, 766, 751, 736, 693, 681, 658, 600, 570,
1
48, 548, 843, 468, 462; H-NMR (DMSO-d ),
δ
: 11.98 (s, 1H), 10.76 (s, 1H), 10.11 (s, 1H), 8.39
6
(
dd, J = 8.2 Hz, J = 1.4 Hz, 1H), 7.94 (d, J = 8.9 Hz, 1H), 7.54 (dd, J = 8.2 Hz, J = 1.4 Hz, 1H),
7
.45–7.44 (m, 2H), 7.42–7.35 (m, 5H), 7.15 (td, J = 7.7 Hz, J = 1.7 Hz, 1H), 7.03 (dd, J = 8.9 Hz
,
13
J = 2.1 Hz, 1H), 5.17 (s, 2H); C-NMR (DMSO-d6)
δ: 163.92, 157.54, 153.12, 144.10, 136.34,
1
35.45, 131.37, 129.31, 128.48, 128.21, 128.18, 127.79, 124.99, 123.44, 122.86, 112.18, 109.76,
+
105.15, 66.04; HR-MS: C H O N Cl [M + H] calcd 397.0950 m/z, found 397.0949 m/z.
21
18
4
2
(
10) Benzyl{4-[(3-chlorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (10)
◦
−1
Yield: 64%; Mp 231–233 C; HPLC purity: 98.85%; IR (ATR, cm ): 3403, 2981, 2883,
739, 1611, 1599, 1545, 1522, 1456, 1428, 1382, 1306, 1246, 1214, 1202, 1142, 1111, 1074,
1
1
1
053, 964, 912, 888, 849, 794, 778, 767, 748, 696, 679, 600, 578, 565, 541, 491, 462; H-NMR
(
DMSO-d6),
δ
: 11.92 (s, 1H), 10.33 (s, 1H), 10.10 (s, 1H), 7.90 (s, 1H) 7.89 (d, J = 8.9 Hz, 1H),
.59 (d, J = 8.2 Hz, 1H), 7.45–7.34 (m, 6H), 7.26 (br.s, 1H), 7.18 (d, J = 7.6 Hz, 1H), 7.03 (d,
: 166.47, 159.64, 153.11, 144,28, 139.77,
7
J = 8.2 Hz, 1H), 5.17 (s, 2H); 13C-NMR (DMSO-d6)
δ

Int. J. Mol. Sci. 2021, 22, 3444
16 of 27
1
1
36.31, 133.00, 130.38, 129.93, 128.49, 128.24, 128.17, 123.64, 120.25, 119.18, 111.08, 109.11,
+
05.31, 66.08; HR-MS: C H O N Cl [M + H] calcd 397.0949 m/z, found 397.0948 m/z.
21
18
4
2
(
11) Benzyl{4-[(4-chlorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (11)
◦
−1
Yield: 54%; Mp 209–212 C; HPLC purity: 98.87%; IR (ATR, cm ): 3430, 3409, 3315,
988, 2900, 1743, 1725, 1618, 1572, 1525, 1489, 1461, 1453, 1434, 1420, 1398, 1368, 1311, 1276,
2
1
6
205, 1090, 1057, 1028, 1010, 985, 963, 905, 892, 871, 831, 825, 819, 762, 734, 706, 694, 683,
1
57, 574, 543, 513, 506, 443; H-NMR (DMSO-d ),
δ: 12.0 (s, 1H), 10.31 (s, 1H), 10.09 (s, 1H),
6
7
.89 (d, J = 8.9 Hz, 1H), 7.73–7.71 (m, 2H), 7.45–7.39 (m, 6H), 7.37–7.34 (m, 1H), 7.24 (d,
J = 2.1 Hz, 1H), 7.03 (dd, J = 8.9 Hz, J = 2.1 Hz, 1H), 5.17 (s, 2H); 13C-NMR (DMSO-d6)
δ:
1
66.48, 159.80, 153.11, 144.22, 137.19, 136.31, 129.79, 128.62, 128.49, 128.23, 128.17, 127.65,
+
1
12.47, 110.96, 109.04, 105.32, 66.07; HR-MS: C H O N Cl [M + H] calcd 397.0949 m/z,
21 18 4 2
found 397.0952 m/z.
(
12) Benzyl(3-hydroxy-4-{[2-(trifluoromethyl)phenyl]carbamoyl}phenyl)carbamate (12)
◦
−1
Yield: 65%; Mp 173–175 C; HPLC purity: 98.74%; IR (ATR, cm ): 3367, 2981, 2883,
1
9
716, 1643, 1616, 1594, 1530, 1506, 1458, 1434, 1386, 1309, 1260, 1218, 1180, 1170, 1102, 1057,
1
61, 870, 819, 783, 765, 731, 693, 658, 634, 574, 545, 462; H-NMR (DMSO-d ),
δ
: 11.99 (s, 1H),
6
1
0.64 (s, 1H), 10.11 (s, 1H), 8.19 (d, J = 8.2 Hz, 1H), 7.92 (d, J = 8.9 Hz, 1H), 7.75 (d, J = 7.6 Hz,
H), 7.70 (t, J = 7.9 Hz, 1H), 7.45–7.44 (m, 2H), 7.42–7.34 (m, 5H), 7.03 (dd, J = 8.9 Hz,
1
J = 1.4 Hz, 1H), 5.18 (s, 2H); 13C-NMR (DMSO-d6)
δ
: 164.51, 157.92, 153.12, 144.24, 136.33,
35.63, 133.21, 131.27, 128.48, 128.22, 128.16, 126.10, 126.21–126.13 (m), 124.97, 123.91 (q,
J = 273.1 Hz), 120.74 (q, J = 28.9 Hz), 111.66, 109.69, 105.12, 66.06; HR-MS: C H O N F
1
22
18
4
2 3
+
[
M + H] calcd 431.1213 m/z, found 431.1205 m/z.
(
13) Benzyl(3-hydroxy-4-{[3-(trifluoromethyl)phenyl]carbamoyl}phenyl)carbamate (13)
◦
−1
Yield: 53%; Mp 213–216 C; HPLC purity: 98.84%; IR (ATR, cm ): 3440, 3320, 1739,
613, 1584, 1571, 1524, 1452, 1414, 1343, 1325, 1268, 1250, 1238, 1214, 1202, 1185, 1176, 1165,
1
1
4
111, 1069, 1054, 964, 913, 897, 870, 846, 795, 768, 747, 695, 659, 601, 574, 565, 542, 524, 491,
1
61; H-NMR (DMSO-d ),
δ: 11.90 (s, 1H), 10.47 (s, 1H), 10.11 (s, 1H), 8.19 (s, 1H), 7.94–7.90
6
(
(
m, 2H), 7.60 (t, J = 7.9 Hz, 1H), 7.47–7.44 (m, 3H), 7.42–7.39 (m, 2H), 7.37–7.34 (m, 1H), 7.27
d, J = 2.1 Hz, 1H), 7.05 (dd, J = 8.6 Hz, J = 1.7 Hz, 1H), 5.18 (s, 2H); C-NMR (DMSO-d6) δ:
13
1
1
1
4
66.71, 159.73, 153.12, 144.37, 139.13, 136.32, 129.94, 129.92, 129.44 (q, J = 31.8 Hz), 128.49,
28.25, 128.17, 124.38, 124.12 (q, J = 273.1 Hz), 120.24 (q, J = 4.3 Hz), 116.90 (q, J = 4.3 Hz),
+
11.04, 109.13, 105.34, 66.09. HR-MS: C H O N F [M + H] calcd 431.1213 m/z, found
22
18
4
2 3
31.1206 m/z.
(
14) Benzyl(3-hydroxy-4-{[4-(trifluoromethyl)phenyl]carbamoyl}phenyl)carbamate (14)
◦
−1
Yield: 61%; Mp 233–235 C; HPLC purity: 98.81%; IR (ATR, cm ): 3354, 2981, 2970,
2
888, 1701, 1637, 1620, 1604, 1589, 1556, 1530, 1501, 1461, 1434, 1411, 1327, 1282, 1237, 1180,
1
1156, 1113, 1100, 1067, 1014, 964, 855, 841, 823, 770, 761, 695, 678, 665, 622, 587, 510; H-NMR
(
1
(
DMSO-d6),
δ
: 11.88 (s, 1H), 10.50 (s, 1H), 10.11 (s, 1H), 7.93–7.92 (m, 2H), 7.91 (d, J = 8.9 Hz,
H), 7.72 (d, J = 8.9 Hz, 1H), 7.45–7.44 (m, 2H), 7.42–7.39 (m, 2H), 7.37–7.34 (m, 2H), 7.28
: 166.46,
s, 1H), 7.04 (dd, J = 8.9 Hz, J = 2.1 Hz, 1H), 5.18 (s, 2H); 13C-NMR (DMSO-d6)
δ
1
59.45, 153.11, 144.35, 142.01, 136.30, 130.17, 128.48, 128.24, 128.17, 126.16 (q, J = 271.7 Hz),
1
25.99 (q, J = 4.3 Hz), 123.79 (q, J = 31.8 Hz), 120.54, 111.28, 109.19, 105.29, 66.08; HR-MS:
+
C H O N F [M + H] calcd 431.1213 m/z, found 431.1207 m/z.
22
18
4
2 3
(
15) Benzyl(3-hydroxy-4-{[2-(trifluoromethoxy)phenyl]carbamoyl}phenyl)carbamate (15)
◦
−1
Yield: 49%; Mp 185–187 C; HPLC purity: 98.27%; IR (cm ): 3524, 3290, 3032, 1707,
1
8
607, 1540, 1521, 1455, 1436, 1414, 1327, 1305, 1240, 1203, 1178, 1166, 1153, 1093, 1057, 964, 927,
1
58, 753, 734, 692, 630, 579, 551, 461; H-NMR (DMSO-d ),
δ: 11.98 (s, 1H), 10.84 (s, 1H), 10.12
6
(s, 1H), 8.49 (dd, J = 8.2 Hz, J = 1.4 Hz, 1H), 7.93 (d, J = 8.2 Hz, 1H), 7.45–7.39 (m, 7H), 7.37–7.34
(m, 1H), 7.21 (td, J = 7.7 Hz, J = 1.7 Hz, 1H), 7.04 (dd, J = 8.2 Hz, J = 2.1 Hz, 1H), 5.18 (s, 2H);

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13C-NMR (DMSO-d6)
δ: 163.55, 157.27, 153.14, 144.13, 137.99, 136.33, 131.65, 131.52, 128.49,
1
28.25, 128.17, 128.12, 124.9, 122.20, 121.30, 120.26 (q, J = 257.2 Hz), 112.31, 109.85, 105.17, 66.07;
+
HR-MS: C H O N F [M + H] calcd 447.1162 m/z, found 447.1153 m/z.
22
18
5
2 3
(
16) Benzyl{4-[(2,3-difluorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (16)
◦
−1
Yield: 52%; Mp 208–210 C; HPLC purity: 98.46%; IR (ATR, cm ): 3446, 3356, 3029,
748, 1637, 1618, 1575, 1530, 1476, 1456, 1432, 1405, 1310, 1268, 1219, 1206, 1176, 1149, 1104,
096, 1063, 1048, 998, 983, 964, 882, 851, 834, 758, 730, 692, 684, 662, 639, 562, 531, 472, 462;
1
1
1
H-NMR (DMSO-d ) δ: 12.01 (s, 1H), 10.64 (s, 1H), 10.13 (s, 1H), 8.01–7.99 (m, 1H), 7.92 (d,
6
J = 8.2 Hz, 1H), 7.45–7.44 (m, 2H), 7.42–7.39 (m, 2H), 7.37–7.34 (m, 2H), 7.24–7.18 (m, 2H),
7
.03 (dd, J = 8.2 Hz, J = 2.1 Hz, 1H), 5.18 (s, 2H); 13C-NMR (DMSO-d6)
δ: 164.50, 158.09,
1
1
53.12, 149.79 (dd, J = 244.2 Hz, J = 10.1 Hz), 144.33, 141.7 (dd, J = 245.7 Hz, J = 14.5 Hz),
36.31, 131.06, 128.48, 128.23, 128.16, 124.57–124.48 (m), 118.58 (d, J = 2.9 Hz), 112.26 (d,
+
J = 15.9 Hz), 111.50, 109.70, 105.18, 66.07; HR-MS: C H O N F [M + H] calcd 399.1151
21
17
4
2 2
m/z, found 399.1145 m/z.
(
17) Benzyl{4-[(2,4-difluorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (17)
◦
−1
Yield: 61%; Mp 218–219 C; HPLC purity: 98.33%; IR (ATR, cm ): 3325, 3257, 1700,
651, 1619, 1592, 1573, 1537, 1497, 1455, 1436, 1362, 1346, 1298, 1277, 1244, 1209, 1187, 1142,
1
1
6
105, 1086, 1066, 1030, 993, 974, 962, 906, 887, 842, 825, 814, 788, 769, 759, 733, 695, 673,
1
58, 615, 599, 575, 549, 504, 473, 463, 454; H-NMR (DMSO-d ),
δ
: 12.02 (s, 1H), 10.45 (s,
6
1
(
H), 10.1 (s, 1H), 8.09 (td, J = 9.1 Hz, J = 6.5 Hz, 1H), 7.91 (d, J = 8.9 Hz, 1H), 7.46–7.44
m, 2H), 7.42–7.34 (m, 4H), 7.33 (d, J = 1.4 Hz, 1H), 7.14–7.10 (m, 1H), 7.03 (dd, J = 8.9 Hz,
J = 2.1 Hz, 1H), 5.17 (s, 2H); 13C-NMR (DMSO-d6),
: 165.00, 158.54, 158.45 (dd, J = 242.8 Hz
J = 13.0 Hz), 153.82 (dd, J = 247.1 Hz, J = 13.0 Hz), 153.14, 144.25, 136.33, 130.69, 128.50,
28.25, 128.18, 124.99 (dd, J = 10.1 Hz, J = 2.9 Hz), 122.73 (dd, J = 11.6 Hz, J = 4.3 Hz), 111.37
δ
,
1
(
d, J = 2.9 Hz), 111.23, 109.55, 105.22, 104.11 (dd, J = 27.5 Hz, J = 24.6 Hz), 66.09; HR-MS:
+
C H O N F [M + H] calcd 399.1151 m/z, found 399.1146 m/z.
21
17
4
2 2
(
18) Benzyl{4-[(2,5-difluorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (18)
◦
−1
Yield: 65%; Mp 215–216 C; HPLC purity: 98.32%; IR (ATR, cm ): 3326, 2968, 2880,
360, 1702, 1643, 1614, 1572, 1540, 1505, 1483, 1436, 1369, 1314, 1287, 1247, 1195, 1170, 1115,
2
1
4
067, 1030, 1000, 988, 967, 909, 884, 839, 824, 787, 762, 737, 696, 667, 630, 620, 591, 553, 513,
1
64; H-NMR (DMSO-d ),
δ: 11.73 (s, 1H), 10.44 (s, 1H), 10.10 (s, 1H), 7.85 (d, J = 8.2 Hz,
6
1
(
H), 7.49 (dd, J = 9.6 Hz, J = 2.7 Hz, 2H), 7.46–7.44 (m, 2H), 7.42–7.39 (m, 2H), 7.37–7.39
m, 1H), 7.28 (d, J = 2.1 Hz, 1H), 7.03 (dd, J = 8.9 Hz, J = 2.1 Hz, 1H), 6.97 (tt, J = 9.3 Hz,
J = 2.4 Hz, 1H), 5.18 (s, 2H); 13C-NMR (DMSO-d6),
: 166.34, 162.41 (d, J = 244.2 Hz),
62.31 (d, J = 242.8 Hz), 159.23, 153.12, 144.36, 140.98 (t, J = 13.0 Hz), 136.31, 130.18, 128.49,
δ
1
1
28.24, 128.18, 111.40, 109.24, 105.29, 103.39 (m, 2C), 98.89 (t, J = 26,0 Hz), 66.09; HR-MS:
+
C H O N F [M + H] calcd 399.1151 m/z, found 399.1149 m/z.
21
17
4
2 2
(
19) Benzyl{4-[(2,6-difluorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (19)
◦
−1
Yield: 69%; Mp 175–178 C; HPLC purity: 98.48%; IR (ATR, cm ): 3405, 3326, 3035,
736, 1632, 1615, 1591, 1511, 1497, 1470, 1419, 1372, 1309, 1294, 1270, 1242, 1215, 1198, 1111,
1
1
4
058, 1012, 990, 963, 915, 848, 826, 776, 760, 741, 721, 700, 650, 628, 558, 528, 509, 483, 455,
1
44; H-NMR (DMSO-d ),
δ
: 12.09 (s, 1H), 10.14 (s, 1H), 10.12 (s, 1H), 7.95 (d, J = 8.9 Hz,
6
1
3
1
H), 7.46–7.34 (m, 6H), 7.27–7.21 (m, 3H), 7.06 (d, J = 8.2 Hz, 1H), 5.19 (s, 2H); C-NMR
(
DMSO-d6), : 167.20, 160.56, 158.11 (dd, J = 249 Hz, J = 5.8 Hz), 153.16, 144.73, 136.31,
δ
1
1
3
29.75, 128.52, 128.30, 128.20, 114.05 (t, J = 17.3 Hz), 111.95 (dd, J = 20.2 Hz, J = 4.3 Hz),
+
09.43, 109.22, 105.35, 66.17; HR-MS: C H O N F [M + H] calcd 399.1151 m/z, found
21
17
4
2 2
99.1144 m/z.

Int. J. Mol. Sci. 2021, 22, 3444
18 of 27
(
20) Benzyl{4-[(3,5-difluorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (20)
◦
−1
Yield: 57%; Mp 224–225 C; HPLC purity: 98.51%; IR (ATR, cm ): 3327, 2968, 1708,
1
9
643, 1614, 1573, 1540, 1505, 1483, 1437, 1369, 1314, 1287, 1247, 1195, 1169, 1115, 1067, 1000,
88, 963, 884, 839, 824, 768, 761, 737, 696, 666, 630, 620, 591, 553, 513, 465; H-NMR (DMSO-
1
d6),
.49 (dd, J = 9.8 Hz, J = 2.2 Hz, 1H), 7.28 (d, J = 1.9 Hz, 1H), 7.03 (dd, J = 8.7 Hz, J = 2.1 Hz
H), 6.96 (tt, J = 9.3 Hz, J = 2.4 Hz, 1H), 5.17 (s, 2H); 13C-NMR (DMSO-d6),
: 166.34, 162.35
dd, J = 242.8 Hz, J = 15.3 Hz), 159.23, 153.10, 144.35, 140.97 (t, J = 13.8 Hz), 136.31, 130.16,
δ: 11.73 (s, 1H), 10.44 (s, 1H), 10.10 (s, 1H), 7.84 (d, J = 8.7 Hz, 1H), 7.53–7.31 (m, 6H),
7
,
1
δ
(
1
28.48, 128.22, 128.16, 111.38, 109.23, 105.30, 103.58–103.20 (m), 98.88 (t, J = 26.3 Hz), 66.09;
+
HR-MS: C H O N F [M + H] calcd 399.1151 m/z, found 399.1147 m/z.
21
17
4
2 2
(
21) Benzyl{4-[(2,3-dichlorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (21)
◦
−1
Yield: 48%; Mp 219–222 C; HPLC purity: 98.53%; IR (ATR, cm ): 3412, 3260, 3065,
029, 1744, 1619, 1585, 1577, 1534, 1519, 1453, 1414, 1367, 1329, 1298, 1204, 1191, 1140, 1114,
3
1
5
080, 1061, 1045, 1030, 980, 962, 886, 849,829, 778, 762, 722, 703, 694, 684, 590, 565, 555,
1
54, 549, 500; H-NMR (DMSO-d ),
δ
: 12.05 (s, 1H), 10.90 (s, 1H), 10.12 (s, 1H), 8.41 (dd,
6
J = 7.6 Hz, J = 2.7 Hz, 1H), 7.93 (d, J = 8.9 Hz, 1H), 7.46–7.43 (m, 2H), 7.42–7.38 (m, 5H),
13
7
.37–7.34 (m, 1H), 7.04 (dd, J = 8.2 Hz, J = 2.1 Hz, 1H), 5.18 (s, 2H); C-NMR (DMSO-d6), δ:
1
1
4
63.90, 157.47, 153.11, 144.27, 137.46, 136.32, 131.73, 131.52, 128.48, 128.43, 128.20, 128.15,
+
25.17, 121.69, 121.07, 112.08, 109.86, 105.14, 66.06; HR-MS: C H O N Cl [M + H] calcd
21
17
4
2
2
31.0560 m/z, found 431.0558 m/z.
(
22) Benzyl{4-[(2,5-dichlorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (22)
◦
−1
Yield: 62%; Mp 173–175 C; HPLC purity: 98.57%; IR (ATR, cm ): 3259, 3112, 3034,
1
9
713, 1699, 1636, 1619, 1577, 1532, 1435, 1405, 1378, 13001, 1276, 1231, 1196, 1140, 1088, 1058,
1
87, 916, 865, 795, 773, 741, 695, 684, 585, 535, 457, 442; H-NMR (DMSO-d ),
δ: 12.04 (s,
6
1
H), 10.93 (s, 1H), 10.14 (s, 1H), 8.61 (d, J = 2.7 Hz, 1H), 7.93 (d, J = 8.2 Hz, 1H), 7.58 (d,
J = 8.2 Hz, 1H), 7.45–7.39 (m, 5H), 7.37–7.34 (m, 1H), 7.21 (dd, J = 8.6 Hz, J = 2.4 Hz, 1H),
.03 (dd, J = 8.9 Hz, J = 2.1 Hz, 1H), 5.17 (s, 2H); 13C-NMR (DMSO-d6)
: 163.68, 157.21,
7
δ
1
53.12, 144.35, 136.75, 136.32, 132.03, 131.71, 130.57, 128.49, 128.21, 128.16, 124.14, 121.22,
+
1
21.19, 112.16, 109.97, 105.09, 66.07; HR-MS: C H O N Cl [M + H] calcd 431.0560 m/z,
21
17
4
2
2
found 431.0559 m/z.
(
23) Benzyl{4-[(2,6-dichlorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (23)
◦
−1
Yield: 58%; Mp 186–189 C; HPLC purity: 98.89%; IR (ATR, cm ): 3306, 3083, 3031,
718, 1660, 1584, 1522, 1444, 1408, 1375, 1353, 1304, 1277, 1248, 1224, 1203, 1162, 1104,
1
1
1
053, 963, 928, 839, 801, 765, 727, 689, 667, 621, 615, 597, 574, 542, 512, 495, 453; H-NMR
(DMSO-d6),
δ: 11.73 (s, 1H), 10.40 (s, 1H), 10.11 (s, 1H), 7.84 (d, J = 8.9 Hz, 1H), 7.83 (m,
2
H), 7.45–7.44 (m, 2H), 7.42–7.39 (m, 2H), 7.37–7.34 (m, 1H), 7.33 (t, J = 2.1 Hz, 1H), 7.28 (d,
J = 2.1 Hz, 1H), 7.03 (dd, J = 8.2 Hz, J = 2.1 Hz, 1H), 5.17 (s, 2H); 13C-NMR (DMSO-d6)
δ:
1
66.42, 159.34, 153.11, 144.40, 140.80, 136.30, 134.00, 130.15, 128.50, 128.25, 128.18, 123.00,
+
1
18.74, 111.28, 109.22, 105.27, 66.10; HR-MS: C H O N Cl [M + H] calcd 431.0560 m/z,
21 17 4 2 2
found 431.0561 m/z.
(
24) Benzyl{4-[(3,4-dichlorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (24)
◦
−1
Yield: 54%; Mp 255–257 C; HPLC purity: 98.47%; IR (ATR, cm ): 3407, 3308, 2961,
739, 1615, 1594, 1520, 1476, 1448, 1414, 1380, 1303, 1277, 1251, 1214, 1199, 1136, 1127, 1104,
053, 1025, 973, 963, 875, 849, 813, 794, 767, 747, 715, 697, 688, 600, 580, 567, 493, 460, 439;
1
1
1
H-NMR (DMSO-d6),
.86 (d, J = 8.2 Hz, 1H); 7.66 (dd, J = 8.9 Hz; J = 2.7 Hz, 1H); 7.61 (d, J = 8.9 Hz, 1H); 7.45–7.44
m, 2H); 7.41–7.39 (m, 2H); 7.37–7.34 (m, 1H); 7.26 (d, J = 1.4 Hz, 1H); 7.03 (dd, J = 8.9 Hz,
J = 2.1 Hz, 1H); 5.17 (s, 2H); 13C-NMR (DMSO-d6)
: 166.42, 159.50, 153.10, 144.35, 138.49,
36.30, 130.92, 130.58, 130.00, 128.49, 128.24, 128.17, 125.36, 121.92, 120.74, 111.12, 109.16,
δ: 11.83 (s, 1H); 10.39 (s, 1H); 10.10 (s, 1H); 8.10 (d, J = 2.7 Hz, 1H);
7
(
δ
1
1
+
05.29, 66.08; HR-MS: C H O N Cl [M + H] calcd 431.0560 m/z, found 431.0558 m/z.
21
17
4
2
2

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(
25) Benzyl{4-[(3,5-dichlorophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (25)
◦
−1
Yield: 66%; Mp 183–185 C; HPLC purity: 98.59%; IR (ATR, cm ): 3406, 3331, 2980,
970, 2883, 1708, 1671, 1648, 1615, 1582, 1532, 1446, 1432, 1532, 1446, 1432, 1415, 1312, 1284,
2
1
5
241, 1193, 1152, 1132, 1112, 1068, 1029, 962, 932, 861, 832, 796, 765, 737, 694, 665, 643, 610,
1
66, 545, 494; H-NMR (DMSO-d ),
δ: 11.73 (s, 1H), 10.40 (s, 1H), 10.11 (s, 1H), 7.84 (d,
6
J = 8.9 Hz, 1H), 7.83 (d, J = 2.1 Hz, 2H), 7.45–7.44 (m, 2H), 7.42–7.39 (m, 2H), 7.37–7.34 (m,
H), 7.33 (t, J = 2.1 Hz, 1H), 7.28 (d, J = 2.1 Hz, 1H), 7.03 (dd, J = 8.2 Hz, J = 2.1 Hz, 1H), 5.17
s, 2H); 13C-NMR (DMSO-d6),
: 166.41, 159.34, 153.10, 144.40, 140.80, 136.29, 133.99, 130.14,
28.48, 128.24, 128.17, 122.99, 118.72, 111.27, 109.21, 105.27, 66.09; HR-MS: C H O N Cl
1
(
δ
1
21
17
4
2
2
+
[
M + H] calcd 431.0560 m/z, found 431.0564 m/z.
(
26) Benzyl{4-[(2,4-dibromophenyl)carbamoyl]-3-hydroxyphenyl}carbamate (26)
◦
−1
Yield: 52%; Mp 199–200 C; HPLC purity: 98.73%; IR (ATR, cm ): 3415, 3090, 1741,
693, 1630 1618, 1591, 1576, 1521, 1457, 1434, 1417,01, 1373, 1309, 1294, 1238, 1210, 1200, 1150,
1
1
1
132, 1107, 1081, 1058, 1035, 959, 861, 832, 764, 737, 696, 677, 567, 549, 503, 462; H-NMR
(DMSO-d6),
δ: 12.0 (s, 1H), 10.70 (s, 1H), 10.10 (s, 1H), 8.32 (d, J = 8.8 Hz, 1H), 7.94 (d,
J = 2.1 Hz, 1H), 7.92 (dd, J = 8.9 Hz, J = 2.1 Hz, 1H), 7.61 (dd, J = 8.9 Hz, J = 2.1 Hz, 1H),
7
5
.45–7.43 (m, 2H), 7.41–7.39 (m, 3H), 7.36–7.34 (m, 1H), 7.03 (dd, J = 8.9 Hz, J = 2.1 Hz, 1H),
.17 (s, 2H); 13C-NMR (DMSO-d6),
: 163.92, 157.54, 153.11, 144.24, 136.34,136.32, 134.33,
δ
1
31.45, 131.17, 128.48, 128.20, 128.15, 124.48, 116.00, 114.98, 112.00, 109.61, 105.15, 66.05;
+
HR-MS: C21 H O N Br [M + H] calcd 518.9549 m/z, found 518.9548 m/z.
17
4
2
2
(
27) Benzyl{4-[(3,5-di(trifluoromethyl))carbamoyl]-3-hydroxyphenyl}carbamate (27)
◦
−1
Yield: 53%; Mp 189–191 C; HPLC purity: 98.91%; IR (ATR, cm ): 3327, 2981, 2970,
2
883, 1752, 1719, 1643, 1615, 1588, 1535, 1473, 1446, 1431, 1381, 1276, 1224, 1166, 1124,
1
1
166, 1124, 1094, 1058, 996, 937, 884, 866, 840, 761, 734, 670, 683, 613, 605, 541; H-NMR
(
7
7
DMSO-d6),
δ
: 11.67 (s, 1H), 10.68 (s, 1H), 10.13 (s, 1H), 8.45 (s, 2H), 7.87 (d, J = 8.2 Hz, 1H),
.81 (s, 1H), 7.45–7.44 (m, 2H), 7.42–7.40 (m, 2H), 7.37–7.35 (m, 1H), 7.30 (d, J = 1.4 Hz, 1H),
.04 (dd, J = 8.2 Hz, J = 2.1 Hz, 1H), 5.18 (s, 2H); 13C-NMR (DMSO-d6)
: 166.75, 159.43,
δ
153.11, 144.54, 140.42, 136.29, 130.64 (q, J = 33.2 Hz), 130.19, 128.49, 128.25, 128.18, 133.26,
(
q, J = 273.1 Hz), 120.43 (q, J = 4.3 Hz), 116.55 (m), 111.17, 109.24, 105.26, 66.11; HR-MS:
+
C H O N F [M + H] calcd 499.1087 m/z, found 499.1082 m/z.
23
17
4
2 6
(
28) Benzyl{3-hydroxy-4-[(2,4,6-trifluorophenyl)carbamoyl]phenyl}carbamate (28)
◦
−1
Yield: 68%; Mp 203–206 C; HPLC purity: 98.17%; IR (ATR, cm ): 3443, 3363, 1739,
651, 1606, 1508, 1466, 1448, 1416, 1378, 1363, 1336, 1310, 1252, 1211, 1188, 1172, 1117, 1085,
1
1
5
052, 1039, 1030, 997, 979, 900, 853, 833, 825, 801, 756, 736, 711, 699, 691, 658, 636, 629, 608,
1
44, 512, 497; H-NMR (DMSO-d ),
δ: 11.97 (s, 1H), 10.13 (s, 1H), 10.03 (s, 1H), 7.90 (d,
6
J = 8.9 Hz, 1H), 7.45–7.43 (m, 2H), 7.42–7.39 (m, 2H), 7.37–7.33 (m, 3H), 7.25 (d, J = 2.1 Hz
,
1
1
1
1
4
H), 7.03 (dd, J = 8.9 Hz, J = 2.1 Hz, 1H), 5.18 (s, 2H); 13C-NMR (DMSO-d6),
δ: 167.10,
60.33, 160.29 (dt, J = 245.7 Hz, J = 14.5 Hz), 158.23 (ddd, J = 250 Hz, J = 15.9 Hz, J = 7.2 Hz),
53.11, 144.70, 136.29, 129.80, 128.50, 128.27, 128.18, 111.1 (td, J = 17.3 Hz, J = 4.3 Hz), 109.41,
+
09.0, 105.26, 100.98 (m), 66.13; HR-MS: C H O N F [M + H] calcd 417.1057 m/z, found
21
16
4
2 3
17.1050 m/z.
(
29) Benzyl{3-hydroxy-4-[(3,4,5-trifluorophenyl)carbamoyl]phenyl}carbamate (29)
◦
−1
Yield: 80%; Mp 206–207 C; HPLC purity: 98.22%; IR (ATR, cm ): 3429, 3334, 3040,
1
8
726, 1616, 1543, 1527, 1438, 1393, 1313, 1285, 1237, 1208, 1182, 1135, 1044, 991, 964, 894,
1
73, 850, 834, 796, 763, 741, 697, 663, 620, 566, 513, 466, H-NMR (DMSO-d ),
δ: 11.72 (s,
6
1
7
1
H), 10.40 (s, 1H), 10.10 (s, 1H), 7.83 (d, J = 8.9 Hz, 1H), 7.73–7.75 (m, 2H), 7.46–7.43 (m, 2H),
.42–7.39 (m, 2H), 7.36–7.34 (m, 1H), 7.28 (d, J = 2.1 Hz, 1H), 7.03 (dd, J = 8.2 Hz, J = 2.1 Hz
H), 5.18 (s, 2H); 13C-NMR (DMSO-d ), δ: 166.30, 159.24, 153.11, 149.97 (ddd, J = 244.2 Hz,
,
6
J = 10.1 Hz, J = 5.8 Hz), 144.38, 136.31, 135.01 (dt, J = 245.7 Hz, J = 15.9 Hz), 134.76 (td,
J = 11.6 Hz, J = 4.3 Hz), 130.10, 128.49, 128.23, 128.17, 111.24, 109.23, 105.27, 104.98 (dd,

Int. J. Mol. Sci. 2021, 22, 3444
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+
J = 20.2 Hz, J = 5.8 Hz), 66.09; HR-MS: C H O N F [M + H] calcd 417.1057 m/z, found
21
16
4
2 3
417.1054 m/z.
(
30) Benzyl{4-[(2,4,5-trichlorophenyl)carbamoyl]phenyl}carbamate (30)
◦
−1
Yield: 62%; Mp: 222–225 C; HPLC purity: 98.85%; IR (ATR, cm ): 3268, 3119, 1710,
696, 1619, 1594, 1574, 1519, 1453, 1434, 1360, 1300, 1275, 1235, 1196, 1138, 1071, 1052, 1028,
1
9
67, 914, 890, 876, 867, 852, 823, 791, 776, 741, 717, 697, 682, 649, 597, 581, 561, 543, 517, 497;
1
H-NMR (DMSO-d6),
δ: 12.06 (s, 1H), 10.95 (s, 1H), 10.13 (s, 1H), 8.79 (s, 1H), 7.92–7.90 (m,
2
H), 7.45–7.38 (m, 5H), 7.36–7.33 (m, 1H), 7.03 (dd, J = 8.9 Hz, J = 2.1 Hz, 1H), 5.17 (s, 2H);
13
C-NMR (DMSO-d6),
28.20, 128.15, 125.37, 122.18, 121.89, 112.03, 110.02, 105.06, 66.08; HR-MS: C H O N Cl
3
δ: 163.62, 157.18, 153.10, 144.43, 136.33, 135.67, 131.71, 130.24, 128.47,
1
21
16
4
2
+
[
M + H] calcd 465.0170 m/z, found 465.0172 m/z.
(
31) Benzyl{3-hydroxy-4-[(2,4,6-trichlorophenyl)carbamoyl]phenyl}carbamate (31)
◦
−1
Yield: 54%; Mp 254–256 C; HPLC purity: 98.81%; IR (ATR, cm ): 3311, 3266, 1711,
1
8
643, 1599, 1507, 1463, 1414, 1372, 1312, 1235, 1188, 1098, 1072, 1055, 967, 915, 853, 818,
1
03, 771, 755, 739, 693, 654, 561, 530, 458; H-NMR (DMSO-d ),
δ
: 12.05 (s, 1H), 10.33 (s,
6
1H), 10.13 (s, 1H), 7.94 (d, J = 8.9 Hz, 1H), 7.82 (s, 2H), 7.46–7.44 (m, 2H), 7.42–7.39 (m, 2H),
7
.37–7.34 (1H), 7.25 (d, J = 1.4 Hz, 1H), 7.04 (dd, J = 8.6 Hz, J = 1.7 Hz, 1H), 5.18 (s, 2H);
13
C-NMR (DMSO-d6),
δ
: 166.98, 160.63, 153.12, 144.78, 136.29, 134.78, 132.75, 132.09, 129.70,
+
128.50 128.33, 128.28 128.19, 109.29, 109.21, 105.35, 66.15; HR-MS: C H O N Cl [M + H]
21 16 4 2 3
calcd 465.0170 m/z, found 465.0170 m/z.
(
32) Benzyl{3-hydroxy-4-[(2,4,6-tribromophenyl)carbamoyl]phenyl}carbamate (32)
◦
−1
Yield: 60%; Mp 244–245 C; HPLC purity: 98.78%; IR (ATR, cm ): 3272, 1711, 1643,
1
5
600, 1540, 1506, 1456, 1413, 1370, 1311, 1234, 1189,49, 1054, 846, 770, 739, 693, 650, 570,
1
48, 522, 456; H-NMR (DMSO-d ),
δ
: 12.12 (s, 1H), 10.36 (s, 1H), 10.11 (s, 1H), 8.07 (s, 2H),
6
7
.94 (d, J = 8.9 Hz, 1H), 7.46–7.44 (m, 2H), 7.42–7.39 (m, 2H), 4.37–7.34 (m, 1H), 7.23 (d,
J = 2.1 Hz, 1H), 7.04 (dd, J = 8.6 Hz, J = 1.7 Hz, 1H), 5.18 (s, 2H); 13C-NMR (DMSO-d6),
δ:
1
67.04, 160.92, 153.09, 144.76, 136.27, 135.14, 134.33, 129.45, 128.49, 128.28, 128.18, 125.37,
+
1
21.37, 109.12, 109.10, 105.34, 66.14; HR-MS: C H O N Br [M + H] calcd 596.8654 m/z,
21 16 4 2 3
found 596.8652 m/z.
3.3. Determination of Lipophilicity by HPLC
The HPLC separation system Agilent 1200 series equipped with a DAD SL (Agilent
®
Technologies) was used. A chromatographic column Symmetry C18 5
µ
m, 4.6 × 250 mm
,
Part No. W21751W016 (Waters Corp., Milford, MA, USA) was applied. The HPLC sepa-
ration process was monitored by the ChemStation for LC 3D chromatography software
(
Agilent Technologies). Isocratic elution by a mixture of MeOH p.a. (72%) and H O-HPLC
2
Mili-Q grade (28%) as a mobile phase was used for the determination of the capacity factor
k. The total flow of the column was 1.0 mL/min, injection 20 L, column temperature
0 C, and sample temperature 10 C. The detection wavelength of 210 nm was chosen.
A KI methanolic solution was used for the determination of the dead times (t ). Retention
times (t ) were measured in minutes. The capacity factors k were calculated according to
the formula k = (t )/t , where t is the retention time of the solute and t is the dead
µ
◦
◦
4
D
R
R
−
t
D
D
R
D
time obtained using an unretained analyte. Each experiment was repeated three times. The
log k values of individual compounds are shown in Table 1.
3.4. Evaluating In Vitro AChE and BChE-Inhibition Potencies
The ability of all the prepared compounds to inhibit AChE from electric eel (Electropho-
rus electricus) and BChE from equine serum (both purchased from Sigma, St. Louis, MO,
USA) was determined in vitro using a modified Ellman’s method, as described in detail
elsewhere [47]. This is a simple, rapid, and direct method to determine the SH and -S-S-
group content in proteins. The enzyme activity is measured indirectly by quantifying the

Int. J. Mol. Sci. 2021, 22, 3444
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concentration of the 5-thio-2-nitrobenzoic acid ion formed in the reaction between the thiol
reagent 5,5’-dithiobis-2nitrobenzoic acid (DTNB) and thiocholine, which is a product of
acetylthiocholine hydrolysis catalyzed by cholinesterases [48
tested inhibitors is expressed as the IC50 values.
–50]. The effectiveness of the
The enzyme activity in the final reaction mixture (2 mL) was 0.2 U/mL, the con-
centration of the substrate (acetylthiocholine or butyrythiocholine) was 40 M, and the
µ
concentration of DTNB was 0.1 mM for all reactions. All reactions were carried out at
◦
2
5 C in the presence of phosphate-buffered saline (0.1 M, pH 7.4) in a glass cuvette with
a 1 cm optical path. All the studied inhibitors were dissolved in DMSO (concentration
.01 M) and then diluted in demineralized water as necessary. For all tested compounds
0
and standards, rivastigmine and galanthamine in at least four different concentrations of
inhibitor in the final reaction mixture were used. All measurements were carried out in
triplicate, and the average values of the reaction rate (v —reaction in the absence of the
0
inhibitor, v —reaction in the presence of the inhibitor) were used for construction of the
i
dependence v /v vs. concentration of the inhibitor. The IC values were calculated from
0
i
50
an obtained equation of the regression curve for y = 2 (resulting from the definition of
IC ) [14,15,25]. The results are presented in Table 1.
50
3.5. In Vitro Viability Assay
Human monocytic leukemia THP-1 cells were used for in vitro antiproliferative as-
says. Cells were obtained from the European Collection of Cell Cultures (ECACC, Salis-
bury, UK) and routinely cultured in Roswell Park Memorial Institute RPMI 1640 medium
supplemented with 10% fetal bovine serum (FBS), 2% L-glutamine, 1% penicillin and
◦
streptomycin (all from Sigma-Aldrich) at 37 C with 5% CO . Cells were passaged twice a
2
week. The effect of the compounds on cell viability was determined using a Cell Count-
ing Kit-8 (CCK-8, 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-
2
H-tetrazolium, monosodium salt, Sigma) according to the manufacturer’s instructions.
The compounds were dissolved in DMSO and added to the cell suspension in the RPMI
5
1
(
3
640 medium without FBS (5
×
10 cells/mL). The maximum concentration of DMSO
Sigma) in the assays never exceeded 0.1% (v/v). Subsequently, the cells were incubated at
7 C with 5% CO for 24 h. After this incubation period, the relative cell viability (the ratio
◦
2
between cells treated with compounds and cells treated only with DMSO) was determined
by CCK-8 kit.
3.6. Model Building and Molecular Modeling
CACTVS/csed and CORINA editors were applied to generate molecule structural
model with its 3D geometry. The data format conversion was performed using an Open-
Babel (inter)change file format converter. The Sybyl-X 2.0/Certara package installed on a
DELL workstation with Ubuntu 20.10 operating system was engaged in order to conduct
the molecular modeling simulations. The MAXMIN2 module implemented in Sybyl-X was
used to initially optimize the compound spatial geometry with the standard Tripos force
field (POWELL conjugate gradient algorithm) with a 0.01 kcal/mol energy gradient conver-
gence criterion. The electrostatic potential values were calculated using Gasteiger–Hückel
method. One 18-ordered atom trial alignment was produced on the most active compound
2
(active analogue approach) with an FIT procedure to cover the entire bonding topology in
the maximal common structure (MCS). SONNIA software was employed to simulate 10
×
10 to 30
×
30 self-organizing maps (SOMs) with a winning distance in the range from 0.2 to
2.0 in CoMSA analysis. Spatial coordinates of the molecular surfaces and the corresponding
potential values were used as input to Kohonen SOM network to generate a 2D map of the
electrostatic potential (MEP) for the set of superimposed molecules. The produced maps
were reshaped into a 100- to 900-element vector subjected to the PLS method implemented
in the MATLAB environment.

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3.7. Principal Component and Hierarchical Custering Analysis
The human-friendly 2D/3D illustrations of the compound distribution in the experimental-
based (FCS) and virtual-derived (VCS) molecular space might be displayed by Principal Com-
ponent Analysis (PCA). PCA is a linear projection method that can be engaged to model
multidimensional data with a relatively small number of so-called principal components
(scores and loadings) produced to maximize the description of variance within the input data.
The PCA model with f principal components for a data matrix X can be calculated as follows
in Equation (1):
T
X = TP + E
(1)
where X is a data matrix with m objects and n variables, T is the score matrix with dimen-
T
sions (m
×
f), P is a transposed matrix of loadings with dimensions (f
×
n), and E is a
matrix of the residual variance (m
On the whole, the first few principal components (PCs) frequently describe sufficiently
×
n) not explained by the first f principal components.
data variance and reveal the groups of objects.
Hierarchical Clustering Analysis (HCA) enables us to examine the (dis)similarities
between objects in the variable space. Hence, the similarity measure as well as the way
of resulting sub-clusters linkage should be specified a priori. The produced findings
are illustrated as a dendogram, where the OX axis presents the indices of the clustered
objects, while the OY one corresponds to the linkage distances between two objects linked,
respectively. Moreover, the visualization method can be applied to the empirical data
sorted according to the order of objects with the generation of the color-coded variable
maps. A mutual interpretation of objects sorted with the Ward linkage method and the
color-coded variable maps facilitate the (dis)similarity assessment of objects in terms of the
input parameters.
3.8. Theoretical Lipophilicity Evaluation
A number of freely/commercially available in silico estimators can be employed to
calculate the theoretical partition coefficients (clogP) as follows:
AlogPS—procedure proposed by Tetko et al. based on atom-type electrotopological-
state (E-state) indices and neural networks (NN);
milogP—method invented by Molinspiration for practical logP calculations of almost
all organic molecules as a sum of fragment-based contributions and correction factors;
ClogP—fragment-based procedure for predicting lipophilicity based on structure-
dependent correction values retrieved from Hansch and Leo’s database that is implemented
in Sybyl/Centara software;
HyperChem logP—an atom-additive method that approaches lipophilicity using the
individual atomic contribution based on procedure proposed by Ghose, Prichett, and Crippen;
MarvinSketch logP—the overall lipophilicity of a molecule is composed of the con-
tributing values of its atom types that were redefined to accommodate electron delocaliza-
tion and contributions of ionic forms;
ChemSketch logP—a comprehensive fragment-based algorithm with high-quality
models retrieved using the empirical data. Well-characterized logP contributions have
been compiled for atoms, structural fragments, and intramolecular interactions provided
3
for more than 12 × 10 experimental logP values;
Dragon AlogP—the statistical estimators of the Ghose-Crippen-Viswanadhan model
were calculated on the basis of known experimental logP for the training set of 8364
compounds. The overall estimation of the lipophilic atomic-based constant is evaluated
with the contribution of 115 atom types;
Dragon MlogP—the theoretical partition coefficient includes VdW volume and Moriguchi
polar parameters as correction factors. A regression MlogP model is based on 13 structural
parameters, which were evaluated on the training group of 1230 organic molecules;
Kowwin—estimates the log octanol–water partition coefficient of chemicals using the
atom/fragment contribution approach;

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XlogP3—an atom-additive procedure with well-defined correction factors that em-
ploys an optimized atom typing approach calibrated on a big training set;
OSIRIS clogP—in-house approach based on the cumulative sum of atom contributions
calculated for more than 5000 compounds with experimentally determined logP values as
training set. Predicting engine distinguishes 369 atom types;
ChemBio clogP—the algorithms for prediction of partition coefficient based on a
training set of compound provides coverage for a broad chemical space;
3
Percepta clogP—based on >12
×
10 of experimental logP values with the algorithm
that uses the principal of isolating carbons.
The redundant descriptors of QSAR/QSPR investigations were selected and erad-
icated using the modified version the uninformative variable elimination (UVE–PLS)
method, namely iterative variable elimination (IVE–PLS). Briefly, the entire algorithm is
composed of the following stages: (1) standard PLS analysis with leave-one-out crossvali-
dation LOO–CV to evaluate the performance of the PLS model; (2) elimination of the matrix
column with the lowest abs(mean(b)/std(b)) value; (3) standard PLS analysis of the new
matrix without the column eliminated in (2); (3) iterative repetition of (
1)–(
3) to maximize
2
q
value.
cv
3
.9. Similarity-Based Activity Landscape Index
The numerical profiling of similarity-related structure–activity landscape index (SALI)
can be quantitatively expressed according to the following Equation (2)
ꢀ
ꢀ
ꢀ
Ax − Ayꢀ
SALIx,y =
(2)
1
− sim(x, y)
where Ax and Ay are the activity profiles for the x-th and y-th molecule and sim(x,y) is the
pair-wise similarity measure. The Tanimoto coefficient was engaged for the fingerprint-
based similarity estimation, where the structural pair-wise molecular relatedness is calcu-
lated as follows; in Equation (3):
nxy
T(x, y) = ꢁ
ꢂ
(3)
nx + ny − nxy
where nxy is the number of bits set into 1 shared in the fingerprint of the molecule x and y,
nx is the number of bits set into 1 in the molecule x, ny is the number of bits set into 1 in the
molecule y, respectively.
3.10. Ligand/Structure-Based Analysis
The selection-driven surface analysis of a structural space can be performed using
the set of descriptors that grab information about the spatial pharmacophoric pattern.
In practice, some machine learning methods conjugated with weighting and selecting
procedures were engaged to indicate the minimal ensemble of pharmacophoric features
that are potentially important in description of guest-host interactions. The comparative
molecular surface analysis (CoMSA) was applied in order to meaningfully compare the
shape and charges variations generated on the molecular surface of the receptor and ligand,
respectively. Briefly speaking, a single layer of neurons is arranged in a 2D plane with well-
defined topology to produce self-organized maps (SOMs). The spatially closed objects are
located in the proximal neurons of the square map in the process of SOMs adaptation to the
input data. In consequence, a 2D image of the property space is produced, where structurally
related molecules are placed in neighboring neurons. The electrostatic/steric features
that are potentially important in the ligand–receptor recognition and complementarity
phenomena can be specified using the iterative variable selection approaches. The backward
column eradication was recurrently repeated until the optimal number of variables included
2
within the model was accomplished—the moment that the
q
deterioration specified the
cv

Int. J. Mol. Sci. 2021, 22, 3444
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set of potentially relevant columns. The cumulative sum of the common columns for all of
the investigated BChE models was calculated and normalized to a range of (0,1).
The crystallographic structure of butyrylcholinesterase determined using X-ray diffrac-
tion at 2.6 Å resolution co-crystalized with a rivastigmine analogue (liganded state) was
downloaded from the PDB repository (PDB code: 6eyf). Apart from the RIV-based analogue
([3-[(1~{R})-1-(dimethylamino)ethyl]-4-oxidanyl-phenyl]~{N}-ethyl- ~{N}-methylcarbamate),
all remaining heteroatoms (including water molecules) were eradicated prior to docking
in the AutoDock Vina programe. Initially, the ligand/enzyme structures were prepared
in the pdbqt file format with the calculated Gasteiger charges. The grid box was centered
on the central atom of the RIV analogue (BY2). In AutoDock Vina, docking simulations in
different poses (default nine) were generated progressively from a single conformer (an
energy-minimized molecule). Then, the resulting molecular conformations and orientations
with the preferred torsion angles and the rotatable bonds were evaluated by the united-
atom (UA) scoring function. VMD, PyMol, Schrödinger Maestro graphical viewers, and
Protein–Ligand Interaction Profiler (PLIP) were employed to illustrate the foreseen 2D/3D
binding modes, respectively.
Sybyl-X 2.0/Certara modeling software was used to conduct the dynamic simulations
of the ligand–enzyme systems. The host–guest structures were analyzed and fixed by the
Prepare Protein Structure module. The Berendsen algorithm was used in MD simulations
on the NVT ensemble in order to control temperature. The initial atom velocities were
assigned at 293K; however, the system was then heated and the corresponding trajectories
were determined at the temperature of 300K. The total length of MDs was arranged to
2
0 ns with 10 fs time step, respectively. The RedMDStream 1.0 program with coarse-
grained molecular dynamics models was engaged for the macromolecular simulations in
the same conditions.
4. Conclusions
In summary:
1.
A library of novel 4-{[(benzyloxy)carbonyl]amino}-2-hydroxybenzoic acid amides
was designed and synthesized as potential acetyl- and butyrylcholinesterase in-
hibitors; hence, the in vitro inhibitory profile and selectivity index were determined.
Benzyl(3-hydroxy-4-{[2-(trifluoromethoxy)phenyl]carbamoyl}phenyl)carbamate (15
showed the best AChE inhibition (IC50 = 36.05 M) and benzyl{3-hydroxy-4-[(2-
methoxyphenyl)carbamoyl]phenyl}carbamate ( ) demonstrated the best BChE inhibit-
)
µ
2
ing activity (IC50 = 22.23 µM) with the highest selectivity for BChE (SI = 2.26).
The distance-oriented property evaluation was conducted using the Principal Com-
ponent Analysis (PCA) and Hierarchical Clustering Analysis (HCA), respectively.
2
.
.
The dissimilarities of molecules 12
–14, 27 with trifluoromethyl substituent(s) (-CF3)
and compound 15 with trifluromethoxy group (-OCF ) from the remaining ones was
3
observed. Noticeably, objects 2–4 that are methoxy-based (-OCH ) positional isomers
3
and molecule 5 with methyl substituent (-CH ) were also clustered together.
3
3
The distribution of the Tanimoto coefficients was analyzed for the triangular T3
2×32
matrix. Interestingly, the potent molecules
T = 0.93). In fact, molecule 15 is characterized by low values of SALI indexes
with the remaining compounds in the dataset, while the most potent molecule is
accompanied by gray pots of the inactive molecules (positional isomers), and
(unsubstituted analogue), respectively. Noticeably, three pairs of fairy similar
fluorine/bromine-containing compounds (17 vs. 19 and 28 26 vs. 32) can potentially
form the activity cliff that is manifested formally by high SALI numerical values.
The preferential selection of the active molecules ( and ) and inactive ones (
, and 26) that are basically ortho- and meta-substituted analogues resulted in the
2 and 15 are marked as fairly similar
(
2
3, 4
1
,
4
5
.
.
2
6
3, 13,
23
generation of the robust models with the acceptable predictive power for the test set.
The molecular docking approach was engaged for the most potent AChE/BChE
inhibitors in order to get comprehensive knowledge of the binding mode. The hy-

Int. J. Mol. Sci. 2021, 22, 3444
25 of 27
drophobic interactions were overwhelmingly generated with Gln119, Asp70, Pro285,
Thr120, and Trp82 aminoacid residues, while the HB-donor ones were dominated
with Thr120. π-stacking interactions were specified with Trp82 aminoacid residue
of chain A as well. 6. The stability of some fused ligand–enzyme systems were eval-
uated using the molecular dynamic simulations. The trifluoromethoxy substituent
of molecule 15 is characterized by lower spatial flexibility compared to methoxy
group of compound 2. Obviously, –OCF is considerably heavier and can generate
3
greater steric hindrance in comparison to –OCH , as the most electronegative element
3
fluorine can interact with the surrounding aminoacid residues, which stiffens the
ligand–enzyme system.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/
1
0.3390/ijms22073444/s1, Table S1: Theoretically estimated partition coefficient calculated by set
of alternative methods for ring-substituted benzyl[4-(arylcarbamoyl)phenyl-3-hydroxy]carbamates
32. Table S2: Predicted values—physicochemical properties of rivastigmine, galanthamine and
1
–
compound 2.
Author Contributions: J.K. and D.P. synthesized and characterized the compounds. M.O. and T.J.
performed analytical measurement. S.S. and J.H. performed biological screening. J.K. and J.J. designed
the compounds. A.B. and V.K. and A.S. performed theoretical lipophilicity calculations, CoMSA, PCA,
IVE-PLS, molecular docking, molecular dynamic simulations. J.K., A.B. and J.J. wrote the paper. All
authors have read and agreed to the published version of the manuscript.
Funding: This study was supported by the Slovak Research and Development Agency (projects APVV-
1
7-0373 and APVV-17-0318). The HPLC/HRMS system forms a part of the National Infrastructure
CzeCOS ProCES CZ.02.1.01/0.0/0.0/16_013/0001609; M.O. was supported by SustES (CZ.02.1.01/0.0/
.0/16_019/0000797). The work of J.H. was also supported by the Ministry of Education, Youth and
0
Sports of the Czech Republic under the project “FIT” CZ.02.1.01/0.0/0.0/15_003/0000495, and Czech
Ministry of Agriculture grant no. RO0518. The authors wish to acknowledge financial support from the
University of Pardubice, Faculty of Chemical Technology.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: The authors thank Johann Gasteiger for facilitating access to the SONNIA
programs and Schrödinger company for the Maestro academic license.
Conflicts of Interest: The authors declare no conflict of interest.
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