Novel benzene-based carbamates for ache/bche inhibition: Synthesis and ligand/structure-oriented sar study

Article abstract of DOI:10.3390/ijms20071524

A series of new benzene-based derivatives was designed, synthesized and comprehensively characterized. All of the tested compounds were evaluated for their in vitro ability to potentially inhibit the acetyl-and butyrylcholinesterase enzymes. The selectivity index of individual molecules to cholinesterases was also determined. Generally, the inhibitory potency was stronger against butyryl-compared to acetylcholinesterase; however, some of the compounds showed a promising inhibition of both enzymes. In fact, two compounds (23, benzyl ethyl(1-oxo-1-phenylpropan-2-yl)carbamate and 28, benzyl (1-(3-chlorophenyl)-1-oxopropan-2-yl) (methyl)carbamate) had a very high selectivity index, while the second one (28) reached the lowest inhibitory concentration IC₅₀ value, which corresponds quite well with galanthamine. Moreover, comparative receptor-independent and receptor-dependent structure–activity studies were conducted to explain the observed variations in inhibiting the potential of the investigated carbamate series. The principal objective of the ligand-based study was to comparatively analyze the molecular surface to gain insight into the electronic and/or steric factors that govern the ability to inhibit enzyme activities. The spatial distribution of potentially important steric and electrostatic factors was determined using the probability-guided pharmacophore mapping procedure, which is based on the iterative variable elimination method. Additionally, planar and spatial maps of the host–target interactions were created for all of the active compounds and compared with the drug molecules using the docking methodology.

Full text of DOI:10.3390/ijms20071524

International Journal of
Molecular Sciences
Article
Novel Benzene-Based Carbamates for AChE/BChE
Inhibition: Synthesis and Ligand/Structure-Oriented
SAR Study
Andrzej Bak 1,
* ,
, Violetta Kozik 1, Dariusz Kozakiewicz 1, Kamila Gajcy 1, Daniel Jan Strub 2
Aleksandra Swietlicka 1, Sarka Stepankova 3 , Ales Imramovsky 4 , Jaroslaw Polanski 1,
Adam Smolinski 5 and Josef Jampilek 6,7
1
Institute of Chemistry, University of Silesia, Szkolna 9, 40 007 Katowice, Poland;
violetta.kozik@us.edu.pl (V.K.); dkozakiewicz@us.edu.pl (D.K.); kamila.gajcy@us.edu.pl (K.G.);
aswietlicka@us.edu.pl (A.S.); jaroslaw.polanski@us.edu.pl (J.P.)
Department of Bioorganic Chemistry, Faculty of Chemistry, Wrocław University of Science and Technology,
Wyb. Wyspian´skiego 27, 50-370 Wrocław, Poland; daniel.strub@pwr.edu.pl
2
3
4
5
6
Department of Biological and Biochemical Sciences, Faculty of Chemical Technology,
University of Pardubice, Studentska 573, 532 10 Pardubice, Czech Republic; sarka.stepankova@upce.cz
Institute of Organic Chemistry and Technology, Faculty of Chemical Technology, University of Pardubice,
Studentska 573, 532 10 Pardubice, Czech Republic; ales.imramovsky@upce.cz
Department of Energy Saving and Air Protection, Central Mining Institute, Plac Gwarkow 1,
40 166 Katowice, Poland; smolin@gig.katowice.pl
Division of Biologically Active Complexes and Molecular Magnets, Regional Centre of Advanced
Technologies and Materials, Faculty of Science, Palacky University, Slechtitelu 27, 78371 Olomouc,
Czech Republic; josef.jampilek@gmail.com
7
Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, 84510, Bratislava, Slovakia
Correspondence: andrzej.bak@us.edu.pl; Tel.: +48-032-359-1397
*
ꢀꢁꢂꢀꢃꢄꢅꢆꢇ
ꢀꢁꢂꢃꢄꢅꢆ
Received: 1 March 2019; Accepted: 23 March 2019; Published: 27 March 2019
Abstract: A series of new benzene-based derivatives was designed, synthesized and comprehensively
characterized. All of the tested compounds were evaluated for their in vitro ability to potentially
inhibit the acetyl- and butyrylcholinesterase enzymes. The selectivity index of individual molecules to
cholinesterases was also determined. Generally, the inhibitory potency was stronger against butyryl-
compared to acetylcholinesterase; however, some of the compounds showed a promising inhibition of
both enzymes. In fact, two compounds (23, benzyl ethyl(1-oxo-1-phenylpropan-2-yl)carbamate and 28,
benzyl (1-(3-chlorophenyl)-1-oxopropan-2-yl) (methyl)carbamate) had a very high selectivity index,
while the second one (28) reached the lowest inhibitory concentration IC50 value, which corresponds
quite well with galanthamine. Moreover, comparative receptor-independent and receptor-dependent
structure–activity studies were conducted to explain the observed variations in inhibiting the potential
of the investigated carbamate series. The principal objective of the ligand-based study was to
comparatively analyze the molecular surface to gain insight into the electronic and/or steric factors
that govern the ability to inhibit enzyme activities. The spatial distribution of potentially important
steric and electrostatic factors was determined using the probability-guided pharmacophore mapping
procedure, which is based on the iterative variable elimination method. Additionally, planar and
spatial maps of the host–target interactions were created for all of the active compounds and compared
with the drug molecules using the docking methodology.
Keywords: benzene-based carbamates; in vitro cholinesterase inhibition; CoMSA; IVE-PLS;
molecular docking study
Int. J. Mol. Sci. 2019, 20, 1524; doi:10.3390/ijms20071524
www.mdpi.com/journal/ijms

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1. Introduction
Several clinically implemented drug and pesticide molecules contain amide (–CONH–) and/or
carbamate (–OCONH–) groups that can be variously substituted to form privileged structural
fragments [1–4]. These molecular motifs are able to interact with a wide range of receptors/enzymes
to induce a biological response [
compounds is being observed among medicinal chemists [8,9
5
–
7
]. As a result, a growing interest in amide- and carbamate-based
]. Based on the cholinergic hypothesis
that assumes the increase in the acetylcholine (ACh) level, carbamate-like drugs can be regarded
as one of the mainstays for the contemporary pharmacotherapy for Alzheimer’s disease (AD) by
inhibiting cholinesterases (ChEs) [10]. AD is a progressive central nervous system (CNS) disorder that
irreversibly degenerates the memory and cognitive abilities (dementia), which causes the everyday
activities of older adults to be severely limited. Even though the etiology of AD has not yet been
comprehensively revealed, a few factors are postulated as playing valid roles in the pathogenesis of
this disease, including the aggregation and accumulation of amyloid-β peptide deposits in the brain,
oxidative stress and a low level of ACh [11]. Basically, two types of the ChE enzymes that belong
to the group of serine hydrolases occur in the human nervous system: acetylcholinesterase (AChE)
and butyrylcholinesterase (BChE) [12]. In fact, both enzymes are able to catalyze ACh hydrolysis in
the cholinergic synapses, but BChE can hydrolyze other esters as well; therefore, its concentration in
the brain seems to be important for those suffering from AD. It is reported that AChE has a higher
hydrolytic capacity, but its levels decrease as the changes become more pronounced during the course
of the disease, while the BChE levels are supposedly increased or unchanged, which can somewhat
compensate for the higher AChE activity [13]. Inhibiting both enzymes produces an increment of the
ACh concentration in the cholinergic synapses that show a symptomatic efficacy in AD treatment;
therefore, competitive cholinesterase inhibitors (ChEIs), e.g., galanthamine or rivastigmine as well as
non-competitive ChEIs, e.g., tacrine or donepezil are clinically applied to alleviate the neuromuscular
symptoms of AD [14]. In fact, novel BChE selective inhibitors such as anti-Alzheimer agents have
recently been proposed, but the potential effectiveness that they offer is frequently accompanied by
the occurrence of central and peripheral side effects [15–18]. In this context, further research for new
ChEIs seems to be required to achieve success in the treatment of AD.
Potency modeling of prospective drug molecules seems to be useful as an initial screening test
(rank order) and a decisive factor in avoiding “chasing” false positives and/or eradicating “bad actors”
at the early stages of drug design or development [19,20]. The art of specifying molecules that have a
potential therapeutic value at the “pre-synthesis” stage can be assisted by computer-aided molecular
design (CAMD) techniques for the comprehensive mapping of the topology and/or topography of
a compound into the property-based chemical space. A key question that confronts computational
chemists is how to quantitatively transform a myriad of feature-based descriptors that have been a
priori calculated for the compounds into an ADMET-related molecular potency. From among the more
optimistic approaches, modeling the multidimensional quantitative structure–activity relationship
(mD–QSAR) techniques seem to be pragmatic provided that a congeneric series of compounds is
produced—the analogy is crucial in chemistry, especially in cases in which the investigated process is
not fully understood or when the variety of observed guest–host interactions makes the optimization of
the pharmacological response a cost/resources/knowledge challenging issue [21–23]. The “Holy Grail”
of in silico modeling is to produce statistically robust models capable of making accurate quantitative
predictions that include the binding affinity, metabolic fate and pharmacokinetic or toxicity parameters
(ADMET) on the route “towards the prediction paradise” [24]. Hence, huge attempts have been
undertaken to implement “direct” receptor-dependent (RD) and “indirect” receptor-independent (RI)
in silico protocols, respectively. The working paradigm of fragment-based design, or fragonomics,
assumes that well-placed fragments account for the crucial ligand–receptor interactions, while the
rest of the molecule primarily serves as a scaffold that holds the “active” fragments together [25].
With regard to the structure of a ligand as a “negative” image of an active target site (pharmacophore),
the question about a reliable quantitative measure of its “intrinsic” activity and its correspondence to

Int. J. Mol. Sci. 2019, 20, 1524
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robust empirical receptor–ligand interaction data naturally appears. In the target-guided QSAR
procedures. the complementary (bio)effector-binding mode is retrieved based on the intrinsic
dependence of the atomic coordinates of both the receptor and ligand in the binding/active site
while the target spatial arrangement of the atoms is also available [26]. The adopted spatial distribution
of the ligand property space is mediated by the corresponding mapping of the target steric, electronic
or lipophilic patterns. In fact, drugs are generally engineered to have a tightly fitting interface with the
receptor/enzyme bonding/active site; however, the “goodness” of a favorable guest–host binding
assessment is still questionable. In other words, the Achilles’ heel of docking procedures is the
deficiency of “truly” selective scoring functions to rank the receptor–ligand complexes according to
their actual binding affinities [27,28]. To overcome these constraints, it is advisable to use both ligand-
and structure-based approaches on the path from the relationships to the models.
The current paper can be regarded as a follow-up of the recently reported carbamates as
prospective ChEIs [29–31]; therefore, the synthesis and biological assessment of a new series of benzene
and naphthalene derivatives is provided. Moreover, the electronic/steric/lipophilic determinants,
which are potentially valid for characterizing the structure-inhibitory potency for a set of the carbamate
analogs, are discussed using RI and RD in silico procedures: comparative molecular surface analysis
(CoMSA) and molecular docking [32]. Additionally, a systematic space inspection was performed
to investigate the statistical estimators using the stochastic model validation (SMV) method to
derive the “average” pharmacophore pattern in the probability-guided pharmacophore approach [33].
The molecular pharmacokinetic profiles were comprehensively screened using several descriptors that
revealed the physicochemical and structural features, which might be essential to map the inhibition
potency of carbamates.
2. Results and Discussion
2.1. Design and Synthesis
The concept of the synthesis of the designed compounds was based on a modification of the
structure of the selected α-aminoketones. Cathinone is biologically active natural product and its
derivatives have broad pharmacological properties. All carbamates were formed in a four-step
synthesis that begins with the basic reagents (see Schemes 1 and 2).
In the first step, the corresponding aromatic ketone was obtained in the Friedel–Crafts reaction.
Next, the bromination of the obtained aromatic ketone with elemental bromine led to a 2-bromo
derivative, which was subjected to the ammonolysis of the obtained halide without secretion. In the
final step, the amine in a free form reacted with the appropriate chloroformate to produce desired
carbamate. The final products were purified using column chromatography with a mixture of ethyl
acetate and hexane, respectively. The structures of the obtained compounds are presented in Table 1.
All of the derivatives were analyzed in order to confirm their chemical structures and were also tested
for their biological activity.
Scheme 1. Synthesis of benzene derivatives
CH2Cl2; (c) HCl; and (d) CH2Cl2.
1–34. Reagents and conditions: (a) AlCl3, CH2Cl2; (b) Br2,
Scheme 2. Synthesis of the naphthalene derivatives 35–41. Reagents and conditions: (a) AlCl3, CH2Cl2;
(b) Br2, CH2Cl2; (c) HCl; and (d) CH2Cl2.

Int. J. Mol. Sci. 2019, 20, 1524
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)
Table 1. Structures of the carbamate derivatives 1–41; in vitro AChE and BChE inhibition (IC50
compared with the standards rivastigmine (RIV) and galanthamine (GLT). ChE inhibitions are
expressed as the mean ± SD (n = three experiments).
Benzene derivatives
AChE
IC50 [µM]
BChE
IC50 [µM]
No.
X
R1
R2
R3
SI *
methyl carbamates
1.
2.
3.
4.
5.
6.
7.
8.
9.
H
H
H
CH3
C2H5
C2H5
CH3
CH3
CH3
CH3
CH3
CH3
CH3
C2H5
CH3
C2H5
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
245.33 ± 0.75
328.86 ± 9.72
273.74 ± 2.16
133.98 ± 2.30
329.68 ± 42.06
146.91 ± 1.51
50.63 ± 0.63
308.80 ± 6.16
282.74 ± 0.71
225.46 ± 2.72
227.91 ± 0.90
440.56 ± 7.54
362.87 ± 7.41
115.89 ± 2.16
113.61 ± 0.29
186.60 ± 1.55
180.77 ± 11.99
131.57 ± 4.55
224.08 ± 2.19
194.93 ± 1.93
1.08
0.75
0.75
1.16
2.90
0.79
0.28
2.35
1.26
1.16
3-CH3
3-Cl
4-OCH3
4-CH3
4-Cl
4-Br
3,4-OCH2O
10.
phenyl carbamates
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
H
H
H
H
3-CH3
3-Cl
4-OCH3
4-CH3
4-Cl
4-Br
3,4-CH3
3,4-OCH2O
CH3
C2H5
C2H5
C3H7
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
C2H5
Ph
Ph
Ph
Ph
Ph
Ph
Ph
Ph
Ph
Ph
Ph
Ph
166.72 ± 1.75
27.64 ± 0.55
52.96 ± 1.06
15.62 ± 0.06
16.55 ± 0.13
48.29 ± 0.31
14.81 ± 0.01
105.73 ± 1.94
87.63 ± 2.37
38.96 ± 0.28
44.05 ± 1.71
26.41 ± 0.05
53.56 ± 0.72
6.03
3.38
6.13
5.92
3.43
7.35
0.85
1.58
1.14
1.17
1.82
1.57
CH3
C2H5
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
179.02 ± 6.21
95.69 ± 5.37
97.97 ± 0.36
165.62 ± 2.75
108.81 ± 1.10
90.21 ± 0.73
138.64 ± 1.45
44.24 ± 2.35
51.46 ± 1.38
47.95 ± 2.09
84.00 ± 0.43
benzyl carbamates
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
H
H
H
H
3-CH3
3-Cl
4-OCH3
4-CH3
4-Cl
4-Br
3,4-CH3
3,4-OCH2O
CH3
C2H5
C2H5
C3H7
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
C2H5
Bn
Bn
Bn
Bn
Bn
Bn
Bn
Bn
Bn
Bn
Bn
Bn
158.41 ± 0.87
11.37 ± 0.05
21.38 ± 0.26
21.29 ± 0.48
16.30 ± 0.17
12.50 ± 0.24
5.51 ± 0.20
57.39 ± 0.02
26.43 ± 0.40
7.02 ± 0.15
11.28 ± 0.15
16.87 ± 0.22
51.89 ± 1.18
13.93
4.87
5.61
8.26
6.67
15.31
1.09
1.52
7.53
2.84
4.55
1.46
CH3
C2H5
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
104.15 ± 2.91
119.47 ± 1.23
134.65 ± 0.70
83.32 ± 2.49
84.37 ± 2.27
62.57 ± 0.65
40.06 ± 0.01
52.83 ± 0.34
32.01 ± 0.54
76.78 ± 2.76
75.57 ± 1.25
Naphthalene derivatives
methyl carbamates
35.
H
CH3
CH3
CH3
126.82 ± 1.74
150.36 ± 7.13
0.84
phenyl carbamates
36.
37.
38.
H
Cl
Br
CH3
CH3
CH3
CH3
CH3
CH3
Ph
Ph
Ph
99.58 ± 5.88
12.72 ± 0.19
13.54 ± 0.47
12.97 ± 0.28
7.83
2.26
4.76
30.64 ± 1.57
61.68 ± 1.42
benzyl carbamates
39.
40.
41.
RIV
GLT
H
Cl
Br
CH3
CH3
CH3
CH3
CH3
CH3
Bn
Bn
Bn
105.20 ± 0.99
18.94 ± 0.18
42.75 ± 0.31
235.91 ± 16.3
38.4 ± 1.97
2.77 ± 0.15
5.55
2.42
0.54
1.46
0.56
103.28 ± 0.41
128.27 ± 0.88
56.1 ± 1.41
1.54 ± 0.02
* SI (index selectivity) = IC50 (AChE)/IC50 (BChE). A higher selectivity to BChE is indicated in bold.

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2.2. In Vitro Assessment of the AChE- and BChE-Inhibitory Profiles
The AChE- and BChE-inhibiting activity for all of the tested carbamates was evaluated and
compared with the internal standards rivastigmine (RIV, Exelon®) and galantamine (GLT, Reminyl®).
These standards were selected because of their different structures since rivastigmine is a classical
acylating pseudo-reversible carbamate ChEI that inhibits both AChE and BChE, whereas galanthamine
is a non-acylating competitive reversible ChEI as well as an allosteric ligand at the nicotinic ACh
receptors. The choice of these reference drugs, which have different mechanisms of action, can
provide relevant results. The findings are summarized in Table 1 and expressed as the 50% inhibitory
concentration (IC50 [µM]) or the concentration of the inhibitor that was required for the 50% inhibition
of the mentioned enzymes.
All of the considered derivatives exhibited a very good to moderate inhibitory activity. The IC50
values for AChE ranged from 32.01
±
0.54 to 329.68
±
42.06
µ
M and for BChE from 5.51
±
0.20
to 440.56 7.54 M. It was observed that some of them had an even better activity than a known
±
µ
drug—rivastigmine. Surprisingly, more of the compounds exhibited a better inhibitory potency
towards BChE than to AChE. Some of the compounds proved to be highly selective for BChE with
respect to AChE (11, 13, 14, 16, 23–28, 31, and 33). Specifically, two compounds, 23 and 28, had a
very high selectivity index (SI = 13.93 and 15.31). In fact, compound 28 had the lowest IC50 value
and had an approximately seven-fold higher inhibitory activity against BChE than RIV (IC50 = 5.51
vs. 38.40
µM), which corresponds quite well with GLT (IC50 = 2.77
µM). Similarly, a five-fold higher
inhibitor activity against BChE relative to RIV was also observed for compound 31 (IC50 = 7.02
µM)
and was more than three-fold higher for compounds 23 and 32 as well. It should be emphasized
that compound 32 exhibited a promising inhibitory activity against AChE with an IC50 value of
32.01
µM—approximately two times better compared to RIV (IC50 = 56.1
µM). Among the naphthalene
derivatives, phenyl carbamates 36
–38 revealed a three-fold higher inhibitor activity towards BChE
relative to RIV, whereas only one benzyl carbamate, 39, exhibited a two-fold higher anti-BChE activity
to RIV. In fact, compounds 36 and 39 might serve as selective inhibition agents towards BChE with
respect to AChE (SI = 7.83 vs. 5.55). Moreover, compounds 37 and 38 also exhibited a high activity
against AChE. Not surprisingly, the substitution of the aryl ring had a direct impact on the variations
in the potency of the investigated compounds. It was observed that the methyl-substituted carbamates
generally exhibited a lower inhibitory ability compared to their phenyl or benzyl counterparts.
Interestingly, compounds 17 and 29, which have the methoxy group in the same position were
weaker inhibitors—the presence of a hydrophilic electron-donating -OCH3 substituent of the phenyl
ring at the para-position decreased the potency of the compounds, especially against the BChE enzyme
(see Table 1). On the other hand, the presence of methyl group(s) in the meta/para-position(s) of the
phenyl ring (compounds 15, 18, 21, 27, 30, and 33) resulted in an improvement in the IC50 value for
the BChE enzyme, thus suggesting the significance of the hydrophobic interactions with the enzyme.
The placement of an electron-withdrawing chlorine substituent in the meta-position of the phenyl ring
appeared to be strongly preferable, especially for the BChE inhibition activity (compounds 16 or 28).
2.3. In Silico Evaluation of AChE- and BChE-Inhibitory Profile
The principal objective of the ligand-based study was to comparatively analyze the molecular
surface (CoMSA) to gain insight into the electronic and/or steric factors that potentially determine
the inhibitory AChE/BChE activities of the investigated compounds. The findings for the surface
descriptors were compared with their force field counterparts (CoMFA), when modeling thinhibiting
potency for the multiple training/test subsets. First, the q2cv performance of the AChE and BChE
profiles for the entire benzene and naphthalene carbamate dataset 1–41 was investigated as the training
set using the CoMFA and CoMSA approach. In this case, both methods performed comparably for the
AChE/BChE activity (expressed in a logarithmic scale as pAChE and pBChE); however, in general,
the modeling of pBChE data produced superior outcomes of the statistical metrics (CoMFA q2cv = 0.61
vs. CoMSA q2cv = 0.61), irrespective of the map size (20
×
20 to 50
×
50) or the probe atom that

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was used (CH3 or H+). It was clear that relying exclusively on data fitting with cross-validated
leave-one-out (CV-LOO) procedure is not satisfactory, and therefore the external validation by splitting
the molecules into training/test subsets was conducted to assess the predictive power of a model
using the SDEP, MAE and q2test statistics. The naphthalene-based carbamates were excluded from the
training set in order to form the test set. We observed an improvement in the q2cv performance (CoMFA
+
q2cv = 0.72 vs. CoMSA q2cv = 0.76); however, the poor predictive abilities of the models (q2test < 0.5
confirmed the dichotomic nature of the q2cv q2test parameters for which a high value of q2cv did not
)
/
automatically imply a high model predictability [34]. From a philosophical point of view, it is not
possible to determine an “absolute” measure of predictivity, as it significantly depends on the choice
of datasets and the statistical approach that is used, but the great advantage of the QSAR paradigm
lies not in the extrapolation, as was stated by Hansch [35]. In other words, the separation of objects
into training/test subgroups is not a trivial issue, and therefore, an additional evaluation, namely the
Stochastic Model Validation (SMV), was performed as a kind of “perturbation” procedure to examine
the data structure [36]. Hence, the fluctuations of the statistical estimators during the CoMSA BChE
modeling were examined since the original dataset of 41 molecules was repeatedly sampled into
30/11 training/test subseries (ratio 3:1). Unfortunately, it was not technically feasible to scrutinize
11
the entire pool of systematically generated training/test populations (
C
≈
3
×
109), and, therefore,
41
the overall number of samplings was restricted to a relatively small fraction of approximately 3 × 106
systematically generated populations (1 out of 1000). Not surprisingly, the generated q2cv vs. q2test
fluctuation pattern revealed the areas with a greater modeling ability that were depicted for the
BChE potency (q2cv
≥
0.75) and were accompanied by areas that had a higher (q2test > 0.5) or lower
(
q2test < 0.5) predictive power. Obviously, the preferential selection of objects into the training set
resulted in a decrease in the predictive performance of the test set. Figure 1 illustrates the frequency
distribution of the compounds in the test subgroup when sampling the models that had the best
statistical parameters (q2cv
≥
0.7 and q2test > 0.5). A relatively smooth compound frequency distribution
within the test set population was disrupted by the larger numbers of compounds 2, 5, 6, 13, 15, 28, 31,
38, and 41, respectively.
Figure 1. Histogram indicating number of individual compounds that accured in test set within the
regions of q2cv > 0.7 and q2test 0.5 for the training set against q2test L–7–O CV for modeling the BChE
potency of the carbamate derivatives (1–41) using CoMSA approach.
>
In general, the specified molecules covered the entire structural space as well as the BChE activity
range (5.51–440.65 M) of the investigated compounds evenly, which partially explains the good ability
and predictability of the model.
µ

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Additionally, the similarity was analyzed using the PCA procedure on the pool of descriptors
that was derived from Dragon 6.0 software, in which the final dataset of 2777 variables was arranged
in matrix X41×2777 with the rows representing molecules (objects) and the columns preserving the
numerical values of the variables (parameters) [37,38]. The experiment was carried out for the centered
and standardized data to illustrate any meaningful variations in the modeling performance of the
examined set of carbamate derivatives with respect to their structure and inhibitory profiles. The PCA
model with the first four PCs described 80.66% of the total data variance, whereas the first two PCs
accounted for 67.37%.
Figure 2 presents the respective score plots of PC1 vs. PC2 (Figure 2a) and of PC1 vs. PC3
(Figure 2b), which indicated that the methyl-based carbamate derivatives (
into one structurally related group along the first principal component (PC1 <
the naphthalene-based derivatives were generally grouped together (PC1 > 40), which indicated major
structural variations from all of the remaining ones.
1
–
10 and 35) can be classified
20). Moreover,
−
Figure 2. Projection of carbamates 1–41 on a plane defined by two principal components for the Dragon
descriptors: the first vs. second (a); and the first vs. third (b).
A detailed inspection of the experimental BChE inhibitory profile and compound lipophilicity,
which were color-coded according to the values of logP (calculated in Sybyl-X) for the objects that
were projected on the plane specified by the two first components, revealed some correlation (R = 0.67).
This was especially visible for the methyl-based carbamates (1–10 and 35), for which the was the lower
activity (pBChE < 4) the lower was the lipophilicity (clogP < 3). On the other hand, some of the more
active naphthalene derivatives were accompanied by fairly high values of the calculated lipophilicity
(clogP > 5) as observed in Figure 3.
One can conclude that the lipophilic profile for compounds should be directly related with their
chemical structure, and, therefore, structurally similar molecules (chemotypes) might have similar
properties. This tendency can be confirmed for halogenated naphthalene derivatives (37, 38, 40,
and 41), which violated the Lipinski’s Rule of Five (Ro5), as can be observed in Figure 4a. Obviously,
the above chlorine- and bromine-based molecules were characterized by the greatest values of the
surface area descriptors, which were obtained by the Sybyl-X software, as visualized in Figure 4b.

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Figure 3. Projection of carbamates
components with: BChE activity in the logarithmic scale (
1
–
41 on the plane defined by the first vs. second principal
); and the calculated lipophilicity profile ( ).
Colors indicate the numerical values of the pBChE activity and clogP.
a
b
Figure 4. Projection of carbamates
components with: Ro5 rule violations (
1
–
41 on the plane defined by the first vs. second principal
); and the calculated surface area ( ). Colors indicate the
a
b
number of Ro5 violations and the numerical values of surface area.
2.4. Probability-Guided Pharmacophore Mapping
It is characteristic that a vast set of highly inter-correlated topologic/topographic descriptors is
generated for mD-QSAR studies, and, therefore, the uninformative variable elimination procedure
can be regarded as a pre-processing stage to prune the input data ensemble. As a result, the iterative
variable elimination procedure (IVE-PLS) was used as a filter to specify the descriptors with the highest
individual weightings towards the inhibitory activity [39]. Thus, 10% of the 30/11 training/test
samples was randomly selected from regions that had a fairly high model ability (q2cv > 0.70) and
predictability (q2test > 0.5) to produce “consensus-based” pharmacophore maps. Taking the number of
molecules into account, the maximum number of PLS components to generate the model was truncated
to seven. The variables with the highest stability abs(mean(b)/std(b)) for each of the randomly selected
models were specified using the IVE-PLS algorithm. In general, the predictive power of a model
that was monitored by q2test was stable for a considerable range of the variables that were eliminated,
whereas there was a slight improvement of the q2cv performance when the columns with the lowest
values of stability were extracted. The backward column elimination was iteratively looped until

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the optimal number of variables to be included within the model was accomplished—the moment
that the q2cv deterioration determined the number of relevant columns. The cumulative sum of the
common columns for all of the investigated 1661 BChE models was calculated and normalized to a
range of <0,1>. The set of columns with a value above the pre-selected cut-off of 0.4 was selected;
however, the spatial pattern, as depicted in Figure 5, was produced by filtering a further 80% of the
CoMSA descriptors that had a relatively small statistical significance for the BChE inhibitory activity.
To simplify the visual inspection of the key pharmacophore patterns, colors were used to indicate the
impact of the descriptors on the inhibitory potency to show the areas with a positive and/or negative
activity contribution (Figure 5a) as well as the four possible combinations of the mean regression
coefficients and charge values (Figure 5b), respectively.
Figure 5. The spatial sectors with the greatest contribution into the BChE inhibitory potency specified as
CoMSA IVE-PLS for the selected 30/11 training/test set samplings. Colors indicate their effect (
a) and
the four possible combination of the mean charge q and correlation coefficient b values (
b). Reference
compound 32 is plotted in two different orientations.
The spatial areas with a potentially detrimental impact on the BChE inhibitory potency, primarily
due to steric or electrostatic factors, are indicated by the dark spheres in Figure 5a, whereas the bright
polyhedrals depict the 3D pattern that was predicted to be positioned by an atom or substituent to
reinforce the molecular inhibitory profile. It appeared that the elongation of the side chains R1 and R2
bonded to the peptide-bond-like motif in the scaffold contributed favorably to the BChE inhibition
activity of the analyzed carbamates, as was suggested by the bright areas close to the nitrogen attached
directly to the carbonyl group. This confirms the tendency that was observed among the carbamate
homologs in which an additional methylene group improved the BChE inhibition affinity slightly
(see Table 1). The obtained findings demonstrate the significance of the side chain R3, where the
mixed (positive/negative) steric contribution to the inhibitory potency was observed, as shown in
Figure 5a. The increase in the bulkiness in this area appears to be a favorable structural variation that
partially elucidates the increase in the BChE potency for phenyl and benzyl carbamates compared to the
methyl counterparts, as presented in Table 1. The presence of negatively charged motifs (delocalized
π
electrons) probably contributed favorably (negative regression coefficients) to the inhibition profile
of the analyzed series, as depicted in Figure 5b. Basically, the spatially disallowed areas that were
attributed by the negative mean charge values of the IVE-PLS CoMSA models were specified for

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the para position of the phenyl ring (X substituent). In other words, the indicated area might have
been occupied by negatively charged atoms/groups, but an increase in the volume/surface may be
detrimental to the inhibition potential, which corresponds quite well with the depletion of the BChE
inhibitory abilities that were observed for the bromine- vs. the chlorine-based carbamates (see Table 1).
The visualization of the pharmacophore maps, which were generated using the consensus
3D-QSAR methodology, provided valuable knowledge about the interaction mode that was required
to enhance the inhibitory potential of the analyzed carbamates as prospective ChEIs.
2.5. Molecular Docking
Site-directed computer-assisted docking algorithms optimally position a (bio)effector molecule in
a binding/active site, which is based on the intrinsic relationship of the target–host atomic coordinates,
while the spatial geometry, or at least the homology models of macromolecule, are accessible [40].
In fact, target-guided docking algorithms mainly employ descriptor-matching approaches in which
the ligand property space is adapted to the corresponding map of the target electronic, steric and
lipophilic features. How to in silico estimate the “natural” forces that combine the building blocks
together in structure-based drug-design (SBDD) is still unclear, since the method does not always
produce a reliable quantitative correlation between the experimental and predicted activity assays [41].
In fact, the computational procedures that attempt to reconstruct a ligand–receptor complex using the
molecular docking approaches involve an efficient search procedure and effective scoring function,
respectively [42]. Rapidly covering the relevant conformational space at the search stage as well as
the effective discrimination between native or non-native docked conformation at the scoring stage
are basically the two critical elements of the docking procedure; however, the SBDD methodology is
typically used for hit virtual screening (flash docking) and lead to optimization [43,44].
The geometries of human acetyl (AChE) and butyrylcholinesterase (BChE) enzymes that are
co-crystallized with pharmacologically used drugs (e.g., galanthamine or rivastigmine) are still under
intense scrutiny, which enabled us to compare the findings of the ligand- and structure-based protocols.
On the other hand, a comprehensive examination of the guest–target interactions using molecular
dynamic simulations (MDs) was beyond the scope of this study.
The crystallographic data of butyrylcholinesterase with a catalytic core that is specified at
a higher resolution of 2.6 Å in the liganded state (holo) with the rivastigmine (RIV) analog
([3-[(1~{R})-1-(dimethylamino)ethyl]-4-oxidanyl-phenyl]~{N}-ethyl-~{N}-methyl-carbamate) were
retrieved from the Protein Data Bank repository (PDB entry: 6eul). The drug analog was subsequently
(re)docked in the active site AC2 of the enzyme chain A, which is composed of six amino acid residues
(Asn68, Ile69, Asp70, Trp82, Thr120, and Pro285) and a 1,2-ethanediol molecule (EDO605), using
the AutoDock Vina program [45]. Moreover, the population of the BChE inhibitors (IC50 < 30 µM)
that are comparatively active to rivastigmine was investigated to compare the interacting mode of
the carbamate derivatives with the drug–enzyme spatial interaction pattern. In fact, the specific
conformations and orientations (poses) of the most active compounds (11
and 36 39), as illustrated in Figure 6, confirmed our intuitive selection of the trial alignment in the
ligand-based study.
, 13, 14, 16, 23–28, 30–33,
–
Moreover, a run-through was performed to investigate the interacting mode for the most active
compounds, which basically revealed two types of non-binding interactions: hydrophobic and
hydrogen bond formation. Planar (2D) and spatial (3D) maps of the host–target interactions were
created for all of the active compounds using Schrödinger Maestro software and the Protein-Ligand
Interaction Profiler (PLIP) and were then compared with the drug (GLT and RIV) molecules [46].
Overall, the hydrophobic interactions were primarily generated with Asp70 (94%), Trp82 (28%), Pro285
(22%) and Tyr332 (22%), while Thr120 (83%) was basically specified as the hydrogen bond donor
(HBD) that interacted with one of the oxygen atoms in the molecule carboxylic group. The 2D- and
3D-binding patterns for GLT, RIV and the most active BChE inhibitor (28) that was tested are illustrated
in Figures 8 and 9, respectively. The hydroxyl substituent of Thr120 appeared to be crucial to form

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the hydrogen bond with the ether (Figure 9a,b) or carbonyl oxygen (Figure 9c). For the examined
BChE inhibitors, the regions close to the nitrogen atom (R1 substituent) seemed to be valid for the
hydrophobic interactions with the Asp70 amino acid residue, which is in line with our previous
receptor-independent findings (Figure 5).
Unfortunately, the obtained docking findings did not provide a clear explanation of the variations
that the meta/para-positioned carbamate derivatives exerted on the enzyme reaction site; however,
the close proximity of a positively charged nitrogen atom of His438 (Figure 8c) might have been
potentially beneficial to the inhibition potential, especially the negatively charged chlorine- or
bromine-based carbamates, which corresponds relatively well with the IVE-PLS CoMSA results.
The electrostatic repulsion between the negatively charged atoms and the oxygen of Ser79 can partially
explain the detrimental impact of the –OCH3 or –OCH2O- groups attached to the phenyl ring. On the
other hand, the postulated hydrophobic interactions with Phe329 might favorably contribute to the
inhibitory potential, as was observed for (di)methyl derivatives.
Obviously, we should beware of q2 [47] and beware of docking [48]; therefore, a combination of
the consensus pharmacophore mapping with a systematic screening of multifaceted guest–host
interactions with target-tailored procedures is highly advisable in rational drug design.
Figure 6. Comparison of the poses for the most active BChE inhibitors in the enzyme active site
AC2. As an additional experiment galanthamine (GLT), which is a very active AChE/BChE inhibitor
(
IC50 < 3 µM) was docked in the enzyme active site AC2. Although the structural variations between
GLT and RIV and the most active molecules are easily noticeable, similar spatial patterns of the
host–BChE complex within the catalytic core was observed for the negatively charged atoms (nitrogen
and oxygen), as presented in Figure 7.
Figure 7. Structure of the BChE enzyme (displayed as ribbons) bound with GLT (depicted as balls and
sticks), RIV (presented as thin sticks) and the most active molecule 28 (illustrated as thick sticks).

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Figure 8. Planar (2D) comparision of the binding mode for: rivastigmine (a); galanthamine (b); and the
most active BChE inhibitor 28 that was tested (c), as generated by the Maestro software.
Figure 9. Spatial (3D) comparison of the binding mode for: rivastigmine (a); galanthamine (b); and the
the most active BChE inhibitor 28 that was tested (c), as generated by the PLIP software.
3. Materials and Methods
3.1. Chemistry
All of the reagents and solvents were purchased from ACROS Organics, Asta-Tech, Maybridge,
Santa Cruz Biotechnology and Sigma-Aldrich. The melting points were measured in an
OptiMelt—Automated Melting Point System (MPA 100, Stanford Research Systems). The 1H and 13
C
NMR spectra were recorded on a Bruker Ascend 500 MHz spectrometer at frequencies of 500 MHz
and 126 MHz and a Bruker Avance III 400 MHz FT-NMR spectrometer at frequencies of 400 MHz and
101 MHz using CDCl3-d6 or DMSO-d6 as the solvent and TMS as the internal standard. The NMR
solvents were purchased from ACROS Organics. The chemical shifts (δ) are given in ppm and the
values of the coupling constants (J) are reported in hertz (Hz). The purity of all of the compounds
was tested using the HPLC/MS method. The HPLC-MS analyses were performed on a Varian model
920 liquid chromatograph equipped with a Varian 900-LC model autosampler, a gradient pump,
a Varian Pro Star 510 model column oven and a Varian 380-LC model evaporative light scattering
detection (ELSD) detector. This was coupled with a Varian 500-MS IT. The HRMS were determined
using a Waters LCT Premier XE high resolution mass spectrometer with electrospray ionization (ESI).

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3.2. General Procedure Used to Synthesize Carbamates 1–41
A mixture of a suitable acid chloride (1 moL) and an anhydrous aluminum chloride (1.05 moL)
was stirred until the aluminum chloride was completely dissolved, and then cooled in a water bath
and diluted in 250 mL of methylene chloride. Next, a suitable aromatic hydrocarbon (1.10 moL) was
added dropwise to a magnetically stirred solution. The reaction mixture was poured into ice-cooled
water, the dish was washed with distilled water three times and the organic layer was separated.
Methylene chloride was evaporated and the reaction product was crystallized or purified by distillation.
In the next step, 1.1 moL of bromine was added dropwise to the solution of the suitable aromatic
ketone (1 moL) dissolved in 300 mL of methylene chloride. After the bromine was dropped into
the magnetically stirred mixture, 300 mL of distilled water was added and mixed until the color
of the mixture changed to white or cream. Then, the organic layer was separated and used in that
form in the next step. A bromine derivative (1 moL) was dissolved in methylene chloride, a suitable
amine (2 moL) was added to the obtained aromatic ketone and then sodium hydroxide was added
dropwise. After 12 h of stirring, the organic layer was separated and poured into a beaker filled with
ice and hydrochloric acid. An aqueous sodium hydroxide solution was then added to the separated
aqueous phase. The extracted amine was washed three times with water, dried with anhydrous
magnesium sulfate, diluted with diethyl ether and treated with gaseous hydrogen chloride until an
acidic pH solution was obtained. The isolated hydrochloride of the 2-amino-1-phenylpropane-1-one
derivative in the form of a white solid was washed with acetone and dried. Then, 0.01 mol of
2-amino-1-phenylpropane-1-one derivative dissolved in 50 mL of methylene chloride and 0.01 mol of
triethyloamine was added to the cooled and magnetically stirred mixture, after which approximately
9 mmoL of suitable chloroformate was added dropwise. After dropping, the mixture was washed
with diluted hydrochloric acid to remove any excess unreacted 1-phenylpropylamine derivative.
The organic layer was separated and dried with anhydrous magnesium sulfate. The solvent was
evaporated under reduced pressure and the product was purified on a column chromatography using
hexane: ethyl acetate 9:1 as the eluent.
Methyl ethyl(1-oxo-1-phenylpropan-2-yl)carbamate (
7.99 (t, H, aromat, J = 20.6 Hz); 7.83 (d, H, aromat, J1 = 51.2 Hz); 7.57 (ddd, H, aromat, J1 = 8.5 Hz,
1
). Yield 88%;1H-NMR (CDCl3, 400 MHz, ppm):
δ
J2 = 2.4 Hz, J3 = 1.2 Hz); 7.46 (dd, H, aromat, J1 = 10.5 Hz, J2 = 4.7 Hz); 7.28 (s, H, aromat); 5.75 (d, H,
CH, J = 6.6 Hz); 3.75 (s, 3H, CH3); 3.30 (s, H, CH2); 3.16 (m, H, CH2); 1.71–1.24 (m, 3H, CH3); 1.10–0.78
(m, 3H, CH3); 13C-NMR (CDCl3, 100 MHz, ppm):
δ 199.2; 155.6; 135.6; 133.3; 128.6; 128.6; 128.4; 128.4;
55.2; 52.8; 38.5; 14.9; 14.3; HR-MS (ESI): for C13H17NO3Na [M+Na]+ calculated: 258.1106. m/z, found:
258.1102 m/z.
Methyl methyl(1-oxo-1-phenylbutan-2-yl)carbamate (
7.90 (dd, 2H, aromat, J1 = 21.8 Hz, J2 = 7.5); 7.71–7.41 (m, 3H, aromat), 5.35 (ddd, 1H, CH, J1 = 52.6 Hz,
J2 = 9.6 Hz, J3 = 5.1 Hz), 3.47 (d, 3H, CH3, J = 111.4 Hz), 2.79–2.44 (m, 2H, CH2), 1.92-1.62 (m, 3H, CH3),
2
). Yield 83%; 1H-NMR (DMSO, 400 MHz, ppm):
δ
1.05-0.72 (m, 3H, CH3); 13C-NMR (DMSO, 100 MHz, ppm):
δ 204.0; 161.8; 140.7; 138.4; 133.9; 133.9;
133.1; 133.1; 65.9; 57.8; 34.9; 25.6; 15.5; HR-MS (ESI): for C13H17NO3Na [M+Na]+ calculated: 258.1106
m/z, found: 258.1103 m/z.
Methyl ethyl(1-oxo-1-phenylbutan-2-yl)carbamate (
8.01 (dd, 2H, aromat, J1 = 48.8 Hz, J2 = 6.8); 7.62–7.25 (m, 3H, aromat), 5.54 (dd, 1H, CH, J1 = 65.9 Hz,
3
). Yield 85%; 1H-NMR (CDCl3, 400 MHz, ppm):
δ
J2 = 58.5 Hz), 3.79 (d, 3H, CH3, J = 17.3 Hz), 3.14 (dd, 2H, CH2, J1 = 22,1 Hz, J2 = 7,1 Hz), 2.10–1.92
(m, 2H, CH2), 1.08–0.81 (m, 3H, CH3), 1.08-0.81 (m, 3H, CH3); 13C-NMR (CDCl3, 100 MHz, ppm):
δ
198.8; 157.3; 135.9; 133.3; 128.5; 128.5; 127.9; 127.9; 60.1; 52.9; 38.1; 21.8; 15.0; 10.4; HR-MS (ESI): for
C14H19NO3 Na [M+Na]+ calculated: 272.1263 m/z, found: 272.1263 m/z.
Methyl methyl(1-oxo-1-(m-tolyl)propan-2-yl)carbamate (
). Yield 86%; 1H-NMR (CDCl3, 400 MHz,
4
ppm): 7.75 (dd, 2H, aromat, J1 = 42.0 Hz, J2 = 8,6); 7.44–7.28 (m, 2H, aromat); 5.79–5.25 (m, 1H, CH),
3.95–3.45 (m, 3H, CH3), 2.77 (d, 3H, CH3, J = 34,6 Hz), 2.30 (d, 3H, CH3, J = 91,9); 1.69–1.22 (m, 3H,
CH3); 13C-NMR (CDCl3, 101 MHz, ppm): 199.5; 156.9; 138.5; 135.4; 134.1; 128.8; 128.5; 125.6; 55.0;

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52.9; 29.3; 21.3; 13.5; HR-MS (ESI): for C13H17NO3 Na [M+Na]+ calculated: 258.1106 m/z, found:
258.1105 m/z.
1
Methyl (1-(3-chlorophenyl)-1-oxopropan-2-yl)(methyl)carbamate (
5
). Yield 89%; H-NMR (CDCl3,
500 MHz, ppm): 8.15–7.69 (m, 2H, aromat); 7.64–6.99 (m, 2H, aromat); 5.77–5.28 (m, 1H, CH), 3.76
(d, 3H, CH3 J = 16.0 Hz), 2.76 (d, 3H, CH3, J = 42.1 Hz), 1.46–1.19 (m, 3H, CH3); 13C-NMR (CDCl3,
126 MHz, ppm): 198.1; 156.8; 136.9; 135.0; 133.2; 130.0; 128.4; 126.5; 55.3; 53.1; 29.4; 13.3; HR-MS (ESI):
for C12H14ClNO3 [M+H]+ calculated: 256.0740 m/z, found: 256.0741 m/z.
Methyl (1-(4-methoxyphenyl)-1-oxopropan-2-yl)(methyl)carbamate (6
). Yield 79%; 1H-NMR (CDCl3,
400 MHz, ppm): 7.97 (dd, 2H, aromat, J1 = 42.0 Hz, J2 = 8.4); 7.01–6.87 (m, 2H, aromat); 5.84–5.14 (m,
1H, CH), 3.88 (s, 3H, CH3); 3.76 (d, 3H, CH3, J = 11.1 Hz), 2.93-2.58 (m, 3H, CH3), 1.37 (d, 3H, CH3,
J = 6.9 Hz); 13C-NMR (CDCl3, 101 MHz, ppm): 197.3; 163.7; 156.8; 130.8; 130.5; 128.2; 113.9; 113.9;
55.4; 54.4; 53.0; 29.1; 13.4; HR-MS (ESI): for C13H17NO4 Na [M+Na]+ calculated: 274.1055 m/z, found:
274.1043 m/z.
◦
1
Methyl methyl(1-oxo-1-(p-tolyl)propan-2-yl)carbamate (7). Yield 87%; m.p.: 36-37.5 C; H-NMR
(CDCl3, 500 MHz, ppm): 7.87 (dd, 2H, aromat, J1 = 53.2 Hz, J2 = 7.8); 7.28 (dd, 2H, aromat, J1 = 5.2 Hz,
J2 = 4.6); 5.65 (dt, 1H, CH, J1 = 156.3 Hz, J2 = 6.8), 3.76 (d, 3H, CH3 J = 8.6 Hz), 2.76 (d, 3H, CH3,
J = 49.2 Hz), 2.44 (d, 3H, CH3, J = 15.6); 1.81–0.70 (m, 3H, CH3); 13C-NMR (CDCl3, 126 MHz, ppm): 198.7;
156.8; 144.2; 132.8; 129.4; 129.4; 128.6; 128.6; 54.8; 53.0; 29.2; 21.7; 13.4; HR-MS (ESI): for C13H17NO3 Na
[M+Na]+calculated: 258.1106, m/z, found: 258.1108 m/z.
1
Methyl (1-(4-chlorophenyl)-1-oxopropan-2-yl)(methyl)carbamate (
8
). Yield 87%; H-NMR (CDCl3,
500 MHz, ppm): 7.91 (dd, 2H, aromat, J1 = 55.8 Hz, J2 = 8.1); 7.50–7.16 (m, 2H, aromat); 5.72–5.31 (m,
1H, CH), 3.75 (s, 3H, CH3), 2.75 (d, 3H, CH3, J = 53.5 Hz), 1.49–1.23 (m, 3H, CH3); 13C-NMR (CDCl3,
126 MHz, ppm): 197.9; 156.8; 139.8; 133.6; 129.9; 129.9; 129.0; 129.0; 54.0; 53.1; 29.2; 13.2; HR-MS (ESI):
for C12H14ClNO3 [M+H]+ calculated: 256.0740 m/z, found: 256.0743 m/z.
1
Methyl (1-(4-bromophenyl)-1-oxopropan-2-yl)(methyl)carbamate (
9
). Yield 87%; H-NMR (DMSO,
400 MHz, ppm): 7.77 (dd, 4H, aromat, J1 = 23.0 Hz, J2 = 8.6); 5,35 (dt, 1H, CH, J1 = 21.8 Hz, J2 = 6.7 Hz),
3.58–3.26 (m, 3H, CH3), 2.85–2.44 (m, 3H, CH3), 1.35–1.24 (m, 3H, CH3); 13C-NMR (DMSO, 100 MHz,
ppm): 203.3; 160.8; 139.8; 136.9; 136.9 135.1; 135.1; 132.3; 61.9; 57.9; 35.8; 18.2; HR-MS (ESI): for
C12H14BrNO3 [M+H]+ calculated: 300.0235 m/z, found: 300.0222 m/z.
Methyl (1-(benzo[d][1,3]dioxol-5-yl)-1-oxopropan-2-yl)(methyl)carbamate (10). Yield 76%; 1H-NMR
(CDCl3, 500 MHz, ppm): 7.54 (ddd, 1H, aromat, J1 = 57.3 Hz, J2 = 45.8, J3 = 16.5); 7.28 (s, 1H, aromat);
6.87 (d, 1H, aromat, J = 8.2); 6.06 (s, 2H, CH2); 5.77–5.26 (m, 1H, CH), 3.77 (d, 3H, CH3, J = 15.2);
2.94–2.48 (m, 3H, CH3); 1.61 (s, 3H, CH3); 13C-NMR (CDCl3, 126 MHz, ppm): 197.0; 156.8; 152.4
148.2; 129.9; 125.0; 108.2; 101.8; 100,4; 54.5; 53.0; 29.1; 13.5; HR-MS (ESI): for C13H15NO5Na [M+Na]+
calculated: 288.0848 m/z, found: 288.0842 m/z.
Phenyl ethyl(1-oxo-1-phenylpropan-2-yl)carbamate (11). Yield 87%; 1H-NMR (CDCl3, 400 MHz, ppm):
8.03 (dd, 2H, aromat, J1 = 27.1 Hz, J2 = 7.5); 7.68–6.71 (m, 8H, aromat); 5.69 (dq, 1H, CH, J1 = 92.3,
J2 = 6,8); 3.56-3.15 (m, 2H, CH2), 1.56 (dd, 3H, CH3, J1 = 36.3 Hz, J2 = 6.6); 1.20 (dt, 3H, CH3, J1 = 22.3 Hz,
J2 = 7.1); 13C-NMR (CDCl3, 101 MHz, ppm): 199.1; 154.9; 151.4 135.5; 133.4; 129.4; 129.4; 128.8; 128.8;
128.5; 128.5; 125.5; 121.6; 121.6; 55.5; 39.1; 15.6; 14.6; HR-MS (ESI): for C18H19NO3 Na [M+Na]+
calculated: 320.1263m/z, found: 320.1258 m/z.
1
Phenyl methyl(1-oxo-1-phenylbutan-2-yl)carbamate (12). Yield 89%; H-NMR (CDCl3, 500 MHz,
ppm): 8.07 (ddd, 2H, aromat, J1 = 21.3 Hz, J2 = 7.4, J3 = 1.2); 7.65–7.59 (m, 1H, aromat); 7.51 (td, 2H,
aromat, J1 = 7.4 Hz, J2 = 3.7); 7.43-7.35 (m, 2H, aromat); 7.29–7.20 (m, 1H, aromat); 7.14–7.09 (m, 2H,
aromat); 5.62 (ddd, 1H, CH, J1 = 34.9, J2 = 9.5, J3 = 5.6); 2.91 (d, 3H, CH3, J = 32.3); 2.09–1.81 (m, 2H,
CH2); 1.08 (dt, 3H, CH3, J1 = 35.9 Hz, J2 = 7.4); 13C-NMR (CDCl3, 126 MHz, ppm): 197.8; 155.5; 151.3
135.7; 133.5; 129.3; 129.3; 128.8; 128.8; 128.6; 128.6; 125.5; 121.7; 121.7; 60.4; 29.8; 20.9; 10.4; HR-MS (ESI):
for C18H19NO3Na [M+Na]+ calculated: 320.1263 m/z, found: 320.1260 m/z.
Phenyl ethyl(1-oxo-1-phenylbutan-2-yl)carbamate (13). Yield 88%; 1H-NMR (CDCl3, 500 MHz, ppm):
8.21–7.90 (m, 2H, aromat); 7.62 (dd, 1H, aromat, J1 = 14.2 Hz, J2 = 6.9); 7.54–7.48 (m, 2H, aromat);

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7.46–7.37 (m, 2H, aromat); 7.33–7.21 (m, 1H, aromat); 7.18–7.09 (m, 2H, aromat); 5.61 (ddd, 1H, CH,
J1 = 44.6, J2 = 8.7, J3 = 6.1); 3.63–3.12 (m, 2H, CH2); 2.12-1.82 (m, 2H, CH2); 1.13 (td, 3H, CH3, J1 = 7.2 Hz,
J2 = 3.4); 1.06 (dt, 3H, CH3, J1 = 14.8 Hz, J2 = 7.2); 13C-NMR (CDCl3, 126 MHz, ppm): 198.6; 155.4; 151.4
135.8; 133.5; 129.4; 129.4; 128.8; 128.8; 128.6; 128.6; 125.4; 121.6; 121.6; 60.4; 38.8; 21.8; 15.3; 10.5; HR-MS
(ESI): for C19H21NO3Na [M+Na]+ calculated: 334.1419 m/z, found: 334.1411 m/z.
1
Phenyl methyl(1-oxo-1-phenylpentan-2-yl)carbamate (14). Yield 76%; H-NMR (CDCl3, 500 MHz,
ppm): 8.11–8.02 (m, 2H, aromat); 7.62 (dd, 1H, aromat, J1 = 14.4 Hz, J2 = 7.2 Hz); 7.54–7.47 (m, 2H,
aromat); 7.47–7.35 (m, 2H, aromat); 7.32–7.20 (m, 1H, aromat); 7.13–7.08 (m, 2H, aromat); 5.73 (ddd,
1H, CH, J1 = 28.4, J2 = 9.0, J3 = 5.8); 2.92 (d, 3H, CH3, J = 30.6); 1.98–1.81 (m, 2H, CH2); 1.49–1.40 (m,
2H, CH2); 1.04 (t, 3H, CH3, J = 7.4 Hz); 13C-NMR (CDCl3, 126 MHz, ppm): 199.0; 155.4; 151.4; 135.7;
133.5; 129.5; 129.5; 128.8; 128.8; 128.6; 128.6; 125.4; 121.7; 121.7; 58.6; 30.2; 29.8; 19.2; 13.9; HR-MS (ESI):
C19H21NO3 [M+H]+ calculated for: 312.1600 m/z, found: 312.1605 m/z.
Phenyl methyl(1-oxo-1-(m-tolyl)propan-2-yl)carbamate (15). Yield 84%; m.p.: 52.5-53.5 ◦C; 1H-NMR
(CDCl3, 400 MHz, ppm): 7.94–7.73 (m, 2H, aromat); 7.40 (dt, 2H, aromat, J1 = 15.7 Hz, J2 = 7.5); 7.28 (s,
2H, aromat); 7.23 (t, 1H, aromat, J = 7.4 Hz); 7.09 (t, 2H, aromat, J = 8.3 Hz); 5.74 (dq, 1H, CH, J1 = 50.8,
J2 = 6.9); 2.94 (d, 3H, CH3, J = 10.2 Hz); 2.44 (s, 3H, CH3); 1.48 (d, 3H, CH3, J = 7,0 Hz); 13C-NMR
(CDCl3, 101 MHz, ppm): 199.3; 154.9; 151.4; 138.6; 135.4; 134.3; 129.3; 129.3 128.9; 128.7; 125.7; 125.4;
121.6; 121.6; 55.4; 30.0; 21.3; 13.6; HR-MS (ESI): for C18H19NO3Na [M+Na]+ calculated: 320.1263 m/z,
found: 320.1277 m/z.
1
Phenyl (1-(3-chlorophenyl)-1-oxopropan-2-yl)(methyl)carbamate (16). Yield 92%; H-NMR (CDCl3,
500 MHz, ppm): 8.01 (dd, 1H, aromat, J1 = 7.5 Hz, J2 = 5.7 Hz); 7.91 (dd, 1H, aromat, J1 = 27.4Hz,
J2 = 7.8 Hz); 7.63–7.56 (m, 1H, aromat); 7.49–7.42 (m, 1H, aromat); 7.42–7.35 (m, 2H, aromat); 7.27–7.19
(m, 1H, aromat); 7.13–7.06 (m, 2H, aromat); 5.68 (dq, 1H, CH, J1 = 61.2, J2 = 6.9); 2.93 (d, 3H, CH3,
J = 17.1 Hz); 1.49 (d, 3H, CH3, J = 6.9 Hz); 13C-NMR (CDCl3, 126 MHz, ppm): 198.0; 155.0; 151.3;
137.0; 135.1; 133.5; 130.2; 129.4; 129.4; 128.5; 126.5; 125.6; 121.6; 121.6; 55.6; 30.0; 13.4; HR-MS (ESI): for
C17H16ClNO3 Na [M+Na]+ calculated: 340.0717 m/z, found: 340.0714 m/z.
Phenyl (1-(4-methoxyphenyl)-1-oxopropan-2-yl)(methyl)carbamate (17). Yield 78%; m.p.: 53–56 ◦C;
1H-NMR (CDCl3, 400 MHz, ppm): 8.04 (dd, 2H, aromat, J1 = 17.9 Hz, J2 = 8.8); 7.39 (dd, 2H, aromat,
J1 = 10.7 Hz, J2 = 5.1); 7.30–7.19 (m, 1H, aromat); 7.11 (t, 2H, aromat, J = 8.8 Hz); 6.97 (d, 2H, aromat,
J = 8.9 Hz); 5.74 (dq, 1H, CH, J1 = 53.9, J2 = 6.9); 3.90 (s, 3H, CH3); 2.93 (d, 3H, CH3, J = 8,2 Hz); 1.47
(d, 3H, CH3, J = 6.7 Hz); 13C-NMR (CDCl3, 101 MHz, ppm): 199.3; 154.9; 151.4; 138.6; 135.4; 134.3;
129.3; 129.3 128.9; 128.7; 125.7; 125.4; 121.6; 121.6; 55.4; 30.0; 21.3; 13.6; HR-MS (ESI): for C18H19NO4Na
[M+Na]+ calculated: 336.1212 m/z, found: 336.1222 m/z.
Phenyl methyl(1-oxo-1-(p-tolyl)propan-2-yl)carbamate (18). Yield 82%; m.p.: 42.5–44 ◦C; 1H-NMR
(CDCl3, 500 MHz, ppm): 7.94 (dd, 2H, aromat, J1 = 22.3 Hz, J2 = 8.2); 7.39 (t, 2H, aromat, J = 7.9 Hz);
7.30 (dd, 2H, aromat, J1 = 11.7 Hz, J2 = 3.7); 7.23 (t, 1H, aromat, J = 7.4 Hz); 7.14–7.07 (m, 2H, aromat);
5.74 (dq, 1H, CH, J1 = 72.3, J2 = 6.9); 2.93 (d, 3H, CH3, J = 9.3 Hz); 2.44 (s, 3H, CH3); 1.48 (d, 3H, CH3,
J = 6.9 Hz); 13C-NMR (CDCl3, 126 MHz, ppm): 198.5; 154.9; 151.4; 144.4; 132.8; 129.5; 129.5; 129.3;
129.3; 128.7; 128.7; 125.5; 121.7; 121.7; 55.0; 29.9; 21.7; 13.6; HR-MS (ESI): for C18H19NO3Na [M+Na]+
calculated: 320.1263 m/z, found: 320.1262 m/z.
◦
Phenyl (1-(4-chlorophenyl)-1-oxopropan-2-yl)(methyl)carbamate (19). Yield 91%; m.p.: 52–54 C;
1H-NMR (CDCl3, 500 MHz, ppm): 7.98 (dd, 2H, aromat, J1 = 27.4 Hz, J2 = 8.5 Hz); 7.48 (dd, 2H, aromat,
J1 = 11.8 Hz, J2 = 4.9Hz); 7.43–7.36 (m, 2H, aromat); 7.24 (t, 1H, aromat, J = 7.4 Hz); 7.08 (dd, 2H, aromat,
J1 = 22.8 Hz, J2 = 7.8 Hz); 5.68 (dq, 1H, CH, J1 = 82.3, J2 = 6.9); 2.93 (s, 3H, CH3); 1.48 (d, 3H, CH3,
J = 6.9 Hz); 13C-NMR (CDCl3, 126 MHz, ppm): 197.7; 154.9; 151.3; 140.0; 133.7; 130.0; 130.0; 129.4; 129.4;
129.1; 129.1; 125.6; 121.6; 121.6; 55.2; 29.9; 13.4; HR-MS (ESI): for C17H16ClNO3Na [M+Na]+ calculated:
340.0717 m/z, found: 340.0712 m/z.
◦
Phenyl (1-(4-bromophenyl)-1-oxopropan-2-yl)(methyl)carbamate (20). Yield 88%; m.p.: 54–55 C;
1H-NMR (CDCl3, 400 MHz, ppm): 7.90 (dd, 2H, aromat, J1 = 21.9 Hz, J2 = 8.5 Hz); 7.65 (dd, 2H,
aromat, J1 = 8.3 Hz, J2 = 6.5 Hz); 7.40 (dd, 2H, aromat, J1 = 11.0 Hz, J2 = 4.8 Hz); 7.25 (dd, 1H, aromat,

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J1 = 15.9 Hz, J2 = 8.5 Hz); 7.08 (dd, 2H, aromat, J1 = 18.7 Hz, J2 = 7.8 Hz); 5.67 (dq, 1H, CH, J1 = 66.0,
J2 = 6.9); 2.93 (s, 3H, CH3); 1.48 (d, 3H, CH3, J = 6.9 Hz); 13C-NMR (CDCl3, 101 MHz, ppm): 198.0; 155.0;
151.3; 134.1; 132.1; 132.1; 130.0; 130.0; 129.4; 129.4; 128.8; 125.6; 121.6; 121.6; 55.2; 29.9; 13.4; HR-MS
(ESI): for C17H16BrNO3Na [M+Na]+ calculated: 384.0211 m/z, found: 384.0226 m/z.
Phenyl (1-(3,4-dimethylphenyl)-1-oxopropan-2-yl)(methyl)carbamate (21).
Yield 74%; m.p.:
94.5–95.5 ◦C; 1H-NMR (CDCl3, 500 MHz, ppm): 7.84–7.74 (m, 2H, aromat); 7.42–7.36 (m, 2H, aromat);
7.29–7.19 (m, 2H, aromat); 7.11 (dd, 2H, aromat, J1 = 11.6, J2 = 4.1); 5.75 (dq, 1H, CH, J1 = 57.6, J2 = 6.9);
2.92 (d, 3H, CH3, J = 14.5); 2.41–2.25 (m, 6H, CH3); 1.47 (d, 3H, CH3, J = 6.9); 13C-NMR (CDCl3,
126 MHz, ppm): 198.8; 154.9; 151.4; 143.2; 137.2; 133.2; 130.0; 129.6; 129.3; 129.3; 126.3; 125.4; 121.7;
121.7; 55.1; 29.9; 20.1; 19.8; 13.7; HR-MS (ESI): for C19H21NO3Na [M+Na]+ calculated: 334.1419 m/z,
found: 334.1418 m/z.
Phenyl (1-(benzo[d][1,3]dioxol-5-yl)-1-oxopropan-2-yl)(methyl)carbamate (22). Yield 76%; m.p.:
94–95 ◦C; 1H-NMR (CDCl3, 500 MHz, ppm): 7.75–7.63 (m, 1H, aromat); 7.54–7.48 (m, 1H, aromat);
7.39 (dd, 2H, aromat, J1 = 10.8 Hz, J2 = 5.1); 7.24 (dt, 1H, aromat, J1 = 13.8 Hz, J2 = 5.9Hz); 7.12 (dd,
2H, aromat, J1 = 12.5 Hz, J2 = 4.8 Hz); 6.89 (dd, 1H, aromat, J1 = 8.2 Hz, J2 = 4.9 Hz); 6.08 (d, 2H, CH2,
J = 7.7 Hz); 5.69 (dq, 1H, CH, J1 = 58.8, J2 = 6.9); 2.93 (d, 3H, CH3, J = 19.9 Hz); 1.46 (d, 3H, CH3,
J = 6.9 Hz); 13C-NMR (CDCl3, 126 MHz, ppm): 196.8; 154.9; 152.2; 151.4; 148.4; 129.1; 129.4; 129.4;
125.5; 125.0; 121.7; 121.7; 108.2; 108,0; 102.0; 54.7; 29.8; 13.7; HR-MS (ESI): for C18H17NO5Na [M+Na]+
calculated: 350.1004 m/z, found: 350.0998 m/z.
Benzyl ethyl(1-oxo-1-phenylpropan-2-yl)carbamate (23). Yield 82%; 1H-NMR (CDCl3, 500 MHz, ppm):
8.01 (d, 2H, aromat, J = 7.6); 7.81 (d, 1H, aromat, J = 7.0 Hz); 7.60–7.26 (m, 7H, aromat); 5.76 (q, 1H,
CH, J = 6.8 Hz); 5.31–5.05 (m, 2H, CH2); 3.32–3.10 (m, 2H, CH2), 1.43 (t, 3H, CH3, J = 8.0); 1.05–0.98
(m, 3H, CH3); 13C-NMR (CDCl3, 101 MHz, ppm): 199.3; 156.2; 136.7 135.5; 133.3; 128.7; 128.7; 128.5;
128.5; 128.5; 128.5; 128.0; 127.6; 127.6; 67.4; 55.4; 38.7; 15.5; 14.6; HR-MS (ESI): for C19H21NO3 [M+H]+
calculated: 312.1600 m/z, found: 312.1645 m/z.
Benzyl methyl(1-oxo-1-phenylbutan-2-yl)carbamate (24). Yield 87%; 1H-NMR (d6-aceton, 500 MHz,
ppm): 8.03-7.87 (m, 2H, aromat); 7.62 (dt, 1H, aromat, J1 = 22.0, J2 = 7.4); 7.52–7.29 (m, 7H, aromat); 5.52
(ddd, 1H, CH, J1 = 46.7, J2 = 9.6, J3 = 5.3); 5.29–5.10 (m, 2H, CH2); 2.76 (s, 3H, CH3); 1.99–1.73 (m, 2H,
CH2); 0.98-0.92 (m, 3H, CH3); 13C-NMR (d6-aceton, 126 MHz, ppm): 198.4; 156.4; 137.3 136.1; 133.1;
128.6; 128.6; 128.4; 128.4; 128.2; 128.2; 127.8; 127.5; 127.5; 66.8; 60.7; 29.6; 20.5; 9.8; HR-MS (ESI): for
C19H21NO3Na [M+Na]+ calculated: 334.1419 m/z, found: 334.1418 m/z.
Benzyl ethyl(1-oxo-1-phenylbutan-2-yl)carbamate (25). Yield 83%; 1H-NMR (CDCl3, 500 MHz, ppm):
8.10-7.83 (m, 2H, aromat); 7.60-7.26 (m, 8H, aromat); 5.62 (dd, 1H, CH, J1 = 8.3, J2 = 6.6); 5.22 (q,
2H, CH2, J = 12.5 Hz) 3.14 (dd, 2H, CH2, J1 = 14.1, J2 = 7.0); 2.07–1.72 (m, 2H, CH2); 0.99 (dq, 6H,
2CH3, J1 = 13.5 Hz, J2 = 6.9); 13C-NMR (CDCl3, 126 MHz, ppm): 198.8; 156.7; 136.7; 135.9; 133.3; 128.7;
128.7; 128.5; 128.5; 128.5; 128.5; 128.0; 127.6; 127.6; 67.5; 60.3; 38.3; 21.7; 15.1; 10.5; HR-MS (ESI): for
C20H23NO3 [M+H]+ calculated: 326.1756 m/z, found: 326.1763 m/z.
1
Benzyl methyl(1-oxo-1-phenylpentan-2-yl)carbamate (26). Yield 80%; H-NMR (CDCl3, 500 MHz,
ppm): 8.06-7.82 (m, 2H, aromat); 7.56 (dt, 1H, aromat, J1 = 23.9 Hz, J2 = 7.4 Hz); 7.48–7.27 (m, 7H,
aromat); 5.60 (ddd, 1H, CH, J1 = 115.5, J2 = 9.2, J3 = 5.6); 5.33-5.12 (m, 2H, CH2); 2.78 (d, 3H, CH3,
J = 15.1); 1.93-1.72 (m, 2H, CH2); 1.43-1.30 (m, 2H, CH2); 0.99 (dt, 3H, CH3, J1 = 15.0 Hz, J2 = 7.4 Hz);
13C-NMR (CDCl3, 126 MHz, ppm): 199.0; 156.7; 136.7; 135.7; 133.3; 128.7; 128.7; 128.5; 128.5; 128.3;
128.3; 128.0; 127.6; 127.6; 67.5; 58.5; 29.7; 29.3; 19.1; 13.9; HR-MS (ESI): for C20H23NO3Na [M+Na]+
calculated: 348.1576 m/z, found: 348.1574 m/z.
Benzyl methyl(1-oxo-1-(m-tolyl)propan-2-yl)carbamate (27). Yield 81%; 1H-NMR (CDCl3, 400 MHz,
ppm): 7.83–7.58 (m, 2H, aromat); 7.41–7.18 (m, 7H, aromat); 5.63 (dq, 1H, CH, J1 = 104.2, J2 = 6.8);
5.29-5.10 (m, 2H, CH2); 2.80 (d, 3H, CH3, J = 22.4 Hz); 2.36 (d, 3H, CH3, J = 25.2 Hz); 1.41 (d, 3H, CH3,
J = 6.9); 13C-NMR (CDCl3, 101 MHz, ppm): 199.4; 156.2; 138.5; 136.7; 135.4; 134.1; 128.9; 128.5; 128.5;
128.5; 128.5; 128.0; 127.6; 125.6; 67.4; 55.2; 29.4; 21.3; 13.5; HR-MS (ESI): for C19H21NO3Na [M+Na]+
calculated: 334.1419 m/z, found: 334.1419 m/z.

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1
Benzyl (1-(3-chlorophenyl)-1-oxopropan-2-yl)(methyl)carbamate (28). Yield 91%; H-NMR (CDCl3,
500 MHz, ppm): 8.00–7.86 (m, 1H, aromat); 7.84–7.63 (m, 1H, aromat); 7.56–7.49 (m, 1H, aromat);
7.47–7.44 (m, 1H, aromat); 7.42–7.23 (m, 5H, aromat); 5.55 (dq, 1H, CH, J1 = 141.1, J2 = 6,8); 5.27–5.11
(m, 2H, CH2); 2.79 (d, 3H, CH3, J = 30.2 Hz); 1.41 (d, 3H, CH3, J = 6.9 Hz); 13C-NMR (CDCl3, 126 MHz,
ppm): 198.0; 156.2; 136.9; 136.5; 136.1; 135.0; 133.2; 130.0; 128.4; 128.4; 128.7; 127.7; 127.7; 126.5; 67.6; 55.5;
29.5; 13.3; HR-MS (ESI): for C18H18ClNO3Na [M+Na]+ calculated: 354.0873 m/z, found: 354.0863 m/z.
◦
Benzyl (1-(4-methoxyphenyl)-1-oxopropan-2-yl)(methyl)carbamate (29). Yield 79%; m.p.: 48–50 C;
1H-NMR (CDCl3, 400 MHz, ppm): 7.91 (dd, 2H, aromat, J1 = 74.8 Hz, J2 = 8.6); 7.43–7.24 (m, 5H,
aromat); 6.85 (dd, 2H, aromat, J1 = 55.0 Hz, J2 = 8.6); 5.67 (dt, 1H, CH, J1 = 93.9, J2 = 6.8); 5.36-5.08 (m,
2H, CH2); 3.87 (d, 3H, CH3, J = 9.7 Hz); 2.77 (d, 3H, CH3, J = 14.7 Hz); 1.42-1.35 (m, 3H, CH3); 13C-NMR
(CDCl3, 101 MHz, ppm): 197.3; 163.7; 156.2; 136.7; 130.9; 130.9; 128.5; 128.5; 128.2; 128.0 127.7; 127.7;
113.9; 113.9; 67.4; 55.4; 54.6; 29.2; 13.4; HR-MS (ESI): for C19H21NO4 [M+H]+ calculated: 328.1549 m/z,
found: 328.1537 m/z.
◦
1
Benzyl methyl(1-oxo-1-(p-tolyl)propan-2-yl)carbamate (30). Yield 84%; m.p.: 62–63 C; H-NMR
(d6-aceton, 500 MHz, ppm): 7.89-7.74 (m, 2H, aromat); 7.42-7.21 (m, 7H, aromat); 5.60 (dq, 1H, CH,
J1 = 44.4, J2 = 6.9); 5.24-5.04 (m, 2H, CH2); 2.86–2.76 (m, 3H, CH3); 2.07 (tt, 3H, CH3, J1 = 4.4, J2 = 2.2);
1.43-1.33 (m, 3H, CH3); 13C-NMR (d6-aceton, 126 MHz, ppm): 198.1; 155.7; 143.8; 137.3; 133.2; 129.2;
129.2; 128.4; 128.4; 128.3; 128.3; 127.8; 127.5; 127.5; 66.8; 55.6; 30.0; 20.7; 12.8; HR-MS (ESI): for
C19H21NO3Na [M+Na]+ calculated: 334.1419 m/z, found: 334.1419 m/z.
◦
Benzyl (1-(4-chlroophenyl)-1-oxopropan-2-yl)(methyl)carbamate (31). Yield 90%; m.p.: 72–73 C;
1H-NMR (d6-aceton, 500 MHz, ppm): 7.90 (dd, 2H, aromat, J1 = 50.6 Hz, J2 = 8.4 Hz); 7.47 (dd, 2H,
aromat, J1 = 38.9 Hz, J2 = 48.4 Hz); 7.40–7.27 (m, 5H, aromat); 5.53 (dq, 1H, CH, J1 = 26.0, J2 = 6.7);
5.22–5.02 (m, 2H, CH2); 2.87–2.76 (m, 3H, CH3); 1.39 (t, 3H, CH3, J = 8.3 Hz); 13C-NMR (d6-aceton,
126 MHz, ppm): 197.6; 155.7; 138.5; 137.2; 134.6; 129.9; 129.9; 128.7; 128.7; 128.4 128.4; 127.9; 127.9; 127.5;
66.8; 56.3; 29.7; 12.5; HR MS (ESI): for C18H18 ClNO3Na [M+Na]+ calculated: 354.0873 m/z, found:
354.0864 m/z.
◦
Benzyl (1-(4-bromophenyl)-1-oxopropan-2-yl)(methyl)carbamate (32). Yield 92%; m.p.: 63–64 C;
1H-NMR (d6-aceton, 500 MHz, ppm): 7.90–7.74 (m, 2H, aromat); 7.63 (dd, 2H, aromat, J1 = 38.6 Hz,
J2 = 8.3 Hz); 7.40–7.27 (m, 5H, aromat); 5.57–5.45 (m, 1H, CH); 5.22–5.02 (m, 2H, CH2); 2.82 (dd, 3H,
CH3, J1 = 12.5 Hz, J2 = 4.2 Hz); 1.39 (t, 3H, CH3, J = 8.2 Hz); 13C-NMR (d6-aceton, 126 MHz, ppm):
197.8; 155.7; 137.2; 134.9; 131.7; 131.7; 129.9; 129.9; 128.4; 128.4; 127.8; 127.5; 127.5; 127.2; 66.8; 56.3; 29.8;
12.5; HR-MS (ESI): for C18H18BrNO3 Na [M+Na]+ calculated: 398.0368 m/z, found: 398.0367 m/z.
Benzyl (1-(3,4-dimethylphenyl)-1-oxopropan-2-yl)(methyl)carbamate (33). Yield 79%, 1H-NMR
(d6-aceton, 500 MHz, ppm): 7.78–7.59 (m, 2H, aromat); 7.41–7.28 (m, 5H, aromat); 7.21 (dd, 1H,
aromat, J1 = 30.0 Hz, J2 = 7.8 Hz); 5.61 (dq, 1H, CH, J1 = 45.9, J2 = 6.8); 5.15 (ddd, 2H, CH2, J1 = 31.2 Hz,
J2 = 15.7 Hz, J3 = 8.9 Hz) 2.88-2.74 (m, 3H, CH3); 2.29 (dd, 3H, CH3, J1 = 24.4 Hz, J2 = 15.7 Hz); 2.07 (ddt,
3H, CH3, J1 = 6.6 Hz, J2 = 4.4 Hz, J3 = 2.2 Hz) 1.41-1.33 (m, 3H, CH3); 13C-NMR (d6-aceton, 126 MHz,
ppm): 198.2; 155.7; 142.5; 137.3; 136.9; 133.6; 129.7; 129.7; 129.3; 128.4; 128.4; 127.8; 127.5; 125.9; 66.7;
55.5; 30.0; 19.1; 18.9; 12.9; HR-MS (ESI): for C20H23NO3Na [M+Na]+ calculated: 348.1576 m/z, found:
348.1575 m/z.
Benzyl (1-(benzo[d][1,3]dioxol-5-yl)-1-oxopropan-2-yl)(methyl)carbamate (34). Yield 79%; m.p.:
◦
60–61.5 C; 1H-NMR (CDCl3, 500 MHz, ppm): 7.67 (dd, 1H, aromat, J1 = 8.2 Hz, J2 = 1.4 Hz); 7.51-7.26
(m, 6H, aromat); 6.85-6.64 (m, 1H, aromat); 6.06 (s, 2H, CH2); 5.58 (dq, 1H, CH, J1 = 130.5, J2 = 6.8);
5.33-5.12 (m, 2H, CH2) 2.78 (d, 3H, CH3, J = 20.4 Hz); 1.44-1.33 (m, 3H, CH3); 13C-NMR (CDCl3,
126 MHz, ppm): 197.0; 156.2; 152.0; 148.2; 136.6; 130.0; 128.5; 128.5; 128.0; 127.7; 127.7; 125.0; 108.3;
108.1; 101.8; 67.5; 54.6; 29.2; 13.6; HR MS (ESI): for C19H19NO5 [M+H]+ calculated: 342.1342 m/z,
found: 342.1354 m/z.
1
Methyl methyl(1-(naphthalen-2-yl)-1-oxopropan-2-yl)carbamate (35). Yield 69%; H-NMR (CDCl3,
400 MHz, ppm): 8.41 (dd, 1H, aromat, J1 = 29.5, Hz, J2 = 8.3); 8.00 (d, 2H, aromat, J = 8.3 Hz); 7.93–7.87
(m, 1H, aromat); 7.63–7.46 (m, 3H, aromat); 5.80-5.29 (m, 1H, CH), 3.68 (d, 3H, CH3, J = 13.5); 2.87

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(d, 3H, CH3, J = 42.2 Hz); 1.53-1.42 (m, 3H, CH3); 13C-NMR (CDCl3, 101 MHz, ppm): 203.7; 157.1;
134.2 134.0; 132.5; 130.5; 128.5; 127.8; 127.2; 126.4; 125.3; 124.5; 58.0; 52.9; 30.0; 13.6; HR-MS (ESI): for
C16H17NO3Na [M+Na]+ calculated: 294.1106 m/z, found: 294.1107 m/z.
1
Phenyl (1naphthalen-1-yl)-1-oxopropan-2-yl)(methyl)carbamate (36). Yield 68%; H-NMR (CDCl3,
400 MHz, ppm): 8.38 (dd, 1H, aromat, J1 = 56.8, Hz, J2 = 8.6); 7.94 (ddd, 3H, aromat, J1 = 28.4 Hz,
J2 = 14.9 Hz, J3 = 7.9 Hz); 7.65-7.44 (m, 3H, aromat); 7.41-7.22 (m, 5H, aromat); 5.63 (gq, 1H, CH;
J1 = 103.9, Hz, J2 = 7.0); 2.95–2.82 (m, 3H, CH3); 1.50–1.45 (m, 3H, CH3); 13C-NMR (CDCl3, 101 MHz,
ppm): 203.5; 156.4; 136.6; 134.2; 134.0; 132.5; 130.6; 128.5; 128.5; 128.5; 128.5; 127.9; 127.8; 127.6; 127.2;
126.4; 125.3; 124.5; 58.2; 30.2; 13.6; HR-MS (ESI): for C21H19NO3 [M+H]+ calculated: 334.1443 m/z,
found: 334.1450 m/z.
Phenyl (1-(4-chloronaphthalen-1-yl)-1-oxopropan-2-yl)(methyl)carbamate (37). Yield 71%; m.p.:
◦
1
85-87 C; H-NMR (CDCl3, 500 MHz, ppm): 8.50-8.35 (m, 2H, aromat); 7.87 (dd, 1H, aromat,
J1 = 73.5 Hz, J2 = 7.8 Hz); 7.72–7.58 (m, 3H, aromat); 7.39–7.18 (m, 3H, aromat); 7.06–6.92 (m, 2H,
aromat) 5.66 (dq, 1H, CH, J1 = 63.0 Hz, J2 = 7.0 Hz); 3.03 (d, 3H, CH3, J = 15.8); 1.54 (d, 3H, CH3,
J = 7.1 Hz); 13C-NMR (CDCl3, 126 MHz, ppm): 202.8; 155.2; 151.3; 136.7; 133.4; 131.7; 131.3; 129.3; 129.3;
128.6; 127.6; 127.0; 125.7; 125.5; 125.0; 124.8; 121.6; 121.6; 58.6; 30.9; 13.6; HR-MS (ESI): for C21H18ClNO3
[M+H]+ calculated: 368.1053 m/z, found: 368.1067 m/z.
Phenyl (1-(4-bromonaphthalen-1-yl)-1-oxopropan-2-yl)(methyl)carbamate (38). Yield 72%; m.p.:
103–104 ◦C; 1H-NMR (CDCl3, 500 MHz, ppm): 8.46–8.30 (m, 2H, aromat); 7.88-7.83 (m, 1H, aromat);
7.72–7.56 (m, 3H, aromat); 7.39–7.19 (m, 3H, aromat); 7.06–6.91 (m, 2H, aromat); 5.64 (dq, 1H, CH,
J1 = 61.2 Hz, J2 = 7.1 Hz); 3.03 (d, 3H, CH3, J = 16.6); 1.54 (d, 3H, CH3, J = 7.1 Hz); 13C-NMR (CDCl3,
126 MHz, ppm): 203.0; 155.2; 151.2; 134.3; 132.5; 131.6; 129.3; 129.3; 128.8; 128.6; 127.9; 127.8; 127.1;
126.2; 125.7; 125.5; 121.6; 121.6; 58.7; 30.9; 13.6; HR MS (ESI): for C21H18BrNO3Na [M+Na]+ calculated:
434.0368 m/z, found: 434.0352 m/z.
1
Benzyl methyl(1-(naphthalen-1-yl)-1-oxopropan-2-yl)carbamate (39). Yield 68%; H-NMR (CDCl3,
500 MHz, ppm): 8.48–8.27 (m, 1H, aromat); 7,.(ddd, 3H, aromat; J1 = 35.0 Hz, J2 = 16.7 Hz, J3 = 9.3);
7.65–7.45 (m, 3H, aromat); 7.41–7.22 (m, 5H, aromat); 5.63 (dq, 1H, CH, J1 = 130.5 Hz, J2 = 7.0 Hz);
5.24–5.02 (m, 2H, CH2); 2.89 (d, 3H, CH3, J = 33.7 Hz); 1.48 (dd, 3H, CH3, J1 = 7.0 Hz, J2 = 3.9 Hz);
13C-NMR (CDCl3, 126 MHz, ppm): 203.6; 156.4; 136.6; 134.2; 134.0; 132.6; 130.5; 128.5; 128.5; 127.9;
127.8; 127.8; 127.6; 127.6; 127.2; 126.4; 125.3; 124.5; 67.4; 58.2; 30.2; 13.6; HR-MS (ESI): for C22H21NO3
[M+H]+ calculated: 348.1600 m/z, found: 348.1608 m/z.
B◦enzyl (1-(4-chloronaphthalen-1-yl)-1-oxopropan-2-yl)(methyl)carbamate (40). Yield 72%; m.p.:
65–66 C; 1H-NMR (CDCl3, 400 MHz, ppm): 8.50–8.28 (m, 2H, aromat); 7.87 (d, 1H, aromat; J = 7.8 Hz,);
7.70–7.46 (m, 3H, aromat); 7.43-7.19 (m, 5H, aromat); 5.55 (dq, 1H, CH, J1 = 91.4 Hz, J2 = 7.0 Hz);
5.26-4.98 (m, 2H, CH2); 2.88 (d, 3H, CH3, J = 30.1 Hz); 1.48 (t, 3H, CH3, J = 5.8 Hz); 13C-NMR (CDCl3,
101 MHz, ppm): 202.9; 156.3; 136.5; 136.4; 133.3; 131.7; 131.3; 128.7; 128.5; 128.5; 128.5; 128.5; 128.0; 127.6;
126.9; 126.2; 125.8; 124.9; 67.4; 58.3; 30.3; 13.4; HR-MS (ESI): for C22H20ClNO3Na [M+Na]+ calculated:
404.1029 m/z, found: 404.1014 m/z.
B◦enzyl (1-(4-bromonaphthalen-1-yl)-1-oxopropan-2-yl)(methyl)carbamate (41). Yield 73%; m.p.:
59–60 C; 1H-NMR (CDCl3, 400 MHz, ppm): 8.47–8.22 (m, 2H, aromat); 7.78 (d, 1H, aromat; J = 7.9 Hz,);
7.70–7.51 (m, 3H, aromat); 7.44–7.16 (m, 5H, aromat); 5.54 (dq, 1H, CH, J1 = 89.1 Hz, J2 = 7,0 Hz);
5.26-4.96 (m, 2H, CH2); 2.88 (d, 3H, CH3, J = 29.8 Hz); 1.48 (t, 3H, CH3, J = 6.1 Hz); 13C-NMR (CDCl3,
101 MHz, ppm): 203.0; 156.3; 136.5; 134.1; 132.5; 131.6; 128.7; 128.7; 128.5; 128.5; 128.5; 128.5; 128.0;
127.8; 127.7; 127.6; 127.0; 125.8; 67.4; 58.4; 30.4; 13.4; HR-MS (ESI): for C22H20BrNO3 Na [M+Na]+
calculated: 448.0524 m/z, found: 448.0512 m/z.
3.3. Evaluating the In Vitro AChE- and BChE-Inhibition Potency
The ability of all the prepared compounds to inhibit AChE from electric eel (Electrophorus
electricus) and BChE from equine serum (both purchased from Sigma, St. Louis, MO, USA) was
determined in vitro using a modified Ellman’s method. The effectiveness of the inhibitors, which

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are expressed as the IC50 values, represent the concentration of an inhibitor that was required for
reduction of enzyme activity (or reaction rate) to 50% (sometimes referred to as the negative logarithm
of the molar concentration required to inhibit the enzyme activity by 50%, pIC50 = log 1/IC50).
The Ellman’s method is widely used for measuring cholinesterase activity and the effectivity of
ChEIs [49]. It is a simple, quick and direct method to determine the content of the SH and
–S–S– groups in proteins [50]. Cholinesterase activity is measured indirectly by quantifying the
concentration of the 5-thio-2-nitrobenzoic acid (TNB) ion that is formed in the reaction between
disulfide reagent 5,50-dithiobis-2-nitrobenzoic acid (DTNB) and thiocholine, which is a product of
substrate (i.e., acetylthiocholine, ATCh) hydrolysis that is catalyzed by cholinesterase [51].
All of the examined compounds were dissolved in DMSO (concentration 0.01 M) and diluted
in demineralized water (concentrations 0.001 M and 0.0001 M). The ability of the tested compounds
to inhibit AChE (from el◦ectric eel) and BChE (from equine serum) was determined using a modified
Ellman’s method at 25 C in the presence of phosphate buffered saline (PBS, 0.1 M, pH 7.4) in a
glass cuvette with a 1 cm optical path. The enzyme activity in the total reaction mixture (2 mL) was
0.2 U/mL, the concentration of the substrate ATCh 40 µM and the concentration of DTNB 0.1 mM for
all of the reactions. The IC50 values were obtained from the dependence of v0/vi on the concentration
of the tested compound (inhibitor), where v0 is the reaction rate of an uninhibited reaction and vi
is the reaction rate of an inhibited reaction (for a given concentration of the inhibitor). First, v0 was
determined. PBS (0.1 M, pH 7.4), DTNB and ATCh were put into the cuvette. The enzymatic reaction
was started by adding the enzyme. The dependence of absorbance (
for 70 s (the reference solution contained PBS, DTNB and ATCh), and then the reaction rate (v0) was
calculated (v = A/ t). The measurement was performed at least in triplicate, and average v0 was
λ = 412 nm) on time was observed
∆
∆
determined. Then, vi (for a given concentration of the inhibitor) was determined. DTNB, ATCh
and the selected volume of a suitably diluted inhibitor (to achieve the required concentration of the
inhibitor in the total reaction mixture) and a certain volume of PBS (to achieve the total volume of the
reaction mixture 2 mL after adding the enzyme) were put into the cuvette. The enzymatic reaction was
started by adding the enzyme. The dependence of absorbance (λ = 412 nm) on time was observed for
70 s (the reference solution was the same as for uninhibited reaction), and then the reaction rate (vi)
was calculated. To determine the IC50 values, twelve different concentrations of the inhibitor were
used and each measurement was performed at least in triplicate. Finally, the dependence of v0/vi
on the concentration of the inhibitor was determined, and the IC50 values were calculated from the
obtained equation of the regression curve for y = 2 (resulting from the definition of the IC50 value) [52].
The obtained results are presented in Table 1.
3.4. Comparative Molecular Surface Analysis using Iterative PLS-Based Variable Elimination
Self-organizing neural mapping (SOM) is regarded as being a nonlinear projection tool that
decreases the dimensionality of the input object, e.g., converts 3D objects to 2D, while preserving the
topological relationships between the input and output data. Moreover, a trained network can be used
to project the specified molecular property (expressed as a vector) by generating a 2D color-coded
clustering pattern that is called a feature map. Hence, the SOM algorithm was used to generate an
electrostatic potential map in the form of a 2D topographic pattern produced from input signals (points)
that were sampled randomly at the molecular surface [53]. In this case, specifying the closest neighbor
and then projecting the signals into this particular neuron is based on a comparison of each 3D input
vector that consists of x, y and z coordinates with a three-element weight vector that describes each
neuron. The shape of a specific molecular surface (template) that is encoded in the weights of the
trained Kohonen network can be used to process the signals coming from the surface of the other
molecule(s) (counter-template), thereby producing a series of comparative SOM maps can be used to
compare/contrast the superimposed molecular geometry.

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Applying SOMs to compress/visualize/classify the structural data has been widely reported, e.g.,
for the 2D mapping of the electrostatic potential on 3D molecular surfaces or partial atomic charges for
atomic molecular representation [54].
The set of CoMSA shape/electronic descriptors is subsequently processed using the PLS method
and expresses the relationship between variable y and the set of predictors X in a form that is
represented by the following equation:
y = X × b + e
(1)
where b is the vector of the regression coefficients and e is the vector of the errors. Generally, PLS
models are constructed for centered/autoscaled data, and their complexity is estimated using, e.g.,
the leave-one-out cross-validation (LOO-CV) procedure. The cross-validated q2cv is calculated as:
m (obsi − predi)2
∑
q2cv = 1 −
(2)
i
m(obsi − mean(obsi))2
∑
i
where obs is the observed value; pred is the predicted value; mean is the mean value of obs; and i refers
to the object index, which ranges from 1 to m.
The quality of the external predictions was measured using the standard deviation of error of the
prediction (SDEP), q2test and the mean absolute error (MAE) parameters, which are defined, respectively, as:
s
n(predi − obsi)2
∑
i
SDEP =
(3)
n
n (obsi − predi)2
∑
q2cv = 1 −
(4)
(5)
i
n(obsi − mean(obsi))2
∑
i
n
i |predi − obsi|
∑
MAE =
n
where n is the number of objects in a test set.
Redundant variables can impede the interpretation of a model by increasing its complexity;
therefore, reducing the number of variables is advisable. The iterative variable elimination (IVE-PLS)
procedure, which is a modification of the UVE-PLS algorithm (developed by Centner et al.) has been
proposed, to analyze the stability of the regression coefficients, which are expressed as the mean(b)/s(b)
ratio, where s(b) represents the standard deviation of the regression coefficient b [55]. Generally,
the entire procedure is composed of the following steps: (i) a standard PLS analysis with LOO-CV to
assess the performance of the PLS model (q2cv); (ii) elimination of the matrix column with the lowest
stability value; (iii) a standard PLS analysis of the new matrix without the column that was eliminated
in Step (ii); and (iv) the recurrent repetition of Steps (i)–(iii) to maximize the LOO q2cv parameter.
A molecule might be encoded by an ensemble of structural (S) and physicochemical (P) properties
that are organized in a vector, which represents an object in the chemical space (CS). The distribution of
the empirically (FCS) and virtually (VCS) produced compounds can be visually scrutinized using, e.g.,
a linear projection procedure called Principal Component Analysis (PCA). PCA is regarded as being a
classical method to explore data that permits the data dimensionality to be reduced, visualized and the
relationships between the objects (molecules) and parameters (descriptors) to be interpreted. On the
condition that the reduction of the data dimensionality is efficient, it is possible to capture interesting
information about the data structure (uncover groups of objects and atypical objects) to indicate
the importance of the original data variables that contribute to the observed structure, and finally,
to illustrate and interpret the relationships between the objects and the parameters in the X matrix
(using the first few principal components PCs) [56,57].

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3.5. Building the Model and Molecular Modeling
The same laboratory was employed to specify all of the pharmacological data to eliminate
any potential data noise that might have been introduced by pooling the datasets from various
sources. The in vitro AChE and BChE inhibition values (IC50) for the set of carbamate derivatives
are listed in Table 1. The CACTVS/csed molecular editor was used to build the set of the respective
compound models. The spatial geometry of the molecules was specified using a CORINA 3D generator.
The (inter)change file format converter OpenBabel was used to convert the chemical data.
The majority of the modeling studies were performed with a Sybyl-X 2.0/Certara software package
running on a HP workstation with a Debian 6.0 operating system. The standard Tripos force field
(POWELL conjugate gradient algorithm) with a 0.01 kcal/mol energy gradient convergence criterion
and a distant dependent dielectric constant was used to optimize the initial geometry of each compound
(MAXMIN2 module). For the electrostatic potential calculations, the Gasteiger–Hückel method
(implemented in Sybyl-X) was initially used to produce the partial atomic charges. One 13-ordered
atom trial alignment on 32 was selected to cover the entire bonding topology in the maximal common
structure (MCS) using the atom FIT method, which is based on matching the positions of the atoms
between the corresponding atom pairs.
The SONNIA software was used in the CoMSA analysis to simulate 20
×
20 to 50
×
50 SOMs with
a winning distance that varied within a range of 0.2–2.0. The Cartesian coordinates of the molecular
surfaces for the superimposed molecules were produced by a SOM network to form a 2D map of the
electrostatic potential. Pretty active against AChE and BChE 32 was selected to form the template
molecules. The output maps were subsequently transformed into a 400–2500-element vector, which
was used by the PLS method implemented in the MATLAB programming environment.
The crystallographic structure of BChE, which contained one amino acid chain and rivastigmine
analog molecule, was retrieved from the PDB repository (code 6eul). Only the 1,2-ethanediol molecule
was retained, because it was specified as being valid in the enzyme active site AC2. The rest of
the heteroatoms, including the crystallographic waters, were extracted prior to the calculations.
The ligand/protein structures for the docking study were prepared in the pdbqt file format with
the Gasteiger charges calculated. During the AutoDock simulation, various poses (default nine)
were generated progressively from a single conformer (an energy-optimized molecule) by applying
a collection of the preferred torsion angles to the rotatable bonds and were evaluated using the
united-atom scoring function. All of the predicted binding 2D/3D modes, including the positions of
the flexible side chains, were visualized using PyMol, Maestro and VMD molecular graphics viewers
and the Protein-Ligand Interaction Profiler (PLIP).
4. Conclusions
To summarize, a series of novel benzene-based derivatives was designed, synthesized and
characterized using 1H NMR spectroscopy and HRMS. All 41 of the tested compounds were evaluated
for their in vitro ability to potentially inhibit AChE and BChE, respectively. The selectivity index of
the individual molecules to cholinesterases was also specified. Roughly speaking, a rather moderate
inhibitory effect against AChE was revealed; however, some of the compounds (11, 13, 14, 16, 23–28,
31, and 33) proved to be very selective for the BChE enzyme. In fact, two compounds (23 and
28) had a very high selectivity index (SI = 13.93 and 15.31). Specifically, compound 28 had the
lowest IC50 value revealing an approximately seven-fold higher inhibitory activity against BChE
than RIV (IC50 = 5.51 vs. 38.40 µM), which corresponds quite well with GLT (IC50 = 2.77 µM).
It was observed that the methyl-substituted carbamates generally exhibited a lower inhibitor ability
compared to their phenyl or benzyl counterparts. Interestingly, compounds 17 and 29, which had
a methoxy group in the same position, were weaker inhibitors—the presence of the hydrophilic
electron-donating -OCH3 substituent of the phenyl ring at the para-position decreased the potency
of the compounds, especially against the BChE enzyme. On the other hand, the presence of methyl
group(s) in the meta/para-position(s) of the phenyl ring (compounds 15, 18, 21, 27, 30, and 33) resulted

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in an improvement of the IC50 value for the BChE enzyme, thus suggesting the significance of a
hydrophobic character for the interactions with the enzyme. Placing the electron-withdrawing chlorine
substituent in the meta-position of the phenyl ring appeared to be strongly preferable, especially for
the BChE inhibition activity (compounds 16 or 28). To partially explain the observed variations in the
anti-AChE/BChE potential of the investigated carbamate series, comparative receptor-independent (RI)
and receptor-dependent (RD) structure–activity studies were conducted, respectively. The principal
purpose of the ligand-based study was to comparatively analyze the molecular surface (CoMSA) to
gain insight into the electronic and/or steric factors that govern the ability of the tested compounds to
inhibit the AChE/BChE activities. The findings for the surface descriptors were compared with their
force field counterparts (CoMFA) when modeling the inhibiting potency for multiple training/test
subsets using the stochastic model validation (SMV) procedure. Moreover, a similarity analysis was
performed using the PCA approach on the pool of Dragon descriptors. The spatial distribution of the
potentially important steric and electrostatic factors on the BChE inhibitory potency was specified
using the probability-guided pharmacophore mapping procedure based on the iterative variable
elimination (IVE-PLS) method. Finally, a comprehensive scrutiny of the guest–target interactions for
the inhibitors that were comparatively active to rivastigmine BChE (IC50 < 30 µM) was conducted
using the site-directed computer-assisted docking methodology. Planar (2D) and spatial (3D) maps
of the host–target interactions were created for all of the active compounds and compared with
the marketed drug (GLT and RIV) molecules, which generally revealed two types of non-binding
interactions—hydrophobic and hydrogen bond formation, respectively. The hydroxyl substituent
of Thr120 appeared to be crucial in forming the hydrogen bond with the ether or carbonyl oxygen
(Figure 9c). For the examined BChE inhibitors, the regions in close proximity to the nitrogen atom
(R1 substituent) seemed to be valid for hydrophobic interactions with the Asp70 amino acid residue,
which is in line with ligand-based findings. Regrettably, a clear explanation of the variations that
the meta/para-positioned carbamate derivatives exerted on the BChE reaction site was not revealed;
however, some regularities were observed in the ligand–receptor interaction pattern. It appeared that
the close proximity of the positively charged nitrogen atom of His438 may potentially be beneficial
to the inhibition potential, especially the negatively charged chlorine- or bromine-based carbamates,
which corresponds relatively well with the IVE-PLS CoMSA results. The electrostatic repulsion
between the negatively charged atoms and the oxygen of Ser79 can partially explain the detrimental
impact of the –OCH3 or –OCH2O- groups that were attached to the phenyl ring. On the other
hand, the postulated hydrophobic interactions with Phe329 can favorably contribute to the inhibitory
potential, as was observed for (di)methyl derivatives.
The combination of consensus pharmacophore mapping with a systematic screening of the
multifaceted guest–host interactions using target-tailored approaches seems to be the path towards an
intelligent drug design system.
Author Contributions: A.B., V.K.; CoMSA, CoMFA, and SMV: docking analysis, and writing, editing, reviewing
of the paper. D.J.S.: HRMS analyses. D.K., K.G., and J.P.: design, synthesis and compound characterisations. A.S.
(Aleksandra Swietlicka); descriptor calculations. and editing of the paper. A.S. (Adam Smolinski), J.J.: editing and
writing of the paper. S.S. and A.I.: AChE/BChE evaluation.
Funding: This study was supported by the Slovak Research and Development Agency (projects APVV-17-0373
and APVV-14-0547), and by the Ministry of Education of the Czech Republic (LO1305).
Acknowledgments: We would like to acknowledge the Schrödinger, OpenBabel and OpenEye for the free
academic software licenses. The authors wish to acknowledge the financial support to the Faculty of Chemical
Technology, University of Pardubice as well as to the project of Grant Agency of Czech Republic—Project No.:
GA 18-03847S.
Conflicts of Interest: The authors declare no conflict of interest.

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Abbreviations
AChE
BChE
CAMD
ADMET
CoMSA
SMV
Acetylcholinesterase
Butyrylcholinesterase
Computer Asisted Molecular Design
Absorption Distribution Metabolism Excretion Toxicity
Comparative Molecular Surface Analysis
Stochastic Model Validation
RIV
Rivastigmine
GLT
Galanthamine
PCA
IVE-PLS
PLIP
Principal Component Analysis
Iterative Variable Elimination Partial Least Squares
Protein Ligand Interaction Profiler
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