SAR-mediated similarity assessment of the property profile for new, silicon-based AChE/BChE inhibitors

Article abstract of DOI:10.3390/ijms20215385

A set of 25 novel, silicon-based carbamate derivatives as potential acetyl-and butyrylcholinesterase (AChE/BChE) inhibitors was synthesized and characterized by their in vitro inhibition profiles and the selectivity indexes (SIs). The prepared compounds w

Full text of DOI:10.3390/ijms20215385

International Journal of
Molecular Sciences
Article
SAR-mediated Similarity Assessment of the Property
Profile for New, Silicon-Based AChE/BChE Inhibitors
Andrzej Bak 1,
* *
, Hana Pizova 2, , Violetta Kozik 1, Katarina Vorcakova 3 , Jiri Kos 4,
Jakub Treml 5, Klara Odehnalova 2, Michal Oravec 6, Ales Imramovsky 7 , Pavel Bobal 2
,
Adam Smolinski 8 , Zdeneˇk Trávnícˇek 4 and Josef Jampilek 4,
*
1
Institute of Chemistry, University of Silesia, Szkolna 9, 40 007 Katowice, Poland; violetta.kozik@us.edu.pl
Department of Chemical Drugs, Faculty of Pharmacy, University of Veterinary and Pharmaceutical Sciences,
Palackeho 1, 612 42 Brno, Czech Republic; odehnalovak@vfu.cz (K.O.); pavelbobal@yahoo.com (P.B.)
Department of Biological and Biochemical Sciences, Faculty of Chemical Technology, University of
Pardubice, Studentska 573, 532 10 Pardubice, Czech Republic; katarina.vorcakova@upce.cz
Division of Biologically Active Complexes and Molecular Magnets, Regional Centre of Advanced
Technologies and Materials, Faculty of Science, Palacky University, Slechtitelu 27, 783 71 Olomouc,
Czech Republic, jiri.kos@upol.cz (J.K.); zdenek.travnicek@upol.cz (Z.T.)
Department of Molecular Biology and Pharmaceutical Biotechnology, Faculty of Pharmacy, University of
Veterinary and Pharmaceutical Sciences, Palackeho 1, 612 42 Brno, Czech Republic; tremlj@vfu.cz
Global Change Research Institute CAS, Belidla 986/4a, 60300 Brno, Czech Republic; oravec.m@czechglobe.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
2
3
4
5
6
7
8
Department of Energy Saving and Air Protection, Central Mining Institute, Plac Gwarkow 1, 40 166 Katowice,
Poland; smolin@gig.katowice.pl
*
Correspondence: andrzej.bak@us.edu.pl (A.B.); pizovah@gmail.com (H.P.); josef.jampilek@gmail.com (J.J.)
ꢀꢁꢂꢀꢃꢄꢅꢆꢇ
ꢀꢁꢂꢃꢄꢅꢆ
Received: 15 October 2019; Accepted: 27 October 2019; Published: 29 October 2019
Abstract: A set of 25 novel, silicon-basedcarbamate derivatives as potential acetyl- and butyrylcholinesterase
(AChE/BChE) inhibitors was synthesized and characterized by their in vitro inhibition profiles and the
selectivity indexes (SIs). The prepared compounds were also tested for their inhibition potential on
photosynthetic electron transport (PET) in spinach (Spinacia oleracea L.) chloroplasts. In fact, some of the
newly prepared molecules revealed comparable or even better inhibitory activities compared to the
marketed drugs (rivastigmine or galanthamine) and commercially applied pesticide Diuron®, respectively.
Generally, most compounds exhibited better inhibition potency towards AChE; however, a wider activity
span was observed for BChE. Notably, benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(2-hydroxyphenyl)
carbamoyl]ethyl]-carbamate (2) and benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(3-hydroxyphenyl)
carbamoyl]ethyl]-carbamate (3) were characterized by fairly high selective indexes. Specifically, compound
2 was prescribed with the lowest IC50 value that corresponds quite well with galanthamine inhibition activity,
while the inhibitory profiles of molecules
[(4-hydroxyphenyl)carbamoyl]ethyl]carbamate (
3
and benzyl-N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-
) are in line with rivastigmine activity. Moreover,
4
a structure–activity relationship (SAR)-driven similarity evaluation of the physicochemical properties for
the carbamates examined appeared to have foreseen the activity cliffs using a similarity–activity landscape
index for BChE inhibitory response values. The ‘indirect’ ligand-based and ‘direct’ protein-mediated
in silico approaches were applied to specify electronic/steric/lipophilic factors that are potentially valid
for quantitative (Q)SAR modeling of the carbamate analogues. The stochastic model validation was
used to generate an ‘average’ 3D-QSAR pharmacophore pattern. Finally, the target-oriented molecular
docking was employed to (re)arrange the spatial distribution of the ligand property space for BChE and
photosystem II (PSII).
Keywords: silicon-based carbamates; in vitro cholinesterase inhibition; CoMSA; IVE-PLS; molecular
docking; similarity-activity landscape index
Int. J. Mol. Sci. 2019, 20, 5385; doi:10.3390/ijms20215385
www.mdpi.com/journal/ijms

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1. Introduction
Finding critical in vitro and/or in silico parameters (descriptors or properties) of the
lead seed drug route is still a long-term aspiration and monumental challenge in drug discovery
projects, mainly due to the intrinsic multidimensionality of the problem [ ]. In concert with the medicinal
hit→
→
→
1
chemist’s intuition (or serendipity), the computer-assisted molecular design (CAMD) approaches are
regardedasanintegralmethodfortheexhaustivetransformationofthecompoundtopologyortopography
and/or the electronic properties into the property-based chemical space [2]. The systematic observational
of trends of structural chemical modifications that produce corresponding changes in the biological
responses for a congeneric series of molecules are fundamental for the multidimensional quantitative
structure–activity (mD-QSAR) studies [3]. Moving from complex biological relationships to simple
QSAR models seems to be a ‘triumph of hope over experience,’ since modelers sometimes are playing a
‘glass bead game.’ The quantitative mapping of experimental/calculated properties/descriptors into the
ADMET-driven molecular potency is frequently a ‘hit-or-miss affair.’ Therefore, some structure–activity
relationship (SAR)-mediated guides are urgently needed at the ‘pre-synthesis’ stage [4].
Molecular similarity is unarguably at the core of many SAR-based methods, assuming that small
variations in structure lead to small changes in activity. In other words, the working tenet of QSAR is
that molecules with high similarity share common effects (similarity principle); however, the validity of
the assumption is still questionable—there is no absolute standard (or metric) of similarity [
similarity is a subjective concept directly related to aspects of human cognition; this phenomena is
sometimes called ‘psychological proximity’ [ ]. Maggiora’s provocative statement that, “Similarity, like
pornography, is difficult to define, but you know it when you see it,” is probably a better illustration
of cognitive aspects of similarity [ ]. Despite the presence of fluid scientific boundaries, e.g., some
5]. In fact,
6
7
similarity metrics exhibit size-dependent behavior—similarity quantification is still a powerful concept
that allows us to specify (with some confidence) the shared host–quest interactions. In fact, the idea of
specifying a numerical measure of the intermolecular similarity based on the degree of the resemblance
between two objects, each described by a number of attributes, has been widely applied in chemical
information systems. A set of characteristics might be denoted by a bit-string representation, sometimes
augmented with the weighting factors, assigning varying importance to the particular descriptors to
calculate a quantitative measure of the structural resemblance between objects, for instance Tanimoto
coefficient [8]. Obviously, a crucial question that confronts drug ‘hunters’ is, which properties or
features should be employed in the receptor-dependent (RD) methods that rely on the principle of
complementarity or in the receptor-independent (RI) ones that are mainly based on the principle of
similarity? Hence, many attempts have been undertaken in order to implement some rough guidelines,
sometimes called ‘rules of thumb’ for both the ligand and structure-based in silico protocols; however,
even meeting the rules does not guarantee the final success of drug discovery projects [9].
A confusing aspect of many SAR analyses is that the ‘fragile event’ can occur, when even small
structural changes (colloquially termed ‘magic methyl’) can result in a boost of potency for the target,
or in a more detrimental manner, can completely destroy biological response—a phenomenon known
as activity ‘hotspot’ or activity ‘cliff’ in structure-activity landscape [10]. In other words, finding
the optimal balance between ADMET-related properties and desired drug potency (‘sweet spot’)
can be rationalized graphically by extension of the 2D similarity-based projection with activity data
in the form of a ‘biological response surface’ or SAR landscapes [11]. Intuitively, we feel that the
structure–activity landscapes are heterogeneous in nature with unevenly distributed regions that
are reminiscent of Tolkien’s Misty Mountains with gently slopes, steep cliffs and stony bridges [12].
Geographically, a successful voyage (or exploration) requires a map indicating smooth and flat regions
that are easy to navigate or jagged parts that are tough to traverse. Chemically, gently sloped hills
pinpoint areas where gradual structural variations are accompanied by moderate changes in activity,

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whereas in flat SAR landscapes, the biological data varies little over the wide modifications in the
compound structure—further substitutions are not likely to result in more potent molecules. In fact,
disallowed structural modifications can be highlighted in the sharply non-uniform regions where
chemically related compounds are characterized by huge response variations, forming steep cliffs in the
activity landscape. Moreover, chemical bridges can be observed with molecules connecting different
local SAR models [13]. The detection of the activity hotspots provides reasonable hints to the key
question of medicinal chemistry regarding the impact of molecule rearrangement on the activity profile.
The numerical quantification of activity cliffs is frequently performed using a variety of fingerprint
representations or similarity metrics; for instance, the structure–activity landscape index (SALI) has
been proposed to evaluate SAR smoothness [14].
Differently substituted amide (-CONH-) or carbamate motifs (-OCONH-) are regarded as privileged
structural fragments in a vast range of marketed drugs [15] and pesticides [16,17], that are capable of
interacting with multiple targets (polypharmacology concept) to modulate a biological response [18–21].
Obviously, polypharmacology also implies various side-effects, as was noticed by an anonymous
reviewer [22]; however, carbamate or amide-like molecules are attractive research aims to be studied
by medicinal chemists; for instance, as potential cholinesterase inhibitors (ChEIs) that are used in
Alzheimer’s disease (AD) pharmacotherapy [23–25]. Dysfunction in cognition and memory loss
(dementia) are characteristic symptoms of this progressive degeneration of the central nervous system
(CNS). According to the cholinergic hypothesis, a low level of acetylcholine (ACh) is postulated to be
one of the crucial factors in the pathogenesis of AD; therefore, ChEIs can have a symptomatic effect in
AD [26,27]. Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) are serine hydrolases that
catalyze Ach’s hydrolysis in the cholinergic synapses. The higher hydrolytic activity of AChE is partly
compensated by the increased or constant level of BChE during the course of Alzheimer’s disease [28].
The second generation of competitive (e.g., galanthamine, rivastigmine) or non-competitive (e.g.,
tacrine and donepezil) ChEIs are clinically used to reduce AD symptoms [29]. Moreover, novel anti-AD
agents have been designed, revealing some central and peripheral side effects; therefore, new targets
are being scrutinized extensively [30–35]. The drugs for the treatment of AD must overcome the
blood–brain barrier (BBB), a serious obstacle for the permeation of drugs that require the CNS action.
However, the rapid development of various nanosystems employed for nano-based drug delivery
systems has great potential to facilitate the movement of drugs across all barriers, and thus, these drug
carriers have been extensively studied within a strategy of direct drug delivery to the CNS [29,36].
The photosystem II core complex (PSIIcc) is a light-driven water-plastoquinone-oxidoreductase
that is harbored in the thylakoid membrane. It is the primary electron donor of the photosynthetic
electron transport (PET) due to its unique property of abstracting electrons from water molecules [37,38].
Over 50% of commercially available herbicides that belong to different chemical chemotypes (e.g., urea,
triazine, or phenol derivatives) act by reversible binding to the reaction center (RC) of the PSII complex
in order to compete with the native plastoquinone (PQ) molecules QA and QB. Strictly speaking,
herbicides that target PSII and inhibit PET compete with the electron acceptor PQ for binding at the
QA site in the subunit D1 in order to block the electron transfer from QA to QB [39,40]. The most
potent PET inhibitors contain an amide and/or carbamate fragment that can be substituted with mutual
displacement of QB from the binding pocket in the D1 subunit; therefore, the effects of differently
substituted amides/carbamates as anti-invasive agents that reveal the herbicidal properties are being
investigated thoroughly [41–43]. Obviously, the clarification of the specific herbicide–amino acid
residue interactions is crucial for the proper understanding of the molecular mechanism of inhibition.
In the current paper, the synthesis and SAR-driven similarity evaluation of physicochemical
properties for a novel series of 25 silicon-based carbamates as potential AChE/BChE/PET inhibitors
is reported. Moreover, a SAR-driven similarity evaluation of physicochemical properties for the
carbamates examined is reported to foresee the activity cliffs using a similarity–activity landscape
index for BChE inhibitory response values. The ‘indirect’ ligand-based and ‘direct’ protein-mediated in
silico approaches were applied to specify electronic/steric/lipophilic factors that were potentially valid

Int. J. Mol. Sci. 2019, 20, 5385
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for the (Q)SAR modeling of the investigated carbamate analogues. The stochastic model validation
(SMV) procedure was used to generate an ‘average’ CoMSA pharmacophore pattern [44]. Moreover,
the target-oriented molecular docking was employed to (re)arrange the spatial distribution of the
ligand property space for BChE and PSII, respectively. As a matter of fact, a consensus methodology as
the combination of the pharmacophore mapping with target-tailored procedures was proposed in the
investigation of the multifaceted carbamate-enzyme interactions.
2. Results and Discussion
2.1. Design and Synthesis
The synthesis of compounds 1–25 consists of two steps. In the first step, silylation with
tert-butyldimethylsilyl chloride (TBDMSCl) and imidazole in DMF was conducted according to
literature, resulting in good yields (Scheme 1) [45].
Scheme 1. Synthesis of (2S)-2-{[(benzyloxy)carbonyl]amino}-3-[(tert-butyldimethylsilyl)oxy]- propanoic
acid. Reagents and conditions: (a) TBDMSCl, imidazole, DMF.
In the second step involving amide bond formation, several chemical approaches were tested.
The only known amide from the series 1–25 was compound 25 with a nitro group in position 4 of the
aromatic ring [46
was used. Only amide 25 was prepared to satisfactory yield since the attempts to apply this method
for the synthesis of other amide derivatives were unsuccessful [49 50]. The major problem in these
–48]. In the original procedure for the synthesis of compound 25, POCl3 in pyridine
,
syntheses seems to be desilylation and subsequent uncontrolled coupling reactions. Finally, ethyl
chloroformate in the presence of triethylamine was used as a coupling reagent (see Scheme 2) [23].
Desired amides were isolated in good yields and sufficient purities.
O
O
H
H
N
R
O
N
O
NH2
OH
N
1-24
25
)
a (
), b (
H
O
+
O
O
O
R
Si
Si
1-25
R = H, OH, OCH3, CH3, F, Cl, Br, CF3, NO2
Scheme 2. Synthesis of benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[arylcarbamoyl]-ethyl]carbamate.
Reagents and conditions: (a) Ethyl chloroformate, TEA and THF (1–24); (b) POCl3, pyridine (25).
2.2. Lipophilicity Measurement
The lipophilicity indexes (log kw) were measured by means of RP-HPLC method and their values
are given in Table 1. The correlation coefficients of linear regression were greater than 0.98 with
covariances equal or less than 0.2 units (n = 2). As reported in Table 1, the lipophilicity increases in
order from hydroxy-substituted to trifluoromethyl-substituted derivatives.

Int. J. Mol. Sci. 2019, 20, 5385
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Table 1. Structures of the carbamate derivatives
1
–
25, experimental log kw values and electronic
M]) compared with
standards 3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU) rivastigmine (RIV) and galanthamine
Hammett’s
σ parameters; in vitro PET, AChE and BChE inhibition (IC50 [µ
(GLT) and in vitro viability (WST) assays (IC50
[µM]) of investigated compounds. ChE inhibition is
expressed as the mean ± SD (n = 3 experiments).
O
H
R
O
N
N
H
O
O
Si
PET
IC50 [µM]
AChE
IC50 [µM]
BChE
IC50 [µM]
WST
IC50 [µM]
SI b
No.
R
log kw
σ a
1.
2.
3.
4.
5.
6.
7.
8.
9.
H
6.52 ± 0.13
6.00 ± 0.04
5.59 ± 0.03
5.40 ± 0.03
0
4.90
0.48
4.94
483
>500
234
>500
>500
447
53.52 ± 0.40
42.15 ± 2.06
59.70 ± 0.43
36.22 ± 1.65
38.63 ± 1.81
29.39 ± 0.44
28.37 ± 0.74
46.41 ± 0.14
48.51 ± 3.77
45.04 ± 1.76
43.03 ± 1.07
45.10 ± 1.43
62.65 ± 1.41
47.15 ± 0.92
48.12 ± 1.63
42.56 ± 0.23
46.27 ± 1.09
54.37 ± 0.83
53.18 ± 0.01
38.08 ± 0.16
51.16 ± 1.03
42.79 ± 2.45
46.14 ± 2.62
49.42 ± 0.47
26.35 ± 0.84
–
73.29 ± 0.28
8.37 ± 0.03
0.73
5.04
3.13
1.60
0.47
0.23
0.19
0.44
0.37
0.34
0.53
0.28
0.35
0.44
0.50
0.68
0.74
0.51
0.73
0.37
0.58
0.55
0.73
0.25
0.14
–
>30
2-OH
3-OH
4-OH
2-OCH3 5.85 ± 0.21
3-OCH3 6.26 ± 0.03
4-OCH3 6.09 ± 0.03
−0.38
0.12
−0.37
−0.28
0.12
−0.27
−0.17
−0.07
−0.17
0.06
0.34
0.06
0.22
0.37
0.23
0.22
0.39
0.23
0.51
0.43
0.51
0.77
0.71
0.78
–
3.08 ± 0.07
19.08 ± 0.17
22.70 ± 0.24
81.47 ± 0.21
130.30 ± 1.37
152.54 ± 4.54
105.40 ± 2.78
131.22 ± 2.88
132.75 ± 2.46
81.52 ± 4.81
160.29 ± 7.89
181.13 ± 10.05
108.29 ± 1.74
96.23 ± 5.29
115.98 ± 0.60
62.60 ± 5.59
107.33 ± 0.43
72.58 ± 3.72
104.17 ± 0.36
88.87 ± 0.71
77.76 ± 0.97
63.12 ± 1.14
197.00 ± 11.28
190.35 ± 6.61
–
3.47 ± 0.08
6.43 ± 0.29
–
>30
>30
–
2-CH3
3-CH3
4-CH3
2-F
3-F
4-F
2-Cl
3-Cl
4-Cl
2-Br
3-Br
4-Br
2-CF3
3-CF3
4-CF3
2-NO2
3-NO2
4-NO2
–
6.24 ± 0.02
6.61 ± 0.03
6.62 ± 0.03
6.32 ± 0.02
6.44 ± 0.02
6.30 ± 0.02
6.74 ± 0.03
6.90 ± 0.02
6.94 ± 0.03
6.82 ± 0.03
7.05 ± 0.03
7.16 ± 0.04
6.90 ± 0.04
7.32 ± 0.03
7.53 ± 0.02
6.53 ± 0.06
6.40 ± 0.04
6.58 ± 0.04
–
–
–
>30
>30
>30
–
–
–
–
–
>30
–
–
–
>30
–
>30
–
–
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
DCMU
RIV
GLT
452
51.5
49.2
22.9
>500
332
>500
354
390
42.3
453
432
>500
43.2
>500
253
2.1
–
–
–
–
–
–
–
–
50.10 ± 3.08
19.95 ± 0.31
1.46
0.56
4.0 ± 0.13
7.96 ± 0.59
–
calculated using ACD/Percepta 14.0.0; b SI (index selectivity) = IC50 (AChE)/IC50 (BChE). A higher selectivity to BChE is
a
indicated in bold.
2.3. In Vitro Evaluation of AChE and BChE-Inhibitory Profiles
The in vitro assessment of AChE/BChE and PET inhibitory profiles for the investigated
pool of compounds was conducted and compared with marketed drugs; e.g., rivastigmine (RIV,
Exelon®, Chicago, Illinois, USA), galanthamine (GLT,) and the commercially used herbicide
3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU). Despite different mechanisms of action, RIV and
GLT are the 2nd generation of ChEIs that pseudo-irreversibly inhibit AChE and BChE [51], respectively.
DCMU is a sensitive and specific inhibitor of photosynthesis [52]. Table 1 reports the IC50 values [µM]
that express quantitative measure of inhibitor concentration needed to reduce the biological process
by 50%.

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As illustrated in Table 1 (in bold) several of the newly synthesized molecules revealed comparable
or even better inhibitory activities compared to marketed drugs (RIV and GLT) and herbicide (DCMU),
respectively. Generally, more compounds exhibited a better inhibition potency towards AChE; however,
a wider activity span (maximal value minus minimal one) was observed for BChE (∆IC50(AChE) = 36.3
versus
BChE with respect to AChE. Notably, molecules
(SI = 5.04 and SI = 3.13). Specifically, compound
corresponds quite well with GLT inhibition activity (IC50 = 7.96
∆
IC50(BChE) = 188.63
µ
M). Moreover, some of compounds proved to be highly selective for
and are characterized by fairly high selective indices
revealed the lowest IC50 value (IC50 = 8.37 M) that
M), while the inhibitory profiles of
2
2
3
µ
µ
molecules 3 and 4 (IC50 = 19.08 and IC50 = 22.70 µM) are in line with RIV activity (IC50 = 19.95 µM).
The selectivity index of the tested compounds is not as high as for some other inhibitors investigated
before. For example, Liston et al. proved that donepezil is a more than two hundred times better
inhibitor of AChE than of BChE. On the other hand, tacrine is only about four times better at the
inhibition of AChE [53]. However, there have been published several studies describing the less
significant selectivity of carbamate inhibitors towards AChE or BChE [24,54–57]. As mentioned above,
reaching the site of action is key for drugs; in the case of anti-AD drugs, this is penetration into the
CNS via the BBB. A crucial step in the design of new drugs is the evaluation of their ADME/Tox
profiles, frequently using predictive computational models. For all effective BChE and AChE inhibitors
(compounds 2–4, 25, 7 and 6), permeation via the BBB using ACD/Percepta 14.0.0 was predicted and
compared to clinically-used drug profiles (see Tables S1–S10) and for all newly designed molecules,
brain penetration sufficient for CNS activity was predicted as well.
2.4. Similarity-Based Assessment of Property Profiles
Molecular similarity is commonly used for estimation of the applicability domain of compounds
being analyzed. The similarity-mediated evaluation of property profiles was conducted using the PCA
procedure on the pool of 2774 descriptors provided by Dragon 6.0 program. The data arranged in the
X25×2774 matrix with columns that represent parameters (variables) and rows molecules (objects) were
centered and standardized. The first four principal components (PCs) characterized 72.02% of the total
variable variance, while the first two PCs described 56.70%. In fact, the efficiency of data compression
in PCA method is strongly dependent on the number of uncorrelated variables. In multi-dimensional
data, a high percentage of variance described by the first few principal components suggests that the
variables are highly inter-correlated. Conversely, smaller values of total variance characterized by the
first PCs indicates a higher number of independent descriptors. To illustrate any meaningful similarity
variations within the group of the investigated carbamate analogues, the projection of the chosen
properties on the plane defined by the first two PCs with respect to their chemotype was carried out.
The silicon-based carbamate derivatives
1–25 can be classified into structurally-related groups
along the first principal component revealing, basically, two clusters: the first one (PC1 > 20) that
contains compounds 20
–
25 and the second one (PC1 < 20) with molecules
1–19 (see Figure 1a). The most
potent BChE inhibitors
2–
4
and unsubstituted molecule create one group along the second principal
1
component (0 < PC2 < 10), as shown in Figure 1a. Interestingly, the structurally related molecules 2–4
(the positional isomers) do not violate the Lipinski’s rule of five (Ro5), as illustrated in Figure 1b.
The detailed investigation of the most active BChE objects 2–4, projected on the PC1 versus
PC2 plain, and color-coded according to calculated lipophilicity (Figure 2a) and molecular weight
(Figure 2b), indicated that lower lipophilicity (clogP < 5) was accompanied by lower molecular weight
(MW < 450 Da). Not surprisingly, the lipophilic profile for compounds is directly related to their
structural composition (chemotypes) and is partially mirrored by the MW values as well.
In silico estimation and experimental specification of the lipophilic values for the ensemble of
silicon-based carbamates was carried out using a few logP predictors; e.g., clogPS, Molinspirations,
OSIRIS, HyperChem 7.0, Sybyl X, MarvinSketch 15, ACD/ChemSketch 2015, Dragon6.0, Kowwin
and XlogP3. The numerical values of the theoretically-estimated partition coefficients and the
empirically-specified logk parameters are reported in Table S11 in the Supplementary Materials.

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The corresponding logP estimators deduced by the set of alternative programs were (inter-)correlated
with each other and cross-compared with the experimental values, as shown in Figure 3 and Table S12.
Figure 1. Projection of carbamates
components for Dragon descriptors with BChE activity on the logarithmic scale (
violations ( ). Colors indicate the numerical values of pBChE activity and rule of five (Ro5) violations.
1–
25 on the plane defined by the first versus second principal
a) and rule of five
b
Figure 2. Projection of carbamates 1–25 on plane defined by the first versus second principal components
for Dragon descriptors with calculated lipophilicity clogP (a) and molecular weights MW (b). Colors
indicate the numerical values of clogP and MW.
Basically, a relatively high correlation of the estimated values of logP with the experimental logk
was recorded (r > 0.85) for the investigated set of compounds with the exception of HyperChem 7.0
and OSIRIS clogP predictor. As a matter of fact, some observed variations in clogP values are probably
the consequence of various computational principles implemented in the software; e.g., descriptor,
atom or fragment-based and/or training databases used at the modelling stage (models are as good as
the training data applied) [58]. Moreover, we employed the iterative variable elimination procedure
(IVE-PLS) on the overall clogP matrix (X25×11) to select clogPS, Molinspirations, OSIRIS, HyperChem
7.0, Sybyl X and Dragon6.0 software as significant contributors to the final model in, namely, consensus
clogP specification. The averaged values of the indicated clogP estimators were correlated with logk
data with the correlation coefficient r = 0.82, because not only the best inter-correlated logP estimators
were indicated.

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Figure 3. Matrix of correlation coefficients of linear relationships between experimental lipophilicity
(logk) and lipophilicity calculated using different programs.
The idea of specifying a numerical measure of the intermolecular similarity based on the degree
of the resemblance between two objects, each described by a number of attributes, has been widely
applied in chemical information systems. Chemical structures are frequently represented by descriptors
encoding molecular structure/property in a numerical format [59]. The descriptor-based similarity
comparison is used as a quantitative assessment of the pair-wise structural relatedness, since it is
difficult to evaluate molecular similarity ‘by the eye’. In fact, various types of the similarity measures
have been described so far [60]. Hamming or Euclidean measures are basically used for the ‘relative’
distance comparisons, whereas the Tanimoto coefficient is preferred as a similarity measure between a
pair of the independent molecules in, namely, the ‘absolute’ comparison. In this study, the similarity
between two molecules was quantified using Tanimoto coefficient evaluated between pairs of the
OpenBabel fingerprint [61]. The distribution of Tanimoto coefficients for the investigated series of
compounds is illustrated in Figure 4a, with the highest frequency recorded in range of 0.84 < T < 0.89,
respectively. Given a set of 25 molecules, a symmetrical matrix of T25×25 was calculated showing the
structural dissimilarities of nitro-substituted isomers (compounds 23–25) from the remaining ones,
as illustrated in Figure 4b.
Figure 4. Distribution of Tanimoto coefficients (a) and the triangular matrix of Tanimoto coefficients (b)
for silicon-based carbamates 1–25.
The integration of the pairwise comparison of the chemical structure and biological response profile
provides a consistent visualization framework for the elucidation of SAR trends; e.g., continuity regions
and/or activity cliffs [10–14]. A graphical representation of chemical similarity versus biological activity

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can be derived by a systematic profiling of structure–activity landscape indexes (SALI). The identification
of activity cliffs depends critically on the accessibility of chemically-related compounds with noticeably
large activity variations; therefore, BChE values were taken into consideration in the SALI calculation.
Obviously, for closely related molecules (e.g., stereoisomers where T
→
1), SALI
→
infinity; thus, such
values were replaced with the next largest SALI value. The symmetrical heatmap representation of
SALI in the grayscale for the scrutinized series of compounds is presented in Figure 5a, with axes
that correspond to molecules sorted in increasing BChE inhibitory activity and a legend indicating
the range of SALI values. With the molecules ordered by BChE activity, two types of spots can be
immediately noted. The white ones represent pairs of compounds exhibiting the highest SALI values,
whereas the black ones indicate pairs revealing the minimum SALI values. The lighter block located in
the right lower part of the map indicates pairs of molecules that potentially form the BChE activity
cliff, where potent molecules
(-OCH3), 10 (-CH3), 12 13 (-F) and 24
modifications can be detected on the neighborhood plot, where the similar pairs of compounds
(T > 0.85) are plotted versus variations in the biological activity ( > 100) and color-coded by
2
–4
are accompanied by the inactive meta/para substituted compounds
6/7
9/
/
/
25 (-NO2), respectively. Moreover, the unfavorable structural
∆
BChE
higher SALI values, as illustrated in Figure 5b. The hints provided, based on the graphical/numerical
SALI representations, prompt further dense sampling of the indicated SAR-variations with the potential
compound synthesis as well as the activity specification.
Figure 5. The gray-scaled structure–activity landscape indexes (SALI) plot with compounds ordered
by increasing BChE activity (a), and a neighboring plot (b) for silicon-based carbamates 1–25.
2.5. Probability-Oriented Pharmacophore Mapping
The systematic probing of the crucial functional motifs in order to possibly specify a set of essential
3D steric/electronic/lipophilic features, namely pharmacophore, is common for the majority of SAR
studies. Basically, the specific congeneric series of compounds that shares the same structural core
(chemotype scaffold) is scrutinized. In fact, QSAR modelers frequently aspire to predict the activities
of new molecules; however, the predictive ability of model is directly dependent on the training/test
division. In real-world drug discovery, a model with ‘good retrospective performance does not
necessarily exhibit good prospective performance’ (Kubinyi paradox) [62]. Restricting ourselves to
single numerical measure of qc2v
pharmacophore generation using stochastic model validation (SMV) methodology [
the original dataset of 25 compounds was recurrently sampled into 17 training/test subgroups
/
q2 can be misleading; therefore, we proposed ‘consensus-based’
test
9,24,44]. Firstly,
/
8
(ratio 2 to 1). Basically, it is usually problematic to arbitrarily specify the training/test ratio, especially
in case where the external validation should be carried out a number of times [63]. Luckily, it was
technically feasible to investigate the whole pool of systematically generated training/test populations
(
C825
≈
106) for CoMSA BChE modeling. Obviously, the predictive performance of the test subset

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was related to the preferential selection of objects into the training set. The frequency distribution of
compounds in the test group for models with acceptable qc2v
0.5 (better than flipping a coin) revealed
that mostly inactive para isomers ( 10 13 and 16) were indicated. Subsequently, the regions of fairly
≥
5
,
7
,
,
high model ability and predictability were sampled to produce an ‘average’ pharmacophore pattern
according to the procedure described elsewhere [64]. The pharmacophore pattern illustrated in Figure 6
was produced using the pre-selected cut-off = 0.5 value, and further filtering of 50% of the CoMSA
descriptors with a negligible statistical importance for the BChE inhibitory profile was applied. Colors
code the impact of the descriptors on the BChE inhibitory potency to indicate the areas of positive
and negative potency contribution (Figure 6a) and the four combinations of charge values versus the
mean regression coefficients (Figure 6b), respectively. The potentially negative impact on the BChE
inhibitory potency, mainly due to steric and/or electronic factors, is graphically represented by the dark
3D polyhedrals, while the bright ones depict the spatial areas that were foreseen to be occupied by
atom/substituent in order to improve the molecular inhibitory profile, as shown in Figure 6a. It appears
that bulky substitution at ortho position of phenyl group can increment the BChE inhibitory potency,
as illustrated by white areas next to the hydroxyl group of the most potent compound 2. On the other
hand, the continuous increase in bulk at ortho region does not necessarily lead to further potency
growth that is in line with the observed inhibitory response reported in Table 1 (e.g., -OH versus
-OCH3). Noticeably, the increase in the bulkiness at meta substitution seems to be unfavorable structural
modification, as illustrated by the dominant dark spheres in the close proximity of this position in
Figure 6a. The above observation confirms the tendency recorded for the positional isomers of the
examined carbamates where BChE IC50 can be generally ranked as meta < ortho. Similarly, substituents
attached directly to phenyl ring at para position contribute unfavorably to the BChE inhibition activity,
as suggested by the dark spheres in Figure 6a and data reported in Table 1. A similar tendency was
observed for the frequency distribution in the test set for the best models, as mentioned earlier; not
only is the bulk of substituents an important factor, but also the distribution of the electron density.
One can conclude that the presence of the positively as well as negatively charged groups at the meta
position is unfavorable to the BChE inhibition, as illustrated by magenta and cyan color in Figure 6b.
The spatially-allowed areas at ortho position should be occupied by positively charged atoms/groups,
as depicted by red color in Figure 6b; however, the surface/volume enhancement can be detrimental to
the inhibition profile of the investigated compounds.
Figure 6. Spatial sectors with the largest contribution into BChE inhibitory potency selected by
CoMSA IVE-PLS for 17
combinations of mean charge q and correlation coefficient b values (
1–25. Reference compound 2 is illustrated as the most potent molecule.
/
8
training/test set samplings. Color coded contributions (
a) and four possible
b) for silicon-based carbamates

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To some extent, the ‘consensus’ 3D-QSAR approach provides a graphical representation of an
‘average’ pharmacophore pattern, with some hints indicating the functional groups that can potentially
increase/decrease the inhibitory potency of the silicon-based carbamates.
2.6. Molecular Docking Study of BChE and PET Activities
In order to derive more detailed insight into the guest–host interactions, 3D ligand-based
approaches can be combined with the site-directed protein-based docking procedures, especially
beneficial when a spatial geometry (or homology model) of the target responsible for the ADMET
profile is accessible [65]. In the docking-driven SAR modeling, the enthalpically and/or enthropically
favorable ligand-targeting factors can be deduced directly from the protein (or enzyme) binding site
and the ligand property/descriptor–coded structural space, respectively. How to accurately relate the
ligand-target interactions to pharmacological or toxicological effects is still questionable; however,
the utility of the intuitive docking procedures for generating guest-host poses in the structure-based
drug-design (SBDD) is widely accepted as a complimentary approach to the traditional ligand-guided
procedures (LBDD) [66]. To compare SBDD and LBDD findings for the AChE/BChE crystallographic
data with commercially-marketed drug molecules (e.g., GLT or RIV) is urgently needed.
The crystallographic geometry of BChE with a catalytic core (CC) determined at a resolution of
2.6 Å in the liganded state (holo) with the RIV analog 3-[(1R)-1-(dimethylamino)ethyl]-4-hydroxyphenyl
ethyl(methyl)carbamate was downloaded from the Protein Data Bank repository (PDB entry code:
6EUL) [51,67]. The AutoDock Vina program was used to (re)dock the RIV analogue in the active site
(AC2) of the enzymatic chain A that is comprised of six amino acids: Asn68, Ile69, Asp70, Trp82,
Thr120 and Pro285, along with a 1,2-ethanediol molecule (EDO605). The implementation of a new
scoring function within AutoDock Vina improved, considerably, the speed and accuracy of docking
algorithm compared to other programs that were tested extensively [68]. Obviously, the ligand-enzyme
interactions are dependent on the pose (conformation and the relative orientation) of both the host
and guest molecule; therefore, the entire anti-BChE population was scrutinized for rough guidelines
to be compared with the drug–enzyme interaction pattern, although the silicon-based carbamates
are structurally distinct from the reference RIV analogue. In fact, an automated docking procedure
confirmed our intuitive scaffold-driven alignment selection, as illustrated in Figure 7.
Figure 7. Docking-driven alignment of BChE silicon-based inhibitors in the enzyme active site AC2.
GLT, as the most potent BChE inhibitor, was docked in the active site of the BChE as well. Despite
structural differences between RIV, GLT and 1–25 carbamate series, some similarities in the atom spatial
distribution can be observed for negatively charged atoms (nitrogen and oxygen), as shown in Figure 8.
The visualization of the host–guest interacting modes was conducted using planar (2D) and
spatial (3D) maps generated by Schrödinger Maestro software and the Protein–Ligand Interaction
Profiler (PLIP) [69], revealing, generally, two types of non-bonding interactions: hydrogen-bond and
hydrophobic ones. The two and three-dimensional binding patterns for the marketed drug molecules
(GLT and RIV) and the most potent anti-BChE molecule 2 are displayed in Figures 9 and 10, respectively.

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Figure 8. Active site of BChE enzyme (depicted as ribbons) bounded with GLT (displayed as balls and
sticks), RIV (illustrated as thin sticks) and the most potent molecule 2 (presented as thick sticks).
Figure 9. 2D-based comparison of binding pattern for RIV (a), GLT (b) and the most active BChE
inhibitor, 2, produced by the Maestro program.
Figure 10. 3D-based comparison of the binding pattern for RIV (a), GLT (b) and the most active BChE
inhibitor 2, produced by PLIP program.
The hydroxyl group of Thr120 seems to play a vital role as a hydrogen donor when forming
the hydrogen bond (HB) with the ether (Figure 10a,b) and carbonyl oxygen of molecule , as shown
in Figure 10c. Moreover, both terminal phenyl rings were specified as crucial building sub-blocks
2

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for the hydrophobic interactions with Pro285 or Tyr332 that partially correspond to our previous
ligand-oriented findings. Regarding all interactions foreseen for the entire set of the silicon-based
1–25 carbamates, the hydrophobic interactions were dominated by the following order of amino acid
residues: Tyr332, Ala328, Pro285, Phe329, Trp82 and Asp70, whereas Thr120 was generally indicated as
the hydrogen bond donor (HBD) to the negatively charged oxygen of the carbamate carbonyl or etheric
group. Interestingly, for the most potent BChE carbamates 3 and 4, the Thr120-based hydrogen bond is
additionally accompanied by a hydrogen bond generated with Asn289. Unfortunately, there was no
clear explanation of the differences in the anti-BChE profile for ortho/meta/para-positioned carbamates
provided by the docking study.
Due to the lack of the X-ray data on PSII-herbicide complexes, the experimental orientation
and conformation of herbicides targeting the QB binding pocket still remain unknown [70]. In the
theoretical studies of herbicide-resistant mutants, different interaction modes have been suggested for
various families of herbicides [71]. In consequence, the molecular docking study can provide valuable
knowledge about the mechanism of herbicide binding indicating the specific amino acids in the QB site
as well. The initial atomic coordinates of the D1 protein were retrieved from the monomeric X-ray
structure of cyanobacterial Photosystem II at 2.9 Å resolution (PDB entry code: 3BZ1). The docking
pocket was set to a 20 Å cube centered at the QB ring in EC9 binding site the central part of which
is formed with hydrophobic Phe and Leu residues; e.g., Phe(211, 255, 265, 274) and Leu(218, 271),
respectively. Carbamate and amide-like herbicides are regarded as PET inhibitors in PSII that cause
displacement of QB from the binding site of the D1 protein [72–74]. Hence, the molecular docking for
the most potent DCMU herbicide resulted in it interacting with the OH group of D1-Ser264 and the
backbone NH group of D1-Phe265 with the DCMU carbonyl group (see Figure 11a,b). That is in line
with the previously published findings of DCMU resistance by Ser264 mutations [42].
Figure 11. 2D-based (a) and 3D-based (b) interacting modes of DCMU in the photosystem II (PSII)
binding pocket produced by Maestro software.
Moreover, the docking studies were conducted for the whole set of silicon-based 1–25 carbamates
to examine the host–guest interacting mode, indicating basically hydrophobic interactions, with Phe275
and Phe265 as the dominant type of non-binding interactions. Interestingly, only the most potent PET
inhibitor (molecule 2) was predicted to be hydrogen-bonded with D1-Ser264, as illustrated in planar
(Figure 12a) and spatial (Figure 12b) interacting modes, respectively.
Obviously, we are profoundly aware that both ligand and structure-driven approaches have their
own limitations; therefore, a consensus methodology i.e., the combination of the pharmacophore
mapping with target-tailored procedures was proposed in the investigation of the multifaceted
inhibitor-enzyme interactions [75].

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Figure 12. 2D-based and 3D-based interacting modes for the most potent PET inhibitor (molecule
produced by Maestro (a) and PLIP (b) programs.
2)
2.7. In Vitro Viability Assay
The preliminary in vitro screening of the most effective compounds and their influence on
cell viability was performed using the human monocytic leukemia THP-1 cell line, as described
previously [23]. The effect on viability was evaluated as the IC50 value (compound concentration
causing 50% inhibition of cell population proliferation) (see Table 1). A compound is considered as
cytotoxic when it demonstrates a toxic effect on cells at the concentration up to 10 µM [76], and the
highest tested concentration that was used for the toxicity assay was three times this value.
Treatment with the selected compounds led to decrease in viability of the cells only in case of
compounds
2
,
3
and
4
; nevertheless, for comparison, the IC50 of camptothecin was 0.16
±
0.07
µ
M [77].
The IC50 values of these compounds are shown in Table 1, and ranged from 3 to ca. 6.5
µ
M. The
antiproliferative effect is probably related to substitution of anilide ring by hydroxy moiety, and this
effect decreased as follows: ortho > meta > > > para. Surprisingly, the nitro derivatives 23 and 25
showed a non-significant antiproliferative effect, as described recently [78]. No lethal effect was shown
by compounds 6, 7 and 25, the potent AChE inhibitors. Additionally compound 1, a potent inhibitor
of PET, did not lead to a significant lethal effect on THP-1 cells. Based on these observations, it can
be concluded that these compounds can be considered non-toxic agents for the subsequent design of
novel drugs.
3. Materials and Methods
3.1. General Methods
AllreagentswerepurchasedfromSigma-AldrichandAcros, respectively. Anhydrousdichloromethane
was distilled from calcium hydride. TLC experiments were performed on alumina-backed silica gel 60
F254 plates (Merck, Darmstadt, Germany). The plates were illuminated under UV (254 nm). The melting
points were determined on Böetius PHMK 05 (Franz Küstner Nachf). Infrared (IR) spectra were collected
on a Smart MIRacleTM ATR ZnSe for the NicoletTM Impact 410 FT-IR Spectrometer (Thermo Scientific).
All 1H, 19F and 13C spectra were specified using a JEOL ECZR 400 MHz NMR spectrometer (400 MHz
for 1H, 101 MHz for 13C and 376 MHz for 19F, JEOL) in CDCl3. Chemical shifts (
δ) are reported in ppm.
High-resolution mass spectra were measured on LTQ Orbitrap XLTMHybrid Ion Trap-Orbitrap Fourier
Transform Mass Spectrometer (Thermo Scientific) with injection into HESI II in the positive mode.

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3.2. Chemistry
3.2.1. General Procedure Used to Synthesize the Carbamates 1–24
(2S)-2-{[(Benzyloxy)carbonyl]amino}-3-hydroxypropanoic acid (10.01 g, 41.84 mM), tert-butyldimethylsilyl
chloride (9.50 g, 63.31 mM) and imidazole (11.42 g, 167.75 mM) were dissolved in dry DMF (400 mL).
◦
The reaction mixture was heated to 60 C under inert gas atmosphere for 72 h. The reaction was
monitored by thin-layer chromatography (ethyl acetate: methanol/9:1). The solvent was evaporated in
vacuo and petroleum ether was added to the rest (400 mL), and subsequently extracted with 5% NaHCO3
(3 × 140 mL). Aqueous layers were acidified to pH 3 with 1M KHSO4 and extracted with EtOAc (3 × 150 mL).
Combined organic layers were washed with brine (200 mL) and dried with MgSO4. The solvent was
evaporated under reduced pressure and the residue was triturated with EtOAc/petroleum ether to give white
crystals of (2S)-2-{[(benzyloxy)carbonyl]amino}-3-[(tert-butyldimethylsilyl)oxy]propanoic acid. Yield 87% [79];
Mp 80–82 ◦C; 1H NMR (CDCl3)
4.46 (d, J = 7.78 Hz, 1 H), 4.13 (dd, J = 10.06, 2.29 Hz, 1 H), 3.86 (dd, J = 10.06, 3.20 Hz, 1 H), 0.87 (s, 9 H),
0.05 (s, 6 H); 13C NMR (CDCl3) 5.6; IR (cm−1):
: 175.0, 156.1, 136.1, 128.5, 128.2, 128.1, 67.2, 63.3, 55.6, 25.7, 18.2,
δ: 7.31–7.40 (m, 5 H), 5.62 (d, J = 8.23 Hz, 1 H), 5.14 (d, J = 5.03 Hz, 2 H),
δ
−
3651w, 3437w, 3241w, 2992 w, 2782w, 1706s, 1567m, 1467w, 1378w, 1282w, 1229m, 1090s, 894s, 781m; HR-MS:
for C17H28NO5Si [M + H]+ calculated 354.1731 m/z, found 354.1733 m/z.
(2S)-2-{[(Benzyloxy)carbonyl]amino}-3-[(tert-butyldimethylsilyl)oxy]propanoic acid (353.50 mg,
1.00 mM) and dry TEA (202.00 mg, 2.00 mM) were dissolved in dry THF (8 mL) and cooled down to
−
5 ◦C. Ethyl chloroformate (108.52 mg, 1.00 mmol) was added dropwise to this solution during 15 min.
The reaction mixture was stirred for 1 h at 0 ◦C, and then a specific derivative of aniline (1.00 mM) was
added. The reaction mixture was monitored by thin-layer chromatography and reaction times were
from 7 to 24 h according to used aniline. When the reaction was finished, water (10 mL) was added
and the solution was extracted with EtOAc (3
×
15 mL). Combined organic layers were washed with
1M HCl (12 mL), saturated NaHCO3 (12 mL) and brine (10 mL), and dried with anhydrous Na2SO4.
The solvent was evaporated in vacuo and the residue was purified using column chromatography to
give compounds (1–24). For each product, the used mobile phases are presented in Table 2.
Table 2. Mobile phases used for purification of the compounds 1–24.
Comp.
Mobile phase
n-hexane/ethyl acetate (6:1)
2% TEA in 10% methanol in dichloromethane
2% TEA in 10% methanol in dichloromethane
2% TEA in 10% methanol in dichloromethane
n-hexane/ethyl acetate (4:1)
1
2
3
4
5
6
n-hexane/ethyl acetate (4:1)
7
n-hexane/ethyl acetate (4:1)
8
n-hexane/ethyl acetate (4:1)
9
n-hexane/ethyl acetate (6:1)
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
n-hexane/ethyl acetate (4:1)
1% DEA in n-hexane/ethyl acetate (6:1)
1% DEA in n-hexane/ethyl acetate (6:1)
1% DEA in n-hexane/ethyl acetate (6:1)
8% TEA in n-hexane/ethyl acetate (7:1)
n-hexane/ethyl acetate 9:1 → 6:1 → 4:1
1% DEA in n-hexane/ethyl acetate (6:1)
12% TEA in n-hexane/ethyl acetate (5:1)
1% DEA in n-hexane/ethyl acetate (6:1)
1% DEA in n-hexane/ethyl acetate (6:1)
1% DEA in n-hexane/ethyl acetate (6:1)
4% TEA in dichloromethane/n-hexane (5:1)
1% DEA in n-hexane/ethyl acetate (6:1)
1% DEA in n-hexane/ethyl acetate (2:1 → 3:1)
n-hexane/ethyl acetate (6:1 → 4:1)

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Benzyl-N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-(phenylcarbamoyl)ethyl]carbamate (
1
): Yield 83%;
Mp 93–96 ◦C; 1H NMR (CDCl3)
δ
: 8.47 (br s, 1 H), 7.50 (d, J = 7.89 Hz, 2 H), 7.30–7.42 (m, 7 H), 7.13
(t, J = 7.43 Hz, 1 H), 5.82 (br s, 1 H), 5.17 (s, 2 H), 4.35 (br s, 1 H), 4.16 (d, J = 6.40 Hz, 1 H), 3.74
(dd, J = 9.43, 8.18 Hz, 1 H), 0.93 (s, 9 H), 0.13 (s, 6 H); 13C NMR (CDCl3)
: 168.3, 156.1, 137.3, 136.0,
129.0, 128.6, 128.3, 128.1, 124.5, 119.8, 67.2, 63.2, 56.1, 25.8, 18.1,
5.5; IR (cm−1): 3170w, 2988w, 2885w,
δ
−
1710w, 1678m, 1613s, 1571s, 1478m, 1421m, 1321m, 1150m, 1061m, 890s, 798s, 766m, 736m; HR-MS:
for C23H32N2O4Si [M + H]+ calculated 429.2204 m/z, found 429.2207 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(2-hydroxyphenyl)carbamoyl]ethyl]carbamate (
2
):
◦
1
Yield 45%; Mp 115–117 C; H NMR (CDCl3)
δ: 8.71 (s, 1 H), 8.40 (s, 1 H), 7.34–7.41 (m, 5 H),
7.10–7.16 (m, 1 H), 6.99–7.06 (m, 2 H), 6.87 (t, J = 7.78 Hz, 1 H), 5.76 (br s, 1 H), 5.18 (s, 2 H), 4.45
(br s, 1 H), 4.18 (dd, J = 9.78, 3.14 Hz, 1 H), 3.79 (dd, J = 9.83, 6.86 Hz, 1 H), 0.90 (s, 9 H), 0.11 (s, 6 H);
13C NMR (CDCl3)
63.1, 56.3, 25.7, 18.2,
δ
: 170.0, 156.1, 148.6, 135.7, 128.7, 128.4, 128.2, 127.4, 124.9, 122.3, 120.4, 119.5, 67.6,
−
5.5; IR (cm−1): 3736w, 3440w, 3387w, 3362w, 2906w, 1742m, 1695s, 1628s, 1567s,
1510s, 1471m, 1421m, 1296s, 1222s, 1036m, 1004m, 944s, 883s, 801s, 795s; HR-MS: for C23H32N2O5Si
[M + H]+ calculated 445.2153 m/z, found 445.2156 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(3-hydroxyphenyl)carbamoyl]ethyl]carbamate (
3):
Yield 47%; oil; 1H NMR (CDCl3)
δ
: 8.50 (s, 1 H), 7.50 (br s, 1 H), 7.37 (br s, 6 H), 7.14 (t, J = 8.06 Hz, 1 H),
6.73 (d, J = 5.49 Hz, 1 H), 6.64 (dd, J = 8.06, 1.31 Hz, 1 H), 5.82 (br s, 1 H), 5.16 (s, 2 H), 4.40 (br s, 1 H),
4.10 (dd, J = 9.89, 3.60 Hz, 1 H), 3.78 (dd, J = 9.95, 7.09 Hz, 1 H), 0.88 (s, 9 H), 0.08 (s, 6 H); 13C NMR
(CDCl3)
45.6, 25.7, 18.1, 9.9,
δ: 168.9, 157.1, 156.3, 138.1, 135.8, 129.8, 128.6, 128.3, 128.2, 112.0, 111.2, 107.4, 67.5, 63.2, 56.7,
−
5.6; IR (cm−1): 3535m, 3361w, 3002w, 2907w, 2812w,1742w, 1684w, 1632m, 1579m,
1478w, 1414m, 1346m, 1166s, 1138s, 1067w, 889s, 798s, 752m; HR-MS: for C23H32N2O5Si [M + H]+
calculated 445.2153 m/z, found 445.2154 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(4-hydroxyphenyl)carbamoyl]ethyl]carbamate (
Yield 50%; Mp 66–69 ◦C; 1H NMR (CDCl3)
: 8.28 (br s, 1 H), 7.37 (br s, 5 H), 7.21–7.29 (m, 2 H), 6.75 (d,
J = 8.80 Hz, 2 H), 5.81 (br s, 1 H), 5.16 (s, 2 H), 4.33 (br s, 1 H), 4.08–4.17 (m, 1 H), 3.74 (dd, J = 9.55,
7.60 Hz, 1 H), 2.06 (s, 1 H), 0.9 (s, 9 H), 0.1 (s, 6 H); 13C NMR (CDCl3)
: 168.4, 156.2, 153.4, 135.9, 129.6,
128.6, 128.3, 128.2, 122.3, 115.8, 67.4, 63.2, 56.2, 45.5, 25.8, 18.1, 10.0,
5.5; IR (cm−1): 3629m, 3355w,
4):
δ
δ
−
3134w, 2992w, 2899w, 2824w, 1738w, 1667m, 1578s, 1482m, 1307m, 1140m, 1058w, 880s, 791m, 759m;
HR-MS: for C23H32N2O5Si [M + H]+ calculated 445.2153 m/z, found 445.2154 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(2-methoxyphenyl)carbamoyl]ethyl]carbamate (
5
):
◦
1
Yield 76%; Mp 50–53 C; H NMR (CDCl3)
δ: 8.70 (br s, 1 H), 8.37 (d, J = 8.0 Hz, 1 H), 7.39
(br s, 5 H), 7.03–7.10 (m, 1 H), 6.97 (t, J = 7.66 Hz, 1 H), 6.88 (d, J = 8.00 Hz, 1 H), 5.82 (br s, 1 H),
5.11–5.23 (m, 2 H), 4.37 (br s, 1 H), 4.14 (d, J = 7.09 Hz, 1 H), 3.84 (s, 3 H), 3.76 (t, J = 7.78 Hz, 1 H), 0.89
(s, 9 H), 0.10 (s, 6 H); 13C NMR (CDCl3)
114.1, 67.2, 63.2, 56.0, 55.4, 25.8, 18.1,
δ
: 168.0, 156.5, 156.1, 136.0, 130.4, 128.6, 128.3, 128.2, 121.6,
−
5.5; IR (cm−1): 3305w, 2971w, 2913w, 1711w, 1689w, 1619m,
1569m, 1476w, 1379w, 1312w, 1231m, 1091s, 889s, 792m, 707m; HR-MS: for C24H34N2O5Si [M + H]+
calculated 459.2310 m/z, found 459.2312 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(3-methoxyphenyl)carbamoyl]ethyl]carbamate (
6):
Yield 61%; Mp 55–57 ◦C; 1H NMR (CDCl3)
δ: 8.48 (br s, 1 H), 7.38 (br.s., 5 H), 7.19–7.28 (m, 2 H), 6.97
(d, J = 7.78 Hz, 1 H), 6.69 (dd, J = 8.23, 2.06 Hz, 1 H), 5.80 (br s, 1 H), 5.17 (s, 2 H), 4.33 (br s, 1 H), 4.15
(d, J = 5.60 Hz, 1 H), 3.80 (s, 3 H), 3.73 (t, J = 8.86 Hz, 1 H), 0.93 (s, 9 H), 0.13 (s, 6 H); 13C NMR (CDCl3)
δ
: 168.3, 160.1, 156.1, 138.5, 136.0, 129.7, 128.6, 128.3, 128.1, 112.0, 110.5, 105.4, 67.2, 63.2, 56.1, 55.2,
25.8, 18.1,
5.5; IR (cm−1): 3359w, 2909w, 2898w, 1713w, 1682w, 1631m, 1577m, 1503w, 1489w, 1332m,
1177mm 893s, 800m, 757w, 736w; HR-MS: for C24H34N2O5Si [M + H]+ calculated 459.2310 m/z, found
459.2311 m/z.
−
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(4-methoxyphenyl)carbamoyl]ethyl]carbamate (
7
):
: 8.32 (br s, 1 H), 7.32–7.44 (m, 7 H), 6.83–6.90
(m, 2 H), 5.81 (s, 1 H), 5.16 (s, 2 H), 4.33 (br s, 1 H), 4.15 (dd, J = 9.55, 3.37 Hz, 1 H), 3.80 (s, 3 H), 3.73
(dd, J = 9.55, 7.95 Hz, 1 H), 0.92 (s, 9 H), 0.11 (s, 6 H); 13C NMR (CDCl3)
: 168.0, 156.4, 156.1, 135.9,
◦
1
Yield 61%; Mp 118–125 C; H NMR (CDCl3)
δ
δ

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130.4, 128.9, 128.4, 128.1, 121.5, 114.2, 67.2, 63.2, 56.1, 55.4, 25.8, 18.1,
2909w, 2781w, 1670m, 1581m, 1480m, 1379w, 1298m, 1276m, 1208m, 1095s, 893s, 788m, 753m, 707w;
HR-MS: for C24H34N2O5Si [M + H]+ calculated 459.2310 m/z, found 459.2311 m/z.
−
5.5; IR (cm−1): 3468w, 3002w,
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(2-methylphenyl)carbamoyl]ethyl]carbamate (
8): Yield
54%; Mp 106–107 ◦C; 1H NMR (CDCl3)
δ: 8.19 (br s, 1 H), 7.81 (d, J = 7.89 Hz, 1 H), 7.32–7.42 (m, 5 H),
7.16–7.25 (m, 2 H), 7.10 (t, J = 7.09 Hz, 1 H), 5.81 (s, 1 H), 5.12–5.23 (m, 2 H), 4.37 (br s, 1 H), 4.21 (dd,
J = 9.49, 2.97 Hz, 1 H), 3.80 (dd, J = 9.83, 7.20 Hz, 1 H), 2.21 (br s, 3 H), 0.91 (s, 9 H), 0.13 (s, 6 H);
13C NMR (CDCl3)
63.2, 56.5, 25.8, 18.3, 17.7,
δ: 168.3, 156.2, 135.9, 135.2, 130.4, 129.1, 128.6, 128.3, 128.2, 126.7, 125.3, 122.9, 67.3,
−
5.3,
5.5; IR (cm−1): 3348m, 2970m, 2925m, 2885m, 1710w, 1653w, 1453w,
−
1382w, 1328w, 1158w, 1090m, 1047s, 880m, 801w, 737w; HR-MS: for C24H34N2O4Si [M + H]+ calculated
443.2361 m/z, found 443.2366 m/z.
Benzyl N-[(1◦S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(3-methylphenyl)carbamoyl]ethyl]carbamate (
9): Yield
1
65%; Mp 92–93 C; H NMR (CDCl3)
δ
: 8.39 (s, 1 H), 7.33–7.41 (m, 6 H), 7.25–7.30 (m, 1 H), 7.21
(t, J = 7.55 Hz, 1 H), 6.95 (d, J = 7.32 Hz, 1 H), 5.79 (br s, 1 H), 5.17 (dd, J = 12.24, 1.94 Hz, 2 H), 4.33 (br s,
1 H), 4.15 (dd, J = 9.49, 3.20 Hz, 1 H), 3.74 (dd, J = 8.00, 1.49 Hz, 1 H), 2.35 (s, 3 H), 0.93 (s, 9 H), 0.12 (br
s, 6 H); 13C NMR (CDCl3)
67.2, 63.2, 56.2, 25.8, 21.5, 18.1,
δ: 168.2, 156.1, 138.9, 137.2, 136.0, 128.8, 128.6, 128.3, 128.1, 125.3, 120.6, 117.0,
−
5.5; IR (cm−1): 3319m, 2970m, 2888w, 1710w, 1674m, 1599m, 1456m
1325m, 1190m, 1154m, 1090m, 1047s, 880s, 801m, 755m, 727m; HR-MS: for C24H34N2O4Si [M + H]+
calculated 443.2361 m/z, found 443.2365 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(4-methylphenyl)carbamoyl]ethyl]carbamate (10):
◦
1
Yield 72%; Mp 111–112 C; H NMR (CDCl3)
(d, J = 8.12 Hz, 2 H), 5.81 (br s, 1 H), 5.17 (s, 2 H), 4.33 (br s, 1 H), 4.15 (d, J = 6.52 Hz, 1 H), 3.73
(t, J = 8.75 Hz, 1 H), 2.33 (s, 3 H), 0.92 (s, 9 H), 0.12 (s, 6 H); 13C NMR (CDCl3)
: 168.1, 156.1, 136.0,
134.8, 134.1, 129.5, 128.6, 128.3, 128.1, 119.9, 67.2, 63.2, 56.1, 25.8, 20.8, 18.1,
5.5; IR (cm−1): 3309m,
δ: 8.38 (br s, 1 H), 7.37 (d, J = 6.40 Hz, 7 H), 7.13
δ
−
2970m, 2881m, 1710w, 1681w, 1592w, 1446m, 1382m, 1328w, 1086m, 1044s, 880m, 734w; HR-MS:
for C24H34N2O4Si [M + H]+ calculated 443.2361 m/z, found 443.2366 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(2-fluorophenyl)carbamoyl]ethyl]carbamate (11): Yield
47%; Mp 70–72 ◦C; 1H NMR (CDCl3)
δ
: 8.74 (br s, 1 H), 8.31 (t, J = 7.72 Hz, 1 H), 7.31–7.45 (m, 5 H),
7.03–7.19 (m, 3 H), 5.79 (br s, 1 H), 5.12–5.24 (m, 2 H), 4.38 (br s, 1 H), 4.17 (d, J = 6.75 Hz, 1 H), 3.75 (dd,
J = 9.55, 8.06 Hz, 1 H), 0.91 (s, 9 H), 0.13 (s, 6 H); 13C NMR (CDCl3) : 168.5, 156.1, 152.5 (d, 1JCF = 243.7
Hz, 1C), 136.0, 128.6, 128.3, 128.2, 125.9 (d, 3JCF = 10.6 Hz, 2C), 124.6, 124.5 (d, 4JCF = 2.9 Hz, 1C), 121.7,
114.9 (d, 2JCF = 19.3 Hz, 2C), 67.3, 63.3, 56.4, 25.8, 18.2, 5.6; IR (cm−1): 3544w, 3312w, 3163w, 2970w,
δ
−
2881w, 1720m, 1678m, 1628s, 1574s, 1510m, 1414m, 1332m, 1286s, 1143s, 1061m, 1022m, 896s, 801s,
716m; HR-MS: for C23H31FN2O4Si [M + H]+ calculated 447.2110 m/z, found 447.2112 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(3-fluorophenyl)carbamoyl]ethyl]carbamate (12): Yield
73%; Mp 84–88 ◦C; 1H NMR (CDCl3)
δ: 8.54 (br s, 1 H), 7.47 (d, J = 10.75 Hz, 1 H), 7.32–7.42 (m, 5 H),
7.22–7.31 (m, 1 H), 7.10 (d, J = 7.89 Hz, 1 H), 6.82 (td, J = 8.20, 2.23 Hz, 1 H), 5.77 (br s, 1 H), 5.17
(s, 2 H), 4.34 (br s, 1 H), 4.15 (dd, J = 9.43, 3.26 Hz, 1 H), 3.74 (dd, J = 9.66, 7.83 Hz, 1 H), 0.92 (s, 9 H),
0.12 (s, 6 H); 13C NMR (CDCl3) : 168.5, 163.0 (d, 1JCF = 244.7 Hz, 1C), 156.2, 138.8 (d, 3JCF = 10.6 Hz,
δ
1C), 135.9, 130.1 (d, 3JCF = 8.7 Hz, 1C), 128.6, 128.3, 128.2, 115.0, 111.2 (d, 2JCF = 22.2 Hz, 2C), 107.5,
107.2, 67.3, 63.2, 56.3, 25.8, 18.1,
5.5; IR (cm−1): 3512w, 3351w, 2977w, 2913w, 1713m, 1685m, 1628m,
−
1567m, 1460w, 1346w, 1167m, 1090m, 1054m, 887s, 798s, 734m; HR-MS: for C23H31FN2O4Si [M + H]+
calculated 447.2110 m/z, found 447.2112 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(4-fluorophenyl)carbamoyl]ethyl]carbamate (13): Yield
68%; Mp 110–112 ◦C; 1H NMR (CDCl3)
δ
: 8.43 (br s, 1 H), 7.45 (dd, J = 8.35, 4.69 Hz, 2 H), 7.31–7.41
(m, 5 H), 7.02 (t, J = 8.63 Hz, 2 H), 5.77 (br s, 1 H), 5.16 (s, 2 H), 4.33 (br s, 1 H), 4.15 (dd, J = 9.43,
3.14 Hz, 1 H), 3.74 (dd, J = 9.49, 7.89 Hz, 1 H), 0.92 (s, 9 H), 0.11 (s, 6 H); 13C NMR (CDCl3)
: 168.3,
159.4 (d, 1JCF = 244.7 Hz, 1C), 156.2, 135.9, 133.4, 128.6, 128.3, 128.2, 121.6 (d, 3JCF = 7.7 Hz, 2C), 115.7
(d, 2JCF = 23.1 Hz, 2C), 67.3, 63.2, 56.2, 25.8, 18.1, 5.5; IR (cm−1): 3366w, 3184w, 2981w, 2896w, 1738w,
δ
−

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1685m, 1631m, 1588s, 1539m, 1482m, 1428m, 1375w, 1324m, 1289m, 1210m, 1175m, 1143m, 940m, 873s,
801s, 748m; HR-MS: for C23H31FN2O4Si [M + H]+ calculated 447.2110 m/z, found 447.2112 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(2-chlorophenyl)carbamoyl]ethyl]carbamate (14): Yield
68%; oil; 1H NMR (CDCl3)
δ
: 8.75 (br s, 1 H), 8.35 (d, J = 8.12 Hz, 1 H), 7.23–7.40 (m, 7 H), 7.05 (td,
J = 7.75, 1.32 Hz, 1 H), 5.77 (s, 1 H), 5.17 (q, J = 12.20 Hz, 2 H), 4.39 (br s, 1 H), 4.19 (d, J = 7.66 Hz,
1 H), 3.80 (dd, J = 9.89, 6.46 Hz, 1 H), 0.87 (s, 9 H), 0.09 (d, J = 7.66 Hz, 6 H); 13C NMR (CDCl3)
168.6, 156.1, 135.9, 134.1, 129.0, 128.6, 128.3, 128.2, 127.6, 124.9, 123.1, 121.6, 67.4, 63.3, 57.1, 25.8, 18.3,
δ:
−
5.4,
5.5; IR (cm−1): 3530w, 3341w, 2970w, 2881w, 2835w, 1756w, 1678m, 1621s, 1571s, 1482m, 1457m,
−
1410m, 1332m, 1286w, 1161m, 1093w, 1061w, 904s, 801s, 712w; HR-MS: for C23H31ClN2O4Si [M + H]+
calculated 463.1814 m/z, found 463.1819 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(3-chlorophenyl)carbamoyl]ethyl]carbamate (15): Yield
83%; Mp 80–81 ◦C; 1H NMR (CDCl3)
δ
: 8.48 (br s, 1 H), 7.60 (s, 1 H), 7.17–7.38 (m, 7 H), 7.07 (d, J = 7.78
Hz, 1 H), 5.73 (br s, 1 H), 5.13 (s, 2 H), 4.30 (br s, 1 H), 4.11 (dd, J = 9.38, 2.97 Hz, 1 H), 3.71 (dd, J = 9.55,
7.83 Hz, 1 H), 0.89 (s, 9 H), 0.08 (s, 6 H); 13C NMR (CDCl3)
: 168.5, 156.2, 138.4, 135.9, 134.7, 130.0,
128.6, 128.4, 128.2, 124.5, 120.0, 117.7, 67.4, 63.1, 56.3, 25.8, 18.1,
5.5; IR (cm−1): 3355m, 2970m, 2903w,
δ
−
2693w, 1727w, 1674m, 1610s, 1560m, 1499m, 1435w, 1321m, 1289m, 1176m, 1143m, 1097m, 1054s, 899s,
801s, 748m; HR-MS: for C23H31ClN2O4Si [M + H]+ calculated 463.1814 m/z, found 463.1818 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(4-chlorophenyl)carbamoyl]ethyl]carbamate (16): Yield
92%; Mp 101–104 ◦C; 1H NMR (CDCl3)
δ
: 8.47 (s, 1 H), 7.42 (d, J = 8.69 Hz, 2 H), 7.31–7.38 (m, 5 H),
7.24–7.29 (m, 2 H), 5.76 (br s, 1 H), 5.14 (s, 2 H), 4.32 (s, 1 H), 4.12 (dd, J = 9.83, 3.43 Hz, 1 H), 3.72 (dd,
J = 9.61, 7.78 Hz, 1 H), 0.89 (s, 9 H), 0.09 (s, 6 H); 13C NMR (CDCl3)
: 168.4, 156.2, 135.9, 129.4, 129.0,
128.9, 128.6, 128.3, 128.2, 121.0, 67.3, 63.12, 56.3, 25.8, 18.1,
5.5; IR (cm−1): 3366w, 3216w, 3152w, 3091w,
δ
−
2974w, 2878w, 1177w, 1628m, 1571s, 1503m, 1432m, 1325s, 1275m, 1179m, 1154s, 1061s, 890s, 798m,
759m, 716w; HR-MS: for C23H31ClN2O4Si [M + H]+ calculated 463.1814 m/z, found 463.1823 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(2-bromophenyl)carbamoyl]ethyl]carbamate (17): Yield
15%; oil; 1H NMR (CDCl3)
δ
: 8.69 (br s, 1 H), 8.34 (d, J = 8.23 Hz, 1 H), 7.53 (dd, J = 8.00, 1.14 Hz, 1 H),
7.31–7.43 (m, 6 H), 6.96–7.04 (m, 1 H), 5.77 (s, 1 H), 5.12–5.27 (m, 2 H), 4.40 (br s, 1 H), 4.19–4.25 (m, 1 H),
3.83 (dd, J = 9.95, 5.72 Hz, 1 H), 0.87 (s, 9 H), 0.09 (s, 6 H); 13C NMR (CDCl3)
: 168.6, 156.1, 135.9, 135.2,
132.2, 128.6, 128.4, 128.3, 125.4, 121.8, 113.7, 67.4, 63.2, 57.3, 25.8, 18.3, 5.4,
5.5; IR (cm−1): 3512w,
δ
−
−
3355w, 2970w, 2888w, 1770w, 1702m, 1621m, 1560m, 1489w, 1403w, 1328w, 1143s, 1090s, 1054s, 912s,
798s, 734m; HR-MS: for C23H31BrN2O4Si [M + H]+ calculated 507.1309 m/z, found 507.1318 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(3-bromophenyl)carbamoyl]ethyl]carbamate (18): Yield
63%; Mp 79–80 ◦C; 1H NMR (CDCl3)
δ
: 8.52 (br s, 1 H), 7.77 (s, 1 H), 7.38 (br s, 6 H), 7.23–7.28 (m, 1 H),
7.15–7.21 (m, 1 H), 5.77 (br s, 1 H), 5.17 (s, 2 H), 4.34 (br s, 1 H), 4.14 (dd, J = 9.61, 3.20 Hz, 1 H), 3.74 (dd,
J = 9.66, 7.72 Hz, 1 H), 0.92 (s, 9 H), 0.12 (s, 6 H); 13C NMR (CDCl3)
: 168.5, 156.2, 138.6, 135.9, 130.3,
128.6, 128.4, 128.2, 127.5, 122.8, 122.6, 118.2, 67.3, 63.1, 56.2, 25.8, 18.1,
5.5; IR (cm−1): 3376m, 2974m,
δ
−
2899w, 1717m, 1678m 1631m, 1560m, 1507m, 1442w, 1328w, 1286m, 1178m, 1097m, 1054m, 911s, 801m,
744m; HR-MS: for C23H31BrN2O4Si [M + H]+ calculated 507.1309 m/z, found 507.1315 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(4-bromophenyl)carbamoyl]ethyl]carbamate (19): Yield
61%; Mp 117–120 ◦C; 1H NMR (CDCl3)
δ
: 8.49 (br s, 1 H), 7.32–7.46 (m, 9 H), 5.77 (br s, 1 H), 5.16
(s, 2 H), 4.33 (br s, 1 H), 4.14 (dd, J = 9.49, 3.20 Hz, 1 H), 3.73 (dd, J = 9.66, 7.72 Hz, 1 H), 0.91 (s, 9 H),
0.11 (s, 6 H); 13C NMR (CDCl3)
: 168.4, 156.1, 136.4, 135.9, 132.0, 128.6, 128.3, 128.2, 121.4, 117.1, 67.3,
63.1, 56.3, 25.8, 18.1,
5.5; IR (cm−1): 3351w, 3213w, 3145w, 3088w, 2981w, 2885w, 1717w, 1701w, 1628m,
δ
−
1571s, 1503m, 1418m, 1325m, 1268m, 1173m, 1165m, 1090m, 1040s, 887s, 798m, 759w; HR-MS: for
C23H31BrN2O4Si [M + H]+ calculated 507.1309 m/z, found 507.1319 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-{[2-(trifluoromethyl)phenyl]carbamoyl}ethyl]carbamate
(
20): Yield 15%; Mp 54–56 ◦C; 1H NMR (CDCl3)
δ: 8.65 (br s, 1 H), 8.22 (d, J = 8.12 Hz, 1 H), 7.53–7.65
(m, 2 H), 7.39 (br s, 5 H), 7.22–7.29 (m, 1 H), 5.75 (s, 1 H), 5.18 (d, J = 4.92 Hz, 2 H), 4.39 (s, 1 H), 4.23 (d,
J = 8.81 Hz, 1 H), 3.82 (dd, J = 10.01, 5.77 Hz, 1 H), 0.87 (s, 9 H), 0.09 (d, J = 8.69 Hz, 6 H); 13C NMR
(CDCl3) δ
: 168.9, 156.1, 135.9, 134.7, 132.9, 128.6, 128.4, 128.3, 126.1 (q, 3JCF = 4.8 Hz, 2C), 124.7, 124.2,

Int. J. Mol. Sci. 2019, 20, 5385
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123.9 (q, 1JCF = 272.6 Hz, 1C), 120.4 (q, 2JCF = 29.9 Hz, 1C), 67.5, 63.1, 57.1, 25.7, 18.3,
3341w, 3173w, 2981w, 2903w, 2824w, 1749w, 1701w, 1617s, 1578s, 1471m, 1410m, 1343m, 1304m, 1180m,
1147s, 1054m, 883s, 798s, 716m; HR-MS: for C24H31F3N2O4Si [M + H]+ calculated 497.2078 m/z, found
497.2078 m/z.
−
5.6; IR (cm−1):
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-{[3-(trifluoromethyl)phenyl]carbamoyl}ethyl]carbamate
(
21): Yield 52%; Mp 58–61 ◦C; 1H NMR (CDCl3)
δ
: 8.66 (br s, 1 H), 7.79 (s, 1 H), 7.70 (d, J = 7.78 Hz,
1 H), 7.45 (t, J = 7.78 Hz, 1 H), 7.39 (br s, 6 H), 5.77 (br s, 1 H), 5.17 (s, 2 H), 4.37 (br s, 1 H), 4.16 (dd,
J = 9.38, 3.43 Hz, 1 H), 3.76 (dd, J = 9.61, 7.78 Hz, 1 H), 0.92 (s, 9 H), 0.12 (s, 6 H); 13C NMR (CDCl3)
168.7, 156.2, 137.9, 135.9, 131.4 (q, 2JCF = 32.8 Hz, 1C), 129.6, 128.6, 128.4, 128.2, 123.75 (q, 1JCF = 272.6
Hz, 1C), 122.8, 121.0, 116.6 (q, 3JCF = 3.9 Hz, 2C), 67.4, 63.1, 56.3, 25.7, 18.1, 5.5; IR (cm−1): 3177w,
δ:
−
2995w, 2899w, 1727w, 1684w, 1631s, 1597s, 1489s, 1389s, 1286s, 1190s, 1150s, 1036m, 955w, 869m, 816m,
759m, 723m; HR-MS: for C24H31F3N2O4Si [M + H]+ calculated 497.2078 m/z, found 497.2082 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-{[4-(trifluoromethyl)phenyl]carbamoyl}ethyl]carbamate
(
22): Yield 55%; Mp 125–127 ◦C; 1H NMR (CDCl3)
δ
: 8.69 (br s, 1 H), 7.55–7.64 (m, 4 H), 7.38 (br s,
5 H), 5.78 (br s, 1 H), 5.17 (s, 2 H), 4.37 (br s, 1 H), 4.12–4.19 (dd, J = 9.38, 2.86 Hz, 1 H), 3.76 (dd,
J = 9.66, 7.83 Hz, 1 H), 0.92 (s, 9 H), 0.12 (s, 6 H); 13C NMR (CDCl3)
: 168.8, 156.2, 139.5, 135.8, 128.6,
128.4, 128.2, 126.2 (q, 2JCF = 32.8 Hz, 1C), 126.3 (q, 3JCF = 3.9 Hz, 2C), 123.9 (q, 1JCF = 272.6 Hz, 1C),
119.4, 67.4, 63.1, 56.3, 25.8, 18.1,
5.5; IR (cm−1): 3355w, 3213w, 3148w, 3099w, 2977w, 2874w, 1749w,
δ
−
1628m, 1578m, 1482m, 1428m, 1350s, 1286m, 1190s, 1143s, 1076m, 1040m, 887s, 798m, 759m; HR-MS:
for C24H31F3N2O4Si [M + H]+ calculated 497.2078 m/z, found 497.2078 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(2-nitrophenyl)carbamoyl]ethyl]carbamate (23): Yield
23%; oil; 1H NMR (CDCl3)
δ
: 8.81 (d, J = 8.35 Hz, 1 H), 8.24 (d, J = 8.35 Hz, 1 H), 7.31–7.47 (m, 7 H),
7.19–7.25 (m, 1 H), 5.82 (d, 1 H), 5.21 (s, 2 H), 4.43 (s, 1 H), 4.26 (d, 1 H), 3.85 (dd, J = 10.18, 4.46 Hz,
1 H), 0.82 (s, 9 H), 0.04 (d, J = 15.32 Hz, 6 H); 13C NMR (CDCl3)
: 169.0, 156.3, 143.7, 143.0, 135.8, 128.6,
128.5, 128.3, 125.1, 119.2, 67.5, 63.0, 56.5, 25.8, 18.1,
5.5; IR (cm−1): 3462w, 3387w, 3230w, 2967m, 2903w,
δ
−
1735w, 1628s, 1539s, 1446m, 1368s, 1300m, 1236w, 1172m, 1047m, 876s, 798m, 759m, 709m; HR-MS:
for C23H31N3O6Si [M + H]+ calculated 474.2055 m/z, found 474.2056 m/z.
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(3-nitrophenyl)carbamoyl]ethyl]carbamate (24): Yield
53%; Mp 84–86 ◦C; 1H NMR (CDCl3)
δ: 8.83 (br s, 1 H), 8.38 (s, 1 H), 7.97 (dd, J = 8.23, 1.37 Hz, 1 H),
7.88 (d, J = 7.89 Hz, 1 H), 7.49 (t, J = 8.18 Hz, 1 H), 7.38 (br s, 5 H), 5.78 (br s, 1 H), 5.18 (s, 2 H), 4.40
(br s, 1 H), 4.17 (dd, J = 9.66, 3.26 Hz, 1 H), 3.78 (dd, J = 9.83, 7.43 Hz, 1 H), 0.92 (s, 9 H), 0.12 (s, 6 H);
13C NMR (CDCl3)
63.0, 56.4, 25.7, 18.1,
δ
: 168.9, 156.3, 148.5, 138.5, 135.8, 129.9, 128.6, 128.4, 128.2, 125.3, 119.0, 114.6, 67.5,
−
5.5; IR (cm−1): 3351w, 3230w, 2970w, 2871w, 1681w, 1621m, 1553s, 1450s, 1414s,
1375s, 1321m, 1279m, 1136m, 1086m, 1040s, 955w, 879s, 794s, 744m, 715m; HR-MS: for C23H31N3O6Si
[M + H]+ calculated 474.2055 m/z, found 474.2059 m/z.
3.2.2. General Procedure Used for the Synthesis of Carbamate 25
(2S)-2-{[(Benzyloxy)carbonyl]amino}-3-[(tert-butyldimethylsilyl)oxy]propanoic acid (300.00 mg,
0.85 mM) and 4-nitroaniline (176.01 mg, 1.28 mM) were dissolved in dry pyridine (5 mL). The mixture
was cooled down to
−
15 ◦C and phosphorous oxychloride (169.43 mg, 1.11 mM) was added
dropwise. The reaction mixture was stirred under inert atmosphere at this temperature for 2
h and at room temperature overnight. The reaction was quenched with ice water (25 mL) and
extracted with EtOAc (3 20 mL). The combined organic layers were washed with saturated NaHCO3
×
(15 mL), brine (15 mL), dried with MgSO4 and concentrated in vacuo. The residue was purified
by column chromatography (EtOAc/petroleum ether (1:3)) to afford yellow crystals of benzyl
N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(4-nitrophenyl)carbamoyl]ethyl]carbamate (25).
Benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(4-nitrophenyl)carbamoyl]ethyl]carbamate [79
25): Yield 51%; Mp 111–115 ◦C; 1H NMR (CDCl3)
: 8.86 (s, 1 H), 8.19–8.24 (m, 2 H), 7.66 (d, J = 8.69
Hz, 2 H), 7.33–7.42 (m, 5 H), 5.73 (s, 1 H), 5.18 (t, J = 12.35 Hz, 2 H), 4.37 (br s, 1 H), 4.18 (dd, J = 10.06,
3.66 Hz, 1 H), 3.77 (dd, J = 9.83, 7.55 Hz, 1 H), 0.92 (s, 9 H), 0.13 (s, 6 H); 13C NMR (CDCl3)
: 169.0,
]
(
δ
δ

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156.3, 143.7, 143.0, 135.8, 128.6, 128.5, 125.1, 119.2, 67.5, 63.0, 56.5, 25.8, 18.1,
−
5.5; IR (cm−1): 3391w,
3230w, 2977w, 2871w, 2831w, 2831w, 1695m, 1635s, 1578m, 1514m, 1453s, 1375s, 1282s, 1204m, 1133s,
1079s, 1044m, 880s, 798s, 759m; HR-MS: for C23H31N3O6Si [M + H]+ calculated 474.2055 m/z, found
474.2067 m/z.
3.3. Lipophilicity Measurement
The lipophilicity of synthesized compound was evaluated using Agilent 1200 Series HPLC
instrument (Agilent technologies, Waldbron, Germany) controlled through the Chemstation software
(version B.04.03). The separation was performed on ZORBAX Extend-C18 (3
column (Agilent Technologies, Waldbron, Germany). Methanol (constituent A) and purified water
×
150 mm, 3.5 µm)
(constituent B) were used as a mobile phase. Total flow rate was set up to 0.4 mL/min; the injection
volume was 1 µ
L; and the column temperature was maintained at 25 ◦C. The detection wavelength of
210 nm was used. Retention factors of the compounds were measured under five different isocratic
conditions in the 65:35–80:20 range (A:B; v/v), in duplicate. The log kw values (indexes of lipophilicity)
were estimated by linear regression analysis based on the following equation,
log k = −SΦ + log kw,
(1)
where logk represents a logarithm of an individual isocratic retention factor,
concentration and S is a constant derived from linear regression analysis.
Φ
is the organic phase
3.4. Evaluating In Vitro AChE and BChE-Inhibition Potencies
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, as described in detail elsewhere [24
presented in Table 1.
,80–83]. The results are
3.5. Study of Inhibition of Photosynthetic Electron Transport (PET) in Spinach Chloroplasts
Chloroplasts were prepared from spinach (Spinacia oleracea L.) according to Kralova et al. [84].
The inhibition of photosynthetic electron transport (PET) in spinach chloroplasts was determined
spectrophotometrically (Genesys 6, Thermo Scientific), using an artificial electron acceptor
2,6-dichlorophenol-indophenol (DCIPP) according to Kralova et al. [84], and the rate of photosynthetic
electron transport was monitored as a photoreduction of DCPIP. The measurements were carried
out in phosphate buffer (0.02 M, pH 7.2) containing sucrose (0.4 M), MgCl2 (0.005 M) and NaCl
(0.015 M). The chlorophyll content in these experiments was 30 mg/L, and the samples were irradiated
(~100 W/m2 with 10 cm distance) with a halogen lamp (250 W) using a 4 cm water filter to prevent
warming of the samples (suspension temperature 22 ◦C). The compounds were dissolved in DMSO
due to their limited water solubility. The applied DMSO concentration (up to 4%) did not affect
the photochemical activity in spinach chloroplasts. The inhibitory efficiency of the compounds was
expressed by IC50 values; i.e., by molar concentration of the compounds, causing a 50% decrease in the
oxygen evolution rate relative to the untreated control. The comparable IC50 value for the selective
herbicide 3-(3,4-dichlorophenyl)-1,1-dimethylurea, DCMU (Diuron®) was about 2.1
are shown in Table 1.
µM. The results
3.6. In Vitro Viability Assay
Human monocytic leukemia THP-1 cells were used for in vitro antiproliferative assays. Cells
were obtained from the European Collection of Cell Cultures (ECACC, Salisbury, UK) and routinely
cultured in RPMI 1640 (Biosera, France) medium supplemented with 10% fetal bovine serum (Biosera,
◦
France), 2% l-glutamine, 1% penicillin and streptomycin (Biosera, France) at 37 C with 5% CO2.
Cells were passaged at approximately one-week intervals. The effect of the compounds on cell

Int. J. Mol. Sci. 2019, 20, 5385
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viability was determined using a Water Soluble Tetrazolium Salts-1 (WST-1, 2-(4-iodophenyl)-
3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2H-tetrazolium) assay kit (Roche Diagnostics, Mannheim,
Germany) according to the manufacturer’s instructions. The compounds were dissolved in DMSO
and added in five increasing concentrations (0.37, 1.1, 3.3, 10 and 30 µM) to the cell suspension in
the culture RPMI 1640 medium. The maximum concentration of DMSO (Sigma) in the assays never
exceeded 0.1%. Subsequently, the cells were incubated at 37 ◦C with 5% CO2 for 24 h. For WST-1
assays, cells were seeded in triplicate into 96-well plates (5
×
104 cells/well in 100
µL culture medium)
using serum-free RPMI 1640 medium, and measurements were taken 24 h after the treatment with the
compounds. The median inhibition concentration values, IC50, were deduced through the production
of a dose-response curve. All data were evaluated using the Microsoft Excel. The results are shown in
Table 1.
3.7. Building the Model and Molecular Modelling
Each molecular model was generated using the CACTVS/csed molecular editor. The initial spatial
geometry of the molecules was specified using a CORINA 3D generator. The structural data conversion
was conducted with the (inter)change file format converter OpenBabel. Sybyl-X 2.0/Certara software
package running on a HP workstation with a Debian 6.0 operating system was used to perform the
majority of the molecular modelling simulations. 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 were used to optimize the initial geometry of each compound (MAXMIN2 module).
The partial atomic charges were specified with the Gasteiger–Hückel method implemented in Sybyl-X
for the calculation of the electrostatic potential values. One 15-ordered atom trial alignment on the
most active molecule
2 (Active Analogue Approach) was used to cover the whole 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 employed in the CoMSA analysis to simulate 20
SOMs with a winning distance ranging from 0.2 to 2.0 [85]. SOM network was employed using the
Cartesian coordinates of the molecular surfaces to generate a 2D map of the electrostatic potential
×
20 to 50
×
50
for the superimposed molecules. Following the AAA approach, the most active molecule
2 was
selected as a template molecule (reference compound). The output maps were reshaped into a 400
to 2500-element vector that was subsequently transformed by the PLS method implemented in the
MATLAB programming environment.
The input crystallographic data of BChE and PSII inhibitor-enzyme complexes were taken
from Protein Data Bank (PDB entry code: 6EUL and 1BZ1). Only EDO (1,2-ethanediol) and PL9
(2,3-dimethyl-5-(3,7,11,15,19,23,27,31,35-nonamethyl-2,6,10,14,18,22,26,30,34-hexatriacontanonaenyl-2,5
-cyclohaxadiene-1,4-dione-2,3-dimethyl-5-solanesyl-1,4-benzoquinone) with the ionic cofactors (Fe2+
and Cl−) were retained in the active site AC2 of BChE and EC9 of PSII enzymes, respectively.
The remaining heteroatoms, including water molecules, were erased from the input files prior to the
calculations. The ligand/enzyme structural files were prepared in the pdbqt format using MGLTools’
inbuilt python scripts [61]. The ligand charges were calculated with semi-empirical PM3 method
implemented in HyperChem software with default SCF settings. The docking pocket was set to
a 20 Å cube with a 0.2 Å grid spacing centered on the RIV for BChE and PL9 for PSII aromatic
rings, respectively. Different poses (default nine) were specified progressively on the basis of an
energy-minimized ligand conformer using the AutoDock programme with the Lamarckian genetic
algorithm and subsequently evaluated with the in-build united-atom scoring function [68]. Finally,
planar (2D) and spatial (3D) visualizations of the predicted ligand-enzyme binding modes were
produced using PyMol, Maestro, VMD and PLIP software [69].
The input crystallographic data of BChE and PSII inhibitor-enzyme complexes were taken
from Protein Data Bank (PDB entry code: 6EUL and 1BZ1). Only EDO (1,2-ethanediol) and PL9
(2,3-dimethyl-5-(3,7,11,15,19,23,27,31,35-nonamethyl-2,6,10,14,18,22,26,30,34-hexatriacontanonaenyl-2,5

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-cyclohaxadiene-1,4-dione-2,3-dimethyl-5-solanesyl-1,4-benzoquinone) with the ionic cofactors (Fe2+
and Cl−) were retained in the active site AC2 of BChE and EC9 of PSII enzymes, respectively.
The remaining heteroatoms, including water molecules, were erased from the input files prior to the
calculations. The ligand/enzyme structural files were prepared in the pdbqt format using MGLTools’
inbuilt python scripts [61]. The ligand charges were calculated with semi-empirical PM3 method
implemented in HyperChem software with default SCF settings. The docking pocket was set to
a 20 Å cube with a 0.2 Å grid spacing centered on the RIV for BChE and PL9 for PSII aromatic
rings, respectively. Different poses (default nine) were specified progressively on the basis of an
energy-minimized ligand conformer using the AutoDock programme with the Lamarckian genetic
algorithm and subsequently evaluated with the in-build united-atom scoring function [68]. Finally,
planar (2D) and spatial (3D) visualizations of the predicted ligand-enzyme binding modes were
produced using PyMol, Maestro, VMD and PLIP software [69].
3.8. In Silico Lipophilicity Evaluation
A diverse set of freely or commercially accessible computer programs can be employed to specify
the theoretical partition coefficient, for instance:
AlogPS—an approach developed by Tetko et al. that is based on atom-type electrotopological-state
(E-state) indices and neural networks;
milogP—a method provided by Molinspiration that enables practical logP calculations for almost
all organic molecules as the sum of fragment-based contributions and correction factors;
ClogP—a fragment-based procedure implemented in Sybyl/Centara software that is able to
accurately predict lipophilicity based on structure-dependent correction values taken from Hansch
and Leo’s database;
HyperChem logP—an atom-additive method that estimates lipophilicity based on the individual
atomic contributions following Ghose, Prichett and Crippen approach;
MarvinSketch logP—the overall lipophilicity of a molecule consists of the contributing values of its
atoms, the types of which are redefined to accommodate electron delocalization and the contributions
of ionic forms;
ChemSketch logP—a comprehensive, fragment-based algorithm methodology with high quality
models specified on the basis of empirical data. Well-characterized logP contributions were compiled
for atoms, structural fragments and intramolecular interactions derived from >12000 experimental
logP values;
Dragon AlogP—the statistical estimators of the Ghose-Crippen-Viswanadhan model were calculated
based on the known experimental logP on the training set of 8364 compounds. The overall estimation of
the lipophilic atomic-based constant is provided with the contribution of 115 atom types;
Dragon MlogP—the theoretical partition coefficient includes VdW volume and Moriguchi polar
parameters as correction factors. MlogP model is composed of a regression equation that is based on
13 structural parameters which were assessed by a trainings group of 1230 organic molecules;
Kowwin—evaluates the log octanol-water partition coefficient of chemicals applying the
atom/fragment contribution method;
XlogP3—an atom-additive method with well-defined correction factors, that employs an optimized
atom-typing approach calibrated on a large training set.
3.9. Structure Activity Landscape Index
The systematic profiling of potency-similarity relationships in the form of the activity landscape
provides a sharp picture of favorable structural modifications that are necessary to identify the activity
cliffs [11]. The smoothness of this landscape can be numerically quantified using the structure–activity
landscape index (SALI) introduced as follows:

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ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
ꢀ
Ax − Ay
SALIx,y
=
,
(2)
1 − sim(x, y)
where
A
x and
A
y are activities of the x-th and y-th molecules and sim(x,y) is the similarity coefficient
between corresponding molecules [14]. In other words, SALI assigns the score that combines the
pairwise similarity and the variations in the activity, respectively. It was shown that the overall
appearance of the symmetric SALI matrix minimally depends on the specific similarity metric;
therefore, the Tanimoto coefficient was employed in the fingerprint space similarity analysis [7].
Despite many obstacles, the Tanimoto metric shed light on the number of features that two molecules
have in common to the total number of unique features present in each molecule according to the
following formula:
nxy
T(x, y) =
,
(3)
(nx + ny − nxy)
xy is the number of bits set into 1 shared in the fingerprint of the molecule x i y;
where
n
nx is the number
of bits set into 1 in the molecule x; and ny is the number of bits set into 1 in the molecule y. The ratio of
T(x,y) > 0.8 indicates a criterion of a high structural/property similarity to the target structure; however,
the validity of this assumption is still questionable since similar compounds do not necessarily interact
with the same target in the same manner. SALI values should be adequately sampled for structures
that represent similar chemotypes but different potency profiles, to identify activity cliffs.
3.10. Principal Component Analysis and Partial Least Squares with Iterative Variable Elimination
PrincipalComponentAnalysis(PCA)isalinearprojectionmethodthatcondensesamultidimensional
data (organized in matrix Xm×n with the rows corresponding to m objects and the columns corresponding
to n parameters) into a few explanatory principal components (PCs) [86]. PCA is a classic method of
data exploration, which allows one to reduce data dimensionality, because a restricted ensemble of
the orthogonal PCs creates a basis of the lower-dimensional space. In the method, input matrix X is
decomposed into three matrices called scores (T), loadings (P) and the residual matrix (E), respectively.
In other words, the PCA model with f principal components for a data matrix X can be presented
according the following equation:
X = TPT+ E,
(4)
where X is a data matrix.
Provided that the reduction of data dimensionality is effective, it is possible to use the score
vectors and loading vectors to visualize and to interpret the relationships between the objects and the
parameters in the input matrix. The similarities between the original data objects are evaluated using
the distance metrics.
Partial least squares (PLS) is a multivariate statistical method that can analyze the input data
with strong co-linearity and model, simultaneously. A relationship between response data (dependent
variable) Y and an ensemble of descriptors (independent variables) X is expressed briefly in the form
of the following formula:
Y = X b + e,
(5)
where b is a vector of the regression coefficients and e is a vector of errors.
Structural information encoded by a myriad of descriptors shows a significant degree of overlap;
therefore, over-parametrization occurs frequently in QSAR studies. In other words, numerous
descriptors show restrained variability and/or are strongly (inter)correlated as well. To avoid random
noise, the recurrent variable selection method conjugated with the PLS procedure (IVE-PLS) was
effectively employed in the mD-QSAR studies. In fact, it can be regarded as an extension of the
single-step uninformative variable elimination algorithm (UVE) that was originally proposed by
Centner et al. for the selection of the variables to be eradicated [87]. Vaguely, the whole computer
implementation was comprised of four basic step including: (i) standard PLS analysis with LOO-CV

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to evaluate the performance of the PLS model, (ii) elimination of a matrix column with the lowest
abs(mean(b)/std(b)) value, (iii) standard PLS analysis of the new matrix without the column eliminated
in stage (ii) and (iv) recurrent repetition of steps (i)–(iii) to maximize the LOO parameter.
4. Conclusions
(1) A set of novel 25 silicon-based carbamate derivatives as potential acetyl and
butyrylcholinesterase inhibitors was synthesized and were characterized by their in vitro inhibition
profiles and the selectivity indexes. Moreover, the compounds were sampled for their inhibition
potential against photosynthetic electron transport in spinach (Spinacia oleracea L.) chloroplasts.
In fact, some of the newly prepared molecules revealed comparable or even better inhibitory
activities compared to marketed drugs (rivastigmine or galanthamine) and commercially applied
pesticides (Diuron®), respectively. Generally, more compounds exhibited better inhibition potency
towards AChE; however, a wider activity span (maximum value less minimum ones) was observed
for BChE. Notably, benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(2-hydroxyphenyl)carbamoyl]-
ethyl]carbamate (
carbamoyl]ethyl]carbamate
Specifically, compound
galanthamine inhibition activity, while the inhibitory profiles of molecules
2
) and benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(3-hydroxyphenyl)-
are characterized by fairly high selective indexes (SIs).
has the lowest IC50 value that corresponds quite well with
and benzyl
) are in line
(3)
2
3
N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(4-hydroxyphenyl)carbamoyl]ethyl]carbamate (
4
with rivastigmine activity.
(2) The SAR-driven similarity evaluation of physicochemical properties for the carbamates
examined was reported to indicate the activity cliffs using similarity-activity landscape index for BChE
inhibitory response values. The similarity-mediated evaluation of property profiles was conducted
using the PCA procedure on the pool of Dragon descriptors. The corresponding logP estimators
deduced by the set of alternative programs were (inter-)correlated with each other and cross-compared
with the experimental logk values, respectively. In this study, the similarity between two molecules
was quantified using Tanimoto coefficient evaluated between pairs of the OpenBabel fingerprint.
A graphical representation of chemical similarity versus biological activity was derived from the
systematic profiling of structure–activity landscape indexes (SALI). The identification of activity cliffs
depends, critically, on the accessibility of chemically related compounds with noticeably large activity
variations; therefore, BChE values were taken into consideration in the SALI calculation. The hints
provided based on the graphical/numerical SALI representations prompt further dense sampling of the
indicated SAR-variations with the potential compound syntheses as well as the activity specifications.
(3) The ‘indirect’ ligand-based and ‘direct’ protein-mediated in silico approaches were applied
to specify electronic/steric/lipophilic factors that were potentially valid for (Q)SAR modeling of the
carbamate analogues. The stochastic model validation was used to generate an ‘average’ 3D-QSAR
pharmacophore pattern. It seems that bulky substitution of phenyl group at ortho position can
increment the BChE inhibitory potency. On the other hand, the continuous increase in bulk at ortho
region does not necessarily lead to further potency growth that is in line with the observed inhibitory
response (e.g., -OH versus -OCH3). Noticeably, the increase in the bulkiness at meta substitution seems
to be unfavorable for structural modification. The above observation confirms the tendency recorded
for the positional isomers of the examined carbamates where BChE IC50 can be generally ranked as
meta < ortho.
(4) The target-oriented molecular docking was employed to (re)arrange the spatial distribution of
the ligand property space for BChE enzyme and PSII system as well. Despite structural differences
between RIV, GLT and 1–25 carbamate series, some similarities in the atom spatial distribution can
be observed for negatively charged atoms of nitrogen and oxygen, respectively. The OH group of
Thr120 seems to play a vital role as hydrogen donor in forming the hydrogen bond (HB) with the
ether and carbonyl oxygen of the most potent molecule
2. Moreover, both terminal phenyl rings were
specified as crucial building (sub)blocks for the hydrophobic interactions with Pro285 or Tyr332, which

Int. J. Mol. Sci. 2019, 20, 5385
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partially corresponds to our previous ligand-oriented findings. The molecular docking for the most
potent DCMU herbicide resulted in interaction with the OH group of D1-Ser264 and the backbone NH
group of D1-Phe265 with the DCMU carbonyl group. Interestingly, only the most potent PET inhibitor
(molecule 2) was predicted to be hydrogen-bonded with D1-Ser264.
Despite the ligand and structurally-driven approach’s limitations, a consensus methodology
combining the pharmacophore mapping and the target-tailored procedures was proposed for
investigating of the multifaceted inhibitor–enzyme interactions.
Supplementary Materials: Supplementary materials can be found at http://www.mdpi.com/1422-0067/20/21/
5385/s1.
Author Contributions: P.B. and H.P., synthesis and compound characterizations; Z.T., compound characterizations
and editing manuscript; K.O., lipophilicity evaluation and HPLC analysis; M.O., HRMS analyses; A.B. and
V.K., CoMSA, SMV, docking analysis and writing of the paper; A.S., descriptor calculations and writing of the
paper, J.J., design of compounds and writing of the paper; K.V. and A.I., AChE/BChE assay; J.K., PET assay; J.T.,
antiproliferative assay.
Funding: This study was supported by the Ministry of Education, Youth and Sports of the Czech Republic
(LO1305), and by IGA UVPS Brno 320/2018/FaF and 320/2017/FaF. S.S. and A.I. wish to acknowledge the institutional
support from the Faculty of Chemical Technology, University of Pardubice. 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 the
National Sustainability Program (NPU I; grant number LO1415).
Acknowledgments: We would like to acknowledge the Schrödinger, OpenBabel and OpenEye for the free
academic software licenses and J.M. for the participation in the synthesis of the compounds we tested.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
AChE
BChE
PSII
Acetylcholinesterase
Butyrylcholinesterase
Photosystem II
CAMD
ADMET
CoMSA
SMV
Computer Assisted Molecular Design
Absorption Distribution Metabolism Excretion Toxicity
Comparative Molecular Surface Analysis
Stochastic Model Validation
Rivastigmine
RIV
GLT
Galanthamine
DCMU
PQ
3-(3,4-dichlorophenyl)-1,1-dimethylurea
Plastoquinone
PCA
IVE-PLS
PLIP
Principal Component Analysis
Iterative Variable Elimination Partial Least Squares
Protein Ligand Interaction Profiler
Photosynthetic Electron Transport
PET
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