Proline-based carbamates as cholinesterase inhibitors

molecules

Article

Proline-Based Carbamates as Cholinesterase

Inhibitors ^†

Hana Pizova ^1,

*

^ID, Marketa Havelkova ¹, Sarka Stepankova ², Andrzej Bak ³, Tereza Kauerova ⁴,

Violetta Kozik ⁵, Michal Oravec ⁶, Ales Imramovsky ⁷, Peter Kollar ⁴, Pavel Bobal ¹

and Josef Jampilek ^8,

*

ID

1

Department of Chemical Drugs, Faculty of Pharmacy, University of Veterinary and Pharmaceutical Sciences,

Palackeho 1, 612 42 Brno, Czech Republic; F14042@vfu.cz (M.H.); 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; sarka.stepankova@upce.cz

Institute of Chemistry, University of Silesia, Szkolna 9, 40-007 Katowice, Poland; andrzej.bak@us.edu.pl

Department of Human Pharmacology and Toxicology, Faculty of Pharmacy, University of Veterinary and

Pharmaceutical Sciences, Palackeho 1, 612 42 Brno, Czech Republic; tereza.kauerova@gmail.com (T.K.);

kollarp@vfu.cz (P.K.)

2

3

4

5

6

7

8

Department of Synthesis Chemistry, Faculty of Mathematics, Physics and Chemistry, University of Silesia,

Szkolna 9, 40-007 Katowice, Poland; violetta.kozik@us.edu.pl

Global Change Research Institute CAS, Belidla 986/4a, 603 00 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

Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Comenius University, Odbojarov 10,

832 32 Bratislava, Slovakia

*

†

Correspondence: pizovah@gmail.com (H.P.); josef.jampilek@gmail.com (J.J.)

Preliminary results were presented at the 46th Europe Congress on Drug Synthesis and Analysis, Bratislava,

Slovakia, 5–8 September 2017 (paper P-68).

Received: 25 September 2017; Accepted: 10 November 2017; Published: 14 November 2017

Abstract: Series of twenty-ﬁve benzyl (2S)-2-(arylcarbamoyl)pyrrolidine-1-carboxylates was prepared

and completely characterized. All the compounds were tested for their in vitro ability to

inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), and the selectivity of

compounds to individual cholinesterases was determined. Screening of the cytotoxicity of all

the compounds was performed using a human monocytic leukaemia THP-1 cell line, and the

compounds demonstrated insigniﬁcant toxicity. All the compounds showed rather moderate

inhibitory effect against AChE; benzyl (2S)-2-[(2-chlorophenyl)carbamoyl]pyrrolidine-1-carboxylate

(IC₅₀= 46.35

benzyl (2S)-2-[(2-bromophenyl)carbamoyl]pyrrolidine-1-carboxylates expressed anti-BChE activity

(IC₅₀= 28.21 and 27.38 M, respectively) comparable with that of rivastigmine. The ortho-brominated

µM) was the most potent agent. On the other hand, benzyl (2S)-2-[(4-bromophenyl)-] and

µ

compound as well as benzyl (2S)-2-[(2-hydroxyphenyl)carbamoyl]pyrrolidine-1-carboxylate

demonstrated greater selectivity to BChE. The in silico characterization of the structure–inhibitory

potency for the set of proline-based carbamates considering electronic, steric and lipophilic properties

was provided using comparative molecular surface analysis (CoMSA) and principal component

analysis (PCA). Moreover, the systematic space inspection with splitting data into the training/test

subset was performed to monitor the statistical estimators performance in the effort to map the

probability-guided pharmacophore pattern. The comprehensive screening of the AChE/BChE proﬁle

revealed potentially relevant structural and physicochemical features that might be essential for

mapping of the carbamates inhibition ef₀ﬁciency indicating qualitative variations exerted on the

0

reaction site by the substituent in the 3 -/4 -position of the phenyl ring. In addition, the investigation

was completed by a molecular docking study of recombinant human AChE.

Molecules 2017, 22, 1969; doi:10.3390/molecules22111969

ww w .mdpi.com/journal/molecules

Molecules 2017, 22, 1969

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Keywords: proline; carbamates; in vitro cholinesterase inhibition; in vitro cytotoxicity assay; CoMSA;

IVE-PLS; molecular docking study

1. Introduction

An amide (–CONH–) and/or carbamate (–OCONH–) group is present in a number of clinically

used drugs [

a privileged structural fragment [3,4

1

] and pesticides [

2

]. Both terminations can be variously substituted, which results in

]. These moieties interact with a number of enzymes/receptors

and, by means of these target sites, they are able to affect the biological response. Therefore, the reason

for the widespread occurrence of amides and carbamates among new biologically active compounds is

obvious [

(ChEIs) [

5

–

15]. Carbamate-like compounds can be considered as essential cholinesterase inhibitors

–

1,

16 18]. Acetyl-(AChE, EC 3.1.1.7) and butyryl-cholinesterases (BChE, EC 3.1.1.8) are two

important cholinesterases (ChEs) that occur in the human body. ChEs belong to the group of serine

hydrolases [19]. The major role of AChE is to catalyse the hydrolysis of acetylcholine (ACh) in

cholinergic synapses, while BChE can hydrolyse ACh as well as other esters; nevertheless, it seems

its concentration in brain is especially important at Alzheimer’s disease (AD) [20,21]. The inhibition

of both enzymes causes an increase in the ACh concentration in cholinergic synapses and can

subsequently affect a number of pathogenic processes. ChEIs are used in the treatment of various

neuromuscular disorders and have provided the ﬁrst generation of drugs for the treatment of

Alzheimer’s disease, myasthenia gravis and glaucoma. An increase in the concentration of ACh

can result in an alleviation of the symptoms of these diseases [22].

AD is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills,

and eventually the ability to carry out the simplest tasks [23]; moreover, the AD is the most common

cause of dementia among older adults. It can be stated that it is the most devastating central nervous

system disorder, especially in Western civilization. Worldwide, nearly 44 million people have AD or

related dementia, and more than 100 million people worldwide are estimated to be affected by AD by

2050 [24

to have important roles in the pathogenesis of this disease; they include aggregation and accumulation

of amyloid- deposits, oxidative stress and low levels of ACh [26]. In the AD brain, AChE levels

,25]. Although the etiology of the AD is not yet entirely known, several conditions are believed

β

decrease, while BChE levels are reportedly increased or unchanged, with changes becoming more

pronounced during the disease course. Strategies that increase the ACh level show symptomatic

efﬁcacy in AD treatment [21]; therefore competitive ChEIs, such as galantamine and rivastigmine, as

well as non-competitive ChEIs tacrine and donepezil are clinically used for relieving AD. In addition,

memantine, a N-methyl-D-aspartate (NMDA) receptor antagonist, was also approved by the FDA for

relieving AD [25]. Thus, research of new ChEIs may be valuable for further progress in the treatment of

AD. Recently, new preferential or BChE selective inhibitors [26–28] as well as various multifunctional

anti-Alzheimer agents [29–32] were reported.

This is a follow-up paper to the paper devoted to recently reported carbamates as

potential ChEIs [16 18 33] and dealing with synthesis and ChE inhibiting properties of benzyl

(2S)-2-(arylcarbamoyl)pyrrolidine-1-carboxylates 9c. In the context of our previously described

N-heterocycles, simple modiﬁcations of proline, an atypical -amino acid, were investigated.

–

,

1

–

α

The rational production of properties supported by computers is basically regarded as a preliminary

stage or an “intuitive roadmap” on the path from a hit to a drug candidate. A pool of

computer-assisted drug design (CADD) procedures, including multidimensional quantitative-structure

activity relationships (mD-QSAR), diffused quickly into computational medicinal chemistry getting

more and more popular in the rational drug discovery [34].

The in silico characterization of the structure–inhibitory potency for the set of proline-based

carbamates considering electronic, steric and lipophilic properties was provided using

comparative molecular surface analysis (CoMSA) and principal component analysis (PCA) [35].

Molecules 2017, 22, 1969

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Moreover, the systematic space inspection with splitting data into the training/test subset was

performed to monitor the statistical estimators performance in the effort to map the probability-guided

pharmacophore pattern. Consequently, a pseudo-consensus 3D-QSAR approach was applied to

retrieve an “average” pharmacophore hypothesis by investigating the best models for training/test

subpopulations to specify the potentially important factors contributing to the inhibitory activity

of potential ChEIs. In addition, the investigation of these positional isomers of proline-based

carbamates was completed by a molecular docking study of recombinant human AChE (rhAChE).

Thus, the comprehensive screening of the AChE/BChE proﬁle revealed the potentially relevant

structural and physicochemical features that might be essential for mapping of the carbamates

inhibition efﬁciency, indicating qualitative variations exerted on the reaction site by a substituent in

the 3⁰-/4⁰-position of the phenyl ring.

2. Results

2.1. Chemistry

From conventional and emerging chemical approaches for amide bond formation [

3 ,36 –40 ] and from

the methods which have been previously used in our laboratories [ 41 43 ], we have selected the procedure

8 , –

where propylphosphonic anhydride (T3P) has been used as a coupling agent [43 ]. The application of

this one-pot reaction to commercially available (2S)-1-[(benzyloxy)carbonyl]-pyrrolidine-2-carboxylic acid

and a wide variety of substituted anilines led to the formation of corresponding substituted anilides

(see Scheme 1 ) that were isolated in high yields and high purity.

1–8c

Scheme 1. Synthesis of benzyl (2S)-2-(arylcarbamoyl)pyrrolid_◦ine-1-carboxylate

1–8c. Reagents and

conditions: propylphosphonic anhydride, dry ethyl acetate, 80 C, argon atmosphere.

Unfortunately, the method with T3P failed in the case of aminophenols. To overcome this

problem with formation of many side products, the modiﬁed Mukayama’s method [44 45] with use

,

of ethyl chloroformate as an activation reagent in the presence of base has been successfully applied

(see Scheme 2). Hydroxy-substituted anilides 9a–9c were isolated in high yields and sufﬁcient purity.

Scheme 2. Synthesis of benzyl (2S)-2-[(hydroxyphenyl)carbamoyl]pyrrolidine-1-carboxylates 9a–9c.

Reagents and conditions: triethylamine, tetrahydrofuran, ethyl chloroformate, room temperature,

argon atmosphere.

2.2. In Vitro Evaluation of AChE- and BChE-Inhibiting Activity with CoMSA and SMV Procedure

All prepared carbamates were tested for their inhibition of AChE and BChE. The activities

of the compounds were compared with the internal standards rivastigmine and galantamine.

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These standards were chosen because of their different structures. While rivastigmine is a classical

acylating pseudo-reversible carbamate ChEI that inhibits both AChE and BChE, galanthamine is

a non-acylating competitive reversible ChEI as well as an allosteric ligand at nicotinic ACh receptors.

The choice of these reference drugs with different mechanisms of action can provide relevant results.

The results are summarized in Table 1 and expressed as 50% inhibitory concentration (IC₅₀

the concentration of inhibitor required for 50% inhibition of the mentioned enzymes.

(µM)), or

Table 1. Structures of discussed carbamates 1–9c; calculated values of lipophilicity (clogP), polar

surface area (PSA), polar volume (PV) and in vitro AChE and BChE inhibition (IC₅₀) in comparison

with standards rivastigmine (RIV) and galanthamine (GLT) and in vitro cytotoxicity assay (IC₅₀) of

compounds. ChE inhibitions are expressed as mean ± SD (n = 3 experiments).

R

O

HN

N

O

Comp.

R

clogP

PV

AChE IC₅₀(µM)

BChE IC₅₀(µM)

SI *

tox. IC₅₀(µM)

PSA (Å)

1

2a

2b

2c

3a

3b

3c

4a

4b

4c

5a

5b

5c

6a

6b

6c

7a

7b

7c

8a

8b

8c

9a

9b

9c

H

2-OCH₃

3-OCH₃

4-OCH₃

2-CH₃

3-CH₃

4-CH₃

2-F

3-F

4-F

2-Cl

3-Cl

4.1632

3.6486

4.2386

4.0122

4.6622

3.9638

4.5638

4.2838

5.1338

4.4038

5.2838

4.0470

5.4970

0.8712

3.7262

3.4962

–

49.446

51.491

61.506

61.876

45.255

49.460

49.466

48.435

49.474

49.441

48.725

49.486

49.435

49.183

49.440

49.462

42.920

49.450

49.438

110.799

134.854

134.702

93.856

105.477

105.171

–

148.212

160.579

165.704

148.051

141.705

129.946

132.090

144.032

131.146

144.623

135.310

134.686

133.841

137.829

137.166

133.509

142.558

118.742

124.821

195.483

207.158

209.563

164.611

172.921

173.231

–

105.88 ± 2.20

130.99 ± 1.29

126.13 ± 2.79

109.36 ± 0.22

61.94 ± 1.35

81.06 ± 0.03

123.41 ± 0.44

51.87 ± 0.76

69.88 ± 0.21

87.23 ± 1.00

46.35 ± 1.05

48.27 ± 0.11

77.34 ± 3.80

84.14 ± 4.39

74.16 ± 0.20

81.32 ± 0.67

73.24 ± 2.88

68.91 ± 0.06

88.65 ± 1.60

152.21 ± 0.01

64.99 ± 0.97

84.52 ± 0.06

153.40 ± 9.97

122.00 ± 1.38

91.08 ± 0.27

50.10 ± 3.08

4.0 ± 0.13

55.57 ± 1.01

51.53 ± 0.75

58.47 ± 0.42

117.40 ± 0.45

98.27 ± 5.88

114.84 ± 4.83

153.11 ± 2.29

59.97 ± 0.00

34.61 ± 0.11

58.14 ± 1.39

53.47 ± 0.52

64.20 ± 7.14

147.34 ± 1.18

27.38 ± 0.85

47.68 ± 0.08

28.21 ± 0.50

44.89 ± 1.66

39.46 ± 1.40

75.48 ± 2.94

48.69 ± 0.80

55.93 ± 0.63

58.75 ± 0.27

42.01 ± 1.26

86.22 ± 0.40

132.97 ± 0.39

19.95 ± 0.31

7.96 ± 0.59

1.91

2.54

2.16

0.93

0.63

0.71

0.81

0.86

2.02

1.50

0.87

0.75

0.52

3.07

1.56

2.88

1.63

1.75

1.17

3.13

1.16

1.44

3.65

1.41

0.68

25.11

0.50

>30

–

4-Cl

2-Br

3-Br

4-Br

2-CF₃

3-CF₃

4-CF₃

2-NO₂

3-NO₂

4-NO₂

2-OH

3-OH

4-OH

–

RIV

GLT

–

* SI (index selectivity) = IC₅₀(AChE)/IC₅₀(BChE). Greater selectivity to BChE in bold.

The principal aim of the ligand-based investigation was the comparative study of the molecular

surface using the CoMSA approach to model in vitro activity observed for a set of proline-based

carbamates as potential AChE/BCHE inhibitors. The ﬁndings of inhibiting potency modeling

(AChE and BChE) were correlated with surface descriptors regarding multiple training/test subsets

and (in)dependent variables used, respectively. Unfortunately, the q²_cvperformance of AChE and BChE

proﬁle for the entire proline-based carbamates dataset

for CoMSA models (q²_cv< 0.5), irrespective of the map size (20

molecules and 7a . Obviously, a model predictive power cannot be evaluated just by goodness

1–9c in the training dataset is not satisfactory

×

20–50 50) or applied template

×

1

–c

of data ﬁtting with the cross-validated leave-one-out (CV–LOO) procedure [46]. Thus, the external

validation with splitting the molecule subset into a set of training/test collections was performed to

evaluate the model predictive ability with SDEP and q²

statistics as well. The CoMSA performance

test

for models divided arbitrarily into training/test subsets in 2:1 ratio (18/7) ranked according to the

molecule inhibitory activity (AChE and BChE), regarding ortho-, meta-, and para-substitution (or their

Molecules 2017, 22, 1969

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combinations), indicated pretty poor model abilities accompanied with poor model predictive power.

Moreover, the Kennard–Stone procedure on dependent variables was applied to split representatively

the collection of data into training/test subsets, however the statistical characteristics of CoMSA

models for AChE/BChE was not noticeably improved.

The obtained ﬁndings conﬁrmed that the separation of objects into training/test subgroups is

not a trivial issue. Hence, an additional assessment, namely Stochastic Model Validation (SMV),

has been performed as a kind of “perturbation” procedure to investigate the data structure [47].

Thus, the ﬂuctuations of the statistical estimators during CoMSA AChE and BChE modeling were

scrutinized, as the original dataset of 25 molecules was recurrently sampled into 18/7 training/test

series (fraction 2/3 to 1/3). In this case, it was technically viable to investigate the entire pool of

systematically generated training/test populations (C₂⁸₅

≈

10⁶). The observed proﬁle for q²_cvvs. q²

ﬂuctuation pattern, where the areas of

test

distribution conﬁrms the intuitive interpretation of q²_cv

/

q²

higher modeling ability within the training set can be depicted for AChE as well as BChE potency

q²_cv

0.75). On the other hand, the preferential choice of objects into the training set that easily

ﬁt into the model is accompanied with the decline in the predictive performance for the remaining

ones, which conﬁrms the dichotomic nature of q²_cvq²parameters, where high value of q²_cvdoes not

(

≥

/

test

automatically imply a high model predictability [48]. It should be emphasized that the great advantage

of the QSAR/QSPR paradigm lies not in the extrapolation as stated Hansch [49].

Additionally, Figure 1a,b depicts the molecule selection frequency into the test subset

in the function of the compound number, while sampling the best models (q²_cv

≥

0.50).

Surprisingly, a relatively smooth compound distribution within the training/test subpopulations

is disrupted by outnumbering of molecules 8a for AChE vs. 6c 8a for BChE proﬁle (common

compounds 8a ). Generally speaking, the indicated molecules are mainly para-substituted isomers.

–

c

1,

4–

,

–c

–

c

Interestingly, the BChE inhibitory activities for the positional isomers increase progressively and can be

ranked according to the rough relation, where ortho > meta > para, which partly explains the preferential

selection of para-positioned molecules into the test subset.

Figure 1. Histograms specify number of individual compounds appearing in test set within regions

of q²_cv> 0.5 for training set against q²_testL–7–O CV for modeling: AChE (

a); and BChE (b) potency of

proline-based carbamates derivatives with CoMSA method.

As an additional experiment, the PCA procedure for an ensemble of descriptors retrieved from

Dragon 6.0 software (Nuance Communications, Burlington, MA, USA) has been applied to the

analyzed compounds. The ﬁnal dataset was arranged in matrix X_25×2821with rows representing

molecules (called objects) and columns presenting numerical parameters (called variables) for further

Molecules 2017, 22, 1969

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analysis. PCA was applied to visualize major differences in the performance of investigated molecules

with respect to their structure and inhibitory proﬁle. The analysis was performed for centered and

standardized data. The PCA model with ﬁrst four PCs described 71.88% of the total data variance,

while the ﬁrst three PCs account for 64.68%. The respective score plots are presented in Figure 2a,b.

An analysis of score plots PC1 vs. PC2 in Figure 2a and PC1 vs. PC3 in Figure 2b indicates that,

basically, proline-based carbamate derivatives can be classiﬁed into groups considering structural

data—the positional isomers are generally grouped together. PC1, which describes 43.03% of total

variance, reveals the major variations between compounds

in CoMSA analysis) and all the remaining ones. Indeed, molecule

1

and 7a

–

1

c

(used as template molecules

(object no. 1) is unmodiﬁed

(only hydrogen substituent) compared to its derivatives containing CF₃group (compounds 7a–c)

characterized by the highest value of the volume descriptors provided by the Sybyl–X 2.0 software.

Not surprisingly, the visualization of objects encoded by a set of Dragon descriptors (parameters) on

the plane deﬁned by PC1 vs. PC2 indicates a similar relationship within the ensemble of carbamate

analogs along the ﬁrst principal component, especially for molecules 4–6c excluded from the best

models. Interestingly, this group of compounds is characterized by large negative values on PC2 as

illustrated in Figure 2a.

Figure 2. Projection of carbamates

1–9c on plane deﬁned by: ﬁrst vs. second (a); and ﬁrst vs.

third (b) principal components for Dragon descriptors.

Similarly, 15 parameters (variables) produced by the Sybyl software were collected including

count, volume, surface, Ro5 and lipophilicity descriptors (see Table 1 and Table S1 in Supplementary

materials) to examine the variations within the ensemble of carbamate derivatives. The compression

of the data slowly increased with the number of PCs that were included. The ﬁrst two PCs account for

70.30% of the total data variance, and it increases to ca. 81.47% for next four PCs. The score and the

corresponding loading plots are presented in Figure 3a,b. The projection of objects on the plane deﬁned

by PC1 vs. PC2 component conﬁrmed the observed previously tendency to cluster compounds

4–6c

together with noticeable dissimilarities to positional isomers (containing NO₂substituent). On the

8

basis of the loading plots (Figure 3b), it could be concluded that the uniqueness of the above molecules

was caused by the positively correlated variables describing the molecular prosperities (e.g., clogP, MW

or polar volume along PC1). Moreover, compounds 5–7 were also clustered more or less according to

Lipinski rule of ﬁve (Ro5) violations (isomers meta/para) as displayed in Figure 4a.

Basically, one can expect that the lipophilic proﬁle for molecules can be related to their chemical

structure, therefore structurally similar compounds, namely chemotypes, should have similar property

Molecules 2017, 22, 1969

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features. The detailed inspection of compound lipophilicity color-coded accordingly to calculated

values of clogP for objects projected on the plane speciﬁed by two ﬁrst principal components (PC1 vs.

PC2) conﬁrmed this tendency as observed in Figure 4b for compound separation detected along PC1

(compounds 5–7 with clogP > 5).

Figure 3. Projection of carbamates

1–9c on plane speciﬁed by ﬁrst vs. second principal component (a);

and corresponding loadings score for Sybyl descriptors (b).

Figure 4. Projection of carbamates

1

–

9c on plane deﬁned by ﬁrst vs. second principal component with:

). Colors code the number of

number of Ro5 rule violations ( ); and molecule lipophilicity proﬁle (

a

b

Ro5 violations and value of calculated lipophilicity.

2.3. Consensus-Based 3D Pharmacophore Mapping

Thousands of highly correlated topologic/topographic-based descriptors are normally generated

in mD-QSAR studies; however, the informative variable selection is not an indispensable

pre-processing procedure to prune the input assemble of generated descriptors.

The subsequent level of variable elimination was applied in order to generate meaningful and

predictive models. The recursive IVE-PLS procedure was employed as a “sieve” to identify structural

Molecules 2017, 22, 1969

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descriptors having the highest individual weightings to the biological activity [50]. Hence, all 18/7

training/test samplings speciﬁed for regions with pretty high model abilities (q²_cv

0.5) were selected

≥

to produce an “average” pharmacophore. Regarding the number of objects, the maximum number of

PLS components considered for the model generation was truncated to 7. Thus, the columns annotated

with the highest stability for each of the randomly chosen models were identiﬁed using the IVE-PLS

methodology. Basically, a minor improvement of the q²_cvperformance was observed, while columns

from the data matrix assigned with the lowest value of abs(mean(b)/std(b)) were extracted; however,

the model predictability monitored by q²

remains stable for a considerable range of variables

test

eliminated in the majority of training/test samplings. The moment of q²_cvdeterioration determines

the number of the relevant columns, hence the backward column elimination is recurrently repeated

until the optimal number of variables included within the model is accomplished. The cumulative

sum of common columns for all investigated AChE or BChE models was calculated and normalized to

the range of (0–1). Initially, the group of columns with the value above the pre-chosen cut-off of 0.4

was selected, but the spatial pattern illustrated in Figure 5 was generated by further ﬁltering of 50% of

CoMSA descriptors with relatively small statistical signiﬁcance for the inhibitory activities. The relative

contribution of each variable is weighted by the magnitude and the sign of the corresponding regression

coefﬁcient; therefore colors code the sign of the descriptor impact on compound potency. A visual

inspection of the key pharmacophore patterns can provide direct knowledge about the interaction mode

that increases/decreases the compound activity. The sign of inﬂuence is color-coded depicting not

only regions with positive and/or negative activity contribution, but also four possible combinations

of the mean charge/correlation coefﬁcient.

Figure 5. CoMSA IVE–PLS monitored for chosen 18/7 training/test set samplings. Molecular plots

show the spatial sectors of the greatest contribution into the BChE inhibitory potency. Colors code

the sign of inﬂuence (a). Four possible combinations of mean charge and correlation coefﬁcient are

color–coded for charge descriptors (b). Compound 7c was plotted in two different orientations as

a reference molecule.

Molecules 2017, 22, 1969

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The dark spheres in Figure 5a indicate the patterns potentially detrimental for the BChE

inhibitory potency (mainly due to steric hindrance or electrostatic factors), while the bright polyhedral

specify the 3D areas where atom/substituent is predicted to be positioned in order to enhance the

compound’s inhibition proﬁle. In fact, large regions with suggested favorable contribution appear

in the close proximity to the nitrogen attached directly to the carbonyl moiety (peptide-bond-like

motif in the scaffold). It suggests that the positively charged hydrogen bonded to this nitrogen atom

can be signiﬁcant for the afﬁnity of the inhibitor molecule, while interacting non-covalently with

a macromolecular site (hydrogen bond donor) as indicated by the corresponding positive regression

coefﬁcient pool in Figure 5b. Moreover, the obtained ﬁndings demonstrate the importance of the side

chain R directly attached to the phenyl ring (see Table 1); especially positions 3⁰and/or 4⁰seem to be

a crucial structural and physicochemical factor for the activity maintenance of the tested compounds.

As a matter of fact, the mixed (negative/positive) steric contribution to the inhibitory efﬁciency is

observed in Figure 5a; however, the increase of bulkiness at position 4⁰of the phenyl ring seem to be

unfavorable structural variations, which can partially explain the lower BChE potency of para isomers.

In general, the negatively charged atoms/groups in para position primarily favorably contribute

(negative regression coefﬁcients) to the inhibition efﬁciency as illustrated in Figure 5 b. On the other

hand, the spatially allowed areas attributed by the positive regression coefﬁcient of CoMSA models

are occupied by the positively-charged carbon atom of CF₃or nitrogen of NO₂fragments, which

corresponds quite well with the enhanced inhibitory activities (AChE & BChE < 100) observed for CF₃

and NO₂substituted derivatives within the ortho population.

The stochastic SMV protocol for the pharmacophore visualization based on the consensus 3D

QSAR modeling with satisfactory statistical characteristics provides the spatial map of chemical

groups/atoms potentially relevant for increasing/decreasing the activity proﬁle of the proline-based

carbamates as potential ChEIs.

2.4. Molecular Docking Study

In the target-guided QSAR procedures the complementary (bio)effector binding mode is

retrieved based on the intrinsic dependence of atomic coordinates of both receptor and ligand in

the binding/active site, while the target spatial arrangement of atoms is available [51]. The adopted

spatial distribution of the ligand property space is mediated by the corresponding mapping of target

steric, electronic or lipophilic patterns. The promising site-directed QSAR methodology called docking

can be employed when the macromolecular geometry or at least good homology models are available.

Molecular docking is a method extensively used in the structure based drug design (SBDD); however;

this method does not always provide quantitative correlations between in silico calculations and actual

activity assays [52]. On the other hand, the SBDD methodology is a sophisticated tool in the lead

optimization and virtual screening of the hits retrieved from the ligand database in ﬂash docking.

In particular, the structures of human acetylcholinesterase in a complex with pharmacologically

relevant drugs (e.g., galanthamine or rivastigmine) were a matter of previous extensive studies [53

]

that allowed us to make a comparison with ligand-based protocols; however, the detailed investigation

of host–target interactions is beyond the scope of this paper.

The crystallographic data of rhAChE with the catalytic core speciﬁed at higher resolution of 2.4 Å

in the liganded state (holo) with galanthamine was obtained from 4EY6 PDB entry. Subsequently, the

drug molecule was successfully (re)docked into the active site of the enzyme chain A using AutoDock

Vina program (Scripps Research Institute, La Jolla, CA, USA) [54], which represents a ﬂexible platform

for the rapid database screening as presented in Figure 6.

Moreover, the attempt to investigate the spatial host–rhAChE patterns within the active site

of chain A was taken for the population of proline-based carbamates (

galanthamine interacting mode. Firstly, the comparison of particular conformations and mutual

orientations (poses) for compounds and 7a is illustrated in Figure 7, indicating relevant structural

1–9c) and compared with

1

–c

Molecules 2017, 22, 1969

10 of 26

variations in the catalytic binding site. It seems that para-substituted derivatives exert potentially

different impact on the enzyme reaction site, which is in line with our receptor-independent ﬁndings.

Figure 6. Structure of rhAChE bound with galanthamine (blue) and docked with AutoDock Vina (red)

in active site of enzyme chain A (depicted amino acid residues of Trp86, Gly120-122, Tyr124, Glu202,

Ser203, Phe295, Phe297, Tyr337, Phe338, His447).

Figure 7. Comparison of poses for molecules 1 (blue), 7a (green), 7b (magenta) and 7c (red) in active

site of enzyme chain A.

Surprisingly, despite a noticeable structural differences between galanthamine and the most active

AChE molecule 5a in the set of proline-based carbamates, a similar spatial distribution of the negatively

charged atoms (nitrogen and oxygen) is observed in the catalytic core as illustrated in Figure 8. In fact,

regions in the close proximity to the nitrogen atom linked directly to the carbonyl motif were also

indicated to have favorable contribution to the inhibitory proﬁle in the consensus CoMSA study.

Moreover, the obtained results demonstrate the importance of the side chain R, especially for

ortho-positioned analogs directly attached to the phenyl ring. Interestingly, a chlorine atom can

contribute to hydrogen bond interactions with Tyr337 of chain A as postulated in the case of the

galanthamine binding mode [53]. Roughly speaking, the inhibition activity proﬁle for the positional

isomers can be partially explained by the possibility of hydrogen bond interactions in the catalytic core

ranked according to ortho > meta > para substitution. It seems that an increase of bulkiness at the para

Molecules 2017, 22, 1969

11 of 26

position of the phenyl ring is unfavorable for the inhibitory potency of the investigated carbamates as

observed also in the pharmacophore study.

Ironically, most ligand based results do not seem to be consistent with the structure based ﬁndings;

therefore we should beware of docking [55]. On the other hand, a systematic screening of multifaceted

drug-receptor bonding/repulsive forces using target-tailored procedures conjugated with consensus

pharmacophore mapping can lead towards an intelligent drug delivery platform.

Figure 8. Models of galanthamine (displayed as sticks) and molecule 5a (depicted as ball-and-sticks) in

rhAChE catalytic core with nitrogen atom (blue) and oxygen (red), respectively.

2.5. In Vitro Cytotoxicity Assay

The preliminary in vitro screening of the cytotoxicity of the most effective compounds was

performed using the human monocytic leukemia THP-1 cell line, as described previously [8].

The cytotoxicity was evaluated as the IC₅₀value (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 [56], and the highest tested

concentration that was used for the toxicity assay was three times this value. Treatment with 30

µM

of the discussed compounds did not lead to a signiﬁcant lethal effect on THP-1 cells. Based on these

observations, it can be concluded that the discussed compounds can be considered as non-toxic agents

for subsequent design of novel drugs.

3. Materials and Methods

3.1. General Methods

All reagents were purchased from Aldrich. 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, Dresden,

Germany) and are uncorrected. The purity of ﬁnal compounds was analyzed by a Dionex Ultimate 3000

(Thermo Scientiﬁc) HPLC system controlled through the Chromeleon^®Chromatography Data System

(version 7.2, Thermo Scientiﬁc, Waltham, MA USA). The separation was performed on a YMC-Tiart C₁₈

(3

of water and acetonitrile in the ratio of 40:60. The total ﬂow rate was 0.2 mL/min; the injection volume

was 1

L; and the column temperature was maintained at 35 ^◦C. The detection wavelength of 210 nm

µm, 150 mm

×

2 mm) column (Agilent Technologies, Waldbronn, Germany). Mobile phase consists

µ

was chosen. The purity of individual compounds was calculated as the average of relative peak areas

in the chromatograms of the sample solution. The measurement of optical rotations was carried on

Automatic polarimeter AA-10 (Optical Activity, Ramsey, UK). The concentration of samples is given in

g/100 mL. Infrared (IR) spectra were recorded on a Smart MIRacle^TMATR ZnSe for Nicolet^TMImpact

410 FT-IR Spectrometer (Thermo Scientiﬁc). The spectra were obtained by accumulation of 256 scans

Molecules 2017, 22, 1969

12 of 26

with 2 cm⁻¹resolution in the region of 4000–600 cm⁻¹. All ¹H, ¹⁹F and ¹³C spectra were recorded on

a JEOL ECZR 400 MHz NMR spectrometer (400 MHz for ¹H, 101 MHz for ¹³C and 376 MHz for ¹⁹F,

Jeol, Tokyo, Japan) in DMSO-d₆. Chemical shifts (δ

) are reported in ppm. In ¹H-NMR spectra there

are very broad signals due to interconversion of carbamate rotamers. In ¹³C and ¹⁹F NMR spectra

two signals for two rotamers are observed in most cases [57]. High-resolution mass spectra were

measured using a high-performance liquid chromatograph Dionex UltiMate^®3000 (Thermo Scientiﬁc)

coupled with a LTQ Orbitrap XL^TMHybrid Ion Trap-Orbitrap Fourier Transform Mass Spectrometer

(Thermo Scientiﬁc) with injection into HESI II in the positive or negative mode.

3.2. Synthesis

General Procedure for Synthesis of Carbamates 1–8c: To a mixture of an appropriate aniline

(3.01 mmol, 1.5 equivalent) and N-benzyloxycarbonyl-L-proline (2.06 mmol) in dry ethyl acetate (5 mL)

propylphosphonic anhydride (T3P, 50% solution in toluene, 4.01 mmol of T3P, 2 equivalent) was

◦

added dropwise. The reaction mixture was stirred overnight at 80 C under atmosphere of argon.

Reaction was monitored by thin layer chromatography. After disappearance of the starting material,

water (10 mL) and ethyl acetate (10 mL) were added. The pH of the reaction mixture was adjusted to

pH 6 using 5 M NaOH solution. Organic layer was separated, washed twice with water and dried with

anhydrous Na₂SO₄, and solvent was evaporated under reduced pressure. The residue was triturated

with diethyl ether/petroleum ether or puriﬁed by column chromatography on silica gel (hexane/ethyl

acetate; v/v, 2:1) to give 1–8c. All the studied compounds are presented in Table 1.

◦

Benzyl (2S)-2-(phenylcarbamoyl)pyrrolidine-1-carboxylate (

1

) [45]. Yield 80%; m.p. 140–141 C; HPLC

pur. 99.39%; [α]_D²⁵

:

−

123.3 (c 1.0, CHCl₃) H-NMR (DMSO-d₆)

δ

: 10.03 (d, J = 4.4 Hz, 1H),

7.59 (dd, J = 7.2, 5.6 Hz, 2H), 6.97–7.45 (m, 8H), 4.88–5.17 (m, 2H), 4.25–4.47 (m, 1H), 3.21–3.63

1

(m, 2H), 2.09–2.38 (m, 1H), 1.75–2.06 (m, 3H); ¹³C-NMR (DMSO-d₆)

δ

: 170.99, 170.70, 153.69, 139.00,

138.92, 136.95, 136.77, 128.64, 128.62, 128.04, 127.47, 126.86, 123.28, 123.20, 119.32, 119.15, 65.91, 65.84,

60.47, 59.95, 47.22, 46.57, 31.28, 30.20, 23.96, 23.16; IR (cm⁻¹): 3270, 2874, 1698, 1666, 1549, 1425, 1355,

1245, 1179, 1124, 986, 905, 758, 698; HR-MS: for C₁₉H₂₀N₂O₃[M + H]⁺calculated 325.1547 m/z, found

325.1548 m/z.

Benzyl (2S)-2-[(2-methoxyphenyl)carbamoyl]pyrrolidine-1-carboxylate (2a) [57]. Yield 67%; m.p. 74–76 ^◦C;

HPLC pur. 99.57%; [α]_D²⁵

:

−

107.0 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

δ

: 9.20 (s, 1H), 7.90–8.05 (m, 1H),

6.99–7.45 (m, 7H), 6.85–6.96 (m, 1H), 4.92–5.17 (m, 2H), 4.57 (t, J = 9.3 Hz, 1H), 3.69–3.89 (m, 3H),

3.23–3.58 (m, 2H), 2.04–2.32 (m, 1H), 1.76–2.03 (m, 3H); ¹³C-NMR (DMSO-d₆)

: 171.04, 170.58, 153.80,

δ

149.51, 136.84, 128.37, 128.05, 127.42, 126.88, 124.38, 121.68, 121.68, 121.08, 120.21, 119.43, 111.09, 65.98,

65.84, 60.40, 59.82, 55.70, 47.22, 46.63, 31.35, 29.85, 23.96, 23.11; IR (cm⁻¹): 3274, 2963, 1705, 1684, 1536,

1414, 1351, 1251, 1120, 1025, 955, 745, 698; HR-MS: for C₂₀H₂₂N₂O₄[M + H]⁺calculated 355.1652 m/z,

found 355.1654 m/z.

Benzyl (2S)-2-[(3-methoxyphenyl)carbamoyl]pyrrolidine-1-carboxylate (2b). Yield 79%; m.p. 118–120 ^◦C;

HPLC pur. 98.89%; [α]_D²⁵

:

−

115.6 (c 1.0, CHCl₃); H-NMR (DMSO-d₆)

δ

: 10.03 (d, J = 3.7 Hz, 1H),

1

7.29–7.41 (m, 3H), 7.08–7.26 (m, 5H), 6.60–6.68 (m, 1H), 4.92–5.13 (m, 2H), 4.30–4.40 (m, 1H), 3.70–3.75

(m, 3H), 3.30–3.56 (m, 2H), 2.11–2.33 (m, 1H), 1.78–1.99 (m, 3H); ¹³C-NMR (DMSO-d₆)

δ: 171.07, 170.79,

159.48, 154.02, 153.68, 140.21, 140.12, 136.95, 136.79, 129.45, 129.43, 128.38, 128.04, 127.49, 126.86, 111.57,

111.39, 108.89, 108.82, 104.99, 104.80, 65.93, 65.85, 60.50, 60.00, 54.97, 54.95, 47.22, 46.58, 31.27, 30.20,

23.96, 23.15; IR (cm⁻¹): 3262, 2944, 1697, 1671, 1597, 1550, 1425, 1349, 1200, 1121, 1049, 955, 879, 768,

700; HR-MS: for C₂₀H₂₂N₂O₄[M + H]⁺calculated 355.1652 m/z, found 355.1653 m/z.

Benzyl (_◦2S)-2-[(4-methoxyphenyl)carbamoyl]pyrrolidine-1-carboxylate (2c) [45 58]. Yield 58%; m.p.

,

122–124 C; HPLC pur. 98.84%; [α]_D²⁵

:

−

126.0 (c 1.0, CHCl₃) ¹H-NMR (DMSO-d₆)

δ: 9.88 (d, J = 4.8 Hz,

1H), 7.44–7.58 (m, 2H), 7.10–7.42 (m, 5H), 6.82–6.95 (m, 2H), 4.91–5.12 (m, 2H), 4.26–4.38 (m, 1H),

3.72 (d, J = 2.4 Hz, 3H), 3.27–3.57 (m, 2H), 2.08–2.32 (m, 1H), 1.71–2.00 (m, 3H); ¹³C-NMR (DMSO-d₆)

Molecules 2017, 22, 1969

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δ

: 170.50, 170.20, 155.25, 155.16, 154.02, 153.71, 136.96, 136.81, 132.17, 132.04, 128.37, 128.08, 127.46,

126.86, 120.91, 120.68, 113.75, 65.89, 65.82, 60.41, 59.90, 55.15, 47.22, 46.58, 31.31, 30.23, 23.97, 23.17;

IR (cm⁻¹): 3274, 2940, 1696, 1668, 1550, 1509, 1444, 1411, 1358, 1244, 1167, 1119, 1035, 836, 766, 697;

HR-MS: for C₂₀H₂₂N₂O₄[M + H]⁺calculated 355.1652 m/z, found 355.1655 m/z.

Benzyl (2S)-2-[(2-methylphenyl)carbamoyl]pyrrolidine-1-carboxylate (3a) [58]. Yield 73%; m.p. 115–117 ^◦C;

HPLC pur. 98.99%; [α]_D²⁵

:

−

123.0 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

δ

: 9.39 (br. s., 1H), 7.03–7.46

(m, 10H), 5.00–5.15 (m, 2H), 4.43 (t, J = 9.4 Hz, 1H), 3.27–3.61 (m, 2H), 2.04–2.35 (m, 3H + 1H), 1.77–2.03

(m, 3H); ¹³C-NMR (DMSO-d₆)

: 170.93, 170.60, 154.19, 153.82, 136.98, 136.84, 136.03, 135.90, 132.29,

δ

130.19, 128.36, 128.21, 127.20, 125.83, 125.45, 65.94, 60.31, 59.67, 47.18, 46.58, 31.46, 30.16, 24.01, 23.17,

17.69, 17.59; IR (cm⁻¹): 3258, 2948, 1715, 1662, 1528, 1416, 1353, 1130, 995, 737, 697; HR-MS: for

C₂₀H₂₂N₂O₃[M + H]⁺calculated 339.1703 m/z, found 339.1706 m/z.

Benzyl (2S)-2-[(3-methylphenyl)carbamoyl]pyrrolidine-1-carboxylate (3b) [58]. Yield 65%; m.p. 101–103 ^◦C;

HPLC pur. 99.68%; [α]_D²⁵

:

−

122.5 (c 1.0, CHCl₃); H-NMR (DMSO-d₆)

δ

: 9.96 (d, J = 6.9 Hz, 1H),

1

7.29–7.49 (m, 4H), 7.07–7.28 (m, 4H), 6.81–6.93 (m, 1H), 4.90–5.14 (m, 2H), 4.35 (ddd, J = 15.7, 8.3, 3.0 Hz,

1H), 3.31–3.56 (m, 2H), 2.13–2.32 (m, 3H + 1H), 1.77–1.98 (m, 3H); ¹³C-NMR (DMSO-d₆)

δ

: 171.02,

170.73, 154.08, 153.76, 138.97, 138.86, 137.91, 137.85, 137.01, 136.85, 128.46, 128.12, 127.54, 126.94, 124.05,

123.96, 120.00, 119.77, 116.61, 116.40, 65.99, 65.89, 60.52, 60.01, 47.27, 46.63, 31.34, 30.26, 24.01, 23.20,

21.22; IR (cm⁻¹): 3274, 2874, 1670, 1557, 1425, 1351, 1203, 1125, 951, 749, 696; HR-MS: for C₂₀H₂₂N₂O₃

[M + H]⁺calculated 339.1703 m/z, found 339.1706 m/z.

Benzyl (2S)-2-[(4-methylphenyl)carbamoyl]pyrrolidine-1-carboxylate (3c) [58]. Yield 76%; m.p. 143–146 ^◦C;

HPLC pur. 99.81%; [α]_D²⁵

:

−

122.0 (c 1.0, CHCl₃); H-NMR (DMSO-d₆)

δ

: 9.94 (d, J = 5.5 Hz, 1H),

1

7.42–7.52 (m, 2H), 7.28–7.41 (m, 2H), 7.07–7.26 (m, 5H), 4.92–5.13 (m, 2H), 4.35 (ddd, J = 11.4, 8.2, 3.1 Hz,

1H), 3.35–3.57 (m, 2H), 2.21–2.29 (m, 3H + 1H), 1.79–2.01 (m, 3H); ¹³C-NMR (DMSO-d₆)

δ

: 170.76,

170.46, 154.01, 153.70, 136.96, 136.80, 136.40, 132.18, 129.01, 128.98, 128.37, 128.06, 127.46, 126.85, 119.40,

119.17, 65.90, 65.83, 60.44, 59.92, 47.22, 46.57, 31.30, 30.22, 23.96, 23.15, 20.43; IR (cm⁻¹): 3270, 2874, 1695,

1668, 1514, 1428, 1352, 1251, 1123, 955, 819, 749, 697; HR-MS: for C₂₀H₂₂N₂O₃[M + H]⁺calculated

339.1703 m/z, found 339.1705 m/z.

Benzyl (2S)-2-[(2-ﬂuorophenyl)carbamoyl]pyrrolidine-1-carboxylate (4a) [45]. Yield 96%; m.p. 113–115 ^◦C;

HPLC pur. 98.66%; [α]_D²⁵

:

−

110.6 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

δ

: 9.85 (s, 1H), 7.76–7.92 (m, 1H),

7.35–7.40 (m, 2H), 7.29–7.34 (m, 1H), 7.18–7.29 (m, 3H), 7.11–7.18 (m, 2H), 4.96–5.13 (m, 2H), 4.49–4.57

(m, 1H), 3.40–3.56 (m, 2H), 2.15–2.32 (m, 1H), 1.80–1.98 (m, 3H); ¹³C-NMR (DMSO-d₆)

δ: 171.50, 171.13,

154.10, 153.76 (d, ¹J_CF= 244.2 Hz, 1C), 153.68, 153.54 (d, ¹J_CF= 244.2 Hz, 1C), 136.97, 136.79, 128.37,

128.08, 127.75, 127.48, 127.44, 126.94, 125.97 (d, ²J_CF= 22.0 Hz, 1C), 125.89 (d, ²J_CF= 21.7 Hz, 1C), 125.39

(d, ³J_CF= 7.5 Hz, 1C), 125.38 (d, ³J_CF= 7.2 Hz, 1C), 125.23 (d, ³J_CF= 7.5 Hz, 1C), 125.22 (d, ³J_CF= 7.2 Hz,

2

4

2

1C), 124.29 (d, J_CF= 21.7 Hz, 1C), 124.21 (d, J_CF= 3.5 Hz, 2C), 124.15 (d, J_CF= 22.0 Hz, 1C),

115.48, 115.35, 65.94, 65.87, 60.10, 59.54, 47.18, 46.58, 31.34, 30.14, 23.93, 23.11; ¹⁹F NMR (DMSO-d₆)

δ

:

−

124.91–

−

124.76 (m, 1F),

−

124.75–

124.53 (m, 1F); IR (cm⁻¹): 3266, 2878, 1672, 1537, 1421, 1353,

−

1254, 1122, 986, 762, 744, 696; HR-MS: for C₁₉H₁₉FN₂O₃[M

−

H]⁻calculated 341.1307 m/z, found

341.1309 m/z.

Benzyl (2S)-2-[(3-ﬂuorophenyl)carbamoyl]pyrrolidine-1-carboxylate (4b). Yield 96%; m.p. 82–84 ^◦C; HPLC

pur. 98.93%; [α]_D²⁵

:

−

109.3 (c 1.0, CHCl₃); H-NMR (DMSO-d₆)

δ

: 10.26 (s, 1H), 7.59 (t, J = 11.2 Hz,

1

1H), 7.26–7.43 (m, 4H), 7.06–7.25 (m, 3H), 6.83–6.94 (m, 1H), 4.89–5.15 (m, 2H), 4.29–4.41 (m, 1H),

3.26–3.58 (m, 2H), 2.12–2.35 (m, 1H), 1.76–2.03 (m, 3H); ¹³C-NMR (DMSO-d₆)

δ: 171.40, 171.12, 162.10

(d, ¹J_CF= 241.2 Hz, 1C), 162.09 (d, ¹J_CF= 241.4 Hz, 1C), 154.04, 153.65, 140.73 (d, ³J_CF= 6.9 Hz, 1C),

140.59 (d, ³J_CF= 6.6 Hz, 1C), 136.92, 136.73, 130.33 (d, ³J_CF= 9.7 Hz, 1C), 130.28 (d, ³J_CF= 9.4 Hz, 1C),

4

128.38, 128.01, 127.79, 127.48, 127.46, 126.94, 115.05 (d, J_CF= 2.5 Hz, 1C), 114.80 (d, J_CF= 2.7 Hz,

2

1C), 109.76 (d, J_CF= 21.3 Hz, 1C), 109.69 (d, J_CF= 21.3 Hz, 1C), 106.12 (d, J_CF= 26.2 Hz, 1C),

105.95 (d, ²J_CF= 26.5 Hz, 1C), 65.97, 65.91, 60.52, 60.01, 47.22, 46.58, 31.21, 30.15, 23.98, 23.17; ¹⁹F NMR

Molecules 2017, 22, 1969

14 of 26

(DMSO-d₆)

δ

:

−

112.13–

−

112.00 (m, 1 F),

−

111.99–

−

111.88 (m, 1F); IR (cm⁻¹): 3503, 3021, 1671, 1607,

1432, 1356, 1297, 1205, 1122, 961, 874, 769, 699; HR-MS: for C₁₉H₁₉FN₂O₃[M

341.1307 m/z, found 341.1309 m/z.

−

H]⁻calculated

Benzyl (2S)-2-[(4-ﬂuorophenyl)carbamoyl]pyrrolidine-1-carboxylate (4c) [45]. Yield 96%; m.p. 122–124 ^◦C;

HPLC pur. 99.53%; [α]_D²⁵116.8 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

: 10.09 (s, 1H), 7.60

(td, J = 9.6, 5.0 Hz, 2H), 7.08–7.41 (m, 7H), 4.90–5.14 (m, 2H), 4.27–4.39 (m, 1H), 3.37–3.57 (m, 2H),

2.11–2.33 (m, 1H), 1.78–2.01 (m, 3H); ¹³C-NMR (DMSO-d₆) : 170.92, 170.63, 158.01 (d, ¹J_CF= 239.8 Hz,

1C), 157.95 (d, ¹J_CF= 240.1 Hz, 1C), 154.03, 153.69, 136.94, 136.75, 135.39 (d, ⁴J_CF= 2.5 Hz, 1C), 135.29

:

−

δ

4

3

(d, J_CF= 2.5 Hz, 1C), 128.37, 128.05, 127.77, 127.47, 126.90, 121.09 (d, J_CF= 7.5 Hz, 2C), 120.91

3

2

(d, J_CF= 7.7 Hz, 2C), 115.22 (d, J_CF= 22.1 Hz, 2C), 115.19 (d, J_CF= 22.1 Hz, 2C), 65.94, 65.87,

60.46, 59.94, 47.22, 46.58, 31.25, 30.19, 23.98, 23.17; ¹⁹F NMR (DMSO-d₆)

δ

:

−

119.35– 119.25 (m, 1F),

−

119.23–

119.13 (m, 1F); IR (cm⁻¹): 3278, 3080, 1668, 1555, 1426, 1354, 1214, 1196, 1128, 959, 741, 696;

−

HR-MS: for C₁₉H₁₉FN₂O₃[M − H]⁻calculated 341.1307 m/z, found 341.1309 m/z.

Benzyl (2S)-2-[(2-chlorophenyl)carbamoyl]pyrrolidine-1-carboxylate (5a). Yield 85%; m.p. 76–78 ^◦C; HPLC

pur. 98.84%; [α]_D²⁵

:

−

106.0 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

δ: 9.55–9.64 (m, 1H), 7.71 (d, J = 7.6 Hz,

1H), 7.48 (t, J = 6.5 Hz, 2H), 7.24–7.41 (m, 5H), 7.20 (t, J = 7.3 Hz, 1H), 4.99–5.13 (m, 2H), 4.47–4.55

(m, 1H), 3.48–3.56 (m, 1H), 3.44 (t, J = 7.3 Hz, 1H), 2.16–2.33 (m, 1H), 1.96–2.03 (m, 1H), 1.81–1.96

(m, 2H); ¹³C-NMR (DMSO-d₆)

δ: 171.27, 170.89, 154.30, 153.79, 136.92, 136.81, 134.66, 134.55, 129.41,

128.19, 127.46, 127.13, 126.56, 125.85, 66.02, 65.94, 60.35, 59.70, 47.18, 46.60, 31.31, 29.97, 23.96, 23.11;

IR (cm⁻¹): 3266, 1677, 1524, 1423, 1354, 1291, 1181, 1121, 1041, 955, 761, 698; HR-MS: for C₁₉H₁₉ClN₂O₃

[M − H]⁻calculated 357.1011 m/z, found 357.1017 m/z.

◦

Benzyl (2S)-2-[(3-chlorophenyl)carbamoyl]pyrrolidine-1-carboxylate (5b). Yield 79%; m.p. 101–103 C;

HPLC pur. 99.08%; [α]_D²⁵

:

−

117.8 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

δ: 10.23 (br. s., 1H), 7.76–7.85

(m, 1H), 7.44 (dd, J = 18.2, 8.2 Hz, 1H), 7.28–7.39 (m, 3H), 7.16–7.26 (m, 2H), 7.06–7.15 (m, 2H), 5.03–5.14

(m, 1H), 4.93 (d, J = 12.9 Hz, 1H), 4.29–4.38 (m, 1H), 3.42–3.56 (m, 2H), 2.17–2.31 (m, 1H), 1.80–1.98

(m, 3H); ¹³C-NMR (DMSO-d₆)

δ: 171.43, 171.15, 154.06, 153.65, 140.39, 136.72, 132.99, 130.35, 128.39,

128.02, 127.48, 126.96, 123.04, 122.97, 118.91, 118.70, 117.73, 117.56, 65.98, 65.91, 60.55, 60.04, 47.22, 46.59,

31.19, 30.14, 23.98, 23.17; IR (cm⁻¹): 3491, 3041, 1673, 1590, 1484, 1427, 1356, 1299, 1196, 1111, 882, 778,

743, 685; HR-MS: for C₁₉H₁₉ClN₂O₃[M − H]⁻calculated 357.1011 m/z, found 357.1017 m/z.

Benzyl (2S)-2-[(4-chlorophenyl)carbamoyl]pyrrolidine-1-carboxylate (5c) [58]. Yield 76%; m.p. 121–123 ^◦C;

HPLC pur. 98.79%; [α]_D²⁵

:

−

113.6 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

δ

: 10.17 (s, 1H), 7.56–7.68 (m, 2H),

7.27–7.44 (m, 5H), 7.08–7.26 (m, 2H), 5.04–5.14 (m, 1H), 4.90–4.98 (m, 1H), 4.29–4.40 (m, 1H), 3.42–3.57

(m, 2H), 2.12–2.34 (m, 1H), 1.79–1.99 (m, 3H); ¹³C-NMR (DMSO-d₆)

: 171.18, 170.89, 154.04, 153.67,

δ

137.93, 137.84, 136.93, 136.73, 128.59, 128.55, 128.39, 128.07, 127.48, 126.91, 120.91, 120.74, 65.97, 65.90,

60.50, 60.00, 47.23, 46.59, 31.24, 30.17, 23.99, 23.18; IR (cm⁻¹): 3270, 3184, 30₋64, 1704, 1673, 1542, 1445,

1359, 1300, 1167, 1117, 968, 840, 766, 732; HR-MS: for C₁₉H₁₉ClN₂O₃[M

found 357.1015 m/z.

−

H] calculated 357.1011 m/z,

Benzyl (2S)-2-[(2-bromophenyl)carbamoyl]pyrrolidine-1-carboxylate (6a) [59]. Yield 83%; m.p. 77–79 ^◦C;

HPLC pur. 98.82%; [α]_D²⁵93.3 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

(m, 2H), 7.23–7.43 (m, 6H), 7.14 (t, J = 7.3 Hz, 1H), 5.00–5.15 (m, 2H), 4.43–4.54 (m, 1H), 3.39–3.58

(m, 2H), 2.16–2.34 (m, 1H), 2.03 (td, J = 11.3, 4.4 Hz, 1H), 1.81–1.98 (m, 2H); ¹³C-NMR (DMSO-d₆)

:

−

δ

: 9.43–9.60 (m, 1H), 7.60–7.68

δ

: 171.07, 170.70, 154.30, 153.80, 136.90, 136.83, 135.86, 132.57, 128.36, 128.20, 127.21, 126.93, 126.49,

66.03, 65.96, 60.41, 59.76, 47.17, 46.58, 31.21, 29.91, 23.95, 23.09; IR (cm⁻¹): 3239, 1717, 1670, 1522, 1414,

1353, 1201, 1125, 1029, 994, 728, 696; HR-MS: for C₁₉H₁₉BrN₂O₃[M

found 401.0513 m/z.

−

H]⁻calculated 401.0506 m/z,

Benzyl (2S)-2-[(3-bromophenyl)carbamoyl]pyrrolidine-1-carboxylate (6b) [60]. Yield 75%; m.p. 124–126 ^◦C;

HPLC pur. 98.69%; [α]_D²⁵

:

−

92.0 (c 1.0, CHCl₃); H-NMR (DMSO-d₆)

δ

: 10.21 (s, 1H), 7.92–7.99

1

(m, 1H), 7.49 (dd, J = 16.7, 7.9 Hz, 1H), 7.38 (d, J = 4.7 Hz, 2H), 7.16–7.34 (m, 4H), 7.09–7.16 (m, 1H),

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4.91–5.13 (m, 2H), 4.33 (ddd, J = 19.8, 8.4, 3.5 Hz, 1H), 3.40–3.55 (m, 2H), 2.17–2.30 (m, 1H), 1.80–1.98

(m, 3H); ¹³C-NMR (DMSO-d₆)

: 171.41, 171.12, 154.05, 153.64, 140.52, 140.40, 136.91, 136.72, 130.71,

δ

130.64, 128.39, 128.02, 127.48, 126.96, 125.93, 125.87, 121.79, 121.50, 118.11, 117.94, 65.98, 65.91, 60.55,

60.04, 47.22, 46.58, 31.19, 30.14, 23.97, 23.17; IR (cm⁻¹): 3254, 3173, 2878, 1701, 1668, 1591, 1418, 1352,

1258, 1128, 990, 874, 745, 667; HR-MS: for C₁₉H₁₉BrN₂O₃[M

401.0513 m/z.

−

H]⁻calculated 401.0506 m/z, found

Benzyl (2S)-2-[(4-bromophenyl)carbamoyl]pyrrolidine-1-carboxylate (6c) [60]. Yield 93%; m.p. 109–111 ^◦C;

HPLC pur. 99.10%; [α]_D²⁵100.0 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

: 10.17 (s, 1H), 7.43–7.63 (m, 4H),

7.27–7.42 (m, 2H), 7.08–7.26 (m, 3H), 4.90–5.12 (m, 2H), 4.29–4.39 (m, 1H), 3.35–3.58 (m, 2H), 2.13–2.31

(m, 1H), 1.76–1.99 (m, 3H); ¹³C-NMR (DMSO-d₆)

: 171.18, 170.90, 154.02, 153.64, 138.34, 138.24, 136.91,

:

−

δ

136.71, 131.44, 128.37, 128.05, 127.46, 126.89, 121.28, 121.12, 65.95, 65.88, 60.51, 60.00, 47.20, 46.57, 31.21,

30.15, 23.97, 23.16; IR (cm⁻¹): 3270, 1703, 1670, 1538, 1444, 1358, 1299, 1166, 1114, 1068, 1005, 967, 823,

731, 695; HR-MS: for C₁₉H₁₉BrN₂O₃[M + H]⁺calculated 403.0652 m/z, found 403.0658 m/z.

Benzyl (2S)-2-{[2-(triﬂuoromethyl)phenyl]carbamoyl}pyrrolidine-1-carboxylate (7a). Yield 65%; m.p.

82–84 ^◦C; HPLC pur. 99.01%; [α]²_D⁵

:

−

100.0 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

δ

: 9.64 (d, J = 18.8 Hz,

1H), 7.56–7.76 (m, 2H), 7.26–7.52 (m, 6H), 7.14 (d, J = 7.6 Hz, 1H), 5.01–5.17 (m, 2H), 4.41–4.53

(m, 1H), 3.35–3.57 (m, 2H), 2.14–2.32 (m, 1H), 1.80–1.99 (m, 3H); ¹³C-NMR (DMSO-d₆)

δ: 171.86,

171.55, 153.81, 136.91, 135.25, 135.13 (q, ⁴J_CF= 1.1 Hz, 2C), 132.99, 132.86, 129.97, 129.69 (q, ⁴J_CF= 0.9 Hz,

3

2C), 128.37, 128.27, 127.77, 127.69, 127.46, 127.37, 126.82, 126.65, 126.26 (q, J_CF= 5.2 Hz, 2C),

2

3

124.95 (q, J_CF= 31.2 Hz, 1C), 124.94 (q, J_CF= 30.3 Hz, 1C), 124.64 (q, J_CF= 4.3 Hz, 2C), 123.56

(q, ¹J_CF= 273.7 Hz, 1C), 123.54 (q, ¹J_CF= 273.4 Hz, 1C), 122.65, 122.61, 66.01, 65.95, 60.17, 59.52, 47.15,

46.55, 30.99, 29.76, 23.85, 22.98; ¹⁹F NMR (DMSO-d₆)

δ

: -59.24 (s, 1F), -59.31 (s, 1F); IR (cm⁻¹): 325₋4,

1704, 1669, 1527, 1418, 1317, 1278, 1160, 1110, 1092, 909, 766, 691; HR-MS: for C₂₀H₁₉F₃N₂O₃[M

calculated 391.1275 m/z, found 391.1278 m/z.

−

H]

Benzyl (2S)-2-{[3-(triﬂuoromethyl)phenyl]carbamoyl}pyrrolidine-1-carboxylate (7b). Yield 63%; m.p.

86–88 ^◦C; HPLC pur. 99.54%; [α]_D²⁵100.0 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

: 10.38 (d, J = 4.8 Hz,

:

−

δ

1H), 8.10 (d, J = 16.8 Hz, 1H), 7.72–7.85 (m, 1H), 7.49–7.62 (m, 1H), 7.27–7.46 (m, 3H), 6.99–7.24 (m, 3H),

4.85–5.18 (m, 2H), 4.36 (td, J = 8.7, 3.4 Hz, 1H), 3.36–3.59 (m, 2H), 2.14–2.37 (m, 1H), 1.76–2.05 (m, 3H);

¹³C-NMR (DMSO-d₆)

δ: 171.69, 171.41, 154.09, 153.67, 139.74, 139.64, 136.93, 136.73, 130.00, 129.95,

129.44 (q, ²J_CF= 31.8 Hz, 1C), 129.41 (q, ²J_CF= 31.8 Hz, 1C), 128.42, 127.97, 127.82, 127.53, 127.47, 126.99,

124.13 (q, ¹J_CF= 272.6 Hz, 2C), 122.86, 122.72, 119.67 (q, ⁴J_CF= 1.0 Hz, 2C), 115.45 (q, ³J_CF= 3.9 Hz,

3

2C), 115.25 (q, J_CF= 3.9 Hz, 2C), 66.03, 65.95, 60.59, 60.10, 47.24, 46.61, 31.19, 30.15, 24.01, 23.20;

¹⁹F NMR (DMSO-d₆)

δ:

−

61.26 (s, 1F),

−

61.27 (s, 1F); IR (cm⁻¹): 3270, 1694, 1674, 1545, 1419, 1325,

1165, 1123, 1111, 1063, 804, 740, 693; HR-MS: for C₂₀H₁₉F₃N₂O₃[M + H]⁺calculated 393.1421 m/z,

found 393.1424 m/z.

Benzyl (2S)-2-{[4-(triﬂuoromethyl)phenyl]carbamoyl}pyrrolidine-1-carboxylate (7c) [61]. Yield 62%; m.p.

106–109 C; HPLC pur. 99.68%; [α]_D²⁵

:

−

98.8 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

δ: 10.37–10.47 (m, 1H),

◦

7.75–7.86 (m, 2H), 7.68 (d, J = 8.2 Hz, 2H), 7.35–7.40 (m, 1H), 7.28–7.35 (m, 1H), 7.14–7.23 (m, 2H),

7.06–7.13 (m, 1H), 5.07 (s, 1H), 4.90–4.97 (m, 1H), 4.32–4.43 (m, 1H), 3.41–3.57 (m, 2H), 2.16–2.34

(m, 1H), 1.79–1.99 (m, 3H); ¹³C-NMR (DMSO-d₆)

δ: 171.73, 171.44, 154.10, 153.68, 142.58, 142.52, 136.95,

136.73, 128.41, 128.06, 127.82, 127.51 (q, ⁴J_CF= 1.0 Hz, 4C), 126.98, 126.00 (q, ³J_CF= 3.9 Hz, 4C), 124.42

(q, ¹J_CF= 271.7 Hz, 2C), 123.41 (q, ²J_CF= 31.8 Hz, 1C), 123.34 (q, ²J_CF= 31.8 Hz, 1C), 119.24, 119.11, 66.03,

65.98, 60.58, 60.09, 47.24, 46.61, 31.22, 30.17, 24.02, 23.20; ¹⁹F NMR (DMSO-d₆) : -60.19 (s, 1F); IR (cm⁻¹):

δ

3301, 1719, 1672, 1527, 1408, 1324, 1273, 1156, 1110, 1067, 835, 767, 696; HR-MS: for C₂₀H₁₉F₃N₂O₃

[M + H]⁺calculated 393.1421 m/z, found 393.1424 m/z.

Benzyl (2S)-2-[(2-nitrophenyl)carbamoyl]pyrrolidine-1-carboxylate (8a). Yield 72%; oil; HPLC pur. 99.71%;

[α]²_D⁵

:

−

92.3 (c 1.0, CHCl₃); H-NMR (DMSO-d₆)

δ

: 10.46 (d, J = 14.1 Hz, 1H), 7.98 (d, J = 7.0 Hz,

1

1H), 7.56–7.85 (m, 2H), 7.11–7.45 (m, 6H), 4.93–5.18 (m, 2H), 4.31–4.49 (m, 1H), 3.37–3.63 (m, 2H),

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2.12–2.38 (m, 1H), 1.79–2.07 (m, 3H); ¹³C-NMR (DMSO-d₆)

δ: 171.03, 170.77, 164.51, 154.32, 153.76,

141.83, 136.80, 136.68, 134.29, 134.10, 131.15, 128.37, 128.15, 127.15, 125.01, 124.68, 119.43, 66.13, 66.02,

60.69, 60.13, 47.16, 46.59, 30.59, 29.49, 23.90, 23.08; IR (cm⁻¹): 3320, 2955, 2870, 1694, 1606, 1582, 1496,

1398, 1336, 1266, 1072, 866, 742, 691; HR-MS: for C₁₉H₁₉N₃O₅[M + H]⁺calculated 370.1398 m/z, found

370.1401 m/z.

◦

Benzyl (2S)-2-[(3-nitrophenyl)carbamoyl]pyrrolidine-1-carboxylate (8b). Yield 60%; m.p. 101–104 C; HPLC

pur. 99.64%; [α]_D²⁵

:

−

104.3 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

δ

: 10.53 (d, J = 9.0 Hz, 1H), 8.52–8.77

(m, 1H), 7.93 (d, J = 6.6 Hz, 2H), 7.52–7.70 (m, 1H), 7.00–7.43 (m, 5H), 4.83–5.19 (m, 2H), 4.37 (dt, J = 8.2,

4.1 Hz, 1H), 3.39–3.63 (m, 2H), 2.12–2.40 (m, 1H), 1.74–2.04 (m, 3H); ¹³C-NMR (DMSO-d₆)

: 171.81,

δ

171.53, 164.53, 153.63, 147.91, 147.87, 140.04, 139.93, 136.88, 136.65, 130.16, 130.11, 128.38, 127.99, 127.01,

125.30, 125.15, 117.87, 113.50, 113.34, 66.04, 65.97, 60.63, 60.15, 47.22, 46.55, 31.15, 30.11, 24.00, 23.20;

IR (cm⁻¹): 3270, 3087, 2983, 1706, 1681, 1598, 1520, 1446, 1394, 1296, 1243, 1123, 893, 739, 664; HR-MS:

for C₁₉H₁₉N₃O₅[M + H]⁺calculated 370.1398 m/z, found 370.1400 m/z.

Benzyl (2S)-2-[(4-nitrophenyl)carbamoyl]pyrrolidine-1-carboxylate (8c) [62]. Yield 99%; m.p. 144–148 ^◦C;

HPLC pur. 98.52%; [α]_D²⁵

:

−

90.9 (c 1.0, CHCl₃); H-NMR (DMSO-d₆)

δ

: 10.67 (d, J = 6.8 Hz, 1H),

1

8.08–8.36 (m, 2H), 7.70–7.99 (m, 2H), 7.02–7.48 (m, 5H), 4.84–5.19 (m, 2H), 4.28–4.52 (m, 1H), 3.38–3.60

(m, 2H), 2.16–2.39 (m, 1H), 1.75–2.04 (m, 3H); ¹³C-NMR (DMSO-d₆)

δ

: 172.02, 171.71, 164.52, 154.06,

153.58, 145.11, 145.02, 142.29, 136.87, 136.61, 128.38, 128.06, 127.48, 126.96, 124.95, 124.89, 119.02, 118.92,

66.03, 66.00, 60.63, 60.12, 47.20, 46.57, 31.13, 30.08, 23.99, 23.17; IR (cm⁻¹): 3344, 2854, 1705, 1662, 1553,

1507, 1421, 1337, 1252, 1111, 986, 852, 696; HR-MS: for C₁₉H₁₉N₃O₅[M + H]⁺calculated 370.1398 m/z,

found 370.1399 m/z.

General Procedure for Synthesis of Carbamates 9a–9c: N-benzyloxycarbonyl-L-proline (0.50 g,

2.06 mmol) and dry TEA (0.81 g, 8.0 mmol) were dissolved in dry THF (7.5 mL) and cooled to

0 ^◦C. To this solution, ethyl chloroformate (0.22 g, 2.06 mmol) was added dropwise during 15 min.

The reaction mixture was then stirred for 30 min at the same temperature, and aminophenole (0.22 g,

◦

2.06 mmol) was added. The resulting solution was stirred at 0 C for 1 h and then at room temperature

under inert atmosphere overnight. After reaction was ﬁnished, water (10 mL) was added, and the

solution was three times extracted with diethyl ether. Combined organic layers were washed with

1M HCl, saturated NaHCO₃and brine and dried with anhydrous Na₂SO₄. Solvent was evaporated

under reduced pressure, and the residue was triturated with diethyl ether/petroleum ether to give

compounds 9a–9c. All the studied compounds are presented in Table 1.

Benzyl (2S)-2-[(2-hydroxyphenyl)carbamoyl]pyrrolidine-1-carboxylate (9a) [61]. Yield 98%; oil; HPLC pur.

98.51%; [α]²_D⁵

:

−

101.3 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

δ

: 9.81 (br. s., 1H), 9.25 (s, 1H), 7.83 (dd,

J = 13.8, 8.0 Hz, 1H), 7.12–7.43 (m, 6H), 6.70–6.99 (m, 2H), 4.95–5.21 (m, 2H), 4.45–4.65 (m, 1H), 3.38–3.57

(m, 2H), 2.06–2.39 (m, 1H), 1.77–2.04 (m, 3H); ¹³C-NMR (DMSO-d₆)

: 171.17, 170.74, 164.53, 153.87,

δ

153.82, 147.53, 147.30, 136.84, 128.40, 128.11, 127.44, 126.84, 126.20, 124.45, 121.74, 118.92, 115.34, 115.25,

66.01, 65.86, 60.26, 59.89, 47.26, 46.66, 31.39, 30.02, 23.99, 23.14; IR (cm⁻¹): 3658, 3282, 2944, 2870,

1767, 1663, 1597, 1526, 1452, 1414, 1348, 1165, 1103, 975, 746, 637; HR-MS: for C₁₉H₂₀N₂O₄[M + H]⁺

calculated 341.1496 m/z, found 341.1500 m/z.

◦

Benzyl (2S)-2-[(3-hydroxyphenyl)carbamoyl]pyrrolidine-1-carboxylate (9b) [63]. Yield 81%; m.p. 153–157 C;

HPLC pur. 98.61%; [α]_D²⁵

:

−

114.8 (c 1.0, CHCl₃); ¹H-NMR (DMSO-d₆)

δ

: 9.90 (d, J = 17.9 Hz, 1H), 9.36

(d, J = 7.0 Hz, 1H), 7.29–7.42 (m, 3H), 6.93–7.25 (m, 5H), 6.41–6.51 (m, 1H), 4.93–5.13 (m, 2H), 4.21–4.41

(m, 1H), 3.38–3.56 (m, 2H), 2.24 (br. s., 1H), 1.77–1.98 (m, 3H); ¹³C-NMR (DMSO-d₆)

: 170.86, 170.55,

δ

157.55, 154.00, 153.68, 139.96, 136.97, 136.79, 129.25, 128.38, 128.07, 127.46, 126.81, 110.39, 110.34, 110.08,

109.95, 106.52, 106.37, 65.90, 65.83, 60.46, 59.94, 47.22, 46.57, 31.30, 30.21, 23.95, 23.14; IR (cm⁻¹): 3794,

3192, 2948, 2350, 1686, 1675, 1597, 1552, 1438, 1422, 1352, 1176, 1153, 1127, 1032, 1000, 973, 753, 687;

HR-MS: for C₁₉H₂₀N₂O₄[M + H]⁺calculated 341.1496 m/z, found 341.1502 m/z.

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Benzyl (2S)-2-[(4-hydroxyphenyl)carbamoyl]pyrrolidine-1-carboxylate (9c). Yield 60%; m.p. 193–194 ^◦C;

HPLC pur. 98.52%; [α]_D²⁵70.7 (c 1.0, EtOH); ¹H-NMR (DMSO-d₆)

: 9.76 (d, J = 14.1 Hz, 1H), 9.18

(d, J = 6.5 Hz, 1H), 7.28–7.43 (m, 4H), 7.12–7.27 (m, 3H), 6.65–6.73 (m, 2H), 4.93–5.13 (m, 2H), 4.25–4.36

(m, 1H), 3.39–3.55 (m, 2H), 2.12–2.28 (m, 1H), 1.77–1.97 (m, 3H); ¹³C-NMR (DMSO-d₆)

: 170.35, 170.10,

:

−

δ

154.04, 153.77, 153.36, 137.01, 136.88, 130.69, 130.59, 128.40, 128.11, 127.48, 126.89, 121.21, 121.01, 115.01,

65.89, 60.45, 59.93, 47.25, 46.58, 31.35, 30.27, 23.99, 23.20; IR (cm⁻¹): 3685, 3262, 2955, 1677, 1658,

1550, 1516, 1437, 1353, 1246, 1177, 1134, 995, 829, 691; HR-MS: for C₁₉H₂₀N₂O₄[M + H]⁺calculated

341.1496 m/z, found 341.1500 m/z.

3.3. In Vitro Evaluation of AChE- and BChE-Inhibiting Activity

The ability of all prepared compounds to inhibit AChE from electric eel (Electrophorus electricus)

and BChE from equine serum (both purchased from Sigma, St. Louis, MO, USA) was determined

in vitro using a modiﬁed Ellman’s method. The effectiveness of the inhibitors expressed as IC₅₀

value represents the concentration of an inhibitor that is required for reduction of enzyme activity

(or reaction rate) to 50% (sometimes it is referred to as the negative logarithm of the molar concentration

inhibiting the enzyme activity by 50%, pIC₅₀= log 1/IC₅₀). The Ellman’s method [64] is widely used for

measuring cholinesterase activity and the effectivity of ChEIs. It is a simple, rapid and direct method to

determine SH and –S–S– groups content in proteins [65]. Cholinesterase activity is measured indirectly

by quantifying the concentration of 5-thio-2-nitrobenzoic acid (TNB) ion formed in the reaction between

disulﬁde reagent 5,5⁰-dithiobis-2-nitrobenzoic acid (DTNB) and thiocholine, a product of substrate

(i.e., acetylthiocholine, ATCh) hydrolysis catalyzed by cholinesterase [66].

All tested compounds were dissolved in DMSO (concentration 0.01 M) and diluted in

demineralized water (concentrations 0.001 M and 0.0001 M). The ability of tested compounds to

inhibit AChE (from electric eel) and BChE (from equine serum) was determined using the modiﬁed

Ellman’s method at 25 ^◦C in the presence of phosphate buffered saline (PBS, 0.1 M, pH 7.4) in a glass

cuvette with 1 cm optical path. The enzyme activity in total reaction mixture (2 mL) was 0.2 U/mL,

the concentration of substrate ATCh 40 µM and the concentration of DTNB 0.1 mM for all reactions.

The IC₅₀value was obtained from the dependence of v₀/v_ion the concentration of the tested compound

(inhibitor), where v₀is the reaction rate of uninhibited reaction and v_iis the reaction rate of inhibited

reaction (for the given concentration of the inhibitor). First, v₀was determined. Into the cuvette

PBS (0.1 M, pH 7.4), DTNB and ATCh were placed. The enzymatic reaction was started by adding

the enzyme. The dependence of absorbance (

solution contained PBS, DTNB and ATCh), and then the reaction rate (v₀) was calculated (v =

λ

= 412 nm) on time was observed for 70 s (reference

A/ t).

∆

The measurement was performed in triplicate at least, and average v₀was determined. Then, v_i(for the

given concentration of the inhibitor) was determined. Into the cuvette DTNB, ATCh, a chosen volume

of the suitably diluted inhibitor (to achieve the required concentration of the inhibitor in the total

reaction mixture) and a certain volume of PBS (to achieve the total volume of the reaction mixture 2 mL

after adding the enzyme) were placed. The enzymatic reaction was started by adding the enzyme.

The dependence of absorbance (λ = 412 nm) on time was observed for 70 s (reference solution was the

same as for uninhibited reaction), and then the reaction rate (v_i) was calculated. For determination

of IC₅₀values, twelve different concentrations of inhibitor were used and each measurement was

performed in triplicate at least. Finally, the dependence of v₀/v_ion the concentration of the inhibitor

was determined, and IC₅₀was calculated from the obtained equation of the regression curve for y = 2

(coming out from the deﬁnition of IC₅₀) [67]. The obtained results are summarized in Table 1.

3.4. Comparative Molecular Surface Analysis

A self-organizing neural network (SOM) is comprised of a single layer of neurons typically

arranged as a hexagonal or rectangular array of nodes using the unsupervised learning rules initially

proposed by Kohonen. The 2D topology of the neural grid with the deﬁned winning and neighborhood

distances between individual neurons directly speciﬁes the mutual relations between the neurons.

Molecules 2017, 22, 1969

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The presented multidimensional input vector x_s= (x_s1

,

. . .

,

x_sm

)

is distributed between neurons

according to the similarity/correlation weight criteria, where similar inputs are located in the same or

proximal nodes. A classical competitive Kohonen (KNN) approach relies on the comparison of the

input vector with the corresponding multi-element weight vectors w_j= (w_j1, . . . , w_jm) that describe

)б

each neuron in order to select the winning one (out_C) into which a particular input will be projected.

The winning neuron is detected by the optimization of the Euclidean distance between a weight (w)

and a vector (x) according to the following formula:

"

#

2

m

out_C= min

(x_i− w_ij)

(1)

∑

i=1

where j = 1, . . .

,

n

refers to a particular neuron,

n

refers to a number of neurons,

m

is the number of

weights per neuron and s indicates a particular input. Contrary to “winner takes all”, in the “winner

takes most” methodology, the weights of the winning neuron and its neighbors are then modiﬁed to

resemble and subsequently attract similar input vectors. While the next input vector is being presented

to the network, the entire procedure is repeated.

In fact, the self-organizing neural mapping is considered as a nonlinear projection tool, which

reduces the dimensionality of the input object, e.g., converts 3D objects to 2D, while maintaining

the topological relationships between input and output data. Additionally, the trained network can

be employed for the projections of the speciﬁed molecular property prescribed to the input vector

with the generation of the color-coded clustering planar pattern called a feature map. Consequently,

the SOM algorithm was used to generate an electrostatic potential map as a 2D topographic pattern

receiving input signals from points sampled randomly at the molecular surface [68]. In a such

application, the speciﬁcation of the closest neighbor and then projection of signals into this particular

neuron is based on the comparison of each 3D input vector consisting of x, y, and z coordinates with

a three-element weight vector describing each neuron. The shape of the certain molecular surface

(template) encoded in the weights of the trained Kohonen network can be used for processing signals

coming from the surface of other molecule(s) (counter-template) providing a series of comparative

SOM maps to compare/contrast the superimposed molecular geometry.

The implementation of the SOM for the classiﬁcation, visualization and compression of the

structural data has been widely reported, in particular for 2D mapping of the electrostatic potential on

3D molecular surfaces or partial atomic charges for atomic molecular representation [69].

3.5. PLS Analysis

The partial least squares (PLS) method expresses the relation between the variable y and a set of

predictors X in the form represented by the following Equation:

y = X × b + e

(2)

where b is the vector of the regression coefﬁcients and e is the vector of the errors. Generally, PLS

models are constructed for centered/autoscaled data, and their complexity is estimated using, e.g.,

the leave-one-out cross-validation (LOO-CV) procedure. In the LOO-CV, one repeats the calibration

process m times, each time treating the i-th left-out object as the prediction object. The dependent

variable for each left-out object is calculated on the basis of the model with one, two, three, etc. factors.

The root-mean-square error of CV for the model with j factors is deﬁned as:

s

^m(obs_i− pred_i,j)²

∑

i

RMSECV =

(3)

j

m

Molecules 2017, 22, 1969

19 of 26

where obs denotes the observed value of a dependent variable; pred is the predicted value of a dependent

variable; and i refers to the object index, which ranges from 1 to m. A model with k factors, for which

RMSECV reaches a minimum, is considered as the optimal one. The cross-validated q²_CVis calculated as:

^m(obs_i− pred_i)²

∑

q²_CV= 1 −

(4)

i

^m(obs_i− mean(abs_i))²

∑

i

where obs is the observed value; pred is the predicted value; mean is the mean value of obs; and i refers

to the object index, which ranges from 1 to m. The cross-validated standard error of prediction s

amounts to:

s

_i(obs_i− pred_i)²

∑

s =

(5)

m − k − 1

where m is the number of objects and k is the number of the PLS factors in the model.

The quality of external predictions was measured using the standard deviation of error of

prediction (SDEP) and q²

parameter, which are deﬁned respectively as:

test

s

ⁿ(pred_i− obs_i)²

∑

i

SDEP =

(6)

(7)

n

ⁿ(obs_i− pred_i)²

∑

q²_test= 1 −

i

ⁿ(obs_i− mean(obs_i))²

∑

i

where n is the number of objects in a test set.

3.6. Iterative PLS-Based Variable Elimination

Redundant variables may inﬂuence a model and increase its complexity; therefore, the reduction

in the number of variables facilitates the interpretation of the model considerably. To ﬁnd only reliable

variables that signiﬁcantly contribute to the regression model, the modiﬁed PLS procedure with

uninformative variable elimination (UVE-PLS) as well as its modiﬁcation, namely, iterative variable

elimination (IVE-PLS) can be applied successfully [70]. The original UVE-PLS algorithm, developed

by Centner et al., analyzes the stability of regression coefﬁcients expressed as the mean(b)/s(b) ratio,

where s(b) represents the standard deviation of the regression coefﬁcient b that is calculated by the

PLS method [71]. Instead of a single step UVE-PLS procedure, we previously proposed an iterative

algorithm based on the abs(mean(b)/s(b)) criterion to identify the variables to be eliminated. Basically,

the entire procedure consists of the following steps: (i) standard PLS analysis with LOO–CV to

assess the performance of the PLS model (q²_CV); (ii) elimination of the matrix column with the lowest

abs(mean(b)/std(b)) value; (iii) standard PLS analysis of the new matrix without the column cancelled

in Step (ii); and (iv) recurrent repetition of Steps i–iii to maximize the LOO q²_CVparameter.

3.7. PCA Analysis

The mapping of the molecular diversity forming the “inﬁnite” chemical space (CS) into

the corresponding biological or property space generally requires multi-dimensional descriptor

representations. A speciﬁed molecule might be represented by a set of structural (S) and

physicochemical (P) properties organized in a vector, which represents an object in the CS.

The molecular distribution of the empirically (FCS) and virtually (VCS) generated compounds might be

graphically investigated using, e.g., a linear projection procedure called Principal Component Analysis

(PCA). PCA is a projection method that is designed to model multivariate data with a relatively small

number of so-called principal components. PCs are constructed as a linear combination of original

variables to maximize the description of data variance. The PCA model decomposes information

contained in a data matrix into the principal component scores and loadings.

Molecules 2017, 22, 1969

20 of 26

The score matrix contains information about any similarities among the data objects, while the

loading matrix allows the similarities among the variables and their roles in the construction of a given

principal component to be studied. The PCA model with f principal components for a data matrix X

can be presented as follows:

X = TP^T+ E

(8)

where X is a data matrix with m objects and n variables, T is the score matrix with dimensions (m

×

f),

P^Tis a transposed matrix of loadings with dimensions (f

×

n) and E is a matrix of the residual variance

(m

×

n) that is not explained by the ﬁrst f principal components. The ﬁrst few principal components

often capture interesting information about the data structure and uncover groups of objects, atypical

objects, etc., and also indicate the importance of the original data variables that contribute to the

observed structure. Therefore, visualization of scores and loadings and their further simultaneous

interpretation allow insight into a problem being studied to be gained [72].

3.8. Model Builder

The same laboratory was employed to specify all pharmacological data to eliminate potential

data noise that might have been introduced by pooling of data sets coming from various sources.

The in vitro AChE and BChE inhibition values (IC₅₀) for the set of carbamate derivatives are listed

in Table 1. The distributions of the IC₅₀inhibition of carbamates

1–9c response in 12 equally spaced

containers (expressed in M) are presented as histograms in Figure 1. The CACTVS/csed molecular

µ

editor was used to specify the constitution of the respective compound models. The spatial geometry

of molecules was produced using the 3D generator CORINA. The (inter)change ﬁle format converter

OpenBabel was applied to convert the chemical data.

3.9. Molecular Modeling

The principal components of the modeling studies were conducted with the usage of the Sybyl–X

2.0/Certara software package (Certara, Princeton, NJ, USA) running on HP workstation with Debian

6.0 operating system. The standard Tripos force ﬁeld (POWELL conjugate gradient algorithm) with

0.01 kcal/mol energy gradient convergence criterion and a distant dependent dielectric constant was

used to optimize the initial geometry of each compound (MAXMIN2 module). The Gasteiger–Hückel

procedure implemented in Sybyl for the electrostatic potential calculations was initially employed

to calculate the partial atomic charges. The trial alignments are typically deﬁned to systematically

span the common scaffold of the analyzed compounds; therefore, one 15-ordered atom trial alignment

on molecule 1 was selected to cover the entire bonding topology in the maximal common structure

(MCS) by the atom FIT method based on the matching of atoms’ positions between the corresponding

atom pairs.

The SONNIA software was employed in CoMSA analysis to simulate 20

with the winning distance varied in the range of 0.2–2.0. The Cartesian coordinates of the molecular

surfaces for superimposed molecules were proceeded by the SOM network to form a 2D map of

×

20 to 50

×

50 SOMs

electrostatic potential. The structurally simplest analog

1 and molecules with the highest volume

descriptor 7a were used to form the template molecules. The output maps were subsequently

–

c

transformed to a 400- to 2500-element vector, which was processed by PLS method implemented in

the MATLAB programming environment.

The crystallographic structure of rhAChE was retrieved from the PDB repository (code 4EY6)

containing two amino acid chains and two galanthamine molecules. All heteroatoms, including

crystallographic waters, were extracted prior to the calculations. The ligand/protein structures were

prepared for the docking study in the pdbqt ﬁle format with Gasteiger charges calculated. During the

AutoDock simulation, various poses (default 9) were generated progressively from a single conformer

(energy optimized molecule) by applying a collection of preferred torsion angles to the rotatable bonds

Molecules 2017, 22, 1969

21 of 26

and evaluated by united-atom scoring function. All predicted binding modes, including the positions

of ﬂexible side chains, were visualized using the VMD molecular graphics viewer.

3.10. In Vitro Cytotoxicity Assay

Human monocytic leukemia THP-1 cells were used for in vitro toxicity assay. Cells were obtained

from the European Collection of Cell Cultures (ECACC, Salisbury, UK) and routinely cultured in RPMI

1640 (Lonza, Verviers, Belgium) medium supplemented with 10% fetal bovine serum (FBS, Sigma),

2% L-glutamine, 1% penicillin and streptomycin (Lonza, Verviers, Belgium) at 37 ^◦C with 5% CO₂.

Cells were passaged at approximately one week intervals. The cytotoxicity of the compounds was

determined using a WST-1 assay kit (Roche Diagnostics, Mannheim, Germany) according to the

manufacturer’s instructions. The tested 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 for 24 h at 37 C with 5% CO₂. For WST-1 assays, cells

were seeded into 96-well plates (5

×

10⁴cells/well in 100

µL culture medium) in triplicate in

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 GraphPad Prism 5.00 software (GraphPad

Software, San Diego, CA, USA). The results are summarized in Table 1.

4. Conclusions

Twenty-ﬁve benzyl (2S)-2-(arylcarbamoyl)pyrrolidine-1-carboxylates were synthesized and

characterized by IR, ¹H, ¹³C and ¹⁹F NMR spectroscopy and HRMS as well as by optical rotations.

All compounds were tested for their in vitro ability to inhibit AChE and BChE. The selectivity index

of individual compounds to cholinesterases was determined. The screening of the cytotoxicity of all

the compounds was performed using human THP-1 cells, no signiﬁcant changes in the viability of

cells were found up to concentration 30

effect against AChE; benzyl (2S)-2-[(2-chlorophenyl)carbamoyl]pyrrolidine-1-carboxylate (5a

(IC₅₀= 46.35 M) was the most potent agent. On the other hand, benzyl (2S)-2-[(2-bromophenyl)-(6a

µM. All the compounds showed rather moderate inhibitory

)

µ

and benzyl (2S)-2-[(4-bromophenyl)-carbamoyl]pyrrolidine-1-carboxylate (6c) expressed anti-BChE

activity (IC₅₀= 27.38 and 28.21 M, respectively) comparable with that of rivastigmine. Compound 6a

µ

and benzyl (2S)-2-[(2-hydroxyphenyl)carbamoyl]pyrrolidine-1-carboxylate (9a) demonstrated greater

selectivity to BChE. A comparative receptor-independent structure–inhibitory activity study of

proline-based carbamates as potential ChEIs is reported using the 3D neural methodology (CoMSA)

coupled with the IVE-PLS procedure. In fact, the ability of the fuzzy molecular representation

for a variety of training/test subset distribution was examined for a large populations models

generated using the stochastic SMV procedure. A systematic space inspection merged with the variable

elimination method produce the probabilistic pharmacophore geometry specifying descriptors that

have potentially the highest individual weightings to the observed AChE/BChE proﬁles. In silico

activity examination conﬁrmed the signiﬁcant qualitative difference in the inhibitory potency of

positional isomers reﬂected in the empirical data, especially for unfavorable para-substitution of

the phenyl ring. The visual investigation of a pharmacophore pattern gives a simpliﬁed picture of

regions that can be modiﬁed to modulate the desired BChE activity of the compound, providing

valuable hints for the property-oriented synthesis. Moreover, PCA analysis was employed to illustrate

the crucial variations in the inhibitory efﬁciency of the screened molecules with respect to their

structure, lipophilicity and activity proﬁle. The performed molecular docking study demonstrated the

importance of the side chain R, especially for ortho-positioned analogs directly attached to the phenyl

ring. Moreover, a chlorine atom can contribute to hydrogen bond interactions with Tyr337 of chain

A as postulated in the case of the galanthamine binding mode. The inhibition activity proﬁle for the

positional isomers can be partially explained by the possibility of hydrogen bond interactions in the

Molecules 2017, 22, 1969

22 of 26

catalytic core ranked according to ortho > meta > para substitution. It seems that an increase of bulkiness

at the para position of the phenyl ring is unfavorable for the inhibitory potency of the investigated

carbamates as observed also in the pharmacophore study.

Supplementary Materials: The supplementary material is available online.

Acknowledgments: This study was supported by IGA VFU Brno 315/2016/FAF, 312/2017/FAF and

318/2017/FAF. The authors acknowledge the institutional ﬁnancial support of the Faculty of Chemical-Technology,

University Pardubice from the Czech Ministry of Education, Youth and Sports. The HPLC/HRMS system forms

a part of the National Infrastructure CzeCOS ProCES CZ.02.1.01/0.0/0.0/16_013/0001609; Michal Oravec was

supported by the National Sustainability Program (NPU I; Grant No. LO1415). The authors thank Johann

Gasteiger for facilitating access to the SONNIA programs. We would like to acknowledge the OpenEye and

OpenBabel Scientiﬁc Software for the free academic licenses. Andrzej Bak thanks Foundation for Polish Science for

his individual grant. This study was also partially supported by the Slovak Research and Development Agency

(Grant No. APVV-0516-12) and by SANOFI-AVENTIS Pharma Slovakia.

Author Contributions: Hana Pizova, Marketa Havelkova, Pavel Bobal, and Josef Jampilek designed, synthesized

and characterized the compounds, and wrote of the paper. Michal Oravec performed HPLC and HRMS

analyses. Andrzej Bak and Violetta Kozik performed SAR/QSAR, CoMSA, and IVE-PLS, and wrote the

paper. Sarka Stepankova and Ales Imramovsky evaluated AChE/BChE. Tereza Kauerova and Peter Kollar

evaluated cytotoxicity.

Conﬂicts of Interest: The authors declare no conﬂict of interest.

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Proline-based carbamates as cholinesterase inhibitors

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