Novel carbamates as orally active acetylcholinesterase inhibitors found to improve scopolamine-induced cognition impairment: Pharmacophore-based virtual screening, synthesis, and pharmacology

Article abstract of DOI:10.1021/jm100573q

A systematic virtual screening (VS) experiment, consisting of the development of 3D-pharmacophore, screening of virtual library, synthesis, and pharmacology, is reported. The predictive pharmacophore model (correlation = 0.955) with one H-bond donor and three hydrophobic features was developed using HypoGen on a training set of 24 carbamates as AChE inhibitors. The model was validated on a test set of 40 carbamates (correlation = 0.844). The pharmacophore-based VS of virtual library led to the identification of novel carbamates as potent AChE inhibitors. The synthesis and pharmacological evaluation of nine carbamates against three diverse assay systems, namely (i) in vitro Ellman method, (ii) in vivo passive avoidance test, and (iii) aldicarb-sensitivity assay, led to the discovery of orally active novel AChE inhibitors which improved scopolamine-induce cognition impairment in Swiss male mice. Finally, two novel lead compounds 85 and 86 are selected as candidate molecules for further optimization.

Full text of DOI:10.1021/jm100573q

6490 J. Med. Chem. 2010, 53, 6490–6505
DOI: 10.1021/jm100573q
Novel Carbamates as Orally Active Acetylcholinesterase Inhibitors Found to Improve
Scopolamine-Induced Cognition Impairment: Pharmacophore-Based Virtual Screening,
Synthesis, and Pharmacology†
Shailendra S. Chaudhaery,‡ Kuldeep K. Roy,‡ Neeraj Shakya,‡ Gunjan Saxena,§ Shreesh Raj Sammi, Aamir Nazir,
Chandishwar Nath, and Anil K. Saxena*,‡
‡Division of Medicinal and Process Chemistry, §Division of Pharmacology, and Division of Toxicology, Central Drug Research Institute, CSIR,
Lucknow 226001, India
Received May 11, 2010
A systematic virtual screening (VS) experiment, consisting of the development of 3D-pharmacophore,
screening of virtual library, synthesis, and pharmacology, is reported. The predictive pharmacophore
model (correlation=0.955) with one H-bond donor and three hydrophobic features was developed
using HypoGen on a training set of 24 carbamates as AChE inhibitors. The model was validated on a
test set of 40 carbamates (correlation = 0.844). The pharmacophore-based VS of virtual library led to
the identification of novel carbamates as potent AChE inhibitors. The synthesis and pharmacological
evaluation of nine carbamates against three diverse assay systems, namely (i) in vitro Ellman method,
(ii) in vivo passive avoidance test, and (iii) aldicarb-sensitivity assay, led to the discovery of orally active
novel AChE inhibitors which improved scopolamine-induce cognition impairment in Swiss male mice.
Finally, two novel lead compounds 85 and 86 are selected as candidate molecules for further
optimization.
Introduction
Active Site of AChE. The active site of human AChE,
20,21
˚
located at the base of the ∼20 A deep and narrow gorge,
Alzheimer’s disease (ADa) is a progressive irremediable
disorder of elderly patients, which is characterized by extensive
central cholinergic neuronal loss, resulting in loss of cognitive
functions.1,2 The cholinesterase (ChE) enzymes, namely acet-
ylcholinesterase (AChE; EC 3.1.1.7) and butyrylcholinester-
ase (BChE;EC 3.1.1.8), which are foundin the centralnervous
system (CNS), catalyze the hydrolysis of acetylcholine (ACh)
at a rate of >10000 molecules per second.3 The AChE enzyme
is one of promising drug-like targets, which has led to few
palliative drugs presently used for the treatment of AD. These
drugs include tacrine, galanthamine, donepezil, and rivastig-
mine as acetylcholinesterase inhibitors (AChEI) and meman-
tine as a noncompetitive N-methyl-D-aspartate (NMDA)
antagonist for moderate improvement in memory and cogni-
tive function.4-10 However, memantine has recently been
reported to block the R7 receptor in hippocampal neurons
more effectively than NMDA receptors.11,12 However, these
drugs result in only mild to moderate improvement in memory
and cognitive function but lack the ability to prevent or slow
the progressive neurodegeneration.13-19 Hence, newer appro-
aches viz. multitarget directed ligands (MTDLs) and β-amyloid
targeted therapies are under investigation.
consists of two subsites: (i) an “esteratic” subsite containing
the catalytic machinery, and (ii) an “anionic” subsite respon-
sible for binding the quaternary trimethylammonium tail
group of the acetylcholine. The essential catalytic functional
unit of the human AChE is the catalytic triad characterized
by three amino acid residues, namely Ser203, His447, and
Glu334.22 The acetyl headgroup of AChE, which is directly
involved in making and breaking of bonds, is being held in
place by another important functional unit in the esteratic
subsite known as oxyanion hole, formed by the peptidic NH
groups of three crucial residues namely, Gly121, Gly122, and
Ala204.22 Site-directed mutagenesis experiment indicates
that the three amino acid residues, namely Trp86, Glu202,
and Phe337, play an important role in binding of the quater-
nary trimethylammonium tail group in the anionic subsite.23
Current Approaches. In the recent past, two aspects, viz.
the complex etiology of AD and the involvement of different
but related dysfunctions in its progression, have gradually
attracted interests in the development of novel multifunc-
tional drugs for the treatment of AD.24 The therapeutic
potential of AChE inhibitors has been empowered by the
recent evidence illustrating that, apart from their role in the
cognition management, they might aid in the reduction in the
rate of neurodegeneration in the AD affected persons. In
fact, AChE has been reported to exert secondary nonchol-
inergic functions related to its peripheral anionic site25-27 in
cell adhesion28,29 and differentiation.30 Several recent inves-
tigations also support its role in mediating the processing and
deposition of β-amyloid (Aβ) peptides.31-35 In the past few
†CDRI communication number 7948.
*To whom correspondence should be addressed. Phone: (522)
2612412 ext 4386. Fax: (522) 26123405. E-mail: anilsak@gmail.com.
a Abbreviations: AD, Alzheimer’s disease; AChE, acetylcholinester-
ase; BChE, butyrylcholinesterase VS, virtual screening; NMDA, N-
methyl-D-aspartate; CADD, computer-aided drug design; PBVS, phar-
macophore-based VS; SBVS, structure-based VS; SAR, structure-
activity relationship; QSAR, quantitative structure-activity relation-
ship; MTDLs, multitarget-directed ligands.
r
pubs.acs.org/jmc
Published on Web 08/04/2010
2010 American Chemical Society

Article
Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17 6491
decades, drug discovery efforts have shifted toward the
design of more potent and selective cholinesterase (ChE)
inhibitors with fewer side effects. The interest in the disco-
very of novel potent AChE inhibitors is expected to continue
in the future, as current AChE inhibitors lack perfection.
In such a scenario, direct- and indirect- computer-aided
drug design (CADD) techniques are of utmost importance in
terms of being time-saving and cost-effective techniques.
One of the rationale starting points for the design of novel
scaffolds is the development of a ligand-based predictive
model to derive different structural features essential for
receptor binding, using ligand-based molecular modeling
tools such as SYBYL/comparative molecular field analysis
(CoMFA)36 and comparative molecular similarity indices
analysis (CoMSIA);37 Catalyst/HypoGen and HipHop.38 In
the recent past, we have published few systematic 3D-QSAR
CoMFA and CoMSIA studies on carbamate-class of AChE
inhibitors in which important physicochemical requirements
for the potential inhibition of AChE enzyme are reported.39,40
However, the development of a 3D-pharmacophore and its
correct use in the VS of the available databases seem to be a
more relevant and time-saving approach, which may lead
into the identification of new chemotypes with potent AChE
inhibitory activity for the treatment of AD.
inhibitor acts in the peripheral nervous system) to induce
paralysis in the exposed C. elegans nematodes.45 It is well-
known that the AChE enzyme is present in the synaptic cleft,
which catalyzes the hydrolysis of neurotransmitter ACh to
choline and acetate and thus eliminates ACh from synapse.
The presence of aldicarb leads to the inhibition of AChE,
which in turn results in the accumulation of ACh at the
synaptic cleft. Such high level of ACh in the synaptic cleft
causes overactivation of cholinergic receptors, hyper-
contraction of muscles, and ultimately paralysis.46 However,
if any chemical or any mutation affects the ACh-AChE
pathway in the nematodes, that would result in their altered
response to aldicarb induced paralysis;47 compounds in-
creasing ACh release at the synaptic cleft would render the
worms sensitive and compounds decreasing the release of
ACh would lead to resistance of worms against aldicarb
induced paralysis. This widely used assay system helped us in
testing our predictive analysis.
Results and Discussion
Pharmacophore Modeling. Among the generated pharma-
cophore models, based on the training set of 24 compounds
(Figure 1), the results of the top 10 pharmacophore models
are summarized in Table 1.
Pharmacophore modeling is done using a collection of
structurally diverse compounds, known to bind the same
active site of a protein, i.e. same mode of action. The
HypoGen algorithm provides quantitative pharmacophore
models based on a set of compounds with biological acti-
vities spread over 3-4 orders of magnitude or more broader
activity range, whereas the HipHop algorithm provides
qualitative pharmacophore models based on a small set of
known active compounds.41-43 Most of the pharmacophore
modeling tools entertain functional features such as hydro-
gen bond donors (HBD), hydrogen bond acceptors (HBA),
hydrophobic groups (HY), aromatic rings (AR), positively
charged/ionizable groups (PG), and negatively charged/
ionizable groups (NG). Additionally, shape and excluded
volumes can be included into the pharmacophore models to
represent the framework of the active site. Pharmacophore-
based virtual screening methods surpass structure-based
methods by their ability to screen very large databases faster
and by their tendency to retrieve more structurally diverse
leads.
In the present paper, we describe the development of
quantitative pharmacophore model using a congeneric car-
bamate class of AChE inhibitors and its successful applica-
tion in the virtual screening process. This has led to the
identification of novel AChE inhibitors which are synthe-
sized and evaluated for their AChE inhibition potential in in
vitro as well as in vivo assay systems. Apart from the widely
used assay systems, such as Ellman methods and passive
avoidance test, synthesized compounds have also been tested
in a model system of Caenorhabditis elegans, which is a
powerful in vivo model for carrying out studies on neuro-
chemical aspects of the organismal biology.44 The C. elegans
model holds relevance because of its appreciable homology
of gene sequences and conservation of many disease path-
ways with humans and, most importantly, for our studies is
the presence of most of mammalian gene families involved in
neuronal function in C. elegans.44 Hence, by employing this
model system we have assessed whether or not our test
compounds affect the release of neurotransmitter ACh in
the synaptic cleft. The assay employs Aldicarb (an AChE
The total cost of each pharmacophore model was close to
the fixed cost value, which is expected for a good pharma-
cophore model, and hence, for an efficient HypoGen run.
The term “cost” in HypoGen algorithm indicates the number
of binary bits required to generate a particular pharmaco-
phore/hypothesis. The HypoGen algorithm performs three
important theoretical cost calculations, namely fixed, null,
and total costs, as determinants of the superiority of any
particular pharmacophore model over others. The config-
uration cost was also within the allowed range (e17.000).
The difference between the null cost (123.942) and the fixed
cost (96.305) was 27.637, while the difference between the
null cost and the total cost (101.226) of the best pharma-
cophore model (Hypo-01; Figure 2) was 22.716 bits. The
probable explanations for observed lower value of the cost
difference (residual cost) in the present study as well as
previously reported in the literature48,49 may be due to the
presence of fairly rigid and structurally homologous mole-
cules in the training set. The best pharmacophore model
(Figure 2), comprising three hydrophobic (HY) and one
hydrogen bond donor (HBD) features along with 34 ex-
cluded volumes, was found to be the most significant in terms
of its high correlation (r) of 0.955 and low root-mean-square
(rms) error of 0.568 only. A very small difference (only 5 bits)
between the total and fixed costs additionally demonstrated
the significance of the best pharmacophore model. The
observed and estimated AChE inhibitory activities of the
training set compounds, by Hypo-01, is given in Table 2,
while the graph between the two is given in Figure 3.
The training set compounds were estimated very accu-
rately by the best pharmacophore model with considerably
low rms error and were correctly categorized into highly
active, moderately active, and inactive classes, with few
exceptions, e.g. the two active compounds, namely 7 and 8,
were estimated to be moderate active, and the two moderate
active compounds, namely 12 and 13, were estimated to be
inactive (Table 2).
Model Validation. Apart from the internal statistics such
as correlation, cost-function quality, and ability to explain
the training set compounds, an additional validation of the

6492 Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17
Chaudhaery et al.
Figure 1. Chemical structures of the training set compounds.
Table 1. Summary of Top 10 Pharmacophore Models Obtained from Catalyst/HypoGen Run
hypothesis
featuresa
total cost
Δcostb
rmsc
correlation (r)
Hypo-01
Hypo-02
Hypo-03
Hypo-04
Hypo-05
Hypo-06
Hypo-07
Hypo-08
Hypo-09
Hypo-10
HBD, HY, HY, HY
101.226
106.122
111.625
113.413
113.850
114.988
116.334
119.520
119.855
120.314
22.716
17.820
12.317
10.520
10.092
8.950
0.568
0.827
1.115
1.153
1.208
1.240
1.260
1.370
1.250
1.400
0.95
0.90
0.80
0.79
0.77
0.75
0.74
0.69
0.75
0.67
HBA, HBD, HY, HY, HY
HBA, HBD, HY, HY, HY
HBA, HBD, HY, HY, HY
HBD, HY, HY, HY, HY
HBA, HBD, HY, HY, HY
HBA, HBA, HY, HY, HY
HBA, HBD, HY, HY, HY
HBA, HBD, HY, HY, HY
HBA, HBD, HY, HY
7.600
4.422
4.092
3.628
a HBD = hydrogen bond donor, HBA = hydrogen bond acceptor, HY = hydrophobic features. b Cost difference between total cost of the
corresponding hypothesis and the cost of the null hypothesis (123.942). c Root-mean-square deviation (Note: Fixed cost of the hypothesis was 96.305).
model using the test set (referred as test set validation) is one
of the mandatory steps for establishing its competency for
prediction accuracy as well as classification quality among
the test set compounds, which were kept aside during the

Article
Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17 6493
Table 2. Observed and Estimated AChE Inhibitory Activities (IC50, nM) of the Training Set Molecules by the Best Pharmacophore Model (Hypo-01)
activity scalec
IC50 (nM)
pIC50
(estimated; nM)
pIC50
(observed; nM)
compd
fit value
esta
obsdb
10.00
est
obsd
1
5.64
4.90
4.52
4.92
4.88
4.52
4.28
4.29
4.55
4.28
4.23
3.62
2.97
3.53
2.94
3.62
3.79
3.89
3.95
3.07
3.05
2.59
2.81
1.55
5.00
þþþ
þþþ
þþþ
þþþ
þþþ
þþþ
þþ
þþ
þþþ
þþ
þþ
þ
þþþ
þþþ
þþþ
þþþ
þþþ
þþþ
þþþ
þþþ
þþ
þþ
þþ
þþ
þ
-0.699
-1.431
-1.820
-1.415
-1.462
-1.813
-2.079
-2.041
-1.785
-2.041
-2.114
-2.716
-3.362
-2.806
-3.398
-2.716
-2.544
-2.447
-2.380
-3.255
-3.301
-3.740
-3.531
-4.785
-1.000
-1.176
-1.344
-1.415
-1.602
-1.754
-1.892
-1.973
-2.041
-2.206
-2.322
-2.390
-2.919
-3.176
-3.398
-2.455
-2.519
-2.588
-2.839
-2.892
-3.551
-3.633
-3.995
-4.614
2
27.00
66.00
26.00
15.00
22.10
26.00
3
4
5
29.00
65.00
40.00
56.70
6
7
120.00
110.00
61.00
78.00
94.00
8
9
110.00
160.80
210.00
245.30
830.00
1500.00
2500.00
285.00
330.00
387.50
690.00
780.40
3560.00
4300.00
9887.00
41130.00
10
11
12
18
19
20
13
14
15
16
17
21
22
23
24
110.00
130.00
520.00
2300.00
640.00
2500.00
520.00
350.00
280.00
240.00
1800.00
2000.00
5500.00
3400.00
61000.00
þ
þ
þ
þ
þ
þ
þþ
þþ
þþ
þ
þþ
þþ
þþ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
a est = estimated. b obsd = observed. c Activity scale: þþþ (IC50<100 nM, highly active), þþ (100 < IC50 < 500 nM, moderately active), þ (IC50
>
500 nM, poorly active).
Figure 3. Two-dimensional scatter plot between observed and
estimated AChE inhibitory activities (pIC50) of the training (blue
dots) and test set (magenta triangles) compounds.
active compounds, namely 1 and 38 in the training and test
set respectively, revealed that none of the essential pharma-
cophoric features were missed and all features mapped with
the least displacement from the centroid of all features
(Figure 4). The angular methyl group of compounds, namely
1 and 38, mapped well with the HYDROPHOBE_2 feature
of the Hypo_01, while the phenyl ring of the noncarbamate
core of each molecule mapped with the HYDROPHOBE_3.
The carbamoyl NH mapped to the HBD_1 feature, while the
HYDROPHOBE_4 feature mapped to the ethyl and methyl
groups of compounds 1 and 38, respectively. The moderate
and lesser active (inactive) compounds missed to map one or
more pharmacophoric features and thereby recommended
well their corresponding categories.
Figure 2. The best pharmacophore model comprising three hydro-
phobic, one hydrogen bond donor, and a set of 34 excluded volumes.
model development process. Therefore, in the present study,
the best pharmacophore model was further validated using
the test set of 40 compounds (Table 3). As expected, the best
pharmacophore model performed well in this crucial step
and explained well the AChEI activity variation among the
test set compounds with correlation (r) of 0.844 (r2 = 0.713;
Figure 3). This further substantiated its significance and
thereby provided an added assurance of its suitability for
the use in the virtual screening process to identify novel
potent AChE inhibitors. The observed and estimated AChE
inhibitory activities of the test set compounds is given in the
Supporting Information.
Complementarity between Best Pharmacophore and Active
Site Residues of AChE. One of the most remarkable features
˚
The Hypo-01 was further studied for its mapping pattern
and thereby classification among the highly active, mode-
rately active, and inactive (or lesser active) compounds in the
training and test set. The mapping analysis of the highly
of the AChE enzyme is the high aromatic content of ∼20 A
deep and cylindrical active-site gorge, which is lined by 14
conserved aromatic amino acids, namely Phe120, Phe288,
Phe290, Phe330, Phe331, Trp84, Trp233, Trp279, Trp432,

6494 Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17
Chaudhaery et al.
Tyr70, Tyr121, Tyr130, Tyr334, and Tyr442.50 The X-ray
crystallographic studies have also established the involve-
ment of several of these conserved aromatic residues in
interactions with AChE inhibitors via both π-cation and
π-π stacking interactions.50 Therefore, it appears that the
hydrophobic interaction between the AChE enzyme and its
inhibitors is one of the most salient features present, which
was also reported by us previously based on 3D-QSAR
comparative molecular field analysis (CoMFA) and com-
parative molecular similarity indices analysis (CoMSIA)
studies.39,40 Among the known cocrystallized structures of
Torpedo californica AChE available in the RCSB Protein
Data Bank (www.rcsb.org), one is cocrystallized with rivas-
tigmine (PDB: 1GQR51), and other one is with ganstigmine
(PDB: 2BAG52). Among these two cocrystallized AChE
inhibitors, rivastigmine is present in the broken form
(Figure 5) with carbamate part being covalently bound with
Ser200 while the remaining noncarbamate part remains in
the anionic subsite lined by Trp84 and Phe330 residues of the
catalytic site (CAS). In case of 2BAG, the carbamate part is
covalently bound with the Ser200 but the noncarbamate part
is absent. Docking of the most active compound 1 into the
Table 3. Details of the 40 Compounds in the Test Set
AChEI activity (pIC50; nM)
activity scale
compd
R1
R2
R3
R4
R5
obsd
est
obsd
est
25
26
27
28
29
30
31
32
33
34
35
-1.45
-1.34
-1.00
-2.15
-1.15
-1.36
-1.49
-2.89
-2.41
-3.11
-2.88
-1.30
-1.65
-1.87
-3.23
-1.97
-2.67
-2.66
-2.84
-3.22
-3.17
-3.24
þþþ
þþþ
þþþ
þþ
þþþ
þþþ
þþþ
þþ
þþþ
þþ
þþ
þ
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
methyl
H
methyl
methyl
H
methyl
methyl
H
þþþ
þþþ
þþþ
þ
methyl
methyl
H
methyl
H
methyl
H
methyl
H
H
H
methyl
H
þþ
þ
methyl
H
H
methyl
isopropyl
methyl
H
þ
þ
H
H
þ
þ
AChEI activity (pIC50; nM)
activity scale
compd
R
obsd
est
obsd
est
36
37
38
39
40
41
42
43
methyl
ethyl
-1.43
-1.91
-1.11
-3.59
-2.56
-1.75
-1.36
-3.42
-2.90
-1.80
-1.35
-3.62
-2.55
-3.02
-1.12
-3.15
þþþ
þþþ
þþþ
þ
þ
þþþ
þþþ
þ
(2-methyl)phenyl
(4-isopropyl)phenyl
ethyl
þþ
þþ
þ
(2-methyl)phenyl
(2-methyl)phenyl
(4-isopropyl)phenyl
þþþ
þþþ
þ
þþþ
þ
AChEI activity (pIC50 (nM)
activity scale
obsd est
compd
R1
R2
R3
R4
obsd
est
44
45
46
47
48
49
50
H
isopropyl
H
methyl
methyl
methyl
benzyl
H
H
-2.51
-1.23
-2.74
-4.11
-3.31
-2.00
-3.25
-4.15
-1.96
-2.48
-4.65
-3.31
-2.45
-2.79
þþ
þþþ
þ
þ
methyl
methyl
H
H
þþþ
þþ
þ
H
isopropyl
isopropyl
phenethyl
methyl
methyl
methyl
benzyl
þ
H
þ
þ
methyl
methyl
H
H
benzyl
benzyl
þþ
þ
þþ
þ

Article
Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17 6495
Table 3. Continued
AChEI activity (pIC50 (nM)
activity scale
compd
R1
R2
R3
obsd
est
obsd
est
51
52
53
54
55
56
57
58
59
60
61
62
63
64
methyl
phenyl
methyl
phenyl
phenyl
phenyl
methyl
methyl
phenyl
phenyl
methyl
methyl
methyl
methyl
benzyl
benzyl
H
methyl
methyl
methyl
methyl
methyl
H
-3.45
-4.05
-3.34
-3.75
-3.54
-1.14
-1.32
-1.75
-2.36
-1.61
-1.45
-3.17
-3.04
-2.21
-3.47
-4.51
-3.36
-3.74
-3.45
-1.62
-1.71
-2.06
-2.33
-1.84
-2.03
-3.17
-3.23
-2.23
þ
þ
þ
þ
þ
þ
H
þ
þ
methyl
methyl
methyl
H
þ
þ
þþþ
þþþ
þþþ
þþ
þþþ
þþþ
þ
þþþ
þþþ
þþ
þþ
þþþ
þþ
þ
H
methyl
H
H
H
methyl
methyl
H
methyl
H
benzyl
benzyl
benzyl
methyl
þ
þ
þþ
þþ
active site of AChE enzyme, using the GOLD53 program,
revealed that all mapped pharmacophoric features of the
best pharmacophore (Hypo-01) complemented well with the
active site residues (Figure 5), which further substantiated
the acceptability of the best pharmacophore. The two resi-
dues, namely Trp84 and Phe330 of the anionic subsite,
correspond to HYDROPHOBE_2 and HYDROPHOBE_3,
respectively, while the projection of carbamoyl amino group
toward His440 corresponds to HBD_1 feature, and the
hydrophobic group attached to the carbamoyl nitrogen re-
sides in the acyl pocket surrounded by Trp233, Phe288, and
Phe290 residues and corresponds to the HYDROPHOBE_4
feature of the best pharmacophore.
Apart from the test set validation, a set of 13 known potent
AChE inhibitors54-62 (Figure 6) were collected from the
literature for external validation of the best pharmacophore
(Hypo-01). This model well mapped and predicted the
AChEI activity of these additional compounds and further
added to the robustness of the model. The estimated and
observed AChEI activities of these compounds are given in
Table 4, while their pharmacophore mapping patterns are
given in the Supporting Information.
Core Hopping and Virtual Library Generation. Core hop-
ping refers to the process of searching for a novel core which
matches with the known core in the reported highly active
inhibitors/drugs for the particular activity. In the present
study, we applied our state-of-the-art in the design of the core
using a very well applied approach known as substructure
searching. However, in the substructure (core) searching
approach, the most crucial task is the selection of the
substructure (core). In our opinion, the best way to gain an
insight into the possible substructure (core) should be based
on the structures of the reported clinically effective inhibi-
tors/drugs, which has either reached into the market of had
succeeded in the clinical trial phases.
novel core and its use in the generation of virtual combina-
torial library. The strategy adopted for the novel core design
is outlined in Figure 7.
The virtual combinatorial library was generated using
CombiGlide module63 implemented in the Schrodinger
software64 in which substituted/unsubstituted alkyl, aryl, hetero-
aryl, and other groups at the substitution sites (circled in
Figure 7) in the designed cores. The generated virtual library
was subjected to the Build Database protocol implemented
in the Accelry’s DS2.0, which provided a multiconformer
database for the use in the pharmacophore-based virtual
screening (PBVS) protocol.
Pharmacophore-Based Virtual Screening. The PBVS has
been a well adopted in silico high-throughput screening
process for identification and optimization of novel lead
molecules. In search of potent AChE inhibitors, the best
pharmacophore model (Hypo-01) was used in the PBVS
protocol using the multiconformer database, which in turn
led to the identification of novel carbamates as potent AChE
inhibitors. Among the identified molecules, a set of nine
prioritized compounds 85-93 (Table 5) were targeted for
synthesis and pharmacological evaluation to test the best
hypothesis (Hypo-01). Figure 8 shows the overlay of two
most active compounds (92 and 93) over the best pharma-
cophore. These two compounds mapped all features very
well with n-heptyl and chloro groups of compound 92 and 93
respectively mapped to the HYDROPHOBE_4, while their
carbamoyl NH mapped to HBD_1. Two other features, viz.
HYDROPHOBE_2 and HYDROPHOBE_3, mapped to
the pyridyl and phenyl rings of the quinoline respectively
with slightly higher displacement from their centroids.
Figure 9 shows the docked poses of the carbamate and non-
carbamate fragments of compounds 86 and 93, where it is
clear that the two residues, namely Trp84 and Phe330 of the
anionic subsite, correspond to HYDROPHOBE_2 and HY-
DROPHOBE_3, respectively, while the projection of carba-
moyl amino group toward His440 corresponds to HBD_1
feature, and the hydrophobic group attached to the carba-
moyl nitrogen resides in the acyl pocket surrounded by
Common core analysis (green colored parts in Figure 6)
among the known clinically effective carbamate class of
AChE inhibitors54-62 led us to proceed with the 4-carba-
moylphenylmethylamine core (Figure 7) in the design of the

6496 Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17
Chaudhaery et al.
Figure 4. Pharmacophore mapping of the most active compounds viz 1 (left) and 38 (right) in the training and test set, respectively.
Figure 5. Stereoview of binding poses of broken carbamate and noncarbamate fragments of the compound 65 (rivastigmine) and 1 in the active
site of AChE enzyme. Fragments are displayed in the orange colored ball and stick form while the active site residues are displayed as lines.
Trp233, Phe288 and Phe290 residues and corresponds to the
HYDROPHOBE_4 feature of the best pharmacophore.
Chemistry. The synthesis of the intermediates (78-84) and
designed compounds (85-93) is outlined in Scheme 1. The
6-methoxyquinoline (78) was partially reduced to 6-methoxy-
1,2,3,4-tetrahydroquinoline (79) using nickel-aluminum alloy,
ethanol, and 10% NaOH in water (w/v) at ambient tempera-
ture. The N-benzylation of 6-methoxy-1,2,3,4-tetrahydro-
quinoline (79) was done using benzyl chloride in the presence
of potassium carbonate (K2CO3) and potassium iodide (KI)
as inorganic bases in anhydrous N,N-dimethylformamide
(DMF) as the organic solvent. The N-methylation of 6-meth-
oxy-1,2,3,4-tetrahydroquinoline (79) was done using methyl
iodide in the presence of sodium hydride (NaH) as an in-
organic base in anhydrous DMF at -10 to 30 °C. The conver-
sion of 6-methoxyquinoline (79), N-benzyl-6-methoxy-1,2,3,4-
tetrahydroquinoline (80), and N-methyl-6-methoxy-1,2,3,4-
tetrahydroquinoline (81) into respective 6-hydroxyquinoline
(82), N-benzyl-6-hydroxy-1,2,3,4-tetrahydroquinoline (83),
and N-methyl-6-hydroxy-1,2,3,4-tetrahydroquinoline (84)
was accomplished by refluxing corresponding methoxy inter-
mediates in 47% HBr in water (v/v) solution for 5-8 h. The
reaction of corresponding hydroxyl intermediates namely,
82, 83, 84 with substituted or unsubstituted alkyl/aryl
isocyanates using dry tetrahydrofuran (THF) as solvent in
the presence of one of the inorganic bases like NaH, pyri-
dine etc., yielded corresponding carbamate derivatives,
namely 85, 86, 90, 91, 92, 93, etc. as the final desired
compounds. The N-debenzylation was accomplished using
10% Pd-C catalyst and H2 at 50 psi to afford title com-
pounds 87-89.
Pharmacological Results. All compounds were evaluated
using three diverse assay systems: (i) in vitro assay method
described by Ellman et al.,65 (ii) in vivo passive avoidance
test66 conducted in adult Swiss male mice of 3-4 months (wt
20-25 g), and (iii) in vivo assay called aldicarb-sensitivity
assay employing C. elegans as the model system.44,46 Scopo-
lamine induced impairment in passive avoidance test (in
vivo) and the inhibition of AChE in rodents are commonly
employed screening tests to predict the potential of an AChE
inhibitor as a cognitive enhancer (antidementic) drug.67
Table 5 summarizes the in silico estimated and in vitro
observed AChE inhibitory (AChEI) activities and also the
in vivo activity data (% LI; passive avoidance test) of the
synthesized compounds (85-93) and Tacrine and Donepezil
as reference compounds. Figure 10 describes the transfer

Article
Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17 6497
Figure 6. Carbamates reported as AChE inhibitors (note: green colored part in each structure indicates common substructure/core).
Table 4. Observed and Estimated AChEI Activities (IC50, nM) of 13
Compounds of the External Set by the Best Pharmacophore Model
(Hypo-01)
in the synaptic cleft to a greater extent as compared to
Donepezil, a widely used anti-Alzheimer drug. The com-
pound 87 with estimated AChE IC50 of 313.92 nM showed
the % LI of 67.395, which was found to be comparatively
IC50 (nM)
esta
obsdb
ref
more potent than its homologue 88 (estimated AChE IC50
=
name
fit value
441.03 nM and % LI = 33.431), the least active compound in
this series. The compound 90 and 91 were also found to be
potent memory enhancer, with % LI of 75.979 and 61.525,
respectively. Two compounds 92 and 93, with estimated (est)
AChE IC50 of 99.83 and 86.50 nM respectively, showed
excellent in vivo antidementia activity with %LI of 106.768
and 123.06, respectively, at an oral dose of 20 μmol/kg (1%
aqueous suspension in gum acacia) compared to the refer-
ence drug (Donepezil; % LI = 134.416; oral dose = 20
μmol/kg; observed AChE IC50 = 90 nM). The increase in
the availability of neurotransmitter ACh in the synaptic cleft
of C. elegans associated with these two compounds 92 and 93
was almost equal to the ACh level increased by the well-
known AChE inhibitor, Donepezil (Figure 11). As shown in
Figure 12, the three measurements, namely % LI, % increase
in ACh in the synaptic cleft and estimated AChE IC50 (nM)
of compounds 85, 86, 90-93 and Donepezil (reference drug),
well corroborated with each other in terms of trend colinea-
rity for all compounds except compounds 87-89. The exact
reason for such exceptions is not known and requires some
additional studies. However, it may be stated that the main
reason for such exceptions may be their inability to cross the
blood-brain barrier (BBB) because of their higher hydro-
philic factor (hyd factor), which is a measure of hydrophilic
nature of a compound. The hydrophilic factor of each
compound was calculated using DRAGON program.68 The
hydrophilic factor of compound 87, 88, and 89 was -2.87,
-2.34, and -2.87,respectively,whichwasquitehigherthan rest
of the compounds (hyd factor range: -6.87 to -8.43). Also,
65 (rivastigmine)
66 (physostigmine)
3.061
4.520
4.704
4.518
4.88
1900.53
65.00
4150.00 72
56.70 72
22.00 72
22.10 72
40.00 72
32.00 61
125.00 72
148.00 57
100.00 60
8.11 58
67 (eptastigmine)
68 (phenserine)
43.228
66.00
29.00
69 (tolserine)
70
4.526
4.567
4.232
4.449
4.396
4.126
4.556
4.360
65.196
59.316
128.361
77.842
87.86
71 (CHF2819)
72 (quilostigmine; NXX-066)
73 (P10358)
74
75
76
77
163.87
60.784
95.435
30.00 58
17.30 58
7.00 62
a est = estimated. b obsd = observed.
latency time (TLT; seconds) in the first and second trial of the
synthesized compounds (85-93) and Donepezil. Figure 11
shows the in vivo data obtained from the biological assay
employing C. elegans model system (aldicarb-sensitivity
assay). The results of compounds, namely Donepezil, 85,
86, 89, and 93 were significant at p < 0.05, whereas the
compound 88 was significant at p<0.001 in the aldicarb-
sensitivity assay.
The compounds 85 and 86 with estimated AChE IC50 of
87.36 and 141.22 nM also showed good in vivo % LI of
89.190 and 89.597 respectively at an oral dose of 20 μmol/kg
(1% aqueous suspension in gum acacia). In the colorimetric
assay system (Ellman method), the compound 86 showed
observed AChE IC50 = 3310 nM. In the C. elegans model
system, compound 85 was found to increase the level of ACh

6498 Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17
Chaudhaery et al.
Figure 7. Strategies adopted during core hopping and virtual library generation.
Table 5. Chemical Structures of Designed Compounds and Their AChE inhibitory (in Silico and in Vitro) and in Vivo Learning Improvement Activities
passive avoidance test
AChEI activity
(IC50; nM)
trials
compd
type
R1
R2
est
obsd
1st
2nd
% LIc
85
86
87
88
89
90
91
92
93
A
A
A
A
A
A
A
B
n-hexyl
2-chlorophenyl
3-bromophenyl
Bn
Bn
H
87.36
141.22
313.92
411.03
233.88
134.16
328.43
99.83
NDb
3310
ND
ND
ND
ND
ND
ND
ND
92.00
83.50
97.00
205.00
186.40
195.00
222.20
216.40
218.00
242.00
238.00
230.00
147.00
245.00
241.00
89.190
89.597
67.395
33.431
46.697
75.979
61.525
106.768
123.060
0
4-chloro-3-trifluoromethylphenyl
4-bromophenyl
4-chloro-3-trifluoromethylphenyl
4-bromophenyl
n-heptyl
H
133.00
120.00
104.00
124.00
99.00
H
Me
Me
B
2-chlorophenyl
86.50
89.60
scopolaminea
donepezil
tacrine
110.00
91.40
73.40
90.00
800
134.416
194.702
a (3 mg/kg, ip). b ND = not determined due to colored nature of compound and/or solubility problem. c % learning improvement: % learning in
treated group - % learning in scopolamine group, where % learning: [(2nd trial - 1st trial)/1st trial] ꢀ 100.
it may interesting to note that these three exceptional
compounds (87-89) are composed of the same hetero-
cyclic core, 1,2,3,4-tetrahydroquinoline, out of which
the compounds 87 and 89 have meta- and para-bromo-
phenyl groups respectively attached to carbamoyl nitro-
gen (Table 5).
Figure 13 shows the stacked-line plot of the % LI, and %
increase in ACh availability at synaptic cleft, where it is
evident that these three variables, obtained from two differ-
ent assay systems, are following similar trend. Also, it further
proves the fact that the memory (cognition) is highly depen-
dent on the availability of ACh in the synaptic cleft in
between pre- and postsynaptic neurons. In view of it, it
may be noted that the % availability of the neurotransmitter
ACh in the synaptic cleft is the most determinant factor of
memory functioning in a model system.
optimization of these leads is in progress to develop a more
comprehensive structure-activity relationship, which may
lead to the discovery of potential candidate molecule as
AChE inhibitors for the treatment of the AD.
Structure-Activity Relationship (SAR). The SAR analysis
of a set of nine carbamates provided important insights into
the essential structural requirements for effective AChE
inhibition and memory enhancement by increasing the level
of cognitive neurotransmitter ACh in the synaptic cleft. It
was observed that the introduction of bulky hydrophobic
groups at the carbamoyl nitrogen led to compounds with
better AChEI and learning improvement (LI) activity.
Among the substituted phenyl groups attached to carbamoyl
nitrogen, the order of preference should be: ortho (o) > meta
(m)>para (p) for AChE inhibition and memory enhance-
ment. For example, ortho-chlorophenyl should be preferred
over meta- and para-chlorophenyl, as an attachment group
to the carbamoyl nitrogen. Conclusions drawn based on
their structural analysis are in good agreement with our
earlier published structural insights gained from the 3D-
QSAR comparative molecular field analysis (CoMFA) and
comparative molecular similarity indices analysis (CoMSIA)
On the basis of the above results, obtained from three
diverse assay systems, the two compounds 85 and 86
(Table 5) were selected as potential lead compounds for
further consideration. These two compounds are novel sub-
stituted carbamate esters attached to sixth position of a N-
benzyl-1,2,3,4-tetrahydroquinoline core. In our lab, further

Article
Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17 6499
Figure 8. Pharmacophore mapping of the two most active compounds 92 (left) and 93 (right).
Figure 9. Stereoview of binding poses of broken carbamate and noncarbamate fragments of the compound 86 and 93 in the active site of AChE
enzyme. Fragments are displayed in the orange colored ball-and-stick form while the active site residues are displayed as lines.
Figure 10. In vivo results of scopolamine induced deficit (dementia/amnesia) in adult Swiss male mice (passive avoidance test) of the screened
compounds.
studies on diverse carbamates.40 In this study, the presence of
bulky hydrophobic groups at the carbamoyl nitrogen was
found to be essential for potential AChE inhibition. On the
other hand, structural analysis of the lead compounds 85 and
86 suggests the presence of a hydrophobic group with steric
bulk on the nitrogen of the 1,2,3,4-tetrahydroquinoline is

6500 Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17
Chaudhaery et al.
Scheme 1a
a Reagents and conditions: (a) Nickel-aluminum alloy, ethanol, 10% NaOH in water (w/v), 5-6 h; (b) benzyl chloride, dry DMF, baked K2CO3 (or
NaCO3), NaI (or KI), 80 °C, 3-5 h; (c) 47% HBr in water (v/v), reflux, 5-8 h; (d) RNCO, NaH, THF; (e) 10% Pd-C, MeOH, H2 at 50 psi; (f) CH3I,
NaH, DMF, -10 to 30 °C.
Figure 13. Stacked line plot of the estimated AChE pIC50 (nM),
the %LI and the % increase in ACh availability associated with
different compounds.
Figure 11. Effect of different compounds on the ACh availability in
C. elegans model system.
Conclusion
In the present study, an efficient virtual screening has been
performed using a Catalyst/HypoGen-based pharmacophore
and in-house virtual library for discovering novel promising
AChE inhibitors. The quantitative pharmacophore has been
developed using the training set of 24 molecules with the help
of HypoGen module implemented in the Catalyst. The best
pharmacophore model provided a statistically significant
correlation of 0.95 (rms = 0.568) and explained about 91%
AChEI activity variation in the training set compounds. The
best pharmacophore model has also well estimated AChEI
activities of the test set compounds with overall predictive
correlation (r) of 0.814 and explained about 71% AChEI
activity variation. In addition, this model predicted very well a
diverse class of clinically potent AChE inhibitors. The PBVS
of the in-house virtual library using the statistically significant
3D pharmacophore model provided novel potent AChE
inhibitors. The pharmacological evaluation of a set of nine
synthesized carbamates against three diverse assay systems,
Figure 12. Comparative analysis of the biological activities of the
synthesized compounds from three different assay systems.
responsible for their potential activities, which is also corro-
borates well with our reported structural insights.40

Article
Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17 6501
namely (i) in vitro (Ellman method), (ii) in vivo (passive
avoidancetest), and (iii) Caenorhabditis elegansmodel system-
based aldicarb-sensitivityassay, ledintothe discoveryof novel
potent carbamates, which are found to increase availability of
acetylcholine (ACh) in the synaptic cleft by inhibiting AChE
enzyme and also found to enhance the learning tendency
(memory) in Swiss male mice. On the basis of the data derived
from three diverse assays, two novel lead compounds, namely
85 and 86, with desirable physicochemical properties, have
been selected as candidate molecules for further optimization,
which in the recent future may provide novel potent AChE
inhibitors for the therapy of Alzheimer’s disease (AD), an
unmet global need.
100 μg/mL of reaction mixture for 30 min at 37 °C prior to
obtaining the kinetic profile of AChE activity. Donepezil (1 μg/
mL) was used as standard AChE inhibitor (standard control).
The AChE inhibitory activity was calculated on the basis of %
decrease from control values, i.e. AChE activity without incu-
bation with any standard or test drug.
In Vivo Pharmacology. Passive Avoidance Test. The study
was conducted in adult Swiss male mice of 3-4 months (wt
20-25 g) were kept in standard housing conditions with 12 h
light and dark cycle. The food and water were available ad
libitum. Mice were subjected to single trial passive avoidance
test as described by Brioni.66 Passive avoidance test was studied
in a computerized shuttle box (Columbus Instruments, Ohio)
provided with a software program PACS 30. The shuttle box is
comprised of two compartments isolated by an automated door.
After exploration period of 30 s for acclimatization, animal was
subjected to a trial of 270s. Each mouse was placed in the bright
compartment and on transfer into the dark compartment; it was
given an electric shock (0.5 mA for 5 s) through floor grid.
Transfer of mice from the bright to the dark compartment was
recorded as transfer latency time (TLT) in seconds. TLT was
recorded in control and treated groups (1st trial, acquisition)
and then after 24 h (2nd trial, retention). An increase in the TLT
on second trial (retention) as compared to first trial (acquisition)
was taken as the criterion for successful learning and memory
improvement (cognitive activity). Mean values and standard
errors (SE) of the mean were calculated for TLT and specific
activity of AChE in the different regions of brain samples of
each group. The significance of difference between values of
AChE activity and TLT between the groups was determined by
one-way ANOVA test that followed by Dunnett’s test.
Assay Employing C. elegans (Model System). To study the
effect of test compounds on ACh release, a biological assay
employing model system C. elegans,44 was performed as per the
method described in Mahoney et al.46 with slight modifications.
Briefly, the wild type strain N2 (var. Bristol) of C. elegans,
procured from C. elegans Genetics Center (CGC), University of
Minnesota, was used. Healthy gravid population of nematodes
grown on nutrient growth medium (NGM) Agar under stan-
dard conditions80 (Brenner, 1974) were subjected to embryo
isolation by axenization method81 (Stiernagle, 1999). Embryos
were transferred to NGM plates seeded with their standard food
(bacteria Escherichia coli) premixed with the test compounds.
Nematodes were grown under standard conditions on the
treated plates for 48 h and then washed off the plates, pelletted,
and transferred to assay plates containing Aldicarb at a con-
centration of 1 mM premixed with NGM. Assays were per-
formed in duplicates and the percentage of worms paralyzed at a
time point of 3 h was recorded. The percentage of worms
paralyzed under treated conditions was normalized against
control values and the data was analyzed for mean ( SE;
statistical significance of data was calculated by Student’s t test
using Sigma Stat software package.
Experimental Section
Pharmacophore-Based Virtual Screening. Modeling Tools
and Biological Data. All molecular modeling works were accom-
plished using the Window-based Accelrys Discovery Studio
version 2.0 (DS2.0).69 The Catalyst 4.7 was used for pharma-
cophore modeling and virtual screening.69 A structurally diverse
set of 64 compounds, reported as AChE inhibitors with AChE
inhibitory activity data (expressed as IC50) spanning about 3.6
orders of magnitude (10-41130 nM), were considered in the
present study.70-74 The AChE inhibitors were categorized into
highly active (AChE IC50 e 100 nM), moderately active (100 <
IC50 < 500 nM), and poorly active (AChE IC50 g 500 nM). The
data set was classified into the training set (24 compounds:
Figure 1) and test set (40 compounds) in such a way to avoid any
redundancy in terms of structural features or activity range and
also to provide clear and concise information during model
development using the HypoGen algorithm implemented in the
Catalyst software.75
The pharmacophore model development methodology is well
described in the literature.76,77 In brief, the energy minimized 3D
structures (using CharmM forcefield78 until the gradient dropped
below 0.001) were submitted for diverse conformational gen-
eration using the “best quality” conformational search option in
the Catalyst software using a constraint of 20 kcal/mol energy
threshold above the global energy minimum, 255 as the maxi-
mum number of conformations and CharmM force field para-
meters78 to ensure maximum coverage in the conformational
space. All other settings were kept as default. On the basis of the
structural characteristics of the training set compounds, only
HBA, HBD, HY, and RA features from feature dictionary were
considered for model development. The pharmacophore model-
ing, which is done in three subsequent phases, viz. construc-
tive, subtractive, and optimization phases, was accomplished
using HypoGen algorithm based on the training set of 24 com-
pounds. The top 10 scoring pharmacophore models (hypotheses)
were analyzed for their statistical significance and to determine
the best model by analyzing quality of the mapped conforma-
tions as well as analysis of theoretical costs, namely fixed, null,
total, error, weight, configuration, and residual costs.79 The
term “cost” in HypoGen algorithm indicates the number of
binary bits required to generate a particular pharmacophore/
hypothesis. The recommended values of residual, weight, and
configuration costs are g40, e2, and e17, respectively. Further
quality assessment and validation of the pharmacophore model
was done using the test set compounds, which were kept apart
during the model development process.
Chemistry. General Methods. Reagents were purchased from
common commercial suppliers and were used without further
purification. Solvents were purified and dried by standard pro-
cedures, when necessary. Chromatographic separations of the
synthesized intermediates and title compounds were performed
on silica gel (Merck: 100-200 mesh). Thin-layer chromatography
was used to monitor the reactions. Melting points (uncorrected)
€
were determined with Buchi 510 apparatus. Characterization of
the synthesized compounds was accomplished in the Sophisti-
cated Analytical Instrument Facility (SAIF) Department of
CDR, Lucknow. IR spectroscopy was carried out using Perkin-
Elmer 881 spectrophotometer, and the values are expressed as
vmax cm-1. Mass spectra (MS) were recorded on a Jeol (Japan)
SX 102/DA-6000 mass spectrometer. 1H NMR spectra were
recorded on Bruker Spectrospin spectrometer at 300 MHz. The
chemical shifts are reported in δ scale (ppm) and are relative to
tetramethyl silane (TMS) as internal standard. The coupling
In Vitro Pharmacology: Ellman Method. The assay of AChE
inhibition was performed according to method described by
Ellman et al.65 using the human AChE purified from red blood
cells. The kinetic profile of the AChE enzyme activity was
studied spectrophotometrically at a wavelength of 412 nM at
an interval of 15s. The assay for each sample was run in
duplicate and each experiment was performed thrice. The test
substance was incubated with enzyme in the concentration of

6502 Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17
Chaudhaery et al.
constants J are given hertz, and spin multiplicities are expressed
s (singlet), d (doublet), t (triplet), dd (double doublet), quint
(quintet), and m (multiplet). Elemental analyses were performed
on a Carlo Erba model EA-1108 elemental analyzer, and data of
C, H, and N are within (0.4% of calculated values. All reported
title compounds were assessed to be g95% pure by analytical
HPLC method.
Carbamylation. Method A: Synthesis of Hexylcarbamic Acid
1-Benzyl-1,2,3,4-tetrahydroquinolin-6-yl Ester (85). A mixture of
1-benzyl-1,2,3,4-tetrahydroquinolin-6-ol (83, 0.239 g, 0.001 mol),
hexyl isocyanate (0.152 g, 0.0012 mmol), and pyridine (0.5 mL)
in dry THF (10 mL) was heated with stirring at 65 °C for 72 h.
After the completion, the reaction mixture was cooled and
quenched with water (1 mL) and concentrated under vacuum.
The separated solid was washed with water (2 ꢀ 3 mL) and
crystallized with anhydrous ether to give the title product 85.
Yield: 0.30 g (81.9%), mp >335 °C. 1H NMR (CDCl3, 200
MHz): δ 0.89 (bs, 3H), 0.90-1.29 (m, 4H), 1.50-1.56 (m, 4H),
1.98-2.15 (m, 2H), 2.79 (t, J = 6.21 Hz, 2H), 3.17-3.36 (m,
4H), 4.44 (s, 2H), 4.88 (bs, 1H), 6.42 (d, J = 8.72 Hz,1H),
6.67-6.75 (m, 2H), 7.22-7.35 (m, 5H). FTIR (KBr): cm-1 669,
760, 1026, 1217, 1348, 1501, 1615, 1728, 2402, 2857, 3018, 3450.
EIMS: m/z 366 (Mþ). HR-MS: calcd for C23H30N2O2 (Mþ)
366.2307; found 366.2342.
Method B: Synthesis of 2-Chlorophenylcarbamic Acid 1-Ben-
zyl-1,2,3,4-tetrahydroquinolin-6-yl Ester (86). A solution of 1-
benzyl-1,2,3,4-tetrahydroquinolin-6-ol (83, 0.239 g, 0.001 mol)
in dry ether (10 mL) was added to a stirred suspension of sodium
hydride (0.024 g, 0.001 mol) in dry THF (15 mL) at -10 °C
during 10 min. The reaction mixture was stirred for an addi-
tional 20 min. 2-Chlorophenyl isocyanate (0.184 g, 0.001 mol)
was added to the stirring reaction mixture. The stirring was
continued for an additional 2 h, during which the temperature
was allowed to rise to 35 °C. The reaction mixture was quenched
with water (0.2 mL), concentrated under vacuum, diluted with
water (5 mL), extracted with chloroform (2 ꢀ 5 mL), and dried
over sodium sulfate. The combined fractions of chloroform were
concentrated under vacuum. The residue was chromatographed
on silica gel column using chloroform:hexane (20:80) as eluent
to give the title product 86 (0.25g, 63.7%). mp 130-133 °C. 1H
NMR (CDCl3, 200 MHz): δ 1.90-2.02 (m, 2H), 2.82 (t, J = 6.18
Hz, 2H), 3.30-3.35 (m, 2H), 4.46 (s, 2H), 6.43-6.48 (m, 1H),
6.74-6.82 (m, 2H), 7.20-7.25 (m, 5H), 7.44-7.46 (m, 4H).
FTIR (KBr): cm-1 534, 754, 807, 882, 1018, 1059, 1193, 1245,
1303, 1352, 1432, 1506, 1597, 1718, 1749, 2371, 2830, 2922, 3418,
3760. EIMS: m/z 393 (M þ 1)þ. HR-MS: calcd for C23H21-
ClN2O2 (M þ 1)þ 393.1292; found 393.1312.
lin-6-yl ester (0.23 g, 0.005 mol) and 5% Pd-C (0.02 g) in
absolute ethanol (20 mL). The 4-chloro-3-trifluoromethylphe-
nylcarbamic acid 1-benzyl-1,2,3,4-tetrahydroquinolin-6-yl ester
was synthesized using the method B. Yield: 0.16 g (86.4. %), mp
150-151 °C. 1H NMR (DMSO-d6, 200 MHz): δ 1.89-1.95 (m,
2H), 2.76 (t, J = 6.08 Hz, 2H), 3.28 (t, 2H, J = 5.52 Hz),
6.44-6.49 (m,1H), 6.72-6.75 (m, 2H), 7.37- 8.00 (m, 2H), 10.14
(bs, 1H). FTIR (KBr): cm-1 661, 698, 769, 796, 819, 892, 944,
1023, 1072, 1147, 1217, 1290, 1347, 1384, 1424, 1450, 1506, 1599,
1707, 1740, 2364, 2489, 2837, 2950, 3359, 3773, 3888. EIMS: m/z
371 (M þ 1) þ. HR-MS: calcd for C17H14ClF3N2O2 (M þ 1)þ
371.0696; found 371.0681
4-Bromophenylcarbamic Acid 1,2,3,4-Tetrahydroquinolin-6-yl
Ester (89). This compound was synthesized using the same
debenzylation procedure used for the compound 87 by taking
a nitrogen flushed mixture of 4-bromophenylcarbamic acid
1-benzyl-1,2,3,4-tetrahydroquinolin-6-yl ester (0.655 g, 0.0015 mol)
and 5% Pd-C (0.06 g) in absolute ethanol (20 mL). The
4-bromophenylcarbamic acid 1-benzyl-1,2,3,4-tetrahydroqui-
nolin-6-yl ester was synthesized using the method B. Yield:
0.48 g (92.3%), mp 197-199 °C. 1H NMR (DMSO-d6, 200
MHz): δ 1.82-2.00 (bs, 2H), 2.76 (t, J = 6.22 Hz, 2H), 3.27 (t,
J = 5.26 Hz, 2H), 7.01-7.08 (m, 3H), 7.25-7.39 (m, 4H), 10.14
(s, 1H). FTIR (KBr): cm-1 506, 608, 690, 749, 814, 884, 927,
1005, 1211, 1265, 1319, 1354, 1401, 1440, 1501, 1550, 1601, 1748,
1938, 2370, 2779, 2725, 2767, 2823, 2922, 3262, 3420, 3774.
EIMS: m/z 348 (M þ 1)þ. HR-MS: calcd for C16H15BrN2O2
(M þ 1)þ 347.0317; found 347.0338.
4-Chloro-3-Trifluoromethylphenylcarbamic Acid 1-Methyl-1,
2,3,4-tetrahydroquinolin-6-yl Ester (90). This compound was
synthesized using the general carbamoylation method B by
taking a solution of 1-methyl-1,2,3,4-tetrahydroquinolin-6-ol
(84, 0.326 g, 0.002 mol) in dry THF (5 mL), a suspension of
sodium hydride (0.048 g, 0.002 mol) in dry THF (5 mL) at -10 °C,
and 4-chloro-3-trifluoromethylphenyl isocyanate (0.53, 0.002
mol). Yield: 0.52g (67.6%), mp 151-152 °C. 1H NMR (CDCl3,
200 MHz): δ 1.94-2.00 (m, 2H), 2.76 (t, J = 6.50 Hz, 2H), 2.87
(s, 3H), 3.20 (t, J = 5.63 Hz, 2H), 6.52-6.57 (m, 1H), 6.78-6.82
(m, 2H), 7.46-7.81 (m, 3H). FTIR (KBr): cm-1 537, 666, 756,
831, 894, 1029, 1147, 1264, 1423, 1489, 1542, 1596, 1698, 1741,
2373, 2928, 3118, 3232, 3402, 3687, 3760. EIMS: m/z: 384 (Mþ).
HR-MS: calcd for C18H16ClF3N2O2 (Mþ) 384.0852; found
384.0865.
4-Bromophenylcarbamic Acid 1-Methyl-1,2,3,4-tetrahydro-
quinolin-6-yl Ester (91). This compound was synthesized using
the general carbamoylation method B using a solution of 1-
methyl-1,2,3,4-tetrahydroquinolin-6-ol (84, 0.326 g, 0.002 mol)
in dry THF (5 mL), a suspension of sodium hydride (0.048 g,
0.002 mol) in dry THF (5 mL) at -10 °C, and 4-bromo-phenyl
isocyanate (0.734, 0.002 mol). Yield: 0.40 g (52.3%), mp
General Procedure of Debenzylation: Synthesis of 3-Bromo-
phenylcarbamic Acid 1,2,3,4-Tetrahydroquinolin-6-yl Ester (87).
A nitrogen flushed mixture of 3-bromophenylcarbamic acid
1-benzyl-1,2,3,4-tetrahydroquinolin-6-yl ester (0.218 g, 0.005
mol; note: this compound was synthesized using the method A
used for synthesis of compound 85) and 5% Pd-C (0.02 g) in
absolute ethanol (25 mL) was shaken in a par apparatus at 38 °C
under 50 psi pressure of hydrogen for 4 h. Pd-C was then
discarded through filtration. The reaction mixture was concen-
trated under vacuum and the residue was washed with dichloro-
methane (2 ꢀ 3 mL) to give the title product 87 (0.16 g, 92.4%);
mp 196-198 °C. 1H NMR (DMSO-d6, 200 MHz): δ 1.73-1.81
(m, 2H), 2.55 (t, J = 6.34 Hz, 2H), 3.06 (t, J = 5.58 Hz, 2H),
6.67-6.98 (m, 4H), 6.90-7.20 (m, 2H), 7.36 (bs, 1H), 9.39 (bs,
1H). FTIR (KBr): cm-1 509, 694, 758, 815, 886, 1012, 1084,
1149, 1216, 1319, 1352, 1442, 1504, 1549, 1600, 1710, 2365, 2479,
2727, 2827, 2927, 3420, 3780. EIMS: m/z 347 (M þ 1)þ. HR-MS:
calcd for C16H15BrN2O2 (M þ 1)þ 347.0317; found 347.0322.
4-Chloro-3-trifluoromethylphenylcarbamic Acid 1,2,3,4-Tet-
rahydroquinolin-6-yl Ester (88). This compound was synthesized
using the same debenzylation procedure used for the compound
87 taking a nitrogen flushed mixture of 4-chloro-3-trifluoro-
methylphenylcarbamic acid 1-benzyl-1,2,3,4-tetrahydroquino-
1
155-159 °C. H NMR (CDCl3, 200 MHz): δ 1.94-2.03 (m,
2H), 2.76 (t, J = 6.42 Hz, 2H), 2.87 (s, 3H), 3.20 (t, J = 5.64 Hz,
2H), 6.52-6.57 (m, 1H), 6.78-6.88 (m, 2H), 7.25-7.45 (m, 4H).
FTIR (KBr): cm-1 674, 774, 1020, 1365, 1590, 2366, 2834, 2934,
3433, 3757, 3867, 3906. EIMS: m/z 361 (M þ 1)þ. HR-MS: calcd
for C17H17BrN2O2 (M þ 1)þ 361.0473; found 361.0469.
n-Heptylcarbamic Acid Quinolin-6-yl Ester (92). This com-
pound was synthesized using the method A using a mixture of
quinolin-6-ol (82, 0.29 g, 0.002 mol), heptyl isocyanate (0.39 mL,
0.0024 mol), and pyridine (2 mL) in dry THF (10 mL). Yield:
0.30 g (52.4%), mp 77-80 °C. 1H NMR (CDCl3, 200 MHz): δ
0.88 (bs, 3H), 1.33-1.34 (m, 8H), 1.57-1.62 (m, 2H), 3.25-3.35
(m, 2H) 5.10 (s, 1H), 7.36-7.52 (m,1H), 7.47-7.52 (m, 1H),
7.60-7.61 (m, 1H), 8.07-8.12 (m, 2H), 8.86-8.88 (m, 1H).
FTIR (KBr): cm-1 478, 648, 731, 771, 838, 910, 977, 1024, 1157,
1215, 1363, 1464, 1498, 1532, 1600, 1719, 2371, 2861, 2944, 3022,
3359, 3762. FAB-MS: m/z: 287 (M þ 1)þ. HR-MS: calcd for
C17H22N2O2 (M þ 1)þ 287.1681; found 287.1698.
2-Chlorophenylcarbamic Acid Quinolin-6-yl Ester. This com-
pound was synthesized using the method A using a mixture of

Article
Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17 6503
quinolin-6-ol (82, 0.29 g, 0.002 mol), 2-chloro-phenyl isocyanate
(0.33 mL, 0.0024 mmol), and pyridine (0.3 mL) in dry tetrahy-
drofuran (5 mL). Yield: 0.30 g (50.2%), mp 225-228 °C. H
(7) Clegg, A.; Bryant, J.; Nicholson, T.; McIntyre, L.; De Broe, S.;
Gerard, K.; Waugh, N. Clinical and Cost-Effectiveness of Done-
pezil, Rivastigmine and Galantamine for Alzheimer’s Disease:
A Rapid and Systematic Review. Health Technol. Assess. 2001, 5,
1–137.
(8) Clegg, A.; Bryant, J.; Nicholson, T.; McIntyre, L.; De Broe, S.;
Gerard, K.; Waugh, N. Clinical and Cost-Effectiveness of Done-
pezil, Rivastigmine and Galantamine for Alzheimer’s Disease. Int.
J. Technol. Assess. Health Care 2002, 18, 497–507.
(9) Goldlist, B.; Gordon, M.; Naglie, G. Galantamine vs Donepezil in
the Treatment of Alzheimer’s disease. Drugs Aging 2003, 20, 1139–
1140.
(10) Prasher, V. P. Review of Donepezil: Ageing and Health Issues in
Intellectual Disabilities. Int. J. Geriatr. Psychiatry 2004, 19, 509–
515.
1
NMR (pyridine-d5, 200 MHz): δ 6.96-7.57 (m, 9H), 8.62-8.71
(m, 1H), 9.47 (m, 1H). FTIR (KBr): cm-1 903, 943, 1040, 1230,
1291, 1353, 1437, 1474, 1552, 1592, 1646, 2373, 3289, 3759.
EIMS: m/z: 298 (M)þ. HR-MS: calcd for C16H11ClN2O2 (M)þ
298.0509; found 298.0544.
Molecular Docking. The GOLD53 program was used for
calculating the docking modes of the compounds 1, 65, 86,
and 93 into the binding site of AChE enzyme. Atomic coordi-
nates of AChE enzyme was obtained from the crystal structure
of AChE in complex with rivastigmine (PDB: 2GQR51). The
(11) Frantz, S. Drug Discovery: Playing Dirty. Nature 2005, 437, 942–
protein was prepared using Protein Preparation Wizard imple-
€
mine were removed from the PDB file, and the polar hydrogen
atoms were added to the amino acid residues before the docking
study.
Covalent Docking. Covalent docking is a unique feature of
GOLD program, which is able to dock covalently bound
inhibitors. Both protein (AChE) and ligand files were set up
with the Ser200 oxygen (O, link atom), which was assigned in
both the protein and ligand input files. The GOLD program
assumes that there is just one atom linking the ligand to the
protein (e.g., the O in a serine residue). More details about this
docking method (http://www.ccdc.cam.ac.uk/products/life_
sciences/gold/).
63
mented in Schrodinger software, where waters and rivastig-
943.
(12) Van der Schyf, C. J.; Youdim, M. B. Multifunctional Drugs as
Neurotherapeutics. Neurotherapeutics 2009, 6, 1–3.
(13) Scarpini, E.; Scheltens, P.; Feldman, H. Tretament of Alzheimer’s
Disease: Current Status and New Perspectives. Lancet Neurol.
2003, 2, 539–547.
(14) Tariot, P. N.; Federoff, H. J. Current treatment for Alzheimer
disease and future prospects. Alzheimer Dis. Assoc. Disord. 2003, 17
(Suppl 4), S105–S113.
(15) Aracava, Y.; Pereira, E. F. R.; Maelicke, A.; Albuquerque, E. X.
Memantine Blocks {alpha}7* Nicotinic Acetylcholine Receptors
More Potently than N-methyl-D-aspartate Receptors in Rat Hip-
pocampal Neurons. J. Pharmacol. Exp. Ther. 2005, 312, 1195–
1205.
(16) Banerjee, P.; Samoriski, G.; Gupta, S. Comments on “Memantine
Blocks {alpha}7* Nicotinic Acetylcholine Receptors More Po-
tently than N-methyl-D-aspartate Receptors in Rat Hippocampal
Neurons”. J. Pharmacol. Exp. Ther. 2005, 313, 928–929.
(17) Ballmaier, M.; Casamenti, F.; Scali, C.; Mazzoncini, R.; Zoli, M.
Rivastigmine Antagonizes Deficits in Prepulse Inhibition Induced
by Selective Immunolesioning of Cholinergic Neurons in Nucleus
Basalis Magnocellularis. Neuroscience 2002, 114, 91–98.
(18) Barnes, C. A.; Meltzer, J.; Houston, F.; Orr, G.; McGann, K.
Chronic Treatment of Old Rats with Donepezil or Galantamine:
Effects on Memory, Hippocampal Plasticity and Nicotinic Recep-
tors. Neuroscience 2000, 99, 17–23.
(19) Van Dam, D.; Abramowski, D.; Staufenbiel, M.; De Deyn, P. P.
Symptomatic Effect of Donepezil, Rivastigmine, Galantamine and
Memantine on Cognitive Deficits in the APP23 Model. Psycho-
pharmacology (Berlin) 2005, 180, 177–190.
(20) Sussman, J. L.; Harel, M.; Frolow, F.; Oefner, C.; Goldman, A. L,
L. T.; Silman, I. Atomic Structure of Acetylcholinesterase from
Torpedo californica: A Prototypic Acetylcholine-Binding Protein.
Science 1991, 253, 872–879.
(21) (a) Tumiatti, V.; Milelli, A.; Minarini, A.; Rosini, M.; Bolognesi,
M. L.; Micco, M.; Andrisano, V.; Bartolini, M.; Mancini, F.;
Recanatini, M.; Cavalli, A.; Melchiorre, C. Structure-Activity
Relationships of Acetylcholinesterase Noncovalent Inhibitors
Based on a Polyamine Backbone. 4. Further Investigation on the
Inner Spacer. J. Med. Chem. 2008, 51, 7308–7312. (b) Bolognesi,
M. L.; Cavalli, A.; Valgimigli, L.; Bartolini, M.; Rosini, M.; Andrisano,
V.; Recanatini, M.; Melchiorre, C. Multi-Target-Directed Drug Design
Strategy: From a Dual Binding Site Acetylcholinesterase Inhibitor to a
Trifunctional Compound Against Alzheimer's Disease. J. Med. Chem.
2007, 50, 6446–6449. (c) Hardy, J.; Selkoe, D. J. The Amyloid
Hypothesis of Alzheimer's Disease: Progress and Problems on the Road
to Therapeutics. Science 2002, 297, 353–356.
(22) Ordentlich, A.; Barak, D.; Kronman, C.; Flashner, Y.; Leitner, M.;
Segall, Y.; Ariel, N.; Cohen, S.; Velan, B.; Shafferman, A. Dissec-
tion of the Human Acetylcholinesterase Active Center Determi-
nants of Substrate Specificity. Identification of Residues Consti-
tuting the Anionic Site, the Hydrophobic Site, and the Acyl Pocket.
J. Biol. Chem. 1993, 268, 17083–17095.
(23) Radic, Z.; Gibney, G.; Kawamoto, S.; MacPhee-Quigley, K.;
Bongiorno, C.; Taylor, P. Expression of Recombinant Acetylcho-
linesterase in a Baculovirus System: Kinetic Properties of Gluta-
mate 199 Mutants. Biochemistry 1992, 31, 9760–9767.
(24) (a) Amit, T.; Avramovich-Tirosh, Y.; Youdim, M. B.; Mandel, S.
Targeting Multiple Alzheimer’s Disease Etiologies with Multi-
modal Neuroprotective and Neurorestorative Iron Chelators.
FASEB J. 2008, 22, 1296–1305. (b) Cavalli, A.; Bolognesi, M. L.;
Minarini, A.; Rosini, M.; Tumiatti, V.; Recanatini, M.; Melchiorre, C.
Multi-Target-Directed Ligands to Combat Neurodegenerative Diseases.
J. Med. Chem. 2008, 51, 347–372.
Acknowledgment. The authors (S.S.C. and K.K.R.) are
thankful to the Council of Scientific and Industrial Research,
New Delhi, for the financial assistance in the form of a
fellowship and to Sophisticated Analytical Instrument Faci-
lity (SAIF) department of Central Drug Research Institute,
Lucknow for the characterization of compounds. The techni-
cal assistance of A. S. Kushwaha, Zahid Ali, and Dayanand
Vishwakarma is also acknowledged.
Supporting Information Available: Synthetic procedures, char-
acterization (NMR, MASS, IR, and elemental analysis) data of
the key intermediates 79-84, and the overlay of compounds
(65-76, 85-93) over the best pharmacophore (Hypo-01). This
material is available free of charge via the Internet at http://
pubs.acs.org.
References
(1) Butters, N.; Delis, D. C.; Lucas, J. A. Clinical Assessment of
Memory Disorders in Amnesia and Dementia. Annu. Rev. Psychol.
1995, 46, 493–523.
(2) (a) Cummings, J. L.; Doody, R.; Clark, C. Disease-Modifying
Therapies for Alzheimer Disease: Challenges to Early Intervention.
Neurology 2007, 69, 1622–1634. (b) Giacobini, E. Cholinesterases:
New Roles in Brain Function and in Alzheimer's Disease. Neurochem.
Res. 2003, 28, 515–522. (c) Terry, A. V.; Buccafusco, J. J. The
Cholinergic Hypothesis of Age and Alzheimer's Disease-Related
Cognitive Deficits: Recent Challenges and Their Implications for
Novel Drug Development. J. Pharmacol. Exp. Ther. 2003, 306,
821–827.
(3) Bazelyansky, M.; Robey, E.; Kirsch, J. F. Fractional Diffusion-
Limited Component of Reactions Catalyzed by Acetylcholinester-
ase. Biochemistry 1986, 25, 125–130.
(4) Ballmaier, M.; Casamenti, F.; Scali, C.; Mazzoncini, R.; Zoli, M.
Rivastigmine Antagonizes Deficits in Prepulse Inhibition Induced
by Selective Immunolesioning of Cholinergic Neurons in Nucleus
Basalis Magnocellularis. Neuroscience 2002, 114, 91–98.
(5) Barnes, C. A.; Meltzer, J.; Houston, F.; Orr, G.; McGann, K.
Chronic Treatment of Old Rats with Donepezil or Galantamine:
Effects on Memory, Hippocampal Plasticity and Nicotinic Recep-
tors. Neuroscience 2000, 99, 17–23.
(6) Van Dam, D.; Abramowski, D.; Staufenbiel, M.; De Deyn, P. P.
Symptomatic Effect of Donepezil, Rivastigmine, Galantamine and
Memantine on Cognitive Deficits in the APP23 Model. Psycho-
pharmacology 2005, 180, 177–190.
(25) Soreq, H.; Seidman, S. Acetylcholinesterase: New Roles for an Old
Actor. Nature Rev. Neurosci. 2001, 2, 8–17.

6504 Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17
Chaudhaery et al.
(26) Johnson, G.; Moore, S. W. The Adhesion Function on Acetylcho-
linesterase is Located at the Peripheral Anionic Site. Biochem.
Biophys. Res. Commun. 1999, 258, 758–762.
(50) Xu, Y.; Colletier, J. -P.; Weik, M.; Jiang, H.; Moult, J.; Silman, I.;
Sussman, J. L. Flexibility of Aromatic Residues in the Active-Site
Gorge of Acetylcholinesterase: X-ray versus Molecular Dynamics.
Biophys. J. 2008, 95, 2500–2511.
(27) Giacobini, E. Cholinesterases: New Roles in Brain Function and in
(51) Bar-On, P.; Millard, C. B.; Harel, M.; Dvir, H.; Enz, A.; Sussman,
J. L.; Silman, I. Kinetic and Structural Studies on the Interaction of
Cholinesterases with the Anti-Alzheimer Drug Rivastigmine. Bio-
chemistry 2002, 41, 3555–3564.
(52) Bartolucci, C.; Siotto, M.; Ghidini, E.; Amari, G.; Bolzoni, P. T.;
Racchi, M.; Villetti, G.; Delcanale, M.; Lamba, D. Structural
Determinants of Torpedo californica Acetylcholinesterase Inhibi-
tion by the Novel and Orally Active Carbamate Based Anti-
Alzheimer Drug Ganstigmine (CHF-2819). J. Med. Chem. 2006,
49, 5051–5058.
Alzheimer’s Disease. Neurochem. Res. 2003, 28, 515–522.
~
(28) Munoz, F. J.; Aldunate, R.; Inestrosa, N. C. Peripheral Binding
Site is Involved in the Neurotrophic Activity of Acetylcholinester-
ase. NeuroReport 1999, 26, 3621–3625.
(29) Sharma, K. V.; Koenigsberger, C.; Brimijoin, S.; Gigbee, J. W.
Direct Evidence for an Adhesive Function in the Noncholinergic
Role of Acetylcholinesterase in Neurite Outgrowth. J Neurosci.
Res. 2001, 63, 165–175.
(30) Blasina, M. F.; Faria, A. C.; Gardino, P. F.; Hokoc, J. N.; Almeida,
O. M.; de Mello, F. G.; Arruti, C.; Dajas, F. Evidence of Non-
cholinergic Function of Acetylcholinesterase During Development
of Chicken Retina as Shown by Fasciculin. Cell Tissue Res. 2000,
299, 173–184.
(53) Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R.
Development and validation of a genetic algorithm for flexible
docking. J. Mol. Biol. 1997, 267, 727–748.
(54) Greig, N. H.; De Micheli, E.; Holloway, H. W.; Yu, Q.-S.; Utsuki,
T.; Perry, T. A.; Brossi, A.; Ingram, D. K.; Deutsch, J.; Lahiri,
D. K.; Soncrant, T. T. The Experimental Alzheimer Drug Phen-
serine: Pharmacodynamics and Kinetics in the Rat. Acta Neurol.
Scand. 2000, 102, 74–84.
(55) Kamal, M. A.; Greig, N. H.; Alhomida, A. S.; Al-Jafari, A. A.
Kinetics of Human Acetylcholinesterase Inhibition by the Novel
Experimental Alzheimer Therapeutic Agent, Tolserine. Biochem.
Pharmacol. 2000, 60, 561–570.
(56) (a) Brufani, M.; Filocamo, L.; Lappa, S.; Maggi, A. New Acetyl-
cholinesterase Inhibitors. Drugs Future 1997, 22, 397–410.
(b) Imbimbo, B. P.; Martelli, P.; Troetel, W. M.; Lucchelli, F.; Lucca,
U.; Thal, L. J. Efficacy and Safety of Eptastigmine for the Treatment of
Patients with Alzheimer's Disease. Neurology 1999, 52, 700–708.
(57) Snape, M. F.; Misra, A.; Murray, T. K.; De Souza, R. J.; Williams,
J. L.; Cross, A. J.; Green, A. R.A Comparative Study in Rats of the
in Vitro and in Vivo Pharmacology of the Acetylcholinesterase
Inhibitors Tacrine, Donepezil and NXX-066. Neuropharmacology
1999, 38, 181–93.
(58) Bolognesi, M. L.; Bartolini, M.; Cavalli, A.; Andrisano, V.; Rosini,
M.; Minarini, A.; Melchiorre, C Design, Synthesis, and Biological
Evaluation of Conformationally Restricted Rivastigmine Analo-
gues. J. Med. Chem. 2004, 47, 5945–5952.
(59) Trabace, L.; Cassano, T.; Steardo, L.; Pietra, C.; Villetti, G.;
Kendrick, K. M.; Cuomo, V. Biochemical and Neurobehavioral
Profile of CHF2819, a Novel, Orally Active Acetylcholinesterase
Inhibitor for Alzheimer’s Disease. J. Pharmacol. Exp. Ther. 2000,
294, 187–194.
(60) Smith, C. P.; Bores, G. M.; Petko, W.; Li, M.; Selk, D. E.; Rush,
D. K.; Camacho, F.; Winslow, J. T.; Fishkin, R.; Cunningham,
D. M.; Brooks, K. M.; Roehr, J.; Hartman, H. B.; Davis, L.;
Vargas, H. M. Pharmacological Activity and Safety Profile of
P10358, A Novel, Orally Active Acetylcholinesterase Inhibitor
for Alzheimer’s Disease. J. Pharmacol. Exp. Ther. 1997, 280,
710–720.
(31) Munoz-Torrero, D. Acetylcholinesterase Inhibitors as Disease
Modifying Therapies for Alzheimer’s Disease. Curr. Med. Chem.
2008, 15, 2433–2455.
(32) Zhang, H. Y.; Yan, H.; Tang, X. C. Non-Cholinergic Effects of
Huperzine A: Beyond Inhibition of Acetylcholinesterase. Cell.
Mol. Neurobiol. 2008, 28, 173–183.
(33) Inestrosa, N. C.; Alvarez, A.; Calderon, F. Acetylcholinesterase is a
Senile Plaque Component that Promotes Assembly of Amyloid
Beta-Peptide into Alzheimer’s Filaments. Mol. Psychiatry 1996, 1,
359–361.
(34) De Ferrari, G. V.; Canales, M. A.; Shin, I.; Weiner, L. M.; Silman,
I.; Inestrosa, N. C. A Structural Motif of Acetylcholinesterase that
Promotes Amyloid Beta-Peptide Fibril Formation. Biochemistry
2001, 40, 10447–10457.
(35) Bartolini, M.; Bertucci, C.; Cavrini, V.; Andrisano, V. Amyloid
Aggregation Induced by Human Acetylcholinesterase: Inhibition
Studies. Biochem. Pharmacol. 2003, 65, 407–416.
(36) Klebe, G.; Abraham, U.; Mietzner, T. Molecular Similarity Indices
in a Comparative Analysis (CoMSIA) of Drug Molecules to
Correlate and Predict Their Biological Activity. J. Med. Chem.
1994, 37, 4130–4146.
(37) Klebe, G.; Abraham, U. Comparative Molecular Similarity Index
Analysis (CoMSIA) to Study Hydrogen-Bonding Properties and to
Score Combinatorial Libraries. J. Comput.-Aided Mol. Des. 1999,
13, 1–10.
(38) Catalyst, release version 4.1; Accelrys Inc.: San Diego, CA, 2006.
(39) Chaudhaery, S. S.; Roy, K. K.; Saxena, A. K. Consensus Super-
iority of the Pharmacophore-Based Alignment, Over Maximum
Common Substructure (MCS): 3D-QSAR Studies on Carbamates
as Acetylcholinesterase Inhibitors. J. Chem. Inf. Model. 2009, 49,
1590–1601.
(40) Roy, K. K.; Dixit, A.; Saxena, A. K. An Investigation of Structu-
rally Diverse Carbamates for Acetylcholinesterase (AChE) Inhibi-
tion Using 3D-QSAR Analysis. J. Mol. Graphics Modell. 2008, 27,
197–208.
(61) Mustazza, C.; Borioni, A.; Giudice, M. R. D.; Gatta, F.; Ferretti,
R.; Meneguz, A.; Volpe, M. T.; Lorenzini, P. Synthesis and
Cholinesterase Activity of Phenylcarbamates Related to Rivastig-
mine, A Therapeutic Agent for Alzheimer’s Disease. Eur. J. Med.
Chem. 2002, 37, 91–96.
(41) Lopez-Rodriguez, M. L.; Benhamu, B.; de la Fuente, T.; Sanz, A.;
Pardo, L. A Three-Dimensional Pharmacophore Model for 5-Hy-
droxytryptamine 6 (5-HT6) Receptor Antagonists. J. Med. Chem.
2005, 48, 4216–4219.
€
(42) Guner, O.; Clement, O.; Kurogi, Y. Pharmacophore Modeling and
(62) (a) Decker, M. Acetylcholinesterase inhibitors based on carbamic
acid quinolin-6-yl esters. Expert Opin. Ther. Patents 2007, 17, 733–
736. (b) Shakya, N.; Fatima, Z.; Nath, C.; Saxena, A. K. Substituted
Carbamic Acid Quinolin-6-yl Esters Useful as Acetylcholinesterase
Inhibitors. U.S. Patent 0142335, June 2006. (c) Marsais, F; Bohn, P.;
Fur, N. L.; Levacher, V. Heterocyclic Compounds, their Preparation and
their Use as Medicaments, in Particular as Anti-Alzheimer Agents.
U.S. Patent 062279, Mar 2009.
Three Dimensional Database Searching for Drug Design Using
Catalyst: Recent Advances. Curr. Med. Chem. 2004, 11, 2991–3005.
(43) Kim, H. -J.; Doddareddy, M. R.; Choo, H.; Cho, Y. S.; No, K. T.;
Park, W. K.; Pae, A. N. New Serotonin 5-HT6 Ligands from
Common Feature Pharmacophore Hypotheses. J. Chem. Inf.
Model. 2008, 48, 197–206.
(44) (a) Riddle, D. L. C. elegans II; Cold Spring Harbor Laboratory Press:
Plainview, NY, 1997. (b) Kaletta, T.; Hengartner, M. O. Finding
function in novel targets: C. elegans as a model organism. Nature
Rev. Drug Discovery 2006, 5, 387–398.
(45) Sieburth, D.; Ch’ng, Q.; Dybbs, M.; Tavazoie, M.; Kennedy, S.;
Wang, D.; Dupuy, D.; Rual, J. F.; Hill, D. E.; Vidal, M.; Ruvkun,
G.; Kaplan, J. M. Systematic analysis of genes required for synapse
structure and function. Nature 2005, 436, 510–517.
(46) Mahoney, T. R.; Luo, S.; Nonet, M. L. Analysis of synaptic
transmission in Caenorhabditis elegans using an aldicarb-sensitivity
assay. Nature Protoc. 2006, 1, 1772–1777.
(47) Nguyen, M.; Alfonso, A.; Johnson, C. D.; Rand, J. B. Caenorhab-
ditis elegans mutants resistant to inhibitors of acetylcholinesterase.
Genetics 1995, 140, 527–535.
(48) Debnath, A. K. Generation of Predictive Pharmacophore Models
for CCR5 Antagonists: Study with Piperidine- and Piperazine-
Based Compounds as a New Class of HIV-1 Entry Inhibitors.
J. Med. Chem. 2003, 46, 4501–4515.
€
(63) CombiGlide, version 2.5; Schrodinger, LLC: New York, 2009.
€
(64) Schro€dinger Software, version 8.0; Schrodinger, LLC: New York,
2005.
(65) Ellman, G. L.; Courtney, K. D.; Andres, V., Jr.; Feather-Stone,
R. M. A New and Rapid Colorimetric Determination of Acetyl-
cholinesterase Activity. Biochem. Pharmacol. 1961, 7, 88–95.
(66) Brioni, J. D.; Hock, F. J.; McGaugh, J. J. Drug Effects on Learning
and Memory. In, Drug Discovery and Evaluation: Pharmacological
Assays; Vogel, G. H., Vogel, W. H., Eds.; Springer Verlag : New York,
1997; pp 335-336.
(67) Das, A.; Shankar, G.; Nath, C.; Pal, R.; Singh, S.; Singh, H. K.
AComparativeStudyinRodentsofStandardizedExtractsofBacopa
Monniera and Ginkgo Biloba: Anticholinesterase and Cognitive
Enhancing Activities. Pharmacol., Biochem. Behav. 2002, 73, 893–900.
(68) Todeschini, R, Consonni, V. DRAGON Software, version 1.11;
Milano Chemometrics and QSAR Research Group: Milano, Italy, 2001.
(69) Accelrys Discovery Studio, version 2.0; Accelrys Inc.: San Diego, CA,
2007.
(49) Hirashima, A.; Shigeta, Y.; Eiraku, T.; Kuwano, E. Inhibitors of
Calling Behavior of Plodia interpunctella. J. Insect Sci. 2003, 3, 4–13.

Article
Journal of Medicinal Chemistry, 2010, Vol. 53, No. 17 6505
(70) Yu, Q.-S.; Pei, X.-F.; Holloway, H. W.; Greig, N. H. Total
Syntheses and Anticholinesterase Activities of (3aS)-N(8)-Norphy-
sostigmine, (3aS)-N(8)-Norphenserine, Their Antipodal Isomers,
and Other N(8)-Substituted Analogues. J. Med. Chem. 1997, 40,
2895–2901.
(71) Yu, Q.-S.; Greig, N. H.; Holloway, H. W. Syntheses and Antic-
holinesterase Activities of (3aS)-N1,N8-Bisnorphenserine, (3aS)-N1,
N8-Bisnorphysostigmine, Their Antipodal Isomers, and Other Poten-
tial Metabolites of Phenserine. J. Med. Chem. 1998, 41, 2371–2379.
(72) Yu, Q.-S.; Holloway, H. W.; Utsuki, T.; Brossi, A.; Greig, N. H.
Synthesis of Novel Phenserine-Based-Selective Inhibitors of Butyryl-
cholinesterase for Alzheimer’s Disease. J. Med. Chem. 1999, 42,
1855–1861.
(73) Yu, Q.-S.; Holloway, H. W.; Utsuki, T.; Brossi, A.; Greig, N. H.
Methyl Analogues of the Experimental Alzheimer Drug Phenser-
ine: Synthesis and Structure-Activity Relationships for Acetyl-
and Butyrylcholinesterase Inhibitory Action. J. Med. Chem. 2001,
44, 4062–4071.
(74) Luo, W.; Yu, Q.-S.; Zhan, M.; Parrish, D.; Deschamps, J. R.;
Kulkarni, S. S.; Holloway, H. W.; Alley, G. M.; Lahiri, D. K.;
Brossi, A.; Greig, N. H. Novel Anticholinesterases Based on the
Molecular Skeletons of Furobenzofuran and Methanobenzo-
dioxepine. J. Med. Chem. 2005, 48, 986–994.
cophore Perception, Development, and Use in Drug Design; Guner,
O. F., Ed.; International University Line: La Jolla, CA, 2000;
pp 171-189.
(76) Prathipati, P.; Saxena, A. K. Characterization of β3-Adrenergic
Receptor: Determination of Pharmacophore and 3D QSAR Model
for β3-Adrenergic Receptor Agonism. J. Comput.-Aided Mol. Des.
2005, 19, 93–110.
(77) Sprague, P. W. Automated Chemical Hypothesis Generation and
Database Searching with Catalyst. In Perspectives in Drug Dis-
covery and Design; M€uller, K., Ed.; ESCOM Science Publishers BV:
Leiden, The Netherlands, 1995; pp 1-21.
(78) Brooks, B. R.; Bruccoleri, R. E.; Olafson, B. D.; States, D. J.;
Swaminathan, S.; Karplus, M. CHARMm: A Program for Macro-
molecular Energy, Minimization, and Dynamics Calculations.
J. Comput. Chem. 1983, 4, 187–217.
€
(79) Sutter, J.; Guner, O. F.; Hoffman, R.; Li, H.; Waldman, M. Effect
of Variable Weight and Tolerances on Predictive Model Genera-
tion. In Pharmacophore Perception, Development, and Use in Drug
Design; Guner, O. F., Ed.; International University Line: La Jolla, CA,
1999; pp 501-511.
(80) Brenner, S. The genetics of Caenorhabditis elegans. Genetics 1974,
77, 71–94.
(81) Stiernagle, T. Maintenance of C. elegans. In C. elegans: A Practical
Approach; Hope, I., Ed.; Oxford University Press: New York, 1999;
pp 51-67.
(75) Li, H.; Sutter, J.; Hoffman, R. HypoGen: An Automated System
for Generating 3D Predictive Pharmacophore Models. In Pharma-