Rational design of 2-pyrrolinones as inhibitors of HIV-1 integrase

Article abstract of DOI:10.1016/j.bmcl.2011.09.054

HIV-1 integrase is an essential enzyme for viral replication and a validated target for the development of drugs against AIDS. With an aim to discover new potent inhibitors of HIV-1 integrase, we developed a pharmacophore model based on reported inhibitor

Full text of DOI:10.1016/j.bmcl.2011.09.054

Bioorganic & Medicinal Chemistry Letters 21 (2011) 6724–6727
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Bioorganic & Medicinal Chemistry Letters
journal homepage: www.elsevier.com/locate/bmcl
Rational design of 2-pyrrolinones as inhibitors of HIV-1 integrase
Kaiqing Ma a, Penghui Wang a, Wei Fu a, Xiaolong Wan b, Lu Zhou a, Yong Chu a, , Deyong Ye a,
⇑
⇑
a Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, PR China
b Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, PR China
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 30 May 2011
Revised 1 September 2011
Accepted 15 September 2011
Available online 20 September 2011
HIV-1 integrase is an essential enzyme for viral replication and a validated target for the development of
drugs against AIDS. With an aim to discover new potent inhibitors of HIV-1 integrase, we developed a
pharmacophore model based on reported inhibitors embodying structural diversity. Eight compounds
of 2-pyrrolinones fitting all the features of the pharmacophore query were found through the screening
of an in-house database. These candidates were successfully synthesized, and three of them showed
strand transfer inhibitory activity, in which, one compound showed antiviral activity. Further mapping
analysis and docking studies affirmed these results.
Keywords:
HIV integrase
Inhibitors
Ó 2011 Elsevier Ltd. All rights reserved.
Pharmacophore
Structure–activity relationship (SAR)
HIV-1 integrase (IN) is an important enzyme involved in medi-
ating the full integration of proviral DNA into the human genome,
which is essential for retroviral replication.1 This step consists of
two reactions with DNA. (1) The first step is referred to as 30-pro-
cessing which includes the endonucleolytic cleavage of the 30-ends
of the viral DNA. (2) The second step, called strand transfer, results
in the ligation of the 30-processed proviral DNA into host chromo-
some. IN is an attractive target because no known human enzyme
exists with similar activity.2 Therefore, the addition of an IN inhib-
itor to existing components of antiretroviral therapy is expected to
improve the outcome of therapy by potential synergism without
exacerbating toxicity.3
The search for HIV-1 IN inhibitors has spanned more than a dec-
ade.3–8 The b-diketo acids have been identified as strand transfer
specific IN inhibitors and have been shown to be ‘druggable’.9
Many bioisosteres of b-diketo acids have been developed for years.
Some of them, such as 12 (S1360),2 2 (L870810),10 9 (GS-9137),11
6 (GSK-364735)12 shown in Figure 1 have already entered into
clinical phase. Moreover, Raltegravir, 4 (MK-0518)13 has been
recently approved by FDA as the first IN inhibitor. However, the
inevitable emergence of drug resistant substitutions in IN will
require a constant effort toward the discovery of novel inhibitors
to maintain a therapeutic advantage over the virus. Our laborato-
ries focus on finding new potent inhibitors of 30-processing and
strand transfer especially the new selective strand transfer
inhibitor.
diversity have been successfully discovered by this approach.11,16–19
In this study, we focus on the development of the pharmacophore
model, which was constructed based on the chemical features of fif-
teen reported potent IN inhibitors. In fact, several HIV IN pharmaco-
phores have already been published.3,16,17 However, to the best of
our knowledge, no attempts to build the model on these potent b-
diketo acid (DKA)-like strand-transfer-selective inhibitors have
been reported. The resulting model can generate the common fea-
tures of these effective inhibitors, and it is further utilized to predict
novel bioactive molecules through the virtual screening of an in-
house database. Eight representatives selected from a group of hits
have been successfully synthesized for a biological assay, and three
of them exhibit strand transfer inhibition in vitro.
Fifteen compounds (Fig. 1) for the training set were selected
from literatures,19–22 which was based on the principles of struc-
tural diversity and certain coverage of activity range. These com-
pounds were provided to the HipHop module to generate the
common features of the pharmacophore model. The chemical struc-
tures of these drugs/candidates were sketched using ISIS/Draw
(MDL Informations Systems, Inc., San Leandro, CA, USA) as shown
in Table 1, and their 3D structures with hydrogens were converted
by CORINA (a fast generation of high-quality 3D molecular models,
Molecular Networks, http://www.molecular-networks.com/prod-
ucts/corina). Atomic types and bond types of these compounds
were inspected and modified manually, and Gasteiger charges were
assigned to them. Furthermore, the structures were optimized by
means of molecular mechanics, using a Tripos force field encoded
in SYBYL v6.9 (Tripos Associates, St. Louis, MO, USA). A set of ener-
getically reasonable conformations were built within the CATALYST
(Version 4.0; Accelrys, Inc., 2003) CatConf module using the Poling
Algorithm.23 The ‘Best conformer generation’ option was used with
Pharmacophore modeling is one of the widely used analog-based
drug design methods.14,15 Some IN inhibitors with structural
⇑
Corresponding authors.
E-mail address: cy110@fudan.edu.cn (Y. Chu).
0960-894X/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.bmcl.2011.09.054

K. Ma et al. / Bioorg. Med. Chem. Lett. 21 (2011) 6724–6727
6725
O
O
H
N
N
N
O S
O
N
Cl
O
N
N
O
OH
NH
OH
O
NH
N
N
H
N
H
N
O
N
N
N
N
O
F
F
HN
N
N
N
OH O
2
OH O
F
F
4
1
3
OH O
OH O
OH O
F
N
S
N
F
N
OH
Cl
N
H
N
H
N
S
N
H
N
N
H
O
N
O
N
O
N
O
F
O
OH
S
O
O
O2N
7
8
5
6
F
O
O
F
O
N
O
HO
O
N
O
Cl
OH
OH
OH
OH
HN
N
N
O
F
Cl
N
H
O
O
F
HO
O
10
9
11
12
O
O
O
S
N
S
OH
O
N
S
N
OH
O
N
OH
N
O
O2N
O
O
O
O
Cl
O
F
15
14
13
Figure 1. Chemical structures of the 15 training set compounds.
Table 1
The IN inhibitory activities and the pharmacophore fit values of the 2-pyrrolinone derivatives
O
OH
O
R1
R2
N
R3
a
Compound
R1
R2
R3
Inhibition of HIV-1 integrase (IC50
)
Pharmacophore fit valueb
30-Processing (
l
M)
Strand transfer (lM)
20
21
22
23
24
25
26
27
Ph
Ph
2-Py
2-Py
Ph
Ph
Ph
Ph
4-NO2-Ph–
Ph
Ph
89
80
77
>100
>100
>100
>100
>100
44
45
40
>100
100
>100
>100
>100
2.47
2.98
2.97
2.59
2.97
2.23
2.76
2.26
–(CH2)2-Ph-4-OMe
–(CH2)2-Ph-4-OMe
–Me
–(CH2)2-Ph-4-OMe
–Me
3-Cl-Ph
3-Cl-Ph
4-OH-Ph
3-Cl-Ph
4-NO2-Ph–
Ph
–Me
–CH2CH(CH2)2
a
HIV-1 IN inhibitory activity was measured according to the procedure described in Experimental. Values are means of three determinations and deviation from the mean
is <10% of the mean value.
b
The overall fit value is 4.
a 20 kcal/mol energy cut off. Default settings were used for the
other parameters.
structure multiconformers, most of which are based on an avail-
able compound library.
The top ranked pharmacophore model (Hypo1) had the best
predictive power and statistical significance and was characterized
by the lowest total cost value (82.4009), the highest cost difference
(100.0801), the lowest RMSD (0.5003), and the best correlation
coefficient (0.9785). The hypothesis (Hypo1) (shown in Fig. 2A)
has four features, namely a hydrophobic aromatic feature (HRA)
and three H-bond acceptors (HBA). Three excluded volumes were
used to define the entrance of the active site. Compound 6
(GSK-364735) was mapped onto Hypo1 with high pharmacophore
fit values (3.99) between the key chemical features of the com-
pound and the pharmacophore features of Hypo1 (Fig. 2B).
The Hypo1 model was applied to an in-house database as a
query to find the compounds that fitted all the above features.
Our in-house database is a chemical database with searchable 3D
Virtual screening was carried out in CATALYST after the library
compounds were minimized to the closest local minimum. A total
of 89 2-pyrrolinone compounds as primary hits were obtained by
use of a ‘best fit’. Among them, compounds 20–27 show pharmaco-
phore fit values ranging from 2.23 to 2.98 when one feature of the
Hypo1 is allowed to be missing in the mapping process. In order to
find more active compounds based on the corrected pharmaco-
phore model, several representative hits selected on the basis of
the pharmacophore fit value were synthesized and tested for their
HIV-1 IN inhibitory activities.
Synthetic approaches for preparation of the hit compounds are
depicted in Scheme 1. This strategy is concise with just a two-step
process. Firstly, condensation of acetophenone (16) or 1-(pyridin-
2-yl) ethanone (17) with diethyl oxalate in the presence of sodium

6726
K. Ma et al. / Bioorg. Med. Chem. Lett. 21 (2011) 6724–6727
Figure 2. Pharmacophore models. (A) The best-ranked HipHop pharmacophore model (Hypo1). The pharmacophore with four features are color coded as follows: H-bond
acceptors (HBA) as green and hydrophobic aromatic (HRA) features as blue. Interfeature distances are given in Angstroms. (B) The mapping plot of HIV-1 integrase inhibitor 6
from the training set onto best HipHop pharmacophore model (Hypo1).
superior to that of compounds substituted with methyl or isobutyl
(23, 27). The R2 group seems also an impact factor for the activity
(21 vs 24), an observation that warrants further study. For inhibi-
tion of the 30-processing reaction, the compound with pyridinyl as
R1 group (22) is more potent than the compound with a phenyl
substitution (20).
OH
R2
O
OH O
O
O
O
R1
+
a
O
O
b
O
R1
R1
N
O
O
R3
18 R1= Ph
16 R1 = Ph
20 - 27
19 R1 = 2-Py
17 R1 = 2-Py
R
1 = Ph-, 2-Py
R2 = 4-NO2-Ph-, Ph-,
3-Cl-Ph-, 4-OH-Ph-
In order to rationalize the obtained results, we mapped the com-
mon feature hypothesis 1 (Hypo1) onto compound 21. The mapping
plot showed a good agreement between chemical features of the
compound and pharmacophoric features of Hypo1 (Fig. 3A). The
benzene ring linked to the nitrogen atom and two carbonyl groups
were well mapped onto the HRA, HBA1, and HBA3, respectively.
Furthermore, we docked compound 21 into the IN active site ab-
stracted from the IN crystal structures with the PDB entry code
3L2V (Fig. 3B)24 to build the binding model. Docking was performed
with GOLD 4.1.2 (The Cambridge Crystallographic Data Centre:
Cambridge, UK). The carbonyl groups of the ligand formed hydro-
gen bonds with the SER184 and HIS213. The binding mode of com-
pound 21 in the active site was very close to that observed in the
crystal structure of Raltegravir with IN.24 Consequently, these
observations provided an excellent explanation for the in vitro
inhibitory activity of compounds 20–22.
R
3 = Ph-, -Me, -CH2(CH3
-(CH2)2-Ph-4-OMe
)
2
Scheme 1. General route for the synthesis of hits of 2-pyrrolinones. Reagent and
conditions: (a) NaH, toluene, rt, 90%; (b) R3-NH2, R2-CHO, THF, rt, 90%.
hydride furnish 2,4-diketo esters (18, 19). At the second step, the
objects of 2-pyrrolinones (20–27) are successfully prepared via
Mannich reaction following intramolecular ring closure in one
pot just by treatment of such resulting esters with various amines
and aldehydes.
We examined the ability of the eight hits to inhibit IN catalytic
activities using in vitro assays. The chemical structures, IN inhibi-
tory activities, and the pharmacophore fit values of these hits are
shown in Table 1.
Three compounds (20, 21, and 22) show partial selectivity to-
ward the IN strand transfer. This result agreed with the general be-
lief that the character of acyl-diketo can function as a selective
strand transfer inhibitor. The presence of a bulky hydrophobic
group at the nitrogen atom seems to be important due to the lack
of activity of derivatives with short and linear substituents in this
position (20 vs 26, 21 vs 27, and 22 vs 23). The activity of the
compound substituted with phenyl at the nitrogen atom (20) is
Although the IN inhibitory activity was not as potent as we ex-
pected, compound 22 exert potent inhibition on the stimulated
luciferase enzymatic activity of HIV in TZM-bl cells with an EC50
value of 0.317 lM (Table 2). More importantly, this compound pos-
sessed low cytotoxicities with a SI value of 83.
A pharmacophore-based virtual screening of the in-house li-
brary was performed to identify HIV-1 IN inhibitors with novel
Figure 3. The mapping plot of the pharmacophore model and the plot of the docking model. (A) Mapping of common feature hypothesis 1 (Hypo1) onto compounds 21,
which explained HIV-1IN inhibitory activity of 21. (B) Predicted binding orientation of compound 21 inside the HIV-1 IN active site.

K. Ma et al. / Bioorg. Med. Chem. Lett. 21 (2011) 6724–6727
6727
Table 2
Supplementary data
The antiviral effect of the 2-pyrrolinone derivatives
Compound
Anti-HIV-1 activity
SIc
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.bmcl.2011.09.054.
a
b
EC50
(l
M)
CC50 (lM)
20
21
22
24
N/A
N/A
790
830
26.6
459
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83.7
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scaffolds. Firstly, the common feature of six clinical candidates
was visualized by generating a pharmacophore model. Secondly,
based on the application of resulting pharmacophore model, eight
compounds with a common 2-pyrrolinones core were selected
from 89 primary hits to be synthesized in a concise strategy. Their
catalytic IN inhibitory activities were tested as well. The com-
pounds 20, 21, and 22 exhibit strand transfer inhibitory activity
with IC50 values of 44, 45, and 40
the best antiviral effect was exhibited by compound 22 with an
EC50 value of 0.317 M. The mapping analysis and the docking
lM, respectively. Furthermore,
l
study showed that the p-methoxylphenyl moiety was well docked
in the vicinity of the aromatic pocket, forming hydrophobic inter-
actions. The analysis is well supported by the biological activities.
These results provide useful information for the design of new po-
tent antiviral agents. Further structural optimization based on this
pharmacophore model and the potent inhibitor structure is in
progress.
Acknowledgments
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232.
We thank Nouri Neamati, associate professor of University of
Southern California, for performing the biological assays, and
Huifang Liu for the help with the work of pharmacophore modeling
and docking. We gratefully acknowledge Xiaoyan Zhang, professor
of Fudan University, for the antiviral test. We also acknowledge the
financial support from National Drug Innovative Program (Grant
No. 2009ZX09301-011).