Discovery of HIV-1 integrase inhibitors: Pharmacophore mapping, virtual screening, molecular docking, synthesis, and biological evaluation

Article abstract of DOI:10.1111/cbdd.12207

HIV-1 integrase enzyme plays an important role in the life cycle of HIV and responsible for integration of virus into human genome. Here, both computational and synthetic approaches were used to design and synthesize newer HIV-1 integrase inhibitors. Pharmacophore mapping was performed on 20 chemically diverse molecules using DISCOtech, and refinement was carried out using GASP. Ten pharmacophore models were generated, and model 2, containing four features including two donor sites, one acceptor atom, and one hydrophobic region, was considered the best model as it has the highest fitness score. It was used as a query in NCI and Maybridge databases. Molecules having more than 99% Q _fit value were used to design 30 molecules bearing pteridine ring and were docked on co-crystal structure of HIV-1 integrase enzyme. Among these, six molecules, showing good docking score compared with the reference standards, were synthesized by conventional as well as microwave-assisted methods. All compounds were characterized by physical and spectral data and evaluated for in vitro anti-HIV activity against the replication of HIV-1 (IIIB) in MT-4 cells. The used approach of molecular docking and anti-HIV activity data of designed molecules will provide significant insights to discover novel HIV-1 Integrase Inhibitors. Computer-aided drug design approaches like pharmacophore mapping, virtual screening, and molecular docking were used to design novel compounds bearing pteridine ring system. Designed compounds were synthesized by conventional and microwave-irradiated methods showing advantage of MWI method. All synthesized compounds were evaluated as HTV-1 integrase inhibitors and further explored to discover novel HIV-I integrase inhibitors.

Full text of DOI:10.1111/cbdd.12207

Received Date : 28-Feb-2013
Revised Date : 18-Jul-2013
Accepted Date : 12-Aug-2013
Article type
: Research Article
Discovery of HIV-1 integrase Inhibitors: Pharmacophore Mapping, Virtual
Screening, Molecular Docking, Synthesis and Biological Evaluation
Hardik Bhatt 1 *, Paresh Patel 2, Christophe Pannecouque 3
1 Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma University,
Ahmedabad 382 481. India.
2 Department of Pharmaceutical Chemistry, L.J. Institute of Pharmacy, L.J. Campus, S.G.
Highway, Ahmedabad- 382 210. India.
3 Rega Institute for Medical Research, Minderbroedersstraat 10, B-3000 Leuven. Belgium.
Corresponding Author:
Hardik G. Bhatt, Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma
University, S. G. Highway, Chharodi, Ahmedabad 382 481. India.
Contact No.: +91 2717 241900-04 Fax No.: +91 2717 241916
E.mail: hardikbhatt23@hotmail.com
Abstract
HIV-1 integrase enzyme plays an important role in life cycle of HIV and responsible for
integration of virus into human genome. Here, both computational and synthetic approaches
were used to design and synthesize newer HIV-1 integrase inhibitors. Pharmacophore
mapping was performed on 20 chemically diverse molecules using DISCOtech and
refinement was carried out using GASP. Ten pharmacophore models were generated and
model 2, containing 4 features including 2 donor sites, 1 acceptor atom and 1 hydrophobic
region, was considered the best model as it has highest fitness score. It was used as a query in
NCI and Maybridge databases. Molecules having more than 99% Qfit value were used to
design 30 molecules bearing pteridin ring and were docked on co-crystal structure of HIV-1
integrase enzyme. Among these, 6 molecules, showing good docking score compared to the
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reference standards, were synthesized by conventional as well as microwave assisted
methods. All compounds were characterized by physical and spectral data and evaluated for
in vitro anti-HIV activity against the replication of HIV-1 (IIIB) in MT-4 cells. The used
approach of molecular docking and anti-HIV activity data of designed molecules will provide
significant insights to discover novel HIV-1 Integrase Inhibitors.
Keywords: HIV-1 integrase, Pharmacophore mapping, Virtual screening, Molecular docking,
Pteridin, Tripos.
Over the past 30 years, Acquired Immuno Deficiency Syndrome (AIDS), which is caused by
Human Immunodeficiency Virus (HIV), has encouraged research efforts into successful
discovery of a number of effective and clinically efficacious therapeutic agents (1). The
common antiretroviral drugs target viral entry (co-receptor antagonists and fusion inhibitors),
reverse transcription (RT; nucleoside and non-nucleoside inhibitors of the viral reverse
transcriptase), integration (IN: integrase inhibitors) and viral maturation (PR: protease
inhibitors) (2). The highly active antiretroviral therapy (HAART) is currently in use as a
standard therapeutic approach. Integrase (IN) is an essential enzyme in the life cycle of HIV-1
virus, the causative agent of acquired immunodeficiency syndrome (AIDS). Inhibitors of
HIV-1 integrase (IN) have emerged as a promising new class of therapeutics for the treatment
of AIDS as only two drugs are approved as integrase inhibitors (Raltegravir and Elvitegravir).
Integrase functions by catalyzing the insertion of virally transcribed DNA into the host
genome in two-stage processes (3). In the first step, termed ‘30-processing’ (30-P), a GT
dinucleotide pair is cleaved from the viral cDNA 30-terminus. Placement of this processed
cDNA into the host genome occurs in a second step referred to as ‘strand transfer’ (ST) (4).
Although IN is regarded as a potentially attractive target for anti-HIV drug development,
discovery of clinically relevant inhibitors is still challenging (5). Many potent inhibitors
against the IN-catalyzed 3′-processing (3′-P) and strand transfer (ST) reactions were initially
developed using in vitro IN assays. Although a large number of structurally diverse IN
inhibitors were reported like diketo acids, quinolines, purine, pyrimidine, etc., the need to
develop novel molecules still persist (6).
A “pharmacophore” concept was introduced by Paul Ehrlich in the early 1900s referred to a
“molecular framework that carries (phoros) the essential features responsible for a drug's
(pharmacon) biological activity”. A pharmacophore (pharmacophore model / pharmacophoric
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pattern) can be considered as the ensemble of steric and electrostatic features of different
compounds which are necessary to ensure optimal supramolecular interactions with a specific
biological target structure and to trigger or to block its biological response (7,8). There are
two methods to derive a pharmacophore, first is ligand based pharmacophore mapping which
involves the identification of pharmacophore features by alignment of training set molecules
(9). Ligand based pharmacophore approach provides an advantage over structure based
pharmacophore to generate pharmacophore model without knowledge of receptor. Second
approach is known as structure based pharmacophore modeling which is the best method of
pharmacophore modeling. Structure based pharmacophore is based on the three-dimensional
structure of the binding site of the target. Pharmacophore is obtained directly from complex
structure of ligand and receptor (10,11). Pharmacophore mapping is followed by substructure
searching which is considered as the best option to find a range of chemical structures with
viable features by using different databases (12-14).
Molecular docking is proved as to be an important tool of computational chemistry. Most
computational docking applications study binding modes of ligands by utilizing prior
knowledge of binding sites. Docking is a computational method which predicts the preferred
orientation of one molecule to a second when bound to each other to form a stable complex.
Docking process involves two steps: first step is prediction of ligand conformation as well as
its position and orientation in binding sites which is also known as pose while second step is
assessment of binding affinity. Knowledge of the preferred orientation may be used to predict
the strength of association or binding affinity between two molecules using a scoring
function. Docking has been widely used to suggest the binding modes of protein inhibitors.
Most of the docking algorithms are able to generate a large number of possible stable
conformers of the molecules. Thus, they also require a means to score each structure to
identify those which are of the greatest interest (15).
The synthesis of pteridin moiety and its derivatives are of considerable interest. Pteridin
derivatives are known to exhibit a variety of biological effects viz. immunosuppressive, anti-
inflammatory, antibacterial, anticancer, etc. (16-18). The therapeutic importance of this
nucleus has enthused us to develop selective molecules in which substituent could be
arranged in a pharmacophoric pattern to display high order pharmacological activity. In last
few years, the use of microwave irradiation in organic reactions as well as in the synthesis of
pharmaceutical compounds is rapidly increasing due to short duration of time of reaction and
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high yield with purity (19,20). This newer technology is emerging as an alternative energy
source capable enough to complete chemical reactions within few seconds or minutes.
Here, we reported design, synthesis and evaluation of novel pteridin derivatives as anti-HIV
agents. The results obtained from molecular docking studies and experimental anti-HIV
activity data will provide noteworthy considerations to discover novel HIV-1 Integrase
Inhibitors.
Materials and Methods
Pharmacophore Mapping
The pharmacophore mapping and database searching were performed using Sybyl X1.2
software from Tripos Inc., St. Louis, MO, USA (21,22). All compounds were sketched using
SKETCH function of Sybyl X1.2. Partial atomic charges were calculated by the Gasteiger
Huckel method and energy minimization were performed using the Tripos force field with a
distance-dependent dielectric and the Powell conjugate gradient algorithm convergence
criterion of 0.01 kcal/mol Å. Twenty molecules were selected to generate seven (7)
pharmacophore models using DISCOtech from previously published research articles in
which HIV-1 integrase inhibitory activity of compounds were determined by using strand
transferase inhibitory assay (23-38). Chemical structure, chemical class and activity of all 20
compounds are shown in Table-1. Genetic algorithm similarity program (GASP) was used to
refine the generated model which uses a genetic algorithm to identify common feature of
different molecules. The purpose of this step is to align the molecules using the 3D query as a
constraint. Using a model from the previous run as input, GASP will attempt to flatten out the
rings and generate the spatial query. By performing a GASP alignment, it is assured that
conformations which were used as input will appear at least once in optimization. If they are
the fit conformations, they will be retained and propagated to the next iteration of the genetic
algorithm. All parameters were kept as default except population size (125), mutation weight
(96), fitness increment (0.02) and number of alignment (10).
Pharmacophore Validation
The generated pharmacophore model should be statistically significant, and should identify
active compound from a database. Therefore, the derived pharmacophore was validated using
statistical methods like sensitivity and specificity by screening a database containing 7 known
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inhibitors and 54 test inhibitors and was also validated by Receiver Operating Characteristic
(ROC) analysis and Güner-Henry (GH) Scoring method.
Statistical Validation
Sensitivity and specificity are statistical measures of the performance of a binary
classification test, also known in statistics as classification function. Sensitivity measures the
proportion of actual positives which are correctly identified as such. Sensitivity relates to the
test's ability to identify positive results. Specificity measures the proportion of negatives
which are correctly identified.
Sensitivity = Number of true positives / (Number of true positives + Number of false
negatives)
Specificity = Number of true negatives / (Number of true negatives + Number of false
positives)
Receiver Operating Characteristic (ROC) Analysis
A generated pharmacophore model was validated by assessing its ability to select diversified
HIV-1 integrase inhibitors from a large list of decoys (molecules which are presumably
inactive against the examined target) employing ROC analysis (39). Therefore, it was
necessary to prepare database that contains combination of active and decoy compounds. The
testing set include 54 active compounds along with their 20 conformers which were
generated using genetic algorithm based global optimizer to find the low energy
conformations of molecules. A total of 1080 conformers of 54 compounds constitute a test
set. Decoy database contained 200 inactive compounds. The ROC curve is a function of
specificity (Sp) versus sensitivity (Se), and the area under the ROC curve (AUC) value is the
important way of measuring the performance of the test.
Güner-Henry (GH) Scoring Method:
The GH score method (39) has been successfully applied to quantify model selectivity (best
model), accuracy of hits and the recall of actives from a molecule dataset consisting of known
actives and inactives. GH scoring methodology can be successfully applied for selection of
model based on quantitative data. The GH score ranges from 0 to 1, where GH score near to 1
indicate ideal model and it should be above 0.7. It is one of the most relevant methods as it
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takes into account of both percentage yields of actives in database which is known as %Y
recall and percentage ratio of actives in hit list which is known as %A precision. The GH
scoring method was applied to the previously mentioned 54 known inhibitors of HIV-1
integrase with their 1080 conformers and the decoy dataset of 200 molecules to validate the
pharmacophore model. The GH analysis was done by calculating the following parameters:
(a) the percent active (%A), which represents the coverage of activity space by the model, (b)
the percent yield (%Y), which is a measure of the selectivity of the model, (c) the enrichment
factor (E), and (d) the GH score. These variables are determined using information derived
from the total number of compounds in the drug database (D), the number of actives in the
database (A), the number of actives retrieved by the model (Ha), and the total number of hits
retrieved by the model (Ht). The above mentioned parameters are calculated using following
equation.
%A = (Ha/A) * 100
%Y = (Ha/Ht) * 100
E = (Ha/Ht) / (A/D)
GH = [{Ha(3A + Ht)}/{4*Ht*A}] * [1-{(Ht-Ha)/(D-A)}]
Virtual Screening
After 3D-pharmacophore model generation, NCI and Maybridge Hit Finder databases were
used for substructure searching and to locate the appropriate binding orientations and
conformations of these substructures with HIV-1 integrase enzyme. The hits from both
databases were filtered by applying Lipinski's rule-of-five (40) followed by molecular
docking studies using Surflex-Dock. A total number of 274 molecules having Qfit value more
than 99% were used for further studies. The major ring systems obtained were
dihydropyrimidine, piperidine-benzoate, quinoxalin-carboxylic acid, sulphonapthalic acid,
piperidine-anthracin dione, pyrolidin-benzoate, pteridin etc. Compounds with pteridin ring
system have higher Qfit value than other scaffolds, thus novel compounds were designed with
substitution at various positions of pteridin ring using knowledge based structure activity
relationship (SAR), which might act as potent HIV-1 integrase inhibitors.
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Molecular Docking Studies
Surflex-Dock was used for molecular docking. Co-crystallized structure of HIV-1 integrase
enzyme [PDB: 1QS4] was taken from protein data bank (PDB) and modified for docking
calculations (41). Co-crystallized ligand was removed from the structure, water molecules
were removed, H atoms were added and side chains were fixed during protein preparation.
Protein structure minimization was performed by applying Tripos force field and partial
atomic charges were calculated by Gasteiger Huckel method. Twenty conformers were
generated for each molecule. Three different standards, Elvitegravir, Dideoxyinosine and
Zidovudine were used for docking.
Chemistry
In order to obtain effective anti-HIV agents, pteridin derivatives were successfully
synthesized. Synthesis of the pteridin derivatives (42) was achieved by following steps
outlined in Scheme 1. 5,6-diamino-2-mercaptopyrimidin-4-ol (0.158 gm, 1 mmol) was mixed
with various diketo compounds (1 mmol) followed by addition of 0.1 gm sodium acetate and
30 ml of glacial acetic acid. The resulting solution was refluxed for 3-5 hours. After
refluxing, the resulting mixture was cooled at room temperature. Precipitates of 2-mercapto-
6,7-disubstitutedpteridin-4-one derivatives, obtained at room temperature, were collected and
obtained by vacuum filtration. Percentage yield, melting point and Rf value were reported.
Above said conventional synthesis have three major disadvantages like poor yield, longer
reaction time and huge amount of solvents are required which is not eco-friendly. Hence,
rapid synthesis of 2-mercapto-6,7-disubstitutedpteridin-4-one derivatives under microwave
irradiation (MWI) was performed in absence of solvent. Reaction time varies from 12-22
minutes with 90-97% yield. The comparisons of results from conventional and microwave
methods are summarized in Table-2. Spectacular results, obtained in terms of time and yield,
gave clear indications of potentialities and advantages of MWI technique. Performing
reactions in such conditions result in faster and cleaner reactions due to less thermal
decomposition of products. Physical characteristics of synthesized compounds are shown in
Table-3.
Melting points were taken in open capillary on an electrically heated metal block and are
1
uncorrected. FT-IR spectra were recorded on Jasco FT-IR 6100 spectrometer. H NMR
spectra were recorded on a BRUKER Advanced II spectrometer operating at 400 MHz in
CDCl3 and TMS as internal standard. Chemical shift values are reported in δ (ppm). Mass
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spectra were recorded on Waters Micromass Q-Tof Micro. The microwave irradiated
reactions were carried out using Synthos 3000 microwave synthesizer from Anton Par. The
progress of all reactions was monitored by TLC on 2 x 7 cm pre-coated silica gel 60 F254
plates and spots were visualized by exposure to UV radiations and iodine vapors.
2-mercapto-6,7-bisphenylpteridin-4-one (1): FT-IR (KBr, cm-1): 3595 (N–H), 3046 (Ar
1
C=H), 2359 (S-H), 1688 (C=O), 1685 (C=C), 1547 (C=N); H-NMR (400 MHz, CDCl3): δ
2.46 (s, 1H, SH), 7.12-7.18 (d, 4H, J = 24.4 Hz), 7.34-7.38 (t, 4H, J = 6.8 Hz), 7.48-7.53 (t,
2H, J = 13.2 Hz), 12.52 (s, 1H, NH); ESI-MS: m⁄ z 333.0 (M+).
2-mercapto-6,7-bis-(p-tolyl)-pteridin-4-one (2): FT-IR (KBr, cm-1): 3306 (N–H), 3035 (Ar
1
C=H), 2360 (S-H), 1710 (C=O), 1633 (C=C), 1526 (C=N), 2944 (Me C-H); HNMR (400
MHz, CDCl3): δ 2.41 (s, 1H, SH), 3.71 (s, 6H, CH3), 6.89-6.96 (d, 4H, J = 7.2 Hz), 7.21-7.27
(d, 4H, J = 8.4 Hz), 12.68 (s, 1H, NH); ESI-MS : m⁄ z 360.9 (M+).
2-mercapto-6,7-bis-(3-methoxyphenyl)-pteridin-4-one (3): FT-IR (KBr, cm-1): 3144 (N–
1
H), 3035 (Ar C=H), 2361 (S-H), 1695 (C=O), 1622 (C=C), 1533 (C=N); H-NMR (400
MHz, CDCl3): δ 2.39 (s, 1H, SH), 3.91 (s, 6H, OCH3), 7.89 (s, 2H), 7.57-7.60 (d, 2H, J = 7.2
Hz), 7.21-7.25 (t, 2H, J = 7.9 Hz), 6.92-6.97 (d, 2H, J = 8.1 Hz), 12.8 (s, 1H, NH); ESI-MS :
m⁄ z 393.1 (M+).
2-mercapto-6,7-bis-(4-fluorophenyl)-pteridin-4-one (4): FT-IR (KBr, cm-1): 3369 (N–H),
1
3020 (Ar C=H), 2363 (S-H), 1728 (C=O), 1645 (C=C), 1539 (C=N), 1235 (C–F); H-NMR
(400 MHz, CDCl3): δ 2.59 (s, 1H, SH), 7.57-7.61, (d, 4H, J = 7.5 Hz), 7.71-7.74 (d, 4H, J =
7.2 Hz), 12.79 (s, 1H, NH); ESI-MS : m⁄ z 368.8 (M+).
2-mercapto-6,7-bismethylpteridin-4-one (5): FT-IR (KBr, cm-1): 3188 (N–H), 3020 (Ar
1
C=H), 2524 (S-H), 1699 (C=O), 1620 (C=C), 1550 (C=N), 2985 (Me C-H); H-NMR (400
MHz, CDCl3): δ 2.60 (s, 1H, SH), 1.52 (s, 6H, CH3), 12.3 (s, 1H, NH); ESI-MS : m⁄ z 209
(M+).
2-mercaptopteridin-4-one (6): FT-IR (KBr, cm-1): 3374 (N–H), 3045 (Ar C=H), 2361 (S-H),
1
1698 (C=O), 1655 (C=C), 1428 (C=N); H-NMR (400 MHz, CDCl3): δ 2.35 (s, 1H, SH),
7.21 (d, 2H, J = 8.1 Hz), 12.5 (s, 1H, NH); ESI-MS : m⁄ z 182.9 (M+).
Anti-HIV Activity and Cytotoxicity
Synthesized compounds and standard drugs were tested for anti-HIV activity against the
replication of HIV-1 (IIIB) in MT-4 cells. The cells were grown and maintained in RPMI
1640 medium supplemented with 10% heat-inactivated Fetal Calf Serum (FCS), 2 mM-
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glutamine, 0.1% sodium bicarbonate and 20 µg/ml gentamicin (culture medium). HIV-1
(HTLV-IIIB/LAI) was used in all experiments. The virus strains were propagated in MT-4
cells. Titer of virus stock was determined in MT-4 cells and the virus stock was stored at -70°
until used. The inhibitory effects of the compounds on HIV-1 replication were monitored by
inhibition of virus-induced cytopathic effect in MT-4 cells and were estimated by the MTT
method. Briefly, 50 µl of HIV-1 (100-300 CCID50 (50% cell culture infective dose)) were
added to a flat-bottomed microtiter tray with 50 µl of medium containing various
concentrations of the test compounds. MT-4 cells were added at a final concentration of
6×105 cells/ml. After 5 days of incubation at 37°C, numbers of viable cells were determined
by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazoliumbromide (MTT) method.
Cytotoxicity of the compounds for mock-infected MT-4 cells was also assessed by the MTT
method (43, 44).
Results and Discussion
Pharmacophore Mapping and Virtual Screening Analysis
Results of pharmacophore hypothesis generated by DISCOtech are shown in Table-4. Model
6 was used for refinement using GASP. Results of pharmacophore models generated by
GASP are shown in Table-5. GASP results contained 4 information, fitness score which
indicates fitness of model; size indicate number of features present in model; HITS gives
information regarding number of molecules match during search and Dmean gives average
interpoint distance among features. The fitness of the alignment is determined by combining
three criteria using the fitness function which includes number and similarity of overlaid
features, volume overlap of alignment (i.e. common volume of aligned molecules) and
internal van der waals energy of the molecular conformations. Model 2 is considered as the
best model as it has highest fitness score compared to remaining 9 models. A 3-D and 2-D
pharmacophore hypothesis are shown in Figure-1 and Figure-2 respectively. The best model
contained 2 donor sites, 1 acceptor atom and 1 hydrophobic region which are critical and
important features for good HIV-1 Integrase activity (45). These features for inhibitory
activity were also highlighted through 3D space modeling approach which explored
importance of critical inter-feature distances among the features for the HIV-1 integrase
activity. Following methods were used to validate the generated pharmacophore model.
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Validation of Best Pharmacophore Model on Marketed HIV-1 Integrase Inhibitors.
The model was validated by screening a database containing 7 known inhibitors. The model
picked up all 7 known inhibitors taken for validation. The results showed Elvitegravir giving
a Qfit value of 84.25%.
Validation of Best Pharmacophore Model on Test Set Compounds.
The model was further validated by screening a database containing 54 test inhibitors. The
model picked up 54 inhibitors giving a Qfit value up to 96.39%.
Statistical Validation
Sensitivity and specificity of best model was evaluated by using separate in-house database
of 54 compounds, which is made up of 18 active integrase inhibitors and 36 drug-like
compounds. Pharmacophore based screening of decoy database resulted into,
(1) No of false negatives = 4
(2) No of True positives 14
=
(3) No of True negatives = 43 (4) No of False positive = 3
Sensitivity and specificity values were obtained from given equations which are as follows:
Sensitivity = 0.78
Specificity = 0.93
Pharmacophore validation results showed good sensitivity and specificity. This validation
results suggested that generated pharmacophore model can be successfully used in virtual
screening to search the substructures from databases to retrieve initial hits.
Receiver Operating Characteristic (ROC) Studies
Here, the validity of a generated pharmacophore is indicated by the area under the curve
(AUC) of the corresponding ROC curve (Figure-3), as well as the overall accuracy,
specificity, true positive rate and false negative rate of the pharmacophore. In ROC study, the
generated pharmacophore model was considered as the best model because it was able for
picking up active molecules in the database, a spiked database including 200 inactive
compounds and 54 active inhibitors (1080 active conformers). The AUC of ROC curve of
model was 0.781 and overall accuracy (ACC) was 0.95 against the randomly selection value
of “0.5”, demonstrating the reliability of generated pharmacophore model which might be
valuable in identifying diverse compounds from database with HIV-1 integrase activity
(Table 6).
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Güner-Henry (GH) Studies
GH scoring method was used as another method for validation of pharmacophore model.
Values all parameters are shown in Table-7. Here, ratio of actives retrieved in hit list (%A)
was obtained as 77.77%; fractions of hits relative to size of database (hit rate or selectivity)
(%Y) was obtained as 85.17% and enrichment of active bin by model relative to random
screening (E) was obtained as 4.04. From the mathematical equation, GH score was obtained
as 0.81 indicating the good quality of the model. This signifies use of generated model for
virtual screening.
Virtual Screening
The best pharmacophore model was selected to search substructure in NCI and Maybridge
Hit Finder database. All parameters were unchanged except priority was kept high. A total
number of 39179 molecules were obtained after substructure searching from both databases.
These molecules were again screened for drug like compounds by applying Lipinski’s rule of
five and finally 17930 compounds were obtained which might be useful in rational design of
new HIV-1 integrase inhibitors. Among these 17930 molecules, 274 molecules exhibit Qfit
value more than 99%. The flowchart of screening procedure is explained in Figure-4.
Molecular Docking Analysis
In this study, 30 molecules were selected for docking purpose, which were designed by
studying structure activity relationship from literature. Co-crystal structure of HIV-1 integrase
was retrieved from the protein data bank. After running Surflex-Dock, the scores of 20
docked conformers were ranked in a molecular spread sheet. We selected the best total score
conformer and speculated regarding the detailed binding patterns in the cavity. Results of
docking study are shown in Table-8. Docking results revealed that 6 molecules showed
comparative good score as to standard drugs. Surflex-Dock results contained three
informations – total score, crash value and polar value. Total score is the total Surflex-Dock
score expressed as -log(Kd) to represent binding affinities which include hydrophobic, polar,
repulsive, entropic and solvation terms. Crash value is the degree of inappropriate penetration
by the ligand into the protein and of interpenetration between ligand atoms (self-clash) that
are separated by rotatable bonds. Crash scores close to 0 are favourable. Polar value is
contribution of the hydrogen bonding and salt bridge interactions to the total score. The polar
score may be useful for excluding docking results that make no hydrogen bonds.
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Binding pose of both compounds 4 and 6 are shown in Figure-5. Compound 4 made three
important hydrogen bonds. One hydrogen bond is formed between flourine group attached to
phenyl ring and Thr-66 at distance of 2.52 Å. Gln-148 made hydrogen bond with flourine
group attached to another phenyl ring (2.63 Å). Carbonyl oxygen makes hydrogen bond with
Asn-155 at distance of 1.99 Å. Compound 6 complexed with HIV-1 integrase showed
occurrence of two hydrogen bonds. Asn-155 made two hydrogen bonds with two nitrogen
atoms of pteridine ring at distance of 2.45 Å and 2.32 Å. Asn-155 is an important amino acid
because it is highly conserved among HIV-1 strains. It was speculated that Asn-155 interacts
with HIV-1 cDNA during integration of HIV virus into human genome and responsible for
mutation. This conclusion is in agreement with the results of a molecular modeling study
which proposed that Asn-155 might bind DNA.
A query fit (Qfit) value was computed for all 6 compounds which were decided to synthesize
based on docking results in order to ensure that compounds to be synthesized will be
according to the generated pharmacophore model. A generated pharmacophore model in
Figure-1 was used as a query to search 3D flexible UNITY database search. UNITY uses a
Qfit value as a score to measure the feature match value of a hit to the pharmacophore model.
The Qfit score is a value between 0 and 100, where 100 denotes closeness of the features of
the ligand with the query. The corresponding query feature coordinates within a given range
of a spatial constraint tolerance. The closer the fit, the higher the Qfit value (46). The results
obtained after searching the database (Table-9) indicated that all compounds have Qfit value
more than 88.85. This result further signifies the rational to design compounds.
Chemistry
All target compounds were synthesized by conventional as well as MWI method and
characterized by physical and spectral analysis data. FT-IR spectra of all the synthesized
compounds exhibited singlet in the region 3400-3100 cm-1 indicating presence of –NH group
of pyrimidine ring. The peak for carbonyl group is observed around 1730-1690 cm-1
indicating presence of ketone group at C4. Presence of -NH and C=O indicates tautomerism
observed in target molecules. All synthesized compounds showed peaks in the region 1550-
1520 cm-1 for C=N and 1680-1620 cm-1 for C=C. 1H NMR spectra of designed series showed
singlet in the region of δ 12.0 to 13.0 corresponding to -NH group. Singlet is observed in the
region of δ 2.3 to 2.6 corresponding to one -SH proton. Aromatic protons showed peaks in the
region of δ 7.0 to 7.8.
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Biological Results
Inhibitory concentration (IC50) of synthesized compounds on HIV-1 replications was
measured by the inhibition of virus induced cytopathic effect in MT-4 cells. Cytotoxicity
(CC50) of test compounds in mock infected MT-4 cells was also measured by MTT-method.
Table-10 showed anti-HIV activity and cytotoxicity for the 2-mercapto-6,7-
disubstitutedpteridin-4-one derivatives with comparison to standard drugs, Zidovudine and
Dideoxyinosine, against the replication of HIV-1 in acutely infected MT-4 cells.. All
compounds were tested for IIIB strains of HIV virus. Among 6 compounds, compound 1
exhibited better anti-HIV activity as compared to other 5 compounds which indicated that
phenyl ring substitution at C6 and C7 position increase anti-HIV activity while substituted
phenyl ring reduce biological activity which can be observed in compound 2 to 4. Compound
5 and 6 also revealed less anti-HIV activity due to absence of lipophilic group at C6 and C7
position. Compound 1 exhibited 6 percent maximum protection against IIIB strain of HIV-1
virus and showed maximum cytotoxicity as compared to other synthesized compounds.
Cytotoxic concentrations of test compounds were found to be from 1.77 to >125 µg/ml
whereas the standards showed more than 25 µg/ml and 20 µg/ml for Zidovudine and
Dideoxyinosine, respectively in mock infected MT-4 cells.
Conclusion
In course of our research to find novel HIV-1 inhibitors, ligand base pharmacophore
modeling was performed using 20 structurally diversified chemical compounds. A total
number of 10 pharmacophore model was generated using DISCOtech. Among these 10
models, best model was refined using GASP. The best pharmacophore model was validated
using various statistical methods which contained 2 donor sites, 1 acceptor atom and 1
hydrophobic group. The same model was used for substructure searching in NCI and
Maybridge Hit Finder databases. Virtual screening tool was used in order to filter the drug
like compounds. The molecules having Qfit value more than 99% were selected to design 30
novel compounds bearing pteridin ring. These 30 compounds were docked on HIV-1
integrase enzyme. A total 6 compounds were selected for synthesis based on docking score
and binding affinity. The synthesized compounds were characterized by physical and spectral
characteristics. All synthesized compounds were tested for in vitro anti-HIV activity and
cytotoxicity. Based on molecular docking studies of designed compounds on HIV-1 Integrase
enzyme and results of anti-HIV activity, these synthesized novel compounds will provide a
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very good root for the development of new hits in future.
Acknowledgement
Authors would like to acknowledge Director General, Nirma University, Ahmedabad, India
for providing necessary facilities.
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Figure 1: Three Dimension Pharmacophore Hypothesis Generated by GASP
Figure 2: Two Dimension Pharmacophore Hypothesis Generated by GASP.
Figure 3: ROC curve of Validated Pharmacophore Model.
Figure 4: Flow Chart of Virtual Screening Procedure of NCI and Maybridge Hit Finder
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Scheme 1: Synthesis of 2-mercapto-6,7-disubstitutedpteridin-4-one derivatives
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Table-1. Training Set Molecules Used to Develop Pharmacophore Hypotheses For HIV-1
Integrase Inhibitors.
Compound
No.
Structure
Class
IC50
In µM
Azaindole hydroxamic
acid 23
84
1
2
3
β-Diketo acids 24
0.059
Isoquinoline 25
1.35
Benzoic hydrazides 26
Dioxepinones 27
0.7
4
5
123
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Quinoline-3-carboxylic
acid 28
23
6
Quinolines 29
0.37
331
7
8
Thiazolothiazepines 30
Quinolinonyl Diketo
acid 31
2.3
9
Quinone 32
20
10
11
Salicylpyrazolinones 27
7.4
Dihydroxy Pyrimidine
carboxamide 33
0.01
12
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Coumarin 34
122
13
14
Dihydroxy Pyrimidine
carboxamide 33
0.01
Benzoic hydrazide 26
200
293
15
16
Salicylpyrazolinones 27
Cl
OH
O
O
N
N
CH3
Isoindole 35
0.5
17
Benzofuran 36
43.80
0.52
18
19
3-aryl- 1,3-diketo acid
37
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Purine 38
11.2
20
IC50 is concentrations of each compound required to inhibit HIV-I Integrase Enzyme by 50%.
Table-2. Comparison of Conventional Method with Microwave Irradiated Method for
Synthesis of 2-mercapto-6,7-disubstitutedpteridin-4-one derivatives.
Compound
No.
Conventional Method
Microwave Irradiated Method
Time
(hrs)
Yield
(%)
Time
Temperature
(°C)
Yield
(min)
(%)
94.22
96.47
95.29
94.16
92.04
90.4
4
3
40.9
12
120
120
120
120
120
80
1
2
3
4
5
6
53.30
59.67
46.12
34.20
38.54
16
2.5
3.5
4.5
5
16
20
22
22
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Table-3: Physical Characteristics of 2-mercapto-6,7-disubstitutedpteridin-4-one
derivatives.
O
N
N
R
R
HN
HS
N
Compound
No.
Substitution
(R)
Molecular Formula
Melting Point
(o C)
Rf Value
-C6H5
p-CH3-C6H4-
m-OCH3-C6H4-
p-F-C6H4-
-CH3
C18H12N4OS
C20H16N4OS
C20H16N4O3S
C18H10F2N4OS
C8H8N4OS
240-242
293-296
235-238
279-282
245-248
>300
0.46*
0.53*
0.44*
0.62*
0.71#
0.52#
1
2
3
4
5
6
-H
C6H6N4OS
* Solvent system - n-hexane:ethyl acetate - 3:2
# Solvent system - n-hexane:ethyl acetate:methanol - 3:2:1
Table-4: Results of Pharmacophore Hypothesis Generated by DISCOtech.
Model No.
Size
4
Hits
20
Score
0.4485
0.4449
0.3221
0.2805
0.2800
Tolerance
0.2500
0.2500
0.2500
0.2500
0.2500
Dmean
6
7
3
2
5
3.1439
3.1292
4.1985
3.8151
3.8108
4
20
4
20
4
20
4
20
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4
1
4
4
20
20
0.0264
0.0264
0.2500
0.2500
3.2538
3.2538
Table-5: Results of Pharmacophore Hypothesis Generated by GASP.
Model No.
Fitness Score
1464.89
1488.84
1448.31
1476.63
1469.33
1479.22
1473.33
1478.21
1446.12
1483.7
Size
4
Hits
20
20
20
20
20
20
20
20
20
20
Dmean
1
2
2.7912
2.7646
2.7670
2.7079
2.7444
2.7815
2.8069
2.7108
2.8080
2.4165
4
3
4
4
4
5
4
6
4
7
4
8
4
9
4
10
4
Table-6: ROC Performances of Pharmacophore Model.
Sr. No
Parameters
ROC-AUC
ACC
Statistical Values
1
2
3
0.78
0.95
0.86
SPC
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4
5
6
TPR
FNR
0.99
0.01
0.05
Std. error
ROC- Receiver Operating Characteristic; AUC- Area Under The Curve; ACC- Overall Accuracy; SPC- Overall
Specificity; TPR- Overall True Positive Rate; FNR- Overall False Negative Rate.
Table-7: Pharmacophore Model Evaluation Based On the Güner-Henry (GH) Scoring
Method
Sr. No
Parameters
Statistical values
1
2
3
4
5
6
7
8
%A
%Y
E
77.77%
85.17%
4.04
0.81
254
GH
D
A
54
Ht
49
Ha
42
Ht- number of hits retrieved; Ha- number of actives in hit list; D - number of compounds in a database; %A-
ratio of actives retrieved in hit list; %Y is a fractions of hits relative to size of database (hit rate or selectivity),
E-enrichment of active bin by model relative to random screening ;GH - Güner-Henry score.
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Table-8: Docking Results of 2-mercapto-6,7-disubstitutedpteridin-4-one derivatives
Obtained Using Surflex-Dock.
Compound No.
Total Score
5.84
Crash
-0.71
-0.64
-0.18
-0.26
-0.78
-1.36
-0.48
Polar
0.02
1.28
0.47
0.98
1.84
1.05
0.20
Similarity
0.03
4
5.66
0.02
6
5.59
0.09
Elvitegravir
4.92
0.04
1
4.75
0.10
3
4.32
0.18
2’3’ dideoxyinosine
2
3.88
0.06
3.50
3.02
-1.08
-0.74
1.36
0.84
0.38
0.14
Zidovudine
5
Table-9: Qfit Values of Synthesized Compounds.
Compound No.
Qfit
91.7200
93.9500
96.6900
88.8500
87.6200
89.3300
1
2
3
4
5
6
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Table-10: Anti-HIV Activity of 2-mercapto-6,7-disubstitutedpteridin-4-one derivatives.
Compound No.
Strain IIIB
CC50 (µg/ml)
1.77
IC50 (µg/ml)
> 1.77
>12.7
Max. Protection
6
3
1
12.7
2
>63.9
63.9
5
3
>56.9
56.9
3
4
>125.00
>60.4
>125.00
60.4
4
5
6
2
0.00141
2.54
>25
113
128
Zidovudine
Dideoxyinosine
>20
IC50 is concentrations of each compound required to inhibit strain IIIB by 50%; CC50 is concentrations of each
compound required to cause cytotoxicity to 50% of the MT-4 cells.
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