Identification of novel adenosine A2A receptor antagonists by virtual screening

Article abstract of DOI:10.1021/jm201455y

Virtual screening was performed against experimentally enabled homology models of the adenosine A_2A receptor, identifying a diverse range of ligand efficient antagonists (hit rate 9%). By use of ligand docking and Biophysical Mapping (BPM), hits 1 and 5 were optimized to potent and selective lead molecules (11-13 from 5, pK_I = 7.5-8.5, 13- to >100-fold selective versus adenosine A₁; 14-16 from 1, pK_I = 7.9-9.0, 19- to 59-fold selective).

Full text of DOI:10.1021/jm201455y

Article
pubs.acs.org/jmc
Identification of Novel Adenosine A2A Receptor Antagonists by
Virtual Screening
Christopher J. Langmead,*,† Stephen P. Andrews,† Miles Congreve,† James C. Errey,† Edward Hurrell,†
Fiona H. Marshall,† Jonathan S. Mason,† Christine M. Richardson,‡ Nathan Robertson,† Andrei Zhukov,†
and Malcolm Weir†
†Heptares Therapeutics Limited, BioPark, Broadwater Road, Welwyn Garden City, Hertfordshire AL7 3AX, U.K.
‡BioFocus, Great Chesterford Research Park, Saffron Walden, CB10 1XL, U.K.
S
* Supporting Information
ABSTRACT: Virtual screening was performed against experimentally enabled homology
models of the adenosine A2A receptor, identifying a diverse range of ligand efficient
antagonists (hit rate 9%). By use of ligand docking and Biophysical Mapping (BPM), hits
1 and 5 were optimized to potent and selective lead molecules (11−13 from 5, pKI =
7.5−8.5, 13- to >100-fold selective versus adenosine A1; 14−16 from 1, pKI = 7.9−9.0,
19- to 59-fold selective).
of an experimentally enabled (site-directed mutagenesis, SDM)
homology model of the receptor (based on the crystal structure
of the turkey β1 adrenergic receptor in complex with
cyanopindolol3), a virtual screen was performed to attempt to
identify novel hits. In silico screening of 545K compounds,
filtered to focus on compounds with CNS druglike properties
and without undesirable heterocycles such as the furan or
xanthine moieties, resulted in 20 confirmed hits in vitro (9% hit
rate). These hits included a highly potent 1,3,5-triazine
derivative and a chromone scaffold, the latter a completely
novel chemotype for adenosine receptors. The binding modes
of these hits were refined using our Biophysical Mapping
approach, allowing optimal interactions to be identified and
enabling these compounds to be rapidly developed into potent
antagonists suitable for further optimization.4
INTRODUCTION
■
The adenosine A2A receptor is a member of the G-protein-
coupled receptor (GPCR) superfamily that mediates effects of
the purine nucleotide adenosine on cellular signaling. The
receptor is expressed in the CNS and periphery and has been a
long-standing drug target for the treatment of inflammatory
disorders and Parkinson’s disease. The A2A receptor is
expressed in midbrain regions of the CNS where it functionally
opposes the actions of the dopamine D2 receptor. Given that
the primary pathology in Parkinson’s disease is a loss of
dopamine and hence reduced dopamine D2 receptor activation,
adenosine A2A receptor antagonism represents a potential
nondopaminergic therapy for this disorder. Initial work to
identify antagonists focused on purine and xanthine derivatives,
essentially based on adenosine and the naturally occurring
antagonist caffeine. This class of compounds is exemplified by
istradefylline1 (KW-6002), which progressed to phase III
clinical development; however, despite extensive efforts, no
other clinical agents that selectively target the A2A receptor have
emerged from this area of chemistry. Further work has focused
on bicyclic and tricyclic derivatives such as triazolotriazines and
triazolopyrimidines, exemplified by ZM241385 and vipadenant,
respectively.1 The most advanced of this class of compounds is
preladenant, which is currently in phase III clinical trials.1
However, despite good affinity and selectivity across other
adenosine receptor subtypes, these compounds are generally
high molecular weight and all contain a furan group that has
proven difficult to replace by empirical medicinal chemistry.
Such an electron-rich group is prone to oxidative metabolism
and potential reactive metabolite formation.2 Therefore, we
sought an alternative structure-based approach to identify novel
antagonist chemotypes for the adenosine A2A receptor. By use
RESULTS AND DISCUSSION
■
Virtual Screening. At the time of this work, no structural
data were available for the A2A receptor, and therefore
homology models were constructed based on the avian β1
adrenergic GPCR crystal structure bound to cyanopindolol
(PDB code 2VT4).3 Several different computational methods
were used to generate and validate two homology models (see
methods section and Supporting Information) because there is
relatively low percentage identity between the two proteins
(25% overall, <20% around the putative ligand binding site).
The validation step included an assessment of the consistency
in the alignments and of the variability, including which regions
of the models had higher and lower confidence associated with
Received: October 12, 2011
Published: January 17, 2012
© 2012 American Chemical Society
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them. The helical bundles between the two models and the
template agreed closely, with lower confidence in the loop
regions, particularly the extracellular loop 2, which did not align
well to the template. By use of published and in-house SDM
data to validate and improve the models and docking
experiments using Glide5 of a small number of known A2A
receptor ligands and similarly sized decoys, virtual screening
conditions and protocols were established. A discussion of the
SDM data used to improve the models has been described
Table 1. Virtual Screening Hits
hit
pKI
LE
LLE
clogP
PSA
MW
1
8.46
5.15
5.75
6.15
5.65
5.62
5.91
5.33
5.70
5.53
0.52
0.47
0.44
0.36
0.33
0.31
0.30
0.29
0.29
0.27
5.4
4.5
3.9
3.2
3.7
2.6
3.2
3.4
3.9
2.1
3.1
0.7
1.9
3.0
1.9
3.0
2.7
1.9
1.8
3.4
84.9
72.2
61.7
66.6
85.7
76.7
78.4
79.8
80.1
95.9
310.4
222.3
264.3
327.4
331.3
367.9
367.4
340.4
363.4
382.4
2
3
4
5
6
elsewhere and is summarized in the methods section.4
A
7
8
comparison of the homology modeling results with ligand-
complexed crystal structures of the adenosine A2A receptor is
detailed in ref 6.
9
10
Virtual screening was then carried out with compound data
sets of commercially available compounds, filtered to focus on
compounds with desired CNS druglike properties. 545 000
compounds were prepared for screening, and all or a more
filtered and clustered set were docked into each of the models
using the SP algorithm within the Glide software, running on a
28 CPU Linux cluster. Details of the workflows, screening
compound numbers and filters used for the virtual screening,
and postprocessing analyses with each homology model are
detailed in the Supporting Information (Figures S2−S4). The
aim of the screen was to identify novel chemotypes that would
provide starting points for optimization to compounds with
good druglike properties. A number of different protocols were
used to analyze the output from the virtual screens to provide a
set of complementary compound selections, each biased toward
certain features of the binding site; in each case, up to 10−
20000 ligand poses were initially selected based on the score
from Glide. This data set was then sectioned in various ways
including the use of consensus scoring, use of other Glide
generated scores, and overlap with the docked poses of known
small ligands for the receptor. It was of particular interest to
assess the utility of the SDM data in guiding compound
selections. Therefore, most of the compounds were selected
based on balanced polar and lipophilic score components and
proximity to one or more residues chosen based on the
experimental (SDM) data. As part of this process, a bias was
employed to focus on compounds that docked in the most
buried part of the site, remote from the low confidence region
bordered by the extracellular loop 2. Compound sets resulting
from the various selections were combined, and a final selection
was made involving 3D visualization and assessment in the
binding site, including the fit to the binding site shape and key
features and the ligand conformation, and final triage by
medicinal chemistry. As a result of this process, a set of 372
compounds was prioritized. Of these, only 230 were logistically
available commercially and were tested for in vitro binding to
the adenosine A2A receptor. Twenty compounds exhibited
activity (IC50 < 55 μM), giving a 9% hit rate overall. Of the top
10 hits, all have ligand efficiencies (LEs) of >0.27, with seven
compounds having >0.3, three having >0.4, and one notable hit
with LE > 0.5.7 These best hits also have reasonable to good
ligand lipophilicity efficiencies (LLEs) in the range 2.1−5.4,
with eight having LLE ≥ 3.8 A number of structurally distinct
chemotypes were identified in the screen, providing multiple
starting points for potential optimization to generate a new A2A
antagonist series: Table 1 and Figure 1 indicate the top 10 hits
ranked by LE. Full binding curves for these hits are shown in
the Supporting Information, which includes a table of the
nearest published adenosine A2A antagonist to each of the hits.
The most potent and most efficient compounds were all
Figure 1. Structures of virtual screening hits.
identified by the protocols that used key interacting residues
from the SDM data as part of the selection process. This study
highlights the importance of using experimental data, where
available, in analysis and virtual hit selection during a virtual
screen.
The results from this virtual screening exercise have
demonstrated that good hit rates of diverse leadlike compounds
are possible using high-quality GPCR (experimentally enabled
and enhanced) homology models. The utility of virtual
screening is supported by two recent papers from academic
labs documenting this approach using an X-ray structure
(rather than a homology model) of the adenosine A2A
receptor.9,10 One group reported that a hit rate of 41% was
achieved with 23 of the 56 assayed compounds showing activity
better than 10 μM.9 Similarly a second study yielded a hit rate
of 35% with 7 of 20 compounds tested having affinities from
200 nM to 10 μM.10 Interestingly, the top ranked hit from one
of these two screens contains the same chemical scaffold as
observed here from virtual screening of our experimentally
enhanced homology model.9
Hits to Leads. The process for the selection and
optimization of the initial hits to develop leads was driven
from docked ligand poses into the A2A homology models,
coupled with 3D analysis of hotspots in the binding site, which
were determined using small fragment probes. The calculated
GRID maps11 especially showed clearly the shape and
pharmacophoric preferences/constrictions of the binding site
(using a methyl group for shape and an aromatic C−H group, a
carbonyl group, and an amide NH group for lipophilic,
hydrogen-bond acceptor and donor hotspots, respectively).
The relatively high LE and LLE of the hits indicate that the A2A
receptor binding site is quite druggable, and this is reinforced
by the GRID analysis suggesting regions of hydrophobic and H-
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Scheme 1. Optimization of Chromone Hit 5
bonding hotspots close together in the site available for very
small ligands to bind. The key H-bonding residue Asn2536.55
sits centrally and is capable of forming high quality interactions
with a diverse range of heterocyclic compounds. In particular,
two hits were rapidly developed into lead series with potent
activity versus the A2A receptor and good selectivity in key
examples against the adenosine A1 receptor. Introducing at least
moderate selectivity versus the A1 receptor subtype was thought
desirable to minimize potential side effects, such as the
stimulant effects seen with nonselective agents such as caffeine.
The A2A binding site was also analyzed using molecular
Further iterations of purchasing of close analogues of 11
identified 12 and 13 that are highly potent and, in the case of
13, highly selective A2A antagonists. In addition to the BPM
data, a low resolution crystal structure of one member of the
series was solved confirming the binding pose presented here
(data not shown). Despite rapid progress with this series, in
vitro metabolism issues and concerns that the thiazole might
represent a liability in terms of possible generation of reactive
metabolites led us to halt work in this series. Indeed, many
previous adenosine A2A antagonists carry a furan group and we
reasoned that a superior class of compounds should not contain
a similar liability.1 In addition, the potential of the work in the
triazine scaffold (below and in ref 6) allowed us to deprioritize
the chromone template.
dynamics simulations with the WaterMap software (Schro-
̈
dinger),12 shown in ref 6, which demonstrates that our highly
ligand efficient ligands occupy exactly the region where there is
a cluster of waters that are termed “unhappy”, meaning that
energetically they would prefer to be in bulk solvent; this
contrasts with larger ligands such as ZM241385.13
The second series to be optimized was triazine 1 (Scheme 2),
already a highly potent A2A antagonist with excellent LE and
LLE.7,8 Simple outline SAR established the importance of the
amino and phenol functionalities for high potency (data not
shown) and that the olefin could be replaced by a range of
groups, including simplification to alkyl-substituted 14. This
fragment-sized molecule retains much of the affinity for the
receptor and has moderate selectivity over the A1 receptor. A
range of derivatives were synthesized in a hits to leads program
on the chemotype, and one direction of the work was to design
phenyl substituted 15 and 16 containing piperazine and
piperidine solubilizing groups. These derivatives were found
to be highly potent antagonists with moderate to good
selectivity. BPM analysis of this series of compounds was
again used during the optimization process, and a representa-
tive data set is shown in Figure 3 and is described in the legend.
One further area of optimization of the series was to examine
modifications to the triazine scaffold itself to allow more direct
access to the “ribose pocket” from which we believed selectivity
over the A1 receptor could be derived. This is the topic of ref 6.
Comparisons can be made of the binding modes of the
chromone and triazine templates using BPM analysis. Alanine
mutation of Asn2536.55 or His2506.52 abolished the binding of
12 and 15; ligand docking suggested that Asn2536.55 makes key
hydrogen bonding interactions with the amino and phenol
functional groups of 15 while for 12 the interaction with
Asn2536.55 is made by the aromatic C−H of the chromone
template and the nitrogen atom of the thiazole substituent.
Alanine mutation of Ile662.64 (ΔpKD = −0.7) and Tyr2717.36
(ΔpKD = −0.7) reduced the affinity of 12, consistent with these
residues forming a pocket for the alkyl chain of this compound.
However, these mutations had little or no effect on the binding
of 15 (ΔpKD of +0.1 and −0.1, respectively). Conversely,
alanine mutation of Ser2777.42 reduced the affinity of 15 (ΔpKD
= −1.0) but not 12 (ΔpKD = +0.3), consistent with the
piperazine group of 15 being oriented toward the pocket of the
The first hit to be optimized was the chromone 5 (Figure 1,
Scheme 1). Selection of closely related analogues from
commercial suppliers, influenced by the proposed binding
mode from the virtual docking, quickly identified several more
potent compounds including ester 11. In particular, relatively
close analogues lacking the carboxylic acid functionality
(presumed to be undesirable for brain penetration) and also
not significantly higher in molecular weight or lipophilicity were
selected. Biophysical Mapping analysis, published elsewhere4
(chromone analogues are 1 in the earlier publication),
distinguished between the binding mode shown in Figure 2
and a pose in which the compounds were rotated 180° and
interacted with the key Asn2536.55 via the chromone carbonyl.4
Figure 2. Docking of the chromone 12, showing the BPM fingerprint
color coded onto the binding site residues and in graphical form as
change in pKD. Nonbinding is shown in red (N253A, H250A). Next
largest effect is in dark orange (L85A), second largest in amber
(N181A, Y271A, I66A), an increase in binding in green (S277A). H-
bonding between the nitrogen of the thiazole and the aromatic C−H
of the chromone is predicted to Asn2536.55. Selected BPM data are
tabulated showing the change in pKD of each binding site mutation.
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Scheme 2. Optimization of Triazine Hit 1
other in vitro properties made this series unsuitable for further
optimization. The top ranked hit from the screen, containing a
1,3,5-triazine core, was by far the most potent, with LE > 0.5
and LLE > 5 and an affinity of <10 nM for the A2A receptor.
Notably, this core group was also identified in a separate virtual
screening exercise for the A2A receptor using an X-ray
structure.9 SAR in this series demonstrated the importance of
the amino and phenol groups, but the olefin moiety could be
readily replaced by a simple alkyl substituent with minimal loss
of affinity. Such a potent, low molecular weight compound
made an excellent starting point for optimization. Further
analogues were designed using a binding model determined
using Biophysical Mapping, to exploit the “ribose pocket”
within the receptor to improve affinity and selectivity over the
A1 receptor. This process resulted in several highly potent
analogues with favorable selectivity profiles, suitable for further
optimization. Continued exploitation of the results presented in
this article is the subject of ref 6.
Figure 3. Docking of the triazine 15, showing the BPM fingerprint
color coded onto the binding site residues and in graphical form as
change in pKD. Nonbinding is shown in red (N253A, H250A). Next
largest effect is in dark orange (L85A, S277A). H-bonding between the
nitrogen of the triazine and the phenol is predicted to Asn2536.55. The
polar piperazine substituent is proposed to reach into the region of the
binding site occupied by ribose in the natural agonist ligand adenosine
and may be the driver of selectivity versus the A1 receptor, as this
region of the binding site contains some amino acid differences
comparing the two receptors.14 Selected BPM data are tabulated
showing the change in pKD of each binding site mutation.
EXPERIMENTAL PROTOCOLS
■
Virtual Screening Compounds. The compounds used for the
virtual screening were from CAP,15 a collection of commercial vendor
catalogues, together with a subset of the BioFocus SoftFocus library
collections. The hits shown were provided by Chembridge (1, 5),
Interchim (2, 3, 8, 9), Asinex (4), Inter-bioscreen (7, 10), and
BioFocus (6).
receptor occupied by the ribose group of adenosine. The
rationalization of the binding modes of these two chemotypes
represents the first application of BPM analysis4 to lead
generation and has enabled the progression of this chemistry
into lead optimization and beyond.6
Computational Chemistry. Homology models were constructed
from the avian β1 adrenergic GPCR crystal structure bound to
cyanopindolol (PDB code 2VT4).3,16 Owing to the relatively low
percentage identity between the two proteins (25% overall, <20%
around the ligand binding site), two initial homology models of the
adenosine A2A receptor were generated, using different methods. This
provided a means to assess consistency in the alignments, the
variability within the built structures, and which regions of the models
had higher and lower confidence associated with them. One model was
constructed using MODELLER,17,18 while the other was constructed
using MOE19 with manual readjustment of the ClustalW alignment
where necessary.20 The alignment in each case was checked to ensure
consistency with known GPCR conserved motifs21 and particularly the
conserved disulfide bond, common to family A GPCRs, which is
located between the top of helix 3 and the extracellular loop 2. Apart
from the extracellular loop 2, the rest of the modeled structures
showed good agreement and in the MOE model this loop was not
modeled beyond the first few residues up to and including Phe168
because of the very poor alignment in this region. The two homology
models were then further evaluated using two different approaches.
First, SDM data, both from the literature22 and in-house,4 were
mapped onto the modeled protein structures. The majority of these
residues lined the anticipated ligand binding site in each of the models.
The mutation sites showed good consistency in the locations of the
residues when comparing the two structures. Second, both models
were used to dock a small number of known A2A antagonists, including
ZM241385, into each of the structures using Glide as the docking
engine.5,23 This was done to explore the potential docking modes that
CONCLUSION
■
The advent of improved homology modeling and the increasing
availability of structural data for GPCRs are now enabling
virtual screening for receptor targets to be used as a viable
alternative to high-throughput screening. In this study we
aimed to identify novel adenosine A2A antagonists through
virtual screening of a library of 545K compounds using a
homology model based on the turkey β1 adrenoceptor. Hits
were computationally filtered and then cherry picked for
screening, taking into account properties of the binding site
(shape, electronic), the ligand (conformation), and desired
regions for interaction (SDM data), leading to a 9% hit rate
from 230 compounds tested by competition radioligand
binding. Of the top 10 hits, 7 had good LE (>0.3) and LLE
(>3), suggesting that they may represent suitable starting points
for further optimization. Notably, one of the hits was a
chromone (5), a chemotype completely novel in the field of
adenosine receptors antagonists. Using ligand docking and
Biophysical Mapping, latterly supported by X-ray structure
determination, we have been able to identify a credible ligand
binding model.4 A series of optimal ligands that displayed
greatly improved affinities compared to 5 and selectivity over
the adenosine A1 receptor subtype were discovered. However,
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could be achieved and to assist in the development of conditions and
protocols for use in analysis of the virtual screen, with similarly sized
decoys also being used in the studies. It was decided to use the two
models in parallel in the virtual screen.
not significantly different from unity, consistent with a competitive
mode of action.
Chemical Synthesis. Hit compounds 1−10 and follow-up
compounds 11−14 were provided by Chembridge, Interchim, Asinex,
Interbioscreen, or BioFocus. The compounds were supplied with
LCMS purities of >95%, as determined by the vendors. Quality control
data are provided in the Supporting Information. Chemical synthesis
and analysis of 15 and 16 were carried out at Oxygen Healthcare,
India, according to Scheme 3. Full experimental details can be found in
the Supporting Information.
Ligand data sets were drawn from CAP,15 a collection of vendor
catalogues giving details of screening samples for purchase. A subset of
the BioFocus SoftFocus library collections were also screened after
excluding compounds designed to target GPCRs. The compounds
from CAP were prefiltered to remove those molecules containing
unwanted chemical functionality. Physicochemical profiles for the data
set were biased toward a CNS-like profile based on the
recommendations in Pajouhesh et al.24 and the properties of a set of
literature A2A antagonists. 545K compounds were prepared for
screening, and all or a subset from more stringent prefiltering and
clustering docked into each of the models using the SP algorithm
a
Scheme 3. Synthesis of 15 and 16
within the Schrodinger Glide software, running on a 28 CPU Linux
̈
cluster. Details of the workflows, screening compound numbers, and
filters used for the virtual screening and postprocessing analyses with
each homology model are detailed in the Supporting Information
(Figures S2−S4). The protein preparation and docking experiments
were done within the Schrodinger Maestro package. The grid
̈
generation necessary for docking was done within Glide. The residues
highlighted in SDM experiments (in-house and external) were used to
further define the cavity of the grid. However, no constraints were
added in the grid generation to ensure that subsequent dockings were
not biased in any way. As standard, up to 3 poses per molecular
structure were stored for analysis. For some compound subsets, Glide
XP docking was carried out on the ligands with 10 poses per ligand
being stored. A selection of 372 virtual hits was finally prioritized for
purchasing, following manual inspection and subsequent triaging by
medicinal chemistry of the most promising docking solutions.
a
Reagents and conditions: (a) THF, iPr2EtN, NH3. (b) For 16: (i) 3-
(4-methoxypiperidin-1-yl)phenylboronic acid, Na2CO3, 1,4-dioxane/
H2O, Pd(PPh3)4, 90 °C, then (ii) 2-hydroxylphenylboronic acid,
Na2CO3, 1,4-dioxane/H2O, Pd(PPh3)4, 90 °C. (c) For 17: (i) 2-
benzyloxyphenylboronic acid, Na2CO3, 1,4-dioxane/H2O, Pd(PPh3)4,
70 °C, then (ii) 3-(4-methylpiperazine-1-carbonyl)phenylboronic acid
hydrochloride, Na2CO3, 1,4-dioxane/H2O, Pd(PPh3)4, 90 °C; (d) 17,
EtOAc, Pd(OH)2/C, 1,4-cyclohexadiene, 140 °C (microwave).
Subsequent docking experiments on the hits from the radioligand
binding assay and also on analogues of the two hit chemotypes derived
from 1 and 5 were carried out. They were guided by ligand SAR, an
iterative process of assessing SDM data, and also by designing our own
BPM mutants to confirm or rule out possible binding modes, as
previously described.4 As part of this, more detailed modeling work
was carried out, including the use of induced fit docking and restrained
minimization work. For the more active compounds, the MOE derived
model gave more plausible results, and therefore, this was used as the
basis for further improvement and validation work. In particular,
validation and improvement of the homology models for docking were
conducted, focused on ZM241385, because of the wealth of SAR for
this series and the amount of SDM data available for the ligand at the
adenosine A2A receptor.22,25 The induced fit docking (IFD) protocol23
was used within Maestro with an autogenerated box size around the
residues highlighted by SDM as having a large effect on antagonist
ASSOCIATED CONTENT
* Supporting Information
Chemical synthesis protocols, QC data and binding curves for
the top 10 hits, more detailed computational methods including
virtual screening workflows, and a table of calculated blood−
brain barrier prediction and the closest published adenosine A2A
antagonist to each of the hits. This material is available free of
charge via the Internet at http://pubs.acs.org.
■
S
AUTHOR INFORMATION
Corresponding Author
*Phone: +44 (0)1707 358631. Fax: +44 (0)1707 358640. E-
mail: chris.langmead@heptares.com.
■
binding, namely, Ile662.64, Val843.32, Leu853.33, Glu151ECL2
,
,
Leu167ECL2, Glu169ECL2, Asn1815.42, Phe1825.43, His2506.52
ACKNOWLEDGMENTS
■
Asn2536.55, Phe2576.59, Tyr2717.36, Ile2747.39, and His2787.43
.
The authors thank Bissan Al-Lazikani for help with constructing
the first generation of homology models, Benjamin Tehan for
assistance with computational chemistry, and Nat Monck for
assisting with the triaging of screening hits.
Adenosine Receptor Assays. Inhibition binding assays were
performed using 2.5 μg of membranes prepared from HEK293 cells
transiently transfected with human adenosine A2A receptor or 10 μg of
membranes prepared from CHO cells stably transfected with human
adenosine A1 receptor. Membranes were incubated in 50 mM Tris-
HCl (HEK293-hA2A, pH 7.4) or 20 mM HEPES, 100 mM NaCl, 10
mM MgCl2 (CHO-hA1, pH 7.4) in the presence of 5−10
concentrations of test compound and 1 nM [3H]ZM241385
(HEK293-hA2A) or [3H]DPCPX (CHO-hA1) at 25 °C for 1 h. The
DMSO concentration was 0.1% (final). The assay was then terminated
by rapid filtration onto GF/B grade Unifilter plates using a TomTec
cell harvester, followed by 5 × 0.5 mL washes with doubly distilled
H2O. Total binding was defined in the presence of 0.1% DMSO;
nonspecific binding was defined in the presence of 1 μM CGS15943
(HEK293-hA2A) or 1 μM DPCPX (CHO-hA1). Bound radioactivity
was determined by liquid scintillation counting, and inhibition curves
were analyzed using a four-parameter logistic equation. IC50 values
were converted to KI values with the Cheng−Prusoff equation using a
KD derived from saturation binding studies. Compounds were tested
to at least n = 2; concentration response curves displayed Hill slopes
ABBREVIATIONS USED
■
BPM, Biophysical Mapping; PDB, Protein Data Bank; LE,
ligand efficiency; LLE, ligand lipophilicity efficiency; SDM, site
directed mutagenesis
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■
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