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 istradefylline ( 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 .
the most advanced of this class of compounds is preladenant , 
 which is currently in phase iii clinical trials . 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 .
therefore , we sought an alternative 
 structure - based approach to identify novel antagonist chemotypes for 
 the adenosine a2a receptor . by use of an experimentally 
 enabled ( site - directed mutagenesis , sdm ) homology model of the receptor 
 ( based on the crystal structure of the turkey 1 adrenergic 
 receptor in complex with cyanopindolol ) ,
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 .
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 1 adrenergic gpcr crystal structure bound to cyanopindolol ( pdb code 2vt4 ) .
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 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 glide 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 elsewhere and is summarized in the methods 
 section .
a comparison of the homology 
 modeling results with ligand - complexed crystal structures of the adenosine 
 a2a receptor is detailed in ref ( 6 ) .
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 s2s4 ) .
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 1020000 
 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 m ) , 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 .
these best hits also have reasonable to good 
 ligand lipophilicity efficiencies ( lles ) in the range 2.15.4 , 
 with eight having lle  3 .
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 
 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 .
one group reported that a hit rate of 41% was achieved with 23 of 
 the 56 assayed compounds showing activity better than 10 m . similarly a second study yielded a hit rate of 
 35% with 7 of 20 compounds tested having affinities from 200 nm to 
 10 m .
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 .
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 maps 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 - bonding hotspots 
 close together in the site available for very small ligands to bind . 

the key h - bonding residue asn253 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 dynamics simulations with the watermap software 
 ( schrdinger ) , 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 . 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 elsewhere ( 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 asn253 via the chromone carbonyl .
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 .
in addition , the potential of the work in the 
 triazine scaffold ( below and in ref ( 6 ) ) allowed us to deprioritize the chromone template .
docking of the chromone 12 , showing the bpm 
 fingerprint 
 color coded onto the binding site residues and in graphical form as 
 change in pkd .
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 asn253 . selected bpm data are tabulated showing the change in 
 pkd of each binding site mutation .
the second series to be optimized was triazine 1 ( scheme 2 ) , already a highly potent 
 a2a antagonist 
 with excellent le and lle .
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 representative 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 .
comparisons can 
 be made of the binding modes of the chromone and 
 triazine templates using bpm analysis .
alanine mutation of asn253 or his250 abolished the binding of 12 and 15 ; ligand docking suggested that asn253 makes key hydrogen bonding interactions with the amino 
 and phenol functional groups of 15 while for 12 the interaction with asn253 is made by the aromatic 
 c  h of the chromone template and the nitrogen atom of the thiazole 
 substituent .
alanine mutation of ile66 ( pkd = 0.7 ) and tyr271 ( pkd = 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 ( pkd of + 0.1 and 0.1 , respectively ) .
conversely , 
 alanine mutation of ser277 reduced the affinity of 15 ( pkd = 1.0 ) 
 but not 12 ( pkd = 
 + 0.3 ) , consistent with the piperazine group of 15 being 
 oriented toward the pocket of the receptor occupied by the ribose 
 group of adenosine .
the rationalization of the binding modes of these 
 two chemotypes represents the first application of bpm analysis to lead generation and has enabled the progression 
 of this chemistry into lead optimization and beyond .
docking of the triazine 15 , showing the bpm 
 fingerprint 
 color coded onto the binding site residues and in graphical form as 
 change in pkd .
h - bonding between the nitrogen of the triazine and the phenol is predicted 
 to asn253 .
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 . selected bpm data are tabulated showing the 
 change in pkd of each binding site mutation .
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 1 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
a series of optimal ligands that displayed greatly 
 improved affinities compared to 5 and selectivity over 
 the adenosine a1 receptor subtype were discovered . however , 
 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 .
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
the compounds used for 
 the virtual screening were from cap , 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 ) .
homology models were constructed 
 from the avian 1 adrenergic gpcr crystal structure 
 bound to cyanopindolol ( pdb code 2vt4 ) . 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 , while the 
 other was constructed using moe with 
 manual readjustment of the clustalw alignment where necessary .
the alignment in each case was checked to ensure 
 consistency with known gpcr conserved motifs 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 .
first , sdm data , both from the literature and in - house , 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 .
this was done to explore the potential docking modes that 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 .
ligand data sets were drawn 
 from cap , 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 . 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 within the schrdinger 
 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 
 s2s4 ) .
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 ,
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 .
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 .
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 .
the induced fit docking ( ifd ) protocol was used within maestro with an autogenerated box size around the 
 residues highlighted by sdm as having a large effect on antagonist 
 binding , namely , ile66 , val84 , leu85 , glu151 , leu167 , glu169 , asn181 , phe182 , his250 , asn253 , phe257 , tyr271 , ile274 , and his278 .
inhibition binding assays 
 were performed using 2.5 g of membranes prepared from hek293 
 cells transiently transfected with human adenosine a2a receptor 
 or 10 g 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 510 concentrations of test compound and 1 
 nm [ h]zm241385 ( hek293-ha2a ) or [ h]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 m cgs15943 ( hek293-ha2a ) or 1 m dpcpx ( cho - ha1 ) .
bound radioactivity 
 was determined by liquid scintillation counting , and inhibition curves 
 were analyzed using a four - parameter logistic equation .
compounds were tested to 
 at least n = 2 ; concentration response curves displayed 
 hill slopes not significantly different from unity , consistent with 
 a competitive mode of action .
hit compounds 110 and follow - up compounds 1114 were provided by chembridge , interchim , asinex , interbioscreen , 
 or biofocus .
the compounds were supplied with lcms purities of > 95% , 
 as determined by the vendors .
chemical synthesis 
 and analysis of 15 and 16 were carried out 
 at oxygen healthcare , india , according to scheme 3 .
( 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 ) .
 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 
 ( 1113 from 5 , pki = 7.58.5 , 13- to > 100-fold selective 
 versus adenosine a1 ; 1416 from 1 , pki = 7.99.0 , 
 19- to 59-fold selective ) . 
Introduction
Results and Discussion
Conclusion
Experimental Protocols