Structure-Based and Fragment-Based GPCR Drug Discovery

DOI: 10.1002/cmdc.201300382

Structure-Based and Fragment-Based GPCR DrugDiscoveryStephen P. Andrews, Giles A. Brown, and John A. Christopher*[a]

� 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim ChemMedChem 2014, 9, 256 – 275 256

CHEMMEDCHEMREVIEWS

Introduction

G protein-coupled receptors (GPCRs) are key modulators of cel-lular function and, with 390 members in the human genome(excluding olfactory receptors), represent the largest family ofmembrane-bound receptors.[1] The binding of a diverse rangeof endogenous ligands, including hormones, neurotransmit-ters, metabolites and cytokines, mediates a conformationalchange within GPCRs, resulting in cell signaling. These recep-tors all have a similar configuration incorporating an extracellu-lar N terminus and intracellular C terminus connected by seventransmembrane a helices (7TM). They are divided into fourmajor classes based on sequence similarity (classes A, B, C, andFrizzled),[1–3] of which class A, the rhodopsin family, is the larg-est, with approximately 300 members. This large class includesthe aminergic receptors, chemokine receptors, glycoproteinhormone receptors, and neuropeptide receptors. Class BGPCRs, the second-largest class, are further divided into the se-cretin (15 members) and adhesion (33 members) subfamilies.The secretin subfamily includes receptors for peptides such ascalcitonin gene-related peptide (CGRP), corticotropin-releasingfactor (CRF), glucagon, glucagon-like peptide (GLP), parathy-roid peptide hormone (PTH), and secretin. Metabotropic gluta-mate (mGlu) receptors are examples of class C GPCRs, and aredistinguished from the other classes by a very large extracellu-lar N-terminal domain, commonly known as the Venus fly trapdomain, which contains the endogenous ligand binding site.

GPCR drug discovery has been very successful, and a largenumber of small-molecule drugs that either block or imitatethe endogenous ligands have been developed to treat a rangeof different diseases. In 2008, seven of the top-15 prescriptiondrugs and five of the top-15 generic drugs targeted GPCRs.[4]

GPCR drug discovery continues to be an important area forthe pharmaceutical industry, and in the last ten years nearlya quarter of all new chemical entities approved for launchhave been GPCR drugs.[5] However, research in this area contin-ues to be challenging, with less than 20 % of all GPCRs current-ly being drugged with small molecules, and on average onlyone new GPCR per year being drugged in the last decade.[1, 5]

Many clinically relevant GPCRs still remain undrugged, and

there are currently over 100 orphan GPCRs for which the en-dogenous ligands and pharmacology remain unknown.[1] Tradi-tional GPCR drug discovery efforts have focussed on high-throughput screening of large compound libraries using cell-based assays to identify novel hits. For challenging GPCR tar-gets such as neuropeptide receptors, chemokine receptors,metabotropic glutamate receptors, and peptide-hormone re-ceptors, this approach has had limited success.

In 2000 the first crystal structure of a class A GPCR, rhodop-sin, was published.[6] After a gap of seven years, an additional19 medium-to-high-resolution crystal structures of class AGPCRs and one Frizzled GPCR have subsequently been pub-lished in complex with either small-molecule or peptide li-gands [b2-adrenergic receptor (b2AR),[7–12] b1-adrenergic recep-tor (b1AR),[13–17] adenosine A2A receptor (A2A),[18–24] sphingosine1-phosphate 1 receptor (S1P1),[25] the chemokine receptorCXCR4,[26] dopamine D3 receptor (D3),[27] histamine H1 receptor(H1),[28] muscarinic acetylcholine M2 and M3 receptors,[29, 30] neu-rotensin receptor,[31] opioid receptors d,[32] k,[33] m,[34] and noci-ceptin,[35] protease-activated receptor 1 (PAR1),[36] serotonin re-ceptor 1B (5-HT1B),[37] serotonin receptor 2B (5-HT2B),[38] the che-mokine receptor CCR5,[39] and the smoothened receptor(SMO)] .[40] More recently, the first crystal structures of class BGPCRs have been published (corticotropin-releasing factor 1 re-ceptor (CRF1) and the glucagon receptor).[41, 42] Recent insightinto GPCR structure have greatly enhanced the understandingof the molecular mechanisms of activation and constitutive ac-tivity of these receptors,[43] and have enabled the use of struc-ture-based drug design techniques to be applied to the targetclass for the first time.[44]

The recent upsurge in the number of GPCR crystal structuresis due to technical advances that have allowed the isolationand purification of receptors in single homogeneous confor-mations, overcoming the inherent flexibility of the proteins.These approaches, described briefly below, have been thor-oughly reviewed elsewhere.[45–47] The first method formsa fusion protein by introducing a soluble protein into the crys-tallisation construct, usually into the third intracellular loop(ICL3), to improve crystal contacts and help promote crystalgrowth. The fusion protein is often T4 lysozyme (T4L), thoughother partners have been investigated,[48] and the T4L strategywas first demonstrated with the structure of the b2AR,[7] com-bined with lipidic cubic phase (LCP) crystallisation. The secondmethod introduces several point mutations into the receptorsequence which significantly increases the thermostability of

G protein-coupled receptors (GPCRs) are an important familyof membrane proteins; historically, drug discovery in thistarget class has been fruitful, with many of the world’s top-sell-ing drugs being GPCR modulators. Until recently, the moderntechniques of structure- and fragment-based drug discoveryhad not been fully applied to GPCRs, primarily because of theinstability of these proteins when isolated from their cell mem-brane environments. Recent advances in receptor stabilisationhave facilitated major advances in GPCR structural biology

over the past six years, with 21 new receptor targets success-fully crystallised with one or more ligands. The dramatic in-crease in GPCR structural information has yielded an increaseduse of structure-based methods for hit identification and pro-gression, which are reviewed herein. Additionally, a number offragment-based drug discovery techniques have been validat-ed for use with GPCRs in recent years ; these approaches andtheir use in hit identification are reviewed.

[a] Dr. S. P. Andrews, Dr. G. A. Brown, Dr. J. A. ChristopherHeptares Therapeutics Ltd. , BioParkWelwyn Garden City, Hertfordshire, AL7 3AX (UK)E-mail : [email protected]


CHEMMEDCHEMREVIEWS www.chemmedchem.org

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the protein and allows purification in short-chain detergentswhich are required for crystallisation by vapour diffusion.[49–51]

The strategy was first used to determine the structure of theturkey b1AR receptor,[13] and receptors stabilised in this way areknown as StaR proteins.[52] The method biases the GPCR to-wards a specific conformation based on the pharmacology ofthe ligand used during the stabilisation process, potentially al-lowing structures of the same GPCR to be determined in bothinactive and active forms. A final strategy generates a complexof the target GPCR with monoclonal antibody fragments (Fab),as used in the first crystal structure of the b2AR.[8]

SBDD Approaches to GPCR Drug Discovery

The recent rapid growth in crystallographic information has fu-elled structure-based drug discovery (SBDD) approaches in theGPCR field. Many of these approaches are centred on hit iden-tification through virtual screening (VS), both against receptorswith solved crystal structures and also via the construction andscreening of homology models of receptors for which thereare currently no direct crystallographic data.[53, 54] Additionally,structural information is facilitating true structure-based designapproaches, where a clearer understanding of ligand bindingmodes and interactions with receptor binding sites is allowingthe rational optimisation of molecules to drive increased affini-ty and selectivity for the receptor of interest. Reviewed in thissection are SBDD approaches reported between 2007 and theend of June 2013 that use several of the recently elucidatedGPCR crystal structures. Purely ligand-based hit discovery oroptimisation approaches, and virtual screens based on homol-ogy models derived from rhodopsin, are outside the scope ofthis review. Table 1 details the GPCR crystal structures thathave found utility in SBDD campaigns to date, and Table 2 de-tails key compounds resulting from the virtual screens.

b-Adrenergic receptors (bAR)

b-Adrenergic receptors (bAR), of which there are b1, b2, and b3

subtypes, are class A GPCRs activated by the endogenous cate-cholamine ligands adrenaline and noradrenaline.[55] A wealth ofdrug discovery efforts aimed at modulation of this subfamilyhas produced several clinically relevant agonist and antagonistdrugs. b-Adrenergic receptor agonists are used in the treat-ment of respiratory diseases such as chronic obstructive pul-monary disease (COPD) and asthma,[56, 57] and antagonists haveutility in cardiovascular medicine, anxiety, and migraine.[58]

Crystal structures of the b2-adrenergic receptor (b2AR),bound to the inverse agonist carazolol (Figure 1), were pub-lished in 2007 (Table 1). The first structure was facilitated bycomplexation of the receptor with an antibody fragment, re-sulting in 3.4 � resolution.[8] The second structure, solved toa higher resolution of 2.4 �,[7] pioneered the use of insertingT4L into ICL3 as a stabilisation strategy. Subsequently, struc-tures of the antagonist timolol,[9] the inverse agonistICI 118551, alprenolol, and a benzofuran derivative were dis-closed,[10] followed by a structure of the agonist BI-167107(Figure 1) in a nanobody-stabilised active state of the b2AR.[11]

Steve Andrews obtained his PhD from

Cambridge University (UK) in 2006,

where he completed the first total syn-

thesis of thapsigargin with Professor

Steven V. Ley CBE FRS. He then under-

took his postdoctoral studies in transi-

tion-metal-catalyzed enantioselective

organic transformations at ETH-Z�rich

with Professor Erick Carreira before

joining Heptares Therapeutics in 2008.

Since that time, Steve’s research has

focused on applying Heptares’ unique

structural biology capabilities to medicinal chemistry projects with

GPCRs. He has worked in lead chemist and project leader roles in

drug discovery programs with a number of different receptors, in-

cluding the adenosine A2A receptor, for which he co-invented Hep-

tares’ first clinical candidate, an antagonist for the treatment of

CNS disorders.

Giles Brown obtained his PhD at the

University of Bristol (UK) in 2000 work-

ing with Prof. Timothy C. Gallagher on

a novel azomethine ylide approach to

the synthesis of bicyclic b-lactams. He

then carried out postdoctoral studies

with Prof. Gary A. Molander at the Uni-

versity of Pennsylvania (USA) investi-

gating the development of SmI2-pro-

moted sequential reactions. In 2002 he

moved to Evotec OAI to work as a me-

dicinal chemist, and in 2003 he moved

to Cambridge BioTechnology, where he led discovery teams devel-

oping novel drugs for GPCRs and protein–protein interactions. He

joined Heptares Therapeutics in 2010 and is currently an Associate

Director, leading discovery projects focussed on CNS indications.

He is the project leader and co-inventor of Heptares’ muscarinic

M1-selective agonist clinical candidate for the treatment of Alz-

heimer’s disease.

John Christopher obtained his PhD in

Organic Chemistry from the University

of Glasgow (UK) in 2000, working with

Prof. Philip J. Kocienski on the applica-

tion of novel organomolybdenum

complexes to the total synthesis of the

natural product cryptophycin 4. He

then joined GlaxoSmithKline in 2000,

applying structure- and fragment-

based drug discovery techniques to

projects he led across diverse protein

target classes and therapeutic areas.

John joined Heptares Therapeutics in 2010 and is an Associate Di-

rector within the Medicinal Chemistry department, researching

GPCR targets linked to diseases including insomnia, addiction, de-

pression, autism, and headache disorders.



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A subsequent publication revealed the structure of active stateb2AR in complex with BI-167107, a nanobody, and the Gs pro-tein, providing the first high-resolution view of GPCR trans-membrane signalling.[12] The b1 subtype of the adrenergic re-ceptor has also been crystallised using the StaR stabilisationstrategy, first demonstrated in 2008 by the determination ofthe turkey b1AR structure at 2.7 � in complex with the antago-

nist cyanopindolol (Figure 1).[13]

b1AR structures of partial ago-nists salbutamol and dobuta-mine were subsequently pub-lished in 2011, together withthose of the receptor in complexwith full agonists carmoterol andisoprenaline.[14] Further struc-tures with carazolol, iodocyano-pindolol, bucindolol, and carve-dilol have additionally been dis-closed.[15, 16] The latter two struc-tures are of particular interest, asthe ligands are classified asbiased agonists due to their abil-ity to stimulate G-protein-inde-pendent signalling, and eachform additional contacts withthe receptor to helix 7 and extra-cellular loop 2 (EL2), observa-tions which begin to elucidatethe requirements of binding ofbiased ligands.

In an early exploitation ofb2AR structures, Topiol and col-leagues used Glide and GOLDdocking tools to build confi-dence in their ability to producedocking poses of carazolol thatwere in accordance with the X-ray structure.[59] Further valida-tion with other b-antagonistsyielded plausible predicted bind-ing modes, and the approachwas extended to a high-through-put docking regime that wasable to efficiently extract knownantagonists from databases of

proprietary and commercial compounds.[60] Testing in a radioli-gand binding assay of available compounds from the top-150hits in each database yielded hit rates of 36 and 12 % respec-tively, which were each in great excess of the hit rate froma randomly selected set of 320 proprietary compounds.[60] Thehighest-affinity compound (pKi = 6.8), other than carazolol,identified from the screen of the proprietary database is1 (Table 2), which was ranked 30th in the docking exercise.Structures of hits from the commercial database were not dis-closed. In a similar exercise in 2009, Kobilka, Shoichet, and co-workers reported the high-throughput docking of approxi-mately one million compounds from the ZINC database intothe carazolol-bound structure of b2AR.[61] Twenty-five moleculeswere selected from the top-500 ranked compounds and pro-gressed to a radioligand binding assay. Six compounds, 24 % ofthose progressed, yielded full-curve inhibition profiles withpKi>5.4; compound 2 had the highest affinity (pKi = 8.0) andwas ranked sixth by the docking scoring function. The hit isrepresentative of a classical b-blocker motif, wherein an etha-

Table 1. Selected GPCR crystal structures.

Entry Year Receptor,Conformation

Methodology Ligand Resolution [�] PDB ID Ref.

1 2007 b2, inactive T4L fusion carazolol 2.40 2RH1 [7]2 2007 b2, inactive Fab complex carazolol 3.40 2R4R [8]3 2008 b2, inactive T4L fusion timolol 2.80 3D4S [9]4 2010 b2, inactive T4L fusion ICI 118551 2.84 3NY8 [10]5 2010 b2, inactive T4L fusion Benzofuran derivative 2.84 3NY9 [10]6 2010 b2, inactive T4L fusion Alprenolol 3.16 3NYA [10]7 2011 b2, active nanobody

stabilisedBI-167107 3.50 3P0G [11]

8 2008 b1, inactive StaR cyanopindolol 2.70 2VT4 [13]9 2011 b1, inactive StaR salbutamol 3.05 2Y04 [14]10 2011 b1, inactive StaR dobutamine 2.50, 2.60 2Y00, 2Y01 [14]11 2011 b1, inactive StaR carmoterol 2.60 2Y02 [14]12 2011 b1, inactive StaR isoprenaline 2.85 2Y03 [14]13 2011 b1, inactive StaR carazolol 3.00 2YCW [15]14 2011 b1, inactive StaR iodocyanopindolol 3.65 2YCZ [15]15 2012 b1, inactive StaR bucindolol 3.20 4AMI [16]16 2012 b1, inactive StaR carvedilol 2.30 4AMJ [16]17 2013 b1, inactive StaR 4-(piperazin-1-yl)-

1H-indole2.80 3ZPQ [17]

18 2013 b1, inactive StaR 4-methyl-2-(piper-azin-1-yl) quinoline

2.70 3ZPR [17]

19 2008 A2A, inactive T4L fusion ZM241385 2.60 3EML [18]20 2011 A2A, active T4L fusion UK-432097 2.71 3QAK [19]21 2011 A2A, inactive StaR caffeine 3.60 3RFM [20]22 2011 A2A, inactive StaR XAC 3.31 3REY [20]23 2011 A2A, inactive StaR ZM241385 3.30 3PWH [20]24 2011 A2A, active StaR NECA 2.60 2YDV [24]25 2011 A2A, active StaR adenosine 3.00 2YDO [24]26 2012 A2A, inactive StaR 1,2,4-triazine derivative 3.27 3UZA [21]27 2012 A2A, inactive StaR 1,2,4-triazine derivative 3.34 3UZC [21]28 2012 A2A, inactive Fab complex ZM241385 3.10 3VGA [22]29 2012 A2A, inactive Fab complex ZM241385 2.70 3VG9 [22]30 2012 A2A, inactive BRIL fusion ZM241385 1.8 4EIY [23]31 2012 S1P1, inactive T4L fusion ML056 2.80 3V2Y [25]32 2010 CXCR4, inactive T4L fusion IT1t 2.50 3ODU [26]33 2010 CXCR4, inactive T4L fusion CVX15 2.90 3OE0 [26]34 2010 D3, inactive T4L fusion eticlopride 2.89 3PBL [27]35 2011 H1, inactive T4L fusion doxepin 3.10 3RZE [28]

Figure 1. b1AR and b2AR ligands.



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Table 2. Key details of recent GPCR virtual screening campaigns.

Compd VS Size Number of Hits[a] Structures of Notable Hits Affinity (LE) Closest Known Structure[b]

b-Adrenergic receptor virtual screens

1[60] 4 � 105

20 hits �35 %inhibitionat 10 mm

(36 %)

b2ARpKi = 6.8[c]

(0.40)

2[61] 1 � 106 6 hits pKi>5.4(24 %)

b2ARpKi = 8.0[c]

(0.45)

(0.33[d]/adrenergic subset of theWOMBAT database)

3[61] 1 � 106 6 hits pKi>5.4(24 %)

b2ARpKi = 6.0[c]

(0.34)(0.22[d]/adrenergic subset of theWOMBAT database)

4[63] 3.4 � 106 6 hits pEC50�4.5(27 %)

b2ARpEC50 = 4.5[e]

closest molecule not specified(0.29[d]/ChEMBL)

5[107] 5 � 104


(0.7 %)

b2ARpKi = 6.1[c]

(0.40)

(0.38[d]/ChEMBL)

Adenosine receptor virtual screens

6[103] 1.4 � 106


(35 %)

A2A

pKi = 6.7[f]

(0.38)

(0.37[d]/WOMBAT & ChEMBL)

7[103] 1.4 � 106


(35 %)

A2A

pKi = 6.7[f]

(0.38)

(0.30[d]/WOMBAT & ChEMBL)

8[105] 4 � 106 23 hits pKi>5.0(41 %)

A2A

pKi = 7.2[g]

(0.39)

(0.35[h]/GLIDA)

9[105] 4 � 106 23 hits pKi>5.0(41 %)

A2A

pKi = 7.2[g]

(0.43)

(0.33[h]/GLIDA)



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Table 2. (Continued)


10[106] ChemDiv library (Mr <500)

8 hits >30 %inhibitionat 10 mm

(22 %)

A2A

pKi = 6.1[f]

(0.36)(0.63[d]/all known adenosinereceptor ligands)

11[107] 5 � 104


(2 %)

A2A

pKi = 5.9[g]

(0.35)

(0.26[d]/ChEMBL)

12[107] 5 � 104


(2 %)

A2A

pKi = 6.7[g]

(0.28)

(0.26[d]/ChEMBL)

13[108] 1584

2 hits >58 %inhibitionat 10 mm

(50 %)

A2A

pKi = 6.2[g]

(0.28)analysis not performed

14[109] 1 � 105


(11 %)

A2A

pKi = 7.4[g]

(0.50) (0.68[i]/training set of10 999 data points)

15[110] 5.45 � 105

20 hits withpIC50>4.3(9 %)

A2A

pKi = 8.5[g]

(0.53)

(0.31[j]/known adenosinereceptor ligands)

S1P1, dopamine, CXCR4 and histamine receptor virtual screens

16[107] 5 � 104

3 hits �30 %b-arrestinrecruitment(0.3 %)

S1P1

pEC50 = 4.7[k]

(0.22[d]/ChEMBL)

17[123] >3 � 106

6 hits withpKi�5.5(23 %)

D3

pKi = 5.8[l]

(0.38)

(0.23[d]/ChEMBL)

18[123] 3.6 � 106

5 hits withpKi�5.5(20 %)

D3

pKi = 6.5[l]

(0.40)(0.48[d]/ChEMBL)



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nolamine side chain protrudes from an aromatic portion. Incontrast, quinoline 3, which ranked 97th in the docking evalua-tion, represents a new b2AR chemotype, validating the abilityof structure-based VS to identify hits in new areas of chemicalspace.

In work predating the publication of the crystal structure ofthe active form of b2AR, de Graaf and Rognan examined theuse of b2AR inactive (ground state) crystal structures in struc-ture-based VS to distinguish inverse agonists and antagonistsfrom partial or full agonist molecules.[62] When an initial modelbased on the inactive-state crystal structures was modified toreflect the early conformational changes in activation of the re-ceptor and combined with a topological scoring function, thedocking-based virtual screen was able to identify partial andfull agonists from a test set of known compounds. Althoughnot followed up in a ligand-discovery campaign, this studyprovided confidence that customised homology models basedon inactive-state structures can be used in VS campaigns, animportant point given that the majority of GPCR structures arein the inactive state, and the identification of partial or full ag-onist chemotypes remains challenging. In a recently reportedstudy, Lefkowitz, Shoichet, and colleagues used the active formcrystal structure of b2AR (PDB ID: 3P0G) in a prospective virtual

screen of 3.4 million molecules from the ZINC database, return-ing several potent agonists to the exclusion of inverse ago-nists.[63] Twenty-two molecules were experimentally tested infunctional and binding assays, yielding four full agonists andtwo partial agonists, of which compound 4 is the least similarhit to known b2AR ligands. To determine whether the activeb2AR structure would serve as a viable template for the activat-ed conformation of other GPCRs, a model of the dopamine D2

receptor was constructed and a virtual screen conducted. Hitrates and potencies were significantly lower than those for theanalogous b2AR screen, indicating that structural informationfrom the 3P0G structure could not be readily transferred.

Hattori et al. used a receptor homology model of the b3AR,based on the b2AR carazolol structure, to rationalise the ob-served b3-subtype selectivity of a series of biphenyl acylsulfo-namide agonists, providing insight to facilitate further SBDDadvances of the series.[64] As discussed in greater detail in theCase Histories section below, knowledge from several b1ARcrystal structures has been used in our laboratories in the effi-cient structure-based progression of fragment screening hitsidentified in a biophysical screen using a stabilised form of thereceptor.[17]

Table 2. (Continued)


19[128] 3.3 � 106

1 hit withpIC50 = 4.0(4 %)

CXCR4pIC50 = 4.0[m]

(0.36[d]/ChEMBL)

20[128] 4.2 � 106

4 hits withpIC50�4.1(17 %)

CXCR4pIC50 = 4.2[m]

(0.24[d]/ChEMBL)

21[129] 1.09 � 105

19 hits withpKi�5(73 %)

H1

pKi = 8.2[n]

(0.56)

(0.34[d]/ChEMBL)

22[130] 1.56 � 105

18 hits withpKi�5(62 %)

H3

pKi = 6.3[o]

(0.45)(0.40[d]/ChEMBL)

[a] Number of hits that meet the specified affinity criteria and hit rate as a percentage of the molecules taken forward into in vitro screening in parenthe-ses. [b] Where relevant, similarity score and libraries searched are specified. [c] Radioligand binding assay with [3H]dihydroalprenolol. [d] Tanimoto ECFP4fingerprint. [e] GloSensor cAMP accumulation assay. [f] Radioligand binding assay with [3H]CGS21680. [g] Radioligand binding assay with [3H]ZM241385.[h] Tanimoto fingerprint not specified. [i] Tanimoto SCFP4 fingerprint. [j] Tanimoto FCFP4 fingerprint. [k] b-Arrestin recruitment assay. [l] Radioligand bindingassay with [3H]N-methylspiperone. [m] Calcium flux assay. [n] Radioligand binding assay with [3H]mepyramine. [o] Radioligand binding assay with[3H]methylhistamine.



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Adenosine receptors

The adenosine receptors constitute a subfamily of class AGPCRs that are activated by binding to the purine nucleoside,adenosine. There are four adenosine receptor subtypes (A1,A2A, A2B, and A3), which can be grouped into two pairs, witheach of the pairs sharing relatively high sequence similarityand common activation pathways.[65, 66] A2A and A2B share 59 %of their sequence identities and, when activated, stimulate theproduction of cAMP (primarily by coupling to Gs protein)whilst A1 and A3 share 49 % of their sequence identities and in-hibit the production of cAMP when activated (primarily by Gi

protein coupling). Selective agonist and antagonist ligands areknown for the various subtypes, and these have been re-viewed elsewhere,[67] as have allosteric modulators of A1, A2A,and A3.[68]

Experimental evidence from both in vivo animal models andhuman clinical trials suggests that A2A antagonists may be ef-fective, non-dopaminergic treatments for Parkinson’s disease(PD).[69–72] PD is a neurological disease in which motor functionis gradually impaired by the continual loss of dopaminergicneurons in the striatum.[73] Dopamine D2 receptor signallingcan often be restored in the early stage of the disease, mostcommonly by administering l-3,4-dihydroxyphenylalanine (l-DOPA, a dopamine precursor) ; however, as the disease pro-gresses this treatment becomes ineffective and is further com-plicated by the accompaniment of dyskinesias.[74] Thus, non-dopaminergic PD therapies such as A2A antagonists may offerthe potential of disease prevention with the added benefit ofdecreased side effects.

The majority of A2A antagonist ligands have been derivedfrom purines (particularly adenine and xanthine) and otherclosely related heterocycles (Figure 2). The xanthine derivativescaffeine and theophylline are nonselective adenosine receptorligands, whereas istradefylline is a potent and selective A2A an-tagonist,[75] which was recently approved in Japan for use in

the adjunctive treatment of PD. The core heterocycles ofZM241385 and preladenant can be considered homologues ofadenine, and these compounds are potent and selective A2A

antagonists. ZM241385 has been used widely as a toolligand,[76, 77] and preladenant has been evaluated in phase IIIclinical trials for the treatment of PD, although it was recentlydiscontinued by Merck and Co. due to insufficient efficacyversus placebo.[78] This may be due to the properties of prela-denant or issues with the mechanism in the larger popula-tion.[79]

More recently, further non-xanthine A2A antagonists havealso been evaluated in rodent models of PD and in the clinic(Figure 3). The most advanced compound is currently tozaden-

ant, which is in phase II clinical trials to assess its efficacy asa treatment for PD and also for its effects on behaviour in co-caine addiction.[80] Lu AA47070 (a phosphonooxymethyleneprodrug of the active thiazole species) showed efficacy in sev-eral rodent models of PD; however, clinical progress was termi-nated after phase I, as the results did not meet Lundbeck’s ex-pectations.[81] JNJ-1734 and related compounds are potent A2A/A1 antagonists that have also shown efficacy in several rodentmodels of PD, but the series has not been progressed into theclinic by Janssen.[82, 83]

Over the last five years much progress has been made ingenerating A2A fusion and StaR constructs, and this has beenenormously enabling to the goal of generating and solving X-ray diffraction patterns of this receptor. In 2008, the receptorwas successfully fused with T4 lysozyme, facilitating crystallisa-tion and allowing its structure to be elucidated for the firsttime (Table 1).[18] This structure, solved to 2.6 � resolution withthe inverse agonist ZM241385 bound in the orthostericpocket, revealed the receptor in its inactive conformation. In2011, several A2A structures were reported, in both active andinactive conformations. These were enabled, once again, byeither fusing the receptor with T4 lysozyme or by generatingthermostabilised receptors (StaR proteins). Inactive A2A StaRconformations have been elucidated both with weakly bindingsmall molecules such as caffeine, and with larger, more potentligands such as ZM241385.[20] Ligand–receptor complexes havealso been solved with A2A StaR constructs during the structure-Figure 2. Examples of purine-derived A2A antagonists.

Figure 3. Examples of non-purine A2A antagonists.



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based design of novel antagonists (see the Case Histories sec-tion below).[21]

In 2012, the inactive structure of A2A was solved as a complexwith an allosteric inverse agonist antibody and,[22] most recent-ly, a high-resolution (1.8 �) inactive structure was solved usinga BRIL fusion partner,[23] which allowed the characterisation ofan allosteric Na+ pocket, as well as a water network and cho-lesterol binding sites. Active conformations of A2A have beenobserved using a T4L-fusion construct complexed to the largeA2A-selective agonist, UK-432097,[19] and with thermostabilisedA2A bound either to the natural agonist adenosine or NECA.[24]

Comparing these structures to those of the inactive state hasprovided insight to the conformational changes that GPCRsundergo during activation, a topic that has received much at-tention within the GPCR research community.[2, 4, 5, 84–90]

A2A was one of the first GPCRs with a solved 3D structure(PDB ID: 3EML),[18] and directly preceding its publication in2008, a community-wide assessment of GPCR modelling anddocking was undertaken.[91, 92] Twenty nine participants at-tempted to correctly predict the 3D structure of receptor andposition of the ligand, ZM241385. The results revealed varyingdegrees of accuracy, as determined by the RMSD of the ligandposition and also the number of correct ligand–protein con-tacts in the models versus the 3EML structure. The averageRMSD was 9.5 �, and the average number of correct contactswas four (of a possible 75), reflecting the enormity of the chal-lenge involved in GPCR homology modelling, even when start-ing from relatively closely related templates (at that time crys-tallographic data were available for b1AR and b2AR, both ofwhich are class A GPCRs). However, several of the models iden-tified good ligand poses, and the best overall model had anRMSD of 2.8 � and 34 correct contacts, including key hydrogenbonds and stacking interactions.

As delineated above, several crystal structures of A2A arenow available, and access to these data has prompteda steady increase in the number of publications of VS cam-paigns against this receptor over the last five years, as well asseveral more comparisons of the crystal structures to predictedmodels.[93–95] These structures have also been used to retro-spectively predict binding modes of clinical agents such asLu AA47070,[96] to design A2A agonists,[97, 98] and for in silicoscreening with other adenosine receptor subtypes.[99–102]

Jacobson and co-workers used DOCK 3.5.54 to screen A2A

against a virtual library of 1.4 million compounds from theZINC database.[103] The top-500 ranking molecules were in-spected visually, assessed for their physicochemical properties,novelty, and additional interactions not included in dockingscore. From this assessment, the top-20 compounds were pur-chased and screened against hA2A in radioligand binding assay.Seven of these compounds showed �40 % inhibition at 20 mm.Two of the hits, 6 and 7, showed affinities of pKi 6.7 (Table 2),demonstrating that VS with models derived from crystallo-graphic data can be used to prioritise small sets of active com-pounds from very large virtual libraries. Follow-up screening ofclose analogues of 7 identified five related ligands with A2A

binding affinities of pKi 5.2–6.4. The SAR of 1,2,4-triazole 6 wasinvestigated further with structure-based methods to under-

stand the binding mode of this new chemical series and to at-tempt to improve its activity.[104]

In a similar study, Katritch et al. screened a library of fourmillion commercially available compounds in silico using the3EML structure and ICM modelling software.[105] The screenperformance was enhanced by retaining several highly struc-tured water molecules in the binding site, a finding in linewith observations that GPCR druggability assessments are sig-nificantly aided by consideration of water energetics.[86] Fromthis, 56 compounds were prioritised for evaluation in a radioli-gand binding assay, and 23 compounds were found to havepKi values >5. The two most potent hits, 8 and 9, had bindingaffinities of pKi 7.2 and were structurally dissimilar to adenosinereceptor ligands known in the GLIDA database, again demon-strating the ability of high-quality structural data to facilitatethe identification of new ligands for GPCRs.

IJzerman and colleagues used a frequent substructure ap-proach to virtual screening, using features of known ligands toguide in silico screening with the 3EML structure.[106] Using thisapproach, compounds with Mr<500 Da from the catalogue ofa screening compound vendor were ranked, and the top-36compounds were purchased for biological testing. Of these 36,eight compounds were found to inhibit A2A binding of[3H]ZM241385 by �30 % at 10 mm, with 10 showing the great-est affinity; however, as this method used a substructure ap-proach to identify hits, the active compounds showed highersimilarity to known ligands than those identified in some ofthe other studies.

de Graaf and co-workers recently performed prospective vir-tual screens against A2A, b2AR, and S1P1,[107] all of which nowhave published crystal structures available. Both ligand- andstructure-based methods were used to build models and thento rank and select compounds (Snooker pharmacophores andfrequent substructure ranking), each with varying degrees ofsuccess with known ligands for the different receptors. For ex-ample, b2AR ligands bind relatively deeply within the TMdomain, and structure-based searches performed better forthis receptor than for A2A, which had a large training set ofknown binders and performed best with ligand-based meth-ods. Retrospective VS validation was performed with themodels, using 50 diverse actives versus 50 000 compounds as-sumed to be inactive. Prospective VS then selected 300 mole-cules for each of the three receptors. Thus 900 compoundswere purchased and cross-screened against all of the receptorsin biological assays. The highest hit rates were observed withA2A, and this was, in part, attributed to the availability of a di-verse training set for this receptor. In total, 18 A2A, six b2AR,and three S1P1 active ligands were identified. Twelve of the 18active A2A compounds were selected for biological testing, asthey ranked highly during the in silico screening against thisreceptor. Of these, compound 11 was found to have the high-est affinity (pKi = 5.9) at A2A. The remaining six A2A actives wereselected as they ranked highly against one of the other recep-tors in silico, including three compounds that had also beenselected by the A2A models, but with a much lower ranking.For example, sildenafil (12) ranked at position 160 for the S1P1

receptor in silico screen and 2285 in the A2A screen. Interest-



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ingly, this PDE5 inhibitor was found to have an affinity of0.2 mm for A2A and was the most active compound identified inthis study. The highest-affinity b2AR and S1P1 ligands listed inTable 2 were compounds 5 and 16, respectively.

Areias et al. performed a virtual screen of a collection of1584 academic compounds against the four adenosine recep-tor subtypes and identified novel chromenes as ligands forA2A.[108] Following the in silico screen, four derivatives were pri-oritised for biological screening against the adenosine receptorsubtypes, and two were found to be active against A2A in vitro.The most active of these was 13, with a binding affinity ofpKi 6.2. The affinity for A2A was improved approximately 10-foldfollowing optimisation, but these compounds also showedsub-micromolar affinities for A1 and A2B.

A proteochemometric approach was recently used by vanWesten et al. to widen the accessible training set for adenosinereceptors, taking into account published bioactivity data for allfour subtypes from two species (rat and human).[109] The pro-teochemometric modelling (PCM) approach can benefit fromthe phenomenon that closely related targets bind closely relat-ed ligands, and, as the four adenosine receptor subtypes showstructural similarity, it was anticipated that using a modeltrained on all of these subtypes would perform better thana model trained on just one. The model was constructed fromthe published structure of the A2A receptor, 3EML, and refinedwith a number of target and ligand descriptors. For example,receptor subtype residues were sampled from within a 5 or7 � sphere of the solved ligand ZM241385, in the 3EML struc-ture, and the final model was trained with the full data set ofthe eight receptors (four subtypes for two species). The finalvirtual screen was conducted with 791162 compounds fromChemDiv and ZINC databases, and 54 compounds were select-ed for screening in biological assays. Performance varied forthe different receptor subtypes; for example, several high-scor-ing ligands were identified for the A3 receptor, but only one ofthose tested was biologically active. However, the A2A receptor(the only orthologue with available crystallographic structuraldata) performed best, and six new ligands were identified, in-cluding two with sub-micromolar binding affinities. Of note,the highest-affinity hit, 14, contained a 2-amino-1,3,5-triazinemotif which has also been independently identified in high-scoring hits by two other research groups (e.g. , compound15).[105, 110]

As well as using available crystallographic data for virtualscreening to identify new ligands, structural insight has alsobeen used to understand receptor subtype specificity withknown adenosine receptor ligands. For example, Abagyan andcolleagues built homology models of all four adenosine recep-tor subtypes from the 3EML A2A structure and then evaluatedthe ability of these models to selectively bind subtype-selec-tive ligands.[100] A range of sequence divergence is observedbetween members of this subclass, and several key bindingsite residues are conserved across all four subtypes, includingthe p stacking Phe1685.29 (superscripts indicate Ballesteros–Weinstein nomenclature),[111] hydrogen bonding Asn2536.55, andhydrophobic Leu2496.51 and Ile2747.39. Initial models weretested with known subtype-selective adenosine receptor li-

gands and decoy sets, but, with the exception of A2A, theygenerally showed poor performance, particularly A3, whichshowed a pronounced negative selectivity associated with anincorrect rotameric state of the Val1695.30 side chain. Themodels were subsequently optimised; A1 and A3 showeda more reasonable performance, but A2B, for which selective li-gands were confined to xanthine derivatives, gave lower over-all performance. A panel of 88 ligands, each with specificity forone of the adenosine receptor subtypes, was then cross-screened in silico against the panel of A1, A2A, A2B, and A3 ho-mology models. It was found that A2A and A2B ligand setsshowed the lowest selectivity scores when cross-screenedagainst one another.

Structural knowledge from A2A has also been applied to thedesign and rationalisation of selectivity of A3 agonists by Ja-cobson and colleagues.[112] Bicyclo[3.1.0]hexane derivative 23(Figure 4) has high affinity at the A3 receptor with excellent se-

lectivity over A1 and A2A (A3 pKi = 9.3; A1 and A2A: <22 % inhibi-tion at 10 mm). In an initial A3 homology model built from ago-nist-bound A2A structure 3QAK,[19] larger molecules such as 24(A3 pKi = 8.5; A1 and A2A : <30 % inhibition at 10 mm) wereunable to be accommodated in the model using an induced-fitdocking approach, due to steric clashes of the long linear ary-lethynyl C2 substituent with residues on transmembranedomain 2 (TM2). Hydrid models were constructed using ago-nist-bound b2AR and activated opsin crystal structures, whichallowed the extracellular portion of TM2 to be outwardly dis-placed and created a larger pocket for the C2 substituent. Theopsin-modified model allowed molecules such as 24 to be suc-cessfully accommodated, and an inability of the A2A receptorto undergo this rearrangement is hypothesised to underlie theobserved selectivity for A3 over A2A.

In addition, the first ZM241385–A2A structure (PDB ID: 3EML)has allowed the selectivity of adenosine A3 receptor antago-nists such as 25 (A3 pKi = 8.9; A1 and A2A: �5 % inhibition at1 mm) and 26 (A3 pKi = 8.4; A1, A2A and A2B: �10 % inhibition at100 nm) (Figure 4) to be rationalised using homology modelsbuilt on this information.[113, 114] In a similar fashion, adenosinereceptor subtype selectivities in favour of A3 have beenprobed using models derived from A2A.[115–119]

Figure 4. A3 receptor agonists and antagonists.



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Dopamine, chemokine, and histamine receptors

Dopamine receptors, of which there are five subtypes, D1–D5,are class A monoaminergic GPCRs. In the central nervoussystem (CNS), dopamine receptors are critically involved inmany functions including cognition, learning, reward, behav-iours, and motor functions. A crystal structure of the dopamineD3 receptor in complex with the D2/D3 selective antagonist eti-clopride (Figure 5), generated using the T4L fusion strategy,was reported in 2010.[27]

The dopamine D3 receptor has a selective distribution inareas of the brain, especially the nucleus accumbens, providingimpetus for the discovery of selective D3 antagonists for drugaddiction.[120] Discovery of D3-selective ligands is challengingdue to the high sequence homology of the D2 and D3 recep-tors in the orthosteric dopamine binding site. Starting fromthe dopamine D3 receptor crystal structure (PDB ID: 3PBL),which represents an inactive form of the receptor, Newmanand colleagues established an active conformational state byusing differences between the inactive and active structures ofthe b1AR and b2AR; Ser1925.42 and Ser1935.43 in TM5 of D3 wererotated to face toward the orthosteric binding site, into whichdopamine was iteratively docked, and the system was relaxedwith molecular dynamics simulations.[121] The resulting modelexhibited a tightening of the binding site relative to the crystalstructure with the inverse agonist eticlopride, which is inagreement with observations from the inactive and activeb2AR structures.[121] Use of these two models, coupled witha systematic deconstruction and characterisation of D3-selec-tive compounds, allowed interactions in a binding site distal tothat of dopamine to be identified that are crucial for selectivity,together with the elucidation of interactions within the orthos-teric binding site that govern efficacy.

In 2010, prior to release of the coordinates of the eticlopr-ide–D3 receptor structure, a challenge was issued to the GPCRmodelling community to predict the structure of the ligand–re-ceptor X-ray complex.[122] In a similar fashion, predictions of theCXCR4 receptor bound to a small molecule and a peptide ana-logue were sought before release of the crystallographic infor-mation.[122] The imminent release of structures of the two re-ceptors provided opportunities to prospectively compare ho-mology-model-based VS approaches against subsequentscreens using experimentally derived information. Thus, Shoi-chet, Roth, and co-workers instigated a ligand discovery cam-paign using their submitted model of the D3 which had been

derived from b1AR and b2AR structures 2VT4 and 2RH1, respec-tively.[123] More than three million commercially available com-pounds were docked into the receptor, scored for van derWaals and electrostatic complementarity and corrected forterms not included in the docking function such as high ligandinternal energy and receptor desolvation. Twenty six com-pounds selected from the top-0.02 % of docking-ranked struc-tures were subsequently screened in a radioligand bindingassay. Following the release of the coordinates for the eticlopr-ide–D3 receptor structure, a second screening exercise was un-dertaken, using 3.6 million lead-like molecules from the ZINCdatabase. Twenty five of the highest-ranked molecules wereprogressed to affinity measurements. Surprisingly, the resultsof ligand identification from the homology-model-based exer-cise were similar to those using the experimentally derivedstructure: similar hit rates of 23 and 20 %, respectively arisingfrom the 26 and 25 compounds chosen for progression toradioligand binding assays. Not only were the hit rates similar,but the affinity ranges of the identified hits were comparable:pKi 5.5–6.7 for the model-based approach, and pKi 5.5–6.5 forthe structure-based selections. The ligands discovered usingeither approach were antagonists, reflecting the inactive recep-tor conformation represented by the eticlopride–D3 structure.Compounds 17 and 18 represent hits from the docking- andstructure-based approaches. Selection of further commercialanalogues of 17, which was one of the least similar moleculesto known dopaminergic ligands identified, based on a consider-ation of the modelled binding mode allowed the identificationof several further compounds with up to 20-fold higher affinity.In a recent publication, Katritch and co-workers reported theuse of two models based on the 3PBL structure in a VS cam-paign, seeking both orthosteric and putative allosteric ligandsof the human dopamine D3 receptor.[124] The first model, of thereceptor with an empty binding pocket, yielded a 56 % hit rateof orthosteric antagonists. The second model, which character-ised the receptor in complex with dopamine, gave a 32 % hitrate; molecules from this approach were predicted to occupyan allosteric site at the extracellular extension of the bindingpocket.

The chemokine receptors are a subclass of GPCRs that areactivated by chemotactic cytokines (chemokines). Defined bycriteria including amino acid sequence homology, conservedcysteine motifs, and a common fold, chemokines are a familyof related proteins that coordinate the trafficking of leukocytesunder homeostatic and inflammatory conditions. Chemokinesare large (typically 8–10 kDa) proteins, and as such, are verydifficult drug discovery targets with relatively low tractabilityfor drug design. There are currently two marketed drugs forchemokine receptors (maraviroc and plerixafor) ; however,these are large and lipophilic, and finding molecules with ap-propriate biological activity within drug-like chemical space ishighly challenging. Structures of the CXCR4 receptor in com-plex with small-molecule IT1t (Figure 5) and cyclic peptide an-tagonists were disclosed in 2011 and have prompted structure-based drug discovery efforts in the area.[26] Several groupshave used the structures to rationalise the binding modes oftheir cyclic peptide antagonists, in studies which may pave the

Figure 5. Small-molecule ligands crystallised into the D3, CXCR4, and H1 re-ceptors, respectively.



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way for further structure-based optimisation of these mole-cules.[125–127]

In similar fashion to the D3 exercise described above, Shoi-chet, Volkman, and colleagues reported a comparison of ho-mology-model- and crystal-structure-based virtual screens forCXCR4, the former being conducted prior to release of the li-ganded CXCR4 structures.[128] Whilst the two approaches wereshown to be comparably effective for the D3 receptor, thetransmembrane sequence identity between D3 and the nearestother receptor for which structural information had been eluci-dated is 42 %. In contrast, at the time CXCR4 had, at best, 25 %identity to the nearest receptor with a reported crystal struc-ture, presenting a significantly higher hurdle for a homology-based VS approach to match one based on CXCR4 structuralinformation. Coupled with the difficulty of identifying hits forchemokine receptors, this higher hurdle unsurprisingly resultedin a lower hit rate for ligand discovery from virtual screeningthan from the homology model technique. From the ZINC da-tabase, 3.3 million and 4.2 million molecules were screenedusing the model- and crystal-structure-based approaches.Twenty four and 23 molecules, respectively, were purchasedfrom within the top-0.05 % of the docked molecules from eachtechnique, and screened in calcium flux assays. Compound 19,the only active molecule from the homology model dockingscreen, closely resembles known antagonists of CXCR4, andlacks selectivity against the CCR2 receptor. Four molecules, in-cluding the highest-affinity hit 20, arose from the crystal-struc-ture-based screen. These four hits are more dissimilar toknown CXCR4 ligands than 19, and were selective in a coun-ter-screen against CCR2. Taken together, the authors concludedfrom the D3 receptor and CXCR4 prospective VS campaignsthat homology-model-based ligand identification can be suc-cessful if a structurally determined template with reasonablesequence identify (42 % as in the case of the D3 model) is avail-able. In contrast, the poor performance of the CXCR4-model-based VS indicates that structural information for receptorswith closer sequence identity than the 18–25 % range availableat the time will be required to create models with sufficient ac-curacy to be useful for predictive ligand discovery.

The biogenic amine histamine mediates multiple physiologi-cal and pathophysiological conditions including allergy and in-flammation, and acts through four class A histamine receptors:H1, H2, H3, and H4. Using the strategy of T4L insertion into ICL3,the structure of H1-T4L in complex with the inverse agonistdoxepin (Figure 5) was published in 2011, and bears similaritiesto other class A aminergeric receptors, b1AR, b2AR, and D3.[28]

In an elegant exercise using the H1 receptor crystal structure,Leurs and co-workers developed and validated a VS methodthat combined molecular docking with a protein–ligand inter-action scoring function, and demonstrated the ability of theapproach to find fragment-sized molecules with an exceptionalhit rate.[129] Dockings were undertaken with PLANTS, an ap-proach that combines a docking algorithm with an empiricalscoring function, and the resulting docking poses were subse-quently post-processed using molecular interaction finger-prints (IFPs). The IFP, defined using seven interaction types,was then compared by using a Tanimoto coefficient to the

pose of doxepin in the crystal structure, and the VS protocolvalidated by use of a reference set of known H1 ligands anda decoy set of molecules in similar areas of chemical space. Asubset of 108 790 molecules from the ZINC database was se-lected, with the criteria that each required a basic group to in-teract with Asp1073.32, a key ionic interaction within the H1

binding site. A total of 354 molecules were identified that metthe optimised IFP and docking score cutoffs and yielded dock-ing poses capable of making the key ionic interaction. Whilstknown H1 actives were identified, including several FDA-ap-proved molecules, the majority of hits were chemically dissimi-lar to known H1 ligands. Clustering and selection based on IFPand docking scores yielded 26 compounds that were screenedat the receptor, with an exceptional 73 % hit rate (19 of thetested compounds yielded affinities of pKi>5) attributed bythe authors to the combined use of similarity to the experi-mentally derived H1–doxepin binding mode and an energeti-cally favourable H1–ligand configuration, as assessed by the IFPand PLANTS scores, respectively. Compound 21 is the highest-affinity hit: pKi = 8.2.

In a similar fashion, de Graaf et al. reported the use of an in-tegrated ligand and structure-based FLAP (fingerprints of li-gands and proteins) model in virtual fragment screening, di-rected against the histamine H3R.[130] The H3R model used inthe process was derived from the doxepin–H1 crystal structure.Following optimisation and validation of the VS protocol,a subset of 156 090 fragment-sized compounds from the ZINCcommercial database were screened, and the top-ranking hitsfrom the ligand- and structure-based models examined visuallybefore purchase and screening of 29 fragments. Eighteen com-pounds (62 % hit rate) had pKi>5 in a radioligand bindingassay, the highest affinity of which is 22 : H3R pKi = 6.3.

Virtual screening studies using homology models for the H4Rbased on the structure of bovine rhodopsin and carazolol-bound b2AR (PDB ID: 2RH1) have also been reported.[131, 132]

The latter study combined the homology model with ligand-based QSAR and in silico guided site-directed mutagenesis ex-periments to identify molecular features that govern selectivitybetween histamine H3 and H4 receptors, and elucidated thebinding mode of dual H3R/H4R antagonist clobenpropit andanalogues.

FBDD Approaches to GPCR Drug Discovery

Fragment-based drug discovery (FBDD) is a strategy for theidentification of small (typically 100–250 Da), ligand-efficientmolecules. FBDD is now a well-established technique, fre-quently used in combination with SBDD approaches to effi-ciently identify and progress small-molecule chemical matterfor soluble protein target classes such as proteases and kinas-es.[133–135] Several FBDD-derived molecules have recently pro-gressed into clinical trials, and vemurafenib, the first fragment-derived drug molecule, was approved in 2012 for use in thetreatment of melanoma.[136, 137] Implementation of the modernparadigm of FBDD in the GPCR field has lagged significantlybehind soluble protein target classes, primarily because GPCRstypically suffer from low expression and poor stability once re-



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moved from their cellular membrane environment. For thesereasons, until recently, biophysical fragment discovery methodssuch as SPR, NMR, and X-ray crystallography have not beencommonplace in GPCR discovery campaigns. Biochemical ap-proaches such as high-concentration screening have beenrarely used, primarily due to limitations of sensitivity, reliability,and the ability to discriminate true hits from false positives.[138]

Within our laboratories in recent years, the use of StaR proteinshas addressed the issues of poor expression and stability, andbiophysical fragment screening approaches have now been va-lidated for GPCR targets.

Target-immobilised NMR screening (TINS) has been used todetect weak interactions between proteins and small mole-cules and has typically been used with soluble proteins suchas kinases and proteases. As with other NMR methods, TINShas had little success when applied to membrane proteinsuntil recently, when the enhanced stability of StaR proteins hasfacilitated ligand identification in this fashion. In the first exam-ple of TINS fragment screening for GPCRs, the b1AR was ther-mostabilised, solubilised in decyl maltoside (DM) and immobi-lised onto resin.[139] Screening of a small fragment library of579 molecules confirmed that combination of the TINS ap-proach with a StaR protein could be used to detect weak frag-ment hits, a number of which were confirmed to bind to thereceptor in an orthogonal radioligand binding assay. Subse-quently, the TINS methodology has been applied to screeningof an A2A StaR protein, which was immobilised on sepharoseresin and screened at 500 mm with 531 fragments.[140] OmpA (astable membrane protein which is known to exhibit minimalspecific binding to small molecules) was used in a referencecell to determine the specificity of hits. Ninety four fragmentsbound specifically to A2A StaR2 and six fragments specificallyto OmpA (using a cutoff ratio of �0.7 for the well-resolved1H NMR signals). The 94 selective A2A hits were followed up inradioligand binding assays with the wild-type receptor, usingboth [3H]ZM241385 and [3H]NECA.[140] Competitive binding anddissociation rates of the radioligands were examined with thefragments to validate the hits and determine whether theywere acting as orthosteric or allosteric ligands. Five competi-tive orthosteric ligands with diverse chemical structures wereidentified, with pIC50 values in the range of 2.7–4.2 in concen-tration–response studies with [3H]ZM241385. The hits werealso able to displace [3H]NECA, and, in a radioligand bindingassay with A1, were able to displace [3H]DPCPX. The lack of re-ceptor subtype selectivity is not surprising owing to the sizesof the molecules, but further work is warranted to determinewhether it would be possible to generate more potent lead-like molecules from these hits. Several non-competitive ligandswere also noted using the TINS technique, as measurement oftheir effects on the dissociation rates of radioligands at bothA1 and A2A indicated that modulators with different profileshad been identified. Some ligands decreased the off-rate of atleast one of the radioligands, and others increased the off-rate,suggesting that both positive and negative allosteric modula-tors had been identified. In a recent publication, Siegal, Carls-son, and colleagues examined the ability of a docking ap-proach for A2A to identify fragment hits for this receptor. The

docking-based screen was able to identify novel hits that didnot overlap with those obtained from the biophysical ap-proach, suggesting that the two approaches are highly com-plementary and their use in tandem will be a powerful strategyfor future fragment-based hit discovery campaigns forGPCRs.[141]

Surface plasmon resonance (SPR) is a highly sensitive bio-physical technique used to study binding interactions viaa label-free optical method capable of measuring smallchanges in refractive index at the surface of the biosensorchip. Experiments are typically conducted with the proteintarget of interest immobilised on the biosensor surface, withtest compound in solution flowing over the chip. Applicationsof the technique to the characterisation of interactions be-tween membrane proteins and ligands have been recently re-viewed.[142] The methodology has been successfully used toscreen large libraries of screening compounds (including frag-ments) against immobilised GPCRs, including A2A StaR1 andb1AR (see below). The technique typically requires the GPCR tobe initially thermostabilised so that it can withstand immobili-sation and the long cycle times imposed during the screeningof large compound libraries. An exception to this is the cap-ture of wild-type chemokine receptor CCR5 onto a biosensorchip and the subsequent detection of binding of probe mole-cules, which was reported by Navratilova et al.[143] In a recentcommunication, the approach was extended to screening ofa set of 656 fragments against the immobilised wild-typeb2AR.[144] Five fragment hits with binding affinities in the rangepKD 4.7–7.8, of which 27 (Figure 6) is the most active, wereconfirmed in dose–response experiments following primaryscreening at a single concentration.

In a 2011 exercise demonstrating for the first time the use ofstabilised receptors in SPR fragment screening, A2A StaR1 wascaptured onto nickel nitrolotriacetic acid functionalised chipsvia C-terminal His10 affinity tags, and the activity of the proteinwas validated with a set of standard ligands with a range of af-finities, in good agreement with the values measured usingwild-type A2A in cellular assays.[139] A library of fragments wasthen screened at 200 mm using injections of XAC as a positive

Figure 6. b2AR, adenosine A3 receptor, and histamine H4 receptor fragmentscreening hits.



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control at regular intervals. XAC was found to perform consis-tently, and the receptor remained active throughout the study.Ten percent of the fragments were found to bind significantlyabove background and were followed up in a concentration–response format, yielding binding affinities between pKD 2.3and 5.

CEfrag is a highly sensitive capillary electrophoresis screen-ing technique that has been demonstrated to be able to iden-tify low-molecular-weight, low-affinity ligands for heat-shockprotein 90 in mobility-shift competition assays.[145] The tech-nique uses a competitor probe ligand, which binds to the bio-logical target of interest and a capillary and electrodes witha high voltage power supply to separate charged and un-charged particles.[146] In a proof-of-concept study, CEfrag hasalso been extended to detect interactions between GPCRs andsmall molecules. Indeed, with A2A StaR1, it was possible todetect the binding of ligands with a range of potencies, includ-ing the fragment-sized molecule, caffeine.[147]

In contrast to using stabilised, isolated GPCRs in detergent-solubilised form for biophysical fragment screening, Kellam,Hill, and co-workers recently reported a fluorescence-basedbinding assay in living cells for fragment screening.[148] A keyaspect of the technique is that the integrity of the membraneenvironment of the receptor is maintained under physiologicalconditions. This confers potential advantages for ligand discov-ery, as intracellular signalling proteins, which would not bepresent in assays using purified detergent-solubilised recep-tors, can allosterically influence the binding of molecules toGPCRs.[149] Using a fluorescent A3 antagonist molecule derivedfrom XAC with high affinity and a slow off-rate at the receptor,the authors developed a high-content screening approachusing a confocal imaging plate reader to detect binding ofpublished antagonists. Affinities of the antagonists in competi-tion-binding format were in good accordance with potenciesof the molecules in a functional assay readout, and the tech-nique was extended to screen a 248-compound subset ofa commercial fragment library, yielding 38 hits at a single con-centration of 1 mm. In subsequent concentration–response ex-periments, DP 01095 (28, Figure 6) had the highest affinity(pKi = 6.4), with affinity values as low as pKi = 4.0 being measur-able.

de Esch and colleagues reported the identification of frag-ment hits for the histamine H4R using a radioligand bindingassay at 10 mm fragment concentration.[150] Fifty six hits froma library of 1010 molecules (6 % hit rate) were identified, andcounter-screening the library against the ligand-gated ionchannel serotonin 5-HT3A in a fluorescence-based functionalfragment screen revealed a high degree of overlap betweenhits for the two targets. The authors observed that the dualactive fragments had a higher molecular complexity thanthose active at only one of the targets, as exemplified by 29(H4R pKi = 7.0, inactive at 5-HT3AR) and 30 (H4R pKi = 8.2, 5-HT3AR pKi = 5.9) (Figure 6).

SBDD and FBDD Case Histories: A2A and b1AR

Within our laboratories we have been using StaR proteins withSBDD and FBDD strategies to expedite drug discovery cam-paigns for GPCRs. Summarised below are two recent case his-tories detailing structure-based approaches to the identifica-tion and optimisation of A2A antagonists and the identificationof high-affinity arylpiperazine lead molecules for the b1ARusing a combination of biophysical fragment screening andstructure-based drug design.

SBDD of A2A antagonists

In an exercise undertaken prior to the publication of A2A crystalstructures, we conducted a VS campaign with ‘structurally ena-bled’ homology models of A2A. During the process of mutagen-esis to generate a StaR protein, a wealth of SDM data is gener-ated which is used to refine and optimise homologymodels.[147] Using the 2VT4 b1AR structure that was available atthe time, A2A models were built, refined with mutagenesisdata, and 545 000 compounds were screened using Glide soft-ware.[110] In total, 230 prioritised compounds were screenedagainst A2A in a radioligand binding assay leading to the iden-tification of 20 hits with pIC50>4.3 (9 % hit rate). The highest-affinity hit (15, Table 2) had pKi = 8.5 and, as described furtherbelow, was the starting point for a medicinal chemistry cam-paign which efficiently identified a clinical candidate for thetreatment of CNS disorders.

Biophysical mapping (BPM) is a technique that has beenused to refine and optimise the binding modes of receptor-bound ligands in the absence of crystallographic data, and wasused extensively in our progression of hits from the A2A virtualscreen.[151] The structural insight that this methodology pro-vides can facilitate the rational optimisation of these ligands,and, in some cases with larger hit sets, may also highlight com-pounds with novel or interesting binding modes that may oth-erwise have been overlooked or dismissed.

During the BPM process, mutations are made within bindingsites of multiple StaR proteins, and the relative effect of thesemutations is compared across a matrix of ligands and mutants(often considering multiple side chain replacements for eachresidue). In this way, the technique enables the direct compari-son of contributions to binding affinity with different chemo-types as well as near neighbours within a particular chemicalseries. In the case of adenosine A2A, a set of eight StaR proteinswas prepared, each with a different point mutation in the or-thosteric binding site of the StaR1 protein. The receptors werethen separately captured onto SPR chips to allow cross-screen-ing against an array of standard ligands and hits/leads froma medicinal chemistry programme, including several differentchemotypes with a wide range of binding affinities.[151] Thestudy provided key structural information that facilitated leadoptimisation of compounds identified by virtual screeningbefore X-ray crystallography data were available for this recep-tor. In particular, a series of 1,2,4-triazine antagonists wasfound to bind deeply within the receptor, exhibiting a novel



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binding mode and displacing several energetically unfavoura-ble water molecules (see below).[21, 147, 151]

Chromone 31 (Figure 7) was identified through virtualscreening and subsequently optimised, with structural input,to a lead series with nanomolar binding affinity for the A2A re-ceptor and moderate to high selectivity against A1 (32 and33).[110] BPM was used to prioritise a binding mode in whichthe chromone C(3)-H and thiazole N atom hydrogen bond toAsn2536.55 in the binding site;[151] this was later confirmedthrough a low-resolution crystal structure of a related memberof the chemical series.[110]

The 1,3,5-triazine hit 15 (Figure 7) was identified from thesame VS campaign. BPM analysis showed that this chemotypehydrogen bonds to Asn2536.55 via a triazine N atom and theappended amino group, and that key interactions are madedeep in the “ribose pocket” with Ser2777.42 and Leu853.33,[110]

where the saccharide ring of the endogenous ligand binds inthe crystal structure 2YDO.[24] Using structural information toguide iterative chemistry, 15 was optimised into a lead seriesof 1,3,5-triazine antagonists with high LE and selectivity versusA1, such as 34.[110] Subsequently, scaffold hopping to a 1,2,4-tri-azine template 35 allowed better access to the ribose pocketand resulted in a series of A2A antagonists with a differentiatedbinding mode, albeit with eroded A1 selectivity.[21]

During the subsequent lead optimisation of the 1,2,4-triazineantagonist series, structure-based approaches were taken toimprove affinity at the A2A receptor and selectivity versus A1

whilst maintaining a balanced, drug-like profile. GRID maps ofthese two receptor subtypes were constructed using a varietyof molecular probes (e.g. , sp3 carbon, sp2 carbon, NH and C=O)to allow comparison of their shape, size, and electrostatics.[21]

Water maps were also used as part of a druggability analysis,[86]

and comparison of the receptor peptide sequences revealedthat two binding site residues in close proximity to these li-gands have smaller side chains in the A2A binding site than theA1 binding site (A2A: Ser2777.42 and Ala592.57 versus A1: Thr andVal, respectively). Initial SAR indicated that an increase in affini-ty could be achieved by introducing small lipophilic substitu-ents at position 3 of the phenyl ring at position 6 of the tria-

zine to interact with a lipophilichotspot identified by the sp2

carbon GRID probe (e.g. , 36versus 37, Table 3). Affinity couldbe further increased by addinga second substituent at posi-tion 5 of the same ring (e.g. , 37versus 38, Table 3). As the A2A

binding site is proposed to belarger than A1 in this region, a se-lective gain in affinity is ob-served with C2-symmetric 3,5-disubstitutions that neatly fill theA2A pocket, but are too large tobe comfortably accommodatedby the A1 binding site, as shownin Figure 8.

Close inspection of the protein side chains around thisregion of the pocket suggested that it may be possible to in-troduce a polar interaction between a substituent at position 4of the same ring of the ligand and His2787.43. Indeed, 4-pyridylanalogues such as 40 were found to be more potent than theparent phenyl derivatives, and 4-phenols such as 41 had veryhigh affinities for both A2A and A1.

During the lead optimisation campaign with 1,2,4-triazineantagonists, multiple crystal structures were solved with exam-ples in both the pyridyl and phenol sub-series. As predicted byBPM, the ligands were found to sit very deeply within theribose pocket, and the phenol derivative 41 was found to hy-drogen bond to His2787.43 via the hydroxy group (Figure 9).Ligand–receptor binding kinetics were measured by SPR for se-

Figure 7. A2A chromones and 1,3,5-triazines from virtual screening and subsequent scaffold-hopping 1,2,4-triazinehit 35.

Table 3. A2A 1,2,4-triazine SAR.

Compd R A2A pKi A2A LE A1 pKi

36 6.93 0.50 6.56

37 7.29 0.50 7.25

38 8.40 0.55 7.36

39 7.67 0.50 6.71

40 8.11 0.53 7.07

41 8.85 0.57 9.79



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lected 1,2,4-triazines and A2A StaR1.[151] Phenol 41 displayeda very slow receptor off-rate (kd = 0.001 s�1) compared with 36(kd = 1.0 s�1), providing useful insight into the energetics ofbinding which facilitated series progression. Exemplars fromthe series have shown good pharmacokinetic profiles in vivoand are efficacious in rodent disease models of PD.[21] Themost advanced compound from this series is currently under-going preclinical development studies.

b1AR biophysical fragment screening and SBDD progression

The study with A2A described earlier concluded that GPCR frag-ment hit discovery using SPR is a viable approach, and provid-ed encouragement to undertake a fragment screening cam-paign using a subset of the Heptares fragment library againstb1AR and A2A StaR proteins in parallel.[17] Focusing on hits thatare selective binders to b1AR, these were progressed to dose–response experiments, resulting in the identification of arylpi-perazine hits 42 and 43 (Figure 10), which had encouragingb1AR affinities and good ligand efficiencies (pKD = 4.80, LE =

0.41; pKD = 5.25, LE = 0.48, respectively). StaR proteins repre-sent powerful tools for hit discovery, and although the proteinsare engineered in a fashion that does not impact the ligandbinding site, the general strategy is to revert to the use ofwild-type receptors for drug discovery purposes as soon as it isfeasible to do so. In line with this strategy, 43 was profiled ina radioligand binding assay, evaluating the displacement of[3H]dihydroalprenolol from the human wild-type b1AR, and inthis orthogonal assay format the validity of hit 43 was con-

firmed (pKi = 5.20), providing the impetus to progress intoa hit-to-lead campaign.

Using the wealth of available structural information for bothb1AR and b2AR the hits were progressed via SBDD techniques,which commenced with a druggability analysis of the b1ARbinding site using the 2VT4 crystal structure.[86] CH aromatic

Figure 8. Docking of ligand 40 and representation of calculated GRIDs.Colour key: grey mesh = sp3 C probe on A1 surface; beige = sp3 C probe onA2A surface; yellow = sp2 C probe to show lipophilic hotspots in A2A. Themethyl substituent at indicated position 3 pushes back, out of the plane ofthe page, to interact with a lipophilic hotspot, while the methyl substituentat position 5 protrudes out of the A1 binding pocket, imparting selectivityfor A2A.

Figure 10. SPR fragment hits 42 and 43, and selected analogues 44–46.

Figure 9. Crystal structures of A2A StaR2 as co-complexes with a) 41 andb) 40. Both ligands show hydrogen bonding interactions with Asn2536.55,and 40 shows a unique hydrogen bond with His2787.43 via its hydroxygroup.



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and water probes indicated regions of the binding pocket thatwere most favourably occupied by hydrophobic and polar por-tions of a molecule respectively. Combining these with probesfor hydrogen bond donor or acceptor hotspots and calcula-tions of the positions and energies of water molecules guidedthe selection of further analogues of 42 and 43. Docking offragments into b1AR typically resulted in several plausible bind-ing modes. Characterisation of the binding site in the abovefashion provided a framework for analysis of the multiple op-tions, and identified those which presented the ligand in themost complementary fashion with the polar and hydrophobicregions, and with the potential to displace the least energeti-cally favourable waters. Overlaying molecules with the ligand-ed b1AR crystal structures facilitated a structure-based ap-proach and allowed the selection and screening of a smallnumber of analogues of initial hits 42 and 43. Example 44docked into the site is depicted in Figure 11, and examiningthis molecule in conjunction with the carazolol and carmoterolliganded crystal structures of b1AR proved instructive in select-ing analogues with the potential to form additional interac-tions in the binding site.

Example 45 (Figure 10) was selected based on the potentialto form an additional polar interaction with Ser2115.42, as ob-served for example with cyanopindolol in the 2VT4 b1AR crys-tal structure.[13] Quinoline 46 (Figure 10), an analogue of origi-nal hit 43, was envisaged to occupy a similar region of thebinding site to that of carazolol in the 2YCW b1AR structure,[15]

the quinoline portion of 46 overlaying with the b-carbolineportion of carazolol. The binding modes of high-affinity, high-efficiency molecules 45 and 46 were subsequently determinedthrough generation of b1AR co-crystallised structures at 2.8and 2.7 �, respectively. In summary, the SBDD approach yield-

ed compounds with large increases in affinity over the originalhits, demonstrating how the combination of FBDD and SBDDstrategies can impact dramatically upon GPCR fragment hitidentification and progression.

Summary and Outlook

GPCR drug discovery has benefitted enormously from a signifi-cant growth in availability of structural information in recentyears, which has stimulated structure-based approaches forseveral class A targets. The majority of the approaches re-viewed herein centre on hit identification through VS tech-niques, both directly against the target for which the structurehas been reported, and indirectly by refinement of homologymodels of subclass members, or more distantly related GPCRs.Impressive VS hit rates can be achieved, in several instanceshigher than 30–40 %, especially where structures of the directtarget can be leveraged. Although hit rates are understandablylower when indirect crystallographic information is used torefine homology models of distant targets, it is evident thatthe continued growth in the number and diversity of GPCRstructures will continue to aid VS approaches, and it is antici-pated that the more recent class A structures, such as those ofthe muscarinic acetylcholine M2 and M3,[29, 30] neurotensin,[31]

opioid d,[32] k,[33] m,[34] nociceptin,[35] PAR1,[36] 5HT1B,[37] 5HT2B,[38]

and chemokine CCR5 receptors[39] will be leveraged in the nearfuture.[152] Additionally, in an extremely significant recent devel-opment, the first structures of class B GPCRs have been dis-closed.[41, 42] Class B GPCRs are highly challenging drug discov-ery targets, and the CRF1 and glucagon receptor structureshighlight architectural differences between these and class Areceptors which provide insight into why their tractability indrug discovery has been so low. Both receptors reveal large,open binding pockets which, whilst necessary to accommo-date the endogenous agonist peptides, make it difficult fora small-molecule ligand to engage effectively with the recep-tor. Encouragingly though, the CRF1 structure shows an antag-onist binding region which was not anticipated, providinga clear opportunity to target this site using SBDD techniques.Furthermore, the CRF1 and glucagon receptor structures willserve as templates for homology modelling and SBDD effortsdirected at other class B receptors, such as the GLP-1 receptoror gastric inhibitory peptide receptor (GIPR), which, incommon with glucagon receptor, are involved in the regula-tion of glucose. A crystal structure of the smoothened recep-tor, a key component of the hedgehog signalling pathwaywhich is implicated in carcinogenesis, has also been recentlyreported,[40] opening up the Frizzled class of receptors to struc-ture-based approaches.

As demonstrated by the case studies from our own laborato-ries, GPCR structural insight can be leveraged not only for theidentification of hit material, but also for rational optimisationto increase affinity and selectivity for the receptor target. Frag-ment-based drug discovery methods, which have found signifi-cant value in soluble protein classes, have been difficult toapply to membrane proteins such as GPCRs due to difficultiesin obtaining sufficient quantities of protein, and the inherent

Figure 11. Aryl piperazine 44, in a pose selected for comparison with thecrystal structures, docked into the b1AR ligand binding site. A druggabilityanalysis was applied,[86] showing water molecules and their energies relativeto bulk solvent (red: high; blue: low) combined with surfaces depictingwater probe (green) and CH aromatic probe (yellow) hotspots.



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instability of the target material when isolated from the cellmembrane. The use of StaR proteins has been demonstratedin screening exercises using TINS NMR, capillary electrophore-sis, and SPR techniques, which complement rare examples ofbiochemical screening such as the fluorescence-based readoutused to identify adenosine A3 receptor hits, or H4R radioligandbinding fragment screening. In the case of the b1AR exerciseconducted in our own laboratories, biophysical fragmentscreening was combined with structure-based design. The ap-proach yielded high-affinity, ligand-efficient hits with bindingmodes elucidated through crystallography, demonstrating thatthe modern drug discovery approaches of FBDD and SBDDcan be harnessed in tandem in a GPCR drug-discovery pro-gram.

Acknowledgements

We thank Miles Congreve and Fiona Marshall for their contribu-tions to compiling this manuscript. The Heptares name, logo,and StaR are trademarks of Heptares Therapeutics Ltd.

Keywords: fragment-based drug discovery · G protein-coupled receptors · structural biology · structure-based drugdiscovery

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[152] Note added in peer review: Use of structural information on the mus-carinic acetylcholine M2 and M3 receptors in virtual screening is nowpublished in a study by Kobilka, Shoichet, and co-workers : A. C. Kruse,D. R. Weiss, M. Rossi, J. Hu, K. Hu, K. Eitel, P. Gmeiner, J. Wess, B. K. Ko-bilka, B. K. Shoichet, Mol. Pharmacol. 2013, 84, 528 – 540.

Received: September 24, 2013Revised: November 15, 2013Published online on December 18, 2013



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