European Journal of Pharmaceutical Sciences 47 (2012) 941–951

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European Journal of Pharmaceutical Sciences

journal homepage: www.elsevier .com/ locate/e jps

High-throughput screening with a miniaturized radioligand competition assayidentifies new modulators of human a2-adrenoceptors

Adyary Fallarero a,1, Katariina Pohjanoksa b,1, Gloria Wissel c, Ulla-Mari Parkkisenniemi-Kinnunen a,Henri Xhaard c, Mika Scheinin b, Pia Vuorela a,⇑a Pharmaceutical Sciences, Department of Biosciences, Abo Akademi University, BioCity, Artillerigatan 6 A, FI-20520 Turku, Finlandb Department of Pharmacology, Drug Development and Therapeutics, University of Turku, Unit of Clinical Pharmacology, Turku University Hospital, Turku, Finlandc Centre for Drug Research, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland

a r t i c l e i n f o

Article history:Received 3 July 2012Received in revised form 30 August 2012Accepted 31 August 2012Available online 12 September 2012

Keywords:a2-AdrenoceptorsLysergol200;20000-Bisepigallocatechin digallatePartial agonistGPCRChemical libraries

0928-0987/$ - see front matter � 2012 Elsevier B.V. Ahttp://dx.doi.org/10.1016/j.ejps.2012.08.021

⇑ Corresponding author. Tel.: +358 2 215 4267; faxE-mail address: pia.vuorela@abo.fi (P. Vuorela).

1 These authors contributed equally to this work.

a b s t r a c t

Human a2-adrenoceptors (a2-ARs) are rhodopsin-like G-protein coupled receptors, and potential drugtargets. The three different human a2-AR subtypes a2A, a2B and a2C are widely distributed in tissues,but so far only a few subtype-selective ligands have been identified. In this project, we set off to conducta large chemical screen for activity on the human a2B-AR and studied the selectivity of the active com-pounds towards the human a2A- and a2C-AR subtypes. We employed a radioligand competition bindingassay that was optimized and miniaturized into a robotic environment. Membrane fractions containingrecombinant human receptor subtypes were prepared from stably transfected Chinese hamster ovary(CHO) cell lines. Initially identified hits were followed up and characterized, and chemoinformatics toolswere applied to gain better understanding of the relevance of the results. After a primary screen againsta2B-AR, 176 compounds of the 17,952 included in the library were declared as active at 10 lM, of which89 compounds were further selected for potency and affinity determinations using the three human a2-AR subtypes. One of the identified positive hits was 200;20000-Bisepigallocatechin digallate, which was foundto have high affinity at all three human a2-AR subtypes. This represents the first non-protonable mole-cule identified as able to interact with these receptors. Additionally, results obtained with a functionalassay (agonist-induced stimulation of [35S]GTPcS binding) supported the identification of another posi-tive hit, lysergol, as a partial agonist of the human a2-AR subtypes. The dataset of confirmed active chem-ical species represents a readily available, high quality source for follow-up studies. Altogether, theseresults provide novel research approaches for drug discovery of modulators of the a2-AR subtypes.

� 2012 Elsevier B.V. All rights reserved.

1. Introduction

The G-protein coupled receptor (GPCR) superfamily is thelargest known class of molecular targets with proven therapeuticvalue, since about 30–40% of all approved drugs act on GPCRs.GPCRs are cell surface receptors transducing signals from extracel-lular ligands to intracellular effector systems via coupling to het-erotrimeric G-proteins. Mammals have three main families ofGPCRs: the rhodopsin-like family (family A), the secretin-likereceptor family (B) and the metabotropic glutamate receptor-likefamily (C). The rhodopsin-like family accounts for over 80% of allmammalian GPCRs (Davies et al., 2007; Jacoby et al., 2006).

Human a2-adrenoceptors (a2-AR) belong to the rhodopsin-likefamily of GPCRs. There are three different human a2-AR subtypes,a2A, a2B and a2C, with slightly different pharmacological properties

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(Bylund, 1992; Bylund et al., 1992; Lomasney et al., 1991). Thesereceptors are encoded by three different intronless genes locatedon human chromosomes 10, 2 and 4, respectively (Kobilka et al.,1987; Lomasney et al., 1990; Regan et al., 1988). They mediate sig-nals carried by the endogenous neurotransmitter noradrenalineand the hormone adrenaline to intracellular second messengersvia pathways involving G proteins mainly of the Gi/o class, but alsoGs and Gq proteins have been implicated in some of their cellulareffects (Chabre et al., 1994; Conklin et al., 1992; Cotecchia et al.,1990; Eason et al., 1992; Jansson et al., 1994; Kurose et al., 1991;Pohjanoksa et al., 1997).

a2-ARs mediate a wide spectrum of physiological functions andpharmacological effects. In the nervous system, their activationinhibits neurotransmitter release (noradrenaline, acetylcholine,serotonin and dopamine), and on the behavior level, they mediatee.g. locomotor inhibition, sedation, anesthesia, analgesia andsuppression of opiate withdrawal symptoms. By inhibiting theactivity of the sympathetic nervous system, their activationreduces blood pressure and heart rate. In peripheral tissues, they

942 A. Fallarero et al. / European Journal of Pharmaceutical Sciences 47 (2012) 941–951

regulate vascular (arterial and venous) and other smooth musclecontraction, platelet aggregation, gastric emptying and intestinalmotility, secretions in the gastrointestinal tract and functions ofthe urogenital system. They also inhibit renin release and increasesodium and water excretion in the kidney, decrease insulin releasefrom pancreatic b cells, decrease intraocular pressure as well asmediate inhibition of lipolysis in adipose tissue (Aantaa et al.,1995; Docherty, 1989; Ruffolo and Hieble, 1994).

Because a2-ARs are widely distributed in tissues and are in-volved in so many physiological processes, the potential therapeu-tic applications of a2-AR agonists and antagonists are numerous(Gentili et al., 2007). However, only very few subtype-selective li-gands exist that can be used to determine the physiological andpathological significance of the receptor subtypes. Subtype non-selectivity seriously hampers the therapeutic use of currentlyknown a2-AR agonists and antagonists, as undesired side effectsmediated by off-target a2-AR subtypes or other aminergic GPCRsare common. This attests to the importance of screening for sub-type-selective ligands of a2-ARs.

In this project, we set off to conduct a large chemical screeningcampaign for activity on the human a2B-AR. Focusing on this spe-cific subtype was adopted as a primary strategy given the largevoid of subtype-selective a2B-AR antagonists available to targetcardiovascular diseases (Huhtinen and Scheinin, 2008). Humana2B-AR mediates constriction of vascular smooth muscle and theyhave been implicated in cardiovascular diseases where pathologi-cal vasoconstriction could be targeted by subtype-selective a2B-AR antagonists (Snapir et al., 2003). During the primary screeningcampaign, we chose to use a radioligand competition binding assaythat was optimized and miniaturized into a robotic environment,on the grounds that competition binding assays are typically veryrobust and have been extensively used for this purpose. A chemicallibrary containing 17,952 compounds in 96-well microplates wasscreened, in which a subset of 2112 compounds consisted ofFDA-approved drugs as well as drug-like molecules with knownpharmacological and toxicological profiles (referred to as: ‘‘drugsubset’’). Two main reasons justified the inclusion of these knowncompounds in the screening campaign. First, by screening themagainst the human a2B-AR, there was a possibility that new prop-erties could be discovered in known drugs (drug repositioning).Second, several of them could serve as positive controls becausemany were known to bind to human a2B-ARs. The remaining com-pounds in the library (more than 15,000 molecules), which containa reasonably large number of synthetic compounds obtained bycombinatorial chemistry, were referred to as the ‘‘general subset’’.Identified hits were followed up and characterized using competi-tion binding assays with all three human a2-AR subtypes. More-over, chemoinformatics tools were applied to gain a betterunderstanding of the relevance of the results, from a pharmacolog-ical standpoint. Our ultimate aim was not only to identify andcharacterize hit compounds that could provide new molecularleads for further drug discovery, but also to provide a reasonablylarge dataset with quantitative affinity estimates that could berationally exploited in further in silico applications.

2. Materials and methods

2.1. Chemicals

[Ethyl-3H]RS79948-197 was acquired from Amersham (GEHealthcare, London, UK) and [35S]GTPcS was from PerkinElmer(Boston, MA, USA). Penicillin, streptomycin, Geneticin� (G418),trypsin, phentolamine, (�)-adrenaline, pimethixene maleate, pro-methazine hydrochloride, cyclobenzaprine hydrochloride, lysergoland pirenperone were purchased from Sigma–Aldrich (St. Louis,

MO, USA). The cell culture medium a-MEM (Modified Eagle’sMedium) was from Gibco (Carlsbad, CA, USA) and fetal bovineserum was from Autogen Bioclear (Wiltshire, UK). Catechinderivatives were purchased from Extrasynthese (Lyon, France).OptiPhase ‘HiSafe’ III and Super Mix scintillation cocktails werefrom PerkinElmer (Turku, Finland). Additional information onother tested compounds is described in the text below.

2.2. Cell culture and preparation of cell membranes

Adherent Chinese hamster ovary (CHO) cell lines expressing thehuman a2-AR subtypes a2A, a2B and a2C (Pohjanoksa et al., 1997)were cultured in a-MEM supplemented with 26 mM NaHCO3, 5%heat-inactivated fetal bovine serum, penicillin (50 U/ml), strepto-mycin (50 lg/ml) and G418 (150 lg/ml). To produce large quanti-ties of cells for preparation of membrane fractions, cells weregrown in triple-bottom culture flasks (500 cm2, Nunc; Thermo Sci-entific, Roskilde, Denmark) without G418. Cells were grown in 5%CO2 at +37 �C, and confluent cells were detached with 0.25% tryp-sin (containing 0.02% EDTA), collected by centrifugation at 114gfor 5 min at +4 �C and washed once with chilled phosphate-buf-fered saline (PBS). The collected cell pellets were stored at�70 �C until use. The cell membranes were prepared at +4 �C.The harvested cells were suspended in hypotonic lysis buffer(10 mM Tris, 0.1 mM EDTA, 0.32 mM sucrose, pH 7.4) and homog-enized using an Ultra-Turrax homogenizer (3 � 10 s at 8000 rpm).The homogenate was centrifuged at 108g for 15 min to remove cellnuclei, unbroken cells and aggregates. The supernatant was col-lected and the pellet was re-homogenized and re-centrifuged asabove. The two supernatants were pooled and centrifuged at50,227g for 30 min. The pellet was washed with TE buffer(10 mM Tris, 0.1 mM EDTA) and re-centrifuged as above. Themembranes were then suspended in TE buffer, aliquoted andstored at �70 �C. Protein concentration was measured as in (Brad-ford, 1976) using bovine serum albumin as the reference standard.

2.3. Saturation binding assays

Saturation binding assays were performed as described previ-ously (Halme et al., 1995) with the a2-AR antagonist radioligand[3H]RS-79948-197. Briefly, membrane preparations (1.0–2.5 lgper sample) were incubated with serial dilutions (0.03–4 nM) ofthe radioligand in a final volume of 0.25 ml in 50 mM potassiumphosphate buffer (pH 7.4) at +25 �C for 30 min. Non-specific bind-ing was determined in parallel tubes in the presence of 10 lMphentolamine, and specific binding was defined as the differencebetween total and non-specific binding. Incubations were termi-nated using rapid filtration through glass fiber filters (WhatmanGF/B). The filters were washed twice with ice-cold 50 mM Tris/10 mM MgCl2, placed into vials with OptiPhase ‘HiSafe’ III andradioactivity was measured in a liquid scintillation counter(Wallac 1410, PerkinElmer, Turku, Finland). All incubations wereperformed at least in duplicate. A receptor density (Bmax) and anequilibrium dissociation constant (Kd) for the radioligand[3H]RS79948-197 were determined for each membrane batch.

2.4. Miniaturized competition-binding assay for screening of a2B-ARligands

The screening platform was based on a previously describedcompetition-binding assay format (Laurila et al., 2007) and optimalconditions were first determined using a manual assay. Bindingreactions were performed on MultiScreen FB 96-well filtrationplates (Millipore Corporation, Bedford, MA, USA). The radioligand[3H]RS79948-197 (0.3 nM, final concentration) was added fol-lowed by the test compounds (10 lM) in 50 lM phosphate buffer

A. Fallarero et al. / European Journal of Pharmaceutical Sciences 47 (2012) 941–951 943

(PB) pH 7.4, or PB alone (for total binding) or phentolamine(100 lM) (for non-specific binding) and an aliquot of cell mem-branes from a2B-AR-expressing CHO cells (in amounts of 1; 2.5;5 and 10 lg/well). Adrenaline (10 lM) was used as a positive con-trol. The final reaction volume was 60 ll. After 30–40 min of incu-bation at room temperature, the reaction was terminated with asequence of three washing steps with cold PB (250 ll per well)and rapid vacuum filtration with the Millipore Multiscreen Vac-uum Manifold System (Bedford, USA). Plates were left to dry eitherat +37 �C for 5 h or at room temperature overnight. Then, 30 ll ofSuper Mix scintillation cocktail was added, and the samples wereleft to stand for at least 3 h before the radioactivity remaining onthe filters was measured in a b-plate liquid scintillation counter(Wallac 1450, PerkinElmer, Turku, Finland). During this optimiza-tion stage, DMSO compatibility of the assay was also assessed.

2.5. Chemical screenings

Once the optimal assay conditions were established, the proce-dure was transferred to an automated environment. The first step,radioligand addition, was the only one still conducted manually.All subsequent pipetting steps preceding the binding reaction wereperformed using a Biomek 3000 liquid handling workstation (Beck-man Coulter, Fullerton, CA, USA). Chemical compounds were testedat 10 lM in the primary screening stage. Following 30–40 minincubation at room temperature, the washing steps were carriedout with the aid of a Combi Multidrop dispenser (Thermo FisherScientific, Waltham, MA, USA). Subsequent phases of the protocolwere conducted as described in the section above. The compoundsthat were screened during the primary campaign belonged to thecommercial libraries of ChemDiv (San Diego, CA, USA), ChemBridge(San Diego, CA, USA), Tripos Discovery Research (now Exelgen Dis-covery, Bude, UK) and the MicroSource Spectrum collection(MicroSource Discovery Systems, Inc., Gaylordsville, CT, USA). FinalDMSO concentration in the assay was kept below 2%. The chemicallibrary contained 17,952 compounds in 96-well microplates, fromwhich a subset of 2112 compounds were FDA-approved moleculesand other drugs with known pharmacological and toxicologicalprofiles (referred to as: ‘‘drug subset’’). The remaining compoundsare referred to as: ‘‘general subset’’.

2.6. Selectivity testing on the human a2-AR subtypes

All positive hits identified in the primary screen (176) were re-tested at a lower concentration (1 lM) against the three human a2-AR subtypes to exclude possible false positive hits and low-affinitycompounds. Selected hits were then taken up for determination ofbinding affinity values. For that purpose, miniaturized competitionbinding assays were performed. The radioligand, [3H]RS79948-197, was added at a final concentration of 0.3 nM, followed bythe test compounds in 50 lM PB buffer, pH 7.4. Eight serial dilu-tions of the selected compounds (1 pM to 10 lM), or the referencecompounds phentolamine (0.1–10 lM) or (�)-adrenaline (10 nMto 1 mM) were tested. The third addition step consisted of an ali-quot of the membranes obtained from a2B, a2A, a2C-AR-expressingCHO cells (2.5 lg cell membrane protein/well). The total volumewas 60 ll. Compounds were incubated with the radioligand andthe membrane preparations at room temperature for 40 min. Then,the washing steps were performed as detailed in Section 2.4. Theassays were carried out in duplicate wells. Bound radioactivitywas measured with a b-plate liquid scintillation counter (Wallac1450).

2.7. Agonist activity testing using the [35S]GTPcS binding assay

Nine compounds were tested for possible agonist activity ateach of the three a2-AR subtypes. These included five hit com-pounds (pimethixene, promethazine, cyclobenzaprine, lysergoland pirenperone). In addition, since 200;20000-bisepigallocatechindigallate – one of the identified hit compounds – was not availablefor follow-up studies, four structurally related compounds weretested instead: (�)-epigallocatechin gallate, (�)-epigallocatechin,(+)-catechin and (�)-epicatechin. Agonist-induced stimulation of[35S]GTPcS binding was determined as described previously (Pelto-nen et al., 1998). Briefly, membrane preparations obtained froma2B, a2A or a2C-AR-expressing CHO cells, respectively, were thawedand resuspended in binding buffer (25 mM Tris, 1 mM EDTA, 5 mMMgCl2, 20 mM NaCl, 1 lM GDP, 1 mM DTT, 30 lM ascorbic acid, pH7.4). Incubations were performed on 96-well MultiScreen MAFB/MSFB glass fiber filter plates (Millipore Corporation, Bedford, MA,USA). Samples containing 5 lg of cell membrane protein/well wereincubated with 1 lM concentrations of the test compounds and0.1 nM [35S]GTPcS for 30 min at room temperature in a total vol-ume of 90 ll. Reactions were terminated by vacuum filtrationusing the Millipore Multiscreen Vacuum Manifold System. Filterswere washed three times with cold wash buffer (20 mM Tris,1 mM EDTA, 5 mM MgCl2, pH 7.4), left to dry and 50 ll of SuperMixscintillation cocktail was added into each well. The incorporatedradioactivity was measured with b-counting.

2.8. Data processing and statistical analysis of the experimental data

Calculations and statistical data analysis were carried out usingGraphPad Prism program (GraphPad Prism, San Diego, USA). Theaffinities (Ki) of compounds were determined via nonlinear regres-sion analysis, assuming one-site binding. For the conversion of IC50

into Ki values, the Cheng-Prusoff equation was used (Cheng andPrusoff, 1973). When applicable, the results are expressed as arith-metic means and their 95% confidence intervals or meanvalues ± standard deviations (SDs). The normality of the maximaland minimal signals was studied using Kolmogorov–Smirnov(KS) and D’Agostino and Pearson omnibus normality tests, todetect any deviations from a Gaussian distribution. Assay qualityunder optimal assay conditions was characterized using repeat-ability measures (coefficient of variation, CV) as well as typicalstatistical parameters: screening window coefficient (Z0 factor),signal-to-background (S/B) and signal-to-noise (S/N) ratios, usingthe following equations (Bollini et al., 2002; Zhang et al., 1999):

Z0 ¼ 1� ½ð3� SDs þ 3� SDbÞ=jXs � Xbj�

S=B ¼ Xs=Xb

S=N ¼ ðXs � XbÞ=ðSD2s þ SD2

bÞ1=2

where Xs; SDs: corresponded to the average and the standard devi-ation (SD) of the maximum signal, i.e. controls for total binding of[3H]RS79948-197 in the absence of competitors and Xb; SDb: corre-sponded to the average and the standard deviation (SD) of the back-ground signal, i.e. controls for non-specific binding, determined insamples containing 100 lM phentolamine.

Acceptance limits for the three statistical parameters were de-fined and used to monitor changes in assay performance duringthe screening campaign. The hit limit was calculated for eachscreened plate as the mean of the minimum signal + 3 SD (usingcontrols containing 100 lM phentolamine, a known antagonist),which corresponded to at least 60% inhibition of the radioligandbinding. Compounds scoring below this threshold were identifiedas positive hits. The hit rate of the assay was calculated as the ratio

Fig. 1. Comparison of frequency distributions obtained for the manual (straightline) and automated assays (dotted line).

Table 1Comparison of performance between the manual and the automated screening assay.

944 A. Fallarero et al. / European Journal of Pharmaceutical Sciences 47 (2012) 941–951

between the number of hits identified during the primary screen-ing campaign and the number of screened compounds.

2.9. Chemoinformatics analysis

A classification tree was built to cluster together compoundswith similar chemical scaffolds independent of the activity data.Briefly, the two-dimensional structures of the 176 hit compoundswere retrieved from the vendor databases, using the chemicalIDs, on a non-automated basis. The 2D structures were eithersketched, or in the case of the drug subset were built from theirSMILES codes, using the Sybyl-X1.2 molecular modeling platform(Tripos Ltd., El Cerrito, CA, USA). The software Distill implementedin Sybyl X1.2 was used to perform hierarchical clustering using thedefault parameters. The scaffolds were then visually inspected incollaboration with a medicinal chemist who provided expertiseabout the most appropriate naming conventions.

In order to obtain additional information, a set of standardmolecular descriptors was calculated using Volsurf+. Volsurf+ cal-culates a total of 94 molecular descriptors that include simpledescriptors such as molecular weight, polar surface area and vol-ume. Volsurf+ also predicts for each compound the logD and for-mal charges as a function of the pH. An estimate of the numberof charged groups for each compound at pH 7.5 is additionally pro-vided (Supplementary Tables 1 and 2). To provide a better descrip-tion of the charged states, i.e. to estimate the amount of possibleformal negative and positive charges, we also estimated thecharges at pH 13.0 (basic groups are neutral, acidic groups are ion-ized and therefore the amount of charge seen directly relates to thenumber of acidic groups) and pH 1.0 (basic groups are positivelycharged and seen, acidic groups are neutral). Even if these pH con-ditions are not physiologically relevant, they still provide informa-tion about the molecules accounting for the errors in pKa

predictions.

2.10. Molecular modeling

The binding mode of 200;20000-bisepigallocatechin digallate wasinvestigated using our previously published model of the a2B-AR(Laurila et al., 2011), which is based on the b2-AR crystal structure(PDB 2RH1) (Cherezov et al., 2007). The protein model was pre-pared in Sybyl-X1.2 by adding hydrogen atoms. The200;20000-bisepigallocatechin digallate structure was built as abovebut was relaxed prior to docking using 1000 steps of energy mini-mization (Tripos forcefield, Gasteiger – Huckel charges, steepestdescent method). Docking simulation was conducted using Goldversion 5.2 (Jones et al., 1995) with standard parameters. The bind-ing site was centered on a 10 Å sphere from a side chain oxygenatom of the aspartic acid in position 92 (D3.32 according to the Bal-lesteros-Weinstein convention). Twenty independent docking sim-ulations were conducted in order to assess the chemical space ofbinding for 200;20000-bisepigallocatechin digallate, leading to a setof reasonable solutions.

For Z0 , S/N and S/B values, results are expressed as arithmetic means ± SD of 150 wells.The repeatability estimates, expressed as coefficients of variation (CV, percent value)refer to the variation registered in the maximal signal on three plates made on thesame day (plate-to-plate variability) or in three plates made on different days (day-to-day variability).

Manual Automated

Screening window coefficient (Z0) a 0.58 ± 0.08 0.61 ± 0.03Signal-to-noise ratio (S/N) a 9.98 ± 1.91 10.24 ± 0.98Signal-to-background ratio (S/B) a 4.14 ± 1.23 4.48 ± 0.37Well-to-well variability (maximal signal) (CV; %) 6.53 6.85Plate-to-plate variability (CV; %) 6.04 3.33Day-to-day variability (CV; %) 27.3 8.41

a Equations are detailed in Section 2.8.

3. Results and discussion

3.1. Optimization and automation of a miniaturized screening assayfor a2B-AR

After performing the initial membrane preparation steps, the Kd

and Bmax values were determined for all three human a2-AR sub-types using the a2-AR antagonist, [3H]RS79948-197, as radioligand.The Kd values of each receptor subtype were as follows: a2A,0.20 ± 0.04 nM; a2B, 0.33 ± 0.04 nM and a2C, 0.18 ± 0.02 nM. TheBmax values of the cell membrane batches were: a2A,

4.2 ± 0.3 pmol/mg protein; a2B, 9.6 ± 2.0 pmol/mg protein anda2C, 8.6 ± 1.1 pmol/mg protein. These values were in accordancewith earlier data and thus indicated that the receptors were wellpreserved after the membrane preparation steps (Pohjanoksaet al., 1997).

The first step of assay optimization was the elucidation of theeffects of protein concentration and reading time on assay perfor-mance. The screening window coefficient (Z0) was used to monitorthe assay optimization process. This parameter combines the twokey issues contributing to the robustness of a screening assay:the dynamic assay signal window (i.e., the amplitude betweenthe means of maximal and minimal signals) and the signal variabi-lity (i.e., their standard deviations). Z0 is dimensionless and typicallyranges between 1 (an ideal assay with no signal variation) and 0 (anon-discriminating assay, with overlapping maximal and minimalsignals). In a well-performing assay, Z0 should typically reach avalue equal or higher than 0.5. High Z0 values translate into havinga high amplitude between the maximal and the minimal signals(i.e., high signal-to-background ratio, S/B) and low coefficient ofvariation (CV). Lower CVs usually permit lower S/B values, butlow CVs are usually not typical in most experimental settings(Macarrón and Hertzberg, 2009). Consequently, to keep Z0 above0.5, even for assays with low signal variability (i.e., CV � 5%), theS/B ratio should typically be higher than 1.9. During the manualoptimization of the assay, acceptable Z0 values (>0.5) (Zhanget al., 1999) were only obtained with protein amounts higher orequal to 2.5 lg/well, and no significant variation of Z0 values wasnoted with different reading times (results not shown). From thesefindings, 2.5 lg of membrane protein/well was selected as an

A. Fallarero et al. / European Journal of Pharmaceutical Sciences 47 (2012) 941–951 945

optimal quantity and 3 h was set for practical purposes as thereading time.

The performance of screening assays is always evaluatedassuming that the data are normally distributed, which is the caseif the variability is only due to random effects. Normal distribu-tions of the signals were confirmed for the manual assay (Fig. 1).In addition, according to the quality control parameters (Table 1),the assay proved to be satisfactory, although high day-to-day fluc-tuation of the maximal signal was detected (P20%). Thus, in orderto improve assay performance and to make it suitable for profilingof large chemical libraries, the assay was automated using a Biom-ek 3000 liquid handling workstation. A comparison of the perfor-mance of the two types of assays is summarized in Fig. 1 andTable 1. As a result of the automation, lower signal dispersionswere obtained and the assay performance was improved, as re-flected in higher Z0 values. Another important change was the over-all reduction in signal variation, which made the assay bettersuited for high throughput. The acceptance limits of the automatedassay were set as Z0 > 0.5, S/B > 3 and S/N > 8. In addition, DMSOcompatibility tests were conducted with the automated assayand the assay was shown to tolerate DMSO concentrations loweror equal to 1% (results not shown).

3.2. Chemical screenings

Typical examples of the raw data obtained during the screeningcampaign for plates containing or lacking positive hits are pre-sented in the Supplementary Fig. 1 of this article. The extent ofinhibition of radioligand binding on the primary target receptor(human a2B-AR) was calculated for all screened compounds (tested

Fig. 2. Frequency distribution of the entire library (A) and the drug subset (B) basedon the values obtained for binding inhibition during the primary screening. Theareas containing the active hits are indicated with gray boxes.

at 10 lM); the resulting frequency distributions are depicted inFig. 2A, while the results obtained for the drug subset are specifi-cally presented in Fig. 2B. The data were binomially distributedin both cases, and the peaks of these distributions correspondedto 15% inhibition of radioligand binding for the entire library and25% for the drug subset. Positive hits were defined when theyinhibited radioligand binding by at least 60%. From 204 platesscreened (with 88 compounds per plate), a total of 176 hits wereidentified, accounting for an overall hit rate of 0.98%. Most of thepositive hits (107) were found among the drug subset of the li-brary; thus, the hit rate for that specific subset was higher (around5%) than in the general subset (0.43%). Forty of the 176 hits ap-peared to have relatively high potency, causing more than 75%inhibition of radioligand binding in the initial screen. This led usto proceed into the next phase, where the aim was to confirmand characterize the pharmacological activity of the initially iden-tified hits.

3.3. Follow-up studies using the three a2-AR subtypes

Following the primary a2B-AR screening, the 176 hit com-pounds initially identified were retested against all three humanreceptor subtypes (a2B, a2C and a2A-AR) in a single-concentrationassay (1 lM), to further exclude molecules with lower (micromo-lar) binding affinity, as schematically presented in Fig. 3A. Fromthese assays, a total of 89 compounds were tentatively chosen asdisplaying potencies with IC50 values 6 1000 nM for the a2B-ARsubtype, 53 compounds against the a2C-AR subtype and 41 com-pounds against the a2A-AR subtype. All compounds but four iden-tified as active at the a2C-AR and/or a2A-AR subtypes were alsopresent in the set of 89 compounds tentatively identified as havinghigh potency for a2B-AR. All of these compounds were then fol-lowed up for quantitative determination of potency (IC50) andaffinity (Ki) estimates.

Fig. 3B presents the distribution of the potency estimates of thehits identified against the three receptor subtypes. In the case ofthe a2B-AR subtype, there were 54 (out of 89) confirmed com-pounds from the drug subset with IC50 6 1000 nM (sum of all num-bers included in the black bars of Fig. 3B, top graph). Twenty-twocompounds displayed IC50 6 500 nM, of which five were highly ac-tive with IC50 6 100 nM. Nine compounds fell within the middleactivity range, with potency values between 1000 and 500 nMwhile 23 compounds had potency values over or close to1000 nM. From this last group, a total of six compounds also dis-played activity against the other two subtypes. These six hits, to-gether with the 31 displaying nanomolar activity, are listed inTable 2 (total of 37).

When analyzing affinities at the other two subtypes (a2C-ARand a2A-AR) (Table 2), it was noticed that a considerable numberof the hits in the drug subset (25) did not show subtype-selectivity.Among them, potent known a2-AR antagonists with nanomolaraffinities, such as rauwolscine and yohimbine, are included. In con-trast, a total of 12 hits (Table 2, marked in bold) were found toshow some a2-AR subtype selectivity based on a ratio of Ki val-ues P 10. Four hits favored the a2B-AR subtype when comparedto at least one of the other subtypes (periciazine, propiomazine,promethazine and chlorpromazine) and six favored the a2A-AR(2-methoxyidazoxan, guanabenz, naphazoline, tetrahydrozoline,bromocriptine and nicergoline). Indeed, two known agonists ofthe a2A-AR subtype, guanabenz and naphazoline (Hayashi et al.,2001; Piletz et al., 1996), were correctly identified by this method,while chlorpromazine was found to favor the a2B-AR subtype, aspublished earlier (Laurila et al., 2011). A very potent antagonistof a2-ARs, 2-methoxyidazoxan (Pauwels and Tardif, 2002), withnanomolar affinity towards the three subtypes, was also identifiedin our screen and selectivity towards a2A-AR was shown (it was

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Num

ber o

f hi

ts

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50

Low Middle High

Low Middle High

Low Middle High

2326

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22

2

drug subsetgeneral subset

drug subsetgeneral subset

A B

Fig. 3. General view of the screening sequence (A) and distribution of the hits according to potency ranges measured against the a2B-AR, a2C-AR and a2A-AR subtypes (B). Thepotencies were arbitrarily set as low, middle and high, depending on the potency estimates (IC50 > 1000 nM; 1000 nM > IC50 > 500 nM and IC50 < 500 nM, respectively).

946 A. Fallarero et al. / European Journal of Pharmaceutical Sciences 47 (2012) 941–951

24-fold more potent at a2A-AR compared to a2B-AR). Moreover,new information was also obtained. For example, promethazinewas found as a selective a2B-AR ligand, and no prior recognitionof activity on a2-AR has been reported for this compound. Simi-larly, other hits, such as pimethixene, cyclobenzaprine, lysergoland pirenperone were identified with no previously reported activ-ity on a2-AR. These compounds were available for further charac-terization, as will be detailed later.

From the general subset (synthetic compounds obtained bycombinatorial chemistry), as outlined in Fig. 3B, in the case ofthe a2B-AR subtype, there were 35 confirmed compounds withpotencies lower or close to 1000 nM, as opposed to 54 in the drugsubset (giving the total of 89 confirmed hits from the entire li-brary). From the general subset, rather few highly active hits(IC50 < 1000 nM) were detected. On the a2B-AR subtype, two com-pounds were found to be highly active, seven were in the middleactivity range and 26 had low potency. However, from these 26, se-ven compounds, such as ChemDiv 5151-0630, exhibited also sig-nificant activity at least against one of the other two subtypes.

Therefore these seven hits were also included in the list presentedin Table 3, along with the nine most active (total of 16). Two com-pounds (Table 3, in bold) were found to be somewhat selectiveagainst the a2A-AR subtype.

The structural determinants of selectivity between the humana2-AR subtypes are currently not well understood. For spiperone,spiroxatrine and chlorpromazine, changes in the first transmem-brane domain (TM1) have been shown to affect subtype selectivity,probably through an indirect effect (Laurila et al., 2011). On theother hand, for yohimbine analogs, a well-documented structuraldeterminant has been identified in the Cys/Ser variation in position5.43 in the fifth transmembrane segment (TM5) and two other dif-ferences in the second extracellular loop, XL2 (xl2.49-xl2.51). Tar-geted mutagenesis of these residues has had major effects on thesubtype-selectivity of some yohimbine analogs (Laurila et al.,2007). In the design of ligands that aim to be subtype selective, po-lar substituents could for instance improve binding to the a2B-ARsubtype through hydrogen bonding with S5.43 (Laurila et al.,2007).

Table 2Competition binding affinities (Ki) of compounds in the drug subset that showed significant a2B-AR binding affinity (estimated IC50 < 1000 nM). Bold text was used to indicate thecompounds showed some a2-AR subtype selectivity (difference in Ki P 10 times).

Compounds Hit code a2B-AR a2A-AR a2C-AR

Ki (nM) CIa n Ki (nM) CIa n Ki (nM) CIa n

Rauwolscine 98 4.3 2.1–8.9 2 3.0 1.9–5.3 2 0.65 0.27–1.6 2Periciazine 76 5.2 1.5–23 2 307 111–715 2 121 49–287 2Yohimbine 36 10 5–21 2 2.6 1.1–5.7 2 3.9 2.1–7.3 22-methoxyidazoxan 106 16 6–35 2 0.67 0.28–1.4 2 5.1 2.6–8.5 2Clozapine 43 31 13–68 2 132 40–308 2 10 4–26 2Propiomazine 55 74 42–133 2 747 423–1318 1 813 505–1310 1Amitriptyline 12 88 45–179 2 nd 120 61–249 2200;20000-bisepigallocatechin digallate 99 91 46–176 2 14 6–29 2 18 7–45 2Perphenazine 72 95 23–252 2 917 294–3468 2 285 89–633 2Isothipendyl 101 95 49–179 2 379 167–771 2 781 457–1349 2Promazine 53 97 40–211 2 nd 289 00–475 2Doxepin 29 112 46–264 2 nd 264 150–449 2Pimethixene 38 140 68–325 2 153 53–328 2 397 152–836 2Trimeprazine 49 147 41–349 2 nd 432 237–926 2Chlorpromazine 3 151 82–307 2 1762 953–3259 1 442 248–819 2Promethazine 54 160 87–286 2 nd ndTrimipramine 66 166 88–280 2 nd 211 104–404 2Cyclobenzaprine 71 171 65–512 2 nd 213 96–524 2Thiothixene 96 189 83–383 2 357 159–826 2 587 159–826 2Lysergol 109 199 73–433 2 210 35–564 2 267 129–587 2Metergoline 4 204 94–429 2 257 80–694 2 377 180–805 2Pirenperone 59 239 125–469 2 37 18–89 2 44 21–101 2Imipramine 23 289 148–541 2 nd 401 237–653 2Trazodone 68 325 145–628 2 275 131–583 2 389 170–787 2Clomipramine 69 342 119–681 2 nd ndGuanabenz 13 349 94–1013 2 21 11–36 2 239 123–668 2Prochlorperazine 51 359 170–697 2 nd 643 358–791 2Carvedilol 88 439 195–823 2 478 281–813 1 217 102–452 2Spiperone 50 458 250–927 2 nd 237 120–453 2Naphazoline 19 476 109–1613 2 10 4–25 2 410 237–708 1Triflupromazine 67 531 282–1083 2 nd ndBenztropine 20 629 199–3272 2 nd 215 121–397 2Pararosaniline 75 825 315–2041 2 113 54–240 2 ndTetrahydrozoline 42 1034 584–1830 1 44 19–86 2 ndAntazoline 18 1107 527–2326 1 206 76–469 2 ndBromocriptine 25 1260 321–6720 2 98 35–227 2 1770 388–5866 2Nicergoline 44 2699 1425–5608 2 185 49–424 2 362 185–722 2

nd – indicates that Ki was not determined because the initially estimated IC50 was higher than 1000 nM.a Indicates the 95% confidence interval of 1–2 independent experiments.

Table 3Competition binding affinities (Ki) of compounds from the general subset that showed significant a2-AR binding affinity (estimated IC50 < 1000 nM). Bold text indicates that thecompounds showed some a2-AR subtype selectivity (difference in Ki P 10 times). The complete list with structures of these hits is available in the Supplementary Fig. 2 of thisarticle.

Compounds Hit code a2B-AR a2A-AR a2C-AR

Ki (nM) CIa n Ki (nM) CIa n Ki (nM) CIa n

ChemDiv 4466-2048 163 19 8–37 2 133 62–302 2 98 42–219 2ChemDiv 3258-0334 148 244 93–668 2 nd ndChemDiv C712-1094 176 266 127–520 2 230 113–525 2 311 134–560 2ChemDiv 3474-0046 156 375 165–744 2 nd ndChemDiv 5222-3672 168 394 129–1155 2 nd ndTripos 1524-18759 142 448 233–1020 2 nd ndChemDiv 5218-2804 167 512 269–1135 2 612 178–1797 2 ndChemDiv 4852-0626 165 517 289–1075 2 167 87–305 2 ndChemDiv C712-1093 175 521 256–1146 2 314 147–859 2 445 178–980 2ChemDiv C712-1339 177 845 367–2027 2 75 22–220 2 124 44–287 2ChemDiv 5151-0630 169 914 402–2778 2 161 48–386 2 730 223–1641 2Tripos 1547-01166 133 920 415–2482 2 nd 277 133–553 2ChemDiv 1724-0247 144 923 458–2081 2 34 18–62 2 142 86–250 2Tripos 1524-11982 141 1037 472–2122 2 236 99–570 2 263 64–824 2ChemDiv C712-1237 178 1107 543–2220 2 283 158–481 2 350 159–771 2Tripos 1524-13002 119 1965 851–3407 2 351 182–603 2 1379 760–2502 1

nd – indicates that Ki was not determined because the initially estimated IC50 was higher than 1000 nM.a Indicates the 95% confidence interval of 1–2 independent experiments.

A. Fallarero et al. / European Journal of Pharmaceutical Sciences 47 (2012) 941–951 947

3.4. Molecular properties of a2-adrenoceptor binders

The physicochemical properties of the 176 hits initially identi-fied in the primary screening campaign were relatively similar,

and no distinguishable features were found in the molecules laterreconfirmed with potencies lower or close to 1000 nM, as opposedto the rest (Supplementary Tables 1 and 2). For example at thea2B-AR subtype, the average molecular weights of the confirmed

948 A. Fallarero et al. / European Journal of Pharmaceutical Sciences 47 (2012) 941–951

actives versus the non-confirmed actives were: 332 ± 89 g/molversus 334 ± 101 g/mol (mean ± standard deviation). The averagePSA was 46 ± 43 Å2 versus 62 ± 46 Å2 while the average hydropho-bic surface area was 468 ± 70 Å2 versus 446 ± 95 Å2. The com-pounds usually had one or two hydrogen-bond acceptors (81 and75 instances in confirmed versus non-confirmed actives), and atleast one hydrogen bond donor (84 and 52 instances). A notableexception was the nanomolar antagonist 200;20000-bisepigallocatechindigallate that has 16 hydroxyl groups.

An important common property of compounds binding to a2-ARs, as well as other amine GPCRs, is the presence of a protonablenitrogen that can form an strong ionic interaction with the sidechain carboxylate of aspartic acid 3.32 in TM3 of the receptor(aspartic acids in positions 113, 92 and 131 in the a2A-AR, a2B-AR and a2c-AR subtypes, respectively) (Cherezov et al., 2007). Onlyseventeen out of the 176 compounds selected for secondaryscreening lack such a basic protonable group (Supplementary Ta-bles 1 and 2) and they were, as expected, generally weak binders.Out of the 54 confirmed hit compounds of the drug subset (se-lected after retesting at 1 lM on a2B-AR) it was indeed found that52 possess at least one basic group. The following basic functionalgroups were primarily found: piperazine, piperidine, imidazole,guanidium, and secondary or tertiary amines. The ionization statespredicted at pH 7.5 do not suggest all basic compounds to becharged (Supplementary Table 1) but in many cases this cansimply reflect errors in the pKa prediction, for example as in thecase of the tertiary amines in yohimbine or lysergolol that havebasic pKa estimates of about 9.0. A notable exception was200;20000-bisepigallocatechin digallate that does not possess anybasic group (it does not even contain any nitrogen atoms) but itstill binds with nanomolar affinity to all three receptor subtypes(Table 2). Further insight into the molecular features possiblyimplicated in the interactions of this compound with the a2-ARsubtypes is presented in Section 3.7.

3.5. Novel scaffolds for a2-AR binders identified in this screeningcampaign

In order to compare the chemical scaffolds of the moleculeswith nanomolar and micromolar binding affinity, a classificationtree based on maximum common substructure was built (Fig. 4).Defining a scaffold may be a subjective procedure and here classeswere assigned according to unbiased hierarchial clustering (Sup-plementary Table 1).

The tree identified 28 distinct groups among the set of activecompounds. The most populated group contained the phenothia-zine scaffold (14 molecules, e.g. chlorpromazine), followed by ben-zylpiperazines (6 molecules, e.g. trazodone) and methoxydibenzenes(5 molecules, e.g. benztropine). In this study, we identified 10entirely novel scaffolds (found in 23 molecules) that did not relateto any known a2-AR or other GPCR ligands. Those were: thieno-chromene (n = 4), namyl methyl piperazine (n = 4), thienopyrimi-dine (n = 2), benzothiopene (n = 2), di-tri azolo (n = 2), and oneeach with a piperidine, pyrrolidine, thiophenyl, piperidine indole,quinidine and pyridopyrimidine scaffold. The scaffolds with thehighest affinity were thienochromenes and pyridopyrimidines.These scaffolds were found to be present in the molecules favoringthe a2A-AR and a2C-AR subtypes, for instance in C712-1339 and1724-0247 (listed in Table 3 in bold), but all molecules with athienochromene scaffold were not found to be subtype-selective.

Twenty out of the 28 chemical scaffolds described in this studywere found within the set of 54 molecules from the drug subsetthat were deemed active in the secondary screening against a2B-AR (Fig. 3B). Only five of these scaffolds (present in seven mole-cules) were previously annotated to bind to a2-ARs according tothe IUPHAR catalog (Harmar et al., 2009). Based on our prior expe-

rience, another four scaffolds can also be deemed as active ona2-ARs (i.e. tested in Ruuskanen et al., 2005). Of the eleven novelscaffolds from the drug subset, three are entirely novel(200;20000-bisepigallocatechin digallate, hydroquinidine andsulfamerazine) and they have not, to the best of our knowledge,been previously reported as binding to a2-ARs or other amineGPCRs. Results obtained with hydroquinidine and sulfamera-zine are presented in the Supplementary Table 1. In particular,200;20000-bisepigallocatechin digallate contains a catechol substruc-ture. Catechol is the active functional group responsible for trigger-ing the activation of adrenoceptors through interaction with theirfifth transmembrane segment, TM5 (e.g. Nyrönen et al., 2001;Peltonen et al., 2003).

3.6. [35S]GTPS binding assay identifies lysergol as a partial agonist

The full a2-AR agonist (�)-adrenaline and three known partialagonists (brimonidine, dexmedetomidine and oxymetazoline)were used as reference compounds in the [35S]GTPcS bindingassay. Based on availability and on their previously unknown prop-erties as a2-AR ligands, pimethixene, promethazine, cyclobenza-prine, lysergol and pirenperone were then tested in this assay toinvestigate the type of binding to the three a2-AR subtypes. As aresult, the ergoline alkaloid lysergol was the only compound thatacted as an agonist, in addition to the four known agonists thatwere employed as positive controls (results not shown for theother tested compounds). For the a2B- and a2A-AR subtypes, lyser-gol is a partial agonist and for the a2C-AR subtype, it emerged as avery weak partial agonist. Efficacy estimates of lysergol areexpressed as intrinsic activity relative to the full agonist (�)-adrenaline. The estimates (mean ± SD, n = 3) were for the a2B-AR,32 ± 7%, for a2A-AR, 41 ± 4%, and for a2C-AR, 17 ± 14%. In theseexperiments, the known a2-AR agonists exhibited the expectedrank order of efficacy, i.e. (-)-adrenaline = brimonidine > dexmede-tomidine > oxymetazoline (= lysergol) at a2A-AR, dexmedetomi-dine > (-)-adrenaline > brimonidine = oxymetazoline (>lysergol) ata2B-AR, and (-)-adrenaline = dexmedetomidine > brimonidine >oxymetazoline (= lysergol) at a2C-AR. Lysergol was thus compara-ble in efficacy to oxymetazoline at a2A-AR and a2C-AR, but weakerthan oxymetazoline at a2B-AR.

The agonist activity of lysergol makes it a potentially veryimportant molecule towards our understanding of a2-AR agonism.Up to now, identified a2-AR agonists have been smaller in size thanthe known a2-AR antagonists, which agrees well with an activationmechanism whereby a contraction of the binding pocket occursupon activation as seen in the activated crystal structures of theb2-AR and the adenosine A2A receptors (Rosenbaum et al., 2011).Lysergol is chemically related to another ergot alkaloid, bromocrip-tine, which was also included in this study and was found to havenanomolar affinity for a2-ARs (Table 2). Bromocriptine is known asan agonist of the dopamine D2 receptor, a close relative to the a2-ARs (Xhaard et al., 2006).

3.7. Identification of 200; 20000-bisepigallocatechin digallate as a non-nitrogenous a2-AR ligand

From the previously presented screening results,200;20000-bisepigallocatechin digallate, a non-protonable compound,emerged as another highly interesting hit. This molecule does pos-sess a catechol ring substructure as part of its galleate moiety, likethe natural ligands adrenaline and noradrenaline. There is ampleevidence that catechol rings interact with S5.42, C/S 5.43 andS5.46 in a2-ARs as well as in other catecholamine-binding GPCRs(other adrenoceptors and dopamine receptors; Xhaard et al.,2006). The catechol ring itself, even when not connected to an

Fig. 4. Scaffold classification tree for the compounds showing nanomolar or micromolar affinity to at least one human a2-AR subtype (see Supplementary Table 1 for affinityvalues). At left, a classification tree clustering molecular scaffolds is shown. In the center, boxes representing the affinity to the a2-AR subtypes are presented (dark tonedcolors represent affinities in the range 1 nM to 1 lM and light toned colors represent affinities in the range 1 lM to 1 mM). The results for a2B-AR is seen as presented in red;a2A-AR in blue and a2C-AR in green. Empty boxes indicate an affinity in the micromolar range, while the absence of a box represents an untested compound. To the right, inthe first column, representative examples of known drugs identified in the screen are shown (a, c, e, g, j, l, n, o) while the second column (b, d, f, h, i, k, m, p, q) includesrepresentatives of the new molecules identified in this study. The names or company codes of the hits are underlined in the tree.

A. Fallarero et al. / European Journal of Pharmaceutical Sciences 47 (2012) 941–951 949

amine moiety, is known to bind to the closely related b2-AR as afragment (Swaminath et al., 2005).

200;20000-bisepigallocatechin digallate has four chiral centersand a large molecular mass of 911.7 g/mol (Fig. 5A), but it

Fig. 5. An in silico insight into the molecular interactions of 200;20000-bisepigallocatechin digallate (structure showed in A) with the human a2B-AR subtype (docking results inB).

950 A. Fallarero et al. / European Journal of Pharmaceutical Sciences 47 (2012) 941–951

provides valuable proof of the concept that nanomolar affinitycan be reached for a2-ARs with a ligand lacking a positivecharge. In order to get further insight into the binding of200;20000-bisepigallocatechin digallate to the human a2B-AR, a dock-ing study was conducted (Fig. 5B). Despite its large molecularstructure, 200;20000-bisepigallocatechin digallate was well accommo-dated within the a2B-AR binding cavity, constructed on the basis ofthe b2-AR model. Interactions, mostly hydrogen bonds, were foundbetween the hydroxyl groups of the ligand and the carboxylic acidat D3.32, as well as with the above-mentioned serine residues inTM5, S5.42, S.43 and S5.46. The docking simulations, however,did not converge to a single set of solutions, illustrating the diffi-culty to predict the precise binding pose of this compound withhigh pseudo-symmetry.

We have previously suggested that non-protonable ligandsmight be found for a2-ARs (Xhaard et al., 2005) based on analogywith other binding sites able to accommodate catecholamine-likeligands. There are cases in other GPCRs where D3.32 binds non-protonable ligands, as salvinorin A binds to opioid receptors, butsalvinorin A is not suggested to bind to the orthosteric site (Rothet al., 2002). Unfortunately, 200;20000-bisepigallocatechin digallatewas not available for retesting in order to elucidate whether itwas an agonist or antagonist ligand. Instead, some other catechinderivatives: (�)-epigallocatechin gallate, (�)-epigallocatechin,(+)-catechin and (�)-epicatechin were tested in the radioligandbinding assay and the [35S]GTPcS binding assay to represent thiscompound class. However, no detectable binding or agonistactivity was registered for any of these molecules (results notshown). This suggests that a catechol substructure may be animportant but not sufficient feature for the activity of200;20000-bisepigallocatechin digallate.

4. Conclusions

In this contribution, the ultimate goal was not only to identifyand characterize new compounds active at a2B-adrenoceptors,but also to identify new scaffolds that could pave the way for com-pletely new chemical elaborations. To achieve these goals, the pro-ject set off by optimizing and miniaturizing a competition bindingassay, followed by a large screening campaign of a commercially

available chemical library containing more than 17,000 molecules.With the aid of chemoinformatics tools, the large amount of gener-ated data was analyzed and relevant structural features present inthe active molecules were recognized. The collection of confirmedactive compounds represents a highly relevant dataset with rarelyavailable features: it contains a relative large number of com-pounds binding to the three a2-ARs subtypes (89 ligands actingat least on the a2B-AR and 4 on the other subtypes), with affinitiesspanning five orders of magnitude, that were all measured underthe same assay conditions by the same laboratory. This informa-tion, which is now being made accessible to researchers, shouldbe very useful for performing theoretical analyses on structureaffinity relationships of a2-AR ligands.

Moreover, a new molecule was identified and characterized as apartial agonist (lysergol), while a non-protonable molecular struc-ture (200;20000-bisepigallocatechin digallate) was tentatively pro-posed, for the first time, as being able to interact with the threehuman a2-AR subtypes with nanomolar affinity. Altogether, thesefindings provide interesting new approaches for research aimedat drug discovery targeting the a2-AR subtypes.

Acknowledgments

Authors acknowledge the financial support of ‘‘Tor, Joe ochPentti Borgs minnesfond’’ to the research project, as well as thesupport received from the Drug Discovery and Chemical Biology(DDCB) network of Biocenter Finland. The contribution of theAcademy of Finland’s Project ‘Structural Bioinformatics for Effec-tive and Efficient Drug Discovery’ under Grant 114823 (BIOIN-FARM) is greatly appreciated. The Finnish IT-Center for ScientificComputing (IT-CSC) is thanked for providing computer resourcesand software. The Doctoral Programme in Informational and Struc-tural Biology (ISB) is acknowledged for organizing graduatestudies.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.ejps.2012.08.021.

A. Fallarero et al. / European Journal of Pharmaceutical Sciences 47 (2012) 941–951 951

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