Research paper

Nanomolar anti-sickling compounds identified by ligand-basedpharmacophore approach

Odailson Santos Paz a, b, Milena de Jesus Pinheiro b, Renan Fernandes do Espirito Santo c, d,Cristiane Flora Villarreal c, d, Marcelo Santos Castilho a, d, *

a Programa de P!os-graduaç~ao em Biotecnologia, Universidade Estadual de Feira de Santana, Brazilb Faculdade Maria Milza, Brazilc Centro de Pesquisa Gonçalo Moniz - FIOCRUZ-BA, Brazild Faculdade de Farm!acia da Universidade Federal da Bahia, Brazil

a r t i c l e i n f o

Article history:Received 22 March 2017Received in revised form10 May 2017Accepted 11 May 2017Available online 12 May 2017

Keywords:Sickle cell anemiaRA2B antagonistVirtual screeningPharmacophore model

a b s t r a c t

Adenosine receptors are considered as potential targets for drug development against several diseases.The discovery of subtype 2B adenosine receptors role in erythrocyte sickling process proved its impor-tance to neglected diseases such as sickle cell anemia, which affects approximately 29.000 people aroundthe world, but whose treatment is restricted to just one FDA approved drug (hydroxyurea). In order towiden the therapeutic arsenal available to treat sickle cell anemia patients, it is imperative to identifynew lead compounds that modify the sickling course and not just its symptoms. In order to accomplishthis goal, ligand-based pharmacophore models that differentiate true ligands from decoys and enlightenthe structure-activity relationship of known RA2B antagonists were employed screen the lead-like subsetof the ZINC database. Following a chemical diversity analysis, 18 compounds were selected for biologicalevaluation. Among them, one molecule Z1139491704 (pEC50 ¼ 7.77 ± 0.17) has shown better anti-sickling activity than MRS1754 (pEC50 ¼ 7.63 ± 0.12), a commercial RA2B antagonist. Moreover, thesecompounds exhibited no cytotoxic effect at lowmicromolar range on mammalian cells. In conclusion, thesound development of validated ligand-based pharmacophore models proved essential to identify novelchemical scaffolds that might be useful to develop anti-sickling drugs.

© 2017 Elsevier Masson SAS. All rights reserved.

1. Introduction

Sickle Cell Disease (SCD) is a hereditary disease characterized bya point mutation change that occurs at the beta hemoglobin globingene [1]. This mutation results in the substitution of a glutamic acidfor valine in position 6 of the beta chain, generating hemoglobin S(HbS) [2], whose deoxygenated form is prone to aggregation. TheHbS insoluble polymer precipitates in the plasma membrane, thuspromoting distortion in the red blood cells (RBC) shape [3,4] andhemolytic processes that lead to severe anemia and vaso-occlusivecrises, which result in pain and organ damage [5].

Most SCD available treatments have palliative purpose only (e.g.Opioid drugs to control pain due to vaso-occlusive events). An

alternative strategy, based on the only FDA approved drug (hy-droxyurea), aims at increasing fetal hemoglobin (HbF) [6]. In fact,patients treated with this drug have increased fetal hemoglobinlevels as well as reduced number of hospital admissions [7,8].However, hydroxyurea is not free from adverse events; neutropeniaand infertility may occur when high doses are used [9,10].

Considering that drug association might have a synergic effectand diminish hydroxyurea required doses [11], the discovery ofnovel drugs to fight sickle cell anemia is paramount to thousands ofpatients worldwide.

The discovery of adenosine 2B receptor (RA2B) role in theerythrocytes sickling process [12] seems to offer a good opportu-nity to achieve this goal, as it opens the possibility to take advan-tage of previous medicinal chemistry efforts that had RA2B as atarget for asthma treatment [13e15]. Although the lead compoundsfrom such projects (Fig. 1) might be repurposed to speed up SCDdrug development efforts, no big-pharma has made its move intothis arena so far [16]. In order to partly fulfill this gap, we built

* Corresponding author. Laboratory of Bioinformatics and Molecular Modeling,Room 115, Faculty of Pharmacy, Federal University of Bahia, R. Bar~ao de Jeremoabo,147 - Ondina, Salvador, BA, 40170-115, Brazil.

E-mail address: castilho@ufba.br (M.S. Castilho).

Contents lists available at ScienceDirect

European Journal of Medicinal Chemistry

journal homepage: http: / /www.elsevier .com/locate/ejmech

http://dx.doi.org/10.1016/j.ejmech.2017.05.0350223-5234/© 2017 Elsevier Masson SAS. All rights reserved.

European Journal of Medicinal Chemistry 136 (2017) 487e496

pharmacophore models that enlighten the structure-activity rela-tionship for adenosine analogs that block RA2B and proved thatthese models are helpful to identify compounds that have anti-sickling effects in cellular assays. Their mechanism of action wasinvestigated by competition assays with NECA, a known RA2Bagonist and their toxicity to mammalian cells, in vitro, was provedto be minimal.

2. Methodology

2.1. Computational methods

2.1.1. Molecules datasetsFive potent RA2B antagonists, with defined stereochemistry

(Table 1), [13,14,18] were drawn on ChemBioDraw v. 12.0 softwareand then converted to 3D format through the “translate molecularfiles” tools available in SYBYL-X 2.0 platform. Next, the structureswere energy minimized by Conjugate Gradient algorithm usingTripos Force Field (ε ¼ 80.4; maximum of interactions ¼ 50,000)

and Gasteiger-Huckel atomic partial charges were assigned. Thesame steps were undertaken for a larger dataset of 287 knownRA2B antagonists [13,17e21] (supplementary material) and decoymolecules generated within DUD-E server (http://dude.docking.org) [22], which were employed during models validation step.

2.1.2. Pharmacophore hypothesis generationThe pharmacophore hypothesis were built with GALAHAD

software, available in SYBYL-X 2.0 [23]. Accordingly, the alignmentprocess was carried out in two steps: First, 30 random conforma-tions for each ligand (population¼ 30) were generated bymeans ofa genetic algorithm. Then, this population evolved through 90generations, using standard CROSS-OVER and MUTATION rates.Next, mutual flexible alignment of conformers was assessedthrough H-BOND (pharmacophoric overlap between conformers),steric (stereo conformers overlap of each other) and ENERGY (totalenergy of conformers) parameters. High ranked alignments wereemployed to build a hypermolecule representation that maximizesthe overlap of common pharmacophore features (positively/nega-tively charged nitrogen, hydrophobic regions, aromatic ring, H-bond donor/acceptor atoms) [24]. Due to the stochastic nature ofthe evolution process, the steps described above were repeated 20times. Phamacophore hypothesis with high ENERGY values (10Xthe average value) or with pareto ranking different from zero werediscarded.

2.1.3. Pharmacophore hypothesis evaluationPharmacophore hypothesis with pareto ranking equal to zero

were evaluated by two consecutive steps. First, their ability todifferentiate true inhibitors from decoys (ROC-validation) wasinvestigated. Thus, decoy compounds were virtually generated(1:50 active: decoy) with e-DUDE server (http://dude.docking.org)

R1=CH3, R3=CH3, R7=H, R8=C6H5

R1=CH3, R3=CH3, R7=H, R8=4-CH3-C6H4, R9=OH

R1=n.C3H7, R3= n.C3H7, R7=H, R8=4-NH2-C6H4, R9=H

pKi=6.74

pKi=6.29

pKi=8.05

Fig. 1. Molecular scaffold of known RA2B antagonists that were designed for asthmatreatment [17].

Table 1Molecules employed for pharmacophore hypotheses generation.a

Compound Structure Biological activity (pKi)

01 8.22

02 8.74

03 8.88

04 9.10

05 8.98

a Asymmetric carbon atoms were set to R stereochemistry.

O.S. Paz et al. / European Journal of Medicinal Chemistry 136 (2017) 487e496488

(Venkatraman et al., 2010) and subjected to the same minimizationsteps described in section 2.1.1. Next, all molecules were super-posed on each pharmacophore hypothesis (template) using UNITY3D “Flex-search” parameters, as available in SYBYL-X 2.0 software[23]. Molecules' QFIT ranking values were employed to buildReceiver Operating Characteristic (ROC) curves, as available inSigma-plot V.12 software. Pharmacophore hypothesis with sensi-tivity >90%, 1-specificty <1.2% and Area Under the Curve (AUC>0.90) were deemed acceptable for further investigation.

In the second evaluation step, pharmacophoric hypothesis werechallenged to differentiate potent from weak RA2B antagonists.Accordingly, 287 known RA2B antagonists, not employed in thehypothesis generation, were flexibly aligned to each pharmaco-phore hypothesis using GALAHAD software default parameters. TheRA2B antagonists fit to each hypothesis was measured throughtheir MOL_QRY values and compared to antagonists potency(potent (pKi" 8.5), moderate (5.5 # pKi # 8.4) and weak (pKi#5.4)). The hypothesis that also fulfills this requirement was selectedas the best pharmacophore hypothesis for screening purposes.

2.1.4. Pharmacophore based virtual screeningThe virtual screening was carried out on the lead-like subset

[25] of ZINC database (http://zinc.docking.org/) [22], accessed on05.10.2013. Mol2 files were downloaded and converted to UNITY3D database format and then flexibly aligned to the selectedpharmacophore hypothesis. The molecules QFIT values were thenemployed to rank themolecules and thosewith QFIT value> 30 hadtheir steric (PC1), electronic (PC2) and lipophilicity (PC3) propertiessummarized into 3 principal components (PC), as available inCHEMGPS-NP server (http://chemgps.bmc.uu.se/batchelor/queue.php?show¼submit). These PCs were employed to hierarchicallycluster the compounds, using Euclidean distances and completeconnection method. Two Compounds from each cluster, consid-ering 80% similarity, were selected for acquisition and subsequentbiological evaluation.

2.2. Biological methods

2.2.1. ReagentsPutative RA2B antagonist were acquired from ENAMINE (http://

www.enamine.net/), with purity >99% and employed withoutfurther purification. NECA (E2387) and MRS1754 (M6316) werepurchased from Sigma Aldrich. All compounds were diluted in 1%dimethyl sulfoxide (DMSO) solution immediately before use.

2.2.2. Biological sample5 ml blood samples from eight adult patients, aged between 19

and 38 years, diagnosed with SCA, were collected immediatelybefore the biological assays begin. The erythrocytes were separatedfrom serum and plasma by centrifugation (5 min at 16,000 g/4 $C)and thenwashed three times in saline (0.9%). Following these steps,100 ml of erythrocyte cells were diluted in 900 ml saline (0.9%) andemployed in biological assays (RBC solution). All proceduresdescribed above were approved by the Hospital Roberto Santosethics committee (928405/2014).

2.2.3. Single dose anti-sickling assayAnti-sickling assays were carried out as described by EGU-

NYOMI and coworkers [26]. Briefly, 0.2 ml of 2% sodium meta-bisulfite solution was added to 0.1 ml of RBC solution and thesamples were analyzed every two hours, for 14 h, under the mi-croscope (40X magnification) to establish sickling delay time (timerequired for the cells sickling to begin) and sickling maximum ef-fect (percentage of sickled cells). Next, each compound (10 mM finalconcentration) was added to the RBC solution (assay solution),

which was kept at 37 $C for 3 h, with occasional gentle stirringevery 30 min. Next, 0.2 ml of sodium metabisulfite solution (2%)was added to the assay solution. Then, erythrocytemorphologywasanalyzed every 12 h by visual inspection (40X magnification, mi-croscope Olympus CX22RFS2). More than 500 erythrocyte, fromfive different random visual fields, were counted in each assay. Allassays were carried out in triplicate. MRS1754 (10 mM) (RA2Bantagonist) [27,28] was employed as positive control.

2.2.4. Dose-response curve for anti-sickling compoundsCompounds with higher anti-sickling effect (p < 0.05) than

positive control (MRS 1754), in single dose assay, had their potencyevaluated as follows: Sickling inhibition was assessed in theabsence of and presence of seven inhibitor concentrations(0.001e100 mM). Then, EC50 values (concentration required for 50%inhibition of sickling effect of meta-bisulfite) of anti-sickling com-pounds were calculated by non-linear biphasic regression (fixedHill-coefficient (nH1 and nH2 ¼ 1.0)), as available in GraphPadPrism 6.1 software. The reported values represent mean ± SD of fiveindividual experiments that were carried out in triplicate.

2.2.5. Mechanism of action of the anti-sickling effectThe mechanism of inhibition was probed using the same assay

conditions described for EC50 determination, with the additionalrequirement that all compounds were also assayed in the presenceof NECA (RA2B agonist). In order to carry out this assay, NECA effectover cell sickling was previously investigated. Accordingly, RBCsolution was treated with NECA (10 mM), as described in single-dose assay protocol and the erythrocytes morphology wasassessed at different periods (2, 4, 6, 8, 10 and 12 h) under normoxicand hypoxic conditions. Then, NECA dose-response curve(0.0001e100000 mM) was calculated as described previously.Following this standardization steps, compounds with anti-sicklingactivity were assayed in three different concentrations (0.03e1 mM)in the presence of six NECA concentrations (0.001e1000 mM). Thereported values represent mean ± SD of five individualexperiments.

Dose-response curves of NECA in the presence (EC50) orabsence of the bioactive compounds (EC500) were calculated withthe GraphPad Prism 6.1. Schild analysis was carried out to investi-gate competitive behavior between NECA and each compound [29].Briefly, the change in agonist affinity (NECA) was calculated by:

pKb ¼ log ½DR­1& ' log ½B&

where pKb is the negative logarithm of the molar concentration ofthe antagonist required to produce a dose ratio relative to theagonist of 2; DR is the ratio between the EC500 and EC50; [B] is thetest compound concentration (putative antagonist) [29].

2.2.6. Cytotoxicity to mammalian cellsMurine macrophage-like cells (J774 cell line) were plated into

96-well plates at a cell density of 2 ( 105 cells/well in Dulbecco'smodified Eagle medium (DMEM; Life Technologies, GIBCO-BRL,Gaithersburg, MD, USA) supplemented with 10% fetal bovineserum (FBS; GIBCO) and 50 mg/ml of gentamycin (Novafarma,An!apolis, GO, Brazil). The plates were incubated for 2 h at 37 $C and5% CO2. Then, putative antagonists were added to each well at fiveconcentrations, ranging from 6.25 to 100 mM, and plates wereincubated for 72 h. After this period, 20 mL/well of Alamar Blue(Invitrogen, Carlsbad, CA) was added to the plates and incubationcontinued for another 12 h. Colorimetric readings were carried outat 570 and 600 nm. Gentian violet (Synth, S~ao Paulo, Brazil) at10 mM was employed as positive control. All compounds wereassayed in triplicate and the data reported comes from three

O.S. Paz et al. / European Journal of Medicinal Chemistry 136 (2017) 487e496 489

independent experiments.

3. Results

3.1. Pharmacophore models generation and evaluation

Most RA2B antagonists are xanthine derivatives whose chemicaldiversity is located at positions 1, 3, 7, 8, and 9. Then, a small subsetof RA2B antagonists might be employed to capture the pharma-cophore requirements for this class of compounds. Althoughpharmacophore hypothesis can be made with as few as two com-pounds, 5e7 compounds provide non-redundant information toachieve this goal [23,30], without imposing toomuch restrictions tothe final models (i.e. to high specificity for virtual screening pur-poses). Then, five potent ligands (pKi 8.22e9.1) of RA2B wererandomly selected and flexibly aligned to each other withinGALAHAD software. This strategy led to the generation of 45pharmacophore hypotheses (supplementary material) that werestepwise analyzed as follows. Reasonable models usually havebalanced ENERGY (strained conformation), STERICS (Steric overlapof groups) and H-BOND (the pharmacophoric concordance). Then,these parameters are usually combined into a Pareto ranking thatdiscards models with extreme values in at least one parameter [31].Accordingly, this approach allowed the exclusion of 22 models.Next, the match between the pharmacophore groups and thepharmacophore point centers (MOL_QRY) was employed to select10 hypotheses (Table 2) for further evaluation.

Although three hypotheses have considerably higher Mol_Qryvalue than the others do, there is no evidence that they are genericenough to allow the identification of RA2B antagonists outside thechemical space of compounds employed for pharmacophore hy-pothesis generation. Thus, all remaining pharmacophore hypoth-esis (10 hypothesis) were evaluated for their ability to identify trueRA2B ligands among decoy compounds that were generated withinDUD-E (http://dude.docking.org). Although DUD-E was designed asa tool to evaluate docking software, its usefulness to assess phar-macophore models has already been proved [22]. Hence, UNITY 3Dwas employed to align 1000 decoys and 50 true RA2B ligands to thepharmacophore features found in each hypothesis. Compounds'match was ranked according to their QFIT score. Once compoundsranking show no clear separation among true ligands and decoys(data not shown), it is not straightforward to choose a cutoff forvirtual screening. For that reason, ROC curves were built to inves-tigate pharmacophore hypothesis sensibility and specificity [32], sothat false positive compounds selection could be minimizedwithout discarding a significant number of promising hits.Accordingly, optimal hypothesis should have an area under the ROCcurve (AUC) near 1.0, meaning that all true ligands have better QFITvalues than decoys. On the other side, hypothesis with AUC< 0.5would performworse than random selection. Four pharmacophore

hypothesis present AUC> 0.9 (01, 04, 06, 12) (Fig. 2A) and thusshould be minimally suitable for virtual screening purposes.However, it is well established that useful pharmacophore hy-pothesis should enlighten the structure-activity relationship (SAR)for a series of compounds, for instance rationalize the biologicalactivity based on the presence or absence of pharmacophore fea-tures [33]. Although QFIT values depict compounds agreement topharmacophore features, it has poor correlation to the biologicalproperty [34]. For that reason, we undertook a different approachto investigate pharmacophore hypothesis ability to differentiatepotent from weak RA2B ligands. In contrast to Catalyst/Hypogen,GALAHAD does not use information from inactive or weak antag-onists to refine the pharmacophore models [35,36]. Then, theoverlay of 287 RA2B ligands (pKi 4e9.3), not previously employed,onto the four pharmacophore hypothesis that succeeded the ROCcurve analysis, was assessed with GALAHAD software.

This approach allowed us to calculate MOL-QRY values that havebeen shown to have good correlation to biological activity. A similartrend was reported by LIU and coworkers [37]. Accordingly, highMOL_QRY values are expected for potent ligands, whereas weakligands should have low values. Only the pharmacophore hypoth-esis 4 follows this behavior (Fig. 2B). In order to confirm whetherthis hypothesis truly helps to rationalize the RA2B ligands SAR, avisual analysis of potent and weak ligands was carried out. Overall,potent ligands fulfill most of it the pharmacophore requirementsdepicted in pharmacophore hypothesis 4 (three hydrogen-bondacceptor (AA), one hydrogen-bond donor (DA) and three hydro-phobic centers (HY)) (Fig. 3A), as observed for the known high af-finity RA2B antagonist shown in Fig. 3B (upper panel). On the otherhand, weak antagonists have a poor match to the pharmacophorefeatures (Fig. 3B e lower panel). This result is in good agreementwith those from Cheng and coworkers [14] which highlight theimportance of a carbonyl (AA) and hydrophobic center (HY) toxanthine derivatives potency, but suggests that further insights toxanthine derivatives SAR can be gained from a careful analysis ofthae best pharmacophore hypothesis.

3.2. Screening virtual models based pharmacophore

As pharmacophore hypothesis four fulfils all the requirementsto be considered a true pharmacophore model, it was employed asa query to search the lead-like subset of ZINC database (http://zinc.docking.org/). The flexible search within UNIT 3D afforded 88molecules with QFIT values ranging from 50 to 85. In order to selecta representative subset of chemically diverse compounds for bio-logical evaluation a Hierarchical lysis [38] was carried out. Then,80% similarity cutoff was employed to select 18 compounds foracquisition and biological evaluation (Table 3).

3.3. Anti-sickling assay

Considering that RBC sickling causes hemoglobin polymeriza-tion, the effect of the selected compounds over this parameter wasevaluated first. Either temperature change or oxygen starvation cancause hemoglobin polymerization [39], but the only the secondprotocol was investigated here. Similar in vitro assays have beenemployed to screen compounds that prevent RBC sickling [2,40,41].

The time lapse between oxygen deprivation and the appearanceof the first polymers, called the delay time, depends on the RBCsickling polymerization kinetics [42]. Then, we evaluated the timerequired for sodium metabisulfite to induce RBC sickling underhypoxic conditions, at room temperature (37 $C). Our results sug-gest that cells sickling reaches its maximum around 12 h after thestimulus (Fig. 4A). For that reason, putative anti-sickling com-pounds were evaluated within this time frame.

Table 2Statistical parameters of selected pharmacophore hypothesis (pareto ranking ¼ 0)built with RA2B antagonists.

Hypothesis Energy Sterics Mol_qry Hbond

01 9.98 7980.90 31.63 214.7003 11.52 7778.70 36.76 217.4004 11.35 7452.50 29.87 223.6006 7.58 7896.70 14.51 212.1007 9.92 7946.20 16.50 209.2008 6.73 7258.30 51.73 198.3012 9.87 7541.90 19.73 203.0013 7.13 7168.00 27.04 207.3014 6.53 6891.60 45.67 194.3015 9.14 6728.50 29.86 212.20

O.S. Paz et al. / European Journal of Medicinal Chemistry 136 (2017) 487e496490

According to this assay MRS1754, a known RA2B antagonist[28,43], reduces 54% RBC sickling at 10 mM, whereas compoundsZ1139491704, Z1151205940, Z17974526, Z241893728, Z223070016reduces it by >70% at the same concentration (Fig. 4B).

As single dose assays do not allow compounds prioritization bypotency, dose-response studies were carried out for these com-pounds (Fig. 5). Visual analysis of the plots suggests a biphasicbehavior that can be related to off-target binding. In fact, MRS1754is known to block RA2B at nanomolar concentration, but also bindto other adenosine receptors at micromolar range [44]. Then,biphasic regression method, considering Hill-coefficients equal tounity, were employed to evaluate the compounds potency. Ac-cording to this approach, the first pEC50 value is found in thenanomolar range whereas the second EC50 has micromolar scale.This behavior is compatible with high affinity binding to RA2B andlow binding affinity to off-target receptors. Considering this hy-pothesis, compounds Z241893728 (pEC50 ¼ 7.46 ± 0.15) andZ1151205940 (pEC50¼ 7.46 ± 0.11) are slightly less equipotent than

MRS1754 (pEC50 ¼ 7.63 ± 0.12) against RA2B. On the other hand,Z1139491704 (pEC50 ¼ 7.77 ± 0.17) is more potent than MRS1754.

Considering the second pEC50 value, Z241893728(pEC50 ¼ 5.06 ± 0.10) and Z1139491704 (pEC50 ¼ 5.29 ± 0.12) aremore potent against the off-target receptors than MRS1754(pEC50 ¼ 4.17 ± 0.13). Then, these hits would have poorer selec-tivity profile than the positive control. In contrast to that, the po-tency of Z1151205940 against off-target receptors(pEC50 ¼ 4.21 ± 0.12) is similar to the one of MRS1754.

Interestingly, Z17974526 (pEC50¼ 7.33 ± 0.06) and Z223070016(pEC50 ¼ 7.63 ± 0.05) show a classic sigmoidal curve behaviorwhich is compatible with minor binding to off-target receptors.Besides, Z223070016 is slightly more potent than MRS1754.

Although all compounds fulfill the RA2B ligands pharmaco-phore requirements, the results presented up to this point do notprove that their mechanism of action relies on this receptor mod-ulation. In order to further investigate this matter, the effect ofNECA, a known RA2B agonist, over the compounds anti-sickling

Fig. 2. Evaluation of pharmacophore hypothesis (PH). (A) Performance to recognize true ligands among decoys molecules that present similar physical-chemical properties; (B)Correlation between MOL_QRY values and ligands potency (pKi) for an external dataset (287 RA2B antagonists), not employed for PH generation.

Fig. 3. Pharmacophore hypothesis insights towards RA2B antagonist potency. (A) Pharmacophore features: AA ¼ hydrogen bond acceptor; DA ¼ hydrogen bond donor; HY ¼ centerhydrophobic. All distances are depicted in angstrom and sphere size varies according to tolerance radius. (B) Overlay of potent (pKi ¼ 9.0) and weak (pKi ¼ 4.0) over pharmacophorehypothesis four.

O.S. Paz et al. / European Journal of Medicinal Chemistry 136 (2017) 487e496 491

effect was investigated (Fig. 6). Initially, the effect of NECA (30 nM-1000 nM) over RBC sickling was established. Next, the anti-sicklingcompounds were added to assay, so that the concentration-response curve due to NECA is shifted (minimum threefold). Asexpected, all compounds reduce NECA activity in a concentration-dependent manner. Although the curves are shifted to the right,the maximal response does not change. This behavior suggests acompetitive mechanism of action that was confirmed by Schildregression analysis (Fig. 7), as curve slope around 1.0, supportcompetitive antagonism [45]. Although these results suggest thatRA2B plays an important role towards the anti-sickling effect ofthese compounds, the biphasic dose-response curves forZ241893728, Z1151205940 and Z1139491704 hint that other re-ceptors might be involved.

Last but not least, Alamar-Blue colorimetric assay suggests thatZ1139491704 and Z223070016 have no cytotoxic effect on J774macrophages at concentrations up to 100 mM (Fig. 8). Then, it isreasonable to assume that all compounds with anti-sickling activityreported herein, have insignificant cytotoxicity to mammalian cellsat the dose required for RA2B blockage.

4. Discussion

Painful vasoconstriction events are the most common compli-cation occurring in SCA children and adults. However, there are fewtreatments to prevent the development of these events. Most of thedrugs available target the symptoms, not the sickling event itself.This scenario suggests that SCA is a neglected disease that requires

Table 3Compounds selected for biological evaluation.

Compound Structure Compound Structure

Z1139491704 Z215362406

Z1269021565 Z422474352

Z30471922 Z17974520

Z17559310 Z1151205940

Z1139490472 Z104902384

Z270227114 Z17974526

Z1139409670 Z1203678049

Z241893728 Z51257604

Z223070016 Z15855510

O.S. Paz et al. / European Journal of Medicinal Chemistry 136 (2017) 487e496492

Fig. 4. Biological assays of putative anti-sickling compounds. (A) Standardization of time required for biological assay. Time dependent effect of sodium metabisulfite over RBCsickling at 37 $C. Data presented as mean ± SEM of 5 samples from five patients. (B) Anti-sickling evaluation of compounds (10 mM) selected by virtual screening. Data presented asmean ± SEM of 5 samples from five patients. # indicates significant difference P < 0.0001 8 h vs. 10 h and ##P < 0.0001 10 h vs. 12 h **P < 0.01 and ****P < 0.0001 by one-wayANOVA (Tukey's test).

Fig. 5. Concentration response curves of anti-sickling compounds selected by virtual screening. Data are presented as mean ± SEM (n ¼ 5) of five samples from five patients.

O.S. Paz et al. / European Journal of Medicinal Chemistry 136 (2017) 487e496 493

Fig. 6. Curve NECA concentration response to the absence or presence of compounds identified in the screening and biological antagonist known RA2B (MRS1754 - positivecontrol). Data expressed as mean ± SEM (n ¼ 5).

y = -1.15x + 9,96R² = 0.99

-1

0

1

2

3

4

5

2 4 6 8 10

log(

DR

-1)

-log[MRS1754]

y = -1.74x + 12.84R² = 0.99

-1

0

1

2

3

4

5

2 4 6 8 10

log(

DR

-1)

-log[Z241893728]

y = -1.46x + 12.10R² = 0.99

-1

0

1

2

3

4

5

2 4 6 8 10

log(

DR

-1)

-log[Z1139491704]

y = -1.76x + 14.00R² = 0,99

-1

0

1

2

3

4

5

2 4 6 8 10

log(

DR

-1)

-log[Z1151205940]

y = -1.12 + 10.68R² = 0.99

-1

0

1

2

3

4

5

2 4 6 8 10

log(

DR

-1)

-log[Z17974526]

y = -1.09x + 10.31R² = 0.99

-1

0

1

2

3

4

5

2 4 6 8 10

log(

DR

-1)

-log[Z223070016]

pKb= 8.68 pKb= 7.38 pKb= 8.31

pKb= 7.99 pKb = 9.54 pKb= 9.45

Fig. 7. Schild plot for the novel anti-sickling compounds and known RA2B antagonist MRS1754. Five experiments were carried out for each NECA concentration, with samples fromdifferent patients. Vertical bars represent the standard error of the mean.

O.S. Paz et al. / European Journal of Medicinal Chemistry 136 (2017) 487e496494

further drug development efforts from both academia and Big-Pharma companies. Herein we report the first steps to achievethis goal. Although several RA2B antagonists have been reported inthe literature [46], most of them are xanthine derivatives thateither cause gastrointestinal secondary events and tachycardia, orhave low water solubility [47,48].

Moreover, xanthine derivatives (Oxpentifylline and pentoxifyl-line) have shown poor efficacy to reduce symptoms in SCA patients[49,50]. This might be related to compounds' poor pharmacokineticprofile, as they were designed to treat asthma, not SCA or to someother property that limits their efficacy. Nevertheless, it is impor-tant to investigate whether compounds with a different molecularscaffold would present a similar pharmacological profile.

Accordingly, the pharmacophore model described in this paperreveals that pyrimidin derivatives (Z223070016 and Z1139491704)fulfill the pharmacophore requirements to bind to RA2B. As far aswe are aware, this is the first time these scaffolds are reported asRA2B antagonists and it widens the chemical diversity of knownRA2B ligands, not only for SCA drug development purposes, butalso for the development of anti-asthma compounds. Moreover, themodel reported herein has better statistical parameters than theone described by Cheng and coworkers [14]. Of course, a straightcomparison is not correct because the dataset and softwareemployed in the pharmacophore model generation and evaluationsteps are different. However, in both cases, AUC was considered acrucial parameter to judge the pharmacophore model suitability.Then, it is reasonable to assume the pharmacophore model re-ported in this work is, at least, as reliable as the one reportedpreviously. This argument is supported by the fact that it allowed usto screen the lead-like subset of ZINC database and identify nano-molar anti-sickling compounds, whose mechanism of action in-volves the antagonism of RA2B. Finally, this model can be useful torationalize the potency difference among bioactive compounds andguide the development of high affinity RA2B antagonists.

The Schild analysis suggests that all five bioactive compoundshave a similar mechanism of action that is compatible withcompetitive antagonism of RA2B. A similar approach was under-taken by Kim and coworkers [51], with one important difference:those authors employed the decrease in cAMP production, inducedby NECA, in Chinese hamster ovary cell line stably transfected withRA2B as an indicative of compounds mechanism of action. Ourresults rely on the effect each bioactive compound has on the af-finity and binding capacity of MRS1754. Then, they strongly supportthe involvement of RA2B on the mechanism of action of the com-pounds, but they cannot rule out the involvement of off-target re-ceptors in the anti-sickling activity of the compounds.

5. Conclusion

Ligand-based pharmacophore models proved successful toidentify anti-sickling compounds whose mechanism of actionseems to depend on RA2B blockage, despite the fact that they arenot xanthine analogs. Moreover, the best pharmacophore modelsheds some light on the pharmacophore features that warrantRA2B affinity and can be considered another sound contribution todrug development campaigns that aim at identifying RA2B antag-onists. Finally, the biological results show strong evidence that thetwo most potent compounds (Z1139491704, Z223070016) arecompetitive RA2B antagonists with insignificant cytotoxicity onmammalian cells.

Acknowledgments

The authors are grateful for the academic support of PPGBiotec-UEFS and the grants fromCNPq 474386/2012-0, 312009/2014-3 and77002/2013-7 as well as FAPESB PNE 009/2011.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.ejmech.2017.05.035.

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