Design, Virtual Screening, and Synthesis of Antagonists of Î±IIb

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Virtual screening, design and synthesis of antagonists of #IIb#3 as antiplatelet agents Alexandre Varnek, Thierry Langer, Pavel G. Polishchuk, Andrey Krysko, Tetiana Khristova, Olga Klimchuk, Victor Kuzmin, Tatyana A. Kabanova, Vladimir M. Kabanov, Olga L. Krysko, Alexander Kornylov, Georgiy Samoylenko, and Sergei A. Andronati J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.5b00865 • Publication Date (Web): 14 Sep 2015 Downloaded from http://pubs.acs.org on September 14, 2015

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Design, virtual screening and synthesis of antagonists of αIIbβ3 as antiplatelet agents Pavel G. Polishchuk,1 Georgiy V. Samoylenko,1 Tetiana M. Khristova,1,2 Olga L. Krysko,1 Tatyana A. Kabanova,1 Vladimir M. Kabanov,1 Alexander Yu. Kornylov,1 Olga Klimchuk,2 Thierry Langer,3 Sergei A. Andronati,1 Victor E. Kuz’min,1 Andrei A. Krysko,1,* Alexandre Varnek2,* 1

A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine, Lustdorfskaya doroga 86, Odessa, 65080, Ukraine.

2

Laboratory of Chemoinformatics (UMR 7140 CNRS/UniStra), University of Strasbourg, 1, rue B. Pascal, Strasbourg, 67000, France. 3

Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Althanstraße 14, 1090 Vienna, Austria.

Keywords: fibrinogen αIIbβ3 receptor, αIIbβ3 antagonists, platelet aggregation, anti-aggregation, docking, pharmacophore, virtual screening.

Abstract

This paper describes design, virtual screening, synthesis and biological tests of novel αIIbβ3 antagonists which inhibit platelet aggregation. Two types of αIIbβ3 antagonists were developed: those binding either closed or open form of the protein. At the first step, available experimental data were used to build QSAR models, ligand- and structure-based pharmacophore models and

ACS Paragon Plus Environment


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to select the most appropriate tool for ligand-to-protein docking. Virtual screening of publicly available databases (BioinfoDB, ZINC, Enamine datasets) with developed models resulted in no hits. Therefore, small focused libraries for two types of ligands were prepared on the basis of pharmacophore models. Their screening resulted in four potential ligands for open form of αIIbβ3 and four ligands for its closed form followed by their synthesis and in vitro tests. Experimental measurements of affinity for αIIbβ3 and ability to inhibit ADP-induced platelet aggregation (IC50) showed that two designed ligands for the open form 4c and 4d (IC50 = 6.2 nM and 25 nM, respectivly) and one for the closed form 12b (IC50 = 11 nM) were more potent than commercial antithrombotic Tirofiban (IC50 = 32 nM).

Introduction Thrombus

formation

is

the

most

important

pathological

mechanism

underlying

atherothrombotic diseases such as acute coronary syndromes and ischemic stroke/transient ischemic attack, which are responsible for elevated mortality worldwide

1-2

and which are a

platelet-mediated phenomenon. To start form clots platelets should be turned from the rested state to the activated one.3 Rupture of atherosclerotic plaques is supposed to be the main cause of arterial thrombus formation.4-5 This exposes such platelet activating proteins as tissue factor, von Willebrand factor, collagen, etc. Activated platelets are able to excrete other agonists of platelet activation such as adenosine diphosphate and thromboxane A2 which promote activation of adjacent platelets.6 Activated platelets change their shape and expose fibrinogen receptors – integrin αIIbβ3, which change their conformation from bent conformation to extended conformation with closed headpiece (Figure 1). Then β-subunit moves away from α-subunit and the receptor goes into high-affinity state with open headpiece in which it binds fibrinogen and von Willebrand factor, resulting in clot formation and clot adherence, respectively.7 Thus inhibition of αIIbβ3 can prevent clot formation regardless the platelet activation pathway.8-10


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Figure 1. Conformational changes in integrin αIIbβ3 upon activation and ligand binding. Most antagonists of αIIbβ3 represent peptidomimetics which mimic RGD or KGD sequence of fibrinogen and bind to the open form of the integrin (Figure 1, Ligand B). For a long time researchers concentrated their efforts on design of novel RGD-peptidomimetic that resulted in two marketed drugs Tirofiban11 and Eptifibatide.12 The third marketed drug is Abciximab - a monoclonal antibody specific for an epitope on β3 subunit.13 The existed drugs have proven their efficiency in reducing a risk of periprocedural myocardial infarction and in urgent target vessel revascularization during catheterization.14 On the other hand, these compounds have some drawbacks like inducing thrombocytopenia,15-16 which is supposed to be an immunological response of an organism on the conformational changes in integrin αIIbβ3 upon binding with RGD-peptidomimetics.17-18 Recently, a novel antagonist of αIIbβ3, RUC-1, has been discovered during experimental screening of ~33000 small compounds.19 According to mutagenesis studies, RUC-1 binds only to the αIIb subunit of the integrin. As it is shown in gel filtration and dynamic light scattering experiments, it doesn’t induce transformations leading to open headpiece form (Figure 1, Ligand A). Later on, this was confirmed by X-ray study of the complex of RUC-1 and αIIbβ3.20 Notice that RUC-1 has a relatively weak inhibition potency of ADP-induced aggregation tested on human platelet rich plasma (IC50 = 13 µM).19 In order to explore the RUC-1 binding pocket and



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to obtain additional information concerning binding mechanism and induction of conformational changes in the receptor, a series of derivatives of RUC-1 have been synthesized.21 One of them, named RUC-2, was found some 100 times more potent in inhibiting ADP-induced platelet aggregation than RUC-1 (IC50 = 96 nM).21 At the same time RUC-2 doesn’t induce any conformation changes in the αIIbβ3 headpiece

21

which may reduce adverse effects. Recently

more potent ligands of the integrin’s closed form RUC-3 (IC50 = 45 nM) and RUC-4 (IC50 = 33 nM) have been reported.22 According to molecular docking and molecular dynamics simulations they interact with the same residues as RUC-2. Protein-ligand binding patterns in αIIbβ3 open and closed forms differ. Thus, Tirofiban binds with Asp224 residue of the αIIb subunit and with Mg2+ ion of metal ion–dependent adhesion site at the β3 subunit in the open form of integrin (Figure 2). On the other hand, RUC-2 binds to Asp224 residue of the αIIb subunit and to Glu220 residue of the β3 subunit and, thus, it displaces Mg2+ ion. These differences are key factors determining ligands effects on the conformational state of the receptor.

Figure 2. Interaction patterns of Tirofiban and RUC-2 compounds with integrin αIIbβ3 in its open (left) and closed (right) forms. In this paper we report design of novel antagonists of αIIbβ3 which bind to either open or to closed forms of the receptor. To achieve our goal we applied various ligand- and structure-based modeling techniques to screen either commercial databases or generated in this work virtual


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combinatorial libraries. Since antagonists of the open form have been investigated for a long time, a lot of data have been collected from in-house studies and from literature sources. Thus, for the design of the ligands for open form of αIIbβ3, we used both ligand-based and structurebased approaches. Only few ligands of closed form of αIIbβ3 have been reported in the literature. Therefore, here only structure-based approaches were used to design new compounds. Results 1. Design of antagonists of the open form of αIIbβ3 Two datasets have been collected from literature sources23 and in-house studies24-29: (1) the affinity dataset comprising 338 compounds with reported affinity values for αIIbβ3 and (2) the anti-aggregation activity dataset comprising 453 compounds tested under similar conditions. These datasets were used for the development of QSAR and ligand-based pharmacophore models. X-ray structures of complexes of different antagonists with αIIbβ3 have been used for the development of structure-based pharmacophore models and molecular docking. Ensemble of QSAR and pharmacophore models and docking experiments formed a virtual screening pipeline which has been further used for compounds selection and prioritization. 1.1. QSAR models Three individual 2D QSAR models both for ligands’ affinity and anti-aggregation activity have been built using Random Forest30 method in combination with three types of fragment descriptors: simplexes (SiRMS),31-32 ISIDA fragments (SMF)33 and fuzzy pH-dependent pharmacophoric triplets (FPT).34-35 Consensus QSAR models have been developed by averaging predictions of the corresponding individual models. Predictive performance of the models was estimated by 5-fold external cross-validation. Usage of applicability domain (AD) for consensus models didn’t significantly improve prediction performance, see Table 1.



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Table 1. 5-Fold external cross-validation statistics for consensus 2D QSAR models of affinity for αIIbβ3 and anti-aggregation activity

a

R2

RMSE

R2ADa

RMSEADa

AD coverage

Affinity for αIIbβ3

0.75

0.76

0.76

0.72

0.97

Anti-aggregation activity

0.52

0.77

0.54

0.74

0.99

statistical parameters taken into account applicability domain

Relatively large RMSE values of predicted activities could be explained by several reasons. The first one is related to some heterogeneity of experimental data collected from different sources: experimental methods of evaluation of biological responses were not always similar. Different experimental conditions may significantly affect activity values. For example, usage of different agonists and anticoagulants in anti-aggregation experiments may change measured activity values by to up to one order of magnitude.36 Therefore, such data were not included in the datasets. However, we accepted data measured by different experimental methods in order to collect sufficiently big enough dataset of compounds studied under similar conditions. The second reason is related to high interindividual variability of αIIbβ3 population in platelets which can fluctuate in the range 71700-102000 receptors per platelet.37 This can lead to significant difference in both dissociation constants of fibrinogen (KD = 70-255 nM)38 and affinity values of antagonists of αIIbβ3. For example, clinically relevant concentrations of tirofiban which cause inhibition of fibrinogen binding range from 17% to 88%.39 Clearly that the above reasons introduce some inevitable noise in the data which effects predictive performance of obtained QSAR models. 1.2. Pharmacophore models Three structure-based pharmacophore models were obtained with LigandScout40 from 3 available X-ray structures of αIIbβ3 complexes with small molecule antagonists L-739,758, Tirofiban, Eptifibatide (PDB codes 2VC2, 2VDM, 2VDN, respectively).41 The binding pockets


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in these complexes are very similar: all major amino acid residues interacting with ligands are almost at the same positions. The models performance has been assessed in virtual screening of a validation set combining the affinity set (338 compounds) with decoys selected from the ChEMBL database (1518 compounds). We found that the models automatically built by LigandScout contained too many features and by this reason they were not able to select any active in the validation set. Therefore, some features were manually marked as optional and some of them were removed in order to make models less specific (see an example on Figure 3). This significantly improved the models performance reaching precision = 0.76-0.86 and recall = 0.13-0.26. The joint application of all three models to validation set slightly improved overall prediction performance (precision = 0.81 and recall = 0.36), whereas enrichment ratio at 1% and 5% was equal to 5.56 and 6.78, respectively.

Figure 3. Example of initial and manually tuned structure-based pharmacophore models for Eptifibatide complex with αIIbβ3 (2VDN). The following labels for pharmacophore features were used: Red stars – centers of negative charge, blue stars – centers of positive charge, red arrows – H-bond acceptors, green arrows – H-bond donors, yellow spheres – hydrophobic parts. Exclusion volumes are not shown for clarity. Structure-based models have provided with important information about distance (~16Å) between positively and negatively charged centers which are considered as essential for ligand binding by many authors.11, 42-43 This information has been used for pre-selection of conformers



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used for building of ligand-based pharmacophore models on the compounds from the affinity dataset.

Figure 4. Example of a ligand-based pharmacophore model. The following labels for pharmacophore features were used: red star – center of negative charge, blue star – center of positive charge, red arrow or sphere – H-bond acceptor, green arrow or sphere – H-bond donor, yellow sphere – hydrophobic part. Exclusion volumes are not shown for clarity. Notice that all other ligand-based pharmacophore models are given on Figure S3 in Supporting Material. Active compounds (pIC50 ≥ 8) from the affinity dataset have been clustered by LigandScout according to their pharmacophoric representation. Then, for each of 7 clusters, one individual pharmacophore model was produce (see an example on Figure 4 and all other models on Figure S3 in Supplementary material). Joint application of all seven obtained models resulted in a reasonable predictive performance (precision = 0.67 and recall = 0.93) and demonstrated higher enrichment ratios than structure-based models: 9.04 and 7.80 at 1% and 5%, respectively. To speed up a virtual screening of large databases, a simple topological 2D pharmacophore model has been developed on the basis of structure- based pharmacophores. It consists of only two features - centers of positive and negative charges – separated by 13 bonds. As one may see from Figure 5, this roughly corresponds to the distance of 16Å separating these features in 3D pharmacophores.


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Figure 5. 3D structure-based pharmacophore (top) and related 2D pharmacophore (bottom) suggested for virtual screening. 1.3. Docking studies At the first step, the most relevant software tool and protein structure have been chosen according to self-docking and cross-docking results as well as performance of virtual screening on the affinity data set. Three ligand-protein complexes (PDB codes 2VC2, 2VDM, 2VDN) were examined in combination with three different docking programs - FlexX,44 MOE

45

and

PLANTS.46-48 Since the role of two water molecules coordinating Mg2+ cation (Figure 2) was not clear, the docking calculations were performed on the structure with and without these water molecules. Computational experiments reveal overall performance of MOE applied to 2VDM binding site including water molecules (see Figure 6 and Table S2 in Supplementary material). This setup was further used in the virtual screening.



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Figure 6. Performance of molecular docking assessed on the affinity dataset with MOE, PLANTS and FlexX programs applied to 2VDM binding site. 1.4. Design and screening of the focused virtual library Virtual screening of BioinfoDB49 containing about 3 million of commercially available compounds with pharmacophore and QSAR models resulted in no hits. This can be explained by low number of compounds with positively and negatively charged groups in commercial libraries. Even simple 2D pharmacophore representation (Figure 5) returns from BioInfoDB. only 210 compounds. Subsequent screening with 3D pharmacophore and QSAR models resulted in no hits. Therefore the focused virtual compound library has been created using a fragmentbased approach. The main requirements for new antagonists of αIIbβ3 were derived from the pharmacophore models, docking studies and some experimental observations. They are: (i) positively and negatively charged groups should be separated by at least 16Å; (ii) lipophilic fragment should be attached to the acidic part of a molecule; (iii) desirable that the above lipophilic fragment is linked to of H-bond acceptor able to bind the Arg214 residue of αIIbβ3. According to these rules various Arg- and Asp- mimetic fragments and different linker groups were proposed (Figure 7). Combinatorial virtual library was generated by in-house computer


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program. After discarding synthetically irrelevant structures, remaining 6930 compounds were used for the screening.

Figure 7. General scheme of the ligands for open form of αIIbβ3 and examples of building blocks used for generation of virtual combinatorial library. At the first step of virtual screening, pharmacophore and QSAR models were applied in parallel followed by selection of common hits (Figure 8). Hits selected by QSAR models met specified threshold values: for affinity pIC50 ≥ 8.0 and for anti-aggregation activity pIC50 ≥ 7.0. Common application of pharmacophore and QSAR models resulted in 93 hits representing 310 individual stereoisomers. Further selection with docking lead to 83 compounds (164 stereoisomers). All these compounds successfully passed through ADME/Tox filters using water solubility

50-51

and Ames mutagenicity

52

in-house models and toxicity assessment with PASS

program.53 Two synthetically feasible compounds represented by two enantiomers each were chosen for further synthesis and biological evaluation.



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Figure 8. Workflow of the virtual screening of the focused library designed for open form of αIIbβ3.

Figure 9. Docking poses of the designed enantiomers 4c (left) and 4d (right). It is interesting to note that the docking experiments revealed a little difference between binding poses of different enantiomers of selected compounds (Figure 9). In the complex, basic nitrogen of tetrahydroisoquinoline group binds to the Asp224, carboxylic group of ligands binds to Mg2+, whereas sulfonamide group forms H-bond with Arg214 residue. These interaction patterns are very similar to those in the Tirofiban - αIIbβ3 complex. Thus it can be expected that different enantiomers of designed compounds would have similar affinity values.


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1.5. Synthesis and biological evaluation Pure enantiomers of synthesized compounds were synthesized and tested for their affinity for αIIbβ3 receptors and anti-aggregation activity. For the purpose of comparison, experiments were also performed on the reference commercial compound Tirofiban. DCC/SuOH method has been used for the preparation of previously reported RGDF mimetics, derivatives of 1,2,3,4-tetrahydroisoquinoline-7-carboxylic acid.27 This method was used on two stages when the acid 1 or 2 was coupled with sodium salts of appropriate β-alanines. The compound 2 described in this paper was synthesized from the acid 1 and β-alanine using TSTU as a coupling reagent (Scheme 1). Boc-derivatives 3 have been obtained analogously using PFTU. Acidolytic elimination of Boc-protective groups from compounds 3 yielded the target RGDF mimetics 4a-d.

i O

N O

OH 1

O

O

H N

N O

O

OH O

2

ii O

O

N O

H N

H N

HN α

S O R OH

O O O 3a, α-S R = n-C4H9; 3b, α-R R = n-C4H9; + 3c, α-S R = C6H5; Cl H2N 3d, α-R R = C6H5

iii

O H N

O

H N O

HN

S O R OH

α O

4a, α-S R = n-C4H9; 4b, α-R R = n-C4H9; 4c, α-S R = C6H5; 4d, α-R R = C6H5

Scheme 1. Synthesis of target RGDF mimetics 4a-d. Reagents: i) NEt3, TSTU; β-alanine, NaHCO3; H+; ii) NEt3, PFTU; α-sulfonamido-β-alanines, NaHCO3; H+; iii) CH2Cl2, HCl gas.



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Experimental data (Table 1) demonstrate high affinity for αIIbβ3 and anti-aggregation activity of compounds 4a-d. RGDS peptide and Tirofiban were used as standard inhibitors.

Table 2. Predicted and experimentally measured values of affinity for αIIbβ3 and antiaggregation activity of the designed αIIbβ3 antagonist of the open form of integrin Affinity for αIIbβ3, Anti-aggregation IC50, nM activity, IC50, nM

Compound

preda

obs

preda

obs

5.8

62.0±9.0

54

320±50.0

5.8

79.0±12.0

54

670±100.0

4.7

0.22±0.01

43

6.2±0.9

4.7

0.96±0.07

43

25.0±5.0

4a

4b

4c

4d

Tirofiban a

2.4±0.4

32±4

–predicted by consensus 2D QSAR models.

2. Design of antagonists of the closed form of αIIbβ3 In this section we describe design of the analogs of RUC-2 ligand displaying high affinity for closed form of αIIbβ3. Since very few experimental data on ligands for closed form were available, only structure-based pharmacophore and docking methods were used. 2.1. Pharmacophore models


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An initial structure-based pharmacophore model (Figure 10) has been generated with LigandScout using the structure of the RUC-2-αIIbβ3 complex (PDB code 3T3M). This model contains: (i) two positive centers separated by 15.8Å, (ii) five H-bond donors associated with positive centers, two of which directed toward αIIbAsp224 amino acid and three – to β3Glu220, (iii) three H-bond acceptors associated with carbonyl group of the ligand, which binds to αIIbAsp232 residue via two water molecules (see also Figure 2), (iv) H-bond donor bounded with β3Asn215, (v) one H-bond acceptor and hydrophobic feature shifted toward one of positive centers.

Figure 10. Pharmacophore model derived from the RUC-2-αIIbβ3 complex. Description of labels is given in caption for Figure 3. 2D topological pharmacophore has been derived from the 3D structure-based pharmacophore similarly to that previously described in section 1.2. It contains two positively charged centres separated by, at least, 12 bonds. This corresponds to the distance of 15.8Å between two positive centers in the 3D pharmacophore model. 2.2. Docking studies As previously described for the open form ligands, a preliminary study has been performed in order to select the most appropriate docking tool (FlexX or MOE) able to characterize a binding mode of molecules interacting with the binding pocket of RUC-2-αIIbβ3 (3T3M) complex. Selfdocking studies demonstrated the performance of FleX (RMSD = 0.78Å) which retained all



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important ligand-protein interactions whereas MOE (RMSD = 2.2Å) failed to bind ligand to Glu220 residue, which seems to be crucial for ligand-protein recognition. 2.3. Virtual screening of publicly available databases Developed 2D and 3D pharmacophore models were used to screen several large databases of commercially available compounds: (i) advanced and HTS Enamine databases, containing collection of 1.5 million structurally diverse compounds;54 (ii) REAL Enamine database, containing ~17 million synthetically feasible compounds;55 and, (iii) ZINC database56 which ensembles collections of compounds from different vendors with overall more than 17 million compounds. This resulted in 50 compounds which have been docked with FlexX (Figure S5). Only two high score compounds have been selected. One of them is the known drug nafamostat – a serine protease inhibitor.57 This compound was also identified by Negri et al. in their structure-based virtual screening.58 Nafamostat has some clear drawbacks: it doesn’t possess high anti-aggregation activity (IC50 = 12.5 µM) and it may have some side effects because of its ability to bind different proteins, such as thrombin, urokinase, trypsin, plasmin etc.59-61 It should, however, be noted that Nafamostat was introduced as an alternative anticoagulant in continuous renal replacement therapy (CRRT) in 1990, but its usage is mainly limited to Japan.62-63 Another selected

compound,

N‐[(E)‐[1‐(2‐{5‐[(1E)‐1‐(carbamimidamidoimino)ethyl]‐4‐methyl‐1,3‐

thiazol‐2‐yl}‐4‐methyl‐1,3‐thiazol‐5‐yl)ethylidene]amino]guanidine, was not available for purchasing at that moment. 2.4. Design and screening of focused virtual library Since no compounds finally been selected from the commercial databases, a small focused virtual library of RUC-2 analogs has been designed. According to 3D structure analysis, a ligand for the integrin’s closed form should possess: (i) a positively charged part (preferably pyperazine residue) able to interact with the Asp224 residue; (ii) a heterocyclic moiety interacting with the Tyr190 residue; (iii) an acceptor group (preferably carbonyl) interacting with the Asp232


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residue, and, (iv) positively charged part (amino group) displacing Mg2+ ion and, in such a way, providing with interactions with Glu220 residue of the β3 subunit. Potentially, a molecule combining 6-аmino-2-(piperazin-1-yl)-3H-quinazolin-4-one scaffold connected to amino-group, as it is shown on Figure 11, may fulfill these conditions. Notice that substituted quinazolinediones and quinazolinones derivatives are known as platelet aggregation inhibitors and fibrinogen receptor antagonists.64 Based on these considerations, 29 virtual compounds (41 stereoisomers) were designed varying a linker separating 6-аmino-2-(piperazin-1-yl)-3H-quinazolin-4-one scaffold and aminogroup (see Figure 11).

Figure 11. Schematic representation of ligands for closed form of αIIbβ3 used for generation of virtual focused library. Designed compounds were screened against 3D pharmacophore models, followed by docking with FlexX and application of ADME/Tox filters described in section 1.4. This resulted in 20 hits, three of which (compounds 12a-c in Table 3) were selected for the synthesis and biological tests. Their best docking poses (Figure 12) reveal the binding pattern similar to those observed in X-ray RUC-2-αIIbβ3 complex – ligands’ amino group interacts with β3Asn215 and piperazin group – with αIIbAsp224 (Figure 11, right). However, among 10 best poses we also discovered those with opposite orientation: amino group interacts with αIIbAsp224 and piperazin group –



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with β3Asn215 (Figure 11, left). These observations show that RUC-2 analogues may have different binding modes in the integrin’s binding pocket.

Figure 12. Docking poses of the compound 12b. 2.5. Synthesis and biological evaluation In this section we describe a synthesis of compounds 12a-c selected in virtual screening. The synthetic route to a set of 2-piperazin-1-yl-quinazolines 12a-с is summarized in Scheme 2. Quinazoline-2,4-dione (6) was obtained by heating the isatoic anhydride and urea in DMF65. As a result of nitration of the compound 6, there was obtained the 6-nitro-3H-quinazoline-2,4-dione (7). The was carried out the chlorination of compound 7 using POCl3 to obtain 6-nitro-2,4dichloro-quinazoline which was further hydrolyzed to form 6-nitro-2-chloro-3H-quinazolin-4one (8). The piperizyl ring was then conveniently introduced to the 2-position of the intermediate 8 by the reaction of it with the 1-Boc-piperizine, which gave the compound 9. The reduction of the nitro group of compound 9 using H2-Pd(C) gave the amine 10 as a crucial substrate for the construction (assembly) of the target molecules. Condensation of Boc-acids with amine 10 has been conducted using the HATU. The elimination of Boc-protective groups yielded the compounds 12a-c.


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H N

O

H N

i

O

H N

ii

NH 5

O

O

O

O

6

N

8

O

O O

N

vii

N N

9

O

O

O

N N

viii NH

H2N

R ix +

NH2 Cl

12a, R = (CH2)2NH2*HCl; 12b, R = (CH2)3NH2*HCl; +

NH2 Cl

N

O R

N H

N

O

10

O

12c, R =

NH

O2N

7

Cl

vi

NH

O2N

N

iii, iv, v

NH

O 2N

O N

O

N NH

N H

O

N NH

O 11a, R = (CH2)2NHBoc; 11b, R = (CH2)3NHBoc; 11c, R =

N Вос

O

Scheme 2. Synthesis of compounds 12a-c. Reagents: i) (H2N)2CO, DMFA, 150 °C, 3 h, 63 %; ii) HNO3, H2SO4, -5 °C, 1 h, 67 %; iii) POCl3, N,N-dimethylaniline, reflux, 6 h; iv) 1 M NaOH, H2O, room temperature, v) 2 h; 1 M HCl, 61 %; vi) 1-Boc-piperizine, NEt3, ACN, 50 °C, 2 h, 82 %; vii) H2 / Pd (C), MeOH/C6H6, room temperature, 3 h, 92 %; viii) NEt3, HATU, Boc-acids, ACN, 50 °C, overnight, 46-58 %; ix)CH2Cl2, HCl gas, room temperature, 1 h, 92-96 %. Results of in vitro biology testing are summarized in Table 3. As one may see, all synthesized compounds are characterized by high affinity for αIIbβ3 and anti-aggregation activity values.

Table 3. Experimental values of affinity for αIIbβ3 and anti-aggregation activity of the designed

αIIbβ3 antagonists of the closed form of the receptor

Compound

Anti-aggregation Affinity for activity, IC50, αIIbβ3, IC50, nM nM



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12a

5.0 ± 0.8

150 ± 25

12b

2.2 ± 0.3

11 ± 1

12c

3.8 ± 0.4

100 ± 15

2.4 ± 0.4

32 ± 4

Tirofiban

Discussion This study demonstrated that virtual screening of large commercial databases resulted in no (for open form ligands) or very few (for closed form ligands) hits. This is not surprising taking into account that studied peptidomimetics or RUC-2 analogues have very specific features (charged parts separated by a certain distance) which don’t occur in most of commercial compounds. That’s why the only solution was generation of focused libraries followed by their screening. The lack of potential αIIbβ3 binders in commercial databases is confirmed in recent publications by Negri et. al.58 and by Wanga et al.66 reported compounds with relatively weak antiaggregation potency (inhibition of ADP induced platelet aggregation IC50 = 12-47 µM58 and IC50 = 20-90 µM66) resulted from structure-based virtual screening of these data sources. Important feature of the given study is application of different chemoinformatics approaches: QSAR, 2D and 3D pharmacophores, ligand-to-protein docking. Joint application of different modeling techniques provided with very high success rate: almost all designed compounds


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displayed high affinity for αIIbβ3 and anti-aggregation activity and some of them perform better than commercial drug Tirofiban. Notice that the closed form ligand 12b (anti-aggregation activity IC50 = 11 nM) designed in this studies outperforms recently reported RUC-3 (IC50 = 45 nM) and RUC-4 (IC50 = 33 nM) molecules.22 Conclusion. This work is devoted to design, virtual screening, synthesis and in vitro tests of novel αIIbβ3 antagonists. Two types of ligands were designed: those binding to open or to closed form of the protein. Various theoretical approaches were applied: QSAR, structure- and ligand-based pharmacophore and docking. Consensus virtual screening involving all these techniques allowed us to select very few molecules for the synthesis and in vitro tests. Experimental validation demonstrated very high success rate of our approach: 2 out of 4 hits for open-form ligands and 2 out of 4 hits for closed-form ligands demonstrated higher affinity and anti-aggregation activity than commercial antithrombotic Tirofiban.



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Experimental section Description of datasets of RGD-peptiomimetics bound to the open form of αIIbβ3 Two datasets of RGD-peptidomimetics which possess affinity for αIIbβ3 or anti-aggregation activity have been provided by A.V. Bogatsky Physical-Chemical Institute of National Academy of Sciences of Ukraine (PCI). All of those compounds have been synthesized and tested for affinity for αIIbβ3 and anti-aggregation activity at the Medicinal Chemistry Department of A.V. Bogatsky Physical-Chemical Institute by earlier described methods. Anti-aggregation activity of compounds was measured by Born’s method on human platelet rich plasma.67 Affinity for αIIbβ3 was measured as inhibition of fluorescein isothiocyanate-labeled fibrinogen binding to activated human platelets by tested compounds.38 These two datasets contained relatively small number of compounds (45 compounds with reported affinity values and 53 – with reported anti-aggregation activity) and they were significantly imbalanced as they contained mostly active compounds. Due to this fact these datasets have been extended by data taken from CHEMBL database (version 7),23 which is a publicly available collection of organic compounds with reported bioactivity data. The compounds from CHEMBL database have been selected taking into account similarity of the used bioassays to those ones which were used in PCI tests. This was the crucial step, because activity values for the same compound obtained in different assays can differ more than order of magnitude. Thus we expect that in such a way prepared datasets should be less heterogeneous and more reliable for modeling. Curation of the two datasets has been performed by Chemaxon Standardizer tool68: (i) mixtures and inorganics were removed, (ii) salts were cleaned or removed, (iii) normalization of specific chemotypes (aromaticity and nitro groups were checked) were performed, (iv) explicit hydrogen atoms were added, (v) treatment of tautomeric forms were done. The resulted datasets contained achiral and chiral compounds (single stereoisomers and racemic mixtures). During dataset curation single stereoisomers were excluded if corresponding racemic compounds were


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present in the dataset. Duplicates in the datasets have been removed using Chemaxon Instant JChem.69 All values of affinity for αIIbβ3 and anti-aggregation activity of compounds were converted to pIC50 (-lgIC50, IC50 in mol/l units). The extended datasets have become more balanced with better distributed and wider range of activity values (see Figure S1 in Supplementary materials). These datasets are available as Supplementary materials. QSAR modeling of open form αIIbβ3 antagonists Three different approaches for representation of molecular structure on 2D level have been used: Simplex representation31-32, 70 and two types of ISIDA descriptors - Substructure molecular fragments33 and fuzzy pH-dependent pharmacophore triplets.34-35 As these approaches are well described in the literature we didn’t provide their detailed description here. Random Forest method30 (implemented in the CF software71) was used for QSAR models development, because this method has proved its applicability for solution of various QSAR tasks.72-75 Predictive performance of obtained models, expressed as determination coefficient (R2) and root mean-squared error (RMSE) (see equations 1 and 2), has been assessed by 5-fold external cross-validation procedure. To perform 5-fold external cross validation all compounds in the dataset were sorted according to their pIC50 values and each fifth compound went to the separate bin, thus five bins were obtained. Then the compounds of four out of the five bins have been combined together and QSAR model has been developed, compounds from the remaining fifth bin (which is actually an external test set) have been predicted by this model. This procedure was repeated five times to use all bins as an external test set only once. Predictions of external sets were combined and cross-validation statistics were calculated according to the equations 1 and 2. Consensus predictions of affinity values for αIIbβ3 and anti-aggregation activity of novel compounds have been made by averaging predictions of corresponding individual QSAR models.

= 1 −

∑ , ,

∑ ,

,


(1)


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=

!

"!

∑"(*!#$%&',( − #&)$,( ,

(2)

where n – number of compounds in an external set; yexp,i – observed activity value of i-th compound in an external set; ypred,i – predicted activity value of i-th compounds in an external set; #training – mean activity value for compounds of a training set. To check whether compounds were inside or outside of AD of the consensus models we estimated root-mean-square deviation of all predictions made by individual QSAR models. If a calculated standard deviation value was less or equal 0.5 then this compound was inside AD, otherwise it was outside AD. ADME/Tox assessments of novel compounds Some ADME/Tox properties of screening compounds, such as mutagenicity and solubility were estimated using previously reported QSAR models. The classification 2D QSAR model52 based on the Ames mutagenicity dataset76 was applied for prediction of mutagenicity of novel compounds. This model was developed using Random Forest method and based on the simplex representation of molecular structure. The aqueous solubility of screened compounds was predicted by two 2D QSAR models in order to make average consensus prediction. The former is based on ISIDA descriptors and developed using Multiple Linear Regression method50 and the latter was developed using Random Forest method in combination with simplex descriptors.51 Possible adverse effects were assessed by PASS53 program, which predicts probability of many types of activity and toxic effects. Pharmacophore model development Structure-based and ligand-based approaches implemented in LigandScout have been used for developing of 3D feature-based pharmacophore models.40 For generation of structure-based pharmacophore models ligands in complexes were ionized and their energy was minimized using MMFF94 force field afterwards structure-based pharmacophore models were produced.


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Ligand-based models of RGD-peptidomimetics, preparation of the validation set and considered statistical parameters Compounds of the affinity dataset with pIC50 ≥ 8 have been chosen for generation of ligandbased pharmacophore models. The selected compounds have been charged with Filter tool of OpenEye77 and at most 200 conformers within 10 kcal/mol energy gap have been generated using Omega.78 Conformers having distance less then 16Å between positively and negatively charged atoms (these are two key features of αIIbβ3 antagonists according to structure-based pharmacophore models) were discarded using in-house Python script based on OpenEye OEChem library.79 Compounds have been clustered based on their pharmacophoric representation and for each of seven clusters of compounds a shared ligand-based pharmacophore has been generated with the default LigandScout settings. The validation set was prepared from compounds of the affinity dataset and decoys. Compounds from the affinity dataset was split on 234 active (pIC50 ≥ 6) and 104 inactive (pIC50 < 6) ones. The set of inactive compounds has been extended by decoys selected from CHEMBL dataset taking into account compounds similarity to known αIIbβ3 antagonists. At the first step, compounds with reported values of anti-aggregation activity or affinity for αIIbβ3 have been discarded from the whole CHEMBL dataset. Then, compounds which had the number of H-bond donor and acceptors, molecular weight, topological surface area, predicted logD values80 within the range of corresponding values for compounds of the affinity dataset have been chosen. On the last step, only 1518 compounds which had at least one positively and one negatively charged groups have been remained as these are key features according to the structure-based pharmacophore. Thus the whole validation set contained 234 active compounds and 1622 inactives and decoys. Because some of active compounds were used in modeling of ligand-based pharmacophores they have been excluded from the set of actives for validation of ligand-based pharmacophore models.



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All possible stereoisomers were generated for each compound in the validation dataset having unspecified stereocenters. Afterwards at most 200 conformers within 10 kcal/mol energy window were produced using Omega.78 The compound of the validation set was predicted as active if at least one of its stereoisomers fit at least one pharmacophore model. Pharmacophore fit scoring function taking into account only chemical feature overlap was used for ranking of the screening results. Statistical characteristics which were used for estimation of pharmacophore models performance: Recall = TP/(TP+FN) Precision = TP/(TP+FP)

TP TP + FP Enrichment ratio = TP + FN TP + FN + TN + FP Ligand-to-protein docking Three programs were used to carry out the docking studies: PLANTS, FlexX and MOE. PLANTS uses stochastic search algorithm and chemplp scoring function.46-48 For the purpose of correct identification of rotatable bonds, charges and protonation states of atoms SPORES81 (structure recognition and protonation tool) were used. Centre and radius of the binding pocket were determined using PyMOL.82 Tuning parameters such as number of ants and iteration scaling factor were remained by default. Protein preparation for docking with FlexX44 consists of several steps: i) the definition of the binding site by selection of the residues flanking the binding pocket; ii) the check of ionization and tautomeric states, position of polar hydrogen atoms, crystal water molecules and metal coordination type; iii) the addition of hydrogen atoms to fill out the remaining open valences of the receptor. FlexX uses systematic search method and its own empirical scoring function.


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Binding pockets were prepared in Molecular Operation Environment (MOE)45 in several steps: (i) protonation of atoms of the protein, (ii) optimization of the protein-ligand complex, and (iii) removal of redundant water molecules. Stochastic search method and London dG scoring function which is implemented in MOE were used in our docking studies. Quality of self-docking and cross-docking studies was evaluated using RMSD values – root mean squared distance between corresponding atoms in an initial conformer and a docked pose. RMSD values lower or equal 2Å were considered as satisfactory. Docking of RGD-peptidomimetics Binding pockets of three selected complexes (PDB codes 2VC2, 2VDM, 2VDN) of αIIbβ3 headpiece in the open form with three different ligands (L-739,758, Tirofiban, Eptifibatide) were very similar in geometry: the root-mean-squared-distances (RMSD) between corresponding heavy atoms of residues included in binding pockets of different complexes were 0.8Å for 2VDM/2VC2 and 2VC2/2VDN and 0.9Å for 2VDM/2VDN. self- and cross-docking studies have been performed in order to choose the most appropriate binding pocket and estimate importance of water molecules coordinated with Mg2+ ion. Results for Tirofiban and L-739,758 which were more similar to the studied ligands than Eptifibatide indicated that the presence of those water molecules was preferable (in the presence of the water molecules there was a greater number of good poses with RMSD ≤ 2Å). It was found that MOE gives better results (lower RMSD values) for Tirofiban and L-739,758 (1.15-2.14 Å) then FlexX (1.85-3.55 Å) and PLANTS (2.07-8.87 Å) (for details see Table S2 in Supplementary materials). To make a final decision compounds from the affinity dataset were docked into 2VDM binding site using all three programs. To produce ROC curves to estimate docking performance compound of the affinity dataset gave been split on active (pIC50 ≥ 6.5) and inactive (pIC50 < 6.5) ones. MOE demonstrated better performance (AUC = 0.72) against this set of compounds than PLANTS



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(AUC = 0.59) and FlexX (AUC = 0.49) (Figure 6). Therefore MOE software and 2VDM binding site were chosen to perform virtual screening. Docking of RUC-2 and its analogs Re-docking of RUC-2 into the binding pocket in 3T3M has been performed with FlexX and MOE. Results were revealed that water molecules which participate in forming of H-bond of the ligand with the Asp232 residue were important for correct ligand orientation and binding. MOE failed to reproduce the binding pose (RMSD = 2.2Å) and to bind the ligand to the Glu220 residue of the β3 subunit whereas FlexX almost perfectly reproduce ligand pose and all ligandprotein contacts (RMSD = 0.78Å) (Figure S4 in Supplementary material). The latter was chosen to carried out the virtual screening. Virtual screening of publicly available datasets and created focused libraries All compounds before screening were standardized with Chemaxon Standardizer68 and charged with Filter tool of OpenEye.77 The virtual screening workflow was common for both studies. On the first step 2D pharmacophore model implemented as in-house Python script based on OpenEye OEChem library79 has been applied to reduce the number of compounds to the reasonable level (several hundred or thousand). For screening of focused libraries this step was omitted because they had been created taking into account information from 2D pharmacophore. Then, 3D pharmacophores, QSAR models (if available) and docking have been applied. On the last stage some ADME/Tox properties and possible side effects have been estimated and synthetically feasible compounds have been selected for synthesis and experimental evaluation. Chemistry 1

H NMR spectra were recorded on Bruker Avance DRX 500 spectrometer with chemical shifts

in ppm with the internal TMS as a standard. Electron ionization (EI) and fast-atom bombardment (FAB) mass spectra were recorded on a VG Analytical VG 70-70EQ instrument. FAB spectra were performed equipped with an argon primary atom beam, and an m-nitrobenzyl alcohol


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matrix was utilized. The purity was measured by HPLC conducted on an Shumadzu system (System Controller CBM-20A, two pumps LC-8A and Photo-diode Array detector SPD-M20A) using a Hypersil GOLD 3µm (4.6 mm×150 mm) or Hypersil GOLD aQ 3µm (4.6 mm×150 mm) columns. For preparative HPLC was used Fraction Collector FRC-10A. The progress of reactions was monitored by TLC (silica gel 60 F254, Merck). The acid 1 have been synthesized using previously published method.29 The optically active αsulfonamido-β-alanines prepared from L- or D-asparagine using previously published methods.83-84 3-[(2-Boc-1,2,3,4-tetrahydroisoquinoline-7-carbonyl)amino]propionic acid (2) The compound 1 (2.0 g, 0.0072 mol) was dissolved in anhydrous acetonitryle (30 ml). The solution was cooled to -5 °C, and triethylamine (1.0 ml, 0.0072 mol), and then TSTU (2.168 g, 0.0072 mol), were added. The mixture was stirred for 1 h at -5 °C and then solution of β-alanine (1.283 g, 0.0144 mol) and NaHCO3 (1.21 g, 0.0144 mol) in water (20 ml) was added. The reaction mixture was mixed 3 h at room temperature. The solvent was evaporated in vacuo to dryness. Then water (20 ml) for was added, and pH of the mixture was brought to 3. The product was extracted from water by chloroform (2 × 150 ml). The organic layer was washed by 1 N HCl (2 × 50 ml), water (50 ml), then dried with Na2SO4, filtered, and the solvent was evaporated in vacuo. The resulting oily residue was dissolved in ether (50 ml) and the precipitate was collected by filtration and dried. Yield 81 %. mp = 122 ºC. 1H NMR spectral and FAB-MS characteristics of the same product 2 previously obtained.27 General Procedure for a preparation of compounds 3 The compound 2 (0.5 g, 0.0014 mol) was dissolved in anhydrous acetonitryle (30 ml). The solution was cooled to -5 °C, and triethylamine (0.2 ml, 0.0014 mol), and then PFTU (0.6 g, 0.0072 mol), were added. The mixture was stirred for 1 h at -5 °C. The solvent was evaporated in vacuo to dryness. The residue was dissolved in 100 ml of chloroform. The solution was washed



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with aqueous solution of 1 M HCl (40 ml), 5 % aqueous solution of NaHCO3 (40 ml) and water (40 ml). The organic layer was dried over Na2SO4, filtered, and the solvent was evaporated in vacuo to dryness. The resulting oily residue was dissolved in acetonitryle (30 ml) and then solution of α-substituted-β-alanine (0.0028 mol) and NaHCO3 (0.235 g, 0.0028 mol) in water (20 ml) was added. The reaction mixture was mixed 2 h at room temperature. The solvent was evaporated in vacuo to dryness. Then water (20 ml) was added, and pH of the mixture was brought to 3. The product was extracted from water by ethyl acetate (2 × 100 ml). The organic layer was washed by 1 N HCl (2 × 20 ml), water (20 ml), then dried with Na2SO4, filtered, and the solvent was evaporated in vacuo. The resulting oil was dissolved in acetonitrile and was separated by 100 ul portions by preparative HPLC using the conditions: Column: Hypersil GOLD aQ 5µm (20 mm×150 mm); Flow rate: 20 ml/min; Mobile Phase: water/acetonitryle; Isocratic 60/40; Detection: UV 254 nm. The product containing fractions were combined and the solvent was evaporated in vacuo to dryness. 2-(S)-(n-Butylsulfonyl)-3-{3-[(2-Boc-1,2,3,4-tetrahydroisoquinoline-7carbonyl)amino]propionyl}aminopropionic acid (3a) Prepared from acid 2 and 2-(S)-(n-Butylsulfonyl)amino-β-alanine.83 Yield 43 %. A colorless glass substance; 1H NMR δ (500 MHz, d6-DMSO) 0.85 (t, J=7.3 Hz, 3 H), 1.30-1.37 (m, 2 H), 1.42 (c, 9 H), 1.56-1.68 (m, 2 H), 2.36 (t, J=6.7 Hz, 2 H), 2.80 (t, J=5.4 Hz, 2 H), 2.96 (t, J=7.4 Hz, 2 H), 3.23-3.27 (m, 1 H), 3.37-3.45 (m, 3 H), 3.55 (t, J=4.9 Hz, 2 H), 3.82-3.88 (m, 1 H), 4.52 (s, 2 H), 7.20-7.23 (m, 2 H), 7.62-7.64 (m, 2 H), 8.00 (s, 1 H), 8.41 (t, J=5.1 Hz, 1 H); MS (FAB) m/z: 555 [M+H]+. 2-(R)-(n-Butylsulfonyl)-3-{3-[(2-Boc-1,2,3,4-tetrahydroisoquinoline-7carbonyl)amino]propionyl}aminopropionic acid (3b) Prepared from acid 2 and 2-(R)-(n-Butylsulfonyl)amino-β-alanine. Yield 38 %. A colorless oil substance. 1H NMR spectral and FAB-MS characteristics of the same product 3a.


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2-(S)-(Phenylsulfonyl)-3-{3-[(2-Boc-1,2,3,4-tetrahydroisoquinoline-7carbonyl)amino]propionyl}aminopropionic acid (3c) Prepared from acid 2 and 2-(S)-(Phenylsulfonyl)amino-β-alanine.84 Yield 36 %. A colorless glass substance; 1H NMR δ (500 MHz, d6-DMSO) 1.42 (c, 9 H), 2.27 (ddd, J=28.5 Hz, J=14.4 Hz, J=7.6 Hz, 2 H), 2.80 (t, J=5.5 Hz, 2 H), 3.09-3.15 (m, 1 H), 3.29-3.34 (m, 1H), 3.36-3.41 (m, 2 H), 3.55 (t, J=5.0 Hz, 2 H), 3.90 (dd, J=14.7 Hz, J=7.1 Hz, 1 H), 4.53 (s, 2 H), 7.22 (d, J=8.2 Hz, 1H), 7.53-7.63 (m, 6 H), 7.77 (d, J=7.4 Hz, 2 H), 7.99 (t, J=5.5 Hz, 1 H), 8.36 (t, J=5.5 Hz, 1 H); MS (FAB) m/z: 575 [M+H]+. 2-(R)-(Phenylsulfonyl)-3-{3-[(2-Boc-1,2,3,4-tetrahydroisoquinoline-7carbonyl)amino]propionyl}aminopropionic acid (3d) Prepared from acid 2 and 2-(R)-(Phenylsulfonyl)amino-β-alanine. Yield 51 %. A colorless glass substance. 1H NMR spectral and FAB-MS characteristics of the same product 3c. General Procedure for a preparation of compounds 4 The compounds 3 (1 mmol) were dissolved in anhydrous CH2Cl2 (50 ml), and the stream of dry HCl was passed through the solution for 30 min. The solvent was evaporated, and the solid residue was dried in vacuo (2 mm Hg) for 2 h at 40 ºC. Chloride

2-(S)-(n-butylsulfonyl)-3-{3-[(1,2,3,4-tetrahydroisoquinolinium-7-

carbonyl)amino]propionyl}aminopropionic acid (4a) Yield 98 %. A hygroscopic solid; 1H NMR δ (500 MHz, d6-DMSO) 0.86 (t, J=7.1 Hz, 3 H, C4H3 Bun), 1.35 (dt, J=14.3 Hz, J=6.6 Hz, 2 H, C3H2 n-Bu), 1.59-1.69 (m, 2 H, C2H2 Bun), 2.37 (t, J=6.9 Hz, 2 H, NHCH2CH2CO), 2.97 (dt, J=15.4 Hz, J=8.1 Hz, 2 H, C1H2 Bun), 3.04 (t, J=5.2 Hz, 2 H, C4H2 THIQ), 3.22-3.27 (m, 1 H, C’H2CH(NHSO2Bun)CO2H), 3.35-3.44 (m, 5 H, C’’H2CH(NHSO2Bun)CO2H and C3H2 THIQ and NHCH2CH2CO), 3.99 (dd, J=15.3 Hz, J=6.4 Hz, 1 H, CH2CH(NHSO2Bun)CO2H), 4.28 (s, 2 H, C1H2 THIQ), 7.29 (d, J=8.0 Hz, 1 H, C5H THIQ), 7.56 (d, J=9.0 Hz, 1 H, NHSO2Bun), 7.70-7.72 (m, 2 H, C6H and C8H THIQ,), 8.12 (t,



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J=5.1 Hz, 1 H, NHCH2CH(NHSO2Bun)CO2H), 8.48 (t, J=5.1 Hz, 1 H, NHCH2CH2), 9.58 (s, 2 H,

+

NH2 THIQ); HRMS (FAB) calculated for C20H31N4O6S [M+H]+: 455.5573, found:

455.5565. Chloride

2-(R)-(n-butylsulfonyl)-3-{3-[(1,2,3,4-tetrahydroisoquinolinium-7-

carbonyl)amino]propionyl}aminopropionic acid (4b) Yield 95 %. A hygroscopic solid; 1H NMR spectral and HRMS (FAB) characteristics of the same product 4a. Chloride

2-(S)-(phenylsulfonyl)-3-{3-[(1,2,3,4-tetrahydroisoquinolinium-7-

carbonyl)amino]propionyl}aminopropionic acid (4c) Yield 98 %. A hygroscopic solid; 1H NMR δ (500 MHz, d6-DMSO) 2.26 (ddd, J=31.5 Hz, J=14.8 Hz, J=7.3 Hz, 2 H, NHCH2CH2CO), 3.05 (t, J=5.2 Hz, 2 H, C4H2 THIQ), 3.13 (dt, J=19.2

Hz,

J=6.3

Hz,

1

H,

C’H2CH(NHSO2Ph)CO2H),

3.29-3.45

(m,

5

H,

C’’H2CH(NHSO2Ph)CO2H and C3H2 THIQ and NHCH2CH2CO), 3.88 (dd, J=14.6 Hz, J=7.0 Hz, 1 H, CH2CH(NHSO2Ph)CO2H), 4.27 (s, 2 H, C1H2 THIQ), 7.29 (d, J=7.3 Hz, 1 H, C5H THIQ), 7.54-7.62 (m, 3 H, m-CH and p-CH Ph), 7.69-7.72 (m, 2 H, C6H and C8H THIQ), 7.78 (d, J=7.4 Hz, 2 H, o-CH Ph), 8.08 (t, J=5.5 Hz, 1 H, NHCH2CH(NHSO2Ph)CO2H), 8.18 (d, J=8.8 Hz, 1 H, NHSO2Ph), 8.47 (t, J=5.1 Hz, 1 H, NHCH2CH2), 9.68 (s, 2 H, +NH2 THIQ), 11.41 (br s, 1 H, CO2H); HRMS (FAB) calculated for C22H27N4O6S [M+H]+: 475.5477, found: 475.5458. Chloride

2-(R)-(phenylsulfonyl)-3-{3-[(1,2,3,4-tetrahydroisoquinolinium-7-

carbonyl)amino]propionyl}aminopropionic acid (4d) Yield 97 %. A hygroscopic solid; 1H NMR spectral and HRMS (FAB) characteristics of the same product 4c. 6-Nitroquinazolin-2,4-dione (7)


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The compound 6 (12.97 g, 0.08 mol) was dissolved in concentrated H2SO4 (44 mL), and this solution was added to mixture of concentrated H2SO4 (11 mL) and HNO3 (5.6 mL, d = 1.5) at 10 °C. The mixture was stirred for 1 h at -5 °C. The reaction mixture was poured onto crushed ice (200 g). A crystalline solid was collected and washed with water. The filtered product was recrystallized from glacial acetic acid. Yield 67 %. m.p. 352-354°C; MS (EI) m/z: 207. 2-Chloro-6-nitro-3H-quinazolin-4-one (8) A mixture of compound 7 (3 g, 0.0145 mol), freshly distilled of POCl3 (70 mL) and N,Nimethylaniline (1.18 mL) was refluxed to completely dissolve the precipitate, about 6 h. Reaction mixture was allowed to cool to room temperature and the solvent was evaporated in vacuo to dryness. To the residue was added ice-water (100 g). Precipitate obtained was filtered, washed with distilled water and was dissolved in 100 mL of chloroform. The solution was washed with water (100 mL). The organic layer was dried over Na2SO4, filtered off, and the solvent was evaporated in vacuo to dryness. The crude product, 2,4-dichloro-6-nitroquinazoline, was used directly in the next step. 2,4-Dichloro-6-nitroquinazoline was suspended in 2 M aqueous sodium hydroxide solution (20 mL) and the mixture was stirred for 3 h. Reaction mixture filtered to remove unreacted 2,4Dichloro-6-nitroquinazoline. Filtrate was neutralized with dilute acetic, the precipitate was collected by filtration, washed with water and dried in air. Yield 61 %. m.p. 248-250 °C; MS (EI) m/z: 227, 225. 2-(4-Boc-piperazin-1-yl)-6-nitro-3H-quinazolin-4-one (9) The compounds 8 (2.004 g, 0.0089 mol) was dissolved in acetonitrile (50 mL), and to this solution was added 1-Boc-piperizine (1.82 g, 0.0098 mol) and NEt3 (1.36 mL, 0.0098 mol). The mixture was stirred for 6 h at 70 °C. Reaction mixture was allowed to cool to room temperature and the precipitate of crude product was collected by filtration. Recrystallization from mixture of benzene and ethanol (2:1) to give pure compound 9. Yield 82 %. m.p. 214-216 °C; 1H NMR δ



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(500 MHz, d6-DMSO) 1.42 (s, 9 H), 3.43 (br s, 4 H), 3.72 (br s, 4 H), 7.30 (d, J=8.5 Hz, 1 H), 8.27 (d, J=7.4 Hz, 1 H), 8.59 (s, 1 H), 11.52 (br s, 1 H); MS (FAB) m/z: 376 [M+H]+. 2-(4-Boc-piperazin-1-yl)-6-amino-3H-quinazolin-4-one (10) The compound 9 (0.5 g, 0.0013 mol) dissolved in 100 mL mixture of benzene and methanol (1:1) was subjected to catalytic hydrogenation at room temperature for 2-3 h (the reaction was monitored by TLC) in the presence of 3 % palladium on carbon (0.05 g). After the reaction, the stream of gellius was passed through the solution for 20 min. The filtered solution was then evaporated in vacuo to give compound 10. Yield 94 %. m.p. 241 °C (decomposes); 1H NMR δ (500 MHz, d6-DMSO) 1.41 (s 9 H), 3.39 (br s, 4 H), 3.43 (br s, 4 H), 5.17 (s, 2 H), 6.95 (d, J=7.1 Hz, 1 H), 7.06-7.11 (m, 2 H), 11.25 (br s, 1 H); MS (FAB) m/z: 346 [M+H]+. General Procedure for a preparation of compounds 11 The 0.01 mol of Boc-acid was dissolved in anhydrous acetonitryle (25 mL). The solution was cooled to -5 °C, and triethylamine (1.4 mL, 0.01 mol), and then HATU (3.8 g, 0.01 mol), were added. The mixture was stirred for 1 h at -5 °C and then 10 mmol of amine 10 was added. The mixture was stirred for 7 h at 50 °C. The residual amount of the activated ether (At-ether of starting Boc-acid) was destroyed by addition of few drops of N,N-dimethylpropane-1,3-diamine, and the solvent was evaporated in vacuo to dryness. The residue was dissolved in 100 mL of chloroform. The solution was washed with water (40 mL), aqueous solution of 1 M HCl (40 mL) and 5 % aqueous solution of NaHCO3 (40 mL). The organic layer was dried over Na2SO4, filtered off, and the solvent was evaporated in vacuo to dryness. The resulting residue was recrystallization from methanol. 3-N-Boc-amino-N-[2-(4-Boc-piperazin-1-yl)-4-oxo-3H-quinazolin-6-yl]propionamide (11a) Yield 58 %. m.p. 244-245 °C; 1H NMR δ (500 MHz, d6-DMSO) 1.37 (s, 9 H), 1.42 (s, 9 H), 2.51-2.55 (m, 2 H), 3.22 (s, 2 H), 3.40 (br s, 4 H), 3.55 (br s, 4 H), 6.86 (s, 1 H), 7.24 (d, J=6.9


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Hz, 1 H), 7.70 (d, J=6.7 Hz, 1 H), 8.29 (s, 1 H), 10.01 (s, 1 H), 11.41 (s, 1 H); MS (FAB) m/z: 517 [M+H]+. 4-N-Boc-amino-N-[2-(4-Boc-piperazin-1-yl)-4-oxo-3H-quinazolin-6-yl]butyramide (11b) Yield 50 %. m.p. 237-238 °C; 1H NMR δ (400 MHz, d6-DMSO) 1.37 (s, 9 H), 1.42 (s, 9 H), 1.70 (s, 2 H), 2.30 (s, 2 H), 2.97 (s, 2 H), 3.40 (br s, 4 H), 3.55 (br s, 4 H), 6.83 (s, 1 H), 7.25 (d, J=6.1 Hz, 1 H), 7.72 (d, J=5.5 Hz, 1 H), 8.28 (s, 1 H), 9.97 (s, 1 H), 11.41 (1 H); MS (FAB) m/z: 531 [M+H]+.

N-[2-(4-Boc-piperazin-1-yl)-4-oxo-3H-quinazolin-6-yl]-1-Boc-piperidine-4-carboxamide (11c) Yield 46 %. m.p. 241 °C; 1H NMR δ (500 MHz, d6-DMSO) 1.30-1.52 (m, 21 H), 1.77 (d, J=11.0 Hz, 2 H), 2.78 (br s, 2 H), 3.40 (br s, 4 H), 3.55 (br s, 4 H), 4.00 (br s, 2 H), 7.24 (d, J=6.0 Hz, 1 H), 7.73 (d, J=6.3 Hz, 1 H), 8.29 (s, 1 H), 10.01 (s, 1 H), 11.42 (s, 1 H); MS (FAB) m/z: 557 [M+H]+. General Procedure for a preparation of compounds 12 The compounds 11 (10 mmol) were dissolved in 100 mL mixture CH2Cl2 and methanol (10:1), and the stream of dry HCl was passed through the solution for 1 h. The solvent was evaporated, and the solid residue was dried in vacuo (2 mm Hg) for 2 h at 40 ºC. Dichloride

3-ammonium-N-[2-(4-piperazinium-1-yl)-4-oxo-3H-quinazolin-6-

yl]propionamide (12a) Yield 92 %. m.p. >300 °C(decomposes); 1H NMR δ (500 MHz, d6-DMSO) 2.83 (t, J=6.3 Hz, 2 H, C2H2 propionamide), 3.08 (dd, J=11.2, 6.0 Hz, 2 H, C3H2 propionamide), 3.26 (br s, 4 H, CH2 piperazinium), 4.06 (br s, 4 H, CH2 piperazinium), 4.98 (br s, 1 H, NH quinazoline), 7.867.93 (m, 2 H, C7H and C8H quinazoline), 8.15 (s, 3 H, +NH3), 8.46 (s, 1 H, C5H quinazoline), 9.86 (s, 2 H, +NH2 piperazinium), 10.83 (s, 1 H, NH propionamide); HRMS (FAB) calculated for C15H21N6O2 [M+H]+: 317.3736, found: 317.3751.



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Dichloride

4-ammonium-N-[2-(4-piperazinium-1-yl)-4-oxo-3H-quinazolin-6-

yl]butyramide (12b) Yield 94 %. Hygroscopic substance; 1H NMR δ (500 MHz, d6-DMSO) 1.91 (dt, J=14.3, 7.1 Hz, 2 H, C3H2 butyramide), 2.34 (t, J=7.3 Hz, 2 H, C2H2 butyramide), 2.81-2.86 (m, 2 H, C4H2 butyramide), 3.27 (br s, 4 H, CH2 piperazinium), 4.09 (br s, 4 H, CH2 piperazinium), 5.25 (br s, 1 H, NH quinazoline), 7.91-7.96 (m, 2 H, C7H amd C8H quinazoline), 8.17 (s, 3 H, +NH3), 8.46 (s, 1 H, C5H quinazoline), 9.98 (s, 2 H, +NH2 piperazinium), 11.73 (s, 1 H, NH butyramide); HRMS (FAB) calculated for C16H23N6O2 [M+H]+: 331.4007, found: 331.4014. Dichloride

N-[2-(4-piperazinium-1-yl)-4-oxo-3H-quinazolin-6-yl]piperidium-4-

carboxamide (12c) Yield 96 %. m.p. >300 °C(decomposes); 1H NMR δ (500 MHz, d6-DMSO) 1.83-1.99 (m, 2 H, C3H’ piperidium), 1.97-2.01 (m, 2 H, C3H’’ piperidium), 2.69-2.75 (m, 2 H, C2H’ piperidium), 2.88-2.93 (m, 2 H, C2H’’ piperidium), 3.23 (br s, 4 H, CH2 piperazinium), 3.53 (dd, J=27.2, 10.1 Hz, 1 H, C4H piperidium), 3.98 (br s, 4 H, CH2 piperazinium), 7.70 (s, 1 H, C8H quinazoline), 7.89 (d, J=6.5 Hz, 1 H, C7H quinazoline), 8.43 (s, 1 H, C5H quinazoline), 8.83 (s, 1 H, +NH’ piperidium), 9.19 (s, 1 H, +NH’’ piperidium), 9.66 (s, 2 H, N+H2 piperazinium), 10.60 (s, 1 H, NH carboxamide); HRMS (FAB) calculated for C18H25N6O2 [M+H]+ : 357.4390, found: 357.4379.

In vitro biology Functional activity was determined by measuring the inhibition of ADP induced platelet aggregation in human platelet-rich plasma (PRP) by Born’s method.67 Mode of action for some compounds was subsequently revealed in vitro by measuring the ability of compounds to inhibit the binding of fluoresceinisothiocyanate-labeled fibrinogen (FITC-Fg)85 to αIIbβ3 in a suspension of human washed platelets.38 Supporting Information


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Additional figures and tables illustrating the datasets, pharmacophores and molecular docking results. This material is available free of charge via the Internet at http://pubs.acs.org. Author information Corresponding authors: * Alexandre Varnek: [email protected], Andrei A. Krysko: [email protected] Acknowledgment The financial support from bilateral Moldova-Ukraine project 14.820.18.04.05/U and No M/161-2014) is acknowledged. PP thanks French Embassy in Ukraine for scholarship for young scientists. TK thanks French Embassy in Ukraine for PhD scholarship. The authors are grateful to Dr. V.V. Polovinko (Enamine LTD, Ukraine) for the 1H NMR spectra and Dr. I.M. Rakipov (A.V. Bogatsky Physico-Chemical Institute of the National Academy of Sciences of Ukraine) for the FAB-MS spectra. The authors also thank Dr. D. Horvath and Dr. G. Marcou from the University of Strasbourg for valuable comments and fruitful discussion. We acknowledge Inte:Ligand company for providing us with LigandScout software. Authors also thank reviewers whose comments and remarks helped us to significantly improve the manuscript quality and consistence.



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Design, Virtual Screening, and Synthesis of Antagonists of Î±IIb

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