Computer-aided design of multi-target ligands at A1R, A2AR and PDE10A, key proteins in neurodegenerative diseases

Kalash et al. J Cheminform (2017) 9:67 https://doi.org/10.1186/s13321-017-0249-4 RESEARCH ARTICLE Computer-aided design of multi-target ligands at A1R, A2AR and PDE10A, key proteins in neurodegenerative diseases Leen Kalash1, Cristina Val2,3, Jhonny Azuaje2, María I. Loza3, Fredrik Svensson1,4, Azedine Zoufir1, Lewis Mervin1,5, Graham Ladds6, José Brea3, Robert Glen1,7, Eddy Sotelo2* and Andreas Bender1* Abstract Compounds designed to display polypharmacology may have utility in treating complex diseases, where activity at multiple targets is required to produce a clinical effect. In particular, suitable compounds may be useful in treating neurodegenerative diseases by promoting neuronal survival in a synergistic manner via their multi-target activity at the adenosine A1 and A2A receptors (A1R and A2AR) and phosphodiesterase 10A (PDE10A), which modulate intracellular cAMP levels. Hence, in this work we describe a computational method for the design of synthetically feasible ligands that bind to A1 and A2A receptors and inhibit phosphodiesterase 10A (PDE10A), involving a retrosynthetic approach employing in silico target prediction and docking, which may be generally applicable to multi-target compound design at several target classes. This approach has identified 2-aminopyridine-3-carbonitriles as the first multi-target ligands at A1R, A2AR and PDE10A, by showing agreement between the ligand and structure based predictions at these targets. The series were synthesized via an efficient one-pot scheme and validated pharmacologically as A1R/A2AR–PDE10A ligands, with IC50 values of 2.4–10.0 μM at PDE10A and Ki values of 34–294 nM at A1R and/or A2AR. Furthermore, selectivity profiling of the synthesized 2-amino-pyridin-3-carbonitriles against other subtypes of both protein families showed that the multi-target ligand 8 exhibited a minimum of twofold selectivity over all tested offtargets. In addition, both compounds 8 and 16 exhibited the desired multi-target profile, which could be considered for further functional efficacy assessment, analog modification for the improvement of selectivity towards A1R, A2AR and PDE10A collectively, and evaluation of their potential synergy in modulating cAMP levels. Keywords: Multi-target ligands, Adenosine receptor ligands, PDE10A inhibitors, Target prediction, Drug design, Docking, QSAR © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Open Access *Correspondence: e.sot[email protected]; [email protected].uk 1 Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge CB21EW, UK 2 Center for Research in Biological Chemistry and Molecular Materials (CIQUS), University of Santiago de Compostela, 15782 Santiago de Compostela, Spain Full list of author information is available at the end of the article Background Neurodegeneration involves the progressive loss of the structure and function of neurons, which is common in Parkinson’s, Huntington’s disease and schizophrenia [1]. Recently, there has been substantial interest in the search for alternative non-dopamine (non-DA) based approaches for the treatment of neurodegenerative diseases, as the classical DA-based approaches have long been associated with many undesirable side effects such as dyskinesia, hallucinations, and on/off effects [2]. Given that the adenosine neuromodulation system (via the adenosine A1 and A2A receptors) has been identified as a key target for the management of neurodegenerative diseases, this qualifies its targeting as a potential promising non-DA based treatment approach [3, 4]. Indeed, modulation of cAMP levels has proven to have benefits in neuronal survival in an adenosine receptor-dependent manner [5]. In addition, recent findings suggest that phosphodiesterase 10A (PDE10A) also plays a role in Page 2 of 19 Kalash et al. J Cheminform (2017) 9:67 neurodegenerative diseases such as Parkinson’s, Huntington’s disease, and schizophrenia [6–8]. Inhibition of PDE10A resulting in maintenance of elevated intracellular cAMP concentrations, has been suggested to be effective in the treatment of these diseases. Thus multi-target ligands that bind to different adenosine receptors subtypes (A1 and A2A receptors) while simultaneously inhibit PDE10A might be synergistic in modulating cAMP levels, which is of therapeutic potential for neurodegenerative diseases [9–11]. Conceptually, multi-target drugs work by creating a combination effect on multiple targets in the biological network simultaneously, which may (through e.g. synergistic effects) decrease the therapeutic dose required, thus increasing therapeutic efficacy, preventing drug resistance, and reducing target-related adverse effects [12–14]. Also, another advantage of multi-target drugs over other types of treatments such as combination therapies, is a reduced likelihood of drug–drug interactions [15, 16]. However, it remains a challenging task for medicinal chemists to design drugs with a specific multi-target profile and to achieve selectivity for specific targets over offtarget effects with suitable pharmacokinetic properties [17, 18]. In fact, the field of multi-target drug design has recently become an active field of research in the pharmaceutical industry, where around 20 designed multitarget drugs have either reached advanced development stages or are already approved [14, 19, 20]. In particular, for Central Nervous System (CNS) diseases, there has been growing interest in exploiting the multi-target profiles of existing compounds to investigate their potential applicability as drugs. For example, multitarget profiles of drugs and drug candidates affecting the dopaminergic system have been investigated. Examples include Aripiprazole, Amitriptyline, Chlorpromazine, and Clozapine [21]. In addition, various multi-target based virtual screening protocols for multi-target drug design have been developed [13, 22–24]. Examples of ligandbased protocols include in silico target prediction and Chemogenomic and pharmacophore-based approaches, which resulted in the discovery of CNS drugs with multitarget combinations such as MAO-A/MAO-B/AChE/ BuChE, AChE/BuChE, and H3-R/HMT/AChE/BuChE [21–24]. Structure-based approaches such as docking and molecular dynamics calculations have also been employed for the discovery of new multi-target ligands such as BuChE inhibitors/hCB2R and MAO-A/MAO-B/AChE/ BuChE ligands to treat neurodegenerative diseases [25]. In this work, we offer a computational strategy for designing synthetically feasible ligands that bind to A1R and A2AR, and inhibit PDE10A—a novel multi-target combination of G protein-coupled receptors (GPCRs) and an enzyme, which has not, to our knowledge, been previously exploited. The designed ligands with this multi-target combination are intended as starting points for future development of multi-target drugs treating neurodegenerative diseases. It should be noted here that in the current study we only consider affinity of ligands to the above receptors, which we also experimentally validate as outlined below. However, for therapeutically relevant purposes also functional effects and optimization of selectivity towards A1R, A2AR and PDE10A need to be considered, which will be the area of a future study. The workflow of the current study is shown in Fig.1. Starting with a focused chemical space consisting of known actives against A1R, A2AR and PDE10A, new synthetically feasible compounds were established via RECAP (Retrosynthetic Combinatorial Analysis Procedure) [26, 27], which fragments molecules at pre-defined bonds and recombines them in a combinatorial manner, and were then evaluated in silico, using target prediction and ligand/protein docking. Compounds with favorable assessments in both steps were carried forward for substructural analysis. This analysis identified compound series with the highest frequency of prediction as multitarget ligands against the desired set of targets, which is of advantage from the practical side, given their synthetic accessibility via a common synthetic route. A series of 2-aminopyridine-3-carbonitriles were selected for prospective validation of the pipeline, a series which was synthetically accessible via a one pot synthetic scheme i.e. providing products with the desired properties: cost-effective, synthetically efficient and available in a timely fashion [28, 29]. Subsequently the synthesized compounds were experimentally tested and confirmed as A1R/A2AR–PDE10A multi-target ligands. Selectivity against other subtypes of both protein families confirmed the pharmacological profile of the compound series, and structure activity relationships (SAR) were also deduced. Hence, in this work we report a successful computational strategy, which allowed the discovery of the first A1R/ A2AR–PDE10A multi-target ligands. The novel A1R/ A2AR–PDE10A ligands are sought to display a combination effect in modulating the A1R, A2AR, and PDE10A targets simultaneously similar to that of combination compounds of Adenosine receptors and PDEs, reported by Rickles etal., which were synergistic in modulating cAMP levels [10]. Results and discussion Design of synthetically feasible A1R/A2AR–PDE10A multi‑target ligands Human enzyme and receptor data were extracted from ChEMBL 20 [30]. Substructure analysis of A1R, A2AR ligands and PDE10A inhibitors with Ki and IC50 values Page 3 of 19 Kalash et al. J Cheminform (2017) 9:67 less than or equal to 1µM revealed that the most frequently occurring common heterocycles among the actives against the three target classes were pyridine, pyrimidine, piperazine, and 1H-pyrazole (Additional file1: Figure S1). Subsequently, A1R (2104), A2AR (2489) and PDE10A inhibitors (679) containing those frequent heterocycles were subjected to RECAP analysis/synthesis in MOE (see Methods for details) [26]. As a result, 458,839 (potentially) synthetically accessible ligands were formed in silico. This list of candidates was filtered to those retaining the common heterocycles (listed above), in order to create a focused chemical space characteristic Fig. 1 The computational strategy for rational design of A1R/A2AR–PDE10A multi-target ligands started with a focused chemical space consisting of known actives of A1R, A2AR and PDE10A, and formed new synthetically feasible compounds which were subjected to target prediction and docking for synthesis and pharmacological evaluation Page 4 of 19 Kalash et al. J Cheminform (2017) 9:67 of A1R, A2AR and PDE10A (with the simultaneous tradeoff of reduced novelty), giving rise to 22,233 compounds. Target prediction of the designed RECAP library To assess the likelihood of active compounds against A1R, A2AR and PDE10A, PIDGIN 1.0 (Prediction including Inactivity), a tool which uses ECFP 4 circular Morgan fingerprints and trained on ChEMBL actives and PubChem inactives, was used to perform in silico target prediction for the focused RECAP library (22,233 compounds) [24]. Subsequent enrichment analysis of the predictions was done using an estimation score, average ratio as developed by Liggi etal. [31] and via Chi square test [32]. For targets to be considered as enriched according to these methods, the estimation score and the Chi square test p value should be less than or equal to 0.01 and 0.05, respectively. Hence, upon analyzing the enrichment parameters for the A1R, A2AR and PDE10A targets that were predicted for the focused RECAP library (Additional file1: Figure S2), the three targets were predicted with an estimation score equal to 0 (enriched) as well as average ratios less than 0.1 (enriched) with Chi squared p values <0.005. The percentage of RECAP compounds of the focused library that were predicted as actives against the A1R, A2AR and PDE10A targets were 51.1, 52.8, and 24.5% respectively. These numbers are relatively high, which however is understandable given that the input to the RECAP analysis consisted of experimentally established known ligands of the above protein targets. Docking of the compounds predicted as A1R/A2AR–PDE10A multi‑target ligands In the next step docking and further substructure analysis were performed on compounds of the focused RECAP library, which were predicted as A1R/A2AR–PDE10A multi-target ligands from the ligand-based side in the previous step. 2563 compounds were predicted as actives against the three desired targets, and they were subsequently docked against a high resolution (1.8 Å) A2AR protein crystal structure (PDBID: 4EIY) [33] its corresponding A1R homology model (see Methods for details), and PDE10A (PDBID: 4DDL) [34]. Compounds which were carried forward to substructural analysis were selected when their docking score gave a value less than a pre-determined cut-off value computed from the docking scores. This cut-off value was evaluated as the docking score with the best F measure statistic obtained by docking a set of known actives and inactives against the protein crystal structures and the homology model (see Methods for details). As a result, a distribution of RECAP compounds that were favorable as multi-target ligands by target prediction and docking was obtained, where 62.47% of the RECAP compounds that were predicted as A1R/ A2AR–PDE10A multi-target ligands and docked against PDE10A exhibited docking scores lower than − 6.49 (the threshold of the best F measure discriminating between actives and inactives for known ligands). Out of the RECAP compounds which displayed docking scores lower than − 6.49 against PDE10A, 48.89 and 35.23% displayed docking scores lower than −7.26 and −8.49 against A1R and A2AR (the thresholds of the best F measures). Substructure analysis of the compounds predicted as A1R/ A2AR–PDE10A multi‑target ligands Substructure analysis was performed on compounds having a favorable assessment by target prediction and docking (i.e. those compounds whose docking scores were below the threshold for all three targets). The analysis revealed frequently occurring series, which shared the same core structure and which are shown in Fig.2. The chemical series were identified as [1,2,4] triazolo[1,5-c]quinazolines (50.4% of all positively predicted multi-target ligands by in silico target prediction as well as docking), imidazo[1,5-a]quinoxalines (14.4%), 6,7-alkoxyisoquinolines (10.6%), and 2-aminopyridine3-carbonitriles (9.2%). These were in addition to various compounds containing the common and frequent heterocycles identified earlier (15.4%). Each series identified Fig. 2 2563 compounds of the focused RECAP library were predicted as A1R/A2AR–PDE10A multi-target ligands, and docked against the A2AR protein crystal structure (PDB ID: 4EIY), A1R homology model, and the PDE10A protein crystal structure (PDB IB: 4DDL), the RECAP series which showed an agreement between the ligand-based and structure-based predictions were mainly a 6,7-alkoxyisoquinolines b [1,2,4] triazolo[1,5-c]quinazolines c 2-aminopyridine-3-carbonitriles d imidazo[1,5-a]quinoxalines Page 5 of 19 Kalash et al. J Cheminform (2017) 9:67 could be considered for synthesis, SAR studies and validation as A1R/A2AR–PDE10A multi-target ligands. Synthesis of novel 2‑aminopyridine‑3‑carbonitriles Due to both ease of the reaction and anticipated yield, a one-pot synthetic scheme was selected for synthesizing one promising series, 2-aminopyridine-3-carbonitriles. The design resulted in 25 compounds for synthesis of which 21 were novel compounds and four (1, 2, 5, and 17) have previously been reported in the literature [35–38]. Compounds 1–25 were screened against PAINs (PAN Assay Interference Compounds) [39] using FAFDrug3 [40], and none of the compounds exihibited potential PAINs liability. Subsequently, their synthesis was performed as shown in Scheme1, and all products were obtained with good yields, ranging from 46 to 85% (see Methods for details). Pharmacological evaluation of novel 2‑aminopyridine‑3‑carbonitriles Bioactivity testing was performed using A1 and A2A human adenosine receptors expressed in transfected CHO (A1) and HeLa (A2A) cells, as well as AD293 cells that were transiently transfected with human PDE10A. Table1 includes the list of synthesized 4,6-substituted 2-amino-pyridin-3-carbonitriles, along with their Ki values against A1R, A2AR, and IC50 values against PDE10A. It can be seen that 15 compounds of the 25 synthesized 2-amino-pyridin-3-carbonitriles exhibited inhibitory activity against PDE10A below 10μM. In addition, 13 compounds were adenosine receptor binders exhibiting selectivity towards A1R and A2AR, which has not been the case in the previous work reported by Mantri etal., where 2-amino-pyridin-3-carbonitriles were promiscuous towards the four adenosine receptor subtypes [36]. Given that the objective of this work is to find compounds displaying specific multi-target activity, compounds 8, 16, 21, and 25 were identified as A1R/ A2AR–PDE10A multi-target ligands, inhibiting PDE10A with IC50 values of 2.4, 3.2, 10.0, and 5.1µM respectively, and binding to A1R with Ki values of 294 and 34nM (compounds 8 and 16, respectively), and to A2AR with Ki values of 41, 95, and 55nM (compounds 16, 21, and 25, respectively). Notably, compound 16 exhibited the desired multi-target profile as a PDE10A inhibitor and a dual binder to A2AR and A1R. It was previously reported that substituted pyridines exhibited PDE inhibitory activity [41, 42], and 2-aminopyridin-3-carbonitriles are adenosine receptor ligands [36]. In this study we have now identified suitable compounds matching both criteria as A1R/A2AR–PDE10A multi-target ligands, satisfying the original compound design objective. (SAR) structure–activity relationship analysis The purpose of the SAR analysis was to rationalize the variation in activity of the newly discovered A1R/A2AR– PDE10A multi-target ligands against PDE10A, given that 2-amino-pyridin-3-carbonitriles have been discovered as a novel class of PDE10A inhibitors. Also due to the fact that compounds of this substructural class were documented as adenosine receptor ligands [36], computational SAR studies were focused on the PDE10A data, where the variation in potency was rationalized in relation to the physicochemical properties of the compounds (which were computed by FAFDrug3, Additional file1: Table S1) [40]. A trend observed repeatedly in several cases was that when logP decreased, associated with an increase in tPSA, then this led to an improvement in the activity against PDE10A. Initial analysis concentrated on compounds 1–4, which have a phenyl substituent at position 4 of the pyridine ring. Compound 3 was the most potent PDE10A inhibitor with an IC50 of 2.0µM, and a computed logP of 3.1 and tPSA of 103.9Å2. Similarly, for compounds 5–7 having a phenyl substituent at position 6 of the pyridine ring, compound 6 was the most potent against PDE10A with an IC50 of 5.7µM and a computed logP of 4.0 and tPSA of 81.2Å2. For compounds 8–13, which have a cyclohexyl ring at position 4 of the pyridine ring, compound 12 displayed the most potent PDE10A inhibitory activity with an IC50 of 0.9µM and a computed logP of 4.7 and tPSA of 90.9Å2. For compounds 14–17, with a p-methoxyphenyl substituent at position 4 of the pyridine ring, compound 16 with the smallest predicted lipophilicity of 3.1 and tPSA of 85.1Å2 displayed a good PDE10A inhibitory activity with an IC50 value equal to 3.2µM, yet the most potent compound was 15 Scheme 1 The one-pot synthetic route followed for the synthesis of novel 4,6-substituted 2-amino-pyridin-3-carbonitriles Page 6 of 19 Kalash et al. J Cheminform (2017) 9:67 Table 1 Percent inhibition of the synthesized 4,6-substituted 2-amino-pyridin-3-carbonitriles at 10 µM (PDE10A) or IC50 (µM) and percentage displacement at 0.1 µM (A1R and A2AR), or Ki Compound R4R6 % inhibition at 10 µM (PDE10A) or IC50 (µM) % displacement at 0.1 µM (A1R and A2AR) or Ki A1R A2ARPDE10A 1394 ± 12 nM 32% 22% 2142 ±7nM 38% 52% 312% 8% 2.0 ± 0.2 µM 4 26% 32% 3.6 ± 0.3 µM 5 53% 543 ± 13 nM 28% 6 12% 1% 5.7 ± 0.3 µM 7 25 ± 2 nM 5% 17% Page 7 of 19 Kalash et al. J Cheminform (2017) 9:67 Table 1 continued 8 294 ± 10 nM 50% 2.4 ± 0.2 µM 9 84 ± 8 nM 34% 68% 10 17% 18% 3.7 ± 0.3 µM 11 16% 11% 1.2 ± 0.1 µM 12 44% 60% 0.9 ± 0.2 µM 13 70 ± 3 nM 49 ± 4 nM 55% 14 108 ± 6 nM 30% 10% 15 6% 32% 1.5 ± 0.2 µM 16 34 nM ± 2 nM 41 ± 2 nM 3.2 ± 0.4 µM 17 46% 29% 65% 18 78 ± 5nM 948 ± 13 nM 38% Page 8 of 19 Kalash et al. J Cheminform (2017) 9:67 with an IC50 value of 1.5µM and a computed logP of 4.4 and tPSA of 71.9Å2. For compounds 19–22, with an o-methoxyphenyl substituent at position 4 of the pyridine ring, compound 22 displayed PDE10A inhibitory activity with the highest potency (IC50 value of 5.6µM), and a computed logP of 3.7 and tPSA of 92.2Å2. Finally a similar general trend is observed for the compounds 23 and 24 with a 4-hydroxyphenyl substituent at position 6 of the pyridine ring, where compound 24 was a more potent PDE10A inhibitor with an IC50 of 3.1µM and computed logP of 3.4 and tPSA of 103.2Å2. Hence, it could be deduced that in the majority of the series considered, where the substituents on a single position is varied, a decrease in computed lipophilicity associated with an increase in polarity generally improved the activity of compounds against PDE10A. This general trend can be attributed to the hydrophilic nature of the pocket, which favours the interactions between the ligand and the PDE10A protein by compounds exhibiting these properties. Table 1 continued 19 58% 338 ± 12 nM 73% 20 12% 50% 6.4 ± 0.4 µM 21 38% 95 ± 4 nM 10.0 ± 0.6 µM 22 8% 1% 5.6 ± 0.5 µM 23 2% 10% 4.0 ± 0.3 µM 24 19% 7% 3.1 ± 0.4 µM 25 15% 55 ± 2 nM 5.1 ± 0.4 µM IC50 values of the 2-aminopyridines-3-carbonitriles were measured for the four phosphodiesterases PDE7A, PDE7B, PDE9A and PDE10A at 10 μM concentration. For those compounds that showed percentage inhibition greater than 70% and selectivity against other measured isoenzymes, IC50 were determined. Calculation of the Ki values at A1R, A2AR, A2BR and A3R was approximated using the Cheng-Prusoff equation: Ki = IC50/[1 + (C/KD)], where IC50 is the concentration of compound that displaces the binding of the radioligand by 50%, C is the concentration of radioligand, and KD is the dissociation constant of each radioligand Page 9 of 19 Kalash et al. J Cheminform (2017) 9:67 Compound selectivity assessment The selectivity of compounds 1–25 against the selected major off-targets A2BR, A3R, PDE7A, PDE7B, and PDE9A, was predicted using PIDGIN at a threshold for binding greater than or equal to 0.8, and subsequently tested experimentally. It is noted here that the IC50 values were determined for compounds with % inhibition at phosphodiesterases greater than 70%. As shown in Additional file1: Table S2, the synthesized compounds are mostly inactive against those off-targets except for compounds 16, 17, 21, and 23 that exhibited IC50 values of 3.4, 3.5, 15.1 and 1.8µM against PDE7A, and compounds 23 and 25, which exhibited IC50 values of 7.3 and 4.7µM against PDE7B. Remarkably, compound 8 was found to exhibit selectivity over all tested off-targets using the above criterion, with the lowest selectivity measured for PDE7B (of 55% inhibition at 10µM ligand concentration). This can be compared to the IC50 value of 8 at PDE10A, which is 2.4μM (indicating approximately twofold selectivity for 8). In general, the experimental results on off-target prediction for the synthesised 4,6-substituted 2-amino-pyridin3-carbonitriles 1–25 agree with the predictions generated using PIDGIN utilised to bias the compound design towards selective compounds such as 8 (Additional file1: Table S2). This compound would serve as a good starting point for analog modification to improve the selectivity of the synthesized ligands towards PDE10A. Analysis of the molecular docking studies of the synthesized 2‑aminopyridine‑3‑carbonitriles The synthesized 2-aminopyridine-3-carbonitriles were docked against A2AR (PDB ID: 4EIY), A1R homology model, and PDE10A (PDBID: 4DDL). Figure3 shows the common predicted ligand-target interactions for representative multi-target ligands of A1R–PDE10A, A1R– A2AR, and A2AR–PDE10A, namely for compounds 8, 18, and 25. It can be seen that compounds 8 and 25, with IC50 values of 2.4 and 5.1µM respectively, share similarities in predicted binding modes, since their pyridine rings display π-stacking with Phe686 and Phe719 of PDE10A (Fig.3). These are the type of interactions predicted to be exhibited by the majority of the synthesized ligands from this work, as well as the only existing interactions between co-crystallised PDE10A inhibitors discovered by fragment screening (PDB ID: 5C2E, 5C1W, 5C29, 5C2A ligands with Ki values of 2, 8, 700, 880, and 4.8nM, respectively) [43]. It is noted that the ligand of 5C2A exhibits a considerable selectivity towards PDE10A over all the other PDEs (in the range of 100–1000 fold and greater over the majority of PDEs, with the least selectivity observed being in the range of 25–100 fold). This ligand exhibits only π-stacking interactions with Phe686 and Phe719, similar to the mode of interactions of compound 8 with PDE10A, which is relatively selective over all tested PDEs, with the lowest selectivity being measured for PDE7B (of 55% inhibition at 10 µM ligand Fig. 3 Docking studies predicted molecular interactions characteristic of the 4,6-substituted 2-amino-pyridin-3-carbonitriles with the A2AR protein crystal structure (PDB ID: 4EIY), A1R homology model, and PDE10A protein crystal structure (PDB ID: 4DDL), which are displayed for representative multi-target ligands with the following combinations: compound 8 (A1R–PDE10A), 18 (A1R–A2AR), and 25 (A2AR–PDE10A): a interactions with A2AR: the overlaid compounds 18 and 25 exhibit H-bonds via amino and carbonitrile groups with Asn253, and the pyridine rings are π-stacked with Phe168 b interactions with A1R: the overlaid compounds 8 and 18 exhibit H-bonds via amino and carbonitrile groups with Asn254, and the pyridine rings are π-stacked with Phe171 c interactions with PDE10A: the overlaid compounds 8 and 25 have the pyridine rings π-stacked with Phe686 and Phe719. The molecular interactions predicted for the active molecules are consistent with observed interactions between co-crystallised ligands and their corresponding protein crystal structures (PDB ID: 4EIY and 4DDL) [33, 34] and the interactions with the A1R homology model reported in the literature [51, 52] Page 16 of 19 Kalash et al. J Cheminform (2017) 9:67 2‑amino‑6‑(4‑hydroxyphenyl)‑4‑(2‑methoxyphenyl) pyridine‑3‑carbonitrile (22) Purified by column chromatography (n-hexane-ethyl acetate 2:1) and then recrystallized from EtOH to give 0.193g, 60% yield (96% purity by HPLC). MP 210–212°C. 1H NMR (300MHz, DMSOd6), δ (ppm): 9.91 (s, 1H), 7.93 (d, J=9.0Hz, 2H), 7.45 (t, J=7.8Hz, 1H), 7.29 (dd, J=7.4, 1.7Hz, 1H), 7.16 (d, J=8.3Hz, 1H), 7.07 (d, J=7.5Hz, 1H), 7.03 (s, 1H), 6.82 (d, J=8.9Hz, 2H), 6.77 (s, 2H), 3.77 (s, 3H). MS (EI) m/z (%): 317.13 (M+, 100), 300.09 (8), 286.11 (6).Analysis calculated for C19H15N3O2: C, 71.91; H, 4.76; Cl, 13.24; O, 10.08. Found: C, 71.92; H, 4.74; Cl, 13.27; O, 10.05. 2‑amino‑4‑(2‑chlorophenyl)‑6‑(4‑hydroxyphenyl) pyridine‑3‑carbonitrile (23) Purified by column chromatography (n-hexane-ethyl acetate 2:1) and then recrystallized from EtOH to give 0.179g, 59% yield (98% purity by HPLC). MP 215–217°C. 1H NMR (300MHz, DMSOd6), δ (ppm): 9.90 (s, 1H), 8.16–7.22 (m, 2H), 7.69–7.30 (m, 4H), 7.16–6.50 (m, 5H). MS (EI) m/z (%): 320.99 (M+, 100), 286.04 (5). Analysis calculated for C18H12ClN3O: C, 67.19; H, 3.76; Cl, 11.02; N, 13.06; O, 4.97. Found: C, 67.37; H, 3.94; Cl, 11.18; N, 12.88; O, 4.63. 2‑amino‑4,6‑bis(4‑hydroxyphenyl)pyri‑ dine‑3‑carbo‑nitrile (24) Purified by column chromatography (n-hexane–ethyl acetate 2:1) and then recrystallized from EtOH to give 0.151g, 53% yield (97% purity by HPLC). MP 299–300°C. 1H NMR (300MHz, DMSO-d6), δ (ppm) 9.92 (s, 2H), 8.19–7.79 (m, 2H), 7.68–7.37 (m, 2H), 7.42–6.99 (m, 1H), 7.01–6.62 (m, 6H). MS (EI) m/z (%): 303.06 (M+, 100), 184.01 (6). Analysis calculated for C18H13N3O2: C, 71.28; H, 4.32; N, 13.85; O, 10.55. Found: C, 71.40; H, 4.54; N, 13.75; O, 10.31. 2‑amino‑4‑(furan‑2‑yl)‑6‑(thiophen‑3‑yl)pyri‑ dine‑3‑carbonitrile (25) Purified by column chromatography (n-hexane-ethyl acetate 2:1) and then recrystallized from EtOH to give 0.123g, 46% yield (95% purity by HPLC). MP 156–157°C. 1H NMR (300MHz, CDCl3) δ(ppm): 8.01 (dd, J=3.0, 1.2Hz, 1H), 7.66 (dd, J=5.1, 1.2Hz, 1H), 7.62 (dd, J=1.8, 0.6Hz, 1H), 7.48 (dd, J=3.6, 0.6Hz, 1H), 7.45 (s, 1H), 7.40 (dd, J=5.1, 3.0Hz, 1H), 7.40 (dd, J=5.1, 3.0Hz, 1H), 6.61 (dd, J=3.6, 1.8Hz, 1H), 5.26 (s, 2H). MS (EI) m/z (%): 267.06 (M+, 100), 237.98 (6), 210.99 (7). Analysis calculated for C14H9N3OS: C, 62.91; H, 3.39; N, 15.72; O, 5.99; S, 11.99. Found: C, 63.11; H, 3.47; N, 15.58; O, 5.97; S, 11.87. Pharmacological evaluation of novel 4,6‑substituted 2‑amino‑pyridin‑3‑carbonitriles Pharmacological evaluation was performed in a radioligand binding competition assay, using A1, A2A, A2B, and A3 human receptors expressed in transfected CHO (A1), HeLa (A2A and A3), and HEK-293 (A2B) according to the procedure reported by Bosch etal. [78]. The activity measurements against the phosphodiesterases PDE7A, PDE7B, PDE9A and PDE10A were performed using AD293 cells that were transiently and separately transfected with human PDE7A, PDE7B, PDE9A, and PDE10A following the procedure described by Shipe etal. [43]. The IC50 values were obtained by fitting the data with non-linear regression using Prism 2.1 software (GraphPad, San Diego, CA) [79], and the reported results are the mean of 3 experiments (n=3) each performed in duplicate. Abbreviations AR: adenosine receptor; A1R: A1 adenosine receptor; A2AR: A2A adenosine receptor; A2BR: A2B adenosine receptor; A3R: A3 adenosine receptor; PDE10A: cAMP and cAMP-inhibited cGMP 3′,5′-cyclic phosphodiesterase 10A; cAMP: cyclic adenosine monophosphate; PDE7A: high affinity cAMP-specific 3′,5′-cyclic phosphodiesterase 7A; PDE7B: cAMP-specific 3′,5′-cyclic phosphodiesterase 7B; PDE9A: high affinity cGMP-specific 3′,5′-cyclic phosphodiesterase 9A; PDGFR: platelet-derived growth factor receptor; VEGFR: vascular endothelial growth factor receptor; ABL1: tyrosine-protein kinase ABL1; SRC: proto-oncogene tyrosine-protein kinase Src; EGFR: epidermal growth factor receptor; HER2: receptor tyrosine-protein kinase erbB-2; MAO-A: amine oxidase [flavin-containing] A; MAO-B: amine oxidase [flavin-containing] B; AChE: acetylcholinesterase; BuChE: butyrylcholinesterase; H3-R: histamine H3 receptor; HMT: histone methyltransferases; hCB2R: human cannabinoid receptor 2; EtOH: ethanol. Authors’ contributions LK and CV contributed equally to the work. LK developed the computational workflow for designing multi-target ligands at A1R, A2AR, and PDE10A and wrote this manuscript. CV validated experimentally the designed ligands. FS contributed in the validation of the docking models used, AZ helped in implementing Mann–Whitney test and LM in applying the Chi square test. AB and ES conceived the main theme on which the work was performed and ensured that the scientific aspect of the study was rationally valid. RG revised and edited the manuscript. All authors read and approved the final manuscript. Author details 1 Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge CB21EW, UK. 2 Center for Research in Biological Chemistry and Molecular Materials (CIQUS), University of Santiago de Compostela, 15782 Santiago de Compostela, Spain. 3 Center for Research Additional files Additional file 1. Supplementary data describing substructural analysis of extracted ChEMBL compounds, statistical analysis of enriched target prediction of RECAP compounds, separation in medians of active/inactive docking score distributions for the docking models, computed logP and tPSA values and selectivity profiling data for compounds 1–25, docking parameters used, scripts for compound extraction from the ChEMBL database, computation of Mann–Whitney test and F1 scores. Additional file 2. Coordinates of the A1R homology model. Additional file 3. CSV file of computed F1 scores of the A1R docking model. Additional file 4. CSV file of computed F1 scores of the A2AR docking model. Additional file 5. CSV file of computed F1 scores of the PDE10A docking model. Page 17 of 19 Kalash et al. J Cheminform (2017) 9:67 in Molecular Medicine and Chronic Diseases (CIMUS), University of Santiago de Compostela, 15782 Santiago de Compostela, Spain. 4 IOTA Pharmaceuticals Ltd, St Johns Innovation Centre, Cowley Road, Cambridge CB40WS, UK. 5 Discovery Sciences, AstraZeneca R&D, Cambridge Science Park, Cambridge, UK. 6 Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB21QJ, UK. 7 Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, UK. Acknowledgements Dr. Hugo Gutiérrez-de-Terán is thanked for providing the A1R homology model for molecular docking studies. Competing interests The authors declared that they have no competing interests. Consent for publication Not applicable. Ethics approval and consent to participate Not applicable. Funding sources LK thanks the IDB Cambridge International Scholarship for support. This work was financially supported by the ERC Starting Grant to AB (No. 336159), the Consellería de Cultura, Educación e Ordenación Universitaria of the Galician Government: (Grant: GPC2014/03), Centro singular de investigación de Galicia accreditation 2016–2019 (ED431G/09) and the European Regional Development Fund (ERDF). Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Received: 20 June 2017 Accepted: 1 December 2017 References 1. Kovacs GG (2014) Current concepts of neurodegenrative diseases. Eur Med J 1:78–86 2. Lang AE, Obeso JA (2004) Personal view challenges in Parkinson’s disease: restoration of the nigrostriatal dopamine system is not enough. Lancet Neurol 3:309–316 3. Latini S, Pedata F (2001) Adenosine in the central nervous system: release mechanisms and extracellular concentrations. J Neurochem 79:463–484 4. 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