Putative role of the adenosine A3 receptor in the antiproliferative action of N6-(2-isopentenyl)adenosine

We tested a panel of naturally occurring nucleosides for their affinity towards adenosine receptors. Both N6-(2-isopentenyl)adenosine (IPA) and racemic zeatin riboside were shown to be selective human adenosine A3 receptor (hA3R) ligands with affinities in the high nanomolar range (Ki values of 159 and 649 nM, respectively). These values were comparable to the observed Ki value of adenosine on hA3R, which was 847 nM in the same radioligand binding assay. IPA also bound with micromolar affinity to the rat A3R. In a functional assay in Chinese hamster ovary cells transfected with hA3R, IPA and zeatin riboside inhibited forskolin-induced cAMP formation at micromolar potencies. The effect of IPA could be blocked by the A3R antagonist VUF5574. Both IPA and reference A3R agonist 2-chloro-N6-(3-iodobenzyl)adenosine-5′-N-methylcarboxamide (Cl-IB-MECA) have known antitumor effects. We demonstrated strong and highly similar antiproliferative effects of IPA and Cl-IB-MECA on human and rat tumor cell lines LNCaP and N1S1. Importantly, the antiproliferative effect of low concentrations of IPA on LNCaP cells could be fully blocked by the selective A3R antagonist MRS1523. At higher concentrations, IPA appeared to inhibit cell growth by an A3R-independent mechanism, as was previously reported for other A3R agonists. We used HPLC to investigate the presence of endogenous IPA in rat muscle tissue, but we could not detect the compound. In conclusion, the antiproliferative effects of the naturally occurring nucleoside IPA are at least in part mediated by the A3R

The endocannabinoid 2-arachidonylglycerol is a negative allosteric modulator of the human A3 adenosine receptor

Studies of endogenous cannabinoid agonists, such as 2-arachidonylglycerol (2-AG), have revealed their potential to exert modulatory actions on other receptor systems in addition to their ability to activate cannabinoid receptors. This study investigated the effect of cannabinoid ligands on the human adenosine A(3) (hA(3)R) receptor. The endocannabinoid 2-AG was able to inhibit agonist ([125I]N(6)-(4-amino-3-iodobenzyl) adenosine-5'-(N-methyluronamide)--[125I] AB MECA) binding at the hA(3)R. This inhibition occurred over a narrow range of ligand concentration and was characterized by high Hill coefficients suggesting a non-competitive interaction. Furthermore, in the presence of 2-AG, the rate of [125I] AB MECA dissociation was increased, consistent with an action as a negative allosteric modulator of the hA(3)R. Moreover, by measuring intracellular cAMP levels, we demonstrate that 2-AG decreases both the potency of an agonist at the hA(3)R and the basal signalling of this receptor. Since the hA(3)R has been shown to be expressed in astrocytes and microglia, these findings may be particularly relevant in certain pathological states such as cerebral ischemia where levels of 2-AG and anandamide are raised

Phenotypic screening of cannabinoid receptor 2 ligands shows different sensitivity to genotype

The Cannabinoid Receptor 2 (CB2R) is a G protein-coupled receptor (GPCR) investigated intensively as therapeutic target, however no drug has reached the market yet. We investigated personal differences in CB2R drug responses using a label-free whole-cell assay (xCELLigence) combined with cell lines (Lymphoblastoid Cell Lines) from individuals with varying CB2R genotypes. Responses to agonists, partial agonists and antagonists of various chemical classes were characterized. Endogenous cannabinoids such as 2-AG induced cellular effects vastly different from all synthetic cannabinoids, especially in their time-profile. Secondly, the Q63R polymorphism affected CB2R responses in general. Agonists and especially partial agonists showed higher efficacy in a Q63R minor homozygote versus other genotypes. Non-classical cannabinoid CP55940 showed the most pronounced personal effects with highly reduced potency and efficacy in this genotype. Contrarily, aminoalkylindole compounds showed less individual differences. In conclusion, a label-free whole-cell assay combined with personal cell lines is a promising vehicle to investigate personal differences in drug response originating from genetic variation in GPCRs. Such phenotypic screening allows early identification of compounds prone to personal differences ('precision medicine') or more suited as drugs for the general population

Multi-Objective Evolutionary Design of Adenosine Receptor Ligands

A novel multiobjective evolutionary algorithm (MOEA) for <i>de novo</i> design was developed and applied to the discovery of new adenosine receptor antagonists. This method consists of several iterative cycles of structure generation, evaluation, and selection. We applied an evolutionary algorithm (the so-called Molecule Commander) to generate candidate A₁ adenosine receptor antagonists, which were evaluated against multiple criteria and objectives consisting of high (predicted) affinity and selectivity for the receptor, together with good ADMET properties. A pharmacophore model for the human A₁ adenosine receptor (hA₁AR) was created to serve as an objective function for evolution. In addition, three support vector machine models based on molecular fingerprints were developed for the other adenosine receptor subtypes (hA_2A, hA_2B, and hA₃) and applied as negative objective functions, to aim for selectivity. Structures with a higher evolutionary fitness with respect to ADMET and pharmacophore matching scores were selected as input for the next generation and thus developed toward overall fitter (“better”) compounds. We finally obtained a collection of 3946 unique compounds from which we derived chemical scaffolds. As a proof-of-principle, six of these templates were selected for actual synthesis and subsequently tested for activity toward all adenosine receptors subtypes. Interestingly, scaffolds <b>2</b> and <b>3</b> displayed low micromolar affinity for many of the adenosine receptor subtypes. To further investigate our evolutionary design method, we performed systematic modifications on scaffold <b>3</b>. These modifications were guided by the substitution patterns as observed in the set of generated compounds that contained scaffold <b>3</b>. We found that an increased affinity with appreciable selectivity for hA₁AR over the other adenosine receptor subtypes was achieved through substitution of the scaffold; compound <b>3a</b> had a <i>K</i>_<i>i</i> value of 280 nM with approximately 10-fold selectivity with respect to hA_2AR, while <b>3g</b> had a 1.6 μM affinity for hA₁AR with negligible affinity for the hA_2A, hA_2B, and hA₃ receptor subtypes

Identifying Novel Adenosine Receptor Ligands by Simultaneous Proteochemometric Modeling of Rat and Human Bioactivity Data

The four subtypes of adenosine receptors form relevant drug targets in the treatment of, e.g., diabetes and Parkinson’s disease. In the present study, we aimed at finding novel small molecule ligands for these receptors using virtual screening approaches based on proteochemometric (PCM) modeling. We combined bioactivity data from all human and rat receptors in order to widen available chemical space. After training and validating a proteochemometric model on this combined data set (<i>Q</i>² of 0.73, RMSE of 0.61), we virtually screened a vendor database of 100910 compounds. Of 54 compounds purchased, six novel high affinity adenosine receptor ligands were confirmed experimentally, one of which displayed an affinity of 7 nM on the human adenosine A₁ receptor. We conclude that the combination of rat and human data performs better than human data only. Furthermore, we conclude that proteochemometric modeling is an efficient method to quickly screen for novel bioactive compounds

Identifying Novel Adenosine Receptor Ligands by Simultaneous Proteochemometric Modeling of Rat and Human Bioactivity Data

The four subtypes of adenosine receptors form relevant drug targets in the treatment of, e.g., diabetes and Parkinson’s disease. In the present study, we aimed at finding novel small molecule ligands for these receptors using virtual screening approaches based on proteochemometric (PCM) modeling. We combined bioactivity data from all human and rat receptors in order to widen available chemical space. After training and validating a proteochemometric model on this combined data set (<i>Q</i>² of 0.73, RMSE of 0.61), we virtually screened a vendor database of 100910 compounds. Of 54 compounds purchased, six novel high affinity adenosine receptor ligands were confirmed experimentally, one of which displayed an affinity of 7 nM on the human adenosine A₁ receptor. We conclude that the combination of rat and human data performs better than human data only. Furthermore, we conclude that proteochemometric modeling is an efficient method to quickly screen for novel bioactive compounds

Identifying Novel Adenosine Receptor Ligands by Simultaneous Proteochemometric Modeling of Rat and Human Bioactivity Data

The four subtypes of adenosine receptors form relevant drug targets in the treatment of, e.g., diabetes and Parkinson’s disease. In the present study, we aimed at finding novel small molecule ligands for these receptors using virtual screening approaches based on proteochemometric (PCM) modeling. We combined bioactivity data from all human and rat receptors in order to widen available chemical space. After training and validating a proteochemometric model on this combined data set (<i>Q</i>² of 0.73, RMSE of 0.61), we virtually screened a vendor database of 100910 compounds. Of 54 compounds purchased, six novel high affinity adenosine receptor ligands were confirmed experimentally, one of which displayed an affinity of 7 nM on the human adenosine A₁ receptor. We conclude that the combination of rat and human data performs better than human data only. Furthermore, we conclude that proteochemometric modeling is an efficient method to quickly screen for novel bioactive compounds

Identifying Novel Adenosine Receptor Ligands by Simultaneous Proteochemometric Modeling of Rat and Human Bioactivity Data

The four subtypes of adenosine receptors form relevant drug targets in the treatment of, e.g., diabetes and Parkinson’s disease. In the present study, we aimed at finding novel small molecule ligands for these receptors using virtual screening approaches based on proteochemometric (PCM) modeling. We combined bioactivity data from all human and rat receptors in order to widen available chemical space. After training and validating a proteochemometric model on this combined data set (<i>Q</i>² of 0.73, RMSE of 0.61), we virtually screened a vendor database of 100910 compounds. Of 54 compounds purchased, six novel high affinity adenosine receptor ligands were confirmed experimentally, one of which displayed an affinity of 7 nM on the human adenosine A₁ receptor. We conclude that the combination of rat and human data performs better than human data only. Furthermore, we conclude that proteochemometric modeling is an efficient method to quickly screen for novel bioactive compounds

Discovery and Kinetic Profiling of 7‑Aryl-1,2,4-triazolo[4,3‑<i>a</i>]­pyridines: Positive Allosteric Modulators of the Metabotropic Glutamate Receptor 2

We report the synthesis and biological evaluation of a series of 7-aryl-1,2,4-triazolo­[4,3-<i>a</i>]­pyridines with mGlu₂ positive allosteric modulator (PAM) activity and affinity. Besides traditional in vitro parameters of potency and affinity, kinetic parameters <i>k</i>_on, <i>k</i>_off and residence time (RT) were determined. The PAMs showed various kinetic profiles; <i>k</i>_on values ranged over 2 orders of magnitude, whereas RT values were within a 10-fold range. Association rate constant <i>k</i>_on was linearly correlated to affinity. Evaluation of a short, medium, and long RT compound in a label-free assay indicated a correlation between RT and functional effect. The effects of long RT compound <b>9</b> on sleep–wake states indicated long RT was translated into sustained inhibition of rapid eye movement (REM) in vivo. These results show that affinity-only driven selection would have resulted in mGlu₂ PAMs with high values for <i>k</i>_on but not necessarily optimized RT, which is key to predicting optimal efficacy in vivo