Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML).

This work describes the synthesis and pharmacological evaluation of 2-furoyl-based Melanostatin (MIF-1) peptidomimetics as dopamine D2 modulating agents. Eight novel peptidomimetics were tested for their ability to enhance the maximal effect of tritiated N-propylapomorphine ([3H]-NPA) at D2 receptors (D2R). In this series, 2-furoyl-l-leucylglycinamide (6a) produced a statistically significant increase in the maximal [3H]-NPA response at 10 pM (11 ± 1%), comparable to the effect of MIF-1 (18 ± 9%) at the same concentration. This result supports previous evidence that the replacement of proline residue by heteroaromatic scaffolds are tolerated at the allosteric binding site of MIF-1. Biological assays performed for peptidomimetic 6a using cortex neurons from 19-day-old Wistar-Kyoto rat embryos suggest that 6a displays no neurotoxicity up to 100 µM. Overall, the pharmacological and toxicological profile and the structural simplicity of 6a makes this peptidomimetic a potential lead compound for further development and optimization, paving the way for the development of novel modulating agents of D2R suitable for the treatment of CNS-related diseases. Additionally, the pharmacological and biological data herein reported, along with >20 000 outcomes of preclinical assays, was used to seek a general model to predict the allosteric modulatory potential of molecular candidates for a myriad of target receptors, organisms, cell lines, and biological activity parameters based on perturbation theory (PT) ideas and machine learning (ML) techniques, abbreviated as ALLOPTML. By doing so, ALLOPTML shows high specificity Sp = 89.2/89.4%, sensitivity Sn = 71.3/72.2%, and accuracy Ac = 86.1%/86.4% in training/validation series, respectively. To the best of our knowledge, ALLOPTML is the first general-purpose chemoinformatic tool using a PTML-based model for the multioutput and multicondition prediction of allosteric compounds, which is expected to save both time and resources during the early drug discovery of allosteric modulators.

3,4-Dihydropyrimidin-2(1H)-ones as Antagonists of the Human A2B Adenosine Receptor: Optimization, Structure-Activity Relationship Studies, and Enantiospecific Recognition.

We present and thoroughly characterize a large collection of 3,4-dihydropyrimidin-2(1H)-ones as A2BAR antagonists, an emerging strategy in cancer (immuno) therapy. Most compounds selectively bind A2BAR, with a number of potent and selective antagonists further confirmed by functional cyclic adenosine monophosphate experiments. The series was analyzed with one of the most exhaustive free energy perturbation studies on a GPCR, obtaining an accurate model of the structure-activity relationship of this chemotype. The stereospecific binding modeled for this scaffold was confirmed by resolving the two most potent ligands [(±)-47, and (±)-38 Ki = 10.20 and 23.6 nM, respectively] into their two enantiomers, isolating the affinity on the corresponding (S)-eutomers (Ki = 6.30 and 11.10 nM, respectively). The assessment of the effect in representative cytochromes (CYP3A4 and CYP2D6) demonstrated insignificant inhibitory activity, while in vitro experiments in three prostate cancer cells demonstrated that this pair of compounds exhibits a pronounced antimetastatic effect.

Nitrogen-Walk Approach to Explore Bioisosteric Replacements in a Series of Potent A2B Adenosine Receptor Antagonists.

A systematic exploration of bioisosteric replacements for furan and thiophene cores in a series of potent A2BAR antagonists has been carried out using the nitrogen-walk approach. A collection of 42 novel alkyl 4-substituted-2-methyl-1,4-dihydrobenzo[4,5]imidazo[1,2-a]pyrimidine-3-carboxylates, which contain 18 different pentagonal heterocyclic frameworks at position 4, was synthesized and evaluated. This study enabled the identification of new ligands that combine remarkable affinity (Ki < 30 nM) and exquisite selectivity. The structure-activity relationship (SAR) trends identified were substantiated by a molecular modeling study, based on a receptor-driven docking model and including a systematic free energy perturbation (FEP) study. Preliminary evaluation of the CYP3A4 and CYP2D6 inhibitory activity in optimized ligands evidenced weak and negligible activity, respectively. The stereospecific interaction between hA2BAR and the eutomer of the most attractive novel antagonist (S)-18g (Ki = 3.66 nM) was validated.

Trifluorinated Pyrimidine-Based A2B Antagonists: Optimization and Evidence of Stereospecific Recognition.

We report the identification of two subsets of fluorinated nonxanthine A2B adenosine receptor antagonists. The novel derivatives explore the effect of fluorination at different positions of two pyrimidine-based scaffolds. The most interesting ligands combine excellent hA2B affinity (Ki < 15 nM) and remarkable subtype selectivity. The results of functional cAMP experiments confirmed the antagonistic behavior of representative ligands. The compounds were designed on the basis of previous molecular models of the stereoselective binding of the parent scaffolds to the hA2B receptor, and we herein provide refinement of such models with the fluorinated compounds, which allows the explanation of the spurious effects of the fluorination at the different positions explored. These models are importantly confirmed by a synergistic study combining chiral HPLC, circular dichroism, diastereoselective synthesis, molecular modeling, and X-ray crystallography, providing experimental evidence toward the stereospecific interaction between optimized trifluorinated stereoisomers and the hA2B receptor.

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics.

Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70-91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC50, EC50, Ki, etc.) for compounds in assays involving different cell lines used, organisms of the protein target, or organism of assay for proteins in the dopamine pathway.

Effect of Nitrogen Atom Substitution in A3 Adenosine Receptor Binding: N-(4,6-Diarylpyridin-2-yl)acetamides as Potent and Selective Antagonists.

We report the first family of 2-acetamidopyridines as potent and selective A3 adenosine receptor (AR) antagonists. The computer-assisted design was focused on the bioisosteric replacement of the N1 atom by a CH group in a previous series of diarylpyrimidines. Some of the generated 2-acetamidopyridines elicit an antagonistic effect with excellent affinity (Ki < 10 nM) and outstanding selectivity profiles, providing an alternative and simpler chemical scaffold to the parent series of diarylpyrimidines. In addition, using molecular dynamics and free energy perturbation simulations, we elucidate the effect of the second nitrogen of the parent diarylpyrimidines, which is revealed as a stabilizer of a water network in the binding site. The discovery of 2,6-diaryl-2-acetamidopyridines represents a step forward in the search of chemically simple, potent, and selective antagonists for the hA3AR, and exemplifies the benefits of a joint theoretical-experimental approach to identify novel hA3AR antagonists through succinct and efficient synthetic methodologies.

Multi-Target Mining of Alzheimer Disease Proteome with Hansch's QSBR-Perturbation Theory and Experimental-Theoretic Study of New Thiophene Isosters of Rasagiline.

Hansch's model is a classic approach to Quantitative Structure-Binding Relationships (QSBR) problems in Pharmacology and Medicinal Chemistry. Hansch QSAR equations are used as input parameters of electronic structure and lipophilicity. In this work, we perform a review on Hansch's analysis. We also developed a new type of PT-QSBR Hansch's model based on Perturbation Theory (PT) and QSBR approach for a large number of drugs reported in CheMBL. The targets are proteins expressed by the Hippocampus region of the brain of Alzheimer Disease (AD) patients. The model predicted correctly 49312 out of 53783 negative perturbations (Specificity = 91.7%) and 16197 out of 21245 positive perturbations (Sensitivity = 76.2%) in training series. The model also predicted correctly 49312/53783 (91.7%) and 16197/21245 (76.2%) negative or positive perturbations in external validation series. We applied our model in theoretical-experimental studies of organic synthesis, pharmacological assay, and prediction of unmeasured results for a series of compounds similar to Rasagiline (compound of reference) with potential neuroprotection effect.

Novel l-prolyl-l-leucylglycinamide (PLG) tripeptidomimetics based on a 2-azanorbornane scaffold as positive allosteric modulators of the D2R.

An efficient and straightforward orthogonal methodology was successfully developed to achieve constrained l-prolyl-l-leucylglycinamide (PLG) analogues starting from two proline mimetics based on a 2-azanorbornane scaffold. A preliminary dopamine D2 receptor radiolabeled binding assay with [3H]-N-propylnorapomorphine shows that enantiopurity of PLG peptidomimetics based on 2-azanorbornane is a requirement to achieve statistically significant positive modulators of the D2 receptor. This is the first documented active peptidomimetic of PLG whose bioactivity is not correlated with the C-terminal carboxamide pharmacophore and which cannot adopt the hypothesized type II ß-turn conformation.

Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives.

The use of Cheminformatics tools is gaining importance in the field of translational research from Medicinal Chemistry to Neuropharmacology. In particular, we need it for the analysis of chemical information on large datasets of bioactive compounds. These compounds form large multi-target complex networks (drug-target interactome network) resulting in a very challenging data analysis problem. Artificial Neural Network (ANN) algorithms may help us predict the interactions of drugs and targets in CNS interactome. In this work, we trained different ANN models able to predict a large number of drug-target interactions. These models predict a dataset of thousands of interactions of central nervous system (CNS) drugs characterized by > 30 different experimental measures in >400 different experimental protocols for >150 molecular and cellular targets present in 11 different organisms (including human). The model was able to classify cases of non-interacting vs. interacting drug-target pairs with satisfactory performance. A second aim focus on two main directions: the synthesis and assay of new derivatives of TVP1022 (S-analogues of rasagiline) and the comparison with other rasagiline derivatives recently reported. Finally, we used the best of our models to predict drug-target interactions for the best new synthesized compound against a large number of CNS protein targets.

Pyrazin-2(1H)-ones as a novel class of selective A3 adenosine receptor antagonists.

BACKGROUND: A3AR antagonists are promising drug candidates as neuroprotective agents as well as for the treatment of inflammation or glaucoma. The most widely known A3AR antagonists are derived from polyheteroaromatic scaffolds, which usually show poor pharmacokinetic properties. Accordingly, the identification of structurally simple A3AR antagonists by the exploration of novel diversity spaces is a challenging goal. RESULTS: A convergent and efficient Ugi-based multicomponent approach enabled the discovery of pyrazin-2(1H)-ones as a novel class of A3AR antagonists. A combined experimental/computational strategy accelerated the establishment of the most salient features of the structure-activity and structure-selectivity relationships in this series. CONCLUSION: The optimization process provided pyrazin-2(1H)-ones with improved affinity and a plausible hypothesis regarding their binding modes was proposed.

Synthesis by microwave-assisted 1,3-dipolar cycloaddition of 1,2,3-triazole 1'-homo-3'-isoazanucleosides and evaluation of their anticancer activity.

Racemic 1'-homo-3'-isoazanucleosides have been obtained by microwave-assisted 1,3-dipolar cycloaddition of 3,5-disubstituted proline derivative (±)-2 with different alkynes. The compounds obtained were evaluated for their cytotoxic activities in vitro against human breast carcinoma cell lines (MCF-7), human ovary carcinoma cell lines (A2780) and human lung carcinoma cell lines (NCI-H460).

Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates.

In a multi-target complex network, the links (L(ij)) represent the interactions between the drug (d(i)) and the target (t(j)), characterized by different experimental measures (K(i), K(m), IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (c(j)). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%-90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.

Synthesis and allosteric modulation of the dopamine receptor by peptide analogs of L-prolyl-L-leucyl-glycinamide (PLG) modified in the L-proline or L-proline and L-leucine scaffolds.

Novel analogs of L-prolyl-L-leucylglycinamide (PLG) were synthesized wherein the prolyl residue was replaced with other amino acids based on a 3,5-disubstituted proline scaffold. In some examples, the L-leucyl residue was also replaced by L-valine. These analogs were tested for their ability to enhance the binding of [(3)H]-N-propylnorapomorphine to short isoform of human dopamine D2 receptors. Compounds 18b and 19b, increased [(3)H] NPA binding at concentrations between 10(-12) and 10(-9) M, which is similar to the effect of PLG in this assay and, provides evidences that these compounds are acting as allosteric modulators of dopamine D2 receptors.

Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates.

The disappointing results obtained in recent clinical trials renew the interest in experimental/computational techniques for the discovery of neuroprotective drugs. In this context, multitarget or multiplexing QSAR models (mt-QSAR/mx-QSAR) may help to predict neurotoxicity/neuroprotective effects of drugs in multiple assays, on drug targets, and in model organisms. In this work, we study a data set downloaded from CHEMBL; each data point (>8000) contains the values of one out of 37 possible measures of activity, 493 assays, 169 molecular or cellular targets, and 11 different organisms (including human) for a given compound. In this work, we introduce the first mx-QSAR model for neurotoxicity/neuroprotective effects of drugs based on the MARCH-INSIDE (MI) method. First, we used MI to calculate the stochastic spectral moments (structural descriptors) of all compounds. Next, we found a model that classified correctly 2955 out of 3548 total cases in the training and validation series with Accuracy, Sensitivity, and Specificity values>80%. The model also showed excellent results in Computational-Chemistry simulations of High-Throughput Screening (CCHTS) experiments, with accuracy=90.6% for 4671 positive cases. Next, we reported the synthesis, characterization, and experimental assays of new rasagiline derivatives. We carried out three different experimental tests: assay (1) in the absence of neurotoxic agents, assay (2) in the presence of glutamate, and assay (3) in the presence of H2O2. Compounds 11 with 27.4%, 8 with 11.6%, and 9 with 15.4% showed the highest neuroprotective effects in assays (1), (2), and (3), respectively. After that, we used the mx-QSAR model to carry out a CCHTS of the new compounds in >400 unique pharmacological tests not carried out experimentally. Consequently, this model may become a promising auxiliary tool for the discovery of new drugs for the treatment of neurodegenerative diseases.

Copper-catalyzed Huisgen 1,3-dipolar cycloaddition under oxidative conditions: polymer-assisted assembly of 4-acyl-1-substituted-1,2,3-triazoles.

We herein document the first example of a reliable copper-catalyzed Huisgen 1,3-dipolar cycloaddition under oxidative conditions. The combined use of two polymer-supported reagents (polystyrene-1,5,7-triazabicyclo[4,4,0]dec-5-ene/Cu and polystyrene-2-iodoxybenzamide) overcomes the thermodynamic instability of copper(I) species toward oxidation, enabling the reliable Cu-catalyzed Huisgen 1,3-dipolar cycloadditions in the presence of an oxidant agent. This polymer-assisted pathway, not feasible under conventional homogeneous conditions, provides a direct assembly of 4-acyl-1-substituted-1,2,3-triazoles, contributing to expand the reliability and scope of Cu(I)-catalyzed alkyne-azide cycloaddition.

TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases.

The interest on computational techniques for the discovery of neuroprotective drugs has increased due to recent fail of important clinical trials. In fact, there is a huge amount of data accumulated in public databases like CHEMBL with respect to structurally heterogeneous series of drugs, multiple assays, drug targets, and model organisms. However, there are no reports of multi-target or multiplexing Quantitative Structure-Property Relationships (mt-QSAR/mx-QSAR) models of these multiplexing assay outcomes reported in CHEMBL for neurotoxicity/neuroprotective effects of drugs. Accordingly, in this paper we develop the first mx-QSAR model for multiplexing assays of neurotoxicity/neuroprotective effects of drugs. We used the method TOPS-MODE to calculate the structural parameters of drugs. The best model found correctly classified 4393 out of 4915 total cases in both training and validation. This is representative of overall train and validation Accuracy, Sensitivity, and Specificity values near to 90%, 98%, and 80%, respectively. This dataset includes multiplexing assay endpoints of 2217 compounds. Every one compound was assayed in at least one out of 338 assays, which involved 148 molecular or cellular targets and 35 standard type measures in 11 model organisms (including human). The second aim of this work is the exemplification of the use of the new mx-QSAR model with a practical case of study. To this end, we obtained again by organic synthesis and reported, by the first time, experimental assays of the new 1,3-rasagiline derivatives 3 different tests: assay (1) in absence of neurotoxic agents, (2) in the presence of glutamate, and (3) in the presence of H2O2. The higher neuroprotective effects found for each one of these assays were for the stereoisomers of compound 7: compound 7b with protection=23.4% in assay (1) and protection=15.2% in assay (2); and for compound 7a with protection=46.2% in assay (3). Interestingly, almost all compounds show protection values >10% in assay (3) but not in the other 2 assays. After that, we used the mx-QSAR model to predict the more probable response of the new compounds in 559 unique pharmacological tests not carried out experimentally. The results obtained are very significant because they complement the pharmacological studies of these promising rasagiline derivatives. This work paves the way for further developments in the multi-target/multiplexing screening of large libraries of compounds potentially useful in the treatment of neurodegenerative diseases.

3D MI-DRAGON: new model for the reconstruction of US FDA drug- target network and theoretical-experimental studies of inhibitors of rasagiline derivatives for AChE.

The number of neurodegenerative diseases has been increasing in recent years. Many of the drug candidates to be used in the treatment of neurodegenerative diseases present specific 3D structural features. An important protein in this sense is the acetylcholinesterase (AChE), which is the target of many Alzheimer's dementia drugs. Consequently, the prediction of Drug-Protein Interactions (DPIs/nDPIs) between new drug candidates and specific 3D structure and targets is of major importance. To this end, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out a rational DPIs prediction. Unfortunately, many previous QSAR models developed to predict DPIs take into consideration only 2D structural information and codify the activity against only one target. To solve this problem we can develop some 3D multi-target QSAR (3D mt-QSAR) models. In this study, using the 3D MI-DRAGON technique, we have introduced a new predictor for DPIs based on two different well-known software. We have used the MARCH-INSIDE (MI) and DRAGON software to calculate 3D structural parameters for drugs and targets respectively. Both classes of 3D parameters were used as input to train Artificial Neuronal Network (ANN) algorithms using as benchmark dataset the complex network (CN) made up of all DPIs between US FDA approved drugs and their targets. The entire dataset was downloaded from the DrugBank database. The best 3D mt-QSAR predictor found was an ANN of Multi-Layer Perceptron-type (MLP) with profile MLP 37:37-24-1:1. This MLP classifies correctly 274 out of 321 DPIs (Sensitivity = 85.35%) and 1041 out of 1190 nDPIs (Specificity = 87.48%), corresponding to training Accuracy = 87.03%. We have validated the model with external predicting series with Sensitivity = 84.16% (542/644 DPIs; Specificity = 87.51% (2039/2330 nDPIs) and Accuracy = 86.78%. The new CNs of DPIs reconstructed from US FDA can be used to explore large DPI databases in order to discover both new drugs and/or targets. We have carried out some theoretical-experimental studies to illustrate the practical use of 3D MI-DRAGON. First, we have reported the prediction and pharmacological assay of 22 different rasagiline derivatives with possible AChE inhibitory activity. In this work, we have reviewed different computational studies on Drug- Protein models. First, we have reviewed 10 studies on DP computational models. Next, we have reviewed 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking with different compounds to find Drug-Protein QSAR models. Last, we have developped a 3D multi-target QSAR (3D mt-QSAR) models for the prediction of the activity of new compounds against different targets or the discovery of new targets.

Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica.

There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets like proteins. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately, most QSAR models predict activity against only one protein. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 32:32-15-1:1. This MLP classifies correctly 623 out of 678 DTPs (Sensitivity = 91.89%) and 2995 out of 3234 nDTPs (Specificity = 92.61%), corresponding to training Accuracy = 92.48%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 313 out of 338 DTPs (Sensitivity = 92.60%) and 1411 out of 1534 nDTP (Specificity = 91.98%) in validation series, corresponding to total Accuracy = 92.09% for validation series (Predictability). This model favorably compares with other LDA and ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. These mt-QSARs offer also a good opportunity to construct drug-protein Complex Networks (CNs) that can be used to explore large and complex drug-protein receptors databases. Finally, we illustrated two practical uses of this model with two different experiments. In experiment 1, we report prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of 10 rasagiline derivatives promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, SEC and 1DE sample preparation, MALDI-TOF MS and MS/MS analysis, MASCOT search, MM/MD 3D structure modeling, and QSAR prediction for different peptides of hemoglobin found in the proteome of the human parasite Fasciola hepatica; which is promising for anti-parasite drug targets discovery.

Silica-supported aluminum chloride-assisted solution phase synthesis of pyridazinone-based antiplatelet agents.

A solution phase protocol that enabled the synthesis of three diverse libraries of pyridazin-3-ones incorporating α,ß-unsaturated moieties at position 5 of the heterocyclic core has been developed using silica-supported aluminum trichloride as a heterogeneous and reusable catalyst. This robust procedure has facilitated the hit to lead process for these series of compounds and allowed the identification of new potent derivatives that elicit antiplatelet activity in the low micromolar range.

Pyrimidine derivatives as potent and selective A3 adenosine receptor antagonists.

Two regioisomeric series of diaryl 2- or 4-amidopyrimidines have been synthesized and their adenosine receptor affinities were determined in radioligand binding assays at the four human adenosine receptors (hARs). Some of the ligands prepared herein exhibit remarkable affinities (K(i) < 10 nm) and, most noticeably, the absence of activity at the A(1), A(2A), and A(2B) receptors. The structural determinants that support the affinity and selectivity profiles of the series were highlighted through an integrated computational approach, combining a 3D-QSAR model built on the second generation of GRid INdependent Descriptors (GRIND2) with a novel homology model of the hA(3) receptor. The robustness of the computational model was subsequently evaluated by the design of new derivatives exploring the alkyl substituent of the exocyclic amide group. The synthesis and evaluation of the novel compounds validated the predictive power of the model, exhibiting excellent agreement between predicted and experimental activities.