Exploring Potency and Selectivity Receptor Antagonist Profiles Using a Multilabel Classification Approach: The Human Adenosine Receptors as a Key Study

Pyrazolo-triazolo-pyrimidine Scaffold as a Molecular Passepartout for the Pan-Recognition of Human Adenosine Receptors

Adenosine receptors are largely distributed in our organism and are promising therapeutic targets for the treatment of many pathologies. In this perspective, investigating the structural features of the ligands leading to affinity and/or selectivity is of great interest. In this work, we have focused on a small series of pyrazolo-triazolo-pyrimidine antagonists substituted in positions 2, 5, and N8, where bulky acyl moieties at the N5 position and small alkyl groups at the N8 position are associated with affinity and selectivity at the A3 adenosine receptor even if a good affinity toward the A2B adenosine receptor has also been observed. Conversely, a free amino function at the 5 position induces high affinity at the A2A and A1 receptors with selectivity vs. the A3 subtype. A molecular modeling study suggests that differences in affinity toward A1, A2A, and A3 receptors could be ascribed to two residues: one in the EL2, E168 in human A2A/E172 in human A1, that is occupied by the hydrophobic residue V169 in the human A3 receptor; and the other in TM6, occupied by H250/H251 in human A2A and A1 receptors and by a less bulky S247 in the A3 receptor. In the end, these findings could help to design new subtype-selective adenosine receptor ligands.

Enhancing reaction-based de novo design using a multi-label reaction class recommender

Reaction-based de novo design refers to the in-silico generation of novel chemical structures by combining reagents using structural transformations derived from known reactions. The driver for using reaction-based transformations is to increase the likelihood of the designed molecules being synthetically accessible. We have previously described a reaction-based de novo design method based on reaction vectors which are transformation rules that are encoded automatically from reaction databases. A limitation of reaction vectors is that they account for structural changes that occur at the core of a reaction only, and they do not consider the presence of competing functionalities that can compromise the reaction outcome. Here, we present the development of a Reaction Class Recommender to enhance the reaction vector framework. The recommender is intended to be used as a filter on the reaction vectors that are applied during de novo design to reduce the combinatorial explosion of in-silico molecules produced while limiting the generated structures to those which are most likely to be synthesisable. The recommender has been validated using an external data set extracted from the recent medicinal chemistry literature and in two simulated de novo design experiments. Results suggest that the use of the recommender drastically reduces the number of solutions explored by the algorithm while preserving the chance of finding relevant solutions and increasing the global synthetic accessibility of the designed molecules.

Identification of new potent A1 adenosine receptor antagonists using a multistage virtual screening approach

The use of antagonists for each adenosine receptor (AR) subtype as potent clinical candidates is of growing interest due to their involvement in the treatment of various diseases. The recent resolution of several A₁ and A_2A ARs X-ray structures provides opportunities for structure-based drug design. In this study, we describe the discovery of novel A₁AR antagonists by applying a multistage virtual screening approach, which is based on random forest (RF), e-pharmacophore modeling and docking methods. A multistage virtual screening approach was applied to screen the ChemDiv library (1,492,362 compounds). Among the final hits, 22 compounds were selected for further radioligand binding assay analysis against human A₁AR, and 18 compounds (81.82% success) exhibited nanomolar or low micromolar binding potency (K_i). Then, we selected six compounds (pK_i > 6) to further evaluate their antagonist profile in a cAMP functional assay, and we found that they had low micromolar antagonistic activity (pIC₅₀ = 5.51-6.38) for the A₁AR. Particularly, four of six compounds (pK_i > 6) showed very good affinity (pK_i = 6.11-7.13) and selectively (>100-fold) for A₁AR over A_2AAR. Moreover, the novelty analysis suggested that four of six compounds (pK_i > 6) were dissimilar to existing A₁AR antagonists and hence represented novel A₁AR antagonists. Further molecular docking and molecular dynamics (MD) studies showed that the three selective compounds 15, 20 and 22 were stabilized (RMS_lig value ≤ 2 Å) inside the binding pocket of A₁AR with similar orientations to the docking pose in 100-ns MD simulations, whereas they escaped from the binding area of A_2AAR with larger values of RMS_lig (RMS_lig ≥ 2 Å). We hope that these findings provide new insights into the discovery of drugs targeting A₁AR and facilitate research on new drugs and treatments for A₁AR-related human pathologies.

Chemoinformatics Profiling of the Chromone Nucleus as a MAO-B/A2AAR Dual Binding Scaffold

Background:

In the context of the current drug discovery efforts to find disease modifying therapies for Parkinson´s disease (PD) the current single target strategy has proved inefficient. Consequently, the search for multi-potent agents is attracting more and more attention due to the multiple pathogenetic factors implicated in PD. Multiple evidences points to the dual inhibition of the monoamine oxidase B (MAO-B), as well as adenosine A2A receptor (A2AAR) blockade, as a promising approach to prevent the neurodegeneration involved in PD. Currently, only two chemical scaffolds has been proposed as potential dual MAO-B inhibitors/A2AAR antagonists (caffeine derivatives and benzothiazinones).

Methods:

In this study, we conduct a series of chemoinformatics analysis in order to evaluate and advance the potential of the chromone nucleus as a MAO-B/A2AAR dual binding scaffold.

Results:

The information provided by SAR data mining analysis based on network similarity graphs and molecular docking studies support the suitability of the chromone nucleus as a potential MAO-B/A2AAR dual binding scaffold. Additionally, a virtual screening tool based on a group fusion similarity search approach was developed for the prioritization of potential MAO-B/A2AAR dual binder candidates. Among several data fusion schemes evaluated, the MEAN-SIM and MIN-RANK GFSS approaches demonstrated to be efficient virtual screening tools. Then, a combinatorial library potentially enriched with MAO-B/A2AAR dual binding chromone derivatives was assembled and sorted by using the MIN-RANK and then the MEAN-SIM GFSS VS approaches.

Conclusion:

The information and tools provided in this work represent valuable decision making elements in the search of novel chromone derivatives with a favorable dual binding profile as MAO-B inhibitors and A2AAR antagonists with the potential to act as a disease-modifying therapeutic for Parkinson´s disease.

Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D)

Adenosine receptors (ARs) are potential therapeutic targets for Parkinson’s disease, diabetes, pain, stroke and cancers. Prediction of subtype selectivity is therefore important from both therapeutic and mechanistic perspectives. In this paper, we introduced a shape similarity profile as molecular descriptor, namely three-dimensional biologically relevant spectrum (BRS-3D), for AR selectivity prediction. Pairwise regression and discrimination models were built with the support vector machine methods. The average determination coefficient (r²) of the regression models was 0.664 (for test sets). The 2B-3 (A_2Bvs A₃) model performed best with q² = 0.769 for training sets (10-fold cross-validation), and r² = 0.766, RMSE = 0.828 for test sets. The models’ robustness and stability were validated with 100 times resampling and 500 times Y-randomization. We compared the performance of BRS-3D with 3D descriptors calculated by MOE. BRS-3D performed as good as, or better than, MOE 3D descriptors. The performances of the discrimination models were also encouraging, with average accuracy (ACC) 0.912 and MCC 0.792 (test set). The 2A-3 (A_2Avs A₃) selectivity discrimination model (ACC = 0.882 and MCC = 0.715 for test set) outperformed an earlier reported one (ACC = 0.784). These results demonstrated that, through multiple conformation encoding, BRS-3D can be used as an effective molecular descriptor for AR subtype selectivity prediction.

Drug/nondrug classification using Support Vector Machines with various feature selection strategies

In conjunction with the advance in computer technology, virtual screening of small molecules has been started to use in drug discovery. Since there are thousands of compounds in early-phase of drug discovery, a fast classification method, which can distinguish between active and inactive molecules, can be used for screening large compound collections. In this study, we used Support Vector Machines (SVM) for this type of classification task. SVM is a powerful classification tool that is becoming increasingly popular in various machine-learning applications. The data sets consist of 631 compounds for training set and 216 compounds for a separate test set. In data pre-processing step, the Pearson's correlation coefficient used as a filter to eliminate redundant features. After application of the correlation filter, a single SVM has been applied to this reduced data set. Moreover, we have investigated the performance of SVM with different feature selection strategies, including SVM-Recursive Feature Elimination, Wrapper Method and Subset Selection. All feature selection methods generally represent better performance than a single SVM while Subset Selection outperforms other feature selection methods. We have tested SVM as a classification tool in a real-life drug discovery problem and our results revealed that it could be a useful method for classification task in early-phase of drug discovery.

Classifier ensemble based on feature selection and diversity measures for predicting the affinity of A(2B) adenosine receptor antagonists

A(2B) adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A(2B) adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The k-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A(2B) adenosine receptor antagonists, and it can be used to develop other QSAR models.

Revisiting a receptor-based pharmacophore hypothesis for human A(2A) adenosine receptor antagonists

The application of both structure- and ligand-based design approaches represents to date one of the most useful strategies in the discovery of new drug candidates. In the present paper, we investigated how the application of docking-driven conformational analysis can improve the predictive ability of 3D-QSAR statistical models. With the use of the crystallographic structure in complex with the high affinity antagonist ZM 241385 (4-(2-[7-amino-2-(2-furyl)[1,2,4]-triazolo[2,3-a][1,3,5]triazin-5-ylamino]ethyl)phenol), we revisited a general pharmacophore hypothesis for the human A(2A) adenosine receptor of a set of 751 known antagonists, by applying an integrated ligand- and structure-based approach. Our novel pharmacophore hypothesis has been validated by using an external test set of 29 newly synthesized human adenosine receptor antagonists.

The A3 adenosine receptor as multifaceted therapeutic target: pharmacology, medicinal chemistry, and in silico approaches

Adenosine is an ubiquitous local modulator that regulates various physiological and pathological functions by stimulating four membrane receptors, namely A(1), A(2A), A(2B), and A(3). Among these G protein-coupled receptors, the A(3) subtype is found mainly in the lung, liver, heart, eyes, and brain in our body. It has been associated with cerebroprotection and cardioprotection, as well as modulation of cellular growth upon its selective activation. On the other hand, its inhibition by selective antagonists has been reported to be potentially useful in the treatment of pathological conditions including glaucoma, inflammatory diseases, and cancer. In this review, we focused on the pharmacology and the therapeutic implications of the human (h)A(3) adenosine receptor (AR), together with an overview on the progress of hA(3) AR agonists, antagonists, allosteric modulators, and radioligands, as well as on the recent advances pertaining to the computational approaches (e.g., quantitative structure-activity relationships, homology modeling, molecular docking, and molecular dynamics simulations) applied to the modeling of hA(3) AR and drug design.