Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays.

G protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like the Chemical European Molecular Biology Laboratory (ChEMBL) database. This data is also very complex covering changes in drug chemical structure and assay conditions like c0 = activity parameter (Ki, IC50, etc.), c1 = target protein, c2 = cell line, c3 = assay organism, etc., difficulting the analysis of these data bases placed in the borders of a Big Data challenge. One of the aims of this work is to develop a computational model able to predict new GPCRs targeting drugs taking into consideration multiple conditions of assay. Another objective is to carry out new predictive and experimental studies of selective 5-HT2AR agonist, antagonist or inverse agonist in human comparing the results with those from the literature. In this work, we combined Perturbation Theory (PT) and Machine Learning (ML) to seek a general PTML model for this dataset. We analyzed 343,738 unique compounds with 812,072 endpoints (assay outcomes), with 185 different experimental parameters, 592 protein targets, 51 cell lines, and/or 55 organisms (species). The best PTML linear model found has three input variables only and predicted 56,202/58,653 positive outcomes (Sensitivity = 95.8%) and 470,230/550,401 control cases (Specificity = 85.4%) in training series. The model also predicted correctly 18,732/19,549 (95.8%) of positive outcomes and 156,739/183,469 (85.4%) of cases in external validation series. In order to illustrate its practical use, we used the model to predict the outcomes of six different 5-HT2A receptor drugs, TCB-2, DOI, DOB, altanserin, pimavanserin and nelotanserin, in a very large number of different pharmacological assays. 5-HT2A receptors are altered in schizophrenia and represent drug target for antipsychotic therapeutic activity. The model correctly predicted 93.83% (76 out of 86) experimental results for these compounds reported in ChEMBL. Moreover, [35S]GTPγS binding assays were carried out experimentally with the same six drugs with the aim of determining their potency and efficacy in the modulation of G-proteins in human brain tissue. The antagonist ketanserin was included as inactive drug with demonstrated affinity for 5-HT2A/C receptors. Our results demonstrate that some of these drugs, previously described as serotonin 5-HT2A receptor agonists, antagonists or inverse agonists, are not so specific and show different intrinsic activity to that previously reported. Overall, this work opens a new gate for the prediction of GPCRs targeting compounds.