Antitarget

Abstract: Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDArequired procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and wellcharacterized quantitative structure–activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83-0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg).

Abstract: G protein-coupled receptors (GPCRs) form a large protein family that plays an important role in many physiological and pathophysiological processes. However, the central role that the biogenic amine binding GPCRs and their ligands play in cell signaling poses a risk in new drug candidates that reveal side affinities towards these receptor sites. These candidates have the potential to interfere with the physiological signaling processes and to cause undesired effects in preclinical or clinical studies. Here, we present 3D cross-chemotype pharmacophore models for three biogenic amine antitargets: the alpha(1A) adrenergic, the 5-HT(2A) serotonin, and the D2 dopamine receptors. These pharmacophores describe the key chemical features present within these biogenic amine antagonists and rationalize the biogenic amine side affinities found for numerous new drug candidates. First applications of the alpha(1A) adrenergic receptor model reveal that these in silico tools can be used to guide the chemical optimization towards development candidates with fewer alpha(1A)-mediated side effects (for example, orthostatic hypotension) and, thus, with an improved clinical safety profile.

Abstract: Background: hERG K+ channels have been recognized as a primary antitarget in safety pharmacology. Their blockade, caused by several drugs with different therapeutic indications, may lead to QT prol...

Abstract: The evaluation of possible interactions between chemical compounds and antitarget proteins is an important task of research and development process. Here we describe the development and validation of QSAR models for the prediction of antitarget end-points, created on the basis of Multilevel and Quantitative Neighborhoods of Atoms descriptors and self-consistent regression. Data on 4000 chemical compounds interacting with 18 antitarget proteins (13 receptors, 2 enzymes and 3 transporters) were used to model thirty two sets of end-points (IC50, Ki and Kact). Each set was randomly divided into training and test sets in a ratio of 80% to 20%, respectively. The test sets were used for external validation of QSAR models created on the basis of the training sets. The coverage of prediction for all test sets exceeded 95% and for half of the test sets it was 100%. The accuracy of prediction for 29 of the end-points, based on the external test sets was typically in the range of R2test = 0.6–0.9; three tests sets had a lower R2test values, specifically 0.55 – 0.6. The proposed approach showed a reasonable accuracy of prediction for 91% of the antitarget end-points and high coverage for all external test sets. On the basis of the created models we have developed a freely available on-line service for in silico prediction of 32 antitarget end-points: http://www.pharmaexpert.ru/GUSAR/antitargets.html.