Tuning hERG Out: Antitarget QSAR Models for Drug Development

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Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death.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 FDA-required 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 well-characterized 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).

Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening

“…In addition, the most rigorous consensus model (consensus rigor) 46 was built by combining five individual models with more restrictive conditions. A consensus rigor model only considers the outcome to be reliable when a compound was inside the applicability domain (AD) for the five models.…”

“…In addition, we removed compounds with previous bioactivity data reported against Sm TGR or S. mansoni and pan-assay interference compounds (PAINs) 59,60 so that selected compounds would be novel Sm TGR inhibitors and contain no PAINs structures. Finally, the compounds were evaluated by predicting a panel of properties including high aqueous solubility (CIQPlogS), 61 acceptable binding to human serum albumin (QPlogKhsa), 61 acceptable brain/blood partition coefficient (QPlogBB), 61 nonblocking or weak blocking of hERG channel, 46,47 and absence of carcinogenicity and hepatotoxicity. 32 At the end of the VS workflow, 29 putative hits were visually inspected and acquired for biological evaluation (Supporting Information, Table S6).…”

“…82 All in silico steps developed in this study were implemented in a publicly available KSAR workflow (http://labmol.farmacia.ufg.br/ksar). The KSAR workflow is tightly integrated with R and KNIME and includes many modules such as the module for preparing the data, PCA, building of QSAR models, and VS. 46,83 We first retrieved 359841 compounds containing half-maximal inhibitory concentration (IC 50 ) data for the Sm TGR enzyme from the PubChem BioAssay database (AID: 485364). Compounds with inconclusive IC 50 results were considered experimental errors and thus were not included in this study to avoid noise in model building.…”

QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni

“…Five in-house highly-predictive models were developed using large datasets of diverse compounds to cover the chemical space for the prediction of new compounds and are described elsewhere. 53, 54 Table 4 shows the structure, consensus predicted potency against Sm TGR (IC 50 in µM), and some predicted ADME properties of the ten new potential Sm TGR inhibitors.…”

Effects of etifoxine: Chemoreactome simulation

“…Данный калиевый канал яв-ляется важным «антитаргетным» белком, взаимодействия с которым следует избегать при разработке лекарственных средств [25]. Нарушение активности KCNH2 приводит к формированию смертельно опасного «синдрома длинного QT», повышающего риск внезапной остановки сердца вследствие спонтанно развивающейся аритмии.…”