Binding studies and GRIND/ALMOND-based 3D QSAR analysis of benzothiazine type K(ATP)-channel openers

Upgrading furanic platforms to α-enaminones: tunable continuous flow hydrogenation of bio-based cyclopentenones

Continuous flow hydrogenation of trans-4,5-diamino cyclopentenones to diamino cyclopentanones and base-promoted elimination originates α-enaminones. The strategy is applied to the short synthesis of reported ATP-sensitive potassium channel agonist.

New Insights on KCa3.1 Channel Modulation

The human intermediate conductance calcium-activated potassium channel, KCa3.1, is involved in several pathophysiological conditions playing a critical role in cell secretory machinery and calcium signalling. The recent cryo-EM analysis provides new insights for understanding the modulation by both endogenous and pharmacological agents. A typical feature of this channel is the low open probability in saturating calcium concentrations and its modulation by potassium channel openers (KCOs), such as benzo imidazolone 1-EBIO, without changing calcium-dependent activation. In this paper, we proposed a model of KCOs action in the modulation of channel activity. The KCa3.1 channel has a very rich pharmacological profile with several classes of molecules that selectively interact with different binding sites of the channel. Among them, benzo imidazolones can be openers (positive modulators such as 1-EBIO, DC-EBIO) or blockers (negative modulators such as NS1619). Through computation modelling techniques, we identified the 1,4-benzothiazin-3-one as a promising scaffold to develop new KCa3.1 channel modulators. Further studies are needed to explore the potential use of 1-4 benzothiazine- 3-one in KCa3.1 modulation and its pharmacological application.

Computational determination of hERG-related cardiotoxicity of drug candidates

Background

Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates.

Result

In this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which were carried out under constant conditions. Based on our dataset, we developed a computational hERG-related cardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic curve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321, and a specificity of 0.967, when ten-fold cross-validation was performed. The model was further evaluated using ten drug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and a specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models.

Conclusion

The neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high accuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred.

In silico prediction of hERG inhibition

The voltage-gated potassium channel encoded by hERG carries a delayed rectifying potassium current (IKr) underlying repolarization of the cardiac action potential. Pharmacological blockade of the hERG channel results in slowed repolarization and therefore prolongation of action potential duration and an increase in the QT interval as measured on an electrocardiogram. Those are possible to cause sudden death, leading to the withdrawals of many drugs, which is the reason for hERG screening. Computational in silico prediction models provide a rapid, economic way to screen compounds during early drug discovery. In this review, hERG prediction models are classified as 2D and 3D quantitative structure–activity relationship models, pharmacophore models, classification models, and structure based models (using homology models of hERG).

TMACC: interpretable correlation descriptors for quantitative structure-activity relationships

Highly predictive topological maximum cross correlation (TMACC) descriptors for the derivation of quantitative structure-activity relationships (QSARs) are presented, based on the widely used autocorrelation method. They require neither the calculation of three-dimensional conformations nor an alignment of structures. We have validated the TMACC descriptors across eight literature data sets, ranging in size from 66 to 361 molecules. In combination with partial least-squares regression, they perform competitively with a current state-of-the-art 2D QSAR methodology, hologram QSAR (HQSAR), yielding larger leave-one-out cross-validated coefficient of determination values (LOO q2) for five data sets. Like HQSAR, these descriptors are also interpretable but do not require hashing. The interpretation both enables the automated extraction of SARs and can give a description in qualitative agreement with more time-consuming 3D and 4D QSAR methods. Open source software for generating the TMACC descriptors is freely available from our Web site.