Slow delayed rectifying potassium current (IKs ) - analysis of the in vitro inhibition data and predictive model development

Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel

Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when with combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficit in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC₅₀ values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC₅₀ values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling technique. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC₅₀ values for large dataset of molecules and compared it to automated patch clamp system for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost displays excellent performance with a coefficient of determination of up to R² ~0.8 for pIC₅₀ values in evaluation datasets, surpassing other metrics and approaches available in literature. Ultimately, the ML-based platform developed in our work is a scalable framework with automation potential to interact with other developing technologies in cardiotoxicity field, including high-throughput electrophysiology measurements delivering large datasets of profiled drugs, rapid synthesis and drug development via progress in synthetic biology.

Physicochemical Properties of Bosentan and Selected PDE-5 Inhibitors in the Design of Drugs for Rare Diseases

The study provides the physicochemical characteristic of bosentan (BOS) in comparison to tadalafil (TA) and sildenafil citrate (SIL). Despite some reports dealing with thermal characteristic of SIL and TA, physicochemical properties of BOS have not been investigated so far. Recent clinical reports have indicated that the combination of bosentan and PDE-5 inhibitor can improve the effectiveness of pharmacotherapy of pulmonary arterial hypertension (PAH). However, in order to design personalized medicines for therapy of chronic rare diseases, detailed information on the thermal behaviour and solubility of each drug is indispensable. Thus, XRD, DSC and TGA-QMS analyses were applied to compare the properties of the drugs, their thermal stability as well as to identify the products of thermal degradation. The dehydration of BOS started at 70°C and was followed by the chemical degradation with the onset at 290°C. The highest thermal stability was stated for TA, which decomposed at ca. 320°C, whereas the lowest onset of the thermal decomposition process was stated for SIL, i.e. 190°C. The products of the drug decomposition were identified. FT-FIR was applied to study intra- and intermolecular interactions between the drug molecules. FT-MIR and Raman spectroscopy were used to examine the chemical structure of the drugs. Chemoinformatic tools were used to predict the polar surface area, pKa, or logP of the drugs. Their results were in line with solubility and dissolution studies.

Interaction Between Domperidone and Ketoconazole: Toward Prediction of Consequent QTc Prolongation Using Purely In Vitro Information

We aimed to investigate the application of combined mechanistic pharmacokinetic (PK) and pharmacodynamic (PD) modeling and simulation in predicting the domperidone (DOM) triggered pseudo-electrocardiogram modification in the presence of a CYP3A inhibitor, ketoconazole (KETO), using in vitro-in vivo extrapolation. In vitro metabolic and inhibitory data were incorporated into physiologically based pharmacokinetic (PBPK) models within Simcyp to simulate time course of plasma DOM and KETO concentrations when administered alone or in combination with KETO (DOM+KETO). Simulated DOM concentrations in plasma were used to predict changes in gender-specific QTcF (Fridericia correction) intervals within the Cardiac Safety Simulator platform taking into consideration DOM, KETO, and DOM+KETO triggered inhibition of multiple ionic currents in population. Combination of in vitro-in vivo extrapolation, PBPK, and systems pharmacology of electric currents in the heart was able to predict the direction and magnitude of PK and PD changes under coadministration of the two drugs although some disparities were detected.

Tox-database.net: a curated resource for data describing chemical triggered in vitro cardiac ion channels inhibition

BACKGROUND

Drugs safety issues are now recognized as being factors generating the most reasons for drug withdrawals at various levels of development and at the post-approval stage. Among them cardiotoxicity remains the main reason, despite the substantial effort put into in vitro and in vivo testing, with the main focus put on hERG channel inhibition as the hypothesized surrogate of drug proarrhythmic potency. The large interest in the IKr current has resulted in the development of predictive tools and informative databases describing a drug's susceptibility to interactions with the hERG channel, although there are no similar, publicly available sets of data describing other ionic currents driven by the human cardiomyocyte ionic channels, which are recognized as an overlooked drug safety target.

DISCUSSION

The aim of this database development and publication was to provide a scientifically useful, easily usable and clearly verifiable set of information describing not only IKr (hERG), but also other human cardiomyocyte specific ionic channels inhibition data (IKs, INa, ICa).

SUMMARY

The broad range of data (chemical space and in vitro settings) and the easy to use user interface makes tox-database.net a useful tool for interested scientists. DATABASE URL: http://tox-database.net.

Predictive model for L-type channel inhibition: multichannel block in QT prolongation risk assessment

Evaluation of the proarrhythmic potential of an investigated compound is now an integral element of the safety profile required for the approval of new drugs. The human ether-à-go-go-related gene (hERG) channel blocking potency is regarded as a surrogate marker of the proarrhythmic risk at the early stages of the research and development process. However, there is no straight correlation between QT prolongation and TdP occurrence probability, and hERG inhibition potential can be an inadequate predictor of QT prolongation. The L-type calcium channel plays a pivotal role in cardiomyocytes' physiology. Thus the main aim of this study was to develop a predictive model for drug-triggered CaL channel inhibition and also the assessment of drug-multichannel interaction effects on the heart rate-corrected QT interval. The data set, consisting of 123 records describing in vitro experimental settings, measured IC₅₀ values and calculated physico-chemical properties for 72 various chemicals, was collected. The models were tested in a modified 10-fold cross-validation procedure. The generalization ability of the best model was as follows: root mean squared error (RMSE) = 1.10, normalized root mean squared error (NRMSE) = 16.09%. Out of the 10 most important variables, 5 described conditions of the in vitro experiments thus their description and experiment's conditions standardization might be the key to the models better performance. The simulations performed with the ToxComp system showed that the hERG block alone causes concentration-dependent QT prolongation, whereas when multichannel block is regarded, the effect could be reversed. For that reason, the multichannel interaction of tested compounds should be taken into consideration, in order to make the proarrhythmic risk assessment more reliable.