Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning was written by Sahli-Costabal, Francisco;Seo, Kinya;Ashley, Euan;Kuhl, Ellen. And the article was included in Biophysical Journal in 2020.Recommanded Product: 50-52-2 This article mentions the following:
All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here, we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their proarrhythmic potential. We capitalize on recent developments in machine learning and integrate information across 10 orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both exptl. and computationally, that drug-induced arrhythmias are dominated by the interplay between two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 22 common drugs exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the proarrhythmic potential of new drugs. Our study paves the way toward establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders. In the experiment, the researchers used many compounds, for example, 10-(2-(1-Methylpiperidin-2-yl)ethyl)-2-(methylthio)-10H-phenothiazine (cas: 50-52-2Recommanded Product: 50-52-2).
10-(2-(1-Methylpiperidin-2-yl)ethyl)-2-(methylthio)-10H-phenothiazine (cas: 50-52-2) belongs to piperidine derivatives. The piperidine moiety constitutes an important building block for the synthesis of a variety of bioactive natural products, alkaloids and other drugs. Fluorinated piperidines are also the subject of continued interest in medicinal chemistry, for example in the synthesis of selective dipeptidyl peptidase II (DPP II) inhibitors. Piperidine derivatives are also used in solid-phase peptide synthesis (SPPS) and many degradation reactions.Recommanded Product: 50-52-2
Referemce:
Piperidine – Wikipedia,
Piperidine | C5H11N – PubChem