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Related Concept Videos

Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...
Electrocardiogram01:29

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and the T...
Antiarrhythmic Drugs: Class III Agents as Potassium Channel Blockers01:12

Antiarrhythmic Drugs: Class III Agents as Potassium Channel Blockers

Class III antiarrhythmic drugs are a group of medications that can prolong action potentials in the heart. They achieve this by blocking potassium channels or enhancing inward currents from sodium channels. However, these drugs have a unique property of "reverse use-dependence," which is most pronounced at slower heart rates and can lead to torsades de pointes—a specific type of arrhythmia. However, it is essential to note that excessive QT interval prolongation—a measure of the heart's...
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Related Experiment Video

Updated: Jul 7, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Computer-aided prediction of QT-prolongation.

O Filz1, A Lagunin, D Filimonov

  • 1Institute of Biomedical Chemistry of Rus. Acad. Med. Sci., Moscow, Russia. Olfilz@gmail.com

SAR and QSAR in Environmental Research
|March 4, 2008
PubMed
Summary

Predicting drug-induced cardiac arrhythmia is crucial. The PASS program accurately forecasts hERG channel blockade and QT prolongation, aiding drug development by identifying potential risks and indirect mechanisms.

Related Experiment Videos

Last Updated: Jul 7, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Area of Science:

  • Pharmacology
  • Computational Chemistry
  • Drug Development

Background:

  • Drug-induced cardiac arrhythmia, specifically acquired long QT syndrome (LQTS), poses a significant challenge in pharmaceutical development.
  • Existing in silico methods primarily focus on predicting hERG channel blockade and QT prolongation.

Purpose of the Study:

  • To evaluate the efficacy of the computer program PASS for predicting hERG channel blockade and QT prolongation.
  • To explore the potential of PASS in analyzing indirect mechanisms contributing to drug-induced LQTS.
  • To investigate correlations between hERG blockade and other molecular activities.

Main Methods:

  • Utilized the computer program PASS, which estimates probabilities for approximately 3000 biological activities.
  • Augmented the PASS training set with 163 compounds exhibiting QT prolongation data.
  • Re-trained the PASS model and employed the PharmaExpert program to analyze predicted biological activity spectra.

Main Results:

  • Achieved prediction accuracies of 87.1% for hERG blockade and 81.8% for QT prolongation after model re-training.
  • Identified correlations between hERG blockade and other molecular mechanisms via PharmaExpert analysis.
  • Discussed the potential involvement of 1-phosphatidylinositol-4-phospate 5-kinase, dimethylargininase, and progesterone 11 alpha-monooxygenase inhibition in hERG blockade.

Conclusions:

  • The PASS program demonstrates high accuracy in predicting hERG blockade and QT prolongation.
  • PASS facilitates the analysis of indirect mechanisms contributing to drug-induced LQTS, enhancing early drug safety assessments.
  • Computational tools like PASS and PharmaExpert are valuable for identifying potential cardiotoxicity risks during drug development.