Safety Intelligence Program Provides Insight into Drug-Induced Cardiac Effects

The Safety Intelligence Program (SIP) is a comprehensive knowledgebase of intelligence based around adverse effects of drugs and other compounds.  It contains in-depth knowledge of drug effects in various tissues, including the cardiovascular system.

Drug-induced cardiotoxicity

Cardiotoxicity is a major adverse effect of drugs and has led to a number of withdrawals, for example terfenadine (withdrawn due to arrhythmia) and cisapride (sudden cardiac death and arrhythmia), and more recently for sibutramine (myocardial infarction).  It also caused restrictions to be placed on some drugs (such as thioridazine), delays in regulatory approval, and late-stage compound failures.  According to Drug Discovery World¹, drugs causing cardiotoxicity (such as heart damage and arrhythmia) have caused 28% of drug withdrawals in the United States over the past 30 years.
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SIP Webinar: Cardiac Update and New Features

In this SIP series Webinar, Dr Jane Reed of BioWisdom will introduce Version 3.5 of the Safety Intelligence Program and give a demonstration of its new features, including:

  1. Comprehensive coverage of cardiac and vascular system adverse events and the compounds that cause them
  2. Version 3.5 functionality updates, including:
  • Improvements in performance and usability, searching and exporting
  • New, powerful summary views
  • Inclusion of chemical structure properties
Download SIP Webinar - Cardiac Update and New FeaturesSIP Webinar - Cardiac Update and New Features
Download SIP Webinar - Cardiac Update and New Features (Slides) SIP Webinar - Cardiac Update and New Features (Slides)

Old Data, New Insight

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19TH January 2010 Cambridge, UK – BioWisdom. a provider of specialist medical metadata and Intelligence Solutions for Healthcare, is pleased to announce the publication of an exciting new paper in Chem. Res. Toxicol. Using metadata derived from BioWisdom’s Safety Intelligence Program, Alex Tropsha and colleagues at the University of North Carolina have applied QSAR modelling and other chemoinformatics techniques to qualitative assertions abstracted from legacy data.

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