Real Patients & Their Virtual Twins: A New Paradigm for Personalized Drug Safety Assessment

A major challenge in developing efficacious and safe drugs is the ability to understand and effectively predict adverse effects of xenobiotic substances on complex biological systems early in the drug discovery process. An alarming fact is that thirty percent of adverse drug reactions (ADRs) cannot be predicted by current pre-clinical animal testing and existing modeling methodologies. Quantitative systems toxicology and safety (QSTS) modeling is an approach that can inform understanding of the mechanistic basis of ADRs and achieve more predictive and accurate risk assessments.

QSTS is a multidisciplinary approach which, at the juncture of Systems Biology with Toxicology and Chemistry, integrates classical toxicology with quantitative analysis of the molecular and functional changes that occur across multiple levels of biological organization. QSTS aims to characterize ADRs by describing modes of action as adverse outcomes pathways and perturbed networks versus conventional empirical end points and animal-based testing.

In this webinar, Sebastian Polak, Nikunj Patel, and Mark Holbrook introduced the Cardiac Safety Simulator (CSS) platform and explained how they developed a QSTS model for citalopram to serve as a “virtual twin” and help predict the likely occurrence of cardiotoxic events in real patients under different clinical conditions. Citalopram is a widely prescribed antidepressant drug, which has been linked with cardiac toxicity especially at higher doses.

By watching this webinar, you will learn:

  • About the latest version of CSS platform
  • How QSTS models, with the appropriate drug and systems parameters, can bridge the gap between preclinical cardiac safety assessments and clinical toxicology results
  • How QSTS models, in combination with appropriate drug and systems parameters, can pave the way towards personalized safety assessments
  • How such an approach can be leveraged to inform predicting likelihood of drug-mediated renal, hepatic, and CNS toxicities in individual patients
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