FEB 13, 2019 10:30 AM PST

Accurate Clinical Concentration-Response Predictions for Cardiac Arrhythmias Using a Population-Based in Vitro/In Silico Model

Speaker
  • Professor, Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University
    Biography
      Weihsueh A. Chiu, Ph.D. is a professor in the Department of Veterinary Integrative Biosciences in the College of Veterinary Medicine and Biomedical Sciences at Texas A&M University. Before joining the university, he worked at the U.S. Environmental Protection Agency (EPA) for more than 14 years, most recently as chief of the Toxicity Pathways Branch in the Integrated Risk Information System (IRIS) Division of the National Center for Environmental Assessment. Throughout his career, he has been involved in a diverse span of risk-related topics, such as defense against chemical-biological warfare agents, radioactive contamination in biosolids, human health risks from environmental chemical exposures, and the interface between science and policy. His recent research has focused on human health risk assessment, particularly with respect to toxicokinetics, mechanisms of toxicity, physiologically-based pharmacokinetic modeling, dose-response assessment, characterizing uncertainty and variability, systematic review, and meta-analysis. He has a particular interest in the development and use of Bayesian and probabilistic methods. Dr. Chiu has served on a variety of expert advisory committees for U.S. federal, state, and Canadian government agencies; the U.S. National Academies of Sciences, Engineering, and Medicine; the World Health Organization; and the Organisation for Economic Cooperation and Development. Dr. Chiu received an AB in Physics from Harvard University, a MA and PhD in Physics from Princeton University, and a Certificate in Science, Technology, and Environmental Policy from the Woodrow Wilson School of Public and International Affairs.

    Abstract

    Xenobiotic-induced cardiotoxicity is a major concern for both pharmaceuticals and chemicals in the marketplace. For drugs, "Thorough QT/corrected QT (QTc)" (TQT) studies are cornerstones of clinical cardiovascular safety assessment. However, TQT studies are resource intensive, and preclinical models predictive of the threshold of regulatory concern are lacking. For other chemicals, cardiotoxicity is not routinely assessed, and is largely inferred from epidemiologic data.  We hypothesized that an in vitro model using induced pluripotent stem cell (iPSC)-derived cardiomyocytes from a diverse sample of human subjects can serve as a "TQT study in a dish," improving cardiotoxicity assessments for both pharmaceutically and chemicals in commerce. For 10 positive and 3 negative control drugs, in vitro concentration-QTc, computed using a population Bayesian model, accurately predicted known in vivo concentration-QTc. Moreover, predictions of the percent confidence that the regulatory threshold of 10 ms QTc prolongation would be breached were also consistent with in vivo evidence. This "TQT study in a dish," consisting of a population-based iPSC-derived cardiomyocyte model and Bayesian concentration-QTc modeling, has several advantages over existing in vitro platforms, including higher throughput, lower cost, and the ability to accurately predict the in vivo concentration range below the threshold of regulatory concern.  These results demonstrate the potential for replacing a multi-million dollar clinical trial – the Thorough QT/QTc study – with an in vitro-in silico model.  Moreover, because cardiotoxicity clinical trials are not performed for non-pharmaceuticals, such a model could fill a critical gap in chemical toxicity testing.  

    Learning Objectives: 

    1. Describe current practices in cardiotoxicity assessment for xenobiotics.
    2. Explain how induced pluripotent stem cell-derived cardiomyocytes may be useful in advancing cardiotoxicity assessment.


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    FEB 13, 2019 10:30 AM PST

    Accurate Clinical Concentration-Response Predictions for Cardiac Arrhythmias Using a Population-Based in Vitro/In Silico Model


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