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A Bayesian New Approach Method for High Throughput Population-Based Cardiotoxicity Risk Characterization of Environmental and Pharmaceutical Compounds
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Cardiovascular disease is a major cause of morbidity and mortality worldwide, but there is little knowledge on the potential cardiovascular effects for most environmental chemicals. Causal links to cardiovascular disease have only been established for air pollutants and a few well-studied compounds such as lead, and even less is known about population variability in susceptibility to environmental exposure-related cardiovascular disease. While the establishment of both pre-clinical and clinical testing requirements for pharmaceuticals have reduced the number of approved drugs with off-target cardiotoxic effects, there is no such routine testing conducted for environmental chemicals. There is therefore a substantial gap in knowledge as to the cardiotoxicity of environmental chemicals and the variability in susceptibility across the population. As a solution to this problem, we utilize hierarchical Bayesian modelling in a robust analysis pipeline to simultaneously address cardiotoxicity hazard, concentration-response, and population variability. Our model’s strength is derived from the data produced from a previously established and diverse population-based human induced pluripotent stem cell derived cardiomyocyte model combined with a hierarchical Bayesian concentration-response model capable of disentangling true population variability from experimental variability to produce highly accurate estimations of hazard, population toxicodynamic variability, and risk. The outcomes of the three specific aims of this dissertation is a hazard characterization for over 100 chemicals, quantified population variability estimations that can be used to make more accurate risk assessments, and an analysis to better understand the trade-off between accuracy and precision of hazard and variability estimates and human induced pluripotent stem cell derived cardiomyocyte population size. Ultimately, this analysis pipeline is not only capable of filling a large data gap in the regulatory sphere but will also prove to be an invaluable resource for drug development, risk assessment, and the chemical industry.
Blanchette, Alexander Daniel (2021). A Bayesian New Approach Method for High Throughput Population-Based Cardiotoxicity Risk Characterization of Environmental and Pharmaceutical Compounds. Doctoral dissertation, Texas A&M University. Available electronically from