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Abstract
Adverse effects of pharmaceuticals on electrocardiograms (ECGs) are one of the most critical drug safety concerns facing drug development companies today. It is recognized that a better understanding of the interrelationship between preclinical measures of cardiovascular safety biomarkers as well as a more integrated approach to risk assessment could dramatically speed the development of safe and effective medicines for patients in need. Using the Health and Environmental Sciences Institute of the International Life Sciences Institute (ILSI/HESI) dataset, we constructed a Bayesian repeated analysis of covariance model to directly assess the cardiovascular risk in QT interval. With prior distributions derived from in vitro hERG ionic current concentration, posteriors for treatment effects were obtained and the likelihood of risk was calculated. Sensitivity of the proposed Bayesian analysis to prior selection and comparison with the classical method were characterized. The results demonstrate that Bayesian integration of in vitro and in vivo biomarkers provides an effective preclinical risk assessment for QT interval and can reduce unnecessary animal exposure in toxicology studies.
Acknowledgments
The authors thank an associate editor and two anonymous reviewers for their insightful comments and suggestions, which have led to substantive improvements.
Additional information
Notes on contributors
Alan Y. Chiang
Alan Y. Chiang is Senior Research Advisor (E-mail: [email protected]) and Ming-Dauh Wang is Principal Research Scientist (E-mail: [email protected]), Global Statistical Sciences, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN 46285.
Ming-Dauh Wang
Alan Y. Chiang is Senior Research Advisor (E-mail: [email protected]) and Ming-Dauh Wang is Principal Research Scientist (E-mail: [email protected]), Global Statistical Sciences, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, IN 46285.