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Activity Number:
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112
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Type:
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Contributed
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Date/Time:
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Monday, August 7, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #306806 |
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Title:
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A Bayesian Simulation-Based Approach in Investigating Physiologically-Based Drug-Drug Interaction Prediction
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Author(s):
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Zhiping Wang*+ and Lang Li and Stephen Hall
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Companies:
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Indiana University Purdue University Indianapolis and Indiana University and Indiana University
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Address:
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1050 Wishard Blvd., RG4101, indianapolis, IN, 46227,
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Keywords:
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drug-drug interaction ; pharmacokinetic
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Abstract:
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Drug-drug interactions (DDIs) are a significant cause of adverse drug reactions that in turn results in significant morbidity and mortality. We propose to develop a suite of Bayesian tools to predict DDIs based on a general physiologically-based pharmacokinetic (PBPK) model framework, and to develop a web-based interface to implement these tools. This is a population approach designed to characterize DDIs by considering all variation sources. False negative rate (FNR) in DDI prediction is carefully defined. Our data analysis and simulation studies have shown that FNR is not only negatively associated with the DDI parameter, , but also positively associated with between subject variations of PK parameters. Its application is demonstrated with a ketoconazole-midazolam example.
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