Using safety signals to detect subpopulations: a population pharmacokinetic/pharmacodynamic mixture modeling approach
View Presentation Marshall Gagne, FDA/CVM Barbara Leotta, FDA/CVM Marilyn N Martinez, FDA/CVM Sanjia Modric, FDA/CVM Michael Myers, FDA/CVM *Junshan Qiu, FDA/CVM Michele Sharkey, FDA/CVM Lisa Troutman, FDA/CVM Haile Yancy, FDA/CVM Keywords: adverse event, safety signal, subpopulation, pharmacokinetic/pharmacodynamic mixture model Safety signals generated from adverse events data not only can help make drug development decisions but also can help identify subpopulations characterized by different biomarkers. The current research focuses on a pharmacokinetic/pharmacodynamic (PK/PD) mixture modeling approach. A zero-inflated ordinal logistic regression model was used to interpret the PD data and further linked with the PK models. This PK/PD mixture model can be used to analyze the PK/PD data simultaneously. Selection of appropriate statistical algorithms to approximate and maximize the likelihood function of the PK/PD mixture model was performed based upon simulation studies. Further, the PK/PD mixture model coupled with a stochastic approximation of expectation and a maximization algorithm were used to analyze simulated data for a population with different characteristics and dosed orally with drug A.
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Key Dates
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April 30 - May 22, 2013
Invited Abstract Submission Open -
June 4, 2013
Online Registration Opens -
August 9 - August 23, 2013
Invited Abstract Editing -
August 23, 2013
Short Course materials due from Instructors -
August 26, 2013
Housing Deadline -
September 9, 2013
Cancellation Deadline and Registration Closes @ 11:59 pm EDT -
September 16 - September 18, 2013
Marriott Wardman Park, Washington, DC