Using safety signals to detect subpopulations: a population pharmacokinetic/pharmacodynamic mixture modeling approach
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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.