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.