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Nonlinear stochastic differential equation models with unobservable variablesare now widely used in the analysis of PK/PD data. The unobservable variablesare often estimated with extended Kalman filter (EKF), and the unknownpharmacokinetic parameters are usually estimated by maximum likelihoodestimator. However, EKF is inadequate for nonlinear PK/PD models, and MLE isknown to be biased downwards. A density-based Monte Carlo filter (DMF) isproposed to estimate the unobservable variables, and a simulation-basedprocedure is proposed to estimate the unknown parameters in this paper, where agenetic algorithm is designed to search the optimal values of pharmacokineticparameters. The performances of EKF and DMF are compared through simulations,and it is found that the results based on DMF are more accurate than thosegiven by EKF with respect to mean absolute error.

Density-based Monte Carlo filter and its applications in estimation of unobservable variables and pharmacokinetic parameters