MC-PEM (Monte Carlo Parametric Expectation Maximization) is a method for analyzing population pharmacokinetic/ pharmacodynamic (PK/PD) data. Serge Guzy, President POP-PHARM developed the engine part of the program while Globomax developed the corresponding interface.

PK/PD modeling expertise using MC-PEM includes:
· Clinical trial design and simulation
· Population PK/PD
· Statistical Modeling
· Multiple Imputation techniques
· Random number generator algorithm
· Power simulation technique
· Survival Analysis Simulation technique
· Bayesian Algorithms
· Randomization test algorithms
· Disease progression models
· Sequential Trial designs
· Tumor Growth modeling
· Assay statistical validation
· Mathematical modeling of Biological systems

MC-PEM accurately evaluates point estimates of population parameters from pharmacokinetic (PK)/Pharmacodynamic (PD) data without linearizing the expectation step.

The PK/PD parameters are modeled to be multivariate normally or log-normally distributed among subjects, and observed data are modeled to have measurement error that is normally or log-normally distributed about the predicted value for each subject, similar to the manner in which NONMEM models population data.  In addition, population parameters may be modeled to subject characteristics (covariates), and intra-subject error coefficients may also be determined.  


The MC-PEM method was first tested on simulated sparse data (one datum per subject) generated from a simple one-compartment model and was found to accurately estimate the population parameter estimates in all cases.  The MC-PEM method was also tested on simulated data generated from a two compartment PK/sigmoidal Emax PD model with a total of 8 PK/PD population parameters and 36 inter-subject variance-covariance parameters.  The MC-PEM method yielded population parameters and variance-covariance parameters that were similar to the simulated values. MCPEM performance was compared to NONMEM (FOCE with interaction). MCPEM was more stable, less sensitive to initial estimates and with equal accuracy and precision, at least for all successful NONMEM runs. Since then, the MC-PEM method has been successfully used to analyze very complex PK/PD/efficacy models containing up to six differential equations and 16 parameters.


The MCPEM methodology appears to be extremely robust, not sensitive to initial estimates. Due to its robustness, covariance components are never  forced to be zero as it often occurs in NONMEM even though some covariance could be no identifiable. Stochastic  concepts to analyze Population PK/PD data appear to be not only valid but also, in many instances, superior to deterministic algorithms.

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