Abstract: Dosage regimens based on parametric population models use single values to describe each parameter distribution. When a target goal is selected, the regimen to achieve it assumes that it does so exactly. In contrast, MM dosage design is based on nonparametric (NP) population models which have up to one set of parameter values for each subject studied in the population. With this more likely model, multiple predictions are possible. Using these NP models, one can compute the MM dosage regimen which specifically minimizes the predicted weighted squared error with which a target goal can be achieved. With feedback from serum concentrations, each set of parameter values in the NP prior has its probability recomputed. Using that revised model, the new regimen to achieve the target with maximum precision is again computed. A new Interacting Multiple Model (IMM) sequential Bayesian method now estimates posterior densities when parameter values have been changing during the analysis. A new clinical software package is in development.