A methodology is proposed to aid parameter estimation in fundamental
models of pharmaceutical processes. This methodology addresses
situations with insufficient data to reliably estimate all parameters,
when the estimation is complicated by uncertain independent variables.
The proposed method uses an augmented sensitivity matrix to rank the
combined set of parameters and uncertain inputs from most estimable to
least estimable. An updated mean-squared-error criterion is then used to
determine the appropriate parameters and inputs that should be
estimated, based on the ranked list. A model for one step in a batch
pharmaceutical production process with an uncertain initial reactant
concentration is used to illustrate the method, revealing that the
initial reactant concentration in each batch should be estimated along
with three out of six model parameters. Non-estimable parameters are
fixed at their initial values to prevent overfitting. The method will
aid error-in-variables parameter estimation in many situations involving
limited data.