(299d) Estimability Analysis for Improved Parameter Estimation in Deterministic Models: Pharmaceutical Case Studies
- Conference: AIChE Annual Meeting
- Year: 2017
- Proceeding: 2017 AIChE Annual Meeting
- Group: Pharmaceutical Discovery, Development and Manufacturing Forum
Tuesday, October 31, 2017 - 9:15am-9:40am
This work illustrates the application of Estimability Analysis to aid the parameter estimation of a deterministic mathematical model. This technique has proven useful when the information content of the data available is insufficient to obtain a good estimate of all the unknown model parameters. Estimability Analysis ranks the parameters from the most to least estimable; taking into account the uncertainties in both the initial guesses of the parameters and the data . This ranking enables the modeler to determine which parameters to estimate, and which to fix. The obtained model parameters are a good initial guess to a formal model-based experimental design exercise to determine the necessary experiments to optimally parametrize the model.
Case studies are presented from drug substance process development where a kinetic model of a reactive system is being developed; and a third case study from the modeling for a continuous direct compression line for drug product.
 Wu et al., 2011, International Journal of Advanced Mechatronic Systems, 3, 188-197.