A QbD case study: Bayesian prediction of lyophilization cycle parameters. uri icon

abstract

  • As stipulated by ICH Q8 R2 (1), prediction of critical process parameters based on process modeling is a part of enhanced, quality by design approach to product development. In this work, we discuss a Bayesian model for the prediction of primary drying phase duration. The model is based on the premise that resistance to dry layer mass transfer is product specific, and is a function of nucleation temperature. The predicted duration of primary drying was experimentally verified on the lab scale lyophilizer. It is suggested that the model be used during scale-up activities in order to minimize trial and error and reduce costs associated with expensive large scale experiments. The proposed approach extends the work of Searles et al. (2) by adding a Bayesian treatment to primary drying modeling.

published proceedings

  • AAPS PharmSciTech

author list (cited authors)

  • Mockus, L., LeBlond, D., Basu, P. K., Shah, R. B., & Khan, M. A.

citation count

  • 6

complete list of authors

  • Mockus, Linas||LeBlond, David||Basu, Prabir K||Shah, Rakhi B||Khan, Mansoor A

publication date

  • March 2011