Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation

Xiangzhong Xie,René Schenkendorf

doi:10.3390/pr7080509

Abstract

Model-based concepts have been proven to be beneficial in pharmaceutical manufacturing, thus contributing to low costs and high quality standards. However, model parameters are derived from imperfect, noisy measurement data, which result in uncertain parameter estimates and sub-optimal process design concepts. In the last two decades, various methods have been proposed for dealing with parameter uncertainties in model-based process design. Most concepts for robustification, however, ignore the batch-to-batch variations that are common in pharmaceutical manufacturing processes. In this work, a probability-box robust process design concept is proposed. Batch-to-batch variations were considered to be imprecise parameter uncertainties, and modeled as probability-boxes accordingly. The point estimate method was combined with the back-off approach for efficient uncertainty propagation and robust process design. The novel robustification concept was applied to a freeze-drying process. Optimal shelf temperature and chamber pressure profiles are presented for the robust process design under batch-to-batch variation.

Highlights

To implement Quality by Design (QbD) concepts, and to ensure optimally designed processes, over the last two decades model-based process design has become an important tool in pharmaceutical manufacturing and process systems engineering [1,2,3,4,5]
With the existence of parameter uncertainties, the variation of temperature at the sublimation interface will lead to significant violations of the critical temperature which is necessary for maintaining the quality of dried active pharmaceutical ingredients (APIs) product
Results from the robust designs attempt to reduce the temperature of the heating shelf to avoid quality failures of the API cake, while the pressure is maintained at its lower boundary to maximize the efficiency of the freeze-drying process

Summary

Introduction

To implement Quality by Design (QbD) concepts, and to ensure optimally designed processes, over the last two decades model-based process design has become an important tool in pharmaceutical manufacturing and process systems engineering [1,2,3,4,5]. Uncertainties in process models and parameters are considered, and are frequently incorporated in robust process design concepts [9,10,11,12,13], the applied algorithms are commonly based on perfect uncertainty measures, i.e., using specific probability density functions (PDFs). Robust design concepts for pharmaceutical processes, which aim to maximize process performance while satisfying critical process constraints under probabilistic uncertainties, are preferred, to provide the proper trade-off between process performance and robustness [2,17].

Objectives

Results

Conclusion