Data Driven Modelling and Control of Batch Processes in the Pharmaceutical Industry

Abstract

Regulatory changes in the pharmaceutical industry have recast the role of chemical engineering in the development of processes for the large-scale manufacture of Active Pharmaceutical Ingredients. Health authorities' expectations have increased regarding proven demonstration of adequate process performance, process robustness, and quality control across development and manufacturing scales. These expectations have substantially increased the demands of experimental data collection, process monitoring, and multivariate process understanding. To address these requirements, innovative implementation of established and emerging automation, modeling, data management, and process monitoring techniques have been increasingly added to the repertoire of process development tools in particular, those carried out in agitated stirred tanks. This article will introduce the application of these techniques across the range of process development (from early to late phase) challenges. In particular, this article describes the implementation of Data Exploration Analysis to results obtained with automated parallel experimentation for batch reaction characterization and automated batch crystallization processes integrated to population balance modeling. Collectively these examples of new strategies for the automation of experimental batch processes, data analysis, and modeling provide an overview of recent trends in pharmaceutical chemical process development.