Process Analytical Technology, continuous manufacturing, and the development of a surrogate model for dissolution: a pharmaceutical manufacturing statistical engineering case study

Abstract

Statistical engineering is a recent movement that seeks to promote a systematic application of statistical tools combined with a comprehensive view of project management to solve important commercial problems. We discuss how statistical engineering principles were applied in the development of a surrogate model for dissolution (also referred to as in vitro release), an important physical property of solid dose pharmaceutical products required for marketing. The approach relies on spectroscopic analytical methods, referred to more generally as Process Analytic Technology, to enable monitoring of a batch of pharmaceutical product in real time. Real time release (RTR) is the release to the market of a batch of final product, nearly immediately after its production, rather than the weeks to months currently needed using conventional wet chemistry analytical methods. The development of a surrogate model advances the goal of RTR of a batch of pharmaceutical product, and the application of statistical engineering tools enhances a coherent development process as illustrated in a case study.

Keywords:

• continuous manufacture
• process analytic technology
• surrogate model
• statistical engineering

Acknowledgment

The authors express deep gratitude to two anonymous referees for their many cogent comments and suggestions that improved the readability and organization of the article.

Additional information

Notes on contributors

Stan Altan

Stan Altan is Senior Director and Research Fellow in the Manufacturing and Applied Statistics department at Janssen Research & Development, LLC. He is a fellow of the ASA, and has served as associate editor of “Statistics in Biopharmaceutical Research” and “Journal of Biopharmaceutical Statistics”.

Hans Coppenolle

Hans Coppenolle obtained his PhD in Agricultural and Applied Biological Sciences in 2002 from KU Leuven (Belgium) and a Master of Science degree in Applied Statistics from Hasselt University (2005). He supports the chemistry, manufacturing and control area since joining the statistics department at Janssen in 2002. He currently holds a position as scientific director in the Manufacturing and Applied Statistics department within the Statistics and Decision Sciences organization at Janssen.

Lynne Hare

Lynne Hare is a consulting statistician emphasizing business process improvement in R&D, Manufacturing and other strategic functions. Serving a large client base, he has helped bring about culture change in Research by accelerating speed to the successful launch of new products and processes and in Manufacturing through the reduction of process variation. His former positions include the Director of Applied Statistics at Kraft Foods, Chief of Statistical Engineering at the National Institute of Standards and Technology, Director of Technical Services at Unilever, Manager of Statistical Applications there as well, Statistics Group Leader at Hunt-Wesson Foods and Visiting Professor at Rutgers University. Lynne’s technical expertise includes experimental strategies and design of experiments for Research as well as quality and productivity improvement for Manufacturing. He holds M.S. and Ph.D. degrees from Rutgers University and an A.B. in mathematics from Colorado College. Lynne is a Fellow of the American Statistical Association and former chairman of its Section on Quality and Productivity and holder of the Gerald J. Hahn Q&P Achievement Award. He is also a Fellow of the American Society for Quality and former chairman of its Statistics Division. The ASQ has awarded him the William G. Hunter and Ellis R. Ott Awards for excellence in quality management. Kraft Foods presented him with the Technology Leadership Award for career accomplishments. He writes a column for Quality Progress Magazine and has numerous publications in technical journals.

Sarah Nielsen

Sarah Nielsen is a Director of Strategy and Deployment of Innovation within Janssen Supply Chain leading Small Molecule Integrated Quality and Supply Chain Analytics. Sarah and her team/colleagues are responsible for developing and deploying real time release. This strategy utilizes data analytics, sensors, and mathematical modeling to provide assurance of quality for product release in real time. Sarah started her career with J&J 13 years ago as a post-doctoral fellow working on cardiovascular stent research, and more recently in her current role in pharmaceuticals which focuses on integrating data science with manufacturing.

Martin Otava

Martin Otava is an Associate Director in Statistics and Decision Sciences of Janssen Pharmaceutical Companies of Johnson & Johnson. He provides statistical support to research and development activities in pharmaceutical manufacturing, mainly for small molecules projects, from early process characterization to process validation and early commercial production. His major areas of interest are various statistical challenges in implementation of continuous manufacturing and data visualization. From methodology perspective, he is interested in design of experiments, Bayesian approach toward hierarchical modeling and inference on equivalence. Martin received a doctorate in statistics and Master of Science in biostatistics from Hasselt university, Belgium. He also has a Master of Science in mathematical statistics from Charles university in Prague.