Statistics in Pharmaceutical Development and Manufacturing

John J. Peterson, Ronald D. Snee, Paul R. McAllister, Timothy L. Schofield, and Anthony Carella

Abstract: The pharmaceutical industry is undergoing rapid change and facing numerous challenges, including the demands of global competition, the need to speed up the drug development process, and the Food and Drug Administration’s (FDA’s) expectations for the incorporation of the principles of quality by design (QbD) and process analytical technology (PAT) in process and analytical development. Statistical thinking and methods play a significant role in addressing these issues. This article provides an overview of the use of statistical thinking and methods in the R&D and manufacturing functions of the pharmaceutical industry. The exposition includes the history of pharmaceutical quality and regulation, phases of pharmaceutical development and manufacturing and the basic quality and statistical tools employed in each, emerging statistical methods, the impact of statistical software and information technology, and the role of statisticians in pharmaceutical development and manufacturing. Four case studies are included to illustrate how these issues play out in actuality. A summary provides a succinct synopsis of those issues and concludes that the complex, technical nature of pharmaceutical development and manufacturing offers many opportunities for the effective use of statistical thinking and methods and that those who use these methods can become catalysts for both process development understanding and product quality improvement.

Using Experimental Design and Randomisation to Construct a Mixed Model

Simon Bate and Marion Chatfield

In many areas of scientific research scientists routinely use complex experimental designs when conducting their experiments. With the advent of the modern statistical package, and a lack of trained statisticians, the scientist often carries out the analysis of data generated from such experiments. This can lead to incorrect and misleading results, especially if the scientist fails to correctly identify the experimental design they are using and the influence the design has on the statistical analysis.

In this paper we describe a procedure that would allow non-statisticians to identify the structure of the experimental design without an in-depth knowledge of design theory. Once this has been achieved it should then be possible for them to produce an appropriate model for the statistical analysis. By placing experimental design at the centre of the statistical process we are able to simplify the statistical model selection. This procedure should also make the benefits of good experimental design more accessible to both statisticians and non-statisticians alike.

The MedDRA Paradox

Gary H. Merrill, Ph.D.

Abstract: MedDRA (the Medical Dictionary for Regulatory Activities Terminology) is a controlled vocabulary widely used as a medical coding scheme. However, MedDRA’s characterization of its structural hierarchy exhibits some confusing and paradoxical features. The goal of this paper is to examine these features, determine whether there is a coherent view of the MedDRA hierarchy that emerges, and explore what lessons are to be learned from this for using MedDRA and similar terminologies in a broad medical informatics context that includes relations among multiple disparate terminologies, thesauri, and ontologies.