Pharmaceutical quality by design using JMP : solving product development and manufacturing problems / Rob Lievense.

Format:

Book

Author/Creator:

Lievense, Rob, author.

Language:

English

Subjects (All):

JMP (Computer file).

Physical Description:

1 online resource (xiv, 420 pages) : illustrations

Place of Publication:

Cary, North Carolina : SAS Institute, 2018.

Summary:

Solve your pharmaceutical product development and manufacturing problems using JMP . Pharmaceutical Quality by Design Using JMP : Solving Product Development and Manufacturing Problems provides broad-based techniques available in JMP to visualize data and run statistical analyses for areas common in healthcare product manufacturing. As international regulatory agencies push the concept of Quality by Design (QbD), there is a growing emphasis to optimize the processing of products. This book uses practical examples from the pharmaceutical and medical device industries to illustrate easy-to-understand ways of incorporating QbD elements using JMP. Pharmaceutical Quality by Design Using JMP opens by demonstrating the easy navigation of JMP to visualize data through the distribution function and the graph builder and then highlights the following: the powerful dynamic nature of data visualization that enables users to be able to quickly extract meaningful information tools and techniques designed for the use of structured, multivariate sets of experiments examples of complex analysis unique to healthcare products such as particle size distributions/drug dissolution, stability of drug products over time, and blend uniformity/content uniformity. Scientists, engineers, and technicians involved throughout the pharmaceutical and medical device product life cycles will find this book invaluable. This book is part of the SAS Press program.

Contents:

Preparing data for analysis

Investigating trends in data over time

Assessing how well a process performs to specifications with capability analyses

Using random samples to estimate results for the commercial population

Working with two or more groups of variables

Justifying multivariate experimental designs to leadership

Evaluating the robustness of a measurement system

Using predictive models to reduce the number of process inputs for further study

Designing a set of structured, multivariate experiments for materials

Using structured experiments for learning about a manufacturing process

Analysis of experimental results

Getting practical value from structured experiments

Advanced modeling techniques

Basic mixture designs for materials experiments

Analyzing data with non-linear trends

Using statistics to support analytical method development

Exploring stability studies with JMP.

Notes:

Description based on print version record.

ISBN:

1-63526-618-1

1-63526-620-3