Business Knowledge Series course

Presented by Heath Rushing, co-founder and Principal Consultant, Adsurgo LLC

This course focuses on how to establish a systematic approach to pharmaceutical development that is defined by Quality-by-Design (QbD) principles using design of experiments (DOE). In addition, this course teaches the application of statistics for setting specifications, assessing measurement systems (assays), developing a control plan as part of a risk management strategy, and ensuring process control/capability. All concepts are taught within the product quality system framework defined by requirements in regulatory guidance documents. Analyses in this course use the point-and-click interface of JMP software.

Learn how to

• implement QbD principles from discovery through product discontinuation
• apply statistics to set specifications and validate measurement systems (assays)
• utilize risk management tools to identify and prioritize potential critical process parameters
• identify critical process parameters and develop a functional relationship between those process parameters and your critical-to-quality attributes (CQAs)
• establish your design space
• develop a control plan as part of a risk management strategy
• ensure your process is in (statistical) control and capable.

Who should attend

Scientists and engineers who need to integrate Quality-by-Design (QbD) principles into their pharmaceutical development and quality systems

Formats available	Standard Duration
Classroom:	3.0 days
Live Web Classroom:	6 half-day session(s)	System Requirements

Before attending this course, you should attend the JMP® Software: A Case Study Approach to Data Exploration course. It is also strongly recommended that you attend the JMP® Software: Statistical Decisions Using ANOVA and Regression course.

This course addresses JMP software.

Introduction to Quality by Design (QbD)

• Quality by Design (QbD) principles
• Product Quality System framework

Primer on Statistical Analysis

• basic statistics
• hypothesis testing
• ANOVA and regression
• blocking

Foundational Requirements for QbD Studies

• setting specifications
• Measurement Systems Analysis (MSA) for assays

Introduction to Design of Experiments (DOE)

• steps to DOE
• defining critical-to-quality attributes (CQAs)
• identifying and prioritizing potential process parameters

Screening Designs

• factorial designs
• fractional factorial designs
• D-optimal designs

Response Surface Designs

• addition of center points
• face-centered CCD
• Box-Behnken Design
• I-optimal designs

Definitive Screening Designs

• classic response surface designs
• I-optimal designs

Using Systematic Understanding from QbD Studies

• presenting results
• developing a control plan as part of a risk management strategy
• process control and capability

Advanced DOE Methodologies (Optional)

• split-plot designs
• robust parameter design
• supersaturated designs
• constrained designs
• mixture designs