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).
• Use 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 that 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 (duration can vary, see event schedule for details)
Classroom:	3.0 days

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.

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.