Quality by Design (QbD) using DOE (3.0 days)

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  • Quality by Design (QbD) using DOE
    April 10, 2023 - April 12, 2023
    9:00 am - 4:00 pm Your Timezone

Quality by Design (QbD) using DOE (3.0 days):

This course focuses on 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 by SAS.

Quality by Design (QbD) using DOE Objectives:

  • 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.


1. Introduction to Quality by Design (QbD)
• Quality by Design (QbD) principles
• Product Quality System framework

2. Primer on Statistical Analysis
• basic statistics
• hypothesis testing
• ANOVA and regression
• Blocking

3. Foundational Requirements for QbD Studies
• setting specifications
• Measurement Systems Analysis (MSA) for assays

4. Introduction to Design of Experiments (DOE)
• steps to DOE
• defining critical-to-quality attributes (CQAs)
• identifying and prioritizing potential process parameters

5. Screening Designs – Identifying Critical Process Parameters
• factorial designs
• fractional factorial designs
• D-optimal designs

6. Response Surface Designs – Develop Functional Relationships and Establish Design Space
• Central Composite Designs (CCDs)
• Box-Behnken designs (Self Study)
• I-optimal designs

7. Specialized Designs
• Definitive Screening Designs
• A Optimal Designs

8. Utilizing Systematic Understanding from QbD Studies
• presenting results
• developing a control plan as part of a risk management strategy
• process control and capability

9. Advanced DOE Methodologies (Self Study)
• split-plot designs
• robust parameter design
• supersaturated designs
• constrained designs
• mixture designs

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