Industries: Pharma / BiotechSkin & CosmeticsMedical Device

QbD - Product & Process Optimization using Design of Experiments

Course Director: Dr. Philippe Solot, Dr. Stefanie Feiler

Course Fee: $2650.00 Regular Registration / $2450.00 Early Bird (30 Days in Advance)

Course Description -

Quality by Design (QbD) means that, starting from the very first development step, products and processes are designed in a way to ensure a high level of quality and reliability.  One of the main QbD tools is statistical Design of Experiments (DoE), which enables to perform the necessary experiments in an efficient and structured way.  This constitutes the most effective manner to identify the Critical Process Parameters (CPP’s) and Material Attributes (CMA’s) that, together, influence most the quality characteristics of highest concern, the so-called Critical Quality Attributes (CQA’s).  Of course, the methodology can also be applied to optimize existing products or processes.

The training gives a comprehensive introduction to statistical Design of Experiments (DoE): on the one hand, the statistical background is explained, on the other hand, the methods are illustrated with examples from the pharmaceutical and chemical industry, and their application is trained with many exercises based on real-world case studies using a DoE software tool.  A part of the last afternoon is reserved for discussing the participants’ own applications.  The practical aspects are addressed at regular intervals throughout the whole course duration, so that the course overall proposes a balanced combination of methodological knowledge and practical aspect.

Topic areas to be discussed include, but are not limited to:

  • The importance of Quality by Design (QbD) as part of an efficient QA strategy
  • DoE vs. one-factor-at-a-time
  • The sequential approach of DoE: screening, modelling and optimization – which design in which context?
  • Definition of a practical problem as the first step of the application of DoE
  • Factor screening and modelling: how to identify the Critical Process Parameters (CPP’s) and Material Attributes (CMA’s) as well as their interactions
  • Optimization of a response variable with response surface models
  • Graphical visualization and interpretation of the results
    DoE for formulations
  • Defining the Design Space
  • Robustness issues
  • Accounting for real-world challenges: complex restrictions, unsuccessful experiments

Who Should Attend

This comprehensive three-day design of experiments certification course is designed for chemists, engineers, pharmacists and biotechnologists in research, development and production, as well as for laboratory staff involved in the development or optimization of products and processes.  The course covers active ingredients as well as formulated products and is of interest not only to the pharmaceutical and biotechnological sectors, but also to scientists working in the chemical and cosmetics industry.

No previous statistical or mathematical knowledge is necessary.  Since the knowledge acquired is software-independent, the training will be of great benefit to anyone who intends to apply DoE or wants to do it better, whichever software tool he/she uses, even if a specific user-friendly tool (the DoE expert system STAVEX) is employed in the course to highlight the practical aspects of the methodology.

Course Agenda

First Day

  • Background
    • Importance of Quality by Design (QbD) as part of an efficient QA strategy
    • Role of QbD / DoE within the Six Sigma framework
    • Regulatory aspects
  • Concepts of Statistical Design of Experiments
    • Introduction
    • DoE vs. one-factor-at-a-time
    • Strategic approach in phases: screening, modelling, optimization
  • DoE in Practice
    • User input: definition of response variables and factors
    • Effects and interactions
  • Modelling
    • Full and fractional factorial designs
    • Selection of an appropriate design
    • Analysis of the experimental results with multiple linear regression
    • Graphical visualization and interpretation

Second Day

  • Modelling (cont.)
    • Numerical and graphical assessment of the model quality
    • Analysis conclusions
  • Specific Issues with Formulation Problems
    • Concepts and definition of restrictions
    • Designs for formulation problems
    • Result analysis and interpretation
  • Optimization
    • Optimization designs
    • Building response surface models to identify best settings, graphical interpretation
    • Confirmatory experiments

Third Day

  • Screening
    • Selection among many factors
    • Screening designs
    • Analysis with the half-normal plot
  • Simultaneous Optimization of Several Response Variables
    • Target optimization
    • Determining the best compromise and the Design Space with the desirability function
  • Accounting for Real-World Challenges
    • Experimental restrictions, trend
    • Handling of violated factor settings
    • Unsuccessful experiments
  • Practical Recommendations
  • Discussion of the Participants’ Own Applications
    • Questions & Answers

Learning Objectives

After completing the course, the participants will have a thorough understanding of the concepts of statistical Design of Experiments (DoE). They will have gained confidence thanks to the many practical exercises, so that they can solve practical problems easily and efficiently in their daily work, when developing or optimizing products and processes in the QbD context. Among the advanced topics, the participants will in particular learn how to deal with typical complex practical situations such as the simultaneous optimization of several potentially contradictory Critical Quality Attributes (CQA’s), the optimization of formulations or the consideration of technical difficulties like an imprecise setting of the process parameters.