Understanding Design of Experiments (DoE) in the Pharmaceutical Industry
Im Auftrag der ECA Academy

Understanding Design of Experiments (DoE) in the Pharmaceutical Industry Im Auftrag der ECA Academy

Heidelberg, Germany

Seminar Nr. 18390


Dieses Seminar hat leider schon statt gefunden. Wenn Sie aber Interesse an diesem Seminar haben, informieren wir Sie gerne über den neuen Termin oder über weitere Angebote zu dem Thema. Nutzen Sie einfach das folgende Kontaktformular, um uns Ihre Anfrage zu senden.

Rückfragen unter:
Tel.: 06221 / 84 44 0 E-Mail: info@concept-heidelberg.de


Dr Raphael Bar, BR Consulting, Israel
Dr Raluca Ilinca Schmitt, Bayer, Germany


This course will explain the basics of DoE with practicing with factorial and fractional DoE as well as DoE by RSM If you have no or little previous knowledge with DoE, you will learn how to set up an experimental design and how to explore the effect of factors that influence either a development/production process or an analytical procedure  while taking into account interactions between the factors.
To better understand and assimilate the DoE  principles, you will learn first to calculate the main effects and factors  interactions by simple manual calculations (with Excel). Then, you will learn how to use Minitab  software program to create a variety of DoE designs, analyze and interpret them. Multiple exercises and examples from pharmaceutical development and laboratory analysis such as robustness studies will be solved by the participants. The participants will learn how to interpret the output of a DoE programme.


With FDA´s Process Validation Guidance for Industry from 2011 and the Annex 15 Revision 2015 process validation has changed to a life cycle. And the life cycle starts with the development which delivers process knowledge and the critical process parameters. To get there the FDA mentions „Design of Experiments“ (DoE). Therefore, DoE is a tool for implementing the process validation life cycle.
Also, ICH Guidelines Q8 (Pharmaceutical Development) and ICH Q9 (Quality Risk Management) speak about DoE as a tool, also in relation to Quality by Design (QbD= approaches).
Meanwhile, DoE is also common practice in other pharmaceutical areas, i.e. in the analytical development or as a CAPA measure for process optimisation.


The addressees of the event are employees from the development, quality control lab and  quality assurance departments who are using DoE or wanted to use DoE in the future. We address also GMP auditors and inspectors and validation personnel also involved in DoE.


Each participant should bring a laptop with Excel and a previously downloaded 30 day free-trial Minitab 19 program from http://www.minitab.com. This program should be downloaded on a laptop a few days before the beginning date of the course and verified that it works on the laptop.


  • DoE and Quality by Design
  • Regulations (EU and FDA)
  • A factorial experiment
  • DoE vs one-at-a-time experiment
  • Where is DoE applied in development and validation of analytical methods
  • Where is DoE applied in manufacturing process
  • development and validation
DoE by Hand Calculations: Effects and Interactions
  • Factorial experiments (categorical and numeric factors)
  • Two and three factorial designs
  • Manual calculation of main effects
  • Manual calculation of interactions
  • What is an orthogonal DoE
  • Exercises with Excel
Acquaintance with Minitab
  • Basic structure of Minitab software
  • Input of data
  • Running a DoE
  • Plotting output results
  • Practicing with Minitab
Basic Statistical tools for Interpretation of DoE Output
  • F-Test
  • t-Test
  • p-value
  • Diagnostics for goodness of fit to model
  • Exercises with Excel
Are the Factors Significant?
  • Deviations from normality plot
  • Making replicate experiments
  • Adding experiments at centre points
  • Using known variability
  • Exercises with Excel
Full Factorial DoE Experiments with Minitab
  • Two factor full DoE experiments
  • Interactions between two factors
  • Plotting Main effects and Interactions
  • Interpretation of DoE Minitab output
  • Does the linear fit the model?
  • Significance with p values
  • General full factorial DoE
  • Exercises with Excel
  • Exercises in interpretation of Minitab outputs
Screening Design Experiments with Minitab
  • Two and three factor experiments with Minitab
  • Aliasing in DoE experiments
  • Resolution of  DoE experiments
  • 4-7 fractional factorial DoE
  • Blackett-Burmann designs
  • Definitive screening design
  • Exercises with Minitab:
    • Robustness of HPLC method with fractional DoE
    • Optimisation of a process with fractional DoE
Optimisation with Response Surface Methodology
  • 22 factorial experiments with RSM
  • Contour plot
  • Surface plot
  • Concept of Design Space
  • Exercises: optimization of drug solubility with RSM design
  • Effect of process parameters on dissolution assay and variability
Case Study DoE: Development of a Medicinal Product
  • Why we use DoE in the pharmaceutical development?
  • Example: DoE for formulation selection / optimization
  • Example: DoE for manufacturing process optimization
  • DoE vs “traditional” approach – when to use which?
Strategy of DoE in Drug Development Process
  • Screening experiments
  • Fractional experiments
  • Full factorial experiments
  • Optimisation experiments: Surface Response Methodology
  • Design Space versus Proven Operating Range (PAR)
  • Normal Operating Range (NOR)
  • Robustness of experiments of a process/method


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