home   genetic news   bioinformatics   biotechnology   literature   journals   ethics   positions   events   sitemap
 
  HUM-MOLGEN -> Events -> Courses and Workshops
Search  -  prev / next
 
 

QbD - Product & Process Optimization using Design of Experiments

 
  January 18, 2018  
     
 
CfPIE - The Center for Professional Innovation & Education, Berlin, Germany
23 - 25 April, 2018


Course Description - Course runs 9:00 - 5:00 on Day 1 & Day 2 -- 9:00 - 3:00 on Day 3 (Breakfast & Lunch Included)

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 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.

 
 
Organized by: Center for Professional Innovation & Education
Invited Speakers:

Stefanie Feiler, Ph.D.

Specialties: Regulatory, International

Dr. Stefanie Feiler works as Senior Consultant in Applied Statistics at AICOS Technologies since 2005.

Her tasks include consulting and data analysis, as well as teaching. In a multitude of diverse projects that she in particular executes for the pharmaceutical industry, she regularly uses a broad range of statistical methods. Her areas of specialization are statistical Design of Experiments (DoE), Six Sigma, and data mining using CART. In training, her main concern is on the applications of the methods in the participants’ typical working situations.

Dr. Feiler studied mathematics and chemistry at the university of Tübingen, Germany. During her exchange year in Besançon, France, she received a master's degree in pure mathematics. She obtained her PhD degree as member of the Statistics Group of the Institute for Applied Mathematics at the University of Heidelberg, Germany.

 
Deadline for Abstracts: N/A
 
Registration: https://www.cfpie.com/ProductDetails.aspx?ProductID=294
E-mail: info@cfpie.com
 
 
   
 
home   genetic news   bioinformatics   biotechnology   literature   journals   ethics   positions   events   sitemap
 
 
 

Generated by meetings and positions 5.0 by Kai Garlipp
WWW: Kai Garlipp, Frank S. Zollmann.
7.0 © 1995-2018 HUM-MOLGEN. All rights reserved. Liability, Copyright and Imprint.