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Practical Implementation of Quality by Design: Statistical Techniques for Product Development

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This course will provide you with an introduction to the power and practical application of these multi-variate tools and techniques.

Thursday 12 to Friday 13 July 2025
Manchester Marriott Victoria & Albert Hotel, Manchester, UK

£1360.00 excl. VAT – £1632.00 incl. VAT (first booking)
£1088.00 excl. VAT – £1306.00 incl. VAT (additional bookings from same site)

Type: Pharmaceutical

About this course

The science and risk-based approach to the development of pharmaceutical products and processes, which is generally referred to as Quality by Design (QbD), requires the application of multi-variate statistical tools.

The ‘traditional’ empirical approach to the development of products, which changes one variable at a time, suffers from the fundamental weakness that often variables are not independent. In order to explore the interactions between variables a multi-variate approach is needed.

Multi-variate statistical tools, such as Design of Experiments (DoE), Principle Component Analysis (PCA), Partial Least Squares (PLS), etc. have been utilised by other industries for decades. Modern computer software makes performing these analyses simpler than they have ever been in the past.

This course will provide you with an introduction to the power and practical application of these multi-variate tools and techniques.

What You Will Learn

  • How to recognise the potential of DoE in product and process development
  • How to apply DoE for identifying Critical Process Parameters (CPPs), material attributes and interactions as well as understanding the impact of variability on Critical Quality Attributes (CQAs)
  • How to appreciate the role of experimentation and to select the correct experimental design for the intended purpose
  • To understand how DoE can be used to help develop a design space
  • How models based on DoE can be used as surrogates for traditional testing of CQAs
  • How to use basic statistics for development activities
  • How multi-variate data analysis can be used to gain process understanding from a large dataset, e.g. arising from PAT application
  • How to take advantage of increased batch control using large sample sizes

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