Statistics in pharma – QbD: design of experiments as a tool used to ensure quality of medicines manufactured

The course is addressed mainly at persons working in the pharmaceutical industry who intend to use DOE (Design of Experiments) tools for designing medicine formulations and technological processes.

Training description: The course is intended for all persons interested in practical application of the ICH Q8 “Pharmaceutical development” guidelines. It is mainly addressed at persons working in the pharmaceutical industry who intend to use DOE (Design of Experiments) tools for designing medicine formulations and technological processes. During the course, we will discuss issues related to verification of the criticality of impact of variables, optimisation of critical parameters and definition of a design space. The issues will be illustrated with examples of analyses in the Statistica software. The potential participants of the course are pharmacists, engineers, biologists, biotechnologists involved in R&D works, supervision over production processes, process validation, as well as employees of registration departments and persons responsible for quality management systems. The course lasts for two days.

Requirements: ability to work with a computer in the Windows environment.

Training programme:

1.New good manufacturing practice regulations
  • Introduction to PAT and Quality by Design
  • Stages of implementation
  • Concepts:
    • Target Product Profile
    • Target Quality Product Profile
    • Critical Quality Attributes
    • design space
    • scale-up
    • Process control
    • Process monitoring
2.Introduction to design of experiments
  • Design of experiments strategies
  • Basic assumptions of a DOE strategy
  • Rules of creating an experimental matrix
  • Calculation of effects of variables’ impact and of interactions between variables
  • Full, fraction and screening matrices
  • Determination of experiments and repetitions number
3.Selected statistical data analysis issues
  • The ‘population’ and ‘sample’ concepts
  • Measurement and a level of measurement
  • Distribution of variables
  • Statistical inference chart
  • Student’s t-test
  • Analysis of variance
  • Analysis of multi-factor experiments results
  • Multiple regression
  • Model creation and verification
  • Response surface
4.Statistica examples
  • Example 1 – choice of a drug form composition (a mixture design)
  • Example 2 – choice of critical parameters (a screening design)
  • Example 3 – optimisation of critical process parameters (a central composite design)

Questions? Please contact our specialist team.

info@statsoftpharma.com

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