Methods for Developing a Process Design Space Using Retrospective Data.

Area of Science:

• Pharmaceutical Sciences
• Chemical Engineering
• Statistics

Background:

• Collinearity in statistical models can lead to misinterpretation and reduced predictability.
• Prospective experimental designs (DoEs) help prevent collinearity but are not always feasible.
• Retrospective analysis of historical data requires careful methodology to ensure valid statistical modeling.

Purpose of the Study:

• To present a methodology for constructing a design space using retrospective process data.
• To provide guidelines for enhancing statistical modeling with historical data.
• To avoid common pitfalls associated with retrospective data analysis.

Main Methods:

• Development of a methodology for design space construction using process data.
• Application of the methodology to a real-world wet granulation process.
• Illustration of concepts and methods using pragmatic examples.

Main Results:

• Demonstration of a viable approach to statistical modeling using historical data.
• Identification of strategies to mitigate collinearity issues in retrospective analyses.
• Successful construction of a design space from wet granulation process data.

Conclusions:

• Retrospective data analysis can yield reliable statistical models when appropriate guidelines are followed.
• The presented methodology effectively constructs design spaces and enhances model predictability.
• This approach offers a valuable alternative for statistical modeling when prospective DoEs are not possible.