Full Description
Multivariate Analysis in the Pharmaceutical Industry provides industry practitioners with guidance on multivariate data methods and their applications over the lifecycle of a pharmaceutical product, from process development, to routine manufacturing, focusing on the challenges specific to each step. It includes an overview of regulatory guidance specific to the use of these methods, along with perspectives on the applications of these methods that allow for testing, monitoring and controlling products and processes. The book seeks to put multivariate analysis into a pharmaceutical context for the benefit of pharmaceutical practitioners, potential practitioners, managers and regulators.
Users will find a resources that addresses an unmet need on how pharmaceutical industry professionals can extract value from data that is routinely collected on products and processes, especially as these techniques become more widely used, and ultimately, expected by regulators.
Contents
Section I. Background and Methodology
1. The pre-eminence of multivariate data analysis as a statistical data analysis technique in pharmaceutical R&D and manufacturing
2. The philosophy and fundamentals of handling, modeling and interpreting large data sets - the multivariate chemometrics approach
3. Data processing in multivariate analysis of pharmaceutical processes
4. Theory of sampling (TOS) - a necessary and sufficient guarantee for reliable multivariate data analysis in pharmaceutical manufacturing
5. The 'how' of multivariate analysis (MVA) in the pharmaceutical industry: A holistic approach
6. Quality by design in practice
Section II. Applications in Pharmaceutical Development and Manufacturing
7. Multivariate analysis supporting pharmaceutical research
8. Multivariate data analysis for enhancing process understanding, monitoring and control - active pharmaceutical ingredient manufacturing case studies
9. Applications of MVDA and PAT for drug product development and manufacturing
10. Applications of multivariate analysis to monitor and predict pharmaceutical materials properties
11. Mining information from developmental data: process understanding, design space identification, and product transfer
12. A systematic approach to process data analytics in pharmaceutical manufacturing: The data analytics triangle and its application to the manufacturing of a monoclonal antibody
13. Model maintenance
14. Lifecycle management of PAT procedures: Applications to batch and continuous processes
15. Applications of MVA for product quality management: Continued process verification and continuous improvement
16. The role of multivariate statistical process control in the pharma industry
17. Application of multivariate process modelling for monitoring and control applications in continuous pharmaceutical manufacturing
Section III. Guidance Documents and Regulatory Framework
18. Guidance for compendial use - The USP chapter
19. Multivariate analysis and the pharmaceutical regulatory framework
