Multivariate Analysis in the Pharmaceutical Industry

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.

Targets pharmaceutical industry practitioners and regulatory staff by addressing industry specific challengesIncludes perspectives from different software providers to introduce readers to several approachesContains insights from pharmaceutical regulators whose attitudes will shape how multivariate analysis (MVA) is used in years to come

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 <1039> chapter 19. Multivariate analysis and the pharmaceutical regulatory framework