Multivariate Analysis in the Pharmaceutical Industry

Dr. Ferreira has over 10 years of experience in the application of multivariate analysis in the pharmaceutical industry both in R&D and manufacturing, spanning both small and large molecule applications. Her research and publications focus on the use of multivariate analysis for extraction of information from large data sets spanning diverse topics such as near infrared spectroscopy, process analysis and material characterization. Dr. Menezes has over 20 years of experience in academia and pharma/biopharma industries where he conducted multiple projects. He is a pioneer of the application of PAT and QbD principles to the bioengineering field. He is the co-editor of three books and has published more than 75 papers and several book chapters on MVA, PAT, QbD, data and knowledge management. After training as a pharmacist and getting his PhD, Mike Tobyn joined the faculty in the University of Bath, where he studied and worked under Professor John Staniforth. He has worked for, or consulted for, spinout and large pharmaceutical companies, and excipient suppliers. Mike’s fascination with materials has led him to believe that the properties of materials in processes are governed more by their faults than their intrinsic perfect properties, but that these are more difficult to detect than conventional analysis will allow. Mike has over 20 years of experience in academia and the pharmaceutical industry, and has published more than 75 papers in the fields of oral drug delivery, inhalation drug delivery, and MVA.

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