Multivariate Analysis in the Pharmaceutical Industry.

Front Cover -- Multivariate Analysis in the Pharmaceutical Industry -- Copyright Page -- Dedication -- Contents -- List of Contributors -- About the Editors -- Foreword -- I. Background and Methodology -- 1 The Preeminence of Multivariate Data Analysis as a Statistical Data Analysis Technique in Pharmaceutical R&amp -- D and Manufact... -- 1.1 Data Size Glossary (Table 1.1) -- 1.2 Big Data-Overall View -- 1.3 Big Data-Pharmaceutical Context -- 1.4 Statistical Data Analysis Methods in the Pharmaceutical Industry -- 1.5 Development of Multivariate Data Analysis as a Data Analysis Technique within the Pharmaceutical Industry -- 1.6 Current Status of the Use of Multivariate Data Analysis in the Pharmaceutical Space -- 1.7 What MVA Can be Used For/What it Cannot be Used For -- 1.8 Current Limitations and Future Developments -- Acknowledgments -- References -- 2 The Philosophy and Fundamentals of Handling, Modeling, and Interpreting Large Data Sets-the Multivariate Chemometrics App... -- 2.1 Introduction -- 2.1.1 The Nature of this Chapter -- 2.1.2 The History of Metrics -- 2.2 Univariate Data and How it is Handled -- 2.2.1 Data Vectors and Some Definitions -- 2.2.2 Some Statistics on Vectors -- 2.2.3 Some General Thoughts about Univariate Thinking -- 2.3 Multivariate Data With Definitions -- 2.3.1 Data Matrices, Two-Way Arrays -- 2.3.2 Three- and More-Way Arrays -- 2.3.3 Multiblock Data -- 2.3.4 General Thoughts About Multivariate Thinking -- 2.4 Modeling -- 2.4.1 General Factor Models -- 2.4.2 Principal Component Analysis -- 2.4.3 Multivariate Curve Resolution -- 2.4.4 Clustering-Classification -- 2.4.5 Regression Models -- 2.4.6 Model Diagnostics -- 2.4.7 Some General Thoughts About Modeling -- 2.5 Conclusions -- References -- 3 Data Processing in Multivariate Analysis of Pharmaceutical Processes -- 3.1 Introduction.

3.1.1 Pharmaceutical Process Data -- 3.1.2 The Quality-by-Design Principle -- 3.2 Continuous Versus Batch Processes -- 3.3 Data Processing -- 3.3.1 Sampling -- 3.3.2 Noise Cancellation -- 3.3.3 Statistical Process Control -- 3.3.3.1 Implementation -- 3.3.3.2 Examples in the Pharma Industry -- 3.4 Conclusions and Trends -- Acronyms -- References -- 4 Theory of Sampling (TOS): A Necessary and Sufficient Guarantee for Reliable Multivariate Data Analysis in Pharmaceutical ... -- 4.1 Introduction -- 4.2 Heterogeneity -- 4.2.1 Counteracting Heterogeneity: Composite Sampling -- 4.3 Heterogeneity: A Systematic Introduction for Multivariate Data Analysis -- 4.4 Sampling Is Always Involved in PAT and Multivariate Data Analysis -- 4.5 Measurement Uncertainty (MU) -- 4.6 The Role of Reliable Process Sampling in Multivariate Data Analysis -- 4.7 Sample Size, Purpose and Representativeness -- 4.8 Analytical Processes vs. Sampling Processes: A Monumental Difference -- 4.8.1 Case Illustration -- 4.9 TOS: The Necessary and Sufficient Framework for Practical Sampling -- 4.10 Process Sampling in the Pharma Industry -- 4.11 Variographics: A Breakthrough for Multivariate Process Monitoring -- 4.12 Conclusions and Further Resources -- Acknowledgments -- Glossary -- References -- Appendix A -- A1 Pierre Gy (1924-2015): TOS's key concept of sampling errors -- A2 TOS: Governing Principles (GPs) and Sampling Unit Operations (SUOs) -- 5 The "How" of Multivariate Analysis (MVA) in the Pharmaceutical Industry: A Holistic Approach -- 5.1 Background -- 5.2 Why Is a Holistic Approach Needed? -- 5.3 What Stands in the Way? -- 5.4 Key Enabling Tools -- 5.4.1 Voice of the Customer -- 5.4.2 Design of Experiments -- 5.4.3 Metadata -- 5.4.4 Data Handling -- 5.4.5 Model Diagnostics -- 5.4.6 FMEA and Risk Assessment -- 5.4.7 Process Automation -- 5.4.8 Visualization.

5.4.9 Calibration Strategy Space -- 5.4.10 Theory of Sampling -- 5.5 Case Study: Multivariate Calibrations for In-Process Control -- 5.5.1 Background -- 5.5.2 Before MVA -- 5.5.3 Calibration Strategy, v1 -- 5.5.4 Model Deployment and Management -- 5.5.5 Outlier Diagnostics -- 5.5.6 Calibration Strategy, v.2 -- 5.5.7 Building Sustainability -- 5.6 Summary -- Glossary -- References -- 6 Quality by Design in Practice -- 6.1 Process Data and Its Analysis -- 6.1.1 Introduction -- 6.1.2 Traditional Approaches to Experimental Design and Data Analysis -- 6.1.3 Modern Approaches to Experimental Design and Data Analysis -- 6.2 The DoE Toolkit -- 6.2.1 The Right Tool for the Right Job -- 6.2.1.1 Factorial Designs -- 6.2.1.2 Optimization Designs -- 6.2.1.3 Mixture Designs -- 6.2.1.4 Other Design Types -- 6.3 Implementing DoE for QbD -- 6.3.1 Variability Starts With Raw Materials -- 6.3.2 Designed Experiments in Formulation -- 6.3.3 Designed Experiments for Calibration Model Development -- 6.3.4 Designed Experiments for Process Development and Understanding -- 6.3.5 A Practical Roadmap for Applying DoE -- 6.3.5.1 Take a Multivariate Mindset -- 6.3.5.2 Define the Objective of the Project -- 6.3.5.3 Use Risk Management Wisely -- 6.3.5.4 Design the Experimental Plan -- 6.3.5.5 Analyze the Data -- 6.3.5.6 Implementation -- 6.3.5.7 Improvement -- 6.4 Translating DoE Into Process Control: Maintaining the Design Space -- 6.4.1 The Relationship Between DoE and MVA Methods -- 6.4.2 A Short Note on Diametrically Opposed Systems -- 6.4.3 Implementing PAT to Maintain the Design Space -- 6.4.4 Bringing QbD and the Pharmaceutical Quality System Together -- 6.5 Modern Data Acquisition and PAT Management Systems -- 6.5.1 A Model of the Pharmaceutical Quality System -- 6.5.2 Architecture of a Modern Control System -- 6.5.2.1 The PAT and Automation LAN.

6.5.2.2 The Manufacturing LAN -- 6.5.2.3 The QbD Team Environment -- 6.5.3 The QbD Development and Deployment Environment -- 6.5.4 PQS for Continuous Manufacturing Systems -- 6.6 Summary and Future Perspectives -- Terminology and Acronyms -- References -- II. Applications in Pharmaceutical Development and Manufacturing -- 7 Multivariate Analysis Supporting Pharmaceutical Research -- 7.1 Overview of Multivariate Analysis as a Part of Pharmaceutical Product Design -- 7.2 Classification and Experimental High-Throughput Screening -- 7.3 Exploring Complex Analytical Data -- 7.3.1 Imaging: Raman Spectra -- 7.4 Product and Process Understanding -- 7.5 Summary -- Abbreviations -- References -- 8 Multivariate Data Analysis for Enhancing Process Understanding, Monitoring, and Control-Active Pharmaceutical Ingredient ... -- 8.1 Introduction -- 8.2 Process Understanding -- 8.2.1 Univariate Trending -- 8.2.2 Multivariate Trending -- 8.2.2.1 Unsupervised Methods -- 8.2.2.2 Supervised Methods -- 8.2.2.2.1 Direct Approaches -- 8.2.2.2.2 Inverse Approaches -- 8.2.3 Post-Hoc Analyses for Process Improvements and Optimization -- 8.3 Process Control -- 8.3.1 Crystallization Control -- 8.3.2 Reaction Control -- 8.4 Multivariate Statistical Process Control -- 8.4.1 Batch-Wise Unfolding (or Batch Level) -- 8.4.2 Observation-Wise Unfolding (or Observation Level) -- 8.5 Conclusion -- Acronyms -- References -- 9 Applications of MVDA and PAT for Drug Product Development and Manufacturing -- 9.1 Introduction -- 9.2 Method Design and Development -- 9.2.1 Method Requirements and Performance Criteria -- 9.2.2 Risk Assessment -- 9.2.3 Calibration Design -- 9.3 Method Validation -- 9.4 Outlier Detection and System Suitability Test -- 9.5 Method Maintenance and Life Cycle Management -- 9.6 Example Data During Commercial Implementation -- 9.6.1 Blending Homogeneity.

9.6.2 Near-Infrared-Based Tablet Potency and Content Uniformity Measurements -- 9.7 Conclusions -- Acknowledgments -- Abbreviations -- References -- 10 Applications of Multivariate Analysis to Monitor and Predict Pharmaceutical Materials Properties -- 10.1 Introduction -- 10.2 Spray-Dried Dispersions -- 10.3 Case Study 1: Investigate the Impact of Spray-Dried Dispersion Particle Properties on Formulation Performance -- 10.3.1 Material and Methods -- 10.3.1.1 Particle Size Characterization by Imaging With Malvern Morphologi G3 -- 10.3.1.2 Particle Size Characterization by Laser-Light Scattering -- 10.3.1.3 Density -- 10.3.1.4 Surface Area -- 10.3.1.5 Mercury Intrusion Porosimetry -- 10.3.1.6 Flow -- 10.3.1.7 Compactibility -- 10.3.1.8 Dissolution -- 10.3.1.9 Data Analysis -- 10.3.2 Results and Discussion -- 10.3.2.1 Exploring the Relationships Between Spray-Dried Dispersion Particle Properties -- 10.3.2.2 Impact of Particle Properties on Formulation Performance -- 10.3.2.2.1 Flow -- 10.3.2.2.2 Compactibility -- 10.3.2.2.3 Dissolution Rate -- 10.3.3 Summary -- 10.4 Case Study 2: Development of a Surrogate Measurement for Particle Morphology -- 10.4.1 Material and Methods -- 10.4.1.1 Light Transmission Data -- 10.4.1.2 Scanning Electron Microscopy -- 10.4.1.3 Mercury Intrusion Porosimetry -- 10.4.1.4 Data Processing and Analysis -- 10.4.2 Results and Discussion -- 10.4.2.1 High-Pressure Intrusion Volume -- 10.4.2.2 Light Transmission Data -- 10.4.2.3 Regression Analysis and Morphology Factor -- 10.4.3 Summary -- 10.5 Conclusions -- Acknowledgments -- Abbreviations -- References -- 11 Mining Information From Developmental Data: Process Understanding, Design Space Identification, and Product Transfer -- 11.1 Introduction -- 11.2 Latent-Variable Modeling Techniques -- 11.2.1 Principal Component Analysis -- 11.2.1.1 Process Monitoring Using PCA.

11.2.2 Projection to Latent Structures.

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