Editors Walkiria S. Schlindwein is Associate Professor of Pharmaceutics at the School of Pharmacy, De Montfort University. Walkiria is the programme leader of two Postgraduate courses in Pharmaceutical Quality by Design. Mark Gibson is Director of AM PharmaServices Ltd. He is a practicing Pharmaceutical Consultant and was formerly with AstraZeneca.

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• List of Figures xiiiList of Tables xixList of Contributers xxiSeries Preface xxiiiPreface xxv1 Introduction to Quality by Design (QbD) 1Bruce Davis and Walkiria S. Schlindwein1.1 Introduction 11.2 Background 21.3 Science] and Risk]Based Approaches 41.4 ICH Q8–Q12 51.5 QbD Terminology 61.6 QbD Framework 71.7 QbD Application and Benefits 71.8 Regulatory Aspects 81.9 Summary 91.10 References 92 Quality Risk Management (QRM) 11Noel Baker2.1 Introduction 112.2 Overview of ICH Q9 132.2.1 Start QRM Process 152.2.2 Risk Assessment 152.2.3 Risk Control 162.2.4 Risk Review 162.3 Risk Management Tools 172.4 Practical Examples of Use for QbD 222.4.1 Case Study 262.4.2 Pre]work 262.4.3 Scoring Meeting 322.4.4 FMECA Tool 322.4.5 Risk Score 322.4.6 Detectability Score 342.4.7 Communication 352.5 Concluding Remarks 362.6 References 443 Quality Systems and Knowledge Management 47Siegfried Schmitt3.1 Introduction to Pharmaceutical Quality System 473.1.1 Knowledge Management – What Is It and Why Do We Need It? 473.2 The Regulatory Framework 483.2.1 Knowledge Management in the Context of Quality by Design (QbD) 483.2.2 Roles and Responsibilities for Quality System 493.2.3 Roles and Responsibilities for Knowledge Management 503.2.4 Implicit and Explicit Knowledge 503.3 The Documentation Challenge 513.4 From Data to Knowledge: An Example 563.5 Data Integrity 583.6 Quality Systems and Knowledge Management: Common Factors for Success 583.7 Summary 593.8 References 604 Quality by Design (QbD) and the Development and Manufacture of Drug Substance 61Gerry Steele4.1 Introduction 614.2 ICH Q11 and Drug Substance Quality 624.2.1 Enhanced Approach 634.2.2 Impurities 634.2.3 Physical Properties of Drug Substance 644.3 Linear and Convergent Synthetic Chemistry Routes 654.4 Registered Starting Materials (RSMs) 674.5 Definition of an Appropriate Manufacturing Process 684.5.1 Crystallization, Isolation and Drying of APIs 684.5.2 Types of Crystallization 694.5.3 Design of Robust Cooling Crystallization 704.6 In]Line Process Analytical Technology and Crystallization Processes 784.6.1 Other Unit Operations 804.7 Applying the QbD Process 824.7.1 Quality Risk Assessment (QRA) 834.8 Design of Experiments (DoE) 874.9 Critical Process Parameters (CPPs) 884.10 Design Space 884.11 Control Strategy 894.12 References 915 The Role of Excipients in Quality by Design (QbD) 97Brian Carlin5.1 Introduction 975.2 Quality of Design (QbD) 985.3 Design of Experiments (DoE) 1005.4 Excipient Complexity 1025.5 Composition 1055.6 Drivers of Functionality or Performance 1055.7 Limited Utility of Pharmacopoeial Attributes 1065.8 Other Unspecified Attributes 1075.9 Variability 1075.10 Criticalities or Latent Conditions in the Finished Product 1085.11 Direct or Indirect Impact of Excipient Variability 1105.12 Control Strategy 1115.13 Communication with Suppliers 1125.14 Build in Compensatory Flexibility 1135.15 Risk Assessment 1135.16 Contingencies 1145.17 References 1146 Development and Manufacture of Drug Product 117Mark Gibson, Alan Carmody, and Roger Weaver6.1 Introduction 1176.2 Applying QbD to Pharmaceutical Drug Product Development 1196.3 Product Design Intent and the Target Product Profile (TPP) 1206.4 The Quality Target Product Profile (QTPP) 1266.5 Identifying the Critical Quality Attributes (CQAs) 1286.6 Product Design and Identifying the Critical Material Attributes (CMAs) 1336.7 Process Design and Identifying the Critical Process Parameters (CPPs) 1366.8 Product and Process Optimisation 1396.9 Design Space 1456.10 Control Strategy 1506.11 Continuous Improvement 1536.11 Acknowledgements 1546.12 References 1547 Design of Experiments 157Martin Owen and Ian Cox7.1 Introduction 1577.2 Experimental Design in Action 1587.3 The Curse of Variation 1587.3.1 Signal]to]Noise Ratio 1597.4 Fitting a Model 1617.4.1 Summary of Fit 1657.5 Parameter Estimates 1657.6 Analysis of Variance 1667.6.1 Reflection 1687.7 ‘To Boldly Go’– An Introduction to Managing Resource Constraints using DoE 1697.8 The Motivation for DoE 1707.8.1 How Does the Workshop Exercise Work? 1717.8.2 DoE Saves the Day! 1727.9 Classical Designs 1737.9.1 How Do Resource Constraints Impact the Design Choice? 1737.9.2 Resource Implications in Practice 1737.10 Practical Workshop Design 1747.10.1 Choice of Factors and Measurements 1757.10.2 Data Collection and Choice of Design 1757.10.3 Some Simple Data Visualization 1757.10.4 Analysis of the Half Fraction 1777.10.5 How to Interpret Prediction Profiles 1777.10.6 Half Fraction and Alternate Half Fraction 1787.10.7 Interaction Effects 1787.10.8 Full Factorial 1817.10.9 Central Composite Design 1817.10.10 How Robust Is This DoE to Unexplained Variation? 1817.11 How Does This Work? The Underpinning of Statistical Models for Variation 1847.12 DoE and Cycles of Learning 1877.13 Sequential Classical Designs and Definitive Screening Designs 1897.14 Building a Simulation 1907.14.1 Sequential design, Part 1: Screening Design (10 Runs) 1917.14.2 Sequential Design, Part II: Optimization Design (30 Runs) 1917.14.3 Definitive Screening Design 1947.14.4 Robustness Design 1947.14.5 Additional Challenges 1977.15 Conclusion 1977.16 Acknowledgements 1987.17 References 1988 Multivariate Data Analysis (MVDA) 201Claire Beckett, Lennart Eriksson, Erik Johansson, and Conny Wikstrom8.1 Introduction 2018.2 Principal Component Analysis (PCA) 2028.3 PCA Case Study: Raw Material Characterization using Particle Size Distribution Curves 2048.3.1 Dataset Description 2048.3.2 Fitting a PCA Model to the 45 Training Set Batches 2058.3.3 Classification of the 13 Test Set Batches 2068.3.4 Added Value from DoE to Select Spanning Batches 2088.4 Partial Least Squares Projections to Latent Structures (PLS) 2088.5 PLS Case Study: A Process Optimization Model 2108.5.1 Dataset Description 2108.5.2 PLS Modeling of 85]Samples SOVRING Subset 2118.5.3 Looking into Cause]and]Effect Relationships 2128.5.4 Making a SweetSpot Plot to Summarize the PLS Results 2138.5.5 Using the PLS]DoE Model as a Basis to Define a Design Space and PARs for the SOVRING Process 2158.5.6 Summary of SOVRING Application 2178.6 Orthogonal PLS (OPLS® Multivariate Software) 2178.7 Orthogonal PLS (OPLS® Multivariate Software) Case Study – Batch Evolution Modeling of a Chemical Batch Reaction 2188.7.1 Dataset Description 2188.7.2 Batch Evolution Modeling 2188.8 Discussion 2208.8.1 The PAT Initiative 2208.8.2 What Are the Benefits of Using DoE? 2218.8.3 QbD and Design Space 2228.8.4 MVDA/DoE Is Needed to Accomplish PAT/QbD in Pharma 2238.8.5 MVDA: A Way to Power up the CPV Application 2238.9 References 2249 Process Analytical Technology (PAT) 227Line Lundsberg]Nielsen,Walkiria S. Schlindwein, and Andreas Berghaus9.1 Introduction 2279.2 How PAT Enables Quality by Design (QbD) 2299.3 The PAT Toolbox 2299.4 Process Sensors and Process Analysers 2299.4.1 Process Sensors – Univariate 2339.4.2 Process Analysers – Multivariate 2339.4.3 Infrared (IR) 2339.4.4 Near Infrared (NIR) 2389.4.5 Tunable Diode Laser Spectroscopy (TDLS) 2399.4.6 Ultraviolet]Visible (UV]Vis) 2399.4.7 Raman 2399.4.8 Focused Beam Reflectance Measurements (FBRM) and Laser Diffraction 2399.4.9 Particle Vision and Measurement (PVM) 2399.4.10 X]Ray Fluorescence (XRF) 2409.4.11 Imaging Technologies 2409.5 Analyser Selection 2409.6 Regulatory Requirements Related to PAT Applications 2409.6.1 Europe 2429.6.2 United States 2429.7 PAT Used in Development 2429.8 PAT Used in Manufacturing 2439.9 PAT and Real Time Release Testing (RTRT) 2459.10 PAT Implementation 2459.11 Data Management 2469.12 In]Line Process Monitoring with UV]Vis Spectroscopy: Case Study Example 2479.13 References 25310 Analytical Method Design, Development, and Lifecycle Management 257Joe de Sousa, David Holt, and Paul A. Butterworth10.1 Introduction 25710.2 Comparison of the Traditional Approach and the Enhanced QbD Approach 25810.3 Details of the Enhanced QbD Approach 26010.4 Defining Method Requirements 26210.5 Designing and Developing the Method 26410.6 Understanding the Impact of Method Parameters on Performance 26610.7 Defining the Method Control Strategy and Validating the Method 26710.8 Monitoring Routine Method Performance for Continual Improvement 26810.9 Summary 26910.10 Example Case Studies 27010.10.1 Case Study 1 – Establishment of Robust Operating Ranges during Routine Method Use and Justifying the Method Control Strategy (Including SST Criteria) 27010.10.2 Risk Assessment and Definition of Ranges 27010.10.3 Experimental Design 27110.10.4 Evaluate the DoE 27210.10.5 Documenting Method Performance 27410.10.6 Case Study 2 – Evaluation of the Ruggedness of a Dissolution Method for a Commercial Immediate Release Tablet Product 27410.10.7 Case Study Acknowledgements 27811 Manufacturing and Process Controls 281Mark Gibson11.1 Introduction to Manufacturing and Facilities 28111.2 Validation of Facilities and Equipment 28211.2.1 The International Society for Pharmaceutical Engineering (ISPE) Baseline® Guide: Commissioning and Qualification 28211.2.2 ASTM E2500]07: Standard Guide for Specification, Design, and Verification of Pharmaceutical and Biopharmaceutical Manufacturing Systems and Equipment 28411.2.3 Science]Based Approach and Critical Aspects 28511.2.4 Risk]Based Approach 28611.2.5 System and Component Impact Assessments 28811.2.6 URSs for Systems 29011.2.7 Specification and Design 29011.2.8 Verification 29011.3 Drug Product Process Validation: A Lifecycle Approach 29211.3.1 Stage 1: Process Design/Product Development 29511.3.2 Stage 2: Process Qualification 29811.3.3 Stage 3: Continued Process Verification 29911.4 The Impact of QbD on Process Equipment Design and Pharmaceutical Manufacturing Processes 30011.5 Introduction to Process Control in Pharmaceutical Manufacturing 30211.6 Advanced Process Controls (APC) and Control Strategy 30511.7 The Establishment of Continuous Manufacture 30911.8 The Tablet Press as Part of a Continuous Tableting Line 31211.9 Real]Time Release Testing and Continuous Quality Verification 31611.10 Acknowledgments 31711.11 References 31712 Regulatory Guidance 321Siegfried Schmitt and Mustafa A. Zaman12.1 Introduction 32112.2 The Common Technical Document (CTD) Format 32212.2.1 Quality Target Product Profile (QTPP) and Critical Quality Attributes (CQAs) 32412.2.2 Quality Risk Management (ICH Q9) 32412.2.3 Product and Process Development (S.2.6 and P.2) 32512.2.4 Control Strategy 32612.2.5 Design Space (Optional) 32712.3 Essential Reading 32812.4 What Is Not Written, or Hidden, in the Guidance Documents? 32912.5 Post]Approval Change 33012.6 Summary 33112.7 References 332Index

Editors Walkiria S. Schlindwein is Associate Professor of Pharmaceutics at the School of Pharmacy, De Montfort University. Walkiria is the programme leader of two Postgraduate courses in Pharmaceutical Quality by Design. Mark Gibson is Director of AM PharmaServices Ltd. He is a practicing Pharmaceutical Consultant and was formerly with AstraZeneca.

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• List of Figures xiiiList of Tables xixList of Contributers xxiSeries Preface xxiiiPreface xxv1 Introduction to Quality by Design (QbD) 1Bruce Davis and Walkiria S. Schlindwein1.1 Introduction 11.2 Background 21.3 Science] and Risk]Based Approaches 41.4 ICH Q8–Q12 51.5 QbD Terminology 61.6 QbD Framework 71.7 QbD Application and Benefits 71.8 Regulatory Aspects 81.9 Summary 91.10 References 92 Quality Risk Management (QRM) 11Noel Baker2.1 Introduction 112.2 Overview of ICH Q9 132.2.1 Start QRM Process 152.2.2 Risk Assessment 152.2.3 Risk Control 162.2.4 Risk Review 162.3 Risk Management Tools 172.4 Practical Examples of Use for QbD 222.4.1 Case Study 262.4.2 Pre]work 262.4.3 Scoring Meeting 322.4.4 FMECA Tool 322.4.5 Risk Score 322.4.6 Detectability Score 342.4.7 Communication 352.5 Concluding Remarks 362.6 References 443 Quality Systems and Knowledge Management 47Siegfried Schmitt3.1 Introduction to Pharmaceutical Quality System 473.1.1 Knowledge Management – What Is It and Why Do We Need It? 473.2 The Regulatory Framework 483.2.1 Knowledge Management in the Context of Quality by Design (QbD) 483.2.2 Roles and Responsibilities for Quality System 493.2.3 Roles and Responsibilities for Knowledge Management 503.2.4 Implicit and Explicit Knowledge 503.3 The Documentation Challenge 513.4 From Data to Knowledge: An Example 563.5 Data Integrity 583.6 Quality Systems and Knowledge Management: Common Factors for Success 583.7 Summary 593.8 References 604 Quality by Design (QbD) and the Development and Manufacture of Drug Substance 61Gerry Steele4.1 Introduction 614.2 ICH Q11 and Drug Substance Quality 624.2.1 Enhanced Approach 634.2.2 Impurities 634.2.3 Physical Properties of Drug Substance 644.3 Linear and Convergent Synthetic Chemistry Routes 654.4 Registered Starting Materials (RSMs) 674.5 Definition of an Appropriate Manufacturing Process 684.5.1 Crystallization, Isolation and Drying of APIs 684.5.2 Types of Crystallization 694.5.3 Design of Robust Cooling Crystallization 704.6 In]Line Process Analytical Technology and Crystallization Processes 784.6.1 Other Unit Operations 804.7 Applying the QbD Process 824.7.1 Quality Risk Assessment (QRA) 834.8 Design of Experiments (DoE) 874.9 Critical Process Parameters (CPPs) 884.10 Design Space 884.11 Control Strategy 894.12 References 915 The Role of Excipients in Quality by Design (QbD) 97Brian Carlin5.1 Introduction 975.2 Quality of Design (QbD) 985.3 Design of Experiments (DoE) 1005.4 Excipient Complexity 1025.5 Composition 1055.6 Drivers of Functionality or Performance 1055.7 Limited Utility of Pharmacopoeial Attributes 1065.8 Other Unspecified Attributes 1075.9 Variability 1075.10 Criticalities or Latent Conditions in the Finished Product 1085.11 Direct or Indirect Impact of Excipient Variability 1105.12 Control Strategy 1115.13 Communication with Suppliers 1125.14 Build in Compensatory Flexibility 1135.15 Risk Assessment 1135.16 Contingencies 1145.17 References 1146 Development and Manufacture of Drug Product 117Mark Gibson, Alan Carmody, and Roger Weaver6.1 Introduction 1176.2 Applying QbD to Pharmaceutical Drug Product Development 1196.3 Product Design Intent and the Target Product Profile (TPP) 1206.4 The Quality Target Product Profile (QTPP) 1266.5 Identifying the Critical Quality Attributes (CQAs) 1286.6 Product Design and Identifying the Critical Material Attributes (CMAs) 1336.7 Process Design and Identifying the Critical Process Parameters (CPPs) 1366.8 Product and Process Optimisation 1396.9 Design Space 1456.10 Control Strategy 1506.11 Continuous Improvement 1536.11 Acknowledgements 1546.12 References 1547 Design of Experiments 157Martin Owen and Ian Cox7.1 Introduction 1577.2 Experimental Design in Action 1587.3 The Curse of Variation 1587.3.1 Signal]to]Noise Ratio 1597.4 Fitting a Model 1617.4.1 Summary of Fit 1657.5 Parameter Estimates 1657.6 Analysis of Variance 1667.6.1 Reflection 1687.7 ‘To Boldly Go’– An Introduction to Managing Resource Constraints using DoE 1697.8 The Motivation for DoE 1707.8.1 How Does the Workshop Exercise Work? 1717.8.2 DoE Saves the Day! 1727.9 Classical Designs 1737.9.1 How Do Resource Constraints Impact the Design Choice? 1737.9.2 Resource Implications in Practice 1737.10 Practical Workshop Design 1747.10.1 Choice of Factors and Measurements 1757.10.2 Data Collection and Choice of Design 1757.10.3 Some Simple Data Visualization 1757.10.4 Analysis of the Half Fraction 1777.10.5 How to Interpret Prediction Profiles 1777.10.6 Half Fraction and Alternate Half Fraction 1787.10.7 Interaction Effects 1787.10.8 Full Factorial 1817.10.9 Central Composite Design 1817.10.10 How Robust Is This DoE to Unexplained Variation? 1817.11 How Does This Work? The Underpinning of Statistical Models for Variation 1847.12 DoE and Cycles of Learning 1877.13 Sequential Classical Designs and Definitive Screening Designs 1897.14 Building a Simulation 1907.14.1 Sequential design, Part 1: Screening Design (10 Runs) 1917.14.2 Sequential Design, Part II: Optimization Design (30 Runs) 1917.14.3 Definitive Screening Design 1947.14.4 Robustness Design 1947.14.5 Additional Challenges 1977.15 Conclusion 1977.16 Acknowledgements 1987.17 References 1988 Multivariate Data Analysis (MVDA) 201Claire Beckett, Lennart Eriksson, Erik Johansson, and Conny Wikstrom8.1 Introduction 2018.2 Principal Component Analysis (PCA) 2028.3 PCA Case Study: Raw Material Characterization using Particle Size Distribution Curves 2048.3.1 Dataset Description 2048.3.2 Fitting a PCA Model to the 45 Training Set Batches 2058.3.3 Classification of the 13 Test Set Batches 2068.3.4 Added Value from DoE to Select Spanning Batches 2088.4 Partial Least Squares Projections to Latent Structures (PLS) 2088.5 PLS Case Study: A Process Optimization Model 2108.5.1 Dataset Description 2108.5.2 PLS Modeling of 85]Samples SOVRING Subset 2118.5.3 Looking into Cause]and]Effect Relationships 2128.5.4 Making a SweetSpot Plot to Summarize the PLS Results 2138.5.5 Using the PLS]DoE Model as a Basis to Define a Design Space and PARs for the SOVRING Process 2158.5.6 Summary of SOVRING Application 2178.6 Orthogonal PLS (OPLS® Multivariate Software) 2178.7 Orthogonal PLS (OPLS® Multivariate Software) Case Study – Batch Evolution Modeling of a Chemical Batch Reaction 2188.7.1 Dataset Description 2188.7.2 Batch Evolution Modeling 2188.8 Discussion 2208.8.1 The PAT Initiative 2208.8.2 What Are the Benefits of Using DoE? 2218.8.3 QbD and Design Space 2228.8.4 MVDA/DoE Is Needed to Accomplish PAT/QbD in Pharma 2238.8.5 MVDA: A Way to Power up the CPV Application 2238.9 References 2249 Process Analytical Technology (PAT) 227Line Lundsberg]Nielsen,Walkiria S. Schlindwein, and Andreas Berghaus9.1 Introduction 2279.2 How PAT Enables Quality by Design (QbD) 2299.3 The PAT Toolbox 2299.4 Process Sensors and Process Analysers 2299.4.1 Process Sensors – Univariate 2339.4.2 Process Analysers – Multivariate 2339.4.3 Infrared (IR) 2339.4.4 Near Infrared (NIR) 2389.4.5 Tunable Diode Laser Spectroscopy (TDLS) 2399.4.6 Ultraviolet]Visible (UV]Vis) 2399.4.7 Raman 2399.4.8 Focused Beam Reflectance Measurements (FBRM) and Laser Diffraction 2399.4.9 Particle Vision and Measurement (PVM) 2399.4.10 X]Ray Fluorescence (XRF) 2409.4.11 Imaging Technologies 2409.5 Analyser Selection 2409.6 Regulatory Requirements Related to PAT Applications 2409.6.1 Europe 2429.6.2 United States 2429.7 PAT Used in Development 2429.8 PAT Used in Manufacturing 2439.9 PAT and Real Time Release Testing (RTRT) 2459.10 PAT Implementation 2459.11 Data Management 2469.12 In]Line Process Monitoring with UV]Vis Spectroscopy: Case Study Example 2479.13 References 25310 Analytical Method Design, Development, and Lifecycle Management 257Joe de Sousa, David Holt, and Paul A. Butterworth10.1 Introduction 25710.2 Comparison of the Traditional Approach and the Enhanced QbD Approach 25810.3 Details of the Enhanced QbD Approach 26010.4 Defining Method Requirements 26210.5 Designing and Developing the Method 26410.6 Understanding the Impact of Method Parameters on Performance 26610.7 Defining the Method Control Strategy and Validating the Method 26710.8 Monitoring Routine Method Performance for Continual Improvement 26810.9 Summary 26910.10 Example Case Studies 27010.10.1 Case Study 1 – Establishment of Robust Operating Ranges during Routine Method Use and Justifying the Method Control Strategy (Including SST Criteria) 27010.10.2 Risk Assessment and Definition of Ranges 27010.10.3 Experimental Design 27110.10.4 Evaluate the DoE 27210.10.5 Documenting Method Performance 27410.10.6 Case Study 2 – Evaluation of the Ruggedness of a Dissolution Method for a Commercial Immediate Release Tablet Product 27410.10.7 Case Study Acknowledgements 27811 Manufacturing and Process Controls 281Mark Gibson11.1 Introduction to Manufacturing and Facilities 28111.2 Validation of Facilities and Equipment 28211.2.1 The International Society for Pharmaceutical Engineering (ISPE) Baseline® Guide: Commissioning and Qualification 28211.2.2 ASTM E2500]07: Standard Guide for Specification, Design, and Verification of Pharmaceutical and Biopharmaceutical Manufacturing Systems and Equipment 28411.2.3 Science]Based Approach and Critical Aspects 28511.2.4 Risk]Based Approach 28611.2.5 System and Component Impact Assessments 28811.2.6 URSs for Systems 29011.2.7 Specification and Design 29011.2.8 Verification 29011.3 Drug Product Process Validation: A Lifecycle Approach 29211.3.1 Stage 1: Process Design/Product Development 29511.3.2 Stage 2: Process Qualification 29811.3.3 Stage 3: Continued Process Verification 29911.4 The Impact of QbD on Process Equipment Design and Pharmaceutical Manufacturing Processes 30011.5 Introduction to Process Control in Pharmaceutical Manufacturing 30211.6 Advanced Process Controls (APC) and Control Strategy 30511.7 The Establishment of Continuous Manufacture 30911.8 The Tablet Press as Part of a Continuous Tableting Line 31211.9 Real]Time Release Testing and Continuous Quality Verification 31611.10 Acknowledgments 31711.11 References 31712 Regulatory Guidance 321Siegfried Schmitt and Mustafa A. Zaman12.1 Introduction 32112.2 The Common Technical Document (CTD) Format 32212.2.1 Quality Target Product Profile (QTPP) and Critical Quality Attributes (CQAs) 32412.2.2 Quality Risk Management (ICH Q9) 32412.2.3 Product and Process Development (S.2.6 and P.2) 32512.2.4 Control Strategy 32612.2.5 Design Space (Optional) 32712.3 Essential Reading 32812.4 What Is Not Written, or Hidden, in the Guidance Documents? 32912.5 Post]Approval Change 33012.6 Summary 33112.7 References 332Index

Editors Walkiria S. Schlindwein is Associate Professor of Pharmaceutics at the School of Pharmacy, De Montfort University. Walkiria is the programme leader of two Postgraduate courses in Pharmaceutical Quality by Design. Mark Gibson is Director of AM PharmaServices Ltd. He is a practicing Pharmaceutical Consultant and was formerly with AstraZeneca.

Select Guide Rating

• List of Figures xiiiList of Tables xixList of Contributers xxiSeries Preface xxiiiPreface xxv1 Introduction to Quality by Design (QbD) 1Bruce Davis and Walkiria S. Schlindwein1.1 Introduction 11.2 Background 21.3 Science] and Risk]Based Approaches 41.4 ICH Q8–Q12 51.5 QbD Terminology 61.6 QbD Framework 71.7 QbD Application and Benefits 71.8 Regulatory Aspects 81.9 Summary 91.10 References 92 Quality Risk Management (QRM) 11Noel Baker2.1 Introduction 112.2 Overview of ICH Q9 132.2.1 Start QRM Process 152.2.2 Risk Assessment 152.2.3 Risk Control 162.2.4 Risk Review 162.3 Risk Management Tools 172.4 Practical Examples of Use for QbD 222.4.1 Case Study 262.4.2 Pre]work 262.4.3 Scoring Meeting 322.4.4 FMECA Tool 322.4.5 Risk Score 322.4.6 Detectability Score 342.4.7 Communication 352.5 Concluding Remarks 362.6 References 443 Quality Systems and Knowledge Management 47Siegfried Schmitt3.1 Introduction to Pharmaceutical Quality System 473.1.1 Knowledge Management – What Is It and Why Do We Need It? 473.2 The Regulatory Framework 483.2.1 Knowledge Management in the Context of Quality by Design (QbD) 483.2.2 Roles and Responsibilities for Quality System 493.2.3 Roles and Responsibilities for Knowledge Management 503.2.4 Implicit and Explicit Knowledge 503.3 The Documentation Challenge 513.4 From Data to Knowledge: An Example 563.5 Data Integrity 583.6 Quality Systems and Knowledge Management: Common Factors for Success 583.7 Summary 593.8 References 604 Quality by Design (QbD) and the Development and Manufacture of Drug Substance 61Gerry Steele4.1 Introduction 614.2 ICH Q11 and Drug Substance Quality 624.2.1 Enhanced Approach 634.2.2 Impurities 634.2.3 Physical Properties of Drug Substance 644.3 Linear and Convergent Synthetic Chemistry Routes 654.4 Registered Starting Materials (RSMs) 674.5 Definition of an Appropriate Manufacturing Process 684.5.1 Crystallization, Isolation and Drying of APIs 684.5.2 Types of Crystallization 694.5.3 Design of Robust Cooling Crystallization 704.6 In]Line Process Analytical Technology and Crystallization Processes 784.6.1 Other Unit Operations 804.7 Applying the QbD Process 824.7.1 Quality Risk Assessment (QRA) 834.8 Design of Experiments (DoE) 874.9 Critical Process Parameters (CPPs) 884.10 Design Space 884.11 Control Strategy 894.12 References 915 The Role of Excipients in Quality by Design (QbD) 97Brian Carlin5.1 Introduction 975.2 Quality of Design (QbD) 985.3 Design of Experiments (DoE) 1005.4 Excipient Complexity 1025.5 Composition 1055.6 Drivers of Functionality or Performance 1055.7 Limited Utility of Pharmacopoeial Attributes 1065.8 Other Unspecified Attributes 1075.9 Variability 1075.10 Criticalities or Latent Conditions in the Finished Product 1085.11 Direct or Indirect Impact of Excipient Variability 1105.12 Control Strategy 1115.13 Communication with Suppliers 1125.14 Build in Compensatory Flexibility 1135.15 Risk Assessment 1135.16 Contingencies 1145.17 References 1146 Development and Manufacture of Drug Product 117Mark Gibson, Alan Carmody, and Roger Weaver6.1 Introduction 1176.2 Applying QbD to Pharmaceutical Drug Product Development 1196.3 Product Design Intent and the Target Product Profile (TPP) 1206.4 The Quality Target Product Profile (QTPP) 1266.5 Identifying the Critical Quality Attributes (CQAs) 1286.6 Product Design and Identifying the Critical Material Attributes (CMAs) 1336.7 Process Design and Identifying the Critical Process Parameters (CPPs) 1366.8 Product and Process Optimisation 1396.9 Design Space 1456.10 Control Strategy 1506.11 Continuous Improvement 1536.11 Acknowledgements 1546.12 References 1547 Design of Experiments 157Martin Owen and Ian Cox7.1 Introduction 1577.2 Experimental Design in Action 1587.3 The Curse of Variation 1587.3.1 Signal]to]Noise Ratio 1597.4 Fitting a Model 1617.4.1 Summary of Fit 1657.5 Parameter Estimates 1657.6 Analysis of Variance 1667.6.1 Reflection 1687.7 ‘To Boldly Go’– An Introduction to Managing Resource Constraints using DoE 1697.8 The Motivation for DoE 1707.8.1 How Does the Workshop Exercise Work? 1717.8.2 DoE Saves the Day! 1727.9 Classical Designs 1737.9.1 How Do Resource Constraints Impact the Design Choice? 1737.9.2 Resource Implications in Practice 1737.10 Practical Workshop Design 1747.10.1 Choice of Factors and Measurements 1757.10.2 Data Collection and Choice of Design 1757.10.3 Some Simple Data Visualization 1757.10.4 Analysis of the Half Fraction 1777.10.5 How to Interpret Prediction Profiles 1777.10.6 Half Fraction and Alternate Half Fraction 1787.10.7 Interaction Effects 1787.10.8 Full Factorial 1817.10.9 Central Composite Design 1817.10.10 How Robust Is This DoE to Unexplained Variation? 1817.11 How Does This Work? The Underpinning of Statistical Models for Variation 1847.12 DoE and Cycles of Learning 1877.13 Sequential Classical Designs and Definitive Screening Designs 1897.14 Building a Simulation 1907.14.1 Sequential design, Part 1: Screening Design (10 Runs) 1917.14.2 Sequential Design, Part II: Optimization Design (30 Runs) 1917.14.3 Definitive Screening Design 1947.14.4 Robustness Design 1947.14.5 Additional Challenges 1977.15 Conclusion 1977.16 Acknowledgements 1987.17 References 1988 Multivariate Data Analysis (MVDA) 201Claire Beckett, Lennart Eriksson, Erik Johansson, and Conny Wikstrom8.1 Introduction 2018.2 Principal Component Analysis (PCA) 2028.3 PCA Case Study: Raw Material Characterization using Particle Size Distribution Curves 2048.3.1 Dataset Description 2048.3.2 Fitting a PCA Model to the 45 Training Set Batches 2058.3.3 Classification of the 13 Test Set Batches 2068.3.4 Added Value from DoE to Select Spanning Batches 2088.4 Partial Least Squares Projections to Latent Structures (PLS) 2088.5 PLS Case Study: A Process Optimization Model 2108.5.1 Dataset Description 2108.5.2 PLS Modeling of 85]Samples SOVRING Subset 2118.5.3 Looking into Cause]and]Effect Relationships 2128.5.4 Making a SweetSpot Plot to Summarize the PLS Results 2138.5.5 Using the PLS]DoE Model as a Basis to Define a Design Space and PARs for the SOVRING Process 2158.5.6 Summary of SOVRING Application 2178.6 Orthogonal PLS (OPLS® Multivariate Software) 2178.7 Orthogonal PLS (OPLS® Multivariate Software) Case Study – Batch Evolution Modeling of a Chemical Batch Reaction 2188.7.1 Dataset Description 2188.7.2 Batch Evolution Modeling 2188.8 Discussion 2208.8.1 The PAT Initiative 2208.8.2 What Are the Benefits of Using DoE? 2218.8.3 QbD and Design Space 2228.8.4 MVDA/DoE Is Needed to Accomplish PAT/QbD in Pharma 2238.8.5 MVDA: A Way to Power up the CPV Application 2238.9 References 2249 Process Analytical Technology (PAT) 227Line Lundsberg]Nielsen,Walkiria S. Schlindwein, and Andreas Berghaus9.1 Introduction 2279.2 How PAT Enables Quality by Design (QbD) 2299.3 The PAT Toolbox 2299.4 Process Sensors and Process Analysers 2299.4.1 Process Sensors – Univariate 2339.4.2 Process Analysers – Multivariate 2339.4.3 Infrared (IR) 2339.4.4 Near Infrared (NIR) 2389.4.5 Tunable Diode Laser Spectroscopy (TDLS) 2399.4.6 Ultraviolet]Visible (UV]Vis) 2399.4.7 Raman 2399.4.8 Focused Beam Reflectance Measurements (FBRM) and Laser Diffraction 2399.4.9 Particle Vision and Measurement (PVM) 2399.4.10 X]Ray Fluorescence (XRF) 2409.4.11 Imaging Technologies 2409.5 Analyser Selection 2409.6 Regulatory Requirements Related to PAT Applications 2409.6.1 Europe 2429.6.2 United States 2429.7 PAT Used in Development 2429.8 PAT Used in Manufacturing 2439.9 PAT and Real Time Release Testing (RTRT) 2459.10 PAT Implementation 2459.11 Data Management 2469.12 In]Line Process Monitoring with UV]Vis Spectroscopy: Case Study Example 2479.13 References 25310 Analytical Method Design, Development, and Lifecycle Management 257Joe de Sousa, David Holt, and Paul A. Butterworth10.1 Introduction 25710.2 Comparison of the Traditional Approach and the Enhanced QbD Approach 25810.3 Details of the Enhanced QbD Approach 26010.4 Defining Method Requirements 26210.5 Designing and Developing the Method 26410.6 Understanding the Impact of Method Parameters on Performance 26610.7 Defining the Method Control Strategy and Validating the Method 26710.8 Monitoring Routine Method Performance for Continual Improvement 26810.9 Summary 26910.10 Example Case Studies 27010.10.1 Case Study 1 – Establishment of Robust Operating Ranges during Routine Method Use and Justifying the Method Control Strategy (Including SST Criteria) 27010.10.2 Risk Assessment and Definition of Ranges 27010.10.3 Experimental Design 27110.10.4 Evaluate the DoE 27210.10.5 Documenting Method Performance 27410.10.6 Case Study 2 – Evaluation of the Ruggedness of a Dissolution Method for a Commercial Immediate Release Tablet Product 27410.10.7 Case Study Acknowledgements 27811 Manufacturing and Process Controls 281Mark Gibson11.1 Introduction to Manufacturing and Facilities 28111.2 Validation of Facilities and Equipment 28211.2.1 The International Society for Pharmaceutical Engineering (ISPE) Baseline® Guide: Commissioning and Qualification 28211.2.2 ASTM E2500]07: Standard Guide for Specification, Design, and Verification of Pharmaceutical and Biopharmaceutical Manufacturing Systems and Equipment 28411.2.3 Science]Based Approach and Critical Aspects 28511.2.4 Risk]Based Approach 28611.2.5 System and Component Impact Assessments 28811.2.6 URSs for Systems 29011.2.7 Specification and Design 29011.2.8 Verification 29011.3 Drug Product Process Validation: A Lifecycle Approach 29211.3.1 Stage 1: Process Design/Product Development 29511.3.2 Stage 2: Process Qualification 29811.3.3 Stage 3: Continued Process Verification 29911.4 The Impact of QbD on Process Equipment Design and Pharmaceutical Manufacturing Processes 30011.5 Introduction to Process Control in Pharmaceutical Manufacturing 30211.6 Advanced Process Controls (APC) and Control Strategy 30511.7 The Establishment of Continuous Manufacture 30911.8 The Tablet Press as Part of a Continuous Tableting Line 31211.9 Real]Time Release Testing and Continuous Quality Verification 31611.10 Acknowledgments 31711.11 References 31712 Regulatory Guidance 321Siegfried Schmitt and Mustafa A. Zaman12.1 Introduction 32112.2 The Common Technical Document (CTD) Format 32212.2.1 Quality Target Product Profile (QTPP) and Critical Quality Attributes (CQAs) 32412.2.2 Quality Risk Management (ICH Q9) 32412.2.3 Product and Process Development (S.2.6 and P.2) 32512.2.4 Control Strategy 32612.2.5 Design Space (Optional) 32712.3 Essential Reading 32812.4 What Is Not Written, or Hidden, in the Guidance Documents? 32912.5 Post]Approval Change 33012.6 Summary 33112.7 References 332Index