Pharmaceutical Quality by Design

Name: Pharmaceutical Quality by Design | A Practical Approach
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A Practical Approach

Walkiria S. Schlindwein Mark Gibson(Herausgeber*in)

Wiley (Verlag)

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Erschienen am 5. Januar 2018

368 Seiten

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Inhalt

List of Figures xiii

List of Tables xix

List of Contributers xxi

Series Preface xxiii

Preface xxv

1 Introduction to Quality by Design (QbD) 1
Bruce Davis and Walkiria S. Schlindwein

1.1 Introduction 1

1.2 Background 2

1.3 Science?] and Risk?]Based Approaches 4

1.4 ICH Q8-Q12 5

1.5 QbD Terminology 6

1.6 QbD Framework 7

1.7 QbD Application and Benefits 7

1.8 Regulatory Aspects 8

1.9 Summary 9

1.10 References 9

2 Quality Risk Management (QRM) 11
Noel Baker

2.1 Introduction 11

2.2 Overview of ICH Q9 13

2.2.1 Start QRM Process 15

2.2.2 Risk Assessment 15

2.2.3 Risk Control 16

2.2.4 Risk Review 16

2.3 Risk Management Tools 17

2.4 Practical Examples of Use for QbD 22

2.4.1 Case Study 26

2.4.2 Pre?]work 26

2.4.3 Scoring Meeting 32

2.4.4 FMECA Tool 32

2.4.5 Risk Score 32

2.4.6 Detectability Score 34

2.4.7 Communication 35

2.5 Concluding Remarks 36

2.6 References 44

3 Quality Systems and Knowledge Management 47
Siegfried Schmitt

3.1 Introduction to Pharmaceutical Quality System 47

3.1.1 Knowledge Management - What Is It and Why Do We Need It? 47

3.2 The Regulatory Framework 48

3.2.1 Knowledge Management in the Context of Quality by Design (QbD) 48

3.2.2 Roles and Responsibilities for Quality System 49

3.2.3 Roles and Responsibilities for Knowledge Management 50

3.2.4 Implicit and Explicit Knowledge 50

3.3 The Documentation Challenge 51

3.4 From Data to Knowledge: An Example 56

3.5 Data Integrity 58

3.6 Quality Systems and Knowledge Management: Common Factors for Success 58

3.7 Summary 59

3.8 References 60

4 Quality by Design (QbD) and the Development and Manufacture of Drug Substance 61
Gerry Steele

4.1 Introduction 61

4.2 ICH Q11 and Drug Substance Quality 62

4.2.1 Enhanced Approach 63

4.2.2 Impurities 63

4.2.3 Physical Properties of Drug Substance 64

4.3 Linear and Convergent Synthetic Chemistry Routes 65

4.4 Registered Starting Materials (RSMs) 67

4.5 Definition of an Appropriate Manufacturing Process 68

4.5.1 Crystallization, Isolation and Drying of APIs 68

4.5.2 Types of Crystallization 69

4.5.3 Design of Robust Cooling Crystallization 70

4.6 In?]Line Process Analytical Technology and Crystallization Processes 78

4.6.1 Other Unit Operations 80

4.7 Applying the QbD Process 82

4.7.1 Quality Risk Assessment (QRA) 83

4.8 Design of Experiments (DoE) 87

4.9 Critical Process Parameters (CPPs) 88

4.10 Design Space 88

4.11 Control Strategy 89

4.12 References 91

5 The Role of Excipients in Quality by Design (QbD) 97
Brian Carlin

5.1 Introduction 97

5.2 Quality of Design (QbD) 98

5.3 Design of Experiments (DoE) 100

5.4 Excipient Complexity 102

5.5 Composition 105

5.6 Drivers of Functionality or Performance 105

5.7 Limited Utility of Pharmacopoeial Attributes 106

5.8 Other Unspecified Attributes 107

5.9 Variability 107

5.10 Criticalities or Latent Conditions in the Finished Product 108

5.11 Direct or Indirect Impact of Excipient Variability 110

5.12 Control Strategy 111

5.13 Communication with Suppliers 112

5.14 Build in Compensatory Flexibility 113

5.15 Risk Assessment 113

5.16 Contingencies 114

5.17 References 114

6 Development and Manufacture of Drug Product 117
Mark Gibson, Alan Carmody, and Roger Weaver

6.1 Introduction 117

6.2 Applying QbD to Pharmaceutical Drug Product Development 119

6.3 Product Design Intent and the Target Product Profile (TPP) 120

6.4 The Quality Target Product Profile (QTPP) 126

6.5 Identifying the Critical Quality Attributes (CQAs) 128

6.6 Product Design and Identifying the Critical Material Attributes (CMAs) 133

6.7 Process Design and Identifying the Critical Process Parameters (CPPs) 136

6.8 Product and Process Optimisation 139

6.9 Design Space 145

6.10 Control Strategy 150

6.11 Continuous Improvement 153

6.11 Acknowledgements 154

6.12 References 154

7 Design of Experiments 157
Martin Owen and Ian Cox

7.1 Introduction 157

7.2 Experimental Design in Action 158

7.3 The Curse of Variation 158

7.3.1 Signal?]to?]Noise Ratio 159

7.4 Fitting a Model 161

7.4.1 Summary of Fit 165

7.5 Parameter Estimates 165

7.6 Analysis of Variance 166

7.6.1 Reflection 168

7.7 'To Boldly Go'- An Introduction to Managing Resource Constraints using DoE 169

7.8 The Motivation for DoE 170

7.8.1 How Does the Workshop Exercise Work? 171

7.8.2 DoE Saves the Day! 172

7.9 Classical Designs 173

7.9.1 How Do Resource Constraints Impact the Design Choice? 173

7.9.2 Resource Implications in Practice 173

7.10 Practical Workshop Design 174

7.10.1 Choice of Factors and Measurements 175

7.10.2 Data Collection and Choice of Design 175

7.10.3 Some Simple Data Visualization 175

7.10.4 Analysis of the Half Fraction 177

7.10.5 How to Interpret Prediction Profiles 177

7.10.6 Half Fraction and Alternate Half Fraction 178

7.10.7 Interaction Effects 178

7.10.8 Full Factorial 181

7.10.9 Central Composite Design 181

7.10.10 How Robust Is This DoE to Unexplained Variation? 181

7.11 How Does This Work? The Underpinning of Statistical Models for Variation 184

7.12 DoE and Cycles of Learning 187

7.13 Sequential Classical Designs and Definitive Screening Designs 189

7.14 Building a Simulation 190

7.14.1 Sequential design, Part 1: Screening Design (10 Runs) 191

7.14.2 Sequential Design, Part II: Optimization Design (30 Runs) 191

7.14.3 Definitive Screening Design 194

7.14.4 Robustness Design 194

7.14.5 Additional Challenges 197

7.15 Conclusion 197

7.16 Acknowledgements 198

7.17 References 198

8 Multivariate Data Analysis (MVDA) 201
Claire Beckett, Lennart Eriksson, Erik Johansson, and Conny Wikstrom

8.1 Introduction 201

8.2 Principal Component Analysis (PCA) 202

8.3 PCA Case Study: Raw Material Characterization using Particle Size Distribution Curves 204

8.3.1 Dataset Description 204

8.3.2 Fitting a PCA Model to the 45 Training Set Batches 205

8.3.3 Classification of the 13 Test Set Batches 206

8.3.4 Added Value from DoE to Select Spanning Batches 208

8.4 Partial Least Squares Projections to Latent Structures (PLS) 208

8.5 PLS Case Study: A Process Optimization Model 210

8.5.1 Dataset Description 210

8.5.2 PLS Modeling of 85?]Samples SOVRING Subset 211

8.5.3 Looking into Cause?]and?]Effect Relationships 212

8.5.4 Making a SweetSpot Plot to Summarize the PLS Results 213

8.5.5 Using the PLS?]DoE Model as a Basis to Define a Design Space and PARs for the SOVRING Process 215

8.5.6 Summary of SOVRING Application 217

8.6 Orthogonal PLS (OPLS® Multivariate Software) 217

8.7 Orthogonal PLS (OPLS® Multivariate Software) Case Study - Batch Evolution Modeling of a Chemical Batch Reaction 218

8.7.1 Dataset Description 218

8.7.2 Batch Evolution Modeling 218

8.8 Discussion 220

8.8.1 The PAT Initiative 220

8.8.2 What Are the Benefits of Using DoE? 221

8.8.3 QbD and Design Space 222

8.8.4 MVDA/DoE Is Needed to Accomplish PAT/QbD in Pharma 223

8.8.5 MVDA: A Way to Power up the CPV Application 223

8.9 References 224

9 Process Analytical Technology (PAT) 227
Line Lundsberg?]Nielsen,Walkiria S. Schlindwein, and Andreas Berghaus

9.1 Introduction 227

9.2 How PAT Enables Quality by Design (QbD) 229

9.3 The PAT Toolbox 229

9.4 Process Sensors and Process Analysers 229

9.4.1 Process Sensors - Univariate 233

9.4.2 Process Analysers - Multivariate 233

9.4.3 Infrared (IR) 233

9.4.4 Near Infrared (NIR) 238

9.4.5 Tunable Diode Laser Spectroscopy (TDLS) 239

9.4.6 Ultraviolet?]Visible (UV?]Vis) 239

9.4.7 Raman 239

9.4.8 Focused Beam Reflectance Measurements (FBRM) and Laser Diffraction 239

9.4.9 Particle Vision and Measurement (PVM) 239

9.4.10 X?]Ray Fluorescence (XRF) 240

9.4.11 Imaging Technologies 240

9.5 Analyser Selection 240

9.6 Regulatory Requirements Related to PAT Applications 240

9.6.1 Europe 242

9.6.2 United States 242

9.7 PAT Used in Development 242

9.8 PAT Used in Manufacturing 243

9.9 PAT and Real Time Release Testing (RTRT) 245

9.10 PAT Implementation 245

9.11 Data Management 246

9.12 In?]Line Process Monitoring with UV?]Vis Spectroscopy: Case Study Example 247

9.13 References 253

10 Analytical Method Design, Development, and Lifecycle Management 257
Joe de Sousa, David Holt, and Paul A. Butterworth

10.1 Introduction 257

10.2 Comparison of the Traditional Approach and the Enhanced QbD Approach 258

10.3 Details of the Enhanced QbD Approach 260

10.4 Defining Method Requirements 262

10.5 Designing and Developing the Method 264

10.6 Understanding the Impact of Method Parameters on Performance 266

10.7 Defining the Method Control Strategy and Validating the Method 267

10.8 Monitoring Routine Method Performance for Continual Improvement 268

10.9 Summary 269

10.10 Example Case Studies 270

10.10.1 Case Study 1 - Establishment of Robust Operating Ranges during Routine Method Use and Justifying the Method Control Strategy (Including SST Criteria) 270

10.10.2 Risk Assessment and Definition of Ranges 270

10.10.3 Experimental Design 271

10.10.4 Evaluate the DoE 272

10.10.5 Documenting Method Performance 274

10.10.6 Case Study 2 - Evaluation of the Ruggedness of a Dissolution Method for a Commercial Immediate Release Tablet Product 274

10.10.7 Case Study Acknowledgements 278

11 Manufacturing and Process Controls 281
Mark Gibson

11.1 Introduction to Manufacturing and Facilities 281

11.2 Validation of Facilities and Equipment 282

11.2.1 The International Society for Pharmaceutical Engineering (ISPE) Baseline® Guide: Commissioning and Qualification 282

11.2.2 ASTM E2500?]07: Standard Guide for Specification, Design, and Verification of Pharmaceutical and Biopharmaceutical Manufacturing Systems and Equipment 284

11.2.3 Science?]Based Approach and Critical Aspects 285

11.2.4 Risk?]Based Approach 286

11.2.5 System and Component Impact Assessments 288

11.2.6 URSs for Systems 290

11.2.7 Specification and Design 290

11.2.8 Verification 290

11.3 Drug Product Process Validation: A Lifecycle Approach 292

11.3.1 Stage 1: Process Design/Product Development 295

11.3.2 Stage 2: Process Qualification 298

11.3.3 Stage 3: Continued Process Verification 299

11.4 The Impact of QbD on Process Equipment Design and Pharmaceutical Manufacturing Processes 300

11.5 Introduction to Process Control in Pharmaceutical Manufacturing 302

11.6 Advanced Process Controls (APC) and Control Strategy 305

11.7 The Establishment of Continuous Manufacture 309

11.8 The Tablet Press as Part of a Continuous Tableting Line 312

11.9 Real?]Time Release Testing and Continuous Quality Verification 316

11.10 Acknowledgments 317

11.11 References 317

12 Regulatory Guidance 321
Siegfried Schmitt and Mustafa A. Zaman

12.1 Introduction 321

12.2 The Common Technical Document (CTD) Format 322

12.2.1 Quality Target Product Profile (QTPP) and Critical Quality Attributes (CQAs) 324

12.2.2 Quality Risk Management (ICH Q9) 324

12.2.3 Product and Process Development (S.2.6 and P.2) 325

12.2.4 Control Strategy 326

12.2.5 Design Space (Optional) 327

12.3 Essential Reading 328

12.4 What Is Not Written, or Hidden, in the Guidance Documents? 329

12.5 Post?]Approval Change 330

12.6 Summary 331

12.7 References 332

Index

List of Figures

Figure 1.1 A framework for QbD. Figure 2.1 Medicinal product development flowchart. Figure 2.2 Overview of a typical quality risk management process (ICH Q9). Figure 2.3 Example of risk assessment tool: flowchart (basic tool). Figure 2.4 Example of a cause and effect diagram (Ishikawa fishbone diagram). Figure 2.5 Overall QRA process flow for the medicinal product. Figure 2.6 Risk score approach (high level) based on current operating space. Figure 2.7 Risk score approach (detailed level) based on current operating space. Figure 2.8 Tacit benefits of QRM. Figure 3.1 Through the ages. Figure 3.2 ICH QbD timeline. Figure 3.3 From data to knowledge. Figure 4.1 Example of a convergent synthesis. Figure 4.2 Temperature-solubility curve for AZD3342 in 1-propanol:water and IMS:water. Figure 4.3 Solubility-temperature and metastable zone limit curves. Figure 4.4 Effect of cooling rate on the metastable zone width of sibenadet HCl. Figure 4.5 Crystal16 data for AZD3342 polymorphs A and G. Figure 4.6 Cooling crystallization scenarios: (a) natural cooling, (b) linear cooling, (c) controlled cooling. Figure 4.7 Effect of seed loading on particle size. Figure 4.8 UV Data for an unseeded linear cooling crystallization of AZD3342. Figure 4.9 FBRM Lasentec data and Photomicrograph for an unseeded, linear cool crystallization of AZD3342. Figure 4.10 UV Data for the dissolution and crystallization of AZD3342 using seeding and a cubic cooling profile (controlled cooling). Figure 4.11 FBRM Lasentec data and optical microscopy of AZD3342 crystals using seeding and a cubic cooling profile (controlled cooling). Figure 4.12 Typical Ishikawa diagram for an anti-solvent crystallization process. Figure 5.1 Examples of "known and unknowns" to users and suppliers. Figure 6.1 Process of determining how a quality attribute is deemed critical. *A severity scale is used to assess relative magnitude. Figure 6.2 Examples of where CQAs may be impacted in the manufacturing process for an oral solid dosage form. Figure 6.3 PDA decision tree for designating parameter criticality. Figure 6.4 Fishbone diagram recognising material attributes (MAs) and process parameters (PPs) contributing to the quality attribute, 'solubility', of a solid dispersion API/polymer product. Figure 6.5 Use of risk assessment to determine what DoE studies to use. Figure 6.6 Example of a process parameter criticality assessment decision tree. Figure 6.7 The manufacturing process flow for ACE tablets. Figure 6.8 Critical parameters affecting blend uniformity for ACE tablets. Figure 6.9 Schematic representation of the relationship between knowledge, design, and control space. Figure 6.10 Example of terminal sterilisation design space. Figure 6.11 Comparison of traditional controls with advanced controls for the real-time release of an oral solid dosage form. Figure 7.1 Constructing a variability gauge chart. Figure 7.2 Impact of formulation of disintegration time with outlier. Figure 7.3 Impact of formulation on disintegration with the corrected data point. Figure 7.4 Prediction profilers showing the impact of formulation on disintegration time. Figure 7.5 Statistical analysis of the disintegration time data. Figure 7.6 Comparison of the statistical analysis of Study A and Study B. Figure 7.7 Identifying sources of variation. Figure 7.8 The GlaxoSmithKline reaction simulator. Figure 7.9 'Understanding and Optimizing Chemical Processes' 2015 Workshop results. Figure 7.10 Costs, risks and benefits of classical designs. Figure 7.11 Data visualization. Figure 7.12 Analysis of the half fraction. Figure 7.13 Prediction profiles for the four different designs (shaded boxes indicate statistically significant effects). Figure 7.14 Interaction effects. Figure 7.15 Explaining the experimental procedure to the 'novice' workshop delegate. Figure 7.16 Comparison of 'expert' and 'novice' findings for the half fraction design. Figure 7.17 Theoretical model of a specific real-world situation. Figure 7.18 Cycles of learning iterate between the real world and our model of it. Figure 7.19 Resource requirements for central composite design. Figure 7.20 Simulation scenario schematic. Figure 7.21 Analysis of the 10 run fractional factorial experiment. Figure 7.22 Prediction profiler for the 10 run fractional factorial. Figure 7.23 Analysis of the central composite design. Figure 7.24 Analysis of the 13 run DSD experiment. Figure 7.25 Comparison of the central composite and definitive screening design (DSD). Figure 8.1 Notation used in principal components analysis (PCA). Figure 8.2 PCA derives a model that fits the data as well as possible in the least squares sense. Alternatively, PCA may be understood as maximizing the variance of the projection coordinates. Figure 8.3 The raw data curves of the 45 batches of the training set. Figure 8.4 Scatter plot of the two principal components. Each point represents one batch of raw material. The plot is color-coded according to supplier. Figure 8.5 Loading line plots of the two principal components. Figure 8.6 Scores and DModX plots for the particle size distribution data set. Top left: Predicted scores for the test set batches. Top right: Scores for the training set batches. Bottom left: Predicted DModX for the test set batches. Bottom right: DModX for the training set batches. Figure 8.7 Line plot of the power spectral density (PSD) curves of the six early (red color) and three late (black color) batches of supplier L3. Figure 8.8 Spanning batches can be selected using DoE. Such spanning batches can be subjected to a thorough investigation in order to ensure production robustness in all part of the PCA score space. Figure 8.9 (Left) Scatter plot of HS_1 against HS_2, (right) PLS t1/t2 score plot (note the resemblance to Figure 8.8). Figure 8.10 (Left) Summary of fit plot of the PLS model of the SOVRING subset with complete Y-data. Five components were significant according to cross-validation. (Right) Individual R2Y- and Q2Y-values of the six responses. The most important responses are PAR, FAR, %Fe_FAR and %P_FAR. Figure...

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