Pharmaceutical quality by design : a practical approach /: a practical approach. (2018)

• Record Type: Book
• Further Information: Note: Edited by Walkiria S. Schlindwein, Mark Gibson.
• Editors: Schlindwein, Walkiria S, 1961-; Gibson, Mark, 1957-
• Contents: 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
• Edition: 1st
• Publisher Details: Hoboken, New Jersey : John Wiley & Sons, Inc
• Publication Date: 2018
• Extent: 1 online resource
• Subjects: 615.19; Drugs -- Design; Drugs -- Quality control
• Languages: English
• ISBNs: 9781118895214; 9781118895221
• Related ISBNs: 9781118895207
• Notes: Note: Description based on CIP data; resource not viewed.
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