Table Of ContentPharmaceutical Quality by Design:
A Practical Approach
ADVANCES IN PHARMACEUTICAL TECHNOLOGY
A Wiley Book Series
Series Editors:
Dennis Douroumis, University of Greenwich, UK
Alfred Fahr, Friedrich–Schiller University of Jena, Germany
Jürgen Siepmann, University of Lille, France
Martin Snowden, University of Greenwich, UK
Vladimir Torchilin, Northeastern University, USA
Titles in the Series
Hot‐Melt Extrusion: Pharmaceutical Applications
Edited by Dionysios Douroumis
Drug Delivery Strategies for Poorly Water‐Soluble Drugs
Edited by Dionysios Douroumis and Alfred Fahr
Computational Pharmaceutics: Application of Molecular Modeling in Drug Delivery
Edited by Defang Ouyang and Sean C. Smith
Pulmonary Drug Delivery: Advances and Challenges
Edited by Ali Nokhodchi and Gary P. Martin
Novel Delivery Systems for Transdermal and Intradermal Drug Delivery
Edited by Ryan Donnelly and Raj Singh
Drug Delivery Systems for Tuberculosis Prevention and Treatment
Edited by Anthony J. Hickey
Continuous Manufacturing of Pharmaceuticals
Edited by Peter Kleinebudde, Johannes Khinast, and Jukka Rantanen
Pharmaceutical Quality by Design: A Practical Approach
Edited by Walkiria S. Schlindwein and Mark Gibson
Forthcoming Titles:
In Vitro Drug Release Testing of Special Dosage Forms
Edited by Nikoletta Fotaki and Sandra Klein
Characterization of Micro‐ and Nanosystems
Edited by Leena Peltonen
Therapeutic Dressings and Wound Healing Applications
Edited by Joshua Boateng
Process Analytics for Pharmaceuticals
Edited by Thomas de Beer, Jukka Rantanen and Clare Strachan
Pharmaceutical Quality
by Design
A Practical Approach
Edited by
WALKIRIA S. SCHLINDWEIN
De Montfort University
Leicester
United Kingdom
MARK GIBSON
A M PharmaServices Ltd
United Kingdom
This edition first published 2018
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Library of Congress Cataloging‐in‐Publication Data
Names: Schlindwein, Walkiria S., 1961– editor. | Gibson, Mark, 1957– editor.
Title: Pharmaceutical quality by design : a practical approach / edited by Dr. Walkiria S.
Schlindwein, Mark Gibson.
Description: First edition. | Hoboken, NJ : John Wiley & Sons, 2018. | Series: Advances in
pharmaceutical technology | Includes bibliographical references and index. |
Identifiers: LCCN 2017030338 (print) | LCCN 2017043153 (ebook) | ISBN 9781118895221 (pdf) |
ISBN 9781118895214 (epub) | ISBN 9781118895207 (cloth)
Subjects: LCSH: Drugs–Design. | Drugs–Quality control.
Classification: LCC RS420 (ebook) | LCC RS420 .P47 2018 (print) | DDC 615.1/9–dc23
LC record available at https://lccn.loc.gov/2017030338
Cover design by Wiley
Cover image: (Background) © ShutterWorx/Gettyimages;(Graph) Courtesy of Walkiria S. Schlindwein and Mark Gibson
Set in 10/12pt Times by SPi Global, Pondicherry, India
10 9 8 7 6 5 4 3 2 1
Contents
List of Figures xiii
List of Tables xix
List of Contributors 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
vi Contents
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
Contents vii
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
6.12 Acknowledgements 154
6.13 References 154
viii Contents
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 (24 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
Contents ix
8 Multivariate Data Analysis (MVDA) 201
Claire Beckett, Lennart Eriksson, Erik Johansson, and Conny Wikström
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
