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E-grāmata: Pharmaceutical Quality by Design: A Practical Approach

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A practical guide to Quality by Design for pharmaceutical product development

Pharmaceutical Quality by Design: A Practical Approach outlines a new and proven approach to pharmaceutical product development which is now being rolled out across the pharmaceutical industry internationally. Written by experts in the field, the text explores the QbD approach to product development. This innovative approach is based on the application of product and process understanding underpinned by a systematic methodology which can enable pharmaceutical companies to ensure that quality is built into the product.

Familiarity with Quality by Design is essential for scientists working in the pharmaceutical industry. The authors take a practical approach and put the focus on the industrial aspects of the new QbD approach to pharmaceutical product development and manufacturing. The text covers quality risk management tools and analysis, applications of QbD to analytical methods, regulatory aspects, quality systems and knowledge management. In addition, the book explores the development and manufacture of drug substance and product, design of experiments, the role of excipients, multivariate analysis, and include several examples of applications of QbD in actual practice. This important resource:

  • Covers the essential information about Quality by Design (QbD) that is at the heart of modern pharmaceutical development
  • Puts the focus on the industrial aspects of the new QbD approach
  • Includes several illustrative examples of applications of QbD in practice
  • Offers advanced specialist topics that can be systematically applied to industry

Pharmaceutical Quality by Design offers a guide to the principles and application of Quality by Design (QbD), the holistic approach to manufacturing that offers a complete understanding of the manufacturing processes involved, in order to yield consistent and high quality products.

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(10)
Bruce Davis
Walkiria S. Schlindwein
1.1 Introduction
1(1)
1.2 Background
2(2)
1.3 Science-and Risk-Based Approaches
4(1)
1.4 ICHQ8--Q12
5(1)
1.5 QbD Terminology
6(1)
1.6 QbD Framework
7(1)
1.7 QbD Application and Benefits
7(1)
1.8 Regulatory Aspects
8(1)
1.9 Summary
9(1)
1.10 References
9(2)
2 Quality Risk Management (QRM)
11(36)
Noel Baker
2.1 Introduction
11(2)
2.2 Overview of ICH Q9
13(2)
2.2.1 Start QRM Process
15(1)
2.2.2 Risk Assessment
15(1)
2.2.3 Risk Control
16(1)
2.2.4 Risk Review
16(1)
2.3 Risk Management Tools
17(5)
2.4 Practical Examples of Use for QbD
22(14)
2.4.1 Case Study
26(1)
2.4.2 Pre-work
26(6)
2.4.3 Scoring Meeting
32(1)
2.4.4 FMECA Tool
32(1)
2.4.5 Risk Score
32(2)
2.4.6 Detectability Score
34(1)
2.4.7 Communication
35(1)
2.5 Concluding Remarks
36(8)
2.6 References
44(3)
3 Quality Systems and Knowledge Management
47(14)
Siegfried Schmitt
3.1 Introduction to Pharmaceutical Quality System
47(1)
3.1.1 Knowledge Management -- What Is It and Why Do We Need It?
47(1)
3.2 The Regulatory Framework
48(3)
3.2.1 Knowledge Management in the Context of Quality by Design (QbD)
48(1)
3.2.2 Roles and Responsibilities for Quality System
49(1)
3.2.3 Roles and Responsibilities for Knowledge Management
50(1)
3.2.4 Implicit and Explicit Knowledge
50(1)
3.3 The Documentation Challenge
51(5)
3.4 From Data to Knowledge: An Example
56(2)
3.5 Data Integrity
58(1)
3.6 Quality Systems and Knowledge Management: Common Factors for Success
58(1)
3.7 Summary
59(1)
3.8 References
60(1)
4 Quality by Design (QbD) and the Development and Manufacture of Drug Substance
61(36)
Gerry Steele
4.1 Introduction
61(1)
4.2 ICHQ11 and Drug Substance Quality
62(3)
4.2.1 Enhanced Approach
63(1)
4.2.2 Impurities
63(1)
4.2.3 Physical Properties of Drug Substance
64(1)
4.3 Linear and Convergent Synthetic Chemistry Routes
65(2)
4.4 Registered Starting Materials (RSMs)
67(1)
4.5 Definition of an Appropriate Manufacturing Process
68(10)
4.5.1 Crystallization, Isolation and Drying of APIs
68(1)
4.5.2 Types of Crystallization
69(1)
4.5.3 Design of Robust Cooling Crystallization
70(8)
4.6 In-Line Process Analytical Technology and Crystallization Processes
78(4)
4.6.1 Other Unit Operations
80(2)
4.7 Applying the QbD Process
82(5)
4.7.1 Quality Risk Assessment (QRA)
83(4)
4.8 Design of Experiments (DoE)
87(1)
4.9 Critical Process Parameters (CPPs)
88(1)
4.10 Design Space
88(1)
4.11 Control Strategy
89(2)
4.12 References
91(6)
5 The Role of Excipients in Quality by Design (QbD)
97(20)
Brian Carlin
5.1 Introduction
97(1)
5.2 Quality of Design (QbD)
98(2)
5.3 Design of Experiments (DoE)
100(2)
5.4 Excipient Complexity
102(3)
5.5 Composition
105(1)
5.6 Drivers of Functionality or Performance
105(1)
5.7 Limited Utility of Pharmacopoeial Attributes
106(1)
5.8 Other Unspecified Attributes
107(1)
5.9 Variability
107(1)
5.10 Criticalities or Latent Conditions in the Finished Product
108(2)
5.11 Direct or Indirect Impact of Excipient Variability
110(1)
5.12 Control Strategy
111(1)
5.13 Communication with Suppliers
112(1)
5.14 Build in Compensatory Flexibility
113(1)
5.15 Risk Assessment
113(1)
5.16 Contingencies
114(1)
5.17 References
114(3)
6 Development and Manufacture of Drug Product
117(40)
Mark Gibson
Alan Carmody
Roger Weaver
6.1 Introduction
117(2)
6.2 Applying QbD to Pharmaceutical Drug Product Development
119(1)
6.3 Product Design Intent and the Target Product Profile (TPP)
120(6)
6.4 The Quality Target Product Profile (QTPP)
126(2)
6.5 Identifying the Critical Quality Attributes (CQAs)
128(5)
6.6 Product Design and Identifying the Critical Material Attributes (CMAs)
133(3)
6.7 Process Design and Identifying the Critical Process Parameters (CPPs)
136(3)
6.8 Product and Process Optimisation
139(6)
6.9 Design Space
145(5)
6.10 Control Strategy
150(3)
6.11 Continuous Improvement
153(1)
6.12 Acknowledgements
154(1)
6.13 References
154(3)
7 Design of Experiments
157(44)
Martin Owen
Ian Cox
1.1 Introduction
157(1)
7.2 Experimental Design in Action
158(1)
7.3 The Curse of Variation
158(3)
7.3.1 Signal-to-Noise Ratio
159(2)
7.4 Fitting a Model
161(4)
7.4.1 Summary of Fit
165(1)
7.5 Parameter Estimates
165(1)
7.6 Analysis of Variance
166(3)
7.6.1 Reflection
168(1)
7.7 `To Boldly Go' -- An Introduction to Managing Resource Constraints using DoE
169(1)
7.8 The Motivation for DoE
170(3)
7.8.1 How Does the Workshop Exercise Work?
171(1)
7.8.2 DoE Saves the Day!
172(1)
7.9 Classical Designs
173(1)
7.9.1 How Do Resource Constraints Impact the Design Choice?
173(1)
7.9.2 Resource Implications in Practice
173(1)
7.10 Practical Workshop Design
174(10)
7.10.1 Choice of Factors and Measurements
175(1)
7.10.2 Data Collection and Choice of Design
175(1)
7.10.3 Some Simple Data Visualization
175(2)
7.10.4 Analysis of the Half Fraction
177(1)
7.10.5 How to Interpret Prediction Profiles
177(1)
7.10.6 Half Fraction and Alternate Half Fraction
178(1)
7.10.7 Interaction Effects
178(3)
7.10.8 Full Factorial
181(1)
7.10.9 Central Composite Design
181(1)
7.10.10 How Robust Is This DoE to Unexplained Variation?
181(3)
7.11 How Does This Work? The Underpinning of Statistical Models for Variation
184(3)
7.12 DoE and Cycles of Learning
187(2)
7.13 Sequential Classical Designs and Definitive Screening Designs
189(1)
7.14 Building a Simulation
190(7)
7.14.1 Sequential design, Part I: Screening Design (10 Runs)
191(1)
7.14.2 Sequential Design, Part II: Optimization Design (24 Runs)
191(3)
7.14.3 Definitive Screening Design
194(1)
7.14.4 Robustness Design
194(3)
7.14.5 Additional Challenges
197(1)
7.15 Conclusion
197(1)
7.16 Acknowledgements
198(1)
7.17 References
198(3)
8 Multivariate Data Analysis (MVDA)
201(26)
Claire Beckett
Lennart Eriksson
Erik Johansson
Conny Wikstrom
8.1 Introduction
201(1)
8.2 Principal Component Analysis (PCA)
202(2)
8.3 PCA Case Study: Raw Material Characterization using Particle Size Distribution Curves
204(4)
8.3.1 Dataset Description
204(1)
8.3.2 Fitting a PCA Model to the 45 Training Set Batches
205(1)
8.3.3 Classification of the 13 Test Set Batches
206(2)
8.3.4 Added Value from DoE to Select Spanning Batches
208(1)
8.4 Partial Least Squares Projections to Latent Structures (PLS)
208(2)
8.5 PLS Case Study: A Process Optimization Model
210(7)
8.5.1 Dataset Description
210(1)
8.5.2 PLS Modeling of 85-Samples SOVRING Subset
211(1)
8.5.3 Looking into Cause-and-Effect Relationships
212(1)
8.5.4 Making a SweetSpot Plot to Summarize the PLS Results
213(2)
8.5.5 Using the PLS-DoE Model as a Basis to Define a Design Space and PARs for the SOVRING Process
215(2)
8.5.6 Summary of SOVRING Application
217(1)
8.6 Orthogonal PLS (OPLS® Multivariate Software)
217(1)
8.7 Orthogonal PLS (OPLS® Multivariate Software) Case Study -- Batch Evolution Modeling of a Chemical Batch Reaction
218(2)
8.7.1 Dataset Description
218(1)
8.7.2 Batch Evolution Modeling
218(2)
8.8 Discussion
220(4)
8.8.1 The PAT Initiative
220(1)
8.8.2 What Are the Benefits of Using DoE?
221(1)
8.8.3 QbD and Design Space
222(1)
8.8.4 MVDA/DoE Is Needed to Accomplish PAT/QbD in Pharma
223(1)
8.8.5 MVDA: A Way to Power up the CPV Application
223(1)
8.9 References
224(3)
9 Process Analytical Technology (PAT)
227(30)
Line Lundsberg-Nielsen
Walkiria S. Schlindwein
Andreas Berghaus
9.1 Introduction
227(2)
9.2 How PAT Enables Quality by Design (QbD)
229(1)
9.3 The PAT Toolbox
229(1)
9.4 Process Sensors and Process Analysers
229(11)
9.4.1 Process Sensors -- Univariate
233(1)
9.4.2 Process Analysers -- Multivariate
233(1)
9.4.3 Infrared (IR)
233(5)
9.4.4 Near Infrared (NIR)
238(1)
9.4.5 Tunable Diode Laser Spectroscopy (TDLS)
239(1)
9.4.6 Ultraviolet-Visible (UV-Vis)
239(1)
9.4.7 Raman
239(1)
9.4.8 Focused Beam Reflectance Measurements (FBRM) and Laser Diffraction
239(1)
9.4.9 Particle Vision and Measurement (PVM)
239(1)
9.4.10 X-Ray Fluorescence (XRF)
240(1)
9.4.11 Imaging Technologies
240(1)
9.5 Analyser Selection
240(1)
9.6 Regulatory Requirements Related to PAT Applications
240(2)
9.6.1 Europe
242(1)
9.6.2 United States
242(1)
9.7 PAT Used in Development
242(1)
9.8 PAT Used in Manufacturing
243(2)
9.9 PAT and Real Time Release Testing (RTRT)
245(1)
9.10 PAT Implementation
245(1)
9.11 Data Management
246(1)
9.12 In-Line Process Monitoring with UV-Vis Spectroscopy: Case Study Example
247(6)
9.13 References
253(4)
10 Analytical Method Design, Development, and Lifecycle Management
257(24)
Joe de Sousa
David Holt
Paul A. Butterworth
10.1 Introduction
257(1)
10.2 Comparison of the Traditional Approach and the Enhanced QbD Approach
258(2)
10.3 Details of the Enhanced QbD Approach
260(2)
10.4 Defining Method Requirements
262(2)
10.5 Designing and Developing the Method
264(2)
10.6 Understanding the Impact of Method Parameters on Performance
266(1)
10.7 Defining the Method Control Strategy and Validating the Method
267(1)
10.8 Monitoring Routine Method Performance for Continual Improvement
268(1)
10.9 Summary
269(1)
10.10 Example Case Studies
270(8)
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(1)
10.10.2 Risk Assessment and Definition of Ranges
270(1)
10.10.3 Experimental Design
271(1)
10.10.4 Evaluate the DoE
272(2)
10.10.5 Documenting Method Performance
274(1)
10.10.6 Case Study 2 -- Evaluation of the Ruggedness of a Dissolution Method for a Commercial Immediate Release Tablet Product
274(4)
10.10.7 Case Study Acknowledgements
278(1)
10.11 References
278(3)
11 Manufacturing and Process Controls
281(40)
Mark Gibson
11.1 Introduction to Manufacturing and Facilities
281(1)
11.2 Validation of Facilities and Equipment
282(10)
11.2.1 The International Society for Pharmaceutical Engineering (ISPE) Baseline® Guide: Commissioning and Qualification
282(2)
11.2.2 ASTM E2500-07: Standard Guide for Specification, Design, and Verification of Pharmaceutical and Biopharmaceutical Manufacturing Systems and Equipment
284(1)
11.2.3 Science-Based Approach and Critical Aspects
285(1)
11.2.4 Risk-Based Approach
286(2)
11.2.5 System and Component Impact Assessments
288(2)
11.2.6 URSs for Systems
290(1)
11.2.7 Specification and Design
290(1)
11.2.8 Verification
290(2)
11.3 Drug Product Process Validation: A Lifecycle Approach
292(8)
11.3.1 Stage 1: Process Design/Product Development
295(3)
11.3.2 Stage 2: Process Qualification
298(1)
11.3.3 Stage 3: Continued Process Verification
299(1)
11.4 The Impact of QbD on Process Equipment Design and Pharmaceutical Manufacturing Processes
300(2)
11.5 Introduction to Process Control in Pharmaceutical Manufacturing
302(3)
11.6 Advanced Process Controls (APC) and Control Strategy
305(4)
11.7 The Establishment of Continuous Manufacture
309(3)
11.8 The Tablet Press as Part of a Continuous Tableting Line
312(4)
11.9 Real-Time Release Testing and Continuous Quality Verification
316(1)
11.10 Acknowledgments
317(1)
11.11 References
317(4)
12 Regulatory Guidance
321(14)
Siegfried Schmitt
Mustafa A. Zaman
12.1 Introduction
321(1)
12.2 The Common Technical Document (CTD) Format
322(6)
12.2.1 Quality Target Product Profile (QTPP) and Critical Quality Attributes (CQAs)
324(1)
12.2.2 Quality Risk Management (ICH Q9)
324(1)
12.2.3 Product and Process Development (S.2.6 and P.2)
325(1)
12.2.4 Control Strategy
326(1)
12.2.5 Design Space (Optional)
327(1)
12.3 Essential Reading
328(1)
12.4 What Is Not Written, or Hidden, in the Guidance Documents?
329(1)
12.5 Post-Approval Change
330(1)
12.6 Summary
331(1)
12.7 References
332(3)
Index 335
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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