Title Details

Joachim Ermer is Head of Quality Control Services Chemistry at Sanofi in Frankfurt, Germany, and Global Reference Standards Coordinator of Sanofi. He studied biochemistry at University of Halle, Germany, and obtained a PhD in enzyme kinetics in 1988. He has more than 20 years of experience in pharmaceutical analytics including development projects, global responsibilities as Director of Analytical Processes and Technology, and Head of Quality Control. He is member of the USP Expert Panel on Validation and Verification, of the EFPIA Quality by Design Working Group, and of the Focus Group Analytics and Quality Assurance of the International Association of Pharmaceutical Technology (APV). From 2000 till 2008, he was Deputy Head of the Working Group Quality Control / Pharmaceutical Analytics of the German Pharmaceutical Society (DPhG). His special interest has been focused early on analytical validation and related topics, such as performance evaluation, statistics, and transfer of analytical procedures..

Phil Nethercote is the Analytical Head and API Analytical Lead for the Global Manufacturing and Supply division of GSK. He has a degree in chemistry from Herriot Watt University in Edinburgh and obtained a PhD in HPLC retention mechanisms from the University of Stirling in 1987. He is a Chartered Chemist and a member of the Royal Society of Chemistry. He has over 25 years of experience in the pharmaceutical industry the majority of which has been with Glaxo, Glaxo Wellcome and GSK where he has led analytical development and new product introduction teams in the UK and in Singapore. In his current role he provides leadership for analytical systems, processes and standards across GSKs global network of manufacturing sites. He is member of the USP Expert Panel on Validation and Verification, of the EFPIA Analytical Quality by Design Working Group and led the revision of the analytical section of the second edition of the ISPE technology transfer guide He has a passion for ensuring efforts invested in Analytical Method Validation and Transfer add real value in ensuring the methods produce fit for purpose data and has been a strong advocate in applying QbD principles to help achieve that aim.

Foreword XIII

List of Contributors XV

1 Analytical Validation within the Pharmaceutical Lifecycle 1
Phil Nethercote and Joachim Ermer

1.1 Development of Process and Analytical Validation Concepts 1

1.2 Alignment between Process and Analytics:Three-Stage Approach 4

1.3 Predefined Objectives: Analytical Target Profile 5

1.4 Analytical Life Cycle 8

References 9

2 Analytical Instrument Qualification 11

2.1 Analytical Instrument and System Qualification 11
Christopher Burgess and R. D.McDowall

2.1.1 Data Quality and Integrity in a GMP Environment 11

2.1.2 USP General Chapter 12

2.1.3 Enhancement of and Harmonization of a Risk-Based Approach to Instruments and Systems with GAMP Laboratory GPG Second Edition 18

2.1.4 Risk-Based Approaches to Analytical Instrument and System Qualification 20

2.2 Efficient and Economic HPLC Performance Qualification 25

2.2.1 Introduction 25

2.2.2 Development of the Revised OQ/PQ Parameters List 27

2.2.3 Transfer of Modular Parameters into the Holistic Approach 29

2.2.4 OQ/PQ Data in Comparison with SST Data 32

2.2.5 Control Charts 33

2.2.6 General Procedure for Continuous PQ 34

2.2.7 Concluding Remarks 36

Acknowledgment 37

Abbreviations 37

References 38

3 Establishment of Measurement Requirements – Analytical Target Profile and Decision Rules 41
Mary Lee JaneWeitzel

3.1 Introduction 41

3.2 Defining the Fitness for Intended Use 42

3.3 Decision Rules 42

3.4 Overview of Process to Develop Requirements for Procedure Performance 43

3.5 Decision Rules and Compliance 43

3.6 Calculating Target Measurement Uncertainty 45

3.6.1 Coverage Factor, k, and Data Distributions 46

3.7 Types of Decision Rules 47

3.7.1 Decision Rules That Use Guard Bands 48

3.8 TargetMeasurement Uncertainty in the Analytical Target Profile 49

3.8.1 Cost of Analysis 49

3.9 Bias and Uncertainty in a Procedure 50

3.10 ATP and Key Performance Indicators 51

3.11 Measurement Uncertainty 51

3.11.1 What Uncertainty Is 51

3.11.2 Reporting Measurement Uncertainty 52

3.11.3 How Uncertainty is Estimated 54

3.11.4 Uncertainty Contains All Sources of Random Variability 55

3.12 Example 56

3.13 Conclusion 57

References 58

4 Establishment of Measurement Requirements – Performance-Based Specifications 59
Todd L. Cecil

4.1 Introduction 59

4.2 Intended Purpose 60

4.3 Identification 60

4.4 Assay 62

4.4.1 Precision 62

4.4.2 Accuracy 63

4.4.3 Precision and Accuracy 64

4.4.4 Specificity 65

4.4.5 Linearity and Range 67

4.5 Impurities 68

4.6 Limit Tests 69

4.6.1 Limit of Detection 69

4.6.2 Precision 70

4.6.3 Specificity 70

4.7 Quantitative Tests 70

4.7.1 Accuracy 70

4.7.2 Precision 71

4.7.3 Specificity and Range 71

4.8 Summary 71

References 71

5 Method Performance Characteristics 73
Joachim Ermer

5.1 Introduction 73

5.2 Precision 74

5.2.1 Distribution of Data 74

5.2.2 Precision Levels 84

5.2.3 Calculation of Precisions and Variances 89

5.2.4 Concentration Dependency of Precision 93

5.2.5 Precision Acceptance Criteria 95

5.2.6 Precisions Benchmarks 107

5.2.7 Sources to Obtain and Supplement Precisions 116

5.2.8 Precision Highlights 119

5.3 Accuracy and Range 119

5.3.1 Drug Substance 122

5.3.2 Drug Product 126

5.3.3 Impurities/Degradants 129

5.3.4 Acceptance Criteria (ATP Requirements) 132

5.3.5 Joint Evaluation of Accuracy and Precision 136

5.3.6 Accuracy Highlights 137

5.4 Specificity 137

5.4.1 Demonstration of Specificity by Accuracy 140

5.4.2 Chromatographic Resolution 140

5.4.3 Peak Purity (Co-elution) 141

5.4.4 Specificity Highlights 145

5.5 Linearity 145

5.5.1 Unweighted Linear Regression 147

5.5.2 Weighted Linear Regression 160

5.5.3 Appropriate Calibration Models 162

5.5.4 Nonlinear and Other Regression Techniques 162

5.5.5 Linearity Highlights 163

5.6 Detection and Quantitation Limit 164

5.6.1 Requirements in Pharmaceutical Impurity Determination 164

5.6.2 Approaches Based on the Blank 167

5.6.3 Determination of DL/QL from Linearity 167

5.6.4 Precision Based Approaches 174

5.6.5 Comparison of the Various Approaches 175

5.6.6 Quantitation Limit Highlights 176

5.7 Glossary 177

Acknowledgments 182

References 182

6 Method Design and Understanding 191

6.1 Method Selection, Development, and Optimization 191
Melissa Hanna-Brown, Roman Szucs, and Brent Harrington

6.1.1 Introduction 191

6.1.2 Method Selection 192

6.1.3 Method Development 194

6.1.4 Method Optimization 205

Acknowledgments 217

6.2 Analytical Quality by Design and Robustness Investigations 217
Rosario LoBrutto

6.2.1 Introduction 217

6.2.2 Method Validation Requirements 220

6.2.3 Robustness 221

6.2.4 Analytical Quality by Design 223

6.2.5 Design of Experiments (DOE) 225

6.2.6 FMEA (Failure Mode Effect Analysis) 227

6.2.7 Illustrative Case Study 231

6.2.8 Illustrative Example for Statistical Analysis 234

6.2.9 Control Strategy 239

6.2.10 Conclusions 240

Acknowledgments 241

6.3 Case Study: Robustness Investigations 241
Gerd Kleinschmidt

6.3.1 Introduction 241

6.3.2 General Considerations in the Context of Robustness Testing 242

6.3.3 Examples of Computer-Assisted Robustness Studies 245

Acknowledgments 287

6.4 System Suitability Tests 287
Joachim Ermer

6.4.1 Chromatographic System Suitability Parameters 288

6.4.2 Non-chromatographic System Suitability Parameters 293

6.4.3 Design of System Suitability Tests 294

References 295

7 Method Performance Qualification 303

7.1 Introduction 303
Joachim Ermer

7.1.1 Example of a Precision Study 305

7.2 Case Study: Qualification of an HPLCMethod for Identity, Assay, and Degradation Products 308
Gerd Kleinschmidt

7.2.1 Introduction 308

7.2.2 Experimental 310

7.2.3 Qualification Summary 310

7.2.4 Qualification Methodology 314

7.2.5 Conclusion 324

7.3 Design and Qualification of a Delivered Dose Uniformity Procedure for a Pressurized Metered Dose Inhaler 324
Andy Rignall

7.3.1 Introduction 324

7.3.2 Designing a Delivered Dose Uniformity Procedure that will Meet an ATP 326

7.3.3 Performance Characteristics of the Delivered Dose Uniformity Procedure 334

7.3.4 Qualification of the Delivered Dose Uniformity Procedure 335

7.3.5 Summary of the Analytical Control Strategy for a Delivered Dose Uniformity Procedure 336

Acknowledgment 337

7.4 Implementation of Compendial/Pharmacopeia Test Procedures 337
Pauline McGregor

7.4.1 Background of Pharmacopeia Procedures 337

7.4.2 How Pharmacopeia Methods are Generated and Published 338

7.4.3 Challenges with Compendial Procedures and the Need to Verify 338

7.4.4 Using Pharmacopeia Procedures in a Laboratory for the First Time 339

7.4.5 Current Approach to Verification of Pharmacopeia Procedures 339

7.4.6 Integration of the Current Verification Process and the Lifecycle Approach 340

7.4.7 Implementation of a Pharmacopeia Procedure Using the Lifecycle Approach 341

7.4.8 Performance Qualification 347

7.4.9 Conclusion 348

7.5 Transfer of Analytical Procedures 348
Christophe Agut and Joachim Ermer

7.5.1 Transfer Process and Strategy 349

7.5.2 Comparative Testing 355

Acknowledgments 372

References 372

8 ContinuedMethod Performance Verification 377
Phil Nethercote and Christopher Burgess

8.1 Introduction 377

8.2 Routine Monitoring 377

8.2.1 Introduction 377

8.2.2 Establishing a Control Chart 380

8.2.3 Examples of Application of Control Charting to Analytical Procedures 382

8.2.4 Periodic Review 383

8.2.5 Determination of Root Cause Using CuSum Analysis 385

8.3 Investigating and Addressing Aberrant Data 391

8.3.1 Laboratory Failure Investigation 391

8.3.2 Classification of Atypical or Aberrant Results 393

8.3.3 Statistical Outlier Tests for Out-of-Expectation Results 399

8.3.4 Summary 405

8.4 Continual Improvement 406

8.4.1 Introduction 406

8.4.2 Control of Change 406

References 409

Index 411