Method Validation in Pharmaceutical Analysis: A Guide to Best Practice

This second edition of a global bestseller has been completely redesigned and extensively rewritten to take into account the new Quality by Design (QbD) and lifecycle concepts in pharmaceutical manufacturing. As in the first edition, the fundamental requirements for analytical method validation are covered, but the second edition describes how these are applied systematically throughout the entire analytical lifecycle. QbD principles require adoption of a systematic approach to development and validation that begin with predefined objectives. For analytical methods these predefined objectives are established as an Analytical Target Profile (ATP). The book chapters are aligned with recently introduced standards and guidelines for manufacturing processes validation and follow the three stages of the analytical lifecycle: Method Design, Method Performance Qualification, and Continued Method Performance Verification. Case studies and examples from the pharmaceutical industry illustrate the concepts and guidelines presented, and the standards and regulations from the US (FDA), European (EMA) and global (ICH) regulatory authorities are considered throughout. The undisputed gold standard in the field.

Table of Contents:
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 2.1.1.1 Criteria for Quality Data 11 2.1.1.2 Regulatory Rationale for Qualified Analytical Instruments 12 2.1.2 USP General Chapter <1058> 12 2.1.2.1 Data Quality Triangle 14 2.1.2.2 Analytical Instrument Qualification Life Cycle: the Four Qs Model 14 2.1.2.3 Risk-Based Classification of Apparatus, Instruments, and Systems 15 2.1.2.4 Roles and Responsibilities for AIQ 17 2.1.2.5 Software Validation for Group B and C Systems 18 2.1.3 Enhancement of <1058> and Harmonization of a Risk-Based Approach to Instruments and Systems with GAMP Laboratory GPG Second Edition 18 2.1.3.1 Increased Granularity of USP <1058> Groups 18 2.1.3.2 Clarification of AIQ Terminology 19 2.1.3.3 A Continuum of Analytical Apparatus, Instruments, and Systems 19 2.1.3.4 Mapping USP <1058> Instrument Groups to GAMP Software Categories 20 2.1.3.5 Enhanced Data Quality Triangle 20 2.1.4 Risk-Based Approaches to Analytical Instrument and System Qualification 20 2.1.4.1 Expanded <1058> Instrument and System Categories 23 2.2 Efficient and Economic HPLC Performance Qualification 25 Hermann Wätzig 2.2.1 Introduction 25 2.2.1.1 The Importance of Analytical Instrument Qualification 25 2.2.1.2 Terms and Definitions 25 2.2.1.3 Continuous Performance Qualification: More by Less 27 2.2.2 Development of the Revised OQ/PQ Parameters List 27 2.2.3 Transfer of Modular Parameters into the Holistic Approach 29 2.2.3.1 Autosampler 29 2.2.3.2 Solvent Delivery System 29 2.2.3.3 Detector 30 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 Jane Weitzel 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 Target Measurement 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 4.4.3.1 Relationship between Accuracy and Precision 64 4.4.4 Specificity 65 4.4.4.1 Chromatographic Procedures 65 4.4.4.2 Non-chromatographic Procedures 66 4.4.5 Linearity and Range 67 4.4.5.1 Linearity 67 4.4.5.2 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 5.2.1.1 The Normal Distribution and its Parameters 74 5.2.1.2 Robust Parameter 84 5.2.2 Precision Levels 84 5.2.2.1 System or Instrument Precision 85 5.2.2.2 Repeatability 86 5.2.2.3 Intermediate Precision and Reproducibility 86 5.2.3 Calculation of Precisions and Variances 89 5.2.3.1 Analysis of Variances (ANOVA) 90 5.2.3.2 Calculation of Precision from Linear Regression 92 5.2.4 Concentration Dependency of Precision 93 5.2.5 Precision Acceptance Criteria 95 5.2.5.1 Precision of the Reportable Result 95 5.2.5.2 Optimization of the Calibration Format 97 5.2.5.3 Acceptable Precision for Assay 101 5.2.5.4 Acceptable Precision for Impurities and Minor Components 105 5.2.6 Precisions Benchmarks 107 5.2.6.1 Precisions for LC Assay 108 5.2.7 Sources to Obtain and Supplement Precisions 116 5.2.7.1 Precisions from Stability 117 5.2.8 Precision Highlights 119 5.3 Accuracy and Range 119 5.3.1 Drug Substance 122 5.3.1.1 Significance Tests 122 5.3.1.2 Equivalence Tests 124 5.3.1.3 Direct Comparison 125 5.3.1.4 Comparison Examples 125 5.3.2 Drug Product 126 5.3.2.1 Percentage Recovery 127 5.3.2.2 Recovery Function 128 5.3.2.3 Standard Addition 128 5.3.2.4 Accuracy of Drug Product by Comparison 129 5.3.3 Impurities/Degradants 129 5.3.3.1 Recovery of Spiked Impurities 129 5.3.3.2 Accuracy of the Integration Mode 130 5.3.3.3 Response Factors 131 5.3.4 Acceptance Criteria (ATP Requirements) 132 5.3.4.1 Can this Theoretically Obtained Relationship be Supported by Experimental Results? 135 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 5.4.3.1 Rechromatography 141 5.4.3.2 Diode Array Detection 142 5.4.3.3 Lc-ms 143 5.4.4 Specificity Highlights 145 5.5 Linearity 145 5.5.1 Unweighted Linear Regression 147 5.5.1.1 Graphical Evaluation of Linearity 151 5.5.1.2 Numerical Regression Parameters 153 5.5.1.3 Statistical Linearity Tests 155 5.5.1.4 Evaluation of the Intercept (Absence of Systematic Errors) 158 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 5.6.1.1 Intermediate Quantitation Limit 166 5.6.1.2 General Quantitation Limit 166 5.6.2 Approaches Based on the Blank 167 5.6.3 Determination of DL/QL from Linearity 167 5.6.3.1 Standard Deviation of the Response 169 5.6.3.2 95% Prediction Interval of the Regression Line 171 5.6.3.3 Aproach Based on German Standard DIN 32645 172 5.6.3.4 From the Relative Uncertainty 173 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 6.3.2.1 Basic and Intrinsic Parameters 243 6.3.3 Examples of Computer-Assisted Robustness Studies 245 6.3.3.1 Robustness Testing Based on Chromatography Modeling Software 246 6.3.3.2 Robustness Testing Based on Experimental Design 258 Acknowledgments 287 6.4 System Suitability Tests 287 Joachim Ermer 6.4.1 Chromatographic System Suitability Parameters 288 6.4.1.1 Signal-to-Noise Ratio 289 6.4.1.2 Test for Required Detectability 291 6.4.1.3 Injection Precision 292 6.4.1.4 System Precision for Impurities? 293 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 CaseStudy:QualificationofanHPLCMethodforIdentity,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 7.2.4.1 Specificity 314 7.2.4.2 Linearity 314 7.2.4.3 Accuracy 318 7.2.4.4 Precision 320 7.2.4.5 Quantitation Limit 321 7.2.4.6 Range 323 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 7.3.1.1 Analytical Procedures for Complex Dosage Forms 324 7.3.1.2 Human and Environmental Factors Associated with Complex Laboratory Procedures 325 7.3.1.3 Delivered Dose Uniformity Testing for Inhalation Products 325 7.3.2 Designing a Delivered Dose Uniformity Procedure that will Meet an Atp 326 7.3.2.1 Risk Assessment and Classification 327 7.3.2.2 Noise Factors Associated with Dose Collection 331 7.3.2.3 Dose Recovery and Sample Preparation 333 7.3.2.4 Automated Delivered Dose Uniformity Procedure 333 7.3.2.5 Results Calculation and Reporting 334 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 7.4.7.1 Gather Knowledge 341 7.4.7.2 Finalizing the ATP 346 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 7.5.1.1 Regulatory and International Guidance 349 7.5.1.2 Transfer Process 350 7.5.2 Comparative Testing 355 7.5.2.1 Equivalence-Based Methodology 355 7.5.2.2 Direct Comparison 369 Acknowledgments 372 References 372 8 Continued Method 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 8.2.3.1 Example 1 382 8.2.3.2 Example 2 382 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 8.4.2.1 Risk Assessment of Changes 407 References 409 Index 411

About the Author :
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.