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Statistics for Biotechnology Process Development [Kõva köide]

Edited by (MedImmune, LLC, Gaithersburg, Maryland, USA), Edited by (Seattle Genetics, Inc., Bothell, WA, USA)
  • Formaat: Hardback, 358 pages, kõrgus x laius: 254x178 mm, kaal: 684 g, 72 Tables, black and white; 154 Line drawings, black and white; 23 Halftones, black and white; 177 Illustrations, black and white
  • Ilmumisaeg: 05-Jun-2018
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1498721400
  • ISBN-13: 9781498721400
Teised raamatud teemal:
Statistics for Biotechnology Process DevelopmentSuurem pilt
  • Formaat: Hardback, 358 pages, kõrgus x laius: 254x178 mm, kaal: 684 g, 72 Tables, black and white; 154 Line drawings, black and white; 23 Halftones, black and white; 177 Illustrations, black and white
  • Ilmumisaeg: 05-Jun-2018
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1498721400
  • ISBN-13: 9781498721400
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781498721400.html
Märksõnad:
Written specifically for biotechnology scientists, engineers, and quality professionals, this book describes and demonstrates the proper application of statistical methods throughout Chemistry, Manufacturing, and Controls (CMC). Filled with case studies, examples, and easy-to-follow explanations of how to perform statistics in modern software, it is the first book on CMC statistics written primarily for practitioners. While statisticians will also benefit from this book, it is written particularly for industry professionals who dont have access to a CMC statistician or who want to be more independent in the design and analysis of their experiments.











Provides an introduction to the statistical concepts important in the biotechnology industry





Focuses on concepts with theoretical details kept to a minimum





Includes lots of real examples and case studies to illustrate the methods





Uses JMP software for implementation of the methods





Offers a text suitable for scientists in the industry with some quantitative training

Written and edited by seasoned veterans of the biotechnology industry, this book will prove useful to a wide variety of biotechnology professionals. The book brings together individual chapters that showcase the use of statistics in the most salient areas of CMC.
Preface vii
Editors ix
Contributors xi
1 Interpretation and Treatment of Data 1(38)
Harry Yang
Steven J. Novick
Lorin Roskos
1.1 Background
2(1)
1.2 Biopharmaceutical Development
2(1)
1.3 Statistics in Bioprocess Development
3(1)
1.4 Statistical Inferences
4(5)
1.4.1 Example
4(1)
1.4.2 Random Variables
5(1)
1.4.3 Continuous Distributions
5(4)
1.4.3.1 Gaussian Distribution
5(2)
1.4.3.2 Student's t-Distribution
7(1)
1.4.3.3 Chi-Square Distribution
8(1)
1.4.3.4 F-Distribution
8(1)
1.4.4 Discrete Distributions
9(1)
1.4.4.1 Binomial
9(1)
1.4.4.2 Poisson
9(1)
1.5 Sampling Considerations
9(2)
1.5.1 Non-Random Sample
10(1)
1.5.2 Simple Random Sampling
10(1)
1.5.3 Stratified Sampling
10(1)
1.5.4 Systematic Sampling
11(1)
1.6 Statistical Estimation
11(7)
1.6.1 Point Estimate
12(1)
1.6.2 Interval Estimation
12(6)
1.6.2.1 Confidence Interval
12(3)
1.6.2.2 Prediction Interval
15(1)
1.6.2.3 Tolerance Interval
16(2)
1.7 Hypothesis Testing
18(6)
1.7.1 Type I and Type II Errors
19(1)
1.7.2 Significance Test
20(1)
1.7.3 Statistical Significance
21(1)
1.7.4 Equivalence Test
22(2)
1.7.4.1 Issues with Significance Test
22(1)
1.7.4.2 Alternate Test
23(1)
1.8 Sample Size
24(3)
1.8.1 Sample Size for Estimation
24(1)
1.8.2 Sample Size for Significance Test
25(1)
1.8.3 Sample Size for Equivalence Test
26(1)
1.9 Selection of Method for Data Analysis
27(3)
1.9.1 Example
27(2)
1.9.2 Test Model Assumptions
29(1)
1.9.2.1 Q-Q Plot
29(1)
1.9.2.2 Nonparametric Test
30(1)
1.9.3 Data Transformation
30(1)
1.10 Removal of Outliers
30(4)
1.10.1 Definition
31(1)
1.10.2 Outlier Tests
32(8)
1.10.2.1 Grubbs' Test
32(1)
1.10.2.2 Dixon's Test
33(1)
1.10.2.3 Other Outlier Tests
34(1)
1.10.2.4 Model-Based Method
34(1)
1.11 Bayesian Inference
34(2)
1.12 Concluding Remarks
36(1)
References
36(3)
2 Design of Experiments (DOE) for Process Development 39(38)
Todd Coffey
2.1 Introduction and Overview
40(4)
2.1.1 Definitions
41(1)
2.1.2 General Classes of Designs
41(3)
2.2 Before You Start: Planning for Success
44(3)
2.2.1 Defining the Experimental Purpose
44(1)
2.2.2 Selecting the Responses
44(1)
2.2.3 Identifying Factors, Levels, and Ranges
45(1)
2.2.4 Fundamental Design Principles
46(1)
2.3 Design Building Blocks
47(4)
2.3.1 Design Notation
47(1)
2.3.2 Factorial Designs
48(1)
2.3.3 2k-p Fractional Factorial Designs
48(3)
2.4 Choosing a Design
51(6)
2.4.1 Screening Experiments
51(2)
2.4.2 Characterization Experiments
53(2)
2.4.3 Optimization Experiments
55(2)
2.4.4 Advantages and Disadvantages of the Classical Approach
57(1)
2.5 Analyzing a Single Response
57(14)
2.5.1 Linear Regression Fundamentals
57(1)
2.5.2 Linear Regression Model
58(1)
2.5.3 Model Fitting
59(1)
2.5.4 Model Assumptions
60(1)
2.5.5 Statistical Analysis for Screening Experiments
61(6)
2.5.6 Statistical Analysis for Characterization Experiments
67(3)
2.5.7 Statistical Analysis for Optimization Experiments
70(1)
2.6 Analyzing Multiple Responses Simultaneously
71(3)
2.7 Summary
74(1)
References
75(2)
3 Quality by Design Applied in Formulation Development and Robustness 77(16)
Pierre Lebrun
Perceval Sondag
Xavier Lories
Jean-Francois Michiels
Eric Rozet
Bruno Boulanger
3.1 Introduction
77(1)
3.2 Design Space Definition and Visualization
78(8)
3.2.1 Quality Target Product Profile and Critical Quality Attributes
78(1)
3.2.2 Critical Process Parameters
79(1)
3.2.3 Design of Experiments
79(2)
3.2.4 Model
81(1)
3.2.5 Probability of Success
82(1)
3.2.6 Visualization of the Results
83(1)
3.2.7 Determining and Reporting the Design Space
84(1)
3.2.8 Design Space vs. PAR
85(1)
3.2.9 Illustration
85(1)
3.3 Example
86(5)
3.4 Conclusion
91(1)
References
91(2)
4 Analytical Procedure Development and Qualification 93(26)
Richard K. Burdick
4.1 Introduction
93(3)
4.1.1 Description of an Analytical Procedure
93(2)
4.1.2 Description of Life Cycle Approach
95(1)
4.1.3 Measurement Error Models
95(1)
4.2 Stage 1: Procedure Design
96(1)
4.3 Stage 2: Procedure Performance Qualification
97(18)
4.3.1 Individual Qualification for Accuracy and Precision
97(7)
4.3.2 Incorporation of a Ruggedness Factor
104(5)
4.3.3 Power Considerations
109(1)
4.3.4 Holistic Qualification of a Bioassay Method
110(3)
4.3.5 Holistic Qualification of a Relative Purity Method
113(2)
4.3.6 Limit of Detection (LOD) and Linearity
115(1)
4.4 Step 3: Continued Procedure Performance Verification
115(1)
References
116(3)
5 Strategic Bioassay Design, Development, Analysis, and Validation 119(26)
David Lansky
5.1 Introduction
119(3)
5.1.1 Supporting a Biotechnology Product
119(1)
5.1.2 Product Specifications Ensure Efficacy and Safety
120(2)
5.2 Statistical and Strategic Introduction
122(5)
5.2.1 Common Properties of Bioassays
124(2)
5.2.2 Issues with Common Analysis Strategies
126(1)
5.3 A Recommended Bioassay Analysis Strategy
127(5)
5.3.1 Transformation
128(1)
5.3.2 Outlier Management
128(1)
5.3.3 Assay Acceptance Criteria
129(1)
5.3.4 Mixed Models
129(1)
5.3.5 Equivalence Testing for Similarity
130(1)
5.3.6 Model Selection
131(1)
5.3.7 Bioassay Analysis Summary
132(1)
5.4 Strategic Design
132(4)
5.4.1 Design Goals
132(1)
5.4.2 Practical and Strategic Design Constraints
133(2)
5.4.3 Other Bioassay Design Considerations
135(1)
5.4.4 Design Strategies during Bioassay Development
135(1)
5.5 Other Ways to Be Strategic by Combining Design, Analysis, and Use of Bioassays
136(1)
5.6 Qualification/Validation Experiment Design and Analysis
137(3)
5.6.1 Discussion of the Qualification Experiment Results
138(2)
5.7 Summary
140(2)
References
142(3)
6 Setting Specification 145(24)
Harry Yang
6.1 Regulatory Requirements
145(1)
6.2 Identification of Critical Quality Attributes
146(1)
6.3 Selection of Critical Process Parameters and Input Material Attributes
147(1)
6.4 Control Strategies
148(2)
6.4.1 Input Material Control
149(1)
6.4.2 In-Process Control
149(1)
6.4.2.1 Procedural Controls
149(1)
6.4.2.2 Process Parameter Controls
149(1)
6.4.2.3 In-Process Testing
149(1)
6.4.3 Release Testing
150(1)
6.4.4 Stability Trending
150(1)
6.4.5 Comparability Testing
150(1)
6.4.6 Continuous Process Verification
150(1)
6.5 Considerations in Setting Acceptance Criteria
150(15)
6.5.1 Frame of Reference
151(1)
6.5.2 Sources of Variation
151(4)
6.5.3 Impact of Correlation
155(3)
6.5.4 Clinical Relevance Limits
158(3)
6.5.5 Shelf Life
161(1)
6.5.6 Release Limit
162(12)
6.5.6.1 Fixed Effect Model
163(2)
6.6 Multivariate Specifications
165(1)
6.7 Concluding Remarks
166(1)
References
166(3)
7 Statistical Analysis of Stability Studies 169(84)
Laura D. Pack
7.1 Introduction
170(1)
7.2 A Word about Study Design
171(1)
7.3 A Word about Source Data
172(2)
7.4 Stability Models for Use in This
Chapter
174(19)
7.4.1 Fixed Lot Model
174(8)
7.4.1.1 Statistical Assumptions for the Fixed Lot Model
175(1)
7.4.1.2 Fitting the Fixed Lot Model in JMP®
175(2)
7.4.1.3 Obtaining Output from the Fixed Lot Model Fit in JMP®
177(1)
7.4.1.4 Verifying Statistical Assumptions for the Fixed Lot Model in MP®
178(4)
7.4.2 Random Lot Model
182(4)
7.4.2.1 Statistical Assumptions for the Random Lot Model
183(1)
7.4.2.2 Fitting the Random Lot Model in JMP®
183(1)
7.4.2.3 Obtaining Output from the Random Lot Model Fit in JMP®
184(2)
7.4.2.4 Verifying Statistical Assumptions for the Random Lot Model in JMP®
186(1)
7.4.3 Qualitative Predictor Model
186(8)
7.4.3.1 Statistical Assumptions for the Qualitative Predictor Model
188(1)
7.4.3.2 Fitting the Qualitative Predictor Model in JMP®
188(1)
7.4.3.3 Obtaining Output from the Qualitative Predictor Model Fit in JMP®
189(3)
7.4.3.4 Verifying Statistical Assumptions in JMP®
192(1)
7.5 Outline
193(1)
7.6 Is There a Trend on Stability?
194(21)
7.6.1 Can a Particular CQA Be Considered "Stability Indicating"?
195(1)
7.6.2 Is the Change over Time Statistically Significant?
196(4)
7.6.3 Is the Change over Time Practically Significant?
200(2)
7.6.4 Is an Individual Result in Trend?
202(7)
7.6.5 Is a Stability Lot in Trend?
209(3)
7.6.6 How Can I Establish a Stability Trending Program?
212(2)
7.6.7 How Can I Perform an Annual "Trend Analysis" as Required for Commercially Approved Products?
214(1)
7.7 How Is the Stability Profile Related to the Expiry Period?
215(16)
7.7.1 What Is the Appropriate Expiry or Retest Period for My Product?
216(9)
7.7.2 Will an Individual Lot Meet Its Intended Expiry Period?
225(2)
7.7.3 Can I Extend the Expiry Period?
227(1)
7.7.4 How Can I Model Behavior for End-to-End Stability for Multiple Product Stages?
228(3)
7.8 How Does the Stability Profile Relate to the Specification Limit?
231(5)
7.8.1 What Is the Appropriate Specification Limit to Achieve a Desired Expiry or Retest Period?
232(2)
7.8.2 Is It Appropriate to Have a Tighter Specification Limit at Lot Release?
234(1)
7.8.3 Does Changing the Specification Limit Impact the Expiry?
234(1)
7.8.4 What Is the Probability of an Individual 00S Result on Stability?
234(2)
7.9 How Much Exposure to a Particular Condition Can Be Allowed without Impacting the Shelf Life?
236(7)
7.9.1 What Is an Appropriate Limit for Exposure to Temperatures above Recommended Storage?
236(1)
7.9.2 How Much Light Exposure Is Acceptable?
237(2)
7.9.3 Does Temperature Cycling Impact My Product?
239(4)
7.10 Do Two (Or More) Different Permutations of My Molecule Change the Same Way over Time?
243(7)
7.10.1 Can I Apply a Bracketing Approach to Several Different Configurations of the Same Product Formulation?
243(2)
7.10.2 Is One Formulation, Container, or Configuration More Stable at a Given Temperature?
245(1)
7.10.3 Is the Stability Profile the Same after a Manufacturing or Process Change?
246(4)
7.11 Conclusion
250(1)
References
251(2)
8 Continued Process Verification 253(20)
Tara Scherder
Katherine Giacoletti
8.1 Introduction
253(1)
8.2 Statistical Tools for CPV
254(5)
8.2.1 SPC Phases, Control Charts and Process Monitoring
254(1)
8.2.2 Shewhart Control Chart for Individual Measurements
255(3)
8.2.3 Process Capability
258(1)
8.3 Special Considerations for CPV in the Biopharmaceutical Industry
259(4)
8.3.1 Randomness, Independence, and Normality-Lack Thereof
259(4)
8.4 Business Considerations for Implementation of CPV
263(3)
8.5 Other Control Charts
266(5)
References
271(2)
9 Multivariate Analysis for Bioprocess Understanding and Troubleshooting 273(24)
Jianchun Zhang
Harry Yang
9.1 Background
273(3)
9.2 Multivariate Analysis
276(7)
9.2.1 Multiple Regression
276(4)
9.2.1.1 Overlapping Response Surfaces
277(1)
9.2.1.2 Desirability Approach
278(1)
9.2.1.3 Bayesian Approach
279(1)
9.2.2 PCA
280(2)
9.2.3 PLS
282(1)
9.3 Multivariate Process Control
283(3)
9.3.1 Process Control
283(2)
9.3.2 Batch Process Monitoring
285(1)
9.4 Applications
286(7)
9.4.1 Operating Ranges of Chromatography Assay
286(1)
9.4.2 Optimization of Cell Culture System
287(1)
9.4.3 Quantification of Scale-Down Model for Bioreactor
288(2)
9.4.4 PCA-Based Process Control
290(3)
9.5 Concluding Remarks
293(1)
References
293(4)
10 Assessment of Analytical Method Robustness: Statistical versus Practical Significance 297(12)
Binbing Yu
Lingmin Zeng
Harry Yang
10.1 Introduction
297(1)
10.2 Common Steps of a Robustness Assessment
298(1)
10.3 Design of Experiments
299(2)
10.4 Evaluation of Assay Robustness
301(2)
10.4.1 Graphical Evaluation by Half-Normal Plot
301(1)
10.4.2 Dong's Method
301(1)
10.4.3 Linear Model
302(1)
10.4.4 Statistical vs. Practical Significance
302(1)
10.5 Case Study
303(2)
10.6 Discussions
305(1)
References
305(4)
11 cGMP Sampling 309(22)
Harry Yang
11.1 Regulatory Guidance
309(1)
11.2 Risk-Based Sampling Plans
310(1)
11.3 Applications
310(3)
11.3.1 Raw Material, Drug Substance, and Finished Product Release
310(1)
11.3.2 Process Efficiency
311(1)
11.3.3 Environmental Monitoring
311(1)
11.3.4 Stability Testing
311(2)
11.4 Acceptance Sampling for Lot Inspection
313(8)
11.4.1 Acceptance Sampling Plan
313(1)
11.4.2 Quality Level
314(1)
11.4.3 Operating Characteristic Curve
314(1)
11.4.4 Single Attribute Sampling
314(1)
11.4.5 Variables Sampling
315(3)
11.4.6 Selection of Sampling Plan
318(1)
11.4.7 Double Sampling Plan
319(2)
11.5 Acceptance Sampling of Liquid Product
321(3)
11.5.1 Prefiltration Bioburden Control
321(1)
11.5.2 Performance of Prefiltration Test Procedures
322(1)
11.5.3 Risk Mitigation through Sterile Filtration
323(1)
11.6 Sampling for Stability Testing
324(2)
11.7 Sampling and Environmental Monitoring
326(2)
11.7.1 Sampling Site, Timing, and Frequency
326(1)
11.7.2 Alert and Action Limits
327(1)
11.8 Concluding Remarks
328(1)
References
328(3)
Index 331
Todd Coffey (PhD Biostatistics) is the founding chair and an associate professor in the Department of Research and Biostatistics at the Idaho College of Osteopathic Medicine. Previously, he started a university-wide statistical consulting center at Washington State University and spend a decade in the biotechnology industry as a nonclinical statistician, making major contributions to successful worldwide regulatory submissions for multiple innovator biologics.