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Emerging Non Clinical Biostatistics in Biopharmaceutical Development and Manufacturing 1st Edition by Harry Yang ISBN 1498704158 9781498704151

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Emerging Non Clinical Biostatistics in Biopharmaceutical Development and Manufacturing 1st Edition Harry Yang Digital Instant Download

Author(s): Harry Yang
ISBN(s): 9781498704151, 1498704158
Edition: 1
File Details: PDF, 5.39 MB
Year: 2016
Language: english
SKU: EB-7027970 Category: Tag:

Emerging Non Clinical Biostatistics in Biopharmaceutical Development and Manufacturing 1st Edition by Harry Yang – Ebook PDF Instant Download/Delivery: 1498704158, 9781498704151
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ISBN 10: 1498704158 
ISBN 13: 9781498704151
Author: Harry Yang

The premise of Quality by Design (QbD) is that the quality of the pharmaceutical product should be based upon a thorough understanding of both the product and the manufacturing process. This state-of-the-art book provides a single source of information on emerging statistical approaches to QbD and risk-based pharmaceutical development. A comprehensive resource, it combines in-depth explanations of advanced statistical methods with real-life case studies that illustrate practical applications of these methods in QbD implementation.

Emerging Non Clinical Biostatistics in Biopharmaceutical Development and Manufacturing 1st Table of contents:

Section I Background

  1. Quality by Design in Biopharmaceuticals
    1.1 Introduction
    1.2 Evolving Regulatory Environment
    1.3 Bioprocess Development
    1.3.1 Upstream Process
    1.3.2 Downstream Process
    1.3.3 Challenges of Bioprocessing
    1.4 Quality by Design
    1.4.1 Quality Target Product Profile
    1.4.2 Product and Process Design
    1.4.2.1 Critical Quality Attributes
    1.4.2.2 Process Development
    1.4.3 Process Control
    1.4.4 Contrast between Minimal Approaches and QbD
    1.5 Statistical Opportunities
    1.5.1 Analytical Method QbD
    1.5.2 CQAs and Clinical Performance
    1.5.3 Design Space
    1.5.4 Control Strategy
    1.5.5 Continued Improvement
    1.5.6 Predictive Modeling of Bioprocesses
    1.5.7 Statistical Computing Tools
    1.5.8 Regulatory Science
    1.6 New Role of Statisticians
    1.7 Concluding Remarks

Section II Analytical Method
2. Analytical Method Validation
2.1 Introduction
2.2 Regulatory Requirements
2.3 Life Cycle Approach
2.4 Validation Study Design
2.4.1 Accuracy and Precision
2.4.2 Experimental Design
2.5 Current Validation Methods
2.5.1 Point Estimate
2.5.2 Confidence Interval Approach
2.5.3 Total Error Approach
2.5.3.1 “Fit for Purpose” Hypothesis
2.5.3.2 Maximum Likelihood
2.5.3.3 Modified MLE
2.5.3.4 β-Expectation Tolerance Interval
2.5.3.5 β-Content Tolerance Interval
2.6 Generalized Pivotal Quantity Method
2.6.1 GPQ of Quality Level
2.6.2 GPQ Test
2.7 Statistical Test Comparisons
2.8 Other Alternate Methods
2.8.1 Method Based on Capability
2.8.2 Bayesian Approach
2.9 Concluding Remarks

  1. Parallelism Testing of Bioassay
    3.1 Introduction
    3.2 Dose–Response Models
    3.3 Parallelism Testing
    3.3.1 Measure of Parallelism
    3.3.2 Statistical Parallelism Tests
    3.3.2.1 Significance Tests
    3.3.2.2 Equivalence Tests
    3.4 Method Comparison
    3.4.1 Measures of Performance
    3.4.1.1 Consumer and Producer Risks
    3.4.1.2 ROC Curve Analysis
    3.4.1.3 Sensitivity versus Specificity
    3.4.1.4 Estimation of Sensitivity, Specificity, and AUC
    3.4.1.5 Simulation
    3.4.1.6 Simulation Results
    3.5 Determination of Equivalence Limits
    3.5.1 Capability-Based Methods
    3.5.2 ROC Curve Method
    3.5.3 Limits Based on AUC
    3.6 Concluding Remarks

  2. Validation of Method Linearity
    4.1 Introduction
    4.2 Study Design
    4.3 Calibration Model
    4.4 Current Statistical Methods
    4.4.1 Descriptive Summary
    4.4.1.1 Visual Inspection
    4.4.1.2 Pearson’s Correlation Coefficient
    4.4.2 Significance Tests
    4.4.2.1 Lack-of-Fit Test
    4.4.2.2 Mendel’s Method
    4.4.2.3 Kroll’s Method
    4.4.3 Equivalence Tests
    4.4.3.1 Krouwer and Schlain’s Method
    4.4.3.2 Corrected Kroll Method
    4.4.3.3 Liu and Hsieh’s Methods
    4.4.3.4 Novick and Yang’s Method
    4.5 Test Linearity on Concentration Scale
    4.5.1 Extension of Novick and Yang’s Method
    4.5.1.1 Alternative Test
    4.5.1.2 Bias on Concentration Scale
    4.5.2 Statistical Test Methods
    4.5.2.1 Linear versus Quadratic Models
    4.5.2.2 Linear versus Higher-Order Polynomials
    4.5.2.3 Examples
    4.5.3 Single-Point Calibration
    4.6 Concluding Remarks

Section III Process Development
5. Residual Host Cell DNA Risk Assessment
5.1 Introduction
5.2 DNA Inactivation and Removal
5.2.1 Filtration
5.2.2 DNA Inactivation
5.3 Risk Assessment
5.3.1 Peden, Sheng, and Lewis’ Method
5.3.2 Krause and Lewis’ Method
5.3.3 Yang, Zhang, and Galinski’s Method
5.3.3.1 Modeling of DNA Inactivation
5.3.3.2 Residual DNA from Oncogenes
5.3.3.3 Safety Factor Estimation
5.3.3.4 Determination of Enzyme Efficiency
5.3.4 Safety Factor Estimation Method Comparisons
5.3.4.1 Theoretical Results
5.3.4.2 Empirical Comparisons
5.4 Risk Control
5.4.1 Specifications Based on Current Standards
5.4.1.1 Method
5.4.1.2 Example
5.4.2 Specifications Based on Acceptable Patient Risk
5.4.2.1 Acceptable Ranges
5.4.2.2 Applications
5.5 Bayesian Approach
5.5.1 Posterior Distribution
5.5.2 Example
5.6 Concluding Remarks

  1. Evaluations of Viral Clearance
    6.1 Introduction
    6.2 Viral Clearance Studies
    6.2.1 Choices of Viruses for Viral Clearance Evaluation
    6.2.2 Selection of Process Steps
    6.2.3 Scale-Down of Process Steps
    6.2.4 Evaluation of Cytotoxicity and Viral Interference
    6.2.5 Spiking Experiments and Collection of Process Samples
    6.2.6 Calculation of Process Capability
    6.3 Assays for Viral Quantification
    6.3.1 Plaque-Forming Assay
    6.3.2 CPE Assay
    6.3.2.1 Karber Method
    6.4 Virus Titer Estimation for Plaque-Forming Assays
    6.4.1 Estimation Method Based on Normal Distribution
    6.4.2 Estimation Method Based on Poisson Distribution
    6.4.2.1 Maximum Likelihood Estimate
    6.4.2.2 Exact Estimate for Poisson Method
    6.4.2.3 Poisson Regression
    6.5 Reduction Factor Estimation
    6.5.1 Approximate CI
    6.5.2 Exact CI
    6.6 Comparisons of Estimation Methods
    6.6.1 Simulations
    6.6.2 Example
    6.7 Concluding Remarks

  2. Bioburden Testing and Control
    7.1 Introduction
    7.2 Regulatory Guidelines
    7.2.1 FDA Guidance
    7.2.2 EMA Perspective
    7.2.3 Potential Limitations of Regulatory Standards
    7.3 Risk-Based Approach
    7.3.1 Risk Assessment
    7.3.1.1 Risk Factors
    7.3.1.2 Criticality Analysis of Risk Factors
    7.3.2 Statistical Risk Analysis
    7.3.2.1 Modeling Prefiltration Bioburden
    7.3.2.2 Performance of Prefiltration Test Procedures
    7.3.2.3 Modeling Postfiltration Bioburden
    7.3.3 Risk Control
    7.3.3.1 First-Tier Risk Factors
    7.3.3.2 Prefiltration Risk Control
    7.3.3.3 Final Postfiltration Risk Control
    7.3.3.4 Control of Second-Tier Risk Factors
    7.4 Applications of Risk-Based Methods
    7.4.1 Justification of Alternative Sample Volumes and Decision Criteria
    7.4.2 Design Space
    7.4.3 Effects of Risk Factors
    7.4.3.1 EFA and Retention Capability
    7.4.3.2 Effect of Risk Tolerance
    7.4.3.3 Double Filters
    7.5 Concluding Remarks

  3. Process Validation
    8.1 Introduction
    8.2 Process Design
    8.2.1 Identification of CQAs
    8.2.1.1 General Considerations
    8.2.1.2 Example
    8.2.2 Design Space
    8.2.3 Statistical Methods for Design Space
    8.2.3.1 Overlapping Mean Response Surfaces
    8.2.3.2 Bayesian Approach
    8.3 Process Performance Qualification
    8.3.1 Risk-Based Approaches
    8.3.1.1 Determination of Residual Risk Level
    8.3.1.2 Determination of Number of PPQ Batches
    8.3.2 Bayesian Alternative
    8.3.2.1 Generalization of Bayesian Method
    8.3.3 Frequentist Method
    8.3.4 Method Comparisons
    8.3.4.1 Numerical Comparison
    8.4 Continued Process Verification
    8.4.1 Multivariate Control Chart
    8.4.2 Principal Component Analysis
    8.4.3 PLS Modeling
    8.5 Concluding Remarks

Section IV Manufacturing
9. Specifications
9.1 Introduction
9.2 Types of Specifications
9.2.1 Univariate Specifications
9.2.2 Multivariate Specifications
9.3 Specifications for Correlated Quality Attributes
9.3.1 Linking Specifications to Drug Efficacy
9.3.2 Example
9.3.3 Performance Evaluation
9.4 Specification of Ratio
9.4.1 One-Sided Upper Tolerance Interval
9.4.2 Upper Tolerance Limit for X1/X2
9.4.2.1 Approximate Method
9.4.2.2 Exact Methods
9.4.2.3 Method Performance
9.4.2.4 Application

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Tags: Harry Yang, Clinical Biostatistics, Biopharmaceutical