Comprehensive Quality by Design for Pharmaceutical Product Development and Manufacture

 
 
Wiley-Aiche (Verlag)
  • erschienen am 30. August 2017
  • |
  • 416 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-119-35616-5 (ISBN)
 
Covers a widespread view of Quality by Design (QbD) encompassing the many stages involved in the development of a new drug product.
The book provides a broad view of Quality by Design (QbD) and shows how QbD concepts and analysis facilitate the development and manufacture of high quality products. QbD is seen as a framework for building process understanding, for implementing robust and effective manufacturing processes and provides the underpinnings for a science-based regulation of the pharmaceutical industry.
Edited by the three renowned researchers in the field, Comprehensive Quality by Design for Pharmaceutical Product Development and Manufacture guides pharmaceutical engineers and scientists involved in product and process development, as well as teachers, on how to utilize QbD practices and applications effectively while complying with government regulations. The material is divided into three main sections: the first six chapters address the role of key technologies, including process modeling, process analytical technology, automated process control and statistical methodology in supporting QbD and establishing the associated design space. The second section consisting of seven chapters present a range of thoroughly developed case studies in which the tools and methodologies discussed in the first section are used to support specific drug substance and drug-product QbD related developments. The last section discussed the needs for integrated tools and reviews the status of information technology tools available for systematic data and knowledge management to support QbD and related activities.
Highlights
* Demonstrates Quality by Design (QbD) concepts through concrete detailed industrial case studies involving of the use of best practices and assessment of regulatory implications
* Chapters are devoted to applications of QbD methodology in three main processing sectors--drug substance process development, oral drug product manufacture, parenteral product processing, and solid-liquid processing
* Reviews the spectrum of process model types and their relevance, the range of state-of-the-art real-time monitoring tools and chemometrics, and alternative automatic process control strategies and methods for both batch and continuous processes
* The role of the design space is demonstrated through specific examples and the importance of understanding the risk management aspects of design space definition is highlighted
Comprehensive Quality by Design for Pharmaceutical Product Development and Manufacture is an ideal book for practitioners, researchers, and graduate students involved in the development, research, or studying of a new drug and its associated manufacturing process.
1. Auflage
  • Englisch
  • New York
  • |
  • USA
John Wiley & Sons Inc
  • Für Beruf und Forschung
  • 12,37 MB
978-1-119-35616-5 (9781119356165)
1119356164 (1119356164)
weitere Ausgaben werden ermittelt
GINTARAS.V. REKLAITIS, PhD, is Professor of Chemical Engineering and Industrial & Physical Pharmacy at Purdue University, member of the U.S. National Academy of Engineering, and the Deputy Director of the NSF Engineering Research Center on Structured Organic Particulate Systems.
CHRISTINE SEYMOUR, PhD, is Director in Global Regulatory Chemistry, Manufacturing & Controls at Pfizer Inc, the 2018 President of AIChE, and a Director in AIChE's Society for Biological Engineering.
SALVADOR GARCÍA-MUNOZ, PhD, is a Senior Engineering Advisor in Process Modeling and Optimization in Small Molecule Development at Eli Lilly and Company.
  • Title Page
  • Copyright Page
  • Contents
  • List of Contributors
  • Preface
  • Chapter 1 Introduction
  • 1.1 Quality by Design Overview
  • 1.2 Pharmaceutical Industry
  • 1.3 Quality by Design Details
  • 1.4 Chapter Summaries
  • References
  • Chapter 2 An Overview of the Role of Mathematical Models in Implementation of Quality by Design Paradigm for Drug Development and Manufacture
  • 2.1 Introduction
  • 2.2 Overview of Models
  • 2.3 Role of Models in QbD
  • 2.3.1 CQA
  • 2.3.2 Risk Assessment
  • 2.3.3 Design Space
  • 2.3.4 Control Strategy
  • 2.4 General Scientific Considerations for Model Development
  • 2.4.1 Models for Process Characterization
  • 2.4.2 Models for Supporting Analytical Procedures
  • 2.4.3 Models for Process Monitoring and Control
  • 2.5 Scientific Considerations for Maintenance of Models
  • 2.6 Conclusion
  • References
  • Chapter 3 Role of Automatic Process Control in Quality by Design
  • 3.1 Introduction
  • 3.2 Design of Robust Control Strategies
  • 3.3 Some Example Applications of Automatic Feedback Control
  • 3.4 The Role of Kinetics Modeling
  • 3.5 Ideas for a Deeper QbD Approach
  • 3.6 Summary
  • Acknowledgments
  • References
  • Chapter 4 Predictive Distributions for Constructing the ICH Q8 Design Space
  • 4.1 Introduction
  • 4.2 Overlapping Means Approach
  • 4.3 Predictive Distribution Approach
  • 4.4 Examples
  • 4.4.1 A Mechanistic Model Example
  • 4.4.2 An Empirical Model Example
  • 4.5 Summary and Discussion
  • Acknowledgment
  • References
  • Chapter 5 Design of Novel Integrated Pharmaceutical Processes: A Model-Based Approach
  • 5.1 Introduction
  • 5.2 Problem Description
  • 5.2.1 Mathematical Formulation
  • 5.2.2 Solution Approach
  • 5.3 Methodology
  • 5.3.1 Superstructure
  • 5.3.2 Model Development
  • 5.3.3 Decomposition Strategy
  • 5.4 Application: Case Study
  • 5.4.1 Stage 1: Problem Definition
  • 5.4.2 Stage 2: Data/Information Collection/Analysis
  • 5.4.3 Stage 3: Superstructure, Model Development, and Decomposition Strategy
  • 5.4.4 Stage 4: Generation of Feasible Candidates and Screening
  • 5.4.5 Stage 5: Screening by Process Model
  • 5.4.6 Stage 6: Evaluation of the Feasible Options: Calculation of the Objective Function
  • 5.5 Conclusions
  • References
  • Chapter 6 Methods and Tools for Design Space Identification in Pharmaceutical Development
  • 6.1 Introduction
  • 6.2 Design Space: A Multidisciplinary Concept
  • 6.3 Integration of Design Space and Control Strategy
  • 6.4 Case Studies
  • 6.4.1 Design Space of a Continuous Mixer: Use of Data-Driven-Based Approaches
  • 6.4.2 Roller Compaction Case Study: Integration of Control Strategy and Its Effects on the Design Space
  • 6.4.2.1 Deterministic Design Space
  • 6.4.2.2 Stochastic Design Space
  • 6.4.2.3 Effect of Control Strategies on the Design Space
  • 6.5 Conclusions
  • Acknowledgment
  • References
  • Chapter 7 Using Quality by Design Principles as a Guide for Designing a Process Control Strategy
  • 7.1 Introduction
  • 7.2 Chemical Sequence, Impurity Formation, and Control Strategy
  • 7.2.1 Chemical Sequence
  • 7.2.2 Impurity Formation
  • 7.2.3 Control Strategy
  • 7.3 Mass Transfer and Reaction Kinetics
  • 7.3.1 CO2 Mass Transfer Model
  • 7.3.1.1 Determination of Henry's Law Constant
  • 7.3.1.2 Determination of the Mass Transfer Coefficient
  • 7.3.2 Reaction Kinetics
  • 7.3.2.1 Deprotection Reaction Kinetics
  • 7.3.2.2 Calculation of Dissolution Constants
  • 7.3.2.3 Coupling Reaction Kinetics
  • 7.4 Optimal Processing Conditions
  • 7.4.1 Use of Combined Models
  • 7.4.2 Carbon Dioxide Removal Process Options
  • 7.5 Predicted Product Quality under Varied Processing Conditions
  • 7.5.1 Virtual Execution of PAR and Design Space Experiments
  • 7.5.1.1 Process Parameters
  • 7.5.2 Acceptable In Situ Values
  • 7.5.3 PAR Simulation
  • 7.5.4 Design Space Simulation: Interactions
  • 7.5.5 Design Space Simulation: Screening Design Experiment and Multifactor Experiment Simulation and Data Analysis
  • 7.5.6 Confirmation of the Design Space with Experiment
  • 7.6 Conclusions
  • Acknowledgments
  • Notation
  • Acronyms
  • Symbols
  • Notes
  • References
  • Chapter 8 A Strategy for Tablet Active Film Coating Formulation Development Using a Content Uniformity Model and Quality by Design Principles
  • 8.1 Introduction
  • 8.2 Content Uniformity Model Development
  • 8.2.1 Principles of the Model
  • 8.2.2 Total Residence Time and Fractional Residence Time
  • 8.2.3 The RSD Model Derivation
  • 8.2.4 Model Parameters and Their Measurements
  • 8.2.4.1 Tablet Velocity
  • 8.2.4.2 Tablet Number Density
  • 8.2.4.3 Spray Zone Width
  • 8.3 RSD Model Validation and Sensitivity Analysis for Model Parameters
  • 8.3.1 Model Validation
  • 8.3.2 Effect of Spray Zone Width on Content Uniformity
  • 8.3.3 Effect of Tablet Velocity on Content Uniformity
  • 8.3.4 Effect of Tablet Size on Content Uniformity
  • 8.3.5 Effect of Pan Load on Content Uniformity
  • 8.3.6 Effect of Coating Time on Content Uniformity
  • 8.4 Model-Based Design Space Establishment for Tablet Active Film Coating
  • 8.4.1 Establish a Model-Based Process Design Space at a Defined Scale
  • 8.4.2 Model-Based Scale-Up
  • 8.4.3 Model-Based Process Troubleshooting
  • 8.5 Summary
  • Notations
  • References
  • Chapter 9 Quality by Design: Process Trajectory Development for a Dynamic Pharmaceutical Coprecipitation Process Based on an Integrated Real-Time Process Monitoring Strategy
  • 9.1 Introduction
  • 9.2 Experimental
  • 9.2.1 Materials
  • 9.2.2 Equipment and Instruments
  • 9.3 Data Analysis Methods
  • 9.3.1 PCA and Process Trajectory
  • 9.3.2 Singular Points of a Signal
  • 9.4 Results and Discussion
  • 9.4.1 Using Offline NIR Measurement to Characterize the Naproxen-Eudragit L100 Binary Powder Mixing Process
  • 9.4.2 Using In-Line NIR Spectroscopy to Monitor the Alcohol-Water Binary Liquid Mixing Process
  • 9.4.3 Real-Time Integrated PAT Monitoring of the Dynamic Coprecipitation Process
  • 9.4.4 3D Map of NIR Absorbance-Wavelength-Process Time (or Process Sample) of the Coprecipitation Process
  • 9.4.5 Process Signature Identification
  • 9.4.6 Online Turbidity Monitoring of the Process
  • 9.5 Challenges and Opportunities for PCA-Based Data Analysis and Modeling in Pharmaceutical PAT and QbD Development
  • 9.6 Conclusions
  • Acknowledgments
  • References
  • Chapter 10 Application of Advanced Simulation Tools for Establishing Process Design Spaces Within the Quality by Design Framework
  • 10.1 Introduction
  • 10.2 Computer Simulation-Based Process Characterization of a Pharmaceutical Blending Process
  • 10.2.1 Background
  • 10.2.2 Goals
  • 10.2.3 Material and Methods
  • 10.2.3.1 Application of QbD Concepts
  • 10.2.3.2 Model and Numerical Simulation
  • 10.2.3.3 Process Characterization Experimental Design
  • 10.2.4 Results and Discussion
  • 10.2.5 Conclusion
  • 10.3 Characterization of a Tablet Coating Process via CFD Simulations
  • 10.3.1 Introduction
  • 10.3.2 Background
  • 10.3.3 Methods
  • 10.3.3.1 Model and Numerical Simulation
  • 10.3.3.2 Simulation Design and Characterization
  • 10.3.3.3 Potentially Critical Input Parameters
  • 10.3.4 Results and Discussion
  • 10.3.4.1 Time Development of Mean Thickness and RSD
  • 10.3.4.2 Knowledge Space
  • 10.3.5 Summary
  • 10.4 Overall Conclusions
  • References
  • Chapter 11 Design Space Definition:: A Case Study-Small Molecule Lyophilized Parenteral
  • 11.1 Introduction
  • 11.2 Case Study: Bayesian Treatment of Design Space for a Lyophilized Small Molecule Parenteral
  • 11.2.1 Arrhenius Accelerated Stability Model with Covariates for a Pseudo-Zero-Order Degradation Process
  • 11.2.2 Design Space Definition
  • 11.3 Results
  • 11.4 Conclusions
  • Appendix 11.A Implementation Using WinBUGS and R
  • 11.A.1 WinBUGS Model
  • 11.A.2 Data Used for Analysis
  • 11.A.3 Calling WinBUGS from R
  • 11.A.4 Calculating the Predictive Posterior Probability of Meeting Shelf Life
  • Notation
  • Acknowledgments
  • References
  • Chapter 12 Enhanced Process Design and Control of a Multiple-Input Multiple-Output Granulation Process
  • 12.1 Introduction and Objectives
  • 12.2 Population Balance Model
  • 12.2.1 Compartmentalized Population Balance Model
  • 12.3 Simulation and Controllability Studies
  • 12.4 Identification of Existing "Optimal" Control-Loop Pairings
  • 12.4.1 Discarding n1
  • 12.4.2 Discarding n2
  • 12.4.3 Discarding n3
  • 12.4.4 Discarding n4
  • 12.4.5 Discussion
  • 12.5 Novel Process Design
  • 12.5.1 Identification of Kernels
  • 12.5.2 Proposed Design and Control Configuration
  • 12.6 Conclusions
  • References
  • Chapter 13 A Perspective on the Implementation of QbD on Manufacturing through Control System: The Fluidized Bed Dryer Control with MPC and NIR Spectroscopy Case
  • 13.1 Introduction
  • 13.2 Theory
  • 13.2.1 Fluidized Bed Dryers (FBDs)
  • 13.2.2 Process Control
  • 13.2.2.1 Proportional Integral Derivative (PID) Control
  • 13.2.2.2 Model Predictive Control (MPC)
  • 13.3 Materials and Methods
  • 13.3.1 Materials
  • 13.3.2 Equipment
  • 13.3.3 MPC Implementation
  • 13.4 Results and Discussion
  • 13.4.1 Process Model
  • 13.4.2 Control Performance with Nominal Process Parameters
  • 13.4.3 Control Performance with Non-nominal Model Parameters
  • 13.5 Continuous Fluidized Bed Drying
  • 13.6 Control Limitations
  • 13.7 Conclusions
  • Acknowledgment
  • References
  • Chapter 14 Knowledge Management in Support of QbD
  • 14.1 Introduction
  • 14.2 Knowledge Hierarchy
  • 14.3 Review of Existing Software
  • 14.4 Workflow-Based Framework
  • 14.4.1 Scientific Workflows
  • 14.4.2 Business Workflows
  • 14.4.3 Comprehensive Workflow-Based Knowledge Management System
  • 14.5 Drug Substance Case Study
  • 14.5.1 Process Description
  • 14.5.2 Workflow-Based Representation of the Semagacestat Study
  • 14.5.3 Using Workflows
  • 14.6 Design Space
  • 14.6.1 Design Space Example
  • 14.6.2 Systematic Approach to Determining Design Space
  • 14.6.3 Workflow-Based Approach to Design Space Development
  • 14.6.4 Drug Product Case Study
  • 14.7 Technical Challenges
  • 14.7.1 Human-Machine Interaction Design
  • 14.7.2 Extraction of Operational Data
  • 14.7.3 Collection of Tacit Knowledge
  • 14.8 Conclusions
  • References
  • Index
  • EULA

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