Predictive Modeling of Pharmaceutical Unit Operations

 
 
Woodhead Publishing
  • 1. Auflage
  • |
  • erschienen am 26. September 2016
  • |
  • 464 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
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978-0-08-100180-6 (ISBN)
 

The use of modeling and simulation tools is rapidly gaining prominence in the pharmaceutical industry covering a wide range of applications. This book focuses on modeling and simulation tools as they pertain to drug product manufacturing processes, although similar principles and tools may apply to many other areas. Modeling tools can improve fundamental process understanding and provide valuable insights into the manufacturing processes, which can result in significant process improvements and cost savings. With FDA mandating the use of Quality by Design (QbD) principles during manufacturing, reliable modeling techniques can help to alleviate the costs associated with such efforts, and be used to create in silico formulation and process design space. This book is geared toward detailing modeling techniques that are utilized for the various unit operations during drug product manufacturing. By way of examples that include case studies, various modeling principles are explained for the nonexpert end users. A discussion on the role of modeling in quality risk management for manufacturing and application of modeling for continuous manufacturing and biologics is also included.


  • Explains the commonly used modeling and simulation tools
  • Details the modeling of various unit operations commonly utilized in solid dosage drug product manufacturing
  • Practical examples of the application of modeling tools through case studies
  • Discussion of modeling techniques used for a risk-based approach to regulatory filings
  • Explores the usage of modeling in upcoming areas such as continuous manufacturing and biologics manufacturingBullet points
  • Englisch
  • Cambridge
Elsevier Science
  • 15,96 MB
978-0-08-100180-6 (9780081001806)
0081001800 (0081001800)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Predictive Modeling of Pharmaceutical Unit Operations
  • Copyright Page
  • Contents
  • List of contributors
  • Predictive modeling of pharmaceutical unit operations
  • Preface
  • 1 Modeling of drug product manufacturing processes in the pharmaceutical industry
  • 1.1 Introduction
  • 1.2 Modeling techniques
  • 1.2.1 First principle predictive models
  • 1.2.1.1 Discrete element method
  • 1.2.1.2 Computational fluid dynamics
  • 1.2.1.3 Finite element method
  • 1.2.1.4 Hybrid models
  • 1.2.1.5 Empirical models
  • 1.3 Process modeling in drug product manufacturing
  • 1.3.1 Problem statement
  • 1.3.2 Modeling technique selection
  • 1.3.3 Model development
  • 1.3.4 Model verification and validation
  • References
  • 2 Quality risk management for pharmaceutical manufacturing: The role of process modeling and simulations
  • 2.1 Introduction
  • 2.2 Quality risk management in pharmaceutical manufacturing
  • 2.2.1 Managing risk to quality
  • 2.2.2 Utilization of models to support quality risk management
  • 2.2.2.1 Sensitivity analysis: a risk assessment tool
  • 2.2.2.2 Feasibility analysis: a tool to evaluate risk mitigation strategies
  • 2.3 Scientific considerations in model development for quality risk management
  • 2.3.1 High-impact models
  • 2.3.2 Medium-impact models
  • 2.3.3 Low-impact models
  • 2.4 Using process models to support quality risk management for emerging technologies
  • 2.4.1 Risk assessment case studies for continuous manufacturing
  • 2.4.1.1 Continuous direct compression risk assessment: a case study
  • 2.4.1.2 End-to-end risk assessment: a case study
  • 2.4.2 Risk mitigation case studies for continuous manufacturing
  • 2.5 Conclusions
  • References
  • 3 Powder flow and blending
  • 3.1 Critical role of the powder blending step in pharmaceutical manufacturing
  • 3.2 Common challenges in powder blending
  • 3.3 Granular mixing fundamentals
  • 3.3.1 Mixing mechanisms
  • 3.3.2 Common techniques of mixing powders
  • 3.4 Assessment, measurement, and characterization
  • 3.4.1 Assessment
  • 3.4.2 Measurement
  • 3.4.3 Characterization
  • 3.5 Modeling techniques for powder mixing
  • 3.5.1 Development and usage of computational tools
  • 3.5.1.1 Techniques for modeling the underlying physics and processes
  • The DEM and its application to granular mixing
  • 3.5.1.2 Improvements in the efficiency of solution methods, algorithms, and compute architecture
  • 3.5.1.3 Advancement in analysis techniques with commercial and open source software
  • 3.5.2 Case study: creating a material model
  • 3.6 Summary and outlook
  • Acknowledgements
  • References
  • 4 Dry granulation process modeling
  • 4.1 Introduction
  • 4.2 Challenges in dry granulation modeling and recent progress
  • 4.2.1 Roller compaction technology
  • 4.2.2 Theoretical background
  • 4.2.3 Common problems of roller compaction and progress
  • 4.3 Modeling tools
  • 4.3.1 DEM modeling
  • 4.3.2 FEM modeling
  • 4.3.3 Simulation technique for the roller compaction process
  • 4.3.4 Requirements for roller compaction modeling
  • 4.4 Experimental validation
  • 4.4.1 Heterogeneity of density
  • 4.4.2 Heterogeneity of roll pressure
  • 4.5 Case studies of model application
  • 4.5.1 Case study 1: 2D finite element modeling
  • 4.5.1.1 Introduction
  • 4.5.1.2 Results and discussion
  • 4.5.1.3 Concluding comments
  • 4.5.2 Case study 2: 3D finite element modeling
  • 4.5.2.1 Introduction
  • 4.5.2.2 Results and discussion
  • 4.5.2.3 Concluding comments
  • 4.6 Conclusions
  • References
  • 5 Mechanistic modeling of high-shear and twin screw mixer granulation processes
  • 5.1 Introduction
  • 5.1.1 QbD/Overview/challenges in high-shear granulation modeling
  • 5.1.2 High-shear granulation rate processes/underlying mechanisms
  • 5.1.2.1 Liquid distribution
  • 5.1.2.2 Consolidation and growth
  • 5.1.2.3 Breakage
  • 5.1.3 High-shear granulation equipment
  • 5.1.3.1 Vertical high-shear
  • 5.1.3.2 Twin screw granulation
  • 5.2 Modeling techniques for high-shear wet granulation processes
  • 5.2.1 Population balance modeling
  • 5.2.2 Granulation kernels
  • 5.2.3 Discrete element method
  • 5.2.4 Hybrid PBM-DEM techniques
  • 5.2.5 Compartmental approach for high-shear wet granulation processes
  • 5.2.5.1 Vertical high-shear
  • 5.2.5.2 Twin screw
  • 5.3 Numerical techniques
  • 5.3.1 Monte Carlo solution techniques
  • 5.3.2 Lumped-parameter approach for PBM
  • 5.3.3 Multidimensional cell-average technique
  • 5.3.4 Tensor decomposition method
  • 5.4 Application of high-shear wet granulation models
  • 5.4.1 Case study of parameter estimation
  • 5.4.2 Case study of compartment model of high-shear granulation
  • 5.4.3 Case study of PBM-DEM coupling
  • 5.5 General discussion and conclusions
  • References
  • 6 Fluid bed granulation and drying
  • 6.1 Introduction
  • 6.2 Granulation modeling
  • 6.2.1 Granulation conditions
  • 6.2.1.1 Granulation under saturated conditions
  • 6.2.1.2 Granulation under subsaturated conditions
  • 6.2.2 Heat loss
  • 6.2.3 Granule properties
  • 6.3 Drying modeling
  • 6.3.1 Microscopic models
  • 6.3.2 Macroscopic models
  • 6.4 FluidBeG: an integrated granulation and drying model
  • 6.4.1 Model background
  • 6.4.2 Case studies
  • 6.4.2.1 MK-D: DOE studies and scale-up
  • 6.5 Future developments
  • References
  • 7 Modeling of milling processes via DEM, PBM, and microhydrodynamics
  • 7.1 Introduction
  • 7.2 Microhydrodynamic modeling of wet media milling
  • 7.3 DEM for modeling of dry milling
  • 7.4 PBM for process-scale modeling of milling
  • 7.4.1 Calibration of the PBM via parameter estimation (the inverse problem)
  • 7.4.2 Applications of the PBM for continuous milling
  • 7.5 Multiscale modeling approaches for dry media (ball) milling
  • 7.6 Case study: application of the microhydrodynamic model to preparation of drug nanosuspensions
  • 7.7 Case study: application of the multiscale DEM-PBM approach to rolling ball milling
  • 7.8 Concluding remarks
  • Acknowledgments
  • References
  • 8 Modeling of powder compaction with the drucker-prager cap model
  • 8.1 Introduction
  • 8.2 The particulate nature of compacts and the modeling of their behavior
  • 8.3 Constitutive models
  • 8.3.1 Hydrostatic pressure dependence in compaction
  • 8.3.2 Drucker-Prager cap (DPC) model
  • 8.4 Parameter identification
  • 8.4.1 Compaction simulators
  • 8.4.2 Standard procedures for parameter extraction
  • 8.4.3 Extrapolation to low- and high-relative densities
  • 8.4.3.1 High-density extrapolation
  • 8.4.3.2 Low-density extrapolation
  • 8.5 Finite element modeling
  • 8.5.1 Review of the technique
  • 8.6 Case studies
  • 8.6.1 Model validation
  • 8.6.2 Excipient characterization
  • References
  • 9 Modeling approaches to multilayer tableting
  • 9.1 Introduction
  • 9.2 Models
  • 9.2.1 Theories focusing on understanding layer adhesion/layer strength
  • 9.2.1.1 Empirical correlations: use of mixing rules to estimate bilayer tablet strength
  • 9.2.1.2 Application of fracture mechanics concepts
  • 9.2.2 Theories focusing on understanding the role of material relaxation on delamination
  • 9.2.3 Numerical simulation
  • 9.3 Conclusions
  • References
  • 10 Computational modeling of pharmaceutical die filling processes
  • 10.1 Introduction
  • 10.2 Background of pharmaceutical die filling
  • 10.2.1 Powder flow from a shoe
  • 10.2.2 Powder packing inside a die
  • 10.2.3 Segregation during die filling
  • 10.3 Computational setup of die filling
  • 10.3.1 Coupled DEM-CFD method
  • 10.3.2 Numerical model of die filling
  • 10.4 Computational analysis of die filling
  • 10.4.1 Effect of air on powder flow
  • 10.4.2 Suction filling
  • 10.4.3 Segregation
  • 10.5 Summary
  • References
  • 11 Modeling tablet film-coating processes
  • 11.1 Introduction
  • 11.2 Thermodynamic modeling
  • 11.2.1 Description and motivation
  • 11.2.2 Model framework
  • 11.2.2.1 Similarity of tablet bed and exhaust air conditions
  • 11.2.3 Model verification and application
  • 11.2.3.1 Case study: immediate release film coating of Drug A tablets
  • 11.2.3.2 Case study: immediate release film coating of Drug B tablets
  • 11.3 Spray atomization modeling
  • 11.3.1 Description and motivation
  • 11.3.2 Model framework
  • 11.3.2.1 Effect of pattern air on liquid breakup
  • 11.3.2.2 Materials characterization
  • 11.3.3 Model verification and application
  • 11.3.4 Global sensitivity analysis
  • 11.4 Tablet mixing modeling
  • 11.4.1 Introduction
  • 11.4.2 Intertablet mixing models
  • 11.4.2.1 Description and motivation
  • General tablet motion
  • 11.4.2.2 Model frameworks
  • DEM models
  • Monte Carlo models
  • Population balance modeling
  • Renewal theory modeling
  • Hybrid models and suggestions
  • 11.4.2.3 Model verification and application
  • 11.4.3 Intratablet mixing models
  • 11.4.3.1 Description and motivation
  • 11.4.3.2 Model frameworks
  • DEM models
  • Monte Carlo-DEM
  • Probabilistic models
  • 11.4.3.3 Model verification and application
  • 11.5 Prospects for an integrated film-coating process model
  • References
  • 12 Modeling in pharmaceutical packaging
  • 12.1 Introduction
  • 12.2 Container WVTR of pharmaceutical packaging
  • 12.2.1 Moisture permeation
  • 12.2.2 Determination of WVTR
  • 12.2.2.1 Single weight gain method for WVTR determination
  • 12.2.2.2 Steady-state method for WVTR determination
  • 12.2.3 Estimation of container WVTR
  • 12.3 Moisture sorption isotherm of pharmaceutical products
  • 12.3.1 Moisture sorption isotherm
  • 12.3.2 Determination of moisture sorption isotherm
  • 12.3.3 Estimation of moisture sorption isotherm
  • 12.4 Moisture uptake modeling of packaged pharmaceutical products
  • 12.4.1 Overview of moisture uptake modeling
  • 12.4.2 Moisture uptake model using polynomial equations for packaged product in storage
  • 12.5 Case studies
  • 12.5.1 Estimation of equilibrium moisture sorption isotherm of a tablet formulation
  • 12.5.2 Moisture uptake modeling for a packaged tablet product in storage
  • 12.5.3 Moisture uptake modeling for a packaged capsule product in storage
  • 12.6 Summary
  • Acknowledgments
  • References
  • 13 Continuous secondary process selection and the modeling of batch and continuous wet granulation
  • 13.1 Paradigm shift to continuous processing for solid dose manufacture
  • 13.2 Selection of the appropriate process based on powder flow and compressibility
  • 13.3 Introduction to modeling batch high shear granulation
  • 13.4 Modeling batch high shear granulation by sampling during granulation
  • 13.5 Impact of raw material particle size and surface area changes on high shear granulation modeling
  • 13.6 Models describing scale-up and equipment transfer of batch high shear granulation
  • 13.7 Evaluating the significance of work, Xsat and the amount of water added within scale
  • 13.8 A single equation to model granulation-SaWW model
  • 13.9 Modeling twin screw continuous wet granulation
  • 13.10 The impact of feeder variability on twin screw wet granulation
  • 13.11 Conclusion
  • References
  • Appendix A DoE and Repeat Run Data Tables
  • 14 Process modeling in the biopharmaceutical industry
  • 14.1 Introduction
  • 14.1.1 Manufacturing process overview
  • 14.1.2 Current modeling techniques
  • 14.1.3 Scope
  • 14.2 Theoretical foundations
  • 14.2.1 Conservation equations
  • 14.2.1.1 Continuity
  • 14.2.1.2 Momentum
  • Turbulence
  • 14.2.1.3 Heat transfer
  • 14.2.1.4 Species transport
  • 14.2.1.5 Multiphase modeling
  • Types of multiphase models
  • Lagrangian
  • Volume of fluid (VOF)
  • Eulerian
  • Discrete element method (DEM)
  • 14.2.2 Introduction to CFD
  • 14.3 Bioreactor operation and modeling
  • 14.3.1 Introduction
  • 14.3.2 Mixing in bioreactors: physics and key parameters
  • 14.3.2.1 Blend time
  • 14.3.2.2 Mass transfer coefficient
  • 14.3.2.3 Just suspension speed
  • 14.3.3 CFD modeling of mixing in bioreactors
  • 14.3.3.1 Single-phase analysis
  • Flow patterns
  • Blend time analysis
  • Impeller performance data
  • 14.3.3.2 Multiphase analysis
  • Gas distribution
  • Bubble size distribution
  • Mass transfer rate (kLa)
  • 14.3.4 Case study: Creating a design space for QbD using simulation
  • 14.3.4.1 Problem description
  • 14.3.4.2 Model setup
  • 14.3.4.3 Results
  • 14.3.4.4 Estimating the design space
  • 14.4 Liquid chromatography
  • 14.4.1 Introduction
  • 14.4.2 Non-CFD modeling of chromatography
  • 14.4.3 Model applications
  • 14.4.4 Industry perspective
  • 14.5 Lyophilization (freeze drying)
  • 14.5.1 Introduction
  • 14.5.2 Non-CFD modeling of lyophilization
  • 14.5.3 Model applications
  • 14.5.4 Industry perspective
  • 14.6 Conclusions
  • References
  • Index
  • Back Cover

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