Process systems engineering for pharmaceutical manufacturing

• Other Authors: Singh, Ravendra (Research assistant professor) (Editor), Yuan, Zhihong (Assistant professor of chemical engineering) (Editor)
• Format: Electronic eBook
• Language: English
• Published: Amsterdam, Netherlands : Elsevier, 2018.
• Series: Computer-aided chemical engineering ; 41.
• Subjects: Systems engineering.; Pharmaceutical industry > Management.; Manufacturing processes.; elektronické knihy; electronic books
• ISBN: 9780444639660; 0444639667; 9780444639639
• Physical Description: 1 online resource

Table of Contents:

• Front Cover
• Process Systems Engineering for Pharmaceutical Manufacturing
• Copyright
• Contents
• Contributors
• Preface
• Chapter 1: New Product Development and Supply Chains in the Pharmaceutical Industry
• 1. Introduction
• 2. Typical Features of Pharmaceutical Industry
• 2.1. Analysis of the Product Development Process
• 2.2. Life Cycle of a Drug
• 2.3. Drug Market Features
• 2.4. Supply Chain Management
• 2.4.1. Typical features of pharmaceutical supply chains
• 2.4.2. Process systems engineering contribution
• 3. Management of Product Development Pipeline
• 3.1. Methodological Approaches
• 3.2. Related Optimization Works
• 3.2.1. Optimization of early-phase testing
• 3.2.2. Optimization of portfolio management
• 3.2.3. Clinical trial supply chain management (CTM)
• 4. Capacity Planning
• 5. Management of the Whole Pharmaceutical Supply Chain
• 6. Conclusions
• References
• Chapter 2: The development of a pharmaceutical oral solid dosage forms
• 1. Introduction
• 2. Pharmaceutical Preformulation and Its Significance in the Development of Solid Dosage Forms
• 2.1. Solid-State Properties
• 2.2. Solubility
• 2.2.1. pKa
• 2.2.2. Partition coefficient (log P)
• 2.3. Dissolution Studies
• 2.4. Stability Studies
• 2.5. Drug-Excipient Compatibility Studies
• 2.6. Physical Properties of Pharmaceutical Solids
• 3. Drug Product Manufacturing
• 3.1. Diluents
• 3.2. Binders
• 3.3. Disintegrating Agents
• 3.4. Lubricant
• 3.5. Coating Materials
• 3.5.1. Sugar coating
• Sealing
• Subcoating
• Smoothing
• Coloring
• Polishing
• 3.5.2. Film coating
• 4. Manufacturing Methods for Oral Solid Dosage Form
• 4.1. Direct Compression (Shangraw et al., 1989)
• 4.2. Granulation
• 4.2.1. Dry granulation
• 4.2.2. Wet granulation
• High shear mixture granulation (Gokhale et al., 2005).
• Fluidized Bed Granulation (Parikh and Mogavero, 2005)
• 5. Type of Unit Operation
• 5.1. Pharmaceutical Process Design Methodology
• 5.2. Unit Operation Design
• 5.2.1. Crystallization
• 5.2.2. Filtration and drying
• 5.2.3. Screening and size reduction
• 5.2.4. Blending
• 5.2.5. Tabletting process
• 6. Batch Versus Continuous Processing
• 7. Process Analytical Technology
• 8. Conclusions
• References
• Chapter 3: Innovative process development and production concepts for small-molecule API manufacturing
• 1. Introduction
• 2. Pharmaceutical Production Processes
• 2.1. Production of High-Molecular-Weight Pharmaceutical Products
• 2.2. Production of Low-Molecular-Weight Pharmaceutical Products
• 3. Innovative Solutions to Accelerate the Development of API Production Processes
• 3.1. Virtual Experimentation
• 3.2. Databases and Property Prediction
• 3.3. Template Processes
• 3.4. Summary
• 4. Innovative Solutions to Improve API Production Processes
• 4.1. Process Analytical Technology
• 4.2. Process Integration and Intensification
• 4.3. Solvent Selection
• 4.4. Biocatalysis
• 4.5. Flow Chemistry
• 5. Example: Sitagliptin
• 6. Future Perspectives
• References
• Chapter 4: Plantwide technoeconomic analysis and separation solvent selection for continuous pharmaceutical manufacturing ...
• 1. Introduction
• 2. CPM of Ibuprofen, Artemisinin, and Diphenhydramine
• 2.1. Continuous-Flow Syntheses
• 2.2. Batch and Continuous Separation Schemes
• 3. Economic Analysis
• 4. Results and Discussion
• 4.1. API Recoveries and Material Efficiencies
• 4.1.1. Ibuprofen (IBU)
• 4.1.2. Artemisinin (ART)
• 4.1.3. Diphenhydramine (DPH)
• 4.2. Economic Analysis
• 4.2.1. CapEx and OpEx Savings
• 4.2.2. Sensitivity Analyses: NPV, ROI, and PBP
• 5. Conclusions
• Acknowledgments
• Appendix A. API Recoveries and PMIs.
• Appendix B. CapEx, OpEx and Sensitivity Analyses
• References
• Chapter 5: Flowsheet modeling of a continuous direct compression process
• 1. Introduction
• 1.1. Flowsheet modeling
• 2. Continuous Direct Compression
• 2.1. Powder Feeding
• 2.2. Methods of Modeling for Powder Feeding
• 2.2.1. Perfect feeding
• 2.2.2. Perfect feeding with random variation
• 2.2.3. Feeding with control strategy
• 2.2.4. Feeding with process parameters and material properties
• 2.3. Powder Blending
• 2.3.1. Convective blenders
• 2.3.2. Gravity-driven blenders
• 2.4. Modeling Methods for Powder Blending
• 2.4.1. Population balance equation
• 2.4.2. Convolution
• 2.4.3. Tanks in series
• 2.5. Tablet press
• 2.6. Modeling methods for the Tablet Press
• 2.6.1. Feed frame
• 2.6.2. Tablet compaction
• References
• Further Reading
• Chapter 6: Applications of a plant-wide dynamic model of an integrated continuous pharmaceutical plant: Design of the rec ...
• 1. Introduction
• 2. Process Description
• 3. Plant-Wide Model
• 4. Results and Discussions
• 4.1. Impact of Wash Factors
• 4.2. Impact of Purge Ratio
• 5. Conclusions
• References
• Chapter 7: Advanced multiphase hybrid model development of fluidized bed wet granulation processes
• 1. Introduction to Granulation Modeling
• 1.1. Fluid Bed Model Development: Multiphase Flow and Granulation
• 1.2. Different Modeling Techniques
• 1.2.1. Population balance modeling
• 1.2.2. Discrete element modeling
• 1.2.3. Computational fluid dynamics
• 1.2.4. Coupled CFD-DEM modeling
• 2. Multiphase Model Development and Implementation: Fluidized Bed Wet Granulation
• 2.1. CFD-DEM: Model Development
• 2.1.1. Flow and energy models
• 2.1.2. Lagrangian multiphase models
• 2.1.3. Implicit unsteady-state model
• 2.1.4. Lagrangian passive scalar model
• 2.2. PBM: Compartmental Model Development.
• 2.2.1. Aggregation
• 2.2.2. Breakage
• 2.2.3. Liquid addition
• 2.2.4. Consolidation
• 2.2.5. Particle flux between compartments
• 2.3. CFD-DEM-PBM: Model Implementation
• 3. Results and Discussion
• 3.1. CFD-DEM Simulation Results
• 3.1.1. Effect on particle velocities
• 3.1.2. Effect on particle temperatures
• 3.1.3. Effect on collision frequency and circulation of particles
• 3.1.4. Effect on the particles residence time in spray zone
• 3.2. PBM Results and Validation of Hybrid Model
• 4. Summary
• References
• Chapter 8: Global sensitivity, feasibility, and flexibility analysis of continuous pharmaceutical manufacturing processes
• 1. Introduction
• 2. Global Sensitivity Analysis
• 2.1. Methods
• 2.1.1. Screening methods
• 2.1.2. Regression-based methods
• 2.1.3. Variance-based methods
• Sobol' method
• FAST and eFAST method
• 2.1.4. Metamodel-based methods
• 2.2. Visualization of Sensitivity Results
• 3. Feasibility and Flexibility Analysis
• 3.1. Methods
• 3.1.1. Traditional simulation-based approach
• 3.1.2. Surrogate-based adaptive sampling approach
• Kriging-based adaptive sampling approach
• RBF-based adaptive sampling approach
• 3.2. Visualization of Results
• 3.3. Extensions
• 4. Software
• 5. Conclusion and Future Perspectives
• Acknowledgments
• References
• Chapter 9: Crystallization process monitoring and control using process analytical technology
• 1. Introduction
• 2. QbD and PAT
• 3. Liquid- and Solid-Phase Monitoring
• 3.1. ATR-FTIR and Ultraviolet-Visible Spectroscopy
• 3.2. Conductivity Measurements
• 3.3. Refractive Index Measurement
• 3.4. Turbidity Measurement
• 3.5. FBRM
• 3.6. PVM and Endoscopy
• 3.7. Raman Spectroscopy
• 3.8. Acoustic Spectroscopy (Ultrasound)
• 4. Monitoring and Control of Batch Crystallization Processes.
• 4.1. Optimal Switching Between Nucleation and Seed Ripening Using Control Charts
• 4.2. Concentration Feedback Control
• 4.3. ADNC
• 4.4. Polymorphic Feedback Control
• 4.5. Polymorphic Control by Optimal Solvent Selection
• 5. Monitoring and Control of Continuous Crystallization Processes
• 5.1. ADNC of Continuous Crystallization Processes
• 5.2. Polymorphic Control in Continuous Crystallization
• 5.3. Encrustation Monitoring in Continuous Crystallization
• References
• Further Reading
• Chapter 10: BioProcess performance monitoring using multiway interval partial least squares
• 1. Motivation and Background
• 2. Combining data Unfolding and Interval Splicing Techniques
• 2.1. Three-Dimensional Data Unfolding
• 2.2. Combining Data Unfolding and Interval Splicing
• 3. Fed-Batch Penicillin Simulator Prediction and Fault Monitoring
• 3.1. Fed-Batch Penicillin Production Process Simulator Overview
• 3.2. Prediction and Process Monitoring
• 4. Prediction and Monitoring Results
• 4.1. Predictive Model Performance
• 4.2. Process Performance Monitoring
• 5. Conclusions
• Funding Sources
• References
• Chapter 11: Process dynamics and control of API manufacturing and purification processes
• 1. Introduction, Objectives, and Background
• 2. Integrated Process
• 3. Model Development
• 3.1. Population Balance Model
• 3.2. Crystallizer
• 3.3. Filter
• 3.4. Dryer
• 3.5. Mixer
• 3.5.1. DEM simulation
• 3.6. Principal Component Analysis-Based ROM
• 3.7. Numerical Technique
• 4. Design Strategy of the Hybrid MPC-PID and PID Only Control System
• 4.1. Hybrid MPC-PID Design
• 4.2. PID Only Design
• 4.3. Design of Controller
• 4.4. MPC-PID Controller Equations
• 5. Performance of the Hybrid Control System
• 5.1. Comparison of Hybrid MPC-PID Scheme With PID Only Scheme
• 6. Conclusions
• Acknowledgments
• References.