Predictive modeling of pharmaceutical unit operations / edited by Preetanshu Pandey, Rahul Bharadwaj.

Format:

Book

Contributor:

Pandey, Preetanshu, editor.

Bharadwaj, Rahul, editor.

Language:

English

Subjects (All):

Drugs--Coatings.

Drugs.

Pharmaceutical industry.

Physical Description:

1 online resource (465 pages) : illustrations

Edition:

1st ed.

Place of Publication:

Amsterdam, Netherlands : Woodhead Publishing, 2017.

Summary:

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 Qualityby 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

Contents:

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

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

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

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

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

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

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

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

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

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

Monte Carlo-DEM

Probabilistic models

11.4.3.3 Model verification and application

11.5 Prospects for an integrated film-coating process model

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.

Notes:

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

9780081001806

0081001800