Comprehensive Quality by Design for Pharmaceutical Product Development and Manufacture E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 611

Veröffentlichungsjahr: 2017

Beschreibung

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.

Leseprobe

Comprehensive Quality by Design for Pharmaceutical Product Development and Manufacture

Edited by Gintaras V. Reklaitis, Christine Seymour, and Salvador García‐Munoz

This edition first published 2017

Copyright © 2017 by American Institute of Chemical Engineers, Inc. All rights reserved.A Joint Publication of the American Institute of Chemical Engineers and John Wiley & Sons, Inc.Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

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The right of Gintaras V. Reklaitis, Christine Seymour, and Salvador García‐Munoz to be identified as the editors of this work has been asserted in accordance with law.

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Library of Congress Cataloguing‐in‐Publication Data

Names: Reklaitis, G. V., 1942– editor. | Seymour, Christine, 1967– editor. |  García‐Munoz, Salvador, 1971– editor.Title: Comprehensive quality by design for pharmaceutical product development and  manufacture / edited by Gintaras V. Reklaitis, Christine Seymour, Salvador García‐Munoz.Description: Hoboken, NJ : John Wiley & Sons, 2017. | Includes bibliographical  references and index. |Identifiers: LCCN 2017016418 (print) | LCCN 2017025889 (ebook) |  ISBN 9781119356165 (pdf) | ISBN 9781119356172 (epub) | ISBN 9780470942376 (cloth)Subjects: LCSH: Drugs–Design. | Pharmaceutical technology–Quality control.Classification: LCC RS420 (ebook) | LCC RS420 .C653 2017 (print) | DDC 615.1/9–dc23LC record available at https://lccn.loc.gov/2017016418

Cover Design: Wiley

Cover Images: (Background) © BeholdingEye/Gettyimages; (Graph) From Chapter 2, Courtesy of Chatterjee, Moore and Nasr

List of Contributors

Siegfried AdamResearch Center Pharmaceutical Engineering GmbHGraz, Austria

Prabir K. BasuQbD ConsultantMt Prospect, IL, USA

Richard D. BraatzDepartment of Chemical EngineeringMassachusetts Institute of TechnologyCambridge, MAandDepartment of Chemical and Biomolecular EngineeringUniversity of Illinois at Urbana‐ChampaignUrbana, IL, USA

Christopher L. BurchamSmall Molecule Design and DevelopmentEli Lilly and CompanyIndianapolis, IN, USA

Shih‐Ying ChangDrug Product Science and TechnologyBristol‐Myers Squibb CompanyNew Brunswick, NJ, USA

Sharmista ChatterjeeOffice of New Drugs Quality AssessmentFood and Drug AdministrationSilver Spring, MD, USA

Wei ChenDrug Product Science and TechnologyBristol‐Myers Squibb CompanyNew Brunswick, NJ, USA

Divyakant DesaiDrug Product Science and TechnologyBristol‐Myers Squibb CompanyNew Brunswick, NJ, USA

Mitsuko FujiwaraDepartment of Chemical and Biomolecular EngineeringUniversity of Illinois at Urbana‐ChampaignUrbana, IL, USA

Rafiqul GaniDepartment of Chemical and Biochemical EngineeringTechnical University of DenmarkLyngby, Denmark

A. GiridharDavidson School of Chemical EngineeringPurdue UniversityWest Lafayette, IN, USA

Li May GohDepartment of Chemical and Biomolecular EngineeringUniversity of Illinois at Urbana‐ChampaignUrbana, IL, USA

Neil HodnettGlaxoSmithKline PharmaceuticalsBrentford, UK

Marianthi G. IerapetritouDepartment of Chemical and Biochemical EngineeringRutgers UniversityPiscataway, NJ, USAFernando J. MuzzioDepartment of Chemical andBiochemical EngineeringRutgers UniversityPiscataway, NJ, USA

Mo JiangDepartment of Chemical EngineeringMassachusetts Institute of TechnologyCambridge, MA, USA

G. JoglekarDavidson School of Chemical EngineeringPurdue UniversityWest Lafayette, IN, USA

Nicholas C. S. KeeDepartment of Chemical and Biomolecular EngineeringUniversity of Illinois at Urbana‐ChampaignUrbana, IL, USAandNational University of Singapore, Block E5andInstitute of Chemical and Engineering SciencesJurong Island, Singapore

Paul J. A. KenisDepartment of Chemical and Biomolecular EngineeringUniversity of Illinois at Urbana‐ChampaignUrbana, IL, USA

Mansoor A. KhanDivision of Product Quality Research (DPQR, HFD‐940)OTR, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food and Drug AdministrationSilver Spring, MD, USACurrent affiliationRangel College of Pharmacy, Texas A&M University Health Science CenterCollege Station, TX, USA

Johannes G. KhinastResearch Center Pharmaceutical Engineering GmbHandInstitute for Process and Particle EngineeringGraz University of TechnologyGraz, Austria

San KiangDrug Product Science and TechnologyBristol‐Myers Squibb CompanyNew Brunswick, NJ, USA

Mark LaPackMark LaPack & Associates Consulting, LLC, Lafayette IN, USA

David LeBlondApplied Statistics consultantWadsworth, IL, USA

Kevin LiefGlaxoSmithKline PharmaceuticalsBrentford, UK

Olav LyngbergChemical Development, Research and DevelopmentBristol‐Myers Squibb CompanyNew Brunswick, NJ, USA

Joseph R. MartinelliSmall Molecule Design and DevelopmentEli Lilly and CompanyIndianapolis, IN, USA

Neil McCrackenSmall Molecule Design and DevelopmentEli Lilly and CompanyIndianapolis, IN, USAandBioproduct Research and DevelopmentEli Lilly and CompanyIndianapolis, IN, USA

Linas MockusDavidson School of Chemical EngineeringPurdue UniversityWest Lafayette, IN, USA

Christine M. V. MooreGlobal CMC PolicyMerck, Inc.Philadelphia, PA, USA

Fani BoukouvalaDepartment of Chemical and Biochemical EngineeringRutgers UniversityPiscataway, NJ, USA

Zoltan K. NagySchool of Chemical EngineeringPurdue University, Forney Hall of Chemical EngineeringWest Lafayette, IN, USA

Steven L. NailPharmaceutical DevelopmentBaxter Pharmaceutical Solutions, LLCBloomington, IN, USA

Moheb M. NasrGlaxoSmithKline PharmaceuticalsBrentford, UK

Luis ObregónPharmaceutical Engineering Research laboratory, Chemical Engineering DepartmentUniversity of Puerto Rico at MayaguezMayaguez, Puerto Rico

Tim PaulPharmaceutical DevelopmentBaxter Pharmaceutical Solutions, LLCBloomington, IN, USA

Nathan PeasePharmaceutical DevelopmentBaxter Pharmaceutical Solutions, LLCBloomington, IN, USA

John J. PetersonGlaxoSmithKline PharmaceuticalsBrentford, UK

Leonel QuiñonesPharmaceutical Engineering Research laboratory, Chemical Engineering DepartmentUniversity of Puerto Rico at MayaguezMayaguez, Puerto Rico

Rohit RamachandranDepartment of Chemical and Biochemical EngineeringRutgers, The State University of New JerseyPiscataway, NJ, USA

Gintaras V. ReklaitisDavidson School of Chemical EngineeringPurdue UniversityWest Lafayette, IN, USA

Alicia Román‐MartínezDepartment of Chemical and Biochemical EngineeringTechnical University of DenmarkLyngby, DenmarkandFacultad de Ciencias QuímicasUniversidad Autónoma de San Luis PotosíSan Luis Potosí, Mexico

Christine SeymourGlobal Regulatory Chemistry and Manufacturing ControlsPfizer Inc.Groton, CT, USA

Daniele SuzziResearch Center Pharmaceutical Engineering GmbHGraz, Austria

Reginald B. H. TanNational University of Singapore, Block E5andInstitute of Chemical and Engineering SciencesJurong Island, Singapore

Joshua D. TiceDepartment of Chemical and Biomolecular EngineeringUniversity of Illinois at Urbana‐ChampaignUrbana, IL, USA

Gregor ToschkoffResearch Center Pharmaceutical Engineering GmbHGraz, Austria

Carlos VelázquezPharmaceutical Engineering Research laboratory, Chemical Engineering DepartmentUniversity of Puerto Rico at MayaguezMayaguez, Puerto Rico

Jennifer WangDrug Product Science and TechnologyBristol‐Myers Squibb CompanyNew Brunswick, NJ, USA

Xing Yi WooThe Jackson LaboratoryBar Harbor, ME, USA

John M. WoodleyDepartment of Chemical and Biochemical EngineeringTechnical University of DenmarkLyngby, Denmark

Huiquan WuDivision of Product Quality Research (DPQR, HFD‐940)OTR, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food and Drug AdministrationSilver Spring, MD, USACurrent affiliationProcess Assessment Branch II, Division of Process Assessment 1Office of Process and Facilities, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food and Drug AdministrationSilver Spring, MD, USA

Mohammad YahyahGlaxoSmithKline PharmaceuticalsBrentford, UK

Lifang ZhouDepartment of Chemical EngineeringMassachusetts Institute of TechnologyCambridge, MA, USA

Charles F. ZukoskiDepartment of Chemical and Biomolecular EngineeringUniversity of Illinois at Urbana‐ChampaignUrbana, ILandDepartment of Chemical and Biological EngineeringUniversity at BuffaloBuffalo, NY, USA

Preface

Quality by design (QbD) is a scientific and risk‐based approach to pharmaceutical product development and manufacturing that is becoming firmly established in the pharmaceutical industry. This volume contains chapters covering the various tools and considerations that come into play when the QbD approach is employed. The contributions are based on presentations from the series of AIChE topical conferences on the theme Comprehensive Quality by Design (QbD). The AIChE QbD Symposium was first held in 2009, was continued for several years, and then in 2013 transitioned to sessions organized under a new AIChE entity, the Pharmaceutical Discovery, Development and Manufacturing (PD2M) Forum. While the initial directions envisioned for QbD has evolved in the course of its adoption and adaption by the industry, the stimulus for innovation in development and manufacture that it provided has been very valuable. The chapters in this volume capture some of the evolution of QbD that has occurred.

The idea for compiling the perspectives of a number of prominent contributors to the development and application of QbD into a coherent volume has to be credited to Dr. Salvador Garcia‐Munoz. His enthusiasm for the approach stimulated our collective recognition that QbD needs to be actively promulgated as appropriate not just for the leading organization in this industry but by the entire pharmaceutical industry. We appreciate the willingness of the authors of the chapters contained herein to share their views and experiences on QbD, the support of the AICHE Publications Committee in approving this project, and the patience of the publisher, Wiley, in dealing with the various delays that our poor time management skills brought about.

March 31, 2017

Gintaras V. Reklaitis and Christine Seymour

1Introduction

Christine Seymour1 and Gintaras V. Reklaitis2

1 Global Regulatory Chemistry and Manufacturing Controls, Pfizer Inc., Groton, CT, USA

2 Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, USA

1.1 Quality by Design Overview

QbD emerged as a cultural change in the pharmaceutical industry, which promoted a scientific and risk‐based approach to pharmaceutical product development and manufacturing. Historically, pharmaceutical development and manufacturing had emphasized checklist‐based operations rather than scientific understanding. The high attrition rates of drug candidates during development and the high value of pharmaceutical products, along with extremely high regulatory burden, had led to business practices that minimized risk and restricted process changes and the implementation of new technology.

Traditionally, pharmaceutical process and product development utilized empirical and univariate experimentation and pharmaceutical processes operated at fixed process conditions with offline analytical testing (with a long feedback timeline) and end‐product testing. In addition, it was typical of pharmaceutical companies to provide the regulatory agencies with minimal process and scientific information, and regulatory agencies responded with a wealth of detail queries.

QbD is a “systematic approach to pharmaceutical development and manufacturing that is based on science and quality risk management and begins with predefined objectives and emphasizes product and process understanding as well as process control” [1]. QbD emphasizes multivariable experimentation, design of experiments, process modeling, kinetics, thermodynamics, online analytical testing, and so on. In addition, it has become an improved regulatory paradigm in which the scientific understanding of the product and process has to be provided to the regulatory agencies. This new regulatory model is intended to allow for higher transparency, higher quality, and the implementation of modern manufacturing techniques, such as continuous processing, as well as continuous improvement of commercial pharmaceutical processes.

1.2 Pharmaceutical Industry

The active pharmaceutical ingredient or drug substance is the active component of the pharmaceutical product, and typically small‐molecule drug substances are produced by a multistep synthesis, which involves a sequence of chemical reactions followed by purification/isolation unit operations. Historically, drug substance pharmaceutical processes consisted of batch operations such as reactions, extraction, distillation, crystallization, filtration/centrifugation, drying, and milling.

The drug product is the pharmaceutical formulation that the patient receives and is often in the form of tablets or capsules; other common formulations are oral solutions, topical transdermal patches, and lyophiles or sterile solutions for injection. Historically, drug product processes also consisted of batch operations such as blending, granulation, drying, tableting, encapsulation, or filling depending on the final formulation.

The history of regulations [2, 3] shows an increase in regulatory control after catastrophes; one of the most tragic incidents in the United States was the elixir sulfanilamide incident of 1937 where diethylene glycol was used as a solvent in a pediatric cough syrup and resulted in more than a hundred deaths. This incident led to the Food, Drug, and Cosmetic Act, which increased the US Food and Drug Administration (FDA)’s authority to regulate drugs and required premarketing safety approval for new medications. Another tragic incident was the thalidomide disaster of 1961 where approximately 12 000 infants in over 50 countries were born with severe malformations. This incident led to the Kefauver–Harris Drug Amendment, which increased the FDA’s authority to require safety and efficacy prior to marketing (and tighter controls of clinical trials). These and other incidents led to tighter regulations and, in the 1960s and 1970s, to a rapid increase in national pharmaceutical regulations; simultaneously, many pharmaceutical companies were also globalizing.

The global harmonization of pharmaceutical guidelines across the developed economics was initiated in 1990 through the International Council for Harmonization (ICH) of Technical Requirements for Pharmaceuticals for Human Use [4]. ICH is cosponsored by regulatory agencies and industrial organizations, as well as many observing organizations. These include the European Commission; the US FDA; Ministry of Health, Labour and Welfare of Japan; the European Federation of Pharmaceutical Industries and Associations; the Japan Pharmaceutical Manufacturers Association; the Pharmaceutical Research and Manufacturers of America; Health Canada; Swissmedic; ANVISA of Brazil; Ministry of Food and Drug Safety of the Republic of Korea; the International Generic and Biosimilar Medicines Association; the World Self‐Medication Industry; and the Biotechnology Innovation Organization. ICH has set a structure and process for the proposal, review, and implementation for efficacy, safety, and quality guidelines as well as dossier format requirements. The initial ICH guidelines set common structure stability, analytical methods, impurity control and drug substance, and drug product specifications requirements for drug substance and drug product, and the early ICH guidelines emphasized testing for quality.

1.3 Quality by Design Details

QbD was introduced through the “QbD tripartite” of ICH guidelines: ICH Q8 (R2) Pharmaceutical Development, ICH Q9 Quality Risk Management, and ICH Q10 Pharmaceutical Quality Systems. ICH Q8 Pharmaceutical Development describes the principles of QbD, outlines the key elements, and provides illustrative examples for pharmaceutical drug products. ICH Q9 Quality Risk Management offers a systematic process for the assessment, control, communication, and review of risks to the quality of the drug product. In addition, it states that “the evaluation of the risk to the quality should be based on scientific knowledge and ultimately linked to the protection of the patient” [5]. ICH Q10 Pharmaceutical Quality Systems describes a “comprehensive model for an effective pharmaceutical quality system that is based on International Standards Organization quality concepts and includes applicable Good Manufacturing Practices” [6]. The fourth QbD ICH guideline (considered the drug substance equivalent of ICH Q8) for enhanced active pharmaceutical ingredient synthesis and process understanding, Q11 Development and Manufacturing of Drug Substances (Chemical Entities and Biotechnological/Biological Entities), was approved in November 2012 [7]. The fifth QbD ICH guideline (ICH Q12), Technology and Regulatory Considerations for Pharmaceutical Product Lifecycle Management, is currently under development.

The QbD approach begins with Quality Target Product Profile, which is a prospective summary of the quality characteristics of the pharmaceutical product that ensures the desired quality, safety, and efficacy, and works backward through the drug product and drug substance processes establishing a holistic understanding of which attributes are linked to patients’ requirements and functional relationships of these attributes.

The next step in QbD is a systematic approach to determine the aspects of the drug substance and drug product manufacturing processes that impact the Quality Target Product Profile. A risk assessment is conducted to identify the quality attributes and process parameters that could potentially impact product safety and/or efficacy, utilizing prior scientific knowledge gained from first principles, literature, and/or similar processes.

The output of the risk assessment is a development plan, in which multivariable experiments, kinetics, and/or modeling is typically utilized. The goal of the plan is to establish a holistic understanding of how attributes and parameters are functionally interrelated throughout the entire drug substance and drug product processes. The result is control strategy, which links parameters and attributes to the Quality Target Product Profile.

This approach provides a comprehensive understanding of the critical quality attributes, which is a “physical, chemical, biological, or microbiological property or characteristic that should be in an appropriate limit, range or distribution to ensure the desired product quality,” and of how the process parameters are related to the quality attributes and how probable they can impact quality.

The enhanced understanding of products and processes, along with quality risk management, leads to a product control strategy, which might include a design space (that is optional in ICH Q8/11), which is “the multidimensional combination and interaction of input variables and process parameters that have been demonstrated to provide assurance of quality.” The control strategy is a planned set of controls, which can be process parameters, process attributes, design space, facility and equipment operating conditions, and process testing that ensures process performance and product quality.

The regulatory QbD landscape continues to evolve, and AIChE conferences and sessions will continue to provide a platform to discuss and debate the latest QbD concepts and implementations.

1.4 Chapter Summaries

The contributions in this book can be divided into three sets: Chapters 2–6 address the role of key technologies, process models, process analytical technology (PAT), automatic process control, and statistical methodology, supporting QbD and establishing associated design spaces. Chapters 7–13 present a range of thoroughly developed case studies in which tools and methodologies are used to support specific drug substance and drug product QbD‐related developments. Finally, Chapter 14 discusses the needs for initial efforts toward systematic data and knowledge management to support QbD and related activities. More specifically:

Chapter 2, An Overview of the Role of Mathematical Models in Implementation of Quality by Design Paradigm for Drug Development and Manufacture (Chatterjee, Moore, and Nasr), reviews the categories of mathematical models that can be exploited to support QbD and presents literature examples of various types of model formulations and their use. The authors emphasize that models are valuable tools at every stage of drug development and manufacture. Examples presented span early‐stage risk assessment, design space development, process monitoring and control, and continuous improvement of product quality.

Chapter 3, Role of Automatic Process Control in Quality by Design (Braatz and coworkers), outlines how robust automatic control is an important element in actual implementation of QbD. Using phenomenologically based or data‐driven models, automatic control strategies provide the active mechanism that maintains the operation of the manufacturing process within the design space despite the disturbances that inevitably arise. The authors illustrate the use of feedback control methodology combined with online process measurement for controlling critical quality attributes such as polymorphic form and particle‐size distribution in batch crystallization operations.

Chapter 4, Predictive Distributions for Constructing the ICH Q8 Design Space (Peterson and coworkers), reviews the reported applications of response surface methodology for constructing design spaces and identifies risks associated with the use of “overlapping mean response” constructions, which are typically used in building multivariate design spaces. The authors outline two predictive distribution approaches that overcome these risks by taking into account the uncertainty associated with the experimental results used in building the response surfaces as well as the correlation that may exist among the responses. A Bayesian and a parametric bootstrapping approach are presented and illustrated with examples.

Chapter 5, Design of Novel Integrated Pharmaceutical Processes: A Model‐Based Approach (Roman‐Martinez, Woodley, and Gani), builds on model‐based development of design space advanced in Chapter 2 to present a systematic strategy for identifying the best design of a process for developing an active pharmaceutical ingredient. The strategy employs a library of unit operation models and physical/chemical property prediction tools, which are used as components within a process synthesis strategy that employs mixed integer nonlinear optimization methods. This optimization‐based strategy generates the optimal selection and sequence of reaction and separation unit operations as well as the associated design space. A case study is reported involving the synthesis of neuraminic acid.

Chapter 6, Methods and Tools for Design Space Identification in Pharmaceutical Development (Boukouvala, Muzzio, and Ierapetritou), reviews the tools and methods that have been developed in the process systems engineering literature to address issues of process feasibility and flexibility under uncertainty. It is shown that process design under uncertainty can be posed as a stochastic optimization problem. Moreover, the review notes that in general the design of the process and its automatic control system need to be treated in an integrated fashion since the automatic control system generally can serve to increase the design space. The concepts are illustrated with two single‐unit examples: a powder blender and a roller compactor.

Chapter 7, Using Quality by Design Principles as a Guide for Designing a Process Control Strategy (Burcham and coworkers), reports a comprehensive process engineering study involving the implementation of an impurity control strategy for a new drug substance. The study makes extensive use of predictive models to determine optimal processing conditions and to map the design space around those conditions. The mechanistic models developed include complex reaction kinetics and mass transfer processes, which were developed through intensive experimentation using appropriate PAT and traditional analytical methods. The process models were used as an integral part of an in silico approach to identify the boundaries of the design space requiring experimental confirmation.

Chapter 8, A Strategy for Tablet Active Film Coating Formulation Development Using a Content Uniformity Model and Quality by Design Principles (Chen and coworkers), presents in detail the development of a mechanistic model to predict the relative standard deviation of table content uniformity of an active film coating unit operation. Systematic studies to identify the most important operating variables, develop model parameters, and validate model prediction across scales are reported. The model is shown to be an effective tool for developing the design space for this unit operation, including establishing the effects of scale‐up.

Chapter 9, Quality by Design: Process Trajectory Development for a Dynamic Pharmaceutical Coprecipitation Process Based on an Integrated Real‐Time Process Monitoring Strategy (Wu and Khan), describes the features, strengths, and limitations of principal component analysis of real‐time process measurements as a means for process trajectory monitoring, identification of singular points of the trajectory, and development of understanding of important phenomena occurring during the dynamic process. A case study of a coprecipitation process monitored using near‐infrared (NIR) and turbidity measurements is detailed. Implications for design space development are discussed.

Chapter 10, Application of Advanced Simulation Tools for Establishing Process Design Spaces Within the Quality by Design Framework (Khinast and coworkers), reports on the value added by advanced simulation tools in building fundamental process understanding, especially of the impact of critical sources of process variability. The use of discrete element modeling (DEM) is described to investigate a powder blending operation with the goal of screening and prioritizing potentially critical input variables and mapping out a blending experimental space. Similarly, computational fluid dynamics (CFD) is used to construct a detailed three‐zone model of film formation on a tablet surface and then to characterize the critical process parameters of the coating operation. In each case, Design of Experiment (DOE) on the most important parameters are utilized to develop a design space.

Chapter 11, Design Space Definition: A Case Study—Small‐Molecule Lyophilized Parenteral (Mockus and coworkers), builds on Chapter 4 and