Computer-Aided Applications in Pharmaceutical Technology: Delivery Systems, Dosage Forms, and Pharmaceutical Unit Operations

Computer-Aided Applications in Pharmaceutical Technology: Delivery Systems, Dosage Forms, and Pharmaceutical Unit Operations, Second Edition covers the fundamentals of experimental design application and interpretation in pharmaceutical technology, chemometric methods with an emphasis on their applications in process control, neural computing, data science, computer-aided biopharmaceutical characterization, as well as the application of computational fluid dynamics in pharmaceutical technology. Completely updated, the book introduces the theory and practice of computational tools through new case studies. Chapters cover Quality by Design in pharmaceutical development, overview data mining methodologies, present computer-aided formulation development, cover experimental design applications, and much more.

• Presents a comprehensive review of the current state of the art on various computer-aided applications in pharmaceutical technology
• Includes case studies to facilitate understanding of various concepts in computer-aided applications
• Covers applications such as the development of dosage forms and/or delivery systems, pharmaceutical unit operations, and relevant physiologically based pharmacokinetic simulations

• Language: English
• Publisher: Elsevier Science
• Release date: Sep 7, 2023
• ISBN: 9780443186561

Book preview

9780443186561_FC

Computer-Aided Applications in Pharmaceutical Technology

Delivery Systems, Dosage Forms, and Pharmaceutical Unit Operations

Second Edition

Jelena Djuris

Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

publogo

Table of Contents

Cover image

Title page

Copyright

Contributors

Introduction

Chapter 1: Quality by design in the pharmaceutical development

Abstract

1.1: Introduction

1.2: ICH Q8–Q14 guidelines

1.3: Scientifically based QbD—State of the art and future prospects

1.4: Regulatory and industry views on QbD

1.5: From QbD to QbC

1.6: Conclusions

References

Chapter 2: Overview of data science and computational modeling concepts in pharmaceutical technology

Abstract

2.1: Introduction

2.2: Mathematical modeling

2.3: Modeling and quality by design/control

2.4: Data science concepts in pharmaceutical technology

2.5: Conclusions

References

Chapter 3: Computer-aided formulation development of microemulsion drug delivery systems

Abstract

3.1: Introduction

3.2: Development of microemulsion drug delivery systems using artificial neural network (ANN) modeling

3.3: Application of statistical design of experiments (DoE) in microemulsion optimization

3.4: Conclusions

References

Chapter 4: Experimental design application and interpretation in pharmaceutical technology

Abstract

4.1: Introduction

4.2: Theory

4.3: Interplay of formulation factors and process parameters in experimental design: Case studies on emulsions

4.4: Additional examples

References

Chapter 5: Chemometric methods in pharmaceutical technology

Abstract

5.1: Introduction

5.2: Theory

5.3: Examples

5.4: Illustrative examples

References

Chapter 6: Artificial intelligence applications in pharmaceutical technology

Abstract

6.1: Artificial intelligence

6.2: Fuzzy logic

6.3: Decision trees

6.4: Evolutionary computing and genetic algorithms

6.5: Self-organizing maps

6.6: Conclusions

References

Chapter 7: Computer-aided biopharmaceutical characterization: Gastrointestinal absorption simulation

Abstract

7.1: Introduction

7.2: Theoretical background

7.3: Model construction

7.4: Sensitivity analysis

7.5: Population simulations/virtual trials

7.6: Fed vs fasted state

7.7: Mechanistic interpretation of drug absorption pattern

7.8: Guidance on formulation strategy

7.9: In vitro dissolution and in vitro-in vivo correlation/relationship (IVIVC/R)

7.10: Biowaiver considerations

7.11: Special populations

7.12: Conclusions

References

Chapter 8: Computational fluid dynamics: Applications in pharmaceutical technology

Abstract

8.1: Introduction

8.2: Theoretical background

8.3: Application of CFD in pharmaceutical technology

8.4: Conclusions

References

Appendix

Reference

Index

Copyright

Woodhead Publishing is an imprint of Elsevier

50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States

The Boulevard, Langford Lane, Kidlington, OX5 1GB, United Kingdom

Copyright © 2024 Elsevier Ltd. All rights reserved.

No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions.

This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).

Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.

ISBN: 978-0-443-18655-4 (print)

ISBN: 978-0-443-18656-1 (online)

For information on all Woodhead publications visit our website at https://www.elsevier.com/books-and-journals

fm01-9780443186554

Publisher: Mica H. Haley

Acquisitions Editor: Andre G. Wolff

Editorial Project Manager: Michaela Realiza

Production Project Manager: Kamesh R

Cover Designer: Mark Rogers

Typeset by STRAIVE, India

Contributors

Ivana Aleksić     Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Sandra Cvijić     Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Ljiljana Djekic     Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Jelena Djuris     Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Zorica Đurić     Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Svetlana Ibric     Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Jelisaveta Ignjatović     Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Ivana Kurcubic     Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Jelena Parojčić     Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Dragana Vasiljevic     Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Introduction

In the years since the first edition of this book, the field of pharmaceutical technology has continued to evolve at a rapid pace. Innovations in data analysis, modeling, and simulation have emerged, providing researchers with an expanded tool kit for understanding and improving products and processes. In response to these developments, we are excited to present this second edition, which incorporates the latest techniques and examples to further support the implementation of computer-aided applications in pharmaceutical technology.

This edition maintains the original goal of providing useful information on the background of various computer-aided tools and illustrative examples of their application in pharmaceutical technology. However, it was important to incorporate recent advances in data analysis that have been developed over the past decade. In this updated edition, we have expanded our coverage of methods presented in chapters on experimental design, multivariate analysis, artificial intelligence, physiologically based pharmacokinetic modeling, and computational fluid dynamics. We have also included a new chapter dedicated to the general concept of data science. While the theoretical parts of the book remain condensed, they have been updated to reflect current understanding and best practices. As before, readers seeking further insight into specific tools are advised to consult cited references.

The field of pharmaceutical technology has undergone significant developments in the past decade, with an increasing emphasis on the collection and analysis of data to improve product development and registration. In this context, computer-aided applications have emerged as essential tools for the analysis of complex data and the simulation of products and processes. This edition aims to provide an updated and comprehensive overview of the latest computer-aided techniques, examples, and applications in pharmaceutical technology.

The book also provides a list of open source and commercial software packages that have been used in the field, offering readers an opportunity to explore and implement different tools and techniques. We encourage the readers to use multiple tools in their projects, as each tool provides specific insight and knowledge of the product and/or process studied.

Our hope is that this book will serve as a valuable resource for beginners and experienced users of in silico applications. By providing an up-to-date overview of the latest developments in the field, we aim to inspire and motivate readers to make further progress and investigation in this exciting field of research.

Chapter 1: Quality by design in the pharmaceutical development

Jelena Djuris; Svetlana Ibric; Zorica Đurić    Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, University of Belgrade, Belgrade, Serbia

Abstract

This first chapter introduces the quality-by-design (QbD) concept and its role in pharmaceutical product development. QbD assures the quality of a pharmaceutical product through scientific development and risk management tools, and can eventually enable real-time release, regardless of the formulation type. Several guidelines on pharmaceutical development, quality risk management, and pharmaceutical quality systems are presented that are applicable throughout the product life cycle. Design space appointment and control strategies for risk management are introduced. The meaning of the QbD concept is presented from academic, regulatory, and industrial points of view. Several illustrative examples are provided to facilitate the understanding of the QbD concept and the ease of its application.

Keywords

Quality by design (QbD); Design space; Risk management tools; Control strategies

Abbreviations

ANDA abbreviated new drug applications

CM continuous manufacturing

CMA critical materials attributes

CPP critical process parameters

CTD common technical document

EMA European Medicines Agency

FDA Food and Drug Administration

FMEA failure mode and effects analysis

GMP good manufacturing practices

HACCP hazard analysis and critical control points

ICH International Conference on Harmonization

KASA knowledge-aided assessment and structured applications

NOR normal operating ranges

PAR proven acceptable ranges

PAT process analytical technologies

QbC quality by control

QbD quality by design

QTTP quality target product profile

R&D research and development

RTRT real-time release testing

1.1: Introduction

The pharmaceutical industry is one of the most strictly regulated professions, and its products are expected to be of, undoubtedly, excellent quality. However, there are numerous reported issues suggesting that pharmaceutical development and manufacturing can and should be improved. This is especially noticeable in terms of quality defects and batch failures, out-of-specification (OOS) results, regulatory issues, shortages of pharmaceutical products on the market, implementation of new technologies, etc. The current state of the pharmaceutical industry, in terms of yield and defects (e.g., relation of quality and productivity), is not comparable to some of the more advanced industries (e.g., petrochemical or semiconductor industry). Various defects in pharmaceutical product quality can be encountered such as low manufacturing process yield or, more dangerously, some may affect the therapeutic performance of the drug (or both). Furthermore, the effects of scale-up, and technology transfers, in general, on the final product are often not understood and reasons for manufacturing failures are not analyzed (Shah, 2009). The quality of a pharmaceutical product can be defined as an acceptably low risk of failing to achieve the desired clinical attributes of the drug (Shah, 2009). It is recognized that reasonable product quality in the pharmaceutical industry sometimes comes with the price of great efforts, time, and cost. In the contemporary context of the development of sophisticated drug delivery systems (such as nanotechnology products and/or mRNA vaccines), it is even more important to understand the effect of material attributes and production process parameters that could jeopardize the final products’ quality and/or performance.

Quality by design (QbD) is a concept introduced by the International Conference on Harmonization (ICH) Q8 guideline, as a systematic approach to development, which begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management. The pharmaceutical industry is somewhat still slow in accepting the QbD concept and its benefits, even though the traditional trial-and-error approach is significantly less efficient (Table 1.1). Predefined objectives, in a pharmaceutical product development, make up the quality target product profile (QTPP), that is, the summary of the drug product quality characteristics that ideally should be achieved. According to the ICH Q8 guideline, QTPP is a prospective summary of the quality characteristics of a drug product to ensure the desired quality, taking into account safety and efficacy of that drug product. Therefore, QTPP is not the same as the product quality specification, although, of course, they may share some of the same requirements. Critical process parameters (CPPs) and critical quality attributes (CQAs) of the product are identified through the scientifically based process of product development, which is the core of the QbD concept. CQA is any physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality. CQA can refer to a starting material, the intermediate, or the final product. CPP is a process parameter whose variability has an impact on a CQA. The identification of a CQA from the QTPP is based on the severity of harm to the patient if the product falls outside the acceptable range for that attribute. This is where quality risk management strategies are especially important and come into place, as introduced in the ICH Q9 guideline. QTPP is defined initially in product development, based on the properties of the drug substance, characterization of the reference product (if it exists), and intended patient population. It is important to, once again, emphasize that QTPP does not necessarily need to include all of the product quality specification tests. A QTPP for immediate release tablets may include the following requirements: assay, content uniformity, and dissolution rate, which should be in accordance with the predefined limits in order to assure safety and efficacy during the shelf life; tablets should be robust in order to withstand transport and handling, and of a suitable size to aid patient acceptability and compliance. According to the defined QTPP, CQAs may include API (active pharmaceutical ingredient) particle size distribution, assay, content uniformity, dissolution, and degradation products; whereas CPPs could be the compression force and speed used for tableting. The definition of QTPP for different pharmaceutical dosage forms and drug delivery systems is described elsewhere in more detail (Zhang and Mao, 2017; Simoes et al., 2018; Buttini et al., 2018; Grangeia et al., 2020; Butreddy et al., 2021; Cunha et al., 2020; Zagalo et al., 2022; Destro and Barolo, 2022).

Table 1.1

The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to assure quality is defined as the design space. The emphasis of the ICH Q8 guideline was to shift pharmaceutical product development from the empirical, trial-and-error approach, to the scientifically based process of design space appointment, in order to gain more control over the final product quality. Definition of the design space also requires implementation of various risk management tools, as well as definition of specifications and manufacturing controls. Fig. 1.1 shows a diagram of a QbD approach, combining design space development and risk management tools. Table 1.1 represents a comparison between the traditional and QbD approaches, regarding different aspects of pharmaceutical development and product life-cycle management (according to the ICH Q8 guideline). QbD has another companion, i.e., a set of tools that are designed to monitor, analyze, and control a process, known as the Process Analytical Technologies (PAT) tools. PAT is a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final product quality.

Fig. 1.1

Fig. 1.1 A QbD approach combining design space appointment and risk management tools ( CPP —critical process parameter, CQA —critical quality attribute, PAT —process analytical technology, QTTP —quality target product profile).

Implementation of the QbD concept is important for all products, including generics and biotechnological products (Nasr, 2011). There are detailed reports on the pharmaceutical QbD concept in general (Lionberger et al., 2008; Yu, 2008; Beg and Hasnain, 2019). The reader is advised to consult relevant reviews and textbooks on regulations and quality in the pharmaceutical industry (Gad, 2008; Schlindwein and Gibson, 2018; Beg and Hasnain, 2019), the QbD concept in the pharmaceutical industry (Am Ende, 2010; Mishra et al., 2018), application of QbD in biopharmaceuticals (Rathore and Mhatre, 2009; Finkler and Krummen, 2016), QbD issues in process understanding for scale-up and other technology transfer activities in manufacture of active ingredients and finished products (Houson, 2011; Gallo and Konagurthu, 2019; Yazdanpanah, 2020), as well as reviews on QbD in pharmaceutical and biopharmaceutical development (Herwig and Menezes, 2013; Reklaitis, 2013; Rathore et al., 2018). Furthermore, links between process analytical technology (PAT) and QbD are elaborated (Bakeev, 2010; Aucamp and Milne, 2019), with special emphasis on biopharmaceuticals (Undey et al., 2012). Ter Horst et al. (2021) have provided a valuable review on the implementation of QbD principles in the regulatory filings of medicinal products in the EU in the period 2014–2019. Although the QbD approach has been known by pharmaceutical industry for almost two decades, it is still predominantly voluntary, and there are a number of companies that are reluctant in applying such principles. Testas et al. (2021) have used a real product to showcase how QbD can accelerate the time frame for a drug product to reach the market.

1.2: ICH Q8–Q14 guidelines

The ICH Q8 guideline on scientifically based pharmaceutical development serves to provide opportunities for pharmaceutical manufacturers to seek regulatory flexibility and mitigation of some activities required for product registration and/or subsequent postapproval change process. The ICH Q8 guideline describes good practices for pharmaceutical product development. Working within the defined design space is not recognized as a change that would require regulatory approval. This paradigm can be used to significantly improve productivity and quality assurance in the pharmaceutical industry. Even though the primary intention of the ICH Q8 document, and QbD itself, was to provide guidance on the contents of Section 3.2.P.2 (Pharmaceutical Development) for drug products defined in the scope of Module 3 of the Common Technical Document (CTD), this concept is now broadened to the whole drug product life-cycle. It is often emphasized that the quality of a pharmaceutical product should be built in by design rather than by testing alone. Development of the manufacturing process should include its continuous verification, meaning that rather than one-time process validation, an alternative approach should be employed whereby the manufacturing process performance is continuously monitored and evaluated. The ICH Q8 guideline suggests that those aspects of drug substances, excipients, container closure systems, and manufacturing processes that are critical to product quality should be determined, as well as justified strategies for making sure that the product quality is under control. If an adequately organized development study is conducted, the pharmaceutical manufacturer can gain a reduction in both postapproval submissions and reviews/inspections by the regulatory authorities. Furthermore, real-time quality control is recommended, leading to a reduction inend-product release testing. Some of the tools that should be applied during the design space and control strategies appointment include experimental designs, PAT, prior knowledge, quality risk management principles, data analytics, computational modeling, etc.

More details on quality risk management tools are provided in the ICH Q9 guideline. This document recommends several techniques for risk analysis, starting from the simple checklists and fishbone diagrams to more advanced techniques such as Failure Mode and Effect Analysis (FMEA) or Hazard Analysis and Critical Control Points. The first revision of the ICH Q9 guideline will become official in 2022, and will introduce some terminology changes and will also aim to reduce the subjectivity in risk assessments. Furthermore, in order for the risk management activities to be more robust, the necessary level of formality in analyses will also be specified. Illustrative examples of the application of risk management tools in the development of pharmaceutical products can be found in references: Kaljević et al. (2016), Stocker et al. (2017), and Casian et al. (2017).

The ICH Q8 guideline has provided an invaluable foundation for the QbD principle. However, it was oriented predominantly toward research and development (R&D) activities and there was a lack of actual correlation between the control strategy, constituted from the design space, and routine manufacturing of the product. Manufacturing in the pharmaceutical industry is inseparably linked to Good Manufacturing Practices (GMP) guidelines. There was a need to bridge the gap between R&D and routine manufacturing, especially in the context of knowledge management and risk management within the whole life-cycle of a product, including its discontinuation. The link was successfully made with the ICH Q10 guideline, which introduced a pharmaceutical quality system and elaborated further on the necessity to establish and maintain the manufacturing process in the state of control, as well as to facilitate continuous process improvement. Orpana (2019) has highlighted the necessity of applying appropriate statistical metrics to verify the state of control in pharmaceutical manufacturing.

The following ICH guidelines were issued based on the relevant needs for the QbD concept specified in more detail for the development and manufacturing of drug substances (both chemical entities and biotechnological/biological entities)—ICH Q11; or more elaborated technical and regulatory considerations for pharmaceutical product life-cycle management—ICH Q12. Popkin et al. (2019) presented the benefits and addressed the versatility of ICH Q11 guideline application, in the context of identification, evaluation, and control of API impurities. The ICH Q12 guideline is especially important from the perspective of the implementation of QbD principles in practice, since it introduces the concepts of established conditions for manufacturing processes and analytical procedures, postapproval change management protocol, product life-cycle management document and change management across the supply chain and product life-cycle. Control strategy elements, as the most important QbD principle, are considered to be established conditions. Jagga et al. (2021) have provided a detailed review on the ICH Q12 implementation in various geographical regions, as well as illustrative case studies on site-transfer variations and postapproval changes for drug substance process parameters and raw materials.

The time frame for issuing ICH Q8–Q14 guidelines is represented in Fig. 1.2.

Fig. 1.2

Fig. 1.2 ICH guidelines relevant for the QbD concept ( ICH Q8 R2, 2009; ICH Q9 R1, 2023; ICH Q10, 2008; ICH Q11, 2012; ICH Q12, 2019; ICH Q13, 2022; ICH Q14, 2022) (ICH—International Conference on Harmonization, Q—quality, QbD—quality by design, R—revision).

The emergence of new trends in manufacturing and equipment design has oriented the pharmaceutical industry toward continuous manufacturing (CM). In CM, raw materials are fed into the manufacturing system, followed by continuous transformation to the output. In order for such an approach to manufacturing to be fully functional, it is necessary to continuously monitor and, if necessary, adjust the process parameters. This is where process monitoring and assurance of the state of control are of vital importance. The ICH Q13 guideline on continuous manufacturing of drug substances and drug products recognizes that PAT is well-suited for CM. PAT should be thought of as a complex set of tools for process monitoring and control through chemometric in−/on line measurements and multivariate data analysis. These topics are elaborated on in more detail in Chapter 5. PAT facilitates QbD-based pharmaceutical development, supports CM, and enables real-time release testing (RTRT). The idea behind RTRT is that if all quality-related risks are addressed and the process is well monitored, then there is no need for the finished products` testing to assure their quality. The relationship between QbD and PAT is represented in Fig. 1.3.

Fig. 1.3

Fig. 1.3 Relationships between the QbD concept and PAT ( CMA —critical material attributes, CPP —critical process parameters, CQA —critical quality attribute, PAT —process analytical technologies, QbD —quality by design, QRM —quality risk management, QTPP —quality target product profile, RTRT —real time release testing).

Ferreira and Tobyn (2015) have provided an in-depth review on enabling process understanding and improvement through PAT and QbD. Continuous manufacturing and related PAT tools also pave the ground for the concept of Quality by Control (QbC) and smart manufacturing in Industry 4.0 (Jelsch et al., 2021). It is believed that this concept will be fully realized through digitalization and automation of real-time data analysis. Su et al. (2019) have provided a very informative perspective, including discussion of challenges related to QbC application in continuous pharmaceutical manufacturing.

The last of ICH quality-related guidelines that have been issued for comments in 2022 is ICH Q14, which is related to the development of analytical procedures. Analytical QbD (aQbD) is one of the hot topics for researchers, industry, and regulators, since it represents versatile tools for the development of robust analytical methods and regulatory flexibility (Peraman et al., 2015; Beg et al., 2021).

1.3: Scientifically based QbD—State of the art and future prospects

The ICH Q8 annex provided examples of the implementation of QbD concepts. Elements of pharmaceutical development (QTPP, CQAs, and risk assessment tools) are defined in more detail. Pharmaceutical manufacturers are encouraged to describe the design space in their submission by using a variety of terms, for example, ranges of materials attributes and process parameters, complex mathematical relationships, time-dependent functions, multivariate models, etc. Furthermore, independent design spaces can be defined for one or more unit operations or a single design space can be established that spans the entire manufacturing process. In order to ensure that a product of the required quality is produced consistently, various control strategies are designed. These strategies are based on product, formulation, and process understanding and include control of the CQAs and/or CPPs. Control strategies can be implemented for both real-time and end-product testing. Several illustrative examples are provided in the ICH Q8 guideline on the use of risk assessment tools, depiction of interactions, and representations of design space.

Some of the issues encountered by the regulatory agencies during the assessment of a QbD-based registration dossier are lack of relevant explanations of the conclusions reached, insufficient graphical presentations of factor interactions, design space boundaries not clearly described, no information on statistical validity of models, and not enough structure in the presented data (Korakianiti, 2011). Collaboration between scientists in industry, academia, and regulatory bodies’ experts is necessary to overcome the abovementioned issues. Many scientific projects are devoted to design space appointment, in-line process monitoring, and modeling of products and processes. This knowledge should serve to provide a foundation for the scientifically based QbD concept application. Some of the peer-reviewed examples of QbD elements’ development are presented below.

The QbD approach was used to establish a relationship between the CPPs, CQAs, and clinical performance of the drug (Short et al., 2011). Extended-release theophylline tablets were analyzed, showing that some of the compendial tests are insufficient to communicate the therapeutic consequences of product variability. Both critical and noncritical attributes were used as inputs to the design space, which was conditioned on quantitative estimates of inefficacy and toxicity risk.

A combined QbD and Discrete Element Model (DEM) simulation approach was used to characterize a blending unit operation, by evaluating the impact of formulation parameters and process variables on blending quality and blending end point (Adam et al., 2011). QbD was used to establish content uniformity as CQA and link it to blend homogeneity, to identify potential critical factors that affect blending operation quality and risk-rank these factors to define activities for process characterization. The results obtained were used to map a three-dimensional knowledge space, providing parameters to define a design space and set up an appropriate control strategy.

A quantitative approach was developed to simultaneously predict particle, powder, and compact mechanical properties of a pharmaceutical blend, based on the properties of the raw materials (Polizzi and García-Muñoz, 2011). A multivariate modeling method was developed to address the challenge of predicting the properties of a powder blend while enabling process understanding.

An integrated PAT approach for process (coprecipitation) characterization and design space development was reported (Wu et al., 2011). CPPs were investigated and their effect on CQAs was analyzed using linear models and artificial neural networks (ANN). Contour plots illustrated design space via CPP ranges.

QbD was applied in the development of liposomes containing a hydrophilic drug (Xu et al., 2011, 2012). The usage of risk assessment facilitated formulation and process design, with the eight factors being recognized as potentially influencing liposome drug encapsulation efficiency and particle size (CQAs). Experimental design was used to establish the design space, resulting in a robust liposome preparation process.

QbD principles were applied to an existing industrial fluidized bed granulation process (Lourenço et al., 2012). PAT monitoring tools were implemented at the industrial scale process, combined with the multivariate data analysis of process to increase process knowledge. Scaled-down designed experiments were conducted at a pilot scale to investigate the process under changes in CPPs. Finally, design space was defined, linking CPPs to CQAs within which product quality is ensured by design, and after scale up, enabling its use at the industrial process scale.

A Bayesian statistical methodology was applied to identify the design space of a spray-drying process (Lebrun et al., 2012). A predictive, risk-based approach was set up, in order to account for the uncertainty and correlations found in the process and in the derived CQAs. Within the identified design space, validation of the optimal condition was affected. The optimized process was shown to perform as expected, providing a product for which the quality is built in by the design and controlled set up of the equipment, regarding identified CPPs.

The QbD approach was used in the formulation of dispersible tablets (Charoo et al., 2012). Critical material and process parameters were linked to CQAs of the product. Variability was reduced by product and process understanding, which translated into quality improvement, risk reduction, and productivity enhancement. The risk management approach further led to a better understanding of the risks, ways to mitigate them, and control strategy proposed, commensurate with the level of the risk.

The production bioreactor step of an Fc-Fusion protein-manufacturing cell culture process was characterized following QbD principles (Rouiller et al., 2012). Using scientific knowledge derived from the literature and process knowledge gathered during development studies and manufacturing to support clinical trials, potential critical and key process parameters with a possible impact on product quality and process performance, respectively, were determined during a risk assessment. The identified process parameters were evaluated using a design of experiment approach. The regression models generated from the data characterized the impact of the identified process parameters on quality attributes. Models derived from characterization studies were used to define the cell culture process design space. The design space limits were set in such a way as to ensure that the drug substance material would consistently have the desired quality.

QbD principles were used to investigate the spray drying process of insulin intended for pulmonary administration (Maltesen et al., 2008). The effects of process and formulation parameters on particle characteristics and insulin integrity were investigated. The design of experiments and multivariate data analysis were used to identify important process parameters and correlations between particle characteristics. Principal component analysis was performed to find correlations between dependent and independent variables.

A multiparticulate system, designed for colon-specific delivery of celecoxib for both systemic and local therapy, was developed using QbD principles (Mennini et al., 2012). Statistical experimental design (Doehlert design) was employed to investigate the combined effect of four formulation variables on drug loading and release rate. A desirability function was used to simultaneously optimize the two responses.

A QbD approach was also used to study the process of a nanosuspension preparation (Verma et al., 2009), to establish appropriate specifications for highly correlated active substance properties (Cui et al., 2011), to develop analytical methods (Vogt and Kord, 2011), and its usage in the optimization of lead drug candidates is proposed to address productivity in drug discovery (Rossi and Braggio, 2011). The role of predictive biopharmaceutical modeling and simulation in drug development, in the context of QbD, was also presented (Jiang et al., 2011).

As illustrated by provided examples, the potential for the application of QbD principles is infinite. The emergence of soft sensors for process monitoring, fast data acquisition, and powerful computational methods shifted the paradigm to a whole new level. We are currently witnessing a new era of automation, digitalization, and introduction of smart manufacturing through the QbC and digital twins. Digital twins refer to complex systems that contain a physical component, its virtual representation, and a channel for communication (data processing) between the two. Once a physical system (e.g., a granulator, mixer, reactor, etc.) is fully understood, and mathematically represented, its virtual counterpart can be used as a surrogate for the simulation of various scenarios and outcomes. More specifically, the virtual counterpart can be utilized for the exploration of the design space and control strategies. The interconnection between the two should be thought of as a powerful ICH Q10 and Q12 tool that provides assurance for the process monitoring and control, and also enables fast response and adjustment to external variations. Chen et al. (2020) have critically addressed how the pharmaceutical industry will need to transform in order to fully adapt from the original QbD initiative to the industry 4.0 concepts. Although a slight controversy on digital twins is present, in terms of seemingly shifting from the original QbD and PAT concepts, the majority of experts claim that QbC achieved by digital twins is the next desired state. Furthermore, some authors believe that regulators will only allow RTRT and full benefits of QbD/QbC if a digital twin is present (Zobel-Roos et al., 2019). Uhlenbrock et al. (2020) have provided an excellent case study on the utilization of digital twins for extraction process design and operation, with special emphasis on the sustainable green extraction procedures for phytochemicals. Ntamo et al. (2022) have presented a case study on the digitalization of a continuous wet granulation and tableting process.

1.4: Regulatory and industry views on QbD

Since the introduction of 21st-century initiative (A Risk-Based Approach) by the Food and Drug Administration (FDA) in 2004, early adoption of new technologies, and risk-based approaches in pharmaceutical product development, are encouraged (FDA, 2004). As defined by an FDA official (Woodcock, 2004), the QbD concept represents product and process performance characteristics scientifically designed to meet specific objectives, not merely being empirically derived from the performance of test batches. Another FDA representative (Shah, 2009) stated that the introduction of the QbD concept can lead to cost savings and efficiency improvements for both industry and regulators. QbD can facilitate innovation, increase manufacturing efficiency, reduce cost/product rejects, minimize/eliminate potential compliance actions, enhance opportunities for first cycle approval, streamline postapproval changes and regulatory processes, enable more focused inspections, and provide opportunities for continual improvement (Shah, 2009). The FDA has provided examples for the implementation of QbD concepts in abbreviated new drug applications (ANDA) for both immediate and modified release dosage forms. Illustrative examples can be obtained through the FDA website, presented in the form of Section 3.2.P.2 Pharmaceutical Development part of the CTD file Module 3 (Quality). Pharmaceutical development of acetriptan