This document discusses Quality by Design (QbD) and its role in pharmaceutical product development. QbD aims to ensure product quality through scientific development and risk management tools. Key aspects of QbD include defining quality target product profiles, identifying critical quality attributes and critical process parameters, and using this information to establish a design space for manufacturing. The document provides examples of how QbD has been applied in various pharmaceutical development and manufacturing case studies.

1. Quality by Design (QbD) in Product Development
Abstract: (QbD) and its role in pharmaceutical product development. QbD assures the quality of a
pharmaceutical product through scientific development and risk management tools, and will 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
lifecycle. Design space appointment and control strategies for risk management are introduced. The meaning of
the QbD concept is presented fromboth regulatory and manufacturers’ points of view.
Key words: quality- by-design (QbD), design space, risk management tools, control strategies.
Introduction
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. Predefined objectives 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. Through the scientifically based process of product
development, critical process parameters (CPPs), and critical quality attributes (CQAs) of the product are
identified. CQA is a physical, chemical, biological, or microbiological property or characteristic that should be
within an appropriate limit, range, or distribution to ensure the desired product quality. 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. QTPP is
initially defined, based upon properties of the drug substance, characterization of the reference product (if it
exists), and intended patient population. It is important to emphasize that QTPP does not necessarily need to
include all of the product specification tests. A QTPP for immediate release tablets may include the following
requirements: assay, content uniformity, and dissolution should be in accordance with the specifications to
assure safety and efficacy during the shelf life; tablets should be robust in order to withstand transport and
handling, and a suitable size to aid patient acceptability and compliance. According to the defined QTPP, CQAs
may include assay, content uniformity, dissolution, and degradation products, whereas CPPs could be the
compression force and speed used for tableting. The multidimensional combination and interaction of input
variables (e.g. material attributes) and process parameters that have been demonstrated to provide assurance of
quality is denoted as the design space. The emphasis of the ICH Q8 guideline is to shift pharmaceutical product
development from the empirical, trial- and-error approach, to the scientifically based process of design space
appointment. Definition of design space also requires implementation of various risk management tools, as well
as definition of specifications and manufacturing controls.
ICH Q8 guideline
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 post approval change process. The ICH Q8 guideline describes good
practices for pharmaceutical product development. Working within the defined design space is not recognized
as the 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 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 lifecycle. 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

2. 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 and control
strategies justified. If an adequately organized development study is conducted, it is possible for the
pharmaceutical manufacturer to gain reduction in both post- approval submissions and reviews/inspections by
the regulatory authorities. Furthermore, real- time quality control is recommended, leading to a reduction of
end- product release testing. Some of the tools that should be applied during the design space appointment
include experimental designs, PAT, prior knowledge, quality risk management principles, etc. More details on
quality risk management tools are provided in the ICH Q9 guideline. QbD and quality risk management tools
are often linked to form a pharmaceutical quality system (ICH Q10 guideline). 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 fi
nal product quality. PAT brought the possibility to evaluate and ensure the acceptable quality of in- process
and/or fi nal product based on the measured process data, allowing real- time release of the products. The ICH
Q8 annex provides examples of implementation of QbD concepts. Elements of pharmaceutical development
(QTPP, CQAs, 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
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 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 use of risk assessment tools, depiction of interactions, and presentations of design
space. Quality has been given abundant significance by all regulatory bodies for manufacturing of
pharmaceutical products and drug delivery systems. Quality means customer satisfaction in terms of service,
product, and process. Customer demands the perfection in quality, reliability, low cost and timely performance
of the drug product. But merely analyzing the final product does indicate the quality; however it should be
designed in the product .Thus the quality has to be built in the product through proper planning, so that the
impending failure can be circumvented. Initially, quality by test (QbT) was the solitary approach to assure the
quality of drug products which was based on methods without clear understanding of the processes. But after
the launching of guidelines for current good manufacturing practice (cGMP) by FDA this problem was solved
as FDA developed generalized quality by design (QbD) in the field of pharmacy.
Pharmaceutical Objectives Of Quality By Design
The goals of pharmaceutical QbD may include the following:
1. To achieve meaningful product quality specifications that is based on clinical performance.
2. To increase process capability and reduce product variability and defects by enhancing product and process
design, understanding, and control.
3. To increase product development and manufacturing efficiencies.
4. To enhance root cause analysis and post approval change management.
Scientifically based QbD – examples of application
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 the
factor interactions, design space boundaries not clearly described, no information on statistical validity of
models, and not enough structure in the presented data, etc. (Korakianiti, 2011). Collaboration between
scientists in industry, academia, and regulatory bodies’ experts is necessary to overcome the above- mentioned

3. 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 the 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. 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 (co- precipitation) 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
CPPs ranges. QbD was applied in 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 the 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 exercise. 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. The 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. Design of experiments and
multivariate data analysis were used to identify important process parameters and correlations between particle

4. characteristics. Principal component analysis was performed to fi nd correlations between dependent and
independent variables. A multi particulate 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. Desirability function was used to simultaneously optimize the two responses. A QbD
approach was also used to study the process of a nano suspension 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 lead drug candidates optimization 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.
QbD Tools – Risk Assessment
Why risk assessment in product development?
 To identify relative risk levels at the beginning of product development
 To prioritize limited development resources
 To document the decision making process throughout development
 To assess the needs of additional studies for scale up and technology transfer
 To identify appropriate specifications, critical process parameters and manufacturing controls
 To decrease variability of critical quality attributes
Risk Assessment
Risk assessment for
 Formulation – starting material properties, levels of components
 Manufacturing process
Steps for risk assessment
 List out all components / processes
 Prepare the process flow chart
 Identify all potential failure modes for each item with risk query (what might go wrong?)
 Risk analysis
 Risk evaluation
CMAs, CPPs and CQAs
What factors affect drug product CQAs?
 Properties of Input Materials- Identify Critical Material Attributes (CMAs)
 Properties of in-process materials- CQAs of one step become CMAs for a downstream unit operation
 Manufacturing process parameters- Identify Critical Process Parameters (CPPs)

5. Control Strategy
“A planned set of controls, derived from current product and process understanding that ensures process
performance and product quality…..”
ICH Q8 (R2) & Q10
Control Strategy includes following elements (but not limited to):
 Input material attributes (e.g. drug substance, excipients, container closure)
 Equipment operating conditions (process parameters)
 In-process controls
 Finished product specifications
 Controls for each unit operations
 Methods and frequency of monitoring and control.
Regulatory And Industry Views On Qbd
Since the introduction of the Food and Drug Association (FDA) 21stcentury initiative (A Risk-Based Approach)
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
empirically derived from performance of test batches. Another FDA representative (Shah, 2009) states that
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 post
approval changes and regulatory processes, enable more focused inspections, and provide opportunities for
continual improvement (Shah, 2009). The FDA has provided examples on 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 web site, presented in the form of section 3.2.P.2 Pharmaceutical
Development part of CTD fi le Module 3 (Quality). Pharmaceutical development of acetriptan immediate
release and an example of modified release tablets are presented. European Medicines Agency (EMA)
representatives (Korakianiti, 2009) stressed that it is the uncontrolled variability in, for example, properties of
the starting materials or the manufacturing process that affect the quality of the pharmaceutical product. Once
the increased process and product understanding is obtained, it is possible to identify and appropriately manage
critical sources of variability, and design effective and efficient manufacturing processes that allow quality
assurance in real time. EMA representatives (Korakianiti, 2009) point out that it is preferable for a design space
to be complemented by an appropriate control strategy. An example of a QbD application in pharmaceutical
product development is presented in the Exam plain Mock P2 document, available online. The review of
variations regulations and the revised Variations Classifications Guideline (2008) has taken into account QbD
submissions, to enable easier updates of the registration dossier. EMA templates and guidance documents used
for the assessment of any new drug application in the centralized procedure include the possibility of design
space appointment (e.g. Day 80 Quality AR Template). EMA, FDA, and ICH working groups have appointed
the ICH quality implementation working group (Q-IWG), which prepared various templates, workshop training
materials, questions and answers, as well as a points- to-consider document (issued in 2011) that covers ICH
Q8(R2), ICH Q9, and ICH Q10 guidelines. This document provides an interesting overview on the use of
different modeling techniques in QbD. In a QbD context, the model is defined as a simplified representation of a
system using mathematical terms. Models are expected to enhance scientific understanding and possibly predict
the behavior of a system under a set of conditions. For the purposes of regulatory submissions, the ICH Q-IWG
document classifies the models according to their relative contribution in assuring the quality of a product

6. Development and implementation of models include definition of the model purpose, decision on the type of
modeling approach (e.g. mechanistic or empirical), selection of variables for the model, understanding of the
model assumptions limitations, collection of experimental data, development of model equations and parameters
estimation, model validation, and documentation of the outcome of the model development. It is also
recommended to set the acceptance criteria for the model relevant to the purpose of the model and to its
expected performance. Also, accuracy of calibration and accuracy of prediction should be compared and the
model should be validated using an external data set. The ICH Q-IWG document also suggests that a design
space can be updated over the product lifecycle, as additional knowledge is gained. It also notes that in
development of design spaces for existing products, multivariate models can be used for retrospective evaluation
of the production data. An important issue of design space scale- up is addressed in the ICH Q-IWGdocument.
Since design spaces are typically developed at a small scale, an effective control strategy helps manage potential
residual risk after development and implementation. While the entire design space does not have to be re-
established at a commercial scale, design spaces should be initially verified as suitable prior to commercial
manufacturing. Design space verification includes monitoring or testing of CQAs that are influenced by scale-
dependent parameters. Additional verification of a design space, which might be triggered by changes (e.g. site,
scale, equipment) is typically guided by the results of risk assessment of the potential impacts of the change(s)
on design space. Joint efforts of EMA and FDA resulted in a pilot program for parallel assessment of QbD
applications in 2011 (EMA-FDA Pilot Program for Parallel Assessment of Quality by Design Applications,
2011). Certain parts of registration fi les will be assessed in parallel, being relevant to QbD, such as
development, design space, real- time release testing, etc. Pharmaceutical manufacturers should always bear in
mind that sufficient details of development and manufacturing information should be included in regulatory
submissions. However, regulatory decisions must be based on scientific and quality risk management principles
(Nasr, 2011). It has been stated (Nasr, 2011) that current challenges of QbD concept implementation include
lack of clarity of regulatory expectations, reluctance to share information in regulatory submissions, and lacking
in links appointed between control strategies and pharmaceutical development, etc. There were several EMA
marketing authorization applications (MAA) with QbD and PAT elements (for the following products:
Avamys®, Torisel®, Tyverb®, Norvir®, Exjade®, Revolade®, Votrient®, etc.). Up to 2011, there was a total
of 26 QbD submissions to EMA (for the new chemical entities); 18 of them were initial MAAs (4 including the
real time release), 6 of them were concerning post- authorization, and 2 were scientific advice requests. An
additional two MAAs were submitted for biological products, but none of the submissions were related to the
generics industry (Korakianiti, 2011). Up to 2011, there were approximately 50 QbD related applications to the
FDA (Miksinski, 2011). FDA authorities state that QbD is to be fully implemented by January 2013 (Miksinski,
2011). Pfizer was one of the first companies to implement QbD and PAT concepts. Through these concepts, the
company gained enhanced process understanding, higher process capability, better product quality, and
increased flexibility to implement continuous improvement changes (Migliaccio, 2011). Also, much of the QbD
investment occurs in process development, and the benefit is realized in commercial manufacturing (Migliaccio,
2011). Another important issue addressed by the Pfizer researchers is that some compendial specifications may
not be adequate to analyze physical, chemical, microbiological, and biological properties of materials that may
impact product quality or process performance (potential CQAs). The process performance index Ppk of the
first QbD Pfizer product was 1.2 (3–4 σ ) at launch and 1.8 (5–6 σ ) 6 months after launch (Migliaccio, 2011),
which indicates that QbD results in robust processes and is able to rapidly improve process capability. Also,
QbD resulted in lower deviation rates in the first year after launch than achieved through traditional continuous
improvement efforts (Migliaccio, 2011). There are a variety of opportunities for the QbD concept to be applied
to existing products: processes can be redesigned, partial design s paces can be defined, enhanced control
strategies can be appointed (including real- time release), or new technologies (i.e. continuous manufacturing)
can be developed (Migliaccio, 2011).

7. Conclusion
Concepts presented in this chapter suggest that there is an ever- growing need for better understanding of the
formulation and process development by pharmaceutical scientists. Benefits of QbD application for both
regulatory agencies and manufacturers have been proven. It is clear the QbD will become a necessity, therefore
all the stakeholders should adapt to its implementation. Quality has been given abundant significance by all
regulatory bodies for manufacturing of pharmaceutical products and drug delivery systems. Quality means
customer satisfaction in terms of service, product, and process. Customer demands the perfection in quality,
reliability, low cost and timely performance of the drug product.
But merely analyzing the final product does indicate the quality; however it should be designed in the product
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