1
Quality- by-design in pharmaceutical
development
Jelena Djuris, Svetlana Ibric, and Zorica Djuric,
Department of Pharmaceutical Technology
and Cosmetology, Faculty of Pharmacy,
University of Belgrade
Abstract: T his fi rst chapter introduces the concept of quality-b y-
design (QbD) and its role in pharmaceutical product development.
QbD assures the quality of a pharmaceutical product through
scientifi c development and risk management tools, and will
eventually enable real-t ime 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 from
both regulatory and manufacturers’ points of view. Several
illustrative examples are provided to facilitate the understanding of
the QbD concept and ease of its application.
Key words: q uality- by-design (QbD), design space, risk management
tools, control strategies.
1.1 Introduction
The pharmaceutical industry is one of the most strictly regulated and its
products are of excellent quality. However, there are issues suggesting
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Computer-aided applications in pharmaceutical technology
that pharmaceutical development and manufacturing can be improved.
These facts are especially noticeable in cases of batch failures and
reworks, regulatory issues, 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. the semiconductor industry). Defects
in pharmaceutical product quality can be encountered such as low
manufacturing process yield or, more dangerously, some which may
affect the therapeutic performance of the drug (or both). For some
products, waste can be as high as 50%. Furthermore, the effects of
scale-u p on the fi nal product are often not understood and reasons for
manufacturing failures are not analyzed (Shah, 2009). The quality of a
pharmaceutical product can be defi ned 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 effort and cost.
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 predefi ned objectives and
emphasizes product and process understanding and process control,
based on sound science and quality risk management. Predefi ned
objectives make up the quality target product profi le (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 effi cacy of that drug
product. Through the scientifi cally based process of product development,
critical process parameters (CPPs), and critical quality attributes (CQAs)
of the product are identifi ed. 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 identifi cation 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 defi ned, 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 specifi cation tests.
A QTPP for immediate release tablets may include the following
requirements: assay, content uniformity, and dissolution should be in
accordance with the specifi cations to assure safety and effi cacy during the
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Quality-by-design in pharmaceutical development
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 defi ned 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-a nd-error approach, to the
scientifi cally based process of design space appointment. Defi nition of
design space also requires implementation of various risk management
tools, as well as defi nition of specifi cations and manufacturing controls.
Figure 1.1 shows a diagram of a QbD approach, combining design space
development and risk management tools.
Implementation of the QbD concept is important for all products,
including generics and biotechnological products (Nasr, 2011). There are
detailed reports on pharmaceutical QbD (Lionberger et al., 2008; Yu,
2008). The reader is advised to consult relevant textbooks on regulations
and quality in the pharmaceutical industry (Gad, 2008), QbD concept in
chemical engineering in the pharmaceutical industry (Am Ende, 2010),
application of QbD in biopharmaceuticals (Rathore and Mhatre, 2009),
QbD issues in process understanding for scale-u p and manufacture of
active ingredients (Houson, 2011), as well as upcoming reviews on QbD
in pharmaceutical and biopharmaceutical development (Herwig and
Menezes, 2013; Reklaitis, 2013). Furthermore, links between process
analytical technology (PAT) and QbD are elaborated on (Bakeev, 2010),
with special emphasis on biopharmaceuticals (Undey et al., 2012).
1.2 ICH Q8 guideline
The ICH Q8 guideline on scientifi cally based pharmaceutical development
serves to provide opportunities for pharmaceutical manufacturers to seek
regulatory fl exibility 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 defi ned design space is not recognized
as the change that would require regulatory approval. This paradigm can
be used to signifi cantly improve productivity and quality assurance in the
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Figure 1.1 QbD approach, combining design space development and risk management tools
Quality-by-design in pharmaceutical development
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 defi ned
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 verifi cation, meaning that rather
than one-t ime 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 and control strategies justifi ed. If an adequately organized
development study is conducted, it is possible for the pharmaceutical
manufacturer to gain reduction in both post-a pproval submissions and
reviews/inspections by the regulatory authorities. Furthermore, real-t ime
quality control is recommended, leading to a reduction of end-p roduct
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-p rocess 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-p rocess and/or fi nal
product based on the measured process data, allowing real-t ime 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 defi ned 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 defi ned 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.
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Computer-aided applications in pharmaceutical technology
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-t ime 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. T able 1.1 represents comparison between the traditional
and QbD approaches, regarding different aspects of pharmaceutical
development and product lifecycle management (according to the
ICH Q8 guideline).
Table 1.1 Comparison between the traditional and QbD
approach (ICH Q8 guideline)
Aspect Traditional Enhanced, QbD approach
Overall – Mainly empirical – Systematic, relating mechanistic
pharmaceutical – Developmental understanding of material
development research often attributes and process parameters
conducted one to drug product CQAs
variable at a time – Multivariate experiments to
understand product and processes
– Establishment of design space
– PAT tools utilized
Manufacturing – Fixed – Adjustable within design space
process – Validation primarily – Lifecycle approach to validation
based on initial and, ideally, continuous process
full- scale batches verifi cation
– Focus on optimization – Focus on control strategy and
and reproducibility robustness
– Use of statistical process control
methods
Process – In- process tests – PAT tools utilized with appropriate
controls primarily for go/no go feed forward and feedback controls
decisions – Process operations tracked and
trended to support continual
post- approval improvement efforts
Product – Primary means of – Part of overall quality control
specifi cations control strategy
– Based on batch data – Based on desired product
available at time of performance with relevant
registration supportive data
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Quality-by-design in pharmaceutical development
Control – Drug product quality – Drug product quality ensured by
strategy controlled primarily by risk- based control strategy for well
intermediates (in- understood product and process
process materials) and – Quality controls shifted upstream,
end- product testing with the possibility of real- time
release testing or reduced
end- product testing
Lifecycle – Reactive (i.e. problem – Preventive actions
management solving and corrective – Continual improvement facilitated
action)
1.3 Regulatory and industry
views on QbD
Since the introduction of the Food and Drug Association (FDA) 21st-
century 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 defi ned by an FDA offi cial
(Woodcock, 2004), the QbD concept represents product and process
performance characteristics scientifi cally designed to meet specifi c
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 effi ciency improvements
for both industry and regulators. QbD can facilitate innovation, increase
manufacturing effi ciency, reduce cost/product rejects, minimize/eliminate
potential compliance actions, enhance opportunities for fi rst 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 modifi ed 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 modifi ed release tablets are
presented. 1
European Medicines Agency (EMA) representatives (Korakianiti,
2009) stressed that it is the uncontrolled variability in, for example,
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Computer-aided applications in pharmaceutical technology
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 effi cient 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 Examplain
Mock P2 document, available online. 2 The review of variations regulations
and the revised Variations Classifi cations 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-t o-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 defi ned as a simplifi ed representation of a
system using mathematical terms. Models are expected to enhance
scientifi c 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 classifi es the models according to their relative
contribution in assuring the quality of a product (T able 1.2 ). Development
and implementation of models include defi nition 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,
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Quality-by-design in pharmaceutical development
Table 1.2 Classifi cation of models’ contribution in assuring
product quality (according to ICH Q-IWG document)
Models class Description
Low- impact These models are typically used to support product and/or
models process development (e.g. formulation optimization).
Medium- impact These models can be useful in assuring quality of the
models product, but are not the sole indicators of product quality
(e.g. most design space models, many in- process controls).
High- impact A model can be considered high impact if prediction from the
models model is a signifi cant indicator of quality of the product (e.g.
a chemometric model for product assay, a surrogate model
for dissolution).
multivariate models can be used for retrospective evaluation of the
production data. An important issue of design space scale-u p is addressed
in the ICH Q-IWG document. 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 verifi ed as suitable prior to commercial
manufacturing. Design space verifi cation includes monitoring or testing
of CQAs that are infl uenced by scale- dependent parameters. Additional
verifi cation 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.
Amenities of the QbD concept for both industry and regulatory bodies
are summarized in Table 1.3 . Pharmaceutical manufacturers should
always bear in mind that suffi cient details of development and
manufacturing information should be included in regulatory submissions.
However, regulatory decisions must be based on scientifi c 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
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Table 1.3 QbD for industry and regulatory bodies
Industry R egulatory agency
Development of scientifi c Scientifi cally based assessment of product
understanding of critical process and manufacturing process design and
and product attributes. development.
Controls and testing are designed Evaluation and approval of product quality
based on limits of scientifi c specifi cations in light of established
understanding at development standards (e.g. purity, stability, content
stage. uniformity, etc.).
Utilization of knowledge gained Evaluation of post- approval changes based
over the product’s lifecycle for on risk and science.
continuous improvement.
Source: Shah, 2009
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-a uthorization, and 2 were
scientifi c 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).
Pfi zer was one of the fi rst companies to implement QbD and PAT
concepts. Through these concepts, the company gained enhanced process
understanding, higher process capability, better product quality, and
increased fl exibility to implement continuous improvement changes
(Migliaccio, 2011). Also, much of the QbD investment occurs in process
development, and the benefi t is realized in commercial manufacturing
(Migliaccio, 2011). Another important issue addressed by the Pfi zer
researchers is that some compendial specifi cations 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 fi rst QbD
Pfi zer product was 1.2 (3–4 σ ) at launch and 1.8 (5–6 σ ) 6 months after
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Quality-by-design in pharmaceutical development
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 fi rst 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 spaces
can be defi ned, enhanced control strategies can be appointed (including
real- time release), or new technologies (i.e. continuous manufacturing)
can be developed (Migliaccio, 2011).
1.4 Scientifi cally 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, insuffi cient 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 issues.
Many scientifi c 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 scientifi cally
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 insuffi cient 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 ineffi cacy 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
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homogeneity, to identify potential critical factors that affect blending
operation quality, and risk-r ank these factors to defi ne activities for
process characterization. Results obtained were used to map a three-
dimensional knowledge space, providing parameters to defi ne 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-p recipitation)
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 artifi cial 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 infl uencing liposome drug encapsulation effi ciency 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 fl uidized 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
defi ned, linking CPPs to CQAs within which product quality is ensured
by design, and after scale-u p, 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
identifi ed 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 identifi ed CPPs.
The QbD approach was used in the formulation of dispersible tablets
(Charoo et al., 2012). Critical material and process parameters were
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Quality-by-design in pharmaceutical development
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 scientifi c 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 identifi ed process parameters were evaluated using a design
of experiment approach. The regression models generated from the data
characterized the impact of the identifi ed process parameters on quality
attributes. The models derived from characterization studies were used to
defi ne 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
characteristics. Principal component analysis was performed to fi nd
correlations between dependent and independent variables.
A multiparticulate system, designed for colon-s pecifi c 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 nanosuspension
preparation (Verma et al., 2009), to establish appropriate specifi cations
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 (Jiang et al., 2011).
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1.5 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. Benefi ts 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.
1.6 Notes
1. http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/How
DrugsareDevelopedandApproved/ApprovalApplications/AbbreviatedNew
DrugApplicationANDAGenerics/UCM304305.pdf
http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/How
DrugsareDevelopedandApproved/ApprovalApplications/AbbreviatedNew
DrugApplicationANDAGenerics/UCM286595.pdf
2. http://www.efpia.eu/Content/Default.asp?PageID=559&DocID=2933
1.7 References
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Quality-by-design in pharmaceutical development
Food and Drug Administration ( 2004 ) P harmaceutical CGMPs for the 21st
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