HOLISTIC APPLICATION OF QUALITY BY DESIGN (QbD) FOR PHARMA PRODUCT DEVELOPMENT EXCELLENCE AND REGULATORY COMPLIANCE

towards generics, decreased post-approval
changes and efficient regulatory oversight.

One of the integral tools in the QbD
armamentarium while developing
optimized products and processes has been
“Design of Experiments (DoE)” employing
apt usage of experimental designs [6].
Amidst a multitude of plausible
interactions of the drug substance with a
plethora of functional and non-functional
excipients and processes, adoption of
systematic approaches lead to evolution of
the breakthrough systems with minimal
expenditure of time, developmental effort
and cost. With the objective of developing
an impeccable products or processes,
earlier this task has been attempted through
trial and error, supplemented with the
previous knowledge, wisdom and
experience of the formulator, termed as the
short-gun approach or one factor at a time
(OFAT) approach [7, 8]. Using this
methodology, the solution of a specific
problematic product or process
characteristic cannot be achieved and
attainment of the true optimal solution was
never guaranteed. However, the QbDbased
approach usually provides
systematic drug product development
yielding the best solutions. Such
approaches are far more advantageous,
because they require fewer experiments to
achieve an optimum formulation, reveal
interaction among the drug-excipientprocess,
simulates the product performance
and subsequent scale-up. Figure 2
illustrates the QbD-oriented development
of drug product embarking upon the
comprehensive understanding of the
quality traits associated with a product(s)
and process(es).

Figure 2: QbD leads to product and
process understanding and continual
improvement

With the percolation of such systematized
approaches, the domain of pharmaceutical
product development has endowed a newer
look towards drug formulation
development and subsequent patient
therapy. Owing to the immense benefits,
the applications of QbD are galore such as
in drug substance manufacturing,
formulation development, analytical
development, stability testing,
bioequivalence trials, etc.
The holistic QbD-based philosophy of
product development revolves around five
fundamental elements viz. defining the
quality target product profile (QTPP),
identification of critical quality attributes
(CQAs), critical formulation attributes
(CFAs) and critical process parameters
(CPPs), selection of apt experimental
designs for DoE-guided, precise definition
of design and control spaces to embark

upon the optimum formulation, postulation
of control strategy for continuous
improvement [9, 10]. Figure 3 illustrates
the five step methodology for drug product
development employing QbD-based
approach.

Figure 3: Five-step QbD methodology
Step I: Ascertaining Drug Product
Objective(s)

The target product quality profile (QTPP)
is a prospective summary of quality
characteristics of the drug delivery product
ideally achieved to ensure the desired
quality, taking into account the safety and
efficacy of the drug product. During drug
product development, QTPP is embarked
upon through brain storming among the
team members cutting across multiple
disciplines in the industry. Critical quality
attributes (CQAs) are the physical,
chemical, biological or microbiological
characteristic of the product that should be
within an appropriate limit, range or
distribution to ensure the desired product
quality. The identification of CQAs from
the QTPP is based on the severity of harm

a patient may get plausibly owing to the
product failure. Thus after defining the
QTPP, the CQAs which pragmatically
epitomize the objective(s), are earmarked
for the purpose.

Step II: Prioritizing Input Variables for
Optimization

Material attributes (MAs) and process
parameters (PPs) are considered as the
independent input variables associated
with a product and/or process, which
directly influence the CQAs of the drug
product. Ishikawa-Fish bone diagram are
used for establishment of cause-effect
relationship among the input variables
affecting the quality traits of the drug
product. Figure 4 illustrates a typical
cause-effect diagram highlighting the
plausible causes of product variability and
their impact on drug product CQAs.

Figure 4: A typical Ishikawa-fish bone
diagram depicting sources of variability
Prioritization exercise is carried out
employing initial risk assessment and
quality risk management (QRM)
techniques for identifying the “prominent
few” input variables, termed as critical
material attributes (CMAs) and critical
process parameters (CPPs) from the
“plausible so many”. This process is

popularly termed as factor screening.
Comparison matrix (CM), risk estimation
matrix (REM), failure mode effect analysis
(FMEA) and hazard operability analysis
(HAZOP) are the examples of commonly
employed risk assessment techniques.
Using these techniques, various MAs and
PPs are assigned with different risk levels
viz. low, medium and high risk based on
their severity and likelihood of occurrence.
The moderate to high risk factors are
chosen from patient perspectives through
brainstorming among the team members
for judicious selection of CMAs.

Figure 5: Prioritization using QRM and
factor screening is necessary to identify
CMAs and CPPs as a prelude to DoE
optimization

QRM is rational approach which not only
provides holistic understanding of the risks
associated with each stages of product
development, but also facilitates mitigation
of risks too. Experimental designs and risk.

assessment techniques are used during
QRM exercise for factor screening,
respectively (Figure 5). Figure 6 portrays
the flow layout of overall risk assessment
plan employing risk assessment and risk
management for identifying the potential
CMAs employing a prototype REM model.

Figure 6: Layout of risk management
strategy employing a typical risk
estimation matrix

The low-resolution first-order
experimental designs (e.g., fractional
factorial, Plackett-Burman and Taguchi
designs) are highly helpful for screening
and factor influence studies. Before
venturing into product or process
optimization, prioritization of CMAs/CPPs
using such QRM and/or screening is
obligatory.
Step III: Design-guided
Experimentation & Analysis

Response surface methodology is
considered as a pivotal part of the entire
QbD exercise for optimization of product
and/or process variables discerned from
the risk assessment and screening studies.
The experimental designs help in mapping
the responses on the basis of the studied
objective(s), CQAs being explored, at
high, medium or low levels of CMAs.

Figure 7 provides bird’s eye view of key
experimental designs employed during
QbD-based product development.
Factorial, Box-Behnken, composite,
optimal and mixture designs are the
commonly used high resolution secondorder
designs employed for drug product
optimization.

Figure 7: Key instances of experimental
designs used during QbD optimization

Design matrix is a layout of experimental
runs in matrix form generated by the
chosen experimental design, to guide the
drug delivery scientists. The drug
formulations are experimentally prepared
according to the design matrix and the
chosen response variables are evaluated
meticulously.
Step IV: Modelization & Validation of
QbD Methodology

Modelization is carried out by selection of
apt mathematical models like linear,

quadratic and cubic models to generate the
2D and 3D-response surface to relate the
response variables or CQAs with the input
variables or CMAs/CPPs for identifying
underlying interaction(s) among them.
Multiple linear regression analysis
(MLRA), partial least squares (PLS)
analysis and principal component analysis
(PCA) are some of the key multivariate
chemometric techniques employed for
modelization to discern the factor-response
relationship. Besides, the model diagnostic
plots like perturbation charts, outlier plot,
leverage plot, Cook’s distance plot and
Box-Cox plot are also helpful in
unearthing the pertinent scientific minutiae
and interactions among the CMAs too. The
search for optimum solution is
accomplished through numerical and
graphical optimization techniques like
desirability function, canonical analysis,
artificial neural network, brute-force
methodology and overlay plot. Subsequent
to the optimum search, the optimized
formulation is located in the design and
control spaces. Design space is a
multidimensional combination of input
variables (i.e., CMAs/CPPs) and out
variable (i.e., CQAs) to discern the optimal
solution with assurance of quality.

Figure 8: Interplay of knowledge, design
and control spaces
 Singh B. et al : Holistic Applications of QbD for Product Development

Figure 8 illustrates the interrelationship
among various spaces like, explorable,
knowledge, design and control spaces.
Usually in industrial milieu, a narrower
domain of control space is construed from
the design space for further implicit and
explicit studies.
Step V: QbD Validation, Scale-up and
Production

Validation of the QbD methodology is a
crucial step that forecasts about the
prognostic ability of the polynomial
models studied. Various product and
process parameters are selected from the
experimental domain and evaluated as per
the standard operating conditions laid
down for the desired product and process
related conditions carried out earlier,
commonly termed as checkpoints or
confirmatory runs. The results obtained
from these checkpoints are then compared
with the predicted ones through linear
correlation plots and the residual plots to
check any typical pattern like ascending or
descending lines, cycles, etc. To
corroborate QbD performance, the product
or process is scaled-up through pilot-plant,
exhibit and production scale, in an
industrial milieu to ensure the
reproducibility and robustness. A holistic
and versatile “control strategy” is
meticulously postulated for “continuous
improvement” in accomplishing better
quality of the finished product.
Software Usage during QbD

The merits of QbD techniques are galore
and their acceptability upbeat. Putting such

rational approaches into practice, however,
usually involves a great deal of
mathematical and statistical intricacies.
Today, with the availability of powerful
and economical hardware and that of the
comprehensive QbD software, the
erstwhile computational hiccups have been
greatly simplified and streamlined. Figure
9 enlist the select computer softwares
available commercially for carrying out
QbD studies in industrial milieu. Pertinent
computer softwares available for DoE
optimization include Design-Expert®,
MODDE®, Unscrambler®, JMP®,
Statistica®, Minitab®, etc., are at the rescue,
which usually provide interface guide at
every step during the entire product
development cycle. Softwares providing
support for chemometric analysis through
multivariate techniques like MNLRA,
PCA, PLS, etc. encompass, MODDE®,
Unscrambler®, SIMCA®, CODDESA®. For
QRM execution using Fish-bone diagrams,
REM and FMEA matrices during risk
assessment studies, etc., softwares like,
Minitab®, Risk®, Statgraphics, FMEA-Pro,
iGrafx, etc., can be made use of.

Figure 9: Select computer software used
during QbD implementation for product
and process optimization

QbD is an inimitable quality-targeted
approach for developing efficacious, costefficacious,
safe and robust drug products,
generics as well as innovator’s. On
industrial fronts, a formulation scientist
can derive its stellar benefits at each stage
of product development lifecycle and
beyond, even after commercial launch and
post-marketing surveillance. Figure 10
pictorially illustrates the application of
strategic principles of QbD during various
stages of drug product development.

Figure 10: QbD is useful overall product
development even after the product
launch

Formulation by Design (FbD)
Formulation by Design (FbD) is a recent
QbD-based paradigm, applicable
exclusively for development of
pharmaceutical dosage forms. Product and
process understanding are the twin
keystones of FbD. It also requires holistic
envisioning of the formulation
development, including how CMAs and

CPPs tend to impact CQAs during
laboratory scale, production and exhibit
scale leading to a robust and stable drug
product [8]. Defining such relationships
between these formulation or process
variables and quality traits of the
formulation is almost an impossible task
without the application of FbD model.
More the formulator knows about the
system, the better he can define it, the
higher precision he can monitor it with.
Such approach has been widely employed
in the development of drug formulations of
diverse kinds. Table 1 and Table 2
illustrates the select literature instances on
the product and process optimization of
drug delivery products employing FbD
approach enlisting their QTPP, CMAs,
CPPs, CQAs and type of experimental
design employed, respectively.
Analytical QbD (AQbD)
AQbD, on the heels of QbD, endeavors for
understanding the predefined analytical
objectives. These comprise, quality target
method profile (QTMP) of an analytical
method and identifying the critical method
variables (CMVs) affecting the critical
analytical attributes (CAAs) for attaining
enhanced method performance, like high
robustness, ruggedness and flexibility for
continual improvement within the ambit of
analytical design space [33, 34]. Besides,
AQbD helps in reducing and controlling
the source of variability to gain in-process
information for taking

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products has undoubtedly spiced up over
the past a few decades, yet it is far from
being adopted as a standard practice.
Federal regulations for generic drug
products are already in place. Several
initiatives still need to be undertaken to
inculcate mundane use of diverse QbD
paradigms in the holistic domain. Apart
from these, the synergistic use of inprocess
PAT and RTRT tools in tandem
with process engineering approaches like
extensometry and chemometry, can also be
helpful in ameliorating product and
process understanding and enhancing the
process capability for efficient
manufacturing. With the growing
acceptance of QbD paradigms, in a
nutshell, it is rationally prophesized that
soon these QbD philosophies will be
required to be implemented to innovators,
biosimilars, analytical development, API
development and even beyond.
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