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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5441_Библиотеки_им_академика_М_И_Перельмана.pdf
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quadratic, and full quadratic assessment along with the p-value of input variables.
The design of the expert (DoE) tool is a statistical method that determined the inu-
ence of independent variables on dependent variables. The most commonly used
DoE methods are screening design (Plackett–Burman, fractional factorial design,
and full factorial design) and response surface methodology (RSM). In the pharma-
ceutical eld, central composite design (CCD) and the Box–Behnken design (BBD)
RSM are the most widely used methods for optimization. Doehlert design is also as
effective as CCD and BBD, but it was less studied for the nanotechnology-based
products (Luiz etal. 2021).
This chapter discussed the QbD approach for pharmaceutical product develop-
ment, the role of QbD and FbD in product development, various experimental
designs employed in the optimization of drug delivery systems, key elements of
QbD, various tools used in FbD optimization, FbD Opti-tactics for drug delivery
systems, software employed for QbD optimization, and applications of QbD and
FbD in different elds.
3.2 Role ofQbD andFbD intheDevelopment
ofPharmaceutical Products
The conventional optimization method is studying the effect of one factor at a time
(OFAT) while maintaining the other parameters constant. This method provides the
solution for a specic problematic property but does not assure the true optimum
composition or process. This may be attributed to the existence of interactions or the
impact of one or more factors on other factors. After optimization, the nal product
is satisfactory but mostly suboptimal because a better formulation may still exist for
the studied conditions. Thus, the traditional OFAT method has various problems
such as being time-consuming, expensive, and ineffective at revealing the interac-
tions. Further, this method only provides “just satisfactory” results because detailed
studies of all factors are not possible in this approach. This approach cannot study
the simultaneous effects of all variables on responses and may result in inconsistent
products. To overcome these problems, recently, DoE techniques were investigated
for pharmaceutical product development. These methods include the application of
appropriate experimental design along with mathematical equations and graphic
outcomes that depict a clear picture of variation of the response(s) as a function of
the factor(s). These methods provided the best possible solution and need fewer
experimental runs to obtain an optimum formulation as compared to traditional
methods. Additionally, the screening methods used in the DoE aid in identifying the
most critical input variables. Using model equations, one can simulate the behavior
of the product or process, saving time, effort, materials, and money. The comparison
between conventional and QbD system is depicted in Fig.3.1.
The most remarkable features of formulation by design (FbD) are that it can
predict the response of formulation and may detect and measure the possible inter-
action and synergistic effects between the variables. The FbD method helps in the
understanding of the formulation and can identify the problems and eliminate them
3 Optimization Techniques fortheDevelopment ofPharmaceutical Products
58
more easily. The FbD techniques provide high-quality drug products at an afford-
able price and ultimately achieved the QbD objectives (Namjoshi etal. 2020).
The use of QbD approaches is to provide quality products and help industry to
speed up the regulatory approval process. The development of a successful product
needs an understanding of QbD principles and the tools utilized in QbD methods.
Various tools such as the design of experiments, risk assessment tools, and PAT
(process analytical technology) are employed for the establishment of QbD princi-
ples. After the implementation of QbD, changes to the product and process can be
managed more effectively.
3.3 Key Experimental Designs Employed forOptimization
ofDrug Delivery Systems
The designing of an ideal pharmaceutical product requires multiple objectives. In
the case of complex drug delivery systems (DDSs), a variety of variables such as
drugs, excipients, polymers, and processes are involved (Singh etal. 2020). For the
last many years, this task has been carried out via trial and error, accompanied by
previous data, experience, and understanding of the formulator (Singh etal. 2005a).
The conventional approach involves studying the inuence of the corresponding
composition and process variable by varying one variable at a time (OVAT) while
keeping other factors constant. This technique, at times, is also denoted as one fac-
tor at a time (OFAT) or “shotgun” approach (Singh etal. 2005a). In OVAT studies,
the rst variable is xed at a favorable value, and the next is observed until no
Conventional productdevelopment andsupply
system
Product development
Fixed, batch manufacturing process
Fixedpackaging process
Product distribution
Product quarantine
Patient
Fixed parameter
In processqualitycontrol and
documentation
Releasetesting document integrity
In processqualitycontrol and
documentation
QbDproduct developmentand supply system
Productdevelopment
Responsive batchcontinuous
manufacturing process
Responsive
e
packagingprocess
Productdistribution
Patient
Design space
Control strategy usingPAT
Real time releasetesting
Fig. 3.1 Comparison between conventional and QbD system
S. Rathee et al.
59
further improvement is achieved in the response variables. This approach can
accomplish the solution of a specic problem, but the fulllment of the true opti-
mum composition or process is never certain. It may be attributed to the presence of
interaction(s) such as the synergistic or antagonistic effect of one or more variable(s)
on others.
The execution of DoE optimization methods invariably includes the use of
experimental designs and the generation of calculated equations and graphic out-
comes. This leads to producing a complete picture of the variation of the product/
process response(s) as a function of the input variable(s). The use of different types
of combination strategies for formulation variables, along with Design of
Experiments (DoE), ts experimental data into statistical equations, utilizes these
models to predict formulation performance, and optimizes critical responses (Rao
and Srinivas 2011).
The general scientic methodology is determined via the conduct of an experi-
ment and following interpretation of its experimental result. There are the twin
essential characteristics of both the experimental and outcomes, which lead to gen-
erating the scientic methodology (Singh etal. 2006). Different runs or trials are the
experiments conducted according to the selected experimental design (ED). Several
kinds of EDs are employed for the optimization of different drug delivery systems
(Singh et al. 2005a). Commonly used EDs for response surface methodology
(RSM), screening, and factor-affecting studies during the pharmaceutical product/
process development include the following:
3.3.1 Factorial Designs (FD)
It is the most commonly used ED and involves all levels of a given factor combined
with all levels of every other factor in the experiment. Full factorial design (FFD)
involves studying the effect of all the factors (k) at various levels (x). It includes
various interactions among them, with the total number of experiments which is
denoted as xk. There are two types of FD: it can be either “symmetric” if the number
of levels is the same for each factor or “asymmetric” in cases of a different number
of levels for different factors (Singh and Ahuja 2000).
3.3.2 Fractional Factorial Designs (FFDs)
The fractional factorial design (FFD) involves a nite fraction (1/xr) of a com-
plete or full FD, where r is the degree of fractionation and xk-r is the total num-
ber of experiments needed (Doornbos and Haan 1995). When the number of
required experiments exceeds the adaptable levels due to an increase in the num-
ber of factors or factor levels, then this design is particularly preferred over FD
(Singh etal. 2005a). An FFD with r= 1, on the other hand, will require only
23-1, i.e., four experiments, estimating a total of three effects are estimated
(Singh etal. 2005b).
3 Optimization Techniques fortheDevelopment ofPharmaceutical Products
60
3.3.3 Plackett–Burman Designs (PBDs)
The Plackett–Burman designs (PBDs) are specialized two-level FFDs used com-
monly for screening of K, i.e., N−1 factors, where N is a multiple of 4 (Plackett
and Burman 1946). It is also known as Hadamard designs or symmetrically reduced
2k-r FDs; the designs can easily be created by using a minimum number of trials
(Loukas 2001). It is also known as a nongeometric design because this design can-
not be represented as cubes. The PBDs are relatively employed during the screening
processes.
3.3.4 Central Composite Designs (CCD)
The central composite design is also known as the Box–Wilson design; the central
composite design (CCD) is most widely used for quadratic models (Singh and
Ahuja 2000). The design consists of a combination of two-level factorial points
(2n), axial or star points (2n), and a central point (Box and Wilson 1992). Hence,
2n+2n+1 was used to denote the total number of factors combined in CCD.
3.3.5 Box–Behnken Designs (BBD)
Box–Behnken design (BBD) is specially designed to optimize various product and
process variables. BBD requires only three levels (−1, 0, 1) (Box and Behnken
1960). It is used to overcome the limitations associated with the CCD, where each
factor has been considered at ve levels. Hence, increasing the number of factors
leads to an increase in the number of experiments. BBD is considered an economi-
cal alternative to CCD (Singh and Ahuja 2000).
3.3.6 Equiradial Designs
The equiradial designs consist of N points on a circle about the center of interest in
the form of a regular polygon (Singh etal. 2005a). Some limitations are associated
with this design, i.e., only a limited number of parameters (p=3) have been selected
in this model. This design has special characteristics; it may be rotated by any angle
and still have the same properties.
3.3.7 Mixture Designs
In a drug delivery system (DDS), simultaneous variation of all factors at all levels
may not be possible under many circumstances. The characteristics of a drug deliv-
ery system (DDS) formulated with different excipients depend not only on the pro-
portions of the substances but also on the absolute quantities of each component
S. Rathee et al.
61
present in the formulation. In this study, the total sum of the excipients is unity, and
none of the fractions can be negative. Hence, the levels of different components can
be changed with the limit that the total should not exceed one. In previous studies,
the mixture design is most widely used (Singh etal. 2005b). The design region for
mixture proportions is simple. The method consists of rst generating data from the
n+1 experiment, where n is the number of factors (Araujo and Brereton 1996).
Based on the n+1 response and predetermined rules, one outcome is eliminated and
a new study is performed.
The mixture design (SMD) is a type of mixture design and is denoted as Scheffe’s
design. It can either be a centroid or lattice design (SLD). The design points are
homogeneously distributed over the factor space and form the lattice (Lachman
etal. 1976).
3.3.8 Taguchi Designs
The Taguchi design was invented by a Japanese engineer, who proposed several
strategies for experimental design that are known as the “Taguchi method.” This
design utilized two-, three-, and mixed-level fractional factorial designs. Taguchi is
denoted as an ED as “ofine quality control,” as it is a method to conrm good
performance in the design stage of products or processes. The objective is to make
a product or process less variable (i.e., more robust) in the face of variation over
which we have little control. In the Taguchi data analysis, the response variable is
not the common raw response or quality characteristics but the signal-to-noise ratio
(S/N ratio) (Jain 2014).
3.3.9 Optimal Designs
It is a type of computer-aided design particularly useful when classical designs do
not apply. Generally, custom designs are produced based on specic optimality cri-
teria, such as D-, A-, G-, I-, and V-optimality criteria (Singh etal. 2005b). The most
widely used criterion in custom designs is D-optimality. This optimality criterion
results in minimizing the generalized variance of the parameter estimates for a pre-
specied model. The D-optimal designs are based on the principle of minimization
of parameter variance and covariance.
3.4 Elements ofQbD
3.4.1 Quality Target Product Profile (QTPP)
The ICH guideline Q8 denes the QbD as “A methodical strategy for development
that starts with predetermined goals and emphasizes knowledge of the product and
process as well as process control, based on sound science and quality risk
3 Optimization Techniques fortheDevelopment ofPharmaceutical Products
62
management.” This rational aspect of product designing involves the rst step to
dening a list of quality requirements which is termed as quality target product
prole (QTPP). QTPP is dened by ICH Q8 as “A prospective summary of the qual-
ity parameters of a pharmaceutical drug product that ideally will be achieved to
ensure the desired quality while considering the safety and efcacy of the drug
product.” These specications for quality are known as quality attributes, and it is
crucial to identify the most critical quality attributes (CQAs) of a formulation to
accurately characterize the various QTPP components, or physicochemical proper-
ties (Namjoshi etal. 2020).
3.4.2 Critical Quality Attributes (CQAs)
As per FDA, the denition of CQA comprises any physical, chemical, biological, or
microbiological property or characteristics that should be within a suitable range or
distribution to assure the desired quality of the developed product such as product
purity, strength, stability, and drug release (Jain 2014). CQA is having a direct effect
on different parameters such as quality, safety, or efcacy of the product. These
parameters are associated with drug substances, excipients, and in-process materi-
als, i.e., intermediates and products (Sangshetti etal. 2017). The dosage form and
delivery system greatly affect the CQAs.
An Ishikawa shbone diagram is used to create the “cause-and-effect” relation-
ship among multiple input factors and drug product CQAs (Singh etal. 2005a). The
Ishikawa diagram showed the cause–effect relationship for CQAs. The ranking
exercise aims to dene statistically signicant variables among material attributes
(MAs) and critical process parameters (CPPs), which greatly affect various CQAs.
The risk estimation matrix (REM) is a method to determine the most prevalent risk
factor. In this method, the MAs and process parameters (PPs) are assigned varied
degrees of risk, viz., high, medium, and low. The degree of risk is based on risk
severity, frequency of incidence, and, at times, its detectability (Singh etal. 2005a,
2017). The prioritization strategy is a factor screening, which aids in choosing stati-
cally signicant CMAs and CPPs while ignoring the “idler” ones. It is useful in
subsequent optimization studies which used the DoE approach (Singh et al.
2006, 2017).
3.4.3 Risk Management
Risk assessment is a crucial component of quality by design (QbD) and is an inte-
gral part of the quality risk management (QRM) process (Fig.3.2). During risk
assessment, the focus is on identifying the critical quality attributes (CQAs) and
their role in achieving the predetermined target. The safety and efcacy of the prod-
uct are highly dependent on the CQAs, making them of utmost importance.
Additionally, the critical material attributes (CMA) and critical process parameters
(CPP) are linked to the CQAs in this process (Patil and Pethe 2013).
S. Rathee et al.
63
There are various qualitative and quantitative tools available for risk assessment.
Brainstorming is an effective technique used in the risk assessment process. Other
tools include shbone/Ishikawa diagrams, road maps, preliminary hazard analysis
(PHA), fault tree analysis (FTA), RRMA (risk ranking and ltering), FMEA (failure
mode and effects analysis), hazard and operability analysis, hazard analysis and
critical control points, etc. Among these tools, the shbone/Ishikawa diagram, FTA,
and road maps are used for qualitative risk identication, while FMEA and PHA are
used as quantitative tools to categorize the risk (Kalyane etal. 2020).
PHA is a semiquantitative method used for preliminary risk analysis, in which a
rank/score is given according to the severity and probability of hazards (Wang etal.
2014). FMEA is a vital technique for risk analysis, involving the ranking of CQA
based on the multiplication of severity, occurrence, and detectability in terms of the
risk priority number (Lipol and Haq 2011). RRMA is a qualitative technique for risk
analysis that categorizes risk as high, low, or medium.
In addition to risk identication and risk analysis, risk evaluation plays a critical
role in qualitative risk management. Bar charts and Pareto charts are commonly
employed tools used for risk evaluation. These charts provide information regarding
the level of signicance of the risk (Borgonovo and Smith 2011). Another tool used
for risk evaluation is the as low as reasonably practicable (ALARP) approach, which
evaluates risk based on the cost-to-benet ratio. Risks are categorized as unaccept-
able, broadly acceptable, or tolerable. Unacceptable risks need to be eliminated or
reduced to a broadly acceptable or tolerable level. Broadly acceptable risks are well
Initiate QRMprocess
Risk assessment
Risk Identification
Risk Analysis
Risk control
Risk reduction
Risk acceptable
Output/resultofQRM process
Risk Review
Risk events
Risk
sk
Communication
Risk management tools
Unacceptable
Fig. 3.2 Details of the quality risk management process
3 Optimization Techniques fortheDevelopment ofPharmaceutical Products
64
controlled because of their insignicant lower risk, and a tolerable risk criterion is
determined through regulatory guidance documents, history, standards, and com-
pany policies (Moore and Nicholson 2005).
3.4.4 Design Space
Design space is a crucial aspect of quality by design (QbD) that establishes the
range of control for a product or process during manufacturing. QbD offers several
benets that can only be realized through the use of a design space. ICH Q8 (R2)
guidelines dene the design space as a combination of input variables and process
parameters that interact in multiple dimensions to ensure product quality (Mishra
etal. 2018). Performing within the design space is not considered a change, while
moving outside of it is deemed a change that would typically trigger a regulatory
post-approval change process. The design space can be an invariant, experimentally
veried design space, where a single design space applies to all production batches,
or it can be a fully model-dependent dynamic design space that calculates different
areas for individual production batches (Chatterjee etal. 2017).
Dynamic design space is a portion of acceptable spaces for an individual batch,
which provides more operating exibility and can be adjusted based on predened
process parameters. However, the lack of standards for predictive model verication
creates uncertainty in developing these types of design spaces. Additionally, creat-
ing and maintaining dynamic design space requires more resources, and the busi-
ness aspect needs to be considered for its exibility regarding investment. The
majority of case studies are available in the literature about the design space genera-
tion of the nal product formulation or active pharmaceutical ingredient (API)
development, with some related to biopharmaceuticals. The number of experiments
required for design space generation is based on the complexity of the process. For
less complex processes like dry granulation or tablet complexation, fewer experi-
ments are required for the generation of design space compared to more complex
processes like biological or API crystallization, which involve multiple factors.
Thorough quality risk assessment and thoughtful experimental setup are necessary
for the generation of quality design space. Blind experiments can increase the cost
of product or process development and take more time than usual.
3.4.5 Control Strategy
The control strategy is a fundamental element of quality by design (QbD), a system-
atic approach to pharmaceutical development that emphasizes a science-based and
risk-based approach to product and process design. The control strategy is a com-
prehensive plan that identies and controls the critical quality attributes (CQAs) of
a product and process to ensure consistent quality. The control strategy includes all
the measures that are used to ensure that the product meets the required specica-
tions and quality standards. It includes the critical process parameters (CPPs), the
S. Rathee et al.
65
critical quality attributes (CQAs), the methods used to monitor and control the pro-
cess, and the specications for the nished product. The control strategy also takes
into account the various risks associated with the product and process and includes
measures to mitigate those risks. The control strategy is developed through a pro-
cess of iterative experimentation and analysis. The initial design of the control strat-
egy is based on the existing knowledge and understanding of the product and
process. This initial design is then rened and optimized through experimentation
and analysis, using tools such as the design of experiments (DOE) and statistical
process control (SPC). The control strategy is implemented throughout the product
life cycle, from development to commercialization. The control strategy is continu-
ously monitored and updated to ensure that it remains effective and relevant as the
product and process evolve. The control strategy is a planned set of measures that
are derived from the existing understanding of the product and the process. These
measures are implemented to ensure consistent product quality by controlling vari-
ous parameters and attributes related to drug substance, drug product materials,
facility, and equipment operating conditions. The control strategy can include in-
process controls, nished product specications, associated methods, and the fre-
quency of monitoring and control. This approach is implemented in the
pharmaceutical development eld to maintain product quality (Bhoop 2014).
A proper understanding of the process helps to minimize the variability of input
materials, allowing for tighter process control and more consistent end products.
The understanding of product performance can also justify alternative approaches
to determine the quality of input materials, such as using disintegration instead of
dissolution for quick-disintegrating solid dosage forms. In-process control mea-
sures, such as dose uniformity, coupled with a near-infrared assay, can enable real-
time release testing, providing a higher level of quality assurance than conventional
end-product testing. The control strategy includes controlling the input material
attributes based on their impact on processability or product quality, as well as the
control of unit operations that affect downstream processing or product quality. It
also includes in-process real-time release testing and a monitoring program to ver-
ify multivariate prediction models (Pawar etal. 2016). Overall, the control strategy
is a key element of QbD, ensuring that the product and process are designed and
controlled to consistently meet the required quality standards. It provides a system-
atic approach to product and process design that is grounded in science and helped
to minimize the risks associated with pharmaceutical development (Jiang etal. 2017).
3.4.6 Product Life Cycle Management
andContinual Improvement
Product life cycle management and continual improvement are important elements
of quality by design (QbD), a systematic approach to pharmaceutical development
that emphasizes a science- and risk-based approach to product and process design.
The product life cycle begins with product development and continues through
commercialization and discontinuation. Throughout this cycle, it is important to
3 Optimization Techniques fortheDevelopment ofPharmaceutical Products
66
manage the product and process to ensure that they meet the required quality stan-
dards. This involves monitoring the product and process performance and making
adjustments as necessary to maintain consistent quality. The ICH Q10 guidelines
provide a framework for product life cycle management and continual improve-
ment. This framework involves establishing a quality management system that
incorporates quality planning, quality control, and quality assurance. The quality
management system should also include processes for monitoring and analyzing the
product and process performance to identify opportunities for improvement.
Continual improvement is a key component of the product life cycle management
process. This involves using the knowledge gained from monitoring the product and
process to identify areas for improvement and implementing changes to improve the
quality and efciency of the product and process. Throughout the product life cycle,
there are always opportunities for companies to enhance the quality of their prod-
ucts. The ICH Q10 guidelines emphasize the management of the product life cycle
and the continuous improvement of product quality. To achieve this, it is important
to monitor whether the product or process performance is meeting the specied
benets as outlined in the design space. This monitoring process could involve ana-
lyzing the manufacturing process trends during routine production or using a math-
ematical model and periodic maintenance. The company’s internal quality system
manages the maintenance of the model while keeping the design space unchanged
(Åsberg etal. 2016). As additional product or process knowledge is gained, there
may be a need for the expansion, reduction, or redenition of the design space. This
would enable companies to continue to improve the quality of their products
throughout the product life cycle.
Overall, product life cycle management and continual improvement are essential
elements of QbD. They provide a systematic approach to product and process
design and help to ensure that the product meets the required quality standards
throughout its life cycle. By continuously monitoring and improving the product
and process, companies can ensure that they remain competitive in the marketplace
and meet the evolving needs of their customers. The critical components of quality
by design (QbD) and their roles across product development phases are depicted in
Fig.3.3.
S. Rathee et al.