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3.5 Various Approaches Employed inQbD Optimization
Modern QbD approaches for developing drug products include desired state of
manufacturing, end-product testing for validation, testing balances with the design,
quality is always accomplished, complements well with Federal QbR, reduced
expenditure of resources, and wider operating ranges (Bhoop 2014). QbD success is
QTPP
(Quality characteristicsofthe productthatwill ensure safety andefficacy)
CQAs (CMAs, CPPs)
(For drug substance, excipients,intermediates,drugproduct)
Risk assessment
(ToidentifyCMA and CPP Linkingmaterialattributes andprocessparameterstoCQA
S
)
Design space
(Linkage between inputvariableand processparameter andCQA
S
)
Controlstrategy
(Using acombinationofappropriate elements such as controlofinput material, product
specification, in Processcontrols, in ProcessorrealTimetests,and monitoring program
basedonenhancedproduct andprocess understandingand qualityriskassessment)
Continualimprovement
(Manageproduct lifecycle,including continualimprovement (ICH Q10))
Fig. 3.3 “Critical components of quality by design (QbD) and their roles across product develop-
ment phases”
3 Optimization Techniques fortheDevelopment ofPharmaceutical Products
68
due to main ve vital strengths, viz., (1) meticulous drug product, apt choice of
experimental designs, (2) recognition of CQAs, (3) critical formulation attributes
(CFAs), (4) critical process parameters (CPPs), and (5) precise denition of design
and control space and precise computer-aided optimization (Singh etal. 2005a,
2011). The QbD optimization approaches are more advantageous because they
require less number of experiments to attain an optimum formulation; locating the
main problem and rectication is easier and showed the interactions between drugs,
excipients, and processes lead to stimulating the product performance
(Ghaemmaghamian etal. 2022).
3.6 Various Approaches Employed inFbD Optimization
Formulation-based design (FbD) optimization involves the systematic optimization
of the formulation of a product to achieve specic desired properties while meeting
constraints such as cost, availability of ingredients, and regulatory requirements.
Here are some approaches that can be employed in FbD optimization of formulation
without plagiarism:
3.6.1 Design ofExperiments (DoE)
In this approach, the formulation space is divided into smaller subspaces, and a
subset of the subspaces is selected for experimentation. The results of the experi-
ments are then used to build a predictive model of the formulation properties, which
can be optimized using FbD techniques.
3.6.2 Constraint-Based Optimization
In this approach, the formulation is optimized subject to a set of constraints such as
the cost of the ingredients, the availability of the ingredients, and the desired proper-
ties of the nal product. The optimization can be performed using techniques such
as linear programming or mixed-integer programming.
3.6.3 Multi-objective Optimization
Multi-objective optimization involves optimizing multiple formulation properties
simultaneously, which can be conicting. This approach can be used to nd the
optimal trade-off between different properties, such as taste, texture, and shelf life.
Surrogate modeling: Surrogate modeling can be used in FbD optimization to
construct a surrogate model of the formulation properties that can be optimized
using standard optimization techniques.
S. Rathee et al.
69
3.6.4 Expert Systems
Expert systems can be used to incorporate domain knowledge into the optimization
process. This approach involves creating a knowledge base of formulation rules and
using an inference engine to select the optimal formulation based on the desired
properties.
3.6.5 Evolutionary Algorithms
Evolutionary algorithms such as genetic algorithms can be used to optimize the
formulation by selecting the optimal combination of ingredients. This approach
works well when the formulation space is large and complex.
It is important to ensure that the FbD optimization process for formulation does
not involve plagiarism by using appropriate attribution methods and ensuring that
the nal optimized formulation is unique and not copied from existing
formulations.
3.7 Tools Used inFormulation-Based Design
(FbD) Optimization
Formulation-based design (FbD) optimization involves breaking down a complex
function or formulation into smaller sub-functions or sub-formulations, which can
be optimized independently. Here are some tools that can be used in FbD
optimization:
(A) Design of experiments (DoE) software: DoE software such as JMP, Minitab,
and Design-Expert can be used to plan and execute experiments in FbD optimi-
zation. These tools can help to identify signicant factors and interactions
between factors in the formulation or function.
(B) Optimization software: Optimization software such as MATLAB Optimization
Toolbox, Gurobi, and CPLEX can be used to solve the optimization problem in
FbD optimization. These tools can help to nd the optimal combination of sub-
functions or sub-formulations that satisfy the optimization objective and
constraints.
(C) Statistical modeling software: Statistical modeling software such as R, SAS,
and SPSS can be used to build predictive models of the sub-functions or sub-
formulations in FbD optimization. These tools can help to identify signicant
factors and interactions between factors and predict the response of the sub-
functions or sub-formulations to changes in the input variables.
(D) Surrogate modeling software: Surrogate modeling software such as Surrogate
Model Toolbox, DACE, and MOE can be used to construct surrogate models of
the sub-functions or sub-formulations in FbD optimization. These tools can
3 Optimization Techniques fortheDevelopment ofPharmaceutical Products
70
help to reduce the computational cost of the optimization by replacing the
expensive sub-functions or sub-formulations with a cheaper surrogate model.
(E) Expert systems software: Expert systems software such as Expert System
Shell, Drools, and CLIPS can be used to create and deploy expert systems in
FbD optimization. These tools can help to incorporate domain knowledge into
the optimization process and make intelligent decisions based on the desired
properties of the product.
(F) Machine learning software: Machine learning software such as TensorFlow,
PyTorch, and scikit-learn can be used to build predictive models of the sub-
functions or sub-formulations in formulation-based design (FbD) optimization.
These tools can help to identify signicant factors and interactions between
factors and predict the response of the sub-functions or sub-formulations to
change in the input variables.
It is important to choose the appropriate tools for formulation-based design
(FbD) optimization based on the problem at hand and the available resources. The
use of these tools can help to accelerate the optimization process and achieve opti-
mal results.
3.8 FbD Opti-tactics forDrug Delivery Systems
Liposomes are spherical structures composed of a double-layered lipid membrane
that closely resembles natural biological membranes. They are formed by using
phospholipids with mixed lipid chains (such as egg PE) or other surfactants via a
process known as sonication. Liposomes are being widely investigated for their
potential as drug delivery systems for a variety of diseases. To develop liposomes
suitable for use, it is crucial to optimize their invivo kinetics, ensure high-through-
put production conditions, and design specic liposome compositions. One promis-
ing approach for identifying appropriate liposome compositions is to use
high-throughput screening methodologies and apply the principles of quality by
design (QbD) to the vital elements (factors) of liposome production. In terms of
drug development, there are two main ways to achieve selectivity for the drug: dis-
covering new compounds with high selectivity and innovating novel drug adminis-
tration methods with high selectivity through integration and harmonization of
various techniques. Takahara et al. (1997) proposed the “3H” formula for innovative
drug administration, which involves combining hybrid, high-quality, and husbandry
approaches. In addition, an overview of the optimization studies conducted on vari-
ous topical mucosal adhesive dosage forms and the parenteral administration of
peptide drugs, including insulin and erythropoietin, is also discussed. In 1988,
Gregoriadis reported on the initial optimization studies for liposomes.
S. Rathee et al.
71
3.9 Available Software Employed forQbD Optimization
There are several software tools available for quality by design (QbD) optimization,
including the following:
3.9.1 Design-Expert
A statistical software package that allows users to design experiments and analyze
data. It provides a graphical interface to visualize results and can be used for opti-
mization of process parameters.
3.9.2 SIMCA
It is a multivariate data analysis software that is commonly used for process model-
ing, monitoring, and optimization. It can be used to analyze large data sets and
identify key process variables that impact quality.
3.9.3 Minitab
A statistical software package that provides a wide range of statistical tools for data
analysis, design of experiments, and quality control. It also provides graphical tools
for visualization and communication of results.
3.9.4 JMP
A data analysis and visualization software that allows users to explore and model
data, design experiments, and optimize processes. It provides a user-friendly inter-
face and can be used for both statistical analysis and machine learning.
3.9.5 MATLAB
A programming language and software environment that is widely used for
data analysis, modeling, and simulation. It provides a wide range of tools for
optimization, including linear programming, nonlinear programming, and genetic
algorithms.
3 Optimization Techniques fortheDevelopment ofPharmaceutical Products
72
3.9.6 Aspen Plus
A process simulation software that is commonly used in the chemical and process
industries. It can be used to simulate and optimize chemical processes, including
process parameters, equipment design, and product quality.
3.9.7 AutoCAD
A computer-aided design software that can be used for designing and optimizing
processes and equipment. It provides a graphical interface to create 2D and 3D
designs and can be used to simulate and optimize process ow and equipment layout.
3.10 Applications ofQbD andFbD inDifferent Fields
Quality by design (QbD) and formulation by design (FbD) are systematic approaches
to product and process development that are widely applicable in various elds.
Here are some examples of their applications:
3.10.1 Pharmaceutical Industry
QbD and FbD have become an essential tool for product development in various
scientic elds. It is widely used for analytical method development utilizing differ-
ent techniques such as HPLC, ultrahigh-performance liquid chromatography, capil-
lary electrophoresis, mass spectrometry, and near-infrared spectroscopy. QbD and
FbD are extensively used in the pharmaceutical industry to ensure the quality and
consistency of drug products. By designing the product and process based on scien-
tic principles and risk assessment, companies can minimize the variability in drug
quality and increase process efciency. QbD and FbD also help to optimize drug
formulation, enhance drug delivery, and improve the safety prole of the drug. In
the domain of pharmaceutical formulation development, QbD is employed to opti-
mize the processes of various formulations, including sterile manufacturing, solid
dosage forms, modied-release products, gel manufacturing, tableting processes,
ANDAs, and API and excipient analysis (Hasnain etal. 2016; Wen and Park 2011;
Lionberger etal. 2008; Karmarkar etal. 2011; Orlandini etal. 2014; Musters etal.
2013; Basalious etal. 2011).
Moreover, biopharmaceutical applications of QbD include protein manufactur-
ing, purication of proteins using different chromatographic techniques, and pro-
duction and characterization of monoclonal antibodies. QbD can also be applied in
the elds of genetics and clinical applications (Rouiller etal. 2012; Finkler and
Krummen 2016).
Pharmaceutical unit operations and dosage forms can be optimized using the
QbD concept, which involves input material, process attributes, and quality
S. Rathee et al.
73
Table 3.1 Applications of QbD in the manufacturing of different dosage forms by using different pharmaceutical unit operations
S.No.
Dosage form
Pharmaceutical
unit operations Model drug
Design of
experiment
(DoE)
Critical
material
attributes
(CMA)
Critical process
parameters
(CPP)
Critical quality
attributes (CQA)
References
1. Solid
nanocrystalline
dry powder
Spray drying Indomethacin Full factorial
design
Not mentioned Inlet
temperature, ow
rate, and
aspiration rate
Particle size,
moisture content,
percent yield, and
crystallinity
Kumar
etal.
(2014)
2. Controlled-
release tablets
Physical mixture,
solvent
evaporation
Felodipine Box–Behnken
design
Amount of
polymer
HPMC,
amount of
polymeric
surfactants,
amount of
Pluronic F127
Preparation
technique
Maximum
solubility after
30min,
equilibrium
solubility after
24h, dissolution
efciency
Basalious
etal.
(2011)
3. Tablets Roller
compaction
Not
mentioned
Fractional
factorial
statistical
design
API
composition,
API excipient
ratio
API ow rate,
lubricant ow
rate, pre-
compression
pressure
Tablet weight,
tablet dissolution,
hardness, ribbon
density
Singh etal.
(2012)
(continued)
3 Optimization Techniques fortheDevelopment ofPharmaceutical Products
74
Table 3.1 (continued)
S.No.
Dosage form
Pharmaceutical
unit operations Model drug
Design of
experiment
(DoE)
Critical
material
attributes
(CMA)
Critical process
parameters
(CPP)
Critical quality
attributes (CQA)
References
4. Tablets Fluid bed
granulation
Not
mentioned
Fractional
factorial
design
(screening)
central
composite
design
(optimization)
Viscosity,
temperature,
and
concentration
of the binder
aqueous
dispersion
Inlet air
temperature,
binder spray rate,
and air ow rate
Particle size
distribution
(PSD), bulk and
tapped densities,
owability, and
angle of repose
Lourenço
etal.
(2012)
5. Coated tablets Film coating Placebo
tablets
Central
composite—
face
centered—
response
surface design
Solid percent
of the coating
dispersion
Inlet air
temperature, air
ow rate, solid
level, coating pan
speed, spray rate
Appearance
(coating defects,
gloss, and color
uniformity),
disintegration
time (dissolution
of the lm
coating)
Teckoe
etal.
(2013)
6. Orodispersible
lms
Homogenate
membrane
method
Theophylline Central
composite
design
Percentage of
HPMC,
percentage of
glycerol
Drying
temperature
Tensile strength,
elongation at
break, Young’s
modulus,
disintegration
time
Zhang and
Mao (2017)
S. Rathee et al.
75
S.No.
Dosage form
Pharmaceutical
unit operations Model drug
Design of
experiment
(DoE)
Critical
material
attributes
(CMA)
Critical process
parameters
(CPP)
Critical quality
attributes (CQA)
References
7. Solid lipid
nanoparticles
(SLN)
Hot-melt
extrusion (HME)
Fenobrate
(FBT)
Plackett–
Burman (PB)
screening
design
Lipid
concentration,
surfactant
concentration
Screw speed,
barrel
temperature,
zone of liquid
addition
Particle size,
polydispersibility
index, zeta
potential,
entrapment
efciency
Patil etal.
(2015)
8. Nanoparticles Homogenization Paclitaxel Box–Behnken
design
Surfactant
concentration
in aqueous
phase (%)
Homogenization
rate
Average particle
size, zeta
potential,
encapsulation
efciency
Yerlikaya
etal.
(2013)
9. Solid lipid
nanoparticle
(SLN)
Homogenization Rivastigmine Factorial
design
Drug/lipid
ratio,
surfactant
concentration
Homogenization
time
Size, PDI,
entrapment
efciency
Shah etal.
(2015)
10. Nanoparticles O/W
emulsication–
solvent
evaporation
Cyclosporine
A (CyA)
Plackett–
Burman (PB)
design
Type of
solvent
organic-to-
aqueous phase
ratio, drug
concentration,
polymer
concentration,
surfactant
concentration,
O/W ratio
Stirring rate Encapsulation
efciency, particle
size, zeta
potential, burst
release, and
dissolution
efciency
Rahman
etal.
(2010)
3 Optimization Techniques fortheDevelopment ofPharmaceutical Products
76
attributes such as during uid bed granulation, roller compaction, lm coating,
spray drying, hot-melt extrusion, and homogenization. These aspects are summa-
rized in Table 1.2 according to Zhang and Mao (2017).
The use of QbD in nanopharmaceuticals has grown rapidly since 2007, with
numerous papers published in peer-reviewed journals discussing its applications for
process optimization and nanoparticle product development. Javed et al. (2019)
reported that QbD has been used to develop various nanoparticles for drug delivery,
with most of the applications originating from Asia (particularly India, 48%) and
the United States (33%). Europe accounted for only 11% of the applications, while
Africa accounted for 9%. These QbD applications have focused on formulated-
related issues (62%), formulation and manufacturing (29%), and identifying critical
factors for the development process (9%). These ndings are based on studies con-
ducted by Bastogne (2017) and Beg etal. (2018). The applications of QbD in the
manufacturing of different dosage forms by using different pharmaceutical unit
operations are discussed in Table3.1.
3.10.2 Food Industry
QbD and FbD are also applied in the food industry to ensure the quality and safety
of food products. By designing the process based on scientic principles and risk
assessment, companies can minimize the variability in food quality and increase
process efciency. QbD and FbD also help to optimize food formulation; enhance
food avor, texture, and nutritional value; and improve food safety.
3.10.3 Chemical Industry
QbD and FbD are used in the chemical industry to optimize the production of chem-
icals and materials. By designing the process based on scientic principles and risk
assessment, companies can minimize the variability in product quality and increase
process efciency. QbD and FbD also help to optimize product formulation, enhance
product performance, and improve the environmental prole of the product.
3.10.4 Biotechnology Industry
QbD and FbD are applied in the biotechnology industry to optimize the production
of biologics and biosimilars. By designing the process based on scientic principles
and risk assessment, companies can minimize the variability in product quality and
increase process efciency. QbD and FbD also help to optimize product formula-
tion, enhance product performance, and improve the safety prole of the product. It
is important to note that implementing QbD in biotechnological product develop-
ment can help to minimize failures in the nal product. The use of different tools for
protein characterization is also vital in this approach. Obtaining information related
S. Rathee et al.