the whole, Box 18.2 enlists various vital technical terms usually employed during

FbD and their corresponding explanations.

Box 18.2 A Holistic Overview of Key Terminology often Employed

During Formulation by Design (FbD) of Various Drug Delivery Products

Term

Precise explanation

Quality target product

profile (QTPP)

Ideal quality characteristics to achieve required levels of

efficacy and safety

Critical quality

attributes (CQAs)

Physicochemical or biological parameters of a drug product,

ranging within apt limits, to ensure apt level of quality

Factors

Independent variables, notably influencing the product

characteristics or process output. These can be material

attributes or process parameters

Coding

Transformation of a variable into a non-dimensional coded

form

Levels

Values assigned to factors usually coded as +1, 0 and
1

Critical material

attributes (CMAs)

Physicochemical or biological characteristics of drug

significantly impacting the quality of drug products

Critical process

parameters (CPPs)

Influential independent process parameters which need to be

monitored to ensure the desired quality

Interaction

Lack of additive nature of factors on their simultaneous validation

Synergism

An overall positive change owing to factor interaction(s)

Antagonism

An overall negative change owing to factor interaction(s)

Design of Experiments

(DoE)

Systematic execution of a planned stratagem to establish

factor-response relationship(s)

Experimental design

Systematic and statistical strategy for designing the

experimental studies to maximize information to

experimentation ratio

Design matrix

Strategic layout of experimental runs in a matrix form, planned

as per a particular experimental design

Experimental runs

Experimental studies conducted as per an experimental design

Quality risk

management (QRM)

A systematic process for identification, assessment and control

of various risks to the drug product quality

Risk assessment

Process to identify and mitigate risks, find varied root causes of

process failure and prevent problems to improve product

quality and reliability

Response surface plot

3-D graphical representation of a response plotted between two

independent variables and one response variable

Contour plot

Geometric 2-D illustration of a response by plotting one

independent variable against another, holding the values of

response and other variables as constant

Explorable space

Possible dimensional space, defined by different variables for

various factors being investigated

Knowledge space

Scientific elements to be explored based upon previous

knowledge of product attributes and/or process parameters

Design space

Multidimensional integration of varied input variables and product/

process parameters, demonstrated to provide quality assurance

Control space

Part of design space selected for detailed investigations

Control strategy

Comprehensive plan to ensure the final product meets

requirements

(continued)

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B. Singh et al.