time (OFAT) stratagem (Lewis et al. 1998; Singh et al., 2005b, 2011a, c; Aksu et al.

2015). During such OFAT studies, the first factor is affixed at a nominated value, and

the next one is scrutinized until there is no betterment observed in the response(s),

further. The use of the conventional OFAT approach has verily been a unidimen-

sional optimization plan, producing “just satisfactory” solution(s), with hardly much

scope at finding errors and their plausible corrections (Lewis et al. 1998; Singh et al.

2005b, 2011a, c; Aksu et al. 2015; Singh et al. 2017a, b).

The aforesaid OFAT approach though accomplishes the solution to a particular

challenging trait, yet attainment of its true optimum solution can never be warranted.

This could invariably be ascribed to the prevalence of interactions, i.e. positive

(synergistic) or negative (antagonistic) influence of one or more of input factors on

the responses. The presence of such variable interactions renders the usage of OFAT

methodology as unsustainable, usually fetching a solution way distant from the

desired optimum (Lewis et al. 1998; Montgomery 2001; Singh et al., 2005b,

2017a, b). The eventual product accomplished using this methodology though

may look to be acceptable, yet is usually sub-optimal. Not being systematic, this

OFAT paradigm needs expensive and extensive experimental “pains” in order to

achieve diminutive informational “gains” on the product or process getting devel-

oped (Cochran and Cox 1992; Lewis et al. 1998; Singh et al. 2005b; Aksu et al.

2015; Durakovic 2017). The OFAT approach, in a nutshell, has proved not only as

untenable on account of exorbitant investments like experimental effort, time and

cost but also owing to its incompetency to offer the real-time results by mending the

flaws, poor predictability and many a time even attainment of successful outcomes.

The erstwhile expertise, experimental know-how and experiential wisdom of the

formulation scientist have been the essential requisites while developing the drug

products for catering to the tailored requirements.

Despite incessant novelties brought forth by the pharmaceutical industry from

time to time, recurrent incidences of product recalls, rejects and failures have been

encountered,

acceptably

owing

to

their

not-up-to-the-mark

quality

and

manufacturing standards (ICH Harmonised Tripartite Guideline 2009; Singh et al.

2013; Singh 2014; Aksu et al. 2015). The conventional Quality-by-Testing (QbT)

approach has been found to involve a great deal of expenditure of time, materials and

manpower, but intermittent testing for monitoring the quality of drug delivery

products is crucial throughout their development cycle (Singh 2014; Singh et al.

2017a, b).

18.3

Formulation by Design (FbD): Vital Precepts

Of late, a holistic and systematic paradigm of pharma Quality by Design (QbD) has

been trending in drug formulation development (ICH Harmonised Tripartite Guide-

line 2009; Singh 2014; Aksu et al. 2015; Beg et al. 2019). As per ICH Q8 (R2), QbD

is a methodical stratagem to assess, comprehend and improve the quality of product

(s) and process(es) and their pertinent quality attributes (ICH Harmonised Tripartite

Guideline 2009). This QbD approach has gained phenomenal popularity among

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