Statistical experimental design has been employed for optimization of complex multivariate processes central to pharmaceutical product development for several decades.
Statistical
Experimental Design
INTRODUCTION
Statistical
experimental design has been employed for optimization of complex multivariate
processes central to pharmaceutical product development for several decades
(Box et al., 1978; Cochran and Cox, 1957). The intent of experimental design is
to rapidly and efficiently study many parameters to identify the combination of
conditions that most efficiently and reproducibly generates a product with
desired properties. These properties are most frequently uniform and
reproducible drug delivery from a stable dosage form, but can extend to more
subtle phenomena such as control of particular physicochemical proper-ties or
even cost and time efficiency of the process.
The
general evolution of experimental design techniques begins with conventional
randomized or Latin square approaches, factorial design and fractional
factorial design, which yields significant and useful information with regard
to the limits of input variables with respect to particular output prop-erties.
The results of these studies can be employed to identify regions of
combinations of input parameters, so-called design space, that give rise to
desirable output properties. Assessment by central composite experimental
design yields more information regarding the curvature of design space,
ulti-mately leading to complete response surface maps, which allow
interpolation of changes in output as a continuous dependent function of the
independent variable inputs. The following sections will describe each of these
approaches in more detail. All lead to the concept of process design space,
which leads to normal operating range from which product specifications can be
developed.
Once
input variables have been identified, according to the approach described in
chapter 17, experiments can be designed that evaluate their con-tribution to
the critical quality attributes of the product under development. Therefore, it
is important to identify the output parameters and the techniques (see chap.
19) that will be employed to measure these properties to allow their response
to the input variables to be characterized. Having considered these practical
elements of the experimental design, the statistical approach has then to be
selected for its relevance to the process under consideration.
TH 2019 - 2024 pharmacy180.com; Developed by Therithal info.