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useful information as possible is obtained by performing a small number
of trials. Instead of using the trial-and-error approach, where independent
variables (ingredients of a formulation, processing parameters, etc.) are
varied by chance, pharmaceutical scientists are nowadays urged to apply
experimental design in their product/process development to demonstrate
their knowledge of it. This knowledge of product/process is defi ned by
so-called
design space
, a multidimensional combination and interaction
of input variables (e.g. material attributes) and process parameters, which
have been demonstrated to provide assurance of quality (ICH Q8R2,
2009). Defi nition of a design space is done by applying concepts of
experimental design (design of experiments, DoE). DoE was fi rst used as
a tool mainly by academic researchers, whereas development of
pharmaceuticals in the industry was done mostly on an empirical basis or
by relying on previous experience. With the introduction of user-friendly
software tools, and encouraged by regulatory guidelines and advices,
DoE is surely fi nding its way to becoming an everyday tool in the
pharmaceutical industry.
Proper organization of experiments is a foundation of every thoughtful
research. The number of experiments conducted is not always a
direct measurement of the amount of information gained about the
problem being studied. If multiple independent factors are being varied
in an unorganized manner, then it is impossible to determine
what affected the outcome. If factors are varied in an unreasonable
range, optimization strategies can become diffi cult to manage. These,
among others, are some of problems that can easily be anticipated
by DoE.
There are many purposes of DoE application: screening studies to
determine the most infl uential factors affecting the product/process being
studied; full or fractional designs to quantify factorial effects; in-depth
response surface studies particularly useful for optimization; mixture
designs, etc. The selection of a particular experimental design depends
upon the nature of the problem being studied and the desired level of
information to be gained.
It is proposed that, in the case of pharmaceutical product development,
screening designs are used at the beginning of the experimental
procedure for investigation of a large numbers of factors, with the aim
of revealing the most important ones. Optimization is used for fi nding
a factor combination corresponding to an optimal response profi le,
and robustness testing is used as a last test before the release of a
product, to ensure that it stays within the specifi cations (Eriksson
et al., 1998).
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