Quality- by-design in pharmaceutical development - Computer-Aided Applications in Pharmaceutical Technology - page 12

homogeneity, to identify potential critical factors that affect blending
operation quality, and risk-rank these factors to defi ne activities for
process characterization. Results obtained were used to map a three-
dimensional knowledge space, providing parameters to defi ne a design
space and set up an appropriate control strategy.
A quantitative approach was developed to simultaneously predict
particle, powder, and compact mechanical properties of a pharmaceutical
blend, based on the properties of the raw materials (Polizzi and García-
Muñoz, 2011). A multivariate modeling method was developed to
address the challenge of predicting the properties of a powder blend,
while enabling process understanding.
An integrated PAT approach for process (co-precipitation)
characterization and design space development was reported (Wu et al.,
2011). CPPs were investigated and their effect on CQAs was analyzed
using linear models and artifi cial neural networks (ANN). Contour plots
illustrated design space via CPPs ranges.
QbD was applied in development of liposomes containing a hydrophilic
drug (Xu et al., 2011; 2012). The usage of risk assessment facilitated
formulation and process design, with the eight factors being recognized
as potentially infl uencing liposome drug encapsulation effi ciency and
particle size (CQAs). Experimental design was used to establish the
design space, resulting in a robust liposome preparation process.
QbD principles were applied to an existing industrial fl uidized bed
granulation process (Lourenço et al., 2012). PAT monitoring tools were
implemented at the industrial scale process, combined with the
multivariate data analysis of process to increase the process knowledge.
Scaled-down designed experiments were conducted at a pilot scale to
investigate the process under changes in CPPs. Finally, design space was
defi ned, linking CPPs to CQAs within which product quality is ensured
by design, and after scale-up, enabling its use at the industrial process
scale.
A Bayesian statistical methodology was applied to identify the design
space of a spray-drying process (Lebrun et al., 2012). A predictive, risk-
based approach was set up, in order to account for the uncertainty and
correlations found in the process and in the derived CQAs. Within the
identifi ed design space, validation of the optimal condition was affected.
The optimized process was shown to perform as expected, providing a
product for which the quality is built in by the design and controlled set
up of the equipment, regarding identifi ed CPPs.
The QbD approach was used in the formulation of dispersible tablets
(Charoo et al., 2012). Critical material and process parameters were

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