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is granule particle size - this batch has signifi cantly coarser particle
size after drying and fi nal blend.
The loading plot (Figure 4.3) displays relationships among
different variables. Variables close to each other are positively
correlated, while those opposite to each other from the origin are
negatively correlated. Variables close to the origin are less
infl uential to the model, while those further away are more
infl uential. Interpretation of loading plots should be done with
care, since variability is often not fully explained by the fi rst two
principal components. The study concluded that overall quality
attributes, such as particle size, exhibit more variations than
process parameters, as they are further away from the origin of
axes - particle size and distribution span major variations in the
data set.
PLS was used to establish a relationship between 65 X-variables
(material attributes/process parameters) and 5 Y-variables (time
points of dissolution profi les). Dissolution profi les of different
batches were successfully predicted, and a score contribution plot
indicated that a slower dissolution rate is correlated to good
granule fl owability (note that correlation does not imply causation).
These results are consistent with the DoE effect analysis and
optimization. A compromise has to be reached between blend fl ow
and tablet dissolution to achieve optimal results.
Example
2
PCA was used, in addition to DoE, to better understand lactose
particle size effect on the properties of dry powder inhaled product
formulations (Guenette et al., 2009). The purpose of this
investigation was to examine the effects of different lactose size
fractions on fi ne particle dose (FPD), formulation stability, and the
ability to process and fi ll the material in the preferred device.
Figure 4.4 represents principal component plot for scores
(shown in blue and labeled blends 1-10) and loadings (shown in
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