Chemometric methods application in pharmaceutical products and processes analysis and control - Computer-Aided Applications in Pharmaceutical Technology - page 78

time), on response variables (blend fl ow, compressibility, and
tablet dissolution). Multivariate analysis of all variables from the
DoE batches was conducted to study relationships between the
variables and to evaluate the impact of material attributes/process
parameters on manufacturability and fi nal product critical quality
attributes (CQAs). Quality risk assessment, historical data analysis
of previous development batches, and several screening DoE
analyses have identifi ed that high shear wet granulation is the
most critical unit operation that impacts downstream intermediate
and fi nal product quality attributes. Three critical process
parameters were selected as design factors: granulation water
amount and wet massing time identifi ed from the granulation
process, and lubrication time from magnesium stearate lubrication
operation. The ultimate goal was to optimize these three critical
process parameters to achieve desired fl owability, compressibility,
and dissolution profi les. Apart from the variables studied in the
DoE effect analysis, many other variables across all unit operations,
including both process parameters and quality attributes, were
studied using PCA and PLS methods (70 variables in total, some of
them are particle size/distribution, bulk/tapped density, LOD,
hardness, different time points of dissolution test, etc.).
On the left side of Figure 4.2, score plots for batch analysis are
represented. The scores t1, t2, and t3 are the orthogonal LVs, or
principal components summarizing the X-variables. The fi rst
component explains the largest variation of the X space, 33.4%,
followed by t2 explaining 15.7% and t3 13.5%. Observations close
to each other are more similar, while those further away are more
dissimilar. The ellipse defi nes the 95% confi dence interval,
therefore no outliers are present in this example. Batches colored
in red are DoE center points and are indicative of good reproducibility.
Batch 12a appears more different from the rest of the batches, as
it is located further away from them. In order to identify the reason
for the batch 12a variability, a score contribution plot is presented
(right side of Figure 4.2), since it displays variables contributing to
variability. It is clear that the most differing variable for batch 12a

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