36 Pharmaceutical Technology LABORATORY BEST PRACTICES 2018 P h a r mTe c h . c o m
The development data suggested the vortexer
model would have a significant effect on the particle
size, which was proven to be the case. Likewise, it was
hypothesized acicular particles, which are typically
more easily fractured than irregular particles, would
be affected the greatest by the vortexer, leading to a
significant decrease across all three responses.
The prediction profiler within the commercial sta-
tistics software package was utilized to predict all three
responses. The acicular and irregular particles would
be clearly differentiated, leading to separated bimodal
distributions. The vortexer model could further di-
vide each predicted mode into bimodal distributions;
Sample Preparation
Figure 7: Predicted D[v,0.1] populations.
400000
300000
200000
100000
21 23 25 27 29 31 33 35 37 39
D[v,0.1] (μm)
Count
Figure 8: Predicted D[v,0.5] populations.
200000
150000
100000
50000
Count
7880 828486889092 9496 98 101104 107
D[v,0.5] (μm)
Figure 9: Predicted D[v,0.9] populations.
1000000
800000
600000
400000
200000
Count
200 220 240260 280300320340 360380400
D[v,0.9] (μm)
Table VIII: Effect tests, D[v,0.9].
Source Nparm DF Sum of Squares F Ratio Prob > F
Vortexer level(3,7) 1 1 83.0547 0.2189 0.6413
Vortexer duration (s)(0,60) 1 1 266.1045 0.7015 0.4051
Vortexer 1 1 916.0159 2.4147 0.1246
Morphology 1 1 496082.8719 1307.7220 <.0001
Vortexer level*Vortexer duration (s) 1 1 4073.4793 10.7381 0.0016
Vortexer level*Vortexer 1 1 2490.2719 6.5646 0.0125
Vortexer duration (s)*Vortexer 1 1 1525.2721 4.0208 0.0487
Vortexer level*Morphology 1 1 1303.6406 3.4365 0.0679
Vortexer duration (s)*Morphology 1 1 550.5871 1.4514 0.2322
Vortexer*Morphology 1 1 351.4501 0.9265 0.3390
Vortexer level*Vortexer level 1 1 1458.4419 3.8446 0.0538
