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HSWG-Brivanib Alaninate: DFF Sensor Identifies Optimal
Formulation, Determines Granulation End-Point, and
Enables Scale Up
Valery Sheverev and Vadim Stepaniuk, Lenterra, Inc.
Ajit Narang, Genentech Inc.
Brivanib Alaninate Granulation
To delineate the pharmaceutical relevance of
measuring wet mass consistency, experiments with
the DFF sensor were carried out in a wet granulation
formulation of a model drug, brivanib alaninate.
Batches were prepared at high drug load (62% w/w
of brivanib alaninate) using 5% w/w HPC as a binder,
4% w/w croscarmellose sodium as a disintegrant,
29% w/w microcrystalline cellulose as a filler, and
water as the granulating fluid. Process design space
for this formulation was defined (Badawy et al., 2012)
at 40% v/v fill of the high shear granulator with the
variables of impeller tip speed (3.6 – 6.0 m/s, target
4.8 m/s), and amount of water used for granulation
(55% w/w to 61% w/w, target 58% w/w of granule
composition)., and wet massing time (10 – 50 s,
target 30 s).
To investigate the effect of substantial changes in
process parameters outside the design space, two
batches of brivanib alaninate granulation were
manufactured with water concentration at 48% w/w
and 67% w/w of the granulation. All the other
process parameters were at the center point of the
design space. Two DFF sensors were used
simultaneously, one installed through the granulator
lid and the other one – from the side port as shown
in the figure. The formulation in the center point of
design space (58% water) was scaled-up from a 10-
liter GEA PharmaConnect® granulator to a 60-liter
one, maintaining volume fill (40% v/v) and blade tip
speed (4.8 m/s) at target parameters identified at the
center of the design space.
Four batches were manufactured to investigate:
1. whether the DFF sensor signal of wet mass
consistency is able to distinguish granules
produced with changes in process parameters;
2. whether the DFF sensor signal can indicate an
optimal end-point of granulation;
3. whether the DFF sensor signal is able to define a
scale independent parameter in the scale-up of
the granulation from the 10-liter to the 60-liter
granulator.
Wet Mass Consistency Plots
Wet mass consistency results obtained from DFF
sensors are illustrated below with time evolution
plotss of force pulse magnitude (FPM). In these
plots, the side sensor data is overlaid with the top
sensor data in lighter color. The duration of water
addition is shown in shaded blue area and accepted
end of wet granulation at the center point of design
space, 30 seconds after the end of water addition, is
shown with a vertical line.
The similarity in pattern and profile of plots indicate
stability of change in wet mass consistency over time
Granulator volume
48% w/w water
58% w/w water
(design space)
67% w/w water
10 L Batch 1 Batch 2 Batch 3
60 L Batch 4
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as measured either by the side or top sensor, even
though the amplitude of peaks was higher for the
side sensor – attributable to its proximity to the
rotating blades. Expectedly, the signal magnitude
increased with increasing water concentration at the
10-liter granulator scale. For all four batches, the DFF
signal response also showed a pattern of increase in
wet mass consistency during the phase of water
addition, and a decline during extended wet
massing. Such evolution is consistent with the
modern model of granulation process (Iveson et. al.,
2001).
The FPM evolution for the center point batches
(water 58%) manufactured in a 10-liter and 60-liter
granulator show similar shape profile between
themselves, but noticeably different from the signals
obtained for batches with 48% and 67% of water.
This common shape of 58% water signal features
two salient points. The first one occurs at 0-30
seconds after end of water addition, exactly at the
central point of the design space, after which the
FPM signal decreases for about a minute, just to pick
up afterwards to reach an absolute maximum at
about 3 minutes of granulation for the 10-liter and 6
minutes for the 60-liter granulator. The other two
batches, for 48% and 67% water, demonstrate no
delayed maximum. The fact that this delayed
maximum feature appears only for the central point
of the design space is common for both 10-liter and
60-liter granulators and that it is independent of the
sensor placement, indicates that DFF sensor could
provide a convenient way of identifying an optimal
formulation by observing features on the FPM
evolution over extended granulation time.
0
0.25
0.5
0.75
1
1.25
1.5
1.75
2
-6 -3 0 3 6 9 12 15 18 21 24 27 30 33
FPM
, N
Time from start of water addiiton, min
10L: 48% water
side sensor
top sensorW
ater
ad
ded
Accepted granulation end-point
0
0.25
0.5
0.75
1
1.25
1.5
1.75
2
-6 -3 0 3 6 9 12 15 18 21 24 27 30 33
FPM
, N
Time from start of water addition, min
10 L: 58% water
side sensor
top sensor
Accepted granulation end-point
Wat
er a
dd
ed
0
0.25
0.5
0.75
1
1.25
1.5
1.75
2
-6 -3 0 3 6 9 12 15 18 21 24 27 30 33
FPM
, N
Time from start of water addition, min
10L: 67% water
side sensor
top sensor
Accepted granulation end-pointW
ater
ad
ded
0
0.25
0.5
0.75
1
1.25
1.5
1.75
2
-6 -3 0 3 6 9 12 15 18 21 24 27 30 33
FPM
. N
Time from start of water addition, min
60L: water 58%
side sensor
top sensor
Wat
er a
dd
ed
Accepted granulation end-point
Recommended granulation end-point
Recommended granulation end-point
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Granulation end-point
The first salient feature of the FPM vs. time
dependence for brivanib alaninate formulation at
58% water occurs approximately at the end of water
addition. This point manifests itself as a local
maximum for 10L granulator, and as an inflection
point for 60L granulator. Apparently, this point
corresponds to transition from the first stage of the
granulation process (wetting and nucleation) to the
consolidation and coalescence stage (Iveson et. al.,
2001).
Another salient feature of the FPM vs. time plots for
the 58% water formulations appears at
approximately 3 minutes for the 10-liter and 6
minutes for the 60-liter granulator. The
dependencies demonstrate absolute maxima of FPM
at these instants.
The HSWG processing is typically stopped several
seconds to a minute after the end of water addition
(indication on the plots as “Accepted granulation
end-points”). This conventional practice ensures
complete dispersion of water within the powder bed
mass and it is generally understood that optimum
granule quality is obtained upon wet massing of the
powder mass after completion of water addition. In
the case of brivanib alaninate granulation, the wet
massing time of 30 seconds was selected as the
center point and 10 to 50 seconds as the range in
the process DoE study. However, the optimum
duration of wet massing time remains a matter of
tradition among different industries and
practitioners, and can vary from a few seconds to
several minutes. In this context, determination of wet
mass consistency peak several minutes after the end
of water addition by the DFF sensor enables
identification of a potential second inflection point
after which the granule quality may deteriorate
(Narang et al, 2015, Iveson et. al, 2001).
Scale independent measure
Similarity of FPM signal for the 58 % water batches
manufactured in a 10-liter and a 60-liter granulator,
respectively, include differences within time-domain
and peak-amplitude. The peak amplitude differences
are due to different wet-mass pressure between the
two granulators at the point of measurement.
The time domain differences is due to the difference
in frequency of blade rotation (RPM) in two
granulators. The first two requirements of the
granulation scale up were maintaining volume fill
(40% v/v) and blade tip speed (4.8 m/s). Maintaining
blade tip speed leads to a smaller shaft rotation
speed in a larger granulator. Therefore, the
granulation process is inherently slower in a larger
granulator.
After measuring FPM evolution in both granulators,
one can find a coefficient that would allow for
identifying the granulation end point in a larger
granulator if such a point in time is known for a
smaller granulator.
0
0.2
0.4
0.6
0.8
1
-6 -3 0 3 6 9 12 15 18 21 24 27 30 33
FPM
(m
ovi
ng
avar
age
30
0),
N
Time from start of water addition, min
FPM vs. time
side sensor, 10L: water 58%top sensor, 10L: water 58%side
sensor, 60L: water 58%top sensor, 60L: water 58%
Wat
er a
dd
ed
Accepted granulation end-point
0
0.2
0.4
0.6
0.8
1
-5000 0 5000 10000 15000 20000 25000
FPM
(m
ovi
ng
avar
age
30
0),
N
Blade count (f⸱τ) from end of water addition
FPM vs. blade number
slide sensor, 10L: water 58%top sensor, 10L: water 58%slide
sensor, 60L: water 58%top sensor, 60L: water 58%
3000 blades
Recommended granulation end-points
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Assuming that the granulation starts at the end of
water addition (at about 3 min in the FPM vs. Time
plots above) we can find the granulation time to the
granule deterioration point that is characterized by a
delayed maximum on the plots. In other words, we
find time delay (τ) between the first and second
inflection point, for each granulator separately.
Granulator
Volume
DFF
sensor
position
Time
delay, τ
sec
Average
τ, sec
Ratio
τ60/ τ10
10L top 187
190
1.8 side 193
60L top 342
345 side 348
Therefore, in the case of the Brivanib formulation,
the granulation end point in a 60L granulator should
be selected as the granulation time in a 10L
granulator multiplied by 1.8, as counted from the
end of water addition.
It is interesting to notice that the blade frequency
ratio in both granulators is approximately same as
the ratio τ60/ τ10. Introducing parameter f⸱τ which is
simply number of blades passing the sensor, one can
plot FPM vs. f⸱τ (see above). In this scale the delayed
maximum appears for both granulator sizes, and for
both top and side sensors at approximately 3,000
blade count, indicating that the time-domain
differences in sensor response between the two
scales was attributed to differences in number of
impeller rotations per unit time as the impeller tip
speed was kept constant.
This data highlights the fact that keeping the same
impeller tip speed is a viable scale-up method for
the brivanib alaninate granulation.
There could be scale-up methods that are different
from that proposed above, since the FPM vs. time
curves may vary for different formulations
depending upon the API characteristics, drug
loading, and the excipients used. But the example of
brivanib allaniate analyzed here indicates that use of
DFF sensor can help design an appropriate scale up
protocol.
Discussion and Conclusion
Differences in the rate of granule densification
across different scales of HSWG presents a
significant challenge in the scale-up of wet
granulation process for formulations of new drug
products. This challenge manifests in the unknown
adjustment that may be needed in one or more
process parameter (as the granulation is scaled-up)
to achieve similar granule attributes at the end of
granulation (end-point). Measurement of wet mass
consistency using the DFF sensor appears to be a
scale-independent attribute.
For a brivanib alaninate granulation, the DFF sensor
response is able to distinguish between a process
variable (% w/w water used for granulation), which
does not correlate with changes in granule particle
size distribution (Badawy et al., 2016), but does
correlate with granule densification. Granule
densification is a critical material attribute (CMA)
that correlates with tablet dissolution as a critical
quality attribute (CQA) of the drug product (Badawy
et al., 2012). These data demonstrate the utility of
DFF sensor as a real-time in-line PAT probe that
responds to a property of the wet mass, which is
called wet mass consistency that provides a metric of
granulation progress different from the size
distribution of the granules and correlates with
granule densification (Narang et al, 2015). Therefore,
DFF sensor can be used as a tool to study the effect
of formulation and process parameters during
formulation and process development, and to
monitor reproducible manufacture of granulations.
Time delay to the peak of the DFF signal response
correlated well with the expected granulation
processes at different water levels and was
consistent for the side-sensor as well as the top-
sensor. Wet granulation mechanistically involves
several simultaneous processes that proceed at
different rates during different stages of granulation.
Three major processes involved in wet granulation
include wetting and nucleation, consolidation and
granule growth, and attrition and breakage (Iveson
et. al., 2001). For example, at the stop of water
addition, if the water level is lower than optimum
(48% w/w), the initial granule growth processes of
nucleation and aggregation would be predominant.
Similarly, if the water level is higher than optimum
(67% w/w), latter stage processes of attrition and
breakage would be predominant. In both these
cases, no further granule densification may be
expected at the end of water addition. On the other
hand, at optimum water concentration (58% w/w),
various granulation mechanisms will be in an
equilibrium and the granule densification would be
sustained for a while. In the current set of
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experiments, this behavior is evident in the higher
delay time to peak at 58% w/w water level,
compared to 48% w/w or 67% w/w water level.
Interestingly, the time to peak was delayed even
further when the formulation was scaled-up to the
60-liter granulator at the center point of water
concentration (58% w/w). This delay in the time to
peak DFF signal was consistent with reduced number
of impeller rotations for a given period of time, when
the process is scaled up with constant impeller tip
speed. The greater time-to-peak lag at the larger
scale indicated slower rate of granule densification at
the larger scale. These findings were consistent with
the observed changes in granule porosity as a
function of time at 10-liter versus 60-liter scale and
the exponential fit to this porosity data (Badawy et
al., 2012). These correlations indicated that the value
of the DFF sensor time-to-peak response, τ, as a
parameter of interest that can inform the rate of
granule densification and robustness of the HSWG
process. Although the exact state of granulation and
the responsible factor for the peak in the DFF
response is not known, it is likely to be the
culmination of dominance of granulation growth
and consolidation mechanisms (over attrition and
breakage) in the powder state. Thus, the peak of DFF
sensor may serve as an indicator of desirable ranges
of wet massing times that may be acceptable as an
end point of the granulation. The time to peak can
be utilized to derive parameters that can assist scale
up of granulations.
References
Badawy, S.I., Narang, A.S., Lamarche, K.,
Subramanian, G., Varia, S.A., Lin, J., Stevens, T., Shah,
P.A., 2016. Integrated application of quality-by-
design principles to drug product development: a
case study of brivanib alaninate film–coated tablets.
Journal of Pharmaceutical Sciences, 105 (1), 168-181
Badawy, S.I., Narang, A.S., Lamarche, K.,
Subramanian, G., Varia, S.A., 2012. Mechanistic basis
for the effects of process parameters on quality
attributes in high shear wet granulation.
International journal of pharmaceutics, 439, 324-333.
Iveson, S.M., Litster, J.D., Hapgood, K., Ennis, B.J.,
2001. Nucleation, growth and breakage phenomena
in agitated wet granulation processes: a review.
Powder Technol. 117, 3-39.
Narang, A.S., Sheverev, V.A., Stepaniuk, V., Badawy,
S., Stevens, T., Macias, K., Wolf, A., Pandey, P., Bindra,
D., Varia, S., 2015. Real-Time Assessment of Granule
Densification in High Shear Wet Granulation and
Application to Scale-up of a Placebo and a Brivanib
Alaninate Formulation. Journal of pharmaceutical
sciences 104, 1019-1034
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