LIU 937 2010 7 Granulation BMS Data

8/3/2019 LIU 937 2010 7 Granulation BMS Data

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Pharmaceutical Research Institute

GranulationGranulation -- An OverviewAn Overview

I - Liquid AbsorptionII - Liquid bridge formationIII - Interparticular void space fill-upS4 - Better wetted granules (denser)IV - Part of the mass is liquid saturatedV - System changes from Liquid in solid to Solid in Liquid


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How does Acoustic Emission work ?How does Acoustic Emission work ?Amplified AC

signal

AC to RMS

conversion

DC signalenvelope


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Comparison of Impeller TorqueComparison of Impeller Torquedata and Mean Acoustic Power datadata and Mean Acoustic Power data

200

250

300

350

400

450

500

550

600

AE

Power/Tor

que

AE Data

Torque Data


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Acoustic MonitoringAcoustic Monitoring650 kHz Acoustic Emission sensors were applied to a650 kHz Acoustic Emission sensors were applied to a

MiMi--Pro High Shear Granulator (900ml bowl)Pro High Shear Granulator (900ml bowl)

Acoustic data was sampled at 100Hz from 128 pointsAcoustic data was sampled at 100Hz from 128 points(64 per sensor)(64 per sensor)

Granulation conditions:Granulation conditions:

!! 120g Batch size (Placebo formulation)120g Batch size (Placebo formulation)

!! 800rpm impeller speed800rpm impeller speed

!! 1000rpm chopper speed1000rpm chopper speed

!! 6ml/min liquid dose rate (water)6ml/min liquid dose rate (water)


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Comparison of Impeller TorqueComparison of Impeller Torquedata and Mean Acoustic Power datadata and Mean Acoustic Power dataMi-Pro Placebo Granulation

-1.50E+00

-1.00E+00

-5.00E-01

0.00E+00

5.00E-01

1.00E+00

Time

Meanacousticpower

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

ImpellerTorque Dry Blend

Water start

12-32ml

32-38ml

38-48ml

Torque


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Test of Prediction Model for aTest of Prediction Model for a

Placebo GranulationPlacebo Granulation

19 data points were identified as indicators of the19 data points were identified as indicators of theendpoint (23 seconds of acoustic data)endpoint (23 seconds of acoustic data)

EndEnd--point data was then modelled using SIMCApoint data was then modelled using SIMCA

software to create the prediction modelsoftware to create the prediction model

A batch was then manufactured using theA batch was then manufactured using theprediction modelprediction model

A class boundary of 3 standard deviations from theA class boundary of 3 standard deviations from themodel endmodel end--point was selectedpoint was selected


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Results for a placebo granulation usingResults for a placebo granulation usingthe prediction modelthe prediction model

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

Time

Torque(m

Nm

)

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

SD'dtoendpoint

Impeller Torque

Liquid Volume

Prediction

Class Boundary (SD)


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Testing of Prediction modelTesting of Prediction model

To further test the model,To further test the model,batches of placebo granulebatches of placebo granule

were manufactured usingwere manufactured usingvarying dose rates, impellervarying dose rates, impellerand chopper speeds, andand chopper speeds, andbatch sizesbatch sizes

In each case the granulationIn each case the granulationwas halted at the predictedwas halted at the predictedendend--point (2.5 standardpoint (2.5 standarddeviation class boundarydeviation class boundary

applied)applied)

Wet and dried granule wereWet and dried granule werethen analysed for a range ofthen analysed for a range of

solid state characteristicssolid state characteristics(density, particle size etc)(density, particle size etc)

5 3 800 120 10006 9 800 120 1000

7 6 800 120 1000

8 6 1000 120 1000

9 6 600 120 100010 6 800 120 1000

11 6 800 120 1000

12 6 800 100 1000

13 6 800 140 100014 6 800 120 500

15 6 800 120 1500

Batch No.

Impeller

speed

(rpm)

Dose

Rate

(ml/min)

Batch

Size (g)

Chopper

speed

(rpm)


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6ml/min; 800rpm6ml/min; 800rpm

Mi-Pro 900ml placebo granulation monitored using acoustic emission in prediction mode stopping at the predicted end-

point (6ml/min dose rate; 795rpm)

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

450.0

500.0

10:04:48 10:06:14 10:07:41 10:09:07 10:10:34 10:12:00 10:13:26 10:14:53

Time

ImpellerTorque

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

SDstoEnd

-point/Dosedvolume

(ml)

Impeller Torque

Dose Volume (ml)

Prediction (SD)

Class Boundary


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6ml/min; 600rpm6ml/min; 600rpm

Mi-Pro 900ml placebo granulation monitored using acoustic emission in prediction mode stopping at the predicted end-

point (6ml/min dose rate; 595rpm)

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

450.0

500.0

10:06:14 10:07:41 10:09:07 10:10:34 10:12:00 10:13:26 10:14:53

Time

ImpellerTorque

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

SDstoEnd

-point/Dosedvolume

(ml)

Impeller Torque

Dose Volume (ml)

Prediction (SD)

Class Boundary


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Granulation ResultsGranulation Results

The system was able to identify endThe system was able to identify end--points (acousticpoints (acoustic

data passes the class boundary) for each of thedata passes the class boundary) for each of thebatches at 800rpm, despite varying dose rates,batches at 800rpm, despite varying dose rates,chopper speeds and batch sizeschopper speeds and batch sizes

However, the system did not predict an endHowever, the system did not predict an end--point forpoint forthe batches manufactured at 600 and 1000rpmthe batches manufactured at 600 and 1000rpm


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Physical Characterisation ResultsPhysical Characterisation Results

for the Granulefor the Granule

Bulk

Density

Mean

Rheology

(mJ)

SpecificSurface

Area

(m/g)

Bulk

Density

Tapped

Bulk

Density

Compressibility

(%)

5 3 800 120 1000 09:54 29.9 0.39 76.3 91.9 84.1 0.42 0.47 0.62 24.19

6 9 800 120 1000 04:11 37.6 0.46 87.3 100 93.65 0.47 0.48 0.6 207 6 800 120 1000 05:48 34.8 0.4 93.5 80.4 86.95 0.56 0.45 0.6 25

8 6 1000 120 1000 05:10 37.4 0.44 111 92 101.5 0.43 0.5 0.62 19.35

9 6 600 120 1000 05:01 30.4 0.36 84.5 74.5 79.5 0.54 0.35 0.51 31.37

10 6 800 120 1000 05:26 33.1 0.39 84.1 95.8 89.95 0.53 0.4 0.57 29.8211 6 800 120 1000 05:50 35.4 0.4 81.3 83.6 82.45 0.42 0.43 0.56 23.21

12 6 800 100 1000 04:56 30.1 0.35 77.4 101 89.2 - 0.42 0.55 23.64

13 6 800 140 1000 06:08 37.3 0.39 99.6 86.8 93.2 - 0.43 0.57 24.56

14 6 800 120 500 04:13 25.6 0.31 74 92.5 83.25 - 0.32 0.45 28.89

15 6 800 120 1500 04:08 25.2 0.35 85.1 65.3 75.2 - 0.33 0.47 29.7916 6 800 120 500 05:47 34.8 0.39 77.6 71.6 74.6 - 0.44 0.58 24.14

17 6 800 120 1500 05:14 31.8 0.39 77.7 79.3 78.5 - 0.44 0.55 20

Rheology (mJ)

Wet Mass testing

Batch No.

Water

content

(ml)

Impeller

speed

(rpm)

Dose

Rate

(ml/min)

Time to

End-point

(mins)

Batch

Size (g)

Chopper

speed

(rpm)

Dry Mass Testing


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Physical Characterisation ResultsPhysical Characterisation Results

for the Granulefor the Granule

1000rpm

Impeller

speed

600rpm

Impeller speed


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Discussion of ResultsDiscussion of Results

Main physical differences between the granuleMain physical differences between the granulebatches manufactured using varied impeller speedsbatches manufactured using varied impeller speeds

are:are:

Particle size distributionParticle size distribution

RheologyRheology

Bulk density (Dried granule)Bulk density (Dried granule)

Factors such as particle size (and particle velocity),Factors such as particle size (and particle velocity),will have an impact on the acoustic frequencies andwill have an impact on the acoustic frequencies andso affect the models predictive capabilityso affect the models predictive capability


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ConclusionsConclusions

The prediction model has been demonstrated to beThe prediction model has been demonstrated to be

robust in that it is able to identify the process endrobust in that it is able to identify the process end--point using differing dose rates and batch sizespoint using differing dose rates and batch sizes

The model has been able to provide granule withThe model has been able to provide granule with

the same physical characteristics despite variedthe same physical characteristics despite varieddose rates and batch sizesdose rates and batch sizes

The model was affected by a change in excipientThe model was affected by a change in excipient

batch, highlighting the importance of control ofbatch, highlighting the importance of control ofphysical properties for this technique.physical properties for this technique.

Acoustic emission could be a valuable addition toAcoustic emission could be a valuable addition to

the PAT toolbox for the monitoring of wetthe PAT toolbox for the monitoring of wetgranulationsgranulations

LIU 937 2010 7 Granulation BMS Data

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