Benoit Igne, Sameer Talwar, Brian Zacour, Carl Anderson, James Drennen
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Transcript of Benoit Igne, Sameer Talwar, Brian Zacour, Carl Anderson, James Drennen
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DEVELOPMENT OF QUALITY BY DESIGN (QBD) GUIDANCE ELEMENTS ON DESIGN
SPACE SPECIFICATIONS ACROSS SCALES WITH STABILITY CONSIDERATIONS
Blending
Benoit Igne, Sameer Talwar, Brian Zacour, Carl Anderson, James Drennen
Duquesne University Center for Pharmaceutical Technology
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Objectives
• Multi-sensor blend monitoring– 2 NIR sensors on a V-blender– Global decision criterion
• Development of efficient calibration strategies– Limited sampling– Alternative calibration algorithm
• Multi-component based end-point criteria
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Instrumentation
• 3.5 quarts stainless-steel V-blender• 2 Near-infrared sensors (SpectralProbe,
ThermoFisher)• Real-time data collection
and blend homogeneity monitoring
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Formulation
• Fluid bed dried granules (72%) were blended with extra-granular excipients– MCC (11.3 %)– Starch (6.8 %)– HPC (4.5 %)– Crospovidone (2.5 %)– Poloxamer (1.25 %)– Talc (1 %)– Magnesium Stearate (0.8%)
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Modeling
• Models based on an “efficient calibration” approach using Classical Least Squares (CLS)
• 3 to 4 design points: 0, 100%, nominal(s) (target(s)) concentration(s)
• Idea:– Take advantage of pure component spectra– Limit sample handling
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Blend end point
• Root Mean Square Error to the Nominal Value
Weighted, cumulative, pooled standard deviation that takes into account the deviation of the predicted concentration of the major components of a mixture to their target concentration, over a given number of rotations. 2 2 2
1 1 1 2 2 2ˆ ˆ ˆ...
t t t
j j j j n nj njj t i j t i j t it
t i t
j t i
w Y Y w Y Y w Y YRMSNV
i w
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Workflow
Router
Decision
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Calibration performances
• Calibration performances (Instr. 1 and 2)
0 0.2 0.4 0.6 0.8 10
0.1
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Reference (%, w/w)
Pre
dict
ed (%
, w/w
)
APIMCCHPCStarchdata5
0 0.2 0.4 0.6 0.8 10
0.1
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Reference (%, w/w)
Pre
dict
ion
(%, w
/w)
APIMCCHPCStarchdata5
API MCC HPC StarchRMSEC (%) = 1.40 0.95 1.08 0.70RMSECnom (%) = 1.66 1.09 1.25 0.75
API MCC HPC StarchRMSEC (%) = 1.59 1.13 0. 78 1.13RMSECnom (%) = 1.91 1.32 0. 87 1.27
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1600 1800 2000 2200 24000.1
0.2
0.3
0.4
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0.9Raw Spectra
1600 1800 2000 2200 2400-2.5
-2
-1.5
-1
-0.5
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2Preprocessed spectra
0 20 40 60 80 100 120
0
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Gabapentin
0 20 40 60 80 100 120
0
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MCC
0 20 40 60 80 100 120
0
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HPC
0 20 40 60 80 100 120
0
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Starch
0 50 100 150 200 2500
0.02
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0.08
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0.12
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GOOD
RMSNV Trend
Sensor 1 output
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Pooled RMSNV
• Combination of RMSNVs from both sensors for decision making
0 500 1000 1500 20000
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Time (s) - 15 rpm
RM
SN
V (%
, w/w
)
Instrument 1277Instrument 1502Pooled RMSNV
(c)
Blend end-point
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Scale up considerations
• Significant challenges– Different blender shape– Different NIR sensors
• Only 1 available• Only access to predictions
• CLS modeling with efficient approach– Ready in 1 day– Powder properties comparable to those
observed at small scale
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Conclusions
• The use of multiple sensors helped understand powder behaviors and limit risks of under or over blending
• Efficient modeling techniques allowed for a simpler method implementation at scale up
• The multi-component blend end point statistic helped stop the blend consistently, even when properties of the granules were altered (different design of experiments), by relying on other components