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

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

Instrumentation

• 3.5 quarts stainless-steel V-blender• 2 Near-infrared sensors (SpectralProbe,

ThermoFisher)• Real-time data collection

and blend homogeneity monitoring

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%)

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

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

Workflow

Router

Decision

Calibration performances

• Calibration performances (Instr. 1 and 2)

0 0.2 0.4 0.6 0.8 10

0.1

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1

Reference (%, w/w)

Pre

dict

ed (%

, w/w

)

APIMCCHPCStarchdata5

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

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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

1600 1800 2000 2200 24000.1

0.2

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0.9Raw Spectra

1600 1800 2000 2200 2400-2.5

-2

-1.5

-1

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1.5

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

0.2

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0.8

Starch

0 50 100 150 200 2500

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0.1

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GOOD

RMSNV Trend

Sensor 1 output

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

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

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