1. 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

2. 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

3. Instrumentation 3.5 quarts stainless-steel V-blender 2 Near-infrared sensors (SpectralProbe, ThermoFisher) Real-time data collection and blend homogeneity monitoring

4. 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%)

5. 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

6. 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.

7. Workflow Route r Decision

8. Calibration performances Calibration performances (Instr. 1 and 2) API MCC HPC Starch RMSEC (%) = 1.40 0.95 1.08 0.70 RMSECnom (%) = 1.66 1.09 1.25 0.75 API MCC HPC Starch RMSEC (%) = 1.59 1.13 0. 78 1.13 RMSECnom (%) = 1.91 1.32 0. 87 1.27

9. Sensor 1 output

10. Pooled RMSNV Combination of RMSNVs from both sensors for decision making Blend end-point

11. 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

12. 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