Risk-Based Development for Quality by Design

1. Risk-Based Development for Quality by Design Ken Morris Purdue University Department of Industrial and Physical Pharmacy FDA SAB Manufacturing Sub-Committee September, 17th, 2003

2. Pharmaceutical cGMPs for the 21st Century: A Risk-Based ApproachA science and risk-based approach to product quality regulation incorporating an integrated quality systems approach • Risk-based orientation • Science-based policies and standards • Integrated quality systems orientation • International cooperation • Strong Public Health Protection

3. Pharmaceutical cGMPs for the 21st Century: A Risk-Based ApproachWhat’s New? • Good science isn’t new, we all do it now • Some technologies, techniques, and models are • Computers • Sensors • Chemometrics • Phenomenological and Fundemental Models • The mutual FDA-Industry-Academic recognition of the technical “way forward “ in application of the state of the science

4. The Issue: API, Formulation, and Process Variables and Dosage Form Performance Low Solubility - High Permeability - Acidic compound in SIF Ajaz Hussain, Arden House 2003

6. Property Purity Solubility/dissolution Partitioning Stability Solid state form/shape Hygroscopicity Initial Drug Substance Characterization Theory-method • Chemistry - HPLC • Thermodynamics, Kinetics - traditional and automated measurement • Thermo - various • Chemistry and HPLC - SS methods • Crystallography SS physics - screening, prediction control • BET - Automated systems

7. Pharmaceutical Technology Europe, 17, June 1994 “Formulations and processes are only as robust as their ability to accommodate changes in the raw materials” KRM

8. Form Screening, Selection, and Control Hilden et.al., Crystal Growth and Design, 2003, in press

9. Cefotaxim Sodium Moisture Uptake - Ulrich Griesser, Univ. of Innsbruck, Simultaneous Multi-sample instrument Ulrich Griesser, PHANTA 9/03

10. Single Crystal Structure PXRD Pattern simulated BFDH Morphology +PXRD Pattern experimental +Index Major Faces Comb. Simple Forms Morphology SPO/DIFRAC Model Average Shape

11. Summary of Estimated Average Shapes and Areas 110 = 64% 001 = 31% -201 = 5% 110 = 43% 011 = 29 % 200 = 15% 001 = 7% -201 = 6% 002 = 60% 102 = 33% 100 = 4%

12. Formulation element Dosage form selection Excipient selection Stability to processing Mechanical properties Flow Compaction Initial processing Formulation Design and API Process Development Theory-method • Medical processability • Excipient properties – interaction studies, phsico-chemical properties • PIT – • ME/MSE – • flow correlations, • heckel analysis • Process models – prototypes and PAT

13. Powder Flow Avalanche testing TSI Inc. Shear Cell Virendra M. Puri, Penn State Powder Rheology Freeman Tech.

14. A Development of the Heckel Equation P From Heckel, Trans. AIME, 221: 1961.

15. PROCESS 1 PROCESS 2 Shape and Flow

16. TREND BETWEEN MASS FLOW AND SHAPE

17. Operation Particle size reduction Charging Blending Dry granulation (RC) Wet granulation Fluid bed High shear Drying Segregation CU Hardness Coating Modeling Surface energy-size laws Triboelectric series model Cascade Model, DEM Density-Strength Various Size-Moisture-Attrition Water Environ Model Heat/Mass transfer/FAST Sinusoidal Variation Partial volume analysis Density response Geometric Growth Compensation Processing/PAT

18. Particle Size Reduction Models Rittinger’s law: The work required in crushing is proportional to the new surface created. Where: P=power required, dm/dt=feed rate to crusher, Dsb = ave diameter before crushing, DSQ=ave after crushing, Kr=Rittinger’s coef. Kick’s law: the work required for crushing a given mass of material is constant for the same reduction ratio, that is the ratio of the initial particle size to the finial particle size Kk=Kick’s coef.                              

19. Powder Charging:Qualitative Trends in a Faraday Pail-Blender System David Engers, unpublished data Purdue

20. Modeling Blending: Cascade Region Characteristic region For fine grains, the boundary between the characteristic region and the remaining powder bed is parabolic in shape Blender head space The powder bed below the boundary rotates with the mixer as a solid body.

21. 16kg Run 180kg Run Blending Scaled “Down”

22. Dry Granulation by Roller CompactionUnpublished CAMP data – A.Gupta • The strength is a linear function of the density which is monitored by NIR • Semi Empirically F=(SNIR-0.17)/0.37

23. Dry Granulation by Roller Compaction Unpublished CAMP data – A.Gupta • The particle sizes of the milled material is also manifest in the slope of the NIR signal (as predicted)

24. Monitoring and Modeling of Fluid Bed GranulationPaul Findlay, Ph.D dissertation, Purdue Univ, 2003

25. Modeling Wet Granulation Funicular Pendular Over Wetting Droplet Capillary Drying At the capillary stage, the water may interact with the surface in such a way as to change the two prominent NIR bands (1450 and 1940 nm) differently.

26. X1=110 g (=X3) X2=255 rpm NIR Treated Response NIR during granulation–wet massing and Particle sizeUnpublished CAMP data, Dr. Jukka Rantanen –

27. 180 65 63 160 Moisture Content Temperature 61 140 Critical moisture 59 57 120 55 100 53 51 80 49 60 47 40 45 DRYING : NIR -Exit Temp vs. Time for APAP Granulation Evaporative T Temperature (°C) MM55 Reading Diffusive MM55 0 5 10 15 20 25 30 Drying Time (min) K.R. Morris, S.L. Nail, G.E. Peck, S.R. Byrn, U.J. Griesser, J.G. Stowell, S.-J. Hwang, K. Park Pharm Sci Tech Today16 235–245 (1998).

28. Full Scale FastDrying Trials of an Ibuprofen Granulation Morris et.al., Drug Dev. Ind. Pharm., 26 (9):985-988 (2000)

29. Drying Excursions and DissolutionCAMP unpublished data

30. Tablet CU: Testing a Model CU for constant size portions of tablets must be larger than for the whole, so in spec using real time monitoring of “part” of the tablets means in spec for the whole tablet T. Li, et. al., in press Pharm. Res. BioMed Anal.

31. COATINGHPMC, Sulfanilamide and, Moisture Real-Time Measurements Unpublished CAMP data, P. Findlay,In prep for JPS

32. Where do we stand? • Taken individually these theories and techniques look independent • Together, however, they show a concerted effort to describe contributions to the overall process of drug development. • These principles and techniques are applicable to batch and continuous processing and may be linked by multi-variate (chemometric) methods after univariate conformation. • Ultimately this give us the ability to understand how development variables interact to influence the final product and to design in the quality.

33. The Business Case • Using existing scientific principles, monitoring and modeling capabilities one will understand more about processes and be able to detect variations quickly • The earlier you start collecting information the more you’ll know the more comfortable everyone will be • Given this level of knowledge and communication with FDA, you will be at the lowest risk (as proposed) possible for your product/process • If your studies show up variability, the sooner you know the better. There is no such thing as what you don’t know won’t hurt you in science based development. • The companies have many of the tools to lower their risk levels RIGHT NOW This will only improve with more research.