Quality by design

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Quality by design

  1. 1. Risk-Based Development for Quality by Design Ken Morris Purdue University Department of Industrial and Physical Pharmacy FDA SAB Manufacturing Sub-Committee September, 17 th , 2003
  2. 2. Pharmaceutical cGMPs for the 21st Century: A Risk-Based Approach A science and risk-based approach to product quality regulation incorporating an integrated quality systems approach <ul><li>Risk-based orientation </li></ul><ul><li>Science-based policies and standards </li></ul><ul><li>Integrated quality systems orientation </li></ul><ul><li>International cooperation </li></ul><ul><li>Strong Public Health Protection </li></ul>
  3. 3. Pharmaceutical cGMPs for the 21st Century: A Risk-Based Approach What’s New? <ul><li>Good science isn’t new, we all do it now </li></ul><ul><li>Some technologies, techniques, and models are </li></ul><ul><ul><li>Computers </li></ul></ul><ul><ul><li>Sensors </li></ul></ul><ul><ul><li>Chemometrics </li></ul></ul><ul><ul><li>Phenomenological and Fundemental Models </li></ul></ul><ul><li>The mutual FDA-Industry-Academic recognition of the technical “way forward “ in application of the state of the science </li></ul>
  4. 4. The Issue: API, Formulation, and Process Variables and Dosage Form Performance Ajaz Hussain, Arden House 2003 Low Solubility - High Permeability - Acidic compound in SIF
  5. 6. Initial Drug Substance Characterization <ul><li>Property </li></ul><ul><li>Purity </li></ul><ul><li>Solubility/dissolution </li></ul><ul><li>Partitioning </li></ul><ul><li>Stability </li></ul><ul><li>Solid state form/shape </li></ul><ul><li>Hygroscopicity </li></ul><ul><li>Theory-method </li></ul><ul><li>Chemistry - HPLC </li></ul><ul><li>Thermodynamics, Kinetics - traditional and automated measurement </li></ul><ul><li>Thermo - various </li></ul><ul><li>Chemistry and HPLC - SS methods </li></ul><ul><li>Crystallography SS physics - screening, prediction control </li></ul><ul><li>BET - Automated systems </li></ul>
  6. 7. <ul><li>“ Formulations and processes are only as robust as their ability to accommodate changes in the raw materials” KRM </li></ul>Pharmaceutical Technology Europe, 17, June 1994
  7. 8. Form Screening, Selection, and Control Hilden et.al., Crystal Growth and Design, 2003, in press
  8. 9. Cefotaxim Sodium Moisture Uptake - Ulrich Griesser, Univ. of Innsbruck, Simultaneous Multi-sample instrument Ulrich Griesser, PHANTA 9/03
  9. 10. Single Crystal Structure +PXRD Pattern experimental PXRD Pattern simulated BFDH Morphology Comb. Simple Forms Morphology +Index Major Faces SPO/DIFRAC Model Average Shape
  10. 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%
  11. 12. Formulation Design and API Process Development <ul><li>Formulation element </li></ul><ul><li>Dosage form selection </li></ul><ul><li>Excipient selection </li></ul><ul><li>Stability to processing </li></ul><ul><li>Mechanical properties </li></ul><ul><ul><li>Flow </li></ul></ul><ul><ul><li>Compaction </li></ul></ul><ul><li>Initial processing </li></ul><ul><li>Theory-method </li></ul><ul><li>Medical processability </li></ul><ul><li>Excipient properties – interaction studies, phsico-chemical properties </li></ul><ul><li>PIT – </li></ul><ul><li>ME/MSE – </li></ul><ul><ul><li>flow correlations, </li></ul></ul><ul><ul><li>heckel analysis </li></ul></ul><ul><li>Process models – prototypes and PAT </li></ul>
  12. 13. Powder Flow Avalanche testing TSI Inc. Powder Rheology Freeman Tech. Shear Cell Virendra M. Puri, Penn State
  13. 14. Development of the Heckel Equation P A
  14. 15. Shape and Flow PROCESS 1 PROCESS 2
  15. 16. TREND BETWEEN MASS FLOW AND SHAPE
  16. 17. Processing/PAT <ul><li>Operation </li></ul><ul><li>Particle size reduction </li></ul><ul><li>Charging </li></ul><ul><li>Blending </li></ul><ul><li>Dry granulation (RC) </li></ul><ul><li>Wet granulation </li></ul><ul><ul><li>Fluid bed </li></ul></ul><ul><ul><li>High shear </li></ul></ul><ul><li>Drying </li></ul><ul><li>Segregation </li></ul><ul><li>CU </li></ul><ul><li>Hardness </li></ul><ul><li>Coating </li></ul><ul><li>Modeling </li></ul><ul><li>Surface energy-size laws </li></ul><ul><li>Triboelectric series model </li></ul><ul><li>Cascade Model, DEM </li></ul><ul><li>Density-Strength </li></ul><ul><li>Various </li></ul><ul><ul><li>Size-Moisture-Attrition </li></ul></ul><ul><ul><li>Water Environ Model </li></ul></ul><ul><li>Heat/Mass transfer/FAST </li></ul><ul><li>Sinusoidal Variation </li></ul><ul><li>Partial volume analysis </li></ul><ul><li>Density response </li></ul><ul><li>Geometric Growth Compensation </li></ul>
  17. 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, D sb = ave diameter before crushing, D SQ =ave after crushing, K r =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                K k = Kick’s coef.                              
  18. 19. Powder Charging: Qualitative Trends in a Faraday Pail-Blender System David Engers, unpublished data Purdue
  19. 20. Modeling Blending : Cascade Region For fine grains, the boundary between the characteristic region and the remaining powder bed is parabolic in shape The powder bed below the boundary rotates with the mixer as a solid body. Characteristic region Blender head space
  20. 21. Blending Scaled “Down” 180kg Run 16kg Run
  21. 22. Dry Granulation by Roller Compaction Unpublished CAMP data – A.Gupta <ul><li>The strength is a linear function of the density which is monitored by NIR </li></ul><ul><li>Semi Empirically </li></ul><ul><li>F=(S NIR -0.17)/0.37 </li></ul>
  22. 23. <ul><li>The particle sizes of the milled material is also manifest in the slope of the NIR signal (as predicted) </li></ul>Dry Granulation by Roller Compaction Unpublished CAMP data – A.Gupta
  23. 24. Monitoring and Modeling of Fluid Bed Granulation Paul Findlay, Ph.D dissertation, Purdue Univ, 2003
  24. 25. 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. Modeling Wet Granulation Pendular Funicular Capillary Droplet Drying Over Wetting
  25. 26. NIR during granulation–wet massing and Particle size Unpublished CAMP data, Dr. Jukka Rantanen – X2=255 rpm X1=110 g (=X3) NIR Treated Response
  26. 27. DRYING : NIR -Exit Temp vs. Time for APAP Granulation Drying Time (min) MM55 Reading Temperature (°C) T MM55 0 30 25 20 15 10 5 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 Today 1 6 235–245 (1998). Evaporative Diffusive Critical moisture Temperature Moisture Content 40 60 80 100 120 140 160 180 45 47 49 51 53 55 57 59 61 63 65
  27. 28. Full Scale Fast Drying Trials of an Ibuprofen Granulation Morris et.al., Drug Dev. Ind. Pharm., 26 (9) :985-988 (2000)
  28. 29. Drying Excursions and Dissolution CAMP unpublished data
  29. 30. Tablet CU : Testing a Model T. Li, et. al., in press Pharm. Res. BioMed Anal. 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
  30. 31. COATING HPMC, Sulfanilamide and, Moisture Real-Time Measurements Unpublished CAMP data, P. Findlay,In prep for JPS
  31. 32. Where do we stand? <ul><li>Taken individually these theories and techniques look independent </li></ul><ul><li>Together, however, they show a concerted effort to describe contributions to the overall process of drug development. </li></ul><ul><li>These principles and techniques are applicable to batch and continuous processing and may be linked by multi-variate ( chemometric) methods after univariate conformation. </li></ul><ul><li>Ultimately this give us the ability to understand how development variables interact to influence the final product and to design in the quality. </li></ul>
  32. 33. The Business Case <ul><li>Using existing scientific principles, monitoring and modeling capabilities one will understand more about processes and be able to detect variations quickly </li></ul><ul><ul><li>The earlier you start collecting information the more you’ll know the more comfortable everyone will be </li></ul></ul><ul><li>Given this level of knowledge and communication with FDA, you will be at the lowest risk (as proposed) possible for your product/process </li></ul><ul><li>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. </li></ul><ul><li>The companies have many of the tools to lower their risk levels RIGHT NOW This will only improve with more research. </li></ul>

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