The Changing Face of Product and Process Development in the QbD Era James Kraunsoe AstraZeneca Product Development UK/US
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Presentation outline Designing in R&D a manufacturing process for Operations What do Manufacturing require? Illustrate approach taken by R&D Use of appropriate p tools How to work smarter Case studies Examples for tablet products Summary and reflections
Requirements of manufacturing Operations driven by cost pressures and need to reduce waste This requires development of Lean/6 Sigma processes and supply chain Operations have 3 key requirements of R&D Robust and capable processes Defined eg, CpK for CQAs and controls >1.67 Operational flexibility Equipment type, scale, operating ranges Flexible or reduced control strategy Exemplify Ability to facilitate innovation and to tolerate variation Use of newer more innovative technology Eg, via a site transfer Natural variation eg, raw materials including API
Development Tools: Paper based Define Quality Target Pharmaceutical Profile Define critical quality attributes cf ICHQ8 Target for for a safe, efficacious and quality product Define process targets that define success wrt manufacturabilty Process metrics eg, Cpks Define Quality Risk Managment Reflect product and prior knowledge (or experience) Prioritise development activities Identifies sources of variation and develop the understanding to adapt Impact of equipment scale and type Changes to raw material grades, suppliers
Development Tools: Practical 8 12 Design of Experiments studies Prioritise variables by QRA Perform at small, pilot and large scale Evaluate formulation/materials and process Examples Water Amount (%w/w) IR tablets 1.25 Wet granulation Example of an evolving design Milling Reduced factorial design 0.75 Time (min) Mg stearate Amount (%w/w) No of revs 45 7 11 6 5 1 100 9 4 2 3 Impeller tip speed (m/s) 10 5 8 Low Milled granule SA (cm 2 /g) High
Development Tools: Practical Product and process characterisation Imaging NMR of tablets t Dissolution of modified release tablet Understanding the impact of raw materials and/or process on the dissolution rate of polymers Presence of inclusions in immediate release tablets Time Flow characterisation Understanding how material properties affect bulk flow of powders 90 - φ w ( ) 66 High cohesion Prosolv 64 Low adhesion 90HD Emocel 50 62 Avicel Good flow PH102 properties 60 Avicel 58 PH101 L-HPC LH22 56 L-HPC LH11 54 HPC-LF Low cohesion 52 Poor flow High adhesion 50 properties FF c (-)
Development tools - Practical Process Analytical Technologies Particle size during granulation 1-21 um In-line use FBRM Monitoring of fine and larger particles with increasing water amount and time 21-100 um Median 100-1000 um Time Granule moiture content during drying In-line NIR Single variable monitoring against 0.50 known moisture peak 0.70 0.60 VAR_337 0.40 09:57 09:58 09:59 10:00 10:01 10:02 10:03 10:04 10:05 10:06 10:08 10:09 10:10 10:11 10:12 10:13 10:14 10:15 10:16 10:17 10:18 10:19 10:20 10:21 10:22 10:23 10:24 10:25 10:27 10:28 10:29 10:30 10:31 10:32 10:33 10:34 10:35 10:36 10:38 10:39 10:40 10:41 10:42 10:43 10:44 10:45 10:46 10:47 10:48 10:49 10:50 11:01 Objective: To confirm process is proceeding as intended Obs ID (DateTime)
Manufacturing: Operational flexibility Roller compaction model: Scale independence Empirical (from DOE) vs mechanisitic (from theory) Ribbon Porosity: 20 25% Throughput: > 17 kg/h Theoretical operating region Raw material characterisation Roller compaction model Experimental operating space: Full scale experiments Intermediate characterisation
Manufacturing: Managing variability In-process Granule Size Control for IR tablet intermediate Combined Feed Forward/Feed Back Loop STAGE 2: WET GRANULATION STAGE 3: WET MASS DE- LUMPING STAGE 4: DRYING STAGE 5: DRY MILLING Change water quantity to x for next batch Y Is the granule size distribution too fine? Granule Size Distribution Measurement Wet Granulation model N Y Comil model: Current parameters acceptable? N Change Comil Parameters
Summary and reflections Manufacturing is a customer R&D need to know what is important Output from R&D is choice or flexibility Operations need to understand why there are constraints Being smarter about how R&D deliver Using Quality Risk Management effectively Prioritise and rationalise Maximising opportunities to collect data Using PATs Minimising number of experiments Using DOEs in conjunction with all manufactures Use of models to understand and predict relationships