Design Space
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Design Space

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Example of implementation of design Space

Example of implementation of design Space

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  • 1. Case Study: Implementation of Design Space Concepts in Development of an Active-Coated Tablet Robert A. Lipper, Ph.D., Divyakant Desai, Ph.D., San Kiang, Ph.D. Bristol-Myers Squibb Pharmaceutical Research Institute Real World Applications of PAT and QbD in Drug Process Development and Approval – September 11, 2006 – Arlington, VA
  • 2. Outline
    • Philosophy/Things to Ponder
    • Technical Challenge and Approach
    • Analysis of Variables
      • Formulation
      • Coating Process
        • Uniformity of Input
        • Spray Characterization
        • DOE
        • Control of Coating
    • Results
    • Scale-Up and Technology Transfer
    • Model Development
    • Summary
  • 3. Design Space Ponderables
    • “ The multidimensional combination and interaction of input variables. . .and process parameters that have been demonstrated to provide assurance of quality.” [from ICH Q8]
      • How many dimensions?
      • How many combinations and interactions?
      • How demonstrated?
    • Homogeneous vs. heterogeneous systems
    • Likelihood of “stealth” variables
  • 4. QbD Philosophy
    • QbD is about connecting the molecule and the patient .
      • Science-based product/process design begins with the API molecular entity and is geared to meet patient needs for pharmacotherapy which is safe, effective, convenient and of consistently high quality.
    • All products are designed and developed to be of high quality ; QbD provides a structured framework for documenting and presenting development rationale, experience and knowledge of the formulation and the process, and to ensure manufacture of products consistently fit for patient use.
  • 5. Patient Requirements
    • Content Uniformity
    • Potency
    • Stability
    • Purity
    • Consistent Bioavailability
    • Cover Range of Potencies
    • Readily Available in Distribution Channels
    • Convenient and Pharmaceutically Elegant
  • 6. Properties of the Molecule: Technical Challenge
    • pKa near neutral
    • BCS Class III
    • Hydrochloride Salt
    • Acidic pH favors stability
    • Dose ≤ 10 mg
    • Primary degradation reaction occurs both in solid state and in solution
      • Accelerates in presence of commonly used tablet excipients
      • Accelerates with common processing conditions (roller compaction, wet granulation, compression)
  • 7. QbD Approach--Tablet Formulation Active Film Coating Inert tablet core Inner layer: seal coat of coating material Middle layer: drug + same coating material Outer layer: with same coating material
    • This approach avoids
      • compaction process
      • granulation process
      • direct drug contact with excipients
    • Protects drug from environmental moisture
    • Acidic environment used for all three layers
    [SCHEMATIC]
  • 8. Manufacturing Process Conventional Excipients Lubricant COURT OY R - 100 COURT OY R - 100 COURT OY R - 100 200 mg tablet cores Polymer coat pH 2 Polymer coat pH 2 API Polymer coat pH 2 Tablet Printing Blend (5 minutes) Blend (3 minutes) Tablet Compression Middle Layer Coat Outer Layer Coat In process monitoring: Weight gain Weight gain Weight gain Raman HPLC Inner Layer Coat
  • 9. CQAs: Content Uniformity & Potency
    • Formulation optimization
    • API-to-polymer ratio
    • Suspension pH
    • Suspension Uniformity
    • Suspension viscosity and solids content
    • Coat thickness
    • Spray Nozzle optimization
      • Optimize air flow of spray gun for droplet size and spatial distribution
      • Angle and distance of nozzles
    • Thermodynamic optimization
      • Spray rate
      • Inlet temperature
      • Air flow
    • Tablet bed optimization in coater
    • Baffle configuration
    • Pan load
    • Pan speed
    Variables Considered to Define Design Space
  • 10. Formulation Factors
    • HPMC- and PVA-based coating formulations were evaluated.
      • Opadry II, a PVA-based coating formulation, provided the best stability
    • Tablets were most stable when pH of the coating suspension was adjusted to around 2 for all three layers
    • Three layer-coated tablets were more stable than those coated with two layers (i.e., omission of either inner or outer layer decreased stability)
  • 11. Elements of QbD: Preliminaries to Studying the Coating Process
    • Placebo cores are subjected to 100% on-line weight check; controlled to 200 mg ± 2%.
    • A round biconvex tablet shape was chosen for durability, ease of coating and improved content uniformity.
    • The process for dissolution of API in the coating vehicle was qualified using a UV fiber-optic probe.
    • A re-circulation loop was designed in the tank to prevent sedimentation of pigments in the coating dispersion. A Raman probe was used to confirm the homogeneity of the coating dispersion. (Mixing design was optimized before undertaking spray characterization.)
    • Brooks air flow controllers are used for monitoring atomization air feed into the spray guns.
  • 12. Proprietary Information In-Line Raman Monitoring for Coating Suspension System Design
  • 13. Spray Characterization and Design
    • Goal
    • Identify spray system hardware, configuration and operating parameters to produce:
      • Flat, focused spray pattern with uniform droplet size and intensity (narrow RSD)
      • Uniform and stable spray cone
    • Approach
    • Characterize spray with two-camera imaging system (Off-line)
      • Profile camera measures spray cone angle, spray intensity, and spray axis angle
      • Droplet camera measures droplet size distribution (DV50 and DV90), mean droplet speed, and droplet density
    • Effect of various parameters (Off-line)
      • Nozzle types
      • Nozzle operation under controlled air flow rates
      • Solid content in coating suspension
      • Air/liquid ratio
  • 14. Comparison of Nozzle Types I and II : Selection criteria: flat, focused spray pattern with uniform droplet size and intensity. Cone angle of (A) Type I and (B) Type II nozzles Effect of Nozzle Type A B
  • 15.
    • Cone angle, droplet size, and droplet density are affected by suspension formulation (API-to-polymer ratio) and flow rate. Spray parameters are customized to assure content uniformity at different API-to-polymer ratios.
    • Droplet size and cone angle decrease with increased air flow rate
    • Too high a ratio of pattern air to atomizing air can create a hollow spray cone which (in this case) would adversely affect content uniformity
    • Air volume is much preferred over air pressure to control the droplet size distribution and pattern of the spray, and is independent of coater scale or “plumbing”
    Spray Characterization: Summary
  • 16. Coating Control: Use of Raman to Monitor the Inner Layer A U Wave number (cm-1) Opadry A.U. Coating Progress Tablet Core
  • 17. Setup of Raman Probe for In-Line Coating Monitoring
    • Pan size: 36”
    • Pan speed: 12 rpm
    • Distance from the bed: ~ 4 inches
    • Scan time: 24 sec
  • 18. Coating Kinetics of the Inner Layer Followed by In-Line Raman Spectroscopy El Hagrasy, A., Chang S-Y., Desai, D. and Kiang, S. American Pharmaceutical Review 9(1):40-45 (2006) Bed Temperature Adjustment Coating Initiated
  • 19. Middle (Active) Layer Monitoring (2.5 mg API Coated tablets) % Target Weight Gain % Potency 50.0 47.8 73.5 70.9 90.3 87.5 102.0 99.7
  • 20. Raman Prediction of the Inner Layer from Different Spatial Locations 60”(I) 48” (I) 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 1 2 3 4 5 6 Location % Weight Gain Location % Weight Gain 60” (II) 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 1 2 3 4 5 6 Location % Weight Gain 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 1 2 3 4 5 6 Location % Weight Gain 48” (II) 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 1 2 3 4 5 6 El Hagrasy, A., Chang S-Y., Desai, D. and Kiang, S. Journal of Pharmaceutical Innovation. Accepted (2006) Raman Gravimetric Raman Gravimetric Raman Gravimetric Raman Gravimetric
  • 21. Monitoring/Control of Coating: Summary
    • First coating layer can be monitored using a Raman probe (feasible to do at-line as well) and/or weight gain
    • Second coating layer (active layer): API deposition can be monitored using weight gain and/or an off-line rapid HPLC or UV fiber-optic method
    • Third coating layer can be monitored using an off-line Raman probe and/or weight gain
    • The process has been successfully scaled using 24 inch, 36 inch, 48 inch, and 60 inch Compu-Lab coaters (Batch sizes: 14 to 215 kg)
  • 22. Classification of Process Variables PAR = Proven Acceptable Range; NOR = Normal Operating Range; EOF = Edge of Failure *If frank failures are observed, EOF should be estimated Inherently Variable Tightly Controllable Non-Critical Critical Fix Fix, determine PAR (EOF*) Determine target, NOR and PAR Determine target, NOR, PAR (EOF*)
  • 23. Parameters Fixed for DOE
    • 36 inch Compu-Lab coater
    • Batch size 50 kg (250,000 tablets)
    • Large baffles
    • Type I nozzles
    • Nozzle distance from tablet bed
    • Ratio of pattern air to atomizing air
    • Coating pan speed
    • Tablet bed temperature
    • Dew point  10C
  • 24. Formulation and Process Optimization DOE Design: API Film Coated Tablets 2^(5-1) Fractional Factorial with 3 Center Points Designs – 19 Runs
  • 25. Creating the Process Design Space: Process Parameters Studied Screening for Parameter Ranges for Optimal Content Uniformity Atomizing/Pattern Air Volume Air Volume Inlet Air Temperature Spray rate % API in suspension API/Opadry Ratio Spray rate % API in suspension Critical Parameters: API application rate was most critical for content uniformity . 60 75 0.75 5 0.75 8 1:1 1:8 200 300 525 600 50 55 60 105 DOE
  • 26. Summary of Potency and Content Uniformity Results of Second Layer Coated Tablets
  • 27. Towards Process Understanding
    • Based on risk assessment around the chosen formulation and processing approach, content uniformity and potency of tablets are considered to be the most critical product quality attributes
    • Variables were systematically analyzed for their potential to influence the critical quality attributes
    • Controlled experiments including DOE were conducted around the key variables to establish reliable operating ranges
    • The process has been shown to be capable of consistently achieving content uniformity RSD of 2.8-3.9%
  • 28. Utilization of Design Space for Tech Transfer Coating Suspension Homogeneity Nozzle Characterization Coating Process
    • Optimal design of mixing tank
    • Raman spectroscopy
    • Real-time Imaging Technology
    • Raman Spectroscopy
    Final Product Coating Suspension Homogeneity Nozzle and tablet flows Characterization
    • Optimization of spray pattern
    • Support scale-up
    • Specify process parameters to enhance tech transfer
    Coating Process
    • Real-time monitoring of the coating kinetics
    • Effect of process variables on coating uniformity
    • Fast check of coating uniformity
    • Develop an index of mixing efficiency
    • Determination of coating end point
    Final Product
    • Minimize risk of sedimentation
    • Continuous verification of TiO 2 content
    DEM and PBE models are being developed to predict coating uniformity and coating weight in production coater
    • fast-HPLC
    • UVFO
    • Raman / NIR
  • 29. DEM-1M model PBE-2 zone model RSD model for Production Coater Workflow for Coating Process Model PAT applications Thermodynamics & mass transfer Formulation 1.Predict RSD 2.Reduce DoE batches 3.Provide added insight to design space for CMC Nozzle optimization Feed tank optimization and scale-up At-line uniformity analysis tablet velocity characterization
  • 30. Summary
    • A product design approach was chosen to address the chemical instability of the API in traditional formulations
    • A clear and complete process understanding is being created during product development to assure process robustness
    • Several PAT techniques, some with potential for in-line process control, are being utilized to develop a deeper process understanding
    • Reliable operating ranges have been established for key process variables
    • Process understanding gained through the QbD approach is being leveraged in scale-up and technology transfer
  • 31. Project Leads: Divyakant Desai San Kiang Formulation and Drug Product Process: William Early Charles Van Kirk Howard Stamato Srinivasa Paruchuri Sanjeev Kothari API Process: Steven Chan John Korzun Analytical R&D: Harshad Patel Leon Liang Xujin Lu PAT: Arwa El-Hagrasy Don Kientzler Wei Chen Shih-Ying Chang Technical Operations: Howard Miller Megan Schroeder DEM modeling: Fernando Muzzio (Rutgers University) Regulatory Sciences: Steve Liebowitz Acknowledgements
  • 32.  
  • 33. Backup slides
  • 34. Formulation and Process Optimization DOE - API versus Polymer Amounts -- Each batch was coated up to 10-mg potency. Tablets corresponding to 2.5-mg and 5-mg were collected at the appropriate times (theoretical weight gain) and results were treated separately. -- Effectively, three separate DOEs were performed for the three strengths: 2.5 mg, 5 mg and 10-mg, respectively. 20 16 2 1:8 20 16 4 1:4 16 8 8 1:1 Total Solids Dissolved / Suspended Opadry II White API % w/w in Coating Suspension API/Polymer Ratio 10 10 5 5 2.5 2.5 40 10 20 5 10 2.5 80 10 40 5 20 2.5 Opadry II White API Opadry II White API Opadry II White API 10 mg Tablets 5 mg Tablets 2.5 mg Tablets Amount per Tablet (mg)
  • 35. Representative Tablet Formulations 7 mg Color D 7 mg Color C 7 mg Color B 7 mg Color A Opadry II Color 200 mg 200 mg 200 mg 200 mg Inert Core Outer Layer 10 mg 20 mg 20 mg 8 mg Opadry II White 10 mg 5 mg 2.5 mg 1 mg API Middle Layer 6 mg 6 mg 6 mg 6 mg Opadry II White Inner Layer 10 mg 5 mg 2.5 mg 1 mg Ingredient