The document summarizes a case study on the implementation of Quality by Design (QbD) principles in developing a film-coated tablet with an active layer to protect an unstable drug. Key aspects included identifying critical quality attributes of content uniformity and potency. Design of experiments was used to establish a design space. In-line monitoring techniques like Raman spectroscopy were utilized to ensure consistent coating quality during scale-up.

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

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

12. Proprietary Information In-Line Raman Monitoring for Coating Suspension System Design

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

16. Coating Control: Use of Raman to Monitor the Inner Layer A U Wave number (cm-1) Opadry A.U. Coating Progress Tablet Core

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

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*)

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

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

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

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