Design Space

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

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

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

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.

Patient Requirements Content Uniformity Potency Stability Purity Consistent Bioavailability Cover Range of Potencies Readily Available in Distribution Channels Convenient and Pharmaceutically Elegant

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)

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]

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

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

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)

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.

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

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

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

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

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

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

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

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

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

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)

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*)

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

Formulation and Process Optimization DOE Design: API Film Coated Tablets 2^(5-1) Fractional Factorial with 3 Center Points Designs – 19 Runs

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

Summary of Potency and Content Uniformity Results of Second Layer Coated Tablets

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%

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

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

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

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

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)

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

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