1. Material Characterization and Control and Knowledge Management Considerations for Continuous Manufacturing with the Real-Time Release Material Characterization and Control and Knowledge Management Considerations for Continuous Manufacturing with the Real-Time Release October 4, 2016 FDA, Silver Spring, MD Stephen W. Hoag, Ph.D. University of Maryland, Baltimore School of Pharmacy Phone 410-706-6865 Email: shoag@rx.umaryland.edu

2. Outline Role of material properties in model development – Identification of CQA – PAT example – RTRT data fusion models Database structure and usage – PharmaHub – Database views – Applications

3. Dosage Form Variability API Dosage Forms Excipients Manufacturing Processing Conditions Spec. range

4. Which Raw Material Inputs → CQA Critical Quality Attributes Hardness Disintegration time Dissolution CU/mixing Stability Excipient Properties Crystal form Particle size Bulk density Etc. Relationships Not well understood

5. Raw Data Hidden Info & Patterns Data Mining Raw Data Def: Discovery of previously unknown information and patterns from set of data Stage 1 Define problem or subject of study Stage 2 Select data Visualize Data Remove noise, & Irrelevant Data Stage 3 Data preprocessing Stage 4 Data Analysis Data Mining Analysis of Results Assimilation of Knowledge

6. Sources of Excipient Variability  Intentional (designed into excipient) – i.e., different grades and vendors – Manufactures produce to improve performance – Many examples  MCC, HPMC, lactose, et al. – Important to understand for:  Optimizing a formulation performance, in vitro and during production  Make regulatory decisions, e.g., excipient substitution and change control  Random – Lot-to-lot variation  Introduced from all factors disused previously – Within a lot variation (container-to-container)  During production process parameters can drift – Especially continuous process where a lot maybe several days of production – Important to understand: so can produce robust formulations

7. PH-200 PH-102 SCG PH-102 PH-101 PH-105 PH-103PH-113 PH-112 Decrease moisture content NMT 1.5% NMT 2% NMT 3% NMT 5% Particle size 180 um 150 um 100 um 50 um 20 um PH-302 PH-301 0.3g/cc 0.4g/cc Loose bulk density NMT= not more than FMC Intentional Variation—Grades of MCC

8. Intentional Variability– Manufacturers of MCC Manufactures GradesParticle Size, µ mMoisture, % Loose Bulk Density, g/cc FMC Avicel PH101503.0-5.00.26-0.31 JRS Vivapur 101 65-- 0.26-0.31 Emcocel 50M0.25-0.37 AKC PH-101 502.0-6.0 0.22 UF-7110.21 KG-8020.12 KG-10000.29 FMC Avicel PH-1021003.0-5.00.28-0.33 JRS Vivapur 102 100-- 0.28-0.33 Emcocel 90M0.25-0.37 AKC PH-102902.0-6.00.30 FMC = FMC B IOPOLYMERS AKC = Asahi Kasei Corporation JRS = J Rettenmaier & Söhne GmbH and Co.KG

9. Tracking of Random Variability Track random variability overtime and plot in histogram

10. Example of Lubricants  Formulation type - binary mixture – MCC ductile / plastic – Lactose intermediate – Dical brittle  Formulation properties – Ejection force (EF) – Breaking force (BF) – Disintegration time (DT)  Excipient properties – Density, true, poured and tapped – Loss on drying (LOD) – Solid state crystal type via DSC – Particle size and size distribution (PSD) – Specific surface area (SSA) – Fatty acid analysis – Microscopy

11. SampleSourceGradeLot No.Sample No. 1Covidien Mallinckrodt (CM)5712812000262NA 2Covidien Mallinckrodt (CM)57121005000629NA 3Covidien Mallinckrodt (CM)22571008000310NA 4Covidien Mallinckrodt (CM)2257MO5676641079 5Covidien Mallinckrodt (CM)2257MO6062NA 6Covidien Mallinckrodt (CM)1729JO397070323926 7Covidien Mallinckrodt (CM)5716LO4224NA 8Covidien Mallinckrodt (CM)1726MO8729NA 9Peter Greven (PG)MF-2-V-BIC917002NA 10Peter Greven (PG)MF-2-VC019038NA 11Peter Greven (PG)MF-3-VCO16795NA 12Spectrum (S)MgStOX0283NA 13Spectrum (S)MgStWQ0272NA MgSt Variability Random Samples Collected from Vendors

12. Mg Stearate Specs. MF-2V SSA 6-10 m 2 /g D50 7-11  m MF-3V SSA 8-12 m 2 /g D50 5-9  m MF-2-V-BI SSA 6-8 m 2 /g D50 7-11  m

13. Sample Mean Particle Size (um) Particle Size Span Specific Surface Area (m2/g) 1 7.48 ± 0.031.865.30 ± 0.29 2 8.50 ± 0.101.885.59 ± 0.02 3 12.54 ± 0.021.665.25 ± 0.05 4 10.02 ± 0.011.765.42 ± 0.04 5 10.27 ± 0.031.545.75 ± 0.06 6 11.88 ± 0.021.623.03 ± 0.06 7 9.10 ± 0.131.446.81 ± 0.27 8 18.79 ± 0.121.574.12 ± 0.02 9 9.44 ± 0.023.534.91 ± 0.04 10 10.14 ± 0.064.134.64 ± 0.02 11 7.71 ± 0.042.936.50 ± 0.03 12 10.04 ± 0.062.452.92 ± 0.02 13 10.12 ± 0.032.642.55 ± 0.03 Property differences between Mg Stearate samples

14. Lubricity of Mg Stearate

15. Solid State Characterization Forms: Anhydrous, monohydrate or dihydrate Dihydrate MonohydrateMixture

16. Excipient Variability & Performance Lactose Formulation, manufactured under identical conditions

17. PCA Analysis of Differences Valid prediction for properties such as Disintegration time Requiring analysis of material properties

18. ICH Q8/Q9/Q10 IMPLEMENTATION  I. Low-Impact Models: – Support product and/or process development  e.g., formulation optimization  II. Medium-Impact Models: – Assuring product quality but not sole indicator of product quality  e.g., most design space models, many in-process controls  III. High-Impact Models: – Model prediction is a significant indicator of product quality  e.g., chemometric model for product assay, dissolution, etc.

19. Spray Coating Beads Drug Layered Bead Spray Coated Bead Cured Bead Polymer Spray Coating Coat Drying & Curing Bead CMA/CQA - Thickness - Uniformity - Water content - Extent of curing API Layer Uncured EC coat Cured EC coat - API Content - API Homogeneity - Particle Size CPP - Air flow - Air temp - Curing time - LOD - Atomization - Spray rate - Air flow - Air temp. Models API assay Process Efficiency Moisture Content Curing Time Dissolution Rate

20. 1) Evaporative Drying & Ordering 2) Particle Deformation 3) Coalescence & Polymer Inter-diffusion Not to scale Latex Particle Interface Rate is very Temp. Dependent Coat @ T ~ Tg

21. 28 32 30 34 26 Inlet Air V (m 3 /h) 3844404246 Spraying T 54 57 60 63 66 Curing T 1 h = -2 2 h = -1 3 h = 0 4 h = +1 5 h = +2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Paper 2

22. Curing and Dissolution rate

23. Product Temp. Curing Time Curing Temp. Air Volume Relative Humidity NIR of Spraying Process NIR of Curing Process Spraying Scores Parafa PCA Parafc PAC Curing Scores Process Param. Spraying ProcessCuring Process Data Fusion Spraying Scores Air Volume Curing Temp. Curing Time Relative Humidity PLS Model For Dissolution Dissolution Results

24. Dissolution 1Hour Parafac Decomposition Parafac = Parallel Factor Analysis Parafac is similar to 3D multidimensional PCA and process time is the 3 rd dimension -Dimensions are: time, wavelength and batch No.

25. Excipients Database Where to Host the Database ? pharmaHUB: a cyber infrastructure developed at Purdue University to support digital scholarship, dissemination, collaboration and outreach for the pharmaceutical engineering community.

26. Analysis Tools make decisions and solve problems Analysis Tools make decisions and solve problems Derivations & Plots of Measurements histograms, yield loci, powder flow function, frequency & cumulative distributions Derivations & Plots of Measurements histograms, yield loci, powder flow function, frequency & cumulative distributions Measurements raw data, chemical and test descriptions Measurements raw data, chemical and test descriptions Catalogs excipients, vendors, products, lots, test methods, equipment, properties Catalogs excipients, vendors, products, lots, test methods, equipment, properties Web Interface

27. Database Structure Excipients - MCC Products - PH101 Lots - Lot # 1 Properties - particle size Test method - Light scattering Equipment - Malvern ← USP compendial category ← Actual product on the market

28. A Publicly Available Database Excipients Database

29. A Publicly Available Database Excipients Database

30. A Publicly Available Database Excipients Database

31. A Publicly Available Database Excipients Database

32. The Mechanical Property Group Particle Size Distribution True Density Bulk Density Cohesiveness Elastic Modulus Compactibility Brittleness 32 A.Hlinak, NIPTE-NIST Meeting, April, 2005

33. Development Lots Based upon random selection of lots: Domain of prior Experience: typical production lots 3 lots used for development can tell if develop formulation using typical material Source: Kindly supplied by Joe Kushner

34. Supplier’s Data User’s Data Data MiningDatabase User Friendly Web based Interface Targeted Experiments to Support Database Relational Modeling Literature Data Database Management Tools User feedback

35. Acknowledgements  FDA & NIPTE for funding  Ting Wang  Ann Christine Catlin  Sumudinie Fernando  Sudheera R. Fernando  Carl Wassgren  Kristine Alston  Linas Mockus  Vadim Gurvich  Paribir Basu  Individuals with invaluable advice  FMC, Brian Carlin  Pfizer, Bruno Hancock  Roquette, Leon Zhou  Colorcon Dave Schoneker  IPEC  And many others