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lR P ig fize ht r s In R c es er ve d Role of Excipient Variability in QbD Drug Product Development Al Dr. Joseph Kushner, IV Pfizer, Inc. 1 Co-Contributors Beth Langdon Jon Hiller Glenn Carlson Fasheng Li Daniel Song Gautam Ranade – – – – Ian Hicks Kam Agarwal Lalji Kathiria Anil Kane Al – – – – – – • Patheon lR P ig fize ht r s In R c es er ve d • Pfizer 2 Outline lR P ig fize ht r s In R c es er ve d • Background on QbD concepts • Approaches for assessing excipient variability in Drug Product Design – Case Study 1 - using vendor CoA datasets to understand variability – Case Study 2 – risk-based and experimental investigations of excipient variability and drug product performance – Case Study 3 - Quality by Design evaluation for robustness of an immediate release tablet platform Al • Closing thoughts: Interaction with regulatory bodies 3 Outline lR P ig fize ht r s In R c es er ve d • Background on QbD concepts • Approaches for assessing excipient variability in Drug Product Design – Case Study 1 - using vendor CoA datasets to understand variability – Case Study 2 – risk-based and experimental investigations of excipient variability and drug product performance – Case Study 3 - Quality by Design evaluation for robustness of an immediate release tablet platform Al • Closing thoughts: Interaction with regulatory bodies 4 Quality-by-Design Paradigm lR P ig fize ht r s In R c es er ve d • Change from single set of process conditions to a design space (range of process conditions) to achieve acceptable product output. • Simple example: Pre-QbD Time: 15 min Speed: 12 rpm QbD Al Blending Time: 10-20 min Speed: 6-18 rpm Compression: 10 kN Tableting Compression: 5-15 kN Spray Rate: 150 g/min Pan Load: 70% Film Coating Spray Rate: 100-200 g/min Pan Load: 50-90% 5 Pictorial of Design Space Unknown Space lR P ig fize ht r s In R c es er ve d Knowledge Space Tested, but fails quality specifications Design Space Demonstrated quality product Al Normal Operating Domain Note: Developing a design space is optional 6 Al lR P ig fize ht r s In R c es er ve d ICH Q8(R2) Pharmaceutical Development 7 Developing Robust Products lR P ig fize ht r s In R c es er ve d API – Target Attribute Profile API Properties Excipients – Vendor Specs Excipient Properties Al Process Parameters Drug Product Quality Target Product Profile (ensures consistent delivery of safe and efficacious drug products to the patient) How can we incorporate excipient understanding into robust drug product design? 8 Outline lR P ig fize ht r s In R c es er ve d • Background on QbD concepts • Approaches for assessing excipient variability in Drug Product Design – Case Study 1 - using vendor CoA datasets to understand variability – Case Study 2 – risk-based and experimental investigations of excipient variability and drug product performance – Case Study 3 - Quality by Design evaluation for robustness of an immediate release tablet platform Al • Closing thoughts: Interaction with regulatory bodies 9 One Approach to Incorporating Excipient Variability into QbD Drug Product Development lR P ig fize ht r s In R c es er ve d Control Strategy Goal: Ensure Product Robustness Confirm Criticalities (if necessary) Work with suppliers for materials Goal: Identify Potential CMAs Prior product knowledge Literature review Goal: Manage variability to consistently supply safe, efficacious products to patients and providers DoEs on Critical Attributes Risk Assessment Gain Development Experience Goal: Reduce Potential CMAs Increase experience domain Confirm excipient suitability Rational lot selection Al Understand Sources of Variability in Excipients Goal: Select Excipients and Composition Understand specification range vs. actual variability Evaluate site-to-site, annual/seasonal, and raw material sources of variability 10 Case Study #1 Utilizing Vendor Generated Data lR P ig fize ht r s In R c es er ve d • QbD filings: Determine domain where proven acceptable product has been manufactured. – Applies to both process conditions and raw material variability. • Need to understand raw material (excipient) variability. – Site differences, year-to-year variation, seasonal variation • Following examples utilize quantitative datasets (ex: Certificate of Analysis data). Al – Readily available from vendor. – Requires no additional, in-house material characterization. – May leverage multivariate methods (i.e. PCA) to simplify data analysis. Joseph Kushner IV. (2013) Utilizing quantitative certificate of analysis data to assess the amount of excipient lot-to-lot variability sampled during drug product development. Pharm Dev Tech, 18(2):333-342. 11 Al lR P ig fize ht r s In R c es er ve d Example Certificates of Analysis Utilize quantitative material physico-chemical properties. 12 Normal Variability vs. Specs 35 Mag Stearate - Stearic Acid % (Spec not less than 40%) Mag Stearate - Tapped Density (Spec: 0.18-0.33 g/cc) lR P ig fize ht r s In R c es er ve d 80 30 70 Typical Property Variability Distribution of values observed in 154 lots from 2004-2012. 40 30 20 20 15 10 5 10 0 0 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 Bin Normal lot-to-lot variability is small compared to specification range. Bin Normal lot-to-lot variability is comparable to specification range. Al Frequency 50 Frequency 25 60 Knowledge of extent of normal variability compared to specification range can aid future planning to ensure drug product robustness. 13 Understanding Excipient Variability Site CorkA Site Newark B 4 2 0 -2 • -4 -3 -2 Al -5 lR P ig fize ht r s In R c es er ve d Principal Component 2 6 -1 0 1 2 3 4 5 6 7 Principal Component 1 Example: Avicel PH102 – PC1 shows site differences – Development Strategy: Source from both sites throughout development to build in robustness 14 Understanding Excipient Variability Principal Component 2 3 2 1 0 -1 -2 -3 -4 2002 2005 2008 2003 2006 2009 lR P ig fize ht r s In R c es er ve d 2001 2004 2007 -3 -2 -1 0 1 2 3 4 Principal Component 1 SIMCA-P 11 - 4/13/2010 11:10:19 AM Al • Example: Mg St (HyQual) – PC1 and PC2 show year-to-year differences (particle size, density, chemical composition) – Uncertainty if material used in development will be similar to future material used in commercial mfg 15 Outline lR P ig fize ht r s In R c es er ve d • Background on QbD concepts • Approaches for assessing excipient variability in Drug Product Design – Case Study 1 - using vendor CoA datasets to understand variability – Case Study 2 – risk-based and experimental investigations of excipient variability and drug product performance – Case Study 3 - Quality by Design evaluation for robustness of an immediate release tablet platform Al • Closing thoughts: Interaction with regulatory bodies 16 Case Study #2 – Risk and Experimental Approach lR P ig fize ht r s In R c es er ve d • Dry granulated, immediate release tablet formulation • Formulation components: API Microcrystalline Cellulose (Avicel PH102) Spray-dried Lactose (Fast Flo 316) Sodium Starch Glycolate (Glycolys, Explotab) Magnesium Stearate (HyQual) Al – – – – – Joseph Kushner IV, Beth A. Langdon, Jon I. Hillier, Glenn T. Carson. (2011) Examining the impact of excipient material property variation on drug product quality attributes: A quality-by-design study for a roller compacted, immediate release tablet. J Pharm Sci, 100:2222-2239. 17 How Many “Knobs” Do Excipients Have? Spray-Dried Lactose Disintegrant Loss on Drying Loss on Drying Loss on Drying Loss on Drying Loose Bulk Density Bulk Density Loose Bulk Density Specific Surface Area lR P ig fize ht r s In R c es er ve d Microcrystalline Cellulose Magnesium Stearate Tapped Density Degree Substitution Stearic Acid Content Residue on Ignition pH Stearic + Palmitic Acid Content Water Content Settling Volume Mg Assay Residue on Ignition Absorbance 210-220 nm Ash % through 325-mesh Water Soluble Substances Absorbance 270-300 nm Water Soluble Substances Bulk density Ether Soluble Substances Acid/Alkaline Ether Soluble Substances Tapped density Specific Rotation Sodium Glycolate Acid Value Wt% on 60-mesh Sodium Chloride D50 Wt% on 140-mesh Purity D90 Wt% on 200-mesh Humidity Degree of Polymerization pH Conductivity Wt% on 60-mesh Wt% on 200-mesh D10 D50 D90 Wt% on 200-mesh Wt% on 325-mesh Al D10 Vendor CoA data lists over 50 potential factors! D50 D90 18 Risk-based Analysis: Literature Review Spray-Dried Lactose Sodium Starch Glycolate Excipient Property Affected Product Attribute Landin et al.16 Lignin (%) Dissolution Suzuki and Nakagami 17 % Crystallinity, Particle Size Moisture Content, Yield Pressure, Tablet Hardness, Dissolution Kothari et al. 18 % Crystallinity Moisture Content Vromans et al. 19 Particle size, % Crystallinity Tablet Hardness Sebhatu et al. 20 Moisture Content Tablet Hardness Rudnic et al. 21 Degree of Cross-linking, Degree of Substitution Water Uptake, Disintegration, Dissolution Bolhuis et al. 22 Degree of Substitution Disintegration Bolhuis et al. 23 Raw Material Source, Degree of Cross-linking Water Uptake, Disintegration Dansereau and Peck 24 Particle Size, Specific Surface Area Tablet Tensile Strength, Friability Leinonen et al. 25 Specific Surface Area, Moisture Content Ejection Work Barra and Somma 26 Particle Size Tablet Hardness, Ejection Work Al Magnesium Stearate Study lR P ig fize ht r s In R c es er ve d Excipient Microcrystalline Cellulose Rao et al. 27 Agglomerates, Particle Size, Specific Surface Area, Polymorph Particle size, specific surface area, and polymorph were identified as the highest risk material properties for further study. 19 Benefits of Risk-Based Analysis Spray-Dried Lactose Disintegrant Loss on Drying Loss on Drying Loss on Drying Loss on Drying Loose Bulk Density Bulk Density Loose Bulk Density Specific Surface Area lR P ig fize ht r s In R c es er ve d Microcrystalline Cellulose Magnesium Stearate Tapped Density Degree Substitution Stearic Acid Content Residue on Ignition pH Stearic + Palmitic Acid Content Water Content Settling Volume Mg Assay Residue on Ignition Absorbance 210-220 nm Ash % through 325-mesh Water Soluble Substances Absorbance 270-300 nm Water Soluble Substances Bulk density Ether Soluble Substances Acid/Alkaline Ether Soluble Substances Tapped density Specific Rotation Sodium Glycolate Acid Value Wt% on 60-mesh Sodium Chloride D50 Wt% on 140-mesh Purity D90 Wt% on 200-mesh Humidity Polymorph Degree of Polymerization pH Conductivity Wt% on 60-mesh Wt% on 200-mesh D10 D50 D90 Wt% on 200-mesh Wt% on 325-mesh D10 Al 52 factors down to 5 Omitted 40+ possible factors… D50 D90 20 Executed Experimental Plan Microcrystalline Cellulose Spray-Dried Lactose Batch Number Particle Size Particle Size Polymorph Particle Size Specific Surface Area 1 High Low Dihydrate Low Low 2 High High Monohydrate High Low 3 High Low Monohydrate Low High 4 High Low Monohydrate High Low 5 High High Monohydrate Low High 6 High High Dihydrate Low Low 7 High Low Dihydrate High High 8 High High Dihydrate High High 9 (Control 1) High Medium Monohydrate Medium Medium 10 Low Low Monohydrate High Low 11 Low High Monohydrate Low High K-1 Low Low Monohydrate High Low K-2 (Control 2) Medium Medium Monohydrate Medium Medium K-3 High High Monohydrate Low High Al lR P ig fize ht r s In R c es er ve d Magnesium Stearate Properties Batches 1-9 were used in the initial screening analysis, Batches 4, 5, and 9-11 in the second screening analysis, and Batches K1 through K-3 were used in the kilo-scale confirmation run Executed experimental plan a result of challenges in obtaining excipients with suitable properties – not an issue for process parameter experiments. 21 Executed Experimental Plan Spray-Dried Lactosea d50 (m) 94 126 94 94 126 126 94 126 116 94 126 94 116 126 Magnesium Stearateb Polymorph Dihydrate Monohydrate Monohydrate Monohydrate Monohydrate Dihydrate Dihydrate Dihydrate Monohydrate Monohydrate Monohydrate Monohydrate Monohydrate Monohydrate lR P ig fize ht r s In R c es er ve d Al Batch Number 1 2 3 4 5 6 7 8 9 (control 1) 10 11 K-1 K-2 (control 2) K-3 Microcrystalline Cellulosea d50 (m) 127 127 127 127 127 127 127 127 127 95 95 108 121 131 d50 (m) 12.8 24.0 4.9 24.0 4.9 12.8 15.6 15.6 10.6 24.0 4.9 24.0 10.6 4.9 SSA (m2/g) 4.4 6.3 10.7 6.3 10.7 4.4 6.0 6.0 6.2 6.3 10.7 6.3 6.2 10.7 a – Values obtained from laser diffraction measurements. b – Values obtained from vendor’s Certificate of Analysis. 22 Results: Ribbon Attributes 7 Sampled excipient particle size variation yield a 0.25 MPa over the tensile strength vs. solid fraction profiles. open diamonds ( ) – high PS MCC; low PS lactose; high PS, low SSA MgSt open circles (○) – low PS MCC, highPS lactose; low PS, high SSA MgSt open triangles ( ) – high PS MCC and lactose; low PS, high SSA MgSt closed circles (●) – control batch (high PS MCC; medium PS lactose; medium PS and SSA MgSt) Tensile Strength (MPa) lR P ig fize ht r s In R c es er ve d open squares (□) – low PS MCC and lactose; high PS, low SSA MgSt 6 5 4 3 2 1 0 Al 0.55 0.60 0.65 0.70 0.75 0.80 0.85 Solid Fraction Conclusion: Excipient variability is not a critical material attribute for ribbon properties. 23 Results: Hardness-Compression Profiles 150 mgW Tablets a 200 b 300 Hardness (N) lR P ig fize ht r s In R c es er ve d 250 160 120 80 40 Hardness 0 MgSt PS Filler PS 200 150 100 50 0 0 3 6 9 12 Compression (kN) 15 18 0 5 10 15 20 Compression (kN) 25 30 open squares (□) – low PS MCC and lactose; high PS, low SSA MgSt, open diamonds ( ) – high PS MCC; low PS lactose; high PS, low SSA MgSt, open circles (○) – low PS MCC, highPS lactose; low PS, high SSA MgSt, open triangles ( ) – high PS MCC and lactose; low PS, high SSA MgSt, closed circles (●) – control batch (high PS MCC; medium PS lactose; medium PS and SSA MgSt) Al Hardness (N) 300 mgW Tablets Interaction between particle size of excipients appear to drive the observed minor differences in the hardness-compression profiles. Conclusion: Excipient variability does impact tablet strength, but not critically. 24 Example Results: Compression Force vs Disintegration Time 480 420 Disintegration is a potential CQA Excipient variability had no impact on disintegration times Disintegration Time (sec) lR P ig fize ht r s In R c es er ve d c Overall Conclusion Al Drug product formulation and manufacturing process were robust to variation in the excipient properties studied 25 360 300 240 180 120 60 0 0 3 6 9 12 15 18 Compression (kN) Legend MCC PSD Lactose PSD Mg Stearate PSD/SSA Open squares (□) Low Low High/Low Open triangles ( ) High High Low/High Control - Closed circles (●) High Medium Medium Managing and Evaluating Available Data Spray-Dried Lactose Loss on Drying Loss on Drying Loss on Drying Loss on Drying Loose Bulk Density Bulk Density Loose Bulk Density Specific Surface Area Degree of Polymerization Disintegrant lR P ig fize ht r s In R c es er ve d Microcrystalline Cellulose Magnesium Stearate Tapped Density Degree Substitution Stearic Acid Content Residue on Ignition pH Stearic + Palmitic Acid Content Water Content Settling Volume Mg Assay Residue on Ignition Absorbance 210-220 nm Ash % through 325-mesh Water Soluble Substances Absorbance 270-300 nm Water Soluble Substances Bulk density Ether Soluble Substances Acid/Alkaline Ether Soluble Substances Tapped density Specific Rotation Sodium Glycolate Acid Value Wt% on 60-mesh Sodium Chloride D50 Wt% on 140-mesh Purity D90 Wt% on 200-mesh Humidity Polymorph pH Conductivity Wt% on 60-mesh Wt% on 200-mesh D10 D50 D90 Wt% on 200-mesh Wt% on 325-mesh D10 Al 52 factors down to 5 Omitted 40+ possible factors… … but are they truly “unknowns”? D50 D90 26 Quantifying Domain of Prior Experience • Rationale: Evaluate the amount and domain of excipient variability sampled during drug product development. Example: Avicel PH102 – 3 Drug product development • Development, Process Understanding • Ph3 Clinical batches • ICH batches – 15 lots used • 4 Newark lots • 11 Cork lots 2 P rincipal Component 2 • lR P ig fize ht r s In R c es er ve d 4 1 0 -1 -2 Domain of Prior Experience – – – – -3 Evaluated with MATLAB scripts -4 -3 -2 -1 0 1 2 Principal Component 1 All PCs: 58% of all lots Only PCs 1 and 2: 79% of all lots Communication tool with commercial sites and regulatory agencies 3 4 5 6 Al • 27 Univariate Approach 80 70 Frequency 60 50 40 30 20 10 0 lR P ig fize ht r s In R c es er ve d Mag Stearate - Stearic Acid % (Spec not less than 40%) Domain of Prior Experience Prior demonstration of quality product Typical Property Variability Distribution of values observed in 154 lots from 2004-2012. 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 Al Bin Prior experience covers ~15% of the specification range, but 97% of the typical variability of Stearic Acid % observed in Mag Stearate. 28 How representative are the batches used in development? (Multivariate) 3.0 8 (Residue on Ignition) 4 2 0 -2 1.0 0.0 -1.0 -2.0 -3.0 -4.0 -4 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 -6 -4 -2 0 2 4 Principal Component 1 6 8 10 Al (Wt% on 200-mesh & 140-mesh, Bulk & Tapped Density) 82% of lot-to-lot variability captured for only Principal Components 1 and 2 (physical properties). 29 Principal Component 6 2.0 6 Principal Component 2 (Wt% on 60-mesh, Water Content) lR P ig fize ht r s In R c es er ve d Filled blue circles (●) represent the 13 batches of spray-dried lactose used in development of an IR tablet formulation; earliest development batch from 2006 – all 13 batches made good quality tablets 12 Principal Component 4 SIMCA-P+ 12 - 2010-12-20 13:20:05 (UTC-5) (Loss on Drying, Acidity/Alkalinity) 28% of lot-to-lot variability for all Principal Components (all physico-chemical properties), due to poor Principal Components 4 and 6 coverage Expanding Domain of Experience 1 - Rational Lot Selection Rationale: Select lots from vendor inventory that expand domain of prior experience during development Example: Avicel PH102 lR P ig fize ht r s In R c es er ve d Principal Component 5 5 4 3 7545C 2 Domain of Prior Experienc e 1 0 -1 -2 7143C Al 7513C -5 -4 -3 -2 -1 0 1 2 3 Principal Component 3 SIMCA-P 11 - 4 /14/2010 10:17: 54 AM Lot 7143C most improves domain of prior experience. 30 Expanding Domain of Experience 2 - Alternate Vendors Rationale: Evaluate alternative vendors of same excipient for similarities/ differences in CoA data Example: Mg St - Mallinckrodt vs. 1 lot of Peter Greven Mg St lR P ig fize ht r s In R c es er ve d 5 3 2 1 0 -1 -2 -4 Al Principal Component 5 4 -3 -2 -1 0 1 2 3 4 5 6 7 Principal Component 1 SIMCA-P 11 - 8/27/2010 11:54:15 AM 31 Expanding Prior Experience: 3 - Using Alternate Grades 4.0 3.0 2.0 0.0 -1.0 -2.0 -3.0 -4.0 -6 -5 -4 -3 -2 -1 0 1 Al t[2] 1.0 lR P ig fize ht r s In R c es er ve d PH101 PH102 PH200 2 3 4 5 6 7 8 9 Domain of Prior Experience Prior demonstration of quality product Normal Operating Range Ideal to be able to use all PH102 lots 10 t[1] SIMCA-P+ 12 - 2011-06-28 04:29:51 (UTC-5) Examination of smaller PS (PH101) and larger PS (PH200) in DP development broadens knowledge space and may enable all lots of PH102 to be within design space for PS. 32 Impact on Other Components 3.0 2.0 0.0 -1.0 -2.0 -3.0 -4.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 Al PC 5 1.0 PH102 PH200 lR P ig fize ht r s In R c es er ve d PH101 0.5 1.0 1.5 2.0 2.5 3.0 Prior Experience Prior demonstration of quality product 3.5 PC 4 SIMCA-P+ 12 - 2011-06-28 04:39:56 (UTC-5) Data for other CoA parameters is intermixed for other PCs. Only a fraction of PH102 lots are within the domain of prior experience for other parameters. 33 Outline lR P ig fize ht r s In R c es er ve d • Background on QbD concepts • Approaches for assessing excipient variability in Drug Product Design – Case Study 1 - using vendor CoA datasets to understand variability – Case Study 2 – risk-based and experimental investigations of excipient variability and drug product performance – Case Study 3 - Quality by Design evaluation for robustness of an immediate release tablet platform Al • Closing thoughts: Interaction with regulatory bodies 34 How Much Impact do Excipients Have? API Excipient lR P ig fize ht r s In R c es er ve d C. Moreton, APR (2010) API Excipient Excipient API Process Process Al Excipients are minor contribution Control strategy focuses on API and process parameters Process Excipients are primary contribution Control strategy focuses on excipient properties 35 Quality by Design Evaluation for an Immediate Release Tablet Platform Study Objectives • • Factors • API Properties • Type: Ibuprofen and Theophylline • Loading: 1, 5, and 25% • Particle Size: 8 – 114 microns • Excipients • • • • 2 parts Microcrystalline cellulose 1 part Spray-dried lactose 1% Magnesium stearate 3% Croscarmellose Sodium Varied diluent:lube particle size ratio (3.4 – 41.6) based on results of prior dry granulated IR tablet excipient variability study. • Manufacturing Method Al • Investigate the impact of excipient lot-to-lot variability on drug product performance and manufacturability, relative to the impact of API properties and manufacturing method Evaluate robustness of an IR tablet platform. lR P ig fize ht r s In R c es er ve d • • Direct Compression (DC) • Dry Granulation (DG) Joseph Kushner IV, Beth A. Langdon, Ian Hicks, Daniel Song, Fasheng Li, Lalji Kathiria, Anil Kane, Gautam Ranade, Kam Agarwal. (2014) A quality-by-design study for an immediate-release tablet platform: examining the relative impact of active pharmaceutical ingredient properties, processing methods, and excipient variability on drug product quality attributes. J Pharm 36 Sci, 103:527-538. Materials (BASF, Ludwigshafen, Germany) Theophylline API (BASF, Ludwigshafen, Germany) Microcrystalline cellulose (FMC Biopolymer, Philadelphia, PA) Lactose (spray dried) (DMV Fontera, Goch, Germany) Magnesium stearate (Mallinckrodt, Hazelwood, MO) Vendor Lot # D[4,3] (µm) Factor Level 25 38 50 Anhydrous powder 200M PLV Micronized Avicel 200 Avicel 102 - C5b Avicel 102 - N3b Avicel 102 - N4a Avicel 101 Lactopress 250 (screened coarse) Lactose SD11 (NZ) Lactopress 260 Lactose SD11 (EU) Lactopress 250 (screened fine) Lactopress 250 Lactopress 250 Mg Stearate 434 Mg stearate KP 5712 Mg stearate KP 5712 Mg stearate KP 5712 Mg stearate VG 1726 IB1V0817 IB1V0311 IB1V1089 179921AX20 169321AX20 198721AX20 PN12824026 71138C5BC P211823545 P212824256 P112824137 N/A HV120027 600656 10648454 N/A 600722 600848 1207000026 1110000870 1005000629 1203000003 1203000005 29.1 42.6 55.3 8.0 37.6 113.5 244.9 136.2 126.2 101.9 62.9 159.4 148.3 137.6 125.9 77.6 124.1 122.9 19.9 12.1 11.7 10.6 5.2 LOW MID HIGH LOW MID HIGH 1 2 3 4 5 1 2 3 4 5 N/A N/A 1 2 3 4 5 lR P ig fize ht r s In R c es er ve d Ibuprofen API Vendor Grade Al Material Name (Vendor, location) Examined effect of both intra-grade and inter-grade excipient particle size variability on IR tablet product performance. Experimental Plan Al lR P ig fize ht r s In R c es er ve d Design: Resolution V 25-1 fractional factorial with axial and center runs 38 Methods • Manufacturing Process – Blending (5-kg batch) Blend – 10 min, 12 rpm Mill – 1000 rpm Blend - 10 min, 12 rpm Lube – 3 min, 12 rpm – Granulation • Roll force – 6 kN/cm • Roll speed – 2 rpm • Roll gap – 2 mm – Tablets – Blends • Particle size • Flow lR P ig fize ht r s In R c es er ve d • • • • • Evaluated Attributes Al • 8-station Manesty • 100 mg flat-faced tablets • 50 rpm speed – Granulation • Ribbon solid fraction • Particle Size • Flow – Tablets • Weight, thickness, hardness, friability • Solid fraction, tensile strength • Disintegration time • Potency Al lR P ig fize ht r s In R c es er ve d Sample Results: Blend Flow Very poor flow (1.46-1.59) observed for high drug load, low drug PS, and low diluent:lube PS ratio. 60% of observed variation due to API properties. 35% due to excipient particle size variation. 40 Al lR P ig fize ht r s In R c es er ve d Sample Results: Granulation Flow Granulation is satisfactory (0.7) to very good (0.95), across all API loadings and particle sizes. 10% of observed variation due to API properties. 47% due to excipient particle size variation. 42% due to API and excipient PS interaction. lR P ig fize ht r s In R c es er ve d Sample Results: Content Uniformity Within-grade Particle Size Variation Al High acceptance values (AV>15) for low drug loading, large drug PS, and high diluent:lube ratio. Within grade variations meet AV criteria. 58% of observed variation due to API properties. 22% due to manufacturing method. 16% due to excipient particle size variation. 5% due to API load, excipient interaction. 42 Robustness Domain for IR Tablets 1 1.2 1.4 1.6 1.8 2 Granulation Flow (Carr) 0 10 20 30 40 50 Tablet Weight (mg) Tablet Weight RSD (%) Tablet Potency (%LC) lR P ig fize ht r s In R c es er ve d Blend Flow (Hausner) 97 98 0 99 0.5 85 1 100 1.5 90 101 2 95 2.5 100 102 3 103 3.5 105 4 110 115 Acceptance Value 0 Tensile Strength (MPa) 0 1 2 3 4 5 Friability (%) 0 0.2 0.4 0.6 0.8 1 Solid Fraction 0 1 2 Al Disintegration (min) 5 0.75 3 10 4 0.8 5 6 7 15 8 0.85 9 10 11 0.9 20 12 13 14 15 0.95 All batches satisfactory except for 25% loading of micronized 8 micron theophylline API. (i.e., very poor flow led to high tablet weight RSD for 100 mg tablets) Case Study #3 Conclusions Excipient particle size variability had the most (~50%) impact on blend and granulation particle size, and granulation flow. • API property variation dominated (~60%) blend flow, ribbon solid fraction and tablet properties. • Acceptable product quality with immediate-release tablet platform achieved for within-grade excipient particle size variations. lR P ig fize ht r s In R c es er ve d • – Some challenges observed for extreme DC formulation cases: • Flow: High drug loading, lower API and excipients particle size • Tablet weight uniformity: Low drug loading, larger API and excipients particle size Control strategy implications: – Use of GMP procedures for formulation composition and within-grade excipients should provide adequate product robustness for an immediate release tablet over a wide range of API loadings and particles sizes. Al • 44 Outline lR P ig fize ht r s In R c es er ve d • Background on QbD concepts • Approaches for assessing excipient variability in Drug Product Design – Case Study 1 - using vendor CoA datasets to understand variability – Case Study 2 – risk-based and experimental investigations of excipient variability and drug product performance – Case Study 3 - Quality by Design evaluation for robustness of an immediate release tablet platform Al • Closing thoughts: Interaction with regulatory bodies 45 Example Questions from Major Regulatory Agencies lR P ig fize ht r s In R c es er ve d – Describe the effect of the physical properties (e.g., particle size, density) of excipients on the particle size distribution of the granulation, sieve cut potency, and tablet core content uniformity. – How particle size of excipients (and other excipient properties) affect compressibility? Al – Discuss if lot-to-lot variability in excipient properties (e.g. bulk density, particle size, surface area) would have any adverse impact on finished product quality. If there is an adverse impact, describe your control strategy. 46 lR P ig fize ht r s In R c es er ve d Impact on Drug Product CQAs (safety and efficacy) No Impact on Drug Product CQAs (safety and efficacy) Impact on Business requirements (yield etc.) Variability observed but: No Impact on Drug Product CQAs (safety and efficacy) No Impact on Business requirements (yield etc.) Al Excipient Variability Excipient Variability – Regulatory and Business 47 Area for Regulatory Focus Area for Business Focus What is “Acceptable Risk Management” for Excipient Variability? Multivariate Analysis PH101 PH102 PH200 8 4.0 3.0 2.0 1.0 -1.0 -2.0 1.0 t[2] 0.0 lR P ig fize ht r s In R c es er ve d Principal Component 2 4 2 0.0 -1.0 0 -2.0 -3.0 -3.0 -2 -4.0 -4.0 -6 -4 Principal Component 4 SIMCA-P+ 12 - 2010-12-20 13:20:05 (UTC-5) Higher Risk 35 30 20 15 10 5 0 Bin Univariate Analysis -4 Frequency 25 -6 -2 0 2 4 Principal Component 1 6 8 10 -5 -4 -3 12 35 35 30 30 25 25 Frequency -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 20 15 -1 0 1 2 3 4 5 6 7 8 9 10 5 0 0 10 t[1] SIMCA-P+ 12 - 2011-06-28 04:29:51 (UTC-5) Minimal Risk 15 5 Bin -2 20 10 Al Principal Component 6 2.0 Frequency 3.0 6 Bin Future Question: What level of risk associated with excipient variability is acceptable for patient safety and for drug product efficacy and supply? 48 Summary lR P ig fize ht r s In R c es er ve d • Gather knowledge on excipients to reduce risks to patient safety and drug product efficacy and supply. – Work with material suppliers to better understand the variability present in their materials. – Leverage pre-existing data and models to support the knowledge and design spaces. – Multivariate analysis methods can help manage the large amounts of excipient material property data. Al • Excipient understanding is as important as API and process knowledge in the QbD world. – Need to balance business needs with regulatory expectations. 49 Acknowledgements • Pfizer – – – – Stanley Wu Wei Zhou Ebenezer Otoo Bo Wang lR P ig fize ht r s In R c es er ve d Dan Gierer Anthony Carella Cindy Oksanen Scott Herbig Kim Vukovinsky Kurt Speckhals Angela Hausberger Tom Garcia Roger Weaver Bruno Hancock Craig Bentham Graham Cook Vince McCurdy Al – – – – – – – – – – – – – • Patheon