CHALLENGES & OPPORTUNITIES OF ICHQ8 (PHARMACEUTICAL DEVELOPMENT) AN INDUSTRY PERSPECTIVE

CHALLENGES & OPPORTUNITIES OF ICHQ8 (PHARMACEUTICAL DEVELOPMENT) AN INDUSTRY PERSPECTIVE Paul Stott, PhD Head of US Product Development AstraZeneca ICH Quality Guidelines Workshop BioKorea 2007 Sept 13-14

Overview ICH vision Background Principles and concepts developed by EFPIA PAT Topic Group Fictitious example EFPIA Mock P2 AstraZeneca example from the FDA CMC Pilot Program Cost Savings from AZ QbD examples to date Conclusions

The Vision of ICHQ8 (and 9 & 10) Pharmaceutical and Manufacturing Sciences leading to continuous product and process improvement A transparent, science and risk based approach to: product development and dossier submission, review, approval and post-approval changes Manufacturers empowered to effect continual improvement throughout the product life-cycle and supply chain More efficient and effective Regulatory oversight

Present Position Lots of check box guidelines in West Specifications set based on batch data Q6A Large numbers of post approval submissions, Timescales different between regions causing difficulties for a global supply chain Lack of clear understanding of some terms: critical quality attributes Regulatory Agreement Design Space etc.

The Way Forward? ICH - Q8, Pharmaceutical Development (Step 5) - Q8 (R) (Step 1) - Q9, Quality Risk Management (QRM) (Step 5) - Q10, Quality Systems (Step 2) FDA - Pharmaceutical cgmps for the 21 st Century A Risk Based Approach - Quality Systems Approach to Pharmaceutical Current Good Manufacturing Practice Regulations - PAT A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance

ICH Q8: Pharmaceutical Development Design Space Definition The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality Regulatory Approach Working within the design space is not considered as a change Procedure Design space is proposed by the applicant and is subject to regulatory assessment and approval.

Structured Approach to Development & Flow In Dossier Target Product Profile Product/ Prior Process Knowledge Dev. Product/ Process Design Space Control Strategy Regulatory Flexibility Definition of Product Intended Use and predefinition of Quality targets (wrt clinical relevance, efficacy and safety) Summary of Prior Scientific Knowledge (drug substance, excipients; similar formulations and processes). Initial Risk Assessment Overview of Quality by Design key actions and decisions taken to develop New Scientific Knowledge, e.g. DoE, PAT, Risk Assessment and Control Summary of Scientific Understanding of Product and Process. Justification and description of Multi-dimensional Space that assures Quality (interrelation-ships and boundaries of Clinical Relevance). Definition of Control Strategy based on Design Space leading to Control of Quality and Quality Risk Mgmt. (Process Robustness) Proposal of Regulatory Flexibility based on Product and Process Scientific Knowledge and Quality Risk Mgmt. (Materials,Site Scale etc) taken from the EFPIA PAT Topic Group

Case Study #1 Examplain Mock P2 Submission: fictitious product developed by the EFPIA PAT Topic Group to illustrate the concepts of QbD

Examplain Tablets Brief Description Examplain an immediate release solid dosage form Tablet of 200 mg compression weight containing 20 mg drug substance Biopharmaceutics Class I (highly soluble, highly permeable) Conventional, wet granulated tablet formulation Some potential for degradation (hydrolysis) - fluid-bed drying is a critical step Drug substance properties Low bulk density potential issues with content uniformity crystalline, single stable polymorph Primary amine salt

Process Development Drying Curves Drying experiments at 1 kg scale Using wet granulate with water content of 18±0.5% (as is routinely produced by the granulation process) Fluid bed drier inlet temperature and air flow were varied Stopped when the water content was in the range 1.5-2.0% Water content of the granules, and their particle size distribution were monitored on-line Confirmed at larger scale On monitoring system scale independent

Process Development Degradation and generation of fines Degradation Fines Inlet temperature Inlet temperature Air flow Air flow Red = does not meet quality requirements 1 kg scale

Process Development Combination of Failure Modes Degradation and fines Inlet temperature Air flow

Process Development Drying Process Trajectories (1 kg Scale) 18.5% 17.5% 1 2 % 3 H 2 O 4 5 2.0% 1.5% 9 8 7 6 Drying time

Design Space for Fluid Bed Drying Summary Design Space comprises: Critical Process Parameters Inlet temperature Air flow Drying Time Quality Attributes Degradation Disintegration Uniformity of Content Multivariate process parameters represented by process trajectories for water content Change of scale understood Areas of failure found in this case Clear control strategy

Design Space for Drying Graphical Description 18.5% 17.5% % H 2 O Regions of uncertainty Known edge of failure due to fines Known edge of failure due to degradation Trajectories describing the boundaries of the design space where product quality is assured 2.0% 1.5% Drying time

Based on: Regulatory Flexibility Mechanistic process understanding Consequent application of Risk Management Development and implementation of Design Space for each unit operation e.g. Fluid bed drying Derivation of the critical to control attributes Regulatory flexibility is proposed for the following topics: Process validation Scale and equipment change Site changes Real time release

Case Study #2 An example of an AstraZeneca Development using the Principles of ICHQ8 from the FDA CMC Pilot Program

QbD approach Quality Risk Management used throughout to direct and focus development work and review the impact of increased knowledge and understanding IVIVC study Testing of the highest risk product and process variables in vivo and development of a clinically relevant dissolution test (underpins proposed Design Space) Multivariate experiments use of PAT tools to measure in-process product attributes, and suitable data analysis tools to determine the most relevant raw materials and process variables linked to Primary Product Attributes and Secondary Product Attributes The Product Design Space Knowledge and understanding used with risk-based approach to propose regulatory flexibility Used to assure patient safety and efficacy (clinical quality) - not linked to physical quality or manufacturability The Product Control Strategy Used to assure clinical and physical quality Managed by internal change control procedures

Drug Substance Properties (part of initial prior knowledge) Amount dissolved in 250 ml (mg) 1000 Molecular Weight: 475 pka: dibasic displays high permeability dissolved in 250 ml (mg) >300mg 100 10 1 0 1 2 3 4 5 6 7 8 9 ph BCS Class II Poor compression properties Yield pressure (slow) MPa Yield pressure (fast) MPa Strain rate sensitivity % Cpd X Ideal 21.7 ~120 64.5 197 ~140 <15

Initial High Level Risks for the Drug Product Initial Quality Risk Assessment: 1. Impact of product / process variables on in vivo performance (BCS Class II) 2. Compression properties leading to poor physical quality

Clinical Quality Risk Assessment 120 (FMEA) Risk Product Number 100 80 60 40 20 Used to choose the highest risk tablet variants (incorporating both product and process variables) to be included in an IVIVC study 0 Changes in Increased level Decreased Impeded Variability in Insufficient Wet mass - Excessive Incorrect Excessive Increased Variability in API particle of binder level of wetting due to filler level or mixing = poor over- water added or granule milling blend time - compression coating size/properties disintegrant Mg Stearate properties blend granulation holding the wet parameters h'phobic coat force thickness variability uniformity mass for too of mg stearate long - around decrease in granules disintegrant performance Potential Failure Mode Table 1 Highest risk failure modes with the potential to impact in vivo performance Manufacture of Tablet Variants A to D Tested in vivo and in vitro Failure mode Changes in drug substance (vandetanib) particle size Failure to control granulation end-point; overgranulation, Increased level of binder in the formulation Decreased level of disintegrant in the formulation. Dissolution retardation mechanism Impact of drug substance surface area on rate of dissolution Impact of granule density and porosity on the rate of ingress of water Impact of slowed tablet disintegration rate on subsequent drug dissolution

In Vitro In Vivo Correlation? Equivalent in vivo performance Couldn't develop an IVIVC as highest risk variants (with different in vitro profiles) shown to perform the same as an oral solution in vivo Clinically relevant dissolution test developed that could be used to test future variants in vitro % Dissolution 110 100 90 80 70 60 50 40 30 20 10 Variant A (Standard tablet) Variant B (Larger particle size) Variant C (Process variant) Variant D (Formulation variant) 0 0 20 40 60 80 100 120 Time (minutes) Boundary of clinical quality based on dissolution profile of Tablet Variant D Broad applicability based on different mechanisms of dissolution retardation investigated

Output of clinical evaluation of product & process variables The studies provide a tool to define the boundaries of the Design Space based on in vivo performance (safety & efficacy) An understanding that there s a low probability of originally perceived high risk changes impacting in vivo PK An increased detectability as the dissolution method has been shown to be a suitable surrogate for in vivo performance (in conjunction with Assay and Uniformity of Dosage Unit) Product dissolution limits using this method will be based on the profile from Variant D, not on process capability The dissolution method can be used to evaluate other product and process variables in the establishment of the Design Space Future changes such as site, scale, equipment, method of manufacture can be qualified using this dissolution method and limit

Process Evaluation studies Impact of extremes of the manufacturing process on dissolution performance: 120 100 80 % Dissolved 60 40 Variant D 20 Variant C 0 0 10 20 30 40 50 60 70 Time (minutes) Also no impact on Assay or UoDU

Output from Process Evaluation Studies 1. Clinically relevant dissolution test used in conjunction with Assay and UoDU tests to demonstrate that remaining material and process variables have no impact on in vivo PK 2. Detailed understanding of the most relevant parameters wrt product physical quality 120 100 Risk Product Number 80 60 40 20 0 Changes in API particle size/properties Increased level of binder Decreased level of disintegrant Increased wetting due to Mg Stearate variability Variability in filler level or properties Insufficient mixing = poor blend uniformity Wet mass - Excessive water over-granulation added or holding the wet mass for too long Potential Failure Mode Incorrect granule Excessive blend milling time - h'phobic parameters coat of mg stearate around granules Increased compression force Variability in coating thickness

Demonstrated multivariate cause and effect relationships for product physical quality 0.055 Impeller Speed =1200 rpm 0.051 B: Mesh Size 0.047 2 0.043 Tabs/punch to picking: 9000 0.039 7.00 7.19 7.38 7.56 7.75 A: Water Quantity Process parameters Granule attributes Tablet attributes 0.055 Impeller Speed = 900 rpm Water quantity GSA Hardness B: Mesh Size 0.051 Wtd Capping: 5 0.047 Comil screen size Comil impeller speed % fines fraction 0.043 Capping MgSt addition method 0.039 7.00 7.19 7.38 7.56 7.75 A: Water Quantity Pre-compression Press speed 850 um sieve fraction Picking

Using Product Knowledge to Develop the Design Space Primary Attributes (Clinical Quality Design Space) Secondary Attributes (Physical Quality Control Strategy) Dissolution Assay Uniformity of Dosage Units (surrogates for in vivo exposure as demonstrated) These attributes directly impact on patient safety and efficacy These will be key elements of the proposed Design Space Appearance Picking Capping Hardness Do not impact on patient safety and efficacy and as such will not constitute a boundary of the Design Space We will use our in depth manufacturing knowledge to control these & share with the Regulatory Agencies (in the dossier) for information

The Drug Product Design Space: Is a combination of Input Boundaries and Primary Attributes boundaries: The Input Boundaries were defined to ensure: a) Low probability* of failure against Primary Attributes throughout the shelf-life of the product b) Low risk* of diminishing the clinical relevance of the clinical quality test methods (especially dissolution) * Assessed using experimentation and prior knowledge The Primary Attribute boundaries were defined to ensure in vivo performance Input Boundaries Constraints on: Drug substance particle size Formulation Process Type Primary Product Attributes (Outputs) Constraints on: Assay Uniformity of Dosage Units Dissolution Ensure correct and consistent dosing to patient Ensures appropriate in vivo performance. No constraints on: Site Scale Equipment and Process Parameters Low probability + High detectability = low risk within Design Space

Proposed Regulatory Flexibility The Design Space had no constraints on: Process parameters Equipment type Site of manufacture Scale of Manufacture any change to the above will be qualified by the dissolution method (in conjunction with Assay and UoDU) and managed by internal change control processes Process type (wet gran) and pack were fixed to ensure the clinical relevance of dissolution test and negate the need for further stability studies when working within the Design Space All backed by a sound scientific and risk-based understanding of the impact of product and process variables on the Primary (linked to clinical quality) and Secondary (linked to physical quality) attributes This has the potential to offer Operations real flexibility and will facilitate continual improvement

Case Study Conclusions The two case studies demonstrate a systematic approach to establish Design Space Design Space will be different for each product Two very different examples presented One based on process parameters one on product attributes Risk Management has been used to direct each stage of the development process Highly desirable to have boundaries linked to safety and efficacy Writing the Dossier will be a challenge - there is not one way

Have AstraZeneca s investment in QbD and Control Strategies been worth the effort? YES! A few examples to illustrate the internal value.

Raw Materials polymer quality 100 80 In-house specs Released (%) 60 40 20 RFT = 55 % 0 1997 1998 1999 Year 2000 2001 2002 Root Cause: new polymer batches changing release rate Steps: use chemometrics to understand critical polymer properties & implement simple evaluation tools for QC Operations Result: Right First Time increased from 55 % to 93 % (2006) Continuous Improvement: towards 100% RFT

In-process control with ACDRA (ACcelerated Dissolution Rate Analysis) >50 000 analyses and tests performed since 1993! Losec 1993- Losec MUPS 1998- Nexium 2000- Seloken ZOC 2000-2002

Improved quality Losec capsules 1995-2002 Fraction of rejected batches based on ACDRA results: 1995: 3.8 % (of 369 batches) 1996: 6.5 % ( 540 ) 1997: 3.8 % ( 639 ) 1998: 0.7 % ( 592 ) 1999: 0.5 % ( 411 ) 2000: 0.0 % ( 521 ) 2001: 0.0 % ( 508 ) 2002: 0.0 % ( 447 ) Why? Improved processes due to feedback to the operators

Savings Losec capsules Dissolution tests moved from Q-lab to IPC-lab (ACDRA) Less Q-analyses: 1.300 kusd (1995-2000) Reduced lead time: 86 kusd (2002)

Faster trouble shooting PRODUCT PROBLEM ROOT CAUSE SAVINGS Losec caps (1996) Slow drug release Variation in approved raw material 35-50 batches 2.0 3.0 MUSD Nexium caps (2000) Poor acid resistance EC solution not homogeneous 10 15 batches 1.4 2.0 MUSD Nexium tabl (2002) Poor acid resistance Fluid bed coater malfunction 30 40 batches 2.0 3.0 MUSD Savings thanks to immediate detection of problem by the ACDRA system

At-line application NIR for content and moisture Replace IPC-lab HPLC and KF analysis with at-line NIR API content and moisture in granules Benefits (samples not sent to QC-lab): Cost savings: Analysis (ca 0.5 MUSD pa) Cut lead time: ~4 days (ca 0.1 MUSD pa)

Challenges & Opportunities of ICHQ8 Challenges: Linking product and process variables to in vivo performance Every QbD development will be different Changing skill requirements (process engineers, biopharmaceutics etc.) Need to demonstrate long term financial & quality benefits to gain senior management buy-in Inherent conservatism of the Pharma Industry Opportunities: enables us to focus on those aspects which have the greatest potential to affect the patient facilitates continuous improvement in the manufacturing process provides flexibility of the supply chain and so ensures an efficient & reliable supply of high quality product Significant efficiency gains & financial savings are possible e.g. reduced batch failures and reduced lead times

Acknowledgements Chris Potter (& the EFPIA PAT Topic Group) Ryan Gibb Paul Dickinson Linda Billett Christer Karlsson Staffan Folestad Arne Torstensson and many other colleagues at AZ