an industry perspective - ICH

Paul Stott, AstraZeneca – BioKorea 2007

CHALLENGES & OPPORTUNITIES OFICHQ8 (PHARMACEUTICAL DEVELOPMENT) –

AN INDUSTRY PERSPECTIVE

Paul Stott, PhDHead of US Product Development

AstraZeneca

ICH Quality Guidelines WorkshopBioKorea 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 21st Century – A RiskBased Approach

- Quality Systems Approach to Pharmaceutical CurrentGood Manufacturing Practice Regulations

- PAT – A Framework for Innovative PharmaceuticalDevelopment, 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

TargetTargetProductProduct

ProfileProfile

ControlControlStrategyStrategy

PriorPriorKnowledgeKnowledge

Product/Product/ProcessProcessDev.Dev.

Product/Product/ProcessProcessDesignDesignSpace Space

Definition of Product Intended Use and pre-definition of Qualitytargets (wrt clinical relevance, efficacy and safety)

Summary of Scientific Understanding of Product andProcess.Justification and description of Multi-dimensional Space that assures Quality(interrelation-ships and boundaries of Clinical Relevance).

Definition ofControl Strategybased on Design Space leading to Control of Qualityand Quality Risk Mgmt.(Process Robustness)

Overview ofQuality by Design key actions and decisions taken to develop New Scientific Knowledge, e.g. DoE, PAT, Risk Assessmentand Control

Summary ofPrior Scientific Knowledge(drug substance, excipients; similar formulations and processes). Initial Risk Assessment

RegulatoryRegulatoryFlexibilityFlexibility

Proposal of Regulatory Flexibility based on Product and Process Scientific Knowledgeand 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 DevelopmentDrying 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 DevelopmentDegradation and generation of fines

Air flow

Inle

t tem

pera

ture

Fines

Air flow

Inle

t tem

pera

ture

Degradation

Air flow

Inle

t tem

pera

ture

Fines

Air flow

Inle

t tem

pera

ture

Fines

Air flow

Inle

t tem

pera

ture

Degradation

Air flow

Inle

t tem

pera

ture

Degradation

Red = does not meet quality requirements1 kg scale


Air flow

Inle

t tem

pera

ture

Degradation and fines

Air flow

Inle

t tem

pera

ture


Air flow

Inle

t tem

pera

ture


Process DevelopmentCombination of Failure Modes


Process DevelopmentDrying Process Trajectories (1 kg Scale)

% H2O

2.0%1.5%

18.5%

Drying time

17.5% 12

34

5

6789

% H2O

2.0%1.5%

18.5%

Drying time

17.5% 12

34

5

6789


Design Space for Fluid Bed DryingSummary

• Design Space comprises:

Critical Process Parameters Quality AttributesInlet temperature DegradationAir flow DisintegrationDrying Time 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 DryingGraphical Description

Known edge of failure due to fines

% H2O

2.0%1.5%

18.5%

Drying time

Known edge of failure due to degradation

Regions of uncertainty17.5%

Trajectories describing the boundaries of the design space where product quality is assured

Known edge of failure due to fines

% H2O

2.0%1.5%

18.5%

Drying time

Known edge of failure due to degradation

Regions of uncertainty17.5%

Trajectories describing the boundaries of the design space where product quality is assured


Regulatory FlexibilityBased on:

– 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)

• Molecular Weight: 475• pKa: dibasic• displays high permeability

• BCS Class II• Poor compression

properties

Amount dissolved in 250 mL (mg)

1

10

100

1000

0 1 2 3 4 5 6 7 8 9

pH

diss

olve

d in

250

mL

(mg)

>300mg

Cpd X Ideal21.7 ~120

~140

<15

64.5

197

Yield pressure (slow) MPaYield pressure (fast) MPaStrain rate sensitivity %


Initial High Level Risks for the Drug Product

Initial Quality Risk Assessment:

1. Impact of product / process variables on in vivoperformance (BCS Class II)

2. Compression properties leading to poor physical quality


Clinical Quality Risk Assessment (FMEA)

• Used to choose the highest risk tablet variants (incorporating both product and process variables) to be included in an IVIVC study

• Manufacture of Tablet Variants A to D

• Tested in vivo and in vitro

Table 1 Highest risk failure modes with the potential to impact in vivo performance

Failure mode Dissolution retardation mechanism

Changes in drug substance (vandetanib) particle size

Impact of drug substance surface area on rate of dissolution

Failure to control granulation end-point; over-granulation,

Impact of granule density and porosity on the rate of ingress of water

Increased level of binder in the formulation

Decreased level of disintegrant in the formulation.

Impact of slowed tablet disintegration rate on subsequent drug dissolution

0

20

40

60

80

100

120

Changes inAPI particle

size/properties

Increased levelof binder

Decreasedlevel of

disintegrant

Impededwetting due toMg Stearate

variability

Variability infiller level orproperties

Insufficientmixing = poor

blenduniformity

Wet mass -over-

granulation

Excessivewater added orholding the wet

mass for toolong -

decrease indisintegrantperformance

Incorrectgranule milling

parameters

Excessiveblend time -

h'phobic coatof mg stearate

aroundgranules

Increasedcompression

force

Variability incoating

thickness

Potential Failure Mode

Ris

k Pr

oduc

t Num

ber


In Vitro In Vivo Correlation? – Equivalent in vivoperformance

• 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

0

10

20

30

40

50

60

70

80

90

100

110

0 20 40 60 80 100 120

Time (minutes)

% D

isso

lutio

n

Variant A (Standard tablet)Variant B (Larger particle size)Variant C (Process variant)Variant D (Formulation variant)

• 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


• Impact of extremes of the manufacturing process on dissolution performance:

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70

Time (minutes)

% D

isso

lved

• Also no impact on Assay or UoDU

Variant D

Variant C

Process Evaluation studies


Output from Process Evaluation Studies1. 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

0

20

40

60

80

100

120

Changes in APIparticle

size/properties

Increased levelof binder

Decreased levelof disintegrant

Increasedwetting due toMg Stearate

variability

Variability in fillerlevel or

properties

Insufficientmixing = poor

blend uniformity

Wet mass -over-granulation

Excessive wateradded or holdingthe wet mass for

too long

Incorrect granulemilling

parameters

Excessive blendtime - h'phobic

coat of mgstearate around

granules

Increasedcompression

force

Variability incoating

thickness

Potential Failure Mode

Ris

k Pr

oduc

t Num

ber

2. Detailed understanding of the most relevant parameters wrt product physical quality


Demonstrated multivariate cause and effect relationships for product physical quality

Processparameters

Granule attributes

Tablet attributes

Water quantity

Comil screen size

Comil impeller speed

MgSt addition method

Pre-compression

Press speed

GSA

% fines fraction

850 um sieve fraction

Hardness

Capping

Picking

Processparameters

Granule attributes

Tablet attributes

Water quantity

Comil screen size

Comil impeller speed

MgSt addition method

Pre-compression

Press speed

GSA

% fines fraction

850 um sieve fraction

Hardness

Capping

Picking

7.00 7.19 7.38 7.56 7.75

0.039

0.043

0.047

0.051

0.055Impeller Speed =1200 rpm

A: Water Quantity

B: M

esh

Siz

eTabs/punch to picking: 9000

22

7.00 7.19 7.38 7.56 7.75

0.039

0.043

0.047

0.051

0.055Impeller Speed = 900 rpm

A: Water Quantity

B: M

esh

Siz

e

Wtd Capping: 5


Primary Attributes(Clinical Quality – Design Space)

• Dissolution• Assay• Uniformity of Dosage Units

(surrogates for in vivo exposure – as demonstrated)

These attributes directly impact on patient safety and efficacyThese will be key elements of the proposed Design Space

Secondary Attributes(Physical Quality – Control Strategy)

• Appearance• Picking• Capping• Hardness

Do not impact on patient safety and efficacy and as such will not constitute a boundary of the Design SpaceWe will use our in depth manufacturing knowledge to control these & share with the Regulatory Agencies (in the dossier) for information

Using Product Knowledge to Develop the 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 productb) Low risk* of diminishing the clinical relevance of the clinical quality test methods (especially

dissolution)

• The Primary Attribute boundaries were defined to ensure in vivoperformance

Input Boundaries

Constraints on:• Drug substance particle size• Formulation• Process Type

No constraints on:• Site• Scale• Equipment and Process

Parameters

Primary Product Attributes (Outputs)

Constraints on:• Assay• Uniformity of Dosage Units• Dissolution

* Assessed using experimentation and prior knowledge

The Drug Product Design Space:

Ensures appropriate in vivoperformance.

Ensure correct and consistent dosing to patient

Ensures appropriate in vivoperformance.

Ensure correct and consistent dosing to patient

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

0

20

40

60

80

100

1997 1998 1999 2000 2001 2002Year

Rel

ease

d (%

)

In-house specs

RFT = 55 %

Root Cause: new polymer batches changing release rateSteps: use chemometrics to understand critical polymer properties & implement simple evaluation tools for QC OperationsResult: Right First Time increased from 55 % to 93 % (2006)Continuous Improvement: towards 100% RFT


Losec 1993-Losec MUPS 1998-Nexium 2000-Seloken ZOC 2000-2002

In-process control with ACDRA(ACcelerated Dissolution Rate Analysis)

>50 000 analyses and tests performed since 1993 !


Improved quality

Why?Improved processes due to feedback to the operators

Losec capsules 1995 -2002Fraction 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 )


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 shootingPRODUCT PROBLEM ROOT CAUSE SAVINGS

Losec caps(1996)

Slow drug release

Variation in approved raw material

35 - 50 batches2.0 – 3.0 MUSD

Nexium caps(2000)

Poor acid resistance

EC solution not homogeneous

10 –15 batches1.4 – 2.0 MUSD

Nexium tabl(2002)

Poor acid resistance

Fluid bed coater malfunction

30 – 40 batches2.0 – 3.0 MUSD

Savings thanks to immediate detection of problem by the ACDRA system


At-line applicationNIR for content and moisture

Replace IPC-lab HPLC and KF analysis with at-line NIRAPI 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

Documents

an industry perspective - ICH