strategy and approach to quality by design - · pdf filestrategy and approach to quality by...

Strategy and Approach

to Quality by Design

9th Drug Evaluation Forum, Tokyo, Japan

Dr Talia Buggins, PhD, AstraZeneca R&D Macclesfield, UK

Ms Maria Edebrink, MSc, AstraZeneca R&D Södertälje,

Sweden

11 April 2012

From Viewpoints of Simultaneous Global Development

Strategy and Approach to Quality by Design

From Viewpoints of Simultaneous Global Development

Overview of Presentation

Why Quality by Design?

QbD Foundations

Basis for QbD Development

Drug Product Case Study

Drug Substance Case Study

Design Space in Marketing Application

Summary

Acknowledgment

Questions

Dr Talia Buggins Ms Maria Edebrink

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To perform development work in a more efficient, streamlined way

To provide opportunities for developing more reliable, robust processes and products and thus deliver increased value to our patients and the business

To allow continuous improvement of manufacturing processes and control strategies throughout the product lifecycle with reduced need for post-approval changes.

4 Dr Talia Buggins Ms Maria Edebrink

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QbD Foundations


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QbD Foundations


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Q8

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Basis for Drug Substance

and Drug Product QbD

Development


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QbD Development Basis for Drug Substance & Drug Product QbD Development


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Drug Product

CQA

Drug Substance

CQA

Drug Product

CQA

Drug Substance

CQA DS Design Space

DS Control Strategy

DP Design Space

DP Control Strategy

QRA Process understanding

Several iterations

may be needed

Patient Safety and Efficacy

Quality Target Product Profile

based on Clinical/Toxicological data

Desired DP properties

Product Risk

Product Knowledge

Overview of Steps in a typical QbD Development

Collate Prior Knowledge

Perform High Level Risk Assessment

Conduct Experimental Evaluation

2nd Iteration of Risk Assessment

Evaluate impact of highest risk variables on in vivo performance

Develop detailed process understanding

Review Risk Assessment

Construct Design Space

Construct Quality Target Product Profile

Establish Control Strategy

9

Product Risk

Product Knowledge












10

QbD Development Quality Risk Assessment, DoE & Kinetics


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Quality Risk Assessment – Failure Mode, Effects and Criticality Analysis

(FMECA)

How we applied FMECA:

Each Critical Quality Attribute (CQA) assessed separately

Based on Experimental data and scientific judgment

Risk priority number = Probability * Severity * Detectability (P*S*D)

Criticality number = Probability * Severity (P*S*Detectability API Spec only)

Iterative process delivering Method of manufacture & Control Strategy



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Initial QRA

Experiments and simulations

Enough data

aquired?

Criticality P*S*D (spec only)

Risk Priotiry P*S*D

All risks Low?

Apply Control Strategy

Yes

No

Established Process

Yes

No

Steer development work

Finalize the Control Strategy



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Table 1 Overview of the criticality

CQA Charging Reaction Work-up Distillation Crystallisation Isolation Drying

Description Low Low Low Low Low Low Low

Identity Low Low Low Low Low Low Low

Assay Low Low Low Low Low Low Low

Absolute

configuration

Low Low Low Low Low Low Low

Organic impurities Low Medium Low Low Low Low Low

Solvents Low Low Low Medium Low Low Low

Water Low Low Low Low Low Low Low

Sulphated ash Low Low High Low Low Low Low

Polymorphic form Low Low Low Medium Low Low Low

Particle size Low Low Low Medium Medium Low Low

Microbiologya Low Low Low Low Low Low Low

Drug Product Case Study:

Linking the Design

Space to clinical performance


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Case study: linking the Design Space to clinical performance

Understanding the in vivo impact of product and process variables is

an important foundation of any QbD development

When linked to meaningful in vitro tests, this enables:

evaluation multiple aspects of the Design Space and

development of science and risk based specifications

One approach is to confirm mechanistic understanding by producing

product variants that incorporate the highest risk variables, and then

evaluating their performance

A case study will be presented here which focuses on the risks

relating to dissolution and bioequivalence, but the principles apply

equally to all Critical Quality Attributes


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Product Risk

Product Knowledge

Case study: linking the Design Space to clinical performance





















1. Conduct Quality Risk Assessment

2. Develop Appropriate CQA tests

3. Understand the in vivo importance of changes

4. Establish Appropriate CQA limits

5. Use the product knowledge in subsequent

QbD steps


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Compound properties

Formulation

Immediate release (IR) tablet, manufactured by a wet granulation process

API properties

Low solubility in buffers <10g/mL across the physiological pH range

Moderate permeability

Absolute bioavailability: F = 36%, estimated fabs = 51 %

However, highly solubility in human intestinal fluid = 0.5 mg / mL

Believed to be due to presence of micelles

BCS class 4.

Dissolution

Poorly soluble in aqueous buffers therefore surfactant media chosen

For this drug surfactant media may have more in vivo relevance, as they are

likely to mimic the solubilisation mechanism in the intestinal environment due

to presence of micelles


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Step 1: Risk assessment

An FMECA-based QRA identified highest risk process and formulation parameters for in vivo performance

In vitro screening in several dissolution media confirmed that these factors did impact dissolution in a variety of media

We now needed to confirm if these changes in in vitro dissolution

would result in changed bioavailability

Used the BCS scientific framework to structure

this discussion / decision on whether further

investigations are required


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Dissolution limits which assure exposure by BCS Class for a QbD based design space (Dickinson et al. 2008)

1 3

2 4

Complete dissolution

within 15 minutes in most

discriminating ‘simple’

media (physiological pH

range).

If slower: bioavailability

data or additional

mechanistic information

Complete dissolution within

30 minutes in most

discriminating ‘simple’

media (physiological pH

range).

If slower: bioavailability data

or additional mechanistic

information

Limit set based on clinical

‘bioavailability’ data

Limit set on case by case

basis:

Solubility High Low

Bioequivalence Study;

Or follow principles of

BCS2 or BCS3 if can

demonstrate that

compound behaves more

like BCS2 or BCS3 in vivo

Dickinson et al. (2008) AAPS Journal. 10: 380-90. Dr Talia Buggins Ms Maria Edebrink

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Step 1: Risk assessment

The case study compound is BCS4 – therefore a clinical IVIVC-type

study will be needed to determine the relevance of in vitro changes

for in vivo performance, and define clinically meaningful in vitro tests

and specifications

Tablet variants were produced incorporating the highest risks from

the QRA for use in a clinical study

In vivo exposures from these variants were compared to the standard

Phase 3/commercial tablet, and an oral solution, in healthy

volunteers

Aims of study:

1. Demonstrate the impact of highest risk parameters on in vivo performance, to help define the Design Space

2. Provide in vivo data to enable selection of a relevant QC dissolution test which could assure in vivo performance


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Tablet variants selected for in vivo study

Description Dissolution risks addressed from initial QRA

Variant A Process variant : Over granulated and over-compressed

Granulation (high risk);

Compression (medium risk).

Change in filler grade causing change in granulation or compression behavior (medium).

Variant B Process variant : Over granulated (extreme) and over-compressed, only large (>1 mm) particles used for compression

Granulation (high);

Compression (medium).

Change in filler grade causing change in granulation or compression behavior (medium).

Variant C Formulation variant Vary the amount of binder and disintegrant

Binder - change in grade (high); Disintegrant - change in grade (medium).

It is not necessary to study all possible parameters in vivo, since

several lead to the same mechanism of slowing dissolution

API quality attributes are fixed by the Drug Substance Design Space Dr Talia Buggins Ms Maria Edebrink

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Step 2: Develop appropriate CQA tests

4x

Standard Tablet

Variant A

Variant B

Variant C

x

1.5x

1.5x

Surfactant 1: Quite similar discrimination

(rank order). Standard tablet very rapid

Surfactant 2: Similar discrimination,

slower profiles

Surfactant 3: Similar discrimination, Slower

profiles

FaSSIF: Media qualitatively similar to in vivo

environment. Useful to build confidence &

understanding

Same rank order for all media

Dissolution of the tablet variants was performed in several surfactant media:


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Step 3: Understand the in vivo importance of changes

+ = standard and side

batches based on most

relevant manufacturing

variables.

*For this type of

approach to be

acceptable the most

relevant risks to clinical

quality need to have

been assessed (i.e. in a

QbD setting).

Cha

nge

in C

max o

r A

UC

(%

)

0

-10

-20

-30

Time to x% dissolution (min)

-40

-50

+

+ +

+ +

+

+

+

1. ‘IVIVC’

2. ‘Safe Space’ 3. ‘Mixed safe space / IVIVC’

3 possible outcomes from the in vivo study:

1. An IVIVC is established, and a specification that controls Cmax and AUC by maximum +/-10% can be set.

2. “Safe space” - no effect seen in the clinical study, specification is set based on the slowest dissolution profile tested in the clinical study (i.e. set at the boundary of knowledge rather than on a biological effect).

3. Mixed safe space / IVIVC result - clinical pharmacokinetics only affected for a few of the variants tested clinically. Again this would allow a dissolution specification to be set that allowed Cmax and AUC to be controlled to +/- 10%.

Dickinson et al. (2008) AAPS Journal. 10: 380-90


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Clinical study results

• The tablet variants all fall within standard bioequivalence limits

compared to the standard tablet, indicating that exposures were

equivalent

• ”Safe space” result – the differences in in vitro performance were

not sufficiently large to affect in vivo performance

Treatment: Solution Standard Tablet Variant A Variant B Variant C

Pla

sma

Co

nce

ntr

ati

on

(n

g/m

L)

0

100

200

300

400

500

600

Protocol Time (hr) 0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72

Tablet comparison

AUC Geomean ratio (90% CIs)

Cmax Geomean ratio (90% CIs)

Variant A/ Standard

0.97 (0.90, 1.05) 1.05 (0.95, 1.16)

Variant B/ Standard

1.02 (0.94, 1.10) 1.04 (0.94, 1.15)

Variant C/ Standard

0.97 (0.89, 1.05) 0.91 (0.83, 1.00)


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Step 4: Using the in vivo data to establish appropriate dissolution limits

The in vitro dissolution methods are over-discriminatory

with respect to in vivo performance

relatively large changes in in vitro dissolution

performance only corresponded to very minor changes

in vivo, which are not sufficient to impact bioavailability

‘Safe Space’ dissolution window

The study data can therefore be used in conjunction with

the discriminatory dissolution methods to allow a

specification to be set that assures in vivo performance

i.e., assures that tablets from the design space will

have equivalent performance to batches used in the

pivotal safety and efficacy studies


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Step 4: Using the in vivo data to establish appropriate dissolution limits

Any tablet with a dissolution profile faster than Variant C would be expected to give equivalent in vivo exposures to the standard tablet

a specification set anywhere above this will guarantee equivalent safety and efficacy to the pivotal trials

This is a wider specification than would be derived using a traditional batch history approach

but it is scientifically supported by the increased understanding gained from a QbD development

Only relevant for this Design Space, as the risk assessment included certain assumptions specific for this e.g. process type, formulation inputs etc.


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Step 5: Using the knowledge gained in subsequent QbD steps

The dissolution method and in vivo relevant specification

were used during subsequent QbD development to:

1. Further develop mechanistic understanding of the process and

formulation

2. Define the Design Space to ensure that every tablet produced

within it will have equivalent performance to those in the pivotal

clinical studies

Extent to which highest risks could parameters can be varied

without impacting clinical performance understood following

clinical study

Assured that lower risk variables had no impact on in vivo

performance through in vitro testing only

3. Assure product quality during routine production as part of the

Control Strategy


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Linking the Design Space to clinical performance: Conclusions

The Design Space and Control Strategy should be developed to

guarantee efficacy and safety equivalent to the pivotal trials

CQAs, including dissolution, should be linked to safety and

efficacy

Need to develop product-specific understanding of in vivo

performance in order to achieve this

Based on this, specifications lower than clinical batch history may be

appropriate, as long as safety and efficacy remains unchanged

Major regulatory agencies have accepted the principal of linking

dissolution to clinical performance, but applied it to specification

setting in different ways

Potential to end up with different specifications in different regions

Still an emerging area of regulatory science


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Drug substance Case Study

Basis for Drug Substance & Drug Product QbD Development

Registered Starting Materials

Quality Risk Assessment, Design of Experiments and Kinetics

Scale & equipment dependence


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QbD Development Basis for Drug Substance & Drug Product QbD Development


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Drug Product

CQA

Drug Substance

CQA

Drug Product

CQA

Drug Substance

CQA DS Design Space

DS Control Strategy

DP Design Space

DP Control Strategy

QRA Process understanding

Several iterations

may be needed

Patient Safety and Efficacy

Quality Target Product Profile

based on Clinical/Toxicological data

Desired DP properties

Registered Starting

Materials


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Product Risk

Product Knowledge












32

QbD Development

RSM


Foundations for RSM

Stereochemistry investigated in-depth

and described in detail

Comprehensive understanding of

impurities and their fate in the process

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QbD Development RSM

Stereochemistry

In-depth literature survey

Ab-initio calculations

Experimental work to support conclusions from literature

Manufacture of possible stereoisomers

Chiral control of registered starting materials (RSM)

Statistics to eliminate analysis of the other enantiomer in API

Specification of enantiomers in RSM

Diastereomer control

Comprehensive understanding shared with regulatory authorities to

support knowledge based controls


0.5% 0.5% = 25 ppm

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QbD Development

RSM

Impurity tracking

Identification of impurities

Tracking of impurities

Fate of impurities

Spiking above specification limits

or

Selection of batches with elevated levels

Analysis in FI’s and/or Drug Substance

Knowledge used to support specifications related to process capability for RSMs


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Quality Risk Assessment,

Design of Experiments &

Kinetics


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Method:

Failure mode, effects and criticality analysis (FMECA) for risk assessment

Design of Experiments (DoE) incl Factorial Experimental Design

Statistic Evaluation

Kinetics

Scale independence justification


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Organic

impuritiesIPC control: volume and water Control to

Specification

Crystallisation

Dissolution time

Chargting rate

Antisolvent amount

Final temperature

Distillation DryingIsolation

Washing amount

Reaction

Acid volume

Solvent volume

Reaction temperature

Reaction time

Work-Up

QbD Development

Factors:

Time, temperature, amount of acid,

solvent volume

Statistical evaluation:

Identify significant parameters and

interactions

Create a statistical model

Evaluate contour plots

Included in S2.6 in the application:

Coefficient plots (all factors)

Coefficient plots (significant factors)

Contour plots


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Quality Risk Assessment, DoE & Kinetics


Investigations of kinetics:

Justify design space for a chemical reaction

Experimental data at different temperatures used to determine the rate expression for the main reaction and the competing side reaction

Rate expressions used to determine and visualise the design space


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Aspirations:

Use kinetics to develop a graphical representation of the Design Space

Assumptions:

The main reaction is an equilibrium

Acetone is formed/removed

Acid concentration is constant

No mass transport limitations

The rate expression for this equilibrium (constant acid concentration)

r=rate; k1=rate constant (product); k2=rate constant (starting material);

x=conversion in reaction

40 Dr Talia Buggins

Ms Maria Edebrink

xxkxkr 21 )1(

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QbD Development QRA, DoE & Kinetics

Method:

Conversion of starting material

investigated

Conversion measured as a function

of time

From 15 C up to 60 C

Result:

Rate expression for the conversion

of starting material to product


xxexer TRTR

3326549303

8 8626)1(10051.2

x = conversion in reaction; R = gas constant (8.31); T = temperature in Kelvin

AstraZeneca R&D


Method:

Reaction kinetics investigated

Impurity formation measured as a

function of time

15 C up to 60 C

Result:

Rate expression for the formation of

the impurity A


x = conversion to impurity A; R = gas constant (8.31); T = temperature in Kelvin

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min

1:1108.3

94724

12 unitxer TR


Result:

Graphical representation of Design

Space based on rate expression

developed from experimental data


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Scale & Equipment

Dependence


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QbD Development

Scale and Equipment

Dependence

Matrix Risk Assessment

CQAs v/s Unit operations

Supported with experimental data

Scale and equipment flexibilities

within the predicted range claimed in

S2.2

Flexibilities supported by data

presented in S2.6 in the Marketing

Application


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Reaction – Stability, establishment of

power input per volume

Distillation – Thermal stability and

distillation time simulation

Isolation – Provoked levels of impurities,

poor mixing, extreme wash conditions

Drying – Thermal Stability

Design Space in the Marketing Application


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Product Risk

Product Knowledge












47

Drug Product Design Space - an example

Design Space presented in P2.01 and P2.3

Supporting experimental data and risk assessments in P2.2 and P2.3

Fixed formulation inputs and process type

Drug substance CQAs assured by the drug substance Design Space

Represented as a series of manufacturing process steps

Design Space boundaries for equipment and process parameters are given for each unit operation

Scale independent parameters have been used to constrain some process steps in a multivariate manner eg. Spray Flux for the granulation step


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Drug Product Design Space

Represented as a series of process steps

Design Space boundaries for equipment and process parameters are given for each unit operation


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Equipment Intermediate product attributes or parameters

Direct heating; fluidised solids bed

Inlet air ≤ 85 C, moisture content ≤ 2%w/w

eg. Drying step

Drug Product Design Space

Scale independent parameters have been used to constrain some process steps in a multivariate manner eg. Spray Flux for the granulation step


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Equipment Intermediate product attributes or parameters

Wet high shear granulation; vertical. Scale up to 1800 L

Water: 20-30%w/w Speed: 3-8 m/s Time: 6-20 min Impeller tip speed 4.0 to 9.4 m/s Spray flux ratio 0.2 to 1.4

(GEA)

eg. Wet granulation step

Scale independent parameters in Design Space: Spray Flux for control of granulation step

Area flux of liquid

- Liquid flowrate (water quantity,

spray time, number of nozzles)

- Droplet size

Area flux of powder - Powder velocity

- Projected spray width

Y = Hapgood (2004) Y


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Scale independent parameters in Design Space: Spray Flux for control of granulation step


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Scale independent Design Space for granulation presented in the file

Granulation constrained by spray flux (within experimental experience) and extreme parameter ranges

Proposed scale constraints beyond our experience

Flexibility up to 1800L (2.25 x) requested

Limited acceptance by Health Authorities to include this flexibility as part of the Design Space

Some authorities required data from >800L prior to increase in scale AZ obliged to follow-up post-approval legislation

QbD Development

Drug Substance Design Space in Marketing Application

S2.6 Development History CQA description

Risk Assessment Summaries

Step-by-Step Narrative

DoE summaries including statistics

Kinetics

Scale and equipment dependence

Product Quality System

S2.2 Method of Manufacture Process Description

Graphical Representation of Design Space

Scale & Equipment limitations included

53 Dr Talia BugginsMs Maria Edebrink

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Organic

impuritiesIPC control: volume and water Control to

Specification

Crystallisation

Dissolution time

Chargting rate

Antisolvent amount

Final temperature

Distillation DryingIsolation

Washing amount

Reaction

Acid volume

Solvent volume

Reaction temperature

Reaction time

Work-Up

Global QbD filings

A knowledge based development can lead to a control strategy that effectively assures product quality, that is different from a traditional approach.

However this is open to different interpretation by regulatory authorities globally

Health Authorities are still learning in this area and may have different perspectives

Can lead to multiple rounds of questions

May lead to different outcomes in different territories eg. different specifications.

Different approaches may be needed for countries that are ICH vs Non-ICH

Industry’s approach to QbD is also still evolving, as we learn from each development and regulatory interaction

Keen to work with Health Authorities to ensure our approaches to QbD evlove in a harmonious manner

Dr Talia BugginsMs Maria Edebrink

AstraZeneca R&D 54

Summary


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Summary

Why QbD •Efficiency, Flexibility and Continous

Improvement

Foundations •Guidelines working together; Development,

Quality Risk Management, Product Quality

System

QbD Development A more consistent and structured approach to

development

•Risk assessments focus effort on the important

factors


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Drug Product Case Study •Design Space assures consistent quality , safety

and efficacy of all batches manufactured

•Understanding relationship of CQAs to

clinical performance enables meaningful

specifications to be set

•Improved robustess of commercial supply

•Facilitated scale up and scale down

Drug Substance Case Study •Comprehensive understanding & data of

chemistry and impurities

•Factorial Experimental Design & Kinetics

•Comprehensive understanding and data of scale-

up effects

QbD Marketing Application •Comprehensive, structured knowledge shared

•Divergent reviews can impeed innovation and

implementation of QbD

Questions?


57 AstraZeneca R&D

Acknowledgements

Ms Kristina Bredin

Dr Martin Bohlin

Mr Daniel Edvardsson

Ms Emma Friman

Dr Martin Hedberg

Ms Helena Hellström

Mr Nils Iverlund

Mr Jeroen Koningen

Dr Magnus Larsson

Mr Lars Lilljeqvist

Mr Joakim Ludvigsson

Dr Magnus Sjögren

Ms Anna Karin Sverdrup

Ms Malin Vågerö

Dr Mikael Wernersson

Mr Andreas Westermark

And the rest of the AstraZeneca team


58 AstraZeneca R&D

Dr Paul Stott

Dr Gavin Reynolds

Dr James Kraunsoe

Dr Paul Dickinson

Dr Jonathan Sutch

Dr Renli Teng

Mr Mark Hindle

Dr Ian Bromillow

Dr David Holt

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