07 The Case Study

Implementation of ICH Q8, Q9, Q10
Case Study
International Conference on Harmonisation of Technical

Requirements for Registration of Pharmaceuticals for Human Use
ICH Quality Implementation Working Group - Integrated Implementation Training Workshop
Case Study
Disclaimer
The information within this presentation is based

on the ICH Q-IWG members expertise and
experience, and represents the views of the ICH
Q-IWG members for the purposes of a training
workshop.
© ICH, November 2010 slide 2

Case Study
Purpose of Case Study

This case study is provided as an example to help
illustrate the concepts and integrated implementation of
approaches described in ICH Q8, Q9 and Q10. It is not
intended to be the complete information on development
and the manufacturing process for a product that would
be presented in a regulatory filing, but focuses mainly on
Quality by Design aspects to facilitate training and
discussion for the purposes of this workshop.
Note: this example is not intended to represent the

preferred or required approach
Case Study
Basis for Development Information

• Fictional active pharmaceutical ingredient (API)
• Drug product information is based on the ‘Sakura’
Tablet case study
- Full Sakura case study can be found at
http://www.nihs.go.jp/drug/DrugDiv-E.html
• Alignment between API and drug product
- API Particle size and drug product dissolution
- Hydrolytic degradation and dry granulation /direct
compression

Case Study
Organization of content
• Quality Target Product Profile (QTPP)

• API properties and assumptions
• Process and Drug product composition overview
• Initial risk assessment of unit operations
• Quality by Design assessment of selected unit
operations

Case Study
Technical Examples
Process focus Quality attribute focus
• API - Final crystallization step - Particle size control
• Drug Product - Blending - Assay and content uniformity

- Direct compression - Dissolution
API Real Time

Blending Compression Release testing
Crystallization
(Assay, CU, Dissolution)

Case Study
Process Step Analysis

• For each example
- Risk assessment
- Design of experiments
- Design space definition
- Control strategy
- Batch release
QRM Design of Design Control Batch

Experiments Space Strategy Release

Case Study
QbD Story per Unit Operation
QTPP Process Quality

& CQAs Variables Risk Management
Design of Design Control Batch

Illustrative Examples of Unit Operations:

API Real Time
Crystallization

Case Study
Quality Target Product Profile

defines the objectives for development
Dosage form and strength Immediate release tablet taken orally

containing 30 mg of active ingredient
Specifications to assure safety Assay, Uniformity of Dosage Unit (content
and efficacy during shelf-life uniformity) and dissolution
Description and hardness Robust tablet able to withstand transport and
handling
Appearance Film-coated tablet with a suitable size to aid
patient acceptability and compliance
Total tablet weight containing 30 mg of active
ingredient is 100 mg with a diameter of 6 mm
• QTPP: A prospective summary of the quality characteristics of a drug product that ideally will
be achieved to ensure the desired quality, taking into account safety and efficacy of the drug
product. (ICH Q8 (R2))

Case Study
Quality Target Product Profile (QTPP)
Safety and Efficacy Requirements
Translation into
Characteristics /
Tablet Quality Target Product Profile
Requirements
(QTPP)
Dose 30 mg Identity, Assay and Uniformity
No off-taste, uniform color, Appearance, elegance, size,

Subjective Properties
and suitable for global market unit integrity and other characteristics
Acceptable hydrolysis degradate levels

Impurities and/or degradates
Patient Safety – chemical purity at release, appropriate manufacturing
below ICH or to be qualified
environment controls
PSD that does not impact

Patient efficacy – Acceptable API PSD
bioperformance or pharm
Particle Size Distribution (PSD) Dissolution
processing
Chemical and Drug Product Degradates below ICH or to be qualified

Hydrolysis degradation & dissolution
Stability: 2 year shelf life and no changes in bioperformance
changes controlled by packaging
(worldwide = 30ºC) over expiry period
QTPP may evolve during lifecycle – during development and commercial manufacture - as new knowledge is
gained e.g. new patient needs are identified, new technical information is obtained about the product etc.

Case Study
Assumptions for the case

• API is designated as Amokinol
- Single, neutral polymorph
- Biopharmaceutical Classification System (BCS)
class II – low solubility & high permeability
- Dissolution rate affected by particle size
- Potential for hydrolytic degradation
• In vitro-in vivo correlation (IVIVC) established –
allows dissolution to be used as surrogate for clinical
performance

Case Study
API Unit Operations

Coupling Reaction Coupling of API Starting Materials
Removes unreacted materials Done

Aqueous Extractions cold to minimize risk of degradation
Distillative Removes water, prepares API

Solvent Switch for crystallization step
Semi Continuous Addition of API in solution and

Crystallization anti-solvent to a seed slurry
Centrifugal Filtration Filtration and washing of API
Drying off crystallization solvents

Rotary Drying

Case Study
Tablet Formulation
Pharmacopoeial
or other
compendial
specification

Case Study
Drug Product Process

API and Excipients
Amokinol
D-mannitol Blending
Calcium hydrogen phosphate hydrate
Sodium starch glycolate
Lubricant
Magnesium Stearate Lubrication
Compression
Coating
HPMC ， Macrogol 6000
titanium oxide Film coating
iron sesquioxide

Case Study
Overall Risk Assessment for Process

Process Steps
CQA

Case Study
Initial Risk Assessment
Process Steps
• Focus on
Impact to
CQA’s CQA
• Drug Substance Risks

- Hydrolysis degradation product not removed by crystallization
- Particle size control needed during crystallization
- Prior knowledge/first principles shows that other unit operations
(Coupling reaction, aqueous workup, filtration and drying) have low risk
of affecting purity or PSD
- Knowledge from prior filings (data/reference)
- Knowledge from lab / piloting data, including data from other
compounds using similar technologies
- First principles knowledge from texts/papers/other respected sources
- Thus only distillation (i.e., crystallizer feed) and crystallization itself are
high risk (red)
Case
ICH Quality Implementation Working Group - Integrated Implementation Training Study
Workshop Organization
Case Study
API: The Story



API Crystallization API Crystallization
Hydrolysis Degradation Particle size

Case Study
API Crystallization Example

• Designed to control hydrolysis degradate
- Qualified in safety trials at 0.3%
• Designed to control particle size
- D90 between 5 and 20 microns
- ‘D90’ means that 90% of particles are less than that value
- Qualified in formulation Design of Experiments (DOE)
and dissolution studies

Case Study
Hydrolysis Degradation
O
O
H2 O
R'
R O OH
R OH + R'
• Ester bond is sensitive to hydrolysis

• More sensitive at higher levels of water and at elevated temperatures
• Prior knowledge/experience indicates that no degradation occurs during
the distillative solvent switch due to the lower temperature (40ºC) used
for this step
• Degradates are water soluble, so degradation prior to aqueous workup
does not impact API Purity
• After Distillative Solvent Switch, batch is heated to 70ºC to dissolve (in
preparation for crystallization). Residual water in this hot feed solution
can cause degradation and higher impurities in API.

Case Study
Crystallization Process
• For Risk Assessment (FMEA)
- Only crystallization parameters
considered, per scientific rationale
in risk assessment
- All relevant parameters considered
based on first principles
• Temperature / time / water content

have potential to affect formation
of hydrolysis degradate
• Charge ratios / agitation /
temperature / seed characteristics
have potential to affect particle
size distribution (PSD)

Case Study
Risk Assessment (FMEA): Purity Control

Case Study
Experimental Setup -
Hydrolysis Degradation
• Crystallization Process Requirements
- API feed solution held at 60ºC, to maintain solubility of product, allows for
passage through extraneous matter filters.
- Batch fed to crystallizer slowly (to ensure particle size control). If fed too slowly
(over too much time), hydrolysis degradate can form in crystallizer feed.
- Batch will contain some level of residual water (thermodynamics)
- No rejection of hydrolysis degradate seen in crystallization (prior
knowledge/experience)
• Process Constraints
- Factory process can control well within +/- 10ºC. 70ºC is easily the worst case
temperature
- The batch must be held hot during the entire feed time (~ 10 hours), including
time for batch heat up and time for operators to safely start up the crystallization.
A total hold time of 24 hours at temperature is the worst case.

Case Study
Experimental Plan –
Hydrolysis Degradation (contd.)
• Univariate experiments justified
- Only upper end of ranges need to be tested, as first principles dictates this is worst
case for degradation rate
- Lower water content, temperature and hold times will not increase hydrolytic
degradation
- Upper end of range for batch temperature and hold time can be set based on
capabilities of a typical factory
- Therefore, only the water content of the batch needs to be varied to establish the
design space
• Experimental Setup
- Set maximum batch temperature (70ºC)
- Set maximum batch feed time (include heat up time, hold time, etc.) = 24 hours
- Vary residual water level
- Monitor degradation rate with criteria for success = max 0.3% degradate (qualified
limit)

Case Study
Design Space Defined

Experimental Data Max Temp: 70ºC
Max Feed Time = 24 hr
Max Water content = 1.0%
At these conditions,
degradate level remains
below qualified limit of 0.3%
Water Content Degradate Level at

(volume% by KF 24 hrs
titration) (LC area%)
0.1% 0.04%
0.5% 0.16%
1.0% 0.27%
2.0% 0.52%

Case Study
Particle Size Distribution Control -

Process History
• Changes in formulation drive
changes in API process
• Ph I and II trials performed with
API-excipient mixture filled in hard
gelatin capsules (liquid filled
capsules = LFC)
• First API Deliveries
- Simpler Crystallization Process
- No PSD control; crystal
agglomeration observed, but
acceptable for LFC formulation
• Ph III trials performed with tablets,
requiring small PSD for processing
and dissolution

Case Study
Particle Size Distribution Control -
Process History (contd.)
• Changes to crystallization process
• Develop semi-continuous crystallization to
better control PSD (narrow the distribution)
and control agglomeration
• Add air attrition milling of seed to lower the
final API PSD
• API Particle Size Distribution Specification:
5 to 20 micron D90
• Risk Assessment
• Charge ratios/agitation/temperature/
seed characteristics have potential to
affect PSD
• Based on data in a previous filing
and experience with this technology.
• Per prior knowledge, other unit
operations (including filtration and
drying) do not affect PSD.
• Lab data and piloting experience
demonstrate that growing crystals
are sensitive to shear (agitation) in
the crystallizer, but not during
drying.

Case Study
Risk Assessment:
Particle Size Distribution (PSD) Control

Case Study
Risk Assessment:
Particle Size Distribution (PSD) Control
To be investigated
in DOE

Case Study
Experimental Design, PSD Control

Half Fraction Factorial
• Test: feed addition time
amount API seed (wt%)
agitation tip speed
crystallization temperature
• Experimental ranges based on
QTPP and chosen by:
- Prior knowledge: estimates of
what ranges would be successful
- Operational flexibility: ensure that
ranges are suitable for factory
control strategy
•Experimental Results: D90 minimum = 2.2 microns; maximum = 21.4 microns

- Extremes are outside of the desired range of 5 to 20 microns for D90

Case Study
PSD Control -- Design Space

• Statistical Analysis of crystallization data allows for determination of
the design space
• Analysis of DOE data generates a predictive model
- PSD D90 =
19.3 - 2.51*A - 8.63*B + 0.447*C - 0.0656*A*C + 0.473*A^2 + 1.55*B^2
- where A = seed wt%, B = agitator tip speed (m/s) and C =
temperature (ºC)
- Statistical analysis shows that crystallization feed time does not
impact PSD across the tested range
• Model range across DOE space = 2.2 to 21.4 microns
- Model error is +1 micron
• Model can be used to create a design space using narrower ranges
than used in the DOE
- Adjust ranges until model predicts acceptable D90 value for PSD

Case Study
Options for Depicting a Design Space

• In the idealized example at left, the
oval represents the full design
space. It would need to be
represented by an equation.
• Alternatively, the design space can
Seed wt%
be represented as the green

rectangle by using ranges
- a portion of the design space is not
utilized, but the benefit is in the
simplicity of the representation
Large square shows the ranges tested in the DOE

Red area shows points of failure
Green area shows points of success.

Case Study
Options for Depicting a Design Space

• Other rectangles can be drawn within
the oval at top left, based on multiple
combinations of ranges that could be
chosen as the design space
• Exact choice from above options can
Seed wt
be driven by business factors

- e.g., keep seed charge narrow,
%
maximizing temperature range, since

temperature control is less precise than
a seed charge
For purposes of this case study, an acceptable “squared off” design space can be chosen
Temperature = 20 to 30ºC
Seed charge = 1 to 2 wt%
Agitation = 1.1 to 2.5 m/s
Feed Rate = 5 to 15 hr (limit of knowledge space)
Monte Carlo analysis ensures that model uncertainty will be effectively managed throughout the range
Since the important variables affecting PSD are scale independent, model can be confirmed at scale with
“center point” (optimum) runs

Case Study
Options for Expanding a Design Space

• Why expand a Design Space?
- Business drivers can change, resulting in a
different optimum operating space
• When is DS Expansion possible?

- Case A: When the original design space was
artificially constrained for simplicity
- Case B: When some edges of the design

space are the same as edges of the
knowledge space

Case Study

Case A
• When the original design space
was artificially constrained for
simplicity
- Alternate combinations of ranges
could be chosen as the new design
space, based on original data.
- e.g. the range for seed wt% could
be constrained, allowing widening
of the temperature range
The large square represents the ranges tested in the DOE. The red area represents points of
failure. The green area represents points of success.
The boxes represent simplified design spaces within the points of success

Case Study

Case B
• When some edges of the
design space are the same as
edges of the knowledge space
- Additional experiments could be
performed to expand the upper
limits of seed wt% and
temperature
The large square represents the ranges tested in the DOE. The red area represents points of
failure. The green area represents points of success.

Case Study
API Crystallization:
Design Space & Control Strategy
• Control Strategy should address:
- Parameter controls
- Distillative solvent switch achieves target water content
- Crystallization parameters are within the design space
- Testing
- API feed solution tested for water content
- Final API will be tested for hydrolysis degradate
- Using the predictive model, PSD does not need to be routinely tested
since it is consistently controlled by the process parameters
• Quality systems
- Should be capable of managing changes within and to the design space
- Product lifecycle can result in future design space changes

Case Study
API Crystallization:
Design Space & Control Strategy

Case Study
Batch Release for API

• Testing conducted on the final API
- Hydrolysis degradate levels are tested by HPLC
- Particle size distribution does not need to be tested, if the design space and
associated model are applied
- In this case study, PSD is tested since the actual PSD result is used in a
mathematical model applied for predicting dissolution in the following drug
product control strategy
- Additional quality tests not covered in this case study
• Verify that the crystallization parameters are within the design space
- Temperature = 20 to 30º C
- Seed charge = 1 to 2 wt%
- Agitation = 1.1 to 2.5 m/s
- Feed time = 5 to 15 hr
- API feed solution water content < 1 wt%

Case Study Case Study Organization
QbD Story per Unit Operation



Real Time

Case Study
QTPP and CQAs

QTPP
Immediate release tablet
Dosage form and strength containing 30 mg of active ingredient.
Specifications to assure safety Assay,

and efficacy during shelf-life Uniformity of Dosage Unit (content uniformity) and
dissolution.
Description and hardness Robust tablet able to withstand transport and handling.
Appearance Film-coated tablet with a suitable size to aid patient

acceptability and compliance.
Total tablet weight containing 30 mg of active ingredient is
100 mg with a diameter of 6 mm.
Drug Product CQAs

•Assay
CQAs derived using Prior Knowledge •Content Uniformity
(e.g. previous experience of developing tablets)
•Dissolution
CQAs may be ranked using quality risk assessment.
•Tablet Mechanical Strength

Case Study
CQAs to Focus on for this Story
• Drug Product CQAs
- Assay & Content Uniformity
- Dissolution

Case Study
Rationale for Formulation & Process

Selection
• Amokinol characteristics
- BCS class II (low solubility, high permeability)
- Susceptible to hydrolysis
- 30 mg per tablet (relatively high drug loading)
• Direct compression process selected
- Wet granulation increases risk of hydrolysis of Amokinol
- High drug loading enables content uniformity to be achieved without dry
granulation operation
- Direct compression is a simple, cost-effective process
• Formulation Design
- Excipient compatibility studies exclude lactose due to API degradation
- Consider particle size aspects of API and excipients
- Dual filler system selected and proportions optimised to give good dissolution and
compression (balance of brittle fracture and plastic deformation consolidation
mechanisms)
- Conventional non-functional film coat selected based on prior knowledge

Case Study
Tablet Formulation
Pharmacopoeial or
other compendial
specification.
May have additional
requirements for
Functionality Related
Characteristics

Case Study
Direct Compression Process
Focus of
Story

Case Study
Initial Quality Risk Assessment

• Impact of formulation and process unit operations on
Tablet CQAs assessed using prior knowledge
- Also consider the impact of excipient characteristics on the
CQAs

Case Study
Example 1:
Real Time Release Testing (RTRT)
for Dissolution

Case Study
Developing Product and Process

Understanding
Investigation of the effect of API particle size on
Bioavailability and Dissolution
Drug Substance with particle size D90 of 100
microns has slower dissolution and lower
Cmax and AUC
In Vivo In Vitro correlation (IVIVC) established
at 20 minute timepoint
Early time points in the dissolution profile

are not as critical due to PK results

Case Study
Developing Product and Process
Understanding: DOE Investigation of factors affecting Dissolution
Multifactorial DOE study of Exp No
1
Run Order
1
API
0.5
MgSt
3000
LubT
1
Hard
60
Diss
101.24
2 14 1.5 3000 1 60 87.99
variables affecting dissolution 3 22 0.5 12000 1 60 99.13
4 8 1.5 3000 10 60 86.03
• Factors: 5 18 0.5 12000 10 60 94.73
-
6 9 1.5 12000 10 60 83.04
API particle size [API] 7 15 0.5 3000 1 110 98.07
unit: log D90, microns 8
9
2
6
0.5
1.5
12000
12000
1
1
110
110
97.68
85.47
- Mg-Stearate Specific Surface Area 10
11
16
20
0.5
1.5
3000
3000
10
10
110
110
95.81
84.38
[MgSt] 12 3 1.5 12000 10 110 81
unit: cm2/g 13 10 0.5 7500 5.5 85 96.85
- Lubrication time [LubT] unit: min

14
15
17
19
1.5
1
7500
3000
5.5
5.5
85
85
85.13
91.87
- Tablet hardness [Hard] unit: N 16
17
21
7
1
1
12000
7500
5.5
1
85
85
90.72
91.95
• Response: 18
19
4
5
1
1
7500
7500
10
5.5
85
60
88.9
92.37
- % API dissolved at 20 min [Diss] 20
21
11
12
1
1
7500
7500
5.5
5.5
110
85
90.95
91.95
• DOE design: 22
23
13
23
1
1
7500
7500
5.5
5.5
85
85
90.86
89
- RSM design
Note: A screening DoE may be used first to identify
- Reduced CCF (quadratic model) which of the many variables have the greatest effect
- 20+3 center point runs

Case Study
Factors affecting Dissolution

Scaled & Centered Coefficients for Diss at 60min
• Key factors influencing -1
in-vitro dissolution:
-
-2
API particle size is the

dominating factor
-3
%
(= CQA of API) -4
-5
-
-6
Lubrication time has a
small influence
MgSt*LubT
Hard
MgSt
API
LubT
API Mg Lubrication Tablet Mg St*LubT
(= low risk parameter) Particle Stearate Blending Hardness
Size N=23 SSA

R2=0.986 R2time
Adj.=0.982
DF=17 Q2=0.981 RSD=0.725 Conf. lev.=0.95
MODDE 8 - 2008-01-23 10:58:52
Acknowledgement: adapted from Paul Stott (AZ) – ISPE PQLI Team

Case Study
Predictive Model for Dissolution

• Prediction algorithm
- A mathematical representation of the design space for
dissolution
- Factors include: API PSD D90, magnesium stearate
specific surface area, lubrication time and tablet
hardness (linked to compression pressure)
Prediction algorithm:
Diss = 108.9 – 11.96 × API – 7.556×10-5 × MgSt – 0.1849 × LubT –
3.783×10-2 × Hard – 2.557×10-5 × MgSt × LubT

Case Study
Predictive Model for Dissolution

• Account for uncertainty
- Sources of variability (predictability, measurements)
• Confirmation of model
- compare model results vs. actual dissolution results for batches
- continue model verification with dissolution testing of production
material, as needed
Batch 1 Batch 2 Batch 3
Model prediction 89.8 87.3 88.5
Dissolution testing 92.8 90.3 91.5

result (88.4–94.2) (89.0-102.5) (90.5-93.5)

Case Study
Dissolution: Design Space

• Response surface plot for effect of API particle size
and magnesium stearate specific surface area (SSA)
on dissolution
Diss (% at 20 min)
Area of potential risk

Design for dissolution failure
Space
Graph shows interaction between
two of the variables: API particle
size and magnesium stearate
specific surface area
API particle size (Log D90)
Acknowledgement: adapted from Paul Stott (AZ)

Case Study
Dissolution: Control Strategy

• Controls of input material CQAs
- API particle size distribution
- Control of crystallisation step
- Magnesium stearate specific surface area
- Specification for incoming material
• Controls of process parameter CPPs

- Lubrication step blending time
- Compression pressure (set for target tablet hardness)
- Tablet press force-feedback control system
• Prediction mathematical model

- Use in place of dissolution testing of finished drug product
- Potentially allows process to be adjusted for variation in API particle size,
for example, and assure dissolution performance

Case Study
Example 2:
Real Time Release Testing (RTRT)
for Assay and Content Uniformity

Case Study
Quality Risk Assessment

Impact on Assay and Content Uniformity CQAs
• QRA shows API particle size, moisture control, blending and lubrication
steps have potential to affect Assay and Content Uniformity CQAs
- Moisture is controlled during manufacturing by facility HVAC control of
humidity (GMP control)

Case Study
Blending Process Control Options

Decision on conventional vs. RTR testing

Case Study
Process Control Option 1

DOE for the Blending Process Parameter Assessment to
develop a Design Space
- Factors Investigated:
Blender type, Rotation speed, Blending time, API Particle size
Blending time Rotation speed Particle size D90
Experiment Run Condition Blender
(minutes) (rpm) (m)
No.
1 2 varied 2 10 V type 5
DOE design
5 6 varied 2 10 Drum type 40
9 3 standard 9 20 V type 20
10 12 standard 9 20 Drum type 20
11 9 standard 9 20 V type 20
12 4 standard 9 20 Drum type 20

Case Study

Blend uniformity monitored using a process analyser
• Control Strategy to assure homogeneity of the blend
- Control of blending
end-point by NIR
and feedback control
of blender
- API particle size
In this case study, the

company chooses to use
online NIR to monitor blend
uniformity to provide
efficiency and more flexibility

Case Study

Blend uniformity monitored using a process analyser
• On-line NIR spectrometer used 0.045
to confirm scale up of blending 0.04
mean spectral standard deviation

• Blending operation complete 0.035
when mean spectral std. dev. 0.03
Pilot Scale
Full Scale
reaches plateau region 0.025
- Plateau may be detected 0.02
using statistical test or rules
0.015
• Feedback control to turn off 0.01
Plateau region
blender 0.005
• Company verifies blend does 0
not segregate downstream 0 32 64 96 128
Revolution (block number)
-
Number of Revolutions of Blender
Assays tablets to confirm
uniformity Data analysis model will be provided
- Conducts studies to try to Plan for updating of model available
segregate API Acknowledgement: adapted from ISPE PQLI Team

Case Study
Tablet Weight Control in Compression Operation
Conventional automated control of Tablet Weight using feedback loop:

Sample weights fed into weight control equipment which sends signal to filling
mechanism on tablet machine to adjust fill volume and therefore tablet weight.

Case Study
RTRT of Assay and Content Uniformity

• Real Time Release Testing Controls
- Blend uniformity assured in blending step (on-line NIR spectrometer for
blending end-point)
- API assay is analyzed in blend by HPLC
- API content could be determined by on-line NIR, if stated in filing
- Tablet weight control with feedback loop in compression step
• No end product testing for Assay and Content

Uniformity (CU)
- Would pass finished product specification for Assay and Uniformity of
Dosage Units if tested because assay assured by combination of blend
uniformity assurance, API assay in blend and tablet weight control (if
blend is homogeneous then tablet weight will determine content of API)

Case Study
Control Strategy
• Input materials meet specifications and are tested
- API PSD
• Assay calculation
- Verify (API assay of blend by HPLC) X (tablet weight)
- Tablet weight by automatic weight control (feedback loop)
- For 10 tablets per sampling point, <2% RSD for weights
• Content Uniformity
- On-line NIR criteria met for end of blending (blend homogeneity)
- Tablet weight control results checked
• Dissolution
- Predictive model using input and process parameters for each batch calculates
whether dissolution meets acceptance criteria
- Input and process parameters are all within the filed design space
- Compression force is controlled for tablet hardness

Case Study
Drug Product Specifications

• Use for stability, regulatory testing, site change, whenever RTR testing
is not possible
- Assay acceptance criteria: 95-105% of nominal amount (30mg)
- Uniformity of Dosage Unit acceptance criteria
- Test method: HPLC
• Input materials meet specifications and are tested
- API PSD
• Assay calculation (drug product acceptance criteria 95-105%)
- Verify (API assay of blend by HPLC) X (tablet weight)
- Tablet weight by automatic weight control (feedback loop)
- For 10 tablets per sampling point, <2% RSD for weights
• Content Uniformity (drug product acceptance criteria meets compendia)
- On-line NIR criteria met for end of blending (blend homogeneity)
- Tablet weight control results checked
• Dissolution (drug product acceptance criteria min 85% in 30 minutes)
- Predictive model using input and process parameters for each batch calculates whether
dissolution meets acceptance criteria
- Input and process parameters are all within the filed design space
- Compression force is controlled for tablet hardness
• Water content (drug product acceptance criteria NMT 3 wt%)
- Not covered in this case study

Case Study
Iterative risk assessments

High Risk Medium Risk Low Risk
Initial QRA Design Control

PHA
Beginning FMEA Space FMEA strategy FMEA
API
API PSD API PSD API PSD model
Crystallization
Blend Blending time

Blending Blending time
homogeneity Feedback control
Lubricant Lubricant
Mg stearate SSA
amount
Lubrication
Lubrication time Lubrication time Lubrication time
Hardness Pressure Pressure

Compression
Content Automated
Tablet weight
uniformity Weight control

Case Study
Batch Release Approach

QA / Qualified Person assures
• Batch records are audited under the PQS
- Parameters are within the filed design space
- Proper process controls and RTRT were performed
and meet approved criteria
• Appropriate model available for handling process
variation which is subject to GMP inspection
• Predictive models are further confirmed and
maintained at the production site
Case Study
Conclusions
• Better process knowledge is the outcome of QbD development
• Provides the opportunity for flexible change management
• Use Quality Risk Management proactively
• Multiple approaches for experimental design are possible
• Multiple ways of presenting Design Space are acceptable
- Predictive models need to be confirmed and maintained
• Real Time Release Testing (RTRT) is an option

- Opportunity for efficiency and flexibility

Key Steps for a product under Quality by Design (QbD)
Pharmaceutical Quality Target
Development QTPP : Definition of intended use & product
Product Profile
Prior Knowledge (science, GMP,

regulations, ..) CQA : Critical Potential CQA (Critical Quality Attribute) identified &
Quality Attribute CPP (Critical Process Parameters) determined
Product/Process Development
CPP : Critical Design to meet CQA using Risk Management &
DOE : Design of Experiment Process Parameter experimental studies (e.g. DOE)
Link raw material attributes and process parameters

QRM principle apply at any stage Risk Management to CQAs and perform Risk Assessment Methodology
Product/Process Understanding Opportunities Design Space (DS), RTR testing
Control Strategy Marketing Authorisation

Quality System PQS
Technology Transfer Commercial Manufacturing

PQS & GMP Batch Release Quality Unit (QP,..) level support by PQS
Local Environment Strategy
Continual Manage product lifecycle, including

improvement continual improvement

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What is the purpose of the case study?

What is the purpose of the case study?

What are the main steps in the organization of content in the case study?

What are the main steps in the organization of content in the case study?

Implementation of ICH Q8, Q9, Q10

International Conference on Harmonisation of Technical

The information within this presentation is based

© ICH, November 2010 slide 2

Purpose of Case Study

Note: this example is not intended to represent the

Basis for Development Information

© ICH, November 2010 slide 4

• Quality Target Product Profile (QTPP)

© ICH, November 2010 slide 5

• API - Final crystallization step - Particle size control

• Drug Product - Blending - Assay and content uniformity

API Real Time

© ICH, November 2010 slide 6

Process Step Analysis

QRM Design of Design Control Batch

© ICH, November 2010 slide 7

QbD Story per Unit Operation

QTPP Process Quality

Design of Design Control Batch

Illustrative Examples of Unit Operations:

© ICH, November 2010 slide 8

Quality Target Product Profile

Dosage form and strength Immediate release tablet taken orally

© ICH, November 2010 slide 9

No off-taste, uniform color, Appearance, elegance, size,

Acceptable hydrolysis degradate levels

PSD that does not impact

Chemical and Drug Product Degradates below ICH or to be qualified

© ICH, November 2010 slide 10

Assumptions for the case

© ICH, November 2010 slide 11

API Unit Operations

Removes unreacted materials Done

Distillative Removes water, prepares API

Semi Continuous Addition of API in solution and

Centrifugal Filtration Filtration and washing of API

Drying off crystallization solvents

© ICH, November 2010 slide 12

© ICH, November 2010 slide 13

Drug Product Process

© ICH, November 2010 slide 14

Overall Risk Assessment for Process

© ICH, November 2010 slide 15

• Drug Substance Risks

API: The Story

QTPP Process Quality

Design of Design Control Batch

Illustrative Examples of Unit Operations:

© ICH, November 2010 slide 17

API Crystallization Example

© ICH, November 2010 slide 18

• Ester bond is sensitive to hydrolysis

© ICH, November 2010 slide 19

• Temperature / time / water content