Quality by Design & Design For Six Sigma:
Allies in Pharmaceutical Development
ISPE Breakfast Seminar
Toronto: May 12, 2009
Montreal: May 13, 2009
Presented by: Murray Adams
Contact: murray.adams@rogers.com
1-905-796-8514
Operational Excellence Consulting Ltd.
Guidance Documents
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ICH and FDA have published several guidance documents
on pharmaceutical development and validation
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ICH – Q8, Q8(R1), Q9, Q10
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FDA – Process Validation: General Principles and Practices
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Common theme of systematic, science based approach to
obtaining “enhanced” process knowledge and managing risk
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Focus for today: Q8(R1) Pharmaceutical Development
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Quality by Design (QbD)
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QbD alignment with Design For Six Sigma (DFSS) principles
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QbD Key Concepts and Principles
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Quality cannot be tested into products it must be built in
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Enhanced scientific knowledge of products and processes
in development can be used to assure product quality
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Critical Quality Attributes (CQA) and factors which affect
them must be well understood and controlled
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Drug substance
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Excipients
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Intermediates
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Drug product
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QbD Key Concepts and Principles (Cont’d)
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Risk assessment should be used throughout development
to guide and justify development decisions
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Material and process parameters must be linked to CQAs
of the finished product
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Design space used to define the acceptable limits for
operational parameters to assure the product quality
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Control strategies implemented to manage risk
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QbD and DFSS Alignment
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Ensure quality on the basis of detailed process knowledge
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Understanding the relationships between product/process
inputs and outputs
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Design of Experiments (DoE) and/or historical regression analysis
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Modeling designs produce prediction equations / transfer functions
Transfer functions can then be used to perform:
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Expected Value Analysis (EVA a.k.a. Monte Carlo Simulation)
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Robust design
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Tolerance allocation studies (setting appropriate specifications)
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What’s DoE ?
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Purposefully changing settings of process inputs (X’s) in order to
observe the effect on process outputs (Y’s or CQA’s).
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Changing multiple inputs to produce an equation which describes
the relationships between inputs, outputs and interactions
Transfer Functions
Inputs
X1
Outputs
e.g. Materials
Y1 = f1 (X1, X2, X3)
X2
e.g. Parameters
Y1
Process
X
3 e.g. Environmental
Y2 = f2 (X1, X2, X3)
Y2
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DOE Terminology
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Inputs, Factors, X’s – variables which if changed or
uncontrolled will affect one or more responses
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Outputs, Responses, Y’s – metrics of interest (quality,
performance, cycle time, cost) which are affected by inputs
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Interactions – combination effect when the effect of one
factor depends upon the setting of another factor
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Levels – the number of settings evaluated for each factor
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Experimental Run – a trial conducted using a carefully
chosen combination of settings for each factor
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Finding the “Critical Few”
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Before conducting experiments we must identify the most
important factors affecting our process
ƒ
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Find the “critical few among the trivial many”
Prior knowledge, literature search, experience
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Map the process at a relatively high level (8 – 12 steps)
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Cause and Effect Analysis (a.k.a. Fishbone / Ishikawa diagram)
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Risk assessment – Failure Mode and Effects Analysis (FMEA)
ƒ
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Identify risk areas with materials and process parameters
Which factors and parameters must be controlled?
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Cause & Effect Example
Method
Measurement
Machine
(X) Pre-compression
(X) Blend Time
(X) Main Compression
(C) Test method
(X) Lubrication Time
(X) Compression Speed
(C) SOP / PBR
(C) Punch Size / Shape
Dissolution
Rate
(C) Avicel Grade
(N) Relative Humidity
(N) Experience
(X) Mg Stearate Mesh
C = Constant
N = Noise
X = Experimental
Factor
(C) Temperture
(C) Training
(X) API PSD
Manpower
Materials
Environment
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FMEA Example
1. Steps / Components
Product
Function /
or
Purpose
Process
Failure Mode
Failure Effects
S
E
V
Causes
Controls
D
E
T
R
P
N
Actions
6. List all causes
leading to failure
2. What’s the
intended function
or purpose?
Plans
P
S
P
O
P
D
p
r
p
n
10. Calculate RPN =
Sev * Occ * Det
7. Probability
of occurrence?
(1 to 5)
3. How could it
fail to do this?
4. What are the
consequences?
5. How bad
is it? (1 to 5)
¾
O
C
C
8. What controls
are in place today?
9. Probability of
escaped detection
(1 to 5)
Calculate RPN, prioritize, assign corrective actions, calculate PRPN
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FMEA Examples
Formulation Components
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Formulation
Function
Component
Failure Mode
Failure Effects
S
E
V
Drug
Substance
Active
Poor bioavailability Reduced effectiveness
Ingredient too little release
5
Magnesium
Stearate
Lubricant Over lubricated
Poor bioavailability
(reduced effectiveness)
5
Under lubricated
Causes
O
C
C
Controls
PSD Spec & test
Incorrect Particle
3
method
Size Distribution
Wrong mesh
size - too small
5 Content too high
2 Content too low
D
E
T
R
P
N
4
- MSA on test method
60 - Confirm PSD spec
appropriate
Actions
2
- Spec
1
10 - Confirm spec
1
1
- Spec
- Spec
1
1
5
2
Causes
O
C
C
Controls
D
E
T
R
P
N
Poor bioavailability
Blended too
4
(reduced effectiveness)
much
2
- Spec
- Timer on blender
3
24 - Confirm spec
3
12 - Confirm spec
Tablet picking
Plans
Fred,
May 25
- None
- None
Process Steps
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Process
Step
Lubrication
Blending
Purpose
Lubricate
granules
Failure Mode
Over lubricated
Failure Effects
Under lubricated Picking
S
E
V
2 Blended too little 2
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Actions
Plans
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Data Collection
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The knowledge gained in a DoE is only as good as the data
we collect for the analysis.
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Measurement System Analysis (MSA)
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ƒ
¾
Sampling plan
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ƒ
ƒ
ƒ
ƒ
¾
How big is the measurement error?
Accuracy, repeatability, reproducibility, P/Tol, P/Tot
What kind of data are we collecting (continuous or discrete)?
How big a sample do we need?
Is the sample representative?
How are we going to collect the samples? (random, intervals, other)?
Is everyone collecting the samples in the same way?
What about PAT’s? (Process Analytical Technologies)
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Snapshots vs. a Movie
¾
¾
Conventional analytical testing provides “snapshots” of quality attributes
at various points in time
Traditionally we test the finished product at the end of the process
+
+
Beginning
¾
¾
Middle
End
PATs can provide real time, or near real time, sequence of pictures
showing the evolution of the process
Allows continuous, “real time quality assurance”
End
Beginning Continuous Monitoring
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Which process is yours?
“Textbook”
380
Sensor 1
360
Sensor 3
340
2
320
1/2
(Ws /m K)
Thermal Effusivity
400
300
Sensor 4
How much do we really know about
what’s going on inside the blender?
280
260
240
220
200
0
3
6
9
12
15
18
21
24
27
30
“Variable”
Blend Time (minutes)
Thermal Effusivity vs Blend Time
410
5.0%
4.5%
400
Effusivity
*Tables, graphs and data reproduced
with permission from Patheon Inc. and
Mathis Instruments
3.5%
3.0%
380
2.5%
370
2.0%
1.5%
360
%RSD (Across Sensors)
4.0%
390
1.0%
350
0.5%
340
0.0%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Time (Minutes)
%RSD
Sensor B23
Sensor B18
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Sensor B25
Mean
Overall Mean
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Types of DoE Designs
Screening Designs
Modeling Designs
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Typically 6 – 11 factors
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Typically 2 – 5 factors
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Many factors in few runs
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Narrow the focus
Prediction equation
(Transfer function)
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2 or 3 levels
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2 or 3 levels
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Optimization, robust design,
tolerance allocation
Examples
Examples
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Taguchi L12 (or PB12)
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Full / Fractional Factorial
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Taguchi L18 (3 levels)
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Central Composite Design
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PB 20
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Box Behnken
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Fractional Factorials
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L12 Design Matrix
Factor
A
B
Blend
Time
(Min)
Row #
1
2
3
4
5
6
7
8
9
10
11
12
5
5
5
5
5
5
15
15
15
15
15
15
C
D
E
F
G
Mg
Pre-comp
Lubrication
Main Comp
Press
Stearate API PSD Force
Time (Min)
Force (KN)
Speed
Mesh
(KN)
2
-1
2
5
30
2000
2
-1
2
5
40
5000
2
1
5
10
30
2000
5
-1
5
10
30
5000
5
1
2
10
40
2000
5
1
5
5
40
5000
2
1
5
5
30
5000
2
1
2
10
40
5000
2
-1
5
10
40
2000
5
1
2
5
30
2000
5
-1
5
5
40
2000
5
-1
2
10
30
5000
Dissolution
Y1
Y2
Y3
----
Y12
Y bar
S
Random Order = 4 ,11 ,5 ,1 ,3 ,2 ,9 ,7 ,12 ,6 ,8 ,10
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Identify factors affecting response(s) average, variation, both or neither
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Check up to 11 factors in 12 runs
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High level information on main effects only
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No information on interactions
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Possible to monitor multiple responses
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23 Full Factorial Example
Factor
A
API PSD
(d50)
Row #
1
2
3
4
5
6
7
8
B
Blend
Time
2
2
2
2
5
5
5
5
Dissolution
C
Main Comp
Force (KN)
10
10
15
15
10
10
15
15
Y1
Y2
Y3
.- - - -
Y12
Y bar S
30
40
30
40
30
40
30
40
Random Order = 7 ,4 ,3 ,2 ,5 ,8 ,1 ,6
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Full Factorial designs produce prediction equation / transfer function
describing relationship between factors and the response(s)
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Main effects of 3 factors, all 2 way interactions, 3 way interaction
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Process optimization
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Multiple response optimization
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Transfer Functions
(a.k.a. Prediction Equations)
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The transfer function is a equation which mathematically describes
the relationship between the process inputs and outputs
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This empirical model is extremely valuable knowledge
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Process optimization
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Robust design
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“What if” scenarios
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Setting specifications
2 level designs
Y = b0 + b1X1 + b2X2 + b3X1X2 ……
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3 level designs
2
2
Y = b0 + b1X1 + b2X2 + b3X1X2 + b4X1 + b5X2 ……
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Design Space
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Definition of Design Space according to ICH Q8:
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“The multidimensional combination and interaction of input variables
that have been demonstrated to provide assurance of quality.”
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Translation – what combination(s) of input settings will meet the
specifications for the output(s)?
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Multiple response optimization determines the optimum set of
conditions to meet specs of 2 or more outputs simultaneously
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“Weighting” the outputs provides opportunity to allow for relative
importance in “trade-off” situations
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Design Space Example
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Consider two Outputs – e.g. dissolution (Y1) and dosage uniformity (Y2)
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There may be two inputs (X1 and X2) which are important to both outputs
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Which combinations of X1 and X2 are acceptable?
Y 1 = dissolution
In spec
Y 2 = dosage uniformity
5.0
5.0
4.8
4.8
4.6
4.6
4.3
4.3
4.1
4.1
3.9
3.9
3.7
3.7
3.4
3.4
3.2
3.0
2.8
3.2
X2
3.0
2.8
2.6
2.3
2.3
2.1
2.1
1.9
1.9
1.7
1.7
1.4
1.4
1.2
1.2
1.0
20 23 26 28 31 34 37 39 42 45 48 51 53 56 59 62 64 67 70
Out of Spec
1.0
20 23 26 28 31 34 37 39 42 45 48 51 53 56 59 62 64 67 70
X1
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X2
2.6
X1
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Design Space Example (Cont’d)
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Which combination(s) of X1 and X2 will meet both the dissolution
and dosage uniformity specifications?
Y 2 = dosage uniformity
Y 1 = dissolution
X2
X2
X1
X1
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Expected Value Analysis
(a.k.a. Monte Carlo Simulation)
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Unfortunately many inputs are not constants!
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EVA applies the distribution of inputs to the transfer functions to
predict output distribution
X1
Y1 = b0 + b1X1 + b2X2 + b3X1X2
X2
Y1
Process
X3
Y2 = b7 + b4X1 + b5X2 + b6X12
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Y2
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Robust Design Studies
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Helps to identify conditions which make the process tolerant of variation
of certain inputs which are difficult or costly to control
X
X
1
2
Process
Process
Y
1
X
3
¾ Initial settings produce outputs which are sometimes out of spec
¾ Shifting input average settings may achieve the same average
response but reduce the output variation even though the variation of
the inputs remains the same
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Tolerance Allocation
¾ Determines the affect of input variation on the response.
¾ How much would the process improve if we tightened the input spec?
X
X
1
2
Process
Process
Y
1
X
3
¾ Intuitively,
ƒ
ƒ
ƒ
we think tightening input specs will improve the process, but…..
Do all inputs have the same amount of influence?
Tighter specs often cost more money, so let’s choose the best option
We might find that it’s possible to relax some specs with no ill effects!
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Control Strategies
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SOPs, PBRs, training programs help to ensure consistency
ƒ
¾
Policies, process limits, RM specs, testing, facilities, maintenance etc.
FMEA – “living document” updated throughout product life cycle
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Investigate problems and implement corrective actions
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Effective transfer of process knowledge from development to
production (Tech Transfer)
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Process Qualification – confirm design is functional as predicted
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“Before commercial distribution begins, a manufacturer is expected to
have accumulated enough data and knowledge about the commercial
production process to support post-approval distribution.” 1.
(1. Joseph C. Famulare; Deputy Director, Office of Compliance FDA/CDER)
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Control Strategies (Cont’d)
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Extensive testing (at least initially) to increase data base of
knowledge
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Control charts alert us to “abnormal” conditions and trends
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PATs can be used to monitor and control some critical process
steps in “real time”
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On-going monitoring provides opportunity for process
improvement as more data is accumulated
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Historical regression analysis
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Annual product reviews
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Change Control – ensure data available to support modifications
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Some Benefits of QbD & DFSS
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Assures the quality of product and reliable delivery of its intended
performance
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Identifies critical controls and potential areas of risk early in the
product life cycle (development)
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reduces / eliminates need for significant post approval changes
minimizes cost of change
¾
Helps to ensure efficient use of resources; both before and after
product launch
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“Enhanced” product/process understanding provides opportunities
for “more flexible regulatory approaches”
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ƒ
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risk-based regulatory decisions (reviews and inspections)
ease of process improvement within the approved design space
real-time QC / reduced end-product release testing
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Q&A
Presented by: Murray Adams
Operational Excellence Consulting Ltd.
Email: murray.adams@rogers.com
Bus. Tel: 1-905-796-8514
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