Date post: | 12-Nov-2014 |
Category: |
Documents |
Upload: | realmayank |
View: | 3,769 times |
Download: | 4 times |
DEVELOPMENT, OPTIMIZATION AND ROBUSTNESS BY DESIGN
QUALITY BY DESIGN
Mayank
Global initiatives
1. ICH, Q8(R1) Pharmaceutical Development (Geneva, Switzerland, Nov. 10, 2005; Rev. 2008).
2. ICH, Q9 Quality Risk Management (Geneva, Switzerland, Nov. 9. 2005).
3. J. Agalloco et al., "FDA's Guidance for Industry: Process Validation: General Principles and Practices," presented at PDA, Jan. 14, 2009.
4. FDA, Draft Guidance for Industry—Process Validation: General Principles and Practices (Rockville, MD, Nov. 2008).
5. W. Charlton, T. Ingallinera, and D. Shive, "Validation of Clinical Manufacturing," and Validation Chapter, in Validation of Pharmaceutical Process, J. Agalloco and F. Carleton, eds. (Informa Healthcare, New York, 3rd ed., 2008), pp. 542–544.
References
Global initiatives
What is QbD?
Quality by design (QbD)
Product and process performance characteristics are scientifically designed to meet specific objectives, not merely empirically derived from performance of test batches
Focus during development
Critical Quality Attributes (CQA)
oCell viabilityoCell countoTitreoProduct characteristics (eg Glycocylation)oImpurity profile
Critical Process Parameter (CPP)oTemperatureopHoAgitationoDOoMedium compositionoOsmolarityoFeed typeoProcess type (eg Batch, fed batch or perfustion)
oOverall purityoType of impurity (eg HCP, endotoxins, DNA,)oYield
oColumn bed height and packing efficiencyoMedia selectivityoMedia particle sizeoDynamic capacityoBuffer conditions (eg pH, conductivity)oTemperatureoFlow rateoSample load
eg. USP DSP
Tools for successful implementation of QbD
Quality by design (QbD)
Team:
Analytical equipments
oOnline/Atlineo NIR detectorso Methanol sensorso CO2/O2 probeso Conductivity probeso Osmolarity probeso Turbidity sensoro Cell count/contamination analyzer
oEngineersoBiologistsoAnalystsoChemistsoIndustrial pharmacistoSatiations
oOfflineo HPLC/UPLC*o LC/MSo Ion analyzer*o C/N analyzero Gel doco Multi well plate readero ELISA
Powerful Statistical tools
Process flow:
Quality by design (QbD)
Screening
Optimization
Validation
Identification of significant parametersFinding parameter ranges
Finding interactions of parametersDefining models
Production
Identification of CPP
Continuous monitoring and development
Characterization range
Acceptable range
Operating range
Process design space
Set point
Identification of noise factorsProcess/ product Development:
RobustCost effectiveFeasible
Defining control strategies
Quality by design (QbD)
Defining Design space
Quality by design (QbD)
Defining Design space
Parameter Controllability
1 Easy
2 Difficult
Parameter Controllability
1 Difficult
2 Easy
Quality by design (QbD)
Defining Design space
Screening
Parameter selection
PhysicalChemicalRaw materialComponent/EquipmentProcess (time, type)EnvironmentalFacility
Categorical
Continuous
Screening
Level selection
Digging for a fossil
Parameter
Resp
onse
Screening
Fractional Factorial
No of exp Factors
A B
1 + -
2 - -
3 + +
4 - +
22
Screening
No of exp Factors Interaction
A B AB
1 + - -
2 - - +
3 + + +
4 - + -
22
Fractional Factorial
Screening
No of exp Factors
A B C=AB
1 + - -
2 - - +
3 + + +
4 - + -
23-1C=AB
C is confounding with AB
Fractional Factorial
Screening
No of exp Factors and interaction
A B=AC C=AB
1 + - -
2 - - +
3 + + +
4 - + -
23-1C=AB
C is confounding with ABB=AC
B is confounding with AC
Fractional Factorial
Screening
No of exp Factors and interaction
A=BC B=AC C=AB
1 + - -
2 - - +
3 + + +
4 - + -
23-1C=AB
C is confounding with ABB=AC
B is confounding with ACA=BC
A is confounding with BC
Fractional Factorial
Screening
Plackett Burman
2 level fractional factorial designsResolution III designEfficient estimationsInteractions between factors ignoredUsed in Matrix formMultiple of 4 not power of 2Saturated orthogonal array
Fractional Factorial
Screening
Plackett Burman
2/
)1()1(
N
yyEx
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11
1 1 1 1 1 1 1 1 1 1 1
1 1 1 -1 -1 -1 1 -1 -1 1 -1
1 1 -1 -1 -1 1 -1 -1 1 -1 1
1 -1 1 1 1 -1 -1 -1 1 -1 -1
1 -1 -1 1 -1 1 1 1 -1 -1 -1
1 -1 -1 -1 1 -1 -1 1 -1 1 1
-1 1 1 1 -1 -1 -1 1 -1 -1 1
-1 1 -1 1 1 1 -1 -1 -1 1 -1
-1 1 -1 -1 1 -1 1 1 1 -1 -1
-1 -1 1 -1 1 1 1 -1 -1 -1 1
-1 -1 1 -1 -1 1 -1 1 1 1 -1
-1 -1 -1 1 -1 -1 1 -1 1 1 1
Fractional Factorial
Matrix
Before deciding whether to build a response surface model, it is important to assess the adequacy of a linear model
The error term ε in the model is comprised of two parts:1. modeling error, (lack of fit, LOF)2. experimental error, (pure error, PE), which can be calculated from
replicate points
The lack of fit test helps us determine if the modeling error is significant different than the pure error
Screening
Lack of fit
Before deciding whether to build a response surface model, it is important to assess the adequacy of a linear model
The error term ε in the model is comprised of two parts:1. modeling error, (lack of fit, LOF)2. experimental error, (pure error, PE), which can be calculated from
replicate points
The lack of fit test helps us determine if the modeling error is significant different than the pure error
Screening
Lack of fit
Response surface methodology
Original System
x1
x2
1
DOE and Experiments
Black BoxedSystem
Input
1x
2x
Response
y
RS Model
Response surface methodology
RSM characteristics
Models are simple polynomials
Include terms for interaction and curvature
Coefficients are usually established by regression analysis with a computer program
Insignificant terms are discarded
Y = β0 constant + β1X1 + β2X2 main effects + β3X1
2 + β4X22 curvature
+ β5X1X2 interaction + ε error
Model equation for 2 factors
Y = β0 constant + β1X1 + β2X2 + β3X3 main effects + β11X1
2 + β22X22 + β33X3
2 curvature + β12X1X2 + β13X1X3 + β23X2X3 interactions + ε error
Model equation for 3 factors
Higher order interaction termsare not included
Response surface methodology
Central composite circumscribed (CCC)
Central composite inscribed (CCI)
Central composite face centered (CCF)
5 Levelsα (star point) are beyond levels
3 Levelsα (star point) are within levels (center)
5 Levelsα (star point) are within levelsScale down of CCC
Central composite design (CCD)
eg. 2 factor
Response surface methodology
3 factors
Total exp: 20Pattern X1 X2 X3+++ 1 1 1++− 1 1 -1+−+ 1 -1 1+−− 1 -1 -1−++ -1 1 1−+− -1 1 -1−−+ -1 -1 1−−− -1 -1 -10 0 0 00 0 0 00 0 0 00 0 0 00 0 0 00 0 0 0
00a 0 0 -1.68179
00A 0 01.68179
30a0 0 -1.68179 0
0A0 01.68179
3 0
A001.68179
3 0 0a00 -1.68179 0 0
++-
+++
--- +--
--+
-++
-+-
+-+
Central composite design (CCD)
Full factorial 8
Axial points 6
Center points 6
Central composite circumscribed (CCC)
Response surface methodology
Randomization:
To avoid effect of uncontrollable nuisance variablesPattern X1 X2 X3000 0 0 0
A001.68179
3 0 0++− 1 1 -100a 0 0 -1.68179−−+ -1 -1 1000 0 0 00a0 0 -1.68179 0−−− -1 -1 -1−+− -1 1 -1000 0 0 0+−+ 1 -1 1000 0 0 0000 0 0 0a00 -1.68179 0 0000 0 0 0+−− 1 -1 -1−++ -1 1 1
00A 0 01.68179
3
0A0 01.68179
3 0+++ 1 1 1
Central composite design (CCD)
++-
+++
--- +--
--+
-++
-+-
+-+
Central composite circumscribed (CCC)
Response surface methodology
To avoid effect of controllable nuisance variablesPattern X1 X2 X3 Block−−− -1 -1 -1 1−++ -1 1 1 1++− 1 1 -1 1+−+ 1 -1 1 1000 0 0 0 1000 0 0 0 1−+− -1 1 -1 2000 0 0 0 2−−+ -1 -1 1 2000 0 0 0 2+++ 1 1 1 2+−− 1 -1 -1 2000 0 0 0 3000 0 0 0 3a00 -1.63299 0 0 30a0 0 -1.63299 0 3
A001.63299
3 0 0 3
0A0 01.63299
3 0 300a 0 0 -1.63299 3
00A 0 01.63299
3 3
---
-++
++-
+-+
+++
--+
+--
-+-
Central composite design (CCD)
Blocking:Central composite circumscribed (CCC)
Response surface methodology
Box Behnen
12 experiments
eg. 3 factor
It is portion of 3k Factorial 3 levels of each factor is used Center points should be included It is possible to estimate main effects and second order terms Box-Behnken experiments are particularly useful if some boundary areas of the design region are infeasible, such as the extremes of the experiment region
Response surface methodology
* One third replicate is used for a 3k factorial design and one-half replicate is used for a 2k factorial design with the CCD for 5, 6 and 7 factors.
Comparison of RSM experiments
Robust process development
Who is better shooter?
BA
Robust process development
Goal post vs Taguchi view
LSL USL LSL USL
Robust process development
Reducing variation
Robust process development
Objective of robust process
Smaller-the-Better S/N Ratio = – 10 Log10 ( 1/n Yi2 )
e.g. defects, impurity, process time, cost
Larger-the-Better S/N Ratio = – 10 Log10 ( 1/n 1/Yi2 )
e.g. titre, yield, resolution, profit
Nominal-the-Best S/N Ratio = – 10 Log10 [ 1/n (YIDEAL- Yi ) 2 ]
e.g. target
Signal-to-Noise S/N Ratio = 10log[μ2/σ2]e.g. trade-off
Robust process development
Identification of Signal and noise
Parameters Plant Laboratory Agitation +++ +++ Feed rate +++ +++
Recipe +++ +++ Aeration ++ +++ Pressure ++ +++
pH ++ +++ Mass transfer + +++ Temperature + +++ Raw material + +++
Operator + +++ Scale up - ++
Environmental - -
What can be controlled in plant and laboratory
Signal:
Noise:
What can not be controlled in plant but in laboratory
eg: Fermentation
Robust process development
Developing robust process
To find a signal settings in presence of noise that minimize response variation while adjusting of keeping the process on target
Signal:
Noise:
Taguchi approach
Inner array
Outer array
OA IA
A B C - - - + + - ++
1 + + +
2 + + -
3 + - +
4 + - -
5 - + +
6 - + -
7 - - +
8 - - -