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Quality by Design
ProtAffin Biotechnologie AG
www.protaffin.com
2PROTAFFIN
Quality in Pharmaceutical Industry
20th Century : mostly reactive - validation
– 1950, US : wrong dosage in children syrup– 1960, Europe : thalidomide– Modern drugs are highly active, but also in a wrong way
• Initiation of regulation (EMA, FDA,…)– Acceleration of publications (1960-1980)– Rationalisation (1980-1990) Release of GMP guidelines
• Harmonisation (199O, birth of ICH : Europe, US and Japan)
• Still one of the most unsuccessfull industry
– Only 10 % in preclinical trial reach Market
3PROTAFFIN
But we are in 21th century
• Bill Clinton‘s hobby horse– Safe, effective and accessible drugs for all US citizen– Complete reorganisation of FDA– 2002 : Pharmaceutical Industry for the 21th century
• PAT, 2004
• Quality system, September 2006
• OOS, October 2006
• Process Validation : General Principle and Practices (draft 2008)
• ICH : Quality by Design (QbD) ICH Q8 : Pharmaceutical Development (2005), annex (2007), R1 (2007), R2 (2009) ICH Q9 : Quality Risk Management (2005) ICH Q10 : Pharmaceutical Quality System (2008) ICH Q11 : Development and Manufacture of Drug Substance (concept paper, 2008, draft
expected in 2010)
4PROTAFFIN
A lot of questions from industry
• Authorities :– ICH : Quality Implementation Working Group on Q8, Q9 and Q10
Questions & Answers, last update May 2010 Meetings
• Associations (PDA, EFPIA,...) Meetings, Workshops, training,...
• Meetings (Bioprocess, Bioproduction,...) Meetings, Workshops, training,...
• Industry (GSK,Amgen..) Publication of Mock examples (A mAb, a case study)
5PROTAFFIN
Quality by Design
• Science based– Prior knowledge, platform technology,... Product understanding
Target Quality Product Profile Critical Quality Attributes Control Strategy
Process understanding : Design Space Design of Experiment, multivariate statistics, Baysian statistics,...
• Risk Assessment based Product Risk Assessment Process Risk Assessment (FMEA,...)
• During the entire lifecycle of the product
6PROTAFFIN
Holistic effect of Risk Assessment and Statistics
Product/Process Risk assessment
Critical questions to be solved
DoE and multivariate statistical analysis
What is really critical ? How to master the criticality ?
7PROTAFFIN
Minimal Product Risk Assessment
ICH Q8(R2) : Pharmaceutical development should include, at a minimum, the following elements:
• Defining the quality target product profile (QTPP) as it relates to quality, safety and efficacy, considering e.g., the route of administration, dosage form, bioavailability, strength, and stability
• Identifying potential critical quality attributes (CQAs) of the drug product, so that those product characteristics having an impact on product quality can be studied and controlled;
• Determining the critical quality attributes of the drug substance, excipients etc., and selecting the type and amount of excipients to deliver drug product of the desired quality;
• Selecting an appropriate manufacturing process ;
• Defining a control strategy.
8PROTAFFIN
Enhanced Product Risk Assessment
An enhanced, quality by design approach to product development would additionally include the following elements:
• A systematic evaluation, understanding and refining of the formulation and manufacturing process, including;
o Identifying, through e.g., prior knowledge, experimentation, and risk assessment, the material attributes and process parameters that can have an effect on product CQAs;
o Determining the functional relationships that link material attributes and process parameters to product CQAs;
• Using the enhanced product and process understanding in combination with quality risk management to establish an appropriate control strategy which can, for example, include a proposal for a design space(s) and/or real-time release testing.
As a result, this more systematic approach could facilitate continual improvement and innovation throughout the product lifecycle (See ICH Q10).
9PROTAFFIN
Process Risk Assessment
• Despite no formal Regulatory requirement, Failure Mode Effect Analysis (FMEA) is the tool the most used in Pharmaceutical Industry
• Ishikawa diagrams largely utilized as starting point
Chemicals Water
Buffers Batch record
Technical staf Chromatographic media
Starting Material Semi purified product
SOP External supply
Equipment Compressed air Electricity Waste
Chromatographic Column system
10PROTAFFIN
FMEA
• Identification of all potential Hazard/failure (what can go wrong)
• What may be the effect of each hazard/failure and severity (S)
• Possible cause and the probability associated (P)
• Way to reduce the Risk to an acceptable level and the change of detecting it (D)
• Ranking of the potential hazards according to their Risk Priority Number
RPN = S x P x D
11PROTAFFIN
FMEA : easy but
• How many level ?
• Which Risk Level is acceptable ?
• Bioprocess are complex : classical FMEA usually 300 – 400 sheets
How to review it regularly during the life cycle of the product ?
• Lack of Homogeneity in term of both wording and scoring
• Solutions may be found in other industries (automobile,...) or closer in Medical Device (ISO 13485 and 14971)
12PROTAFFIN
FMEA : example
PA401 FMEA analysis of Down Stream in 19 sheets
13PROTAFFIN
Second part of QbD : Statistics
• Design of Experiment (DoE) and multivariate analysis
• Monte Carlo simulation and Baysian statistics
• Neuronal network
•Six Sigma
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14PROTAFFIN
Statistics in Industry
• 1940 : World war II Bullets manufactured in one state must fit with Rifles assembled in other states
• Manual inspection of all products
• Statistical analysis of samples
• 1945 : end of WWII, reconstruction of Japan economyVery disappointing results
• Japonese product = poor quality = gadget
Deming/Juran (US citizen) build new Quality Systems (Total Quality Management)• Quality results from organisational process
• Successfully used for automobile, electronic,
• …
• Success of Japanese industry became a serious threat for US Economy
– If Japanese can, why can’t we ?
15PROTAFFIN
Statistics in Industry
• 1986 : Methodology Six Sigma (Motorola) : DMAICBased on customer’s satisfaction
< 3.4 defect/1 000 000
• 1987 : Release of Iso 9000 Reaction against Japanese success
Reviewed in 2000 (Iso9000:2000)
• 1987- Evolution of Six Sigma and ISO (alone or combined)DFSS, Lean Six Sigma, Toyota Way,, …to Process Ninja
16PROTAFFIN
Statitics in Pharmaceutical industry
18th century 1946 1985
(De Moivre) (Placket Burman) FMEA
Normal Law DoE
1944 1960 1988
Monte Carlo Simulation Bayesian Statistics (Harry)
Six Sigma
2000
Neuronal Network
2005 2006 2009(?)
ICH Q8 ICH Q9 FDA
validation
QbD
Regulatory
Pharmaceutical development becomes a modern Science
17PROTAFFIN
Design of Experiments
• Design of Experiment (DoE) and multivariate analysis
Classical approach DoE Process understanding
18PROTAFFIN
• Interactions • Real optimum• Quality of information
Design of Experiments
Design of Experiment (DoE)
19PROTAFFIN
But also
Decrease of the number of experiments
Faster and cheaper development (time, manpower, reagents,...)
20PROTAFFIN
DoE in Production Process for BioPharmaceuticals
W C B
L B
A c id
B a se
A n t if o a m
I n o c ulum
M e dium
Sup e r n a t a n t
C e ll p a st e
S h ak e F las kS F -1 0 1
F erm en to rF E R -1 0 2
C en t ri fu g eC C -1 0 3 & 1 0 4
H ig h P res s u reH P -1 0 4
C en t ri fu g eC C -1 0 4
P e lle t
St o r a ge
St o r a ge
A c id p r e c ip it a t io n
C en t ri fu g eC C -1 0 4
A c id
H ig h P res s u reH P -1 0 4C en t ri fu g e
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P e lle tP e lle t
So luble p r o t e in s
F i l t rat io nD E -1 0 6
C I P buf f e r
B uf f e r A
B uf f e r B
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D iafi l t rat io nD F -2 0 5
D i lu t io nD F -2 0 6
B uf f e r A
F i l t rat io nD E -2 0 7
W a st e
W a st e
W a st e W a st e
21PROTAFFIN
DoE in BioPharmaceutical Life Cycle
Discovery Preclinical Phase I Phase II Phase III Commercial
R IV Factorial Design : identification of Critical Factor
RSM : Optimisation
R III Factorial Design
(Design Space and
Proven Acceptable Range)
Multivariate Analysis
Process Design Process Continued Process
Qualification Verification
CTD
22PROTAFFIN
DoE – 1 : Define
•Define the goal/target of the experiment :
Identification of critical factors (and interactions), optimisation, characterisation,...
Minimal level of improvement
Justification of the step and analytical procedure to be used
Use of the product : screening, reagent, pharmaceutical development
Ressource (Man power, timelines,...)
What is possible (number of experiments and analysis,...)
Prior knowledge (patents, publication, existing process,...)
23PROTAFFIN
DoE – 2 : Measure
•For existing process :
Mesure current actual performance
Multivariate analysis, principal componant analysis, Matrix chart
Justification of the step and analytical procedure to be used
Use of the product : screening, reagent, pharmaceutical development
Ressource (Man power, timelines,...)
What is possible (number of experiments and analysis,...)
Prior knowledge (patents, publication, existing process,...)
24PROTAFFIN
DoE – 3 : Analyse
3a- Run your experiments
25PROTAFFIN
Design-Expert® SoftwareFVIIam capacity
Error from replicates
Shapiro-Wilk testW-value = 0.997p-value = 0.992A: Contact TimeB: pH loadC: Column VolumeD: ConductivityE: Elution temperature
Positive Effects Negative Effects
Half-Normal Plot
Ha
lf-N
orm
al
% P
rob
ab
ilit
y
|Standardized Effect|
0.00 142.38 284.75 427.13 569.50
010
20
30
50
70
80
90
95
99
C
E
CE
DoE – 3 : Analyse
3b- Identify the critical factor and interactions
Critical factorsPlace for improvement
Reproducibility
Half Normal Plot
26PROTAFFIN
Analysis of variance table [Partial sum of squares - Type III]Sum of Mean F p-value
Source Squares df Square Value Prob > FModel 2502 3 834 24,75 0.0020 significant B-Conductivity sample722 1 722 21,42 0.0057 C-Load 722 1 722 21,42 0.0057 BC 1058 1 1058 31,39 0.0025Curvature 160 1 160 4,75 0.0812 not significantResidual 168,5 5 33,7Lack of Fit 156 4 39 3,12 0.3984 not significantPure Error 12,5 1 12,5Cor Total 2830,5 9
Factors Variation degree of SS/df MS/residual associated
selected associated freedom probability
DoE – 3 : Analyse
3c- Statistical significance of selected factors and interactions
27PROTAFFIN
DoE – 3 : Analyse
3c- Controls of the models
Design-Expert® SoftwareHCP peak
Color points by value ofHCP peak:
519.2
2.5
Internally Studentized Residuals
No
rma
l %
Pro
ba
bil
ity
Normal Plot of Residuals
-1.43 -0.71 0.00 0.71 1.43
1
5
10
20
30
50
70
80
90
95
99
Design-Expert® SoftwareRP cor pic 2
Color points by value ofRP cor pic 2:
97.4616
0
Internally Studentized Residuals
No
rma
l %
Pro
ba
bil
ity
Normal Plot of Residuals
-2.56 -1.03 0.50 2.03 3.56
1
5
10
20
30
50
70
80
90
95
99
If all factors affecting the process are identified, residuals are random and distributed according a normal law
28PROTAFFIN
DoE – 3 : Analyse
3c- Controls of the models
Design-Expert® SoftwareHCP peak
Color points by value ofHCP peak:
519.2
2.5
Predicted
Inte
rna
lly
Stu
de
nti
ze
d R
es
idu
als
Residuals vs. Predicted
-3.00
-1.50
0.00
1.50
3.00
3.10 128.59 254.08 379.56 505.05
Design-Expert® Softwareyield
Color points by value ofyield:
5769
275
Predicted
Inte
rna
lly
Stu
de
nti
ze
d R
es
idu
als
Residuals vs. Predicted
-3.00
-1.50
0.00
1.50
3.00
568.13 1534.59 2501.06 3467.53 4434.00
OK Will requires data transformation
29PROTAFFIN
DoE – 3 : Analyse
3c- Controls of the models
30PROTAFFIN
DoE – 3 : Analyse
Load Flow rate Gradient pH Particle size% cm/h CV µm ppm50 50 15 8 30 450 150 15 6 30 350 50 5 8 90 211
150 150 5 8 30 491150 50 5 6 30 519100 100 10 7 60 157150 150 15 8 90 43150 50 15 6 90 950 150 5 6 90 249
100 100 10 7 60 143
HCP contamination
Design-Expert® Software
HCP peakDesign points below predicted value519.2
2.5
X1 = A: LoadX2 = C: Gradient
Actual FactorsB: Flow rate = 100.00D: pH = 7.00E: Particle size = 60.00
50.00
75.00
100.00
125.00
150.00
5.00
7.50
10.00
12.50
15.00
-70
60
190
320
450
H
CP
pe
ak
A: Load C: Gradient
31PROTAFFIN
DoE – 4 : Improve
• 4- Optimize significant factors even with complex/contradicory results
X
X
32PROTAFFIN
DoE – 4 : Improve
• 4- Optimize significant factors even with complex/contradicory results
X
X
33PROTAFFIN
DoE – 5 : Control
Current conditionsCurrent conditions Optimised conditionsOptimised conditions
Wash 1 : 25 % A 0.2 M B in 8 % A
Wash 2 : 0.5 M B
Elution : 0.5 M B in 25 % A 0.75 M B in 22.5 % A
Only a mathematical model, results must be controled
Yield : 68 – 85 % 85 %
HCP Clearance : 2.9 – 3.1 Log 4.1 Log
34PROTAFFIN
Not only DoE :
• Define
• Mesure
• Analyse
• Improve
• Control
DMAIC : Six SigmaNot anymore sufficient : ICH Q8(R2)
The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality
Baysian statistics and Monte Carlo simulation
35PROTAFFIN
Monte Carlo Simulations
36PROTAFFIN
Monte Carlo simulation for initial specifications
• 10 000 calculations with A and B randomly chosen within initial target specifications
• Nearly 20 % of failure
37PROTAFFIN
How to improve the process robustness?
AB
A B
38PROTAFFIN
Optimized specifications
No Failure (< 3.4/106 : Six Sigma robust process)
39PROTAFFIN
Neuronal Network
• DoE has been developped 100 years ago
• DoE, despite serious improvment is now an ‘’old’’ technique and suffers from many disadvantages
– Number of experiments may be further reduced
– Model may be wrong in the real world
– Difficulty to select the best model (complexity)
– Predictability of model difficult to estimate
40PROTAFFIN
Number of experiments may be further reduced
• DoE : With n experiments, you can calculate the coefficients for SIGNIFICANT n-1 factors and interactions
• Exemple : 5 factors tested : – Factorial : 2^5 = 32 experiments– Half Factorial : 16 experiments– If only A, B and interaction AB are found significant, evaluation of their
parameter would have required only 4 experiments
41PROTAFFIN
Number of experiments may be further reduced
• RSM model with interactions may required a lot of expirements.
• Yi = naaa*A3+ nbbb*B3+ naab*A2B + nabb*AB2 + naa*A2+ nbb*B2+ nab*AB + na*A+ nb*B + ……..
• 9 parameters for only 2 factors
42PROTAFFIN
Model may be wrong in the real world
• 3 Factors : A, B, C, RSM• Real = 3A2 + 2 B2 + C + AB + AC
Run A B C Real Alea Real1 0,0 2,0 1,0 11,0 2,7 13,72 5,0 2,0 1,0 96,0 18,2 114,23 5,0 4,0 1,0 132,0 10,2 142,24 0,0 4,0 1,0 37,0 4,7 41,75 2,5 3,0 3,5 58,3 -14,3 43,96 2,5 3,0 -0,7 41,4 -17,4 24,07 2,5 3,0 3,5 58,3 17,1 75,38 2,5 1,3 3,5 33,6 2,0 35,79 2,5 3,0 3,5 58,3 -0,9 57,3
10 6,7 3,0 3,5 187,0 -4,5 182,511 2,5 3,0 3,5 58,3 -12,5 45,812 5,0 2,0 6,0 111,0 6,1 117,113 2,5 3,0 3,5 58,3 -13,0 45,314 2,5 3,0 3,5 58,3 -8,3 49,915 0,0 4,0 6,0 62,0 -7,0 55,016 2,5 4,7 3,5 94,2 -2,4 91,817 5,0 4,0 6,0 157,0 -18,9 138,118 -1,7 3,0 3,5 35,6 -6,7 28,919 0,0 2,0 6,0 26,0 11,2 37,220 2,5 3,0 7,7 75,1 8,3 83,4
Equation CoefficientFactor EstimateInterceptA-AB-BC-C 1 1,0AB 1 1,1AC 1 1,0BCA^2 3 3,1B^2 2 1,8C^2
43PROTAFFIN
Model may be wrong in the real world
• Real world + 20% (precision of the process / analytics)
Run A B C Real Alea Real1 0,0 2,0 1,0 11,0 2,7 13,72 5,0 2,0 1,0 96,0 18,2 114,23 5,0 4,0 1,0 132,0 10,2 142,24 0,0 4,0 1,0 37,0 4,7 41,75 2,5 3,0 3,5 58,3 -14,3 43,96 2,5 3,0 -0,7 41,4 -17,4 24,07 2,5 3,0 3,5 58,3 17,1 75,38 2,5 1,3 3,5 33,6 2,0 35,79 2,5 3,0 3,5 58,3 -0,9 57,3
10 6,7 3,0 3,5 187,0 -4,5 182,511 2,5 3,0 3,5 58,3 -12,5 45,812 5,0 2,0 6,0 111,0 6,1 117,113 2,5 3,0 3,5 58,3 -13,0 45,314 2,5 3,0 3,5 58,3 -8,3 49,915 0,0 4,0 6,0 62,0 -7,0 55,016 2,5 4,7 3,5 94,2 -2,4 91,817 5,0 4,0 6,0 157,0 -18,9 138,118 -1,7 3,0 3,5 35,6 -6,7 28,919 0,0 2,0 6,0 26,0 11,2 37,220 2,5 3,0 7,7 75,1 8,3 83,4
Equation CoefficientFactor EstimateIntercept 56,7A-A 44,1B-B 17,6C-C 1 11,0AB 1 3,8AC 1 -4,1BC 4,0A^2 3 20,0B^2 2 3,6C^2 -0,4
44PROTAFFIN
Best Model and Predictibility
• Real model : A, linear , tested at 4 levels
0123456789
10
0 1 2 3 4 5
Theoretical Real Mean Linear cubic
0123456789
10
0 1 2 3 4 5
Real cubic Next time
If complexity of model increase, precision to data increase (diminution of Sum of Square) but predictibility to other results decrease (increase of bias)
45PROTAFFIN
Bayes limit : Bias/variance dilemna
0
20
40
60
80
100
120
0 2 4 6 8
Complexity of model
Sum of Square Bias Bayes Limit
Best Model
46PROTAFFIN
DoE models : which is the best ?
47PROTAFFIN
DoE / Neuronal Network
DoE Neuronal Network
Factors
Response
Constant Function
48PROTAFFIN
Functions in Neuronal Network
• Several types of fonctions, only two parameters/function• Reduced number of experiments for modeling (statistical learning)• If classical DoE require 36 experiments for modelisation, neuronal
network may use this number of experiment to– Statistical learning (modelisation), ex : 12 experiments +– Evaluation of the model on other data not used (validation), ex 12
experiments +– Evaluation of the model on final data remaining (test), ex 12
experiments• Allow Bootstrap : instead of a single analysis, perform i.e 200
statistical modeling/validation/testing analysis using each time 12 random experiments for each step
49PROTAFFIN
Conclusions
• Avoid Muri (overworked men or overstrechted equipment)
• Due to Mura (inconsistency, irregular production)
• To decrease Muda (waste, wrong product)
• In a Kaizen (continuous improvement) spirit
Toyota Way ProtAffin Way
50PROTAFFIN
Quality by Design
– Regulatory : to offer flexibility in the Design Space but...
– New for Biopharmaceutical Industry and still only poorly used
51PROTAFFIN
QbD strongly requested by Authorities, lack of implementation may lead not only to a Dossier Assessment Refusal Report but to the discontinuation of GMP authorisation for Manufacturing of Facility