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1
2eme Masters ISSBA 2010
Angers, the 3d of February 2010
Quality by Design
Statistics and Modelisation in Pharmaceutical Development
Alain Poncin
LFB Biotechnologies
Process Development Unit Manager
2
Introduction
3
Professional Experience
█ 1988 : MSc in Biochemistry, University of Liege
█ 1988 – 2008 : Eurogentec (Belgium)2 Business Units : Reagents and Tools for Research
Contract Research Organisation (CMO)
Development of more than 70 proteins in
Clinical trials (Phase I to IV)
█ 2008 - 2010 : LFB Biotechnologies (France)2 Companies : LFB Biomedicaments : Plasma fractionation
LFB Biotechnologies : mAbs and transgenic animals
Development of production process for TG
Introduction of QbD
█ March 2010 - : Protaffin (Austria)Small Biotech, production of recombinant proteins in e. Coli
4
Expertise in QbD
█ Implementation of QbD in Down Stream Proces for plasma derived recombinant proteins.
A.Poncin, P. Paolantanocci, M. Ollivier
Web Seminar PDA on Quality by Design, 3 March 2010.
█ Study of the anion Exchange chromatographic step operating conditions of the new IgG manufacturing process-Characterisation by Design of Experiment.
P. Paolantanocci, D Gachelin, S. Nakache, A.Poncin, A. Sauger, M. Ollivier
Plasma Product Biotechnology, 11-15 May 2009, Menorca, Spain.
█ Risk Assesment and DoE must be used in sinergy for Quality by Design succes. A. Poncin
PDA conference on Quality by Design, 7-8 October 2008, Frankfurt, Germany
█ Eurogentec current validation strategy A. Poncin
5th International conference on HIC/RPC chromatography, 2007 Interlaken
5
Pharmaceutical Development
Preclinical
From Drug discovery to animal testing
(Toxicology)
Phase I Phase II Phase III
Safety Safety
Efficacy
Efficacy
DoseIND/IMPD
(First in Man)
(e)CTD
(AMM)
Commercial
Process Development Production
Laboratory GMPValidation
3 Batches
Traditionnal Development
(Minimal Approach)
Phase IV
PharmacoVigilance
Process Design QualificationContinuous Verification
Enhanced Quality by Design Approach
6
Enhanced Quality by Design Approach
Preclinical Phase I Phase II Phase III Phase IV
Process Design QualificationContinuous Verification
Product Target Quality Profile
Potential Critical/Key Quality Attributes
Process
Design
Potential Critical/Key Parameters
Design SpacePrior Knowledge
ScienceDoE
Risk Management (wc)Critical/Key Parameters
Critical/Key Quality Attributes
Control Strategy
7
Statistics and Modelisation
Preclinical Phase I Phase II Phase III Phase IV
Product Life Cycle
Process DevelopmentDoE : Factorial DesignIdentification of Critical Parameters
Design-Expert® SoftwareHCP peak
Error from replicates
Shapiro-Wilk testW-value = 0.992p-value = 0.968A: LoadB: Flow rateC: GradientD: pHE: Particle size
Positive Effects Negative Effects
Half-Normal Plot
X2: Half-Normal % ProbabilityX1: |Standardized Effect|
0.00 88.26 176.53 264.79 353.05
010
20
30
50
70
80
90
95
99
A
C
E
8
Statistics and Modelisation
Preclinical Phase I Phase II Phase III Phase IV
Product Life Cycle
Process OptimisationDoE : Response Surface ModelOptimisation and ModelisationIn silico modelisationTo establish Range and Specifications
Design-Expert® Software
YieldDesign points above predicted valueDesign points below predicted value96.8
0
X1 = A: Factor AX2 = B: Factor B
0.00
0.50
1.00
1.50
2.00
0.00
12.50
25.00
37.50
50.00
-20
15
50
85
120
Y
ield
A: Factor A B: Factor B
9
Statistics and Modelisation
Preclinical Phase I Phase II Phase III Phase IV
Product Life Cycle
Process CharacterisationScale Down ValidationDoE : Factorial DesignDemonstration of Range and Specifications Definition of Design Space
Sequential Model Sum of Squares [Type I]Sum of Mean F p-value
Source Squares df Square Value Prob > FMean vs Total 3,8571 1 3,8571 12,432 0.0021 SuggestedLinear vs Mean 12,6390 15 0,8426 8,3612 0.15022FI vs Linear 0,3256 2 0,1628 2,7393 0.2105Quadratic vs 2FI 0,0594 1 0,0594 1,0000 0.4226Cubic vs Quadratic 0,1189 2 0,0594 1,0000 0.4226
10
Statistics and Modelisation
Preclinical Phase I Phase II Phase III Phase IV
Product Life Cycle
Process Validation/characterisationMultivariate AnalysisDemonstration of scale up andreproducibility
1 2 3 4 5 6 7
50 kDa
30 kDa
20 kDa
15 kDa
1 L
350 L
Analysis of Variance for EFT - Type III Sums of Squares Source Sum of Squares Df Mean Square F-Ratio P-Value MAIN EFFECTS A:Scale 0,00666667 1 0,00666667 0,02 0,8842 RESIDUAL 1,10667 4 0,276667 TOTAL (CORRECTED) 1,11333 5
Analysis of Variance for Viable cells - Type III Sums of Squares Source Sum of Squares Df Mean Square F-Ratio P-Value MAIN EFFECTS A:Scale 0,0266667 1 0,0266667 0,18 0,6965 RESIDUAL 0,606667 4 0,151667 TOTAL (CORRECTED) 0,633333 5
Analysis of Variance for Expr level - Type III Sums of Squares Source Sum of Squares Df Mean Square F-Ratio P-Value MAIN EFFECTS A:Scale 0,00666667 1 0,00666667 0,40 0,5614 RESIDUAL 0,0666667 4 0,0166667 TOTAL (CORRECTED) 0,0733333 5
11
Statistics and Modelisation
Preclinical Phase I Phase II Phase III Phase IV
Product Life Cycle
Continued Process Verification Multivariate Analysis Graph Plot
X-bar Chart for Protéines-Rendement
0 20 40 60 80 100 120
Subgroup
-8
-4
0
4
8
12
16(X 10000,0)
X-b
ar
CTR = 26045,57UCL = 124007,09
LCL = -71915,95
12
UMFP - formulation
Preclinical Phase I Phase II Phase III Phase IV
Product Life Cycle
Empirical development
Qualitative Justification (//Factorial)
Quantitative Justification (//RSM)
13
Analytics/Quality Control
Preclinical Phase I Phase II Phase III Phase IV
Product Life Cycle
Development Validation Qualification (?) (including Robustness by DoE)
14
All that in a highly regulated environment
(EMA, FDA, ICH,…)
15
Regulation in the 20th Century : Reactivity
█ A long list of accidents linked to drugs1902 : Biologics Control Act, creation of CBER (US, 13 children dead))
1930 : Creation of the FDA agency (US, 107 adults dead)
1950 : First publication of FDA (Guidance for Industry)
1960 : Europe : thalidomide : 10,000 malformed children
Modern drugs are highly active, but also in a wrong way Initiation of regulation (EMA, FDA,…)
Acceleration (1960-1980)
Rationalisation (1980-1990), release of GMP guidelines
Harmonisation (199O, birth of ICH)
█ No Change
█ Still one of the most unsuccessfull industry Only 10 % of success between pre-clinical and market
16
Regulation in the 21th Century : science based
█ Bill Clinton‘s hobby horseSafe, effective and accessible drugs for all US citizen
Government responsibles for both regulation/accessibility
Complete reorganisation of FDA2002 : Pharmaceutical Industry for the 21th century
PAT 2004 Quality system, September 2006 OOS, October 2006…
█ 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)
█ FDA : Process Validation : General Principle and Practices (draft 2008)
█ Freedom to operate in the Design Space
17
Regulatory and Science
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 In Place in LFB Training
(SOP, training)
To be extended
Regulatory
Phamaceutical development becomes a modern Science
18
In other industries : Quality is a long story
█ Starting in the 13TH century (craftsmen and guilds)
█ 1750-1900 : industrial revolutionFrom guilds copy to supervisor and engineersTarget : increase productivity with stable employmentCreation of Inspection DepartmentWhen a faulty product reached a customer :
Why did we let this product get out? Why did we make it this way?
19
Quality in the 20th centuries (1/2)
█ 1900 – 1920 : notion of Input Process Output
█ 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 ?
20
Quality in the 20th centuries (2/2)
█ 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
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
21
Cost of Low Quality
█ real cost of low quality = 10 x cost of known defects/failures
2006 2007 2008 2009
Cost of Quality History
0
400
800
1200
1600
CO
Q
PreventionAppraisalInt. FailureTotal COQTotal Failure
22
Six Sigma (6several methodologies
█ DMAIC (existing Product/Process)Define
Mesure
Analyse
Improve
Control
█ DFSS (Design For Six Sigma)DCOV : Define, Characterize, Optimize, Verify
IDOV : Identify, Design, Optimize, Validate/verify
IDDOV : Identify, Define, Develop, Optimize, Verify
DFSS : Software
█ Lean Six Sigma : ‘’Lean’’ manufacturing and Six Sigma
Will be used to organize this presentation
23
Six Sigma organisation
█ Six Sigma training is certifiedYellow Belt : Technical Staff
Green Belt : Project Manager
Black Belt : Head of Department (Operational Excellence,…)
Master Black Belt : Trainers
█ Expensive (5000 – 10 000 $/ level) and time consumming (1 – 2 weeks/level)
█ Main difficulty : statistical part
24
Statistics
Normal Law and basic tests
25
Normal law
█ A long story :
DeMoivre, 1667-1754 : description of games of chance (money)
Laplace, 1749-1827 and Gauss, 177-1855 : errors in astronomical calculations
Quetelet, 1796-1874 : biological and social data
Galton, 1822-1911 : psychological data
█ Reasons of success :
Easy (as compared to other laws or non parametric analysis)
Law of large number (central limit theorem)
random error distributed following a normal law
26
Normal law presentation
█ Normal law :
defined by 2 parameters average (mean), m or standard deviation, s.d or
█ several graphical illustrations :
non cumulative
0
0,02
0,04
0,06
0,08
0,1
0,12
0,14
0,16
0 5 10 15 20 25 30
X
Pro
bab
ility
cumulative
0
0,2
0,4
0,6
0,8
1
0 5 10 15 20 25 30
X
Cum
ulat
ive
prob
abili
ty
22/2.
2.
1)(
xexf
0,00%10,00%20,00%30,00%40,00%50,00%60,00%70,00%80,00%90,00%
100,00%
0 5 10 15 20 25
X
Pro
babi
lity
-6
-4
-2
0
2
4
6
Log
it(p
roba
bili
ty)
27
Normal law : average and s.d
Normal law
0
0,02
0,04
0,06
0,08
0,1
0,12
0,14
0,16
-10 0 10 20 30 40 50
X
Prob
abili
ty
Average : x position s.d : width
Normal law
0
0,1
0,2
0,3
0,4
0,5
0,6
0 5 10 15 20 25 30 35
X
pro
bab
ility
s.d=2,8 s.d=1,4 s.d=0,7
Area under the curve = 1
28
Standard/studentised Normal law
Standard normal law :
0
0,1
0,2
0,3
0,4
0,5
-10 -5 0 5 10 15 20 25 30 35
X
prob
abili
ty
Normal distribution standard normal distribution
average = 0
s.d. = 1 = varianceTable : probability versus z
29
Outliers
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07
0,08
0,09
-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
pro
bab
ility
99 %1 s.d. : 64 %
2 s.d. : 95 %
3 s.d. : 99 %
…….
6 s.d. : 99.99996%
6defects/millions
30
Outliers
█ Outliers : soundly studied
allowed detection of Mars’ satellites
economic (stock exchange, speculation,…..)
military (radioactivity,…)
never delete outliers without careful studies (can mask unexpected effect)
31
t test
█ Comparison of two groupsCalculation of average and s.d. of group values
Intersection of P9X confidence interval
00,020,040,060,080,1
0,120,140,16
0 5 10 15 20 25 30 35 X1 X20
0,2
0,4
0,6
0,8
1
0 5 10 15 20 25 30 35 X1 X2
32
ANOVA
█ Comparison of several groups
several presentations : values
Bar graph (Nested)
Line (DOE)
TableTable
33
Define
34
Define : 6
█ Define : ‘’The Customer’s Voice’’Your customer (internal or external)
General customers (‘’supermarket’’), Head of Production,…
Your target (if the market is segmented) Male 40-50 years old, teenagers,…
Their requirements to be satisfied (and encourage them to buy your product, your project,..)
Quality, Price, Special requirements,…
Your objectives, your goals, the product characteristics Memory > 4 mb, autonomy > 48h, productivity > 20%...
The way you will quantify the success of the product, of the project
█ In Pharmaceutical Industry : QbD : ICH Q8(R2)Quality Target Profile (intended use, route of administration, dosage strength,…)Drug Product Quality CriteriaPotential Critical Quality AttributesControl Strategy
35
Define : examples of tools
█ 1- Process Map
Preculture Fermentation Harvest Filtration Chromatography 1 Viral inactivation
Chromatography 2UltrafiltrationNano filtration Chromatography 3Vialing 0.22 µm filtration
Process Flow Chart
36
Define : examples of tools
█ 2- Ishikawa fishbone
37
Define : examples of tools
█ 3- Quality Function Deployment
InteractionsStrong PositivePositiveNegativeStrong Negative
Viru
s
Bio
burd
en/
End
otox
ins
Re
sidu
al D
NA
HC
P
Oxy
dise
d/d
eam
idat
ed
form
s
Mu
ltim
ers/
aggr
egat
es
Pos
t Tra
duct
ionn
al M
odifi
catio
ns
Re
sidu
al S
/D
Re
sidu
al P
rote
in A
De
grad
atio
n
Yie
ld
Re
prod
uci
bilit
y
Prio
rity
Safety 10
Efficacy 8
Low cost 6
Absolute weight 19 11 11 13 15 19 19 11 13 13 15 9 24
Relative weight 152 104 104 120 132 168 168 104 120 116 108 72 120
Fre
e
Ste
rile
an
d a
pyr
ogen
< 1
00 p
g/do
se
< 1
00 p
pm
< 1
%
Co
mpe
titor
Sta
y eq
uiv
alen
t
TB
D
< L
OD
No
vose
ven
> 1
g/L
Re
prod
uci
ble
Design requirements
Cus
tom
er
De
man
ds
Relationship :
Strong = 9.0 Medium = 3,0 Weak = 1,0
Development Target :
Decrease
Maintain
Increase
38
‘’Define’’ in QbD
█ Define the Target Product Profile (TPP)Based on prior knowledge, literature (including patents), the expected dosage,
way of administration,….establish the Target Product Profile
Example :
Protein X is a recombinant coagulation factor produced in the milk of transgenic animals to treat bleeding episodes in Hemophilia patients with inhibitors. It will be administrated by IV injection at 100 µg/kg.
█ From TPP identify potential Critical Quality Attributes (CQA) and the way to analyse/quantify them.
█ Rank the potential CQA (Risk Assesment for Product)
39
Potential CQA
Potential Quality Attribute
Product related Process related
Bioactive contaminants Contaminants without
Biological activity
Criticality Determination Safety assesment
Control Strategy and provisional Specifications
40
Potential CQA : example
Type Impurities Potential effect Analytical Tool Provisional specifications CommentsGlycosylation variants Modification of half life MS-HPLS must stay comparable
ImmunogenicityDes-Gla variants Lower activity MS-HPLC > 9 Gla domainsDeamidation Not known IEX-HPLC must stay comparableAggregates Immunogenicity SE-HPLC < 3 %Degradation Lower activity SE-HPLC Comparable to competitor
SDS-PAGEVirus Contamination / Free validation of viral clearanceBacteria Contamination Sterility SterileEndotoxins Fever to anaphylactic LAL < 5 EU/mgHCP Immunogenicity ELISA < 5 ppmanimal related protein Immunogenicity ELISA < LODDNA ? qPCR < 100 pg/doseresidual solvent stability/tolerance specific test < 0,5 mg/Lresidual detergent stability/tolerance specific test < 50 mg/Lparticles stability/tolerance Visual Free
Product related
Bioactive Process Related
Process without bioactivity
41
Classification of potential Quality attributes
A-Mab : a Case Study in BioProcess Development, CMC Biotech Working Group, 30th October 2009.
█ Tool 1 : criticality is evaluated using a risk ranking approach (ICHQ9) which assesses the possible impact of each potential attribute on safety and efficacy.
The ranking is determined by the IMPACT and the UNCERTAINTY
█ Tool 2 : criticality is also evaluated using a risk ranking approach (ICHQ9) which assesses the possible impact of each potential attribute on safety and efficacy.
The ranking is determined by the SEVERITY and the LIKELIHOOD
█ Tool 3 : Impurity Safety Factor (ISF)
ISF = LD50 (or MRL) / Level in product dose
LD50 : Lethal Dose for 50 % of animals
MRL : Maximum Residue Limit
42
Tool 1 : IMPACT and UNCERTAINTY
Impact (Score) Efficacy PK/PD Immunogenicity Safety
Very High = 20 Very significant change
Significant change on PK
ATA detected and confers limits on safety
Irrevesible AES
High = 16 Significant change
Moderate change with impact on PD
ATA detected and confers limits on efficacy
Reversible AES
Moderate = 12 Moderate change
Moderate change with no impact on PD
ATA detected with in vivo manageable effect
Manageable AES
Low = 4 Acceptable change
Acceptable change with no impact on PD
ATA detected with minimal in vivo effect
Minor AES
None = 2 No change No impact on PK/PD
ATA not detected No AES
AES : Adverses Events, ATA : Anti Therapeutic Antibody
43
Tool 1 : IMPACT and UNCERTAINTY
Uncertainty Description (variants and HCP)
Description (Process Raw Material)
Very High = 7 No information (new variant) No information (new impurity)
High = 5 Published external literature for variant in related molecule
---
Moderate = 3 Non clinical or in vitro data with this variant, (data in vitro, non clinical or clinical from similar class
Component used in previous process
Low = 2 Variant present at same level in batches used in clinical trials
---
Very low = 1 No impact of specific variant present at higher level in batches used in clinical trials
GRAS or studied in clinical trials
GRAS : generally recognised as safe
44
Tool 1 : example
█ Potential CQA : glycosylation variantsThe potential biological affect depends of the variation of glycosylation
Immunogenic variants : high mannose and 1-3 gal-gal Modified half life mono-sialic < di sialic terminal glycans
Impact score Biological efficacy : moderate change = 12 PK/PD : significant change on pK =20 Immunogenicity : ATA detected and confer limits on safety =20 Safety : Irreversible AES = 2 Highest Impact score = 20
Uncertainty Published external literature : High = 50
Criticality (Risk score) = 20 x 5 = 100
45
Tool 2 : SEVERITY AND LIKELIHOOD
Severity score
Severity (impact to Product Efficacy and Patient Safety)
9 Very High-death, microbiology related infections, hypersensitivity immune reaction
7 Bleeding not stopped due to lower efficacy or serious immune response
5 Moderate immunogenicity or reduction in efficacy
3 Low immunogenicity potential or small reduction in efficacy
1 Very Low – no mesurable impact
46
Tool 2 : SEVERITY AND LIKELIHOOD
Likelihood score
Likelyhood of severity
9 Very High
7 High
5 Moderate
3 Low
1 Very Low or never observed
47
Tool 2 : example
█ Potential CQA : glycosylation variantsThe potential biological affect depends of the variation of glycosylation
Immunogenic variants : high mannose and 1-3 gal-gal Modified half life mono-sialic < di sialic terminal glycans
Severity Bleeding not stopped due to lower efficacy or serious immune response = 7
Likelihood Initial : High = 7
Criticality (Risk Priority Number) = 7 x 7 = 49
48
Tool 2 : SEVERITY AND LIKELIHOOD
Likelihood score
Likelyhood of severity
9 Very High
7 High
5 Moderate
3 Low
1 Very Low or never observed
49
Tool 3 : Impurity Safety Factor
█ Example : residual Solvent : MRL < 0.5 mg/L1 dose : 7 mg = 7 ml.
Maximum detergent = 0.0035 mg = 3.5 µg/dose
[solvent] for S/D viral clerance = 1 % = 10 g/L = 106 µg/L
At this step : 10 L of product for 10 00 doses = 107 µg/10 000 dose
• if no clearance (copurification) : 103 µg/dose
• ISF = 3.5/103 = 10-2.45
50
Product Quality Risk Assesment Summary
pCQA Tool #1 Tool #2 Tool #3 Potential process step
Control strategy
Glycosylation variants
100 49 / / Pooling strategy MS-HPLC
Des-Gla variants 36 12 / IEX, Ca elution
a/Ag, SDS
Virus free 100 49 / 2 viral clearance
Validation of viral clearance
… … … …
Detergent / / 10-2.45 AEX Quantification of detergent
51
Product Quality Risk Assesment Summary
█ Continuum of risk scoreFrom high risks which require a priority evaluation to low risk which may be
adressed later on.
Needs to establish internal policy : what is acceptable, what is not
█ Not fixedEvolution due to better understanding (clearance studies), better control
█ Allow to draw basic requirements for process and early justification of the steps to investigate
52
From pCQA to process development
Preculture Fermentation Harvest Filtration Chromatography 1 Viral inactivation
Chromatography 2UltrafiltrationNano filtration Chromatography 3Vialing 0.22 µm filtration
Figure 1 : Process Flow Chart
53
And justification of steps to develop
█ Simplified QFD Matrix (from EMeA Mock Dossier Examplain)
54
Early Process Risk Assesment
█ Several tools/methodologyFood : HACCP (Hazard Analysis and Critical Control Point)
Automobile : FMEA (Failure Mode Effect Analysis, AMDEC in French)
Medical Device : FMEA
Pharmaceutical : mainly FMEA but not regulated
…
█ Used for years in industry, new and difficult for Pharmaceutical Implementation usually with consultancy (1 year, 90 000 €)
55
FMEA principle
█ For Each process Step/Substep :
█ Identify Potential Failure Mode (5 M, Ishikawa, Prior Knowledge)
█ Describe the possible effects of the failure and its possible cause
█ Quantify the Severity (S) and the Probability (P) of the potential failure
█ (Quantify the Detectability (D) of the potential failure)
█ Calculate the Risk Priority Number (RPN) of the potential failure
RPN = S x P (x D)
█ Identify Measures, Controls,… to reduce the failure
█ Recalculate the Risk Priority Number (RPN)
56
FMEA easy but…
█ How many levels for quantification?1 – 3
1 – 5
1 -10
█ RPN : what is acceptable, what is not
█ Easy to draw the initial FMEA analysis but usually non homogeneous, > 300 sheets, a nightmare to update.
57
FMEA : possible strategy : RPN and Policy
S : 1 -10
P : 1 -10
D : 1 -10
RPN : 1 - 1000
o 1 to 100 : broadly acceptable region o 101 to 150 : as low as reasonable practicable region (ALARP), part I o 151 to 250 : as low as reasonable practicable region (ALARP), par II
o 251 to 1000 : intolerable region
58
FMEA : possible strategy : Homogeneity
█ Define the various failure modes (5M) :Human error
Material deficiency
…
█ Describe the various levels for S, P and DP : 1 never,…, 5 sometimes,…, 10 always
P might also be based on Process Capability Indices
█ Describe the Controls and Measures of Risk ReductionHuman error : staff training, double check, detailled Standard Operating Procedures,…
Material deficiency : trained staff, qualified equipment, preventive maintenance,…
59
FMEA : possible strategy : Size
█ Other initial Risk Assessment tools(HACCP, initial Risk assessment, FMEA only on critical failure)
First FMEA based only on Probability, all failure with Low Probability Indice not treated
3 D Risk Assessment Model (J Olivier, Journal of Validation Technology, 2008) Distance along product stream (effect of bacterial contamination from fermentation to F&F) Distance from product (WFI, HVAC, GMP area, Product Tank,…) System complexity
Use of generic FMEA : from 300 to only 30 sheets containing the same information Containers preparation Buffers/reagents preparation General Equipment assembly and calibration … For each Process Unit/Subunit : identify only specific failures
60
FMEA : example
247
30
30
RPN
7
3
3
D
7
5
5
S
5
2
2
P
Identification of critical factors
Trained staff
WrittenSOP/method of production
Automation
Description/QC of raw material
Approvedsuppliers
Risk Control, Measures of Risk reduction, Tests
To be determinedContamination of Drug Product
Ineffective purification
Purification5.5.6
Human errorContamination of Drug product
Wrongbuffer (pH, conductivity)
Purification5.5.1
Reagentsidentity/Quality
Purification failure,
Production stopped
Wrongpreparation(saltaddition, …
Samplepreparation
5.4.5
Possible causePossible effect(harm) of the hazard/failure
Possible hazard/
failure
Product, Part, System, Function, Process
N#
247
30
30
RPN
7
3
3
D
7
5
5
S
5
2
2
P
Identification of critical factors
Trained staff
WrittenSOP/method of production
Automation
Description/QC of raw material
Approvedsuppliers
Risk Control, Measures of Risk reduction, Tests
To be determinedContamination of Drug Product
Ineffective purification
Purification5.5.6
Human errorContamination of Drug product
Wrongbuffer (pH, conductivity)
Purification5.5.1
Reagentsidentity/Quality
Purification failure,
Production stopped
Wrongpreparation(saltaddition, …
Samplepreparation
5.4.5
Possible causePossible effect(harm) of the hazard/failure
Possible hazard/
failure
Product, Part, System, Function, Process
N#
61
If improvment of existing process
6 : Measure(Only for existing Products/Process)
62
Measure
█ Make sure you have data in accordance with ‘’Define’’
█ process input (parameter)
█ process output (results)
█ Link graphically (Matrix Plot) Inputs and Outputs
█ Example : yield of precipitation of proteins related topH
Temperature
Conductivity
%alcool
Initial proteins
Historical data, 106 productions
63
Measure : Temperature variation
0 20 40 60 80 100 120
Row
-5,5
-5
-4,5
-4
-3,5
-3
-2,5
Tem
pera
ture
Time Sequence Plot
Useful to detect shift
64
Measure : output (yield x-bar Chart)
X-bar Chart for Yield
51 53 55 57 59 61
Subgroup
0,31
0,33
0,35
0,37
0,39
X-b
ar
CTR = 0,33UCL = 0,35
LCL = 0,31
65
Measure : Radar/Spider Plot
Radar/Spider Plot
Scale: (-6,0-74,0)pH
Temperature
ConductivityAlcool
Proteins
Yield0,3168550,3109310,3190790,3110270,3107070,325940,3087450,2998860,3307450,3362780,3222890,3195420,3188260,3110370,3322780,335541
66
Measure : Matrix Plot
67
6 : Analyse
68
Analyse : Capability indices for Inputs
█ Capability indices indicated how well you master/control your process
█ Pc, Pck, Cc, Cck,,…P : initial analysis
C : when the process is under control
c : when symetric (upper and lower) results
ck : lower value of c upper/c lower
Upper specifications
Lower specifications Low Control High Control
69
Capability indices and statistics
NormalMean=6,0667Std. Dev.=0,0342462
Cp = 0,94Pp = 0,97Cpk = 0,63Ppk = 0,65K = -0,33
Process Capability for pH
LSL = 6,0; Nominal = 6,1; USL = 6,2
5,9 5,95 6 6,05 6,1 6,15 6,2
pH
0
10
20
30
40
50
freq
uenc
y
Observed Estimated Defects Specifications Beyond Spec. Z-Score Beyond Spec. Per Million USL = 6,2 0,000000% 3,89 0,004964% 49,64 Nominal = 6,1 0,97 LSL = 6,0 0,943396% -1,95 2,573095% 25730,95 Total 0,943396% 2,578058% 25780,58
70
Analyse : multivariate analysis
Input 1 (pH)
Monovariate
Outp
u
t
Input 2 (°C)
multivariateInput 1 (pH)
Multivariate
71
What is DoE
Classical approach DoE Process understanding
72
DoE : nearly a century
73
For me, only 20 years
74
DoEDoE
- interactions
- real optimum
- quality of information
X Interaction Optimum
75
From simple (fractionnal) design to RSM
X1X2
X3
Factorial Box Behnken Doehlert
76
From linear to Surface Response Model (non linear)
Design-Expert® Software
ConversionDesign points above predicted valueDesign points below predicted value97
51
X1 = A: TimeX2 = B: Temperature
Actual FactorC: Catalyst = 2.50
40.00
42.50
45.00
47.50
50.00
80.00
82.50
85.00
87.50
90.00
75.0
79.0
83.0
87.0
91.0
C
on
ve
rsio
n
A: Time B: Temperature
77
█ Several familiesn = number of Factor tested and L : level/factor Semi Factorial Design : the lowest number of experiments required : 2n-k
Used for a first screening of mains factors and at least single interactions Used for demonstration of a Proven Acceptable Range (PAR) or Design Space Don’t be afraid by the number of factor.
Factorial Design : higher number of experiments : 2n
For both Design, only two levels (L = 2) + eventual central point(s), Models will always be linear.
Response Surface Model : higher number of experiments : Ln. Non linear models.The number of experiments can be decreased by historical methods or by computer optimisation (D Optimal).
Used for optimisation/modeling of a process Used for searching the ‘’edge of failure’’
Mixture : RSM + constraint (sum of component = fixed value) Used in chemistry, formulation,…
Combined : Mixture + (semi) Factorial or RSM Used for combines mixture/process such as formulation (excipents) and freeze drying
conditions.
78
DoE, an other spirit
█ The kind of question to answer must be understood :Critical parameters
Interactions
Optimisation
Demonstration of Proven Acceptable Range
Modeling
█ The experiments are planned before starting
█ Apparently a high number of experiments, more work, more time, more money.
█ In reality, far less experiments (semi factorial or reduction for RSM) to obtain far less valuables results. Allow a better planning of experiments including Analytical.
79
How to build a Design Space
█ Factorial design
█ Semi factorial design
STD Factor A Factor B Factor C ALEA1 -1 -1 -1 32 1 -1 -1 53 -1 1 -1 74 1 1 -1 85 -1 -1 1 26 1 -1 1 17 -1 1 1 48 1 1 1 6
STD Factor A Factor B Factor C ALEA1 -1 -1 1 32 1 -1 -1 53 -1 1 -1 74 1 1 1 85 -1 -1 1 26 1 -1 1 17 -1 1 1 48 1 1 1 6
C = A x B
Lack of orthogonality
Introduction of ‘’aliase’’
Usefull to know to detect made up/false results
80
High collinearity : regression by least square not efficient
Ridge parameter Factor 1 Factor 2 Factor 3
0.0 (classical regression) 4.2637 -1.5614 -2.9287
0.01 (ridge regression) 0.6741 -0.1870 -0.2684
Use of Ridge statistics allows to analyse non
orthogonal Design
Lack of Orthogonality is not a problem
81
Design-Expert® Software
OLS8.7538
-8.7538
X1 = A: pHX2 = B: Conductivity
Actual FactorC: Load = 0.00
-1
-0.5
0
0.5
1
-1.00
-0.50
0.00
0.50
1.00
-6
-3
0
3
6
O
LS
A: pH B: Conductivity
Design-Expert® Software
Ridge1.1295
-1.1295
X1 = A: pHX2 = B: Conductivity
Actual FactorC: Load = 0.00
-1
-0.5
0
0.5
1
-1.00
-0.50
0.00
0.50
1.00
-0.9
-0.45
0
0.45
0.9
R
idg
e
A: pH B: Conductivity
OLS Ridge
Same overall topology, but completely different precision of the model (Monte Carlo simulation…)
82
DoE : number of experiments
83
DoE : Number of experiments
█ With n experiments, you can calculate the coefficients for n-1 factors and interactions
For 2 factors, Factorial design requires 22 = 4 experiments, you can calculate the coefficients for 3 factors and interactions : A, B and interaction AB (green color), it’s not interesting to erase 1 experiment and loose informations on possible interaction between A and B.
For 3 factors, Factorial design requires 23 = 8 experiments, you can calculate the coefficients for 7 factors and interactions : A, B, C and interactions AB, AC, BC and ABC. Using semi Factorial design (22 = 4 experiments) informations on possible interaction are also lost (red color).
For more than 3 factors, the number of experiments should be limited to the number of factors tested + the number of single interactions. I never found (up to now) significant triple interactions (ABC), Why loose time, money for a large number of such interactions ABC…F, ABD…F, ACD…F, AED…F…(yellow color)
84
Center points are useful to estimate
Reproducibility Linearity Extension of Design
Center points
- +
Factor A
- +
Factor A
- +
Factor A
85
Example of semi factorial design
86
Représentations :For each factor (and each response), estimation of the slope
between the average at low and high value of the factor or interaction.
Identification of critical factors
Standard order Factor A Factor B Factor C AB AC BC ABC Response-1 -1 -1 1 1 1 -1 221 -1 -1 -1 -1 1 1 28-1 1 -1 -1 1 -1 1 261 1 -1 1 -1 -1 -1 84-1 -1 1 1 -1 -1 1 921 -1 1 -1 1 -1 -1 86-1 1 1 -1 -1 1 -1 101 1 1 1 1 1 1 56
Average Low 37,5 57,0 40,0 52,0 53,5 70,5 50,5Average High 63,5 44,0 61,0 63,5 47,5 29,0 50,5Slope 26,0 -13,0 21,0 11,5 -6,0 -41,5 0,0Effect 13,0 -6,5 10,5 5,8 -3,0 -20,8 0,0
87
3D View of critical factors and interactions
C A
B
A B C
AB AC BC
ABC
88
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
Identification of Critical Factors and interaction
Identification of critical factors and interactions
Critical factorsPlace for
improvement
Reproducibility
Half Normal Plot
89
Design-Expert® SoftwareFVIIam capacity
A: Contact TimeB: pH loadC: Column VolumeD: ConductivityE: Elution temperature
Positive Effects Negative Effects
Pareto Chart
t-V
alu
e o
f |E
ffe
ct|
Rank
0.00
1.21
2.41
3.62
4.82
Bonf erroni Limit 4.38176
t-Value Limit 2.57058
1 2 3 4 5 6 7
E
CE
C
Identification of Critical Factors and interaction
Pareto Chart
90
Factorial - semi factorial Design
pH Temperature Stability6 4 604 4 306 37 204 37 154 4 286 4 756 37 154 37 17
Factorial
pH Temperature Stability4 4 306 37 204 37 154 4 28
Semi factorial
91
Factorial analysis - semi factorial
DESIGN-EXPERT PlotStability
A: pHB: temperature
Half Normal plotHa
lf No
rmal
% p
roba
bility
|Effect|
0.00 7.88 15.75 23.63 31.50
0
20
40
60
70
80
85
90
95
97
99
A
B
AB
DESIGN-EXPERT PlotStability
A: pHB: temperature
Half Normal plot
Half
Norm
al %
pro
babil
ity
|Effect|
0.00 4.04 8.08 12.12 16.17
0
20
40
60
70
80
85
90
95
97
99
A
B
Term AliasesRequire Intercept ABModel A ABModel B ABAliased AB
92
Factorial - semi factorial Design
2 3 4 5 6 7 8
4 Full 1/28 Full 1/2 1/4 1/8 1/16
16 Full 1/2 1/4 1/8 1/1632 Full 1/2 1/4 1/864 Full 1/2 1/4
128 Full 1/2256 Full
Num
ber of experiments
Number of factor
Factorial analysis : NL
Semi Factorial analysis : N(L-X)
Loss of resolution (aliase)
93
But how many factors to select?
Design-Expert® SoftwareFT
Error from replicates
Shapiro-Wilk testW-value = 0.798p-value = 0.039A: LoadB: Flow rateC: GradientD: pHE: Particle size
Positive Effects Negative Effects
Half-Normal Plot
Ha
lf-N
orm
al
% P
rob
ab
ilit
y
|Standardized Effect|
0.00 3.59 7.18 10.77 14.36
010
20
30
50
70
80
90
95
99Warning! No terms are selected.
94
Recherche des paramètres critiques :
95
- +
Factor X
Are the factors selected significant ?
Analyse statistique :Ttest (P95)
Comparison of Alow –Ahigh 5%
Comparison of Blow –Bhigh 5%
Comparison of Clow –Chigh 5%
Comparison of ABlow –ABhigh 5%
…
Ttest not applicable
Anova
96
ANOVA (Table)
Analyse statistique :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
97
Demonstration of PAR
Design-Expert® SoftwarePurity
Error from replicates
Shapiro-Wilk testW-value = 0.847p-value = 0.116A: pH sampleB: Conductivity sampleC: LoadD: pH elutionE: Gradient
Positive Effects Negative Effects
Half-Normal Plot
Ha
lf-N
orm
al
% P
rob
ab
ilit
y
|Standardized Effect|
0.00 5.59 11.18 16.77 22.36
0
10
20
30
50
70
80
90
95
99Warning! No terms are selected.
98
Analysis of variance table [Partial sum of squares - Type III]Sum of Mean F p-value
Source Squares df Square Value Prob > FModel 661,88 7 95 0,33 0.8755 not significant A-pH sample 210,13 1 210 0,73 0.5500 B-Conductivity sample45,13 1 45 0,16 0.7601 C-Load 66,13 1 66 0,23 0.7155 D-pH elution 15,13 1 15 0,05 0.8566 E-Gradient 15,13 1 15 0,05 0.8566 BC 10,13 1 10 0,04 0.8820 BE 300,13 1 300 1,04 0.4934Curvature 30,63 1 31 0,11 0.7993 not significantPure Error 288,00 1 288Cor Total 980,50 9
If a model is found significant, estimation of the impact on product quality can be studied by in silico simultation
99
Significant model found…
Final Equation in Terms of Coded Factors:
Specifc activity =77,59,5 * B9,5 * C
-11,5 * B * C
Final Equation in Terms of Actual Factors:
Specifc activity =4,338,67 * Conductivity sample2,87 * Load
-0,31 * Conductivity sample * Load
But what is its accuracy/validity ?
100
Accuracy/validity of the model : residuals
█ 1- Normal Plot of residuals
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
101
█ 2- Distribution of residuals (homo/heterodiasticity)
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 require data transformation
102
Box – Cox Plot
Design-Expert® Softwareyield
LambdaCurrent = 1Best = 0.23Low C.I. = -0.31High C.I. = 0.84
Recommend transform:Log (Lambda = 0)
Lambda
Ln
(Re
sid
ua
lSS
)
Box-Cox Plot for Power Transforms
15.11
17.04
18.98
20.92
22.86
-3 -2 -1 0 1 2 3
If heterodiasticity, = f ()
The transformation = 1- will reduce that effect :
= -1 : inverse, = 0 : Log
= 0.5 : square root, = 1 : no transformation
103
█ 3- Distribution of residuals (vs Run or time)
Design-Expert® Softwareyield
Color points by value ofyield:
5769
275
Run Number
Inte
rna
lly
Stu
de
nti
ze
d R
es
idu
als
Residuals vs. Run
-3.00
-1.50
0.00
1.50
3.00
1 2 3 4 5 6 7 8 9 10
104
Accuracy/validity of the model
105
Weight of runs on the model
Design-Expert® Softwareyield
Color points by value ofyield:
5769
275
Run Number
Ex
tern
all
y S
tud
en
tiz
ed
Re
sid
ua
lsExternally Studentized Residuals
-4.32
-2.16
0.00
2.16
4.32
1 2 3 4 5 6 7 8 9 10
106
DoE and Regulatory Agencies
█ EMA and FDA expect to find informations not dataLoad 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
107
Factorial analysis - semi factorial
█ Loss of resolution (aliase)
Lower detection of interaction
Main factors ‘’aliased’’ to interaction
108
Improve - DoE
109
Design-Expert® Software
AS
Design Points
X1 = D: Conductivity
Actual FactorsA: Contact Time = 90B: pH load = 7.5C: Column Volume = 8E: Elution temperature = 20
20 25 30 35 40
8
25.75
43.5
61.25
79
D: Conductivity
AS
One Factor
25 + 5 mS/cm
NOR
PAR
Edge of failure
?
Definition of Critical Factors
PAR/NOR
Access to CP
110
Optimisation of an affinity chromatography step
█ Define :Affinity chromatography step based on VHH ligand
Prior knowledge : 2 washes : 25 % compound A followed by 0.5 M compound B Elution : 0.5 M compound B in 25 % A
█ Mesure :Yield : 65 – 85 % (mainly affected by number of use and quality of the starting material)
HCP : Clearance : 2.9 – 3.1 Log
111
Optimisation of an affinity chromatography step
█ AnalyseDesign : RSM, 5 level/compound
Compound A : 0 – 25 % (not tested at higher % due to high viscosity)
Compound B : 0 – 1 M
Use of D optimal design : only 14 experiments including replicates
Chromatography in // by 96 well format (Atoll GmBh) in a single day
1 model for yield
1 model for HCP clearance
112
Model for yield and HCP clearance
113
Optimisation of washes
█ Optimised wash : minimize ‘’yield’’ (low loss of target protein) – maximize HCP
X
114
Optimisation of elution
█ Optimised wash : maximize ‘’yield’’ (low loss of target protein) – minimize HCP
X
X
115
Optimisation of chromatographic conditions
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 (C in 6sigma)
Yield : 68 – 85 % 85 %
HCP Clearance : 2.9 – 3.1 Log 4.1 Log
116
Quality of results depends also of analytics
117
DoE is not anymore sufficient
█ Results of DoE expresses an average, not individual results
█ What about the robustness of the process?
█ ICH Q8(R2) requires to provide assurance of quality
█ Bayesian Statistical approch including Monte Carlo Simulation is able to add this assurance
118
Monte Carlo simulation
119
120
Monte Carlo theory
Y = f(a,b,c)
121
Example 1 : area calculation
Precision increased with number of shoots
Only valid if shoots randomized
122
Example 2 : NovoNordisk
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
Example 3 : specifications for optimized affinity chromatography
Optimized Wash : 0.2 M B in 8 % A
0.2 M + ? B
8 % + ? A
Clearance must be > 3.75 to reach a final contamination < 5 ppm HCP
Equation (by DoE) Initial specification tested
A : 8 + 2 %
B : 0.2 + 0.05 M
Final Equation in Terms of Actual Factors:HCP Clearance = 2,48
12,395,17 * Compound A 0
-0,91 * Compound B 01,40 * Compound A* Compound B
-25,83 * Compound A^20,04 * Compound B^2
-0,36 * Compound A^2 * Compound B-0,01 * Compound A* Compound B^210,15 * Compound A^3
0,00 * Compound B^3
138
Monte Carlo simulation for initial specifications
█ 10 000 calculations with A and B randomly chosen within initial specifications
█ Nearly 20 % of failure
139
How to improve the process?
A
B
A B
140
Optimized specifications
Initial specifications Optimized specification
A : 8 + 2 % A : 7.5 + 1 %
B : 0.2 + 0.05 M B : 0.2 + 0.05 M
20 % failure No Failure (< 3.4/106 : Six Sigma robust process)
141
Validation of optimal specifications
█ These theoretical specifications for Compound A and B have been confirmed/validated by DoE during Process characterisation studies
Sequential Model Sum of Squares [Type I]Sum of Mean F p-value
Source Squares df Square Value Prob > FMean vs Total 3,8571 1 3,8571 12,432 0.0021 SuggestedLinear vs Mean 12,6390 15 0,8426 8,3612 0.15022FI vs Linear 0,3256 2 0,1628 2,7393 0.2105Quadratic vs 2FI 0,0594 1 0,0594 1,0000 0.4226Cubic vs Quadratic 0,1189 2 0,0594 1,0000 0.4226
142
Mono/multi-dimensionnal specifications
█ Additionnal advantage of DoE : multidimensionnal
Monodimensionnal
Factor 1 : 45 -51
Factor 2 : 1 – 1.45
In case of Deviation
Factor 1 : 53
Factor 2 : 0.7
Both out of specifications but no impact
X
143
Conclusions
144
█ Statistic = math = truth
█ Presented as nice graphics
█ But…
Design-Expert® Software
ConversionDesign points above predicted valueDesign points below predicted value97
51
X1 = A: TimeX2 = B: Temperature
Actual FactorC: Catalyst = 2.50
40.00
42.50
45.00
47.50
50.00
80.00
82.50
85.00
87.50
90.00
75.0
79.0
83.0
87.0
91.0
C
on
ve
rsio
n
A: Time B: Temperature
145
█ Presentation
Experiment Results1 102 253 14 505 1006 257 508 109 110 20
Experiment Results1 10,052 25,13 1,00984 50,25 1006 25,5787 50,878 10,29 1,0059810 20,02
146
█ Number of experiment
147
█ Scale
148
█ Design
0
2
4
6
8
10
12
0 2 4 6 8 10 12
X
Y
-200
0
200
400
600
800
1000
1200
0 100 200 300 400 500 600
X
Y
Y=0.113+0.981X
R = 0.9904
Y=-12.51+2.021X
R =0.9993
149
Design
Assays from world class company
150
Design-Expert® Sof tware
y ield5769
274.5
X1 = A: LoadX2 = B: Flow rate
Actual FactorsC: Gradient = 10.00D: pH = 7.00E: Particle size = 60.00
50
75
100
125
150
50.00
75.00
100.00
125.00
150.00
500
1500
2500
3500
4500
yie
ld
A: Load B: Flow rate
Design-Expert® Sof tware
Corrected HCP498
5
X1 = A: LoadX2 = B: Flow rate
Actual FactorsC: Gradient = 10.00D: pH = 7.00E: Particle size = 60.00
50
75
100
125
150
50.00
75.00
100.00
125.00
150.00
142
164.75
187.5
210.25
233
Co
rre
cte
d H
CP
A: Load
B: Flow rate
Graphical interpretation
151
Run Temperature Pressure Duration1 25 5 162 20 5 243 25 5 244 20 5 165 25 25 246 20 25 167 25 25 168 20 25 24
Design-Expert® Software
ConversionDesign points above predicted valueDesign points below predicted value97
51
X1 = A: TimeX2 = B: Temperature
Actual FactorC: Catalyst = 2.50
40.00
42.50
45.00
47.50
50.00
80.00
82.50
85.00
87.50
90.00
75.0
79.0
83.0
87.0
91.0
C
on
ve
rsio
n
A: Time B: Temperature
Présentation d’un concurrent, QbD, Dusseldorf, Octobre 2008
Factorial Design
RSM
152
█ Transformation of Response
0
2
4
6
8
10
12
-200 0 200 400 600 800
days
mo
rtal
ity
non treated treated Linear (non treated) Linear (treated)
0
5
10
15
20
0 0,5 1 1,5 2 2,5 3 3,5
log(days)
mo
rtal
ity
non treated treated Linear (non treated) Linear (treated)
Drug commercialised 4 years
153
And in the future
154
QbD now and tomorrow
█ ICH meeting, Bruxelles; November 2008
155
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
156
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
157
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
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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
159
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
160
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)
161
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
162
DoE models : which is the best ?
163
DoE / Neuronal Network
DoE Neuronal Network
Factors
Response
Constant Function
164
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
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Neuronal neutwork
█ Modern statistical modelisation
█ Succesfully applied to learning ofQuantitative models (// to DoE)
Qualitative model : oral/picture recognition (classification)
█ Mathematical optimisation no more problematic and user-friendly
dedicated software on the market
█ Introduced in one of the next ICH ?
166
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