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Connecting People, Science and Regulation ®

Risk Assessment and DoE must be used
in Synergy for the success of QbD

Alain Poncin
Process Development Unit Manager
LFB Biotechnologies

Quality by Design, Frankfurt 1


Quality by Design
Science
Statistics Knowledge
Risk Management
- of
the production
process
- of the product
summarized in the Risk
Assessment Report

2


Risk Management
Technical report 42, PDA (2005)
Pharma : ICH Q8 : Pharmaceutical Development (2005) and annex (2007)
ICH Q9 : Quality Risk Management (2005)
Compliance
ICH Q10 : Pharmaceutical Quality System (2008)
ICH Q11 : Development and Manufacture of Drug Substance (concept
paper, 2008, draft expected in 2009)
Focused on Product Quality

Medical Device ISO13485 : Medical devices – Quality management systems – Requirements
Compliance for regulatory purposes
ISO14971 : Medical devices – Application of Risk Management to medical
Economy devices
Focused on Product Quality + Product Availability
3


Risk Management at LFB Biotechnology
When
Standard initial Risk analysis (FMEA) as soon as a first lab process gives
satisfactory results
Updated during product/process development, clinical development and post
approval.
How
Based on standardised ‘’blocks’’ (Upstream, Harvest, Chromatography,
Ultra/diafiltration,…)
‘’Personnalised’’ using what is known : protein stability, ease/difficulty of steps,
occurrence of hasard,…

Ranks the work to be performed during development and process characterisation
Reflects and summarises all what is known about the protein and the process
4


Initial Risk analysis
1- Process Flow Chart Preculture Fermentation Harvest Filtration

Chromatography 2 Viral inactivation Chromatography 1

Filtration Ultrafiltration Filtration

Vialing

5


2- Table of content

N° System Sub system Justification
5 Capture by Capture of target
Chromatography product after harvest
5.1 Cleaning before
chromatography
5.2 System assembly
5.3 Storage of intermediates
5.4 Sample preparation
5.5 Purification
5.6 Cleaning after purification

6


3- Risk Analysis

Based on previous experience, building of new blocks for this
first Risk Analysis at LFB Biotechnologies
Copy and paste to a ‘’white’’ Risk Assessment

Missing blocks (viral inactivation, nanofiltration,…) specially
for plasma product
Not adapted to LFB Biotechnologies (history,…)
Final analysis not homogeneous

7


Lack of homogeneity :
N Product, Part, Possible Possible S Possible cause P Risk control D RPN
# System, Hazard/ Effect
Fonction Failure (harm)
2.2 Buffer Wrong buffer Ultra/diafiltrati 6 Defective 3 Qualified 3 54
preparation (pH, on failure, equipment (pH, equipment,
conductivity) contamination scale,…) preventive
or degradation maintenance
of target Trained staff
product
Calibration before
use
5.5 Purification Wrong buffer Contamination 4 Human error 2 Trained staff 3 24
for of target (inversion of Written
equilibration/ product buffers) SOP/method of
wash and or production
elution
6.8 Ultra/ Wrong buffer Contamination/ 7 Human error 3 Written SOP 5 105
Diafiltration for loss of sample Trained staff
equilibration

8


To increase homogeneity and quality of RA:
Define risks
For the Product safety/efficacy/availability
-contamination (physical, chemical,…)
-degradation (lower yield, production failure, immunogenicity)

Identify risks for a general ‘’process’’

Identify Possibles causes (human, material,…)

Identify the measures to reduce the risk

Adapt for each kind of process : fermentation, chromatography,
ultrafiltration,…

9


5 Capture by Capture of target
Chromatography product after harvest
5.1 System assembly and calibration
5.2 Cleaning before
chromatography
5.3 Storage and expiration time of
intermediates
5.5 Purification
5.6 Product recovery
5.7 Cleaning after purification
5.8 Storage of Matrix/column
10


2- Table of content

6 Ultra/diafiltration Buffer Exchange
6.1 System assembly and
calibration
6.2 Cleaning before
ultra/diafiltration
6.3 Storage and expiration time of
intermediates
6.5 Ultra/diafiltration
6.6 Product recovery
6.7 Cleaning after
ultra/diafiltration
6.8 Membrane/carter Storage 11


5- Quantification of Residual Risk
N# Product, Possible Possible effect S Possible cause P Risk Control, D RPN
Part, hazard/ (harm) of the Measures of
System, failure hazard/failure Risk reduction,
Function, Tests
Process
5.4.5 Sample Wrong Purification 5 Reagents 2 Description/QC of 3 30
preparation preparation failure, identity/Quality raw material
(salt Production Approved
addition, … stopped suppliers
5.5.1 Purification Wrong Contamination 5 Human error 2 Trained staff 3 30
buffer (pH, of Drug product Written
conductivity) SOP/method of
production
Automation
5.5.6 Purification Ineffective Contamination 7 To be determined 5 Identification of 7 247
purification of Drug Product critical factors

12


Risk Priority Number

RPN = Severity x Probability x Detectability

Require Internal Policy Definition
At LFB Biotechnology : 4 levels :
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
13


6- Initial Risk Analysis conclusions

Step System Current status/notes
3 Clarification Need further comparability studies to assess
starting material equivalency
4 0.22 µm filtration Final choice of filter type

5 Capture by Not used in standard conditions, need identification
Chromatography of critical parameters to obtain a reproducible
process
6 Viral Inactivation Need first evaluation of viral clearance before First
in Man
7 Chromatography Well known and controlled step, yet close to the
optimum (load, wash and elution)
8 0.22 µm filtration Final choice of filter type
14


From initial Risk analysis : identification of capture
purification step as a high risk Step
To reduce this risk : identification of critical factors and
critical quality attributes.

Experience (int./ex.) Process development Initial Risk Assesment
Publication review First lab scale production
Patent review

Identification of
critical factors by
DoE
15


Particularity of DoE in Process Development

Protein folding : protein concentration, pH, Salt, Organic solvent,… 14 factors

Chromatography : pH and conductivity for equilibration and elution, sample load, matrix and
column resolution,… 10 factors

Ultrafiltration : pump speed, Pin, Pout, membrane, temperature, volume of buffer,… 8 factors

Design space highly multidimensional

2 n experiments (full factorial) cannot be used,
2 n-k experiments (semi factorial) used for identification of critical factors
16


Even 2 n-k experiments are a huge amount of work (process
+ analytics)

Before starting experiments :

Are Analytic tools sufficient ?
Which Design : (semi) factorial ?
Which factors to test ?
Which Range (Design Space) ?
17


Which Design ?

Full

Semi

18


Which factors and which Design space ?

19


Experimental Work

20


Output (Quality attributes)
capture step : high yield, reproducible
Quantification of target protein biological activity
Quantification of total proteins
Purity by SDS-PAGE
On Load, Flow Through and Peak (23 samples)
Calculation of step yield and specific activity

21


Results : usually nice graphics

Design-Expert® Software

Yield
92

10
100
X1 = B: Conductivity sample
X2 = D: pH elution
75

Actual Factors
A: pH sample = 7.07 50
Y ie ld

C: Load = 30.00
E: Gradient = 15.41
25

0

8.50 10.00
8.00 8.13
7.50 6.25
7.00 4.38
D: pH elution B: Conductivity sample
6.50 2.50

22


But in another region of the space…


Yield Yield
92 92

10 10
100 100
X1 = B: Conductivity sample X1 = B: Conductivity sample
X2 = D: pH elution
75 X2 = D: pH elution
75
Actual Factors
A: pH sample = 7.07 Actual Factors
50
A: pH sample = 7.07
Y ie ld

C: Load = 30.00 50

Y i e ld
E: Gradient = 15.41 C: Load = 10.00
25 E: Gradient = 15.41
25
0

0

8.50 10.00
8.00 8.13
7.50 6.25 8.50 10.00

7.00 4.38 8.00 8.13
6.50 2.50 7.50 6.25
7.00 4.38
6.50 2.50

23


Results : critical factors : half normal plot of effects
Design-Expert® Software Half-Normal Plot
Yield

Error from replicates

H a lf-N o rm a l % P ro b a b ility
99

E Shapiro-Wilk test
W-value = 0.962
p-value = 0.794 95
A: pH sample E
B: Conductivity sample 90
C: Load
D: pH elution
80 A
E: Gradient
Positive Effects 70
C
Negative Effects
A 50

30
20
10
0

C 0.00 7.63 15.25 22.88 30.50

|Standardized Effect|

- +
Factor Effect = slope/2
24


Or pareto chart
Design-Expert® Software Pareto Chart
Yield E
6.2 4
A: pH sample
B: Conductivity sample A
C: Load
D: pH elution

t-V a lu e o f |E ffe c t|
4.6 8
C
E: Gradient Bonf erroni Limit 4.38176
Positive Effects
Negative Effects

3.1 2

t-Value Limit 2.57058

1.5 6

0.0 0

1 2 3 4 5 6 7

Rank

25


Are they significants ? ANOVA

ANOVA for selected factorial model
Analysis of variance table [Partial sum of squares - Type III]
Sum of Mean F p-value
Source Squares df Square Value Prob > F
Model 4129 3 1376 28,8 0.0014
A-pH sample 1301 1 1301 27,2 0.0034
C-Load 968 1 968 20,3 0.0064
E-Gradient 1861 1 1861 38,9 0.0015
Curvature 960 1 960 20,1 0.0065
Residual 239 5 48
Lack of Fit 207 4 52 1,6 0.5244
Pure Error 32 1 32
Cor Total 5328 9

- +
Factor

26


ANOVA : Statistical analysis : can you do it ?
Design-Expert® Software Normal Plot of Residuals
Yield

Color points by value of
Yield: 99

N o rm a l % P ro b a b i lity
82
95
5 90

80
70

50

30
20

10

5

1

-1.94 -0.97 0.00 0.97 1.94

Internally Studentized Residuals

27


What is the value of the model obtained ?
Design-Expert® Software Predicted vs. Actual
Yield
82.00
Color points by value of
Yield:
82
2
62.75
5

P re d ic te d
43.50

24.25

5.0 0

5.00 24.25 43.50 62.75 82.00

Actual

28


Finally, capture step can be optimised

29


Capture step by DoE and ranges
Design-Expert® Software PAR Factor
One
AS
79
Design Points
NOR
X1 = D: Conductivity ? Edge of
Actual Factors
A: Contact Time = 90
B: pH load = 7.5
61.25
failure
C: Column Volume = 8
E: Elution temperature = 20
AS
43.5

25.75

Definition of
25 + 5 mS/cm
Criticals 8

Parameters
20 25 30 35 40

Nice results isn’t ?
D: Conductivity 30


But following purification results did not correlated well
with prediction from this model.

A new analysis was performed with more powerfull tools.

Two main raisons were implied in the failure of the first
model
One critical factor not identified
High collinearity of some factors
31


How many factors to select ?
Adjusted R-Squared or Mallows’CP statistics

Selection of number of factor

100 2000

80
1500
R Squared

60

Cp
1000
40
500
20

0 0
0 1 2 3 4 5
Number of factor
32


High collinearity : regression by least square not efficient

Use of Ridge statistics

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

33


Lack of detection of one critical factor

- +
Factor
Effect = 0 D : pH elution

The new Model is predictible for the purifications performed (up to now)
34


Finally, update of the initial Risk Analysis
N# System, Hazard Possible effect S Possible cause P Risk Control, D RPN
5.4 Sample Wrong Purification 5 Reagents 2 Description/QC of 3 30
preparation preparation failure, identity/Quality raw material
(salt Production Approved
addition, … stopped suppliers
5.5.6 Purification Ineffective Contamination 7 To be determined 5 Identification of 7 247
purification of Drug Product critical factors

5.4 Sample Wrong Increase of 5 Reagents 5 Quantification of 5 125
preparation preparation proteolytic identity/Quality proteolytic
(buffer activities activities
addition) Production Written SOP
failure Automation
5.5.6 Purification Ineffective Contamination 6 Contact Time too 4 Qualification of 5 120
purification of Drug Product short (< 2 min) equipment,
Other factors preventive
under control maintenance
Trained staff
35


Synergy of Risk Management and DoE
Experience (int./ex.) First lab scale Initial Risk Assesment
Publication review Production
Patent review Process
Identification of critical
factors by DoE Process Development
Risk Assesment Update First in Man
Process optimisation GMP for Phase I
Process capability indices Risk Assesment Update Phase I
(Six Sigma)
Process Characterisation

Risk Assesment update Phase II/IIII
Process Validation
CTD
Statistics Risk Assesment update Phamacovigilance Phase IV
(trends,…)
Product discontinuation
36


And in the future
Increased number of experiments (// chromatography, 96
wells technology)
Application to ultra/diafiltration
Introduction of additional statistic tools (bayesian
statistics, Monte Carlo simulation, …)
More applications of Six Sigma
PAT

37


Economical impact of QbD : example

Eurogentec s.a.

60
50
40
30
20
10
0
87 88 89 90 91 92 93 94 95 96 97 98 99- 00- 01- 02- 03- 04- 05- 06- 07-
00 01 02 03 04 05 06 07 08

Turnover in millions € Nber of employees x 10

38

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