quality by design (qbd) in process development - …€¦ · process mapping risk analysis doe...

Quality by design (QbD) in process development

DCVMN 10 March 2017

Product quality cannot be ensured by inspections, it has to be designed into the product by a robust production process

Process mapping Risk analysis

DoEDesign space

definition

3

What is QbD?

• Process mapping

– Process flow, fishbone diagrams

– Which factors could potentially affect our process?

• Risk analysis

– Failure Mode and Effects Analysis (FMEA)

– Which parameters should be investigated in detail?

• Design of experiments (DoE)

– Screening extended space in many parameters

– Detailed quantification of cause and effect relationships

• Execution and analysis, definition of design space

Four key steps defining process design space

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4

Why QbD?

Screening

• Which factors are important?• What are the appropriate

ranges?

Optimization

• Detailed quantification of the vital factors. What are the optimal ranges for these factors?

Robustness

• Verify that process is robust to small variations in the vital factors.

Operating space(production)

Characterized space

Design space(quality adequate)

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Risk analysis

6

Example of fishbone analysisDNA removal using an anion-exchange chromatography column for cell-based influenza purification

Cell residues

Host cell used

Column and resinHETP

Assymetry

Resin volume

Flow distribution

Leakage

Bed stability

Ligand density

SamplePrecipitation

DNA concentration

Viral stability

Ferm batch to batch var.

Conductivity variability

Virus strain behaviour

Debris and particles

pH

Hold times

pH

Wash

Conductivity

Buffer composition

Flow rate

Number of column volumes (CV)

CIP

pH, NaOH concentration

Flow rate, contact time

Wash volume (CV)

Number of previous CIP runs

Resin cleaning efficiency

Measurement systems

Conductivity measurement systems

Bias

TCID50 variability

Time from harvest

Assay deviation

Accuracy and precision

HA analysis robustness

Strip

NaCl concentration

Flow rate

Load

DNA load

Load volume

Time from harvest

Collection

Temperature

Manual fractionation

Flow rate

Virus content

System/hardware

Flow path design

Reliability of monitors,

Valves and pumps

System pH

Choice of material (DNA adhesion)

Virus yield

DNA content

UNICORN™ method

Conductivity calibration

Electrical power

Particle size Particle porosity

DNA size, RNA content

Aggregates

CIP = cleaning in placeHA = hemagglutinin

HETP = height equivalent to the theoretical plateTCID50 = 50% tissue culture infective dose

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FMEA: failure modes and effects analysis

Risk analysis and mitigation

1. What might go wrong?

2. How severe are the consequences?

3. What is the likelihood the failure will occur?

4. Can we detect the failure?

To be performed in cross-functional teams (operators, R&D, QA/QC, management, etc.)

7

S = Severity of each failure (1–10)

O = Likelihood of occurrence (1–10)

D = Likelihood of detection (1–10)

RPN = Risk priority number = S × O × D

Define what each level represent

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DoE essentials

What is DoE?

DoE is a systematic way of changing process inputs and analyzing the resulting process outputs in order to quantify the cause and effect relationship between them while using a minimum number of runs.

929256325AA I Mar. 2017

Keywords for DoE

A parameter (variable) that is thought to affect the result

Can usually be controlled

One or more measured variables describing the outcome of the experiment

Factors Responses

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General design constructions with two factors

One factor at the time (OFAT) DoE

11

200

130

190

180

170

160

150

140

0 2.50.5 1.0 1.5 2.0

Te

mp

era

ture

(°C

)

Time (h)

OFAT

Factor X2

Fa

cto

r X

1

L,L

L,H H,H

H,L

Centerpoint (cp)

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OFAT: the classical/intuitive approach

Time (h) Temperature (°C)

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DoE results in more information with less experiments

13

8 16 32 64 128

100DoE-structured approach

Number of experiments

Info

rma

tio

n (

%)

Needed explanation level

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Interactions between factors can be discovered with DoE

14

60

65

70

80

85

90

75

200

130

190

180

170

160

150

140

0 2.50.5 1.0 1.5 2.0

Tem

pe

ratu

re (°C

)

Time (h)

Yield, %

DoE

OFAT

c

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Analysis for vaccines

Single radial immunodiffusion (SRID)

Hemagglutinin (HA) agglutination assay

Enzyme-linked immunosorbent assay (ELISA)

SDS-PAGE/Western blot

qPCR

Plaque assays

TCID50

Sensitivity, detection range

Method variation (precision, accuracy)

Hands on time

Cost

Robustness

Examples of analytical methods for vaccines Challenges with analytical methods for QbD and DoE

15

TCID50 = 50% tissue culture infective dose

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DoE case study:DNA reduction in vaccine processing

Live influenza virus

Sterile filtration

Normal flow filtration

Concentration + buffer exchange

Cross flow filtration

Protein reduction

Core bead chromatography Virus passes and proteins binds

DNA reduction

AIEX chromatography Virus passes and DNA binds

Concentration + sample conditioning

Crossflow filtration

Clarification

Normal flow filtration

Cell culture + influenza virus

MDCK cells, microcarriers, 72 h infection

DNA removal in vaccine process

17

• Process mapping and FMEA showed that loss of product and inefficient DNA removal have highest impact on product quality.

• Host cell DNA is a critical impurity.

• DoE setup with pH and conductivity as factors, and antigen yield and DNA removal as responses.

AIEX = anion exchange chromatography

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DoE: experimental parameters

18

Factors DoE range

pH (equil., load, wash)

7.0–9.0

NaCl (equil., load, wash)

300–800 mM

Responses (supernatant)

MDCK cell DNA (qPCR)

Influenza HA analysis

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DoE: experimental set-up

19

Material

SampleA/Solomon Island/3/2006 (H1N1)A/Wisconsin/67/2005 (H3N2)B/Malaysia/2506/2004 (B)

Sample conditioning PD-10 Desalting columns

Format Prepacked 96-well plates

Capto™ Q resin 50 µL/well

Sample load 400 µL/well

Incubation 10 min/shaker

Supernatant collection(flowthrough)

Centrifugation, 500 × g

Resin

in well

Wash/

Equilibration

Sample

addition

Wash

1-3 times

Elution

1-3 times

Incubation

Vacuum filtration or centrifugation

Mixing Mixing Mixing

Analysis

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DoE reveals optimal DNA reduction

A/Solomon Islands/3/2006 (H1N1) A/Wisconsin/67/2005 (H3N2)

20

• The level of gDNA (%) in the flowthrough is shown in the boxes.

• Conditions were chosen to achieve efficient DNA reduction (red region) and keep the influenza virus in a

non-binding mode.

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21

Chromatography—confirmation of DoE

SampleHA yield

(%)gDNA

(ng/mL) beforegDNA

(ng/mL) afterDNA log

reduction

A/Solomon Island/3/2006 (H1N1) > 85 2010 17 2.1

A/Wisconsin/67/2005 (H3N2) > 85 11 300 16 2.9

B/Malaysia/2506/2004 (B) > 85 96 800 16 3.8

0

1000

2000

3000

4000

A280

mAU

0 50Flowthrough (FT)

virusEluate

H1N1 (A280)

H3N2 (A280)

B (A280)

0

100

200

Cond

mS/cm

0

Cond

mS/cm

200

100

100 mL

Column: XK16/20Volume: 20 mL CaptoTM Q resin

Flow rate: 2.0 mL/min (60 cm/h)

Equil. buffer: 20 mM Tris, 0.5 M NaCl, pH 7.5Elution buffer: 20 mM Tris, 1.5 M NaCl, pH 7.5

CIP: 1 M NaOH

Sample load: 40 mL (2 CV)FT volume: 1.12-fold sample volume

CIP = cleaning in place

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QbD DoE

Systematic approach to identify potential sources of process variability and parameters likely to have greatest impact on product quality.

Summary: benefits of QbD and DoE

22

Systematically vary several experimental parameters simultaneously to obtain sufficient information with minimum number of experiments.

Analytical methods are crucial for a successful DoE

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Introduction to QbD exercice

Process mapping Risk analysis

DoEDesign space

definition

24

What is QbD?

• Process mapping

– Process flow, fishbone diagrams

– Which factors could potentially affect our process?

• Risk analysis

– Failure Mode and Effects Analysis (FMEA)

– Which parameters should be investigated in detail?

• Design of experiments (DoE)

– Screening extended space in many parameters

– Detailed quantification of cause and effect relationships

• Execution and analysis, definition of design space

Four key steps defining process design space

29256325AA I Mar. 2017

Process mapping

25

• Which factors could potentially affect our process?• Tools: High level process maps

Fishbone diagrams

Process mapping

26

Seed N-2 Cell

expansion

Seed N-1 Cell

expansion

Production bioreactor

Virus propagation

Clarification NFF

Removal of cell debris and large particles

TFFConc of

virus

SEC Separation

of virus from small molecular

compounds

AIEX (FT) DNA

removal. virus in flow

through

Virus inactivation formaldehyde

Formulation Sterile

filtration, mixing with

other strains


Fishbone diagrams

Process mapping

27

Seed N-2 Cell

expansion

Seed N-1 Cell

expansion

Production bioreactor

Virus propagation

Clarification NFF

Removal of cell debris and large particles

TFFConc of

virus

SEC Separation

of virus from small molecular

compounds

AIEX (FT) DNA

removal. virus in flow

through

Virus inactivation formaldehyde

Formulation Sterile

filtration, mixing with

other strains


Fishbone diagrams

Process mapping

28


Fishbone diagrams

Break down • Chromatography system / measurements

• Column / resin / packing

• Running conditions:• Equilibrium

• Sample• Wash

• Strip• CIP

Fishbone analysis

FMEA – Failure Modes and Effects Analysis

March 13, 2017Presentation Title 29

Risk analysis and mitigation – To be performed in cross-functional teams

1. What might go wrong?

2. How severe are the consequences?

3. What is the likelihood the failure will occur?

4. Can we detect the failure?

S - Severity of each failure (1-10)

O - Likelihood of occurrence (1-10)

D - Likelihood of detection (1-10)

RPN - Risk Priority Number = S x O x D

Define what each level represent

The components of an FMEA


Action Results

Item / Process

Step

Potential Failure

Mode(s)

Potential Effect(s)

of Failure

S

e

v

Potential

Mechanism(s) of

Failure

O

c

c

u

r

Potential Cause(s) of

Failure Mechanism

Current Process

Controls

D

e

t

R

P

N

Recommended

Action(s)

Responsibility &

Target Completion

Date

Actions Taken

New

Se

v

New

Occ

Ne

w D

et

New

RP

N

Eq. pH Below limit Loss of

product

5 Buffer

blending

3 Operator In-line

monitoring

2 30

Response Plans and Tracking. Beginning with

most critical RPN's and severities (CTQ's), define

recommended actions, responsibilities and target

dates. As a result of the completed actions taken, re-

rate severity, occurrence and detection based on any

changes to the end effect, likelihood of occurrrence,

and the likelihood of the actions taken + controls to

Risk Priority Number - The combined weighting of Severity,

Occurrence, and Detection. RPN = Sev X Occ X Det

Identify the potential failure

mechanism(s).

Severity - On a scale of 1-10, rate

the Severity of the end effect. See

Severity sheet for full definition.

Detection - On a scale of 1-10, rate the Detection. (The likelihood

that the current controls will detect the failure). See Detection sheet

for full definition.

Identify each failure mode.

Identify each potential

consequence(s) (local, next and

end) of that failure.

Occurrence - On a scale of 1-10, rate

the likelihood of Occurrence of the failure

mechanism and subsequent failure mode.

See Occurrence sheet for full definition.

Identify the potential cause(s)

of the failure mechanism(s).

Identify the current

process controls.

Immediate, obvious detectionVerylow/Practically no occurrenceNo or negligible consequences1-2

Will be detected at-line before next unit operation by existing systems

Once yearly/ once in every 50 batches

Small consequences: Additional batch testing, re-work...

3-4

Detection at batch release/equivalenttwice yearly, ~once every 10 batches

Batch involved probably lost/needs to be discarded

5-6

Detection at batch release or at periodical control after batch completion

once in 2-10 batchesLoss of several batches, damage to equipment

7-8

Will probably not be detected by existing systems

Every batch/runPossible harm/injury to patient or operator

9-10

DetectionOccurrenceSeverityLevel

FMEA-Flow through DNA removal on AIEX


•128 failure modes identified

• RPN scores 1-180

Pareto plot of RPN scores


Pareto plot, FMEA Capto Q VFW

0

20

40

60

80

100

120

140

160

180

200

Failure ID

RP

N s

co

re

RPN>50

RPN 16-49

RPN<15

FMEA score card for top 10 RPN’s


Action Results

Item / Process Step

Potential

Failure

Mode(s)

Potential

Effect(s)

of Failure

SPotential

Mechanism(s) of

Failure

OCurrent

Process

Controls

D

R

P

N

Recommended

Action(s)

Actions

Taken

New

S

New

O

New

D

New

RP

N

Analysis assay failure

loss of batch 6

operator error

5 internal std run,

operator

6 180 Consistent re-analysis for OOS

results

Yes 3 5 5 75

Sample DNA level too high

DNA in product 5

Upstream process var

5 DNA assay 5 125 Picogreen screening of sample

pre-run

Yes 4 5 3 60

Bioburden too high

contamination

of systems

8

Improper CIP

2 Bioburden control 7 112 pH/cond/pattern to be checked

at each CIP

Yes 2 5 3 30

Bioburden too high

contamination

of systems

8

improper storage

2 Bioburden control 7 112 mandatory CIP cycle pre-run

after storage

Yes 4 2 3 24

Loading volume Above limit

DNA in product 5

operator error

4 DNA assay 5 100 Picogreen screening of sample

pre-run

Yes 4 5 3 60

Sample DNA level too high

DNA in product 5

Upstream process var

5 A260 4 100 Validate max loading level to

more than 2xmargin

Yes 5 1 5 25

Column/resin HETP/As Out of spec

DNA in product 6

Previous run (air, pressure…)

3 DNA assay 5 90 In-line HETP/As control at start

of each run

Yes 4 3 2 24

Bioburden too highloss of batch 6

improper CIP

2 Bioburden control 7 84 pH/cond/pattern to be checked

at each CIP

Yes 2 5 3 30

Bioburden too highloss of batch 6

improper storage

2 Bioburden control 7 84 mandatory CIP cycle pre-run

after storage

Yes 4 2 3 24

Loading Volume Above limit

DNA in product 5

operator error

4 A260 4 80 Validate max loading level to

more than 2xmargin

Yes 5 1 5 25

System hardware Monitor failureloss of batch 6

breakage

3 operator 4 72 In-line/Calibration of pH, Cond

before each run

Yes 4 3 3 36

Pareto plot after mitigation

0

20

40

60

80

100

120

140

160

180

200

#126 #3

7

#49

#112 #4

3

#51

#1 #2

#105

#108

#121 #2

3

#40

#120 #4

4

#115

#100 #9

9

#114

#128 #3

0

#69

#83

#86

#95

#98

#33

#56

#35

#58

#61

#64

#110 #5

4

#8 #27

#66

#73

#88

#16

#76

#81

#15

Failure ID

RPN score

s

Highest scores reduced from180 to 75 after first mitigation

Group exercise:Fishbone analysis and FMEA on buffer preparation

Practical exercise:Risk analysis for buffer preparations


Contamination

Wrong pH

Wrong conductivity

Wrong water quality

Other?

Fishbone analysis: Buffer preparation


What could possibly go wrong?

Buffer according to spec




Components


buffer chemicals

pH-meter

Operator

vessel

Group exercise




Components


saltbuffer chemicals

pH-meterconductivity -meter

water

termometerscale

Operator

stirrer

vessel

Group exercise

Breakdown of failure modes for components in buffer preparation


Example

Components

Buffer chemicals

Wrong type

Too much

Too little

Contaminated

Cross-contaminated

Wrong quality

Salt

Wrong type

Too much

Too little

Contaminated

Cross-contaminated

Wrong quality

Water

Wrong quality

Too much

Too little

Ranking - Severity


Effect Severity of Effect Ranking

Hazardous

without warning

Possible harm/injury to patient or operator 10

Hazardous with

warning

Possible harm/injury to patient or operator 9

Very High Loss of several batches, damage to equipment 8

High Loss of several batches, damage to equipment 7

Moderate Batch involved probably lost/needs to be

discarded

6

Low Batch involved probably lost/needs to be

discarded

5

Very Low Small consequences: Additional batch testing, re-

work…

4

Minor Small consequences: Additional batch testing, re-

work…

3

Very Minor No or negligible consequences 2

None No or negligible consequences 1

Severity is an assessment of the seriousness of the effects (next, local and end) as felt by the customer.

Ranking - Occurrence


Probability of Process

Failure Ranking

Very High: Failure is almost

inevitable > 1 in 2 10

1 in 5 9

High: Repeated failures 1 in 10 8

1 in 10 7

Moderate: Occasional failures 1 in 20 6

1 in 20 5

1 in 100 4

Low: Relatively few failures 1 in 100 3

1 in 1 000 2

Remote: Failure is unlikely < 1 in 1 000 1

Failure Rates* (failures per batch )

Occurrence is the likelihood that the failure mechanism and subsequent failure mode will occur (based on historical data on similar processes or past experience knowledge).

Ranking - Detection


Detection Likelihood of DetectionLikelihood

of DetectionRanking

Absolute

Uncertainty

Will probably not be detected by existing systems < 1% 10

Very

Remote

Will probably not be detected by existing systems 1% - 5% 9

Remote Detection at batch release or at periodical control after

batch completion

5% - 15% 8

Very Low Detection at batch release or at periodical control after

batch completion.

15% - 30% 7

Low Detection at batch release/equivalent 30% - 50% 6

Moderate Detection at batch release/equivalent 50% - 75% 5

Moderately

High

Will be detected at-line before next unit operation by

existing systems

75% - 85% 4

High Will be detected at-line before next unit operation by

existing systems

85% - 93% 3

Very High Immediate, obvious detection 93% - 99% 2

Almost

Certain

Immediate, obvious detection >99 % 1

Detection is the likelihood that the control will detect the failure.

FMEA


Practical example

Item/ process step

Potential Failure Mode(s)

Potential Effect(s) of Failure

Severity

Potential Mechanism (s)of Failure

Occurance

Current process control

Detectio

n

RP

N (S

x O x D

)Component / Chemical

Contamination

Chemicals 6 Contaminated hardware

3 pH / cond 3 54

Component / Chemical

Contamination

Bioburden 6 Contaminated hardware

3 Bioburden testing 6 108


Contamination

Bioburden 6 Contaminated chemical


Practical exercise


Fill in the table in groups

Contamination

Wrong pH

Wrong conductivity

Wrong water quality

Other?

FMEA


Practical example

Item/ process step



Severity

Potential Mechanism (s)of Failure

Occurance

Current process control

Detectio

n

RP

N (S

x O x D

)Component / Chemical

Contamination

Chemicals 6 Contaminated hardware

3 pH / cond 3 54


Contamination

Bioburden 6 Contaminated hardware



Contamination

Bioburden 6 Contaminated chemical


Group exercise

FMEA


Practical example

Failu

re m

od

e

ID #

Item/ process step



Severity Potential

Mechanism (s)of Failure

Occurance Current

process control

Detectio

n

RP

N (S

x O x D

)

1Component / Chemical

Contamination

chemicals 6contaminated hardware

3 pH / cond 3 54

2Component / Chemical contaminationchemicals 6

contaminated buffer componant

5 pH / cond 3 90

3Component / Chemical contaminationchemicals 6


2 pH / cond 3 36


contaminationbioburden 6contaminated hardware

3 bioburden testing 6 108

5Component / Chemical contaminationbioburden 6



6Component / Chemical contaminationbioburden 6



Group exercise

Pareto plot - Failure mode ID vs RPN

RPN


0

20

40

60

80

100

120

140

160

180

200

#1 #2 #3 #4 #5 #6

RPN score

Failure mode ID#

RPN

RPN

Pareto plot - Failure mode ID vs RPN

RPN

RPN Sorted March 13, 2017Presentation Title 48

0

20

40

60

80

100

120

140

160

180

200

#1 #2 #3 #4 #5 #6

RPN score

Failure mode ID#

RPN

RPN

0

20

40

60

80

100

120

140

160

180

200

#5 #4 #2 #6 #1 #3RPN score

Failure mode ID#

RPN sorted

FMEA risk mitigation


RPN > 80

Failu

re m

od

e

ID#

Item/ process step



Severity Potential

Mechanism (s)of Failure

Occurance Current

process control

Detectio

n

RP

N (S

x O x D

)

Action(s)

Ne

w S

eve

rity

Ne

w O

ccura

nce

Ne

w D

ete

ction

Ne

w R

PN

5Component / Chemical contamination bioburden 6


5bioburden testing

6 180Sterilefiltration

6 1 6 36


contamination bioburden 6contaminated hardware

3bioburden testing

6 108Sterilefiltration

6 1 6 36

2Component / Chemical contamination chemicals 6


5 pH / cond 3 90 Clear SOP 6 2 6 72

Pareto plot after risk mitigation

RPN


0

20

40

60

80

100

120

140

160

180

200

#5 #4 #2 #6 #1 #3

RPN score

Failure mode ID#

New RPN

Pareto plot after risk mitigation

RPN

RPN sorted March 13, 2017Presentation Title 51

0

20

40

60

80

100

120

140

160

180

200

#5 #4 #2 #6 #1 #3

RPN score

Failure mode ID#

New RPN

0

20

40

60

80

100

120

140

160

180

200

#6 #1 #5 #4 #2 #3RPN score

Failure mode ID#

New RPN sorted

#6

#1

#5

#4

#2

#3

Investigation on highest risks after mitigation

Examples


Experimental work

Training

New equipment

Facility upgrade

Business management

DoE

53

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29256325AA I Mar. 2017

quality by design (qbd) in process development - …€¦ · process mapping risk analysis doe...

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