Transcript Molecule Risk Assessment Guidance Document

2009 MBSW, May 18-20
Quality Risk Assessment:
a Lifecycle Approach in Evaluating
Quality Attributes for Bioproducts
Suntara Cahya, PhD
Outline
•
“Biomolecule Diversity”
•
Molecule Risk Assessments
•
Risk Scoring
•
Relation with CQA’s
•
Concluding Remarks
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Molecular Size & Higher Order Structure
Gemzar
MW 300 Da
Insulin
MW 5808 Da
MAb
MW 150 kDa
Fusion Molecule
MW 65kDa
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Molecular Heterogeneity
Translational Events
•
Splicing
•
Frame shifts
•
Intron read-through
Post-Translational Modifications
•
Glycosylation (N- & O-linked)
– Site occupancy
– Glycan structures
•
Phosphorylation
Chemical & Enzymatic Modifications
•
Aggregation
•
Proteolytic clipping
•
Deamidation
•
Isomerization
•
Oxidation
•
Glycation
•
Pyruvylation
•
Pyroglutamatation
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Potential to Impact:
• Manufacturing
consistency (lot-tolot variability)
CM&C
• Stability
• Toxicity
Toxicology
• Efficacy
• Safety
Clinical
• Immunogenicity
• PK/PD
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ADME
4
Monoclonal Antibody Variants
Predominant
pyroglutamation of Gln1
Partial loss of Gln1
Partial succinimide &
isomerization of Asp56 (CDR)
Partial glycation of
several lysines
Partial oxidation
of Met252
Heterogeneous
glycosylation of Asn297
Partial cleavage at
Pro329-Ser330
Partial deamidation of
Asn315, Asn384 & Asn389
Incomplete disulfide
Partial succinimide &
Predominant loss of Lys447
isomerization of Asp401
(2x2x2x22x2x8x2x33x2x2x2)2/2 = >1010 variants
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Molecule Risk Assessments
• Complexity in managing the risks (Safety & Efficacy)
• Impact on control strategies
• Had historically been part of dev process
– Assessments more subjective
– Opinions could vary depending on the individuals
• Motivation:
 More standard & objective ways of performing the
risk assessment
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Molecule Risk Assessments
• Motivation cont’d
•
•
•
•
•
Consistent approach that works for multiple molecules
(Antibodies, Fusion Proteins, Peptides)
Phase appropriate assessments
Documentations at key points during molecule
development
Consistent with ICH guidance on risk assessments
and QbD (ICH Q9 and ICH Q8)
Focused on molecule quality attributes and analytical
control strategy
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Risk Components
• Broken down into three risk components
• Severity:
The impact of the particular quality attribute on the
pharmacological properties of the biomolecule, including
toxicity, safety, efficacy, PK/PD profile, and
immunogenicity
–
–
Location of modification – is it in a region critical to molecule’s
activity
Documented evidence of impact
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Risk Components
• Detectability:
The capability of the analytical methods to detect and
quantify the particular quality attribute (i.e., specific vs.
non-specific method) as well as the use of the method in
the analytical control strategy (i.e., routine vs. non-routine
method)
–
–
–
Routine method developed
Non-routine developed
Attribute is not currently monitored
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Risk Components
• Occurrence:
The likelihood of the particular quality attribute to exceed
a specified level of concern (“Exposure Limit”), based
upon API and drug product manufacturing process
variability as well as the stability of the API and drug
product throughout their respective shelf-lives
–
–
–
–
Stage of development
Platform modification
Data that is available
Growth on stability
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Risk Scoring
• Each risk component has different levels of risk
scoring
• Overall risk scored based on Risk Priority Number
(RPN) = Severity X Occurrence X Detectability
• Flowcharts to facilitate scoring evaluation
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Risk Assessment Flow Chart Severity
Quality Attribute or Modification
Yes
Documented Negative
Impact on toxicity, safety,
efficacy, PK, PD or
immunogenicity?
No
Yes
Located in region likely
to impact activity (e.g.
CDR)?
Impact on in vitro Bioactivity?
Yes or
Unknown
No
No
No
Impact on in vitro
bioactivity OR Impact on
in vivo toxicity, safety, efficacy,
PK, PD or immunogenicity?
Platform Modification?
Yes
Yes
No
9
1
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Detectability
“1”: Modification is detected and accurately quantified by a specific,
routine test method
OR
Modification is detected by a non-specific, routine test method AND
accurately quantified by a specific, non-routine test method,
“3”: Modification is detected by a non-specific, routine method
OR
Modification is detected and accurately quantified by a specific, nonroutine test method
“9”: Appropriate methods are not currently available or used for
detection and accurate quantification of the modification.
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Occurrence – Part 1
Quality Attribute
Pre C1
C1 or after
Measurable or Expected
Growth at Long-Term
Storage Condition?
SPM Gate?
No
Yes
Qualitative
Non-Stability
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Qualitative
Stability
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Quantitative
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Occurrence – Qualitative NonStability
Qualitative
Non-Stability
Y to Either
Question
Are Levels
at Release
Comparable to Similar
Platform Molecules
(OR is Modif. Present at
< 1.0%)?
Is
Modification or Impurity
Common to Molecule
Platform OR Present at
< 1.0%?
N to Both
Questions
N to Both
Questions
Are Data for
Other Clinical/Tox
Lots Available? Or
Relevant Literature
Refs?
N
P score = 7
Y
Y to Either
Question
Are Data for
Other Clinical/Tox
Lots of the Product
Available?
N
P score = 1
*Are Levels
at Release Consistent
w/ Previous Clinical/
Tox Lots (or below)?
N
P score = 7
*consider process &
method variability
Y
Y
P score = 4
*Are Initial Levels
Consistent or Below
Previous Clinical/Tox
Lots (or below)?
N
P score = 7
*consider process &
method variability
Y
P score = 1
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Occurrence – Qualitative Stability
Qualitative
Stability
N
Is Modification
Common to Molecule
Platform?
N
Can an Appropriate
Molecule-Specific Exposure
Limit Be Established for the
QA?
Y
Establish P score on a
case-by-case basis
Y
N
Is Growth
Rate Similar to
(or less than)
Platform
Molecules?
P score = 1
Y
Y
P score = 9
P score = 1
Don’t
Know
N
Clinical/Tox Experience
w/ Modification at Levels
Consistent w/ “Aged”
Material (or below)?
Is There Risk
of Exceeding
Exposure Limit at End of
Shelf-life?
P score = 7
Y
P score = 1
N
P score = 7
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Occurrence – Quantitative
Quantitative
N
Y
Measurable
Growth at Long-Term
Storage Condition?
Growth =
Rate per month x
shelf-life (months)
Growth = 0
0<N<6
How Much
Representative Lot
Data is Available?
N>6
Calculate the max and/or min of the n lots
Calculate the mean & stdev from the n lots
for one-sided upper limit:
for one-sided upper limit:
lim itU  (m ean growth)
kU 
stdev
limitU  max growth
k 
limitU  t arget
U


for one-sided lower limit:
m in growth  lim itL
kL 
target  lim it 
L
for one-sided lower limit:
(m ean growth)  lim itL
kL 
stdev
For two-sided limits
k U , L  min(k U , k L )
for two-sided limits:
k U , L  min(k U , k L )
where:
where:
lim itU  upper limit
lim it  upper limit
U
lim it L  lower limit
lim it L  lower limit
growth  % per mont h shelf - life
Target  nominalor t arget values
e.g. 0% for relsubs, 100%for monomer
growth  % per mont h shelf - life
if
if
k ≤ 5  score = 9
5 < k ≤ 7  score = 4
k > 7  score = 1
k ≤ 0.5  score = 9
0.5 < k ≤ .8  score = 4
k > 0.8  score = 1
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Note: Limits Confidential
used in the k-factor
calculation
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representative of anticipated commercial specifications. Early-phase
limits that are too wide relative to the commercial specs will result in
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Occurrence – Quantitative n<6
lim itU  m ax growth
k 
lim itU  nom inal
U

limitU  max growth

limit
U

 nominal
LimitU
k ≤ 0.5  score = 9
Growth over shelf-life
0.5 < k ≤ .8  score = 4
k = 0.5
x
x
x
k > 0.8  score = 1
k = 0.8
“Nominal”
Init
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Shelf-life
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Occurrence – Quantitative n≥6
lim itU  (m ean growth)
k 
stdev
U
k ≤ 5  score = 9
LimitU
5 < k ≤ 7  score = 4
k > 7  score = 1
k-sigma
3
4
5
6
7
P (exceed limit)
1349.9 / million
31.7 / million
0.3 / million
0.987 / billion
0.001 / billion
Cpk
1.0
1.3
1.7
2.0
2.3
Growth over shelf-life
x
x
x
x
x
x
Init
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Shelf-life
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RPN Scoring
RPN = Severity x Occurrence x Detectability
Risk
Severity
Occurrence
Detectability
High
9
9&7
9
4
3
1
1
Medium
Low
1
Overall risk: High
•
•
Two components are rated high
Minimum RPN = 63
Severity Occurrence Detectability
9
7
1
1
7
9
9
4
3
RPN
63
63
108
Overall risk: Low
•
•
Method exists AND either severity/occurrence rated low
Severity Occurrence Detectability
Highest RPN = 27
9
1
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3
RPN
27
27
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RPN Scoring
RPN Scores
Severity
Occurrence
Detectability
Detectability x
Occurrence
9
9
81
81
729
7
9
63
63
567
4
9
36
36
324
9
3
27
27
243
7
3
21
21
189
4
3
12
12
108
9
1
9
9
81
1
9
9
9
81
7
1
7
7
63
4
1
4
4
36
1
3
3
3
27
1
1
1
1
9
1
9
Chance
of
“Harm”
Degree of
“Harm”
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Relation with CQA’s
• Tool to assess criticality of molecule attributes and identify
potential CQA’s
• List of potential CQA’s are broader than just molecule
modifications
• Some quality attributes are likely to always be “critical”
due to their known potential to impact patient safety and
product efficacy.
• Examples Include: Host Cell Proteins, DNA, Endotoxin,
Bioburden, Sterility, Viruses, Content/Protein
Concentration, etc
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Concluding Remarks
• Risk Mitigations
• Gaining additional information (more data: literature, in-vivo,
additional lots, etc)
• Better detection methods
• Improved mfg process (level and/or variability)
• Improved analytical method precision
• “Exposure limit” vs Specification limit
• Commercial specifications has been historically set based on
process capability, may be tighter than the exposure limit
• May re-assess the occurrence risk  risk of OOS
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Concluding Remarks
• “Lifecycle” approach
• Deliverables at gate reviews throughout development cycle
• Documentations at key gate reviews
• Guidance on roles and responsibilities: RACI diagram
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Summary
• The approach provides the basic framework to
perform risk assessment of biomolecules
• Consistency and objectivity across molecules
• Phase appropriate evaluations
• Continual revisions are expected as more
experience is gained overtime
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Risk Assessment Team
Mike DeFelippis
Bryan Harmon
Cathy Srebalus Barnes
Sarah Demmon
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Acknowledgments
Susan Janes
Matt Hilton
Melody Gossage
Bruce Meiklejohn
Juliana Kretsinger
Jill Olinger
Mandy Dorsey
Owen Van Cauwenberghe
Jeff Schwartzenhauer
Kristi Griffiths
Kamal Egodage
Gary Sullivan
Sacha Storms
Chi Nguyen
Agatha Feltus
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Questions?
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