Quality Risk Assessment: a Lifecycle Approach in Evaluating Quality Attributes for Bioproducts 2009...

Quality Risk Assessment: a Lifecycle Approach in Evaluating Quality Attributes for Bioproducts

2009 MBSW, May 18-20

Suntara Cahya, PhD

Outline

• “Biomolecule Diversity”

• Molecule Risk Assessments

• Risk Scoring

• Relation with CQA’s

• Concluding Remarks

Suntara Cahya, 2009 MBSW Company Confidential Copyright © 2009 Eli Lilly and Company 2

Suntara Cahya, 2009 MBSW Company Confidential Copyright © 2009 Eli Lilly and Company

GemzarMW 300 Da

InsulinMW 5808 Da

MAbMW 150 kDa

Fusion MoleculeMW 65kDa

Molecular Size & Higher Order Structure

3


Potential to Impact:

• Manufacturing consistency (lot-to-lot variability)

• Stability

• Toxicity

• Efficacy

• Safety

• Immunogenicity

• PK/PD

Translational Events• Splicing• Frame shifts• Intron read-through

Post-Translational Modifications• Glycosylation (N- & O-linked)

– Site occupancy– Glycan structures

• PhosphorylationChemical & Enzymatic Modifications

• Aggregation• Proteolytic clipping• Deamidation• Isomerization• Oxidation• Glycation• Pyruvylation• Pyroglutamatation

CM&C

Toxicology

Clinical

ADME

Molecular Heterogeneity

4


Monoclonal Antibody VariantsPredominant

pyroglutamation of Gln1

Predominant loss of Lys447

Heterogeneous glycosylation of Asn297

Partial oxidation of Met252

Partial glycation of several lysines

Partial succinimide & isomerization of Asp56 (CDR)

Partial deamidation of Asn315, Asn384 & Asn389

Partial succinimide & isomerization of Asp401

Partial loss of Gln1

Incomplete disulfide

Partial cleavage at Pro329-Ser330

(2x2x2x22x2x8x2x33x2x2x2)2/2 = >1010 variants 5

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


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


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


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


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


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



Risk Assessment Flow Chart - Severity

12

Documented Negative Impact on toxicity, safety,

efficacy, PK, PD or immunogenicity?

Quality Attribute or Modification

Located in region likely to impact activity (e.g.

CDR)?

Impact on in vitro bioactivity OR Impact on

in vivo toxicity, safety, efficacy, PK, PD or immunogenicity?

1

No

Yes

9

Yes

Platform Modification?

Impact on in vitro Bioactivity?

No

Yes

Yes or Unknown

Yes

No

No

No


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, non-routine test method

“9”: Appropriate methods are not currently available or used for detection and accurate quantification of the modification.

13


Occurrence – Part 1

14

SPM Gate?

C1 or afterPre C1

Measurable or Expected Growth at Long-Term Storage Condition?

No

Yes

QualitativeNon-Stability

Qualitative Stability

Quantitative

Quality Attribute


Occurrence – Qualitative Non-Stability

15

Is Modification or Impurity Common to Molecule

Platform OR Present at < 1.0%?

QualitativeNon-Stability

Are Levels at Release

Comparable to Similar Platform Molecules

(OR is Modif. Present at < 1.0%)?

Y to Either Question

P score = 1

P score = 1NAre Data for

Other Clinical/Tox Lots of the Product

Available?

P score = 7

N*Are Initial Levels Consistent or Below Previous Clinical/Tox

Lots (or below)?

Y

Y

*consider process & method variability

N to Both Questions

Y to Either Question

Y

NP score = 7

P score = 7N

P score = 4

Are Data for Other Clinical/Tox Lots Available? OrRelevant Literature

Refs?

*Are Levels at Release Consistent w/ Previous Clinical/Tox Lots (or below)?

Y

*consider process & method variability

N to Both Questions


Occurrence – Qualitative Stability

16

Can an Appropriate Molecule-Specific Exposure Limit Be Established for the

QA?

Qualitative Stability

Is Modification Common to Molecule

Platform?

Is Growth Rate Similar to (or less than)

Platform Molecules?

Establish P score on a case-by-case basis

N

Y

P score = 7

N

P score = 1Y

Clinical/Tox Experience w/ Modification at Levels

Consistent w/ “Aged” Material (or below)?

YP score = 1

N

Is There Risk of Exceeding

Exposure Limit at End ofShelf-life?

Y

P score = 9

Y

Don’t Know

P score = 7

N

P score = 1

N


Occurrence – Quantitative

Quantitative

Measurable Growth at Long-Term Storage Condition?

How Much Representative Lot Data is Available?

0 < N < 6 N > 6

Note: Limits used in the k-factor calculation should be representative of anticipated commercial specifications. Early-phase limits that are too wide relative to the commercial specs will result in k-factors that are artificially high (and corresponding P scores that are artificially low).

Growth = 0Growth =

Rate per month x shelf-life (months)

N Y

Calculate the mean & stdev from the n lots for one-sided upper limit:

stdev

growth mean limitk

UU )(

for one-sided lower limit:

stdev

limitgrowth meank

LL

)(

For two-sided limits ),min(, LULU kkk where:

life-shelf month per %

limitlower

limitupper

growth

limit

limitL

U

if k ≤ 5 score = 9 5 < k ≤ 7 score = 4 k > 7 score = 1

Calculate the max and/or min of the n lots for one-sided upper limit:

ettlimit

growthmaxlimitk

U

UU

arg

for one-sided lower limit:

L

LL

limittarget

limitgrowthmink

for two-sided limits: ),min(, LULU kkk where:

life-shelf month per %

monomerfor 100% relsubs,for 0% e.g.

valuesor target nominal arget

limitlower

limitupper

growth

T

limit

limitL

U

if k ≤ 0.5 score = 9 0.5 < k ≤ .8 score = 4 k > 0.8 score = 1

17


k = 0.5

k = 0.8

Occurrence – Quantitative n<6

nominallimit

growthmaxlimitk

U

UU

Init Shelf-life

LimitU

“Nominal”

xxx

Growth over shelf-life

nominallimitU

growthmaxlimitU

k ≤ 0.5 score = 9 0.5 < k ≤ .8 score = 4 k > 0.8 score = 1

18


Occurrence – Quantitative n≥6

Init Shelf-life

LimitU

xxx

xxx

Growth over shelf-life

stdev

growth mean limitk

UU )(

k ≤ 5 score = 9 5 < k ≤ 7 score = 4 k > 7 score = 1

k-sigma Cpk3 1349.9 / million 1.04 31.7 / million 1.35 0.3 / million 1.76 0.987 / billion 2.07 0.001 / billion 2.3

P (exceed limit)

19


RPN ScoringRPN = Severity x Occurrence x Detectability

Overall risk: High• Two components are rated high• Minimum RPN = 63

Overall risk: Low• Method exists AND either severity/occurrence rated low • Highest RPN = 27

Severity Occurrence Detectability RPN9 7 1 631 7 9 639 4 3 108

Severity Occurrence Detectability RPN9 1 3 271 9 3 27

Risk Severity Occurrence Detectability

High 9 9 & 7 9

Medium 4 3

Low 1 1 1

20


RPN Scoring

Degree of “Harm”

Chance of

“Harm”

Occurrence DetectabilityDetectability x Occurrence 1 9

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

RPN Scores Severity

21


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

22

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


Concluding Remarks

• “Lifecycle” approach• Deliverables at gate reviews throughout development cycle

• Documentations at key gate reviews

• Guidance on roles and responsibilities: RACI diagram


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



Risk Assessment Team

Mike DeFelippis

Bryan Harmon

Cathy Srebalus Barnes

Sarah Demmon

Suntara Cahya

26


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

27

Questions?


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Quality Risk Assessment: a Lifecycle Approach in Evaluating Quality Attributes for Bioproducts 2009...

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