Company Confidential

© 2012 Eli Lilly and Company

Beyond ICH Q1EOpening RemarksRebecca ElliottSenior Research ScientistEli Lilly and Company

MBSW 2013

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ICH Q1E

• Required analysis for setting specifications• Statistical details• BUT what does the analysis tell us?

• The more data, the narrower the interval on the regression line, the longer the dating.

• Assuming common slopes, the analysis provides an average change for a PRODUCT.

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Estimate of Dating

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0 3 6 9 12 15 18 21 24Age (months)

Linear Fit

Decreasing Property

Dating is 22 months.

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Estimate of Dating with More Data

• Dating is now 24 months.• (Assuming common slopes.)• Slope represents average

change across batches.• Batches are a random

sample from product.• Slope represents

average change for the PRODUCT.

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0 6 12 18 24Age (months)

Linear Fit

Decreasing Property

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Estimate of Dating with More Data

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0 6 12 18 24Age (months)

Linear Fit

Decreasing Property

• AND, we already know some batch results are likely to be outside of spec.• Observed• Projected

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Why Do ICH Q1E?• Batches are released and evaluated individually.

• Individual results must meet specs. • Dating/specifications need to apply to actual test results.• ICH Q1E does not provide analysis for individual results.• ICH Q1E does not consider additional circumstances

that can cause molecule to degrade.• Shipping• Patient/customer use

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Session Agenda• Jim Schwenke

• Consulting Statistician, Applied Research Consultants, PQRI• On the Shelf-Life of Pharmaceutical Products

• Jeff Gardner• President and Principal Consultant, DataPharm Statistical &

Data Management Services• Statistical Considerations for Mitigating the Risk of

Individual OOS Results on Stability• Becky Elliott

• Senior Research Scientist, Eli Lilly and Company• Change During Patient Use—Questions and Challenges

• Question and Answer Period

Company Confidential

© 2012 Eli Lilly and Company

Change During Patient Use—Questions and ChallengesRebecca ElliottSenior Research ScientistEli Lilly and Company

MBSW 2013

9

Stability Model

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0 6 12 18 24 30Age (months)

Linear Fit

Decreasing Property

Release buffer is for assay variability

Release buffer is for change, change variability, assay variability

Is this picture complete?

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More Complex Stability Model

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0 6 12 18 24 30Age (months)

Fit Each Value Batch=="AA"

Decreasing Property

Patient use

Controlled stability chamber

Release buffer: normal change & variability, assay variability, and in-use change

Multi-use products

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In-Use Change

• Can be large• Potentially fewer batches for analysis• Can have a different change model than routine

stability

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Statistics are “easy”

• Determine routine and in-use models• Linear• Quadratic• Nonlinear

• Determine estimates of variability• Model• Assay

• Adjust release buffer(s)

Non-statistical questions are “hard.”

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Today’s Topics

• Modeling in-use change1. Complexity of complete statistical model and

impact on business2. Significance of in-use change3. Correlation of results4. Groups5. Proxy data

• Other uncontrolled conditions

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#1 Analysis Impact to Business

• One-time or yearly studies?• Requirement is often upon registration

• “Fresh” batch• “Aged” batch

• There may be no regulatory requirement to generate data yearly

• One-time estimate or yearly update?• Implications are to WHO does stat analysis

WHEN and HOW.• One complicated model• Two easier models

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#2 Significance of Change

• Change estimates• Errors can be high depending on assay

• Is change significant?• Include estimate of change variability?

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#2 Significance of Change

• Assay variability is included in buffer for long term stability change. Is it “double counting” to include it for in-use change?

• Is there “room” within the specifications?

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0 5 10 15 20 25 30 35Age in Days

Linear Fit

Lot B1

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0 5 10 15 20 25 30 35Age in Days

Linear Fit

Lot B2

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#2 Significance of Change

• Is change meaningful?• Science vs. statistical significance

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Linear Fit

Lot B3

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Linear Fit

Lot B4

p-value = 0.02 p-value = 0.06

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#3 Correlation in Results• Multiple batches can be manufactured close together in

time (e.g., validation batches, special studies).• Timepoints to be assayed are close together.• Lab wants to maximize resources.

• Hold samples• Test them together

• Common timepoints across batches are put on same assay run.

Testing batches together dependent slopes

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#3 Correlation—Shared Assay Dates

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12/15/200802/16/200902/17/2009

Test Date

Linear Fit

Lot A1

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01/01/200902/16/200902/17/2009

Test Date

Linear Fit

Lot A2

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12/28/200802/16/200902/17/2009

Test Date

Linear Fit

Lot A3

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01/09/200902/16/200902/17/2009

Test Date

Linear Fit

Lot A4

Are these 4 independent estimates of the slope?

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#3 Correlation—Solution• Backload samples• E.g.: 30 day study tested on day 0, 7, 15, 30

• Study day 1: put 30-day samples on stability• Study day 15: put 15-day samples on stability• Study day 23: put 7-day samples on stability• Study day 30: test all samples on same assay run

• Independent slope estimates without run-to-run assay variability

• More planning with lab• Protocols are more complicated

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#4 Groups

• What about group differences?• Sites, components, raw materials?• Different testing labs

• Do we have “enough” data to tell meaningful differences?

• Should we expect group differences?

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#4 Groups—Are they different?

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3.0

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0 5 10 15 20 25 30 35Age in Days

Fit Each Value Lot=="B1"Fit Each Value Lot=="B2"Fit Each Value Lot=="B3"Fit Each Value Lot=="B4"Fit Each Value Lot=="B5"Fit Each Value Lot=="B6"

Increasing Property: Groups

No technical or scientific reason for these groups to be different. Therefore, there is no practical difference here. Sums of squares is small due to low variability within batches.

Group x age effect p-value < 0.0001

Age in DaysGroupLot[Group]Group*Age in DaysLot*Age in Days[Group]

Source12323

Nparm12323

DF2.31153643.24465950.01256730.15561130.0253697

Seq SS465.5347326.7311

0.843715.66981.7031

F Ratio<.0001*<.0001*0.4853<.0001*0.1970

Prob > FSequential (Type 1) Tests

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#5 Proxy Data

• Patient use involves simulating dosing regimen• Does this impact the molecule?

• Accelerated studies may be held under the same ambient conditions as patient use• Do these studies have same change?• What are timepoints? Are there enough during

the in-use period?

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In-Use Change

• Non-statistical questions can impact• Conclusions• Analysis• Cost to the business

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Other Uncontrolled Environments

• Manufacturing wait times• Transfer times between production steps• Transfer times to packaging• Packaging/labeling time• Transfer time shipping• Shipping excursions• When in the process are stability samples

assayed?

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Estimating Routine Stability Change

Manufacturing Shipping Customer Use

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Estimating Routine Stability Change

Manufacturing• Controlled temps• Uncontrolled temps• Wait times• Packaging

Shipping Customer Use

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Estimating Routine Stability Change

Manufacturing• Controlled temps• Uncontrolled temps• Wait times• Packaging

Shipping• Controlled temps• Uncontrolled temps• Warehouse• Loading• Shipping excursions

Customer Use

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Estimating Routine Stability Change

Manufacturing• Controlled temps• Uncontrolled temps• Wait times• Packaging

Shipping• Controlled temps• Uncontrolled temps• Warehouse• Loading• Shipping excursions

Customer Use• Controlled temps• Uncontrolled temps

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Estimating Routine Stability Change

Manufacturing• Controlled temps• Uncontrolled temps• Wait times• Packaging

Shipping• Controlled temps• Uncontrolled temps• Warehouse• Loading• Shipping excursions

Customer Use• Controlled temps• Uncontrolled temps

Time 0

• Batch release

Stability Chamber

• End of shelf-life

Where is time 0 sample drawn?Are we missing changes?

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Conclusions

• Estimating stability change goes beyond statistical computations.• Consider business processes

• Impact to statistical modeling• Consider data structure

• Correlated data points• Data groups

• Consider science AND statistical significance• Consider proxy data