Beyond ICH Q1E
Opening Remarks
Rebecca Elliott
Senior Research Scientist
Eli Lilly and Company
MBSW 2013
Company Confidential
© 2012 Eli Lilly and Company

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.
2

Estimate of Dating
Dating is 22 months.
3

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.
4

Estimate of Dating with More Data
• AND, we already know some batch results are likely to be outside of spec.
• Observed
• Projected
5

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
6

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
7

Change During Patient Use —
Questions and Challenges
Rebecca Elliott
Senior Research Scientist
Eli Lilly and Company
MBSW 2013
Company Confidential
© 2012 Eli Lilly and Company

Stability Model
Release buffer is for assay variability
Is this picture complete?
Release buffer is for change, change variability, assay variability
9

More Complex Stability Model
Multi-use products
Controlled stability chamber
Release buffer: normal change & variability, assay variability, and in-use change
Patient use
10

In-Use Change
• Can be large
• Potentially fewer batches for analysis
• Can have a different change model than routine stability
11

Statistics are “easy”
• Determine routine and in-use models
• Linear
• Quadratic
• Nonlinear
• Determine estimates of variability
• Model
• Assay
• Adjust release buffer(s)
Nonstatistical questions are “hard.”
12

Today’s Topics
• Modeling in-use change
1. Complexity of complete statistical model and impact on business
2. Significance of in-use change
3. Correlation of results
4. Groups
5. Proxy data
• Other uncontrolled conditions
13

#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
14

#2 Significance of Change
• Change estimates
• Errors can be high depending on assay
• Is change significant?
• Include estimate of change variability?
15

#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?
16

#2 Significance of Change p-value = 0.02
p-value = 0.06
• Is change meaningful?
• Science vs. statistical significance
17

#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
18

#3 Correlation —Shared Assay Dates
Are these 4 independent estimates of the slope?
19

#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
20

#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?
21

#4 Groups —Are they different?
Group x age effect p-value < 0.0001
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.
22

#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?
23

In-Use Change
• Non-statistical questions can impact
• Conclusions
• Analysis
• Cost to the business
24

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?
25

Estimating Routine Stability Change
Manufacturing Shipping Customer Use
26

Estimating Routine Stability Change
Shipping Customer Use Manufacturing
• Controlled temps
• Uncontrolled temps
• Wait times
• Packaging
27

Estimating Routine Stability Change
Customer Use Manufacturing
• Controlled temps
• Uncontrolled temps
• Wait times
• Packaging
Shipping
• Controlled temps
• Uncontrolled temps
• Warehouse
• Loading
• Shipping excursions
28

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
29

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
Where is time 0 sample drawn?
Are we missing changes?
Stability Chamber
• End of shelf-life
30

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
31