presentation_5-28-2013-8-52-2

Report
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
Patient use
Release buffer: normal
change & variability, assay
variability, and in-use change
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)
Non-statistical 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.
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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?
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
Manufacturing
•
•
•
•
Shipping
Customer Use
Controlled temps
Uncontrolled temps
Wait times
Packaging
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Estimating Routine Stability Change
Manufacturing
•
•
•
•
Controlled temps
Uncontrolled temps
Wait times
Packaging
Shipping
•
•
•
•
•
Customer Use
Controlled temps
Uncontrolled temps
Warehouse
Loading
Shipping excursions
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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
29
Estimating Routine Stability Change
Manufacturing
•
•
•
•
Controlled temps
Uncontrolled temps
Wait times
Packaging
Shipping
•
•
•
•
•
Customer Use
Controlled temps
Uncontrolled temps
Warehouse
Loading
Shipping excursions
• Controlled temps
• Uncontrolled temps
Where is time 0 sample drawn?
Are we missing changes?
Time 0
• Batch release
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
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