risk management in stability studies - · pdf fileproduct stability baxter healthcare risk...

Evald Muraj

Product Stability

Baxter Healthcare

Risk Management in Stability

Studies

Key Questions

• What is risk?

• How do we spot risk?

• How do we avoid risk?

…and…

• What makes it so risky?

More importantly, how do these questions apply to Product Stability and Stability Studies?

2

Risk

• Risk: the combination of the probability ofoccurrence of harm and the severity of that harm(ISO/IEC Guide 51).

• Quality Risk Management (QRM): a systematicprocess for the assessment, the control, thecommunication and the review of risks to qualityof the drug product across the product lifecycle.

• Stability Risk Management: …across the product lifecycle.

3

Question

• How do we spot risk?

– In order to spot Stability-related risk, we must first understand the origins of Stability.

– Afterward, we must understand what Stability entails and, in doing so, locate the weakest links.

1.) What group did/does Stability belong to?

2.) What does Stability generally entail?

4

Risk & Stability

• Stability has its roots in Quality (control).

• Sum up Quality in six letters: GMP/GDP

• Sum up Stability in six words: 1. Identify

2. Store

3. Test4. Review

5. Trend6. Report

5

Risk & Stability

Identify: product, duration, condition, location

Store: GMP, validation, monitoring, contingency

Test: panel, validated, SOP’ed, contract, doc’ed

Review: GDP, turnaround, specs, GDP and GDP

Trend: statistics, SOP’ed, doc’ed, GDP, Out-of-...

Report: GDP, scope, arguments, conclusions

…and defense.

That which can go wrong along the way is risk.6

The Basics

Collect and Organize Info

Formulate Risk Question

Choose Tool

Identify Risk Factors

Define Risk Components

Create Matrix

Determine Action Threshold

Apply Tool

Define Risk Mitigation

Document and Approve

7

The Basics Continued…

• Gather relevant info, references, assumptions.

• Define boundaries of QRM exercise.

• Start with high level statement.

• Choose Tool:– flowchart, criticality, fault tree, risk ranking

• Define severity & probability and create matrix.

High Medium High High

Medium Low Medium High

Low Low Low Medium

Low Medium High

seve

rity

probability 8

Risk & Stability & Common

SenseStability must be defended per GMP/GDP standards.

If you’re unsure, it’s likely that there’s imminent risk (SOS/ICH).

Stability must be a reasonable process first and foremost.

“Time makes more converts than reason.” – Thomas Paine 9

Back to Square One (Logic, Quality

and ICH)

1.) What might go wrong?

2.) What is the likelihood or probability that it will go wrong?

3.) What are the consequences?

1.) Identify, Store, Test, Review, Trend, Report.

2.) Very high if/because it’s based on human beings.

3.) Your product does not go/remain commercial.

10

Identify

• Stability indicating batches (PD, PV, Clinical etc.)

• How many per year? (Lot vs. Batch vs. RM vs. RS)

• Duration of study (shelf-life vs. shelf-life calc)

• Condition of study (intended vs. accelerated)

• Location of study (freezers, chambers, contract)

Is everything GDP’ed? What are some documents that you can identify as necessary from above points?

Programs, Protocols, SOPs, Reports…11

Store

• Location of product

• Segregated stability

• GMP units

• GDP units

• Monitoring of units

• Contingency units

Relevant documents that you can identify?

Programs, Protocols, SOPs, Reports…12

Test

• Stability indicating methods

• Validated methods

• Testing windows

• GDP TRFs

• Turnaround

• Completion

• Failure, Deviation…– retest procedure? reserve procedure?

13

Review

• Turnaround

• GDP

• Other tracking metrics

• OOS

Relevant documents?

14

Trending

1.) Is there risk in analyzing stability data?2.) Is there a universal term for this risk?3.) Are there guidelines for this risk?

1.) Statistics confirms/reveals behavior. Yes.2.) The hazard is OOS. The risk is OOT.3.) There are, but they aren’t spectacular.

Data evaluation is the one area where GMP/GDP cease to apply as didactically. OOT is the primary risk in the analysis of Stability Data.

There is a difference between OE tools and Stat tools.15

OOT (Out-of-Trend) Risk

Evaluation• What is an OOT result?

– A result that does not follow the expected trend,either in comparison with other stability batchesor with respect to previous results collectedduring a stability study.

– More complicated than a comparison tospecification limits.

– Procedures to identify OOT depend on availabledata that define the norm.

16

OOT Results Modus Operandi

• OOT results do not follow the expected trend.– They are not necessarily Out-of-Specification.

• OOT results are somewhat of a rogue topic.– United States vs. Barr Laboratories (1993) forced

an FDA draft guidance on OOS.

– This guidance footnotes that similar guidance canbe used to examine OOT results.

– But there is no clear legal or regulatory basis torequire consideration of data within specificationbut not following expected trends.

17

OOT ID as Preventative

Maintenance• Common sense:

– OOT analysis could predict the likelihood of futureOOS results.

• The Risky Nature of Stability Data

– Stability data is a routine regulatory submission.

– Stability data can set internal release limits.

– Stability data estimates product change to expiry.

• OOT is crucial to both regulatory and business.

18

Types of OOT Identification

• Qualitative

– Graphical

• Quantitative

– Statistical

• Note: OOT identification must be SOP driven

– Specific criteria identified.

– Prevention of false positives.

19

Graphical OOT Evaluation

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70

80

90

100

110

120

0 3 6 9 12 15 18 21 24 27 30 33 36

Res

ult

(un

its)

Time (months)

Lot #4 – Results vs. Time

Lot 4

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Graphical OOT Evaluation

60

70

80

90

100

110

120

0 3 6 9 12 15 18 21 24 27 30 33 36

Res

ult

(un

its)

Time (months)

Lot #4 through Lot #7 – Results vs. Time

Lot 4

Lot 5

Lot 6

Lot 7

21

Risk in Statistical Evaluation of

OOT• Pros

– Data variability

– Assay specific

• Randomness (attractive & statistically baseless)

– Three consecutive aberrant results

– Result is ± 5% of T=0

– Result is ± 3% of previous result

– Result is ± 5% of the mean of all previous results

22

Conservative Statistical Models

• Account for the variability of data– Three types of data.

• Rate of false positives can be set after limits

• Historical database is needed

• Three methods:– Regression Control Chart

– By Time Point

– Slope Control Chart

23

Regression Control Chart

• Uses & Assumptions– Within a batch or between batches

– Normal and independent distribution of data

– Constant variability across time points

– Common linear slope for all batches

• Fit a least-squares regression line to data.

expected result = intercept + (slope × time)

24

• expected result = intercept + (slope × time)

• To find control limits– Calculate the expected result ± (k × s)

– k = multiplier of normal quantiles for protection

– s = square-root of the mean square error of regression

• Results within limits are not OOT

• Results outside limits are OOT and require further investigation


25

Time Point (x) Result (y) XY XX

0 98 0 0

3 104 312 9

6 90 540 36

9 98 882 81

12 97 1164 144

18 100 1800 324

24 98 2352 576

36 97 3492 1296


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Regression Control Chart – expected result ± (k

× s)

70

80

90

100

110

0 3 6 9 12 15 18 21 24 27 30 33 36

Res

ult

(un

its)

Time (months)

Results (units) vs. Time (months) in Regression Control Chart with Regression Control Limits

Lot 4

UTL

LTL

Linear Regression

27

By-Time-Point Chart• Compares based on historical batches

• Assumes

– normal distribution

– all observations at a time point are independent

• Advantages

– level of confidence can be tailored to product

– no assumptions about the shape of the degradation curve

• Challenges

– if current data aren’t tested at nominal time points

• Tolerance interval is computed per time point using historic data

• Calculate: mean (x̄) and standard deviation (s)

interval = x̄ ± ks28

By-Time-Point Chart interval = x

± k s

80

90

100

110

0 3 6 9 12 15 18

Res

ult

(un

its)

Time (months)

Results (units) vs. Time (months)

Lot 4

29

OOT in Degradants & Impurities

• Batches measured for degradation product and impurities.– percent area or percent

• Useful knowledge– shape of trend

– distribution of results

• Differences with regular assaying– linearity

– constant variance of results

– What often happens to degradant level as variability with time increases?

30

OOT in Degradants and

Impurities• Linearity and variance may not hold for

degradants and impurities– consider the relationship between variability with

time and % degradant (both increase).

• Limit of Quantitation (LOQ)– no number below LOQ

– reported: < LOQ [ICH: < RT (Reporting Threshold)]

• What is the result of truncating data?– on variability?

– on valuable statistical information?

31


Impurities• Example: a new peak forms

– should it exist?

– is it OOT?

– it is a new data point.

• Two options

– comparison to previous values from the batch

– comparison to previous values from other batches

32


Impurities• Comparison to previous values from batch

– degradation/impurity all above LOQ

– linear relationship

– assuming normality

• What if one or none of these criteria don’t stand?– Then identifying OOT results from the batch’s data

isn’t recommended.

– Why?• OOT = deviation from the expected

• if T=0M, 3M and 6M are below LOQ and 9M is above LOQ then a possible underlying trend between 0 and 9 can’t be outlined by analyzing only the batch in question.

33


Impurities• Comparison of new value to values from other

batches.

• Three options:

– All values are above LOQ

– All values are below LOQ

– Portions of the data are below LOQ

34

• All values are above LOQ

– By-time-point method

– Interval-normality assumed

• Skewed distribution:

– Plot (x,y)

– Plot (log(x),log(y))

– Analyze transformation

• Fewer points = wider intervals

• Linear trend & constant variance

– regression chart can be used


Impurities

0

0.25

0.5

0.75

1

-1 2 5 8 11 14 17 20

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

-2 -1 0 1 2

Normal

Log

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Impurities• All values are below LOQ

– Use LOQ value.

• any result above LOQ is an OOT result

• requires sufficient amount of data

• A portion of data are below LOQ

– Normalize all values below LOQ to LOQ prior to calculating tolerance interval.

36

Implementation Challenges

• ID during stability is more difficult than ID during release.

– Stability studies are less frequent

– Batch release is one point / stability results change

– Experience with product is required

– Contract vs. in-house evaluation

– Computer systems treat data per time-point

– No set definition of OOT prior to analysis

37


• Definitions:

– a result is OOT if it is at odds with previous test results for that batch

– a results is OOT if it is at odds with previous test results from other batches at that time-point

• No widespread definite agreement currently

– Agreement must be reached and an OOT process must be proceduralized.

38

Extrapolation to Mitigate Risk

• One may be able to use regression and mean response to extrapolate the confidence limits

• This method originates from the application of statistics in medicine

– Patient responds to an applied treatment

– Regressors = treament

– Dependent variable = response

– Average response is quantifiable.

39

Extrapolation

• Let ŷ be the predicted mean for x, i.e.

It so follows:

40

• Therefore:

represents a (1 – α) × 100% confidence interval for the average response.

If we amend the square root with a “1” then the interval will grow wider than the previous instance.

Extrapolation

41

(for the skeptics, those last proofs added last minute on scrap

paper)

42

• We can then plot the regression line confidence interval (CI) as well as the y±CI.

Extrapolation

700

750

800

850

900

950

1000

1050

1100

1150

1200

0 12 24 36 48

Pote

ncy (

IU/m

L)

Time (months)

Lot #4 - Potency vs. Time Regression Chart with 95% Confidence Intervals for Average Response at 5C

Potency

UCI at 95%

LCI at 95%

Specification

43

• Simple Chemical Degradation– Shelf-life determination is oftentimes slow

– Especially during process/formulation changes

– Determination of shelf-life under accelerated conditions is an attractive/efficient option

– Builds confidence in formulation’s future

– Meets regulatory requirements

• Energy is needed for most degradations.– This is referred to as the activation energy, Ea

– Higher temperature = more energetic molecules

Arrhenius Modeling to Mitigate

Risk

44

Arrhenius Modelingactivation energy

average kinetic energy(Gas constant, absolute temperature)

pre-exponential factorRewrite to aid graphically:

familiar format?

rate of reaction

45

• Stability studies will be conducted at accelerated conditions in search of spec failure.– Generate Arrhenius plot (i.e. ln k vs. 1/T)– Plot can be used to predict rate of degradant

formation or API depletion.– Shelf-life correlates with the time required to achieve

shelf-life limiting level of degradant, and shelf life =

– [D] is shelf-life of limiting degradant concentration– [D0] is initial concentration of said degradant

Arrhenius Modeling

46

• Example1:

– Formulation has D0 = 0.05% and D = 0.5%

– Degradant measures in at 0.73% (T=6M/40°C)

– Degradant measures in at 0.50% (T=1M/60°C)

– Determine shelf-life at 25°C

1. Rate Constants:

– 40°C (0.73%-0.05%)/6M = 0.11%/M

– 60°C (0.50%-0.05%)/1M = 0.45%/M

2. Arrhenius parameters ln k vs. 1/T

– 40°C is ln(0.11%/M) = ln A – Ea/(1.987 cal mol-1K-1 313K)

– 60°C is ln(0.45%/M) = ln A – Ea/(1.987 cal mol-1K-1 333K)• Solving for ln A and Ea gives

• ln A = 21.2 A = 1.61 × 109 %/mo

• Ea = 14.6 kcal/mol

3. Use limiting degradation threshold to determine rate at 25°C with

Arrhenius Modeling

K.C. Waterman. ”Understanding and Predicting Pharmaceutical Product." Handbook of Stability Testing in Pharmaceutical Development: Regulations, Methodologies, and Best Practices. Springer 2008. 126-127.

y = -7335.9x + 21.231

-3

-2.5

-2

-1.5

-1

-0.5

0.003 0.0031 0.0032 0.0033

ln k

1/T

ln k vs. 1/T

intercept is ln A and slope is –Ea/R

47

• Considerations– Degradation in some systems does not embody Arrhenius

behavior over a wide range of temperature

• Physical changes can occur

• Multiple pathways lead to multiple activation parameters

• Temperature can change pH

• Humidity can effect kinetics for solid dosage forms

– Can be applied to biologics

• Limiting factor is again temperature range

• Avoid phase changes

• Avoid protein thermal unfolding (>50°C)

Arrhenius Modeling

48

Reporting Stability Data

• Analyst will report assay-results on Test Request Form (TRF)

– This should be an official form, preferably tailored to the product/study.

• Product name, strength, lot number, package, method number, storage condition, time point should be present, assay number, reporting, reviewing spaces

• Per GDP standards, reference to a Reporting SOP is preferred.

• Reported results should be rounded to the specification

49

Stability Analytical Report

Sample Name:Lot #:Study #:Protocol #:Study Start Date:Study Purpose:

Manufacturing Date:Manufacturing Site:Expiration Date:

Testing Site:Packaging Size:

Storage Condition:Sample Orientation:

Packaging Information:Packaging Date:

Test Name Method SpecificationT=0M

ReleaseT=1M T=3M T=6M T=9M T=12M T=18M T=24M

Pull Date

Test Date

LIMS ID

Appearance

Moisture

pH

Potency

Profile

Dissolution

Completed by/date: ___________________ Reviewed by/date: ____________________ Approved by/date: ___________________

Huynh-Ba, Kim, and N. Subbarao. "Evaluation of Stability Data." Handbook of Stability Testing in Pharmaceutical Development:

Regulations, Methodologies, and Best Practices 2008. 278.

50

Final Stability Report

• Data generated from study/studies, are reported either at the end of the study or on an annual basis (depending on the Reporting SOP)

• Content of the stability report is a critical part of regulatory submission, and they are the subject of inspections/audits.– Protocols

– Primary Data

– Secondary Data

– Stability Commitment

– Statistical Evaluation

– Data Summary and Evaluation

• Consolidating data into multiple reports is often suggested– Primary, Secondary, Intermediate, Reference Standard

– Raw Material (e.g. buffers, media) reports are reported separately

– Annual commitment is suggested to show control over raw data 51

Sections• Introduction

– What the report entails

• What was tested, when, where, for how long.

• What data will be presented

• Synopsis of conclusion

• Background

– What the product is, where it’s made etc.

– Historical conclusions from previous batches/phases

– Changes in formulation/manufacturing process

– Changes to stability indicating attributes

• Stability Indicating Parameters

– Table of Methods

– Changes to methods

– Synopsis of methods

• Batches examined and Primary Packaging

• Study Design

• Results and Discussion

– Tabular and Graphical presentation of data

– Statistical analysis of trends

– ID of OOTs

• Conclusion

– Did study meet its purpose?

– How is shelf-life affected, what is the proposed expiry per Q1E?

• Deviations, OOS, OOT

– List

– Summary, CAPAs, Reference

• Appendices/Data Tables 52

Reports can fall prey to Q1E

• ICH has strict guidelines on what data requirements are for claiming shelf-life.

e.g. “if backed by statistical analysisand relevant supporting data…up to2X, but not exceeding X+12months, or if refrigerated up to1.5X but not exceeding X+6months.”

53

What’s the verdict on this graph?

54

0

1

2

3

4

5

0 3 6 9 12 15 18

% D

egra

dan

t

Time (months)

Lot # 7 - % Degradant vs. Time (months)

Spec ( < 1.6%)

Lot #7 Result

Assume that inspectors have the capacity

to notice and request

recalculation/regraphing.

55

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

-1.2 -0.7 -0.2 0.3 0.8 1.3 1.8

Log

(% D

egra

dan

t)

Log (months)

Lot # 7 – Log (% Degradant) vs. Log (Time [months])

Lot #7 Result

Spec ( < 1.6%)


• ID during stability is more difficult than ID during release.

– Stability studies are less frequent

– Batch release is one point / stability results change

– Experience with product is required

– Contract vs. in-house evaluation

– Computer systems treat data per time-point

– No set definition of OOT prior to analysis

56


• Definitions:

– a result is OOT if it is at odds with previous test results for that batch

– a results is OOT if it is at odds with previous test results from other batches at that time-point

• No widespread definite agreement currently

– Agreement must be reached and an OOT process must be proceduralized.

57

Risk Questions on Analysis and

OOT• What is minimum amount of data required?• Is the OOT procedure intended for NDA studies, commercial studies or both?• What is the change over time? Linear or nonlinear?• Are multiple statistical approaches necessary?• What is each analytical method’s precision?• Do ICH reporting thresholds impact impurities?• How are degradants and impurities rounded/reported? How does this affect

statistical tools?• What is the effect of container closure?• Is OOT a moving or still criterion?• Are statistical procedures documented?• Is the integrity of data used to identify future OOT intact?• Can department handle scope of statistical limits as studies multiply?• Can previous time points turn OOT after including later measurements?• Are multiplicity in testing and p-value adjustments necessary as studies progress?• Is the computer code for data extraction validated per 21CFR Part 11 (7).• What are the main points to consider?

58

Core Questions after we’re ICH/GMP

Fluent1. What is the breadth of the stability program?

2. What statistical approaches are used?

3. What data is used to determine/update limits?

4. What are the minimum data requirements?

5. What evaluation is performed if #4 isn’t met?

6. What are the investigation requirements?

7. Who is responsible for evaluating OOT data?

8. How is OOT confirmed, and is it limited to specifications?

9. What is the result of a confirmed OOT?

10. How do OOT investigations contribute to annual product review?

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risk management in stability studies - · pdf fileproduct stability baxter healthcare risk...

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