minimum potency throughout dating and lot release ...€¦ · using stability data and...

Minimum Potency Throughout Dating and Lot Release Practices of

Other Agencies/CountriesHuman Vaccines

Philip R. Krause, M.D.FDA/CBER/OVRR

Ames, IA, April 21, 2015

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The role of statistical models in stability analysis

• Provide an indication of the level of confidence in reported results

• Assessing whether this level of confidence is sufficient is an important regulatory function

• Where feasible, appropriate statistical models should be used to analyze data supporting regulatory decisions

PotencySpecific ability or capacity of the product, as indicated by appropriate laboratory tests or by adequately controlled clinical data obtained through the administration of the product in the manner intended, to effect a given result. -- [21 CFR §600.3 (s)]

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Why do we do a potency assay?• In development, to:

– Assure that safe potencies are not exceeded in clinical trials– Obtain information that will support licensure-including

correlation of potency with clinical response• After licensure, to assure that lots behave similarly to

those tested in the clinical trials that supported licensure– The potency should not be below the lowest potency believed to

be efficacious– The potency should not exceed the highest potency believed to

be safe• The potency assay thus provides a “bridge” between

licensed material and the clinical trials

What does a potency assay tell us?• The assay estimates the mean potency value for a lot• There are two sources of variability in the measured

potency of an individual vial from the same vaccine lot– manufacturing variability and assay uncertainty

• Thus, we can never know the actual potency in an individual vial that was used in a clinical trial

• We can know the characteristics of the lot of vaccine from which that vial came (we routinely estimate the mean potency)

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Necessary attributes for potency assays

• Predictive of “ability of the product . . . to effect a given result” (21 CFR 600.3(s))

• Possess characteristics that are amenable to validation

• Precision sufficient to meet goal of potency assays, i.e., provide assurance that vaccine is safe and effective throughout the dating period– Includes for use in stability studies– Includes for use in the “bridge” between marketed

and clinical trial materials• Stability indicating

Stability-indicating assays• Identify degradation that is related to vaccine

effect• Implies that the assay is relevant to vaccine

effect not only at the beginning of the dating period, but also at the end

• Supportive data can often be obtained in accelerated or forced degradation studies

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Bridging pre-licensure and post-licensure results

• Goal: Make sure that product released after licensure behaves similarly to that tested in pre-licensure clinical studies

• This can be assured if the mean potency of lots produced after licensure is at least as high as that of effective clinical lots

• Assumptions: – Process remains in control: within-lot variability does

not increase after licensure– Potency assay consistently predicts effectiveness

Assay variability and specifications• Vials in a lot released with a mean potency of X

may have actual potency less than (or greater than) X, due to assay variability

• Releasing a lot with a measured potency of X is tantamount to saying that the 95% lower confidence bound on this value is acceptable

• Thus, it is important to know:– the 95% lower (one-sided) bound on mean potency– Whether this 95% lower bound exceeds “LL”, the

lowest potency believed to be effective• Minimum release potency should provide

assurance that the 95% lower bound exceeds LL– More stringent bounds may sometimes be needed

• Similar considerations apply for upper limitsLowest effectivePotency (LL)

Highest safePotency (UL)

X

X-1.65SEM

95%

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Transformation of potency data

• For stability analysis, potency data usually should be analyzed on a log scale– Biological relevance– More likely to allow linear regression models– Often log-transformed potency is normally

distributed

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Setting Specifications on Potency Assays: Two Questions

• How much potency do we need in a dose of product?– How much is needed at end expiry?– How much is needed at release in order to assure

enough potency is present at end expiry?• For unstable products, this must be more than is

needed at end expiry

• How certain do we need to be of that value?

Using stability data and specificationsto set shelf life

• The dating period means the period beyond which the product cannot be expected beyond reasonable doubt to yield its specific results [21 CFR 600.3(l)].

• Goal: Throughout its shelf life, product must be comparable to batches shown to be safe and effective in clinical studies

• Stability data are used to make predictions that can be extrapolated to future batches of product

• The most reliable predictions to support the dating period are based on mathematical modeling of biologically relevant stability-indicating parameters

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Optimizing the stability estimates• If degradation is known to be linear, bunching points at

beginning and end of study gives the most accurate slope• If degradation is known not to be linear (and is not predictable),

bunching points at beginning and end of study provides the best estimate of percent loss over that time period.

• We nonetheless need intermediate time points to provide assurance that decay is linear. For products that have increasing degradation rates over time, any stability estimate can be risky

• The utility of most stability studies could be improved by including additional determinations at the end of the study

• Intermediate time points are mostly helpful in determining whether decay is linear or not, and in defining risk of modeling

Relationship between release potency (specification) and end-expiry potency

Time

U.L.

L.L.

Expiry

} Error termMinimum Release Spec (LRL)

storage

shipping

storage in clinicreconstitution

This figure shows predicted consequences of releasingvaccine at LRL, and alsoshows how LL and other information could be used tocalculate an LRL that assuresvaccine potency does not fall below LL.


Time

U.L.

L.L.

Expiry


storage

shipping


Potency (at LRL) is allocated for product efficacy, stability (“stability budget”) and to account for errors in potency assignment and in stability estimate

Calculation of minimum acceptable potency at release

Time

U.L.

L.L.

Expiry


storage

shipping


}Expected stability lossshould encompass all anticipated conditions


Time

U.L.

L.L.

Expiry


storage

shipping


The minimum acceptable difference between LRL and LL can be defined as “Minimum Release Overage” (MRO).MRO= |expected potency

loss due to stability| + error term M

RO


Time

U.L.

L.L.

Expiry


storage

shipping


If you know minimum potency at which vaccine is released (LRL):

LL < LRL – MROIf you know LL,

LRL> LL + MROIn all cases, MRO > 0

MRO

Release potency window(LRL – URL)

Time

U.L.

L.L.

Expiry


storage

shipping


If product cannot beproduced such that it can be released at potency between LRL and URL , then there is no consistently manufacturable product

} Error termMaximum Release Spec (URL)

MRO

Error term

• If precision of each component of the release model is independently determined, an aggregate error term may be used

• For lower release limit this includes– Error in each stability estimate– Error in initial potency determination

• For upper release limit this includes– Error in initial potency determination

Safety considerations and shelf life

Time

U.L.

L.L.

95% upper bound

• Can also be based on accumulation of a toxic degradation product

When is this easier?• Assay precision is high

– When assay precision is high, error term is lower and it is easier to construct a release model due to improved understanding of release potencies and stability

– Must be careful that assay is measuring the right thing

• Manufacturing variability is low• Therapeutic index is wide

– Assumes we understand the therapeutic index (LL and UL)

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Monitoring

• Primary goals of post-licensure stability monitoring – Assure continued stability sufficient to assure

safety and efficacy– Refine stability understanding– Monitor the manufacturing process?

Stability monitoring

U.L.

L.L.

U.L.

L.L.


U.L.

L.L.

Failure?

Should this be considered a failure?

This could be viewed as an opportunity to better understand the stability, and because it could be due to assay variability, would not necessarily be considered a “failure”


U.L.

L.L.


U.L.

L.L.

Assay variability should not lead to stability monitoring failures!

Lower potency results also could be due to random selection of vials with below-average potency

If monitoring requires all tests to exceed LL, but release potencies are set based on lot mean potencies, stability monitoring failures are likely to result

Results of annual stability studies may be used to refine original stability estimates, potentially influencing the release model


Problems with requiring all stability data points to exceed L.L.

• Initial potency influences likelihood of “failure”–thus, stability monitoring on high potency lots provides very little information

• Increased number of tests increases likelihood of failure, reducing motivation to obtain information and making it difficult to design filling model, since number of subsequent tests is not always known

• In typical testing programs, failures are very difficult to interpret– Investigations often don’t yield much useful information

Stability monitoring approach

• Prospectively identify actions to be taken based on key threshold values

• Retesting can be allowed to address assay variability, if prospectively built in to the monitoring plan

• Most important question on stability monitoring is not if each individual test exceeds L.L., but instead if there is evidence suggesting change in decay slope

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• Compare current stability with previous stability estimate that was used to determine shelf life

• Best comparison may be between regression lines (assuming first order kinetics)

• Need to use statistics to show that regression lines are similar – Need to define similarity – Need some assurance that meaningful differences would be detected

(beta error)– Need to confirm assumptions (e.g., kinetics)

• Can use data from stability monitoring to refine stability estimates, and thus, release potency specs

Using stability monitoring to confirm and update previous stability estimates

Key point about stability monitoring

• The application (license) should describe the stability monitoring program in sufficient detail– What will be tested– How many lots will be tested– What’s the rationale (ideally, in statistical terms–

i.e., what type of changes does this program have a reasonable chance of detecting)

– What will be done with the results

References

• ICH Q1– A(R2) Stability testing of new drug substances and

products– B Photostability testing of new drug substances and

products– C New dosage forms– D Bracketing and matrixing designs– E Evaluation of stability data– F Registration Applications in Climatic Zones III and IV

• WHO “Guidelines on stability evaluation of vaccines”• Biologicals. 2009 Nov;37(6)

Conclusions• Release potency and shelf life are optimally evaluated using

relevant statistical models of product stability under predicted conditions of storage

• Statistical models allow use of all the data to make scientifically supportable predictions that the product will be safe and effective throughout the shelf life, with an indication of the degree of confidence in that conclusion

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