Design and statistical analysis of method transfer studies for biotechnology - Description
products Meiyu Shen Lixin Xu Center for Drug Evaluation and Research US Food and Drug Administration This presentation reflects the views of the author and should not be construed to represent FDAs views or policies ID: 737958 Download Presentation
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Design and statistical analysis of method transfer studies for biotechnology
products. Meiyu Shen, Lixin Xu. Center for Drug Evaluation and Research, . U.S. . Food and Drug . Administration. This presentation reflects the views of the author and should not be construed to represent FDA’s views or policies.
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Design and statistical analysis of method transfer studies for biotechnology
Presentation on theme: "Design and statistical analysis of method transfer studies for biotechnology"— Presentation transcript:
Design and statistical analysis of method transfer studies for biotechnology productsMeiyu Shen, Lixin XuCenter for Drug Evaluation and Research, U.S. Food and Drug Administration
This presentation reflects the views of the author and should not be construed to represent FDA’s views or policiesSlide2
OutlineMethod development and its life cycle managementPurpose of analytical method transfer studiesWhat parameters compared in analytical method transfer studiesTesting materials AnalysisConclusionSlide3
New analytical method developmentParameters evaluated SpecificityLinearityAccuracyPrecisionLimits of detection (LOD) Limits of Quantitation (LOQ)RangeSlide4
Life cycle management of analytical proceduresIncluding, but not limited toTrend analysis on method performance at regular intervalsto optimize the analytical procedure
to revalidate all or a part of the analytical procedure
Development and validation of a new or alternative analytical method
A new impurity
Method transferred to a new testing siteSlide5
Analytical method transfer studiesThe purpose of method transfer studies (internal)To determine if the two laboratories provide comparable results across the range of interest.If so, then to transfer a fully validated analytical method from the originating lab to a new lab (receiving lab)
Once transferred, the method is suitable for its intended use and can be used to ensure process consistency and meet product specifications
Analytical method transfer studiesHow to achieve the goal? Obtaining the comparative data from method transfer studiesChecking the receiving lab’s bias (difference between the true value and the mean of the receiving lab)Determining success of implementation of the fully validated analytical method in the receiving labSlide7
Important Factors in Method transfer studiesSuppose that the same type of instrument from the same manufacturer, same reagents, same experimental conditions, and same testing procedure, we investigate the following factors:operatorsdaysrunsReplicateslotsSlide8
Key parameters in method transfer studiesMean shift (often incorrectly cited as accuracy)Comparing means of two labs PrecisionComparing the standard deviations of two labsBias (accuracy)Slide9
Testing materialsIs the reference standard appropriate material from which comparative data is obtained for method transfer studies?NoSince the method is used to ensure process consistency and meet product specificationsSlide10
Testing materialsMultiple lots of a drug product if the assay is used for drug releasing testsMultiple lots of a drug substance if assay is used for measuring the content in drug substanceForced degradation samples or samples of a drug substance or a drug product containing pertinent product-related impurities if the transferred assay is stability indicating Slide11
Literature review: statistical analysisMany proposals, just name a few here Significance testing approach Comparing the means of two labs by the p-value of rejecting H0: μR=μ
: Discouraging the sponsors to use a large sample size and to obtain more precise measurement
Quality control method
Checking individual values against the control limit
Not quantitative criteria for decision Slide12
Literature review: statistical analysisβ-expected tolerance approachCalculating the tolerance interval in which a proportion (β) of the receiving laboratory population is expected to fall within, Compares the above tolerance interval to acceptance limits around the mean estimate of the sending laboratory
: challenge to define the acceptance limits.
-content tolerance interval
to assure more than 100P% of the
(or percent difference, d) between individual results obtained in the sending
the receiving laboratory are within the
(L, U) with 100(1 - α)% confidence level.
Two-sided tolerance interval
Two one-sided tolerance interval
challenge to define L, U, and PSlide13
Our proposal: Equivalence test for comparing means of two laboratories Denote the means of the response variable of interest by μR and μS , respectively, for the receiving laboratory and the sending laboratory.
Here δ is a pre-specified constant, also called an equivalence margin.Slide14
Challenge of setting equivalence margin for equivalence approachFixed marginBased on the experts’ knowledgeDifferent margin for a different assay 1% , a reasonable margin for HPLCToo stringent for bioassay2.5%, a reasonable margin for a specific bioassayToo liberal for HPLCWider than specification 2% for drug substance assay
Challenge: It is hard to have a numberSlide15
Challenge of setting equivalence margin for equivalence approachNon-fixed margin: a function of assay variabilityUnified rule for many assaysBased on statistical power for rejecting the null hypothesis in the equivalence hypothesis test with a limit number of observations (not exceeding hundreds)All margins sits well within the assay specification.Slide16
How to obtain the assay variabilityLong term quality control data Not appropriate, e.g.,If there is a stability trend over the timeIf there is a drift from assay instruments over the timeOnly good if there is no other confounding factor except operators, days, and runsHard to meet this criteriaComparative method transfer studies
We may estimate the assay variability from studiesSlide17
Statistical analysis for the mean difference of two labsHypothesis testing (1):H0: μS – μR ≤ - c
are the mean responses of the sending lab and receiving lab, respectively, and
> 0 is the constant.
Determined from power function of rejecting the above hypothesis if
Power function for the two one-sided tests procedureLet be the probability of rejecting H0 under Ha in Hypothesis testing (1) when σS
The power function is:
# of obs. in receiving lab
# of obs. in sending lab
: normal cumulative function Slide19
Determination of δ=CσSn
=0.85 is reasonably chosen such that we can achieve about 85% power with a sample size in the range 20 to 30 per laboratory.
An example: internal transfer to a new siteAll equipment moved to the new sitePersonnel transferred to the new site At least 2 lots >2 analysts and >2 daysReasonable sample size per lab: ~20-30Margin: e.g., 0.85σ
Power to pass equivalence test is about 85% under no true mean differenceSlide21
Statistical analysis for the mean difference of two labsOption 1:Treating 0.85 as a constantEstimating from the sending labDefine and Concluding equivalence criteria is met if and ,
the 1-α quantile of t-distribution with degrees of freedom ν, α is the nominal significance level (e.g., 0.05
Inflate both type 1 and 2 error ratesSlide22
Statistical analysis (continued)Option 2:Considering 0.85 as a random variable Define and Where
equivalence criteria is met if and , where is the 1-α quantile of
standard normal distribution,
α is the nominal significance level (e.g., 0.05).Slide23
Head-to-head approach for comparing precisions obtained in two labsHypothesis H0: σR≤σ
Hypothesis testing: small powers to reject H0 for small samples.
Check the point estimate
Receiving lab’s bias verificationImportant to check bias sincethe equivalence margin can be large enough such that 90% confidence interval in mean differences falls within the equivalence margin but the receiving lab’s mean fails the bias criteria. Slide25
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Ségalini A. Transfer of analytical procedures: A panel of strategies selected for risk management, with emphasis on an integrated equivalence-based comparative testing approach. Journal of Pharmaceutical and Biomedical Analysis. 56. 293– 303 (2011).16. Krause S. Validation of Analytical Methods for Biopharmaceuticals: A Guide to Risk-Based Validation and Implementation Strategies. PDA/DHI, River Grove, IL, ISBN-10: 1933722061 (2007).17. Satterthwaite FE. An Approximate Distribution of Estimates of Variance Components. Biometrics Bulletin. 2. 110–114 (1946).
18. Chen Y, Weng Y, Dong X, Tsong Y. Wald Tests for Variance-Adjusted Equivalence Assessment with Normal Endpoints. Journal of Biopharmaceutical Statistics (2016). http://www.tandfonline.com/doi/full/10.1080/10543406.2016.1265542.19. Okamoto M. Assay validation and technology transfer: Problems and solutions. Journal of Pharmaceutical and Biomedical Analysis. 87. 308– 312 (2014).Slide26
AcknowledgementDr. Yi Tsong, CDER/OBDr. Juhong Liu, CDER/OBPDr. Chikako Torigoe, CDER/OBPSlide27