Bioequivalence and Bioavailability Forum

Hi Angus,

❝ We have early partial AUC data from a completed study and the CV (%) for the early partial AUC intrasubject between formulation variance is 30.5%.

If this was a nonreplicated study CV_intra is pooled from CV_WR and CV_WT.

❝ The reason we have measured the partial AUC is that we are required by the FDA to submit BE data on the partial AUC metric when performing a pivotal BE study.

Zolpidem, MPH?

❝ The question arises if we should use a replicate study design.

Are you thinking about RSABE? If CV_intra ~ CV_WR ~ CV_WT with 30.5% you will save something in the sample size (example will follow).

❝ I am thinking "yes"…

Well, would be nice if the FDA accepts RSABE for this drug.

❝ …and thinking in terms of a full replicate design comparing test and reference formulations.

Nothing tells you more about the performance of formulations than a fully replicated design. I like it.

❝ I am thinking in terms of estimating sample size and power. Are there Tables available for estimating the sample size and power of such study designs.

Only one paper.* You need simulations, since there is no explicit formula for power of the mixed pro­ce­dure (no scaling for s_WR <0.294, scaling ≥0.294, T/R within 0.8–1.25). The convergence is slow (the 10,000 sim’s of the paper are to few). In the meantime the two Lászlós themselves recommend the freeware R / package PowerTOST.

❝ I have information here only for nonrepliciate BE study designs.

OK, you can play around with what you got.

• First let’s try unscaled ABE, CV 30.5%, T/R 0.90, 90% power, TRTR|RTRT:
  require(PowerTOST)
sampleN.TOST(CV=0.305, theta0=0.9, targetpower=0.9, design="2x2x4", details=F)
  You should get:
  +++++++++++ Equivalence test - TOST +++++++++++
            Sample size estimation
-----------------------------------------------
Study design:  2x2x4 replicate crossover
log-transformed data (multiplicative model)

alpha = 0.05, target power = 0.9
BE margins        = 0.8 ... 1.25
Null (true) ratio = 0.9,  CV = 0.305

Sample size (total)
 n     power
56   0.902900
  
  
• Now RSABE with FDA’s method:
  require(PowerTOST)
sampleN.RSABE(CV=0.305, theta0=0.9, targetpower=0.9, design="2x2x4", details=F)
  You should get:
  ++++++++ Reference scaled ABE crit. +++++++++
           Sample size estimation
---------------------------------------------
Study design:  2x2x4 (full replicate)
log-transformed data (multiplicative model)
1e+05 studies for each step simulated.

alpha  = 0.05, target power = 0.9
CVw(T) = 0.305; CVw(R) = 0.305
Null (true) ratio = 0.9
ABE limits / PE constraints = 0.8 ... 1.25
Regulatory settings: FDA

Sample size
 n    power
44   0.90338
  

Why do we need only 44 subjects for RSABE when we need 56 for ABE? Since with a CV of 30.5% we are almost at exactly the borders of scaling, i.e., we have a ~50% chance that in the actual study the CV will be higher. More scaling, higher power, less subjects.

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• Tóthfalusi L, Endrényi. Sample Sizes for Designing Bioequivalence Studies for Highly Variable Drugs. J Pharm Pharmaceut Sci. 2011;15(1):73–84. open access.