Helmut, thank you for the detailed response, examples in R, and references! I put the answers to the questions below (I tried to change color on responses, but not sure if that worked). Thanks. Sveta

Which PE did you find in the prior study? Its sample size would be helpful as well.

• This is more a hypothetical example, so really not known

Correct – if you are a believer of the ‘carved in stone’ approach (i.e., that in the planned study the CV will be exactly 23% and the PE exactly 1). I suggest to have a look at Example 3 of the ABE-Vignette of the R package PowerTOST to reconsider your assumptions. See also there (slide 8 and followings).

What do you mean by ‘subsequently’?

• ‘Subsequently’ here refers to the next study on somewhat different population (where we expect smaller value of CV), with higher order of crossover

What is the purpose of the study – dose proportionality? If yes, that’s another pot of tea.

• The purpose is to show dose-normalized equivalence

Rather strong assumptions, right?

• We actually expect a smaller value of %CV, not sure on GMR though

It depends whether you want to show dose-normalized equivalence (i.e., strict dose normality) or dose proportionality by the power model E[Y]=α⋅DβE[Y]=α⋅Dβ.

Say we have four formulations (A, the reference R, B, and C) and three dose levels (x, y, z), where A = R = x, B = y, C = z.

You randomize subjects either to the Latin Square ARBC|RBCA|BCAR|CARB or any one of the six Williams’ designs ACBR|RBCA|BARC|CRAB, ARBC|RCAB|BACR|CBRA, ACRB|RABC|BRCA|CBAR, ABRC|RACB|BCAR|CRBA, ABCR|RCBA|BRAC|CARB, ARCB|RBAC|BCRA|CABR.

• If you want to assess dose-normalized equivalence, follow the ‘Two‐at‐a‐Time Principle’, i.e., perform pairwise comparisons whilst excluding the others.1 Do not use a pooled ANOVA because it may give biased estimates and/or inflate the Type I Error.2,3

That means your first scenario is correct (you estimate the sample size like a 2×2×2 crossover) and get three incomplete block designs to evaluate: A ↔ R, xB/y ↔ R, and xC/z ↔ R.

If you insist in a pooled ANOVA for any reasons, you end up with 20 subjects as well (though with slightly higher power due to the higher degrees of freedom 3n–6 compared to the n–2 in the 2×2×2):

library(PowerTOST)

x <- data.frame(design = c("2x2x2", "4x4"), n = NA, power.pct = NA)

x[1, 2:3] <- sampleN.TOST(CV = 0.23, theta0 = 1, design = "2x2x2",

print = FALSE)[7:8]

x[2, 2:3] <- sampleN.TOST(CV = 0.23, theta0 = 1, design = "4x4",

print = FALSE)[7:8]

x[3] <- round(100*x[3], 2)

print(x, row.names = FALSE)

design n power.pct

2x2x2 20 82.08

4x4 20 84.55

• This is what we are trying to do, so this is exactly the response I was looking for.

I used the following example in R with CV=1.04 and got similar overall SS from either 2x2 or 4x4, so then the overall SS will just need to be split to higher order crossover – about 67 subjects per sequence for 2x2 and about 34 per sequence for 4x4. I assume that if we had to do even higher order crossover, then we would follow similar approach. R does not support 5x5 for example, but if we had to use 5x5 it would be about 26 subjects per sequence, it seems to be,is it correct?

CV=1.04, design = 2x2:

sampleN.TOST(CV = 1.04, theta1=0.67, theta2 = 1.5, theta0 = 1.1, design = "2x2", print = FALSE, logscale=TRUE, targetpower=0.9)[7:8]

Sample size Achieved power

1 134 0.9029653

CV=1.04, design = 4x4

sampleN.TOST(CV = 1.04, theta1=0.67, theta2 = 1.5, theta0 = 1.1, design = "4x4", print = FALSE, logscale=TRUE, targetpower=0.9)[7:8]

Sample size Achieved power

1 132 0.9008229

• if you want to assess dose proportionality, the acceptance range depends on the dose-range (the wider the dose-range the narrower it will be). For the sample size estimation you need the dose-range and an assumed slope of the power model β. Furthermore, it depends whether you plan a confirmatory4 or exploratory5 study.]]>

Dear All,

What is the Bioequivalence Acceptance criteria for in-vitro binding study of Colesevelam for EMA submission?]]>

:ok:]]>

Hi Sveta,

Which PE did you find in the prior study? Its sample size would be helpful as well.

Correct – if you are a believer of the ‘carved in stone’ approach (

`PowerTOST`

to reconsider your assumptions. See also there (slide 8 and followings).What do you mean by ‘subsequently’?

What is the purpose of the study – dose proportionality? If yes, that’s another pot of tea.

Rather strong assumptions, right?

It depends whether you want to show dose-normalized equivalence (

Say we have four formulations (

`A`

, the reference `R`

, `B`

, and `C`

) and three dose levels (`A`

= `R`

= `B`

= `C`

= You randomize subjects either to the Latin Square

`ARBC|RBCA|BCAR|CARB`

or any one of the six Williams’ designs `ACBR|RBCA|BARC|CRAB`

, `ARBC|RCAB|BACR|CBRA`

, `ACRB|RABC|BRCA|CBAR`

, `ABRC|RACB|BCAR|CRBA`

, `ABCR|RCBA|BRAC|CARB`

, `ARCB|RBAC|BCRA|CABR`

.- If you want to assess dose-normalized equivalence, follow the ‘Two‐at‐a‐Time Principle’,
*i.e.*, perform pairwise comparisons whilst excluding the others.^{1}Do not use a pooled ANOVA because it may give biased estimates and/or inflate the Type I Error.^{2,3}

That means your first scenario is correct (you estimate the sample size like a 2×2×2 crossover) and get three incomplete block designs to evaluate:`A`

↔`R`

,*x*`B`

/*y*↔`R`

, and*x*`C`

/*z*↔`R`

.

If you insist in a pooled ANOVA for any reasons, you end up with 20 subjects as well (though with slightly higher power due to the higher degrees of freedom 3*n*–6 compared to the*n*–2 in the 2×2×2):

`library(PowerTOST)`

x <- data.frame(design = c("2x2x2", "4x4"), n = NA, power.pct = NA)

x[1, 2:3] <- sampleN.TOST(CV = 0.23, theta0 = 1, design = "2x2x2",

print = FALSE)[7:8]

x[2, 2:3] <- sampleN.TOST(CV = 0.23, theta0 = 1, design = "4x4",

print = FALSE)[7:8]

x[3] <- round(100*x[3], 2)

print(x, row.names = FALSE)

design n power.pct

2x2x2 20 82.08

4x4 20 84.55

- If you want to assess dose proportionality, the acceptance range depends on the dose-range (the wider the dose-range the narrower it will be). For the sample size estimation you need the dose-range and an assumed slope of the power model β. Furthermore, it depends whether you plan a confirmatory
^{4}or exploratory^{5}study.

- Schuirmann D.
*Two at a Time? Or All at Once?*Pittsburgh: International Biometric Society, Eastern North American Region, Spring Meeting; March 28–31, 2005. Abstract.

- European Medicines Agency, CHMP.
*Guideline on the Investigation of Bioequivalence.*London; 20 January 2010. Doc. Ref. CPMP/EWP/QWP/1401/98 Rev. 1/ Corr **.

- D’Angelo P.
*Testing for Bioequivalence in Higher‐Order Crossover Designs: Two‐at‐a‐Time Principle Versus Pooled ANOVA*. Rockville: 2^{nd}Workshop of the Global Bioequivalence Harmonisation Initiative; 15–16 September, 2016. Some of her slides in this post --> 18888.

- Smith BP, Vandenhende FR, DeSante KA, Farid NA, Welch PA, Callaghan JT, Forgue ST.
*Confidence Interval Criteria for Assessment of Dose Proportionality*. Pharm Res. 2000; 17(19): 1278–83. doi:10.1023/a:1026451721686.

- Hummel J, McKendrick S, Brindley C, French R.
*Exploratory assessment of dose proportionality: review of current approaches and proposal for a practical criterion.*Pharm. Stat. 2009; 8(1): 38–49. doi:10.1002/pst.326.

Hi Fabrice,

An example: The NDA of idelalisib. A lot of Phase I/II studies (women/men, Japanese/Caucasian, food effect, DDIs, renal/hepatic impairment) were performed in 6 (six!) to 12 subjects with early formulations.

There was also a study comparing formulations (n = 15, extremely underpowered for the x̃ CV of 29% in earlier studies). Some comparisons failed (

**2.5.2.2 What are the safety or efficacy issues, if any, for BE studies that fail to meet the 90% CI using equivalence limits of 80–125%?**

None. The exposure for the different drug products is similar.

Since idelalisib is in BCS II (where dissolution cannot predict

Hello,

I would like to obtain an advice on BE study sample size estimation.

Let’s say, we are planning a 2x2 crossover study for BE, and the intra-subject coefficient of variation for our test compound, from a prior study, is 23%. The assumed geometric mean ratio is 1.00, alpha is 0.05, equivalence limits are 80%-125%, and we desire power of 80%. These criteria result in an overall sample size of 20 subjects, or 10 per sequence.

Subsequently, we want to add 2 extra dose levels of our test drug, resulting in a 4x4 crossover trial. Let’s assume that %CV and GMR are not changing, as no further data is available. For this new scenario, is it appropriate to: 1) use the original sample size of N=20 and simply divide it over 4 sequences (5 per sequence), or 2) take the original per sequence sample size of 10 and multiply by 4 to get 40 subjects overall needed? In either case 1) or 2), is there a supporting reference you can provide?

Thanks,

Sveta

Edit: Category and subject line changed; see also this post #1 and #2 --> 16205. [Helmut]]]>

Thanks Helmut for your feed-back!

Indeed.

OK but then how to justify in the dossier the extrapolation of some early phase outcomes, e.g. a food effect or an efficacy and/or safety exposure signal... should these key findings be evaluated/demonstrated again with the final formulation?

Yep.

Thank you!]]>

Hi Fabrice,

Without digging into guidelines: No. What we have in Phase I/II is sometimes not what I would call a ‘formulation’ in the biopharmaceutical sense at all. Anything goes: Manually filled capsules, lab-scale tablet-presses, etc. Doesn’t matter because we are interested in PK (I) and safety (II). Once you move to phase III you are bound to cGMP (though still in pilot-scale). Only when you move from III to the to be marketed formulation, the applicable SUPAC guidance (IR, MR, SS) cut in and very likely you need a BE study.]]>

Dear All,

Sorry for the naive and quite basic question, but I did not succeed in finding a clear and definite answer in any of the FDA guidances :confused::

Is FDA requiring a formal BE demonstration for bridging the early phase with the Phase 3 formulations?

If so, what would be the required actions in case the Phase 3 formulation is not perfectly bioequivalent to the previously used formulation?

Many thanks in advance!]]>

Hi Dan-gium!

"gleicher Bioäquivalenz" - identical bioequivalence? REALLY?

It's just "cover-my-axx" for the primary doc to prescribe the cheapest/the generic the health care provider of the specific patient has a manufacturer contract for all its patients with. The wording is more than "a little offffff"... :-(

If you have a FENCE at the southern border, it's definitely Denmark. Otherwise it might be somethink else... :-D]]>

Ahoy my capt’n!

Quoting Charlie DiLiberti (2

*Ask ten physicians what bioequivalence is,
and eleven of them get it wrong.*

i nobody,

Isn't this ok?

I mean, yes the terminology may be a little off, but the doctor seems to be acknowledging that generic drugs may be equally safe and efficaceous.

The doctor you are quoting is light years ahead of the average doctor here in Denm....uhm...sorry I mean Belgium.]]>

...read this morning in a discharge report from hospital, below the Section "Medication":

" Selbstverständlich können auch Präparate gleicher Bioäquivalenz und Bioverfügbarkeit verwendet werden."

((emphasis on the "und" ("and"), please note!!1elf!!1!))

Is BE/BA really only relevant/understood in regulatory/scientific discussion? Is there anybody out there?

January blues here, going back to my work now...]]>

Hi Helmut,

Thanks for quick reply. I will contact Mr. Charlie DiLiberti for the same.

Dear All,

Can anybody have idea on this topic. Please help me.

Edit: Relax; see also this post #9 --> 16205. [Helmut]]]>

Hi GM,

Ask Charlie DiLiberti, the Chairperson of the SAAMnow Board (charliemontclairbe.com).]]>

Dear All,

First of all,

I have question in my mind from many days...How can we calculate sample size for a replicate study, which has more than two replicates for each treatments?:confused:

In recent days, I am working on In Vitro Permeation Test (IVPT) studies. These study designs are as same as Full replicate study design with more than two replicates for each treatment. Please see the draft guidance on acyclovir for further details.

I am trying to calculate the sample size based on Full replicate study sample size calculation. i.e., (2*sample size for replicate study)/no. of replicates required for IVPT study.

I saw one post by

Anybody in the forum who had knowledge on this topic, please help me.

Also, how to do power calculation for these studies?

Thanks in advance.]]>

Dear all,

we know that the FDA uses

THX to ElMaestro discovering another example. Though I attended the 4

Q1. What is a sample size to achieve 80% power to pass bioequivalence, assuming CV as 100%, T/R true ratio as 5%?

Q2. With a fixed sample size (n=1,000), what are maximum differences in FEV_{1} metrics which pass BE with 80% power?

To address questions 1–2;

- package ‘PowerTOST’ was used to estimate sample size using observed CVs (R ver 3.6.0)

- Sample size was calculated for reference and treatment group

Of note, Robert Lionberger (Director Office of Research and Standards, Office of Generic Drugs) told me that he visits the forum regularly. :thumb up:

- Liang Zhao (Division of Quantitative Methods and Modeling, ORS, OGD, CDER/FDA).
*FEV1 Based Bioequivalence Study for Inhaled Corticosteroids.*

Hi Pharma_88,

This is only acceptable if you

The idea behind is to assess whether you reached (pseudo-) steady state.* Hence, pre-doses on the mornings of day 5, 6, 7.

Similar.

- At last month’s 4
^{th}workshop of the Global Bioequivalence Harmonization Initiative (GBHI) in Bethesda there was a consensus that no statistical test should be performed. Give a table of results, geometric means / CV, and plots.

Dear Pharma_88,

No. The initial cohort is started with 3 patients. If you have 0 or 1 DLT, you enrol 3 more in the same cohort. If you meet the pre-defined criteria, the DMC will allow you to move to the next cohort. There you go through the same process: start with 3, if you have 0 or 1 DLT you add 3 more, and so on until you reach the highest tolerated dose or the highest dose defined in your protocol. All cohorts are treated equally, including the first one.

Two possible situations:

- the withdrawn subject was one of the first 3. If the other 2 patients each had a DLT, no need to replace him: you have reached your maximum tolerated dose anyway. If the other 2 had 0 or 1 DLT: you may replace the patient, if allowed by your protocol. Then look at the number of DLT in your 3 patients and see whether you can add 3 more;

- the withdrawn subject was one of the last 3. If your stopping criteria are met: no need to replace him. If they are not met, and if allowed by your protocol: you may replace him. Then the DMC will see whether you can move to the next cohort. But don't add 3 more to the same cohort (3 + 3 + 3).

Hello All,

A case of Steady state bioequivalence study in which dose is administered twice daily from Day XX to Day XX. Suppose Day 01 to Day 07 where on day 07 only morning dose will be administered. My question is what is the ideal pre-dose time points required to collect to get Cpd. As per guideline, at least 3 pre-dose samples are required to collect. But as we are administrating Drug twice daily, its is necessary to collect pre-dose for morning and evening both for few days i.e Day 01 (morning evening), Day 04 (morning evening) and Day 07 (Morning)?

Also suggest for t.i.d. administration.

Appreciate your valuable suggestion in this regards.

Edit: Category changed; see also this post #1 --> 16205. [Helmut]]]>