Bioequivalence and Bioavailability Forum

Dear All!

Don't confuse the usage of "expected power" here by Helmut with the functions in package PowerTOST which estimate the expected power and / or sample size based on expected power, some sort of Bayesian power, taking into account the uncertainty of an observed point estimate of T vs. R and / or the uncertainty of an observed CV.

See f.i. ?exppower.TOST and references given in that man page.

Daer Helmut,
Dear Colleagues,

It even hurts less (or doesn't hurt at all) in case of scaled bioequivalence.
Last chunk of code modified:
CV  <- 0.42
#des <- "2x2x4"#scaled+mode subjects included for dro-outs
res <- sampleN.scABEL(CV=CV, design=des, print=FALSE)
n   <- res[["Sample size"]] + 8 #Let's assume we compensate for possible drop-outs
pwr <- res[["Achieved power"]]
wo  <- as.integer(substr(des, nchar(des), nchar(des)))-1
el  <- n
for (j in 1:wo) {
  el <- el*(1-dor/100)
}
cat("Study started with", n, "subjects (expected power",
    sprintf("%.1f%%).", 100*pwr),
    "\nStudy ended with", as.integer(el), "eligible subjects (expected power",
    sprintf("%.1f%%).", 100*power.scABEL(CV=CV, design=des, n=el, regulator='EMA')), "\n")

#Study started with 38 subjects (expected power 81.9%).
#Study ended with 32 eligible subjects (expected power 84.1%).


Best regards, VStus

Hi nobody,

❝ ❝ Nobody knows how to deal with this story.

❝

❝ ... sorry, no.

Didn’t mean you. Nobody ≠ nobody (case sensitive).

❝ […] At least you need to write a new, Chapter 2.5-like overview on the pk, pharmacology, safety to make the assessor sleep well next night after reading your application for a generic. On the other hand, assessors for originator's MAs have a much, much better sleep (better sleeping pills?), in my experience...

Well, maybe assessors should take stimulants instead of sleeping pills. Les Benet’s claim “HVD(P)s are safe drugs” was based on two arguments:

1. If an HVD(P) does not have a flat dose-response curve, there would have been safety/efficacy issues in phase III and the originator’s product would not get approved.
   • Correct, if the originator does not ‘forget’ to report serious AEs (remember Boeh­ringer’s dabigatran…).
2. No safety/efficacy issues were found in clinical practice.
   • OK, we all know that pharmacovigilance isn’t very sensitive and the system not well implemented in some countries. However, 32% of 222 novel therapeutics were affected by a postmarket safety event.*

Hence, I would rather say:

Reference-scaling for the innovator’s product

1. Should not be allowed when scaling up to the production batch size.
   
2. Should not be allowed for major formulation changes for a certain time after approval (e.g., five years).
   
3. If there were no relevant issues, it should be allowed.
   Easy for the FDA because the product-specific guidance is issued fast and updated regularly.
   Takes a while for the EMA, but some already exist (e.g., posaconazole).

Reference-scaling for a generic product

• Should generally be allowed (#3 for the innovator is applicable).

Just my two ¢ worth.

---

• Downing NS, Shah ND, Aminawung JA, Pease AM, Zeitoun J-D, Krumholz HM, Ross JS. Postmarket Safety Events Among Novel Therapeutics Approved by the US Food and Drug Administration Between 2001 and 2010. JAMA. 2017; 317(18): 1854–63. doi:10.1001/jama.2017.5150.

Hi M.tareq,

❝ ❝ while ago I reviewed a manuscript exploring the pros and cons of TSDs vs. ABEL. Was very interesting and I hope that the authors submit a revised MS soon.

❝

❝ Can i get the link or the name of the paper when the authors submit it ?

If the revised MS will get accepted and published, for sure.

❝ In General, if a drug isn't known to be HVDP and by definition of HVDP that, they have wide therapeutic index yet the CRO/Sponsor went with replicate design.

Any replicate design can be assessed for conventional ABE. If the sponsor intends to try RSABE it has to be stated in the protocol. Might be worthwhile in borderline cases (CV_wR ~30%).

❝ How the assessor would consider that? mean if the published literature or pilot study suggest low CV of the reference /test product yet the cro/sponsor went with replicate design?

I think you are mixing two things up. Replicate designs are applicable for ABE as well. If the sponsor aims for RSABE – in contrary to data in the public domain (few exist!) which suggests low variability – as a regulator I would be cautious. Note that regulators see a lot of studies. Was the CV_wR caused by poor study conduct? That’s a gray zone.

Pilot studies can be misleading. The estimated CV is not carved in stone. Try this one:

library(PowerTOST)
design <- "2x2x4" # any of "2x2x4", "2x2x3, "2x3x3"
n   <- 16      # total sample size
CV  <- 0.25    # CV as a ratio
df  <- eval(parse(
         text=known.designs()[which(known.designs()$design==design), "df"],
         srcfile=NULL)
       )       # get the degrees of freedom
CL <- CVCL(CV=CV, df=df, side="2-sided")
cat("\ndesign of pilot:", design,
    "\nsample size    :", n,
    "\nestimated CV   :", sprintf("%.2f%%", 100*CV),
    "\nCL of the CV   :", sprintf("%.2f%% to %.2f%%", 100*CL[[1]], 100*CL[[2]]),
    "\n\n")

Gives:

design of pilot: 2x2x4
sample size    : 16
estimated CV   : 25.00%
CL of the CV   : 20.60% to 31.87%

Might be highly variable, right?

❝ Like abusing the use of scABE ?

Maybe.

❝ ❝ Nobody knows how to deal with this story. Regulations ≠ science. Not required by the FDA…

❝

❝ Agree, yet i read an ANDA submission -can't find the link now- where the sponsor detected outliers based on T/R ratio of each subject and redosing of such subjects along with other subjects who exhibited normal pkp profile; though it was after reviewing with FDA regulator.

Yes, that’s an old story (“re-dose the suspected outliers – both with T and R – together with at least five ‘normal’ subjects or 20% of the sample size, whichever is larger”). IMHO, the individual T/R-ratios are not a particularly good idea for assessing outliers (ignoring period effects).

❝ Another published paper about ibandronic acid https://www.ncbi.nlm.nih.gov/pubmed/24756462 …

Highly variable like all bisposphonates. Can’t reproduce the estimated sample. I got 132 and not 138.

❝ … the sponsor/CRO stated the definition of outliers using studentized residuals and boxplot to eliminate subjects with values away from the boxplot by more than 3 IQR.

Not surprising since Susana Almeida was co-chair of the Bioequivalence Working Group, European Generic and Biosimilar Medicines Association (EGA). At the joint EGA/EMA workshop (London, June 2010) I – as a joke! – suggested boxplots, which are nonparametric by nature. The EMA hates nonparametric statistics. But the panelists welcomed the “idea” and my joke made it to the Q&A document. My fault. Never presume humor. Now it’s carved in stone.

This study demonstrated why not accepting reference-scaling for AUC might not be a good idea. Since the sample size is based on AUC, products with extreme T/R-ratios of C_max would pass ABEL due to the high sample size. Given the results of this study: 90% chance to pass with T/R as low as 84.46% or 80% chance to pass with T/R 82.95%. Technically (i.e., according to the GL) nothing speaks against that. But do we want such products on the market?
Wasn’t the case in this study (T/R for C_max 102.56%), but…
BTW, for the FDA the study could have been performed in just 45 (!) subjects – without risking extreme C_max-ratios.

❝ My point is, as your kindly said, it's best to state in the protocol how to deal with outliers especially regarding variability estimation and proving/showing to the assessor the reasons for excluding such outlier(s) from study or reviewing it with the regulator before submission of the data?

I would say so.