Bioequivalence and Bioavailability Forum

Dear Samaya,

❝ Is there any possibility to get through by any means or study to be repeated?

By any means??
Well, your GMRs or T/R ratios itself are on lower side 84% (79.89-89%) and 86% (83-90%) so I don’t think that repeat study is going to be that helpful.

❝ Or we should go ahead with the reformulation??

From the Cmax and AUC data it is very clear that the rate of release and extent both are lesser. So the best approach should be to find a discriminatory dissolution condition and then plan your activities accordingly.

I assume it to be a BCS Class II molecule so why don’t you look into the reference characterization first (use of specific particle size range/use of surfactant etc.) along with API characterization (different polymorph different solubility).
Also is there any information available on saturation metabolism of the molecule? That might also provide you idea about what went wrong.

Hope this will give you some leads.

Agree with Bloom,

Better to reformulate since your T/R ratios are way too low.

Question:

❝ For Product X (IR tablets), full replicated BE study was performed on 56 volunteers under fasting state (EU submission).

Why did you run a full replicate study? Based on your data the drug doesn't look like a HVD (unless your pilot data suggested)

John

Dear Samaya,

❝ When data for first half subjects (N: 28) was analysed, we got following results.

❝ Cmax ratio: 87 (CI: 80.88-95.13)

❝ AUC ratio: 87 (CI: 81.85-94.18)

Did you plan for a two-stage design (which I understand is tricky for full replicate studies) ? If so, why proceed to the second stage though BE was demonstrated ? If not, then what was the reason for doing an interim analysis ? Was it planned in the protocol (with appropriate alpha-correction) ?

Hi Samaya,

❝ Power ~100

Irrelevant in BE. If you insist in post hoc power, your number is wrong. Do you really believe in obtaining ~100% power in a failed study? For C_max:

library(PowerTOST)
CLlo <- 0.7989
CLhi <- 0.8900
PE   <- sqrt(CLlo*CLhi)
n    <- 56
des  <- "2x2x4"
CV   <- CI2CV(lower=CLlo, upper=CLhi, n=n, design=des)
cat(sprintf("%s %.2f%%", "Power:", 100*power.TOST(CV=CV, n=n, theta0=PE,
                                                  design=des)), "\n")
Power: 48.43%*

Or did you assume a PE of 100%?

cat(sprintf("%s %.2f%%", "Power:", 100*power.TOST(CV=CV, n=n, theta0=1,
                                                  design=des)), "\n")
Power: 100.00%

That’s not a good idea.

❝ No outlier detected.

Irrelevant again (EU submission…).

❝ Is there any possibility to get through by any means…

What do you mean by “get through by any means”? Do you want us to teach you dirty tricks?

First of all please answer Ohlbe’s questions. Why didn’t you stop the study after the interim analyis? Or did you dose all 56 subjects already and just wanted to “have a look”?
For me your evaluation smells of Pocock’s group sequential design: fixed sample size N, one interim analysis at N/2, parallel groups, normal distributed data, known variance, testing for a significant difference.
But you have a different cup of tea: full replicate cross-over, lognormal data, unknown variance, testing for equivalence by CI-inclusion.

Which α did you use?

• If 0.05 (90% CI) – wrong (patient’s risk inflated to ~8.1%)
  
• If 0.0294 (94.12% CI), did you explore the overall patient’s risk before the study? If yes, please enlighten us how you did that.

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• Rule of thumb: If one of the confidence limits is exactly at the border of the acceptance range and the other CL = 1 (i.e., PE 0.8944 or 1.1180), power in any design and any sample size is ~50%. Try it.