Bioequivalence and Bioavailability Forum

Hi ElMaestro,

❝ should we submit a dataset to EMA and suggest them to publish it along with a description (numerical example) of how exactly they wish to derive the decision?

Maybe better not only a data set but also a proposed method for evaluation.

❝ When FDA indicated that they were going in the direction of in vitro popBE for inhalanda and nasal sprays they published a dataset and showed exactly how to process the data to figure out the pass / fail criterion that satisfies the regulator.

I have a counter-example. This goody was recommended in Apr 2014 and revised in Jan 2020. Nobody knew how the FDA arrived at the BE-limits and why. Last month I reviewed a manuscript explaining the background. Made sense (based on lots of data of the originator) but for years it was a complete mystery.

❝ If EMA would do the same here we'd have all doubt eliminated.

Utopia. Notoriously the EMA comes up with unsubstantiated ‘inventions’ made up out of thin air and leaves it to us to figure out if and how they work. Examples?

• Rounded regulatory constant k = 0.760 and upper cap at CV_wR = 50% in reference-scaling,
  
• sequence(stage) term in TSDs,
  
• ‘substantial’ accumulation if AUC_0–τ > 90% of AUC_0–∞,
  
• for partial AUCs default cut-off time τ/2,
  
• comparison of C_ss,min for originators but of C_ss,τ for generics,
  
• …

❝ I think we need to know exactly:

❝ 1. Do we use nonparametrics or not?

Guess.

❝ 2. Do we use logs or not?

Logs? Possibly t_max follows a Poisson distribution.  I will try to compile data from my studies.  See Fig. 2 and Fig. 3 in the article.

❝ 3. Is the decision of 20% comparability based on a confidence interval or on something else?

Likely the former; made up out of thin air.

❝ 3a. If there is a CI involved, is it a 90% or 95% CI or something else?

90%.

❝ 4. Are we primarily working on ratio or on a difference?

IMHO, calculating ratios of discrete values with potentially unequal intervals is plain nonsense. Data are on an ordinal scale. Only (‼) allowed operations: addition, subtraction, ranking. Nothing else.

❝ 5. Is the bootstrap involved?

A possible approach but why?

❝ 6. How should we treat datasets from parallel trials, and how should we treat data from XO (i.e. how to handle considerations of paired and non-paired options)?

I would suggest the Mann–Whit­ney U test (parallel) and the Wil­cox­on signed-rank test (paired / crossover). Requires some tricks in case of tied observations (practically always) for the exact tests, e.g., function wilcox_test() of package coin instead of wilcox.test().

❝ My gut feeling is that they want nonparametrics for the Tmax comparability part (yes I am aware of the sentence).

I doubt it. Really.

❝

Actually, perhaps they just want the decision taken on basis of the estimates of medians and ranges from min to max?

I think so (see also Ohlbe’s post). However, that’s statistically questionable (politely speaking). See the updated article and hit F5 to clear the browser’s cached version.

❝ If we submit a dataset, let us make sure we submit one with ties (the one I pasted above had none).

It’s extremely unlikely that you will find one without…

I explored one of my studies. Ibuprofen 600 mg tablets, single dose, fasting, 2×2×2 crossover, 16 subjects (90% target power for C_max), sampling every 15 minutes till 2.5 hours. Re-sampled the reference’s t_max in 10⁵ simulations and applied the ‘≤±20% criterion’:

     median        re-sampled    med. diff (%)      pass.pct     
 Min.   :1.000   Min.   :1.000   Min.   :-50.000   Mode :logical 
 1st Qu.:1.375   1st Qu.:1.375   1st Qu.:-15.385   FALSE:34887   
 Median :1.500   Median :1.500   Median :  0.000   TRUE :65113   
 3rd Qu.:1.750   3rd Qu.:1.750   3rd Qu.: 18.182                 
 Max.   :2.500   Max.   :2.500   Max.   :100.000


Dashed lines 2.5 and 97.5 percentiles

Power compromised. In ≈35% of simulated studies the reference could not be declared equivalent to itself.
Bonus question: Which distribution? Skewness +0.627.

The generic tested in this study was approved 25 years ago and is still on the market. Any problems?

If you want to give it a try:

R <- c(1.25, 2.00, 1.00, 1.25, 2.50, 1.25, 1.50, 2.25,
       1.00, 1.25, 1.50, 1.25, 2.25, 1.75, 2.50, 2.25)

P.S.: Amazing that this zombie rises from the grave. See this post and this thread of June 2013…