vezz
☆    

Erba (CO), Italy,
2019-02-13 07:35

Posting: # 19903
Views: 548
 

 AUC <5% of TEST geometric mean AUC [Outliers]

Several years ago I asked a question on a very similar issue and I think that most of the considerations made at that time applies also to the case that I would like to bring to your attention today. However, the situation is not exactly the same and in the meanwhile you may have gained some useful experience in the application of the EMA guideline on BE.

In a bioequivalence study on an orally inhaled product a case of AUC <5% of the geometric mean AUC was found for the test drug. At the time of the data review it was decided to exclude from the statistical analysis the data from this treatment period. Now the EMA is raising some concerns on this approach.

Of course, we know that the EMA guideline (section 4.1.8) allows for similar exclusions only for the reference product. However, with inhaled products it is not unusual to encounter this kind of issues, often due to a poor inhalation technique of the subject (despite the trainings provided during the study). This situation is very different compared to, say, the intake of a tablet.

I would like to ask you if you have some experiences or considerations to share on this topic.

Thank you very much for your help!

Kind regards,

Stefano
ElMaestro
★★★

Denmark,
2019-02-13 08:36

@ vezz
Posting: # 19904
Views: 474
 

 AUC <5% of TEST geometric mean AUC

Hi vezz,

» In a bioequivalence study on an orally inhaled product a case of AUC <5% of the geometric mean AUC was found for the test drug. At the time of the data review it was decided to exclude from the statistical analysis the data from this treatment period. Now the EMA is raising some concerns on this approach.
»
» Of course, we know that the EMA guideline (section 4.1.8) allows for similar exclusions only for the reference product. However, with inhaled products it is not unusual to encounter this kind of issues, often due to a poor inhalation technique of the subject (despite the trainings provided during the study). This situation is very different compared to, say, the intake of a tablet.

I am involved in a lot of inhaled trials. Most of them fail for a variety of reasons, but almost always on the PE not the width of the CI.
When you say your observation is most likely caused by bad technique then you maybe right but you may also be wrong. Most companies provide a lot of training, much more than patients will get when they after approval get their medication at a pharmacy. If they during the trial can't inhale properly then obviously this problem goes a bit beyond training and the subject her/himself.
If the technique is wrong during the actual dosing then it would be OK to have a checkbox on the CRF to note this and then to exclude the subject per protocol. Do that next time, please.

I can't fix the actual issue for you. But please check the Cmax vs the bioanalytical LLOQ. If the ratio is less than 20 then you may be able to exclude either the period or even the whole subject regardless of treatment. there is wording in the EMA guideline for this. It is not all to uncommon to see this done for OIPs and Nasals and it is widely accepted because of the wording of the guideline. Let me know how you fare with this idea.

if (3) 4

x=c("Foo", "Bar")
b=data.frame(x)
typeof(b[,1]) ##aha, integer?
b[,1]+1 ##then let me add 1


Best regards,
ElMaestro

“(...) targeted cancer therapies will benefit fewer than 2 percent of the cancer patients they’re aimed at. That reality is often lost on consumers, who are being fed a steady diet of winning anecdotes about miracle cures.” New York Times (ed.), June 9, 2018.
vezz
☆    

Erba (CO), Italy,
2019-02-13 09:08

@ ElMaestro
Posting: # 19905
Views: 450
 

 AUC <5% of TEST geometric mean AUC

Hi ElMaestro,

thank you for your prompt reply.

The information on inhalation manoeuvre (performed correctly/incorrectly, if not why, etc.) was collected in the CRF and some data were excluded due to a bad technique. For the case under discussion, no issues were reported.

Could you please expand a little bit on your suggestion related to Cmax? I think you are referring to the following recommendation included in section 4.1.7 (Bioanalytical methodology) of the EMA guideline: "The lower limit of quantitation should be 1/20 of Cmax or lower, as pre-dose concentrations should be detectable at 5% of Cmax or lower". However, it is not completely clear to me how to justify the exclusion of the period based on this statement.

Thank you again for your help!

Kind regards,

Stefano
ElMaestro
★★★

Denmark,
2019-02-13 11:09

@ vezz
Posting: # 19906
Views: 437
 

 AUC <5% of TEST geometric mean AUC

Hi vezz,

» Could you please expand a little bit on your suggestion related to Cmax? I think you are referring to the following recommendation included in section 4.1.7 (Bioanalytical methodology) of the EMA guideline: "The lower limit of quantitation should be 1/20 of Cmax or lower, as pre-dose concentrations should be detectable at 5% of Cmax or lower". However, it is not completely clear to me how to justify the exclusion of the period based on this statement.

When it comes to justifying exclusions, nothing is clear :-)
I think I will abstain from trying to give a recipe that always works. If I could, I most definitely would. I think you need to review the protocol, the bioanalytical plan+report, and the SAP. It is all about wording. Perhaps something is stated somewhere about qualifying subject profiles?

Note also: In BE we usually only evaluate for stats those subjects who contribute at least one Test measurement and one Ref measurement. So in a 222BE trial, if one subject's period is lost, the entire subject is dropped from stats.

I have rather frequently discussed the matter with regulators. There seems to be no widely agreed consensus on the implementation of the guideline (just like the stereoselective bioanalysis :lol2:).

if (3) 4

x=c("Foo", "Bar")
b=data.frame(x)
typeof(b[,1]) ##aha, integer?
b[,1]+1 ##then let me add 1


Best regards,
ElMaestro

“(...) targeted cancer therapies will benefit fewer than 2 percent of the cancer patients they’re aimed at. That reality is often lost on consumers, who are being fed a steady diet of winning anecdotes about miracle cures.” New York Times (ed.), June 9, 2018.
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