Bioequivalence and Bioavailability Forum

Hi sciguy!

❝ […] For exploratory PK for NDA where things like polymorphism haven't been figured out, maybe an IQR would be too restrictive and eye-balling would be the way to go.

Even for an NDA, you can see (sic!) polymorphism.

❝ Still, it would be nice to have a less subjective approach...

Think about the way we estimate λ_z in NCA. Except for the most simple cases (one compartment) automated methods fail more or less often – visual inspection is mandatory.

❝ Just to clarify on analyzing the neighbouring concentrations though, I guess you are referring to analytical variability. So neighbouring conc. are assayed as well and if they (and the suspected value) are all within some specified precision, we can rule out bioanalytical mess-ups?

Exactly. I can’t give you my entire SOP. Basic steps:

• Internal validation – similar to ICSR. If enough sample is available analyze everything in duplicate. If the neighbouring values (i.e., before/after the suspect) are within a certain range (generally ±20%), the repeated values are considered meaningful.
  
• Compare the measured value of the first batch to the one measured in the second batch; if the first value is ≥ ±2SD of the second one - reject it and use the second one.
  
• Get an estimate from the first batch: Linear interpolation between neighbours if increasing and log/linear if decreasing. If the estimate is ≤ ±2SD of the measured one in the second batch you have an even stronger justification for rejection.

To get the SD of the second batch: Have a look of the within-batch variability of calibrators from the method validation. Take a bracketing approach: Find the two concentrations where the suspect lies within. Use the larger CV. Calculate the SD as: measured concentration in the second batch × CV.

❝ ❝ The most nasty combination are studies of drugs with low within-subject variability (=small samples size) and high between-subject variability (e.g., polymorphism). Mix-up of a single sample in the area of C_max will blow the entire study. Guaranteed.

❝

❝ Oh yes, hope to not run into one of those!

Have a look at this one… before the new European guidelines were applicable. We were able to justify the mix-up in the clinical phase by looking at the subjects’ lab-values and comparing them to their pre- and post-study values. The anticoagulant was citrate, so only γ-GT and albumine could be measured in the biosamples. Luckily these two volunteers had different values. When I presented this example, the answer by a regulator was:

“We know that things like this happen. You should have expected it and powered the study accordingly.”

CV of C_max of this drug is ~15% (in my twelve studies and about the same number of studies published). Would have needed 98 instead of 12 subjects (CV 15⇒50%). Ethical?