Bioequivalence and Bioavailability Forum

Hi Fabrice,

❝ In one study (not a BE ) we have to analyze, one patient has all concentrations < LLOQ after one treatment.

❝ As this was not really expected, this case was specifically planned neither in the study protocol nor in the SAP. The SAP however specified that concentrations < LLOQ should be set to zero, which then resulted in C_max and AUC_last = 0.

Though this was not a BE-study, still keep the BE-GL (Section 4.1.8) in mind:

[…] subjects in a crossover trial who do not provide evaluable data for both of the test and reference products (or who fail to provide evaluable data for the single period in a parallel group trial) should not be included.

❝ […] other patients also show a very low concentrations profile (but with at least one or two concentrations > LLOQ).

Well, that’s not what I would call informative profiles (EMA’s term: “reliable estimates of peak and extent of exposure”). Canada’s HPFB/TGD once stated that two points qualify for AUC and one for C_max I’m not sure whether this really make sense. In PK modeling you could deal with censored data, but IMHO in NCA you would be reaching beyond meaningful boundaries.

❝ It therefore makes sense trying to keep these Cmax and AUC = 0 in the outcome of the statistical analysis, but of course, this information is being lost by the log-transformation: log(0)= .

I would exclude the subject and maybe the other subjects with only one or two concentrations as well.

❝ Different workarounds are possible:

❝ 1. Analyzing the data in the original scale without log-transformation

In principle yes. I’m not sure whether regulators would like that.

❝ 2. Imputing C_max and AUC_last by something positive. For C_max, one can think using LLOQ or LLOQ/2, but what for AUC_last?

Personally I don’t like these LLOQ or LLOQ/2 “methods”. In PK modeling it was shown that any of those don’t give better estimates compared to either keeping the values “missing” or work with censored data.

❝ Moreover, the results of the statistical analysis then become quite dependent of the selected imputation rule.

Correct. Slippery ground.

❝ 3. Applying a log(Param+d)- instead of a log(Param)-transformation, where d is a very small positive number. But again the statistical outcome is very dependent of the value chosen for d.

I have seen that in Canada. I don’t think any value of d could be reasonably justified.

❝ As it is the second time I face such a special case over the last few months, I would be very interested in having expert advices on the best (or less worse) practice to deal with it?

Doesn’t help you in the current case, but in the future I would try to improve the analytical method.