Bioequivalence and Bioavailability Forum

Dear Marko!

❝ our general approach for integration of peaks in chromatograms is one generic method per batch, no smoothing and "manual" integrations.

❝ However (as every analyst except regulators knows ), from time to time it is not so easy to integrate all chromatograms within the batch consistently with one generic method.

Well, it’s a cumbersome task to find integration parameters suitable for an entire batch, when you have problems in the lower range. I don’t get the point why such a procedure (trial & error!) should be “better” than manual reintegration.

❝ This time I will […] focus on the "type" of integration method-"raw" data or/and smoothing.

❝ Despite the fact that some agencies prefer the same integration method to be used for validation and study, our approach remains one method per batch.

❝ If we are lucky there is indeed the same method for validation and study, but in reality, we have to adjust certain parameters such as noise level, RT, peak width etc., but never apply smoothing (again historic regulatory tabu).

You are aware that you never use the raw signal of the detector? Without bunching the chromatogram (especially in LC-MS/MS) would not only look awful, but would be impossible to be integrated at all. Either there is an ion in the detector or not… See also this spring’s discussion at David’s PK-List.

❝ If we took a rather conservative/regulated approch, which tests should/could be performed to justify that smoothing (how much?) could be applied?

❝ I was thinking of reintegrating 3xA/P (data used for between run A/P and establishment of regression model) and then comparing the results (raw vs. smoothed data). Since I am far from a statistician expert, how to evaluate the comparison (t-test....)? IMHO, only the "classical bioanalytical" %nominal and %CV wouldn't be enough.

Stupid question: A/P? One possibility would be an approach similar to the one used in cross-validation, namely orthogonal regression. You can test the slope for # 1 and the intercept for # 0. It’s important that you don’t use simple linear regression, because the main assumption there are error-free regressors (x-values).

❝ Anyway, would such a procedure be sufficient for regulatory agencies to allow smoothing or no-smoothing within the same validation or study.

No idea.

❝ Does anyone have any real experience on this topic?

Not me.