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I would very much appreciate your feedback on the interpretation of
the following regulatory issue regarding bioanalytical methods. It is
concerned to the approval of calibration curves during the
application of a validated method .
Supposing a calibration curve is prepared with 8 distinct
concentrations each one in duplicate.
If one looses both determinations of 2 of this 8 concentrations
without being LLOQ or ULOQ AND
one determination of, let's say, other 4 concentrations (the second
replica of each of these 4 concentrations still within the limits).
1) approve the calibration curve, since only 2 concentrations out of
8 were lost and there is still at least one replica "validating" the
other 6 out of 8 concentrations.
2) reject the calibration curse, since, at all, 8 of the 16
determinations were lost, no matter I still have at least one
determination/replica within 15% for 6 out of 8 concentrations of the
Please, have in mind this situation was not found during the
validation of the analytical method, but in a batch/analytical run
during the application of a validated method to routine drug
Thanks in advance,
Cartesius Analytical Unit
(from Guidance for Industry - Bioanalytical Method Validation - FDA -
CDER - May 2001)
section IV - C - 2: Calibration Curve/Standard Curve/Concentration-
The simplest model that adequately describes the concentration-
should be used. Selection of weighting and use of a complex
regression equation should
be justified. The following conditions should be met in developing a
- <= 20% deviation of the LLOQ from nominal concentration
- <= 15% deviation of standards other than LLOQ from nominal
At least four out of six non-zero standards should meet the above
criteria, including the
LLOQ and the calibration standard at the highest concentration.
standards should not change the model used.
On section VI - Acceptance Criteria for the Run
Matrix-based standard calibration samples: 75%, or a minimum of six
back-calculated (including ULOQ) should fall within +/-15%, except
for LLOQ, when it
should be +/-20% of the nominal value. Values falling outside these
limits can be discarded,
provided they do not change the established model.
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