In today's blog article we’d like to deal with the role and application of confidence intervals during method validations.

For drug release, not only the presence of the active pharmaceutical ingredient in claimed concentration and potency is important but also a low amount of impurities. Such impurities can be, for example, production related. An example is biotechnological manufacturing of drugs (e.g. therapeutic proteins) using bacteria. In sterile, e.g. parenterally administered drugs, no germs are allowed to be present and additionally no bacterial "residues". These can be fever-inducing components of the cell membrane of certain bacteria, so-called endotoxins. They can be detected by various methods, one of which is the Limulus Amoebocyte Lysate (LAL) test.

In case of purity tests during method validation, it is necessary to determine the limit of detection (LOD, sometimes also called DL = detection limit) and the limit of quantification (LOQ), respectively. This can be done e.g. based on the calibration curve and is then called “calibration curve procedure” in the (German) literature.

Two examples

Let's start with an example. During validation of an analytical method the linearity should be evaluated. Therefore 5 concentrations with 3 replicates each are examined (in order to evaluate linearity and trueness in one approach). At the examination level corresponding to 120% test concentration, one value is considerably higher than the other two ones. However, even taking this value into account, the acceptance criterion can be met. But the question is what to do. Is it necessary to repeat the entire 120% concentration with all three replicates, are we allowed to exclude the conspicuous one and calculate the mean by using just the two good ones, or do we have to repeat the whole linearity experiment?