The study showed the method was robust for the responses collected (the method characteristics in Table 3). There was one control identified following the RMR study that was required to minimise variability in the level observed
for one of the impurities (B). One of the factors that was most responsible for this variation was the combined injection
temperature/initial oven hold time group factor (Group 4). The most likely cause of this variation is the temperature of the
injector (240–260 °C) rather than the length of time the oven is kept at the minimum temperature of the gradient (140 °C)
because the impurity is known to be thermally labile.
The plot in Figure 7 visually displays the predicted effect of increasing the temperature of the injector (Group 4) combined with the effect of
the other most significant factor (the column - Group 5). Therefore, a tighter control was placed around the injector temperature
to minimise the variability seen for this impurity prior to proceeding to full method validation. Without the careful combination
of factors that preceded this study, it could have been harder to deduce which parameter was the cause of the variability.
Figure 7: predicted effect of Group factors 4 and 5.
Despite being considered lower risk, a difference was also noticed between the two columns tested Figure 7) with some of the impurities being observed at slightly different levels on the two columns. This illustrates the importance
of including parameters in robustness assessment - even if assessed as lower risk. As the age of the column and the column
batch were grouped, it is impossible to identify whether the observed difference is derived from column batch-to-batch variability
or the performance of a column varying with time. It was recommended that this issue was to be further assessed as part of
a method ruggedness study.
The use of risk‑based assessment tools to identify, score and prioritise method parameters coupled with RMR is a novel adaptation
of already established techniques. Reduced method robustness provides an effective means of reducing the number of experiments
required to assess method suitability and performance. This approach has been successfully applied to assess robustness of
a GC‑FID method used for the analysis of a key pharmaceutical starting material. The results from this study allowed the analyst
to identify key method parameters by performing 16 experiments instead of the usual 32 or 40. This simple approach can easily
be applied to any analytical method and provides an analyst with a checkpoint for progression of analytical methods in drug
development. If all of the important parameters are accommodated and testing shows the method is robust then a further robustness
study may not be needed when proceeding to full validation.
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The authors wish to thank Luca Martini, Anna Nicoletti and Jill Trewartha who were involved in the GC–FID RMR study mentioned
in this article.