Reduced method robustness testing of analytical methods driven by a risk-based approach - Pharmaceutical Technology

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Reduced method robustness testing of analytical methods driven by a risk-based approach
A novel approach for assessing method robustness is described that uses risk-based assessment tools to identify, score, prioritise and then group method parameters. These parameters are then studied using reduced fractional factorial designs (termed Reduced Method Robustness) to evaluate the suitability of analytical methods prior to full validation. This simple approach helps to identify high risk method parameters earlier and can potentially save resource later in the development process.


Pharmaceutical Technology Europe
Volume 4, Issue 22

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.


Figure 7: predicted effect of Group factors 4 and 5.
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.

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.

Conclusions

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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Acknowledgements

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.


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