Friday, 23 April 2010

MTS Recommends... Reduced Robustness Testing for Analytical Methods

Reduced method robustness testing of analytical methods driven by a risk-based approach’ by Phil Borman, Marion Chatfield, Patrick Jackson, Alice Laures, and George Okafo in Pharmaceutical Technology Europe, Volume 4, Issue 22

This article details an interesting approach to reducing the number of factors investigated during a robustness study. Basically the factors which are chosen to be studied in an experimental design are further reduced by performing a risk analysis of each factor and combining factors where possible. As we hear more and more about addressing method robustness earlier in the method development process as part of Quality by Design, more efficient approaches to performing these studies become desirable.

The thing that stands out for me about this article, and indeed all discussions of robustness, is that although the statistics and design of experiments is very important (and I think this article describes these quite well) the most important contribution to robustness testing comes from the experience of the analyst who understands the actual effects of the factors involved and can identify those which are most important. The risk assessment performed in the case study described in this article was performed by ‘GC experts’. Without this contribution the studies cannot produce meaningful results.


  1. You're right Oona, input from method/technique experts and stats experts is vital to the success of any method risk assessment/study of this nature.

    In order to maximise the method understanding we can generate compared to the resource we put in we must move away from the old:
    -statisician receives a work package
    -supplies an experimental regime
    -receives experimental data
    -provides a report.
    To a much more inclusive one team approach with everyone providing their own insight to make the studies work as slickly as possible.

  2. Reduction of method factors by combining them (which the authors advocate) is an interesting approach which will certainly help reduce resource. Care must be taken however not to 'group' together factors that may have opposite effects on a given response as this would result in factor effects being masked. To be fair the authors point out "Scientific judgement, therefore, must be used when allocating combinations to ensure that the effects of the parameters do not cancel each other out....", ..."As a rule, parameters can be combined if they have the same direction of effect (e.g., if increasing the pH and the % organic will increase the resolution) or if they affect different method performance characteristics."

    Out of interest the same authors have just written a paper on "Method ruggedness studies incorporating a risk based approach: A tutorial" in Analytica Chimica Acta. This is how I found this blog