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

RMR approach

Robustness studies typically utilise fractional factorial designs to meet validation requirements - although sometimes Plackett Burman designs are used.14 These designs help to estimate the effects of individual method parameters and all their interactions with each other. 15,16 Each parameter is varied over two levels: high or “level +1” and low or “level -1”, and the two levels of each parameter are then systematically combined to create the set of experiments. Full two-level factorial designs permit estimation of the effect of individual parameters and all their interactions. Although the maximum amount of information is be obtained, full factorial designs are not practical because of the elevated number of experiments to be performed. In fractional factorial designs, only a fraction of the full design is studied, which decreases the overall number of experiments and the statistical resolution because not all the single parameter effects or interactions are estimated independently. It is desirable to further reduce the number of experiments used for two reasons:

  • To enable the use of such studies to provide an early indication of robustness and a direction for further method improvement (if required).
  • To most effectively use resources for the demonstration of robustness.

The number of experiments in fractional factorial designs can be reduced in two ways, which can be combined:

  • Reducing the number of factors
  • Reducing the statistical resolution of the design.

Figure 1: Two ways of reducing the number of experiments (runs); illustration from DX7 software.
Figure 1, taken from the DoE software Design-Expert (DX7), shows how many experiments (runs, vertical axis) are required for various numbers of factors (horizontal axis) and for different statistical resolutions (explained later). It illustrates the two ways of reducing the number of experiments.

Reducing the number of factors

Prior to building the statistical design, a risk assessment is performed to help determine whether the number of factors and the resolution of the design can be reduced. A prioritisation matrix tabulates the method parameters and the method performance characteristics, and the impact of each parameter over these characteristics is assessed and scored. Different scoring scales can be used; in this paper, the following scale has been used: 1=very low impact, 3=slight impact, 5=possible impact, 7=likely impact and 9=strong impact.

The scores for each method parameter are then summed to give an importance score, which is used to rank each parameter with respect to risk.

The outcome of this prioritisation exercise determines whether any method parameters can be removed from the design, combined with other parameters, or should be included as a single factor. It also helps decide appropriate design resolution. An example of the prioritisation matrix is shown in the case study that follows.

The acceptability of removing a parameter partly depends on whether it is an early or final robustness assessment. If doubts remain as to whether or not a parameter should be removed, the parameter can be combined with other low‑risk parameters to determine whether these parameters studied together produce an effect (e.g., instrument‑related parameters with narrow ranges).


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