Robustness testing quantitative factors

A tentative list of factors to be evaluated in robustness testing for sample preparation is presented in Table 9. Additional factors can be added. The limits for the factor levels are typical and should be considered as proposals. Responses to be considered are usually related to the quantitative performance of the method. 

In robustness tests, usually the factors are examined at two extreme levels.For mixture-related and quantitative factors, these levels usually are chosen symmetrically around the nominal. The range between the extreme levels is selected so that it represents the variability that can occur when transferring the method.However, specifications to estimate such variability are not given in the ICH guidelines. Often the levels are chosen based on personal experience, knowledge, or intuition. Some define the extreme levels as nominal level +x%. However, this relative variation in factor levels is not an appropriate approach, since the absolute variation then depends on the value of the nominal level. Another possibility is to define the levels based on the precision or the uncertainty, with which... 

In reference 88, response surfaces from optimization were used to obtain an initial idea about the method robustness and about the interval of the factors to be examined in a later robustness test. In the latter, regression analysis was applied and a full quadratic model was fitted to the data for each response. The method was considered robust concerning its quantitative aspect, since no statistically significant coefficients occurred. However, for qualitative responses, e.g., resolution, significant factors were found and the results were further used to calculate system suitability values. In reference 89, first a second-order polynomial model was fitted to the data and validated. Then response surfaces were drawn for... 

In this chapter, the possibilities to set up and treat the results of a robustness test were reviewed (Sections I-VIII). Robusmess usually is verified using two-level screening designs, such as FF and PB designs. These designs allow examining the effects of several mixmre-related, quantitative, and qualitative factors, on one or several responses, describing either quantitative and/or qualitative aspects of the analytical method. 

The responses of main interest are different during both applications. In optimization, responses related to the separation of peaks (Section 6.2) are modelled. In robustness testing the quantitative aspect (the content determination) of the method is of most interest, since it is the one that should remain unaffected by small variations in the variables. Responses related to the separation (resolution, relative retention) or describing the general quality of the chromatogram (capacity factors, analysis times, asymmetry factors, and column efficacy) are often also studied. As recommended by the ICH guidelines the results of a robustness test can be used to define system suitability test limits for some of the responses [82]. 

In robustness testing, the extreme levels are most frequently chosen symmetrically around the nominal for mixture-related and quantitative factors. However, for some factors, an asymmetric interval might better represent the reality or better reflect the change in response occurring. A first example is the capillary temperature. Suppose a capillary temperature of 15 °C is prescribed. Symmetric levels, selected based on uncertainty are, for instance, 10 °C and 20 °C. However, many cooling systems do not allow temperatures of more than 10 °C below room temperature therefore, 10 °C may not be attained accurately by the instrument. The lowest extreme level could then be taken equal to the nominal (15 °C). 

The four factors in Table 2.4 were selected from a robustness test on a CE method to determine rufloxacin hydrochloride in coated tablets (29). AU factors were quantitative (A-D) and their extreme levels are situated symmetrically around the nominal. 

The robustness-test of a quantitative off-line OPLC assay-procedure was recently reported (89). The test was performed by fractional factorial design and evaluated by half-normal probability plot. The effects of seven factors were investigated on two levels. The method was found to be robust. 

Further discussion of method validation can be found in Chapter 7. However, it should be noted from Table 11 that it is frequently desirable to perform validation experiments beyond ICH requirements. While ICH addresses specificity, accuracy, precision, detection limit, quantitation limit, linearity, and range, we have found it useful to additionally examine stability of solutions, reporting threshold, robustness (as detailed above), filtration, relative response factors (RRF), system suitability tests, and where applicable method comparison tests. 

Prior to performing a formal validation, the analytical chemist should have performed some prevalidation during method development. The expectation is that a well-developed HPLC method should subsequently be validated with no major surprises or failures. Prior to validation, specificity and some degree of robustness should be demonstrated. In addition, some form of system suitability criteria will have been established. System suitability evaluates the capability of an HPLC system to perform a specific procedure on a given day. It is a quality check to ensure that the system functions as expected and that the generated data will be reliable. Only if the system passes this test should the analyst proceed to perform the specific analysis. System suitability can be based on resolution of two specified components, relative standard deviation, tailing factor, limit of quantitation or detection, expected retention times, number of theoretical plates, or a reference check. 

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