Method validation is the process of demonstrating that the performance characteristics of an analytical method meet the requirements of the intended use and extended range of interest. Although the parameters may vary depending on the type of the method to be validated, common validation elements that need to be considered are:
- Accuracy
- Precision (repeatability/intermediate precision)
- Specificity (also called Selectivity)
- Limit of Detection
- Limit of Quantification
- Linearity/ Range
- Robustness (also called Ruggedness)
It is important to remember that after determining the validation elements that are best suited to your method, the acceptance criterion for each study must be determined and approved. It is essential to have a full understanding of the method objectives and method requirements prior to commencing the study. In other words, you should fully understand the capabilities and limitations of the method before transitioning it from the development phase to validation.
There are numerous guidelines readily available on method validation. These guidance documents identify and describe typical validation elements to be used, based on the type of analytical method. In my opinion and experience, robustness (or ruggedness) is a key element that is oftentimes not thoroughly investigated or adequately assessed.
Robustness studies should challenge the method by making small but deliberate changes in order to determine what variances can be tolerated. The objective is to demonstrate the reliability of the method with respect to intentional variations in method parameters. It is best to understand the robustness of the method during development in order to set reasonable parameters, which can then be challenged during the validation study. Assessing the robustness in the final stages of validation involves risk (i.e. loss of time, money, and effort).
With that said, in order to effectively challenge the robustness of your method, you will have to determine what variances could occur during normal operation. Those variances could involve instruments, scientists, standards, reagents, columns, etc. If your method specifies a parameter range (e.g. flowrate, pH, incubation time, room temperature, etc.), then your robustness study should demonstrate that the method can optimally perform at the each end of the stated range.
Quite often, the results of a robustness study will indicate important limitations to the method that may not have necessarily surfaced without such a challenge. It is important to remember that all methods have limitations. Because a method does not perform optimally during a robustness study should not mean the validation is a failure. There is value to be gained by understanding of the tolerance of a method. When writing a validation protocol, be mindful of the types of slight variances that could possibly occur during the course of daily operation and be sure to include them as part of the robustness challenge. The validation of an analytical method should not only be designed to demonstrate suitability and reliability, but it should also be designed to define its limitations.