46
May/June 2013
Why is my Method Not Robust? Chromatography Today Help Desk
With the first edition of Chromatography Today Helpdesk, it was thought one of the most common issues, namely of non-robust methods, should be addressed. Why do assays keep falling over and what can be done to make them stable is a question that keeps many scientists and lab managers awake at night. One of the biggest issues that users face though is that the assay has been validated and submitted to a variety of regulatory authorities across the globe, and the cost implication of revalidation becomes prohibitive.
So how is it possible that assays can become unstable, what needs to happen in the method development part of the process to ensure that the assay is more stable, and what are the key experimental parameters to monitor? There are different approaches that can be employed here, and the one that follows is a suggestion that can be used explicitly or used as a general guideline.
When an assay starts to fall over, information is the key to understanding what the real problem is. The information that is gathered should include;
• Data from previous sample batches analysed • Data from the validation study • Data from the method development study
• Physiochemical properties of the compounds being analysed (if known)
• An understanding of what parameters have changed
In terms of the latter comment, some care has to be taken when interpreting this information. Sometimes “Why was my assay working?” may be a more relevant question than “Why is my assay not working?” There is a natural assumption that if an assay has been working for some time and then fails, that the cause of the failure is due to the one parameter that is being changed, and whilst this is usually correct it is not always so. An important issue here is that some assays are just not stable, so relatively small changes, which may be out of the control of the analyst or supplier, result in the failure of the assay. This is very akin to the problem faced by Edward Lorenz [1,2] when he developed his atmospheric models and discovered that
minute changes in the input parameters had a significant effect on the global weather system, the so called ‘butterfly effect’, Figure 1. In this case Lorenz observed that very small changes in the input parameters into a seemingly simple mathematical model for global weather, involving only three parameters, could have a dramatic effect on the final weather system. Lorenz stated that this was akin to a butterfly flapping its wings and causing a hurricane in another part of the world. The following problem exemplifies this scenario.
Figure 2 shows a real issue that separation scientists can experience. The original assay had been validated on one column and
Figure 1: The classic solution to the Lorenz equation, highlighting that small difference in the input parameters can have a significant effect on the output data.
subsequently the same column had been used to run the initial set of samples. The
Figure 2: Stability of the assay is lost which can be seen through the lack of retention time stability for peaks 1 and 4 (doxylamine and doxepin) Experimental conditions:
Mobile phase:A – 30 mM KH2PO4 (pH= 7), B – MeCN, Gradient : 10 to 80% B in 10 min, Flow rate: 0.2mL/min, Temperature: 30°C, UV Detection: 210nm, Injection volume: 5µL Analytes:
1. Doxylamine (B), 2. Hydroxyisophthalic acid (A), 3. Benzamide (N), 4. Doxepin (B) 5. Flavone (N), 6. Fenoprofen (A)
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