Expert Insight: The future of method development is in Analytical Quality by Design, but which tools do we need to get there?

Watch this on-demand webinar to learn about AQbD flow and the benefits of using design of experiments software during chromatographic method development

10 Aug 2020

Dr. Mark Eggink, principal scientist at Byondis

Analytical Quality by Design (AQbD) will start to play a more predominant role in the analytical field. Implementing AQbD in your workflows can have several advantages, for example, it can make post-approval changes of an analytical method less complicated and time consuming.

An AQbD flow is built up of different parts, the most important being the analytical target profile (ATP), which defines the requirements of the critical quality attribute (CQA). From there, an analytical technique can be selected. Furthermore, the use of a design of experiment (DoE) approach is increasing in popularity in analytical method development, as it enables fast and efficient development of robust methods.

In this on-demand webinar, Dr. Mark Eggink, principal scientist, Byondis, will present on the analysis of an ATP, the AQbD flow, and the benefits of using DoE software in combination with separation software during method development of a size-exclusion chromatography method for a mAb/ADC.

Read on for highlights from the live Q&A session or register here to watch it any time that suits you.

Q: What Quality by Design software was used: DryLab, Fusion, or something else?

ME: We use the QbD’s Fusion Software from S-Matrix.

Q: With the pending ICH Q14 for post-approval change flexibility, are you also using ICH Q12?

ME: As ICH Q14 is still pending, we cannot use this right now. For changing analytical methods, we do not have that much flexibility. You cannot just change an analytical method post-change. Then, you need to file a change. Hence, we are using the Fusion software for this, which works in our case very nicely.

Q: What is the time period to revalidate the analytical method?

ME: You start revalidating a method when it's not working properly and if you generate bad quality data or are having a lot of issues with the method, you need to change the method. If you change the method, then you need to revalidate and that is how it works currently. With the new guideline, you can have most preferably some flexibility if you’re still keeping in your design space and still fitting your ATP.

Q: Could you clarify the difference between method design space and method robustness? How do you report method parameters if you want to include design space?

ME: The robustness is typically a little bit smaller. So, the parameters you choose to check your robustness should be in your design space. Your design space is the whole wide area that I showed and that is the space where you can move around. Typically, with the robustness of your assay, in principle, the whole wide space is a robust space. You should be there, but you should take into account that you are not coming too close to the borders because you could get issues. You want to always be on the safe side.

Q: How many parameters can be changed or modified per one run?

ME: You can change a lot of parameters in your model. The only advice is to keep changing only the critical ones, so not everything, because your model, your DoE model is getting big where you have to do a lot of runs. You can make it as big as you want but you also have to ask yourself the question, "The parameters that I change, is that really necessary?”

Q: In method development, can we not do robustness testing and instead be sure of method robustness from optimization?

ME: Yes and no. Doing the robustness is really checking around the points which you set which you determined in your optimization. So, you do need to check it once more. The robustness exercise is verifying your design space with actual injections. The optimization is built up from the design of experiment, which has a lot of statistical calculations behind it. So, it's good to verify and prove that the data is okay. Furthermore, for the optimization you do not perform your Monte Carlo simulation, that's what you do in the robustness part.

Q: How do you use Quality by Design and transfer of analytical methods from standard HPLC to UHPLC?

ME: In theory, when you set your criteria in your design space and you change your equipment and your criteria are still valid, or your reportable result is still the same and falling within your design space, I think then you have a good case to change your method from an HPLC to a UPLC or vice versa.

Q: What is the best strategy for method monitoring in QC to make sure the method continues to meet its ATP requirements?

ME: Trending. Trend your data. Monitor your data. Determine your data. For variance of the assay, you need to build on historical data to show the performances of you assay. It could be that methods are drifting, or if you have some jump in your data, for instance, when you're starting to use a new column, the batches of columns can change over time, which is impacting your results. You can only see that when you have properly trending data. If things are changing, yes, it could be that you need to adapt your ATP because your data is still valid, but it's not fulfilling your ATP requirements anymore, but still your reportable result is valid. Then you need to start updating your ATP.

Q: If Quality by Design is followed, what about validation?

ME: Yes, you still need to do a validation to develop your methods. You develop a robust method, but you still need to follow the ICH Q2. A validation is obligatory.

Q: Can you explain further the reassessment part if it's not always verified against the ATP and how can design of experiment software help with this?

ME: The reflection of your ATP, if you are making small changes, then you need to check if the goal upfront is still the same. So, you need to reflect. DOE software can help to show you if you have parameters, specific effects in your methods which can help you answer these questions. For instance, if one of your parameters is behaving or the result is behaving strangely, then you can look if there is something you had missed. For instance, when you have a critical parameter which you did not assess at the start as critical because it always works, then you need to go back because you missed or you get a new critical parameter and you have to add that into your development. Sometimes you can get it out of the software to look if you have some cross-reactions between some parameters which you already selected in your DoEs, but if not, you have to start over.

Q: If we are using a pharmacopeial monograph, how will Quality by Design help?

ME: Currently, you have to follow the monographs. So, if you are following it on the letter, you do not have to do it. You only need to verify the method and you must follow according to the monograph, but if you are deviating from your monograph, then it can help you because you are doing additional things because you think it's needed. A monograph, like a companion text, is sometimes quite broad written down, which gives you a lot of flexibility. The flexibility can also be a disadvantage because you do not control your method well enough. Then the Quality by Design approach can help, because you need to be at that point more specific or more selective than the monograph is describing. So, it can help but it can also be your disadvantage because the monographs are too broad.

Q: At what point during the method life cycle should the full validation report be completed? Should this be just before filing, before releasing clinical supplies, or at a different point?

ME: That is the company strategy; companies are doing that differently. I think the latest point you need to validate your methods is when you enter commercial. Then, your methods need to be validated. Before that, you can decide yourself what fits the best or what is the company strategy that you use, but before you go commercial or before you send in your filing to the FDA or EMA, you need to validate your methods.

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