Extractable and leachable studies is highly complex and often includes a number of different parameters, each of which can introduce variability to the final data set.
Data from extractable and leachable studies are used for the evaluation of potential health risks associated with exposure to components used to manufacture, store, or deliver a product. It is critically important that the data supporting these evaluations are accurate and enables the proper evaluation of the test article. Inaccuracies in the data (i.e. errors in identification of analytes or quantification of levels) can impact patient and/or user safety.
Whilst the sources of variability cannot always be eliminated, they can be understood and controlled. Some have more impact on variability than others.
We see six different categories that commonly cause variability. Laying them out in an Ishikawa (or fishbone) diagram creates a clearer picture of what these categories are.
Fig 1 – Ishikawa Diagram showing 6 categories that cause data variability
Sources of analytical variability can lead to inaccuracies in the analytical data. If they are not controlled and understood, they can be detrimental to the analysis. Variability isn’t always a problem, as long as it is well understood and the variability doesn’t impact the objective of the study.
An ICH guideline for extractables and leachables was recently announced. It provides significant guidance for best practise in extractable and leachable study design and, importantly, has the potential to reduce some of these sources of variability.
Look out for further detailed discussion on some of these sources of variability in the coming months. In the meantime, talk to us about extractable and leachable studies.