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USP Chapter <1032>, Design and Development of Bioassay provides a framework for bioassay development, though many of the statistical concepts are not implemented from lack of understanding. This talk will cover the use of different statistical designs and analysis for developing a bioassay that is robust.
In biomarker diagnostic work, an analyte assay may lead to a binary or qualitative outcome such as disease or not-disease, or to-treat or not-to-treat. It is important to know what level of random change in the analytes can be tolerated to guarantee the claimed diagnostic performance. We can define the maximum allowable deviation (MAD) such that the binary endpoint claim is maintained. It can be determined from previously run validation datasets by varying analyte signals in small increments and then creating ruggedness plots. These can then be used to set acceptance limits for validation studies of operational factors. For multi-analyte assays this is not entirely straightforward, but summary measures including logistic regression scores make it possible. It still remains to confirm if such limits are achievable given the underlying variability of the analytes. An accelerated stability example is presented.
N-Glycosylation of biotherapeutics is an important critical quality attribute. Glycans can influence protein stability, serum clearance, immunogenicity and pharmacokinetics. Therefore it is crucial to monitor the full glycan profile of a glycoprotein throughout discovery, clinical and manufacturing phases. A High-throughput RapifluorMS N-glycan method has been employed in our lab to address the challenges that are encountered with product quality support and meet aggressive timelines. This presentation will provide an overview of the development workflow to automate all the steps from sample preparation to data analysis of the RapifluorMS released N-glycan analysis.
An overlook of the evolution of our automation line addressing scientific as well as data handling matters.