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App Note: Computer-aided Assay Development for Charge Heterogeneity Analysis by iCE

Posted: 13 October 2015 |

Due to protein therapeutic complexity, techniques like isoelectric focusing have become indispensable tools in evaluating biologic preparations…

Unlike chemically synthesized drugs, protein therapeutics are a dynamic heterogeneous mix of active compounds.

Due to their complexity, analytical techniques like isoelectric focusing have become indispensable tools in evaluating biologic preparations. The resulting surge in charge isoform analysis has led to major advances in instrumentation, such as Imaged Capillary Electrophoresis (iCEā„¢). However, to obtain the full benefit from improved instrumentation requires the coinciding development of robust assays. Initially implemented in biopharmaceutical manufacturing, the holistic process characterization philosophy known as Quality by Design (QbD) has the potential to transform assay development. Proper adaptation of these techniques will provide a tremendous benefit to the robustness and predictability of assay performance. Key to QbD is comprehensively gauging the effects of process inputs on critical to quality (CTQ) attributes of the output.

To this end, the Design of Experiments (DOE) methodology has proven itself to be a highly efficient tool in modelling the relationship between input and output. Though statistical analysis packages such as SAS JMP and MinitabĀ® have lowered the computational barriers to executing DOE, generating meaningful results still requires a working knowledge of the model building process. The goal of this note is to promote the successful application of DOE tools in the assay development process by offering a stepwise example. The roadmap contained in the following pages has purposely captured enough technical detail to provide a comprehensive reference guide for both the statistician and analytical biochemist. The subjects that will be covered include initial factor screening, construction of a central composite DOE, response surface modelling, assay optimization, model validation and assay performance.

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