![]() Nevertheless, performing DoEs with a large number of factors may be challenging, time-consuming, and expensive. Several scientific publications have proven the successful application of design-of-experiment (DoE) in chromatography method development. CEX separations are complex and governed by multiple factors. However, the development of CEX methods in a time and resource-efficient manner constitutes a bottleneck in product characterization. In this presentation, we demonstrate how to use JMP Pro 16 Profiler Simulation feature with Graph Builder to achieve an extensive and insightful exploration of the formulation space applicable to diverse fields.Ĭation-exchange chromatography (CEX) is the industry gold standard for the analysis of biopharmaceutical charge variants. Indeed, under certain assumptions, this coding strategy enables one to interpolate and consider missing compositions not present in the original DOE. So, we recoded those two factors as continuous and mixture variables, derived the equivalent regression model, and reran the simulations. At that point, to be able to identify promising subregions, we needed to overcome the discreteness of the space. However, at the data analysis stage, even after considering thousands of simulated hypothetical formulations, none of them was predicted to meet the desired properties. We created the DOE treating them as categorical due to the experimental constraints. ![]() The first three variables represent different compositions for making Chemical A, B and C, respectively, and as such, can be coded both as categorical factors, as well as continuous mixture variables. The formulation space consists of four input variables: Chemical A Type, Chemical B Type, Chemical C Type, and Chemical D Content. This case study investigates chemical mixtures to achieve optimal properties using design of experiment (DOE) data. Whether your work can be planned or is a wild path of exploration and serendipity, JMP has the modes to make your work speedy, efficient and productive, while keeping you in an environment of discovery. But there can be “easy buttons” along the way, and you can create your own “easy buttons” for the steps that you plan to take again. The analytic process is fundamentally interactive, and JMP supports interactivity. You must be able to pursue clues, identify things, drill down to details, and adapt to new conditions. The problem is that the steps are not always the same in each situation or in each set of data – the analysis has to be interactive. JMP is built for discovery, but discovery is not just happening to see something in a graph it usually involves a lot of work and many process steps. JMP Presenters: John Sall, Julian Parris, Mandy Chambers, Ryan Lekivetz, Rebecca Lyzinski and Aurora Tiffany-Davis The Flow of Discovery: Automating the standard paths and enabling the non-standard paths.
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