Hi everyone,
We’re developing a highly complex, multi-unit manufacturing process with a large number of CPPs across the various unit operations, together with continuous CQAs such as time-dependent drug release profiles.
We’ve built up quite a lot of historical data from previous DoEs, which we’re currently looking at as one combined dataset, and we also have the possibility to generate substantial additional data.
We’re considering upgrading to JMP Pro, mainly to explore whether FDE and Bayesian Optimization could work together for this type of problem.
I’d particularly like to hear from people who have used these approaches in similarly complex, multi-step process development, rather than relatively simple optimization problems.
A few things I’m curious about:
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FDE → BayesOpt: How practical is it to reduce continuous profiles to FPC scores and then use these as target Ys in BayesOpt?
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Historical DoE data: How well does BayesOpt work with data combined from several DoEs, particularly when the factor ranges and designs are different?
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Multi-unit constraints: How easy is it to deal with process boundaries and combinations of factors that are not feasible across different unit operations?
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BayesOpt vs. traditional DoE: For this type of problem, have you found the iterative approach of BayesOpt to be a real advantage compared with augmenting a conventional DoE?
I’d really appreciate hearing about real-world experiences, workflows, or lessons learned from anyone who has tackled something similar. Even if the experience was that it didn’t work as expected, that would be very useful to know.
Thanks!
Ricardo Costa
