Hi Vaneeta,
I have previously designed a cell culture study with similar parameters you have described. I looked at main effects only and designed the study using JMP DOE builder. To determine number of runs, I increased the number of runs to achieve at least 80% power for each of the main effects. I typically add a few runs to this number in case of error resulting in loss of a data point. When analyzing the data, each raw material was treated as a random effect to understand the variability attributed to changing lots. This also allows for inference to be drawn across all lots, rather than the tested lots. I would also include other covariates in the model that could impact my response (for cell culture models, starting seed density can be highly variable).
One question that I always have in this study, is where do the lots come from? It is common for a new lot to be sent to a customer, but this may be a repack from a larger bulk (ie 1000 kg were made and but only 100, 1 kg containers were filled and assigned a lot number, then a new order came in and the same powder was packed but given a different lot number). It is important to understand if the material packaged is from different batches or just repacked, as this could skew the results if multiple lots are from the same manufacturing batch.
I hope this helps and I would be happy to discuss further.
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Jonathan Ross
Director MSAT and CMC Statistics
Rezolute Bio
Bothell WA
[email protected]Disclaimer: Opinions expressed are solely my own and do not express the views or opinions of my employer.
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Original Message:
Sent: 07-01-2024 16:45
From: Vaneeta Grover
Subject: Lot-to-lot variability of raw materials
I have a question. We have 4 different raw materials and we want to understand the effect of lot variability of these 4 raw materials. For each raw material, we have 3- 4 different lots that we can vary to study it's impact. What is the best way to set up the study and analyse it.
Thank you so much for your help in advance.
Regards,
Vaneeta.