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As you can imagine, the manufacturing process takes about six weeks. With the process I described earlier, which includes transduction, it takes approximately seven to 15 days. This is a lengthy process compared to an electroporation platform. Essentially, with MaxCyte, you're reducing the timeline. You take your patient material, follow all the steps, and when your cells have expanded, you load your mRNA CAR with the electroporation platform. This speeds up the process from seven to 15 days to one to two days.
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Research is ongoing, and many publications are emerging, especially from Carl Jones' Group. They are doing a lot of work with the electroporation platform. Several groups in Sweden are also heavily involved with MaxCyte's electroporation platform. As awareness increases and more commercially approved therapies emerge, more people will lean towards the electroporation platform. During my time at MaxCyte, they received approval for one commercial therapy in Japan for dendritic cells, autologous dendritic cell therapy. This is public knowledge. They also have over 40 commercial agreements with large pharmaceutical companies. MaxCyte is involved with nine out of 10 pharmaceutical companies, to give a rough estimate.
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Lonza would be a significant competitor. They have the Amaxa, now known as the Amaxa Nucleofector platform. The advantage with Amaxa is its ability to perform high-throughput transfections. Their processing assemblies can handle up to 384 samples, so one could do 384 samples, 96 samples, and 1 sample at a time. During my time in the field, Lonza was also heavily involved with gene editing platforms, similar to MaxCyte. The main drawback with Lonza's technology is the cost. With Lonza's instrument, customers must purchase cell type-specific consumables, including media-specific reagents. This can become quite expensive. They claim their technology is scalable, meaning what you do on a small scale should be replicable on a large scale. However, I have not worked with their equipment. From what I've heard in the field, the results can sometimes deviate. What you see on a small scale may not be exactly what you see on a large scale. There could be some deviation in the results when a customer optimizes its process on a small scale and then tries to upscale to larger samples.
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