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  "documentTitle": "Berkeley Lights (BLI)",
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      "text": "I think for the vast majority of people, using some other combination of technologies is a much better place to start.",
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      "text": "Use cases are \"very limited,\" generally to low risk, low value assays; anything beyond that requited \"a crop ton of validation work\" to double-check the data using traditional methods\nA: \"Its use is very limited. People see it as being good for very, very, very specific things. I think using it for a secretion assay is perfectly fair if it's been developed appropriately, so some of those antibody screening type assays. Again, it's very limited, and you have to have a very limited view of it.\"\nQ: \"Anything else besides secretion assays? Is that it?\"\nA: \"In my mind, that's the safest thing to use it for. We certainly branch outside of that for certain things with the knowledge that we're going to have to do a crop ton of validation work.\"",
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      "text": "Expert does not recommend the BLI machine for \"the vast majority of people\"\n\"I would certainly not start by using Beacon. I think it's one of those situations where Berkeley Lights had a better model for getting pilot studies, and I was really convinced that was just the way to go, that I was really getting that much more specific data from the instrument, certainly it would be something I would consider. But I think for the vast majority of people, using some other combination of technologies is a much better place to start. Beacon has a lot of really niche applications, and if you get all of those parameters right, you get some really great data out of it. But I think the vast majority of people who start with it probably shouldn't.\"",
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      "text": "Machine is not suited for commercial pharma/biotech customers; \"really incredibly challenging for them\"\n\"The idea that well, we've invested in this, so now we better figure out what to do with it; that's not the first time I've heard that. We clearly didn't make the full investment in order to have a Beacon. But we're an academic institute that can fail 9 times out of 10, and be totally fine. Our 10th success gets published. But one of the frustrations that a lot of my pharmaceutical peers have expressed, somebody at some level decides to make this investment, and then it becomes their problem to make it work right out of the gate with amazing data that demonstrates great cost-savings and throughput increase, and all these other kinds of things, which is really incredibly challenging for them.\"",
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      "text": "I would certainly not start by using Beacon. I think it's one of those situations where Berkeley Lights had a better model for getting pilot studies, and I was really convinced that it was just the way to go... But I think for the vast majority of people, using some other combination of technologies is a much better place to start. — Leading Academic Institution/Ex-BLI Scientist",
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      "text": "Source: Scorpion Capital consultation calls with experts",
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      "text": "LEADING ACADEMIC INSTITUTION/EX-BLI SCIENTIST (cont'd): Wouldn't recommend the machine for \"the vast majority of people\"; other technologies are a \"much better place to start\"; not suited for commercial pharma/biotech customers; \"really incredibly challenging for them\" to get the machine to work\"; use cases are \"very limited,\" generally to low risk, low value assays; anything beyond that requires \"a crop ton of validation work\" to double-check the data with legacy tools.",
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