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I am relatively new to the cancer IO space, but unless you discovered a new check-point idea (e.g., TIGIT) a lot of the ideas are flushed out already. If you're

I am relatively new to the cancer IO space, but unless you discovered a new check-point idea (e.g., TIGIT) a lot of the ideas are flushed out already. If you're thinking cell-therapy, there are a number of autologous ideas already on CMC for p1 testing. If you're trying to make TILs angry, or modulate M1/M2 CD11b macrophages, anything that requires transcriptional re-programming, you have a lot of catching up to do.

jl2718 6y ago
Most of the stuff you mention here is more biological, which is outside of my full understanding as an ML hobbyist in the area (not my fund, just helping). There is some low-hanging fruit in the portfolio like an improved ligand for an existing checkpoint target. I worked mostly on predicting ligands to stimulate the HLA->APC->TCR->proliferation pathway, mostly using data from binding assays and HPLC/MS isolation. There is a lot just on the computational side that is required to apply well-known vaccination methods to cancer. For seed startups, I'd think it's more likely to find fundable companies making progress on improving the application of existing leading-edge methods than attempting entirely new approaches. CAR-T comes to mind as a prohibitively expensive therapy that invites approaches to reduce cost and improve access, which gives them a simpler path to commercialization than starting with the FDA from scratch. Again, not the expert, not my fund, so I can't comment on the need to catch up, but the people I'm trying to help are some of the luminaries in the field, so I don't think unfamiliarity is the major roadblock; it's more a matter of whether their time is best spent on this or on research.