For your repurposing efforts, have you considered partnering with one of the large-scale EHR data providers and running causal inference algorithms to try to identify potential unexpected effects of certain therapeutics or combinations thereof in longitudinal outcome data?

Do you see any use cases for looking at metagenomics data in your drug discovery or lead optimization efforts?

To what extend (if any) do you think that a database profiling common human genetic variation in eg KRAS tumors would be helpful so that you can design antibodies that will be broadly applicable? Do you analyze mass datasets from eg TCGA or Genomics England and try to design antibodies considering common variants or do you pick a canonical target and work from there?

Are you limited by capital or by discovery? Eg have you discovered what you think are disease targets with unmet need where you’re reasonably confident you have a real target, but you have to deprioritize it due to trial costs? Or is the limiting factor finding targets and agonists/antagonists for them?

What evidence exists that the insights gained via single-cell perturbations can help uncover novel disease targets? A critic might say a single cell perturbations are simply not a good model for complex multicellular disease processes as the disease phenotype is rarely a linear sum of single cell phenotypes. Is the method most applicable to rare diseases with a clearly understood gene driver or also to highly prevalent diseases? I think Yumanity failed recently with their yeast disease model in neurology so I’m curious of how you address this criticism