Dr. Emilia Javorsky

And I wish we were in that world, Tristan, where that was the data standard of where the gap was and what we needed.

But it's so much more crude than that.

And I think that's something people don't realize.

Like, we don't even have a national sort of data commons of cancer genetics and imaging data and things that scientists could learn from that's interoperable and people can work with.

just the simple things that we already collect in clinic.

And I think this is a piece that Silicon Valley gets wrong about medicine too, is really overestimating the data that we have and the strength of that data in representing what's actually happening in a patient.

Yeah.

So it is probably the best time in human history to be a mouse in the sense that we can cure cancer in mice.

We've done a really great job of that over the years.

And we have a lot of drugs that are able to do that.

The problem is when we take those things that look good in mice, it looks like it's curing the cancer.

It looks like it's going to be safe.

This looks like it can actually get where it needs to go in the body.

And then test them in humans.

it falls apart and they don't work.

And 90 plus percent of the things that are going to cure cancer or save the life of a mouse are not actually going to move the needle at all in a human being.

And so that's the piece that I think is a missing link, which is from what we know in the lab bench, does it actually work in the bedside, right?

Does it work in for the patient?

And that gap is something that we've yet to bridge.

So I think a fundamental assumption most people have is that if a drug looks promising to treat a disease, then that means we'll get it to patients and it'll make it through the FDA and it'll make it to be able to actually help people.