Sam Fazeli

So let me bring this to the point that I think maybe you and I discussed a little bit when we saw each other is you don't know what you don't know.

Is it possible that some of the novel targets that you look at were actually tested in some lab in a GSK or at Pfizer or at Lilly or one of these big, big places, which has a ton of work going on preclinical that never sees the light of day.

And of course, if they've failed or because they shut down those projects, they never come out.

Does that matter even if that's the case?

And then added to that, do you feel that you have a disadvantage not having access to those massive data sets that are in-house within large pharma?

Just talk about that subject a little bit, please.

Sorry, we don't have a huge amount of time left and I've got a huge number of questions still.

Let's talk about, I think we've really explored well the idea of and the use of AI up to the preclinical candidate stage.

So I was listening to Patrick Schwab, who's at GSK AI, and he was talking about the value or the impact of AI on the clinical development part.

So there's two questions I'm going to load in here.

One is the FDA wants to get rid of animal testing in preclinical, and it wants to replace it by AI.

Your view on that, because you obviously said that's something that I don't think we can replace, so we have to go through it, et cetera.

And then the Patrick Schwab white space, going from phase one to phase two to phase three.

We've talked about the time saving and the value of AI in discovery and to the PCC point.

Talk to us about these two things.

First, replacing the preclinical models with AI.

And two, and how much would that happen?

And what's your view?

And how much time will it save?

And secondly, the clinical development path, which you are into now, can you not cut?