Back in the world of drug discovery, it's important to remember that we can't create complete virtual animals, but we can at least recreate parts of them.

So what's possible today is the idea that We physically have a lot of animal data that describes their behavior, their body weight, the pathology of their organs, what normal looks like, right? We have that data.

And so there's really very little purpose to replicating that data over and over again.

It's different to the idea of building a sort of entirely accurate representation of the biology of an organism, a molecular, a tissue and an organ level, right? And I think that's genuinely where we are today in terms of feasibility.

When you think about what matters when it comes to understanding the safety of a potential therapeutic disease, You would essentially blow everyone's mind if you really tried to build a virtual human or a virtual animal based upon the availability of data today and our fundamental understanding of biology.

So we are much more likely to go at it piecemeal.

So we're much more likely to, if we think about the safety assessment context, What are the toxicities that routinely cause us problems when we put new therapeutics into humans? And by inference, I'm saying, what are the things that our current workflows with animals miss on a consistent basis, right? And if you take it from that perspective, you think about things like liver injury, cardiac consequences, kidney injury.

This includes novel approaches like the ones we've been talking about, AI, machine learning and digital twins.