Neurocrine Biosciences Inc Morgan Stanley 24th Annual Global Healthcare Conference

That's a great question, very timely at Neurocrine, I know industry-wide. My view of China is that they are a competitive force, but at the same time, they represent an important source of innovation. I think that those views, I think at a high level, seem to be diametrically opposed. A lot of us, I think, would share that same sentiment. Even on the competitive side of the equation, competition can be a good thing. It can bring out the best in all countries, parties involved. I've seen that at Neurocrine over the years, and particularly when it comes to things that we're developing in competitive spaces or commercially. I'm confident that the U.S. will do well to compete in this area. Coming back to China, what I've seen over the course of the past decade, a lot of changes in the region.

The speed, the quality, the breadth of the science has really improved. I think that's something that we appreciate. The competitive bar has been risen, in particular in areas of validated biology. For us, that really expands the universe of innovation that Neurocrine and other companies can access. We have done that in the past couple of years. We have been in China and been successful bringing in mainly earlier-stage assets that are not material for the organization at a high level. We have used some of the work that they've done to help us accelerate some of our interest in research. How does that change things at Neurocrine today? I would say fundamentally, it doesn't change our view on the mix of internal R&D versus business development. It does change in one important way, and that is the importance of speed and selectivity.

Selectivity, I mean by that, what you choose to invest in. For validated biology, being right about the science is no longer the most important thing. It's really developing a compelling and efficient drug development plan that allows you to be first to market, or nearly so, and also having that same plan be one that shines a light on all your points of differentiation throughout each point of development. That's something that you have to keep in mind now as China opens up access to a new source of assets and technologies for companies in the sector. The other thing I would call out that I think we'll see more and more of, perhaps even at Neurocrine, is leaning into the Chinese ecosystem to help accelerate development.

This can occur in one of two ways, either directly through a more zero type of relationship or, from what I am seeing out there, companies striking collaborations on the same assets and technologies that I just referred to, but allowing their Chinese partner to develop the asset that comes from that collaboration, say, through the end of phase I. Then the company here in the U.S. catches that program as it comes over the ocean and finishes the development plan here in the U.S. and then commercializes it. The benefits, again, speed allows the company to look at that as a multiplier in terms of programs that they can bring into the portfolio per unit of time. It usually comes at a less of a cost, which is a benefit as well, so they can do more with their R&D dollars.

I think it goes back to an earlier remark. It is also done with very high quality. So for Neurocrine, that doesn't mean we are moving our R&D to China, but it does put China squarely in the global community of what we want to access to make Neurocrine better and faster for drug development.

Yeah, no, I think it is another great question. For AI and machine learning technologies, it has been something that we have been very deliberate and intentional incorporating through our organizations. Actually, in our corporate goals now, there is an element of that last year and this year as well. We have brought in, over a year ago, I should say, in 2025, OpenAI, and that is certainly a tool that is used broadly across the organization now. We also have Anthropic through Claude that we are using across the organization now. So both of those are available for our employees. I would separate productivity gains from claims about AI suddenly changing or transforming research and development. I would like to touch on the productivity gains piece first, and I will come back to R&D. But on the productivity gains side of the equation, there is a lot of near-term opportunity I see for Neurocrine.

The employees have adopted it in several different ways, but I can see it accelerating some of their workflow. I have seen it being used in areas of the company that interrogate large datasets. They are able to move through that more quickly. There is a lot of redundant tasks that are just very tedious in nature that have been minimized. Just generally, information and knowledge the individual groups might have has been made much more accessible across the organization, and that has benefited us as well. I have seen it in medicinal chemistry, chemical development, chemistry broadly, translational science, clinical development, clinical trial execution. I would expect that to continue as we look at the next year or so. The challenge I think where most of us run into problems is how do you monetize or see if you are making improvements across your organization with the technology.

For us, it is all about outcomes and KPI standards that we can attach to specific problems. In every organization, there are just repetitive decisions that come up. For us using AI, can we make those decisions faster? When it comes to repetitive tasks or activities, can we get through those more quickly or in a more cost-effective manner? When it comes to clinical trials, can we enroll our trials more quickly with AI tools? Ultimately, what it is going to come down to when we think about an organization in its entirety, can you move through more science without having to proportionately increase the size of your organization? I know we often talk about increases in headcount or decreases in headcount for AI.

I think in the near term, the opportunity is can you minimize your growth using AI based on some of the outcomes I just described? Those are the areas that we will look for improvements and how we will look to measure them over the next couple of years. I do want to come back to the R&D side of the equation. I think right now it is still early days in how we think about improving probabilities of technical success across the R&D portfolio. The reason for that is the whole idea of machine learning, AI, is that you need to have data populate your project in order for it to get reasonable outputs.

If you are working in novel areas of biology, it is very unreasonable to expect what you get from that project or from that programming, if you will, is going to be helpful in improving your probability of technical success. That is a more mid to longer-term view. Right now, I think there is a lot of near-term opportunities for us.