And that whole end-to-end process can be immensely expensive, costing billions of dollars and taking, you know, up to a decade to do. And in many cases, it still fails. You know, there are countless diseases that have no vaccine for them, that have no treatment. And it’s not that people haven’t tried, it’s that they are, they’re a challenge.
And so we built the company thinking about: how can we reduce these deadlines? How can we aim for many, many more things? And that’s how I got into the company. You know, my background is in software engineering and data science. I actually have a PhD in what’s called information physics, which is very closely related to data science.
And I started when the company was very young, maybe a hundred, 200 people at the time. And we were building the first preclinical engine of a company, which is how we can target a bunch of different ideas at once, do some experiments, learn really fast, and do it again. We do a hundred experiments at a time and learn quickly and then take that learning to the next stage.
So if you want to do a lot of experiments, you need to have a lot of mRNA. So we built this massively parallel robotic processing of mRNA and we had to integrate it all. We needed systems to drive all these, er, robotics together. And, you know, as things evolved as you capture data in these systems, that’s where AI starts to come into play. You know, instead of just capturing, you know, this is what happened in an experiment, now you’re saying let’s use that data to make some predictions.
We take the decision-making away from scientists who don’t want to look at themselves and look at data over and over and over. But we use their ideas. We build models and algorithms to automate their analysis and, you know, do a much better and much faster job of predicting outcomes and improving the quality of our data.
So when Covid came along, it was really, uh, a powerful moment for us to take everything that we had built and everything that we had learned, and the research that we had done and really apply it in this very important scenario. Um, and so when this sequence was first released by the Chinese authorities, it only took 42 days to go from taking this sequence, identifying, you know, these are the mutations that we want to make. This is the protein we want to target.
Forty-two days from now until we create a human-safe, clinical-grade manufacturing batch and send it to the clinic, which is unprecedented. I think a lot of people were surprised at how quickly it moved, but it’s really… We’ve spent 10 years getting to this point. We’ve spent 10 years building this engine that allows us to advance research as quickly as possible. But it didn’t stop there.
We thought about how we can use data science and AI to really inform how best to get the best result from our clinical studies. And so one of the first big challenges that we had was we had to do this big phase three trial to show in a large number, you know, it was 30,000 subjects in this study to show that this works, right?
