And that whole process from start to finish 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 out there right now that don’t have a vaccine for them, that don’t have a treatment for them. And it’s not that people haven’t tried, it’s just that they are challenging.
And so we built the company thinking about: how can we reduce those deadlines? How can we target many, many more things? And that’s how I got into the company. You know, my background is in software engineering and data science. In fact, I have a Ph.D. in what is called information physics, which is very much 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 that early preclinical engine of a company, which is how we can focus on a bunch of different ideas at once, run some experiments, learn very quickly, and do it again. Let’s 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 needed to integrate all of that. We needed systems to handle all of these, uh, robotics together. And, you know, as things evolved as you capture data into these systems, that’s where AI starts to come in. 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.
Let’s remove the decision making from, you know, scientists who don’t want to just look and look at the data over and over and over again. But let’s use your ideas. Let’s build models and algorithms to automate your analyzes 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 really important scenario. Um, so when the Chinese authorities first published this sequence, it only took us 42 days to take that sequence and identify, you know, these are the mutations that we want to make. This is the protein we want to target.
Forty-two days from that time to actually build manufacturing, batching, and shipping to the clinic is clinical grade and human safe, which is unprecedented. I think a lot of people were surprised at how fast it moved, but it’s really… We spent 10 years to get to this point. We spent 10 years building this engine that allows us to move research as quickly as possible. But he did not stop there.
We thought, how can we use data science and AI to really inform the best way to get the best result from our clinical studies? So one of the first big challenges that we had was that we had to do this big phase three trial to test in large numbers, you know, it was 30,000 subjects in this study to prove that this works, right?