And the whole process from start to finish can be incredibly expensive, costing billions of dollars, and you know, taking up to a decade to do that. And in many cases, it still fails. You know, there are countless diseases that have no vaccine, no treatment. And it’s not that people haven’t tried, it’s just, they’re challenging.
And so we built the company thinking: how can we reduce those timelines? How can we target many, many more things? And that’s how I joined the company. You know, my background is in software engineering and data science. I actually have a PhD in so 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’re building a company’s original pre-engineering engine, that is, how can we target a bunch of different ideas at the same time, run some tests, learn for real? Hurry and do it again. Run hundreds of experiments at once and learn quickly and then move that learning to the next stage.
So if you want to run multiple tests you must have multiple mRNAs. So we built mRNA processing using this giant parallel robot and we need to integrate all of that. We need systems to control all of those, uh, robots together. And, you know, as things evolve as you collect data in 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.
You know, get rid of the decision making, you know, scientists don’t want to just stare and look at the data over and over again. But use their insights. Let’s build models and algorithms to automate their analyses, and you know, do a much better and faster job at predicting outcomes and improving our data quality.
So when Covid came, it was a really powerful moment for us to take advantage of everything we’ve built and everything we’ve learned, and the research we’ve done, and really apply it. Use it in this really important scenario. Well, and so when this sequence was first published by the Chinese authorities, it took us only 42 days from taking that sequence, identifying, you know, these are the mutations that we want to do. This is the type of protein we want to target.
Forty-two days from that point to actually build production, mass-produce, and be safe for humans at the clinical level, and have it shipped to the clinic — this is completely unprecedented. I think a lot of people were surprised at how fast it moved, but it really was… It took us 10 years to get to this point. We spent 10 years building this tool that allows us to move research as quickly as possible. But things don’t stop there.
We thought, how can we use data science and AI to really inform, the best way to get the best results for our clinical studies. And so one of the first big challenges that we had was that we had to do this big, third phase trial to demonstrate with a large number of, you know, 30,000 subjects in this study. proved that this works, right?
