A $4.5 billion biotech working on tech-driven cancer treatments told us how it's using AI in areas rivals are ignoring, and why companies that aren't could struggle
- Tech is a major part the $4.5 billion biotech Moderna's efforts to develop a new kind of personalized cancer treatment.
- But the company's successes with AI have been not with "these large moonshot programs but all the little things that make it really hard for our scientists to do their jobs," Dave Johnson, the biotech's senior director of informatics, told Business Insider.
- A key example of that can be seen with the complex planning required to manufacture the company's experimental cancer vaccines. AI allowed Moderna to go beyond "guessing" to make those decisions, Johnson said.
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For biotech company Moderna, which went public last year in the biggest biotech IPO in history, the emphasis is as much on the tech as the biology.
That extends from the tablets that monitor operations on the floor of its Norwood, Massachusetts manufacturing facility to the algorithms that are used in its experimental cancer vaccines.
"The success we've had has been not focusing on these large moonshot programs but all the little things that make it really hard for our scientists to do their jobs" so they "can focus on their core jobs of being innovative," he said.
Moderna has been able to see and seize those opportunities because the company digitized all its operations from the very beginning, which allowed it to generate data enabling the use of algorithms, he said. At Moderna, digital initiatives are emphasized at every level, from the company's Chief Digital Officer, Marcello Damiani, to the amount of internal resources that are available, Johnson says.
"I think there are a lot of applications of machine learning, AI, that people are ignoring," Johnson said. Those smaller problems, which are achievable, get overlooked, "and I think a lot of companies in the industry are going to struggle with that."
AI is being used across the biopharmaceutical industry to take on some of its most persistent challenges. With massive data sets at its disposal, the industry hopes machine learning and AI tech can make better predictions about subjects like which experimental drugs are most likely to succeed.
That's especially important in the pharmaceutical industry, where productivity from the research and development activities that lead to new drugs has been on the decline for decades. Moderna's experience with AI could prove a useful case study for other biopharmaceutical companies.
Read more: The $1.2 trillion pharma industry has big ambitions for AI technology
Using algorithms to deploy manufacturing resources more effectively
Moderna is developing experimental products called personalized cancer vaccines that are being tested out for conditions like melanoma, and must be custom made for each patient.
Because of that, and because patients enroll in research trials at variable times, planning the manufacturing process for the vaccines, from capacity for orders to even aspects like how much raw material the biotech needed, was especially complicated. The biotech also wanted to know what would happen if aspects were changed, and how much that would affect the product.
So Moderna turned to an algorithm, feeding in metrics from research trials to simulate hundreds of trials with thousands of pretend patients, asking questions like, "What if we change this timeline from five days to three days? What would that mean?" Johnson said.
The results allowed Moderna to match its manufacturing capacity to its needs, including through making large, crucial decisions about where to invest capital funds.
"Before we had this tool, they were kind of guessing," he said.
The machine learning-based approach allows Johnson and team to supply decision-makers with concrete metrics, like what percentage of patients will need a particular dose. Those, in turn, go into designing a research trial and ensuring capacity is large enough to meet patient needs, without spending so much on capital that expensive manufacturing capacity gets wasted.
This particular project began when Moderna's clinical team, which takes charge of all matters related to its research programs, was having difficulty figuring out where to begin.
Johnson's group analyzed both the problem and how achievable it was. It also considered the project's value, or how great the return on investment could be.
Quantifying that was hard. But because the project involved large capital decisions being made around the company's future, "it was clear that it was an overwhelming return on investment," Johnson says.
So the team set it into motion, with work beginning in February of 2018, all done in house. The initial release came about a month later. The algorithm has been used consistently in the time since for various projects around the company, including to plan for Moderna's phase 2 research.
Because this project addressed a more or less straightforward problem, and could be handled with a simple algorithm, trained using a forecasting technique and the right data, Johnson doesn't recall any large hurdles. But that hasn't been the case with every algorithm.
The duration of AI projects can be highly variable, depending on their complexity and the availability of relevant data, according to Johnson.
One especially tenacious, complex problem, for instance, has held up one project for years. The AI solution Johnson and his team devised has been working and is being used to make key decisions across four different teams at the company, including the manufacturing unit, he says.
That can be tracked easily, because Moderna came up with a plan and can follow whether there are any discrepancies as research develops.
Johnson says that "if there was a mismatch we would absolutely see that, and so far we haven't seen those challenges."
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