What enabled Moderna to move so fast with their COVID-19 vaccine? Describe Moderna’s business model. How does it contrast with th

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HBS Professors Marco Iansiti and Karim R. Lakhani, Post-Doctoral Fellow Hannah Mayer (University of St. Gallen), and Director Kerry Herman (Case Research & Writing Group) prepared this case. It was reviewed and approved before publication by a company designate. Funding for the development of this case was provided by Harvard Business School and not by the company. Professor Lakhani has been a paid key opinion leader for the company. HBS cases are developed solely as the basis for class discussion. Cases are not intended to serve as endorsements, sources of primary data, or illustrations of effective or ineffective management. Copyright © 2020, 2021 President and Fellows of Harvard College. To order copies or request permission to reproduce materials, call 1-800-545- 7685, write Harvard Business School Publishing, Boston, MA 02163, or go to www.hbsp.harvard.edu. This publication may not be digitized, photocopied, or otherwise reproduced, posted, or transmitted, without the permission of Harvard Business School.

M A R C O I A N S I T I

K A R I M R . L A K H A N I

H A N N A H M A Y E R

K E R R Y H E R M A N

Moderna (A)

We’re a technology company that happens to do biology.

— Stéphane Bancel, CEO, Moderna

Noubar Afeyan, CEO of Flagship Pioneering (Flagship) and Moderna co-founder and chairman, and Moderna CEO Stéphane Bancel (MBA 2000), took a quick break between interviews with CNN and CNBC. It was late July 2020, and Moderna had just announced that their vaccine candidate for the novel coronavirus (COVID-19) had entered Phase 3 clinical trials in the U.S., signaling that the much- anticipated drug was only one step away from commercialization.1

Based in Cambridge, Massachusetts, Moderna was a biotechnology (biotech) company with an innovative platform approach to mRNA science. The last few months had seen an unprecedented acceleration of biological work around COVID-19 at Moderna. Bancel had read about the new infectious agent in early January that was causing a pneumonia like disease in early January, and emailed National Institute of Allergy and Infectious Diseases (NIAID) Director Dr. Anthony Fauci’s team at the National Institutes of Health (NIH). Bancel said, “The day after, we learned it was not the flu, not bacteria. A day later, we learned it was a coronavirus, but not SARS or MERS.”a Early in the second week of January, Chinese scientists in Wuhan announced they had isolated and fully sequenced the virus, setting off calls for full release of the details. On January 11, the gene sequencing data was posted on Virological.org.b

Two days later, January 13, using the genetic sequence posted online, the Moderna team finalized the design of a corona vaccine candidate against this new virus. By February 7, Moderna’s engineers made a Phase 1 clinical study vaccine, and on February 24, after passing quality control testing, they shipped it to the NIH in preparation for preliminary tests (Phase 1 clinical study) in volunteers in

a SARS = Severe Acute Respiratory Syndrome; MERS = Middle East Respiratory Syndrome.

b Virological.org was a hub for prepublication data designed to assist with public health activities and research.

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Seattle, Washington. Bancel said, “We did all this in two months. The fastest time anyone had done this before was 20 months, with SARS. It was a 90% reduction in time.”

In some respects, Moderna’s response to the crisis of the pandemic had been business as usual. After all, the company was organized to respond to health challenges like COVID-19. Founded in 2010 and designed from the ground up as a digital biotech company with a factory for in-house manufacturing capabilities, Moderna looked different from most biotechs. Yet despite $5.1 billion raised in funding, (of which $750 million came from several key strategic partners) and its innovative approach, Moderna had yet to bring a drug or vaccine to market.

In February, when the reality of the pandemic hit, Moderna pursued its COVID vaccine development within its existing organizational structure, and ran in parallel to its other drug development activities. All of its plans and investments over the past ten years—including a manufacturing facility opened in Norwood, Massachusetts, in July 2018—had situated the firm in a sweet spot to carry out rapid research, drug development, clinical trials and manufacturing.

Now, after a long day of virtual interviews with some of the nation’s biggest news outlets, Afeyan and Bancel considered the next frontier for Moderna. As the vaccine headed into Phase 3 trials, and they looked to the reality of possibly distributing a vaccine to billions around the world, they considered the impact their efforts had on Moderna as an organization. Moderna’s digital environment meant they continuously learned from every step on the production chain. But making enough vaccine for billions could transform the organization dramatically. The unprecedented scale of the challenge could swamp the organization and overshadow Moderna’s other pipeline candidates. Further, was there risk in becoming branded a COVID company? Bancel noted, “We have seven vaccines in development, all of which could provide important medicines, as no vaccines exist against any of these viruses.” He added, “How do we position ourselves outside of this? Should we do something orthogonal? What about our other vaccines?” Should Moderna set up a separate organization dedicated to COVID-19 vaccine development?

Traditional Vaccines and Drugs Vaccines of all types aimed to expose the body to an antigen that might not cause the disease but

could lead to an immune response to block or kill the virus once a person was infected. But the exact approaches to achieving this effect varied. Traditional pharmaceutical (pharma) companies typically worked on virus vaccines, injecting a weakened or inactivated form of the virus into the human body. Viruses typically caused diseases by reproducing thousands of times once in the human body. When weakened viruses were injected in the human body, they reproduced fewer than 20 times; because of their limited ability to reproduce, the disease did not break out, yet the low level of viral replication was enough to trigger the body to generate antibodies that would protect the vaccinated individual against that same strain of viral infection in the future. Vaccines for diseases such as chickenpox, measles, and polio all followed this approach. Proven to reduce disease, disability, death and inequity worldwide,2 few medical interventions could compete with vaccines for their cumulative impact on public health and well-being of entire populations. Immunization efforts had eradicated smallpox globally, and eliminated measles, polio, and rubella from the Americas.3

An alternative to a weakened virus was to inject altogether inactivated viruses, which could not reproduce at all or cause the disease. Because the immune system still perceived that a virus had entered the body, it generated antibodies to protect against this interloper. While this type of vaccine typically required multiple doses to achieve immunity, its advantage lay in not subjecting the recipient

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to even a mild form of the disease; it could thus be given to people with weakened immune systems. This approach had been used, among others, in Hepatitis A and rabies vaccines.4

Vaccine and Drug Development

Big pharma players, including industry leaders Johnson & Johnson, Merck, Pfizer and Roche, generally relied on traditional approaches to develop drugs, including vaccines. Traditional vaccine development typically began with research endeavors in university labs, medical science centers or smaller biotech firms, relied on years of research and were usually funded by government grants or private foundations.5 This initial period of discovery was largely dedicated to isolating a pathogen and reducing its potency or inactivating it for use in a possible vaccine. These potential vaccines would eventually make it through the research phase, be tested on small animals, such as mice or rabbits, followed by larger animals like pigs or monkeys. Such preclinical testing helped to understand how the vaccine worked and how it might affect the human body.6 (See Exhibit 1.)

During these periods of drug research with parallel pre-clinical (i.e., animal testing) efforts, multiple groups around the world could be working on similar ideas, including the development of vaccines against a virus or bacteria. Researchers conveyed progress through presentations at science conferences or peer-reviewed journal articles. Big pharma companies stayed close to such developments. Scientists working at pharma companies often came in only at this stage, attending such conferences and reading journal articles to get an overview of the most promising candidates. (See Exhibit 2 for an overview of the top pharma companies.) When convinced of the idea’s success and commercial viability, pharma representatives partnered with scientists to expand their research toward product development. This process could take up to 10 years, and many of ideas never moved past initial research.

Vaccine candidates that passed the research and pre-clinical testing stages moved into clinical trials, (i.e., tests in humans). This happened across three phases. In Phase 1, a small group of people volunteered to evaluate the safety, immune effect and tolerance across different drug dosages. In Phase 2, a larger group was used to confirm formulations and dosages. Phase 3 involved several thousand human volunteers to evaluate the protection provided by the vaccine. Such clinical trials often took multiple years, and could be conducted by third-party providers. Following successful completion of these trials, companies sought regulatory approval; in the U.S., at the national level from the Food and Drug Administration (FDA) and in the EU, at the international level. Regulators granted authorization for manufacturing new drugs or vaccines.

Once the regulators gave approval, manufacturing and shipping could commence. Many big pharma companies relied on third-party partners for these services, including licensing and contract manufacturers. They used elaborate demand-forecasting schemes to ensure that supply met demand; these often necessitated that pharma companies produce large quantities of a vaccine. This stage continued to see close cooperation between the pharma companies and regulators to ensure the accuracy of the demand forecasts. Upon introduction into the market, a new drug or vaccine continued to be monitored closely by the drug manufacturer and regulatory bodies.

Using mRNA as a Drug

Moderna’s approach to developing vaccines (and drugs in general) was fundamentally different from that of big pharma. It relied on using messenger RNA (mRNA), a molecule that carried a genetic sequence, and lipid nano-particles (LNPs), a fat used to wrap up and protect the mRNA and deliver it into cells. The body’s natural use of mRNA revolved around its role in providing the information needed to make corresponding proteins. The human body created hundreds of thousands of proteins— such as insulin or growth hormones—to function and survive.

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To produce a protein, the body used the information contained in DNA (i.e., genes). Each cell contained only two copies of each DNA gene, but the body needs many more copies to make a protein. When a body needed to produce a particular protein, it made many copies of that protein’s gene, in the form of RNA. These temporary RNA copies, called messenger RNAs, or mRNAs, move out of the cell’s nucleus and into the cytoplasm, where they instruct the cell’s ribosomes (the small particles that serve as protein factories) to produce the desired protein. Melissa Moore, chief scientific officer of platform research at Moderna said, “mRNA is basically a template that holds a code, or instructions, for a cell on how to manufacture proteins. It doesn’t have anything to do with gene editing, because it doesn’t function in the nucleus, so doesn’t touch the DNA. But it does get the cell to behave in a certain way.”

Moderna, and other companies that followed, took the use of mRNA to the next level by turning it into a drug. Following this logic, once an externally manufactured mRNA was injected into the body as a drug, the ribosomes in the cells read the injected mRNA like a code and made the protein the mRNA instructed them to make, thus leveraging the power of mRNA for the targeted production of proteins needed to fight diseases. mRNA functioned like a software instruction manual, instructing the body how to produce its own drugs.

LNPs were also important for the development of Moderna’s drugs. LNPs served as the “delivery vehicles” for mRNA, clothing the mRNA in fats to ensure proper delivery into the human body. “mRNA is the software and LNPs are the hardware needed to make our therapeutics work,” Moore said. “So we are not just an mRNA company; we are also a delivery company.” If Moderna could prove that its approach worked in one single drug, it would work for all others as well. “mRNA is a platform like the iPhone,” she added. “The individual drugs—preventative vaccines or therapeutic treatments— are like apps. If we get the platform to work, many, many apps can be developed on our platform.”

Moderna’s nature as a platform company meant that similar technologies could be used to develop multiple medicines in parallel, and learnings from one drug could spill over directly into others. This was in contrast to typical biopharmac companies that administered their research and development efforts and clinical trials in sequence, and the learning across drug candidates was minimal at best.

Moderna’s Conception

Flagship Pioneering

Moderna started out as project LS18 in 2010 at Cambridge-based Flagship, at the time called Flagship Ventures. Though based on venture capital-type investment pools, Flagship was more than simply an early-stage investor providing money to promising business ventures. “We operate as an institutional innovation enterprise,” said Afeyan. Trained as a bioengineer with a PhD from the Massachusetts Institute of Technology (MIT), Afeyan had founded Flagship in 2000 after serving as the founder and CEO of PerSeptive Biosystems, a leader in bio-instrumentation, and as senior vice president and chief business officer Applera, which acquired PerSeptive in 1998. Committed to spearheading radical innovations in medicines, Flagship followed a strict methodology for creating ideas through scientific innovation and entrepreneurship in revolutionary, untapped spaces. They were the founders, funders and owners of their portfolio companies, taking the leading role in the conception and growth of these firms, which initially start out as projects. “To understand the genesis of Moderna, one must understand Flagship,” Afeyan explained.

c Biopharmaceutical companies manufactured their drugs by biological methods (i.e., in living organisms such as yeast, bacteria or mammalian cells).

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Flagship followed a structured four-stage process for pioneering innovations through scientific discovery. First, they generated hypotheses believed to yield break-through innovations, asking the question “What if?” (e.g., “What if mRNA could be a drug?”). They called these “explorations.” Second, if these explorations proved promising, they became prototype companies (ProtoCos). Flagship formed to test the feasibility of the concept. Third, if the explorations validated the science underlying the venture, ProtoCos turned into new companies (NewCos) and Flagship committed significant capital. At this point, while still an internal venture, Flagship and the venture team would recruit a broader team to develop the business further. Fourth, the NewCo was spun out to become a growth company (GrowthCo). At this stage, the company began forging investor relationships and outside partnerships. (See Exhibit 3 for Flagship’s process for pioneering.) Eventually, some of these GrowthCos ended up operating as public companies. Between 2013 and 2020, 20 Flagship GrowthCos completed IPOs,7 including Moderna in December 2018, at a valuation of $7.5 billion.8

“This is not your typical unmet-need/breakthrough-solution approach to coming up with a business,” Afeyan explained. “We subscribe to a Darwinian process, covering creation, iteration and selection of groundbreaking innovations for which no precedent exists. Asking ‘What if?’ questions propels you far into the future. It may be unrealistic or overly optimistic, but that’s how radical innovation happens.” Afeyan also believed that science in business was uniquely positioned to ask these questions and pursue such an approach. “Academic science searches for knowledge; science done in business searches for solutions. It goes where value is believed to exist imminently,” he said, adding, “Our approach of thinking about the future state first and then developing solutions backwards is not necessarily a better way of doing science than the incremental innovations academia strives for—it’s just different.” Though there was no expressed need for mRNA as a therapeutic at the start, Afeyan believed in its potential, noting, “We deliberately looked into mRNA as a novel medicinal modality.”

In 2010, Robert Langer, David H. Koch Institute Professor at MIT, an accomplished and decorated researcher and a key scientific advisor to Afeyan, directed Afeyan’s attention to Dr. Derrick Rossi’s recent research. At the time, Rossi was an investigator at Boston Children’s Hospital and an Assistant Professor in the Stem Cell and Regenerative Biology Department at Harvard Medical School. His research on using mRNA to reprogram cells9 led Afeyan to wonder: might it be possible not just to reprogram cells but to get patients to produce their own biological drug through the use of mRNA? Could Rossi’s research insights provide a basis for developing a biotherapeutic drug rather than laboratory stem cells? Afeyan wanted to get these questions answered through more research and experimentation, so he set up a team to explore whether mRNA could be turned into a therapeutic. Langer and Rossi provided Moderna with key scientific direction early on, making them academic co- founders. In 2020, Langer continued to serve on Moderna’s board.

“We had long had a vision of creating new types of medicines for humans,” Afeyan said. “Plus, we had an interest in creating platform companies.” mRNA fit all the bills: it was an unproven medicinal modality that could serve as a multi-product platform. “Developing drugs is the most rewarding thing to do in the pharma industry. But at this point, big pharma only pursues incremental innovations, which leaves the riskier, less proven approaches unexploited,” Afeyan noted. Given the time and costs, pharma companies often focused on one product, committing enormous resources to them. Following Flagship’s logic of a platform meant that if one drug worked under the given conditions, then many would work, allowing much faster time-to-market of new drugs once a proven method was in place.

Moderna’s set-up as a multi-product platform company meant developing a different narrative for investors as well. “Our story to investors was different than what they knew from incumbent pharma companies. And so we also happened to attract different kinds of investors than you might see in big pharma,” said Lavina Talukdar, head of Investor Relations. Moderna’s platform nature was more

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attractive to generalist investors accustomed to investing in pre-revenue, pre-earnings ventures, where the use of price-per-earnings ratios to value an investment—a standard indicator for big pharma companies—was less relevant. “These types of investors understand the value of digital platforms and learning effects, from having seen such effects in tech companies,” Talukdar added. Traditional pharma investors required more information on the unique dynamics of Moderna’s business model. “We also talk to investors focused on developmental stage biotech companies, who are interested in probability of success and time-to-market. This is a very different conversation than those we have with a tech or finance investor, who has experience with platform companies like ours,” Talukdar concluded.

Deciding to Go Digital

As Moderna came together as a venture, Afeyan reached out to Stéphane Bancel, then-CEO at French diagnostics company bioMérieux, whom he had known for several years. Afeyan had been trying to recruit Bancel into Flagship as a senior partner for some time. He recalled, “Early on I saw he was a special person. I noticed how creative and impassioned he was, which was counterintuitive, given he was the CEO of a large public company.” With Moderna taking shape, Afeyan called Bancel, saying, “I’ve got the thing you should work on. If you’re not taking this, and it succeeds in a big way as I expect it will, then someday you’re going to regret it.” He managed to persuade Bancel to come onboard at Flagship and join Moderna as a board member; soon after, he became the company’s CEO. Afeyan recalled, “He’s resourceful, impatient and wants to achieve impact fast.”

A native of France and an engineer by training with hands-on coding experience, Bancel had spent his entire professional career in the life sciences field. Having served in sales and manufacturing roles at Eli Lilly before joining bioMérieux as CEO in 2006, Bancel understood the power of digitization in the pharma field. “I have seen the disaster unintegrated data and systems can cause,” he said. “I’ve been in places where I knew more about computers than the IT guys. I wanted Moderna to be a digital company from day one. For that to happen, we needed the IT to be built right, even if it meant considerable investments at a time when we didn’t have revenue streams. Digitizing right from the get-go is much easier than doing this ex post on a legacy system.”

By 2012, Moderna had a staff of about 20 people. Bancel quickly recognized the need to digitize when he walked into the office of one the small start-up’s few scientists. The scientist had been working on an Excel spreadsheet, spread across two large screens, manually inputting a nucleotide sequence. He was proud of his progress but Bancel was horrified: “If any of the Excel cells had been wrong, the entire design of the drug would have been useless.” To do away with the manual process, Bancel had a drug design studio set up, where drugs could be crafted easily and reliably on the computer rather than through manual work.d

To enable full digitization to further accelerate research and results, Bancel realized he needed a formal chief digital officer (CDO) to drive this. In 2015, he brought former colleague and ex-CIO at bioMérieux Marcello Damiani onboard. Fluent in digital technology and pharma, Damiani believed that business process engineering was the key to digitizing a firm and needed to come first before digitizing, and Bancel agreed. “Enabling Marcello to design the processes was key. Digitization only makes sense once the processes are done. If you have crappy analog processes, you’ll get crappy digital processes.” Damiani added “You cannot blindly translate manual processes into digital processes. The processes, once set, have to be redesigned to fit in a digital environment. It is important to do this exercise holistically rather than in silos, or else optimization will only happen in individual pieces.”

d See https://vimeo.com/151182986 for more information on Moderna’s drug design studio.

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Damiani’s dual title as Moderna’s chief digital and operational excellence officer gave him a mandate for process engineering across Moderna’s departments.

By 2016, Moderna had made quick progress towards becoming a fully digital biotech company. The digitization of processes was the first step in Bancel’s vision for Moderna, followed by automating as many processes as possible. “We are at the beginning of a new S-curve.e How quickly we learn is the most important indicator for us. We have to keep learning really fast. That’s what motivates me to push for automating as much as we can,” Bancel explained. Going digital, however, was only the first step towards Bancel’s vision of utilizing artificial intelligence (AI) in Moderna’s processes. Becoming an AI- driven company required more than simply digitizing operations and promoting a digital cultural as part of Moderna’s DNA. A layered, multi-step logic was key to becoming an AI factory, and doing it right from the outset would take time and resources.

Building the AI Factory

Damiani knew there were several principles that mattered in an AI-driven company. First: the cloud. “Operating in the cloud rather than building our own infrastructure was foundational to everything else we did. It was the first decision we made,” Damiani said. (See Exhibit 4 for Moderna’s digitization building blocks.) He believed that storing data in the cloud was more secure than hosting it locally; cheaper than maintaining the infrastructure in-house; and more agile and resilient, with better disaster recovery solutions and less downtime. Moderna started using Amazon Web Services (AWS) in 2013, and deepened the relationship over time.

The second principle was integration. Both Damiani and Bancel had seen what siloed data could do to efficiency and productivity, so having data harmonized across systems, entered once and with the ability to flow freely to whichever team needed the data, was crucial. “At bioMérieux, before we undertook an IT transformation, just getting the current headcount was a feat,” Damiani explained, adding, “The systems didn’t work well together. Lots of data was just stored locally in Excel spreadsheets. At Moderna, we wanted business processes and data to be integrated. Having our lab instruments connected to each other through the Internet of Things also enabled data integration.”

Automation and robotics were the next steps, although Damiani was cautious of automating and robotizing too early. “We needed to have maturity in our processes first. Removing error-prone manual activities was the initial step,” Damiani noted. His team spearheaded “islands of automation,” connecting them into a larger automated whole once each island was stable. The team operated the systems as manual-automated hybrids until they achieved automation across the board. Dave Johnson, head of Informatics, Data Science and AI, said, “Automating too much and too early is dangerous too.”

As the whole system became connected, analytics and AI came into play. “AI is really the holy grail,” Damiani said, adding, “We relied on digitization early on, not for the sake of digitization but for generating data. Today, we have a lot of structured data, for instance in research and pre-clinical production. When we run experiments, we collect even more data. This allows us to build better algorithms, which helps build the next generation of medication. It’s a virtuous cycle.” (See Exhibit 5 for digital integration at Moderna.)

Moderna employed a mixed approach to sourcing digital solutions. They bought most of their systems to support undifferentiated processes, such as finance and human resources (HR) tools off- the-shelf. Damiani estimated that 85% of these kinds of tools were existing Software as a Service (SaaS)

e An S-curve in innovation management refers to the start of a new technological era in which performance increases rapidly.

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solutions, such as Workday which Moderna used in HR. He estimated that around the same share of tools to support their company-specific processes and innovation, such as in research or technical development, were custom built.

Functions across Moderna adopted digital and AI at different rates, with pre-clinical leading the way, yet Damiani felt the organization was on pace. “We are at maybe 60% or 70% of my vision,” he said. “Granted, recently established areas, such as clinical and commercial, lag behind, but overall we are in a great position.” By early 2020, preclinical production had largely been automated and more automation, especially for clinical and research activities, was underway. This significantly reduced cycle times, allowing Moderna to move at a faster pace than pharma or even other biotech companies.

Johnson believed the use of AI gave Moderna a competitive advantage. “We use algorithms to support our decision making in different areas. For instance, in the clinical trial space, they give us predictions that humans wouldn’t be able to make in a reasonable time frame,” he said. “This allows us to scale with

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