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Celata Bioinnovations Adaped from Wells, John R. and Weinstock, Benjamin, (2020) “Celata Bioinnovations”. Harvard Business School

Introduction In December 2019, Jon Hu (Harvard Business School MBA, 2019) and Dr. Samantha Dale Strasser, co-founders of Celata Bioinnovations, were raising $1 million to launch their company. They had founded Celata less than six months earlier with the aim of redefining the drug discovery process. Celata’s platform used novel data types and a unique data integration process to generate hypotheses about proteins that were involved in the formation and progression of disease. They believed the platform was better and faster at identifying actionable biological targets, increasing the speed of discovery and reducing the chances of failure, promising to lower costs and bring drugs to market more quickly. Their platform had achieved proof of concept during Strasser’s PhD research at Massachusetts Institute of Technology (MIT), when it helped identify and validate a target related to Inflammatory Bowel Disease (IBD) in mouse models. However, full validation wouldn’t come until their platform was used to develop a drug that successfully treated a disease in humans, a process that could take several years. In the first instance, Celata’s platform was best suited to identify targets for cancers, inflammatory diseases, and neurodegenerative diseases, which collectively presented a global market opportunity of over $200 billion. However, Hu and Strasser were debating which opportunities to pursue. Should they try to improve the efficacy of current drugs, or go for brand new opportunities, indications for which there were no solutions on the market? And if they focused on the latter, should they just look for large market opportunities, or include “orphan” diseases, for which there was a relatively small market? Should they look for small molecule drug candidates, or include large molecule biologics in their search? And where should they operate in the drug development pipeline? At one extreme, they could sell potential drug candidates to established pharmaceutical companies; at the other, they could “go full pharma” and build a new pharmaceutical company themselves.

The Basics of Disease Collective agreement on what constitutes disease has shifted over time and been the subject of much debate.1 But, at the most basic level, disease could be characterized by the persistent abnormal functioning of a biological structure or system. These abnormalities could occur at any point in biological pathways – defined as, “[the] series of actions among molecules in a cell that [lead] to a certain product or a change in the cell” – which were responsible for sending signals to cells, regulating metabolism, turning genes on and off, and a whole host of other functions.2 At the beginning of a biological pathway was deoxyribonucleic acid (DNA), the building block for all life. Francis Crick, one of the discoverers of DNA, formulated the “Central Dogma” of molecular biology, which described how biological pathways descend from DNA: Basic instructions of biological function were encoded in DNA, which were then transcribed to molecules called ribonucleic acid (RNA), which governed the creation of proteins in a process called translation. After proteins were built, they were subject to chemical changes typically catalyzed by enzymes (a type of protein) in a process called post-translational modification (PTM). These modified proteins were the basis for all normal cellular function.3 An error, or series of errors, somewhere in a biological pathway could disrupt the “normal” balance of protein functions and cause disease.

Drugs to Counter Disease Historically, the drug discovery process followed the model of “classical,” or “forward,” pharmacology, where researchers would identify certain compounds that produced a desirable change in a model (e.g., curing or mitigating the effects of a disease in a mouse). Then, they would work to identify the biological targets causing the disease – typically aberrant protein structures. The goal was to identify the mechanism of action (MOA) by which the compound was having a positive effect, and find ways of improving its efficacy. The Human Genome Project, a vast task tackled by the international community between 1990 and 2003, promised a new approach to drug discovery. The goal of the project was to map the base pairs of DNA that define a human being. These DNA pairs provided the instructions for assembling proteins to create a healthy individual. Any critical deviations from the norm could possibly lead to aberrant protein structures that caused disease. However, proteins

underwent many chemical changes as they were created and distributed through the body, so the biological target causing a disease might not be revealed in DNA, while any changes from the norm in DNA would not necessarily create aberrant protein structures that caused disease. Nevertheless, a greater understanding of protein formulation and distribution popularized a new approach to drug discovery called “reverse” pharmacology.4 In this process, researchers hypothesized certain biological targets involved in the formation or progression of disease and then screened for compounds with therapeutic effects.

Drug Development In both forward and reverse pharmacology, researchers had to follow a process overseen by the United States Food & Drug Administration (FDA) to bring a new drug to the US market and keep it there. It consisted of a number of steps, which were increasingly expensive, demanding a clear choice as to whether it made sense to proceed with development as each stage was completed. The steps were as follows: 1. 2.

3.

4. 5.

6. 7.

8.

Target Discovery and Candidate Development. Identifying and validating a biological target and screening it against compounds that could modify it in some way; Preclinical Research. Testing a drug candidate in vitro (e.g., cells in Petri dishes) and then in vivo (e.g., nonhuman animals – typically mice) to establish basic safety and to optimize efficacy by improving how the drug attached to a target, while limiting side effects; IND Application. Submitting an Investigational New Drug (IND) application to the FDA to gain approval to test the drug in humans. INDs were complex and time consuming, requiring detailed information on preclinical pharmacology and toxicology studies, drug manufacturing, and clinical protocols. They could amount to over 1,000 pages of documentation. Clinical Research. Approved INDs went on to clinical trials where the drug was tested in humans. Clinical trials were typically comprised of three phases, involving increasingly large- scale trials, during which patient selection and dosing schedules were fine-tuned. FDA Review. Developers submitted a New Drug Application (NDA) to the FDA, who then reviewed all relevant experimental data and results and either approved or rejected the drug. Those drugs that were approved were allowed to come to market, and some were then subjected to an extra step, FDA Post-Market Drug Safety Monitoring.5 The time to get to drug approval typically took between 10 and 15 years. Late-stage drug failures were a major concern for drug developers because they were the most expensive to bear, significantly increasing the costs of research and development. Drugs failed for a range of reasons: the target selected turned out to be incorrect; the drug itself was not an ideal candidate to improve the disease; or negative side effects outweighed therapeutic benefits. It was also common for drug candidates to fail because of mistakes in dosing or patient selection. The switch to the reverse pharmacology model of drug discovery was widely expected to improve research efficiency by limiting the number of falsely-identified targets. In reality, the opposite proved true, as R&D productivity and returns plummeted, primarily due to increasing costs (mostly from clinical trials) and reduced sales forecasts due to smaller patient populations for increasingly niche and rare diseases. Between 2010 and 2018, the average cost of bringing a new drug to market for large biopharmaceutical companies increased from $1.2 billion to $2.2 billion, while forecast peak annual sales declined from $816 million to $407 million.6 For smaller companies, the average cost of bringing a new drug to market had ballooned to $2.8 billion.7

The Phosphorylation Opportunity As described above, there were many different types of PTMs, to which proteins were subjected after formation, but the most common was called phosphorylation, which could affect one-third of all human proteins. During phosphorylation, a kinase (a type of enzyme) catalyzed the transfer of a phosphate group from a high-energy molecule called adenosine triphosphate (ATP) to a substrate protein, resulting in a phosphorylated protein and adenosine diphosphate (ADP).8 Many proteins could be phosphorylated multiple times at different “sites” in their structures. The process could be reversed in what was known as dephosphorylation, when a phosphorylated protein released a phosphate group, catalyzed by a type of enzyme known as phosphatase.9 Of the roughly 20,000 identified human proteins, there were approximately 500 identified kinases and 200 identified phosphatases. 10 In most instances, protein phosphorylation was a vital part of governing healthy biological functions; however, “aberrant phosphorylation” had been associated with several cancers, inflammatory diseases, and neurodegenerative

diseases.11 Understanding the biological pathways involved in such diseases would enable researchers to develop new and improved drugs. There was one major problem, however. Fewer than 3% of the 100,000+ known phosphorylation sites had an identified function. Similarly, only between 3-4% of these phosphorylation sites had an identified kinase responsible for the modification.12 The opportunity to match kinases to phosphorylation sites and modified-protein function in order to develop new drugs was massive. But, the task of doing so was daunting and would require new computational approaches. This is where Celata was aiming to compete.

Celata Founders Jon Hu Jon Hu was born in China and moved to the United States when he was five years old. Initially, he didn’t know English, so he spent most of his time in libraries, reading as much as he could in order to learn English. Of all the famous people he read about, one stuck out above the rest. Hu explained, “I became obsessed with Bill Gates. Here was somebody who built something that fundamentally changed society – and is still at it! From that moment on, I knew I wanted to start my own business.” When Hu was in elementary school, his teacher asked the class to do a familiar exercise – to write down what they wanted to be when they grew up. “Most kids wrote down what was expected of them – doctor, lawyer, astronaut, professional athlete,” Hu remarked. “If you could time travel back to that classroom, you’d see on that wall ‘Jon Hu, Business Owner.’” In high school, Hu began trying to identify what was going to be the next big thing in the world of technology. He settled on two possibilities: the connection of computers – “computers talking to one another,” he clarified – and advances in biotechnology. At first, he began thinking of opportunities with computers. “My dad was a computer programmer, so I asked him to teach me how to code. We spent hours writing the classic simple script that spits out ‘hello world.’” Hu continued, “I was frustrated and took out a piece of paper and wrote down, ‘hello world,’ then said, ‘computers are useless! Look how quickly I could do that! Of course this was extremely naïve, but from then on, I shifted my focus to biotech.” Hu then went on to study biomedical engineering and economics at Northwestern University in Evanston, Illinois, where he received his degrees in 2011. At Northwestern, Hu was active with several on-campus business and research organizations, including Students Consulting for Nonprofit Organizations (SCNO) – where he created a national nonprofit which acted as the umbrella that linked all the school chapters around the country – the Institute for Student Business Education (ISBE), and the Northwestern Undergraduate Research Journal (NURJ). Hu commented, “All of these taught me lessons in different fields that would help me reach my dream of innovating in the biotech space. I also attended several industry conferences in the area, learning as much as I could about what opportunities there were for disruption.” He continued, “I even spent time as an insurance salesman to learn more about sales. I hated it but I learned a ton. This was all about developing the skills I’d need later.” After graduation, Hu joined Bain & Co., where he worked on “a bit of everything” He then joined Guild Capital, a hybrid VC fund, where he was placed in key roles in start-ups around the world. Hu explained, “At this point, I had gotten business 101 experience and start-up 101 experience. Now the idea was that I’d spend five or six years in big pharma, learning as much as I could, and then go off on my own.” To get that experience, in April 2015, Hu moved from Chicago to Boston, where he joined the pharmaceutical giant Shire to work on R&D strategy. He quickly realized that it wasn’t a good fit. “My second week there,” Hu remarked, “I went up to my manager and said, ‘I’ll get done what I promised to do when I signed on, but if I do, will you write me a letter for business school?’ He laughed and agreed.” In the fall of 2017, Hu started at Harvard Business School, where he received his MBA in 2019. He reflected, “I learned loads at HBS and really got the confidence I needed to try and start something big. And even though it was tough being at Shire, it gave me some key insights.” He explained, “I realized that it always seemed that scientists and engineers were speaking at rather than with the finance and business folks – and vice versa – with both sides thinking the other didn’t understand the bigger picture.” He continued, “This made for a very dysfunctional relationship. But it also presented a unique opportunity to improve communication and bring expertise from each domain.” Hu concluded, “After that, I knew Samantha, who I first met at Northwestern and worked on my senior design project with, would be the perfect partner going forward.”

Dr. Samantha Dale Strasser Samantha Dale Strasser was born in Wisconsin, where her first fascination was wanting to be an astronaut. She explained, “It was the questions of the unknown I’ve always been really excited about. Nothing seemed more unknown than space! But, fast forward to the end of high school, I realized that there were loads of problems that we needed to solve here, on our own planet, especially in biology and energy.” After high school Strasser left Wisconsin to study biomedical engineering and applied mathematics at Northwestern University. She had her first research opportunity freshman year, when she was mentored by two professors – Dr. Allen Taflove and Dr. Vadim Backman – working on early detection of cancer, as part of an interdisciplinary project involving electrical engineers, biomedical engineers, and biologists. Strasser’s specific project was to quantify properties of cells at increasingly small levels to better understand changes during disease progression. Her research was published and got Strasser thinking about the potential to develop the research into a diagnostic tool. This was carried out for an engineering “capstone” project during her senior year. She explained, “During this project, Jon and I first had the opportunity to work in-depth together. We realized early on not only how well we worked as a team, but that we shared a common goal. We wanted to do something like this – take ground- breaking research to market – and make a big impact on the world to help people. Exactly what that meant in practice...we were still figuring out.” She continued, “We had the same goal of wanting to innovate in the biotech space, but we came at it from different perspectives. In the end, we realized that we couldn’t commercialize this particular research as a diagnostic tool – it was too early stage. But the bigger takeaway was that we wanted to find ways to work together in the future and eventually start a company. We kept in touch during the years to come.” After Northwestern, Strasser received a Churchill Scholarship to study at the University of Cambridge in England. There, she received her MPhil in Physics. Asked about her decision to pivot to physics, Strasser said, “I wanted to try something new, and I was really interested in the alternative energies space. Our research group in Cambridge focused on advancing polymer solar cells – which have the potential to be printed - with different materials to improve their efficiency.” She continued, “It was interesting work, but I realized I missed healthcare and that I wanted to focus on it for my doctorate. We’re at a really exciting time in biology, right on the cusp of achieving major change towards answering many of the ‘unknowns’ in the field with the help of new technologies computing abilities.” Determined to focus on the healthcare space again, Strasser moved to Cambridge, MA, where she began her PhD at MIT in the Electrical Engineering and Computer Science department (EECS). Her research – housed in the Biological Engineering (BE) department and mentored by Dr. Douglas Lauffenburger - focused on improving analysis and interpretation of data on phosphorylated proteins (i.e., phosphoproteomics). She explained, “Better understanding when and why various proteins are phosphorylated allows us to tease out what’s happening with certain diseases and how they’re progressing.” She continued, “A big question is how do we take this data type and find something meaningful that allows us to successfully take action against disease?” While at MIT, Strasser and Hu – who was in Boston at Shire at the time – caught up more regularly, and she began discussing her research. She was eager to show him a platform that she had built to interpret phosphoproteomic datasets to identify novel targets for treating diseases. Hu was impressed and realized that Strasser’s research represented a big opportunity. The two agreed that as soon as they finished their degrees – Hu, the MBA; Strasser, the PhD – they would find a company that leveraged and built upon Strasser’s platform. In September 2019, they launched Celata Bioinnovations.

The Platform The methodology central to Celata’s platform was called SKAI, or Substrate-based Kinase Activity Inference. SKAI coupled phosphoproteomics data with prior knowledge about kinase-substrate interactions integrated across a variety of different organisms – namely humans, mice, and rats. This data integration process expanded the information available to interpret phosphoproteomic datasets and created organism specific kinase-substrate sets – both of which improved the target identification process. With those sets, kinases involved in various diseases could be inferred using a computational method called GSEA, or Gene Set Enrichment Analysis. Those inferences were then used to provide new hypotheses about potential drug targets. The platform achieved proof of concept after insights from the SKAI approach identified a kinase called MK2 as a potential therapeutic target in Inflammatory Bowel Disease (IBD) and subsequently validated the target using a small

molecule inhibitor called ATI450 as a potential therapy for the disease.13 Mice that were treated with ATI450 saw a significant reduction in the inflammation from active colitis (a subtype of IBD).14 Strasser commented on the research, “We were particularly excited about the MK2 proof of concept. Without our method of data integration, MK2 would not have been identified in our analysis. This clearly illustrates the potential of our approach. However, moving forward, the platform still needs stronger validation across a variety of different disease systems. The platform we’re working with now is an expanded version of the one we used to ...