Empress Therapeutics is a pioneer of metagenome-based discovery

The Therapeutic EmpressEmpress Therapeutics, which came out of stealth mode a year ago, has already discovered 15 drug leads across multiple diseases and target classes. Launched in 2020, the Flagship Pioneering startup is now on track to file multiple IND applications within the next 24 months.

While AI has played a role in compressing these timelines, the company is focusing on more than efficiency gains. “There are two aspects to how you make drug discovery better, and AI has a role to play. Speed ​​and efficiency are one part, but predictability and safety are probably the best areas to focus on,” said Jason Park, CEO and co-founder of Empress Therapeutics.

Chemistry: Combining patient data, DNA and AI

Empress shares Flagship Pioneering’s core philosophy of evolutionary and genetics research to drive therapeutic breakthroughs. Similar to how Moderna used the information molecule, mRNA, to program cells to make proteins, Empress has developed a platform known as Chemilogics that combines patient data, DNA, AI and synthetic biology to help small molecule or drug chem.-based precision determination. the candidates.

While interest in large molecule drugs has increased in recent years, small molecules have a number of advantages. Their inherent properties make them uniquely suited to cross cell membranes, reaching intracellular targets that are often inaccessible to larger molecules. In addition, their potential for oral bioavailability makes them a cornerstone of modern medicine.

Tackling the challenge of small molecule discovery

Jason Park

Jason Park, Ph.D. [Empress Therapeutics]

“Small molecules make great drugs,” says Jason Park, CEO of Empress Therapeutics. The challenge is finding a good starting point for small molecule development. In drug discovery, a widely cited estimate states that there are 10^60 possible combinations of drug-like compounds. “It’s effectively infinite,” Park said. The sheer amount of this makes traditional drug discovery a monumental undertaking and contributes to the significant failure rate in moving from initial hit identification to validated lead compounds to commercialized therapeutics. “There has to be a better way to do this,” Park said. At Empress, that “better way” begins with a fundamental question: “What if you could bring the power of genetics to the discovery and generation of chemistry? What if, instead of screening billions of components, we could decipher genetic blueprints for the production of small molecules already present in the human body?”

Rather than grappling with the astronomical number of potential drug-like compounds, Empress focuses on the rich chemical diversity already encoded within the human metagenome, which includes the individual’s genome as well as the collective genome of the microorganisms that inhabit and inhabit the human body. . The human genome contains about 20,000 protein-coding genes. Conversely, recent estimates suggest that there are more than 170 million unique protein sequences in the gut microbiome alone.

Mining the human metagenome for drug candidates

The rich diversity of protein sequences within the metagenome implies a wide variety of enzymes with potentially novel functions. “Another way of thinking about it — basically every chemical compound in your body, other than the ones you eat and get through that pathway, is affected by enzymes when they’re made or modified,” Park said.

Thus, Empress uses genetic data to explore how DNA programs cells to produce chemical compounds, via enzymes, allowing her to identify small molecules that have evolved within the human body to interact with specific disease targets. . “If you imagine the central dogma – DNA codes for RNA that codes for proteins.” The Empress set out to shed light on the role of enzymes in catalyzing chemical reactions. The big question was, what if you could start predicting how the compounds in your body are ultimately related to your genetic sequence? Can you go from DNA to chemistry?”

From DNA to chemistry

To this end, the company uses techniques such as natural language processing to read DNA and investigate the role that particular sequences play in coding for the many enzymes that play a role in the production of molecules of interest. Nature has already verified such molecules over millennia.

“We know that the parent compound is already inside the human body, so there’s reason to believe it’s safe,” Park says. “And we know if it’s conserved, it’s dysregulated in disease, it’s encoded in genetics—that gives us a lot of confidence and certainty that this molecule is important.”

A convergence of technologies and talents

The company’s strategy to mine the metagenome to explore “the most privileged group of chemistry” would not have been possible a decade ago, Park said. This is because the company relies on the convergence of several elements. “You need the data,” Park points out. The recent explosion of metagenomic data provides powerful fodder for AI algorithms. “But you also need things like synthetic biology, advances in NLP, genome mining, causal discovery, causal inference tools,” Park said.

“It’s similar to what we saw in the technology industry,” Park said, referring to the convergence of technology streams — advances in algorithms including the emergence of transformer models, GPU advances, the availability of elastic cloud storage and processing, and cooperation. of multidisciplinary experts driven by an entrepreneurial mindset.

This convergence is embodied in Empress’ own leadership team, which includes industry veterans such as Chief Scientific Officer Murray McKinnon, who brings more than 35 years of experience in drug discovery and early stage development. McKinnon has contributed to the development of numerous blockbuster drugs, including Orencia, Nulojix, Remicade, Simponi, Stelara and Tremfya.

Questioning boundaries with experiments

This blend of experience and a willingness to challenge conventional wisdom is at the heart of Empress’s approach. “We have entrepreneurs, a number of scientists who know that what we call scientific dogma is really just a veneer for a series of experiments,” Park said. “We have entrepreneurial scientists who maybe have a start-up experience or have come out of academia. They really don’t know what the boundaries are.”

“We have that entrepreneurial spirit of ‘we can do anything,’ the capabilities of the platform to make drug discovery repeatable and an engineering problem rather than just a scientific endeavor, and then the people who know what a good medicine,” Park said. “That’s part of what’s fun about working in a small biotech. You bring all those people together and focus on the mission of making great medicine, putting everything in place to increase the safety and speed of doing so. Because if we’re right about this, and the data looks good so far, we’re not just going to make one drug—we’re going to make drug after drug.


Filed under: clinical trials, machine learning and AI


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