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Engine Biosciences Using CRISPR, Machine Learning to Build Drug Candidate Pipeline

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CHICAGO – With a fresh $43 million in the bank thanks to a Series A funding round announced last week, drug discovery-focused bioinformatics firm Engine Biosciences has mapped out its plan for the next 18 to 24 months as it eyes its first clinical trials in 2023.

Engine Biosciences said it will apply the new funding to widen its portfolio of potential therapeutics for precision oncology, prepare for its first clinical programs, and scale up its technology.

Cofounder and CEO Jeffrey Lu said that the Series A funding should enable Engine's work for at least another year or two as the firm pursues commercial contracts and possibly additional venture capital for the future. "The primary objective is for us to continue to push a therapeutics pipeline," Lu said.

The company, which is based in Singapore and has a presence in San Carlos, California, has built two artificial intelligence-enabled technology platforms: NetMappr, a machine learning-driven search engine for biology that uncovers gene combinations and potential drug targets; and CombiGem, which physically probe hundreds of thousands of gene combinations, measure biological changes from perturbations, and provide confirmation of drug targets and gene combinations.

Lu described CombiGem as a wet lab-based genetic screening platform that can work with CRISPR as well as other perturbation techniques. While Engine typically uses CRISPR, the firm has applied CombiGem to siRNA, shRNA, and microRNA overexpression.

Most of the work has been for generating targets for the company's internal pipeline in oncology, but Lu said that Engine has been collaborating with one undisclosed, US-based company.

In general, Engine wants to work with researchers who are either searching for novel targets for their pipelines or who are trying to get a better understanding of the biological mechanisms of compounds that have targets, according to Lu.

Both the Singapore and Silicon Valley sites have wet laboratories as well as computational bioinformatics professionals.

The wet labs perform functional genomic screening with combinatorial CRISPR technology. The combinatorial perturbation "really allows us to get at the genetic interactions and genetic networks that underlie ultimately the biology of disease," Lu said.

"We are very much built at the intersection of the dry and wet lab," Lu said.

Engine Biosciences has been in business since 2018, when it raised a $10 million seed round and effectively spun out of the Massachusetts Institute of Technology. Some of the founding team also has ties to Mayo Clinic, the Wyss Institute for Biologically Inspired Engineering at Harvard University, and the University of California, San Diego, and those institutions remain among Engine's academic research partners.

Engine Biosciences now has about 30 employees, more than half of whom have Ph.D.s, according to company promotional material.

With its labs and bioinformatics technology, the firm has since developed pipelines to investigate potential targeted therapies for liver, ovarian, colorectal, and breast cancers. Engine started with precision oncology because cancer is where the company's research and development team has the most experience.

"That is something that we believe can bear fruit in the relatively near term," Lu said.

The firm has also done some work in neurodegeneration, including Parkinson's disease, Alzheimer's disease, and amyotrophic lateral sclerosis, as well as in dermatology.

In ALS and Alzheimer's specifically, Lu noted that there has long been a dearth of good targets for drug developers. "That's where the platform is well suited," he said.

"Because there is a lack of good data to train those algorithms to actually make good predictions and do good analytics, we actually use our CRISPR and CombiGem platform to run screens in relevant disease models," Lu explained. This goes beyond typical cell lines to include induced pluripotent stem cells.

"The founding mission of the company was to ultimately use the technology platform to enable many therapeutics for many disease areas," Lu said, acknowledging that it is a broad goal shared by plenty of others.

"We've taken a very specific approach toward a problem that we think we can solve scientifically, and that is understanding genetic interactions and genetic networks, which is a massive, complex, and large mathematical space to sort through," Lu said.

He said that the proper way to approach this problem is to build a platform that employs machine learning-based analytics to generate and then validate computational predictions from experimental data. Engine has reached this stage.

"We're trying to use data from various sources, whether they're from clinical centers, publicly available from third parties, as well as from the datasets that we generate internally through our functional genomics platform," Lu said. "It's the foundation for us to launch multiple therapeutic development efforts."

Lu said that the Engine has been testing some small-molecule targeted therapies for cancer targets and has nominated a series of lead compounds that the firm will begin optimizing later this year. He did not offer specifics.

Engine's current business plan is to support drug discovery and development, mostly internally.

"I think that the targets and the therapeutics we've identified in our oncology pipeline are a big attraction for the investors that have come this round," Lu said. Those include lead investor Polaris Partners, as well as previous investors 6 Dimensions Capital, WuXi AppTec, DHVC, EDBI, Baidu Ventures, Vectr Ventures, Goodman Capital, WI Harper, and Nest.Bio, that also participated in the Series A.

Engine's primary objective right now, Lu said, is to bring some of the targets to preclinical and clinical development phases as the firm looks to start the first clinical trials on targets and compounds in its pipeline the year after next.

"As many platform-based companies do, we also seek to have strategic partnerships that will bring in additional capital to use the platform to generate possible therapeutics in other areas," he added. The hope at Engine is that such partnerships would give the firm a stake in royalties for any compounds that make it to market.

"We're in the early stages of doing that right now with our scientific founders," Lu said. The academic labs have produced a successful proof of concept, he said, and he expects that work to migrate to the commercial realm in the next year or two.

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