The marriage of biology and technology is booming as companies seek new ways to bring computing to drug research. The goal is to make the iterative process of drug discovery scalable, making the entire effort faster and more efficient. But no matter how much of the work is done with software and mouse clicks, at some point a new molecule must eventually find its way into real living mice. That’s where startup Manifold Bio wants to stand out.
Early work in traditional drug discovery was a series of laboratory tests. Manifold co-founder and CEO Gleb Kuznetsov said the results were more predictive of how the drug would work, but it also became more expensive with each successive test. The puzzle is what the molecule does in mammals. Companies typically select their best few molecules for animal testing: one molecule per mouse. Manifold’s technology can test many molecules, possibly hundreds, in a single mouse.
“In vivo tests are usually done later,” Kuznetsov said. “If we could try a lot [molecules] Faster, in vivo, this could be a breakthrough strategy. “
Investors agree.Manifold debuts on Thursday $40 million in financinga Series A financing led by Triatomic Capital.
Boston-based Manifold spun out of Harvard University, and Kuznetsov and company co-founder Pierce Ogden were graduate students in the lab of geneticist George Church.Over the years, the lab has formed a number of biotech startups, Kuznetsov said, and a common theme is Combining cutting-edge molecular biology with other technologies. These companies are not only developing new drugs, but also trying to Challenging traditional drug discovery paradigmsHe says.
Manifold is developing protein medicines. The technology that allows the startup to test multiple drugs in a single mouse is a protein “barcode,” a marker placed on an experimental molecule that can be tracked. With this ability, scientists at Manifold can determine where molecules are going in animals and which ones are working. Scientists can also identify which molecules are off-target and potentially triggering toxic effects. Kuznetsov said the technique could find promising molecules earlier and cheaper than traditional drug discovery methods.
Manifold first unveiled its approach in 2020, when it raised $5.4 million seed financing Led by Playground Global. The company also participated in a Series A round, which added new investors Section 32, FPV Ventures, Horizons Ventures and Tencent. Earlier investors Fifty Years and GETTYLAB’s FAST were also involved.
Jory Bell, global general partner at Playground, said he immediately recognized the potential of Manifold’s technology. Manifold learned from a dozen mice that its molecules required hundreds of mice using traditional methods, he said. Earlier tests on animals also gave addicts more options. Bell explained that when a startup conducts animal testing, it’s usually locked into one or two molecules. Deriving animal data from a wealth of molecules means Manifold can use what it learns and apply that insight to its drug candidates earlier.
“So, because you can choose the best drug early, the chances of clinical success are greatly increased,” Bell said.
Manifold’s technology can be applied to many indications. Cancer was the company’s initial focus, an area chosen because it offered many opportunities for rapid clinical testing to prove the company’s technology works, Kuznetsov said. The research has yielded some drug plans, but he said the company is not yet ready to provide details about them or say when they might be tested in humans.
Kuznetsov described Manifold’s drugs as antibody-like molecules. The company is designing them to address two challenges of cancer antibody drugs: specificity and toxicity. In addition to building out its in-house pipeline, Manifold is now looking to partner with other companies interested in applying barcode technology to their own drug research, Kuznetsov said.
Several new companies have raised funds in the past year Support calculation method to protein drug discovery. A startup, oligonucleotide-focused Creyon Bio, claims its AI-based technology can predict how drugs will work in humans Could enable companies to skip animal testing entirely.
Computers can be powerful tools in the early stages of drug design, Bell said. But he added that the research quickly returned to traditional testing of molecular pharmacokinetics and pharmacodynamics — what a drug does to the body and what the body does to a drug. That would require animal testing, he said. Kuznetsov saw the limits of what computers could achieve. The tests that best predict how a drug candidate will work in patients are those in live mammals, not in computer simulations, he said.
“Philosophically, we believe that the best test environment is reality,” Kuznetsov said.
Public area Photos by Flickr users Yu-Chan Chen
