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February 22, 2016

twoXAR: Disrupting Drug Discovery

twoXAR’s analytics combine radically different data sets to match up diseases with potential drug candidates.

Eric Bender

“We’re looking to build a next-generation biopharmaceutical company that brings a data science-first approach to drug discovery,” says Andrew M. Radin, co-founder and chief business officer of twoXAR. “This is where we can have the biggest impact in reducing the time and costs associated with finding more effective medicines across many complex diseases, especially in rare diseases where there is a lack of investment.”

Andrew M. Radin
CBO & Co-Founder

A startup in Palo Alto, California, twoXAR integrates and analyzes massive biological, chemical and clinical data sets to prioritize drug compounds as candidates for treating specific illnesses. “We’re computer scientists solving a biology problem, as opposed to biologists trying to solve a computer science problem,” says Radin. “We have no wet labs and conduct no animal studies.”

The company was founded by two Andrew Radins in 2014 — Andrew M. Radin, who graduated from MIT’s Sloan School that year, and Andrew A. Radin (no relation), a data scientist who has worked as chief technical officer for several startup firms.

twoXAR’s roots go back to a graduate school class Andrew A. took in bioinformatics at Stanford University. Given a homework assignment to extract findings from sets of biomedical data, he created a big-data algorithm to study type 2 diabetes, and produced a surprisingly successful model for predicting which drugs might work for the disease.

That success eventually led to the formation of twoXAR, which has built a commercial computational drug discovery platform based on the algorithm Andrew A. developed for that initial classroom project. Since then, they have worked painstakingly to evolve the technology and validate the results it generates.

In Parkinson’s disease, for example, twoXAR’s platform sifted through an extremely broad collection of data from pre-clinical and clinical research, along with a library of drug compounds, and produced a list of intriguing drug candidates.

At the time, the company didn’t have the expertise in Parkinson’s to evaluate these results. However, it found that one of the top candidates was being studied in the lab of Tim Collier, a leading Parkinson’s researcher at Michigan State University. After examining the twoXAR results in detail, Collier and his colleagues agreed to begin an ongoing collaboration with twoXAR. The Collier lab is now running animal studies to examine the efficacy of some of the compounds the company has identified.

twoXAR also works with scientists at the University of Chicago and Mount Sinai Hospital in New York City. In these academic collaborations, researchers provide the company with data around a disease they’ve been studying. twoXAR puts that data into its system, along with related data sets from various public and private sources, and generates promising candidates. “Basically, at this stage we’ll file indication patents for these candidates, our partners will run animal studies on them, and we’ll share the upside from any discoveries we make together,” Radin says.

Additionally, the startup is engaging with a number of biopharmaceutical firms in drug discovery collaborations. “Each one of those conversations looks very different, depending on the disease in which they’re interested and the stage they’re at in adopting large-scale data sciences,” Radin comments. “We’re not looking for folks to convince; we want to find companies that recognize that the analysis of large data is the path to the future.”

twoXAR offers to help its partners identify new candidates and new targets for a specific disease, prioritize existing candidates for a disease, or validate existing compounds repurposed for another disease.

Bringing data science to bear

From a drug developer’s perspective, “you can think of our platform as similar to high-throughput screening of drug candidates, but much faster, with a much broader set of drugs and data,” says Radin. “Because, when we look at biological data, chemical data and clinical data, these are radically different data sets. Any one of those data sets independently might not provide enough information, but when we look at the overlaps between them, and we start to see the same signal out of all that noise, that’s a strong indication that a drug might treat that disease.”

The twoXAR drug discovery software platform works in a four-step process. The first step is collecting biological data from sources such as gene expression microarrays, chemical data such as molecular structural information, and clinical data to see if the drug might be protective against a similar disease. The data collection also draws on libraries of molecular drug candidates, often using one library with more than 25,000 compounds.

Next, the platform takes that data and plugs it into a network model of the illness. Third, its proprietary algorithms identify relevant features. Finally, these features are plugged into a machine-learning algorithm, which produces a list of the compounds ranked on the probability that they can effectively treat the disease.

Compounds that post the highest scores but are neither known treatments nor under study are particularly interesting candidates for study. “Some may lead to new mechanisms of action, which are what actually cures these diseases and doesn’t just treat the symptoms,” Radin says.

Although current drugs for Parkinson’s, for instance, only treat symptoms, “we can tune our algorithm to focus on things that are potentially neuroprotective — stopping or reversing the progression of disease,” he says. “We’ve filed for patents for several candidates that we’re looking to move forward in studies.”

So far, twoXAR has run analyses for compounds addressing more than 20 diseases. And as the firm broadens the scope of illnesses under study, it also steadily widens the data sets on which it draws. “The more data we have, the better,” Radin notes. “As this universe expands and more people are willing to share their data from public and private sources, we are able to make even more robust predictions.”

Not all of these data sets are equally trustworthy, but “we have algorithms to determine which data sets are noisy and which aren’t, and we throw out the ones that are not relevant,” Radin says. “So it’s not a huge problem for us.”

Connecting and competing

MIT connections have been key for twoXAR’s launch. In the company’s earliest days, Andrew M. drew on conversations with MIT alumni in senior management positions in biopharma companies for guidance on the pharmaceutical industry. He now works with the Industrial Liaison Program, the Martin Trust Center for MIT Entrepreneurship, and former professors at Sloan to find partners and hone business strategies.

Given the rapid changes and deep competition in drug discovery, entrants such as twoXAR have their work cut out for them. “Our competition is basically any pharmaceutical company that’s discovering new drugs,” Radin says. “But really, our biggest challenge is changing the perception of big data-driven approaches within the industry. It is still early days and a lot of skepticism remains about this radically new approach to drug discovery.”

“We're just doing what scientific researchers have always done,” he comments. “But advances in statistical methods, our proprietary algorithms, and secure cloud computing allow us to do it orders of magnitude faster across disease areas where there are real needs for new medicines.”

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