Cool Startup: twoXAR

Andrew Radin x 2
Andrew M. Radin (left) with friend and twoXAR business parter Andrew A. Radin.


It’s not every day that you meet someone with the same name as you. And it’s even less likely this person will have similar interests and be someone with whom you might want to start a business.

But that’s exactly the story of the two Andrew Radins, founders of twoXAR.

Chief Business Officer Andrew M. Radin met his co-founder and Chief Executive officer Andrew A. Radin battling over a domain name–you guessed it–andrewradin.com.  About six or seven years ago, the former asked the latter, who owned the domain, if he could buy it from him and was told (in not so many words) to get lost.

Somehow, this exchange sparked a friendship, first on Facebook, then through commonalities such as travel to China, working in science and tech and their independent, entrepreneurial pursuits.  A little over a year ago, as Andrew A. Radin completed work on a computational method to enhance drug and treatment discovery, he naturally thought of joining forces with his namesake and friend, Andrew M. Radin.

For Andrew M. who was just completing his MBA from MIT Sloan, the timing was right and the discovery compelling enough to turn down other appealing job offers and join Andrew A. in forming the aptly named twoXAR (pronounced TWO-czar). Based in Silicon Valley, the company predicts efficacy of drug candidates by applying statistical algorithms to various data sets.We caught up with Andrew M. Radin recently to hear about their exciting new venture and their progress.

Tell us about what you do at twoXAR.

We take large diverse, independent data sets including biological, chemical, clinical etc.–some subsets include gene expression assays, RNA-seq, protein binding profiles, chemical structure, drug libraries (tens  of thousands of drugs), whatever we can get our hands on–and use statistical algorithms to predict efficacy of drug candidates in a human across therapeutic areas. The raw output from our technology (DUMA Drug Discovery Platform) is the probability of a given drug to treat a given disease. It all takes only a matter of minutes.

Where do you get your data sets?  Are they from clinical trials?

Some of our data comes from clinical trials, but we pride ourselves on using data sets that are largely independent from each other and come from a variety of sources along the biomedical R&D chain–as early as basic research and as late as clinical data from drugs that have been on the market for 30 years.  All of these data sets are extremely noisy, but we specialize in identifying signal in this noise then seeking overlapping evidence from radically different data sets to strengthen that signal.

These data come from proprietary and public sources. The more data we have, the better results DUMA delivers.

Could you give an example of how you could use this tool in pharmacologic research?

Our technology allows us to better characterize the attributes of a disease beyond just gene expression. We can examine how a drug might be related to a myriad of informational evidence streams allowing a researcher to build more confidence on a prediction for drug efficacy.

Let’s take Parkinson’s Disease as an example. Existing treatments focus on managing the symptoms. The real societal win would be to stop, and possibly reverse, the progression of the disease altogether. This is what we are focusing on.

In Parkinson’s disease, we’ve acquired gene expression data on over 200 Parkinson’s patients sourced from the NIH and examined over 25,000 drug candidates and have found a handful of promising candidates across a variety of mechanisms of action.

So you can “test out” a drug before actually running a clinical trial?

That’s the idea. Using proprietary data mining techniques coupled with machine learning, we’ve developed DUMA, an in silico drug discovery platform that takes a drug library and predicts the probability of each of those drugs to treat the disease in question in a human body. We can plug in different drug libraries (small molecules, biologics, etc.) and different disease data sets as desired.

At this stage we are taking our in silico predictions to in vivo preclinical studies before moving to the clinic. Over time we aim to demonstrate that computational models can be more predictive of efficacy in humans than animal models are.

It seems, intuitively, that this would be really valuable, but I would imagine that your clients would want to see proof that this model works.  How do you prove that you have something worthwhile here?

Validation is critical and we are working on a number of programs to demonstrate the effectiveness of our platform. First, we are internally validating the model by putting known treatments for the disease into DUMA, but blinding the system to their current use. If in the results the known treatments are concentrated at the top of our list we know it’s working. Second, we take the drug candidates near the top of the list that are not yet known treatments and conduct preclinical studies with clear endpoints to demonstrate efficacy in the physical world. We are currently conducting studies with labs who have experience with these animal models to publish methods for peer-reviewed journals.

You have a really advanced tool to come up with potentially great treatments, but what’s to say that’s better than what’s going on out there now?  How do you prove it’s better or faster? 

If you look at drug industry trends, the top drug companies have moved out of R&D and become marketing houses–shifting the R&D risk to startups and small and medium drug companies. Drug prospecting is recognized to be extremely risky and established methods have produced exciting results in the past but have, over time, become less effective in striking the motherlode. Meanwhile, the drug industry suffers from the same big data woes as many industries–they can produce and collect petabytes and petabytes of data, but that goldmine is near-worthless if you don’t have the tools to interpret it and extract the gold. Advances in data science enable twoXAR to analyze, interpret, and produce actionable results with this data orders of magnitude faster than the industry has in the past.

It seems that this could be scaled up to have many different applications.  How do you see twoXAR transforming the industry? 

In regards to scale, not only can computational platforms look at more data faster than humans without bias, much smaller teams can accomplish more. At twoXAR, we have a handful of people in a garage and we can essentially do the work of many wet lab teams spanning multiple disease states. Investors, researchers, and patient advocacy groups are very interested in what we are doing because they see the disruptive potential of our technology and how it will augment the discovery of new life-saving treatments for our families and will completely recast the drug R&D space. One of the things I learned at MIT from professors Brynjolfsson and Little is that the increasingly exponential growth of technological progress often takes us by surprise. I predict that tectonic shifts in the drug industry will be coming much quicker than many folks expect.

To learn more about twoXAR, visit their website and blog.

This article was originally published on MedTech Boston.

10 Genius Ideas to Improve Healthcare

Photo courtesy Anna Shaynurova
Photo courtesy Anna Shaynurova

10 Genius Ideas to Improve Healthcare from MIT Sloan’s Bioinnovations Conference

The MIT Sloan School of Management held its 11th annual Bioinnovations Conference at the Boston Marriott Cambridge Hotel on September 20th, featuring influential speakers from the healthcare, life sciences, research, and regulatory sectors. This year’s theme was “Value in Healthcare” and brought an impressive turnout of over 350 attendees.

“Our goal for the conference was to bring together industry leaders across business, science and medicine to discuss some of the most pressing issues in healthcare,” said conference organizer Anita Kalathil. “MIT and Sloan are passionate about how to improve healthcare, whether at the molecular or systems level, and we know that any solutions are going to have to be cross functional. Our goal was to make the MIT Sloan Bioinnovations conference the connecting point for these different groups.”

There were many great takeaways from this conference, but here are 10 of the most noteworthy:

1. Delivering true value in healthcare.

Neel Shah, founder and executive of Costs of Care, was the conference’s opening speaker. “There’s a misperception that considering cost is not aligned with patient interests,” he said. Cost consideration is becoming ever more important in healthcare, as policymakers demand greater accountability and patients demand greater transparency in pricing.

2. Refocusing the future of research & development.

Mark Fishman, President of Novartis Institutes of Biomedical Research, shared in his opening keynote that aging, cancer, brain disease and genetic therapies hold the greatest promise for future research. He also shared his unique approach for R&D, which is to focus less on cost-benefit and more on areas with the greatest patient needs and solid scientific knowledge.

3. Putting Big Data to good use.

There was a lively discussion during the Big Data, Policy, and Personalized Medicine panel, highlighting the need for better ways of collecting, analyzing and interpreting the huge amounts of data that are being generated from various sources, including medical records, diagnostics, genomics, and sensory data from patient devices. The panel members represented a number of impressive companies (TwoXAR, Privacy Analytics and Genospace) that are attempting to do just that.

4. Researching therapies (and prevention).

In his keynote address, Gary Kelloff, Special Advisor to the National Cancer Institute and the National Institute of Health, shared that the present approach in cancer research involves discovering and developing targeted therapies to biomarkers of cancer. While acknowledging the importance this research, Dr. Kelloff also urged participants to invest in researching the prevention of disease.

5. Improving health IT.

In his keynote, John Halamka, CIO at Beth Israel Deaconess, discussed the ongoing challenges in health information technology that need to be addressed: lack of interoperability, providing transparency while also ensuring privacy, harnessing HIT and Big Data to improve quality of care, and facing the ongoing threat of accelerating security incidents.

6. Considering a team approach.

During a panel about medical device development, Ramesh Raskar, Associate Professor at MIT Media Lab & Head of Camera Culture Research Group, shared that he felt the sciences needed to move away from independent research (which can be slow to produce innovations) and toward a culture that allows individuals to work more collaboratively in teams (which can be faster). He also shared a memorable quote: “The innovator may or may not be an entrepreneur,” which again highlighted the advantage of a diverse team approach.

7. Incorporating patient-centered design.

Kristian Olson, Medical Director at the Consortium for Affordable Medical Technology, recommended that “patients be in the room” when designing medical innovations. And Elizabeth Johansen, Director of Product Design and Implementation at Diagnostics for All, shared her techniques for creating user-friendly devices. Particularly helpful was her advice to observe how patients interact with their devices in their own surroundings.

8. Overhauling healthcare delivery.

According to Mikki Nasch, co-founder of The Activity Exchange, “Your zip code is still a better predictor of your health than your genetic code.” Social and environmental factors are huge determinants of health, and the delivery of healthcare in old models doesn’t address this issue. Healthcare needs urgently to transition away from traditional paradigms and into newer models of care, such as ACOs, that better address these social factors.

9. Finding collaboration between payers and pharma.

There was a lively debate during one of the panels about specialty drug pricing. Panel members suggested that payers and pharma need to come together at a systems level to help advance development of treatments and cures. Dr. Winton from Biogen Idec Market Access suggested new payer-pharma models and shared risk plans.

10. Driving innovation with patients at the wheel.

The final keynote of the day was given by Jamie Heywood, Co-Founder and Chairman of PatientsLikeMe, an online platform that allows patients to share information about their medical conditions and treatments and connect with others with similar conditions. Not only does this novel website help patients, but the open platform also allows healthcare and industry professionals to better understand patients’ experiences and conditions and may help to accelerate the development of new treatments. Conference attendee Dimple Mirchandani was impressed with Heywood’s emphasis on continuous learning to better understand diseases and their treatments, and by his inspiring vision for caregivers and patients to use “data for good.”

This article was originally posted on MedTech Boston.