How Proprietary Data Analytics Is Used to Identify and Advance Translational Research at Scale

Using data models to grade assets for investment isn’t new. Both private investors and government funding agencies use analytics to make investment decisions. Research Bridge Partners’ data analytics program has taken these best practices and applied them to principal investigators (PIs) and their labs. The result is a data model and set of algorithms that gives us unique insight on not only the research but the personality, productivity, and commercial potential of universities, PIs and their labs.

Academic research is roughly a $72 billion per year industry, and the Administration recently proposed an exponential uptick in funding. We bring data analytics to it, and we share this analysis with academic researchers and their institutions.

Our unit of analysis is the PI, not the university, but we grow the model by “island hopping” from university to university. We started with 14 universities in 2017 and now have over 100. As of Spring 2021, we had about 75,000 PIs in our model, had used the model to develop approximately 400 targets, and have interviewed about half of those to determine our first set of fellows.

We receive two questions about our data analytics program often enough that it makes sense to address them here.

The first question: “Why are we using a proprietary search method instead of the conventional university channels?” In addition to their education and research programs, American universities have built licensing offices, startup incubators and accelerators, and corporate partnering offices. The staff who support these programs work hard to find and adequately resource academic inventors.

We use these channels, both algorithmically and on the ground. Research Bridge Partners’ algorithms weight participation in these programs, and we make sure to talk with the translation professionals and academic leadership wherever we go. In addition, we use our model to independently search for faculty entrepreneurs. Research Bridge Partners’ principals used to run “access point” programs, and none of us were immune from blind spots caused by institutional politics, personal interest, personality fit, limited time, or whatever.

The second question: “What are the underlying features collected on each lab, and how do you assemble your algorithms?”

What we can share are some insights, including the following:

  • Collaboration really, really matters. We looked at this from several angles and have developed an analytic perspective on collaboration that generates high correlations with other objective and subjective measures of entrepreneurial potential.
  • Gender-related access to commercialization still needs to be addressed at many universities. Academia is now hiring and promoting many more women PIs than in the past. But even after we adjusted for field, career stage, and research quality, women PIs are still commercializing at a much lower rate, overall, than their male peers.
  • Research quality is key. The most important thing that an academic co-founder can do, is to invent something amazing. One of the reasons that Stanford is “good at” commercialization is that it has 156 members of the National Academy of Sciences on its faculty. Great research innovation is the prerequisite for great commercialization.
  • Both human and artificial intelligence are needed. Algorithms are good at identifying salient trends in vast swaths of research, but they have their limitations. Understanding the disparity in the keywords alternatively preferred in academia and industry, for example, but also from campus to campus. For these and other essential tasks, only human intelligence will do.

We cannot be successful without Research Bridge Partners’ proprietary analytics, but our success does not depend on them alone. Analytics are essential because they enable us to identify individual researchers who have the rare potential to be the scientific co-founders of a success biomedical startup. However, understanding that potential, let alone realizing it, can only be achieved through a deep, collaborative relationship. In other words, our people pick up where our data leave off.

It’s People, Not Money, When Commercializing Academic Research

What matters most in our approach isn’t money; it’s people. People are our core bet – they are what we “bridge,” at Research Bridge Partners.

Researchers at universities across the country generate innovations that have the potential to benefit society in significant ways. Research leaders at mid-continent universities are generally as productive as their counterparts on the coasts. But when it comes to commercialization, Massachusetts and California produce spin-outs at a much higher rate than the rest of the country. For example, the University of California System creates spin-outs at about twice the rate per dollar of research as the University of Texas System.

This delta gets talked about using dollars, because dollars are easy to measure and obviously important. We often point out that the Bay Area has $15 of investible venture capital for every $1 of research done by Bay Area universities, but Minneapolis only has $0.09 of investible VC for every dollar of research done by Twin Cities universities. We talk about this investment capital gap because it illustrates the larger capability and performance gap between the tech hubs and the rest of the country.

The problem is that such data points lead people to conclude that dollars are the problem. “If only we could get more VC in our local community, that would close the gap between us and Palo Alto.” This gets it backwards. The dollar gap is a symptom; the people gap is the problem.

Commercializing technology is much, much more than matching promising patents with venture capital or industry dollars. Successful startups require tight collaboration between world-leading innovators and savvy, well-connected business leaders. In turn, startups need the additional support of a talented and knowledgeable ecosystem of business talent, professional service providers, investors, and others. They also need a deep enough reservoir of these resources so that a lot of people can say “no” to the deal before the right people say “yes.” Still, many institutions and states nevertheless assume that the problem can be solved with a new building or a far-reaching marketing campaign. Or by launching a $100 million fund that covers a million square miles and most of the industry sectors in the SIC index.

The right resources are hard to build and require scale. But once the necessary scale is reached, it’s reinforcing. The scientists whose innovations can change the world are working at universities all over the country. The minimum efficient scale for their innovation is low, and so the U.S. innovation infrastructure is diffuse. But their commercialization counterparts, the rare business people capable of turning that innovation into a billion dollars’ worth of impact, overwhelmingly operate in the Bay Area or Boston. Given the velocity and depth of both markets, good opportunities and good payoffs are far more plentiful there.

It’s those men and women – the people who can turn scientific innovation into scaling startups – who are what most heartland university communities are missing. The cost to move dollars is zero. It’s all electrons now. What’s expensive to move, and non-fungible across geographies, is people with options who could be doing something else with their time.

The most powerful economic force on the planet is the search for yield. Money follows opportunities. If money isn’t flowing to local deals, it’s because the local deals often aren’t shaped to attract that capital. And if the local deals aren’t good enough, the reason is either that the science isn’t good enough – which the data doesn’t support – or that the way the mid-continent innovation has been formed into an investible opportunity isn’t good enough. We bet on the latter, and we built Research Bridge Partners to bridge those great innovations and that great talent.

University Entrepreneurship Topography

Successfully Navigating the Topography of University Commercialization

University Entrepreneurship Topography

[dropcap]U[/dropcap]niversities around the country are launching initiatives to promote “commercialization” and “entrepreneurship”. But those terms cover a lot of ground. Initiatives that fail to tease out the differences between them are likely to fail, disappointing critical constituencies and leaving important opportunities fallow.

At Research Bridge Partners, we developed a framework that captures the topography of university commercialization and entrepreneurship.  It is useful in making commercialization decisions, especially early in the process when path dependencies and lock-in occur. You need to know where you are starting to know where you are going.

We look at the commercial activity at a university along two axes:  who creates an innovation at a university, and who owns that innovation.  Here is our 2x2 university entrepreneurship topography framework:

Who innovates:  as a university leader, you have different duties to students and faculty, duties that contextualize your relationship with them regarding commercial matters. In our framework, the left two examples were launched by students and the right two examples by faculty.

Who owns:  the university has different claims on th[dropcap][/dropcap]e innovation that gets commercialized on its campus depending on a number of factors – most importantly who funded the innovation, where the innovation happened, and what contractual relationship(s) the university has with the innovator. In our framework, the bottom two examples are owned by the inventor and the top two examples are owned by the University.

The stakes for you, as a university leader, are higher in some boxes than others.  In particular, faculty-generated/university-owned innovation can be especially high stakes.  Commercialization activities in this box have outsized potential to:

  1. Change the world in ways that are directly aligned with the university mission – the advancement of science in the public interest and the promotion of human health, for example.
  2. Generate significant licensing revenue.
  3. Expose the university to significant cost (from, e.g., patent prosecution) and risk (from, e.g., litigation).
  4. Directly impact faculty satisfaction, which gets reflected in recruitment and retention.

(Despite the institutional criticality of the upper-right box, it seems that a disproportionate amount of university effort goes towards supporting the lower-left box:  student innovators/no university ownership.  We are puzzled by this … but then again, we were not early investors in Microsoft, Dell, Facebook, or Snap.)

Let’s go more deeply into each box of the university commercialization landscape framework.  As examples, because they have been well publicized, we will use company examples from Stanford’s entrepreneurship experience.

Lower-left box: Student inventors, non-university-owned IP

Snap, Inc.

When it comes to students, most of us involved in commercialization and entrepreneurship seem to agree:  the university’s education obligations are paramount.   It is wonderful that Evan Spiegel and Bobby Murphy could come up with and evolve the idea for Snapchat while at Stanford, but Stanford’s core duty to them was pedagogical, not commercial – to the students, not to their innovation.

This box is filled with good news.  Your campus and your local ecosystem probably offer an abundance of solutions and support.  National partners, such as Blackstone and MassChallenge, can provide best practices, links to mentors, and potentially institutional startup capital.  Also, alumni are waiting to be asked to mentor your students.  There is a pretty good playbook here, including both curricular and co-curricular initiatives:  entrepreneurship classes, incubators, business plan competitions, etc.  Our advice is to implement as much of that playbook as possible, package these initiatives with success stories from your campus, and send the resulting student startups special delivery to the Development office for fundraising wins.  (Be careful, though:  this playbook doesn’t work very well for the other segments in the commercialization landscape.)

Lower-right box: Faculty inventors, non-university-owned IP


In the 1990s, Paul Romer, an economist, had an idea for an education tools company.  Although this idea grew out of his work as a Stanford professor, the university did not have a claim on the underlying intellectual property.  Romer formed the company, Aplia, in 2000 and raised $10 million of launch capital, which allowed Aplia to hire a strong team.  Aplia grew quickly and was eventually bought by Thompson Learning.

Aplia is a great story – not only was it successful, it was successful doing something that advances the mission of universities (education), and the faculty member who started it went on to win a Nobel Prize!

However, this box can be dangerous.  Here is why:

  1. Faculty sometimes pretend that they are in this box, when they are really in the upper-right box (university owns the IP.) Maybe they want to do commercialization without the hassle of dealing with the university’s compliance architecture or without sharing value with the university.  Maybe they just don’t know how to use the university’s commercialization apparatus, or the commercialization bureaucracy is really difficult to use.  Regardless, if they are wrong about university ownership of the underlying invention, then it can be extremely expensive to clean up after the fact … and it can expose both the university and the faculty member to significant risk.
  2. Faculty who start here are usually on their own. For Paul Romer, that worked, in part because he is super capable, had a great idea and, in part, (frankly) because he had the Stanford brand behind him and was located in Silicon Valley.  Most faculty don’t have those advantages.  They might benefit from the supportive know-how and time relief their university could provide as a collaborator.

We love this box for its opportunity and flexibility, but as a university administrator, you need to make sure you gate it properly.  Administrative conflict issues should be narrowed down early, and this won’t always be collaborative.  When it comes to commercial work, most conflicts between a university and its faculty are due to ownership and use of IP.  Calling out these conflicts is important.  But when you do this, reach out as a colleague rather than with a reprimand.

When faculty who we work with seem to be roaming down this path, our message is: “Engage the university, because if you don’t launch this company well from the start, you’re killing your options. The world of autonomy you are picturing probably does not exist.”

Upper-right box: Faculty inventors, university-owned IP


One of us, Isaac, grew up on the Stanford campus, and he remembers dinner table discussions when Norm Cohen (Stanford) and Herb Boyer (Berkeley) were launching, based on their recombinant DNA and restriction enzyme technologies, what became Genentech … and with Genentech, the entire biotechnology sector.  At the time, patenting the IP from basic research, not to mention putting that IP into a startup and spinning it out, was extremely unusual and not universally approved.  Fast forward to now, and Genentech is the poster child for university spin-outs.

The university – not the inventor – owns innovations in the top row.  This ownership creates hard obligations formalized in the Bayh-Dole Act and soft (but real!) expectations from other key stakeholders that the university will move innovation rapidly and effectively into the public markets.  It also creates compliance, conflicts management, and other oversight obligations.

Unfortunately, this ownership position can put the university into an adversarial relationship with the faculty inventor.   Although there is no way around these difficult conversations, some universities seem to have them more productively.  These universities emphasize support over compliance in their relationships with faculty – acting as colleagues rather than cops.

This box is where Research Bridge Partners does most of our work.  We have developed some counterintuitive perspectives.  For example, we think that the trend is for universities to create too many spin-out companies, that many EIR programs are wastes of money, and that fast-track licenses can be a disaster.  We will cover these perspectives more deeply in other blog posts.

In general, though, our core view of this segment of commercial activity:  if done right, it should be a powerful tool to attract and retain outstanding faculty and directly advance the university’s mission.

Upper-left box: Student inventors, university-owned IP

Alphabet, Inc. (Google)

Alphabet, Inc., is one of the world’s most valuable companies, with a market cap pushing $1 trillion.  Famously, though, when Brin and Page disclosed their search ranking algorithms to Stanford’s TLO, they were greeted with no fanfare and (if anecdote is to be believed) did not even make the office’s “top 10 list” for the year.

This box is hard:

  1. This population of entrepreneurs – especially, the post-doctoral students – have low status at most universities, and as a result often have a hard time getting mindshare from the university’s entrepreneurship programs and licensing bureaucracy.
  2. On the flip side, post docs often have the past experience in industry, the combination of personal maturity and networks, and the risk preferences (read: career desperation) that make them likely to be more effective as commercializers – not just inventors, but entrepreneurs.
  3. Some universities have not clearly defined the rules for student IP ownership, even for graduate or post-doctoral students. As a result, there can be extra confusion and tension in this box.

A lot of value gets left on the table, here.  Since, in our experience, few university programs are tailored to these inventors, and few administrators understand how to value the importance of these co-founders in licensing deals, undervalued or lost opportunities are common.

We handle this box by making the lab – not the IP – our unit of analysis.  We look for IP-plus-post-doc combinations as the core of the spin-outs that we will help catalyze and invest in.  This is not a natural act for most universities; in fact, it’s part of the value that good VCs with deep experience in academic spin-outs bring to the table in the markets where they operate.


One last point, about people.  Our university entrepreneurship topography framework emphasizes that the most important action that a university can take to support entrepreneurship and commercialization, is to curate the university community.  The common action that Stanford University took in all four of the example cases discussed above?  It got those people onto its campus.  It takes Cohens to make Genentechs.  If you want Snapchats, admit Spiegels.  For any university administrator, that’s job one.

My life-saving miracle took twenty years of hard work


Ipilimumab (now branded as Yervoy), which is based on the research of Noble Laureate James Allison, is the first-in-class immunotherapeutic for blockade of CTLA-4 and significantly benefits overall survival of patients with metastatic melanoma.


[dropcap]W[/dropcap]hen I was a sophomore in college, I was diagnosed with melanoma, an aggressive form of cancer. Within 24 hours, I was on a plane home, and 24 hours after that I had surgery to remove the tumor.

That initial whirlwind of activity stood in stark contrast to what happened next: nothing. As my doctors explained, melanoma doesn't respond to chemotherapy, radiation, or any other therapy they had to offer. After surgery, my only option was to wait and hope the cancer didn't come back.

But it did. Three months later I found a lump in my neck. The disease had returned and progressed to stage IIIc. At age 20, survival statistics gave me a 40% chance of making it past my 25th birthday.

However, those statistics couldn't account for a groundbreaking development during the brief window before my cancer returned. Just weeks before I found the new tumor in my neck, a team of researchers published a paper revealing that a drug called ipilimumab improved survival in patients with advanced melanoma. For the first time, oncologists could offer melanoma patients a pharmaceutical therapy.

Although ipilimumab (which is now branded as Yervoy) wasn't approved until the next year, I was able to access the drug through a clinical trial - a development that very well may have saved my life. Had I not received the drug, there's a one-in-five chance that I wouldn't be here writing these words today, according to the study's results.

The movie-like appearance of ipilimumab in my life was the culmination of a multi-decade process that started when I was a toddler. The drug's development is rooted in university research James Allison performed in the early 1990s. (Allison shared The Nobel Prize for Physiology and Medicine 2018 for that work.)

Most accounts of ipilimumab's development fast forward to the successful large-scale trial that led to its FDA approval, overlooking the decade and a half of heavy lifting that is typically required to develop a life-saving drug. Researchers like Allison need to find a corporate home for their breakthroughs (or build one themselves.) They also need to raise capital and negotiate licenses before they can even begin to solve manufacturing challenges and run clinical trials.

These are formidable challenges. Allison told NPR that he was "depressed" by his initial failure to find a company willing to commercialize his research. Fortunately, he worked at Berkeley, in the middle of a major technology startup hub, where he was surrounded by the resources required to overcome those challenges.

Because I survived cancer, I was able to finish my degree in math at Yale, and I went on to work at the Boston Consulting Group and then Google. I'm married now, and I'm able to support my wife as she trains to be an oncologist. (This frequently takes the form of sending her pictures of our dog and cats while she's working in the hospital.)

Unlike my wife, I don't expect to work directly with cancer patients. But as the lead data scientist for Research Bridge Partners, I'm doing my part to identify and commercialize breakthrough treatments. Our non-profit has built a one-of-a-kind data model that lets us search for future James Allisons - only those who don't happen to be next door to Silicon Valley. We use data to find the researchers whose science could save lives, but who might not get the chance to do so because they work in the wrong part of the country.

We use a lot of math and a lot of domain expertise to find these men and women. Then we do our best to ensure that the science behind the next ipilimumab gets bridged to the resources and people who can turn it into Yervoy-calibre impact in the lives of patients - like me - throughout the world.