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Fla. Research, Tech-Transfer Activity Reach All-Time High, According to FSU-FRC Survey

NAME: John Fraser
TITLE: Executive Director of the Office of IP Development and Commercialization, Florida State University
BACKGROUND: President, Association of University Technology Managers (2006-2007); Director, University/Industry Liaison Office, Simon Fraser University; Executive Vice President and co-founder, UTC; President and CEO of UTI; Vice President, TDC; President, Burnside Development
CHICAGO — Research and technology-transfer activity at research universities based in Florida is at an all-time high, according to survey data released last week by Florida State University’s Office of IP Development and Commercialization.
According to the data, which was gathered from the 13 schools that make up the Florida Research Consortium, Florida research universities last year collectively spent $1.6 billion on research, filed 803 patent applications, won 171 US patents, and inked 135 licensing or option agreements — all records since the schools began reporting data as a group in 2000.
FSU also reported that revenues from licensing deals spiked slightly from the previous year: In 2007, members of the FRC took in $53.7 million in licensing revenues, compared to $47.7 million in 2006. However, the FRC schools have yet to approach the amount of revenues realized in the early part of the decade: $95.4 million in 2000, $92.2 million in 2001, and $86 million in 2002.
The member institutions of the FRC are: Florida A & M University, the University of Miami, Florida Atlantic University, the University of Florida, Florida Gulf Coast University, the University of North Florida, Florida International University, the University of South Florida, Florida State University, the University of West Florida, the University of Central Florida, the Florida Institute of Technology, and Nova Southeastern University. The aggregate tech-transfer data can be seen here.
This week, BTW caught up with John Fraser, who heads FSU’s tech-transfer office, at the Licensing Executives Society Spring Meeting here to discuss the FRC’s tech-transfer activity and numbers, and how the organization is using the data to define tech-transfer success.
Following is a transcript of the interview.

Why does Florida aggregate data on tech-transfer activity at all of its research institutions, and what is your role in this?
The reason we decided to aggregate this data is to set a baseline that begins to show the role of universities in Florida’s economy. The first thing we had to look at is what ways universities interact. Part of it is R&D money from the government, and we also work with corporations both inside and outside the state. The whole point was to begin to showcase how tech transfer works in the state.
The Florida Research Consortium was an initiative of Governor Jeb Bush, who said that California and other states seem to have figured out how their universities work with companies in the state to impact and improve the number of jobs and generate wealth. He wound up putting together a group of people to figure out how to do this in Florida, which created the Florida Research Consortium. I am on the executive board, which is largely made up of vice presidents of research at the universities and representatives from a number of the major corporations in Florida.
How did Florida State University become the conduit through which this state tech-transfer data is disseminated?
I arrived in Florida almost 12 years ago. I went around and started talking to my counterparts at the University of Florida and the other big universities and realized that from where I had come from, there was always an association or some other organization in which all the tech-transfer directors got together and talked shop. This hadn’t happened yet in Florida, so I decided to take the initiative. We get together to talk about legislative issues, swap stories about how to deal with particular companies, talk about what language is appropriate in particular situations, and decide to take certain initiatives together. For a number of years we have gone, and will continue to go, to the Biotechnology Industry Organization annual meeting to staff the Florida pavilion. Right now one of the major issues is increasing the number of university start-up companies.
Do you have data from other states that you can compare to the Florida tech-transfer metrics to make meaningful comparisons?
The data we have on Florida is fundamentally data from the Association of University Technology Managers. AUTM collects it nationally, and hasn’t historically broken it down state by state. However, AUTM has put together a data engine on its website called STATT [Statistics Access for Tech Transfer — see BTW, 3/5/2008]. You can go in there, pull up a state, and delve into the data and get the same sort of information that we collect in the state of Florida for comparison’s sake. I have not done that personally yet.
There are ways to compare it but there are a lot of differences. If I compared Massachusetts — which has huge research-intensive hospitals and research institutions that have been there for decades — with Florida, where the presence of such a research base is relatively new; or if I compared Florida with a state like Kansas, or South Dakota, there would be many differences, so it would be hard to compare.
How much university tech commercialization in Florida falls loosely into the life-sciences bucket?
About eight years ago, AUTM asked precisely that question, and gathered data nationally. We learned that approximately two-thirds of the licensing deals that we do are in the life sciences. And in the same year, roughly 80 percent of the tech-transfer revenues were life-science oriented. Obviously, the revenues were from deals done a decade or so before that, but that was the first time we had an answer to this question.
We’ve not followed up, because it is very complex to do. But my guess is that in Florida those numbers have been maintained. When you look at the life sciences sector, historically they have been open innovators and taken innovations from wherever they can, added a lot of value, and gone through the horrific development process. Other industries, like IT, have a very different business model, and a very different approach to working with research universities. They would much rather have a research collaboration in which they can work together, solve a problem quickly, and get it into the marketplace; as opposed to licensing a patent, aggregating it with other things, and over the next 12 years, developing a product for the marketplace. We’re seeing an interest from companies in the science and engineering fields wanting to do deals because the technology they bring to the marketplace will impact the healthcare and life sciences sector, and continue to provide solutions for medical problems. We’ll see more of that, but the preponderance of our deals will likely remain life sciences oriented.
In 2005 and 2006, the total research funding from all sources to Florida universities dipped dramatically from 2004, but the total research funding in 2007 was at an all-time high. Why?
This was actually a data-collection glitch. We realized that for a couple of years one of the schools hadn’t followed through and put the correct data out. We saw that and asked the very same question.
So the research funding has actually slightly risen year over year since 2004?
The same trend is seen in the licensing revenue data. Is that due to the same data-collection glitch?
Yes and no. That’s a different question. Early in the chart, which covers a decade, the Florida State University Taxol license [with Bristol-Myers Squibb] was bringing in $50 million to $60 million per year, and the University of Florida was just beginning to accelerate its program. The Taxol royalty deteriorated rapidly, so you see the numbers high, then going down quickly, and coming back up again as we recovered from the loss of revenue from Taxol, but others started contributing more. This is typical with licensing income – it goes up and down quickly depending on the product.
Of all the tech-transfer metrics that AUTM and the FRC put out, which do you think are the most important for defining tech-transfer success?
I was asked to do a paper that was published in the Licensing Journal in January. I called it ‘Lessons Learned.’ And what I learned is that inside an office, you look at different metrics depending on how mature your office is. Early on, you look at the number of invention disclosures. Five years out, that’s less interesting, and you look at the number of deals. Five years after that, you look at the number of products on the market and the royalty income from those as a metric of success. So the metrics change at the office level over time.
Then there are new initiatives like startup companies, so you begin to incorporate that in the metrics like AUTM has done. But then you realize: what is it strategically that we are attempting to do? We want to measure impact. How do we measure impact? Well, obviously, by the number of deals, and the cash flow. We can control the number of deals that we do, but we can’t control the licensing income. That’s the market, and that’s the company. Fine, but that’s another indicator.
Then you look beyond that to the impact. Products: How many lives saved? Taxol: How many lives saved? The answer from Bristol-Myers Squibb is that 2 million women over a period of about eight years used Taxol in their fight against breast and ovarian cancer and improved the quality of their lives. Now you’re really talking.
Ashley Stevens [director of Boston University’s Office of Technology Development] is doing a study, and he presented a poster at the last AUTM meeting about it, that found that in the last 15 years, 115 [US Food and Drug Administration]-approved drugs were sourced or invented at institutions with the use of public funding. All of a sudden you realize that this is a huge indicator of impact on the pharma industry.
Then you can start measuring improvements on competitiveness and productivity. Or you can look at startup companies. When one of these companies starts growing, what is the average wage? What jobs are created? What wealth or investment income is there? And that will tend to be in the local community. So you have to look beyond our traditional metrics and start thinking about impact.
The last thing is, having done that, how do you communicate the value of what this is all about, which is why AUTM did the Better World Report. AUTM now has a task force underway which focuses on how we can measure beyond our traditional metrics. I’m hoping that over the next year we’ll start issuing new reports on data that we want to gather. We explicitly over the last few years went to decision-makers and policy-makers in Washington, DC, and asked them what kind of data they are interested in. We told them that we think we can gather some of this information, by working with the venture capital community, and with associations like the Association of University Research Parks.
So the metrics are evolving, changing, and broadening as we begin to understand the broader impact of what we are doing. Some of the traditional metrics we will need to keep.
Because we need them to measure the effectiveness of our offices. There may be things that we realize five years from now that we need to start tracking. We’ll need to do more detailed case studies, and understand the networks that impact innovation.
When we look at our own data … for instance, we’ve done a number of quick informal studies that ask, ‘Over a five-year period, how many deals done in a particular year went to startups, how many went to companies with fewer than 500 employees, and how many went to large companies?’ The answers are surprisingly stable numbers. Startups are about 14 or 15 percent; large companies are 33 to 38 percent; and the bulk of the deals we do are with small, innovative companies. Is that a metric? No, it’s a data point after the fact, but boy is it interesting.
AUTM is looking to study its process, to take an inventions disclosure, and ask how long until it patents? How long until there is a deal? How long until there is something in the marketplace? And typically, what is the exit strategy? Also, how many of these disclosures go absolutely nowhere? This is not a good business model in terms of getting positive cash flow. It’s a crapshoot. When it succeeds, which is a modest percent of the time, you can measure impact in terms of things like lives saved. But you also want to learn from the failures.
People are starting to realize that this activity can impact the American economy, and as we’re hearing at this meeting, it can impact other economies, like Chile. There is a process, and you have to measure more than just patents and deals done. You have to ask yourself how this fits in with the economic fabric.

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