Who’s who in university tech transfer offices

If you were anything like me as a graduate student, your impression of your institution’s tech transfer office (TTO) is of a mysterious place that 1) at some point early in your graduate career insists you sign over your rights to patent inventions, and 2) suddenly sprung into action with a flurry of requests for information and data about a week before you were scheduled to present at a conference.

Likewise, if you’re an entrepreneur looking to license intellectual property (IP) from a university, the structure of a university’s TTO may not be clear; who should you talk to and when? Despite this, TTOs are incredibly important if you want an invention or IP from the university to make it into application in the real world. They decide what gets patented and who gets to commercialize a technology.

The purpose of the TTO

At the risk of generalizing, I’ll define the purpose of TTOs by their name: to transfer inventions or technology from the university to a company that can commercialize it, in exchange for royalties or payments that flow back to the university, research, and the faculty/student inventors. How TTOs do that has changed in the last 40 years1 and varies by institution; some TTOs stick strictly to patenting and licensing, while some include corporate alliances, and others incubate startups.  

A TTO’s funding structure is an important element for academics and entrepreneurs to understand because this influences its risk appetite in what it chooses to patent, who it is motivated to work with, and how it prefers to transfer technology. . Some universities provide a majority or all of the TTO budget, freeing these TTOs to patent higher-risk or potentially less commercially-viable inventions without the worry of recouping patent or IP costs from companies that license the technology. Other TTOs are funded primarily through the licensing fees, payments, and royalties that are made when a technology is licensed. These TTOs must consider how likely they are to recoup the costs on a patent, leading them to not patenting higher-risk inventions but have an overall more ‘commercially viable’ IP pool in the university. Similarly, self-funded TTOs will more thoroughly weigh whether a licensee can pay for a license and how likely it will successfully commercialize the IP (leading to more funding support for the TTO).

Another major axis that TTOs will differ along is in their mission statements or existing directives, and whether they are private or public institutions. Public and state-backed institutions often have a mission of economic development within the state, providing financial backing or other incentives for the startup to remain in-state. And institutions are increasingly recognizing the value of startups as a way to move their inventions to real-world application, leading to changes in mission statement or directives to support new ventures.

Roles in TTOs

From my experience working in Yale’s TTO (the Office of Cooperative Research) and chatting with folks at other institutions, I put together the below list of who’s who in the TTO. Note that it is based on Yale’s model, with some generalizability and mix-and-match of roles across institutions. If there’s anything I’ve missed or if you want to share how your TTO is structured, share in the comments below or drop me an email. These structures aren’t secrets and the more we can compare and contrast working TTO models, the more we learn about what works!

The core team

Business Development Professionals (aka, the BDs): The BDs are usually the main contact point for most academics with an invention or entrepreneurs/companies looking to license. On the academic side, the BDs are responsible for maintaining relationships with the faculty and understanding their work, monitoring progress toward an invention that could be patented, assessing whether an invention should be patented, and managing the patenting process. On the entrepreneur/company side, the BDs maintain a portfolio of IP that can be licensed from the university, explain the value of each piece of IP and get answers to questions they don’t know by asking the inventors, and drawing up and negotiating term sheets and licenses. Depending on the university, the BDs may be organized by industry area, type of application, or ad hoc (just who reached out to the faculty member first).

Graduate/Postdoc Volunteers/Fellows: Savvy TTOs (or individual BDs) increasingly have roles for graduate students or postdocs that volunteer or are paid a stipend to assist. These folks help determine the value and patentability of a technology by conducting market research and patent searches. Motivation depends on the person, but they’re typically building experience for a future career in a TTO, patent law, corporate BD, new ventures, or consulting. They often don’t have business or professional experience and may need extensive guidance in their work. But for both academics looking to launch a new venture and entrepreneurs, they can help you get work done — just remember to be clear on your asks, provide them with feedback, and where possible help them move toward their future career goals in return. 

Yale’s official programs2 for graduate students while I was there: Blavatnik Associates, Y-AID Fellows, Canaan Fellows.

Intellectual Property and Patenting: The IP and Patenting people are responsible for getting patents issued, shepherding the process from invention disclosure by a faculty member to the TTO, through provisional patenting or a PCT, to the final patent being issued. Though they may have extensive legal knowledge of patentability and the patenting process, I found that patent writing may still be outsourced to a legal firm (or firms). This legal firm drafts the patent and finalizes it with input from the BDs and the academic faculty/staff/student inventors. As a result, both academics and entrepreneurs/companies tend to have limited direct interactions with IP and Patenting folks.

Licensing and Payments: The Licensing and Payments team are what I would call ‘business end’ of the TTO: they invoice entrepreneurs/companies that are licensing the technology (for fees, royalties, etc.), receive payments, and allocate them as dictated by the technology license and university policy. By extension, they will often also handle assignment of rights for inventions and finalize agreements of royalty allocations between co-inventors. (Side note here for faculty/students/postdocs: universities often have ‘default’ policies for allocations that are available on the TTO website – some examples here, here, here).

Operations: The Operations or Ops team is responsible for the TTO’s operations, so they most frequently interact with other teams in the TTO or other parts of university administration, and rarely interact with academics or entrepreneurs. However, they’re great to know because they often plan TTO events, such as monthly updates  on new discoveries or annual tech showcases. As an academic, these events are a chance to showcase your work and inventions to entrepreneurs or companies that are potential licensees. As an entrepreneur, these events are your chance to browse what the university has and talk directly with inventors.

Communications: Not every TTO has its own communications (Comms) person or team, but they should. The purpose of the Comms Team is to keep the TTO connected to the university community and the entrepreneurial and business communities, as well as raise the profile of the TTO to the outside world. They are looking for stories to highlight the success of inventions and IP from the TTO, so getting to know them is a chance to send your successes to them (either as an academic or as an entrepreneur) for a win-win situation: they get content to publish, and it raises the profile of your research or company.

Administrative assistants: The administrative assistants support the roles of other members of the TTO and while that’s self-explanatory, I wanted to highlight them here because 1) they are underappreciated and 2) knowing the right admin assist to contact can be the difference between getting a meeting the next week or getting radio silence for months. They often manage logistics, scheduling, and much of what needs to get done in the office. They field calls from both inside and outside the university and try to connect individuals with the right resources. So if you’re not sure who you should be in contact with, they’re a great starting point.

Director or VP of Tech Transfer: The Director is the person in charge of the whole TTO, including setting its strategy and facilitating relationships with other parts of the university or other institutions. Their specific responsibilities and level of interaction with academic inventors and entrepreneurs vary by TTO, but typically they are the person with final sign-off or approval on a license to a company or entrepreneur. They are also often founts of wisdom who have seen many seasons in BD and/or in TTO’s and can provide high level context on relationships between the TTO and other parts of the university or with outside entities.

New Ventures

The growing interest in entrepreneurship, startups, and new ventures over the last decade has led to a flurry of new roles in university TTOs aimed at supporting and launching startups from university inventions. The structure of the new ventures component of a TTO can vary significantly, but overall can contain:

Director of New Ventures/Entrepreneurship: Depending on the TTO, this person’s responsibilities can vary from advising startups, to networking and supporting the ecosystem as a whole, to presiding over disbursement of seed funds. If you’re a student or faculty interested in launching a new venture, I would highly recommend getting to know this person and asking their advice on how to proceed. If you’re an entrepreneur, this person is often great for learning what tech may be promising or ready to launch a company around, and for learning what talent may be interested in joining a startup or what resources are available for startups.

New Venture Managers/Program Mangers: These people are still rare in the TTO ecosystem, but they are usually the boots on the ground managing the new venture programs. This includes administering venture development programs (think I-CORPS and accelerator programs) and providing support strategic and operational support to a portfolio of new ventures. The responsibilities of their role often overlap with those of New Entrepreneurs (see below), though Venture Managers aren’t necessarily looking to launch a startup themselves. But some still do make the jump from their role into a startup!

Entrepreneurs in Residence/Mentors in Residence: EIRs and MIRs are folks volunteering their time or have part-time positions to provide advice or consulting to university startups. Some are entrepreneurs looking for their next company, and the EIR role allows them to learn about and keep updated on multiple potential leads from different labs. Others are consultants who donate their time upfront and are looking for paid contracts once the company has launched. And still others are current executives with business experience who just enjoy giving advice and feedback. For academics looking to launch new ventures, EIRs and MIRs can provide invaluable advice on the viability of your startup, what parts are promising, where there are holes in your story or data. EIRS and MIRs can make introductions to others who may be assets for your startup, possible funders, or other people who would be valuable for your network. On the flip side as an entrepreneur, EIRs may provide you with advice and a broadened network, but they may also serve as competition if they see themselves in the same role you envision for yourself. If you’re interested in tech from a particular university, I’d recommend looking into becoming an EIR as a win-win: you’ll get an early look at technologies that may be your next new venture, and you’ll be guiding academic teams to produce technologies and data packages around those technologies that are more ready for investment.

New Entrepreneurs (Blavatnik Fellows, Venture Fellows, etc.): New entrepreneurs differ from EIRs in that they are first-time entrepreneurs and are typically willing to do more legwork than an EIR, who is primarily advisory or executive. They are often coming from a professional role (e.g., operations, consulting) and are making the jump into startups, so they are similarly evaluating the technologies at a university to find one to launch a venture around. Based on their background, the new entrepreneur can help with everything from building pitch decks and honing the startup story, to networking with investors, to finding lab or office space for a new venture. They are often supported by the university for 1-2 years (at Yale, we have the Blavatnik Fellowship, which provides us a 1-year stipend). For academics, they are invaluable to jump start a venture because your time and business expertise is limited, but you don’t yet have funds to pay an employee. For entrepreneurs, they can also be invaluable resources to help launch a company — just remember that they’re looking for a startup to join or cofound, so don’t take advantage of their help and then leave them high and dry.

External Partnerships

I’ll end this post (dangerously) on the group I know the least about but has become increasingly important as government financial support for research has waned: external partnerships. External partnerships are companies or other groups that fund research at the academic institution. There are often rights or restrictions on any IP generated, ranging from an option (e.g., Pharma X has an automatic option of 6 months on any generated IP) to first right-of-refusal on a license. In one extreme case I heard of, the funding stipulated that no IP could be filed on the funded research: an unfortunate situation for the faculty member.

I’m not sure how most institutions handle external partnerships within the tech transfer office versus creating a separate office to handle them. At Yale, the Office of Cooperative Research was involved in helping arrange and execute external partnerships, but the majority of this work occurred through the Office of Sponsored Projects. This office had an entire separate set of staff built around structuring and managing these partnerships. The UNC system also has a separate office for this, known as Industry Partnerships. However, some institutions may also handle these partnerships in the TTO, either in a designated team or across the roles above.


This post started as brain-dump synthesizing some of what I learned as a Blavatnik Fellow at Yale. Along the way, it morphed into a full-form breakdown of TTO roles. Many thanks to Jamie Testai and Kirsten Leute at Osage University Partners for their content contributions and wise editing feedback.

And a huge thank you to (in alphabetical order) Barry Schweitzer, Bill Wiesler, Bridget Martell, Colin Foster, Dave Lewin, Diane Harmon, Jim Boyle, John Puziss, Jon Soderstrom, Morag Grassie, Natasha Samuels, Rich Andersson, Tom Jasinksi, and so many more people at Yale’s Office of Cooperative Research for everything I learned as a Blavatnik Fellow.

1 For an excellent summary of how the role of TTOs has changed over the past 40 years, see https://www.linkedin.com/pulse/evolution-technology-transfer-arundeep-s-pradhan-rttp/

2 For those of you who can’t access these programs but are looking for a role like this, know that it generally doesn’t hurt to reach out to a TTO offering to work in exchange for experience. Just please, be realistic about your time commitment for helping.

What ate the public biotech market?

The usual disclaimer: I’m not investment advice. I hold $XBI. With thanks to Carl West for editing and feedback.

In case you don’t know, the biotech market is down. Like, really down.

With the biotech rout in the public markets, I wondered whether there are any trends that can be pulled out of the plunge in stock prices. I figured this would also be an interesting introduction to data from public markets; I’ve spent most of my time buried in individual SEC filings like 10Ks and haven’t worked with aggregate stock data. So let’s go ask some questions and get some data!

The Question

The commonly cited reason for biotech’s poor performance is that there are “too many preclinical companies” that are far from FDA approval and any revenue. But what do analysts mean when they say this? And are preclinical-stage companies actually the reason the biotech market is down so much?

Skip the next two sections and go to The Results if you want the punchline, but I will silently judge you.

The Data

Google search didn’t turn up any freely available list of all public biotech stocks, so I went with the list of companies published in the Q3 SPDR S&P Biotech ETF, better known as the biotech index fund $XBI. It contains 166 companies of the >700 small and mid-size biotech companies on the market, so upfront the data may be skewed. But $XBI is one of the most commonly-referred to biotech index funds in biotech industry news (particularly when proclaiming the fall of the biotech market), so I figured this would be a good place to start analysis.

I also hold $XBI stock (so, disclosure and all that) and it’s always good to understand more about what you’re investing in.

The Methods

I pulled the 166 companies in the $XBI into a spreadsheet and then manually analyzed each company’s website for therapeutic area keywords, number of preclinical-stage assets, number of clinical-stage assets, and number of approved assets. Some of these may be miscounted because of out-of-date pipeline information or my misinterpretation of the pipeline – many of these companies had an asset in multiple clinical trials for different diseases.

After pulling the disease area and pipeline data, I used Excel’s stock features to pull financial information for each company. This included the stock price on November 30, 2021 (near market high) and June 15, 2022 (near market low), market cap, and P/E (as applicable). While I used all companies in calculating the average drop in stock market, I excluded 13 companies because they no longer existed (e.g., Achillion Pharmaceuticals), or were diagnostic/medtech/lab tool companies whose pipelines were harder to evaluate (e.g., Myriad Genetics, MiMedx Group, Twist Biosciences). The average loss in stock price in the $XBI is 41% when including these companies, and 40% without them.

Finally, I checked company press releases for catalysts occurring on or before June 15, 2022 to identify what events may be driving some of the outliers.

The Results

(1) There aren’t simple indicators of the market

The stock price in $XBI declined an average of 41% in the ~6 months between November 30, 2021 and June 15, 2022. But none of the variables I looked at was a strong predictor of a company’s change in share price. Part of this may be edge cases of positive or negative news, but (unsurprisingly) there probably isn’t a single indicator of the market. This is good news, because it means the time spent digging into a company’s 10k and analyzing the individual markets for potential assets when deciding to invest isn’t wasted. That, or the market is just a mess moving on irrational hopes and fears.

So Analysts, you get to keep your jobs.

It’s okay, your secret is safe with me. All these memes brought to you by reading too much Out-of-Pocket Health.

(2) Prices still move on the usual catalysts of acquisitions and clinical/regulatory events

For example, Turning Point Therapeutics (NASDAQ:TPTX) was up 96.8%. And a quick Google search reveals the reason: an acquisition announcement by BMS for $4.1 billion. There’s a similar story for Biohaven through its acquisition by Pfizer (NASDAQ:BHVN, up 29.2%), though that pathway was been a bit rockier.

Individual stocks (labeled) plotted by their percent change in stock price from November to June (y-axis) by number of clinical-stage and approved assets in their pipeline (x-axis). Orange points are companies for which there were clear catalysts in press releases. The reason for looking at both clinical-stage and approved assets is below.

Similarly, companies are trading based on clinical data readouts. Arcutis (NASDAQ:ARQT) was up 24% on positive Phase 3 data from roflumilast in sebhorreic dermatitis, which is already under consideration for approval in plaque psoriasis. On the opposite end, companies are hurting from negative data readouts; Praxis (NASDAQ:PRAX) was down 88.7% from a failed Phase 2/3 trial for major depressive disorder, and Akebia (NASDAQ:AKBA, down 87%) received a CRL from the FDA for vadadustat, their candidate for anemia in patients with chronic kidney disease.

(3) More clinical + approved assets = less loss in stock price…ish

I was curious to test the analyst refrain of “too many early-stage/preclinical companies on the market.” This could be interpreted in many ways: (A) too many companies who have only preclinical assets, (B) too many companies whose assets are mostly preclinical, or (C) too many companies that are too far from revenue (where “early-stage” just means “many years to approval and making some $”).

Let’s start with (A) too many companies whose assets are mostly preclinical. This doesn’t make a ton of sense, because there are a total of three (yes, three) companies on the XBI whose pipelines only have preclinical assets: Beam Therapeutics (NASDAQ:BEAM), Verve Therapeutics (NASDAQ:VERVE), and Sana Biotechnology (NASDAQ:SANA). This could still be true for biotech stocks not listed on the XBI, but three stocks aren’t pulling the entire XBI index down 41%!

So let’s look at (B) too many companies whose assets are mostly preclinical. To figure this out, I looked first at the proportion of the pipeline that was preclinical, which was calculated as:

[number of preclinical assets]/([number of preclinical assets]+[number of clinical assets]+[number of approved assets])

For example, if a company had 5 preclinical assets, 3 clinical assets, and 2 approved asset, that would be 5/(5+3+2) = 0.5.

Individual stocks plotted by percent change in stock price from November to June (y-axis) by ratio of preclinical assets to total pipeline (x-axis).
Haha that coefficient of variation is pretty much useless.

But the resulting dataset doesn’t show much of a correlation. It was also hard to assess the number of preclinical assets; some small companies will have none because their sole asset has advanced into clinical trials, while some of large biotechs like AbbVie and Vertex have many preclinical assets because of their massive pipelines.

So next I looked at the inverse: the number of clinical-stage AND approved assets in a company’s pipeline. I included both clinical-stage and approved assets in the count because the move into clinical trials is a de-risking step that provides some value. And because the numbers were less variable, I could bin companies by their number of clinical-stage and approved assets (so, if a company has 3 clinical and 2 approved assets, that would be 5 total).  

I then plotted their change in share price by the number of clinical-stage and approved assets, or number of approved assets they have. I was a lazy and stuck all the companies with >10 assets into one big bucket at the end, binned under ’11’ (come at me, stats profs).

Average change in stock price and standard deviation for stocks from November to June (y-axis) by number of clinical-stage and approved assets in the pipeline. Number of companies in each bin is indicated to the right of each data point.
Conclusion: stock market data is noisy AF.

A weak correlation appears when binning companies by number of clinical and approved assets, then plotting this number of clinical/approved assets by the percent change in stock price. The coefficient of determination isn’t anything crazy, but a modest 0.4006.

So then I tried something simple. What if the basis of investors change in stock price is just “how far have you gotten, ever?” We can do this by looking at the furthest stage a company has gotten to with any asset: Preclinical, Phase 1, Phase 2, Phase 3, or Approval. For simplicity, I binned Phase 1/2 and Phase 2/3 studies upward (so they joined Phase 2 or Phase 3 studies).

Average percent change in stock price from November to June (y-axis), with companies binned by the furthest stage of each company’s lead asset.
Error bars to make a PI scream.

There are still some gigantic variations in stock price (as illustrated by the standard deviation bars), but overall it lines up. If the furthest a company has taken an asset is Preclinical or Phase 1, it’s getting clobbered by the market (67% and 64% loss in value). The data get noisier following that, but there’s less loss in stock price for companies who have made it to Phase 2 or 3 (49% and 45% loss), and the least loss in price for companies who have made it to Approval (29% loss). So that suggests the driver behind loss in stock price is the earlier hypothesis (C): too many companies that are still too far from revenue.

One of the biggest criticisms/post-hoc analyses of the biotech rout has been that many companies IPO’d while still at the preclinical stage. Many of these companies would be in Preclinical, Phase 1, or possibly in Phase 2 studies at this point, which would mean they’re still years from revenue. The teams in companies without approved assets also remain ‘unvetted’ – it’s unclear they have what it takes to get an asset across the finish line.

Once a company has taken something through to approval, it has de-risked its team and ability to execute. More importantly, it is also close to the point of making actual sales-based revenue, which is the biotech company version of ‘winning’. And most of these companies are still years from the finish line.

Thoughts on the public biotech bust (and does it matter for startups?)

Disclaimer: THIS POST IS NOT FINANCIAL OR INVESTING ADVICE. If you’re looking for that, consult with someone working as a financial advisor and/or (maybe not) the Twitter/Reddit stonk memes.

Well, THAT’S probably not good…

Public biotech valuations have collapsed, plummeting far beyond the overall market. Much has been written about public biotech bust (here, here, here, and Google if you want more….) Ideas on why it’s happening range from too many pre-clinical companies, too much capital, too many companies, a comedown after the rush of the pandemic, and plain old investor sheep behavior. It could, and probably is, a little of all of the above.

How does this impact biotech startups and private biotech? My instinct is that it’s a bad sign for the private market as well, primarily for late-stage companies. The public market represents one of the two successful exit pathways for startups. Private investors (e.g., venture capital, private equity) get their money back, ideally many times over, when a startup gets acquired or it IPOs. With biotech valuations this low, startups will hesitate to go the IPO route because they won’t be able to raise as much money, or perhaps even enough money to keep going. At these values private investors also won’t make back much of a multiple, or may not even make back the money the put in.

There’s a double whammy here too: cut off the IPO path and what’s left is acquisition. While there are multiple pharmaceutical companies that could acquire startups, there are relatively few (maybe 20-50 companies) compared to the thousands of biotech startups in the U.S. alone. Pharma companies do a limited number of acquisitions each year and a few have indicated they think acquisition prices are still too high. Knowing IPOs aren’t an option, pharma companies can wait until prices drop to a bargain. Again, private investors and founders lose money. At some point acquisition could increase enough to cover the volume of companies that would instead IPO, but that won’t be immediate.

Earlier-stage companies raising Seed and Series A are a bit safer for now, but it will get more challenging the longer this drags on to months or years. They’ll find it harder to raise Series A, Series B or subsequent funding as venture capital firms save funding to help the startups they’ve already invested in weather the storm and wait for better offers from IPOs or pharma acquisitions. There may also be less funding overall available, as private investors see poorer returns from biotech and allocate their money elsewhere.

What would turn this around?

First, there are larger trends that are weighing down the market overall: Fed tightening to reign in inflation, the war in Ukraine and the impact that has had on oil prices, and COVID-related supply chain disruptions are issues (quick explainer here at Reuters). If any or all (ha) of these were to disappear, we would likely see a rebound in the entire market, including some in biotech. But it probably wouldn’t be enough.

Ultimately what we’d need to see are one of two things:

  • Signs of clinical and commercial success from public biotech companies. Remember, these companies are all operating at a loss (and often with little to no revenue) until they have an FDA-approved and marketed therapy. A few success stories just making the leap from no product-based revenue to a quarterly revenue stream that meets or exceed projections would be good news.
  • Increased appetite for acquisition from pharma companies for private biotech companies at valuations that allow private investors to make a multiple on their invested funds. This would provide an alternative exit to an IPO that would keep funds going and private investors focused on biotech.

Will this happen. Who knows?