Several employees at the Office of Technology Transfer extend their expertise in the technology industry to outside professional organizations.

Among these employees are Laura Fritts, Director of Patent and License Strategy and Chief Intellectual Property Officer; Kimberly Dunn, Compliance Associate; Linda Kesselring, Operations Director; Kevin Lei, Director of Faculty and Start-Up Services; Patrick Reynolds, Assistant Director of Faculty and Start-up Services; Quentin Thomas, Marketing Manager; and Sarah Wilkening, Licensing Associate.

The majority of the above OTT employees volunteer with the Association of University Technology Managers (AUTM). AUTM is a nonprofit organization that supports individuals involved in technology transfer through education, professional development, and advocacy, among other activities. Through these methods, the organization provides knowledge of technology transfer beyond Emory and forges connections between members, which is why many employees opted to join.

Each involved OTT member contributes to AUTM in a different capacity, ranging from assisting with marketing to hosting instructional webinars. For example, while Fritts serves on the AUTM Public Policy Legal team, Dunn previously served on the Distance Learning Committee organizing speakers for AUTM-hosted webinars and was Chair of the Intellectual Property Portfolio Management Committee. As chair, Dunn helped develop and manage an annual course for three years before recently joining AUTM’s TOOLs Committee.

Having been an AUTM member for over 20 years, Kesselring currently serves as chair of the website committee. As chair, she works with AUTM staff focused on marketing and communications and organizes work, activities, and conference calls to “meet objectives for the AUTM website.” This is done through tasks like supporting the website’s redesign and providing regular statistics.

Reynolds chairs AUTM’s Better World Project committee, a group that selects stories submitted by technology transfer offices about how their technologies “make the world a better place.”

“It’s easy to get in the cycle of only knowing what is happening in your own office,” he said. “The Better World Project allows me to see the great work that institutions and [technology transfer offices] around the world are doing.”

Thomas, another AUTM volunteer, occasionally serves as a presenter on Annual Meeting webinar sessions and recently left the Marketing Course Committee after working with this group for three years. He noted that his work with AUTM increases exposure of Emory’s OTT office and fuels additional opportunities, while also allowing him to learn new skills and gain knowledge from colleagues at other institutions.

In addition to those who volunteer with AUTM, several OTT employees cite benefits from volunteering with outside organizations. Lei volunteers with Certified Licensing Professional (CLP), Inc. as a member of The CLP Exam Development and Maintenance Committee. The CLP program certifies professionals that demonstrate experience and proficiency in the licensing and commercialization of intellectual property after they take and pass a 150 multiple choice question exam. When the organization recruited volunteers to revise exam questions in 2019, Lei took this opportunity.

“I thought it would be important to be involved and help maintain the high standard of the CLP program, because it takes dedicated volunteers to develop a quality certification program,” he said.

Wilkening primarily volunteers with the Patent Agents of Georgia, an organization she co-founded under the Georgia Intellectual Property Alliance. This group aims to foster community among Georgia’s past, present, and future patent agents and coordinates networking events for “science-lovers that want to stay at the frontlines of research without having to do the research.”

“I wish I would have known about this career path earlier in my life … Being a patent agent can be a rewarding career opportunity,” she said. “This organization has brought together technology transfer and patent professionals from all over Georgia, and I have had the honor to help branch those networking opportunities.”

Fritts has volunteered as a lawyer with the Executive Committee for Atlanta’s IP Inn of Court and the Advisory Board for the United States Patent and Trademark Office (USPTO) Georgia PATENTS. As a volunteer, she has helped inventors file patent applications, saying, “The benefits to the organizations, my office, and me personally far outweigh the burden of the work.”

Dunn also volunteered with the Georgia Association of Paralegals’s Atlanta Legal Aid Society. With this organization, she assisted domestic violence victims in filing appropriate documents involving scenarios like temporary restraining orders and coordinating temporary housing. She listed obtaining a “diverse network of knowledge and experience” among several benefits she received from volunteering.

“Volunteering is like continuing education: we constantly learn something new,” she explained. “The most important benefit is the feeling of being a part of something bigger than yourself.”

Telemedicine is the use of available telecommunications technologies to diagnose and provide care to patients from a remote location. Although frequently mentioned in the news lately due to COVID-19, telemedicine is a field that has been around for more than 50 years.

The need to treat patients without seeing them in person arose mostly because of people in rural areas, who were unable to travel long distances to see a doctor or go to a hospital. To solve this issue, doctors would talk to patients on the phone and even use the telegraph to send and receive medical information. Nowadays, modern video-conferencing is widely used for telemedicine, providing an easy and convenient way to share information with providers. Since file sharing is easier than ever, doctors can electronically send their patients their lab results, X-Rays, and other types of diagnostic information. Furthermore, doctors can share patient records with each other, enabling them to provide better collaborative care to their patients.

Since the use of smart devices has increased in the past few years, many doctors now have remote access to important health information, such as their heart rate, activity levels and even their blood pressure. That way patients can receive medical advice from the comfort of their home, without unnecessary travel and long wait times at doctor’s offices. Telemedicine helps doctors with office-related costs while also saving them precious time and allowing for more appointments. As evidenced during this pandemic, an additional benefit of telemedicine is the reduced risk of exposure to contagious diseases that are frequently found in hospitals and doctor’s offices, therefore protecting both patients and medical personnel.

The telemedicine industry is rapidly growing, with more and more tech companies creating HIPAA-compliant video platforms and e-health environments. With the inevitable future developments in the medical device sector, it is very likely that telemedicine is here to stay as a regular part of medical care.

More resources on telemedicine:

• National Institute of Biomedical Imaging and Bioengineering. https://www.nibib.nih.gov/science-education/science-topics/telehealth

• Centers for Disease Control and Prevention. https://www.cdc.gov/phlp/publications/topic/telehealth.html

• World Health Organization. https://www.who.int/gho/goe/telehealth/en/

Experimental drugs can be a lifeline for those with conditions for which conventional treatments are not working or readily available. At the same time, doctors and patients want to have confidence that they are making safe and informed choices when requesting the use of drugs that have not have fully completed the clinical trial process.  With many doctors turning to experimental treatments for COVID-19 patients, this topic has recently gained renewed attention. Two processes in particular: compassionate use and Right to Try, give patients with serious or terminal conditions the ability to gain access to treatments which have not received FDA approval. These treatments can be potentially lifesaving for those whom conventional treatment is either unavailable or not working.

Compassionate use, formally known as Expanded Access, allows patients access to experimental treatments with approval from their doctor, the FDA, and an Institutional Review Board (IRB). The patient must meet several criteria, including having a serious disease or condition for which no comparable satisfactory treatment is available and being unable to enroll in clinical trials for the drug. If patients decide to pursue compassionate use, they must first speak to their physician, who then files a request to both the FDA and IRB. Eligibility is granted by the FDA if the agency determines that both (1) the benefits of using the experimental treatment outweigh any risks and (2) that its use will not jeopardize ongoing clinical trials. The FDA must respond to such a request within thirty days, and approval has historically been granted in 99% of cases. Secondly, the use must be approved by a registered IRB. Only once both of these steps are complete is the patient allowed access to the drug.

In May 2018 Congress passed the Right to Try Act, establishing a second process for patients to gain access to experimental treatments. Under Right to Try, patients with life-threatening conditions no longer need the approval of the FDA and IRBs to gain access to certain experimental drugs. Those with a terminal illness can submit a request directly to the manufacturer of an experimental treatment with the consent of their doctor. They may then obtain the treatment if the manufacturer approves. Similar to with compassionate use, patients using the Right to Try process must have exhausted all available conventional treatments and be unable to participate in clinical trails involving the experimental drug. The medication must also have completed Phase 1 clinical trials, an additional requirement not present in the compassionate use process. Right to Try has proven controversial in the scientific community since its conception. Proponents claim it cuts red tape patients for terminally ill patients in obtaining treatment, but critics point to the lack of reporting requirements and say that Right to Try removes safeguards in the compassionate use process that ensure patient safety.

While compassionate use and Right to Try have different processes, the programs share several key limitations. Importantly, manufacturers have no obligation to provide experimental drugs under either process, and the FDA has no power to compel companies to do so. Many manufacturers are reluctant to provide patients access to experimental drugs due to concerns that adverse reactions could lead to negative publicity or interfere with the FDA approval process.  Additionally, insurance companies are under no obligation to cover experimental treatments. The fact that many patients must pay out-of-pocket for experimental treatments has led to a stratified system where low-income patients are unable to obtain experimental drugs.

Despite these constraints, both compassionate use and Right to Try continued to be utilized by doctors and patients across the country. Many individuals with severe or terminal illness can’t afford to wait the seven to twelve years it takes for drugs to be approved by the FDA. Through compassionate use and Right to Try, these patients with few options left get a renewed chance.

The healthcare industry generates a lot of data. X-rays, pathology slides, patient vitals, clinical trial information; we have mountains of information accessible at the touch of a button. But it’s costly and time-inefficient for humans to manually pour over it. So what do we do with all of this data?

The field of intelligence (AI) allows not only to let us analyze all our data, but to find subtle and complex patterns in them. Machine learning algorithms are particularly responsible for these advancements. Engineers have developed software that’s able to look at a dataset, find relationships between a bunch of variables, and then make mathematical models that we can use to predict behaviors in the future or analyze other sources of data: images, patient records, etc.

What does that mean in the real world? Computer scientists can make systems that can analyze an image and detect a disease better than humans can. They can make programs that are able to predict the notoriously unpredictable process of how proteins fold, or even make drugs which are currently in human trials. Let’s learn a little more about how machine learning works, and see some of the current applications.

How machine learning systems work

Machine learning systems are complex algorithms. Algorithms are methods of “treating” data: a computer receives an input, some sort of data. The computer recognizes that data and sorts it. For example, let’s say I have a program that can tell me if the color of an animal I’m thinking about makes sense. if I type in the phrase “blue horse,” there might be something in the code that recognizes the word “blue” and the word “horse.” The code then might have some information about the colors a horse can be, and would have an instruction to compare “blue” with “horse.” If “blue” isn’t in the “horse” database, then the computer would have an instruction to tell me “No, horses can’t be blue.” To make it simple, algorithms are sets of instructions that do something with information we give it.

A simple algorithm for calculating interest. Courtesy of Edraw.

They get way more complex than that example. Specific algorithms can take ridiculous amounts of data and look for statistical relationships between them. Machine learning systems are able to make new models by using what are called learning algorithms. There are a bunch of different types, but the most conceptually simple ones involve supervised learning. These learning algorithms are exposed to a “training set” of data with a bunch of examples with known “right answers” — for example, a bunch of CT scan images from patients whom we know have cancer or not. The algorithm “doesn’t know” which patients have the disease. It keeps changing the instructions inside of its algorithm to try to — if the computer sees a white spot in a patient’s liver, it says yes, but this turned out to be wrong, so the next time the computer doesn’t care about that spot when saying yes or no.

Do this a bunch of times, and eventually the algorithm learns what data points lead to the correct output: the machine learns. Over time, these algorithms get very accurate, and we can use them for specific applications.

Radiology and imaging

Image analysis is a very natural application of this sort of software. The cancer example above isn’t science fiction: Google’s already developed a system that’s able to detect breast cancer. A very recent study published information about a deep learning model that’s able to diagnose forms of common hip arthritis at an accuracy rivaling radiologists’ analysis.

But there are concerns about the current state of radiology AI and bringing these technologies to the clinic. Analyzing images is a very complex process, critics argue, and we don’t always know how these machine learning systems actually work. Although applications are rapidly developing, it’s clear that radiologists are still very necessary.

A picture of an image where an AI system found cancer in a human breast. Courtesy of Northwestern University via The New York Times.

Drug discovery

UK-based startup Exscientia became the first company to develop a small-molecule drug entirely designed by artificial intelligence. Small-molecule drugs are tiny chemicals that generally “stick” to proteins of interest. It is very hard to know what sort of molecules will stick and treat a disease and which ones won’t. The scientists at Exscientia use a learning algorithm that takes data from human-run tests to optimize the structure of the chemical they’re making, reducing the amount of time it takes to get a working drug. The compound, titled DSP-181 and targeted toward treating obsessive-compulsive disorder, is set to enter clinical trials in Japan soon.

Point-of-care solutions

AI doesn’t just help us make drugs; it can help us deliver them, too. Electronic health record systems are growing more and more. Nurses say that bureaucratic work cuts their time spent with patients by about 25 percent. Doctors report that less than half of their patients are what they’d describe as “highly engaged” in their course of care. Engineers are already developing systems that can not only manage data, but use electronic information to make predictions about how patients might comply with certain treatments.

From point-of-care solutions and radiology to drug discovery and beyond, it’s clear that artificial intelligence will play an increasingly large role in analyzing healthcare data in the future in ways both known and unknown to us now. New applications of machine learning systems are rapidly developing as engineers aim to create new machine learning algorithms that can analyze and find complex data. Used in tandem with the expertise of doctors, radiologists, and other healthcare professionals, these algorithm-based technologies are already modernizing our healthcare system and have the potential to improve public health on an even larger scale in the future.

Emory has approximately 600 technologies available to license at any given time. In particular, Emory offers a variety of live science resources such as therapeutics, diagnostics, and research tools that are marketed by Emory OTT.  

However, finding new technologies available at a university can be time-consuming and potentially frustrating for startups and established companies alike. Below are simple ways to find and remain up-to-date with technologies coming from Emory University.

1. Subscribe to TechFeed to receive email notifications about products: TechFeed is a notification system where users can sign up to receive emails about recently added technologies. It can be individually customized to get notifications based on what products you’re interested in and how frequently you want to be notified. 

2. Use our Technology Listings page: To get an improved searching experience with more accurate results, use our Google-powered search option. Through this, you can find non-confidential summaries of available technologies. Alternatively, if you’re looking for something around a specific indication or topic, click on Keywords in our word cloud and Technology Categories to get a list of these technologies.

3. Visit our Featured Innovations page: Using these articles, you can get more information about and see real-world applications of available technologies.

4. Contact our knowledgeable Marketing Associate, Quentin Thomas: Reach out to Quentin via email to request a hand-picked selection of technologies related to your needs and areas of interest. Quentin also encourages interested parties to set up a face-to-face conversation with him so he can guide the path from there. 

5. Subscribe to our RSS feed and follow us on Twitter @Emory OTT: By doing this, you can stay up-to-date with all of our new technologies as soon as they’re listed on the website.

Additionally, to find a one-stop shop to find technologies from Emory as well as universities worldwide, there are a number of third-party listing services available to search free of charge, including the Association of University Technology Managers (AUTM) Innovation Market (AIM).

Our goal as technology matchmakers is to simplify the process for industry colleagues to find our technologies. If you have any suggestions on how we can improve this process, please contact us.