Investor at Fusion Fund

Daniel Gomez

What is Fusion Fund’s thesis around healthcare?

Daniel : Fusion Fund is an early stage venture firm based in Palo Alto, CA that invests in companies building on core technical advantages in their business model. We leverage our team’s strong technical background and entrepreneurial and operational experience to identify business opportunities and accelerate growth. The firm focuses on industrial, enterprise, and healthcare applications and sectors.

Given the relevant healthcare innovation background of Lu Zhang, Founding and Managing Partner of Fusion Fund, it is a key area of focus for the firm. Before Fusion Fund, Zhang founded a medical device company for Type II diabetes diagnostics — acquired 2012—based on her own technology at Stanford University. Lu and Fusion Fund have been recognized as a leader in AI in healthcare investment after being the first to publish an industry report on the subject and one of the earlier firms investing in this area since 2017.

As the thesis relates to healthcare, we believe the industry is one of the best for AI to show its full capability. Specifically, AI can solve key problems within the industry such as data overload and low efficiency. Additionally, hardware—medical device, sensor— and software (AI) integration solves for scalable personalization. At Fusion Fund, we invest in AI in healthcare applications that empower rather than replace existing players, equip doctors, andincrease the efficiency and accuracy of service providers while lowering costs.

“Specifically, AI can solve key problems within the industry such as data overload and low efficiency. Additionally, hardware (medical device, sensor) and software (AI) integration solves for scalable personalization”

What high-impact technologies are being developed today? Are there any effective therapies?

Daniel : Today, loss of functionality such as speech or movement is very difficult to restore. In the U.S., 400,000 patients suffer from debilitating diseases such as Duchenne Becker Muscular Dystrophy, Amyotrophic Lateral Sclerosis, Tetraplegia, and other injuries, who’s quality of life is hindered by loss of connectivity to the world. There are a copious number of startups working on solutions varying from bioelectronic medicine, brain-machine interfaces (BMI), peripheral nervous system-machine interfaces (PNS-MI), and bioelectronic medicine. Owing to inherent limitations in acquiring signal detail at a non-invasive distance, we believe non-invasive brain machine interface approaches— EMG,tDCS,fNIRS—are unfeasible with current technologies. In order to improve patients’ lives, the BMI must have a high bandwidth signal with temporal and high spatial resolution. Non-invasive solutions are inherently too limited in their spatial resolution.

The most promising BMI solution we identified is Paradromics, a company developing and bringing to market the first brain-computer interfacing chip capable of massive parallel neural recording and stimulation. The company’s Neural-Input- Output-Bus (NIOB) uses a high bandwidth implantable brain machine interface to restore connectivity. To date, the company has accomplished major milestones such as securing $18M in research funding from the Defense Advances Research Projects Agency (DARPA) that was won as part of DARPA’s Neural Engineering System Design (NESD) program.

How are pharmaceutical companies today innovating around efficacy?

Daniel : Drug manufacturers have virtually no insight into whether patients are self-administering medication correctly or whether they are experiencing any side effects that might cause them to discontinue taking the medication against the advice of the prescribing physician. They use tools such as market research firms and focus groups yet it is not granular enough to provide meaningful insights.

As a result, pharmaceutical companies are looking for solutions that provide customized care, to ensure that patients remain on medication, and monitor improvements in conditions. At Fusion Fund, we invested in —Catalia Health—which uses AI-driven conversations that are delivered over a robot —Mabu— interface to deliver better medical outcomes to patients. By gathering previously unattainable data on patient health, adherence rate, symptoms, and previously unreported side effects the company enables pharmaceutical companies in better understanding the patient journey. Specifically, the company is able to better track the long- term use and efficacy of its products. The company partners with leading pharmaceutical drug providers servicing patients prescribed specialty pharmaceuticals targeting complex medical conditions such as multiple sclerosis, rheumatoid arthritis, renal cell carcinoma, and hemophilia. Prescribing these treatments will require special monitoring and expertise on top of traditional pharmaceuticals.

How are hospitals today increasing productivity, safety, and experience?

Daniel : PET scans and MRIs are used to produce detailed images of a patient’s organs and internal structures, which help doctors diagnose medical conditions. PET scans typically take 20 minutes to capture the full human body, while MRIs can take up to 1hour. As a result, every year in the U.S., 35+ million patients expend billions of dollars and numerous hours on medical scans. By applying deep learning, companies today can improve the acquisition time, processing, and analysis of medical images. This will drastically improve hospital productivity, increase safety, and improve the patient experience. Given these market needs, we invested in Subtle Medical, a medical imaging software company. The company has a suite of deep learning-based software solutions that enable hospitals and imaging centers to run up to 4x faster PET and MRI scans. The software integrates seamlessly into the clinical workflow and significantly reduces cost, imaging time, and radiation/contrast dose. Unlike many companies in the radiology and AI space focused on automating image interpretation, Subtle Medical has uniquely positioned themselves in the acquisition phase, using their software to reduce scan times and increase workflow efficiency, since the vast majority of imaging costs are associated with these technical aspects, rather than with interpretation and diagnostics.

What new technologies are being developed to treat cancer better?

Daniel : Broad-based next generation sequencing (NGS) is typically involved in identifying cancer treatments. This approach provides an average molecular profile of all the cells in the patient’s sample, but often conceals the genetic diversity and neglects to identify the rare cells that can propel disease progression. To effectively over come cancer, we need to comprehend its biology, specifically, the varying genetic composition of every cancerous cell.

The granularity required is now being achieved through single cell genomics, a fast-growing emerging approach in which genomic technologies are applied at the level of single cells, rather than taking the average readout of sample with a variety of different cell populations. Emerging technologies within this sector are allowing for molecular profiling of individual cells and accurate measurement of underlying genetic diversity, with the purpose of enabling development of more precise medicines and targeting diseased cells that may persist after treatment.

As a result of recent market developments, Fusion Fund, invested in Mission Bio, the developer of a precision genomics platform designed to provide scalable single-cell DNA analysis for precision medicine. The company’s Tapestri Platform is a proprietary droplet microfluidics platform that enables scalable detection of genomic variability with access to DNA at the single-cell level, enabling researchers and clinicians to predict and prevent cancer progression and relapse. Tapestri can analyze tens of thousands of individual cells in parallel and is 50 times more sensitive than traditional NGS techniques. One of the key strengths of the platform is its ability to identify whether mutations are found in the same cell (vs. found in different cells in the same tumor); this information on cooperative mutations can help determine the likely aggressiveness of the cancer, how fast it is likely to advance, and how it might respond or evolve to targeted therapies.

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