FY2026 Awardees
The Tufts Launchpad | Accelerator (TLA) funding program provides funding and business development support to enable faculty to advance the commercial viability of promising inventions disclosed to the Tufts Technology Commercialization unit.
In the FY2026 cycle, 14 competitive submissions were received, and the following four projects were selected for funding and business development support.
- Multi-Receptor Agonists for Treatment of Metabolic Syndrome
Team Lead: Krishna Kumar, Robinson Professor of Chemistry; Co-PI: Jennifer Lee, Scientist II, HNRCA
Obesity and type 2 diabetes ("Diabesity") afflicts nearly a billion people worldwide and imposes over $170 billion in annual health care costs in the United States alone. Current incretin-based drugs such as semaglutide and tirzepatide have validated peptide hormone therapies but remain limited by gastrointestinal side effects, variable efficacy, and rapid weight regain after discontinuation. The Kumar laboratory has developed a first-in-class unimolecular tetra-agonist platform that integrates GLP-1R, GIPR, glucagon receptor (GcgR), and Y2R (PYY) agonism within a single peptide framework. This rationally designed approach mimics the multi-hormonal effects of bariatric surgery, offering the potential for durable weight loss without invasive procedures. This TLA supported project will test four tetra-agonist leads in diet-induced obese mice, benchmarking them against semaglutide and tirzepatide. Studies will measure weight loss, glycemic control, body composition, and durability of effect after treatment cessation—directly addressing a central weakness of current therapies. Successful completion will yield one or two clinical candidates with IP-backed differentiation ready for IND-enabling studies. - Membrane Progesterone Receptors as a Therapeutic Target to Treat Anxiety, Major Depressive Disorder and Neuroinflammation
Team Lead: Stephen J. Moss, Professor of Neuroscience; Co-PI: Paul Davies, Research Associate Professor, Department of Neuroscience
Major Depressive disorder (MDD) affects approximately 250 million people globally. Current therapies are only ~30% effective at reducing the symptoms of MDD. Neuroinflammation is a driver of major CNS diseases and disorders and is now known to be responsible for drug-resistant MDD. Progesterone derived neuroactive steroids (NASs) have long been targeted by pharmaceutical companies because of their known antidepressive properties and to be anti-inflammatory. However, these NASs have serious side effects that have limited their commercial success. The TLA supported project will support the development of next generation NASs that have anti-inflammatory properties without the known side-effects of first generation NASs. The drugs target membrane progesterone receptors resulting in a lack of sedation that will have significant clinical benefits and commercial advantages. - CLAIRVUE: Transparent AI-Driven Photoacoustic Imaging for Real-Time Deep Tissue Diagnostics
Team Lead: Srivalleesha (Valli) Mallidi, Associate Professor, Department of Biomedical Engineering
Detecting vascular dysfunction in tumors, ischemic wounds, and infections, at high resolution remains limited by current imaging modalities. Photoacoustic imaging (PAI) uniquely merges the molecular sensitivity of optical imaging with the depth penetration of ultrasound, offering three dimensional, functional vascular insights several millimeters below the surface at high resolution of <300 μm. Despite FDA approval for breast cancer screening, PAI adoption has been constrained by noise sensitivity, weak signal loss at depth, and acquisition speeds that limit real-time use. More importantly, because photoacoustic signals depend on the amount of light reaching the tissue, signal attenuation effects are compounded by skin bias in individuals with darker skin tones. To overcome these barriers, the Mallidi lab developed ClAIrVue, a deep learning–based, hardware-agnostic framework that restores faint signals, accelerates imaging by reducing dataset requirements and broadly applicable to imaging subjects of various skin tones. The TLA supported project will develop and validate scalable, real-time models across diverse patient populations and PAI hardware platforms; advance collaborations with industry partners to align with clinical and commercial needs and deploy the developed AI pipelines in GPU-backed cloud platforms. - Developing Readout Methods for Real-time Olfactory Sensing
Team Lead: Brian Lin, Research Assistant Professor, Department of Developmental, Molecular and Chemical Biology; Key Collaborator: Sameer Sonkusale, Professor of Electrical and Computer Engineering
The Lin Lab is developing a digital sensor that can smell using a fundamentally different approach to competitors, as it utilizes a living, bioengineered olfactory tissue that mimics the mammalian olfactory epithelium, capable of detecting over 30 billion chemicals. This tissue contains functional sensory neurons that trigger calcium signaling and action potentials upon binding their cognate ligand. This tissue will be combined with local field potential electrodes/ emitted light sensors to capture multidimensional volatile organic compounds profiles and analyze them in real-time. The TLA supported project will develop hardware that enables a portable prototype, ultimately facilitating field testing outside the lab setting.