The LaunchPad for Diabetes program is an internal translational research fund supporting collaborative research projects proposing innovative solutions for the treatment of Type 1 or Type 2 diabetes. 

The goal of this program is to support/develop translational research projects that address unmet clinical needs and lead to improvements in care of patients with diabetes mellitus. The LaunchPad for Diabetes Fund supports collaborative translational research projects that propose innovative and viable solutions to curing, treating or diagnosing Diabetes.

Examples of desirable outcomes include: improved diagnosis and treatment of disease through new medical devices; new biomarkers or diagnostics; new therapeutic targets and agents; or new clinical adoption of developed tools. It is expected that outcomes from funded projects will result in new intellectual property, industry partnerships, license deals and/or the creation of a start-up companies.

Program Overview:

  • Fund established in 2009 (15 years)
  • 77 projects (including 18 renewals) including 22 Ignite pilot projects have been funded 
  • Schools/Colleges/Offices of fund researchers: 5 (Medicine, Engineering, Education, Data Science and OVPR)
  • Departments of the funded researchers:  30 Departments/Offices
  • 2024 patent fillings: 5 
  • Transactions in 2024: 4 
  • Publications in 2024: 4  
  • Presentations in 2024-24: 6 
  • Start-up companies launched catalyzed by LaunchPad funding: 6 
    o    Type Zero
    o    Slate Bio
    o    Cyto Recovery (collaboration with Virginia Tech)
    o    HTIC, 
    o    MG Medicine name change to MG Therapeutics
    o    IsletRegen
    o    Early discussions are underway on Voxel3D
     

LaunchPad projects have received  $19,063,822M in follow-on funding to date to to advance the funded projects.

Contact:

 AskLaunchPad4Diabetes@virginia.edu

Two Year Projects ($200,000)

RENEWAL: Voxelated Bioprinting Multiscale Porous Bioscaffolds to Reverse Type 1 Diabetes
Liheng Cai, PhD, Materials Science, Chemical and Biomedical Engineering, Chemistry 
Minglin Ma, PhD, Biological & Environmental Engineering, Cornell University 
Shayn Peirce-Cottler, PhD, Biomedical Engineering 
Meaghan Stumpf, MD, Endocrinology& Metabolism

Decision Support System for Healthcare Providers to Optimize Therapy in People with Type 2 Diabetes: Balancing Risk of Complications and Medication Interruption
Daniel Chernavvsky MD 
Ralf Nass MD, Endocrinology & Metabolism 
Anas El Fathi PhD, Psychiatry & Neurobehavioral Sciences/CDT 
Marc Breton PhD, Psychiatry & Neurobehavioral Sciences/CDT 
Heman Shakeri, PhD, School of Data Science

RENEWAL: Adhesive formulation of a tissue repair protein for treatment of diabetic foot ulcers     

Ki Ho Park, PhD, Surgery 
Liheng Cai, PhD, Materials Science, Chemical and Biomedical Engineering, Chemistry 
Scott Hollenbeck, MD, Plastic Surgery, Maxillofacial and Oral Health

 

Prototyping and Pilot Clinical Testing of a Continuously Adapting Fully Closed-Loop Insulin Delivery System 
Heman Shakeri, PhD, School of Data Science 
Boris Kovatchev, PhD, Psychiatry & Neurobehavioral Sciences/CDT
Marc Breton, PhD, Psychiatry & Neurobehavioral Sciences/CDT 
Sue Brown, MD, Endocrinology& Metabolism 
 

One Year Projects ($100,000)

Insulearn: A Self Learning Bolus Calculator for Simplfied Insulin Therapy for T1D& 
Melissa Schoelwer, MD, Pediatrics 
Anas El Fathi PhD, Psychiatry & Neurobehavioral Sciences/CDT

Automated Insulin Delivery for Basal Insulin Titration in Type 2 Diabetes (AID-BIT)

Boris Kovatchev, PhD, Psychiatry & Neurobehavioral Sciences/CDT 
Anas El Fathi PhD, Psychiatry & Neurobehavioral Sciences/CDT ;

 

 

 

Ignite Projects funded ($40K) 

Alternative splicing modulation of fibrotic genes for the treatment of diabetic cardiomyopathy and heart failure    
Muge Kuyumcu-Martinez, PhD, Molecular Physiology & Biological Physics                   
Sunil Verma, PhD, Molecular Physiology & Biological Physics    

A Machine Learning Approach to Improve Glycemic Variability Estimates for Patients with Self-Monitoring of Blood Glucose    
Jianxin Xi, PhD, School of Data Science                                          
Ben Lobo, PhD, School of Data Science  

Neural network-enabled biophysical cytometry of pancreatic islets for correlation to engraftment potential    
Nathan Swami, PhD, Electrical and Computer Engineering                               
Shayn Peirce-Cottler, PhD, Biomedical Engineering                       
Ken Brayman, PhD, Transplant Surgery