SBIR/STTR Research Award
Genetically Engineered Pancreatic Organoids Composed of Extracellular Matrix Hydrogels for the Purpose of Long-Term Therapeutic Glycemic Maintenance
Award amount
$224,589Program and phase
STTR · Phase IAward year
2019Award details
- Awarding agency
- Department of Defense
- Branch
- Defense Advanced Research Projects Agency
- Recipient company
- ECMedical LLC
- Proposal award date
- 2019-03-18
- Contract end date
- 2020-01-17
- UEI
- X6DRYWC5MGB9
- Solicitation number
- 18.C
- Topic code
- ST18C-004
Research abstract
Glucose abnormalities significantly affect patient morbidity and mortality. Currently, glycemic control is achieved via medications and exogenous insulin regimens that are costly both in terms of qualified personnel and consumables. Furthermore, none of these approaches are curative. Strategies under development such artificial pancreases do not necessarily address these limitations because they would still rely on consumables and in addition, glucose sampling from the interstitium is not feasible. Autologous islet cell transplantation is less than ideal because cell procurement and expansion in culture can not be performed in real time, and these cells would still be susceptible to autoimmune disease such as Type 1 Diabetes. The objective of this proposal is to develop a solution that simultaneously overcomes all of these limitations. We propose a tissue-engineered approach based on implantable ready-to-use xenogeneic organoids that have been genetically engineered to prevent rejection and autoimmune disease, and are able to provide reliable, autonomous, multi-hormonal glycemic control. The substrate for the pancreatic cell organoids will be a hydrogel composed of pancreatic porcine extracellular matrix containing porcine pancreatic alpha and beta cells that will inherently produce insulin and glucagon in response to glycemic status, and will be genetically engineered via CRIPR-Cas9 to avoid rejection.
Technology keywords
Not reported
Awards with the same topic code
Verified by an exact topic code match. This is not a text-similarity match.
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