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Shelf-stable synthetic cannabinoid biosensor

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911NF-17-P-0040
Agency Tracking Number: A17A-014-0082
Amount: $149,997.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: A17A-T014
Solicitation Number: 2017.0
Solicitation Year: 2017
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-06-29
Award End Date (Contract End Date): 2018-01-02
Small Business Information
737 Concord Avenue
Cambridge, MA 02138
United States
DUNS: 557201394
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Dr. Mike Bates
 Senior Scientist
 (617) 621-8500
Business Contact
 Dr. Kateri Paul
Phone: (617) 621-8500
Research Institution
 Northeastern University
 Ms. Kimberly Ozcan
360 Huntington Ave 3 Egremont Rd
Boston, MA 02115
United States

 (617) 373-5600
 Nonprofit College or University

Nano Terra will collaborate with the Center for Drug Discovery (CDD) at Northeastern University, to develop a sensor capable of detecting synthetic cannabinoids independently of their specific molecular structure. The sensor will be portable, label-free and most-importantly shelf stable. Our proposed sensor will employ a structurally stabilized mutant CB1 receptor as the recognition element in fluorescence-based competitive binding assay. We will develop a freeze-dried, shelf-stable form of the CB1 receptor that maintains binding activity after rehydration by encapsulation in a polymersome matrix. The fluorescence assay will be implemented with low-cost LED-based fluorimeter for detecting fluorescently tagged indicator ligands that are displaced by the target cannabinoid. Our approach will capitalize on the CDDs expertise in the synthesis of CB1-ligands, to design an indicator ligand with an optimal binding affinity, low non-specific binding and low detection limit. The Phase 1 work will focus on development of the shelf-stable receptor and demonstrating the feasibility of the proposed fluorescence-based approach for detecting at least three structurally distinct synthetic cannabinoids with >90% accuracy and identifying potential interferences.

* Information listed above is at the time of submission. *

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