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2332121

Cooperative Agreement

Overview

Grant Description
STTR PHASE II: OPTIMIZED MANUFACTURING AND MACHINE LEARNING BASED AUTOMATION OF ENDOTHELIUM-ON-A-CHIP MICROFLUIDIC DEVICES FOR DRUG SCREENING APPLICATIONS.

-The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project is to address the unmet need in the companion diagnostic guided therapy market for sickle cell disease (SCD).
SCD is a lifelong disease affecting millions of people worldwide.
Emerging therapies are estimated to be $150K-$200K per patient each year.

A companion diagnostic cost in SCD anti-adhesive therapies is estimated at least $3,000 per patient.
With improved accessibility to patients living in low- and middle-income countries and scalable curative therapies, the global SCD treatment market size is projected to increase to $8.75B by 2029.
Additionally, companion diagnostic-guided drugs have an increased regulatory approval probability of 50% in Phase III clinical trials.

The proprietary Endothelium-on-a-Chip platform with human donor cells provides a physiologically relevant means to study blood-endothelium interaction.
This platform can be integrated into preclinical studies to screen the effect of novel drug candidates as well as for assessment of drug toxicity.

In Phase I of the STTR project, standards and quality control criteria for experimental conditions on the Endothelium-on-a-Chip were established.
The continuing projects with pharmaceutical companies have highlighted the need to scale the manufacturing process.

This Small Business Technology Transfer (STTR) Phase II project will focus on optimizing the manufacturing process to enable the scale-up of the assay and develop the machine learning system for automated data analysis.
Currently, this assay involves in-house fabrication and is limited to the central laboratory at the company's location.

Manufacturing will be optimized with proper selection of material, fabrication methods, scalable techniques, and systematic integration of different elements of the assay.
This will potentially enable the commercialization and implementation of the technology at a larger scale.

Current methods of analysis include counting adhesion events manually.
This method will be replaced by a machine learning-based system to identify and classify adhesion events and separate those from the endothelial cells in the background.

Automated data analysis will enable faster outcomes and remove user bias.
Since this approach relies on enhancing the capabilities of the existing platform for scale-up and streamlined analysis, it is anticipated that it will improve its accessibility to the broader research community.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
- Subawards are planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH PHASE II (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE II", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23516
Awarding / Funding Agency
Place of Performance
Cleveland, Ohio 44106-2119 United States
Geographic Scope
Single Zip Code
Analysis Notes
Amendment Since initial award the End Date has been extended from 03/31/26 to 09/30/26 and the total obligations have increased 20% from $904,947 to $1,085,488.
Biochip Labs was awarded Cooperative Agreement 2332121 worth $1,085,488 from National Science Foundation in April 2024 with work to be completed primarily in Cleveland Ohio United States. The grant has a duration of 2 years 5 months and was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships. The Cooperative Agreement was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase II Programs (SBIR/STTR Phase II).

SBIR Details

Research Type
STTR Phase II
Title
STTR Phase II: Optimized manufacturing and machine learning based automation of Endothelium-on-a-chip microfluidic devices for drug screening applications.
Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project is to address the unmet need in the companion diagnostic guided therapy market for Sickle Cell Disease (SCD). SCD is a lifelong disease affecting millions of people worldwide. Emerging therapies are estimated to be $150k-$200k per patient each year. A companion diagnostic cost in SCD anti-adhesive therapies is estimated at least $3,000 per patient. With improved accessibility to patients living in low- and middle-income countries and scalable curative therapies, the global SCD treatment market size is projected to increase to $8.75B by 2029. Additionally, companion diagnostic-guided drugs have an increased regulatory approval probability of 50% in Phase III clinical trials. The proprietary Endothelium-on-a-chip platform with human donor cells provides a physiologically relevant means to study blood-endothelium interaction. This platform can be integrated into preclinical studies to screen the effect of novel drug candidates as well as for assessment of drug toxicity. In Phase I of the STTR project, standards and quality control criteria for experimental conditions on the Endothelium-on-a-chip were established. The continuing projects with pharmaceutical companies have highlighted the need to scale the manufacturing process. This Small Business Technology Transfer (STTR) Phase II project will focus on optimizing the manufacturing process to enable the scale-up of the assay and develop the machine learning system for automated data analysis. Currently, this assay involves in-house fabrication and is limited to the central laboratory at the company’s location. Manufacturing will be optimized with proper selection of material, fabrication methods, scalable techniques, and systematic integration of different elements of the assay. This will potentially enable the commercialization and implementation of the technology at a larger scale. Current methods of analysis include counting adhesion events manually. This method will be replaced by a machine learning-based system to identify and classify adhesion events and separate those from the endothelial cells in the background. Automated data analysis will enable faster outcomes and remove user bias. Since this approach relies on enhancing the capabilities of the existing platform for scale-up and streamlined analysis, it is anticipated that it will improve its accessibility to the broader research community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
BM
Solicitation Number
NSF 23-516

Status
(Ongoing)

Last Modified 3/5/25

Period of Performance
4/1/24
Start Date
9/30/26
End Date
60.0% Complete

Funding Split
$1.1M
Federal Obligation
$0.0
Non-Federal Obligation
$1.1M
Total Obligated
100.0% Federal Funding
0.0% Non-Federal Funding

Activity Timeline

Interactive chart of timeline of amendments to 2332121

Transaction History

Modifications to 2332121

Additional Detail

Award ID FAIN
2332121
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
K5TKCGKKA1T9
Awardee CAGE
8RJR8
Performance District
OH-11
Senators
Sherrod Brown
J.D. (James) Vance
Modified: 3/5/25