SBIR/STTR Research Award

SBIR Phase I:Drug discovery using stem cell derived organoids

Award identifier 39532bcb07440e860eb527be46e85c801d21bc921c25789876bcce1eda96ed03Awarding agency National Science Foundation

Award amount

$274,829

Program and phase

SBIR · Phase I

Award year

2023

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Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
ROSEBUD BIOSCIENCES INC.
Proposal award date
2023-08-15
Contract end date
2024-07-31
UEI
WV8CZB3JHU21
Solicitation number
NSF 22-551
Topic code
PT

Research abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable the development of new therapies for rare diseases in children.An estimated 350 million people worldwide - half of which are children - suffer from an estimated 7,000+ known rare diseases annually, yet 95% of rare diseases still lack treatment. Even though greater than80% of rare diseases are genetic in origin, there have been relatively few actual drug discoveries. This is an enormous problem as 30% of children with rare diseases will not live to see their 5th birthdays. To address this, drug development can be accelerated by screening investigational drugs against micro-organs (“organoids”) that resemble young organs and have the same gene mutations as the patients. In a simple petri dish, organoids enable the direct study of the relationship between a patient’s genes and his/her disease.Such organoids present an opportunity to rapidly identify new disease mechanisms and targeted therapies. The team is scaling organoid technology into an automated drug development platform that is high throughput, robust, and applicable to multiple genetic diseases in children. _x000D_ _x000D_ This project advances drug development for pediatric rare diseases by accomplishing two primary objectives: (1) automated heart, liver, and brain organoid derivation from human stem cells, and (2) automated machine learning (ML) detection of disease in organoids derived from patients with genetic diseases affecting any of these three organs. To do this, a combination of robotics and specific protocols will differentiate tissue- and disease-specific organoids under standardized conditions. The organoids are monitored with microscopy as they transition between tissue state. The ML model learns what healthy organoids look like and uses that information to identify when an organoid exhibits a disease phenotype. These objectives are important for reducing batch-to-batch organoid variability and human error that can confound drug discovery efforts. In these same organoid models, drug screening and development will be performed to try to reverse or retard the disease phenotype unique to each disease. The result of this research will be the production of organoids with high accuracy and precision, as well as an automated means for detecting changes in heart, liver, and neural organoids that will be essential for finding new therapies._x000D_ _x000D_ 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.

Technology keywords

Not reported

Agency intelligence

National Science Foundation

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