2304624
Project Grant
Overview
Grant Description
SBIR Phase I: Advanced Deep Learning Technologies for Designing Humanized Antibody - The broader impact / commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to accelerate antibody design and engineering through the development of proprietary computational approaches. Compared to conventional antibody drug development approaches that are often lengthy, costly, and inefficient, this innovation may offer a more efficient and cost-effective alternative.
The proposed approach aims to create better therapeutic-grade antibodies while unlocking novel antibody design possibilities. The market opportunity addressed by the proposed technology is significant, as the global therapeutic antibody market for cancer and infectious diseases is projected to reach $235 billion by 2028. This project has the potential to transform the field of antibody discovery and provide new therapeutic options for patients.
This Small Business Innovation Research (SBIR) Phase I project aims to develop an artificial intelligence (AI)-based platform that efficiently designs novel antibody drug candidates with possible lower toxicity and immunogenicity risks. The research will involve developing novel and proprietary AI-based models to create best-in-class antibody therapeutics and validate them through state-of-the-art in-silico experiments.
To successfully complete this Phase I project, the company plans to:
A) Develop a novel computational model to design antibody hit sequences,
B) Demonstrate the scalability of the proposed computational model in designing antibody hit sequences against diverse targets,
C) Assess biological values of the antibody hit sequences predicted by the computational model.
The expected technical outcomes involve a more rapid and efficient process for designing therapeutic antibodies, resulting in lower development expenses and a quicker path to market. The AI technologies have the potential to design the most promising therapeutic antibodies to treat infectious diseases and cancer in months rather than years, reducing the time and resources needed for the pre-clinical development of therapeutic antibodies.
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 not planned for this award.
The proposed approach aims to create better therapeutic-grade antibodies while unlocking novel antibody design possibilities. The market opportunity addressed by the proposed technology is significant, as the global therapeutic antibody market for cancer and infectious diseases is projected to reach $235 billion by 2028. This project has the potential to transform the field of antibody discovery and provide new therapeutic options for patients.
This Small Business Innovation Research (SBIR) Phase I project aims to develop an artificial intelligence (AI)-based platform that efficiently designs novel antibody drug candidates with possible lower toxicity and immunogenicity risks. The research will involve developing novel and proprietary AI-based models to create best-in-class antibody therapeutics and validate them through state-of-the-art in-silico experiments.
To successfully complete this Phase I project, the company plans to:
A) Develop a novel computational model to design antibody hit sequences,
B) Demonstrate the scalability of the proposed computational model in designing antibody hit sequences against diverse targets,
C) Assess biological values of the antibody hit sequences predicted by the computational model.
The expected technical outcomes involve a more rapid and efficient process for designing therapeutic antibodies, resulting in lower development expenses and a quicker path to market. The AI technologies have the potential to design the most promising therapeutic antibodies to treat infectious diseases and cancer in months rather than years, reducing the time and resources needed for the pre-clinical development of therapeutic antibodies.
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 not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF22551
Grant Program (CFDA)
Awarding Agency
Place of Performance
San Carlos,
California
94070-4002
United States
Geographic Scope
Single Zip Code
Related Opportunity
22-551
Analysis Notes
Amendment Since initial award the End Date has been extended from 08/31/24 to 08/31/25 and the total obligations have increased 7% from $274,822 to $294,822.
Marwell Bio was awarded
Project Grant 2304624
worth $294,822
from in September 2023 with work to be completed primarily in San Carlos California United States.
The grant
has a duration of 2 years and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I:Advanced Deep Learning Technologies for Designing Humanized Antibody
Abstract
The broader impact / commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to accelerate antibody design and engineering through the development of proprietary computational approaches. Compared to conventional antibody drug development approaches that are often lengthy, costly, and inefficient, this innovation may offer a more efficient and cost-effective alternative. The proposed approach aims to create better therapeutic-grade antibodies while unlocking novel antibody design possibilities. The market opportunity addressed by the proposed technology is significant, as the global therapeutic antibody market for cancer and infectious diseases is projected to reach $235 billion by 2028. This project has the potential to transform the field of antibody discovery and provide new therapeutic options for patients. _x000D_ _x000D_ This Small Business Innovation Research (SBIR) Phase I project aims to develop an artificial intelligence (AI)-based platform that efficiently designs novel antibody drug candidates with possible lower toxicity and immunogenicity risks. The research will involve developing novel and proprietary AI based models to create best-in-class antibody therapeutics and validate them through state-of the-art in-silico experiments. To successfully complete this Phase I project the company plans to: a) develop a novel computational model to design antibody hit sequences, b) demonstrate the scalability of the proposed computational model in designing antibody hit sequences against diverse targets, c) assess biological values of the antibody hit sequences predicted by the computational model. The expected technical outcomes involve a more rapid and efficient process for designing therapeutic antibodies, resulting in lower development expenses and a quicker path to market. The AI technologies have the potential to design the most promising therapeutic antibodies to treat infectious diseases and cancer in months rather than years, reducing the time and resources needed for the pre-clinical development of therapeutic antibodies._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.
Topic Code
PT
Solicitation Number
NSF 22-551
Status
(Complete)
Last Modified 1/14/25
Period of Performance
9/1/23
Start Date
8/31/25
End Date
Funding Split
$294.8K
Federal Obligation
$0.0
Non-Federal Obligation
$294.8K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2304624
Additional Detail
Award ID FAIN
2304624
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
PTX1ZT26L8V3
Awardee CAGE
967L9
Performance District
CA-15
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Budget Funding
Federal Account | Budget Subfunction | Object Class | Total | Percentage |
---|---|---|---|---|
Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $274,822 | 100% |
Modified: 1/14/25