CFD RESEARCH CORPORATION — Department of Health and Human Services SBIR Phase I: 400
CFD RESEARCH CORPORATION — SBIR Phase I award from Department of Health and Human Services.
Phase I SBIR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Health and Human Services in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $335,438. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code 400 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $335,438
- Agency
- Department of Health and Human Services · National Institutes of Health
- Program / Phase
- SBIR · Phase I
- Topic
- 400
- Solicitation
- PA22-176
- NAICS
- —
- Place of performance
- AL
- Period
- 2023-09-22 → 2024-08-31
Description
Project Summary/Abstract: Computational techniques for gene engineering, such as codon optimization, use synonymous codon changes to increase protein production. Applications for these computational gene optimizations include recombinant protein drugs, nucleic acid therapies, and mRNA vaccines. Although codon optimization increases protein production in certain systems, synonymous changes to a gene sequence can cause unexpected detrimental results to the protein. Further, researchers have been critical of codon optimization for human therapeutics as the optimization process can affect protein conformation and function, and reduce efficacy. Therefore, codon optimization may not provide an optimal strategy for increasing protein production or designing safe and effective therapeutics. CFDRC has utilized state-of-the-art natural language processing techniques to learn how synonymous codons are used by a target organism and apply this learning to gene engineering. We demonstrated our model could predict the E. Coli synonymous codon usage with 73% accuracy, significantly above prior reports. We believe that using this AI-based approach to gene engineering will provide an optimal strategy for increasing protein production and may increase the efficacy of therapeutics.