SBIR/STTR Research Award

Monoclonal Antibody to FGF2 for Treatment of Hepatocellular Carcinoma and Other C

Award identifier b40b89e844fcdf47fd9e84a94a677a23a8a87937c831004b2df16b7dcc3a0587Awarding agency Department of Health and Human Services · National Institutes of Health

Award amount

$117,637

Program and phase

SBIR · Phase I

Award year

2010

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
Department of Health and Human Services
Branch
National Institutes of Health
Recipient company
GALAXY BIOTECH, LLC
Proposal award date
Not reported
Contract end date
Not reported
UEI
Not reported
Solicitation number
PHS2010-2
Topic code
NCI

Research abstract

DESCRIPTION (provided by applicant): The objective of the proposed research is to characterize a monoclonal antibody (mAb) that binds and blocks Fibroblast Growth Factor 2 (FGF2) for potential use in cancer therapy. The FGF family of growth factors is a large group of factors believed to play a role in the growth of many tumors, as well as in aspects of normal development. In particular, FGF2 (basic FGF) not only stimulates some tumor cells directly, but is a powerful inducer of angiogenesis, the new blood vessel formation needed by tumors to grow. Overexpression of FGF2 and/or correlation of FGF2 level with clinical features or outcome has been reported for melanoma, prostate cancer, pancreatic cancer, liver cancer (hepatoma), esophageal cancer, thyroid carcinoma and other types of cancer. The Applicant has already generated an anti-FGF2 mAb and in preliminary studies shown that it binds a different epitope than other anti-FGF2 mAbs, blocks a bioactivity of FGF2 and, importantly, strongly inhibits growth of a hepatoma tumor xenograft. In the proposed research plan, this anti-FGF2 mAb, designated GAL-F2, will be intensively studied both in vitro and in vivo, in order to provide the scientific justification for moving it toward clinical trials. The ability of GAL-F2 to bind to various forms of FGF2 and to block binding of FGF2 to the four FGF receptors, FGFR1-4, will be determined. The ability of GAL-F2 to inhibit FGF2- induced cell proliferation and migration, and to inhibit soft agar colony formation by human hepatoma cell lines, will be investigated. Another important aim will be to show that GAL-F2 has potent anti-angiogenic properties, for example by showing that the mAb inhibits blood vessel formation induced by xenografts of a highly angiogenic retinoblastoma cell line. Finally, experiments will be conducted to investigate the capability of the GAL-F2 mAb to block growth of human hepatoma tumor xenografts in mouse models; the results will be critical for the potential choice of hepatocellular carcinoma as the initial clinical indication for the mAb. It is expected that in Phase II of this grant proposal, more extensive animal model and mechanism of action studies will be conducted, and the GAL-F2 mAb will be converted into a humanized antibody suitable for clinical development. PUBLIC HEALTH RELEVANCE: Despite recent scientific advances, cancer remains a major medical problem. The objective of the planned program is to study a monoclonal antibody that targets a growth factor believed to be involved in many tumors. The antibody has the potential to be an effective drug for various types of cancer including liver, pancreatic, and prostate.

Technology keywords

Not reported

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Agency intelligence

Department of Health and Human Services

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.