SBIR/STTR Research Award

An In Vitro Robotic Assay for Anti-Estrogenic Activity

Award identifier c6a97f81e5bfda50f10152d2b572208095e83de257d66b85c2169cbca7990f79Awarding agency Department of Health and Human Services · National Institutes of Health

Award amount

$121,756

Program and phase

SBIR · Phase I

Award year

2006

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
CERTICHEM INC
Proposal award date
Not reported
Contract end date
Not reported
UEI
DJCLLPHN8383
Solicitation number
PHS2006-2
Topic code
Not reported

Research abstract

DESCRIPTION (provided by applicant): Recent scientific investigations have shown that many chemicals used in plastics, Pharmaceuticals, pesticides, cosmetics, food additives, etc., are endocrine disrupters (EDs). EDs interfere in various ways with hormones such as estrogens to have significant adverse effects on many behavioral and physiological processes. ED effects (e.g. estrogenic or anti-estrogenic) sometimes occur at very low (picomolar to nanomolar) concentrations, especially on fetal or developing mammals (including humans). The prevalence and actions of EDs in our environment warrant the development of valid assay methods. Consequently, various governmental bodies (e.g., EPA, FDA, and ICCVAM) and proactive corporations have explicitly expressed a desire to have in vitro robotic assays for EDs, such as anti-estrogenic activity (anti-EA). However, no anti-EA assay is commercially available. To begin to meet these governmental, scientific and commercial needs, CertiChem (CCi) has preliminary data suggesting that it is feasible to develop an anti-EA robotic assay that would be valid (i.e., reliable, accurate, versatile, rapid, and cost effective) using MCF-7 cells. In this Phase I application, CCi now proposes to confirm that it is feasible to develop a robotic assay for anti-EA by first determining an optimum robotic protocol for detecting anti-EA using MCF-7 cells. CCi then proposes to estimate the reliability (reproducibility) of this assay by repeatedly (5 times) assaying over a 12 week period a limited set of reference chemicals (10) published by ICCVAM to use for validation of anti-EA assays (Specific Aim #1). CCi also proposes (Specific Aim #2) to estimate the accuracy of this assay by comparing data CCi obtains on these 10 reference chemicals with data published by ICCVAM. We strongly expect data obtained on this Phase I grant to confirm our preliminary data that this anti-EA assay is worthy of more extensive testing and development in a Phase II study to prepare it for commercialization. Development of a robotic screening assay for anti-EA is commercially, scientifically, and socially important because of the large number of chemicals (>10,000) - much less chemical mixtures - that should now be screened for anti-EA by profit, non-profit, or governmental entities. Development of a robotic assay for anti-EA is desired as part of the mission of NIEHS, EPA and ICCVAM.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

10 SBIR/STTR records share this UEI (2002–2016). Reported award amounts total $5,067,084. Reported amounts are not federal obligations.

Phases: Phase II, Phase I

Agencies: Department of Health and Human Services, National Science Foundation

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.