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TeamChip for High-throughput, Predictive Human Metabolism and Toxicology

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41ES018022-01
Agency Tracking Number: R41ES018022
Amount: $150,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: NIEHS
Solicitation Number: PHS2010-2
Timeline
Solicitation Year: 2010
Award Year: 2010
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
1223 PEOPLES AVE
TROY, NY 12180-3511
United States
DUNS: 173649075
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 MOOYEAL LEE
 (415) 978-2160
 LEE@SOLIDUSBIO.COM
Business Contact
 JAMES ROXXELL
Phone: (518) 276-6283
Email: scammr@rpi.edu
Research Institution
 Rensselaer Polytechnic Institute
 
110 Eighth Street
Troy, NY 12180-3590
United States

 () -
 Nonprofit College or University
Abstract

DESCRIPTION (provided by applicant): Solidus Biosciences, Inc. in partnership with Rensselaer Polytechnic Institute is proposing to address a critical need in chemical safety technology through its proprietary Transfected Enzyme and Metabolism Chip (or TeamChip) for high-throughput analysis of systematic drug candidate and chemical metabolism and toxicology. The TeamChip is being developed to mimic the first-pass metabolism of the human liver and to predict enzyme-specific hepatotoxicity. The effects of metabolism on other cell types will also be demonstrated. Thus, the reactivity of target compounds with individual human metabolic enzymes or combinations of enzymes in the human liver or other organ types can be assessed and quantified at speeds commensurate with predictive human toxicity assessment of early stage drug candidates and environmental chemicals. The specific aims of this Phase I STTR project are to: 1. Develop efficient methods for transfecting genes into THLE-2 human liver epithelial cell lines and Beas-2B human lung epithelial cell lines encapsulated in 3D alginate matrices as small as 30 nL. 2. Construct recombinant adenoviruses that carry genes for metabolic enzymes from a human liver cDNA library (e.g., CYP450 isoforms including CYP1A2 and CYP3A4) and demonstrate gene transfection on monolayers of THLE-2 and Beas-2B cells using fluorogenic substrates in a 96-well plate. 3. Demonstrate 3D cellular microarrays containing metabolic enzyme-expressing THLE-2 and Beas-2B cells and identify metabolic genes whose differential expression affects the cellular response to chemicals as proof of concept. In vitro technologies that can be used to quickly assess large numbers of compounds for toxicity remain limited. A critical component of safety evaluation is metabolism and toxicology of chemicals (e.g., drug candidates and environmental chemical toxicants), which reflects the susceptibility of chemicals to be metabolized by human metabolic enzymes and the toxicity of parent compounds and their metabolites. Current approaches to chemical safety assessment are costly, time consuming, and use large amounts of compound and large numbers of animals. Thus, there is great potential and opportunity to apply the TeamChip as a safety assessment tool that can be used to evaluate whether and how specific metabolic enzymes contribute to the toxicity of drug candidates and chemical toxicants. This capability may also be used to predict differences among individuals in drug and chemical metabolism and toxicity. 1 PUBLIC HEALTH RELEVANCE: The drug discovery process is an investment-intensive, high-risk endeavor that results in low yields of effective and safe drugs; a problem that is confounded by the significant lack of information that exists in predicting the metabolic fate of drug candidates, in general, and in predicting the reactivity of drug candidates in the human body. The proposed Phase I STTR project for the development of Solidus Bioscience's TeamChip technology has significant relevance to public health by providing pharmaceutical researchers with the information needed to predict the in vivo metabolism of drug candidates, and thus help to decide which compounds are brought forward for lead optimization and the ultimate development of better and safer drugs. Furthermore, this research is relevant to the prioritization of industrial and environmental chemicals in terms of their safety and use.

* Information listed above is at the time of submission. *

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