Novel Peptide Libraries for Early Cancer Detection

Award Information
Agency:
Department of Health and Human Services
Branch:
N/A
Amount:
$100,000.00
Award Year:
2007
Program:
SBIR
Phase:
Phase I
Contract:
1R43CA130543-01
Agency Tracking Number:
CA130543
Solicitation Year:
2007
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
PEPTIDES INTERNATIONAL, INC.
PEPTIDES INTERNATIONAL, INC., 11621 ELECTRON DR, LOUISVILLE, KY, 40299
Hubzone Owned:
Y
Socially and Economically Disadvantaged:
N
Woman Owned:
Y
Duns:
106895766
Principal Investigator
 KRZYSZTOF DARLAK
 (502) 266-8787
 kd@pepnet.com
Business Contact
 DEANNA LONG
Phone: (502) 266-8787
Email: dwlong@pepnet.com
Research Institution
N/A
Abstract
DESCRIPTION (provided by applicant): Global cancer rates are predicted to increase by 50% to 15 million by 2020. Treatments depend on early detection and identification to select the most effective therapies to end the suffering and loss of life. Biomarkers offer tremendous potential for early detection, though few exist today. Biomarkers of early stage disease are likely to be at very low concentrations in highly complex biological samples containing abundant proteins. Dramatic improvements in technology would be needed for better detection and identification of these specific, but trace, biomarkers. No bioseparation method exists today to accomplish this goal in a simple, cost-effective manner while utilizing existing sample collection and analytical platforms. Thus, new, robust and sensitive processing technology is urgently needed for early detection of biomarkers. In 2001, solid-phase peptide combinatorial libraries were developed at the American Red Cross (ARC) and produced by Peptides International (PI) to generate leads in biomarker discovery and drug development. ARC demonstrated that the libraries could be used to concentrate trace analytes in complex biological mixtures. PI now proposes a more powerful approach for identification of cancer-specific biomarkers with emphasis on protein tyrosine kinases (PTK), which are key regulators of cellular growth and differentiation processes in immune, endocrine, and nervous system physiology, and are of critical importance in the development of many cancers. This requires the selective concentration of classes of proteins, e.g. PTKs using Targeted Combinatorial Peptide Library (TCPL) technology. We propose a new sample preparation device comprising a novel peptide library for the selective enrichment of class specific proteins, initially targeted for PTKs. The Specific Aims of this Phase I feasibility study will be the synthesis and evaluation of the following: (i) a control Combinatorial Peptide Library (CPL); (ii) a tyrosine-enriched TTCPL; (iii) a phosphotyrosine-enriched TCPL and (iv) a phosphate analog-enriched TCPL. The criterion for advancement to Phase II studies is the targeted library that yields at least a10-fold enhancement of PTK detection over the existing unmodified control library which, itself, is anticipated to improve detection of trace proteins by = 10 fold. Upon demonstrated success of this feasibility study, we envision a successful Phase II/III commercialization path to produce Targeted Peptide Libraries for early cancer detection for research organizations and diagnostic companies. This novel device would be a quantum leap in improvement over existing techniques which currently offer no such commercial product. This project seeks to develop a novel approach to biomarker discovery and detection for the early diagnosis of cancer. It addresses the problem of enrichment and selective identification of classes of trace proteins in disease-related biopsy samples. The project focuses on achieving early and more reliable detection of protein tyrosine kinases (PTKs) that are important in the diagnosis of cancer. It achieves this by using novel targeted peptide libraries that will allow patients to receive earlier therapy. While focused on cancer, this technology has broad application to many other disease states.

* information listed above is at the time of submission.

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