Funded Projects

Saliva Analysis with an Array Sensor

Grant Number: 1U01DE015017-01
PI Name: Eric V. Anslyn
PI E-mail: anslyn@ccwf.cc.utexas.edu
PI Title: University Distinguished Teaching Professor
Institution: University of Texas Austin, Austin TX 78712
Department: Chemistry and Biochemistry
Project Start: 30-Sep-2002
Project End: 30-Jun-2006

Abstract

Real-time bedside medical diagnostics presents a revolution in medical care that is undoubtedly coming in the near future. It will have a profound impact on how medicine is practiced, and will greatly improve the general health care of the Nation. As with most medical diagnostic procedures, the testing will require sample collection. Saliva is undoubtedly the most non-invasive bodily fluid that can be collected. Previous research has shown that a large number of various disease markers are present in saliva, and therefore saliva represents an excellent medium for real-time bedside medical diagnostics. At the University of Texas at Austin (UT) a technology has recently been developed that is well suited to an ultimate application in a bed-side diagnostic setting. This technology is a micro machined bead-based array sensor suite well suited to multiplexing sensing arrays. In this application we describe work focused upon extending this technology to saliva analysis. In a collaboration with the University of Kentucky (UK), correlations between ELISA based analyses with the UT technology will be performed. The goal is to demonstrate the feasibility of performing multiplexed assays on saliva for a series of metabolic and disease markers. The study will consist of developing nine chemical assays, combining them into sensing suites, making the assays practical and robust, and correlating the results with that from UK. At the same time three improvements will be made to the UT technology. These consist of fabricating "chips on chips" such that incompatible chemistries can be multiplexed, forming platforms where beads can act as reagent sources in order to minimize sample preparation, and creating an automated, spatially resolved bead sorting process to improve manufacturing.