Abstract 16220: A Novel Bioinformatics Visual-High Throughput Screening to Predict Therapeutic Targets From in-vitro to in-vivo Studies
Background: The early stages of drug discovery research often involve high throughput studies aimed to identify novel therapeutic targets with limited side effects. State-of-the-art mass spectrometry can monitor, for a given disease, the behaviors of thousands of proteins in response to candidate therapies. However, the available informatics strategies used to decipher these protein behaviors lack the innovation to transition from discovery mode to the clinical trial predictive mode. We, therefore, introduce a powerful visual-High Throughput Screening (visHTS) strategy that enables facile analysis of, but not limited to, proteomics data for identification of protein pathways responsive to various stimuli or drug compounds.
Methods and Results: To assess the specific and off-target effects of potential therapeutics, on the proteome during pro-inflammatory activation of mouse and human macrophages (so-called “M1” polarization), we performed relative protein quantification using the isobaric tandem mass tagging system. Our informatics workflow comprises statistical tools that i) normalize the protein abundance with respect to the control conditions, ii) project them into the simplicial domain and iii) perform cluster analysis to classify the protein abundance profiles according to their similarities. visHTS merges the quantified proteomes of various polarization experiments (+/- drug), creating a super-database (9000+ profiles). This procedure provides a simple means to mine the data for proteins that: 1) are not affected by the drug, 2) display diminished inflammatory potential by the drug (Figure) and 3) display off-target behaviors by the drug. visHTS uses a color-coded bulls-eye plot as simple graphic-user-interphase to monitor protein behaviors of interest.
Conclusions: We foresee that our visHTS system will facilitate identification of novel on and off-target pathways and will predict the candidacy of therapeutics for clinical trials.
- © 2013 by American Heart Association, Inc.