Biography
Biography: Oliva Petra
Abstract
Biomarkers are essential for improving the outcomes of clinical trials and accelerating drug development. Mass spectrometry (MS)-based proteomics applied early to clinical samples has the potential to identify and narrow down predictive and pharmacodynamics biomarkers. These markers can then be used in clinical trials for patient stratification and to increase sensitivity of primary endpoints for a better measurement of therapeutic response. Unbiased proteomic profiling is powerful during the exploratory biomarker stage for monitoring hundreds or thousands of proteins, but throughput is low and relative quantitation is variable for low abundant analytes. Here, we describe an integrated, hypothesis-driven strategy that combines unbiased proteomics and literature mining to generate a highly quantitative and reproducible targeted proteomics assay for testing in large, representative patient cohorts for candidate biomarker screening. Combined with appropriate statistical and bioinformatics processes, this strategy will facilitate selection of a robust biomarker panel which may be validated as a companion diagnostic or as a clinical tool.