In a bid to discover biomarkers for multiple sclerosis, SurroMed has agreed to study patient samples for Biogen using its proteomics and metabolite analysis platform, SurroMed said June 20.
Although the companies did not disclose financial details of the collaboration, Chris Becker, SurroMed’s director of chemistry and mass spectrometry, said the project is significant because it is one of the first large-scale analyses of samples collected from patients with multiple sclerosis.
“This is a real landmark opportunity to use a gel-less, broad solution for molecular phenotyping to look across proteomic, metabolomic, and cytometric data in an important disease area with large numbers of clinical samples,” Becker said. “I think it’s the first of its kind.”
Specifically, SurroMed will study an undisclosed number of patient samples in hopes of rooting out potential drug targets and biological markers associated with the diagnosis and prognosis of patients with multiple sclerosis.
Cambridge, Mass.-based Biogen will pay SurroMed a technology access fee, provide research funding, and pay milestone fees upon completion of certain undisclosed objectives. SurroMed can also earn royalties on the sale of diagnostic tests covered under the agreement that result from the research collaboration.
SurroMed, based in Mountain View, Calif., has developed a drug target and biomarker discovery platform centered around separations and mass spectrometry for identifying proteins and metabolites, cytometry for isolating cell-surface markers, and ELISA immunoassays.
To perform its analysis of proteins and metabolites, SurroMed separates sample components into high and low molecular weight fractions, Becker said, and then digests the high molecular weight proteins prior to identification with mass spectrometry. Becker’s group uses both a Micromass Q-TOF and Thermo Finnigan ion trap mass spectrometer, he said.
SurroMed does not use labels as part of its strategy for quantifying the level of certain proteins and metabolites in samples, Becker said. Instead, his group relies on its ability to churn out reproducible spectra and apply quality control techniques to compare expression levels across several samples.
Becker and his colleagues have also devoted effort to writing software for use in comparing spectra to find which peptides are differentially expressed, as well as customized database search software. In particular, Becker said his group has developed a new scoring method for ranking the confidence of the software’s predicted protein identifications.