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Statistical Methodology for Characterization of Macromolecular Similarity


EMSL Project ID
49707

Abstract

As with any drug, approval of generic versions of macromolecular drugs requires rigorous evaluation of therapeutic equivalence to the reference drug in order to assure similar efficacy and safety. Compared with drugs that are small organic molecules, the chemical composition and characteristics of macromolecular drugs—typically proteins or polysaccharides—are inherently more variable because of the way these molecules are produced and isolated. It would be advantageous to have a way to determine molecular similarity and, by implication, equivalence without using costly in vivo testing in animals and humans.
The specific aim of the proposal is to develop and test a robust data-driven statistical methodology for assessing similarity among distinct samples of therapeutic macromolecules, whether from different batches or altered processes, or even if produced by different entities entirely. The methodology is based on a genetic algorithm designed to extract relevant features from large, complex datasets, including an assortment of high-resolution mass-spectrometry methods, high-field nuclear magnetic resonance spectroscopy analyses, and several other spectroscopic and chromatographic methods used to characterize macromolecules in solution.

Project Details

Start Date
2016-12-07
End Date
2017-09-30
Status
Closed

Team

Principal Investigator

John Cort
Institution
Pacific Northwest National Laboratory

Team Members

Mowei Zhou
Institution
Environmental Molecular Sciences Laboratory

Xing Zhang
Institution
Pacific Northwest National Laboratory

Christina Stevenson
Institution
Pacific Northwest National Laboratory

Ernesto Nakayasu
Institution
Pacific Northwest National Laboratory

Charles Ansong
Institution
National Institutes of Health

Erin Baker
Institution
North Carolina State University

Related Publications

Brinson R.G., J.P. Marino, F. Delaglio, L.W. Arbogast, R.M. Evans, A. Kearsley, and G. Gingras, et al. 2018. "Enabling adoption of 2D-NMR for the higher order structure assessment of monoclonal antibody therapeutics." mAbs 11, no. 1:94-105. PNNL-SA-131540. doi:10.1080/19420862.2018.1544454