John Marino, PhD, Leader, Biomolecular Structure & Function Group at the National Institute of Standards and Technology (NIST)
Dr. Marino is the leader of the NIST Biomolecular Structure & Function Group and Co-Director of the Institute for Bioscience and Biotechnology Research (IBBR), a joint research institute of the University of Maryland and NIST, in Rockville, Maryland. Dr. Marino joined NIST in 1997 as a Research Chemist and was appointed Adjunct Professor at the University of Maryland. Prior to coming to NIST, Dr. Marino completed a Ph.D. in Chemistry from Yale University in 1995 and an A.B in Chemistry from Princeton University in 1989. After his PhD, he held an Alexander von Humboldt post-doctoral fellowship for two years at the Goethe Universität in Frankfurt, Germany. Dr. Marino’s current research focuses on precision measurement of biomolecular structure and dynamics, with a focus on NMR techniques for characterization of the higher-order-structure (HOS) of biotherapeutics.
Developing NMR as a Precision Tool for Assessment of Biotherapeutic Structure
Protein therapeutics are a highly successful class of drugs that are currently used to treat a number of serious and life-threatening conditions such as cancer, autoimmune disorders, and infectious diseases including COVID-19. These therapeutics have numerous critical quality attributes (CQA) that must be evaluated to ensure safety and efficacy, including that they must adopt and retain the correct structural fold without forming unintended aggregates. The entirety of structural elements from primary sequence to quaternary interactions is termed by the industry as the ‘higher order structure’ (HOS) of the therapeutic, and the development of analytical techniques for HOS characterization throughout the lifecycle of a protein therapeutic, from development to manufacture, has therefore emerged as a major priority in the pharmaceutical industry. To address this measurement gap, our group has developed, demonstrated, and optimized one-dimensional (1D) and two-dimensional (2D) nuclear magnetic resonance (NMR) spectroscopy methods as robust and fit-for-purpose approaches for spectral ‘fingerprinting’ the HOS of protein therapeutics and their formulations. To utilize these NMR fingerprints in HOS decision making, I will describe how principal component analysis (PCA) with quantitative similarity assessment using PC Euclidean distance and cluster confidence surface overlap can be employed for automated and quantitative classification of one-dimensional (1D) diffusion-edited 1H spectra and 2D 1H-13C methyl spectra as demonstrated using measurements on an IgG1k NIST reference mAb (NISTmAb), and mAbs from biopharma partners. Since this classification approach can be performed without the need to identify signals, results suggest that it is possible to use even more efficient measurement strategies that do not produce spectra that can be analyzed visually, but nevertheless allow useful decision-making that can be timely, objective and automated.