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Principal Investigator: PETER SCHUCK
Organization: NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING
Fiscal Year: 2024
Award: $1,425,266
Funding agency: National Institute of Biomedical Imaging and Bioengineering
In this project we seek to develop experimental methods for the study of macromolecular and particle size-distributions. This includes methods for the study of polymorphic dynamic multi-protein complexes that elude classical structural techniques, as well as methods for high-resolution particle size distributions in nanoparticle and biotechnology applications.
In previous years we have designed computational algorithms for the analysis of sedimentation velocity analytical ultracentrifugation (SV-AUC) data with high hydrodynamic resolution, based on the deconvolution of diffusion and nonideality effects. These were implemented and distributed in the software SEDFIT, which is widely used in research and quality control applications in academic and biopharmaceutical laboratories. For example, SEDFIT analysis has become the de facto standard for the detection of oligomeric impurities of protein pharmaceuticals, as well as the characterization of viral vector preparations for vaccine and gene therapy applications.
A long-standing effort of our group is to ensure the calibration accuracy of analytical ultracentrifuges. Several years ago we have embarked on a collaborative project with colleagues from NIST to design and produce a calibration window to be made available as an SRM by NIST. In the reporting period we have tested prototype windows from the designated manufacturer selected by NIST. They were deemed suitable for radial calibration as designed, and NIST has commenced the production run of the windows.
One other recent project was devoted to the creation of an automated programming interface for sedimentation velocity analysis in SEDFIT. This provides an opportunity to standardize and accelerate the data analysis, and allows SEDFIT analyses to be incorporated into software that satisfies access control and documentation requirements in the GMP environment, thereby broadening application of sedimentation velocity in the biopharmaceutical production. Furthermore, the automated interface sets the stage for future development of expert systems or AI systems to carry out sedimentation analyses. In the reporting period, we have completed the publication of this interface, and added further updates to accommodate newer analyses models.
Lipid nanoparticles (LNPs) have become a major tool for drug delivery in therapeutic and vaccine applications. Examples of lipid nano-medicines include several cancer therapeutics as well as cancer and viral mRNA vaccines. An important step for their well-characterized production is the measurement of the LNP size-distribution. In order to exploit the high hydrodynamic resolution of SV-AUC we have adapted the sedimentation analysis to accommodate flotation processes observed with LNPs. This was accomplished in a multi-method approach, taking advantage of diffusion coefficient measurements easily obtained by dynamic light scattering. The diffusion information was applied to achieve diffusional deconvolution of the flotation boundaries. As an example for the application of this approach we have studied the particle size distribution of SARS-CoV-2 mRNA vaccine products. This work was published in ACS Nano.
Finally, we have continued the development of sedimentation coefficient distribution analysis for highly concentrated protein samples. The goal is to allow the measurement of oligomeric states and weak self-association processes of proteins at concentrations comparable to conditions in the cytosol, serum, and therapeutic protein formulations. To this end, we have developed a new approach to measure and account for obligatory lensing artifacts in sedimentation boundaries of concentrated solutions. It is based on the detailed analysis of the time-invariant noise pattern of Rayleigh interference optical SV-AUC data. This was combined with models for hindered sedimentation and diffusion in concentrated solution, which were adapted from published results in statistical fluid mechanics. These models for concentration-dependent sedimentation were embedded in a mean-field approximation for hydrodynamic interactions enabling size-distribution analysis in SV-AUC, which we have developed previously (Nature Communications 2018). Together, the new combined model for optical aberrations and concentrated sedimentation allows a significant increase in the upper limit of protein concentrations that can be studied by SV-AUC. We have demonstrated this with IgG samples at ~45 mg/ml and serum albumin at ~140 mg/ml. This work was submitted for peer review.
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