Enabled by recent structural and biophysical analyses, researchers have rapidly developed and characterized novel therapeutic and prophylactic approaches for COVID-19 treatment. In this talk, I will highlight our recent work to identify and further engineer single-domain antibodies (nanobodies) capable of SARS-CoV-2 neutralization. Using kinetic analyses and structure-guided design, we show how nanobody multimerization yields exceptional neutralization potency through conformational “control” of the viral Spike protein.

The origins of LC-MS can be traced back to 1968 where direct liquid introduction from a capillary into a high vacuum electron impact source of an MS instrument was implemented. Now mass spectrometry plays a pivotal role throughout all stages of drug development and is now as ubiquitous as other analytical techniques such as SPR and NMR. Native-MS is evolving into a highly enabling analytical technique within biopharma.

Protein stability is critical for the successful development of non-aggregating biopharmaceuticals. We have combined biophysical analyses, protein engineering, formulation screening, and molecular modelling, to characterise factors that influence protein aggregation. Examples will be presented from small-angle X-ray scattering, hydrogen-deuterium exchange mass spectrometry, and molecular dynamics simulations. Insights are being used to develop improved protein engineering and formulation design strategies for the minimisation of aggregation in liquid and freeze-dried forms.

In all biological products, distinguishing aggregated API from other particle types matters for understanding the root cause of instability. Until now, subvisible particle characterization methods have been unreliable, slow, and difficult to use across many workflows. Introducing the Aura, a 96-well, low-volume, high throughput aggregate and particle imaging system can rapidly size, count, and characterize biological particles and identify them as proteins, non-proteins, cellular aggregates, or other types of molecules.

There is an evolving pre-competitive effort to develop protein aggregates that are relevant for the biopharmaceutical industry, from both an early-stage and later-stage perspective. This builds from industry consortia and workshop efforts through the Biomolecular Interaction Technologies Center (BITC) and future associated partners (to be determined). The talk will focus on priorities for current efforts with technology providers and instrument developers, and building a broader community effort in this area.

Aggregation is a concern especially during the reconstitution and administration of therapeutic antibody (TAb) preparations, as it seems to be correlated with anti-drug antibodies (ADA) development in treated patients. This talk will deal with the optimization of separative methods for TAb aggregates detection and characterization, and will then focus on in vitro cell-based assays, to evaluate the potential of aggregated TAbs to induce immune responses that could drive ADA development.

We have developed machine learning methods for identifying lead antibodies and affinity-matured variants with Pareto optimal combinations of affinity and biophysical properties. Our approach combines novel descriptors of antibody molecular features with neural networks to identify antibody variants with different levels of affinity improvement while compromising other key properties (e.g., specificity) by the least amount possible. These methods reduce the required experimentation needed to co-optimize antibody affinity and biophysical properties.

Despite many years of study, the characteristics of effective antibody selection remain incompletely understood. Here we performed large-scale analyses of antibody improvement pathways to explore antibody developmental features. We also mined human antibody repertoire sequences and revealed a functional selection pressure for MHC-II epitope deletion within antibody proteins. These data suggest that HLA-based personalization shapes the composition and durability of human antibody immunity, with important implications for protein therapeutic development.

Here we present precision-based high protein titer measurements and secondary structure prediction in real-time using QCL-Mid IR Culpeo liquid analyzer. This unique analytical tool can serve as an orthogonal method for characterizing higher-order structures (HOS) and conduct Forced degradation studies (FDS) at the key unit operations steps by quantifying the changes in the secondary structures of drug substances.

Phage ELISA has been a workhorse in clone characterization, but it is labor-intensive and consumes large amounts of antigens. We have developed a flow cytometry-based multiplex assay for phage-displayed binders utilizing off-the-shelf reagents. It simultaneously measures binding to five antigens and reduces antigen consumption by >500-fold within shorter experimental time, compared with phage ELISA. This assay accelerates the discovery of clones with desirable specificity profiles early in the development pipeline.

AAV products comprise a protein capsid and a nucleic acid packaged inside. The smaller manufacturing scales of AAV-based products make it particularly challenging to develop methods with very limited sample availability. We describe traditional characterization methods and the challenge of their applicability to AAV-based products. We evaluate new technologies which require minimal sample amounts to inform critical quality attributes and we demonstrate how they are orthogonal to the traditional methods.

We recently collected amino acid sequences of currently marketed antibody-based biologic medicines and used these to derive their homology-based structural models. Availability of both sequence and structural models of these biotherapeutics afforded us an opportunity to analyze their physicochemical attributes from a perspective of developability. These analyses have resulted in a profile that can be used to estimate ‘medicine-likeness’ of the biologic drug candidates currently in discovery and development.

We present a physics-based model to understand the mechanism of antibody viscosity. In this model variable domain interactions lead to the formation of elongated complexes that entangle with each other. A theory accounting for the reptation dynamics of these complexes is able to describe the viscosity as a function of concentration and shear rate. This shows that viscosity can be predicted from measurements of the two-body interaction.

To fully realize the potential of AAV vectors for gene therapy applications, reliable and efficient analytical technologies have become increasingly relevant and urgent to help establish a structure-function relationship, guide the development of manufacturing process, and assess the quality of clinical materials. In this talk, we will highlight the novel LC/MS workflows that are developed for the analysis of recombinant AAV vectors, including aggregation, titer, empty/full ratio, and PTM analysis.