Charge Detection Mass Spectrometry Reveals Conformational Heterogeneity in Megadalton-Sized Monoclonal Antibody Aggregates

Aggregation of protein-based therapeutics can occur during development, production, or storage and can lead to loss of efficacy and potential toxicity. Native mass spectrometry of a covalently linked pentameric monoclonal antibody complex with a mass of ∼800 kDa reveals several distinct conformations, smaller complexes, and abundant higher-order aggregates of the pentameric species. Charge detection mass spectrometry (CDMS) reveals individual oligomers up to the pentamer mAb trimer (15 individual mAb molecules; ∼2.4 MDa) whereas intermediate aggregates composed of 6-9 mAb molecules and aggregates larger than the pentameric dimer (1.6 MDa) were not detected/resolved by standard mass spectrometry, size exclusion chromatography (SEC), capillary electrophoresis (CE-SDS), or by mass photometry. Conventional quadrupole time-of-flight mass spectrometry (QTOF MS), mass photometry, SEC, and CE-SDS did not resolve partially or more fully unfolded conformations of each oligomer that were readily identified using CDMS by their significantly higher extents of charging. Trends in the charge-state distributions of individual oligomers provides detailed insight into how the structures of compact and elongated mAb aggregates change as a function of aggregate size. These results demonstrate the advantages of CDMS for obtaining accurate masses and information about the conformations of large antibody aggregates despite extensive overlapping m/z values. These results open up the ability to investigate structural changes that occur in small, soluble oligomers during the earliest stages of aggregation for antibodies or other proteins

Interrogating heterogeneity of cysteine-engineered antibody-drug conjugates and antibody-oligonucleotide conjugates by capillary zone electrophoresis-mass spectrometry

ABSTRACTProduction of site-specific cysteine-engineered antibody-drug conjugates (ADCs) in mammalian cells may produce developability challenges, fragments, and heterogenous molecules, leading to potential product critical quality attributes in later development stages. Liquid phase chromatography with mass spectrometry (LC-MS) is widely used to evaluate antibody impurities and drug-to-antibody ratio, but faces challenges in analysis of fragment product variants of cysteine-engineered ADCs and oligonucleotide-to-antibody ratio (OAR) species of antibody-oligonucleotide conjugates (AOCs). Here, for the first time, we report novel capillary zone electrophoresis (CZE)-MS approaches to address the challenges above. CZE analysis of six ADCs made with different parent monoclonal antibodies (mAbs) and small molecule drug-linker payloads revealed that various fragment impurities, such as half mAbs with one/two drugs, light chains with one/two drugs, light chains with C-terminal cysteine truncation, heavy chain clippings, were well resolved from the main species. However, most of these fragments were coeluted or had signal suppression during LC-MS analysis. Furthermore, the method was optimized on both ionization and separation aspects to enable the characterization of two AOCs. The method successfully achieved baseline separation and accurate quantification of their OAR species, which were also highly challenging using conventional LC-MS methods. Finally, we compared the migration time and CZE separation profiles among ADCs and their parent mAbs, and found that properties of mAbs and linker payloads significantly influenced the separation of product variants by altering their size or charge. Our study showcases the good performance and broad applicability of CZE-MS techniques for monitoring the heterogeneity of cysteine-engineered ADCs and AOCs

Characterization and Modeling of Reversible Antibody Self-Association Provide Insights into Behavior, Prediction, and Correction

Reversible antibody self-association, while having major developability and therapeutic implications, is not fully understood or readily predictable and correctable. For a strongly self-associating humanized mAb variant, resulting in unacceptable viscosity, the monovalent affinity of self-interaction was measured in the low μM range, typical of many specific and biologically relevant protein–protein interactions. A face-to-face interaction model extending across both the heavy-chain (HC) and light-chain (LC) Complementary Determining Regions (CDRs) was apparent from biochemical and mutagenesis approaches as well as computational modeling. Light scattering experiments involving individual mAb, Fc, Fab, and Fab’2 domains revealed that Fabs self-interact to form dimers, while bivalent mAb/Fab’2 forms lead to significant oligomerization. Site-directed mutagenesis of aromatic residues identified by homology model patch analysis and self-docking dramatically affected self-association, demonstrating the utility of these predictive approaches, while revealing a highly specific and tunable nature of self-binding modulated by single point mutations. Mutagenesis at these same key HC/LC CDR positions that affect self-interaction also typically abolished target binding with notable exceptions, clearly demonstrating the difficulties yet possibility of correcting self-association through engineering. Clear correlations were also observed between different methods used to assess self-interaction, such as Dynamic Light Scattering (DLS) and Affinity-Capture Self-Interaction Nanoparticle Spectroscopy (AC-SINS). Our findings advance our understanding of therapeutic protein and antibody self-association and offer insights into its prediction, evaluation and corrective mitigation to aid therapeutic development

A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties

ABSTRACTIdentification of favorable biophysical properties for protein therapeutics as part of developability assessment is a crucial part of the preclinical development process. Successful prediction of such properties and bioassay results from calculated in silico features has potential to reduce the time and cost of delivering clinical-grade material to patients, but nevertheless has remained an ongoing challenge to the field. Here, we demonstrate an automated and flexible machine learning workflow designed to compare and identify the most powerful features from computationally derived physiochemical feature sets, generated from popular commercial software packages. We implement this workflow with medium-sized datasets of human and humanized IgG molecules to generate predictive regression models for two key developability endpoints, hydrophobicity and poly-specificity. The most important features discovered through the automated workflow corroborate several previous literature reports, and newly discovered features suggest directions for further research and potential model improvement

Development of Anti-CD74 Antibody–Drug Conjugates to Target Glucocorticoids to Immune Cells

Glucocorticoids (GCs) are excellent anti-inflammatory drugs but are dose-limited by on-target toxicity. We sought to solve this problem by delivering GCs to immune cells with antibody–drug conjugates (ADCs) using antibodies containing site-specific incorporation of a non-natural amino acid, novel linker chemistry for in vitro and in vivo stability, and existing and novel glucocorticoid receptor (GR) agonists as payloads. We directed fluticasone propionate to human antigen-presenting immune cells to afford GR activation that was dependent on the targeted antigen. However, mechanism of action studies pointed to accumulation of free payload in the tissue culture supernatant as the dominant driver of activity and indeed administration of the ADC to human CD74 transgenic mice failed to activate GR target genes in splenic B cells. Suspecting dissipation of released payload, we designed an ADC bearing a novel GR agonist payload with reduced permeability which afforded cell-intrinsic activity in human B cells. Our work shows that antibody-targeting offers significant potential for rescuing existing and new dose-limited drugs outside the field of oncology