Reduction of therapeutic antibody self-association using yeast-display selections and machine learning

Abstract

Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis of the enriched bococizumab libraries, along with similar library analysis for antibody affinity, enabled identification of extremely rare variants with co-optimized levels of low self-association and high affinity. This analysis revealed that co-optimizing bococizumab is difficult because most high-affinity variants possess positively charged variable domains and most low self-association variants possess negatively charged variable domains. Moreover, negatively charged mutations in the heavy chain CDR2 of bococizumab, adjacent to its paratope, were effective at reducing self-association without reducing affinity. Interestingly, most of the bococizumab variants with reduced self-association also displayed improved folding stability and reduced nonspecific binding, revealing that this approach may be particularly useful for identifying antibody candidates with attractive combinations of drug-like properties. Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CS-SINS: charge-stabilized self-interaction nanoparticle spectroscopy; FACS: fluorescence-activated cell sorting; Fab: fragment antigen binding; Fv: fragment variable; IgG: immunoglobulin; QD: quantum dot; PBS: phosphate-buffered saline; V H : variable heavy; V L : variable light.

Reduction of monoclonal antibody viscosity using interpretable machine learning

“…While we demonstrated this direction in this study, we envision that our model could be used in concert with other experimental or computational approaches for co-optimizing antibody properties that also strongly depend on the same variable regions, such as affinity, solubility, aggregation, and nonspecific binding. 9 , 34 , 40–46 Given that common antibody properties such as isoelectric impact antibody properties in opposite ways, 34 , 47 we expect our model will help constrain the selection of antibody mutations that co-optimize other key antibody properties while simultaneously minimizing viscosity. These and other applications of our model are expected to improve the predictable generation of drug-like therapeutic antibodies that are well suited for use in high concentration antibody formulations necessary for subcutaneous delivery.…”

Selection of biophysically favorable antibody variants using a modified Flp-In CHO mammalian display platform

“…Another recent case study where the biophysical properties of bococizumab were successfully improved used yeast display and machine learning ( Makowski et al, 2022 ). In this study the selection for antigen binding and low self-association was performed separately using FACS and both positive and negative sequences from both selections were used to train a machine learning model.…”

In vivoAuto-tuning of Antibody-Drug Conjugate Delivery for Effective Immunotherapy using High-Avidity, Low-Affinity Antibodies

“…The trastuzumab antibody fragment was displayed on the surface of yeast along with a library of mutant sequences with several mutations per antibody. For sequence generation, a heavy chain complementarity determining region (HCDR) focused library for trastuzumab was made in single chain Fab (scFab) format as described previously 49 to isolate trastuzumab mimics with high avidity, low affinity. We targeted sites 53 and 56 in HCDR2 and 96, 97, 98, 99, 100, 100A and 100B in HCDR3.…”