Makowski, E. | bioRxiv Science

bioRxiv2026

Self-Interaction Nanoparticle Spectroscopy Predicts High-Concentration Viscosity of Therapeutic IgG1 Antibodies

Predicting high-concentration viscosity of monoclonal antibodies such as IgG1 is crucial for their development as therapeutics for subcutaneous delivery. Unfortunately, traditional experimental rheometry methods for assessing viscosity are low-throughput. This study evaluates Self-Interaction Nanoparticle Spectroscopy (SINS) assays--specifically charge-stabilized SINS (CS-SINS) and PEG-stabilized SINS (PS-SINS)--for high-throughput viscosity prediction. We characterized 96 IgG1 antibodies, assessing SINS against in silico descriptors and dynamic light scattering (DLS) data. CS-SINS showed strong correlation with charge, offering limited additional utility. In contrast, PS-SINS provided orthogonal information; integrating it with in silico data and DLS significantly improved random forest model accuracy for binary viscosity classification. PS-SINS measurements in multiple buffers captured complementary information, achieving comparable accuracy without DLS. Importantly, PS-SINS scores exhibited a strong logarithmic relationship (r=0.98) with high-concentration viscosity in Fc variants of clinical antibodies, suggesting a direct mechanistic link. Furthermore, PS-SINS performed reliably with one column purified (protein A) samples, supporting its early-stage application. These findings establish PS-SINS as a high-throughput tool to accelerate the developability assessment of antibody candidates.

biochemistry↗

bioRxiv2025

Property Enhancer - a data efficient multi-objective approach for functional antibody optimization

In-silico antibody lead optimization remains challenging due to scarce high-quality data, costly experimental validation, and the need to jointly optimize multiple developability properties. Discovery workflows often rely on high-throughput phage, ribosome or yeast display experiments, which yield large but noisy datasets; as leads emerge, strategies shift to low-throughput assays which are precise, yet unscalable. Deep-learning and language-model approaches are hindered by such limited, unreliable measurements. We introduce Property Enhancer (PropEn), a data-efficient framework for low-data, heterogeneous regimes that can simultaneously optimize multiple antibody properties. PropEn proposes a matching-based augmentation that expands the training data with sequence pairs differing by only a few mutations; within each pair the second sequence improves the target value, providing an implicit optimization signal. Extensive in silico and in vitro tests show 10-39x affinity gains across four targets and nine leads, and enable joint multi-property optimization, positioning PropEn as a scalable, general solution.

molecular biology↗