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Self-interaction nanoparticle spectroscopy predicts high-concentration viscosity of therapeutic IgG1 antibodies
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DOI：10.1080/19420862.2026.2709948.png)
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Abstract
En 中文
Predicting high-concentration viscosity of monoclonal antibodies is crucial for their development as therapeutics for subcutaneous delivery, but traditional experimental rheometry methods for assessing viscosity are low-throughput, which limits their utility. 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 with high-concentration viscosity in Fc variants of clinical antibodies (r = 0.72 and r = 0.85 for trastuzumab and omalizumab variants, respectively), 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.
Keywords:
Self-interaction nanoparticle spectroscopy (SINS)
PS-SINS
CS-SINS
developability
antibody screening
antibody viscosity
machine learning
in silico
molecular descriptors
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7.3
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1.8K
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7.2K
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